!pip install matplotlib s3fs "xarray[io]"

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import zarr
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from scipy.signal import stft

Level 2 Data

In this notebook we demonstrate the variety of different data available in the FAIR MAST dataset. In this example we are using level 2 MAST data, which includes cropping, interpolation, calibration, etc. of each signal, as well as mapping each diagnostic group try and follow IMAS naming convetions. The level 2 data is well-indexed data and follows the FAIR principles. Shots are also filtered using the plasma current to remove shots which were only used for testing, comissioning, machine calibration etc.

First we need to connect to the remote S3 storage bucket to access the data. Each shot from MAST is stored as a seperate Zarr file.

Using fsspec and xarray we can remotely read data directly over the web. In the example below we also turn on local file caching, allowing us to avoid reading over the network multiple times.

shot_id = 30421

endpoint_url = 'https://s3.echo.stfc.ac.uk'
url = f's3://mast/level2/shots/{shot_id}.zarr'

# Get a handle to the remote file
store = zarr.storage.FsspecStore.from_url(
    url,
    storage_options=dict(
        protocol='simplecache',
        target_protocol="s3",
        cache_storage='.cache',
        target_options=dict(
            anon=True, endpoint_url=endpoint_url, asynchronous=True
        )
    )
)

Summary Profiles

The summary group provides a collection of general physics quantities for an experiment.

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def plot_1d_profiles(profiles: xr.Dataset):
    """Helper function for plotting 1D profiles"""
    n = int(np.ceil(len(profiles.data_vars) / 2))
    fig, axes = plt.subplots(n, 2, figsize=(10, 2*n))
    axes = axes.flatten()

    for i, name in enumerate(profiles.data_vars.keys()):
        profiles[name].plot(x='time', ax=axes[i])

    for ax in axes:
        ax.grid('on', alpha=0.5)
        ax.set_xlim(profiles.time.min(), profiles.time.max())

    plt.tight_layout()

profiles = xr.open_zarr(store, group='summary')

plot_1d_profiles(profiles)
profiles

<xarray.Dataset> Size: 163kB
Dimensions:              (time: 2906)
Coordinates:
  * time                 (time) float64 23kB -0.0612 -0.06095 ... 0.6648 0.665
Data variables:
    greenwald_density    (time) float64 23kB ...
    ip                   (time) float64 23kB ...
    line_average_n_e     (time) float64 23kB ...
    neutron_rates_total  (time) float64 23kB ...
    power_nbi            (time) float64 23kB ...
    power_radiated       (time) float64 23kB ...
Attributes:
    description:  
    imas:         summary
    label:        Plasma Current
    name:         summary
    uda_name:     AMC_PLASMA CURRENT
    units:        A

xarray.Dataset

• Dimensions:
  • time: 2906
• Coordinates: (1)
  • time
    (time)
    float64
    -0.0612 -0.06095 ... 0.6648 0.665

    units :

    s

    array([-0.0612 , -0.06095, -0.0607 , ...,  0.66455,  0.6648 ,  0.66505],
          shape=(2906,))

• Data variables: (6)
  • greenwald_density
    (time)
    float64
    ...

    description :

    label :

    Greenwald density limit

    name :

    greenwald_density

    uda_name :

    ESM_N_GREENWALD

    units :

    1 / m ** 3

    [2906 values with dtype=float64]

  • ip
    (time)
    float64
    ...

    description :

    imas :

    summary.global_quantities.ip

    label :

    Plasma Current

    name :

    ip

    uda_name :

    AMC_PLASMA CURRENT

    units :

    A

    [2906 values with dtype=float64]

  • line_average_n_e
    (time)
    float64
    ...

    description :

    imas :

    summary.line_average.n_e.value

    label :

    Ne_bar

    name :

    line_average_n_e

    uda_name :

    ESM_NE_BAR

    units :

    1 / m ** 3

    [2906 values with dtype=float64]

  • neutron_rates_total
    (time)
    float64
    ...

    description :

    imas :

    summary.fusion.neutron_rates.total

    label :

    neutrons

    name :

    neutron_rates_total

    uda_name :

    ANU_NEUTRONS

    units :

    Hz

    [2906 values with dtype=float64]

  • power_nbi
    (time)
    float64
    ...

    description :

    imas :

    summary.heating_current_drive.power_nbi

    label :

    P(SS+SW)

    name :

    power_nbi

    uda_name :

    ANB_TOT_SUM_POWER

    units :

    W

    [2906 values with dtype=float64]

  • power_radiated
    (time)
    float64
    ...

    description :

    imas :

    summary.global_quantities.power_radiated.value

    label :

    P_Rad_Pol

    name :

    power_radiated

    uda_name :

    ABM_PRAD_POL

    units :

    W

    [2906 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(Index([-0.06120026111602783, -0.06095026111602783, -0.06070026111602783,
           -0.06045026111602783, -0.06020026111602783, -0.05995026111602783,
           -0.05970026111602783, -0.05945026111602783, -0.05920026111602783,
           -0.05895026111602783,
           ...
             0.6627997388839728,   0.6630497388839728,   0.6632997388839728,
             0.6635497388839728,   0.6637997388839728,   0.6640497388839728,
             0.6642997388839729,   0.6645497388839728,   0.6647997388839728,
             0.6650497388839728],
          dtype='float64', name='time', length=2906))

• Attributes: (6)

  description :

  imas :

  summary

  label :

  Plasma Current

  name :

  summary

  uda_name :

  AMC_PLASMA CURRENT

  units :

  A

Pulse Schedule

profiles = xr.open_zarr(store, group='pulse_schedule')

fig, axes = plt.subplots(2, 1, figsize=(10, 5))
axes = axes.flatten()
profiles['i_plasma'].plot(x='time', ax=axes[0])
profiles['n_e_line'].plot(x='time', ax=axes[1])


for ax in axes:
    ax.grid('on', alpha=0.5)
plt.tight_layout()

profiles

<xarray.Dataset> Size: 70kB
Dimensions:   (time: 2906)
Coordinates:
  * time      (time) float64 23kB -0.0612 -0.06095 -0.0607 ... 0.6648 0.665
Data variables:
    i_plasma  (time) float64 23kB ...
    n_e_line  (time) float64 23kB ...
Attributes:
    description:  
    imas:         pulse_schedule
    label:        /xdc/ip/t/ipref
    name:         pulse_schedule
    uda_name:     /xdc/ip/t/ipref
    units:        A

xarray.Dataset

• Dimensions:
  • time: 2906
• Coordinates: (1)
  • time
    (time)
    float64
    -0.0612 -0.06095 ... 0.6648 0.665

    units :

    s

    array([-0.0612 , -0.06095, -0.0607 , ...,  0.66455,  0.6648 ,  0.66505],
          shape=(2906,))

• Data variables: (2)
  • i_plasma
    (time)
    float64
    ...

    description :

    imas :

    pulse_schedule.flux_control.i_plasma

    label :

    /xdc/ip/t/ipref

    name :

    i_plasma

    uda_name :

    /xdc/ip/t/ipref

    units :

    A

    [2906 values with dtype=float64]

  • n_e_line
    (time)
    float64
    ...

    description :

    imas :

    pulse_schedule.density_control.n_e_line

    label :

    /xdc/density/t/nelref

    name :

    n_e_line

    uda_name :

    /xdc/density/t/nelref

    units :

    1 / m ** 2

    [2906 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(Index([-0.06120026111602783, -0.06095026111602783, -0.06070026111602783,
           -0.06045026111602783, -0.06020026111602783, -0.05995026111602783,
           -0.05970026111602783, -0.05945026111602783, -0.05920026111602783,
           -0.05895026111602783,
           ...
             0.6627997388839728,   0.6630497388839728,   0.6632997388839728,
             0.6635497388839728,   0.6637997388839728,   0.6640497388839728,
             0.6642997388839729,   0.6645497388839728,   0.6647997388839728,
             0.6650497388839728],
          dtype='float64', name='time', length=2906))

• Attributes: (6)

  description :

  imas :

  pulse_schedule

  label :

  /xdc/ip/t/ipref

  name :

  pulse_schedule

  uda_name :

  /xdc/ip/t/ipref

  units :

  A

Magnetics

Magnetic diagnostics for equilibrium identification and plasma shape control.

profiles = xr.open_zarr(store, group='magnetics')

fig, axes = plt.subplots(4, 3, figsize=(8, 10))
axes = axes.flatten()

profiles['b_field_pol_probe_ccbv_field'].plot.line(x='time', ax=axes[0], add_legend=False)
profiles['b_field_pol_probe_obv_field'].plot.line(x='time', ax=axes[1], add_legend=False)
profiles['b_field_pol_probe_obr_field'].plot.line(x='time', ax=axes[2], add_legend=False)


profiles['b_field_pol_probe_omv_voltage'].plot.line(x='time_mirnov', ax=axes[3], add_legend=False)
profiles['b_field_pol_probe_cc_field'].plot.line(x='time_mirnov', ax=axes[4], add_legend=False)
profiles['b_field_tor_probe_cc_field'].plot.line(x='time_mirnov', ax=axes[5], add_legend=False)

profiles['b_field_tor_probe_saddle_field'].plot.line(x='time_saddle', ax=axes[6], add_legend=False)
profiles['b_field_tor_probe_saddle_voltage'].plot.line(x='time_saddle', ax=axes[7], add_legend=False)
profiles['b_field_tor_probe_omaha_voltage'].plot.line(x='time_omaha', ax=axes[8], add_legend=False)

profiles['flux_loop_flux'].plot.line(x='time', ax=axes[9], add_legend=False)
profiles['ip'].plot.line(x='time', ax=axes[10], add_legend=False)

for ax in axes:
    ax.grid('on', alpha=0.5)
plt.tight_layout()

profiles

<xarray.Dataset> Size: 335MB
Dimensions:                                   (b_field_pol_probe_cc_channel: 5,
                                               time_mirnov: 363201,
                                               b_field_pol_probe_ccbv_channel: 40,
                                               time: 3633,
                                               b_field_pol_probe_obr_channel: 18,
                                               b_field_pol_probe_obv_channel: 18,
                                               ...
                                               b_field_tor_probe_omaha_channel: 4,
                                               time_omaha: 7264001,
                                               b_field_tor_probe_saddle_field_channel: 12,
                                               time_saddle: 36321,
                                               b_field_tor_probe_saddle_voltage_channel: 12,
                                               flux_loop_channel: 15)
Coordinates: (12/14)
  * b_field_pol_probe_cc_channel              (b_field_pol_probe_cc_channel) <U13 260B ...
  * b_field_pol_probe_ccbv_channel            (b_field_pol_probe_ccbv_channel) <U10 2kB ...
  * b_field_pol_probe_obr_channel             (b_field_pol_probe_obr_channel) <U9 648B ...
  * b_field_pol_probe_obv_channel             (b_field_pol_probe_obv_channel) <U9 648B ...
  * b_field_pol_probe_omv_channel             (b_field_pol_probe_omv_channel) <U11 132B ...
  * b_field_tor_probe_cc_channel              (b_field_tor_probe_cc_channel) <U13 156B ...
    ...                                        ...
  * b_field_tor_probe_saddle_voltage_channel  (b_field_tor_probe_saddle_voltage_channel) <U15 720B ...
  * flux_loop_channel                         (flux_loop_channel) <U12 720B '...
  * time                                      (time) float64 29kB -0.0612 ......
  * time_mirnov                               (time_mirnov) float64 3MB -0.06...
  * time_omaha                                (time_omaha) float64 58MB -0.06...
  * time_saddle                               (time_saddle) float64 291kB -0....
Data variables:
    b_field_pol_probe_cc_field                (b_field_pol_probe_cc_channel, time_mirnov) float64 15MB ...
    b_field_pol_probe_ccbv_field              (b_field_pol_probe_ccbv_channel, time) float64 1MB ...
    b_field_pol_probe_obr_field               (b_field_pol_probe_obr_channel, time) float64 523kB ...
    b_field_pol_probe_obv_field               (b_field_pol_probe_obv_channel, time) float64 523kB ...
    b_field_pol_probe_omv_voltage             (b_field_pol_probe_omv_channel, time_mirnov) float64 9MB ...
    b_field_tor_probe_cc_field                (b_field_tor_probe_cc_channel, time_mirnov) float64 9MB ...
    b_field_tor_probe_omaha_voltage           (b_field_tor_probe_omaha_channel, time_omaha) float64 232MB ...
    b_field_tor_probe_saddle_field            (b_field_tor_probe_saddle_field_channel, time_saddle) float64 3MB ...
    b_field_tor_probe_saddle_voltage          (b_field_tor_probe_saddle_voltage_channel, time_saddle) float64 3MB ...
    flux_loop_flux                            (flux_loop_channel, time) float64 436kB ...
    ip                                        (time) float64 29kB -6.039e+03 ...
Attributes:
    description:  
    imas:         magnetics
    label:        Plasma Current
    name:         magnetics
    uda_name:     AMC_PLASMA CURRENT
    units:        A

xarray.Dataset

• Dimensions:
  • b_field_pol_probe_cc_channel: 5
  • time_mirnov: 363201
  • b_field_pol_probe_ccbv_channel: 40
  • time: 3633
  • b_field_pol_probe_obr_channel: 18
  • b_field_pol_probe_obv_channel: 18
  • b_field_pol_probe_omv_channel: 3
  • b_field_tor_probe_cc_channel: 3
  • b_field_tor_probe_omaha_channel: 4
  • time_omaha: 7264001
  • b_field_tor_probe_saddle_field_channel: 12
  • time_saddle: 36321
  • b_field_tor_probe_saddle_voltage_channel: 12
  • flux_loop_channel: 15
• Coordinates: (14)
  • b_field_pol_probe_cc_channel
    (b_field_pol_probe_cc_channel)
    <U13
    'xmc/CC/MV/201' ... 'xmc/CC/MV/240'

    array(['xmc/CC/MV/201', 'xmc/CC/MV/210', 'xmc/CC/MV/220', 'xmc/CC/MV/230',
           'xmc/CC/MV/240'], dtype='<U13')

