def post(self):
try:
args = chart_parser.parse_args()
selected_genes = args["gene_set"]
selected_dataset = args["dataset_identifier"]
selected_charttype = args["chart_type"]
print args["chart_type"]
request_to_handle = request.accept_mimetypes.best_match(['application/pdf', 'text/html'])
if request_to_handle == 'application/pdf':
accept = "pdf"
else:
accept = "html"
chart_resource = nc.sample_distribution_for_genes(selected_dataset, selected_genes,
title=selected_dataset,
chart_type=selected_charttype,
chart_rendering=accept)
# returns the write rendering based on generator [bokeh with bokehjs or matplot with mpld3]
if accept == "html":
if chart_resource["generator"] == "bokeh":
return cr.BokehResource(chart_resource["chart"]), 201
if chart_resource["generator"] == "matplot":
return cr.MatPlotLibResource(chart_resource["chart"]), 201
if accept == "pdf":
filename = ig.get_generated_guid_as_string() + ".pdf"
file_path = APP_CONFIG["domain"] + APP_CONFIG["static_documents"]
chart_resource["chart"].savefig("." + APP_CONFIG["static_documents"] + filename)
return cr.StringApiResource(file_path + filename), 201
except:
return cr.StringApiResource("Chart creation failed"), 400
def normalize_gene_counts(data, method, threshold=None, target_identifier="", experiment_identifier=""):
# if exists -> get from db
processing_result = cn.bioconductor_normalization(data, method)
new_intern_identifier = identifier_generator.get_generated_guid_as_string()
hdf_storage.store_hdf(processing_result.frame, new_intern_identifier)
print("compressed pytable storage done")
package = mr.get_package_by_name_and_version(processing_result.package,
processing_result.version).public_identifier
dataset = dr.create_data_set(new_intern_identifier, package + "_" + target_identifier, "genes normalized dataset",
package_identifier=package,
experiment_identifier=experiment_identifier)
print("new dataset saved")
#er.link_dataset_to_experiment(experiment_identifier, dataset.public_identifier)
print("new dataset linked")
return dataset
def post(self):
args = job_parser.parse_args()
if args.local:
try:
print args.compare_datasets
if args.compare_datasets is not None:
return cr.MatPlotLibResource(dp.plot_comparison(args.compare_datasets, args.features, args.chart_type))
except:
return cr.StringApiResource("There was an issue when comparing data sets, check for identifiers or if the lists are equal")
method = args.method_identifier
dataset = args.dataset_identifier
min_distance = args.min_distance
min_samples = args.min_samples
print(args.experiment_identifier)
res = pr.create_mining_task(method + "_" + dataset + "_" + ig.get_generated_guid_as_string(),
method, dataset, min_sample=min_samples, min_distance=min_distance,
experiment_identifier=args.experiment_identifier)
return cr.JsonResource(eto.JobView(res).to_json()), 201
def post(self):
args = dataset_parser.parse_args()
print("dataset post received")
# upload new dataset
if args.dataset_identifier is None:
print("creating new data set")
new_file = args.file
filename = secure_filename(new_file.filename)
print("uploaded" + filename)
intern_identifier = ig.get_generated_guid_as_string()
new_file.save(filename)
try:
with open(filename, "rb") as fl:
frame = dr.get_data_frame_from_csv(fl)
intern_location = dr.store_data_frame_to_hdf(frame, intern_identifier)
# raw counts by default
data_entity = dr.create_data_set(intern_identifier,
public_identifier=filename,
dataset_type="raw gene counts",
experiment_identifier="raw_data_container") # For demo -> add exp identifier
server_hash = ig.md5_for_file(fl)
print(filename + " is saved")
print("file removed")
return cr.JsonResource(
{"filename": filename, "intern_identifier": data_entity.intern_identifier,
"public_identifier": data_entity.public_identifier, "server_md5": server_hash}), 201
except IntegrityError:
return cr.StringApiResource("Public identifier already taken"), 409
except:
return cr.StringApiResource("An error has occured, check if your data set comply with the expected format"), 400
finally:
if filename is not None:
os.remove(filename)
else:
# a new dataset is created based on source
try:
print("pre-processing existing dataset")
source_data = args.dataset_identifier
print("source data : " + source_data)
# target_data = args.target_dataset_identifier
# TODO link to experiment identifier!
# experiment_identifier = args.experiment_identifier
method_identifier = args.preprocessing_method_identifier
print("source " + source_data + "method " + method_identifier)
path = APP_CONFIG["application_files_location"] + APP_CONFIG["application_store_name"]
df = dr.get_data_frame_from_hdf(source_data, path)
print("data frame is loaded")
new_data_set = ed.normalize_gene_counts(df, method_identifier,
target_identifier=source_data,
experiment_identifier=args.experiment_identifier)
return cr.JsonResource(eto.SummaryDatasetView(new_data_set).to_json()), 201
except Exception as e:
print(e.__str__())
return cr.StringApiResource("Explosion! Tool down..."), 400
