...
| Code Block | ||||
|---|---|---|---|---|
| ||||
In order to access the new mdsplus.jetdata.eu service, users will need to make a minor change to their codes, changing the host parameter in the connection calls from "mdsplus.jet.efda.org" or "mdsplus.jet.uk" to "ssh://<username>@mdsplus.jetdata.eu" where <username> is the your JET account username (e.g. jsmith or xy1234) Users will also need to register their SSH public key with us in order for this to work. Please do this by emailing support@it.ukaea.uk with the subject line “MDSplus SSH Key Registration”. Please put your SSH Public key into the body of the message – do not add attachments to the email. It is important that you provide your public key only and do not include your private key (users should take all reasonable steps to protect their private keys). (Due to a limitation of the mdsplus server, please avoid using Ed25519 keys, though ecdsa keys can be used) Suggested email text to use: Please register my SSH Public key for use with mdsplus.jetdata.eu. My username is : <your shortname> My SSH Public key is: Once your key has been registered we will confirm this back to you and you will then be able to test the connection and your clients If you need information about creating SSH keys please see the information here: • There is some general information here: https://www.digitalocean.com/community/tutorials/how-to-set-up-ssh-keys-2 • There is a page on how to create key pairs with openssh at https://www.ssh.com/academy/ssh/keygen • There is a page on how to create key pairs with putty at https://www.ssh.com/academy/ssh/putty/windows/puttygen • The www.ssh.com has pages about generating keys for other ssh clients too. Important Note: when generating SSH key pairs, we’d suggest that most users will find it more convenient not to use a passphrase (i.e. leave it blank). Also, due to a limitation of the mdsplus server, please avoid using Ed25519 keys, though ecdsa keys can be used |
Saving data to IMAS
Before saving the experimental data to imas it is important check which version of imas data dictonary we are using.
You may load (or you may use scripts that load) IMAS environment without specifying the version
| Code Block |
|---|
module purge
module load cineca
module load imasenv |
The last line loads imas data dictionary and also idstools which will be used later.
However, loading imasenv does not necessarily provide you with the latest version of data dictionary. In this case:
Once the pair of keys 'id_rsa_jet' is created it necessary that the system can distinguish between the keys. You can inform the system by editing 'config' file in ~/ .ssh folder.
| Code Block | ||
|---|---|---|
| ||
Host jet
HostName mdsplus.jetdata.eu
User <your jet username>
IdentityFile ~/.ssh/id_rsa_jet |
In an example we fetch JET data (dda='hrts', uid = 'jetppf', seq=0, dtype= ['TE', 'DTE', 'NE', 'DNE', 'Z']) .
Example of script fetching HRTS JET data.
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
#example of saving experimental data to ids
import os
import json
def read_ppf(conn, shot, ppf, seq=0, uid='jetppf',debug=False):
ierr = conn.get('_sig=ppfuid("' + uid +'")')
c = '_sig=jet("ppf/%s/%d",%d)'% (ppf, seq, shot)
if debug: print('\nDEBUG: %s\n' % c)
try:
| ||||||
| Code Block | ||||||
| ||||||
