Python API¶
This notebook demonstrates how to set up a Jetto interpretive simulation and run it via duqtools.
In [1]:
Copied!
import logging
logging.basicConfig(
level=logging.INFO,
format='%(message)s',
)
import logging
logging.basicConfig(
level=logging.INFO,
format='%(message)s',
)
Below is an example duqtools config.
The example below takes t_e (electron temperature) from the template, and multiplies it by 1.1.
It also sets a few machine specific parameters (major_radius, b_field), and adjusts the start (t_start) and end time (t_end) for the simulation.
For more documentation on the parameters, check out the documentation here: https://duqtools.readthedocs.io/en/latest/config/create/
In [2]:
Copied!
from duqtools.api import create
config = {
'tag': 'data_01',
'create': {
'runs_dir':
'/afs/eufus.eu/user/g/g2ssmee/jetto_runs/duqduq/data_123',
'template':
'/afs/eufus.eu/user/g/g2ssmee/jetto_runs/interpretive_esco02',
'template_data': {
'user': 'g2aho',
'db': 'aug',
'shot': 36982,
'run': 2,
},
'operations': [
{
'variable': 't_e',
'operator': 'multiply',
'value': 1.1,
},
{
'variable': 'major_radius',
'operator': 'copyto',
'value': 165.0,
},
{
'variable': 'b_field',
'operator': 'copyto',
'value': -2.5725,
},
{
'variable': 't_start',
'operator': 'copyto',
'value': 2.875,
},
{
'variable': 't_end',
'operator': 'copyto',
'value': 2.885,
},
],
},
'system': 'jetto',
}
from duqtools.api import create
config = {
'tag': 'data_01',
'create': {
'runs_dir':
'/afs/eufus.eu/user/g/g2ssmee/jetto_runs/duqduq/data_123',
'template':
'/afs/eufus.eu/user/g/g2ssmee/jetto_runs/interpretive_esco02',
'template_data': {
'user': 'g2aho',
'db': 'aug',
'shot': 36982,
'run': 2,
},
'operations': [
{
'variable': 't_e',
'operator': 'multiply',
'value': 1.1,
},
{
'variable': 'major_radius',
'operator': 'copyto',
'value': 165.0,
},
{
'variable': 'b_field',
'operator': 'copyto',
'value': -2.5725,
},
{
'variable': 't_start',
'operator': 'copyto',
'value': 2.875,
},
{
'variable': 't_end',
'operator': 'copyto',
'value': 2.885,
},
],
},
'system': 'jetto',
}
13:45:22 [WARNING] Python module 'omas' not found. Submodule 'jams' needs it @jams.py:14 Python module 'omas' not found. Submodule 'jams' needs it 13:45:22 [WARNING] Python module 'netCDF4' not found. Submodule 'transp' needs it @transp.py:25 Python module 'netCDF4' not found. Submodule 'transp' needs it
Running create will create a new run from the template, copy over the template data, and apply the operations to the data. This will return a dictionary with jobs and runs (Job and Run objects).
In [3]:
Copied!
jobs_and_runs = create(config)
job, run = jobs_and_runs['run_0000']
jobs_and_runs = create(config)
job, run = jobs_and_runs['run_0000']
Source data: g2aho/aug/36982/2 - Creating run : /afs/eufus.eu/user/g/g2ssmee/jetto_runs/duqduq/data_123/run_0000 - Copy ids from template to : /gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/imasdb/aug/36982/1 - Copying template to : /afs/eufus.eu/user/g/g2ssmee/jetto_runs/duqduq/data_123/run_0000 - Setting inital condition of : /gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/imasdb/aug/36982/1 Apply operator='multiply' scale_to_error=False variable=IDSVariableModel(ids='core_profiles', path='profiles_1d/*/electrons/temperature', type='IDS-variable', name='t_e', dims=['time', '$rho_tor_norm']) value=1.1 Writing data entry: /gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/imasdb/aug/36982/1 - Writing new batchfile : run_0000 - Updating imas locations of : /afs/eufus.eu/user/g/g2ssmee/jetto_runs/duqduq/data_123/run_0000 - Writing runs : /afs/eufus.eu/g2itmuse/user/g2ssmee/duqtools/docs/examples/runs.yaml - Writing csv
run contains some metadata about the run and the locations to the input and output data for the run.
