pycheops_analysis¶
Module Contents¶
Classes¶
A class used to store the light-curve model of a Dataset. |
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CHEOPS Dataset object |
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CHEOPS Dataset object |
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CHEOPS Dataset object |
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CHEOPS MultiVisit object |
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CHEOPS StarProperties object |
Functions¶
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Function to print the same string to stdout and to a log file |
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Function that plots the CHEOPS photometry of all the apertures. |
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Function that plots the CHEOPS photometry extracted for one aperture. |
Function that plots some selected diagnostics of the photometry extracted for a given aperture. |
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Function that plots all diagnostics of the photometry extracted for a given aperture |
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Function to compute the full photometric model of a pycheops Dataset. |
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Function that computes the root mean square (rms) of the photometric residuals at different time-bin. |
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Computes root mean square (rms) of a pycheops Dataset object for a given parameters set. |
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Computes root mean square (rms) of a pycheops Dataset object for a given parameters set. |
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Estimate the highest probability density interval. |
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Lomb-Scargle power-spectrum of the residuals. |
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Save the current dataset as a pickle file |
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Load a dataset from a pickle file |
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Attributes¶
- pycheops_analysis.my_dpi = 192¶
- pycheops_analysis.module_celerite2 = True¶
- pycheops_analysis.out_color = lightgray¶
- pycheops_analysis.rms_time¶
- pycheops_analysis.global_names = ['P', 'D', 'b', 'W', 'f_c', 'f_s', 'h_1', 'h_2', 'Tref']¶
- pycheops_analysis.transit_names = ['dT', 'T_0', 'c', 'dfdt', 'd2fdt2', 'dfdbg', 'dfdcontam', 'dfdx', 'd2fdx2', 'dfdy', 'd2fdy2',...¶
- pycheops_analysis.detrend_bounds¶
- pycheops_analysis.detrend_default¶
- pycheops_analysis.detrend¶
- pycheops_analysis.params_units¶
- pycheops_analysis.printlog(l, olog=None)¶
Function to print the same string to stdout and to a log file
- Parameters
l (str) – string to print and write.
olog – opened file object to write the string. Default is None.
- Returns
Nothing to return.
- pycheops_analysis.plot_all_apertures(dataset, out_folder)¶
Function that plots the CHEOPS photometry of all the apertures. Each aperture is plotted in an own subplot.
- Parameters
dataset (Dataset) – CHEOPS Dataset object.
out_folder (str) – output folder to save the plot.
- Returns
Nothing to return.
- pycheops_analysis.plot_single_lc(t, f, ef, bjd_ref)¶
Function that plots the CHEOPS photometry extracted for one aperture.
- Parameters
t (list or float array) – time as BJD_TDB - bjd_ref.
f (list or float array) – normalized flux.
ef (list or float array) – error of the normalized flux.
bjd_ref (int or float) – reference time of the time vector. Defined as the integer part of the first time-point.
- Returns
figure object.
- pycheops_analysis.plot_custom_diagnostics(lc)¶
Function that plots some selected diagnostics of the photometry extracted for a given aperture.
- Parameters
lc (dict) – ligth-curve dictionary, see Dataset.lc for the keys.
- Returns
figure object.
- pycheops_analysis.plot_corner_diagnostics(dataset)¶
Function that plots all diagnostics of the photometry extracted for a given aperture in a corner/triangle plot.
- Parameters
dataset (Dataset) – CHEOPS Dataset object.
- Returns
figure object.
- class pycheops_analysis.DatasetModel(n_time)¶
A class used to store the light-curve model of a Dataset.
- Parameters
n_time (int) – Length of the photometric light-curve, needed to initialize the arrays.
- pycheops_analysis.get_full_model(dataset, params_in=None, time=None)¶
Function to compute the full photometric model of a pycheops Dataset.
- Parameters
dataset (Dataset) – pycheops Dataset object. You have to run lmfit or emcee before using this function.
params_in (Parameters or list of Parameter, optional (default None)) – Parameters for which compute the model, if None the lmfit or emcee the params_best will be used.
time (list or float array, optional (default None)) – Time vector needed to compute the light-curve model, if None the dataset.lc[“time”] will be used.
- Returns
DatasetModel object. It returns nothing if params_in == None and (dataset.emcee.params_best == None or dataset.lmfit.params_best == None).