~>module list Currently Loaded Modulefiles: 1) profile/archive 18) cmake/3.5.2 35) IMAS/3.29.0/AL/4.8.3 2) cineca s = conn.get(c) raw 19) mdsplus/7.92.0/gcc/4.8= s.data() dim0 36) itm-blas/3.8.0/intel/17.0 3) intel/pe-xe-2017--binary= conn.get('dim_of(_sig,0)').data() try: 20) blitz/1.0.1 dim1 = conn.get('dim_of(_sig,1)').data() return {'raw': raw, 'x':dim0, 't':dim1} 37) itm-lapack/3.8.0/intel/17.0 4) itm-intel/17.0 except: return {'raw': raw, 't':dim0} except: 21) jaxfront/R1.1 return None def readExperimental(data_exp, host, server='ssh://'): ''' reads experimental data after connecting to the host via ssh server 38) interpos/9.2.0/intel/17.0 5) intelmpi/2017--binary data_exp: data details in dictionary format ''' import sys try: 22) git/2.23 import MDSplus haveMDS = True except: 39) xmllib/3.3.1/intel/17.0 6) itm-intelmpi/2017 print(' No MDSplus support found.\n') print('\n\n\n Exiting...\n') exit(1) try: 23) itm-fftw/3.3.4 conn = MDSplus.Connection(server+host) print('Connection OK') connected = True 40) libfortranparser/0.0.6/intel/17.0except: 7) gnu/7.3.0 print('in readExperimental, MDSplus failed: ',sys.exc_info()[1]) connected = False if connected: 24) szip/2.1--gnu--6.1.0try: EXP={} 41) keplertools/1.8.9 8) itm-gcc/7.3.0 for sig in data_exp['dtype']: print('reading 25) zlib/1.2.8--gnu--6.1.0dtype: ',sig) 42) kepler/2.5p5-3.1.1signal= data_exp['dda']+'/'+sig 9) jdk/1.8.0_111 aux = read_ppf(conn, discharge, signal, seq=data_exp['seq'], uid=data_exp['uid']) 26) itm-hdf5/1.8.17-old # Z coordinate is one dimensional, aux['raw'] is 43) imas-fc2k/4.13.9 10) itm-java/1.8.0_1112D by default if sig=='Z': 27) pspline/20161207 aux['raw'] = aux['raw'].flatten() 44) itm-qt/5.8.0 11) itm-python/3.6 EXP[sig] = { 28) slatec/4.1 'time': aux['t'].tolist(), 45) imas-viz/2.4.4 12) matlab/2018b'x':aux['x'].tolist(), 'data': aux['raw'].tolist(), 29) itm-mkl/2017.1 'signal': signal 46) idstools/1.5.1 13) itm-matlab/2018b } except: 30) itm-matheval/1.1.11 print('in readExperimental, read_ppf failed: ',sys.exc_info()[1]) 47) autoGui/1.15 14) netbeans/7.3 return None del(conn) else: EXP=None 31) itm-netcdf/4.4 return EXP ########### host='jet' discharge=99357 # data to download data={} data['dda']='hrts' data['uid'] = 'jetppf' data['seq']=0 data['dtype']=['TE','DTE','NE','DNE','Z'] EXP=readExperimental(data,host) if EXP: a_file = open('data_jet_fetched_'+str(discharge)+'_'+data['dda']+'_'+str(data['seq'])+'.json', "w") json.dump(EXP, a_file) a_file.close() |
IMAS UDA
It is possible to access and fetch and map data from several experiments WEST, JET , TCV and AUG using UDA protocol which id described in detailed on ITER confulence pages (requires iter account). Accessing data with UDA needs to be adapted due to recent (Aug 2021) change in connection protocol.
Saving data to IMAS
Before saving the experimental data to imas it is important check which version of imas data dictonary we are using.
You may load (or you may use scripts that load) IMAS environment without specifying the version
| Code Block |
|---|
module purge
module load cineca
module load imasenv |
The last line loads imas data dictionary and also idstools which will be used later.
However, loading imasenv does not necessarily provide you with the latest version of data dictionary. In this case:
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
~>module list Currently Loaded Modulefiles: 1) profile/archive 48) ggd/1.9.1/intel/17.0/imas/3.29.0 15) itm-maven/3.3.9 32) nag/mark26--binary 49) libbds/1.0.2/intel/17.0/imas/3.29.0 16) scripts/R4.9 33) itm-nag/mark26--binary 5018) amnscmake/1.3.3/intel/17.0/imas5.2 35) IMAS/3.29.0/AL/4.8.3 172) totalview/2017.3.8cineca 34) uda/2.2.5 19) mdsplus/7.92.0/gcc/4.8 5136) imasenvitm-blas/3.298.0/intel/rc |
...
