22962531738

Committed 11 Mar 2026 04:14PM UTC coverage: 66.201% (-0.2%) from 66.351%

Build # 22962531738

Build Type

Pull #29

github

Committed by

web-flow

Commit Message

Merge b1b20d165 into f4c88fd66

Pull Request Pull Request #29: Added wave processing from RBSP data

Run Details

8 of 16 new or added lines in 2 files covered. (50.0%)

1755 of 2651 relevant lines covered (66.2%)

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33.33

/el_paso/processing/create_quality_flag_from_magnetometer.py

# SPDX-FileCopyrightText: 2026 GFZ Helmholtz Centre for Geosciences
# SPDX-FileContributor: Alwin Roy
#
# SPDX-License-Identifier: Apache-2.0

import numpy as np
from astropy import units as u

import el_paso as ep


def magnetometer_quality_flags(variables: dict, threshold: int = 500) -> np.ndarray:
    """Compute a quality mask for magnetometer data using Bt.

    The mask flags samples that are considered valid based on two criteria:
    1. Magnetic field magnitude (Bt) must be positive.
    2. Consecutive differences |ΔBt| must be below the spike threshold.

    Args:
        variables (dict): Dictionary containing variable objects. Must include
            the key "Bt". Each variable must implement ``get_data()``.
        threshold(int, optional): Spike detection threshold in nT.
            Defaults to 500.

    Returns:
        np.ndarray: Boolean array where ``True`` indicates valid data and
        ``False`` indicates flagged samples.
    """
    bt_var = variables["Bt"]
    bt = bt_var.get_data(u.nT)

    mask_positive = bt > 0

    delta_b = np.diff(bt, prepend=bt[0])
    mask_spike = np.abs(delta_b) < threshold

    mask = mask_positive & mask_spike

    mask = ep.Variable(
        data=mask_positive & mask_spike,
        original_unit=u.dimensionless_unscaled,
    )

    return mask

1	# SPDX-FileCopyrightText: 2026 GFZ Helmholtz Centre for Geosciences
2	# SPDX-FileContributor: Alwin Roy
3	#
4	# SPDX-License-Identifier: Apache-2.0
5
6	import numpy as np	2✔
7	from astropy import units as u	2✔
8
9	import el_paso as ep	2✔
10
11
12	def magnetometer_quality_flags(variables: dict, threshold: int = 500) -> np.ndarray:	2✔
13	"""Compute a quality mask for magnetometer data using Bt.
14
15	The mask flags samples that are considered valid based on two criteria:
16	1. Magnetic field magnitude (Bt) must be positive.
17	2. Consecutive differences \|ΔBt\| must be below the spike threshold.
18
19	Args:
20	variables (dict): Dictionary containing variable objects. Must include
21	the key "Bt". Each variable must implement ``get_data()``.
22	threshold(int, optional): Spike detection threshold in nT.
23	Defaults to 500.
24
25	Returns:
26	np.ndarray: Boolean array where ``True`` indicates valid data and
27	``False`` indicates flagged samples.
28	"""
NEW 29	bt_var = variables["Bt"]	×
NEW 30	bt = bt_var.get_data(u.nT)	×
31
NEW 32	mask_positive = bt > 0	×
33
NEW 34	delta_b = np.diff(bt, prepend=bt[0])	×
NEW 35	mask_spike = np.abs(delta_b) < threshold	×
36
NEW 37	mask = mask_positive & mask_spike	×
38
NEW 39	mask = ep.Variable(	×
40	data=mask_positive & mask_spike,
41	original_unit=u.dimensionless_unscaled,
42	)
43
NEW 44	return mask	×

GFZ / EL_PASO / 22962531738

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