annotate queue_genotype_workflow.py @ 12:0882b7bb3dfc draft

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#!/usr/bin/env python
import argparse
import os
import shutil
import sys
import threading
import time
from datetime import datetime

from bioblend import galaxy

from six.moves import configparser

parser = argparse.ArgumentParser()
parser.add_argument('--affy_metadata', dest='affy_metadata', help='Input Affymetrix 96 well plate metadata file')
parser.add_argument('--annot', dest='annot', help='Probeset annotation file')
parser.add_argument('--api_key', dest='api_key', help='Current user API key')
parser.add_argument('--calls', dest='calls', help='Apt-probeset genotype calls file')
parser.add_argument('--confidences', dest='confidences', help='Apt-probeset genotype confidences file')
parser.add_argument('--config_file', dest='config_file', help='qgw_config.ini')
parser.add_argument('--dbkey', dest='dbkey', help='Reference genome dbkey')
parser.add_argument('--reference_genome', dest='reference_genome', help='Reference genome')
parser.add_argument('--history_id', dest='history_id', help='Encoded id of current history')
parser.add_argument('--output', dest='output', help='Output dataset')
parser.add_argument('--output_nj_phylogeny_tree', dest='output_nj_phylogeny_tree', help='Flag to plot neighbor-joining phylogeny tree')
parser.add_argument('--report', dest='report', help='Apt-probeset genotype report file')
parser.add_argument('--sample_attributes', dest='sample_attributes', help='Sample attributes tabular file')
parser.add_argument('--snp-posteriors', dest='snp-posteriors', help='Apt-probeset genotype snp-posteriors file')
parser.add_argument('--summary', dest='summary', help='Apt-probeset genotype summary file')
args = parser.parse_args()


def add_library_dataset_to_history(gi, history_id, dataset_id, history_datasets, outputfh):
    # Add a data library dataset to a history.
    outputfh.write('\nImporting dataset into current history.\n')
    new_hda_dict = gi.histories.upload_dataset_from_library(history_id, dataset_id)
    new_hda_name = new_hda_dict['name']
    history_datasets[new_hda_name] = new_hda_dict
    return history_datasets


def copy_history_dataset_to_library(gi, library_id, dataset_id, outputfh):
    # Copy a history dataset to a data library.
    outputfh.write('\nCopying history dataset with id %s to data library with id %s.\n' % (str(dataset_id), str(library_id)))
    new_library_dataset_dict = gi.libraries.copy_from_dataset(library_id, dataset_id)
    return new_library_dataset_dict


def copy_dataset_to_storage(src_path, dst_base_path, dataset_name, output_fh):
    # Copy a dataset to a storage directory on disk.  Use the date
    # to name the storage directory to enable storing a file per day
    # (multiple runs per day will overwrite the existing file).
    date_str = datetime.now().strftime("%Y_%m_%d")
    dst_dir = os.path.join(dst_base_path, date_str)
    if not os.path.isdir(dst_dir):
        os.makedirs(dst_dir)
    dst_path = os.path.join(dst_dir, dataset_name)
    shutil.copyfile(src_path, dst_path)
    outputfh.write("Copied %s to storage.\n" % dataset_name)


def delete_history_dataset(gi, history_id, dataset_id, outputfh, purge=False):
    # Delete a history dataset.
    outputfh.write("\nDeleting history dataset with id %s.\n" % dataset_id)
    gi.histories.delete_dataset(history_id, dataset_id, purge=purge)


def delete_library_dataset(gi, library_id, dataset_id, outputfh, purged=False):
    # Delete a library dataset.
    outputfh.write("\nDeleting library dataset with id %s.\n" % dataset_id)
    deleted_dataset_dict = gi.libraries.delete_library_dataset(library_id, dataset_id, purged=purged)
    return deleted_dataset_dict


def get_config_settings(config_file, section='defaults'):
    # Return a dictionary consisting of the key / value pairs
    # of the defaults section of config_file.
    d = {}
    config_parser = configparser.ConfigParser()
    config_parser.read(config_file)
    for key, value in config_parser.items(section):
        if section == 'defaults':
            d[key.upper()] = value
        else:
            d[key] = value
    return d