17.0 3) intel/pe-xe-2017--binary 20) blitz/1.0.1 37) itm-lapack/3.8.0/intel/ |
...
17.0
4) itm-intel/17.0 21) jaxfront/R1.1 38) interpos/9.2.0/intel/17.0
5) intelmpi/2017--binary 22) git/2.23 39) xmllib/3.3.1/intel/17.0
6) itm-intelmpi/2017 23) itm-fftw/3.3.4 40) libfortranparser/0.0.6/intel/17.0
7) gnu/7.3.0 24) szip/2.1--gnu--6.1.0 41) keplertools/1.8.9
8) itm-gcc/7.3.0 25) zlib/1.2.8--gnu--6.1.0 42) kepler/2.5p5-3.1.1
9) jdk/1.8.0_111 26) itm-hdf5/1.8.17-old 43) imas-fc2k/4.13.9
10) itm-java/1.8.0_111 27) pspline/20161207 44) itm-qt/5.8.0
11) itm-python/3.6 28) slatec/4.1 45) imas-viz/2.4.4
12) matlab/2018b 29) itm-mkl/2017.1 46) idstools/1.5.1
13) itm-matlab/2018b 30) itm-matheval/1.1.11 47) autoGui/1.15
14) netbeans/7.3 31) itm-netcdf/4.4 48) ggd/1.9.1/intel/17.0/imas/3.29.0
15) itm-maven/3.3.9 32) nag/mark26--binary 49) libbds/1.0.2/intel/17.0/imas/3.29.0
16) scripts/R4.9 33) itm-nag/mark26--binary 50) amns/1.3.3/intel/17.0/imas/3.29.0
17) totalview/2017.3.8 34) uda/2.2.5 51) imasenv/3.29.0/intel/rc
|
we loaded version 3.29.0/intel/rc . If you want to load the latest version data dictionary check first what versions are available with a command "module avail <required module>":
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
~>module avail imasenv
---------------------------------------------------------- /gw/modules/environment ----------------------------------------------------------
imasenv/3.10.2 imasenv/3.23.1/ual/4.0.3/1.4 imasenv/3.26.0/gcc/7.3.0/rc imasenv/3.31.0/intel/17.0/1.0
imasenv/3.11.0 imasenv/3.23.1/ual/4.0.3/1.5 imasenv/3.26.0/intel/17.0/rc imasenv/3.31.0/intel/17.0/1.1
imasenv/3.12.1 imasenv/3.23.1/ual/4.0.3/1.6 imasenv/3.26.0/intel/rc imasenv/3.31.0/intel/17.0/rc
imasenv/3.15.1 imasenv/3.23.1/ual/4.0.4/1.0 imasenv/3.26.0/rc imasenv/3.31.0/intel/rc
imasenv/3.16.0 imasenv/3.23.1/ual/4.0.4/1.1 imasenv/3.28.0/gcc/7.3.0/rc imasenv/3.31.0/rc
imasenv/3.17.0 imasenv/3.23.1/ual/4.0.4/1.2 imasenv/3.28.0/intel/17.0/rc imasenv/3.32.0/gcc/7.3.0/rc
imasenv/3.17.1 imasenv/3.23.1/ual/4.1.0/1.0 imasenv/3.28.0/intel/rc imasenv/3.32.0/intel/17.0/rc
imasenv/3.18.0 imasenv/3.23.2/rc imasenv/3.28.0/rc imasenv/3.32.0/intel/rc
imasenv/3.19.0 imasenv/3.23.2/ual/4.1.1/1.0 imasenv/3.28.1/gcc/7.3.0/1.0 imasenv/3.32.0/rc