  • b_field_pol_probe_ccbv_channel
    (b_field_pol_probe_ccbv_channel)
    <U10
    'AMB_CCBV01' ... 'AMB_CCBV40'

    array(['AMB_CCBV01', 'AMB_CCBV02', 'AMB_CCBV03', 'AMB_CCBV04', 'AMB_CCBV05',
           'AMB_CCBV06', 'AMB_CCBV07', 'AMB_CCBV08', 'AMB_CCBV09', 'AMB_CCBV10',
           'AMB_CCBV11', 'AMB_CCBV12', 'AMB_CCBV13', 'AMB_CCBV14', 'AMB_CCBV15',
           'AMB_CCBV16', 'AMB_CCBV17', 'AMB_CCBV18', 'AMB_CCBV19', 'AMB_CCBV20',
           'AMB_CCBV21', 'AMB_CCBV22', 'AMB_CCBV23', 'AMB_CCBV24', 'AMB_CCBV25',
           'AMB_CCBV26', 'AMB_CCBV27', 'AMB_CCBV28', 'AMB_CCBV29', 'AMB_CCBV30',
           'AMB_CCBV31', 'AMB_CCBV32', 'AMB_CCBV33', 'AMB_CCBV34', 'AMB_CCBV35',
           'AMB_CCBV36', 'AMB_CCBV37', 'AMB_CCBV38', 'AMB_CCBV39', 'AMB_CCBV40'],
          dtype='<U10')

  • b_field_pol_probe_obr_channel
    (b_field_pol_probe_obr_channel)
    <U9
    'AMB_OBR01' ... 'AMB_OBR18'

    array(['AMB_OBR01', 'AMB_OBR02', 'AMB_OBR03', 'AMB_OBR04', 'AMB_OBR05',
           'AMB_OBR06', 'AMB_OBR07', 'AMB_OBR08', 'AMB_OBR09', 'AMB_OBR10',
           'AMB_OBR11', 'AMB_OBR12', 'AMB_OBR13', 'AMB_OBR14', 'AMB_OBR15',
           'AMB_OBR16', 'AMB_OBR17', 'AMB_OBR18'], dtype='<U9')

  • b_field_pol_probe_obv_channel
    (b_field_pol_probe_obv_channel)
    <U9
    'AMB_OBV01' ... 'AMB_OBV18'

    array(['AMB_OBV01', 'AMB_OBV02', 'AMB_OBV03', 'AMB_OBV04', 'AMB_OBV05',
           'AMB_OBV06', 'AMB_OBV07', 'AMB_OBV08', 'AMB_OBV09', 'AMB_OBV10',
           'AMB_OBV11', 'AMB_OBV12', 'AMB_OBV13', 'AMB_OBV14', 'AMB_OBV15',
           'AMB_OBV16', 'AMB_OBV17', 'AMB_OBV18'], dtype='<U9')

  • b_field_pol_probe_omv_channel
    (b_field_pol_probe_omv_channel)
    <U11
    'xmc/OMV/110' ... 'xmc/OMV/310'

    array(['xmc/OMV/110', 'xmc/OMV/210', 'xmc/OMV/310'], dtype='<U11')

  • b_field_tor_probe_cc_channel
    (b_field_tor_probe_cc_channel)
    <U13
    'xmc/CC/MT/201' ... 'xmc/CC/MT/212'

    array(['xmc/CC/MT/201', 'xmc/CC/MT/206', 'xmc/CC/MT/212'], dtype='<U13')

  • b_field_tor_probe_omaha_channel
    (b_field_tor_probe_omaha_channel)
    <U14
    '/xmo/OMAHA/1LZ' ... '/xmo/OMAHA...

    array(['/xmo/OMAHA/1LZ', '/xmo/OMAHA/3LZ', '/xmo/OMAHA/5LZ', '/xmo/OMAHA/6LZ'],
          dtype='<U14')

  • b_field_tor_probe_saddle_field_channel
    (b_field_tor_probe_saddle_field_channel)
    <U11
    'ASM_SAD/M01' ... 'ASM_SAD/M12'

    array(['ASM_SAD/M01', 'ASM_SAD/M02', 'ASM_SAD/M03', 'ASM_SAD/M04',
           'ASM_SAD/M05', 'ASM_SAD/M06', 'ASM_SAD/M07', 'ASM_SAD/M08',
           'ASM_SAD/M09', 'ASM_SAD/M10', 'ASM_SAD/M11', 'ASM_SAD/M12'],
          dtype='<U11')

  • b_field_tor_probe_saddle_voltage_channel
    (b_field_tor_probe_saddle_voltage_channel)
    <U15
    'XMB/SAD/OUT/M01' ... 'XMB/SAD/O...

    array(['XMB/SAD/OUT/M01', 'XMB/SAD/OUT/M02', 'XMB/SAD/OUT/M03',
           'XMB/SAD/OUT/M04', 'XMB/SAD/OUT/M05', 'XMB/SAD/OUT/M06',
           'XMB/SAD/OUT/M07', 'XMB/SAD/OUT/M08', 'XMB/SAD/OUT/M09',
           'XMB/SAD/OUT/M10', 'XMB/SAD/OUT/M11', 'XMB/SAD/OUT/M12'], dtype='<U15')

  • flux_loop_channel
    (flux_loop_channel)
    <U12
    'AMB_FL/CC03' ... 'AMB_FL/P6L/1'

    array(['AMB_FL/CC03', 'AMB_FL/CC04', 'AMB_FL/CC05', 'AMB_FL/CC07',
           'AMB_FL/CC09', 'AMB_FL/P3L/4', 'AMB_FL/P3U/1', 'AMB_FL/P3U/4',
           'AMB_FL/P4L/1', 'AMB_FL/P4L/4', 'AMB_FL/P4U/4', 'AMB_FL/P5L/1',
           'AMB_FL/P5L/4', 'AMB_FL/P5U/1', 'AMB_FL/P6L/1'], dtype='<U12')

  • time
    (time)
    float64
    -0.0612 -0.061 ... 0.665 0.6652

    units :

    s

    array([-0.0612, -0.061 , -0.0608, ...,  0.6648,  0.665 ,  0.6652],
          shape=(3633,))

  • time_mirnov
    (time_mirnov)
    float64
    -0.0612 -0.0612 ... 0.6652 0.6652

    units :

    s

    array([-0.0612  , -0.061198, -0.061196, ...,  0.665196,  0.665198,  0.6652  ],
          shape=(363201,))

  • time_omaha
    (time_omaha)
    float64
    -0.0612 -0.0612 ... 0.6652 0.6652

    units :

    s

    array([-0.0612, -0.0612, -0.0612, ...,  0.6652,  0.6652,  0.6652],
          shape=(7264001,))

  • time_saddle
    (time_saddle)
    float64
    -0.0612 -0.06118 ... 0.6652 0.6652

    units :

    s

    array([-0.0612 , -0.06118, -0.06116, ...,  0.66516,  0.66518,  0.6652 ],
          shape=(36321,))

• Data variables: (11)
  • b_field_pol_probe_cc_field
    (b_field_pol_probe_cc_channel, time_mirnov)
    float64
    ...

    description :

    Centre column poloidal mirnov array

    imas :

    magnetics.b_field_pol_probe[:].field.data

    label :

    Tesla/sec

    name :

    b_field_pol_probe_cc_field

    uda_name :

    xmc/CC/MV/201

    units :

    T

    [1816005 values with dtype=float64]

  • b_field_pol_probe_ccbv_field
    (b_field_pol_probe_ccbv_channel, time)
    float64
    ...

    description :

    Centre column Bv array

    imas :

    magnetics.b_field_pol_probe[:].field.data

    label :

    CCBV01

    name :

    b_field_pol_probe_ccbv_field

    uda_name :

    AMB_CCBV01

    units :

    T

    [145320 values with dtype=float64]

  • b_field_pol_probe_obr_field
    (b_field_pol_probe_obr_channel, time)
    float64
    ...

    description :

    Outer Br array

    imas :

    magnetics.b_field_pol_probe[:].field.data

    label :

    OBR01

    name :

    b_field_pol_probe_obr_field

    uda_name :

    AMB_OBR01

    units :

    T

    [65394 values with dtype=float64]

  • b_field_pol_probe_obv_field
    (b_field_pol_probe_obv_channel, time)
    float64
    ...

    description :

    Outer Bv array

    imas :

    magnetics.b_field_pol_probe[:].field.data

    name :

    b_field_pol_probe_obv_field

    units :

    T

    [65394 values with dtype=float64]

  • b_field_pol_probe_omv_voltage
    (b_field_pol_probe_omv_channel, time_mirnov)
    float64
    ...

    description :

    Outboard vertical mirnov array

    imas :

    magnetics.b_field_tor_probe[:].voltage.data

    label :

    Volt

    name :

    b_field_pol_probe_omv_voltage

    uda_name :

    xmc/OMV/110

    units :

    V

    [1089603 values with dtype=float64]

  • b_field_tor_probe_cc_field
    (b_field_tor_probe_cc_channel, time_mirnov)
    float64
    ...

    description :

    Centre column toroidal mirnov array

    imas :

    magnetics.b_field_tor_probe[:].field.data

    label :

    Tesla/sec

    name :

    b_field_tor_probe_cc_field

    uda_name :

    xmc/CC/MT/201

    units :

    T

    [1089603 values with dtype=float64]

  • b_field_tor_probe_omaha_voltage
    (b_field_tor_probe_omaha_channel, time_omaha)
    float64
    ...

    description :

    High frequency OMAHA toroidal mirnov array

    imas :

    magnetics.b_field_tor_probe[:].voltage.data

    label :

    arb

    name :

    b_field_tor_probe_omaha_voltage

    uda_name :

    /xmo/OMAHA/1LZ

    units :

    V

    [29056004 values with dtype=float64]

  • b_field_tor_probe_saddle_field
    (b_field_tor_probe_saddle_field_channel, time_saddle)
    float64
    ...

    description :

    imas :

    magnetics.b_field_tor_probe[:].field.data

    label :

    mT

    name :

    b_field_tor_probe_saddle_field

    uda_name :

    ASM_SAD/M01

    units :

    T

    [435852 values with dtype=float64]

  • b_field_tor_probe_saddle_voltage
    (b_field_tor_probe_saddle_voltage_channel, time_saddle)
    float64
    ...

    description :

    imas :

    magnetics.b_field_tor_probe[:].voltage.data

    label :

    V

    name :

    b_field_tor_probe_saddle_voltage

    uda_name :

    XMB/SAD/OUT/M01

    units :

    V

    [435852 values with dtype=float64]

  • flux_loop_flux
    (flux_loop_channel, time)
    float64
    ...

    description :

    imas :

    magnetics.flux_loop[:].flux.data

    label :

    FL/CC03

    name :

    flux_loop_flux

    uda_name :

    AMB_FL/CC03

    units :

    Wb

    [54495 values with dtype=float64]

  • ip
    (time)
    float64
    -6.039e+03 -6.039e+03 ... 427.5

    description :

    imas :

    magnetics.ip[:].data

    label :