- Return type
- pycheops_analysis.binned_rms(stats_dict, t_lc, residuals, rms_time_in, keyword='flux-all (w/o GP)', olog=None)¶
Function that computes the root mean square (rms) of the photometric residuals at different time-bin.
- Parameters
stats_dict (dict) – Dictionary that is already defined and it will be updated with statistics.
t_lc (list or float array) – time vector.
residuals (list or float array) – residuals vector.
rms_time_int (list or float array) – list of the bin size, if >= 1 it is interpreted as hour, otherwise in minutes.
keyword (str) – Name of the statistics, i.e. the model used to create the residuals. Default: “flux-all (w/o GP)”. Change it accordingly.
olog (file object, optional) – Opened log file object where to save prints.
- Returns
Nothing to return, but stats_dict will be modified in place.
- pycheops_analysis.computes_rms(dataset, params_best=None, glint=False, do_rms_bin=True, olog=None)¶
Computes root mean square (rms) of a pycheops Dataset object for a given parameters set.
- Parameters
dataset (Dataset) – Dataset object containing the photometry and model object.
params_best (Parameters or list of Parameter) – set of parameters to model the light-curves (and detrending) to compute the residuals and the rms.
glint (bool, optional, default False) – Specify if glint parameter has been used.
do_rms_bin (bool, optional, default True) – Specify if the statistics and rms have to be computed for the unbinned residuals only (False) or for a given set of bin-sizes (True).
olog (file object, optional) – Opened log file object where to save prints.
- Return statistics
Dictionary containing the statistics (fitness, rms, etc) of the model for given parameters.
- Rtype statistics
dict
- pycheops_analysis.computes_rms_ultra(dataset, params_best=None, glint=False, do_rms_bin=True, olog=None)¶
Computes root mean square (rms) of a pycheops Dataset object for a given parameters set. LBo-WARNING: I DON’T KNOW WHY IT IS EXACTLY THE SAME OF computes_rms
- Parameters
dataset (Dataset) – Dataset object containing the photometry and model object.
params_best (Parameters or list of Parameter) – set of parameters to model the light-curves (and detrending) to compute the residuals and the rms.
glint (bool, optional, default False) – Specify if glint parameter has been used.
do_rms_bin (bool, optional, default True) – Specify if the statistics and rms have to be computed for the unbinned residuals only (False) or for a given set of bin-sizes (True).
olog (file object, optional) – Opened log file object where to save prints.
- Return statistics
Dictionary containing the statistics (fitness, rms, etc) of the model for given parameters.
- Rtype statistics
dict
- pycheops_analysis.plot_and_clip_lcs(datasets, apertures, out_folder, index_to_clip='all')¶
- pycheops_analysis.copy_parameters(params_in)¶
- pycheops_analysis.get_fitting_parameters(params, to_print=False)¶
- pycheops_analysis.get_best_parameters(result, dataset, nburn=0, dataset_type='visit', update_dataset=False)¶
- pycheops_analysis.get_best_parameters_ultranest(result, params, sampler, dataset_type='visit')¶
- pycheops_analysis.model_plot_fit(dataset, par_fit, par_type='median', nsamples=0, flatchains=None, model_filename=None)¶
- pycheops_analysis.init_multi_model(datasets, params, pnames, gnames, tnames, do_fit=True)¶
- pycheops_analysis.check_boundaries(pfit, pnames, params)¶
- pycheops_analysis.set_pycheops_par(pfit, pnames, gnames, tnames, params, dataset, visit)¶
- pycheops_analysis.multi_model(pfit, pnames, gnames, tnames, params_in, datasets)¶
- pycheops_analysis.components_model(pfit, pnames, gnames, tnames, params, datasets, gps, fill_gaps=False)¶
- pycheops_analysis.plot_final_params(pbest, pnames, gnames, tnames, params, datasets, gps, out_folder, fill_gaps=True, emcee=True, pars_type='mle', nsamples=0, flatchain=None)¶
- pycheops_analysis.lnprob_woGP(pfit, pnames, gnames, tnames, params, datasets)¶
- pycheops_analysis.lnprob_wGP(pfit, pnames, gnames, tnames, params, datasets, gps)¶
- pycheops_analysis.init_walkers(lnprob, pfit, pscale, nwalkers, args=(), init_scale=0.001)¶
- pycheops_analysis.sampler_to_dict(sampler, nburn=0, nprerun=0, nthin=1)¶
- pycheops_analysis.do_emcee_analysis(lnprob, pfit, pscale, out_folder, args=(), nwalkers=64, nprerun=0, nsteps=512, nthin=4, nburn=128, progress=True, run_emcee=True, read_sampler=False)¶
- pycheops_analysis.high_posterior_density(trace, cred=0.6827)¶
Estimate the highest probability density interval.