imasenv/3.19.1 imasenv/3.23.2/ual/4.1.2/0.2 imasenv/3.28.1/gcc/7.3.0/rc imasenv/3.32.1/gcc/7.3.0/rc
imasenv/3.20.0 imasenv/3.23.2/ual/4.1.2/1.0 imasenv/3.28.1/intel/17.0/1.0 imasenv/3.32.1/intel/17.0/rc
imasenv/3.21.0/ual/3.8.10/1.0 imasenv/3.23.2/ual/4.1.4/0.2 imasenv/3.28.1/intel/17.0/rc imasenv/3.32.1/intel/rc
imasenv/3.21.0/ual/3.8.5/1.0 imasenv/3.23.2/ual/4.1.4/1.0 imasenv/3.28.1/intel/rc imasenv/3.32.1/rc
imasenv/3.21.0/ual/3.8.8/1.0 imasenv/3.23.2/ual/4.1.5/1.0 imasenv/3.28.1/rc imasenv/3.33.0/gcc/7.3.0/rc
imasenv/3.21.1/ual/4.0.0/1.0 imasenv/3.23.2/ual/4.1.5/1.1 imasenv/3.29.0/gcc/7.3.0/1.0 imasenv/3.33.0/intel/17.0/rc
imasenv/3.21.1/ual/4.0.1/1.0 imasenv/3.23.2/ual/4.1.5/1.2 imasenv/3.29.0/gcc/7.3.0/rc imasenv/3.33.0/intel/rc
imasenv/3.22.0/ual/4.0.2/1.0 imasenv/3.24.0/rc imasenv/3.29.0/intel/17.0/1.0 imasenv/3.33.0/rc
imasenv/3.22.0/ual/4.0.2/1.1 imasenv/3.24.0/ual/4.1.5/1.0 imasenv/3.29.0/intel/17.0/rc imasenv/3.7.4
imasenv/3.22.0/ual/4.0.2/1.2 imasenv/3.24.0/ual/4.2.0/1.0 imasenv/3.29.0/intel/rc imasenv/3.8.0
imasenv/3.22.0/ual/4.0.2/1.3 imasenv/3.25.0/gcc/6.1.0/1.0 imasenv/3.29.0/rc imasenv/3.9.0
imasenv/3.22.0/ual/4.0.2/1.4 imasenv/3.25.0/gcc/6.1.0/rc imasenv/3.30.0/gcc/7.3.0/rc imasenv/3.9.1
imasenv/3.22.0/ual/4.0.2/1.5 imasenv/3.25.0/gcc/7.3.0/1.0 imasenv/3.30.0/intel/17.0/rc imasenv/newLL/3.18.0
imasenv/3.22.0/ual/4.0.2/1.6 imasenv/3.25.0/gcc/7.3.0/rc imasenv/3.30.0/intel/rc imasenv/test
imasenv/3.22.0/ual/4.0.2/1.7 imasenv/3.25.0/gcc/rc imasenv/3.30.0/rc imasenvX/3.19.1/ual/3.8.2/1.0
imasenv/3.22.0/ual/4.0.2/1.8 imasenv/3.25.0/intel/17.0/1.0 imasenv/3.31.0/1.0 imasenvX/3.20.0/ual/3.8.3/1.0
imasenv/3.23.1/ual/4.0.3/1.0 imasenv/3.25.0/intel/17.0/rc imasenv/3.31.0/1.1 imasenvX/3.20.0/ual/3.8.5/1.0
imasenv/3.23.1/ual/4.0.3/1.1 imasenv/3.25.0/intel/rc imasenv/3.31.0/gcc/7.3.0/1.0 imasenvX/3.21.0/ual/3.8.5/1.0
imasenv/3.23.1/ual/4.0.3/1.2 imasenv/3.25.0/rc imasenv/3.31.0/gcc/7.3.0/1.1
imasenv/3.23.1/ual/4.0.3/1.3 imasenv/3.25.0/ual/4.2.0/1.0 imasenv/3.31.0/gcc/7.3.0/rc |
From the above output we conclude that the latest data dictionary version is 'imasenv/3.33.0/rc'. Using the above list you can also choose a different version if there is such a need.