def get_data_library_dict(gi, name, outputfh):
    # Use the Galaxy API to get the data library named name.
    outputfh.write("\nSearching for data library named %s.\n" % name)
    # The following is not correctly filtering out deleted libraries.
    data_lib_dicts = gi.libraries.get_libraries(library_id=None, name=name, deleted=False)
    for data_lib_dict in data_lib_dicts:
        if data_lib_dict['name'] == name and data_lib_dict['deleted'] not in [True, 'true', 'True']:
            outputfh.write("Found data library named %s.\n" % name)
            outputfh.write("%s\n" % str(data_lib_dict))
            return data_lib_dict
    return None


def get_history_status(gi, history_id):
    return gi.histories.get_status(history_id)


def get_history_dataset_id_by_name(gi, history_id, dataset_name, outputfh):
    # Use the Galaxy API to get the bcftools merge dataset id
    # from the current history.
    outputfh.write("\nSearching for history dataset named %s.\n" % str(dataset_name))
    history_dataset_dicts = get_history_datasets(gi, history_id)
    for name, hd_dict in list(history_dataset_dicts.items()):
        name = name.lower()
        if name.startswith(dataset_name.lower()):
            outputfh.write("Found dataset named %s.\n" % str(dataset_name))
            return hd_dict['id']
    return None


def get_history_datasets(gi, history_id):
    history_datasets = {}
    history_dict = gi.histories.show_history(history_id, contents=True, deleted='false', details=None)
    for contents_dict in history_dict:
        if contents_dict['history_content_type'] == 'dataset':
            dataset_name = contents_dict['name']
            # Don't include the "Queue genotype workflow" dataset.
            if dataset_name.startswith("Queue genotype workflow"):
                continue
            history_datasets[dataset_name] = contents_dict
    return history_datasets


def get_library_dataset_file_path(gi, library_id, dataset_id, outputfh):
    dataset_dict = gi.libraries.show_dataset(library_id, dataset_id)
    outputfh.write("\nReturning file path of library dataset.\n")
    return dataset_dict.get('file_name', None)


def get_library_dataset_id_by_name(gi, data_lib_id, dataset_name, outputfh):
    # Use the Galaxy API to get the all_genotyped_samples.vcf dataset id.
    # We're assuming it is in the root folder.
    outputfh.write("\nSearching for library dataset named %s.\n" % str(dataset_name))
    lib_item_dicts = gi.libraries.show_library(data_lib_id, contents=True)
    for lib_item_dict in lib_item_dicts:
        if lib_item_dict['type'] == 'file':
            dataset_name = lib_item_dict['name'].lstrip('/').lower()
            if dataset_name.startswith(dataset_name):
                outputfh.write("Found dataset named %s.\n" % str(dataset_name))
                return lib_item_dict['id']
    return None


def get_value_from_config(config_defaults, value):
    return config_defaults.get(value, None)


def get_workflow(gi, name, outputfh, galaxy_base_url=None, api_key=None):
    outputfh.write("\nSearching for workflow named %s\n" % name)
    workflow_info_dicts = gi.workflows.get_workflows(name=name, published=True)
    if len(workflow_info_dicts) == 0:
        return None, None
    wf_info_dict = workflow_info_dicts[0]
    workflow_id = wf_info_dict['id']
    # Get the complete workflow.
    workflow_dict = gi.workflows.show_workflow(workflow_id)
    outputfh.write("Found workflow named %s.\n" % name)
    return workflow_id, workflow_dict


def get_workflow_input_datasets(gi, history_datasets, workflow_name, workflow_dict, outputfh):
    # Map the history datasets to the input datasets for the workflow.
    workflow_inputs = {}
    outputfh.write("\nMapping datasets from history to workflow %s.\n" % workflow_name)
    steps_dict = workflow_dict.get('steps', None)
    if steps_dict is not None:
        for step_index, step_dict in list(steps_dict.items()):
            # Dicts that define dataset inputs for a workflow
            # look like this.
            # "0": {
            #      "tool_id": null,
            #      "tool_version": null,
            #      "id": 0,
            #      "input_steps": {},
            #      "tool_inputs": {},
            #      "type": "data_input",
            #      "annotation": null
            # },
            tool_id = step_dict.get('tool_id', None)
            tool_type = step_dict.get('type', None)
            # This requires the workflow input dataset annotation to be a
            # string # (e.g., report) that enables it to be appropriatey
            # matched to a dataset (e.g., axiongt1_report.txt).
            # 1. affy_metadata.tabular - must have the word "metadata" in
            #                            the file name.
            # 2. sample_attributes.tabular - must have the word "attributes"
            #                                in the file name.
            # 3. probeset_annotation.csv - must have the word "annotation" in
            #                              the file name.
            # 4. <summary file>.txt - must have the the word "summary" in the
            #                         file name.
            # 5. <snp-posteriors file>.txt - must have the the word
            #                                "snp-posteriors" in the file name.
            # 6. <report file>.txt - must have the the word "report" in the
            #                        file name.
            # 7. <confidences file>.txt - must have the the word "confidences"
            #                             in the file name.
            # 8. <calls file>.txt - must have the the word "calls" in the
            #                       file name.
            # 9. all_genotyped_samples.vcf - must have "all_genotyped_samples"
            #                                in the file name.
            annotation = step_dict.get('annotation', None)
            if tool_id is None and tool_type == 'data_input' and annotation is not None:
                annotation_check = annotation.lower()
                # inputs is a list and workflow input datasets
                # have no inputs.
                for input_hda_name, input_hda_dict in list(history_datasets.items()):
                    input_hda_name_check = input_hda_name.lower()
                    if input_hda_name_check.find(annotation_check) >= 0:
                        workflow_inputs[step_index] = {'src': 'hda', 'id': input_hda_dict['id']}
                        outputfh.write(" - Mapped dataset %s from history to workflow input dataset with annotation %s.\n" % (input_hda_name, annotation))
                        break
    return workflow_inputs