    Plasma Current

    name :

    ip

    uda_name :

    AMC_PLASMA CURRENT

    units :

    A

    array([-6039.37793 , -6039.37793 , -6516.910645, ...,   617.256775,
             427.544525,   427.544525], shape=(3633,))

• Indexes: (14)
  • b_field_pol_probe_cc_channel
    PandasIndex

    PandasIndex(Index(['xmc/CC/MV/201', 'xmc/CC/MV/210', 'xmc/CC/MV/220', 'xmc/CC/MV/230',
           'xmc/CC/MV/240'],
          dtype='object', name='b_field_pol_probe_cc_channel'))

  • b_field_pol_probe_ccbv_channel
    PandasIndex

    PandasIndex(Index(['AMB_CCBV01', 'AMB_CCBV02', 'AMB_CCBV03', 'AMB_CCBV04', 'AMB_CCBV05',
           'AMB_CCBV06', 'AMB_CCBV07', 'AMB_CCBV08', 'AMB_CCBV09', 'AMB_CCBV10',
           'AMB_CCBV11', 'AMB_CCBV12', 'AMB_CCBV13', 'AMB_CCBV14', 'AMB_CCBV15',
           'AMB_CCBV16', 'AMB_CCBV17', 'AMB_CCBV18', 'AMB_CCBV19', 'AMB_CCBV20',
           'AMB_CCBV21', 'AMB_CCBV22', 'AMB_CCBV23', 'AMB_CCBV24', 'AMB_CCBV25',
           'AMB_CCBV26', 'AMB_CCBV27', 'AMB_CCBV28', 'AMB_CCBV29', 'AMB_CCBV30',
           'AMB_CCBV31', 'AMB_CCBV32', 'AMB_CCBV33', 'AMB_CCBV34', 'AMB_CCBV35',
           'AMB_CCBV36', 'AMB_CCBV37', 'AMB_CCBV38', 'AMB_CCBV39', 'AMB_CCBV40'],
          dtype='object', name='b_field_pol_probe_ccbv_channel'))

  • b_field_pol_probe_obr_channel
    PandasIndex

    PandasIndex(Index(['AMB_OBR01', 'AMB_OBR02', 'AMB_OBR03', 'AMB_OBR04', 'AMB_OBR05',
           'AMB_OBR06', 'AMB_OBR07', 'AMB_OBR08', 'AMB_OBR09', 'AMB_OBR10',
           'AMB_OBR11', 'AMB_OBR12', 'AMB_OBR13', 'AMB_OBR14', 'AMB_OBR15',
           'AMB_OBR16', 'AMB_OBR17', 'AMB_OBR18'],
          dtype='object', name='b_field_pol_probe_obr_channel'))

  • b_field_pol_probe_obv_channel
    PandasIndex

    PandasIndex(Index(['AMB_OBV01', 'AMB_OBV02', 'AMB_OBV03', 'AMB_OBV04', 'AMB_OBV05',
           'AMB_OBV06', 'AMB_OBV07', 'AMB_OBV08', 'AMB_OBV09', 'AMB_OBV10',
           'AMB_OBV11', 'AMB_OBV12', 'AMB_OBV13', 'AMB_OBV14', 'AMB_OBV15',
           'AMB_OBV16', 'AMB_OBV17', 'AMB_OBV18'],
          dtype='object', name='b_field_pol_probe_obv_channel'))

  • b_field_pol_probe_omv_channel
    PandasIndex

    PandasIndex(Index(['xmc/OMV/110', 'xmc/OMV/210', 'xmc/OMV/310'], dtype='object', name='b_field_pol_probe_omv_channel'))

  • b_field_tor_probe_cc_channel
    PandasIndex

    PandasIndex(Index(['xmc/CC/MT/201', 'xmc/CC/MT/206', 'xmc/CC/MT/212'], dtype='object', name='b_field_tor_probe_cc_channel'))

  • b_field_tor_probe_omaha_channel
    PandasIndex

    PandasIndex(Index(['/xmo/OMAHA/1LZ', '/xmo/OMAHA/3LZ', '/xmo/OMAHA/5LZ', '/xmo/OMAHA/6LZ'], dtype='object', name='b_field_tor_probe_omaha_channel'))

  • b_field_tor_probe_saddle_field_channel
    PandasIndex

    PandasIndex(Index(['ASM_SAD/M01', 'ASM_SAD/M02', 'ASM_SAD/M03', 'ASM_SAD/M04',
           'ASM_SAD/M05', 'ASM_SAD/M06', 'ASM_SAD/M07', 'ASM_SAD/M08',
           'ASM_SAD/M09', 'ASM_SAD/M10', 'ASM_SAD/M11', 'ASM_SAD/M12'],
          dtype='object', name='b_field_tor_probe_saddle_field_channel'))

  • b_field_tor_probe_saddle_voltage_channel
    PandasIndex

    PandasIndex(Index(['XMB/SAD/OUT/M01', 'XMB/SAD/OUT/M02', 'XMB/SAD/OUT/M03',
           'XMB/SAD/OUT/M04', 'XMB/SAD/OUT/M05', 'XMB/SAD/OUT/M06',
           'XMB/SAD/OUT/M07', 'XMB/SAD/OUT/M08', 'XMB/SAD/OUT/M09',
           'XMB/SAD/OUT/M10', 'XMB/SAD/OUT/M11', 'XMB/SAD/OUT/M12'],
          dtype='object', name='b_field_tor_probe_saddle_voltage_channel'))

  • flux_loop_channel
    PandasIndex

    PandasIndex(Index(['AMB_FL/CC03', 'AMB_FL/CC04', 'AMB_FL/CC05', 'AMB_FL/CC07',
           'AMB_FL/CC09', 'AMB_FL/P3L/4', 'AMB_FL/P3U/1', 'AMB_FL/P3U/4',
           'AMB_FL/P4L/1', 'AMB_FL/P4L/4', 'AMB_FL/P4U/4', 'AMB_FL/P5L/1',
           'AMB_FL/P5L/4', 'AMB_FL/P5U/1', 'AMB_FL/P6L/1'],
          dtype='object', name='flux_loop_channel'))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783,  -0.06100026111602783, -0.060800261116027834,
           -0.060600261116027836,  -0.06040026111602784,  -0.06020026111602784,
            -0.06000026111602784,  -0.05980026111602784,  -0.05960026111602784,
            -0.05940026111602784,
           ...
              0.6633997388839677,    0.6635997388839678,    0.6637997388839678,
              0.6639997388839678,    0.6641997388839678,    0.6643997388839677,
              0.6645997388839677,    0.6647997388839678,    0.6649997388839678,
              0.6651997388839678],
          dtype='float64', name='time', length=3633))

  • time_mirnov
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783,  -0.06119826111602783,  -0.06119626111602783,
           -0.061194261116027826, -0.061192261116027824,  -0.06119026111602782,
            -0.06118826111602782,  -0.06118626111602782, -0.061184261116027816,
           -0.061182261116027814,
           ...
              0.6651817388846986,    0.6651837388846986,    0.6651857388846987,
              0.6651877388846986,    0.6651897388846986,    0.6651917388846986,
              0.6651937388846987,    0.6651957388846986,    0.6651977388846986,
              0.6651997388846986],
          dtype='float64', name='time_mirnov', length=363201))

  • time_omaha
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783,  -0.06120016111602783, -0.061200061116027826,
            -0.06119996111602782,  -0.06119986111602782,  -0.06119976111602782,
           -0.061199661116027815,  -0.06119956111602781,  -0.06119946111602781,
           -0.061199361116027806,
           ...
              0.6651988389048602,    0.6651989389048603,    0.6651990389048603,
              0.6651991389048603,    0.6651992389048602,    0.6651993389048603,
              0.6651994389048603,    0.6651995389048603,    0.6651996389048602,
              0.6651997389048603],
          dtype='float64', name='time_omaha', length=7264001))

  • time_saddle
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783,  -0.06118026111602783, -0.061160261116027834,
           -0.061140261116027834, -0.061120261116027835, -0.061100261116027836,
            -0.06108026111602784,  -0.06106026111602784,  -0.06104026111602784,
            -0.06102026111602784,
           ...
              0.6650197388839426,    0.6650397388839426,    0.6650597388839425,
              0.6650797388839426,    0.6650997388839426,    0.6651197388839426,
              0.6651397388839426,    0.6651597388839425,    0.6651797388839426,
              0.6651997388839426],
          dtype='float64', name='time_saddle', length=36321))

• Attributes: (6)

  description :

  imas :

  magnetics

  label :

  Plasma Current

  name :

  magnetics

  uda_name :

  AMC_PLASMA CURRENT

  units :

  A

Looking at the spectrogram of one of the mirnov coils can show us information about the MHD modes. Here we see several mode instabilities occuring before the plasma is lost.

ds = profiles['b_field_pol_probe_omv_voltage'].isel(b_field_pol_probe_omv_channel=1)
# Parameters to limit the number of frequencies
nperseg = 2000  # Number of points per segment
nfft = 2000  # Number of FFT points

# Compute the Short-Time Fourier Transform (STFT)
sample_rate = 1/(ds.time_mirnov[1] - ds.time_mirnov[0])
f, t, Zxx = stft(ds, fs=int(sample_rate), nperseg=nperseg, nfft=nfft)

fig, ax = plt.subplots(figsize=(15, 5))
cax = ax.pcolormesh(t, f/1000, np.abs(Zxx), shading='nearest', cmap='jet', norm=LogNorm(vmin=1e-5))
ax.set_ylim(0, 50)
ax.set_title(f'Shot {shot_id}')
ax.set_ylabel('Frequency [Hz]')
ax.set_xlabel('Time [sec]')
plt.colorbar(cax, ax=ax)
plt.show()

Spectrometer Visible

Spectrometer in visible light range diagnostic

profiles = xr.open_zarr(store, group='spectrometer_visible')
profiles['filter_spectrometer_dalpha_voltage'].plot.line(x='time')
profiles['filter_spectrometer_bes_voltage'].isel(bes_channel=0).plot.line(x='time_bes')
profiles

<xarray.Dataset> Size: 385MB
Dimensions:                             (time: 36321, bes_channel: 32,
                                         time_bes: 1452801, dalpha_channel: 3)
Coordinates:
  * bes_channel                         (bes_channel) <U13 2kB 'xbt/channel01...
  * dalpha_channel                      (dalpha_channel) <U13 156B 'XIM_DA/HM...
  * time                                (time) float64 291kB -0.0612 ... 0.6652
  * time_bes                            (time_bes) float64 12MB -0.0612 ... 0...
Data variables:
    density_gradient                    (time) float64 291kB ...
    filter_spectrometer_bes_voltage     (bes_channel, time_bes) float64 372MB ...
    filter_spectrometer_dalpha_voltage  (dalpha_channel, time) float64 872kB ...
Attributes:
    description:  
    imas:         None
    label:        Volt
    name:         spectrometer_visible
    uda_name:     XIM_DA/HM10/R
    units:        V

xarray.Dataset

• Dimensions:
  • time: 36321
  • bes_channel: 32
  • time_bes: 1452801
  • dalpha_channel: 3
• Coordinates: (4)
  • bes_channel
    (bes_channel)
    <U13
    'xbt/channel01' ... 'xbt/channel32'

    array(['xbt/channel01', 'xbt/channel02', 'xbt/channel03', 'xbt/channel04',
           'xbt/channel05', 'xbt/channel06', 'xbt/channel07', 'xbt/channel08',
           'xbt/channel09', 'xbt/channel10', 'xbt/channel11', 'xbt/channel12',
           'xbt/channel13', 'xbt/channel14', 'xbt/channel15', 'xbt/channel16',
           'xbt/channel17', 'xbt/channel18', 'xbt/channel19', 'xbt/channel20',
           'xbt/channel21', 'xbt/channel22', 'xbt/channel23', 'xbt/channel24',
           'xbt/channel25', 'xbt/channel26', 'xbt/channel27', 'xbt/channel28',
           'xbt/channel29', 'xbt/channel30', 'xbt/channel31', 'xbt/channel32'],
          dtype='<U13')

  • dalpha_channel
    (dalpha_channel)
    <U13
    'XIM_DA/HM10/R' ... 'XIM_DA/HU10/T'

    array(['XIM_DA/HM10/R', 'XIM_DA/HM10/T', 'XIM_DA/HU10/T'], dtype='<U13')

  • time
    (time)
    float64
    -0.0612 -0.06118 ... 0.6652 0.6652

    units :

    s

    array([-0.0612 , -0.06118, -0.06116, ...,  0.66516,  0.66518,  0.6652 ],
          shape=(36321,))

