!pip install pandas matplotlib fsspec s3fs "zarr<3" pyarrow xarray

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Curated FAIR MAST Data

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

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

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()

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'

# Connect to remote S3 file system, with local file caching.
fs = fsspec.filesystem(
  **dict(
    protocol='filecache',
    target_protocol="s3",
    cache_storage='.cache',
    target_options=dict(anon=True, endpoint_url=endpoint_url)
  )
)

# Get a handle to the remote file
store = zarr.storage.FSStore(fs=fs, url=url)

Summary Profiles

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

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

plot_1d_profiles(profiles)
profiles

<xarray.Dataset>
Dimensions:              (time: 2906)
Coordinates:
  * time                 (time) float64 -0.0612 -0.06095 ... 0.6648 0.665
Data variables:
    ip                   (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    line_average_n_e     (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    neutron_rates_total  (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    power_nbi            (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    power_radiated       (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
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])

• Data variables: (5)
  • ip
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    imas :

    summary.global_quantities.ip

    label :

    Plasma Current

    name :

    ip

    uda_name :

    AMC_PLASMA CURRENT

    units :

    A

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • line_average_n_e
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    imas :

    summary.line_average.n_e.value

    label :

    m^-2

    name :

    line_average_n_e

    uda_name :

    ANE_DENSITY

    units :

    1 / m ** 2

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • neutron_rates_total
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    imas :

    summary.fusion.neutron_rates.total

    label :

    neutrons

    name :

    neutron_rates_total

    uda_name :

    ANU_NEUTRONS

    units :

    Hz

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • power_nbi
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    imas :

    summary.heating_current_drive.power_nbi

    label :

    P(SS+SW)

    name :

    power_nbi

    uda_name :

    ANB_TOT_SUM_POWER

    units :

    W

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • power_radiated
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    imas :

    summary.global_quantities.power_radiated.value

    label :

    P_Rad_Pol

    name :

    power_radiated

    uda_name :

    ABM_PRAD_POL

    units :

    W

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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])
# profiles['vertical_control'].plot(x='time', ax=axes[2])

# for i in range(len(profiles.gas_channel)):
#     profiles['gas'].isel(gas_channel=i).plot(x='time', ax=axes[3])

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

profiles

<xarray.Dataset>
Dimensions:   (time: 2906)
Coordinates:
  * time      (time) float64 -0.0612 -0.06095 -0.0607 ... 0.6645 0.6648 0.665
Data variables:
    i_plasma  (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    n_e_line  (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
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])

• Data variables: (2)
  • i_plasma
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    imas :

    pulse_schedule.flux_control.i_plasma

    label :

    /xdc/ip/t/ipref

    name :

    i_plasma

    uda_name :

    /xdc/ip/t/ipref

    units :

    A

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • n_e_line
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    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

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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>
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 ...
  * b_field_pol_probe_ccbv_channel            (b_field_pol_probe_ccbv_channel) <U10 ...
  * b_field_pol_probe_obr_channel             (b_field_pol_probe_obr_channel) <U9 ...
  * b_field_pol_probe_obv_channel             (b_field_pol_probe_obv_channel) <U9 ...
  * b_field_pol_probe_omv_channel             (b_field_pol_probe_omv_channel) <U11 ...
  * b_field_tor_probe_cc_channel              (b_field_tor_probe_cc_channel) <U13 ...
    ...                                        ...
  * b_field_tor_probe_saddle_voltage_channel  (b_field_tor_probe_saddle_voltage_channel) <U15 ...
  * flux_loop_channel                         (flux_loop_channel) <U12 'AMB_F...
  * time                                      (time) float64 -0.0612 ... 0.6652
  * time_mirnov                               (time_mirnov) float64 -0.0612 ....
  * time_omaha                                (time_omaha) float64 -0.0612 .....
  * time_saddle                               (time_saddle) float64 -0.0612 ....
Data variables:
    b_field_pol_probe_cc_field                (b_field_pol_probe_cc_channel, time_mirnov) float64 dask.array<chunksize=(1, 90801), meta=np.ndarray>
    b_field_pol_probe_ccbv_field              (b_field_pol_probe_ccbv_channel, time) float64 dask.array<chunksize=(20, 1817), meta=np.ndarray>
    b_field_pol_probe_obr_field               (b_field_pol_probe_obr_channel, time) float64 dask.array<chunksize=(9, 3633), meta=np.ndarray>
    b_field_pol_probe_obv_field               (b_field_pol_probe_obv_channel, time) float64 dask.array<chunksize=(9, 3633), meta=np.ndarray>
    b_field_pol_probe_omv_voltage             (b_field_pol_probe_omv_channel, time_mirnov) float64 dask.array<chunksize=(1, 90801), meta=np.ndarray>
    b_field_tor_probe_cc_field                (b_field_tor_probe_cc_channel, time_mirnov) float64 dask.array<chunksize=(1, 90801), meta=np.ndarray>
    b_field_tor_probe_omaha_voltage           (b_field_tor_probe_omaha_channel, time_omaha) float64 dask.array<chunksize=(1, 227001), meta=np.ndarray>
    b_field_tor_probe_saddle_field            (b_field_tor_probe_saddle_field_channel, time_saddle) float64 dask.array<chunksize=(3, 18161), meta=np.ndarray>
    b_field_tor_probe_saddle_voltage          (b_field_tor_probe_saddle_voltage_channel, time_saddle) float64 dask.array<chunksize=(3, 18161), meta=np.ndarray>
    flux_loop_flux                            (flux_loop_channel, time) float64 dask.array<chunksize=(8, 3633), meta=np.ndarray>
    ip                                        (time) float64 dask.array<chunksize=(3633,), meta=np.ndarray>
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])

