savu.data.experiment_collection

Complexity

Total Complexity

97

Size/Duplication

Total Lines	438
Duplicated Lines	0 %

Importance

Changes

0

36 Methods

Rating	Name	Duplication	Size	Complexity
A	Experiment.get()	0	3	1
A	Experiment.__init__()	0	12	1
A	Experiment.create_data_object()	0	12	3
A	Experiment._setup()	0	10	1
A	Experiment.__meta_data_setup()	0	15	3
A	Experiment._setup_iterate_plugin_groups()	0	15	2
A	Experiment._barrier()	0	7	2
A	Experiment._set_process_list_path()	0	7	2
A	Experiment.log()	0	11	3
A	Experiment._set_transport()	0	2	1
A	Experiment._reset_datasets()	0	6	3
A	Experiment._get_collection()	0	2	1
A	Experiment._set_dataset_names_complete()	0	4	1
A	Experiment._set_experiment_for_current_plugin()	0	7	2
A	Experiment._reorganise_datasets()	0	13	3
A	Experiment._get_current_and_next_patterns()	0	14	2
A	Experiment._get_dataset_names_complete()	0	2	1
A	Experiment._finalise_setup()	0	16	4
A	Experiment._create_pre_run_nxs_file()	0	21	4
A	Experiment._create_nxs_entry()	0	7	4
B	Experiment._finalise_experiment_for_current_plugin()	0	26	6
A	Experiment._set_all_datasets()	0	6	3
A	Experiment._get_transport()	0	2	1
A	Experiment.__find_next_pattern()	0	8	4
A	Experiment._add_input_data_to_nxs_file()	0	9	2
A	Experiment.__set_system_params()	0	14	3
A	Experiment._get_modified_command()	0	10	1
A	Experiment._save_command_log()	0	19	3
A	Experiment._check_checkpoint()	0	8	4
A	Experiment.__copy_input_file_to_output_folder()	0	4	3
A	Experiment._merge_out_data_to_in()	0	8	3
B	Experiment._save_pre_run_log()	0	21	8
A	Experiment._set_nxs_file()	0	16	5
A	Experiment._clear_data_objects()	0	3	1
A	Experiment._set_initial_datasets()	0	2	1
A	Experiment.__unreplicate_data()	0	6	3

# -*- 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 <scientificsoftware@diamond.ac.uk>
"""

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.core.iterative_plugin_runner import IteratePluginGroup
from savu.plugins.savers.utils.hdf5_utils import Hdf5Utils
from savu.core.iterate_plugin_group_utils import check_if_in_iterative_loop
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
        self.meta_data.set('iterate_groups', [])

    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._setup_iterate_plugin_groups(transport)

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

    def _setup_iterate_plugin_groups(self, transport):
        '''
        Create all the necessary instances of IteratePluginGroup
        '''
        iterate_plugin_groups = []
        iterate_group_dicts = self.meta_data.plugin_list.iterate_plugin_groups

        for group in iterate_group_dicts:
            iterate_plugin_group = IteratePluginGroup(transport,
                group['start_index'],
                group['end_index'],
                group['iterations'])
            iterate_plugin_groups.append(iterate_plugin_group)

        self.meta_data.set('iterate_groups', iterate_plugin_groups)

    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
            if self.meta_data.get("pre_run"):
                self._create_pre_run_nxs_file()
            else:
                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
        """
        current_path = os.getcwd()
        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"# The directory the command was executed from\n")
                command_log.write(f"{current_path}\n")
                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 _save_pre_run_log(self):
        current_path = os.getcwd()
        folder = self.meta_data.get('out_path')
        log_folder = os.path.join(folder, "run_log")
        filename = os.path.join(log_folder, "pre_run_log.txt")
        if not os.path.isfile(filename):
            with open(filename, 'w') as pre_run_log:
                pre_run_log.write(f"# SAVU PRE-RUN\n")
                pre_run_log.write(f"# During the pre-run, the following process list was run:\n")
                pre_run_log.write(f"{self.meta_data.get('process_file_name')}\n")
                pre_run_log.write(f"# The following statistics were calculated on the input data:\n")
                if "pre_run_stats" in self.meta_data.get_dictionary().keys():
                    for key, value in self.meta_data.get("pre_run_stats").items():
                        pre_run_log.write(f"   {key}: {value}\n")
                if "pre_run_preview" in self.meta_data.get_dictionary().keys():
                    pre_run_log.write(f"# The following value for the preview parameter was calculated from the input data:\n")
                    pre_run_log.write(f"   {self.meta_data.get('pre_run_preview')}")
                if len(self.meta_data.get("warnings")) != 0:
                    pre_run_log.write(f"# Please read the following warnings before deciding whether to continue:\n")
                    for warning in self.meta_data.get("warnings"):
                        pre_run_log.write(f" ~ {warning}")

    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 _create_pre_run_nxs_file(self):
        data_path = self.meta_data["data_path"]

        for name, data in self.index["in_data"].items():
            raw_data = data.backing_file
            folder = self.meta_data['out_path']
            fname = self.meta_data.get('datafile_name') + '_pre_run.nxs'
            filename = os.path.join(folder, fname)
            self.meta_data.set("pre_run_filename", filename)
            self.__copy_input_file_to_output_folder(raw_data, filename)

            if isinstance(raw_data.get(data_path, getlink=True), h5py.ExternalLink):
                link = raw_data.get(data_path, getlink=True)
                location = f'{"/".join(self.meta_data.get("data_file").split("/")[:-1])}/{link.filename}'
                #new_filename = os.path.join(folder, link.filename)
                #with h5py.File(location, "r") as linked_file:
                #    self.__copy_input_file_to_output_folder(linked_file, new_filename)
                with h5py.File(filename, "r+") as new_file:
                    del new_file[data_path]
                    new_file[data_path] = h5py.ExternalLink(location, link.path)
                    pass

    def __copy_input_file_to_output_folder(self, file, new_filename):
        with h5py.File(new_filename, "w") as new_file:
            for group_name in file.keys():
                file.copy(file[group_name], new_file["/"], group_name)

    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')
        if self.meta_data.get("pre_run") == True:
            fname = self.meta_data.get('datafile_name') + '_pre_run.nxs'
        else:
            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'] = []
        if not check_if_in_iterative_loop(self):
            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])
        else:
            # temporary workaround to
            # https://jira.diamond.ac.uk/browse/SCI-10216: don't mark any
            # datasets as "to replace" if the given plugin is in an iterative
            # loop
            logging.debug('Not marking any datasets in a loop as '\
                          '\"to replace\"')
        
        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())