plotypus.lightcurve module

Light curve fitting and plotting functions.

plotypus.lightcurve.make_predictor(regressor=LassoLarsIC(fit_intercept=False), Selector=GridSearchCV, fourier_degree=(2, 25), selector_processes=1, use_baart=False, scoring='r2', scoring_cv=3, **kwargs)

Makes a predictor object for use in get_lightcurve().

Parameters

regressor

: object with “fit” and “transform” methods, optional

Regression object used for solving Fourier matrix (default sklearn.linear_model.LassoLarsIC(fit_intercept=False)).

Selector

: class with “fit” and “predict” methods, optional

Model selection class used for finding the best fit (default sklearn.grid_search.GridSearchCV).

selector_processes

: positive integer, optional

Number of processes to use for Selector (default 1).

use_baart

: boolean, optional

If True, ignores Selector and uses Baart’s Criteria to find the Fourier degree, within the boundaries (default False).

fourier_degree

: 2-tuple, optional

Tuple containing lower and upper bounds on Fourier degree, in that order (default (2, 25)).

scoring

: str, optional

Scoring method to use for Selector. This parameter can be:

• “r2”, in which case use (the default)
• “mse”, in which case use mean square error

scoring_cv

: positive integer, optional

Number of cross validation folds used in scoring (default 3).

Returns

out

: object with “fit” and “predict” methods

The created predictor object.

plotypus.lightcurve.get_lightcurve(data, copy=False, name=None, predictor=None, periodogram=Lomb_Scargle, sigma_clipping=mad, scoring='r2', scoring_cv=3, scoring_processes=1, period=None, min_period=0.2, max_period=32, coarse_precision=1e-5, fine_precision=1e-9, period_processes=1, sigma=20, shift=None, min_phase_cover=0.0, n_phases=100, verbosity=None, **kwargs)

Fits a light curve to the given data using the specified methods, with default behavior defined for all methods.

Parameters

data

: array-like, shape = [n_samples, 2] or [n_samples, 3]

Photometry array with columns time, magnitude, and (optional) error. time should be unphased.

name

: string or None, optional

Name of star being processed.

predictor

: object that has “fit” and “predict” methods, optional

Object which fits the light curve obtained from data after rephasing (default make_predictor(scoring=scoring, scoring_cv=scoring_cv)).

periodogram

: function, optional

Function which finds one or more periods. If period is already provided, the function is not used. Defaults to plotypus.periodogram.Lomb_Scargle()

sigma_clipping

: function, optional

Function which takes an array and assigns sigma scores to each element. Defaults to plotypus.utils.mad().

scoring

: str, optional

Scoring method used by predictor. This parameter can be

• “r2”, in which case use R^2() (the default)
• “mse”, in which case use mean square error

scoring_cv

: positive integer, optional

Number of cross validation folds used in scoring (default 3).

scoring_processes

: positive integer, optional

Number of processes to use for scoring cross validation (default 1).

period

: number or None, optional

Period of oscillation used in the fit. This parameter can be:

• None, in which case the period is obtained with the given periodogram function (the default).
• A single positive number, giving the period to phase data.

min_period

: non-negative number, optional

Lower bound on period obtained by periodogram (default 0.2).

max_period

: non-negative number, optional

Upper bound on period obtained by periodogram (default 32.0).

course_precision

: positive number, optional

Precision used in first period search sweep (default 1e-5).

fine_precision

: positive number, optional

Precision used in second period search sweep (default 1e-9).

period_processes

: positive integer, optional

Number of processes to use for period finding (default 1).

sigma

: number, optional

Upper bound on score obtained by sigma_clipping for a point to be considered an inlier.

shift

: number or None, optional

Phase shift to apply to light curve if provided. Light curve is shifted such that max light occurs at phase[0] if None given (default None).

min_phase_cover

: number on interval [0, 1], optional

Fraction of binned light curve that must contain points in order to proceed. If light curve has insufficient coverage, a warning is printed if “outlier” verbosity is on, and None is returned (default 0.0).

n_phases

: positive integer

Number of equally spaced phases to predict magnitudes at (default 100)

verbosity

: list or None, optional

Verbosity level. See plotypus.utils.verbose_print().

Returns

out

: dict

Results of the fit in a dictionary. The keys are:

• name

  : str or None

  The name of the star.

• period

  : number

  The star’s period.

• lightcurve

  : array-like, shape = [n_phases]

  Magnitudes of fitted light curve sampled at sample phases.

• coefficients

  : array-like, shape = [n_coeffs]

  Fitted light curve coefficients.

• dA_0

  : non-negative number

  Error on mean magnitude.

• phased_data

  : array-like, shape = [n_samples]

  data transformed from temporal to phase space.

• model

  : predictor object

  The predictor used to fit the light curve.

• R2

  : number

  The score of the fit.

• MSE

  : number

  The mean square error of the fit.

• degree

  : positive integer

  The degree of the Fourier fit.

• shift

  : number

  The phase shift applied.

• coverage

  : number on interval [0, 1]

  The light curve coverage.

See also

get_lightcurve_from_file()

plotypus.lightcurve.get_lightcurve_from_file(file, *args, use_cols=None, skiprows=0, **kwargs)

Fits a light curve to the data contained in file using get_lightcurve().

Parameters

file

: str or file

File or filename to load data from.

use_cols

: iterable or None, optional

Iterable of columns to read from data file, or None to read all columns (default None).

skiprows

: number, optional

Number of rows to skip at beginning of file (default 0)

Returns

out

: dict

See get_lightcurve().

plotypus.lightcurve.find_outliers(data, predictor, sigma, method=mad)

Returns a boolean array indicating the outliers in the given data array.

Parameters

data

: array-like, shape = [n_samples, 2] or [n_samples, 3]

Photometry array containing columns phase, magnitude, and (optional) error.

predictor

: object that has “fit” and “predict” methods, optional

Object which fits the light curve obtained from data after rephasing.

sigma

: number

Outlier cutoff criteria.

method

: function, optional

Function to score residuals for outlier detection (default plotypus.utils.mad()).

Returns

out

: array-like, shape = data.shape

Boolean array indicating the outliers in the given data array.

plotypus.lightcurve.plot_lightcurve(name, lightcurve, period, data, output='.', legend=False, color=True, n_phases=100, err_const=0.005, **kwargs)

Save a plot of the given lightcurve to directory output.

Parameters

name

: str

Name of the star. Used in filename and plot title.

lightcurve

: array-like, shape = [n_samples]

Fitted lightcurve.

period

: number

Period to phase time by.

data

: array-like, shape = [n_samples, 2] or [n_samples, 3]

Photometry array containing columns time, magnitude, and (optional) error. time should be unphased.

output

: str, optional

Directory to save plot to (default ‘.’).

legend

: boolean, optional

Whether or not to display legend on plot (default False).

color

: boolean, optional

Whether or not to display color in plot (default True).

n_phases

: integer, optional

Number of phase points in fit (default 100).

err_const

: number, optional

Constant to use in absence of error (default 0.005).

Returns

None