def start_workflow(gi, workflow_id, workflow_name, inputs, params, history_id, outputfh):
    outputfh.write("\nExecuting workflow %s.\n" % workflow_name)
    workflow_invocation_dict = gi.workflows.invoke_workflow(workflow_id, inputs=inputs, params=params, history_id=history_id)
    outputfh.write("Response from executing workflow %s:\n" % workflow_name)
    outputfh.write("%s\n" % str(workflow_invocation_dict))


def rename_library_dataset(gi, dataset_id, name, outputfh):
    outputfh.write("\nRenaming library dataset with id %s to be named %s.\n" % (str(dataset_id), str(name)))
    library_dataset_dict = gi.libraries.update_library_dataset(dataset_id, name=name)
    return library_dataset_dict


def update_workflow_params(workflow_dict, dbkey, output_nj_phylogeny_tree, outputfh):
    parameter_updates = None
    name = workflow_dict['name']
    outputfh.write("\nChecking for tool parameter updates for workflow %s using dbkey %s.\n" % (name, dbkey))
    step_dicts = workflow_dict.get('steps', None)
    for step_id, step_dict in list(step_dicts.items()):
        tool_id = step_dict['tool_id']
        if tool_id is None:
            continue
        # Handle reference_source entries
        if tool_id.find('affy2vcf') > 0:
            tool_inputs_dict = step_dict['tool_inputs']
            # The queue_genotype_workflow tool provides a selection of only
            # a locally cached reference genome (not a history item), so dbkey
            # will always refer to a locally cached genome.
            # The affy2vcf tool allows the user to select either a locally
            # cached reference genome or a history item, but the workflow is
            # defined to use a locally cached reference genome by default.
            reference_genome_source_cond_dict = tool_inputs_dict['reference_genome_source_cond']
            # The value of reference_genome_source_cond_dict['reference_genome_source']
            # will always be 'cached'.
            workflow_db_key = reference_genome_source_cond_dict['locally_cached_item']
            if dbkey != workflow_db_key:
                reference_genome_source_cond_dict['locally_cached_item'] = dbkey
                if parameter_updates is None:
                    parameter_updates = {}
                parameter_updates[step_id] = reference_genome_source_cond_dict
                outputfh.write("Updated step id %s with the following entry:\n%s\n" % (step_id, str(reference_genome_source_cond_dict)))
        if tool_id.find('coral_multilocus_genotype') > 0 and output_nj_phylogeny_tree == 'yes':
            # Reset the default value 'no' of output_nj_phylogeny_tree to 'yes'.
            if parameter_updates is None:
                parameter_updates = {}
            output_nj_phylogeny_tree_dict = {'output_nj_phylogeny_tree': 'yes'}
            parameter_updates[step_id] = output_nj_phylogeny_tree_dict
            outputfh.write("Updated step id %s with the following entry:\n%s\n" % (step_id, str(output_nj_phylogeny_tree_dict)))
    return parameter_updates