  • 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  ])

  • 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])

  • 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 ])

• Data variables: (11)
  • b_field_pol_probe_cc_field
    (b_field_pol_probe_cc_channel, time_mirnov)
    float64
    dask.array<chunksize=(1, 90801), meta=np.ndarray>

    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

    Array Chunk Bytes 13.86 MiB 709.38 kiB Shape (5, 363201) (1, 90801) Dask graph 20 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_pol_probe_ccbv_field
    (b_field_pol_probe_ccbv_channel, time)
    float64
    dask.array<chunksize=(20, 1817), meta=np.ndarray>

    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

    Array Chunk Bytes 1.11 MiB 283.91 kiB Shape (40, 3633) (20, 1817) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_pol_probe_obr_field
    (b_field_pol_probe_obr_channel, time)
    float64
    dask.array<chunksize=(9, 3633), meta=np.ndarray>

    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

    Array Chunk Bytes 510.89 kiB 255.45 kiB Shape (18, 3633) (9, 3633) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_pol_probe_obv_field
    (b_field_pol_probe_obv_channel, time)
    float64
    dask.array<chunksize=(9, 3633), meta=np.ndarray>

    description :

    Outer Bv array

    imas :

    magnetics.b_field_pol_probe[:].field.data

    name :

    b_field_pol_probe_obv_field

    units :

    T

    Array Chunk Bytes 510.89 kiB 255.45 kiB Shape (18, 3633) (9, 3633) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_pol_probe_omv_voltage
    (b_field_pol_probe_omv_channel, time_mirnov)
    float64
    dask.array<chunksize=(1, 90801), meta=np.ndarray>

    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

    Array Chunk Bytes 8.31 MiB 709.38 kiB Shape (3, 363201) (1, 90801) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_tor_probe_cc_field
    (b_field_tor_probe_cc_channel, time_mirnov)
    float64
    dask.array<chunksize=(1, 90801), meta=np.ndarray>

    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

    Array Chunk Bytes 8.31 MiB 709.38 kiB Shape (3, 363201) (1, 90801) Dask graph 12 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_tor_probe_omaha_voltage
    (b_field_tor_probe_omaha_channel, time_omaha)
    float64
    dask.array<chunksize=(1, 227001), meta=np.ndarray>

    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

    Array Chunk Bytes 221.68 MiB 1.73 MiB Shape (4, 7264001) (1, 227001) Dask graph 128 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_tor_probe_saddle_field
    (b_field_tor_probe_saddle_field_channel, time_saddle)
    float64
    dask.array<chunksize=(3, 18161), meta=np.ndarray>

    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

    Array Chunk Bytes 3.33 MiB 425.65 kiB Shape (12, 36321) (3, 18161) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

  • b_field_tor_probe_saddle_voltage
    (b_field_tor_probe_saddle_voltage_channel, time_saddle)
    float64
    dask.array<chunksize=(3, 18161), meta=np.ndarray>

    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

    Array Chunk Bytes 3.33 MiB 425.65 kiB Shape (12, 36321) (3, 18161) Dask graph 8 chunks in 2 graph layers Data type float64 numpy.ndarray

  • flux_loop_flux
    (flux_loop_channel, time)
    float64
    dask.array<chunksize=(8, 3633), meta=np.ndarray>

    description :

    imas :

    magnetics.flux_loop[:].flux.data

    label :

    FL/CC03

    name :

    flux_loop_flux

    uda_name :

