castaneda
a9fd8cebed
integrated with newRun_NNDy.py that is run by lyse and handles the halting of the execution of the script while the shot is running
74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
#this file runs the experiment through runmanager
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from datetime import datetime
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import time
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from os import listdir
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import zmq
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#import for labscript
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from labscript.labscript import labscript_init
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from runmanager import remote
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def run_experiment(routine_name, global_var):
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hdf_path = f"C:\\Users\\DyLab\\PrepLab\\Experiments\\NNDy_TestSetup\\{routine_name}"
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#this way it should be loading the routine based on past executions, with the possibility of only changing the global variables
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#it's possible that there could be some issue if the script is changed before running MLOOP
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#without further insights on this, it's recommended to always run the sequence once from runmanager GUI before running NNDy
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labscript_init(hdf_path,
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new = True)
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runmanager_client = remote.Client()
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runmanager_client.set_view_shots(False)
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runmanager_client.set_globals(global_var)
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#print of all the shot globals
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print(f'globals: \n {runmanager_client.get_globals()}')
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#set the path to the shot that will be created
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hdf_path = runmanager_client.get_shot_output_folder()
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runmanager_client.engage()
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#print('engaged')
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print(f'engaged, now waiting')
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#newRun_NNDy.py will make sure that the script is put on hold until the shot isn't sent to lyse
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#ask Jianshun for further info
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url = "tcp://" + str("127.0.0.1") + ":" + str(24325)
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context = zmq.Context.instance()
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server = context.socket(zmq.REP)
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server.bind(url)
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msg = server.recv_json()
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server.send_json(True)
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server.unbind(url)
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server.close()
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print('waiting time is over')
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#returns and retrieve the hdf5 file of the shot
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try:
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hdf_files = listdir(hdf_path)
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if len(hdf_files) == 0:
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raise ModuleNotFoundError
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else:
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if len(hdf_files) > 1:
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raise ImportError
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hdf_file = hdf_path + "/" + hdf_files[0]
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except (ModuleNotFoundError, ImportError) as e:
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raise Error(f'An error has occured while importing from hdf output folder: {e}')
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return hdf_file
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