outputfh = open(args.output, "w")
config_defaults = get_config_settings(args.config_file)
user_api_key = open(args.api_key, 'r').read()
admin_api_key = get_value_from_config(config_defaults, 'ADMIN_API_KEY')
galaxy_base_url = get_value_from_config(config_defaults, 'GALAXY_BASE_URL')
gi = galaxy.GalaxyInstance(url=galaxy_base_url, key=user_api_key)
ags_dataset_name = get_value_from_config(config_defaults, 'ALL_GENOTYPED_SAMPLES_DATASET_NAME')
ags_library_name = get_value_from_config(config_defaults, 'ALL_GENOTYPED_SAMPLES_LIBRARY_NAME')
ags_storage_dir = get_value_from_config(config_defaults, 'ALL_GENOTYPED_SAMPLES_STORAGE_DIR')
coralsnp_workflow_name = get_value_from_config(config_defaults, 'CORALSNP_WORKFLOW_NAME')
es_workflow_name = get_value_from_config(config_defaults, 'ENSURE_SYNCED_WORKFLOW_NAME')
fags_workflow_name = get_value_from_config(config_defaults, 'FILTER_ALL_GENOTYPED_SAMPLES_WORKFLOW_NAME')
vam_workflow_name = get_value_from_config(config_defaults, 'VALIDATE_AFFY_METADATA_WORKFLOW_NAME')

affy_metadata_is_valid = False
datasets_have_queued = False
filtered = False
stag_database_updated = False
synced = False
lock = threading.Lock()
lock.acquire(True)
try:
    # Get the current history datasets.  At this point, the
    # history will ideally contain only the datasets to be
    # used as inputs to the 3 workflows, EnsureSynced,
    # ValidateAffyMetadata and CoralSNP.
    history_datasets = get_history_datasets(gi, args.history_id)

    # Get the All Genotyped Samples data library.
    ags_data_library_dict = get_data_library_dict(gi, ags_library_name, outputfh)
    ags_library_id = ags_data_library_dict['id']
    # Get the public all_genotyped_samples.vcf library dataset id.
    ags_ldda_id = get_library_dataset_id_by_name(gi, ags_library_id, ags_dataset_name, outputfh)

    # Import the public all_genotyped_samples dataset from
    # the data library to the current history.
    history_datasets = add_library_dataset_to_history(gi, args.history_id, ags_ldda_id, history_datasets, outputfh)
    outputfh.write("\nSleeping for 5 seconds...\n")
    time.sleep(5)

    # Get the EnsureSynced workflow
    es_workflow_id, es_workflow_dict = get_workflow(gi, es_workflow_name, outputfh)
    outputfh.write("\nEnsureSynced workflow id: %s\n" % str(es_workflow_id))
    # Map the history datasets to the input datasets for
    # the EnsureSynced workflow.
    es_workflow_input_datasets = get_workflow_input_datasets(gi, history_datasets, es_workflow_name, es_workflow_dict, outputfh)
    # Start the EnsureSynced workflow.
    start_workflow(gi, es_workflow_id, es_workflow_name, es_workflow_input_datasets, None, args.history_id, outputfh)
    outputfh.write("\nSleeping for 15 seconds...\n")
    time.sleep(15)
    # Poll the history datasets, checking the statuses, and wait until
    # the workflow is finished.  The workflow itself simply schedules
    # all of the jobs, so it cannot be checked for a state.
    while True:
        history_status_dict = get_history_status(gi, args.history_id)
        sd_dict = history_status_dict['state_details']
        outputfh.write("\nsd_dict: %s\n" % str(sd_dict))
        # The queue_genotype_workflow tool will continue to be in a
        # "running" state while inside this for loop, so  we know that
        # the workflow has completed if only 1 dataset has this state.
        if sd_dict['running'] <= 1:
            if sd_dict['error'] == 0:
                # The all_genotyped_samples.vcf file is
                # in sync with the stag database.
                synced = True
                break
        outputfh.write("\nSleeping for 5 seconds...\n")
        time.sleep(5)