    AMB_FL/CC03

    units :

    Wb

    Array Chunk Bytes 425.74 kiB 227.06 kiB Shape (15, 3633) (8, 3633) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ip
    (time)
    float64
    dask.array<chunksize=(3633,), meta=np.ndarray>

    description :

    imas :

    magnetics.ip[:].data

    label :

    Plasma Current

    name :

    ip

    uda_name :

    AMC_PLASMA CURRENT

    units :

    A

    Array Chunk Bytes 28.38 kiB 28.38 kiB Shape (3633,) (3633,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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)

<matplotlib.colorbar.Colorbar at 0x37a248550>

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>
Dimensions:                             (bes_channel: 32, dalpha_channel: 3,
                                         time: 36321, time_bes: 1452801)
Coordinates:
  * bes_channel                         (bes_channel) <U13 'xbt/channel01' .....
  * dalpha_channel                      (dalpha_channel) <U13 'XIM_DA/HM10/R'...
  * time                                (time) float64 -0.0612 ... 0.6652
  * time_bes                            (time_bes) float64 -0.0612 ... 0.6652
Data variables:
    density_gradient                    (time) float64 dask.array<chunksize=(18161,), meta=np.ndarray>
    filter_spectrometer_bes_voltage     (bes_channel, time_bes) float64 dask.array<chunksize=(2, 90801), meta=np.ndarray>
    filter_spectrometer_dalpha_voltage  (dalpha_channel, time) float64 dask.array<chunksize=(2, 18161), meta=np.ndarray>
Attributes:
    description:  
    imas:         None
    label:        Volt
    name:         spectrometer_visible
    uda_name:     XIM_DA/HM10/R
    units:        V

xarray.Dataset

• Dimensions:
  • bes_channel: 32
  • dalpha_channel: 3
  • time: 36321
  • time_bes: 1452801
• 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 ])

  • 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  ])

• Data variables: (3)
  • density_gradient
    (time)
    float64
    dask.array<chunksize=(18161,), meta=np.ndarray>

    description :

    label :

    m-4

    name :

    density_gradient

    uda_name :

    ADG_DENSITY_GRADIENT

    units :

    1 / m ** 4

    Array Chunk Bytes 283.76 kiB 141.88 kiB Shape (36321,) (18161,) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • filter_spectrometer_bes_voltage
    (bes_channel, time_bes)
    float64
    dask.array<chunksize=(2, 90801), meta=np.ndarray>

    description :

    imas :

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

    label :

    Volt

    name :

    filter_spectrometer_bes_voltage

    uda_name :

    xbt/channel01

    units :

    V

    Array Chunk Bytes 354.69 MiB 1.39 MiB Shape (32, 1452801) (2, 90801) Dask graph 256 chunks in 2 graph layers Data type float64 numpy.ndarray

  • filter_spectrometer_dalpha_voltage
    (dalpha_channel, time)
    float64
    dask.array<chunksize=(2, 18161), meta=np.ndarray>

    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

    Array Chunk Bytes 851.27 kiB 283.77 kiB Shape (3, 36321) (2, 18161) Dask graph 4 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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(1, 3)
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>
Dimensions:           (channel: 14, time: 2906)
Coordinates:
  * channel           (channel) <U21 '/xdc/pf/f/p1' ... 'AMC_P5U FEED CURRENT'
  * time              (time) float64 -0.0612 -0.06095 -0.0607 ... 0.6648 0.665
Data variables:
    coil_current      (channel, time) float64 dask.array<chunksize=(7, 2906), meta=np.ndarray>
    coil_voltage      (channel, time) float64 dask.array<chunksize=(7, 2906), meta=np.ndarray>
    solenoid_current  (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
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])

• Data variables: (3)
  • coil_current
    (channel, time)
    float64
    dask.array<chunksize=(7, 2906), meta=np.ndarray>

    description :

    imas :

    pf_active.coil[:].current.data

    label :

    P2il Feed Current

    name :

    coil_current

    uda_name :

    AMC_P2IL FEED CURRENT

    units :

    A

    Array Chunk Bytes 317.84 kiB 158.92 kiB Shape (14, 2906) (7, 2906) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • coil_voltage
    (channel, time)
    float64
    dask.array<chunksize=(7, 2906), meta=np.ndarray>

    description :

    imas :

    pf_active.coil[:].voltage.data

    label :

    /xdc/pf/f/p1

    name :

    coil_voltage

    uda_name :

    /xdc/pf/f/p1

    units :

    V

    Array Chunk Bytes 317.84 kiB 158.92 kiB Shape (14, 2906) (7, 2906) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • solenoid_current
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    label :

