import os, json, requests, h5py from getpass import getpass from APIHandler import APIHandler from classes import * def call_entrypoint_from_elabid(elabid): ''' Calls an entrypoint sample from eLabFTW using its elabid, then returns an object of the Entrypoint class. If the entry is not a sample (category_title not matching exactly "Sample") returns ValueError. ''' try: sample_data = APIHandler(apikey).get_entry_from_elabid(elabid, entryType="items") if not sample_data.get("category_title") == "Sample": raise ValueError("The resource you selected is not a sample, therefore it can't be used as an entrypoint.") sample_object = Entrypoint(sample_data) except ConnectionError as e: raise ConnectionError(e) return sample_object # Entrypoint-class object def call_material_from_elabid(elabid): ''' Calls a material from eLabFTW using its elabid, then returns an object of the Material class. If the entry is neither a PLD Target or a Substrate batch returns ValueError. Such entries always have a category_title key with its value matching exactly "PLD Target" or "Substrate". Because of an old typo, the value "Subtrate" (second 's' is missing) is also accepted. ''' try: material_data = APIHandler(apikey).get_entry_from_elabid(elabid, entryType="items") material_category = material_data.get("category_title") # TO-DO: correct this typo on elabftw: Subtrate → Substrate. if not material_category in ["PLD Target", "Substrate", "Subtrate"]: print(f"Category of the resource: {material_category}.") raise ValueError(f"The referenced resource (elabid = {elabid}) is not a material.") elif material_category == "PLD Target": material_object = Target(material_data) else: material_object = Substrate(material_data) except ConnectionError as e: raise ConnectionError(e) return material_object # Material-class object def call_layers_from_list(elabid_list): ''' Calls a list of (PLD deposition) experiments from eLabFTW using their elabid - which means the input must be a list of integers instead of a single one - then returns a list of Layer-class objects. If one of the entries is not related to a deposition layer (category_title not matching exactly "PLD Deposition") that entry is skipped, with no error raised. ''' list_of_layers = [] for elabid in elabid_list: try: layer_data = APIHandler(apikey).get_entry_from_elabid(elabid, entryType="experiments") if not layer_data.get("category_title") == "PLD Deposition": continue layer_object = Layer(layer_data) list_of_layers.append(layer_object) except ConnectionError as e: nums = [ layer.layer_number for layer in list_of_layers ] nums.sort() print(f"LIST OF THE LAYERS PROCESSED (unordered):\n" + str(nums)) raise ConnectionError(f"An error occurred while fetching the experiment with elabid = {elabid}:\n" + str(e) + f"\nPlease solve the problem before retrying." + "\n\n" + f"Last resource attempted to call: {ELABFTW_API_URL}/experiments/{elabid}" ) return list_of_layers # list of Layer-class objects def chain_entrypoint_to_batch(sample_object): ''' Takes an Entrypoint-class object, looks at its .batch_elabid attribute and returns a Material-class object containing data on the substrate batch associated to the starting sample. Dependency: call_material_from_elabid. ''' material_elabid = sample_object.batch_elabid material_object = call_material_from_elabid(material_elabid) return material_object def chain_entrypoint_to_layers(sample_object): ''' Takes an Entrypoint-class object, looks at its .linked_experiments_elabid attribute (list) and returns a list of Layer-class objects containing data on the deposition layers associated to the starting sample - using the function call_layers_from_list. The list is sorted by progressive layer number (layer_number attribute). Dependency: call_layers_from_list. ''' linked_experiments_elabid = sample_object.linked_experiments_elabid # list of elabid layer_object_list = call_layers_from_list(linked_experiments_elabid) layer_object_list.sort(key=lambda x: x.layer_number) return layer_object_list def chain_layer_to_target(layer_object): ''' Takes a Layer-class object, looks at its .target_elabid attribute and returns a Material-class object containing data on the PLD target used in the deposition of said layer. Dependency: call_material_from_elabid. ''' target_elabid = layer_object.target_elabid material_object = call_material_from_elabid(target_elabid) return material_object def deduplicate_instruments_from_layers(layers): ''' Takes a list of Layer-class objects and for each layer gets the instruments used (laser, depo chamber and RHEED), returns deduplicated list. Ideally, the lists should only contain one element. ''' lasers = [] chambers = [] rheeds = [] for lyr in layers: instruments = lyr.get_instruments(apikey) lasers.append(instruments["laser_system"]) chambers.append(instruments["deposition_chamber"]) rheeds.append(instruments["rheed_system"]) instruments_used_dict = { "laser_system": list( set( lasers ) ), "deposition_chamber": list( set( chambers ) ), "rheed_system" : list( set( rheeds ) ), } # lasers = { f"layer_{lyr.layer_number}": lyr.laser_system for lyr in layers } # chambers = { f"layer_{lyr.layer_number}": lyr.deposition_chamber for lyr in layers } # rheeds = { f"layer_{lyr.layer_number}": lyr.rheed_system for lyr in layers } # instruments_used_dict = { # "laser_system": lasers, # "deposition_chamber": chambers, # "rheed_system": rheeds, # } return instruments_used_dict def make_nexus_schema_dictionary(substrate_object, layers): ''' Main function, takes all the other functions to reconstruct the full dataset. Takes a Substrate-class object (output of the chain_entrypoint_to_batch() function) and a list of Layer-class objects (output of the chain_entrypoint_to_layers() function), returns dictionary with the same schema as the NeXus standard for PLD fabrications. ''' pld_fabrication = { "sample": { "substrate": { "name": substrate_object.name, "chemical_formula" : substrate_object.get_compound_formula(apikey), "orientation" : substrate_object.orientation, "miscut_angle" : { "value": substrate_object.miscut_angle, "units": substrate_object.miscut_angle_unit }, "miscut_direction" : substrate_object.miscut_direction, "thickness" : { "value": substrate_object.thickness, "units": substrate_object.thickness_unit, }, "dimensions" : substrate_object.dimensions, "surface_treatment" : substrate_object.surface_treatment, "manufacturer" : substrate_object.manufacturer, "batch_id" : substrate_object.batch_id, }, "multilayer": {}, }, "instruments_used": deduplicate_instruments_from_layers(layers), } multilayer = pld_fabrication["sample"]["multilayer"] for layer in layers: name = "layer_" + layer.layer_number target_object = chain_layer_to_target(layer) target_dict = { "name": target_object.name, "chemical_formula" : target_object.get_compound_formula(apikey), "description" : target_object.description, "shape" : target_object.shape, "dimensions" : target_object.dimensions, "thickness" : { "value": target_object.thickness, "units": target_object.thickness_unit, }, "solid_form" : target_object.solid_form, "manufacturer" : target_object.manufacturer, "batch_id" : target_object.name, # TO-DO: currently not available: } multilayer[name] = { "target": target_dict, "start_time": layer.start_time, "operator": layer.operator, "description": layer.description, "number_of_pulses": layer.number_of_pulses, "deposition_time": { "value": layer.deposition_time, "units": layer.deposition_time_unit, }, "temperature": { "value": layer.temperature, "units": layer.temperature_unit, }, "heating_method": layer.heating_method, "layer_thickness": { "value": layer.layer_thickness, "units": layer.layer_thickness_unit, }, "buffer_gas": layer.buffer_gas, "process_pressure": { "value": layer.process_pressure, "units": layer.process_pressure_unit, }, "heater_target_distance": { "value": layer.heater_target_distance, "units": layer.heater_target_distance_unit, }, "repetition_rate": { "value": layer.repetition_rate, "units": layer.repetition_rate_unit, }, "laser_fluence": { "value": layer.laser_fluence, "units": layer.laser_fluence_unit, }, "laser_spot_area": { "value": layer.laser_spot_area, "units": layer.laser_spot_area_unit, }, "laser_energy": { "value": layer.laser_energy, "units": layer.laser_energy_unit, }, "laser_rastering": { "geometry": layer.laser_rastering_geometry, "positions": layer.laser_rastering_positions, "velocities": layer.laser_rastering_velocities, }, "pre_annealing": { "ambient_gas": layer.pre_annealing_ambient_gas, "pressure": { "value": layer.pre_annealing_pressure, "units": layer.pre_annealing_pressure_unit, }, "temperature": { "value": layer.pre_annealing_temperature, "units": layer.pre_annealing_temperature_unit, }, "duration": { "value": layer.pre_annealing_duration, "units": layer.pre_annealing_duration_unit, }, }, "post_annealing": { "ambient_gas": layer.post_annealing_ambient_gas, "pressure": { "value": layer.post_annealing_pressure, "units": layer.post_annealing_pressure_unit, }, "temperature": { "value": layer.post_annealing_temperature, "units": layer.post_annealing_temperature_unit, }, "duration": { "value": layer.post_annealing_duration, "units": layer.post_annealing_duration_unit, }, }, } return pld_fabrication def build_nexus_file(pld_fabrication, output_path): # NOTE: look at the mail attachment from Emiliano... with h5py.File(output_path, "w") as f: nx_pld_entry = f.create_group("pld_fabrication") nx_pld_entry.attrs["NX_class"] = "NXentry" # Sample section nx_sample = nx_pld_entry.create_group("sample") nx_sample.attrs["NX_class"] = "NXsample" # Substrate section nx_substrate = nx_pld_entry.create_group("substrate") nx_substrate.attrs["NX_class"] = "NXsubentry" pass if __name__=="__main__": # TO-DO: place the API base URL somewhere else. ELABFTW_API_URL = "https://elabftw.fisica.unina.it/api/v2" apikey = getpass("Paste API key here: ") elabid = input("Enter elabid of your starting sample [default= 1111]: ") or 1111 data = APIHandler(apikey).get_entry_from_elabid(elabid) sample = Entrypoint(data) sample_name = sample.name.strip().replace(" ","_") substrate_object = chain_entrypoint_to_batch(sample) # Substrate-class object layers = chain_entrypoint_to_layers(sample) # list of Layer-class objects result = make_nexus_schema_dictionary(substrate_object, layers) # print(make_nexus_schema_dictionary(substrate_object, layers)) # debug with open (f"output/sample-{sample_name}.json", "w") as f: json.dump(result, f, indent=3)