def validate_bubbleplot_input(_id, feature_table_artifact_path, taxonomy_artifact_path, metadata_path=None, fill_variable=None): """ Do prechecks as to decrease the chance of job failing. Input: - feature_table_artifact_path: feature table in QIIME2 artifact format (either path or FileStorage object) - taxonomy_artifact_path: taxonomy in QIIME2 artifact format (either path or FileStorage object) """ # Save uploaded file in the docker container feature_table_uploaded_path = save_uploaded_file(_id, feature_table_artifact_path) taxonomy_uploaded_path = save_uploaded_file(_id, taxonomy_artifact_path) if(metadata_path is not None): metadata_uploaded_path = save_uploaded_file(_id, metadata_path) else: metadata_uploaded_path = None def validate_artifact(feature_table_uploaded_path, taxonomy_uploaded_path): # Check Artifact type try: check_artifact_type(feature_table_uploaded_path, "feature_table") check_artifact_type(taxonomy_uploaded_path, "taxonomy") except AXIOME3PipelineError as err: message = str(err) return 400, message return 200, "Imported data good!" def validate_metadata(metadata_uploaded_path, fill_variable): # Load metadata via QIIME2 metadata API # It will verify metadata vadlity as well try: metadata_df = load_metadata(metadata_uploaded_path) except MetadataFileError as err: message = str(err) return 400, message # Check user-specified columns actually exist in the metadata file if(fill_variable is not None): try: check_column_exists(metadata_df, fill_variable) except AXIOME3PipelineError as err: message = str(err) return 400, message return 200, "Ok" responseIfError(validate_artifact, feature_table_uploaded_path=feature_table_uploaded_path, taxonomy_uploaded_path=taxonomy_uploaded_path) if(metadata_uploaded_path is not None): responseIfError(validate_metadata, metadata_uploaded_path=metadata_uploaded_path, fill_variable=fill_variable) return feature_table_uploaded_path, taxonomy_uploaded_path, metadata_uploaded_path
def validate_triplot_input(_id, feature_table_artifact_path, taxonomy_artifact_path, metadata_path, environmental_metadata_path, fill_variable): # Save uploaded file in the docker container feature_table_uploaded_path = save_uploaded_file(_id, feature_table_artifact_path) taxonomy_uploaded_path = save_uploaded_file(_id, taxonomy_artifact_path) metadata_uploaded_path = save_uploaded_file(_id, metadata_path) environmental_metadata_uploaded_path = save_uploaded_file(_id, environmental_metadata_path) def validate_metadata(metadata_path, environmental_metadata_path): # Load metadata via QIIME2 metadata API # It will verify metadata vadlity as well try: metadata_df = load_metadata(metadata_path) except MetadataFileError as err: message = str(err) return 400, message try: environmental_metadata_df = load_metadata(environmental_metadata_path) except MetadataFileError as err: message = str(err) return 400, message # Check user-specified columns actually exist in the metadata file try: check_column_exists(metadata_df, fill_variable) except AXIOME3PipelineError as err: message = str(err) return 400, message return 200, "Ok" def validate_artifact(feature_table_uploaded_path, taxonomy_uploaded_path): # Check Artifact type try: check_artifact_type(feature_table_uploaded_path, "feature_table") check_artifact_type(taxonomy_uploaded_path, "taxonomy") except AXIOME3PipelineError as err: message = str(err) return 400, message return 200, "Imported data good!" responseIfError(validate_metadata, metadata_path=metadata_uploaded_path, environmental_metadata_path=environmental_metadata_uploaded_path) responseIfError(validate_artifact, feature_table_uploaded_path=feature_table_uploaded_path, taxonomy_uploaded_path=taxonomy_uploaded_path) return feature_table_uploaded_path, taxonomy_uploaded_path, metadata_uploaded_path, environmental_metadata_uploaded_path
def analysis_precheck(_id, feature_table, rep_seqs, taxonomy, metadata):
"""
Do prechecks as to decrease the chance of job failing.
Input:
- feature_table: QIIME2 artifact of type FeatureTable[Frequency]
- rep_seqs: QIIME2 artifact of type FeatureData[Sequence]
"""
feature_table_path = save_uploaded_file(_id, feature_table)
rep_seqs_path = save_uploaded_file(_id, rep_seqs)
taxonomy_path = save_uploaded_file(_id, taxonomy)
metadata_path = save_uploaded_file(_id, metadata)
def validate_analysis_input(feature_table, rep_seqs, taxonomy):
"""
Precheck input files prior to running denoise step
Input:
- feature_table: Path to QIIME2 artifact of type FeatureTable[Frequency]
- rep_seqs: Path to QIIME2 artifact of type FeatureData[Sequence]
"""
# Check Artifact type
try:
feature_table_artifact = Artifact.load(feature_table)
rep_seqs_artifact = Artifact.load(rep_seqs)
if (str(feature_table_artifact.type) != "FeatureTable[Frequency]"):
msg = "Input Feature Table is not of type 'FeatureTable[Frequency]'!"