    Sol Current

    name :

    solenoid_current

    uda_name :

    AMC_SOL CURRENT

    units :

    A

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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>
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 '/xsx/H...
  * horizontal_cam_upper_channel  (horizontal_cam_upper_channel) <U14 '/xsx/H...
  * tangential_cam_channel        (tangential_cam_channel) <U12 '/xsx/TCAM/1'...
  * time                          (time) float64 -0.0612 -0.0612 ... 0.6652
Data variables:
    horizontal_cam_lower          (horizontal_cam_lower_channel, time) float64 dask.array<chunksize=(3, 45401), meta=np.ndarray>
    horizontal_cam_upper          (horizontal_cam_upper_channel, time) float64 dask.array<chunksize=(3, 45401), meta=np.ndarray>
    tangential_cam                (tangential_cam_channel, time) float64 dask.array<chunksize=(3, 45401), meta=np.ndarray>
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/9'

    array(['/xsx/HCAM/L/1', '/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', '/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'], dtype='<U14')

  • horizontal_cam_upper_channel
    (horizontal_cam_upper_channel)
    <U14
    '/xsx/HCAM/U/1' ... '/xsx/HCAM/U/9'

    array(['/xsx/HCAM/U/1', '/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', '/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'], dtype='<U14')

  • tangential_cam_channel
    (tangential_cam_channel)
    <U12
    '/xsx/TCAM/1' ... '/xsx/TCAM/9'

    array(['/xsx/TCAM/1', '/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', '/xsx/TCAM/2', '/xsx/TCAM/3',
           '/xsx/TCAM/4', '/xsx/TCAM/5', '/xsx/TCAM/6', '/xsx/TCAM/7',
           '/xsx/TCAM/8', '/xsx/TCAM/9'], 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  ])

• Data variables: (3)
  • horizontal_cam_lower
    (horizontal_cam_lower_channel, time)
    float64
    dask.array<chunksize=(3, 45401), meta=np.ndarray>

    description :

    label :

    Volt

    name :

    horizontal_cam_lower

    uda_name :

    /xsx/HCAM/L/1

    units :

    V

    Array Chunk Bytes 49.88 MiB 1.04 MiB Shape (18, 363201) (3, 45401) Dask graph 48 chunks in 2 graph layers Data type float64 numpy.ndarray

  • horizontal_cam_upper
    (horizontal_cam_upper_channel, time)
    float64
    dask.array<chunksize=(3, 45401), meta=np.ndarray>

    description :

    label :

    Volt

    name :

    horizontal_cam_upper

    uda_name :

    /xsx/HCAM/U/1

    units :

    V

    Array Chunk Bytes 49.88 MiB 1.04 MiB Shape (18, 363201) (3, 45401) Dask graph 48 chunks in 2 graph layers Data type float64 numpy.ndarray

  • tangential_cam
    (tangential_cam_channel, time)
    float64
    dask.array<chunksize=(3, 45401), meta=np.ndarray>

    description :

    label :

    Volt

    name :

    tangential_cam

    uda_name :

    /xsx/TCAM/1

    units :

    V

    Array Chunk Bytes 49.88 MiB 1.04 MiB Shape (18, 363201) (3, 45401) Dask graph 48 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • horizontal_cam_lower_channel
    PandasIndex

    PandasIndex(Index(['/xsx/HCAM/L/1', '/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', '/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'],
          dtype='object', name='horizontal_cam_lower_channel'))

  • horizontal_cam_upper_channel
    PandasIndex

    PandasIndex(Index(['/xsx/HCAM/U/1', '/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', '/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'],
          dtype='object', name='horizontal_cam_upper_channel'))

  • tangential_cam_channel
    PandasIndex

    PandasIndex(Index(['/xsx/TCAM/1', '/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', '/xsx/TCAM/2', '/xsx/TCAM/3',
           '/xsx/TCAM/4', '/xsx/TCAM/5', '/xsx/TCAM/6', '/xsx/TCAM/7',
           '/xsx/TCAM/8', '/xsx/TCAM/9'],
          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>
Dimensions:       (major_radius: 120, time: 146)
Coordinates:
  * major_radius  (major_radius) float64 0.3 0.31 0.32 0.33 ... 1.47 1.48 1.49
  * time          (time) float64 -0.0612 -0.0562 -0.0512 ... 0.6588 0.6638
Data variables:
    n_e           (time, major_radius) float64 dask.array<chunksize=(146, 120), meta=np.ndarray>
    n_e_core      (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    p_e           (time, major_radius) float64 dask.array<chunksize=(146, 120), meta=np.ndarray>
    t_e           (time, major_radius) float64 dask.array<chunksize=(146, 120), meta=np.ndarray>
    t_e_core      (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
Attributes:
    description:  
    imas:         thomson_scattering
    label:        core temperature
    name:         thomson_scattering
    uda_name:     AYC_TE_CORE
    units:        eV

xarray.Dataset

• Dimensions:
  • major_radius: 120
  • time: 146
• 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
    dask.array<chunksize=(146, 120), meta=np.ndarray>

    description :

    imas :

    thomson_scattering.channel[:].n_e

    label :

    electron density

    name :

    n_e

    uda_name :

