Data Gallery

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import intake
import pandas as pd
import numpy as np
from scipy.signal import stft
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

plt.rcParams["font.family"] = "sans"
plt.rcParams["font.size"] = 8

Data Gallery#

This notebook contains example plots of data from different diagnostics as a demonstration of what sort data is available in the archive.

First we need to find the url to a particular shot. Here we are going to use shot 30420 as an example.

shot_id = 30420
catalog  = intake.open_catalog('https://mastapp.site/intake/catalog.yml')
shot_df = pd.DataFrame(catalog.index.level1.shots().read())
url = shot_df.loc[shot_df.shot_id == shot_id].iloc[0].url

Plasma Current Data#

Data from the amc source contains

Plasma Current (\(I_p\)): Flows within the plasma, providing initial heating and contributing to the poloidal magnetic field for confinement and stability.
PF Coil Currents: Control the poloidal magnetic field, allowing for plasma shaping, vertical stability, and edge magnetic configuration control.
TF Coil Currents: Generate the strong toroidal magnetic field necessary for primary plasma confinement.

dataset = catalog.level1.shots(url=url, group='amc')
dataset = dataset.to_dask()

dataset

<xarray.Dataset>
Dimensions:            (time: 30000)
Coordinates:
  * time               (time) float32 -2.0 -2.0 -2.0 -1.999 ... 3.999 4.0 4.0
Data variables: (12/46)
    efps_current       (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    error_field_02     (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    error_field_05     (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    p2il_coil_current  (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    p2il_feed_current  (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    p2iu_coil_current  (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    ...                 ...
    p6u_current        (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    plasma_current     (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    sol_current        (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    status             float32 ...
    tf_current         (time) float32 dask.array<chunksize=(30000,), meta=np.ndarray>
    version            float32 ...
Attributes:
    description:  Plasma Current and PF/TF Coil Currents
    file_name:    amc0304.20
    format:       IDA3
    mds_name:     None
    name:         amc
    quality:      Not Checked
    shot_id:      30420
    signal_type:  Analysed
    source:       amc
    uda_name:     AMC
    uuid:         01aad0c4-2a84-59e2-8b1b-168b4bd66aa3
    version:      0

dataset = dataset.isel(time=(dataset.time > 0) & (dataset.time < .35))
fig, axes = plt.subplots(3, 1, figsize=(10, 6))
ax1, ax2, ax3 = axes.flatten()

ax1.plot(dataset['time'], dataset['plasma_current'])
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Plasma Current $I_p$ (kA)')

ax2.plot(dataset['time'], dataset['sol_current'])
ax2.set_xlabel('Time (s)')
ax2.set_ylabel('Solenoid Feed Current (kA)')

ax3.plot(dataset['time'], dataset['tf_current'])
ax3.set_xlabel('Time (s)')
ax3.set_ylabel('TF Feed Current (kA)')

for ax in axes:
    ax.grid(alpha=0.3)

plt.tight_layout()

_images/1a8915864c9fb385003676c01fe5e6d31e6ae18f3c4fc5b6fc3791d3f4ab47b9.png

Thompson Scattering Data#

ayc source holds the Thomspon Scattering data at the core. Thomson scattering diagnostics provide accurate measurements of electron temperature and density.

dataset = catalog.level1.shots(url=url, group='ayc')
dataset = dataset.to_dask()