In [4]:
Copied!
run.model_dump()
run.model_dump()
Out[4]:
{'dirname': PosixPath('/afs/eufus.eu/user/g/g2ssmee/jetto_runs/duqduq/data_123/run_0000'),
'shortname': PosixPath('run_0000'),
'data_in': {'relative_location': None,
'user': '/gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/imasdb',
'db': 'aug',
'shot': 36982,
'run': 1},
'data_out': {'relative_location': None,
'user': '/gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/imasdb',
'db': 'aug',
'shot': 36982,
'run': 2},
'operations': [{'operator': 'multiply',
'scale_to_error': False,
'variable': {'ids': 'core_profiles',
'path': 'profiles_1d/*/electrons/temperature',
'type': 'IDS-variable',
'name': 't_e',
'dims': ['time', '$rho_tor_norm']},
'value': 1.1},
{'operator': 'copyto',
'scale_to_error': False,
'variable': {'type': 'jetto-variable',
'name': 'major_radius',
'lookup': {'doc': 'Reference major radius (R0)',
'name': 'major_radius',
'type': 'float',
'keys': [{'file': 'jetto.jset',
'field': 'EquilEscoRefPanel.refMajorRadius'},
{'file': 'jetto.in', 'field': 'RMJ', 'section': 'nlist1'}]}},
'value': 165.0},
{'operator': 'copyto',
'scale_to_error': False,
'variable': {'type': 'jetto-variable',
'name': 'b_field',
'lookup': {'doc': 'B-field (B0)',
'name': 'b_field',
'type': 'float',
'keys': [{'file': 'jetto.jset',
'field': 'EquilEscoRefPanel.BField.ConstValue'}]}},
'value': -2.5725},
{'operator': 'copyto',
'scale_to_error': False,
'variable': {'type': 'jetto-variable', 'name': 't_start', 'lookup': None},
'value': 2.875},
{'operator': 'copyto',
'scale_to_error': False,
'variable': {'type': 'jetto-variable', 'name': 't_end', 'lookup': None},
'value': 2.885}]}
job contains information about the status of the job and the locations of the input and output files for the simulation.
In [5]:
Copied!
print(job.in_file)
print(job.in_file.exists())
print(job.out_file)
print(job.out_file.exists())
print(job.status())
print(job.in_file)
print(job.in_file.exists())
print(job.out_file)
print(job.out_file.exists())
print(job.status())
/gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/jetto.in True /gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/jetto.out False JobStatus.NOSTATUS
The job can be submitted using the configured submit system.
In [6]:
Copied!
job.submit()
job.submit()
submitting script via slurm ['sbatch', '/gss_efgw_work/work/g2ssmee/jetto/runs/duqduq/data_123/run_0000/.llcmd'] submission returned: b'Submitted batch job 237647\n'
Track the status of the job through job.status().
In [7]:
Copied!
import time
print(job.status())
t0 = time.time()
while not job.is_done:
t1 = time.time() - t0
print(f'{t1:10.2f} - {job.status()}')
time.sleep(1)
print(job.status())
import time
print(job.status())
t0 = time.time()
while not job.is_done:
t1 = time.time() - t0
print(f'{t1:10.2f} - {job.status()}')
time.sleep(1)
print(job.status())
JobStatus.NOSTATUS
0.00 - no status
1.00 - no status
2.14 - no status
3.14 - no status
4.14 - no status
5.14 - no status
6.15 - no status
7.15 - no status
8.15 - no status
9.15 - no status
11.12 - no status
13.23 - no status
15.03 - running
16.17 - running
17.17 - running
18.17 - running
19.18 - running
20.18 - running
21.18 - running
22.18 - running
23.18 - running
24.18 - running
25.19 - running
26.19 - running
27.19 - running
28.19 - running
29.19 - running
30.19 - running
31.20 - running
32.20 - running
33.20 - running
34.20 - running
35.20 - running
36.20 - running
37.20 - running
38.21 - running
JobStatus.COMPLETED
Check that the run has created some data.
In [8]:
Copied!
run.data_out.exists()
run.data_out.exists()
Out[8]:
True
Load the data into an xarray.Dataset and do things with it.
In [9]:
Copied!
ds_out = run.data_out.get_variables(('time', 'rho_tor_norm', 't_e'))
ds_out.isel(time=[
0,
]).plot.scatter('rho_tor_norm', 't_e', marker='.')
ds_out = run.data_out.get_variables(('time', 'rho_tor_norm', 't_e'))
ds_out.isel(time=[
0,
]).plot.scatter('rho_tor_norm', 't_e', marker='.')
Out[9]:
<matplotlib.collections.PathCollection at 0x2b93510eb3a0>