    AYC_NE

    units :

    1 / m ** 3

    Array Chunk Bytes 136.88 kiB 136.88 kiB Shape (146, 120) (146, 120) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • n_e_core
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    core density

    name :

    n_e_core

    uda_name :

    AYC_NE_CORE

    units :

    1 / m ** 3

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • p_e
    (time, major_radius)
    float64
    dask.array<chunksize=(146, 120), meta=np.ndarray>

    description :

    label :

    electron pressure

    name :

    p_e

    uda_name :

    AYC_PE

    units :

    u * a

    Array Chunk Bytes 136.88 kiB 136.88 kiB Shape (146, 120) (146, 120) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_e
    (time, major_radius)
    float64
    dask.array<chunksize=(146, 120), meta=np.ndarray>

    description :

    imas :

    thomson_scattering.channel[:].t_e

    label :

    electron temperature

    name :

    t_e

    uda_name :

    AYC_TE

    units :

    eV

    Array Chunk Bytes 136.88 kiB 136.88 kiB Shape (146, 120) (146, 120) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_e_core
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    core temperature

    name :

    t_e_core

    uda_name :

    AYC_TE_CORE

    units :

    eV

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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>
Dimensions:       (major_radius: 120, time: 146)
Coordinates:
  * major_radius  (major_radius) float64 0.3 0.31 0.32 0.33 ... 1.47 1.48 1.49
  * time          (time) float64 -0.0612 -0.0562 -0.0512 ... 0.6588 0.6638
Data variables:
    n_e           (time, major_radius) float64 dask.array<chunksize=(146, 120), meta=np.ndarray>
    n_e_core      (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    p_e           (time, major_radius) float64 dask.array<chunksize=(146, 120), meta=np.ndarray>
    t_e           (time, major_radius) float64 dask.array<chunksize=(146, 120), meta=np.ndarray>
    t_e_core      (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
Attributes:
    description:  
    imas:         thomson_scattering
    label:        core temperature
    name:         thomson_scattering
    uda_name:     AYC_TE_CORE
    units:        eV

xarray.Dataset

• Dimensions:
  • major_radius: 120
  • time: 146
• 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
    dask.array<chunksize=(146, 120), meta=np.ndarray>

    description :

    imas :

    thomson_scattering.channel[:].n_e

    label :

    electron density

    name :

    n_e

    uda_name :

    AYC_NE

    units :

    1 / m ** 3

    Array Chunk Bytes 136.88 kiB 136.88 kiB Shape (146, 120) (146, 120) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • n_e_core
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    core density

    name :

    n_e_core

    uda_name :

    AYC_NE_CORE

    units :

    1 / m ** 3

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • p_e
    (time, major_radius)
    float64
    dask.array<chunksize=(146, 120), meta=np.ndarray>

    description :

    label :

    electron pressure

    name :

    p_e

    uda_name :

    AYC_PE

    units :

    u * a

    Array Chunk Bytes 136.88 kiB 136.88 kiB Shape (146, 120) (146, 120) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_e
    (time, major_radius)
    float64
    dask.array<chunksize=(146, 120), meta=np.ndarray>

    description :

    imas :

    thomson_scattering.channel[:].t_e

    label :

    electron temperature

    name :

    t_e

    uda_name :

    AYC_TE

    units :

    eV

    Array Chunk Bytes 136.88 kiB 136.88 kiB Shape (146, 120) (146, 120) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • t_e_core
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    core temperature

    name :

    t_e_core

    uda_name :