Let's switch to the latest imas release available on the gateway:
| Code Block |
|---|
module unload imasenv
module load imasenv/3.33.0/rc |
You can now check if there exists ids to store the experimental data by typing:
| Code Block |
|---|
dd_doc |
Above command opens data dictionary by default in Konqueror. If option "Show/hide error bars" doesn't work, copying the address to Firefox will help.
By browsing data dictionaries you will find needed fiields. If there are no required fields then a request has to be raised.
Saving Thomson scattering data
In our example we want to save Thomson scattering data. You can find the corresponding ids in the list above. Let's browse this ids.
We can imediately notice that there are structures introduced in imas version 3.32.1. Apart from experimentally measured values we can also save other experimental data like coordinates of line-of-sight of particular measurement.
Data is structured in such a way that we have to choose channel first to browse further for place to save measured values:
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:
| Code Block | ||
|---|---|---|
| ||
# hrts data with sig='TE','DTE','NE','DNE','Z'
# hrts[sig]={
# 'time': t, #1D float
# 'x':x, #1D float R coordinate
# 'data': data, #2D float except sig='Z'
# 'signal': signal,
# 'seq':sequence,
# 'uid':owner,
# 'dda':dda
# }
# 'data' is 2D data[i,j] where i=time_slice index and j= x_value index |
'data' array consist of 701 time slices (from around t=40s to t=75s) and 63 experimental points.
| Code Block | ||
|---|---|---|
| ||
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, create data_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 is 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'])
#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]
sequence = hrts['TE']['seq']
data_entry.put(thomson,seq)# the last number is the occurence which can be used to store the data sequence number
#closing the Data Entry
data_entry.close() |
Note, that we used (once and outside the loop) only one command 'data_entry.put(thomson)' to save the data. 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 physical memory is 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:
| Code Block |
|---|
> ~/public/imasdb/data_access_tutorial/3/0>ls
ids_150000001.characteristics ids_150000001.tree ids_993570001.datafile
ids_150000001.datafile ids_993570001.characteristics ids_993570001.tree
|
If the data is saved using HDF5 backend, the shot number is included in the data tree folder names and is saved in the following folder:
| Code Block |
|---|
> ~/public/imasdb/data_access_tutorial/3/99357/1>ls
master.h5 thomson_scattering.h5
|
If you used HDF5 backend you can check if the data was stored correctly in the ids directly with the command:
| Code Block |
|---|
