s.identifier,
authors=[s.contributor],
affiliations=[s.group],
publication_year=experiment_date.year)
mdfcc.add_data(box_file.get_shared_link_download_url(access='open'))
# Don't publish specific recipes. Later on, we will bundle datasets and
# and publish an omnibus dataset
# mdfcc.add_service("globus_publish")
mdfcc.set_source_name(s.sample_id)
submission = mdfcc.get_submission()
submission["projects"] = {}
submission["projects"]["nanomfg"] = {
"catalyst": s.catalyst,
"max_temperature": max_temp(s),
"carbon_source": carbon_source(s),
"base_pressure": s.base_pressure,
"sample_surface_area": s.sample_surface_area,
"sample_thickness": s.thickness,
"orientation": "",
"grain_size": ""
}
mdf_source_id = mdfcc.submit_dataset(submission=submission)
print("Submitted to MDF -----> " + str(mdf_source_id))
# print(str(mdfcc.check_status()))
mdfcc.reset_submission()
class MDFAdaptor:
def __init__(self):
self.mdfcc = MDFConnectClient(test=True, service_instance="prod")
def upload_recipe(self, recipe, box_file):
experiment_date = datetime.now()
self.mdfcc.create_dc_block(
title="Graphene Synthesis Sample " + "TBD",
authors=[
"%s, %s" % (auth["last_name"], auth["first_name"])
for auth in recipe.authors
],
affiliations=[auth["institution"] for auth in recipe.authors],
publication_year=recipe.experiment_year,
)
self.mdfcc.add_data_source(
box_file.get_shared_link_download_url(access="open"))
# Don't publish specific recipes. Later on, we will bundle datasets and
# and publish an omnibus dataset
# mdfcc.add_service("globus_publish")
self.mdfcc.set_source_name(str(uuid.uuid4()))
submission = self.mdfcc.get_submission()
submission["projects"] = {}
submission["projects"]["nanomfg"] = {
"catalyst": recipe.catalyst,
"max_temperature": recipe.max_temp(),
"carbon_source": recipe.carbon_source(),
"base_pressure": recipe.base_pressure
# "sample_surface_area": recipe.sample_surface_area,
# "sample_thickness": recipe.thickness,
# "orientation": "",
# "grain_size": ""
}
print("\n\n\n\n------>", submission)
try:
mdf_result = self.mdfcc.submit_dataset(submission=submission)
except Exception as e:
print("Exception submitting dataset to mdf ", str(e))
raise MDFException(e)
if not mdf_result["success"]:
self.mdfcc.reset_submission()
print("\n\n\n--->Error-----> " + mdf_result["error"])
raise MDFException(mdf_result["error"])
print("Submitted to MDF -----> " + str(mdf_result))
self.mdfcc.reset_submission()
return mdf_result["source_id"]
def upload_raman_analysis(self, recipe, recipe_dataset_id, raman_set,
raman_box_file):
self.mdfcc.create_dc_block(
title="Graphene Synthesis Raman Analysis",
authors=[
"%s, %s" % (auth["last_name"], auth["first_name"])
for auth in recipe.authors
],
affiliations=[auth["institution"] for auth in recipe.authors],
publication_year=recipe.experiment_year,
related_dois=recipe_dataset_id,
)
self.mdfcc.add_data_source(
raman_box_file.get_shared_link_download_url(access="open"))
self.mdfcc.set_source_name(str(uuid.uuid4()))
submission = self.mdfcc.get_submission()
submission["projects"] = {}
submission["projects"]["nanomfg"] = {
"d_to_g": raman_set["d_to_g"],
"gp_to_g": raman_set["gp_to_g"],
"d_peak_shift": raman_set["d_peak_shift"],
"d_peak_amplitude": raman_set["d_peak_amplitude"],
"d_fwhm": raman_set["d_fwhm"],
"g_peak_shift": raman_set["g_peak_shift"],
"g_peak_amplitude": raman_set["g_peak_amplitude"],
"g_fwhm": raman_set["g_fwhm"],
"g_prime_peak_shift": raman_set["g_prime_peak_shift"],
"g_prime_peak_amplitude": raman_set["g_prime_peak_amplitude"],
"g_prime_fwhm": raman_set["g_prime_fwhm"],
}
print("\n\n\n\n------>", submission)
try:
mdf_result = self.mdfcc.submit_dataset(submission=submission)
except Exception as e:
print("Exception submitting raman analysis dataset to mdf ",
str(e))
raise MDFException(e)
if not mdf_result["success"]:
self.mdfcc.reset_submission()
print("\n\n\n--->Error-----> " + mdf_result["error"])
raise MDFException(mdf_result["error"])
print("Submitted raman analysis to MDF -----> " + str(mdf_result))
self.mdfcc.reset_submission()
return mdf_result["source_id"]