  • time_bes
    (time_bes)
    float64
    -0.0612 -0.0612 ... 0.6652 0.6652

    units :

    s

    array([-0.0612  , -0.0612  , -0.061199, ...,  0.665199,  0.665199,  0.6652  ],
          shape=(1452801,))

• Data variables: (3)
  • density_gradient
    (time)
    float64
    ...

    description :

    label :

    m-4

    name :

    density_gradient

    uda_name :

    ADG_DENSITY_GRADIENT

    units :

    1 / m ** 4

    [36321 values with dtype=float64]

  • filter_spectrometer_bes_voltage
    (bes_channel, time_bes)
    float64
    ...

    description :

    imas :

    spectrometer_visible.channel[:].filter_spectrometer.photoelectric_voltage.data

    label :

    Volt

    name :

    filter_spectrometer_bes_voltage

    uda_name :

    xbt/channel01

    units :

    V

    [46489632 values with dtype=float64]

  • filter_spectrometer_dalpha_voltage
    (dalpha_channel, time)
    float64
    ...

    description :

    imas :

    spectrometer_visible.channel[:].filter_spectrometer.photoelectric_voltage.data

    label :

    Volt

    name :

    filter_spectrometer_dalpha_voltage

    uda_name :

    XIM_DA/HM10/R

    units :

    V

    [108963 values with dtype=float64]

• Indexes: (4)
  • bes_channel
    PandasIndex

    PandasIndex(Index(['xbt/channel01', 'xbt/channel02', 'xbt/channel03', 'xbt/channel04',
           'xbt/channel05', 'xbt/channel06', 'xbt/channel07', 'xbt/channel08',
           'xbt/channel09', 'xbt/channel10', 'xbt/channel11', 'xbt/channel12',
           'xbt/channel13', 'xbt/channel14', 'xbt/channel15', 'xbt/channel16',
           'xbt/channel17', 'xbt/channel18', 'xbt/channel19', 'xbt/channel20',
           'xbt/channel21', 'xbt/channel22', 'xbt/channel23', 'xbt/channel24',
           'xbt/channel25', 'xbt/channel26', 'xbt/channel27', 'xbt/channel28',
           'xbt/channel29', 'xbt/channel30', 'xbt/channel31', 'xbt/channel32'],
          dtype='object', name='bes_channel'))

  • dalpha_channel
    PandasIndex

    PandasIndex(Index(['XIM_DA/HM10/R', 'XIM_DA/HM10/T', 'XIM_DA/HU10/T'], dtype='object', name='dalpha_channel'))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783,  -0.06118026111602783, -0.061160261116027834,
           -0.061140261116027834, -0.061120261116027835, -0.061100261116027836,
            -0.06108026111602784,  -0.06106026111602784,  -0.06104026111602784,
            -0.06102026111602784,
           ...
              0.6650197388839426,    0.6650397388839426,    0.6650597388839425,
              0.6650797388839426,    0.6650997388839426,    0.6651197388839426,
              0.6651397388839426,    0.6651597388839425,    0.6651797388839426,
              0.6651997388839426],
          dtype='float64', name='time', length=36321))

  • time_bes
    PandasIndex

    PandasIndex(Index([-0.06120026111602783, -0.06119976111602783, -0.06119926111602783,
           -0.06119876111602783, -0.06119826111602783, -0.06119776111602783,
           -0.06119726111602783, -0.06119676111602783, -0.06119626111602783,
           -0.06119576111602783,
           ...
             0.6651952388846987,   0.6651957388846986,   0.6651962388846986,
             0.6651967388846987,   0.6651972388846986,   0.6651977388846986,
             0.6651982388846986,   0.6651987388846986,   0.6651992388846987,
             0.6651997388846986],
          dtype='float64', name='time_bes', length=1452801))

• Attributes: (6)

  description :

  imas :

  None

  label :

  Volt

  name :

  spectrometer_visible

  uda_name :

  XIM_DA/HM10/R

  units :

  V

PF Active

profiles = xr.open_zarr(store, group='pf_active')
fig, axes = plt.subplots(3, 1)
axes = axes.flatten()

profiles['coil_current'].plot.line(x='time', ax=axes[0], add_legend=False)
profiles['coil_voltage'].plot.line(x='time', ax=axes[1], add_legend=False)
profiles['solenoid_current'].plot.line(x='time', ax=axes[2], add_legend=False)

plt.tight_layout()
profiles

<xarray.Dataset> Size: 699kB
Dimensions:           (channel: 14, time: 2906)
Coordinates:
  * channel           (channel) <U21 1kB '/xdc/pf/f/p1' ... 'AMC_P5U FEED CUR...
  * time              (time) float64 23kB -0.0612 -0.06095 ... 0.6648 0.665
Data variables:
    coil_current      (channel, time) float64 325kB ...
    coil_voltage      (channel, time) float64 325kB ...
    solenoid_current  (time) float64 23kB 8.764e+03 8.813e+03 ... 773.8 780.2
Attributes:
    description:  
    imas:         pf_active
    label:        Sol Current
    name:         pf_active
    uda_name:     AMC_SOL CURRENT
    units:        A

xarray.Dataset

• Dimensions:
  • channel: 14
  • time: 2906
• Coordinates: (2)
  • channel
    (channel)
    <U21
    '/xdc/pf/f/p1' ... 'AMC_P5U FEED...

    array(['/xdc/pf/f/p1', '/xdc/pf/f/p2', '/xdc/pf/f/p4', '/xdc/pf/f/p5',
           'AMC_P2IL FEED CURRENT', 'AMC_P2IU FEED CURRENT',
           'AMC_P2OL FEED CURRENT', 'AMC_P2OU FEED CURRENT',
           'AMC_P3L FEED CURRENT', 'AMC_P3U FEED CURRENT', 'AMC_P4L FEED CURRENT',
           'AMC_P4U FEED CURRENT', 'AMC_P5L FEED CURRENT', 'AMC_P5U FEED CURRENT'],
          dtype='<U21')

  • time
    (time)
    float64
    -0.0612 -0.06095 ... 0.6648 0.665

    units :

    s

    array([-0.0612 , -0.06095, -0.0607 , ...,  0.66455,  0.6648 ,  0.66505],
          shape=(2906,))

• Data variables: (3)
  • coil_current
    (channel, time)
    float64
    ...

    description :

    imas :

    pf_active.coil[:].current.data

    label :

    P2il Feed Current

    name :

    coil_current

    uda_name :

    AMC_P2IL FEED CURRENT

    units :

    A

    [40684 values with dtype=float64]

  • coil_voltage
    (channel, time)
    float64
    ...

    description :

    imas :

    pf_active.coil[:].voltage.data

    label :

    /xdc/pf/f/p1

    name :

    coil_voltage

    uda_name :

    /xdc/pf/f/p1

    units :

    V

    [40684 values with dtype=float64]

  • solenoid_current
    (time)
    float64
    8.764e+03 8.813e+03 ... 773.8 780.2

    description :

    label :

    Sol Current

    name :

    solenoid_current

    uda_name :

    AMC_SOL CURRENT

    units :

    A

    array([8764.074219, 8813.039062, 8868.389648, ...,  778.0495  ,  773.791504,
            780.17804 ], shape=(2906,))

• Indexes: (2)
  • channel
    PandasIndex

    PandasIndex(Index(['/xdc/pf/f/p1', '/xdc/pf/f/p2', '/xdc/pf/f/p4', '/xdc/pf/f/p5',
           'AMC_P2IL FEED CURRENT', 'AMC_P2IU FEED CURRENT',
           'AMC_P2OL FEED CURRENT', 'AMC_P2OU FEED CURRENT',
           'AMC_P3L FEED CURRENT', 'AMC_P3U FEED CURRENT', 'AMC_P4L FEED CURRENT',
           'AMC_P4U FEED CURRENT', 'AMC_P5L FEED CURRENT', 'AMC_P5U FEED CURRENT'],
          dtype='object', name='channel'))

  • time
    PandasIndex

    PandasIndex(Index([-0.06120026111602783, -0.06095026111602783, -0.06070026111602783,
           -0.06045026111602783, -0.06020026111602783, -0.05995026111602783,
           -0.05970026111602783, -0.05945026111602783, -0.05920026111602783,
           -0.05895026111602783,
           ...
             0.6627997388839728,   0.6630497388839728,   0.6632997388839728,
             0.6635497388839728,   0.6637997388839728,   0.6640497388839728,
             0.6642997388839729,   0.6645497388839728,   0.6647997388839728,
             0.6650497388839728],
          dtype='float64', name='time', length=2906))

• Attributes: (6)

  description :

  imas :

  pf_active

  label :

  Sol Current

  name :

  pf_active

  uda_name :

  AMC_SOL CURRENT

  units :

  A

Soft X-rays

profiles = xr.open_zarr(store, group='soft_x_rays')
fig, axes = plt.subplots(3, 1)


profiles['horizontal_cam_lower'].plot.line(x='time', ax=axes[1], add_legend=False)
axes[1].set_ylim(0, 0.02)

profiles['horizontal_cam_upper'].plot.line(x='time', ax=axes[2], add_legend=False)
axes[2].set_ylim(0, 0.02)

if "tangential_cam" in profiles:
    profiles['tangential_cam'].plot.line(x='time', ax=axes[0], add_legend=False)
    axes[0].set_ylim(0, 0.2)

plt.tight_layout()
profiles

<xarray.Dataset> Size: 160MB
Dimensions:                       (horizontal_cam_lower_channel: 18,
                                   time: 363201,
                                   horizontal_cam_upper_channel: 18,
                                   tangential_cam_channel: 18)
Coordinates:
  * horizontal_cam_lower_channel  (horizontal_cam_lower_channel) <U14 1kB '/x...
  * horizontal_cam_upper_channel  (horizontal_cam_upper_channel) <U14 1kB '/x...
  * tangential_cam_channel        (tangential_cam_channel) <U12 864B '/xsx/TC...
  * time                          (time) float64 3MB -0.0612 -0.0612 ... 0.6652
Data variables:
    horizontal_cam_lower          (horizontal_cam_lower_channel, time) float64 52MB ...
    horizontal_cam_upper          (horizontal_cam_upper_channel, time) float64 52MB ...
    tangential_cam                (tangential_cam_channel, time) float64 52MB ...
Attributes:
    description:  
    imas:         None
    label:        Volt
    name:         soft_x_rays
    uda_name:     /xsx/TCAM/1
    units:        V

xarray.Dataset

• Dimensions:
  • horizontal_cam_lower_channel: 18
  • time: 363201
  • horizontal_cam_upper_channel: 18
  • tangential_cam_channel: 18
• Coordinates: (4)
  • horizontal_cam_lower_channel
    (horizontal_cam_lower_channel)
    <U14
    '/xsx/HCAM/L/1' ... '/xsx/HCAM/L...

    array(['/xsx/HCAM/L/1', '/xsx/HCAM/L/2', '/xsx/HCAM/L/3', '/xsx/HCAM/L/4',
           '/xsx/HCAM/L/5', '/xsx/HCAM/L/6', '/xsx/HCAM/L/7', '/xsx/HCAM/L/8',
           '/xsx/HCAM/L/9', '/xsx/HCAM/L/10', '/xsx/HCAM/L/11', '/xsx/HCAM/L/12',
           '/xsx/HCAM/L/13', '/xsx/HCAM/L/14', '/xsx/HCAM/L/15', '/xsx/HCAM/L/16',
           '/xsx/HCAM/L/17', '/xsx/HCAM/L/18'], dtype='<U14')

  • horizontal_cam_upper_channel
    (horizontal_cam_upper_channel)
    <U14
    '/xsx/HCAM/U/1' ... '/xsx/HCAM/U...

    array(['/xsx/HCAM/U/1', '/xsx/HCAM/U/2', '/xsx/HCAM/U/3', '/xsx/HCAM/U/4',
           '/xsx/HCAM/U/5', '/xsx/HCAM/U/6', '/xsx/HCAM/U/7', '/xsx/HCAM/U/8',
           '/xsx/HCAM/U/9', '/xsx/HCAM/U/10', '/xsx/HCAM/U/11', '/xsx/HCAM/U/12',
           '/xsx/HCAM/U/13', '/xsx/HCAM/U/14', '/xsx/HCAM/U/15', '/xsx/HCAM/U/16',
           '/xsx/HCAM/U/17', '/xsx/HCAM/U/18'], dtype='<U14')

