SciPy compatibility

When working with space physics data, you often need to combine measurements from different instruments that sample at different rates, or remove low-frequency trends from a signal. Speasy provides ready-to-use functions for these common tasks, built on top of SciPy.

All functions in speasy.signal accept one or more SpeasyVariable objects and return SpeasyVariable objects with metadata preserved.

Interpolation

Instruments on the same spacecraft often sample at different cadences (e.g. magnetic field at 16 Hz, particle moments at 4.5 s). To compare them, you need to bring them onto a common time base.

Interpolate onto a regular time grid:

>>> import speasy as spz
>>> from speasy.signal import resampling
>>> import numpy as np
>>> ace_mag = spz.get_data('amda/imf', "2016-6-2", "2016-6-5")
>>> ref_time = resampling.generate_time_vector("2016-6-2", "2016-6-5", np.timedelta64(5, 's'))
>>> ref_time.shape, ace_mag.time.shape
((51840,), (16200,))
>>> ace_mag_resampled = resampling.interpolate(ref_time, ace_mag)
>>> ace_mag_resampled.shape
(51840, 3)
>>> bool(np.all(ace_mag_resampled.time == ref_time))
True

Interpolate onto another variable’s time base — useful for aligning two instruments:

>>> import speasy as spz
>>> from speasy.signal import resampling
>>> import numpy as np
>>> mms1_products = spz.inventories.tree.cda.MMS.MMS1
>>> b, Tperp, Tpara = spz.get_data(
...    [
...         mms1_products.FGM.MMS1_FGM_SRVY_L2.mms1_fgm_b_gsm_srvy_l2,
...         mms1_products.DIS.MMS1_FPI_FAST_L2_DIS_MOMS.mms1_dis_tempperp_fast,
...         mms1_products.DIS.MMS1_FPI_FAST_L2_DIS_MOMS.mms1_dis_temppara_fast
...     ],
...     '2017-01-01T02:00:00',
...     '2017-01-01T02:00:15'
... )
>>> Tperp_interp, Tpara_interp = resampling.interpolate(b, [Tperp, Tpara])
>>> Tperp_interp.shape, Tpara_interp.shape, b.shape
((240, 1), (240, 1), (240, 4))

Here Tperp and Tpara (sampled at ~4.5 s) are interpolated onto the magnetic field time base (~16 Hz). You can pass a list of variables to interpolate them all onto the same reference in one call.

Resampling

If you simply want to change the sampling rate of a variable without providing an explicit time vector, use resample():

>>> import speasy as spz
>>> from speasy.signal import resampling
>>> import numpy as np
>>> ace_mag = spz.get_data('amda/imf', "2016-6-2", "2016-6-5")
>>> ace_mag.shape
(16200, 3)
>>> ace_mag_5s = resampling.resample(ace_mag, np.timedelta64(5, 's'))
>>> ace_mag_5s.shape
(51837, 3)

Filtering

You can apply any SciPy IIR filter (designed as second-order sections) to a Speasy variable. This is useful for removing trends, isolating frequency bands, or smoothing data.

Example: high-pass filter to remove the DC component

>>> import speasy as spz
>>> from speasy.signal import filtering
>>> from scipy import signal
>>> import numpy as np
>>> ace_mag = spz.get_data('amda/imf', "2016-6-2", "2016-6-5")
>>> my_filter = signal.butter(4, 0.1, 'high', output='sos')
>>> ace_mag_filtered = filtering.apply_sos_filter(my_filter, signal.sosfilt, ace_mag)
>>> ace_mag_filtered.shape
(16200, 3)
>>> round(np.mean(ace_mag), 3), round(np.mean(ace_mag_filtered), 3)
(np.float32(0.355), np.float32(0.0))

The apply_sos_filter function takes three arguments:

The filter coefficients (from scipy.signal.butter, scipy.signal.cheby1, etc. with output='sos')
The filter function to apply (typically scipy.signal.sosfilt or scipy.signal.sosfiltfilt for zero-phase filtering)
The variable(s) to filter (a single SpeasyVariable or a list of them)

Note

Filtering assumes a regular time axis. If your data has gaps or irregular sampling, resample it first using resample() or interpolate().