    AYC_TE_CORE

    units :

    eV

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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>
Dimensions:       (major_radius: 160, time: 73)
Coordinates:
  * major_radius  (major_radius) float64 0.0 0.01 0.02 0.03 ... 1.57 1.58 1.59
  * time          (time) float64 -0.0612 -0.0512 -0.0412 ... 0.6488 0.6588
Data variables:
    t_i           (time, major_radius) float64 dask.array<chunksize=(73, 160), meta=np.ndarray>
    v_i           (time, major_radius) float64 dask.array<chunksize=(73, 160), meta=np.ndarray>
Attributes:
    description:  
    imas:         charge_exchange
    label:        Carbon temperature
    name:         charge_exchange
    uda_name:     ACT_SS_TEMPERATURE
    units:        eV

xarray.Dataset

• Dimensions:
  • major_radius: 160
  • time: 73
• 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 -0.0512 ... 0.6488 0.6588

    units :

    s

    array([-0.0612, -0.0512, -0.0412, -0.0312, -0.0212, -0.0112, -0.0012,  0.0088,
            0.0188,  0.0288,  0.0388,  0.0488,  0.0588,  0.0688,  0.0788,  0.0888,
            0.0988,  0.1088,  0.1188,  0.1288,  0.1388,  0.1488,  0.1588,  0.1688,
            0.1788,  0.1888,  0.1988,  0.2088,  0.2188,  0.2288,  0.2388,  0.2488,
            0.2588,  0.2688,  0.2788,  0.2888,  0.2988,  0.3088,  0.3188,  0.3288,
            0.3388,  0.3488,  0.3588,  0.3688,  0.3788,  0.3888,  0.3988,  0.4088,
            0.4188,  0.4288,  0.4388,  0.4488,  0.4588,  0.4688,  0.4788,  0.4888,
            0.4988,  0.5088,  0.5188,  0.5288,  0.5388,  0.5488,  0.5588,  0.5688,
            0.5788,  0.5888,  0.5988,  0.6088,  0.6188,  0.6288,  0.6388,  0.6488,
            0.6588])

• Data variables: (2)
  • t_i
    (time, major_radius)
    float64
    dask.array<chunksize=(73, 160), meta=np.ndarray>

    description :

    imas :

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

    label :

    Carbon temperature

    name :

    t_i

    uda_name :

    ACT_SS_TEMPERATURE

    units :

    eV

    Array Chunk Bytes 91.25 kiB 91.25 kiB Shape (73, 160) (73, 160) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • v_i
    (time, major_radius)
    float64
    dask.array<chunksize=(73, 160), meta=np.ndarray>

    description :

    label :

    Carbon Velocity

    name :

    v_i

    uda_name :

    ACT_SS_VELOCITY

    units :

    m / s

    Array Chunk Bytes 91.25 kiB 91.25 kiB Shape (73, 160) (73, 160) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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,   -0.05120026111602783,   -0.04120026111602783,
            -0.031200261116027826,  -0.021200261116027824,  -0.011200261116027822,
           -0.0012002611160278204,   0.008799738883972175,   0.018799738883972184,
             0.028799738883972192,    0.03879973888397219,    0.04879973888397218,
              0.05879973888397219,     0.0687997388839722,    0.07879973888397218,
              0.08879973888397219,     0.0987997388839722,    0.10879973888397221,
              0.11879973888397222,     0.1287997388839722,     0.1387997388839722,
              0.14879973888397222,     0.1587997388839722,     0.1687997388839722,
              0.17879973888397221,    0.18879973888397222,    0.19879973888397223,
              0.20879973888397224,     0.2187997388839722,     0.2287997388839722,
               0.2387997388839722,    0.24879973888397222,    0.25879973888397223,
              0.26879973888397224,    0.27879973888397225,    0.28879973888397226,
              0.29879973888397227,     0.3087997388839722,    0.31879973888397223,
              0.32879973888397224,    0.33879973888397225,    0.34879973888397225,
              0.35879973888397226,     0.3687997388839723,     0.3787997388839722,
              0.38879973888397223,    0.39879973888397224,    0.40879973888397225,
              0.41879973888397226,    0.42879973888397227,     0.4387997388839723,
               0.4487997388839723,     0.4587997388839723,     0.4687997388839723,
               0.4787997388839723,     0.4887997388839723,     0.4987997388839722,
               0.5087997388839722,     0.5187997388839722,     0.5287997388839722,
               0.5387997388839723,     0.5487997388839723,     0.5587997388839723,
               0.5687997388839723,     0.5787997388839723,     0.5887997388839723,
               0.5987997388839723,     0.6087997388839723,     0.6187997388839723,
               0.6287997388839723,     0.6387997388839723,     0.6487997388839724,
               0.6587997388839724],
          dtype='float64', name='time'))

