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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()
#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) |
Reading data
Once the data is saved to ids we can open it and for example plot it:
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import imas
import numpy as np
import matplotlib.pyplot as plt
import sys
from imas import imasdef
db = 'data_access_tutorial'
shot=99357
run=1
#creating the Data Entry object which handles the pulse file with shot=15000, run=1, belonging to database 'data_access_tutorial' of the current user, using the MDS+ backend
#data_entry = imas.DBEntry(imasdef.HDF5_BACKEND, db, shot, run, user_name=getpass.getuser())
data_entry = imas.DBEntry(imasdef.MDSPLUS_BACKEND, db, shot, run, user_name=getpass.getuser())
#opening the pulse file handled by the Data Entry object previously created
data_entry.open()
#getting a slice at time=1s using the closest time slice interpolation
time_requested=50.
sslice = data_entry.get_slice('thomson_scattering', time_requested, imasdef.CLOSEST_INTERP)
no_channels=len(sslice.channel)
#[te,dte,ne,dne,r,z] =[np.array([0.0]*no_channels)]*6
te=np.array([])
dte=np.array([])
ne=np.array([])
dne=np.array([])
r=np.array([])
for i in range(no_channels):
te=np.append(te,sslice.channel[i].t_e.data)
dte= np.append(dte,sslice.channel[i].t_e.data_error_upper)
ne=np.append(ne,sslice.channel[i].n_e.data)
dne= np.append(dne,sslice.channel[i].n_e.data_error_upper)
r= np.append(r,sslice.channel[i].position.r)
z[i]= sslice.channel[i].position.z
#closing the Data Entry
data_entry.close()
# plotting
fig1=plt.figure(1)
elinewidth=0.9
capsize=3
ax1=fig1.add_subplot(121)
ax1.errorbar(r,te,yerr=dte,fmt='o',elinewidth=elinewidth,capsize=capsize, color='blue',label=r'$T_e$ at $t='+str(time_requested)+'$ s')
ax1.set_ylim([0.,6.0e3])
ax1.set_xlabel("r (m)",fontsize=14)
ax1.set_ylabel("eV",fontsize=14)
ax1.legend(loc='lower left', bbox_to_anchor=(0.0, 0.0),fontsize=12)
ax2=fig1.add_subplot(122)
ax2.errorbar(r,ne,yerr=dne,fmt='o',elinewidth=elinewidth,capsize=capsize, color='red',label=r'$n_e$ at $t='+str(time_requested)+'$ s')
ax2.set_ylim([0.,7.2e19])
ax2.set_xlabel("r (m)",fontsize=14)
ax2.set_ylabel("$m^{-3}$",fontsize=14)
ax2.legend(loc='lower left', bbox_to_anchor=(0.0, 0.0),fontsize=12)
plt.show() |