>~/public/imasdb/data_access_tutorial/3/99357/1>h5dump thomson_scattering.h5 |less |
| Code Block | ||||||
|---|---|---|---|---|---|---|
| ||||||
HDF5 "thomson_scattering.h5" {
GROUP "/" {
ATTRIBUTE "HDF5_BACKEND_VERSION" {
DATATYPE H5T_STRING {
STRSIZE 10;
STRPAD H5T_STR_NULLTERM;
CSET H5T_CSET_UTF8;
CTYPE H5T_C_S1;
}
DATASPACE SCALAR
DATA {
(0): "1.0"
}
}
ATTRIBUTE "RUN" {
DATATYPE H5T_STD_I32LE
DATASPACE SCALAR
DATA {
(0): 1
}
}
ATTRIBUTE "SHOT" {
DATATYPE H5T_STD_I32LE
DATASPACE SCALAR
DATA {
(0): 99357
}
}
GROUP "thomson_scattering" {
DATASET "channel[]&AOS_SHAPE" {
DATATYPE H5T_STD_I32LE
DATASPACE SIMPLE { ( 1 ) / ( H5S_UNLIMITED ) }
DATA {
(0): 63
}
}
DATASET "channel[]&n_e&data" {
DATATYPE H5T_IEEE_F64LE
DATASPACE SIMPLE { ( 63, 701 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) }
DATA {
(0,0): 1e-20, 1e-20, 1.74005e+18, 3.30191e+18, 4.63574e+18,
(0,5): 5.01502e+18, 7.16228e+18, 9.61972e+18, 1.14838e+19,
(0,9): 1.26156e+19, 1.40738e+19, 1.49135e+19, 1.7819e+19,
(0,13): 1.69414e+19, 1.83607e+19, 1.80031e+19, 1.95752e+19,
(0,17): 1.92397e+19, 1.74838e+19, 1.87457e+19, 1.84743e+19,
(0,21): 1.99327e+19, 1.93234e+19, 1.93735e+19, 2.02663e+19,
(0,25): 1.82691e+19, 1.79814e+19, 1.55414e+19, 1.5825e+19,
(0,29): 1.47801e+19, 1.63837e+19, 1.40053e+19, 1.41187e+19,
(0,33): 1.46383e+19, 1.25988e+19, 1.40001e+19, 1.20421e+19,
(0,37): 1.31313e+19, 1.51261e+19, 1.39026e+19, 1.41953e+19,
(0,41): 1.52628e+19, 1.4697e+19, 1.56486e+19, 1.55934e+19,
(0,45): 1.79571e+19, 1.52815e+19, 1.73831e+19, 1.69241e+19,
(0,49): 1.80319e+19, 1.77864e+19, 1.8345e+19, 1.91198e+19,
(0,53): 1.7536e+19, 2.03026e+19, 1.91662e+19, 1.97445e+19,
: | ||||||
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~>module avail imasenv ---------------------------------------------------------- /gw/modules/environment ---------------------------------------------------------- imasenv/3.10.2 imasenv/3.23.1/ual/4.0.3/1.4 imasenv/3.26.0/gcc/7.3.0/rc imasenv/3.31.0/intel/17.0/1.0 imasenv/3.11.0 imasenv/3.23.1/ual/4.0.3/1.5 imasenv/3.26.0/intel/17.0/rc imasenv/3.31.0/intel/17.0/1.1 imasenv/3.12.1 imasenv/3.23.1/ual/4.0.3/1.6 imasenv/3.26.0/intel/rc imasenv/3.31.0/intel/17.0/rc imasenv/3.15.1 imasenv/3.23.1/ual/4.0.4/1.0 imasenv/3.26.0/rc imasenv/3.31.0/intel/rc imasenv/3.16.0 imasenv/3.23.1/ual/4.0.4/1.1 imasenv/3.28.0/gcc/7.3.0/rc imasenv/3.31.0/rc imasenv/3.17.0 imasenv/3.23.1/ual/4.0.4/1.2 imasenv/3.28.0/intel/17.0/rc imasenv/3.32.0/gcc/7.3.0/rc imasenv/3.17.1 imasenv/3.23.1/ual/4.1.0/1.0 imasenv/3.28.0/intel/rc imasenv/3.32.0/intel/17.0/rc imasenv/3.18.0 imasenv/3.23.2/rc imasenv/3.28.0/rc imasenv/3.32.0/intel/rc imasenv/3.19.0 imasenv/3.23.2/ual/4.1.1/1.0 imasenv/3.28.1/gcc/7.3.0/1.0 imasenv/3.32.0/rc imasenv/3.19.1 imasenv/3.23.2/ual/4.1.2/0.2 imasenv/3.28.1/gcc/7.3.0/rc imasenv/3.32.1/gcc/7.3.0/rc imasenv/3.20.0 imasenv/3.23.2/ual/4.1.2/1.0 