%load_ext autoreload
%autoreload 2
from duqtools.api import ImasHandle
paths = (
'g2vazizi/jet/94875/8000',
'g2vazizi/jet/94875/8001',
'g2vazizi/jet/94875/8002',
)
handles = [ImasHandle.from_string(p) for p in paths]
Defining variables¶
Then we must define the relations between the data. This is done via the Variable model.
ion_temperature is not in the default list of variables, so we must define it. Note that we create an extra dimension with * to select all the ions.
The other variables are defined by duqtools, so we can pass them as string.
from duqtools.api import Variable
variables = (Variable(name='ion_temperature',
ids='core_profiles',
path='profiles_1d/*/ion/*/temperature',
dims=['time', 'ion', '$rho_tor_norm']), 't_i_ave',
'rho_tor_norm', 'time')
Loading the data¶
Load the data using the get_variables() method.
datasets = tuple(handle.get_variables(variables) for handle in handles)
datasets[0]
<xarray.Dataset>
Dimensions: (time: 101, ion: 3, rho_tor_norm: 100)
Coordinates:
* time (time) float64 48.35 48.36 48.36 48.37 ... 48.99 48.99 49.0
* rho_tor_norm (rho_tor_norm) float64 0.005025 0.01508 ... 0.9899 1.0
Dimensions without coordinates: ion
Data variables:
ion_temperature (time, ion, rho_tor_norm) float64 9.499e+03 ... 643.8
t_i_ave (time, rho_tor_norm) float64 9.499e+03 9.493e+03 ... 643.8Time Standardizing¶
Sometimes we have datasets with various starting times, but we want to compare them anyway
for this you can use the standardize_time() function, which is an in-place operation:
from duqtools.ids import standardize_time
for ds in datasets:
standardize_time(ds, start=0.1)
Grid and time rebasing¶
We can interpolate all datasets to the same reference grid and time using standardize_grid_and_time().
from duqtools.api import standardize_grid_and_time
datasets = standardize_grid_and_time(
datasets,
grid_var='rho_tor_norm',
time_var='time',
reference_dataset=0,
)
Data concatenation¶
Finally, we can concatenate along the run dimension. We set the run coordinates to the name of the data so they can be re-used later.
import xarray as xr
dataset = xr.concat(datasets, 'run')
dataset['run'] = list(paths)
Now we have the data in a nicely structured xarray dataset.
dataset
<xarray.Dataset>
Dimensions: (run: 3, time: 101, ion: 3, rho_tor_norm: 100)
Coordinates:
* rho_tor_norm (rho_tor_norm) float64 0.005025 0.01508 ... 0.9899 1.0
* time (time) float64 0.1 0.1077 0.1149 ... 0.7389 0.7449 0.75
* run (run) <U23 'g2vazizi/jet/94875/8000' ... 'g2vazizi/jet/9...
Dimensions without coordinates: ion
Data variables:
ion_temperature (run, time, ion, rho_tor_norm) float64 9.499e+03 ... 760.9
t_i_ave (run, time, rho_tor_norm) float64 9.499e+03 ... 760.9Plotting¶
Now that we have standardized and rebased the grid and time coordinates, plotting and other operations on the data becomes straightforward.
xarray has some built-in functionality to make plots using matplotlib.
dataset.isel(time=50).plot.scatter('rho_tor_norm',
'ion_temperature',
hue='run',
col='ion',
marker='.')
<xarray.plot.facetgrid.FacetGrid at 0x2b01a98a5990>
dataset['ion_temperature'].isel(time=10).plot.line(
x='rho_tor_norm',
hue='run',
col='ion',
)
<xarray.plot.facetgrid.FacetGrid at 0x2b01a9867340>
Duqtools has some built-in plots as well, using altair, that are designed with larger data sets and interactivity in mind.
from duqtools.api import alt_line_chart
chart = alt_line_chart(dataset.isel(time=[0, 1, 2, 3]),
x='rho_tor_norm',
y='ion_temperature')
chart