• 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>
Dimensions:              (time: 146, z: 65, major_radius: 65,
                          n_boundary_coords: 139, n_x_points: 4, profile_r: 65)
Coordinates:
  * major_radius         (major_radius) float64 0.06 0.09 0.12 ... 1.95 1.98
  * n_boundary_coords    (n_boundary_coords) float32 0.0 1.0 2.0 ... 137.0 138.0
  * n_x_points           (n_x_points) <U16 'EFM_XPOINT1_R(C)' ... 'EFM_XPOINT...
  * profile_r            (profile_r) float32 0.0 0.01562 0.03125 ... 0.9844 1.0
  * time                 (time) float64 -0.0612 -0.0562 ... 0.6588 0.6638
  * z                    (z) float32 -2.0 -1.938 -1.875 ... 1.875 1.938 2.0
Data variables: (12/35)
    beta_normal          (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    beta_pol             (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    beta_tor             (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    bphi_rmag            (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    bvac_rmag            (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    da_rating            (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    ...                   ...
    triangularity_upper  (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    vloop_dynamic        (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    vloop_static         (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    whmd                 (time) float64 dask.array<chunksize=(146,), meta=np.ndarray>
    x_point_r            (n_x_points, time) float64 dask.array<chunksize=(4, 146), meta=np.ndarray>
    x_point_z            (n_x_points, time) float64 dask.array<chunksize=(4, 146), meta=np.ndarray>
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
  • n_x_points: 4
  • profile_r: 65
• 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
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.beta_normal

    label :

    betat/(I/ a Bvac_geom)

    name :

    beta_normal

    uda_name :

    EFM_BETAN

    units :

    T

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • beta_pol
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.beta_pol

    label :

    Poloidal Beta

    name :

    beta_pol

    uda_name :

    EFM_BETAP

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • beta_tor
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.beta_tor

    label :

    Toroidal Beta

    name :

    beta_tor

    uda_name :

    EFM_BETAT

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • bphi_rmag
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    Bphi at rmag

    name :

    bphi_rmag

    uda_name :

    EFM_BPHI_RMAG

    units :

    T

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • bvac_rmag
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    Bvac at rmag

    name :

    bvac_rmag

    uda_name :

    EFM_BVAC_RMAG

    units :

    T

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • da_rating
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    EFIT rating by D-alpha p

    name :

    da_rating

    uda_name :

    ESM_EFM_RATING_DA

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • elongation
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].boundary.elongation

    label :

    Elongation

    name :

    elongation

    uda_name :

    EFM_ELONGATION

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • elongation_axis
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    Elongation on Magnetic A

    name :

    elongation_axis

    uda_name :

    EFM_ELONGATION_AXIS

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ip_rating
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    EFIT rating by I_p

    name :

    ip_rating

    uda_name :

    ESM_EFM_RATING_IP

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • j_tor
    (time, z, major_radius)
    float64
    dask.array<chunksize=(37, 33, 33), meta=np.ndarray>

    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

    Array Chunk Bytes 4.71 MiB 314.79 kiB Shape (146, 65, 65) (37, 33, 33) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lcfs_r
    (time, n_boundary_coords)
    float64
    dask.array<chunksize=(146, 139), meta=np.ndarray>

    description :

    imas :

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

    label :

    r-coords of separatrix

    name :

    lcfs_r

    uda_name :

    EFM_LCFS(R)_(C)

    units :

    m

    Array Chunk Bytes 158.55 kiB 158.55 kiB Shape (146, 139) (146, 139) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lcfs_z
    (time, n_boundary_coords)
    float64
    dask.array<chunksize=(146, 139), meta=np.ndarray>

    description :

    imas :

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

    label :

    z-coords of separatrix

    name :

    lcfs_z

    uda_name :

    EFM_LCFS(Z)_(C)

    units :

    m

    Array Chunk Bytes 158.55 kiB 158.55 kiB Shape (146, 139) (146, 139) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • li
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    Plasma Inductance

    name :

    li

    uda_name :

    EFM_LI

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • magnetic_axis_r
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    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

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • magnetic_axis_z
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    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

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • minor_radius
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].boundary.minor_radius

    label :

    Minor Radius

    name :

    minor_radius

    uda_name :

    EFM_MINOR_RADIUS

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • psi
    (time, z, major_radius)
    float64
    dask.array<chunksize=(37, 33, 33), meta=np.ndarray>

    description :

    imas :

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

    label :

    psi(r,z)

    name :

    psi

    uda_name :

    EFM_PSI(R,Z)

    units :

    Wb / rad

    Array Chunk Bytes 4.71 MiB 314.79 kiB Shape (146, 65, 65) (37, 33, 33) Dask graph 16 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q
    (time, profile_r)
    float64
    dask.array<chunksize=(146, 65), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].profiles_1d.q

    label :

    q(r) at z=0.