dataset

<xarray.Dataset>
Dimensions:              (acqiris_time: 73, radial_index: 131, arb: 130,
                          time: 73, spectral_index: 4, instrument_time: 20,
                          time_segment: 73, xyc_time: 73)
Coordinates:
  * acqiris_time         (acqiris_time) float32 0.0 1.0 2.0 ... 70.0 71.0 72.0
  * arb                  (arb) float32 0.0 1.0 2.0 3.0 ... 127.0 128.0 129.0
  * instrument_time      (instrument_time) float32 1.0 2.0 3.0 ... 19.0 20.0
  * radial_index         (radial_index) float32 0.0 1.0 2.0 ... 129.0 130.0
  * spectral_index       (spectral_index) float32 1.0 2.0 3.0 4.0
  * time                 (time) float32 0.004166 0.008332 0.0125 ... 0.3 0.3042
  * xyc_time             (xyc_time) float32 0.0 1.0 2.0 3.0 ... 70.0 71.0 72.0
Dimensions without coordinates: time_segment
Data variables: (12/36)
    acqiris_time_        (acqiris_time) float32 dask.array<chunksize=(73,), meta=np.ndarray>
    angle                (radial_index) float32 dask.array<chunksize=(131,), meta=np.ndarray>
    aspectra             (time, radial_index, spectral_index) float32 dask.array<chunksize=(73, 131, 4), meta=np.ndarray>
    chi2                 (time, radial_index) float32 dask.array<chunksize=(73, 131), meta=np.ndarray>
    covariance_ne_te     (time, radial_index) float32 dask.array<chunksize=(73, 131), meta=np.ndarray>
    instrument_dr        (instrument_time, radial_index) float32 dask.array<chunksize=(20, 131), meta=np.ndarray>
    ...                   ...
    te_error             (time, radial_index) float32 dask.array<chunksize=(73, 131), meta=np.ndarray>
    version_fibre        float32 ...
    version_poly         float32 ...
    version_raman        float32 ...
    xyc_time_            (xyc_time) float32 dask.array<chunksize=(73,), meta=np.ndarray>
    yag_nelint           (time) float32 dask.array<chunksize=(73,), meta=np.ndarray>
Attributes:
    description:  Core Thomson scattering data
    file_name:    ayc0304.20
    format:       IDA3
    mds_name:     None
    name:         ayc
    quality:      Not Checked
    shot_id:      30420
    signal_type:  Analysed
    source:       ayc
    uda_name:     AYC
    uuid:         8d043ece-8bf8-5af8-87e4-d2a1b01716fa
    version:      0

fig, axes = plt.subplots(2,1)
ax1, ax2 = axes

ax1.plot(dataset['time'], dataset['te_core'])
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Core Temperature (eV)')

ax2.plot(dataset['time'], dataset['ne_core'])
ax2.set_xlabel('Time (s)')
ax2.set_ylabel('Peak Core Electron Density ($1 / m^3$)')

for ax in axes:
    ax.grid(alpha=0.3)

plt.tight_layout()

_images/b328d635e94966789292df52254063ca07d6e71a38fbcc25adf4530234698fd0.png

CO2 Interferometers#

CO2 interferometers (ane) are used to measure the electron density in the plasma. By measuring the phase shift of the laser beam as it passes through the plasma, the electron density can be inferred with high precision.

dataset = catalog.level1.shots(url=url, group='ane')
dataset = dataset.to_dask()

dataset

<xarray.Dataset>
Dimensions:        (time: 32768)
Coordinates:
  * time           (time) float32 -0.01 -0.00996 -0.00992 ... 1.301 1.301 1.301
Data variables:
    co2            (time) float32 dask.array<chunksize=(32768,), meta=np.ndarray>
    density        (time) float32 dask.array<chunksize=(32768,), meta=np.ndarray>
    hene           (time) float32 dask.array<chunksize=(32768,), meta=np.ndarray>
    passnumber     float32 ...
    status         float32 ...
    status_detail  float32 ...
    version        float32 ...
Attributes:
    description:  CO2 Interferometry
    file_name:    ane0304.20
    format:       IDA3
    mds_name:     None
    name:         ane
    quality:      Not Checked
    shot_id:      30420
    signal_type:  Analysed
    source:       ane
    uda_name:     ANE
    uuid:         5f814dd8-7336-56d7-a1ea-bac62e5cdcd3
    version:      0

fig, ax = plt.subplots(1, 1)
plt.plot(dataset['time'], dataset['density'])
ax.set_xlabel('Time (s)')
ax.set_ylabel('Integrated Electron Density ($1 / m^2$)')
ax.grid(alpha=0.3)

plt.tight_layout()

_images/77be42e9b3f052a9dfc930e438ace4c2a3ee4ff4d49a42898745971f1d90c003.png

Equillibrium Reconstruction Data#

The source efm contains data from EFIT. EFIT is a computational tool used to reconstruct the magnetic equilibrium configuration of the plasma in a tokamak. It calculates the shape and position of the plasma, as well as the distribution of the current and pressure within it, based on magnetic measurements.

dataset = catalog.level1.shots(url=url, group='efm')
dataset = dataset.to_dask()
dataset