raise ValueError(msg)
if (str(rep_seqs_artifact.type) != "FeatureData[Sequence]"):
msg = "Input Representative Sequences is not of type 'FeatureData[Sequence]'!"
raise ValueError(msg)
except ValueError as err:
message = str(err)
return 400, message
return 200, "Imported data good!"
responseIfError(validate_analysis_input,
feature_table=feature_table_path,
rep_seqs=rep_seqs_path,
taxonomy=taxonomy_path)
return feature_table_path, rep_seqs_path, taxonomy_path, metadata_path
def taxonomic_classification_precheck(_id, feature_table, rep_seqs, classifier=None): """ Do prechecks as to decrease the chance of job failing. Input: - feature_table: QIIME2 artifact of type FeatureTable[Frequency] - rep_seqs: QIIME2 artifact of type FeatureData[Sequence] """ feature_table_path = save_uploaded_file(_id, feature_table) rep_seqs_path = save_uploaded_file(_id, rep_seqs) # default classifier path default_classifier_path = current_app.config["DEFAULT_CLASSIFIER_PATH"] classifier_path = save_uploaded_file(_id, classifier) if classifier is not None else default_classifier_path def validate_taxonomic_classification_input(feature_table, rep_seqs): """ Precheck input files prior to running denoise step Input: - feature_table: Path to QIIME2 artifact of type FeatureTable[Frequency] - rep_seqs: Path to QIIME2 artifact of type FeatureData[Sequence] """ # Check Artifact type try: feature_table_artifact = Artifact.load(feature_table) rep_seqs_artifact = Artifact.load(rep_seqs) if(str(feature_table_artifact.type) != "FeatureTable[Frequency]"): msg = "Input Feature Table is not of type 'FeatureTable[Frequency]'!" raise ValueError(msg) if(str(rep_seqs_artifact.type) != "FeatureData[Sequence]"): msg = "Input Representative Sequences is not of type 'FeatureData[Sequence]'!" raise ValueError(msg) except ValueError as err: message = str(err) return 400, message return 200, "Imported data good!" responseIfError(validate_taxonomic_classification_input, feature_table=feature_table_path, rep_seqs=rep_seqs_path) return feature_table_path, rep_seqs_path, classifier_path
def validate_pcoa_input(_id, pcoa_artifact_path, metadata_path, target_primary, target_secondary=None): metadata_uploaded_path = save_uploaded_file(_id, metadata_path) # Save QIIME2 PCoA artiffact pcoa_uploaded_path = save_uploaded_file(_id, pcoa_artifact_path) def validate_metadata(metadata_path, target_primary, target_secondary): # Load metadata via QIIME2 metadata API # It will verify metadata vadlity as well try: metadata_df = load_metadata(metadata_path) except MetadataFileError as err: message = str(err) return 400, message # Check user-specified columns actually exist in the metadata file try: check_column_exists(metadata_df, target_primary, target_secondary) except AXIOME3PipelineError as err: message = str(err) return 400, message return 200, "Ok" def validate_artifact(pcoa_artifact_path): try: check_artifact_type(pcoa_artifact_path, "pcoa") except AXIOME3PipelineError as err: message = str(err) return 400, message return 200, "OK" responseIfError(validate_metadata, metadata_path=metadata_uploaded_path, target_primary=target_primary, target_secondary=target_secondary) responseIfError(validate_artifact, pcoa_artifact_path=pcoa_uploaded_path) return pcoa_uploaded_path, metadata_uploaded_path
def denoise_precheck(_id, sequence_data):
"""
Do prechecks as to decrease the chance of job failing.
Input:
- sequence_data: sequence data in QIIME2 artifact format
"""
# Save uploaded file in the docker container
upload_path = save_uploaded_file(_id, sequence_data)
def validate_denoise_input(sequence_data):
"""
Precheck input files prior to running denoise step
Input:
- sequence_data: sequence data in QIIME2 artifact format
"""
# Check Artifact type
try:
q2_artifact = Artifact.load(sequence_data)
if (str(q2_artifact.type) !=
"SampleData[PairedEndSequencesWithQuality]"):
msg = "Input QIIME2 Artifact is not of type 'SampleData[PairedEndSequencesWithQuality]'!"
raise ValueError(msg)
except ValueError as err:
message = str(err)
return 400, message
return 200, "Imported data good!"
responseIfError(validate_denoise_input, sequence_data=upload_path)
return upload_path