CONAN._classes.load_lightcurves

class CONAN._classes.load_lightcurves(file_list=None, data_filepath=None, input_lc=None, filters=None, wl=None, nplanet=1, sort=False, verbose=True, show_guide=False, lamdas=None)

lightcurve object to hold lightcurves for analysis

Parameters:

• file_list (list;) – List of filenames for the lightcurves. Files must have 9 columns: time,flux,err,xc,xc,xc,xc,xc,xc. where xc are columns that can be used in decorrelating the flux. Arrays of zeroes are put in xc if file contains less than 9 columns.

• data_filepath (str;) – Filepath where lightcurves files are located. Default is None which implies the data is in the current working directory.

• input_lc (dict;) – dictionary of input lightcurves. If provided, it overrides the file_list and data_filepath parameters. Each key is the name of the lightcurve and the value is a numpy array with the lightcurve data. The array must be of shape (N,M) where N is the number of data points and M is the number of columns (>=3).

• filter (list, str, None;) – filter for each lightcurve in file_list. if a str is given, it is used for all lightcurves, if None, the default of “V” is used for all.

• wl (list, int, float, None;) – central wavelength in microns for each lightcurve in file_list. This is only used for plotting transit depth or occultation depth variation as a function of wl. If int or float is given, it is used for all lightcurves. If None, the default of 0.6 is used for all if same filter, or unique random value per filter.

• nplanet (int;) – number of planets in the system. Default is 1.

• sort (bool;) – if True, sorts the lightcurves based on the median time. Default is False.

• verbose (bool;) – if True, prints out information about the lightcurves. Default is True.

Returns:

lc_obj

Return type:

light curve object

_obj_type

type of object created.

Type:

str;

_nplanet

number of planets in the system.

Type:

int;

_fpath

filepath where lightcurves files are located.

Type:

str;

_names

list of filenames for the lightcurves.

Type:

list;

_nphot

number of lightcurves.

Type:

int;

_filters

list of filters for each lightcurve.

Type:

list;

_wl

list of central wavelengths for each lightcurve.

Type:

list;

_filter_shortcuts

dictionary of filter shortcuts.

Type:

dict;

_filnames

list of unique filters.

Type:

list;

_input_lc

dictionary of the input lightcurves.

Type:

dict;

_rms_estimate

list of rms estimates for each lightcurve.

Type:

list;

_jitt_estimate

list of jitter estimates for each lightcurve.

Type:

list;

Examples

>>> lc_obj = load_lightcurves(file_list = ["lc1.dat","lc2.dat"],
>>>                             filters = ["V","I"],
>>>                                  wl = [0.6,0.8] )

_bases_init

_clipped_data

_detrended = False

_filnames

_filter_shortcuts

_fpath

_input_lc

_masked_points = False

_nphot

_nplanet = 1

_obj_type = 'lc_obj'

_rescaled_data

_show_guide = False

Methods

add_GP([lc_list, par, kernel, operation, amplitude, ...])	Define GP hyperparameters for each lc. The first hyperparameter h1 is amplitude (standard deviation)
add_custom_LC_function([func, x, func_args, ...])	Define custom model to be combined with or to replace the light-curve model. This must be
add_sinusoid([lc_list, trig, n, par, Amp, P, x0, verbose])	Add sinusoid to fit correlation along a column of the data. This fits a sinusoid to the
add_spline([lc_list, par, degree, knot_spacing, ...])	add spline to fit correlation along 1 or 2 columns of the data. This splits the data at the defined knots interval and fits a polynomial of defined degree to each section.
clip_outliers([lc_list, clip, width, select_column, ...])	Remove outliers using a running median method. Points > clip*M.A.D are removed
contamination_factors([cont_ratio, verbose])	add contamination factor for each unique filter defined in load_lightcurves().
detrend_data([overwrite, verbose])	detrend data using total trend model determined from get_decorr(). Note that this does not inflate the flux errors to account for the uncertainties in the detrend model
get_LDs(Teff, logg, Z, filter_names[, unc_mult, ...])	get Kipping quadratic limb darkening parameters (q1,q2) using ldtk (requires internet connection).
get_decorr([T_0, Period, rho_star, Duration, D_occ, ...])	Function to obtain best decorrelation parameters for each light-curve file using the
lc_baseline([fit_offset, dcol0, dcol3, dcol4, dcol5, ...])	Define baseline model parameters to fit for each light curve using the columns of the input data. dcol0 refers to decorrelation setup for column 0, dcol3 for column 3 and so on.
limb_darkening([q1, q2, verbose])	Setup Kipping quadratic limb darkening LD coefficient (q1, q2) for transit light curves.
mask_points([lc_list, condition, show_plot, verbose])	Function to mask points in the lightcurves based on a condition. These points are removed from the injested data
phasecurve([D_occ, Fn, ph_off, A_ev, f1_ev, A_db, ...])	Setup phase curve parameters for each unique filter in loaded lightcurve.
planet_parameters([RpRs, Impact_para, rho_star, ...])	Define parameters and priors of model parameters. By default, the parameters are fixed to
plot([plot_cols, col_labels, nrow_ncols, figsize, ...])	visualize data
print([section])	Print out all input configuration (or particular section) for the light curve object.
rescale_data_columns([method, columns, verbose])	Function to rescale the data columns of the lightcurves. This can be important when
save_LCs([save_path, suffix, overwrite, verbose])	Save each of the loaded light curves to a .dat file
supersample([lc_list, ss_factor, verbose])	Supersample long intergration time of lcs in lc_list. This divides each exposure of the lc into int(ss_factor) subexposures to attain a sampling rate of exp_time/ss_factor.
transit_depth_variation([ddFs, dRpRs, divwhite, verbose])	Include transit depth variation between the different filters. Note 'RpRs' must be fixed to
transit_timing_variation([ttvs, dt, baseline_amount, ...])	include transit timing variation between the transit. Note: Reference 'T_0' and 'P' values
update_planet_parameters([RpRs, Impact_para, ...])	Update parameters and priors of model parameters. By default, the parameters are all set to None for no update on them.