<xarray.Dataset>
Dimensions:            (time: 64, psi_norm: 65, n_iterations: 10,
                        fcoil_seg_n: 938, fcoil_n: 101, ffprime_coefs_n: 2,
                        mag_probe_n: 78, psi_loop_n: 46, r: 65, z: 65,
                        profile_r: 129, lcfs_coords: 147, limiter_n: 37,
                        pprime_coefs_n: 2, profile_z: 65)
Coordinates: (12/13)
  * fcoil_n            (fcoil_n) float32 0.0 1.0 2.0 3.0 ... 98.0 99.0 100.0
  * ffprime_coefs_n    (ffprime_coefs_n) float32 0.0 1.0
  * lcfs_coords        (lcfs_coords) float32 0.0 1.0 2.0 ... 144.0 145.0 146.0
  * mag_probe_n        (mag_probe_n) float32 0.0 1.0 2.0 3.0 ... 75.0 76.0 77.0
  * n_iterations       (n_iterations) float32 0.0 1.0 2.0 3.0 ... 7.0 8.0 9.0
  * pprime_coefs_n     (pprime_coefs_n) float32 0.0 1.0
    ...                 ...
  * profile_z          (profile_z) float32 -2.0 -1.938 -1.875 ... 1.938 2.0
  * psi_loop_n         (psi_loop_n) float32 0.0 1.0 2.0 3.0 ... 43.0 44.0 45.0
  * psi_norm           (psi_norm) float32 0.0 0.01562 0.03125 ... 0.9844 1.0
  * r                  (r) float32 0.06 0.09031 0.1206 0.1509 ... 1.939 1.97 2.0
  * time               (time) float32 -0.05 -0.045 -0.04 ... 0.29 0.295 0.3
  * z                  (z) float32 -2.0 -1.938 -1.875 -1.812 ... 1.875 1.938 2.0
Dimensions without coordinates: fcoil_seg_n, limiter_n
Data variables: (12/151)
    all_times          (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    areap_c            (time, psi_norm) float32 dask.array<chunksize=(64, 65), meta=np.ndarray>
    betan              (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    betap              (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    betapd             (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    betat              (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    ...                 ...
    wpol               (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    xpoint1_rc         (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    xpoint1_zc         (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    xpoint2_rc         (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    xpoint2_zc         (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
    zbdry              (time) float32 dask.array<chunksize=(64,), meta=np.ndarray>
Attributes:
    description:  Basic EFIT
    file_name:    efm0304.20
    format:       IDA3
    mds_name:     None
    name:         efm
    quality:      Not Checked
    shot_id:      30420
    signal_type:  Analysed
    source:       efm
    uda_name:     EFM
    uuid:         1e39c600-8ffb-5f56-900d-2941e352319c
    version:      0

Below we show how to load and plot the plasma current denisty and with the last closed flux surface (LCFS).

d = dataset['plasma_current_rz'].dropna(dim='time')
r = dataset['r']
z = dataset['z']


lcfs_R = dataset['lcfs_r'].sel(time=d.time)
lcfs_Z = dataset['lcfs_z'].sel(time=d.time)

R, Z = np.meshgrid(r, z)

index = 50

# Get the x-point
xpoint_r = dataset['xpoint2_rc'][index]
xpoint_z = dataset['xpoint2_zc'][index]

# Get the current centre
mag_axis_r = dataset['current_centrd_r'][index]
mag_axis_z = dataset['current_centrd_z'][index]

# Get the last closed flux surface (LCFS)
lcfs_r = lcfs_R[index].values
lcfs_r = lcfs_r[~np.isnan(lcfs_r)]
lcfs_z = lcfs_Z[index].values
lcfs_z = lcfs_z[~np.isnan(lcfs_z)]

fig, ax = plt.subplots()
ax.contourf(R, Z, d[index], cmap='magma', levels=20, label='Plasma Current')
ax.plot(lcfs_r, lcfs_z, c='red', linestyle='--', label='LCFS')
ax.scatter(xpoint_r, xpoint_z, marker='x', color='green', label='X Point')
ax.scatter(mag_axis_r, mag_axis_z, marker='o', color='purple', label='Current Centre')

plt.title(f'EFIT Plasma Current & LCFS for Shot {d.attrs["shot_id"]}')
plt.ylabel('Z (m)')
plt.xlabel('R (m)')
plt.legend()

/var/folders/xr/yr8z575s52b4tbg3fj65qwx00000gp/T/ipykernel_88143/1992647380.py:28: UserWarning: The following kwargs were not used by contour: 'label'
  ax.contourf(R, Z, d[index], cmap='magma', levels=20, label='Plasma Current')

<matplotlib.legend.Legend at 0x3791a4d10>

_images/f069502511cfef7db230cafb2b88209730dc8a28af0581914ec3a77bec776224.png

Mirnov Coils#

Mirnov coils are primarily used to measure magnetic fluctuations in the plasma. These fluctuations can provide important information about various plasma instabilities.

They are particularly useful for studying magnetohydrodynamic (MHD) phenomena. MHD activity includes various modes of instabilities, such as kink modes and tearing modes, which can affect plasma confinement and stability.

dataset = catalog.level1.shots(url='s3://mast/level1/shots/29790.zarr', group='xmo')
dataset = dataset.to_dask()
dataset

<xarray.Dataset>
Dimensions:                        (dim_0: 16, dim_1: 2, time: 1400000)
Coordinates:
  * dim_0                          (dim_0) int32 0 1 2 3 4 5 ... 11 12 13 14 15
  * dim_1                          (dim_1) int32 0 1
  * time                           (time) float64 -0.1 -0.1 -0.1 ... 0.6 0.6 0.6
Data variables: (12/18)
    devices_d3_acq216_025_channel  (dim_0) int32 dask.array<chunksize=(16,), meta=np.ndarray>
    devices_d3_acq216_025_range    (dim_0, dim_1) float32 dask.array<chunksize=(16, 2), meta=np.ndarray>
    devices_limit                  (dim_0) float64 dask.array<chunksize=(16,), meta=np.ndarray>
    omaha_1lz                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    omaha_2lt                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    omaha_2lz                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    ...                             ...
    omaha_5lz                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    omaha_5ur                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    omaha_5ut                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    omaha_5uz                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    omaha_6lz                      (time) float32 dask.array<chunksize=(87500,), meta=np.ndarray>
    time1                          (time) float64 dask.array<chunksize=(87500,), meta=np.ndarray>
Attributes:
    description:  Magnetic Field Measurements: OMAHA high frequency Mirnov co...
    file_name:    xmo029790.nc
    format:       CDF
    mds_name:     None
    name:         xmo
    quality:      Not Checked
    shot_id:      29790
    signal_type:  Raw
    source:       xmo
    uda_name:     XMO
    uuid:         ff541ac3-6b1c-5373-8d33-b85fd46bc75b
    version:      -1

We can first look at the line profile for one of the Mirnov coils:

fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.plot(dataset['time'], dataset['omaha_3lz'])
ax.grid()

ax.grid(alpha=0.3)
ax.set_ylabel('Volts (V)')
ax.set_xlabel('Time (s)')

plt.tight_layout()

_images/ef819dc7192eaff4195adff28c401c126c1bf162d1fad28cc27f0c37a9fd6c77.png

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

ds = dataset['omaha_3lz']
# 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[1] - ds.time[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'XMO/OMAHA/3LZ - Shot {ds.attrs["shot_id"]}')
ax.set_ylabel('Frequency [Hz]')
ax.set_xlabel('Time [sec]')
plt.colorbar(cax, ax=ax)

<matplotlib.colorbar.Colorbar at 0x37d0e0a10>

_images/2f37bd6e7e28cf1b16720c827c9e2a4a59db7fb371a3159da4e38e93a4db6f45.png

Photron Camera Data#

RBA#

RBA contains the data from Photron bullet camera A.

A Photron Bullet Camera provides high-speed, high-resolution imaging of fast transient events in the plasma. Its ability to capture detailed images of plasma instabilities, turbulence, and disruptions makes it essential for understanding and controlling plasma behavior, ultimately aiding in the pursuit of sustained nuclear fusion.

dataset = catalog.level1.shots(url=url, group='rba')
dataset = dataset.to_dask()
dataset

<xarray.Dataset>
Dimensions:  (time: 186, height: 912, width: 768)
Coordinates:
  * time     (time) float64 0.000256 0.002256 0.004256 ... 0.3083 0.3093 0.3103
Dimensions without coordinates: height, width
Data variables:
    data     (time, height, width) uint8 dask.array<chunksize=(24, 228, 192), meta=np.ndarray>
Attributes: (12/48)
    CLASS:           IMAGE
    IMAGE_SUBCLASS:  IMAGE_INDEXED
    IMAGE_VERSION:   1.2
    board_temp:      0.0
    bottom:          1024
    camera:          
    ...              ...
    units:           pixels
    uuid:            4a073a8f-aa2d-5deb-95df-91f93194c494
    vbin:            0
    version:         -1
    view:            Hl07 floor mount + FFC2 + 25mm lens + CII filter
    width:           768

plt.imshow(dataset.data[-10], cmap='gray')

<matplotlib.image.AxesImage at 0x3969f8990>

_images/4716b2d06bbcaae5382b5c019f12e2dcde4407d6b0ddc9f94df4f9dc46af940b.png

RBB#

RBB contains the data from Photron bullet camera B, which is looking at the central column.