Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Code Block
titlesaving to imas
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()
#if fails, creates one
if op[0]<0:
     cp=data_entry.create()
     if cp[0]==0:
        print("data entry created")
elif op[0]==0:
    print("data entry opened")	

# Fetched data
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")

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 only one command 'data_entry.put(thomson)' . Since we had all data available at once we didn't need to use put and putSlice commands to save slice by slice. However, the two approaches should be equivalent and provide the same ids. The first one is faster as it saving is done 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:

...