    if synced:
        # Get the ValidateAffyMetadata workflow.
        vam_workflow_id, vam_workflow_dict = get_workflow(gi, vam_workflow_name, outputfh)
        outputfh.write("\nValidateAffyMetadata workflow id: %s\n" % str(vam_workflow_id))
        # Map the history datasets to the input datasets for
        # the ValidateAffyMetadata workflow.
        vam_workflow_input_datasets = get_workflow_input_datasets(gi, history_datasets, vam_workflow_name, vam_workflow_dict, outputfh)
        # Start the ValidateAffyMetadata workflow.
        start_workflow(gi, vam_workflow_id, vam_workflow_name, vam_workflow_input_datasets, None, args.history_id, outputfh)
        outputfh.write("\nSleeping for 15 seconds...\n")
        time.sleep(15)
        # Poll the history datasets, checking the statuses, and wait until
        # the workflow is finished.
        while True:
            history_status_dict = get_history_status(gi, args.history_id)
            sd_dict = history_status_dict['state_details']
            outputfh.write("\nsd_dict: %s\n" % str(sd_dict))
            # The queue_genotype_workflow tool will continue to be in a
            # "running" state while inside this for loop, so  we know that
            # the workflow has completed if only 1 dataset has this state.
            if sd_dict['running'] <= 1:
                if sd_dict['error'] == 0:
                    # The metadata is valid.
                    affy_metadata_is_valid = True
                    break
            outputfh.write("\nSleeping for 5 seconds...\n")
            time.sleep(5)
    else:
        outputfh.write("\nProcessing ended in error...\n")
        outputfh.close()
        lock.release()
        sys.exit(1)

