def formulation_instance_model_collection_train_analysis_service(f_id):
resp = flask.Response(json.dumps({'status': 'failed'}))
if request.args.get('action') == 'train':
training_uuid = str(uuid.uuid1()).replace('-', '')[:8]
logging_uuid = str(uuid.uuid1()).replace('-', '')[:8]
fit_model_task.delay(f_id,
training_uuid,
logging_uuid,
epochs=int(request.args.get('epochs')))
resp = flask.Response(
json.dumps({
'status': 'success',
'formulation_id': f_id,
'training_uuid': training_uuid,
'logging_uuid': logging_uuid
}))
elif request.args.get('action') == 'getPlotData':
resp = flask.Response(r.get(request.args.get('redisTrainingTaskID')))
elif request.args.get('action') == 'getModelList':
fdm = FormulationDataModel(f_id)
saved_model_list = fdm.get_saved_model_list()
if len(saved_model_list) > 0:
resp = flask.Response({'saved_model_list': saved_model_list})
elif request.args.get('action') == 'saveToDB':
fdm = FormulationDataModel(f_id,
model_name=request.args.get('modelName'))
fdm.save_grid_to_db()
resp = flask.Response({'status': 'success'})
return set_debug_response_header(resp)
def formulation_instance_model_instance_analysis_service(f_id, model_name):
fdm = FormulationDataModel(f_id, model_name=model_name)
data_traces, grid_traces = fdm.get_formulation_predict_data()
resp = flask.Response(
json.dumps({
'status': 'success',
'formulation_id': f_id,
'data_traces': data_traces,
'grid_traces': grid_traces,
'model_name': model_name
}))
return set_debug_response_header(resp)
def formulation_instance_data_collection_analysis_service(f_id):
resp = flask.Response(json.dumps({'status': 'failed'}))
if request.method == 'GET':
test_rs = Formulation.query.get(f_id).test
lines = []
for test_r in test_rs:
td_rs = test_r.test_data.order_by(TestData.x_value)
xt, yt, zt = [], [], []
xe, ye, ze = [], [], []
if test_r.measure_type == 'temperature':
for td_r in td_rs:
if td_r.data_type == 'Tan Delta':
xt.append(td_r.x_value)
yt.append(test_r.frequency_min)
zt.append(td_r.y_value)
else:
xe.append(td_r.x_value)
ye.append(test_r.frequency_min)
ze.append(td_r.y_value)
else:
for td_r in td_rs:
if td_r.data_type == 'Tan Delta':
xt.append(test_r.temperature_min)
yt.append(td_r.x_value)
zt.append(td_r.y_value)
else:
xe.append(test_r.temperature_min)
ye.append(td_r.x_value)
ze.append(td_r.y_value)
line = {
'xt': xt,
'yt': yt,
'zt': zt,
'xe': xe,
'ye': ye,
'ze': ze,
'name': test_r.name,
'id': test_r.id
}
lines.append(line)
resp = flask.Response(
json.dumps({
'status': 'success',
'lines': lines,
'formulation_id': f_id,
}))
return set_debug_response_header(resp)
def formulation_instance_log_collection_analysis_service(f_id):
resp = flask.Response(r.get(request.args.get('redisLoggingTaskID')))
return set_debug_response_header(resp)