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Note: channel[i1].position.r and channel[i1].position.z are scalars.
Let's assume we want to save measured HRTS profiles of electron temperature and density with errors for the JET shot 99357.
After fetching data from JET (dda='hrts', uid = 'jetppf', seq=0, dtype= ['TE', 'DTE', 'NE', 'DNE', 'Z']) we saved it in a dictionary with the following structure:
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'data' array consist of 701 time slices (from around t=40s to t=75s) and 63 experimental points. We need to project data onto numpy arrays:
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import json import imas,os,datetime,sys import getpass import numpy as np from imas import imasdef db = 'data_access_tutorial' shot=99357 run=1 #creates the Data Entry object 'data_entry', a kind of handler of the pulse file with sho, run, belonging to database 'data_access_tutorial' of the current user, using the MDS+ backend data_entry = imas.DBEntry(imasdef.MDSPLUS_BACKEND, db, shot, run, user_name=getpass.getuser()) #data_entry = imas.DBEntry(imasdef.HDF5_BACKEND, db, shot, run, user_name=getpass.getuser()) # Open save data_entry to data base # Tries to open data_entry op = data_entry.open() #open() and create(0 return a tuple (x_int,y_int) where x<0, y>0, x number of failures, y number of successes in the current session. #if open fails, createscreate onedata_entry if op[0]<0: cp=data_entry.create() if cp[0]==0: print("data entry created") elif op[0]==0: print("data entry opened") # Open file with fetched data, the data print("data entry opened") # Fetched datais not numpy array format with open("data/data_jet_hrts_99357.json") as json_file: hrts=json.load(json_file) x_coord=np.array(hrts['TE']['x']) # no. of space points nb_points = len(x_coord) #no of time slices nb_slices=len(hrts['TE']['time']) #here, we can perform some read/write operations using the get/put() operations #... #creating the 'thomson_scattering' auxiliary IDS and initializing it thomson = imas.thomson_scattering() #creates a 'thomson scattering' IDS thomson.ids_properties.homogeneous_time=1 #setting the homogeneous time (mandatory) thomson.ids_properties.comment='IDS created for testing the IMAS Data Access layer' #setting the ids_properties.comment attribute #thomson.time=np.array([0.]) #the time(vector) basis must be not empty if homogeneous_time==1 otherwise an error will occur at runtime # since all data is available we can save whole time vector at once thomson.time=np.array(hrts['TE']['time']) thomson.ids_properties.creation_date = datetime.datetime.now().strftime("%y-%m-%d") # the number of channel corresponds to number of data points thomson.channel.resize(nb_points) for j in range(nb_points): thomson.channel[j].t_e.data.resize(1) thomson.channel[j].t_e.data_error_upper.resize(1) thomson.channel[j].n_e.data.resize(1) thomson.channel[j].n_e.data_error_upper.resize(1) # python interface accepts only numpy arrays to be saved in ids te_data = np.array(hrts['TE']['data']) #2D dte_data = np.array(hrts['DTE']['data'])#2D ne_data = np.array(hrts['NE']['data'])#2D dne_data = np.array(hrts['DNE']['data'])#2D z_data = np.array(hrts['Z']['data']) #1D r_data = np.array(hrts['TE']['x']) #1D for j in range(nb_points): thomson.channel[j].position.r=r_data[j] thomson.channel[j].position.z=z_data[j] thomson.channel[j].t_e.data=te_data[:,j] thomson.channel[j].t_e.data_error_upper=dte_data[:,j] thomson.channel[j].n_e.data=ne_data[:,j] thomson.channel[j].n_e.data_error_upper=dne_data[:,j] data_entry.put(thomson) |
Note, that we used (once and outside the loop) only one command 'data_entry.put(thomson)' to save the data once outside the loop. Since we had all data available at once we didn't need to use put and putSlice commands to save time slice by time slice. However, the two approaches should be equivalent and provide the same ids. The first one is faster as procedure of saving the data in the data physical memory is called performed only once.
If the data was saved with MDSPLUS backend , the shot number is included in the data files names and is saved in the following folder:
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