imasenv/3.28.1/intel/17.0/1.0 imasenv/3.32.1/intel/17.0/rc imasenv/3.21.0/ual/3.8.10/1.0 imasenv/3.23.2/ual/4.1.4/0.2 imasenv/3.28.1/intel/17.0/rc imasenv/3.32.1/intel/rc imasenv/3.21.0/ual/3.8.5/1.0 imasenv/3.23.2/ual/4.1.4/1.0 imasenv/3.28.1/intel/rc imasenv/3.32.1/rc imasenv/3.21.0/ual/3.8.8/1.0 imasenv/3.23.2/ual/4.1.5/1.0 imasenv/3.28.1/rc imasenv/3.33.0/gcc/7.3.0/rc imasenv/3.21.1/ual/4.0.0/1.0 imasenv/3.23.2/ual/4.1.5/1.1 imasenv/3.29.0/gcc/7.3.0/1.0 imasenv/3.33.0/intel/17.0/rc imasenv/3.21.1/ual/4.0.1/1.0 imasenv/3.23.2/ual/4.1.5/1.2 imasenv/3.29.0/gcc/7.3.0/rc imasenv/3.33.0/intel/rc imasenv/3.22.0/ual/4.0.2/1.0 imasenv/3.24.0/rc imasenv/3.29.0/intel/17.0/1.0 imasenv/3.33.0/rc imasenv/3.22.0/ual/4.0.2/1.1 imasenv/3.24.0/ual/4.1.5/1.0 imasenv/3.29.0/intel/17.0/rc imasenv/3.7.4 imasenv/3.22.0/ual/4.0.2/1.2 imasenv/3.24.0/ual/4.2.0/1.0 imasenv/3.29.0/intel/rc imasenv/3.8.0 imasenv/3.22.0/ual/4.0.2/1.3 imasenv/3.25.0/gcc/6.1.0/1.0 imasenv/3.29.0/rc |
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 imasenv/3.9.0 imasenv/3.22.0/ual/4.0.2/1.4 imasenv/3.25.0/gcc/6.1.0/rc imasenv/3.30.0/gcc/7.3.0/rc imasenv/3.9.1 imasenv/3.22.0/ual/4.0.2/1.5 imasenv/3.25.0/gcc/7.3.0/1.0 imasenv/3.30.0/intel/17.0/rc imasenv/newLL/3.18.0 imasenv/3.22.0/ual/4.0.2/1.6 imasenv/3.25.0/gcc/7.3.0/rc imasenv/3.30.0/intel/rc imasenv/test imasenv/3.22.0/ual/4.0.2/1.7 imasenv/3.25.0/gcc/rc imasenv/3.30.0/rc imasenvX/3.19.1/ual/3.8.2/1.0 imasenv/3.22.0/ual/4.0.2/1.8 imasenv/3.25.0/intel/17.0/1.0 imasenv/3.31.0/1.0 imasenvX/3.20.0/ual/3.8.3/1.0 imasenv/3.23.1/ual/4.0.3/1.0 imasenv/3.25.0/intel/17.0/rc imasenv/3.31.0/1.1 imasenvX/3.20.0/ual/3.8.5/1.0 imasenv/3.23.1/ual/4.0.3/1.1 imasenv/3.25.0/intel/rc imasenv/3.31.0/gcc/7.3.0/1.0 imasenvX/3.21.0/ual/3.8.5/1.0 imasenv/3.23.1/ual/4.0.3/1.2 imasenv/3.25.0/rc imasenv/3.31.0/gcc/7.3.0/1.1 imasenv/3.23.1/ual/4.0.3/1.3 imasenv/3.25.0/ual/4.2.0/1.0 imasenv/3.31.0/gcc/7.3.0/rc |
From the above output we conclude that the latest data dictionary version is 'imasenv/3.33.0/rc'. Using the above list you can also choose a different version if there is such a need.
Let's switch to the latest imas release available on the gateway:
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module unload imasenv
module load imasenv/3.33.0/rc |
You can now check if there exists ids to store the experimental data by typing:
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dd_doc |
Above command opens data dictionary by default in Konqueror. If option "Show/hide error bars" doesn't work, copying the address to Firefox will help.
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() |
As it can be seen from the plot, the fetched data has not been yet filteredBy browsing data dictionaries you will find needed fiields. If there are no required fields then a request has to be raised.