    name :

    q

    uda_name :

    EFM_Q(R)

    units :

    Array Chunk Bytes 74.14 kiB 74.14 kiB Shape (146, 65) (146, 65) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q100
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    q at Psi_norm=100%

    name :

    q100

    uda_name :

    EFM_Q_100

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q90
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    q at Psi_norm=100%

    name :

    q90

    uda_name :

    EFM_Q_100

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q95
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.q_95

    label :

    q at Psi_norm=95%

    name :

    q95

    uda_name :

    EFM_Q_95

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • q_axis
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].global_quantities.q_axis

    label :

    q on Magnetic Axis

    name :

    q_axis

    uda_name :

    EFM_Q_AXIS

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rpsi100_in
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    i/b Rad of lcfs at Mid

    name :

    rpsi100_in

    uda_name :

    EFM_R(PSI100)_IN

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rpsi100_out
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    o/b Rad of lcfs at Mid

    name :

    rpsi100_out

    uda_name :

    EFM_R(PSI100)_OUT

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rpsi90_in
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    i/b Rad of 90%Psi at Mid

    name :

    rpsi90_in

    uda_name :

    EFM_R(PSI90)_IN

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rpsi90_out
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    o/b Rad of 90%Psi at Mid

    name :

    rpsi90_out

    uda_name :

    EFM_R(PSI90)_OUT

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rpsi95_in
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    i/b Rad of 95%Psi at Mid

    name :

    rpsi95_in

    uda_name :

    EFM_R(PSI95)_IN

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • rpsi95_out
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    o/b Rad of 95%Psi at Mid

    name :

    rpsi95_out

    uda_name :

    EFM_R(PSI95)_OUT

    units :

    m

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • triangularity_lower
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].profiles_1d.triangularity_lower

    label :

    Lower Triangularity

    name :

    triangularity_lower

    uda_name :

    EFM_TRIANG_LOWER

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • triangularity_upper
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    imas :

    equilibrium.time_slice[:].profiles_1d.triangularity_upper

    label :

    Upper Triangularity

    name :

    triangularity_upper

    uda_name :

    EFM_TRIANG_UPPER

    units :

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vloop_dynamic
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    dynamic V_loop

    name :

    vloop_dynamic

    uda_name :

    ESM_V_LOOP_DYNAMIC

    units :

    V

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vloop_static
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    static V_loop

    name :

    vloop_static

    uda_name :

    ESM_V_LOOP_STATIC

    units :

    V

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • whmd
    (time)
    float64
    dask.array<chunksize=(146,), meta=np.ndarray>

    description :

    label :

    Plasma Diamagnetic Energ

    name :

    whmd

    uda_name :

    EFM_WPLASMD

    units :

    J

    Array Chunk Bytes 1.14 kiB 1.14 kiB Shape (146,) (146,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • x_point_r
    (n_x_points, time)
    float64
    dask.array<chunksize=(4, 146), meta=np.ndarray>

    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 :

    Array Chunk Bytes 4.56 kiB 4.56 kiB Shape (4, 146) (4, 146) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • x_point_z
    (n_x_points, time)
    float64
    dask.array<chunksize=(4, 146), meta=np.ndarray>

    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 :

    Array Chunk Bytes 4.56 kiB 4.56 kiB Shape (4, 146) (4, 146) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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'].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>
Dimensions:         (time: 2906)
Coordinates:
  * time            (time) float64 -0.0612 -0.06095 -0.0607 ... 0.6648 0.665
Data variables:
    inboard_total   (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    outboard_total  (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    pressure        (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
    total_injected  (time) float64 dask.array<chunksize=(2906,), meta=np.ndarray>
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])

• Data variables: (4)
  • inboard_total
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    label :

    Total inboard gas

    name :

    inboard_total

    uda_name :

    AGA_INBOARD_TOTAL

    units :

    cps

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • outboard_total
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    label :

    Total outboard gas

    name :

    outboard_total

    uda_name :

    AGA_OUTBOARD_TOTAL

    units :

    cps

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pressure
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    label :

    Fast Ion Gauge molec.den

    name :

    pressure

    uda_name :

    AGA_FIG

    units :

    D2/m3

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • total_injected
    (time)
    float64
    dask.array<chunksize=(2906,), meta=np.ndarray>

    description :

    label :

    Integrated total gas

    name :

    total_injected

    uda_name :

    AGA_INTEG_GAS

    units :

    count

    Array Chunk Bytes 22.70 kiB 22.70 kiB Shape (2906,) (2906,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• 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