In [1]:

%load_ext autoreload
%autoreload 2

%load_ext autoreload %autoreload 2

Working with IMAS files¶

This example shows how duqtools can be used to manipulate IMAS data using ImasHandle and IDSMapping.

Copying data¶

This example shows how duqtools can be used to copy data.

In [2]:

from duqtools.api import ImasHandle

from duqtools.api import ImasHandle

First, we construct and Imas handle, consisting of the user name, database (sometimes referred to as machine name), shot and run number. Together these form an Imas path that point to some data on the system.

We can print the path, and check that the data exists.

Note that the user is optional. Duqtools will fill in the current user by default.

In [3]:

handle = ImasHandle(user='g2ssmee', db='jet', shot=94875, run=8000)
print(handle.path())
handle.exists()

handle = ImasHandle(user='g2ssmee', db='jet', shot=94875, run=8000) print(handle.path()) handle.exists()

/afs/eufus.eu/user/g/g2ssmee/public/imasdb/jet/3/0/ids_948758000.datafile

Out[3]:

True

Let's say we want to make a copy of this data set to modify. We should construct a new Imas handle to specify the target location. In the next cell, we create the Imas handle from a string instead. Again, the user is optional, so the string 'jet/94875/8888' would give the same result.

In [4]:

target = ImasHandle.from_string('g2ssmee/jet/94785/8888')
print(target.path())
target.exists()

target = ImasHandle.from_string('g2ssmee/jet/94785/8888') print(target.path()) target.exists()

/afs/eufus.eu/user/g/g2ssmee/public/imasdb/jet/3/0/ids_947858888.datafile

Out[4]:

True

The template data can be copied to the new location.

In [5]:

handle.copy_data_to(target)
target.exists()

handle.copy_data_to(target) target.exists()

- Copy ids from template to : g2ssmee/jet/94785/8888
INFO:screen:- Copy ids from template to : g2ssmee/jet/94785/8888

Out[5]:

True

If you are unhappy with the outcome, wecan delete the data using the ImasHandle.delete() method. Let's not do that for now.

In [6]:

# target.delete()
target.exists()

# target.delete() target.exists()

Out[6]:

True

Local IMAS databases¶

Duqtools is compatible with local imas databases, so you can specify the path to your imasdb directly:

In [7]:

handle = ImasHandle(user='/afs/eufus.eu/user/g/g2ssmee/public/imasdb',
                    db='jet',
                    shot=94875,
                    run=8000)
print(handle.path())
handle.exists()

handle = ImasHandle(user='/afs/eufus.eu/user/g/g2ssmee/public/imasdb', db='jet', shot=94875, run=8000) print(handle.path()) handle.exists()

/afs/eufus.eu/user/g/g2ssmee/public/imasdb/jet/3/0/ids_948758000.datafile

Out[7]:

True

Exploring, modifying, and saving data¶

We developed duqtools to make it as straightforward as possible to manipulate IDSs. In this section, we show an example of how one may go about doing so.

Let's say we want to look at 'core_profiles' > 'profiles_1d' > 't_i_average'.

First, we retrieve the 'core_profiles' IDS.

In [8]:

cp = target.get('core_profiles')
cp

cp = target.get('core_profiles') cp

Out[8]:

IDSMapping(
  profiles_1d = ...
  vacuum_toroidal_field = ...
  time = ...
)

Duqtools wraps the IMAS database in a dict-like object, so that the different datasets can be accessed like a dict.

In [9]:

cp['profiles_1d/0/t_i_average']

cp['profiles_1d/0/t_i_average']

Out[9]:

array([10873.96082052, 10872.03673416, 10867.81168627, 10857.14694854,
       10839.70936357, 10810.41005674, 10768.73609831, 10717.18121687,
       10656.83014146, 10587.90629975, 10510.8825408 , 10426.32395967,
       10334.37851785, 10235.50941984, 10129.89201314, 10017.95409844,
        9899.86968924,  9776.03781595,  9646.63883809,  9512.05793477,
        9372.48695856,  9228.30672488,  9079.79858345,  8927.2307248 ,
        8771.001231  ,  8611.41002345,  8448.74656226,  8283.47218662,
        8115.83427092,  7946.37952659,  7775.362634  ,  7603.26181556,
        7430.46655014,  7257.31070955,  7084.25627928,  6911.64950335,
        6739.84399765,  6569.20529548,  6399.99075788,  6232.39309061,
        6067.04216858,  5904.90863603,  5746.40291772,  5591.66680358,
        5440.56835546,  5293.2622794 ,  5151.00788034,  5013.84737283,
        4880.92735065,  4752.21217566,  4627.47418283,  4506.80740179,
        4390.03306972,  4276.68716968,  4167.13381318,  4061.02844256,
        3957.9525705 ,  3857.84875559,  3760.31831585,  3665.19036274,
        3572.26780325,  3481.58015413,  3392.99882522,  3306.33913114,
        3221.38281571,  3137.80026139,  3055.85629861,  2975.21039141,
        2895.6526337 ,  2817.71099145,  2741.48366389,  2666.79272628,
        2593.64297709,  2522.08395083,  2452.13261181,  2383.82234115,
        2317.14406963,  2251.5531281 ,  2187.1278063 ,  2124.12496133,
        2062.07507695,  2001.48957193,  1942.76277079,  1886.27239269,
        1831.48816948,  1778.05842728,  1726.6317098 ,  1687.26031293,
        1667.55709752,  1668.17721166,  1586.95127615,  1519.66283828,
        1469.01982524,  1432.43775006,  1398.52365894,  1346.74496774,
        1254.3287541 ,  1108.8514321 ,   917.15362323,   702.3809399 ])

In [10]:

cp['profiles_1d/0/t_i_average'] *= 1.1

cp['profiles_1d/0/t_i_average'] *= 1.1

Once we changed the data, we can sync the changes back to the data file.

In [11]:

cp.sync(target)

cp.sync(target)

Plotting data¶

The example below shows how can plot some data using matplotlib.

In [12]:

x = cp['profiles_1d/0/grid/rho_tor_norm']
y = cp['profiles_1d/0/t_i_average']

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.plot(x, y)
ax.set_xlabel('rho_tor_norm')
ax.set_ylabel('t_i_average')

x = cp['profiles_1d/0/grid/rho_tor_norm'] y = cp['profiles_1d/0/t_i_average'] import matplotlib.pyplot as plt fig, ax = plt.subplots() ax.plot(x, y) ax.set_xlabel('rho_tor_norm') ax.set_ylabel('t_i_average')

Out[12]:

Text(0, 0.5, 't_i_average')

In [ ]: