| name | toksearch-signal-routing |
| description | Read this FIRST when unsure which Signal class or data path a DIII-D quantity needs. Maps physics quantities (Ip, Wmhd, beta_N, q95, NBI power, density, Te, boundary, ...) to the correct Signal class and path, how to discover IMAS paths, and which skill to use for time-alignment/correlation. Avoids the common mistake of using PtDataSignal for a quantity that lives in MDSplus or IMAS. |
Choosing the right signal class for a DIII-D quantity
Picking the wrong backend is the most common failure. PtData only serves raw
PTDATA pointnames; asking it for an analysis quantity (e.g. wmhd, betan)
fails with PtDataError: Shot.extension not found. Use this table.
| Quantity | Class & path | Notes |
|---|
| Plasma current Ip | PtDataSignal('ip') | amps -> /1e6 for MA; times in ms |
| Ip from EFIT | MdsSignal(r'\ipmhd', 'efit01') | MdsSignal is imported from toksearch |
| Stored energy Wmhd | MdsSignal(r'\wmhd', 'efit01') | not a PTDATA pointname; Joules -> /1e6 for MJ |
| Normalized beta (beta_N) | ImasSignal('equilibrium.time_slice.global_quantities.beta_normal') | EFIT01-backed |
| q95 | ImasSignal('equilibrium.time_slice.global_quantities.q_95') | |
| Total NBI power | ImasSignal('nbi.unit.power_launched.data') | object array of per-unit W series (see recipe) |
| Core electron density | ImasSignal('core_profiles.profiles_1d.electrons.density_thermal') | 2-D (time, rho) |
| Thomson Te (per channel) | ImasSignal('thomson_scattering.channel.t_e.data', split_by='channel') | dict keyed by channel |
| Plasma boundary outline | ImasSignal('equilibrium.time_slice.boundary.outline.r') | ragged object array |
PtDataSignal/ImasSignal come from toksearch_d3d; MdsSignal from toksearch.
Discovering IMAS paths (don't guess)
from toksearch_d3d import list_imas_fields
list_imas_fields()
list_imas_fields('nbi')
Supported IDSs: core_profiles, ec_launchers, ece, equilibrium, gas_injection,
magnetics, nbi, reflectometer_profile, tf, thomson_scattering, wall. There is
no summary IDS; line-averaged density is not available via IMAS here.
Recipe: total injected NBI power (object array)
nbi.unit.power_launched.data returns a numpy object array of per-unit
power series (watts). Sum over units, then convert:
import numpy as np
def peak_nbi_mw(rec):
units = rec['pnbi']['data']
total = np.nansum(np.stack([np.asarray(u, float) for u in units]), axis=0)
rec['peak_pnbi_MW'] = float(np.nanmax(total) / 1e6)
Aligning / correlating two signals
Do not hand-roll resampling. Use Pipeline.fetch_dataset + Pipeline.align
(see the toksearch-datasets skill) so both signals share one grid:
pipe.fetch_dataset('ds', {'ip': PtDataSignal('ip'),
'wmhd': MdsSignal(r'\wmhd', 'efit01')})
pipe.align('ds', align_with=10.0, method='pad')
@pipeline.map
def corr(rec):
ds = rec['ds']; a = ds['ip'].values; b = ds['wmhd'].values
m = np.isfinite(a) & np.isfinite(b)
rec['corr'] = float(np.corrcoef(a[m], b[m])[0, 1])
SQL shot metadata
from toksearch_d3d.sql import connect_d3drdb
Canonical shot-type classification is the shots_type table
(shot_type='plasma'), joined on shots.shot = shots_type.shot — not the
shot_type column on shots.
API reminders
- Times are milliseconds.
- Set record fields by item assignment in a
def map: rec['k'] = v
(Record has no .update(); lambdas can't assign).
rec.get('k', default) requires the default argument.
See also
- toksearch-d3d-imas — full ImasSignal API (ragged data,
split_by, dims).
- toksearch-datasets —
fetch_dataset/align for multi-signal grids.
- toksearch-d3d-ptdata / toksearch-mds — per-backend details.