  • tangential_cam_channel
    (tangential_cam_channel)
    <U12
    '/xsx/TCAM/1' ... '/xsx/TCAM/18'

    array(['/xsx/TCAM/1', '/xsx/TCAM/2', '/xsx/TCAM/3', '/xsx/TCAM/4',
           '/xsx/TCAM/5', '/xsx/TCAM/6', '/xsx/TCAM/7', '/xsx/TCAM/8',
           '/xsx/TCAM/9', '/xsx/TCAM/10', '/xsx/TCAM/11', '/xsx/TCAM/12',
           '/xsx/TCAM/13', '/xsx/TCAM/14', '/xsx/TCAM/15', '/xsx/TCAM/16',
           '/xsx/TCAM/17', '/xsx/TCAM/18'], dtype='<U12')

  • time
    (time)
    float64
    -0.0612 -0.0612 ... 0.6652 0.6652

    units :

    s

    array([-0.0612  , -0.061198, -0.061196, ...,  0.665196,  0.665198,  0.6652  ],
          shape=(363201,))

• Data variables: (3)
  • horizontal_cam_lower
    (horizontal_cam_lower_channel, time)
    float64
    ...

    description :

    label :

    Volt

    name :

    horizontal_cam_lower

    uda_name :

    /xsx/HCAM/L/1

    units :

    V

    [6537618 values with dtype=float64]

  • horizontal_cam_upper
    (horizontal_cam_upper_channel, time)
    float64
    ...

    description :

    label :

    Volt

    name :

    horizontal_cam_upper

    uda_name :

    /xsx/HCAM/U/1

    units :

    V

    [6537618 values with dtype=float64]

  • tangential_cam
    (tangential_cam_channel, time)
    float64
    ...

    description :

    label :

    Volt

    name :

    tangential_cam

    uda_name :

    /xsx/TCAM/1

    units :

    V

    [6537618 values with dtype=float64]

• Indexes: (4)
  • horizontal_cam_lower_channel
    PandasIndex

    PandasIndex(Index(['/xsx/HCAM/L/1', '/xsx/HCAM/L/2', '/xsx/HCAM/L/3', '/xsx/HCAM/L/4',
           '/xsx/HCAM/L/5', '/xsx/HCAM/L/6', '/xsx/HCAM/L/7', '/xsx/HCAM/L/8',
           '/xsx/HCAM/L/9', '/xsx/HCAM/L/10', '/xsx/HCAM/L/11', '/xsx/HCAM/L/12',
           '/xsx/HCAM/L/13', '/xsx/HCAM/L/14', '/xsx/HCAM/L/15', '/xsx/HCAM/L/16',
           '/xsx/HCAM/L/17', '/xsx/HCAM/L/18'],
          dtype='object', name='horizontal_cam_lower_channel'))

  • horizontal_cam_upper_channel
    PandasIndex

    PandasIndex(Index(['/xsx/HCAM/U/1', '/xsx/HCAM/U/2', '/xsx/HCAM/U/3', '/xsx/HCAM/U/4',
           '/xsx/HCAM/U/5', '/xsx/HCAM/U/6', '/xsx/HCAM/U/7', '/xsx/HCAM/U/8',
           '/xsx/HCAM/U/9', '/xsx/HCAM/U/10', '/xsx/HCAM/U/11', '/xsx/HCAM/U/12',
           '/xsx/HCAM/U/13', '/xsx/HCAM/U/14', '/xsx/HCAM/U/15', '/xsx/HCAM/U/16',
           '/xsx/HCAM/U/17', '/xsx/HCAM/U/18'],
          dtype='object', name='horizontal_cam_upper_channel'))

  • tangential_cam_channel
    PandasIndex

    PandasIndex(Index(['/xsx/TCAM/1', '/xsx/TCAM/2', '/xsx/TCAM/3', '/xsx/TCAM/4',
           '/xsx/TCAM/5', '/xsx/TCAM/6', '/xsx/TCAM/7', '/xsx/TCAM/8',
           '/xsx/TCAM/9', '/xsx/TCAM/10', '/xsx/TCAM/11', '/xsx/TCAM/12',
           '/xsx/TCAM/13', '/xsx/TCAM/14', '/xsx/TCAM/15', '/xsx/TCAM/16',
           '/xsx/TCAM/17', '/xsx/TCAM/18'],
          dtype='object', name='tangential_cam_channel'))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783,  -0.06119826111602783,  -0.06119626111602783,
           -0.061194261116027826, -0.061192261116027824,  -0.06119026111602782,
            -0.06118826111602782,  -0.06118626111602782, -0.061184261116027816,
           -0.061182261116027814,
           ...
              0.6651817388846986,    0.6651837388846986,    0.6651857388846987,
              0.6651877388846986,    0.6651897388846986,    0.6651917388846986,
              0.6651937388846987,    0.6651957388846986,    0.6651977388846986,
              0.6651997388846986],
          dtype='float64', name='time', length=363201))

• Attributes: (6)

  description :

  imas :

  None

  label :

  Volt

  name :

  soft_x_rays

  uda_name :

  /xsx/TCAM/1

  units :

  V

Thomson Profiles

Thomson scattering measurements in a tokamak provide information about the plasma’s electron temperature and density profiles. The diagnostic analyses the scattering of laser light off free electrons in the plasma from a number of radial channels.

Below we plot the following profiles measured by the Thomson diagnostic

• \(T_e\) - Electron temperature

• \(N_e\) - Electron density

• \(P_e\) - Electron pressure

profiles = xr.open_zarr(store, group='thomson_scattering')
profiles

fig, axes = plt.subplots(3, 1)
axes = axes.flatten()
profiles.t_e.plot(x='time', y='major_radius', ax=axes[0])
profiles.n_e.plot(x='time', y='major_radius', ax=axes[1])
profiles.p_e.plot(x='time', y='major_radius', ax=axes[2])
plt.tight_layout()

profiles

<xarray.Dataset> Size: 425kB
Dimensions:       (time: 146, major_radius: 120)
Coordinates:
  * major_radius  (major_radius) float64 960B 0.3 0.31 0.32 ... 1.47 1.48 1.49
  * time          (time) float64 1kB -0.0612 -0.0562 -0.0512 ... 0.6588 0.6638
Data variables:
    n_e           (time, major_radius) float64 140kB ...
    n_e_core      (time) float64 1kB ...
    p_e           (time, major_radius) float64 140kB ...
    t_e           (time, major_radius) float64 140kB ...
    t_e_core      (time) float64 1kB ...
Attributes:
    description:  
    imas:         thomson_scattering
    label:        core temperature
    name:         thomson_scattering
    uda_name:     AYC_TE_CORE
    units:        eV

xarray.Dataset

• Dimensions:
  • time: 146
  • major_radius: 120
• Coordinates: (2)
  • major_radius
    (major_radius)
    float64
    0.3 0.31 0.32 ... 1.47 1.48 1.49

    description :

    imas :

    thomson_scattering.channel[:].position.r

    label :

    major radius

    units :

    m

    array([0.3 , 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41,
           0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53,
           0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,
           0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77,
           0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89,
           0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  , 1.01,
           1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1 , 1.11, 1.12, 1.13,
           1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.2 , 1.21, 1.22, 1.23, 1.24, 1.25,
           1.26, 1.27, 1.28, 1.29, 1.3 , 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37,
           1.38, 1.39, 1.4 , 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49])

  • time
    (time)
    float64
    -0.0612 -0.0562 ... 0.6588 0.6638

    units :

    s

    array([-0.0612, -0.0562, -0.0512, -0.0462, -0.0412, -0.0362, -0.0312, -0.0262,
           -0.0212, -0.0162, -0.0112, -0.0062, -0.0012,  0.0038,  0.0088,  0.0138,
            0.0188,  0.0238,  0.0288,  0.0338,  0.0388,  0.0438,  0.0488,  0.0538,
            0.0588,  0.0638,  0.0688,  0.0738,  0.0788,  0.0838,  0.0888,  0.0938,
            0.0988,  0.1038,  0.1088,  0.1138,  0.1188,  0.1238,  0.1288,  0.1338,
            0.1388,  0.1438,  0.1488,  0.1538,  0.1588,  0.1638,  0.1688,  0.1738,
            0.1788,  0.1838,  0.1888,  0.1938,  0.1988,  0.2038,  0.2088,  0.2138,
            0.2188,  0.2238,  0.2288,  0.2338,  0.2388,  0.2438,  0.2488,  0.2538,
            0.2588,  0.2638,  0.2688,  0.2738,  0.2788,  0.2838,  0.2888,  0.2938,
            0.2988,  0.3038,  0.3088,  0.3138,  0.3188,  0.3238,  0.3288,  0.3338,
            0.3388,  0.3438,  0.3488,  0.3538,  0.3588,  0.3638,  0.3688,  0.3738,
            0.3788,  0.3838,  0.3888,  0.3938,  0.3988,  0.4038,  0.4088,  0.4138,
            0.4188,  0.4238,  0.4288,  0.4338,  0.4388,  0.4438,  0.4488,  0.4538,
            0.4588,  0.4638,  0.4688,  0.4738,  0.4788,  0.4838,  0.4888,  0.4938,
            0.4988,  0.5038,  0.5088,  0.5138,  0.5188,  0.5238,  0.5288,  0.5338,
            0.5388,  0.5438,  0.5488,  0.5538,  0.5588,  0.5638,  0.5688,  0.5738,
            0.5788,  0.5838,  0.5888,  0.5938,  0.5988,  0.6038,  0.6088,  0.6138,
            0.6188,  0.6238,  0.6288,  0.6338,  0.6388,  0.6438,  0.6488,  0.6538,
            0.6588,  0.6638])

• Data variables: (5)
  • n_e
    (time, major_radius)
    float64
    ...

    description :

    imas :

    thomson_scattering.channel[:].n_e

    label :

    electron density

    name :

    n_e

    uda_name :

    AYC_NE

    units :

    1 / m ** 3

    [17520 values with dtype=float64]

  • n_e_core
    (time)
    float64
    ...

    description :

    label :

    core density

    name :

    n_e_core

    uda_name :

    AYC_NE_CORE

    units :

    1 / m ** 3

    [146 values with dtype=float64]

  • p_e
    (time, major_radius)
    float64
    ...

    description :

    label :

    electron pressure

    name :

    p_e

    uda_name :

    AYC_PE

    units :

    u * a

    [17520 values with dtype=float64]

  • t_e
    (time, major_radius)
    float64
    ...

    description :

    imas :

    thomson_scattering.channel[:].t_e

    label :

    electron temperature

    name :

    t_e

    uda_name :

    AYC_TE

    units :

    eV

    [17520 values with dtype=float64]

  • t_e_core
    (time)
    float64
    ...

    description :

    label :

    core temperature

    name :

    t_e_core

    uda_name :

    AYC_TE_CORE

    units :

    eV

    [146 values with dtype=float64]

• Indexes: (2)
  • major_radius
    PandasIndex

    PandasIndex(Index([                0.3,                0.31,                0.32,
                          0.33,                0.34, 0.35000000000000003,
           0.36000000000000004, 0.37000000000000005, 0.38000000000000006,
           0.39000000000000007,
           ...
             1.400000000000001,   1.410000000000001,   1.420000000000001,
             1.430000000000001,   1.440000000000001,   1.450000000000001,
             1.460000000000001,   1.470000000000001,   1.480000000000001,
             1.490000000000001],
          dtype='float64', name='major_radius', length=120))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783, -0.056200261116027835,  -0.05120026111602784,
            -0.04620026111602784,  -0.04120026111602784, -0.036200261116027845,
           -0.031200261116027847,  -0.02620026111602785, -0.021200261116027852,
           -0.016200261116027855,
           ...
              0.6187997388839719,    0.6237997388839718,    0.6287997388839718,
              0.6337997388839718,    0.6387997388839718,    0.6437997388839718,
              0.6487997388839718,    0.6537997388839718,    0.6587997388839718,
              0.6637997388839718],
          dtype='float64', name='time', length=146))

• Attributes: (6)

  description :

  imas :

  thomson_scattering

  label :

  core temperature

  name :

  thomson_scattering

  uda_name :

  AYC_TE_CORE

  units :

  eV

fig, axes = plt.subplots(2, 1)
profiles['t_e_core'].plot(x='time', ax=axes[0])
profiles['n_e_core'].plot(x='time', ax=axes[1])
for ax in axes:
    ax.grid('on', alpha=0.5)
plt.tight_layout()
profiles

