Fournax

class dorado.Fournax(name, epoch=None, period=None)

Bases: dorado.Zellars

The Fournax class extends the Zellars target class to provide a consistent simple, yet robust interface to targets with regular or semi-regular photometric variability for the purposes of lightcurve/timeseries fourier analysis.

Fournax is an abbreviation of Fourier numerical astronomy extension, its name is a backronym styled to match the constellation ‘fornax’.

Attributes

name: str

name of target in string format.

epoch: float or astropy.Time

Epoch for the targets ephemeris of which the theoretical times of extrema will be extrapolated from.

period: float

The period between extrema of interest corresponding to the ephemeris epoch given.

Methods Summary

OMinusC(filter)	OMinusC takes observed time(s) of max light for a repeating variable star and ephemeris data and returns O-C values as well as the corresponding cycle.
analyze(filter[, graphical])
fourFind(filter[, fitted])
smooth(x[, window_len, window])	smooth the data using a window with requested size. This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal. input: x: the input signal window_len: the dimension of the smoothing window; should be an odd integer window: the type of window from ‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’ flat window will produce a moving average smoothing. output: the smoothed signal example: t=linspace(-2,2,0.1) x=sin(t)+randn(len(t))*0.1 y=smooth(x) see also: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve scipy.signal.lfilter TODO: the window parameter could be the window itself if an array instead of a string NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y. https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html 2017-07-13 (last modified), 2006-10-31 (created) Section author: Unknown[1], GaelVaroquaux, Unknown[142], Unknown[143], Unknown[144], Unknown[145], Unknown[146], Unknown[147], WesTurner, Christian Gagnon, clecocel.
superfit(filter, terms, s)	superfit takes a raw timeseries and performs a multistep smoothed fit of the data which includes a spline fit tailored with the curvature of knots from a spline fit of a Fourier fit. The result is then expanded and then convoluted with a ‘blackman’ window function. Parameters ———- x, y: array timeseries arrays to be fit terms: int terms to retain in the fourier fit. s: int new data array size Returns ——- X, Y: array The superfit timeseries arrays.
tomlFind(filter[, fitted])

Methods Documentation

OMinusC(filter)

OMinusC takes observed time(s) of max light for a repeating variable star and ephemeris data and returns O-C values as well as the corresponding cycle.

Returns

cycle: float, list, or array

The cycle corresponding to the time(s) of max light

OmC: float, list, or array

The O-C value for the time(s) of max light

analyze(filter, graphical=True)

fourFind(filter, fitted=True)

smooth(x, window_len=11, window='hanning')

smooth the data using a window with requested size. This method is based on the convolution of a scaled window with the signal. The signal is prepared by introducing reflected copies of the signal (with the window size) in both ends so that transient parts are minimized in the begining and end part of the output signal. input: x: the input signal window_len: the dimension of the smoothing window; should be an odd integer window: the type of window from ‘flat’, ‘hanning’, ‘hamming’, ‘bartlett’, ‘blackman’

flat window will produce a moving average smoothing.

output: the smoothed signal example: t=linspace(-2,2,0.1) x=sin(t)+randn(len(t))*0.1 y=smooth(x) see also: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve scipy.signal.lfilter TODO: the window parameter could be the window itself if an array instead of a string NOTE: length(output) != length(input), to correct this: return y[(window_len/2-1):-(window_len/2)] instead of just y. https://scipy-cookbook.readthedocs.io/items/SignalSmooth.html 2017-07-13 (last modified), 2006-10-31 (created) Section author: Unknown[1], GaelVaroquaux, Unknown[142], Unknown[143], Unknown[144], Unknown[145], Unknown[146], Unknown[147], WesTurner, Christian Gagnon, clecocel

superfit(filter, terms, s)

superfit takes a raw timeseries and performs a multistep smoothed fit of the data which includes a spline fit tailored with the curvature of knots from a spline fit of a Fourier fit. The result is then expanded and then convoluted with a ‘blackman’ window function. Parameters ———- x, y: array

timeseries arrays to be fit

terms: int

terms to retain in the fourier fit.

s: int

new data array size

X, Y: array

The superfit timeseries arrays.

tomlFind(filter, fitted=True)