PowerSpectrum#

class gadfly.PowerSpectrum(frequency, power, error=None, name=None, norm=None, detrended_lc=None)[source]#

Bases: object

An observed power spectrum.

Parameters

  • frequency (Quantity) – Frequency of each power spectral density measurement

  • power (Quantity) – Power spectral density at each frequency

  • error (Quantity) – Uncertainty on PSD measurements

  • name (str) – Name of the target/instrument

  • norm (Quantity) – Normalization factor

  • detrended_lc (LightCurve) – Detrended light curve to cache along with the PSD.

Attributes Summary

light_curve_rms

Convert from power spectral density to the approximate root mean square of the original light curve.

omega

Angular frequency.

Methods Summary

bin([bins])

Bin the power spectrum.

cutout([frequency_min, frequency_max])

Cut out a section of the power spectrum.

from_light_curve(light_curve[, method, ...])

Compute the power spectrum from a light curve.

plot(**kwargs)

Plot a power spectrum.

Attributes Documentation

light_curve_rms#

Convert from power spectral density to the approximate root mean square of the original light curve.

Returns

rms

Return type

Quantity

omega#

Angular frequency.

Returns a numpy array of \(\omega = 2 \pi f\) with f in units of [uHz].

Returns

omegas

Return type

ndarray

Methods Documentation

bin(bins=None, **kwargs)[source]#

Bin the power spectrum.

Requires scipy. Other keyword arguments passed to bin_power_spectrum().

Parameters

bins (int or ndarray) – Number of bins or an array of bin edges.

Returns

new_ps – A new, binned power spectrum

Return type

PowerSpectrum

cutout(frequency_min=None, frequency_max=None)[source]#

Cut out a section of the power spectrum.

Parameters

  • frequency_min (Quantity) – Cut out any measurements below this frequency

  • frequency_max (Quantity) – Cut out any measurements above this frequency

Returns

new_ps – A new, cropped power spectrum

Return type

PowerSpectrum

classmethod from_light_curve(light_curve, method='fft', include_zero_freq=False, name=None, detrend=True, detrend_poly_order=3, save_detrended_lc=True)[source]#

Compute the power spectrum from a light curve.

Parameters

  • light_curve (LightCurve, LightCurveCollection # noqa) – Light curve(s)

  • method (str, options are "fft" or "lomb-scargle") – Method used to compute the power spectrum.

  • include_zero_freq (bool) – Include frequency=0 in the first entry of the results.

  • name (str) – Name for the power spectrum

  • detrend (bool) – If True, detrend the light curve.

  • detrend_poly_order (int) – Polynomial order used for detrending the light curve

  • save_detrended_lc (bool) – If True, save the detrended light curve in an attribute on the PSD object.

plot(**kwargs)[source]#

Plot a power spectrum.

Requires matplotlib.

Parameters

  • ax (Axes) –

  • kernel (None or subclass of Term) –

  • obs (PowerSpectrum) –

  • freq (Quantity) –

  • figsize (list of floats) –

  • n_samples (int) –

  • p_mode_inset (bool) –

  • legend (bool) –

  • scaling_low_freq (str) –

  • scaling_p_mode (str) –

  • inset_xlim (list of floats) –

  • inset_ylim (list of floats) –

  • title (str) –

  • label_inset (str) –

  • label_obs (str) –

  • label_kernel (str) –

  • kernel_kwargs (dict) –

  • obs_kwargs (dict) –

  • inset_kwargs (dict) –

  • create_new_figure (bool) –

  • inset_bounds (list) –

Returns