<xarray.Dataset> Size: 425kB
Dimensions:       (time: 146, major_radius: 120)
Coordinates:
  * major_radius  (major_radius) float64 960B 0.3 0.31 0.32 ... 1.47 1.48 1.49
  * time          (time) float64 1kB -0.0612 -0.0562 -0.0512 ... 0.6588 0.6638
Data variables:
    n_e           (time, major_radius) float64 140kB ...
    n_e_core      (time) float64 1kB ...
    p_e           (time, major_radius) float64 140kB ...
    t_e           (time, major_radius) float64 140kB ...
    t_e_core      (time) float64 1kB ...
Attributes:
    description:  
    imas:         thomson_scattering
    label:        core temperature
    name:         thomson_scattering
    uda_name:     AYC_TE_CORE
    units:        eV

xarray.Dataset

• Dimensions:
  • time: 146
  • major_radius: 120
• Coordinates: (2)
  • major_radius
    (major_radius)
    float64
    0.3 0.31 0.32 ... 1.47 1.48 1.49

    description :

    imas :

    thomson_scattering.channel[:].position.r

    label :

    major radius

    units :

    m

    array([0.3 , 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41,
           0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53,
           0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62, 0.63, 0.64, 0.65,
           0.66, 0.67, 0.68, 0.69, 0.7 , 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77,
           0.78, 0.79, 0.8 , 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89,
           0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  , 1.01,
           1.02, 1.03, 1.04, 1.05, 1.06, 1.07, 1.08, 1.09, 1.1 , 1.11, 1.12, 1.13,
           1.14, 1.15, 1.16, 1.17, 1.18, 1.19, 1.2 , 1.21, 1.22, 1.23, 1.24, 1.25,
           1.26, 1.27, 1.28, 1.29, 1.3 , 1.31, 1.32, 1.33, 1.34, 1.35, 1.36, 1.37,
           1.38, 1.39, 1.4 , 1.41, 1.42, 1.43, 1.44, 1.45, 1.46, 1.47, 1.48, 1.49])

  • time
    (time)
    float64
    -0.0612 -0.0562 ... 0.6588 0.6638

    units :

    s

    array([-0.0612, -0.0562, -0.0512, -0.0462, -0.0412, -0.0362, -0.0312, -0.0262,
           -0.0212, -0.0162, -0.0112, -0.0062, -0.0012,  0.0038,  0.0088,  0.0138,
            0.0188,  0.0238,  0.0288,  0.0338,  0.0388,  0.0438,  0.0488,  0.0538,
            0.0588,  0.0638,  0.0688,  0.0738,  0.0788,  0.0838,  0.0888,  0.0938,
            0.0988,  0.1038,  0.1088,  0.1138,  0.1188,  0.1238,  0.1288,  0.1338,
            0.1388,  0.1438,  0.1488,  0.1538,  0.1588,  0.1638,  0.1688,  0.1738,
            0.1788,  0.1838,  0.1888,  0.1938,  0.1988,  0.2038,  0.2088,  0.2138,
            0.2188,  0.2238,  0.2288,  0.2338,  0.2388,  0.2438,  0.2488,  0.2538,
            0.2588,  0.2638,  0.2688,  0.2738,  0.2788,  0.2838,  0.2888,  0.2938,
            0.2988,  0.3038,  0.3088,  0.3138,  0.3188,  0.3238,  0.3288,  0.3338,
            0.3388,  0.3438,  0.3488,  0.3538,  0.3588,  0.3638,  0.3688,  0.3738,
            0.3788,  0.3838,  0.3888,  0.3938,  0.3988,  0.4038,  0.4088,  0.4138,
            0.4188,  0.4238,  0.4288,  0.4338,  0.4388,  0.4438,  0.4488,  0.4538,
            0.4588,  0.4638,  0.4688,  0.4738,  0.4788,  0.4838,  0.4888,  0.4938,
            0.4988,  0.5038,  0.5088,  0.5138,  0.5188,  0.5238,  0.5288,  0.5338,
            0.5388,  0.5438,  0.5488,  0.5538,  0.5588,  0.5638,  0.5688,  0.5738,
            0.5788,  0.5838,  0.5888,  0.5938,  0.5988,  0.6038,  0.6088,  0.6138,
            0.6188,  0.6238,  0.6288,  0.6338,  0.6388,  0.6438,  0.6488,  0.6538,
            0.6588,  0.6638])

• Data variables: (5)
  • n_e
    (time, major_radius)
    float64
    ...

    description :

    imas :

    thomson_scattering.channel[:].n_e

    label :

    electron density

    name :

    n_e

    uda_name :

    AYC_NE

    units :

    1 / m ** 3

    [17520 values with dtype=float64]

  • n_e_core
    (time)
    float64
    ...

    description :

    label :

    core density

    name :

    n_e_core

    uda_name :

    AYC_NE_CORE

    units :

    1 / m ** 3

    [146 values with dtype=float64]

  • p_e
    (time, major_radius)
    float64
    ...

    description :

    label :

    electron pressure

    name :

    p_e

    uda_name :

    AYC_PE

    units :

    u * a

    [17520 values with dtype=float64]

  • t_e
    (time, major_radius)
    float64
    ...

    description :

    imas :

    thomson_scattering.channel[:].t_e

    label :

    electron temperature

    name :

    t_e

    uda_name :

    AYC_TE

    units :

    eV

    [17520 values with dtype=float64]

  • t_e_core
    (time)
    float64
    ...

    description :

    label :

    core temperature

    name :

    t_e_core

    uda_name :

    AYC_TE_CORE

    units :

    eV

    [146 values with dtype=float64]

• Indexes: (2)
  • major_radius
    PandasIndex

    PandasIndex(Index([                0.3,                0.31,                0.32,
                          0.33,                0.34, 0.35000000000000003,
           0.36000000000000004, 0.37000000000000005, 0.38000000000000006,
           0.39000000000000007,
           ...
             1.400000000000001,   1.410000000000001,   1.420000000000001,
             1.430000000000001,   1.440000000000001,   1.450000000000001,
             1.460000000000001,   1.470000000000001,   1.480000000000001,
             1.490000000000001],
          dtype='float64', name='major_radius', length=120))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783, -0.056200261116027835,  -0.05120026111602784,
            -0.04620026111602784,  -0.04120026111602784, -0.036200261116027845,
           -0.031200261116027847,  -0.02620026111602785, -0.021200261116027852,
           -0.016200261116027855,
           ...
              0.6187997388839719,    0.6237997388839718,    0.6287997388839718,
              0.6337997388839718,    0.6387997388839718,    0.6437997388839718,
              0.6487997388839718,    0.6537997388839718,    0.6587997388839718,
              0.6637997388839718],
          dtype='float64', name='time', length=146))

• Attributes: (6)

  description :

  imas :

  thomson_scattering

  label :

  core temperature

  name :

  thomson_scattering

  uda_name :

  AYC_TE_CORE

  units :

  eV

CXRS Profiles

Charge Exchange Recombination Spectroscopy (CXRS) measurements provide information about ion temperature and plasma rotation. This diagnostic analyses the light emitted from charge exchange reactions between injected neutral beams and plasma ions.

Below we plot the following profiles measured by the CXRS diagnostic

• \(T_i\) - Ion temperature

• \(V_i\) - Ion velocity

profiles = xr.open_zarr(store, group='charge_exchange')

fig, axes = plt.subplots(2, 1)
profiles['t_i'].plot(x='time', y='major_radius', ax=axes[0], vmax=1000)
profiles['v_i'].plot(x='time', y='major_radius', ax=axes[1], vmax=1000)
plt.tight_layout()
profiles

<xarray.Dataset> Size: 376kB
Dimensions:       (time: 146, major_radius: 160)
Coordinates:
  * major_radius  (major_radius) float64 1kB 0.0 0.01 0.02 ... 1.57 1.58 1.59
  * time          (time) float64 1kB -0.0612 -0.0562 -0.0512 ... 0.6588 0.6638
Data variables:
    t_i           (time, major_radius) float64 187kB ...
    v_i           (time, major_radius) float64 187kB ...
Attributes:
    description:  
    imas:         charge_exchange
    label:        Carbon temperature
    name:         charge_exchange
    uda_name:     ACT_SS_TEMPERATURE
    units:        eV

xarray.Dataset

• Dimensions:
  • time: 146
  • major_radius: 160
• Coordinates: (2)
  • major_radius
    (major_radius)
    float64
    0.0 0.01 0.02 ... 1.57 1.58 1.59

    units :

    m

    array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 , 0.11,
           0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21, 0.22, 0.23,
           0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33, 0.34, 0.35,
           0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47,
           0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59,
           0.6 , 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,
           0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 , 0.81, 0.82, 0.83,
           0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95,
           0.96, 0.97, 0.98, 0.99, 1.  , 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07,
           1.08, 1.09, 1.1 , 1.11, 1.12, 1.13, 1.14, 1.15, 1.16, 1.17, 1.18, 1.19,
           1.2 , 1.21, 1.22, 1.23, 1.24, 1.25, 1.26, 1.27, 1.28, 1.29, 1.3 , 1.31,
           1.32, 1.33, 1.34, 1.35, 1.36, 1.37, 1.38, 1.39, 1.4 , 1.41, 1.42, 1.43,
           1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5 , 1.51, 1.52, 1.53, 1.54, 1.55,
           1.56, 1.57, 1.58, 1.59])

  • time
    (time)
    float64
    -0.0612 -0.0562 ... 0.6588 0.6638

    units :

    s

    array([-0.0612, -0.0562, -0.0512, -0.0462, -0.0412, -0.0362, -0.0312, -0.0262,
           -0.0212, -0.0162, -0.0112, -0.0062, -0.0012,  0.0038,  0.0088,  0.0138,
            0.0188,  0.0238,  0.0288,  0.0338,  0.0388,  0.0438,  0.0488,  0.0538,
            0.0588,  0.0638,  0.0688,  0.0738,  0.0788,  0.0838,  0.0888,  0.0938,
            0.0988,  0.1038,  0.1088,  0.1138,  0.1188,  0.1238,  0.1288,  0.1338,
            0.1388,  0.1438,  0.1488,  0.1538,  0.1588,  0.1638,  0.1688,  0.1738,
            0.1788,  0.1838,  0.1888,  0.1938,  0.1988,  0.2038,  0.2088,  0.2138,
            0.2188,  0.2238,  0.2288,  0.2338,  0.2388,  0.2438,  0.2488,  0.2538,
            0.2588,  0.2638,  0.2688,  0.2738,  0.2788,  0.2838,  0.2888,  0.2938,
            0.2988,  0.3038,  0.3088,  0.3138,  0.3188,  0.3238,  0.3288,  0.3338,
            0.3388,  0.3438,  0.3488,  0.3538,  0.3588,  0.3638,  0.3688,  0.3738,
            0.3788,  0.3838,  0.3888,  0.3938,  0.3988,  0.4038,  0.4088,  0.4138,
            0.4188,  0.4238,  0.4288,  0.4338,  0.4388,  0.4438,  0.4488,  0.4538,
            0.4588,  0.4638,  0.4688,  0.4738,  0.4788,  0.4838,  0.4888,  0.4938,
            0.4988,  0.5038,  0.5088,  0.5138,  0.5188,  0.5238,  0.5288,  0.5338,
            0.5388,  0.5438,  0.5488,  0.5538,  0.5588,  0.5638,  0.5688,  0.5738,
            0.5788,  0.5838,  0.5888,  0.5938,  0.5988,  0.6038,  0.6088,  0.6138,
            0.6188,  0.6238,  0.6288,  0.6338,  0.6388,  0.6438,  0.6488,  0.6538,
            0.6588,  0.6638])

• Data variables: (2)
  • t_i
    (time, major_radius)
    float64
    ...

    description :

    imas :

    charge_exchange.channel[:].ion[:].t_i

    label :

    Carbon temperature

    name :

    t_i

    uda_name :

    ACT_SS_TEMPERATURE

    units :

    eV

    [23360 values with dtype=float64]

  • v_i
    (time, major_radius)
    float64
    ...

    description :

    label :

    Carbon Velocity

    name :

    v_i

    uda_name :

    ACT_SS_VELOCITY

    units :

    m / s

    [23360 values with dtype=float64]

• Indexes: (2)
  • major_radius
    PandasIndex

    PandasIndex(Index([ 0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09,
           ...
            1.5, 1.51, 1.52, 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59],
          dtype='float64', name='major_radius', length=160))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783, -0.056200261116027835,  -0.05120026111602784,
            -0.04620026111602784,  -0.04120026111602784, -0.036200261116027845,
           -0.031200261116027847,  -0.02620026111602785, -0.021200261116027852,
           -0.016200261116027855,
           ...
              0.6187997388839719,    0.6237997388839718,    0.6287997388839718,
              0.6337997388839718,    0.6387997388839718,    0.6437997388839718,
              0.6487997388839718,    0.6537997388839718,    0.6587997388839718,
              0.6637997388839718],
          dtype='float64', name='time', length=146))