    if affy_metadata_is_valid:
        # Get the CoralSNP workflow.
        coralsnp_workflow_id, coralsnp_workflow_dict = get_workflow(gi, coralsnp_workflow_name, outputfh)
        outputfh.write("\nCoralSNP workflow id: %s\n" % str(coralsnp_workflow_id))
        # Map the history datasets to the input datasets for
        # the CoralSNP workflow.
        coralsnp_workflow_input_datasets = get_workflow_input_datasets(gi, history_datasets, coralsnp_workflow_name, coralsnp_workflow_dict, outputfh)
        outputfh.write("\nCoralSNP workflow input datasets: %s\n" % str(coralsnp_workflow_input_datasets))
        # Get the CoralSNP workflow params that could be updated.
        coralsnp_params = update_workflow_params(coralsnp_workflow_dict, args.dbkey, args.output_nj_phylogeny_tree, outputfh)
        outputfh.write("\nCoralSNP params: %s\n" % str(coralsnp_params))
        # Start the CoralSNP workflow.
        start_workflow(gi, coralsnp_workflow_id, coralsnp_workflow_name, coralsnp_workflow_input_datasets, coralsnp_params, args.history_id, outputfh)
        outputfh.write("\nSleeping for 15 seconds...\n")
        time.sleep(15)
        # Poll the history datasets, checking the statuses, and wait until
        # the workflow is finished.  The workflow itself simply schedules
        # all of the jobs, so it cannot be checked for a state.
        while True:
            history_status_dict = get_history_status(gi, args.history_id)
            sd_dict = history_status_dict['state_details']
            outputfh.write("\ndatasets_have_queued: %s\n" % str(datasets_have_queued))
            outputfh.write("\nsd_dict: %s\n" % str(sd_dict))
            # The queue_genotype_workflow tool will continue to be in a
            # "running" state while inside this for loop, so  we know that
            # the workflow has completed if no datasets are in the "new" or
            # "queued" state and there is only 1 dataset in the "running"
            # state.  We cannot filter on datasets in the "paused" state
            # because any datasets downstream from one in an "error" state
            # will automatically be given a "paused" state. Of course, we'll
            # always break if any datasets are in the "error" state.  At
            # least one dataset must have reached the "queued" state before
            # the workflow is complete.
            if not datasets_have_queued:
                if sd_dict['queued'] > 0:
                    datasets_have_queued = True
            if sd_dict['error'] != 0:
                break
            if datasets_have_queued and sd_dict['queued'] == 0 and sd_dict['new'] == 0 and sd_dict['running'] <= 1:
                # The stag database has been updated.
                stag_database_updated = True
                break
            outputfh.write("\nSleeping for 5 seconds...\n")
            time.sleep(5)
        outputfh.write("\nstag_database_updated: %s\n" % str(stag_database_updated))
        if stag_database_updated:
            # Get the id of the "bcftools merge" dataset in the current history.
            bcftools_merge = get_history_dataset_id_by_name(gi, args.history_id, "bcftools merge", outputfh)
            # Get the FilterAllGenotypedSamples workflow
            fags_workflow_id, fags_workflow_dict = get_workflow(gi, fags_workflow_name, outputfh)
            outputfh.write("\nFilterAllGenotypedSamples workflow id: %s\n" % str(fags_workflow_id))
            # Map the history datasets to the input datasets for
            # the FilterAllGenotypedSamples workflow.
            fags_workflow_input_datasets = get_workflow_input_datasets(gi, history_datasets, fags_workflow_name, fags_workflow_dict, outputfh)
            # Start the FilterAllGenotypedSamples workflow.
            start_workflow(gi, fags_workflow_id, fags_workflow_name, fags_workflow_input_datasets, None, args.history_id, outputfh)
            outputfh.write("\nSleeping for 15 seconds...\n")
            time.sleep(15)
            # Poll the history datasets, checking the statuses, and wait until
            # the workflow is finished.  The workflow itself simply schedules
            # all of the jobs, so it cannot be checked for a state.
            while True:
                history_status_dict = get_history_status(gi, args.history_id)
                sd_dict = history_status_dict['state_details']
                outputfh.write("\nsd_dict: %s\n" % str(sd_dict))
                # The queue_genotype_workflow tool will continue to be in a
                # "running" state while inside this for loop, so  we know that
                # the workflow has completed if only 1 dataset has this state.
                if sd_dict['running'] <= 1:
                    if sd_dict['error'] == 0:
                        # The all_genotyped_samples.vcf file is filtered.
                        filtered = True
                        break
                outputfh.write("\nSleeping for 5 seconds...\n")
                time.sleep(5)
        outputfh.write("\nfiltered: %s\n" % str(filtered))
        if filtered:
            # Get the id of the "bcftools view" dataset in the current history.
            bcftools_view = get_history_dataset_id_by_name(gi, args.history_id, "bcftools view", outputfh)
            # Create a new dataset in the All Genotyped Samples data library by
            # importing the "bcftools view" dataset from the current history.
            # We'll do this as the coraldmin user.
            admin_gi = galaxy.GalaxyInstance(url=galaxy_base_url, key=admin_api_key)
            new_ags_dataset_dict = copy_history_dataset_to_library(admin_gi, ags_library_id, bcftools_view, outputfh)
            outputfh.write("\nnew_ags_dataset_dict: %s\n" % str(new_ags_dataset_dict))
            # Rename the ldda to be all_genotyped_samples.vcf.
            new_ags_ldda_id = new_ags_dataset_dict['id']
            outputfh.write("\nnew_ags_ldda_id: %s\n" % str(new_ags_ldda_id))
            renamed_ags_dataset_dict = rename_library_dataset(admin_gi, new_ags_ldda_id, ags_dataset_name, outputfh)
            outputfh.write("\nrenamed_ags_dataset_dict: %s\n" % str(renamed_ags_dataset_dict))
            # Get the full path of the all_genotyped_samples.vcf library dataset.
            ags_ldda_file_path = get_library_dataset_file_path(gi, ags_library_id, ags_ldda_id, outputfh)
            outputfh.write("\nags_ldda_file_path: %s\n" % str(ags_ldda_file_path))
            # Copy the all_genotyped_samples.vcf dataset to storage.  We
            # will only keep a single copy of this file since this tool
            # will end in an error before the CoralSNP workflow is started
            # if the all_genotyped_samples.vcf file is not sync'd with the
            # stag database.
            copy_dataset_to_storage(ags_ldda_file_path, ags_storage_dir, ags_dataset_name, outputfh)
            outputfh.write("\naCopied gs_ldda_file_path: %s to ags_storage_dir %s\n" % (str(ags_ldda_file_path), str(ags_storage_dir)))
            # Delete the original all_genotyped_samples library dataset.
            deleted_dataset_dict = delete_library_dataset(admin_gi, ags_library_id, ags_ldda_id, outputfh)
            outputfh.write("\ndeleted_dataset_dict: %s\n" % str(deleted_dataset_dict))
            # To save disk space, delete the all_genotyped_samples hda
            # in the current history to enable later purging by an admin.
            ags_hda_id = get_history_dataset_id_by_name(gi, args.history_id, "all_genotyped_samples", outputfh)
            outputfh.write("\nags_hda_id: %s\n" % str(ags_hda_id))
            delete_history_dataset(gi, args.history_id, ags_hda_id, outputfh)
        else:
            outputfh.write("\nProcessing ended in error...\n")
            outputfh.close()
            lock.release()
            sys.exit(1)
    else:
        outputfh.write("\nProcessing ended in error...\n")
        outputfh.close()
        lock.release()
        sys.exit(1)
except Exception as e:
    outputfh.write("Exception preparing or executing either the ValidateAffyMetadata workflow or the CoralSNP workflow:\n%s\n" % str(e))
    outputfh.write("\nProcessing ended in error...\n")
    outputfh.close()
    lock.release()
    sys.exit(1)
finally:
    lock.release()

outputfh.write("\nFinished processing...\n")
outputfh.close()