• Attributes: (6)

  description :

  imas :

  charge_exchange

  label :

  Carbon temperature

  name :

  charge_exchange

  uda_name :

  ACT_SS_TEMPERATURE

  units :

  eV

Equilibrium

profiles = xr.open_zarr(store, group='equilibrium')

profile_1d = profiles.drop_vars(['j_tor', 'psi', 'q'])
plot_1d_profiles(profile_1d)

profiles

<xarray.Dataset> Size: 10MB
Dimensions:              (time: 146, z: 65, major_radius: 65,
                          n_boundary_coords: 139, profile_r: 65, n_x_points: 4)
Coordinates:
  * major_radius         (major_radius) float64 520B 0.06 0.09 ... 1.95 1.98
  * n_boundary_coords    (n_boundary_coords) float32 556B 0.0 1.0 ... 138.0
  * n_x_points           (n_x_points) <U16 256B 'EFM_XPOINT1_R(C)' ... 'EFM_X...
  * profile_r            (profile_r) float32 260B 0.0 0.01562 ... 0.9844 1.0
  * time                 (time) float64 1kB -0.0612 -0.0562 ... 0.6588 0.6638
  * z                    (z) float32 260B -2.0 -1.938 -1.875 ... 1.875 1.938 2.0
Data variables: (12/35)
    beta_normal          (time) float64 1kB ...
    beta_pol             (time) float64 1kB ...
    beta_tor             (time) float64 1kB ...
    bphi_rmag            (time) float64 1kB ...
    bvac_rmag            (time) float64 1kB ...
    da_rating            (time) float64 1kB ...
    ...                   ...
    triangularity_upper  (time) float64 1kB ...
    vloop_dynamic        (time) float64 1kB ...
    vloop_static         (time) float64 1kB ...
    whmd                 (time) float64 1kB ...
    x_point_r            (n_x_points, time) float64 5kB ...
    x_point_z            (n_x_points, time) float64 5kB ...
Attributes:
    description:  
    imas:         equilibrium
    label:        q(r) at z=0.
    name:         equilibrium
    uda_name:     EFM_Q(R)
    units:        

xarray.Dataset

• Dimensions:
  • time: 146
  • z: 65
  • major_radius: 65
  • n_boundary_coords: 139
  • profile_r: 65
  • n_x_points: 4
• Coordinates: (6)
  • major_radius
    (major_radius)
    float64
    0.06 0.09 0.12 ... 1.92 1.95 1.98

    description :

    imas :

    equilibrium.time_slice[:].coordinate_system.r

    label :

    r-coordinates of computa

    units :

    m

    array([0.06, 0.09, 0.12, 0.15, 0.18, 0.21, 0.24, 0.27, 0.3 , 0.33, 0.36, 0.39,
           0.42, 0.45, 0.48, 0.51, 0.54, 0.57, 0.6 , 0.63, 0.66, 0.69, 0.72, 0.75,
           0.78, 0.81, 0.84, 0.87, 0.9 , 0.93, 0.96, 0.99, 1.02, 1.05, 1.08, 1.11,
           1.14, 1.17, 1.2 , 1.23, 1.26, 1.29, 1.32, 1.35, 1.38, 1.41, 1.44, 1.47,
           1.5 , 1.53, 1.56, 1.59, 1.62, 1.65, 1.68, 1.71, 1.74, 1.77, 1.8 , 1.83,
           1.86, 1.89, 1.92, 1.95, 1.98])

  • n_boundary_coords
    (n_boundary_coords)
    float32
    0.0 1.0 2.0 ... 136.0 137.0 138.0

    units :

    array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,
            12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,
            24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,
            36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,
            48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,
            60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,
            72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,
            84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,
            96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,
           108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,
           120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,
           132., 133., 134., 135., 136., 137., 138.], dtype=float32)

  • n_x_points
    (n_x_points)
    <U16
    'EFM_XPOINT1_R(C)' ... 'EFM_XPOI...

    array(['EFM_XPOINT1_R(C)', 'EFM_XPOINT1_Z(C)', 'EFM_XPOINT2_R(C)',
           'EFM_XPOINT2_Z(C)'], dtype='<U16')

  • profile_r
    (profile_r)
    float32
    0.0 0.01562 0.03125 ... 0.9844 1.0

    units :

    array([0.      , 0.015625, 0.03125 , 0.046875, 0.0625  , 0.078125, 0.09375 ,
           0.109375, 0.125   , 0.140625, 0.15625 , 0.171875, 0.1875  , 0.203125,
           0.21875 , 0.234375, 0.25    , 0.265625, 0.28125 , 0.296875, 0.3125  ,
           0.328125, 0.34375 , 0.359375, 0.375   , 0.390625, 0.40625 , 0.421875,
           0.4375  , 0.453125, 0.46875 , 0.484375, 0.5     , 0.515625, 0.53125 ,
           0.546875, 0.5625  , 0.578125, 0.59375 , 0.609375, 0.625   , 0.640625,
           0.65625 , 0.671875, 0.6875  , 0.703125, 0.71875 , 0.734375, 0.75    ,
           0.765625, 0.78125 , 0.796875, 0.8125  , 0.828125, 0.84375 , 0.859375,
           0.875   , 0.890625, 0.90625 , 0.921875, 0.9375  , 0.953125, 0.96875 ,
           0.984375, 1.      ], dtype=float32)

  • time
    (time)
    float64
    -0.0612 -0.0562 ... 0.6588 0.6638

    units :

    s

    array([-0.0612, -0.0562, -0.0512, -0.0462, -0.0412, -0.0362, -0.0312, -0.0262,
           -0.0212, -0.0162, -0.0112, -0.0062, -0.0012,  0.0038,  0.0088,  0.0138,
            0.0188,  0.0238,  0.0288,  0.0338,  0.0388,  0.0438,  0.0488,  0.0538,
            0.0588,  0.0638,  0.0688,  0.0738,  0.0788,  0.0838,  0.0888,  0.0938,
            0.0988,  0.1038,  0.1088,  0.1138,  0.1188,  0.1238,  0.1288,  0.1338,
            0.1388,  0.1438,  0.1488,  0.1538,  0.1588,  0.1638,  0.1688,  0.1738,
            0.1788,  0.1838,  0.1888,  0.1938,  0.1988,  0.2038,  0.2088,  0.2138,
            0.2188,  0.2238,  0.2288,  0.2338,  0.2388,  0.2438,  0.2488,  0.2538,
            0.2588,  0.2638,  0.2688,  0.2738,  0.2788,  0.2838,  0.2888,  0.2938,
            0.2988,  0.3038,  0.3088,  0.3138,  0.3188,  0.3238,  0.3288,  0.3338,
            0.3388,  0.3438,  0.3488,  0.3538,  0.3588,  0.3638,  0.3688,  0.3738,
            0.3788,  0.3838,  0.3888,  0.3938,  0.3988,  0.4038,  0.4088,  0.4138,
            0.4188,  0.4238,  0.4288,  0.4338,  0.4388,  0.4438,  0.4488,  0.4538,
            0.4588,  0.4638,  0.4688,  0.4738,  0.4788,  0.4838,  0.4888,  0.4938,
            0.4988,  0.5038,  0.5088,  0.5138,  0.5188,  0.5238,  0.5288,  0.5338,
            0.5388,  0.5438,  0.5488,  0.5538,  0.5588,  0.5638,  0.5688,  0.5738,
            0.5788,  0.5838,  0.5888,  0.5938,  0.5988,  0.6038,  0.6088,  0.6138,
            0.6188,  0.6238,  0.6288,  0.6338,  0.6388,  0.6438,  0.6488,  0.6538,
            0.6588,  0.6638])

  • z
    (z)
    float32
    -2.0 -1.938 -1.875 ... 1.938 2.0

    description :

    imas :

    equilibrium.time_slice[:].coordinate_system.z

    label :

    z-coordinates of computa

    units :

    m

    array([-2.    , -1.9375, -1.875 , -1.8125, -1.75  , -1.6875, -1.625 , -1.5625,
           -1.5   , -1.4375, -1.375 , -1.3125, -1.25  , -1.1875, -1.125 , -1.0625,
           -1.    , -0.9375, -0.875 , -0.8125, -0.75  , -0.6875, -0.625 , -0.5625,
           -0.5   , -0.4375, -0.375 , -0.3125, -0.25  , -0.1875, -0.125 , -0.0625,
            0.    ,  0.0625,  0.125 ,  0.1875,  0.25  ,  0.3125,  0.375 ,  0.4375,
            0.5   ,  0.5625,  0.625 ,  0.6875,  0.75  ,  0.8125,  0.875 ,  0.9375,
            1.    ,  1.0625,  1.125 ,  1.1875,  1.25  ,  1.3125,  1.375 ,  1.4375,
            1.5   ,  1.5625,  1.625 ,  1.6875,  1.75  ,  1.8125,  1.875 ,  1.9375,
            2.    ], dtype=float32)

• Data variables: (35)
  • beta_normal
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.beta_normal

    label :

    betat/(I/ a Bvac_geom)

    name :

    beta_normal

    uda_name :

    EFM_BETAN

    units :

    T

    [146 values with dtype=float64]

  • beta_pol
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.beta_pol

    label :

    Poloidal Beta

    name :

    beta_pol

    uda_name :

    EFM_BETAP

    units :

    [146 values with dtype=float64]

  • beta_tor
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.beta_tor

    label :

    Toroidal Beta

    name :

    beta_tor

    uda_name :

    EFM_BETAT

    units :

    [146 values with dtype=float64]

  • bphi_rmag
    (time)
    float64
    ...

    description :

    label :

    Bphi at rmag

    name :

    bphi_rmag

    uda_name :

    EFM_BPHI_RMAG

    units :

    T

    [146 values with dtype=float64]

  • bvac_rmag
    (time)
    float64
    ...

    description :

    label :

    Bvac at rmag

    name :

    bvac_rmag

    uda_name :

    EFM_BVAC_RMAG

    units :

    T

    [146 values with dtype=float64]

  • da_rating
    (time)
    float64
    ...

    description :

    label :

    EFIT rating by D-alpha p

    name :

    da_rating

    uda_name :

    ESM_EFM_RATING_DA

    units :

    [146 values with dtype=float64]

  • elongation
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].boundary.elongation

    label :

    Elongation

    name :

    elongation

    uda_name :

    EFM_ELONGATION

    units :

    [146 values with dtype=float64]

  • elongation_axis
    (time)
    float64
    ...

    description :

    label :

    Elongation on Magnetic A

    name :

    elongation_axis

    uda_name :

    EFM_ELONGATION_AXIS

    units :

    [146 values with dtype=float64]

  • ip_rating
    (time)
    float64
    ...

    description :

    label :

    EFIT rating by I_p

    name :

    ip_rating

    uda_name :

    ESM_EFM_RATING_IP

    units :

    [146 values with dtype=float64]

  • j_tor
    (time, z, major_radius)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].profiles_2d[:].j_tor

    label :

    J(r,z)

    name :

    j_tor

    uda_name :

    EFM_PLASMA_CURR(R,Z)

    units :

    A / m ** 2

    [616850 values with dtype=float64]

  • lcfs_r
    (time, n_boundary_coords)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].boundary.lcfs.r

    label :

    r-coords of separatrix

    name :

    lcfs_r

    uda_name :

    EFM_LCFS(R)_(C)

    units :

    m

    [20294 values with dtype=float64]

  • lcfs_z
    (time, n_boundary_coords)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].boundary.lcfs.z

    label :

    z-coords of separatrix

    name :

    lcfs_z

    uda_name :

    EFM_LCFS(Z)_(C)

    units :

    m

    [20294 values with dtype=float64]

  • li
    (time)
    float64
    ...

    description :

    label :

    Plasma Inductance

    name :

    li

    uda_name :

    EFM_LI

    units :

    [146 values with dtype=float64]

  • magnetic_axis_r
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.magnetic_axis.r

    label :

    Radius of Magnetic Axis

    name :

    magnetic_axis_r

    uda_name :

    EFM_MAGNETIC_AXIS_R

    units :

    m

    [146 values with dtype=float64]

  • magnetic_axis_z
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.magnetic_axis.z

    label :

    Height of Magnetic Axis

    name :

    magnetic_axis_z

    uda_name :

    EFM_MAGNETIC_AXIS_Z

    units :

    m

    [146 values with dtype=float64]

  • minor_radius
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].boundary.minor_radius

    label :

    Minor Radius

    name :

    minor_radius

    uda_name :

    EFM_MINOR_RADIUS

    units :

    m

    [146 values with dtype=float64]

  • psi
    (time, z, major_radius)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].profiles_2d[:].psi

    label :

    psi(r,z)

    name :

    psi

    uda_name :

    EFM_PSI(R,Z)

    units :

    Wb / rad

    [616850 values with dtype=float64]

  • q
    (time, profile_r)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].profiles_1d.q

    label :

    q(r) at z=0.

    name :

    q

    uda_name :

    EFM_Q(R)

    units :

    [9490 values with dtype=float64]

  • q100
    (time)
    float64
    ...

    description :

    label :

    q at Psi_norm=100%

    name :

    q100

    uda_name :

    EFM_Q_100

    units :

    [146 values with dtype=float64]

  • q90
    (time)
    float64
    ...

    description :

    label :

    q at Psi_norm=100%

    name :

    q90

    uda_name :

    EFM_Q_100

    units :

    [146 values with dtype=float64]

  • q95
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.q_95

    label :

    q at Psi_norm=95%

    name :

    q95

    uda_name :

    EFM_Q_95

    units :

    [146 values with dtype=float64]

  • q_axis
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.q_axis

    label :

    q on Magnetic Axis

    name :

    q_axis

    uda_name :

    EFM_Q_AXIS

    units :

    [146 values with dtype=float64]

  • rpsi100_in
    (time)
    float64
    ...

    description :

    label :

    i/b Rad of lcfs at Mid

    name :

    rpsi100_in

    uda_name :

    EFM_R(PSI100)_IN

    units :

    m

    [146 values with dtype=float64]

  • rpsi100_out
    (time)
    float64
    ...

    description :

    label :

    o/b Rad of lcfs at Mid

    name :

    rpsi100_out

    uda_name :

    EFM_R(PSI100)_OUT

    units :

    m

    [146 values with dtype=float64]

  • rpsi90_in
    (time)
    float64
    ...

    description :

    label :

    i/b Rad of 90%Psi at Mid

    name :

    rpsi90_in

    uda_name :

    EFM_R(PSI90)_IN

    units :

    m

    [146 values with dtype=float64]

  • rpsi90_out
    (time)
    float64
    ...

    description :

    label :

    o/b Rad of 90%Psi at Mid

    name :

    rpsi90_out

    uda_name :

    EFM_R(PSI90)_OUT

    units :

    m

    [146 values with dtype=float64]

  • rpsi95_in
    (time)
    float64
    ...

    description :

    label :

    i/b Rad of 95%Psi at Mid

    name :

    rpsi95_in

    uda_name :

    EFM_R(PSI95)_IN

    units :

    m

    [146 values with dtype=float64]

  • rpsi95_out
    (time)
    float64
    ...

    description :

    label :

    o/b Rad of 95%Psi at Mid

    name :

    rpsi95_out

    uda_name :

    EFM_R(PSI95)_OUT

    units :

    m

    [146 values with dtype=float64]

  • triangularity_lower
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].profiles_1d.triangularity_lower

    label :

    Lower Triangularity

    name :

    triangularity_lower

    uda_name :

    EFM_TRIANG_LOWER

    units :

    [146 values with dtype=float64]

  • triangularity_upper
    (time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].profiles_1d.triangularity_upper

    label :

    Upper Triangularity

    name :

    triangularity_upper

    uda_name :

    EFM_TRIANG_UPPER

    units :

    [146 values with dtype=float64]

  • vloop_dynamic
    (time)
    float64
    ...

    description :

    label :

    dynamic V_loop

    name :

    vloop_dynamic

    uda_name :

    ESM_V_LOOP_DYNAMIC

    units :

    V

    [146 values with dtype=float64]

  • vloop_static
    (time)
    float64
    ...

    description :

    label :

    static V_loop

    name :

    vloop_static

    uda_name :

    ESM_V_LOOP_STATIC

    units :

    V

    [146 values with dtype=float64]

  • whmd
    (time)
    float64
    ...

    description :

    label :

    Plasma Diamagnetic Energ

    name :

    whmd

    uda_name :

    EFM_WPLASMD

    units :

    J

    [146 values with dtype=float64]

  • x_point_r
    (n_x_points, time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].boundary.x_point[:].r

    label :

    Radius of X-point 1

    name :

    x_point_r

    uda_name :

    EFM_XPOINT1_R(C)

    units :

    [584 values with dtype=float64]

  • x_point_z
    (n_x_points, time)
    float64
    ...

    description :

    imas :

    equilibrium.time_slice[:].boundary.x_point[:].z

    label :

    Height of X-point 1

    name :

    x_point_z

    uda_name :

    EFM_XPOINT1_Z(C)

    units :

    [584 values with dtype=float64]

• Indexes: (6)
  • major_radius
    PandasIndex

    PandasIndex(Index([               0.06,                0.09,                0.12,
                          0.15,                0.18,                0.21,
                          0.24,                0.27,                 0.3,
                          0.33,                0.36, 0.38999999999999996,
                          0.42,                0.45,                0.48,
                          0.51,                0.54,  0.5700000000000001,
            0.6000000000000001,  0.6299999999999999,  0.6599999999999999,
                          0.69,                0.72,                0.75,
                          0.78,                0.81,  0.8400000000000001,
            0.8699999999999999,  0.8999999999999999,  0.9299999999999999,
                          0.96,                0.99,                1.02,
                          1.05,                1.08,                1.11,
            1.1400000000000001,                1.17,                 1.2,
                          1.23,                1.26,                1.29,
                          1.32,                1.35,                1.38,
                          1.41,                1.44,                1.47,
                           1.5,                1.53,                1.56,
                          1.59,                1.62,                1.65,
                          1.68,                1.71,                1.74,
                          1.77,                 1.8,                1.83,
            1.8599999999999999,                1.89,                1.92,
                          1.95,                1.98],
          dtype='float64', name='major_radius'))

  • n_boundary_coords
    PandasIndex

    PandasIndex(Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,
           ...
           129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0],
          dtype='float32', name='n_boundary_coords', length=139))

  • n_x_points
    PandasIndex

    PandasIndex(Index(['EFM_XPOINT1_R(C)', 'EFM_XPOINT1_Z(C)', 'EFM_XPOINT2_R(C)',
           'EFM_XPOINT2_Z(C)'],
          dtype='object', name='n_x_points'))

  • profile_r
    PandasIndex

    PandasIndex(Index([     0.0, 0.015625,  0.03125, 0.046875,   0.0625, 0.078125,  0.09375,
           0.109375,    0.125, 0.140625,  0.15625, 0.171875,   0.1875, 0.203125,
            0.21875, 0.234375,     0.25, 0.265625,  0.28125, 0.296875,   0.3125,
           0.328125,  0.34375, 0.359375,    0.375, 0.390625,  0.40625, 0.421875,
             0.4375, 0.453125,  0.46875, 0.484375,      0.5, 0.515625,  0.53125,
           0.546875,   0.5625, 0.578125,  0.59375, 0.609375,    0.625, 0.640625,
            0.65625, 0.671875,   0.6875, 0.703125,  0.71875, 0.734375,     0.75,
           0.765625,  0.78125, 0.796875,   0.8125, 0.828125,  0.84375, 0.859375,
              0.875, 0.890625,  0.90625, 0.921875,   0.9375, 0.953125,  0.96875,
           0.984375,      1.0],
          dtype='float32', name='profile_r'))

  • time
    PandasIndex

    PandasIndex(Index([ -0.06120026111602783, -0.056200261116027835,  -0.05120026111602784,
            -0.04620026111602784,  -0.04120026111602784, -0.036200261116027845,
           -0.031200261116027847,  -0.02620026111602785, -0.021200261116027852,
           -0.016200261116027855,
           ...
              0.6187997388839719,    0.6237997388839718,    0.6287997388839718,
              0.6337997388839718,    0.6387997388839718,    0.6437997388839718,
              0.6487997388839718,    0.6537997388839718,    0.6587997388839718,
              0.6637997388839718],
          dtype='float64', name='time', length=146))

  • z
    PandasIndex

    PandasIndex(Index([   -2.0, -1.9375,  -1.875, -1.8125,   -1.75, -1.6875,  -1.625, -1.5625,
              -1.5, -1.4375,  -1.375, -1.3125,   -1.25, -1.1875,  -1.125, -1.0625,
              -1.0, -0.9375,  -0.875, -0.8125,   -0.75, -0.6875,  -0.625, -0.5625,
              -0.5, -0.4375,  -0.375, -0.3125,   -0.25, -0.1875,  -0.125, -0.0625,
               0.0,  0.0625,   0.125,  0.1875,    0.25,  0.3125,   0.375,  0.4375,
               0.5,  0.5625,   0.625,  0.6875,    0.75,  0.8125,   0.875,  0.9375,
               1.0,  1.0625,   1.125,  1.1875,    1.25,  1.3125,   1.375,  1.4375,
               1.5,  1.5625,   1.625,  1.6875,    1.75,  1.8125,   1.875,  1.9375,
               2.0],
          dtype='float32', name='z'))

• Attributes: (6)

  description :

  imas :

  equilibrium

  label :

  q(r) at z=0.

  name :

  equilibrium

  uda_name :

  EFM_Q(R)

  units :

fig, axes = plt.subplots(1, 3, figsize=(10, 5))

profiles['j_tor'].isel(time=50).plot(ax=axes[0], x='major_radius')
profiles['psi'].isel(time=50).plot(ax=axes[1], x='major_radius')
profiles['q'].isel(time=50).plot(ax=axes[2])
plt.tight_layout()

Gas Injection

profiles = xr.open_zarr(store, group='gas_injection')

plot_1d_profiles(profiles)
plt.tight_layout()
profiles

<xarray.Dataset> Size: 116kB
Dimensions:         (time: 2906)
Coordinates:
  * time            (time) float64 23kB -0.0612 -0.06095 ... 0.6648 0.665
Data variables:
    inboard_total   (time) float64 23kB ...
    outboard_total  (time) float64 23kB ...
    pressure        (time) float64 23kB ...
    total_injected  (time) float64 23kB ...
Attributes:
    description:  
    imas:         gas_injection
    label:        Integrated total gas
    name:         gas_injection
    uda_name:     AGA_INTEG_GAS
    units:        count

xarray.Dataset

• Dimensions:
  • time: 2906
• Coordinates: (1)
  • time
    (time)
    float64
    -0.0612 -0.06095 ... 0.6648 0.665

    units :

    s

    array([-0.0612 , -0.06095, -0.0607 , ...,  0.66455,  0.6648 ,  0.66505],
          shape=(2906,))

• Data variables: (4)
  • inboard_total
    (time)
    float64
    ...

    description :

    label :

    Total inboard gas

    name :

    inboard_total

    uda_name :

    AGA_INBOARD_TOTAL

    units :

    cps

    [2906 values with dtype=float64]

  • outboard_total
    (time)
    float64
    ...

    description :

    label :

    Total outboard gas

    name :

    outboard_total

    uda_name :

    AGA_OUTBOARD_TOTAL

    units :

    cps

    [2906 values with dtype=float64]

  • pressure
    (time)
    float64
    ...

    description :

    label :

    Fast Ion Gauge molec.den

    name :

    pressure

    uda_name :

    AGA_FIG

    units :

    D2/m3

    [2906 values with dtype=float64]

  • total_injected
    (time)
    float64
    ...

    description :

    label :

    Integrated total gas

    name :

    total_injected

    uda_name :

    AGA_INTEG_GAS

    units :

    count

    [2906 values with dtype=float64]

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(Index([-0.06120026111602783, -0.06095026111602783, -0.06070026111602783,
           -0.06045026111602783, -0.06020026111602783, -0.05995026111602783,
           -0.05970026111602783, -0.05945026111602783, -0.05920026111602783,
           -0.05895026111602783,
           ...
             0.6627997388839728,   0.6630497388839728,   0.6632997388839728,
             0.6635497388839728,   0.6637997388839728,   0.6640497388839728,
             0.6642997388839729,   0.6645497388839728,   0.6647997388839728,
             0.6650497388839728],
          dtype='float64', name='time', length=2906))

• Attributes: (6)

  description :

  imas :

  gas_injection

  label :

  Integrated total gas

  name :

  gas_injection

  uda_name :

  AGA_INTEG_GAS

  units :

  count