This function determines the shortest, continuous interval containing the specified fraction (cred) of steps of the Markov chain. Note that multi-modal distribution may require further scrutiny.
# cred = 0.6827 <-> -/+ 1 sigma # cred = 0.9545 <-> -/+ 2 sigma # cred = 0.9973 <-> -/+ 3 sigma # cred = 0.999999426696856 <-> -/+ 5 sigma
- tracearray
The steps of the Markov chain.
- credfloat
The probability mass to be included in the interval (between 0 and 1).
- start, endfloat
The start and end points of the interval.
- pycheops_analysis.single_chain_summary(singlechain, idx)¶
- pycheops_analysis.summary_parameters(samples, pnames, params, mle_within_hdi=True)¶
- pycheops_analysis.trace_plot(sampler_dict, summary, out_folder)¶
- pycheops_analysis.print_pycheops_pars(pars, user_data=True, expr=False)¶
- pycheops_analysis.print_analysis_stats(analysis_stats)¶
- pycheops_analysis.plot_fft(dataset, pars_in, star=None, gsmooth=5, logxlim=(1.5, 4.5))¶
Lomb-Scargle power-spectrum of the residuals.
If the previous fit included a GP then this is _not_ included in the calculation of the residuals, i.e., the power spectrum includes the power “fitted-out” using the GP. The assumption here is that the GP has been used to model stellar variability that we wish to characterize using the power spectrum.
The red vertical dotted lines show the CHEOPS orbital frequency and its first two harmonics.
If star is a pycheops starproperties object and star.teff is <7000K, then the likely range of nu_max is shown using green dashed lines.
- pycheops_analysis.multi_detrending_model(detrending_args)¶
- pycheops_analysis.should_I_decorr_detrending_model(detrending_args)¶
- class pycheops_analysis.PIPEDataset(file_key, force_download=False, download_all=True, configFile=None, target=None, verbose=True, metadata=True, view_report_on_download=True, n_threads=1)¶
Bases:
pycheops.DatasetCHEOPS Dataset object
- Parameters
file_key –
force_download –
download_all – If False, download light curves only
configFile –
target –
view_report_on_download –
metadata – True to load meta data
verbose –
- get_PIPE_lightcurve(self, PIPE_data, reject_highpoints=False, verbose=False)¶
- class pycheops_analysis.FITSDataset(file_key, force_download=False, download_all=True, configFile=None, target=None, verbose=True, metadata=True, view_report_on_download=True, n_threads=1)¶
Bases:
pycheops.DatasetCHEOPS Dataset object
- Parameters
file_key –
force_download –
download_all – If False, download light curves only
configFile –
target –
view_report_on_download –
metadata – True to load meta data
verbose –
- get_FITS_lightcurve(self, visit_args, transit, info, reject_highpoints=False, verbose=False)¶
- class pycheops_analysis.AsciiDataset(file_key, force_download=False, download_all=True, configFile=None, target=None, verbose=True, metadata=True, view_report_on_download=True, n_threads=1)¶
Bases:
pycheops.DatasetCHEOPS Dataset object
- Parameters
file_key –
force_download –
download_all – If False, download light curves only
configFile –
target –
view_report_on_download –
metadata – True to load meta data
verbose –
- get_ascii_lightcurve(self, ascii_data, normalise=False, reject_highpoints=False, verbose=False)¶
- pycheops_analysis.save_dataset(dataset, folder, target, file_key, gp=False)¶
Save the current dataset as a pickle file
- Returns
pickle file name
- pycheops_analysis.load_dataset(filename)¶
Load a dataset from a pickle file
- Parameters
filename – pickle file name
- Returns
dataset object
- class pycheops_analysis.CustomMultiVisit(target=None, datasets_list=None, ident=None, id_kws={'dace': True}, verbose=True)¶
Bases:
pycheops.MultiVisitCHEOPS MultiVisit object
Specify a target name to initialize from pickled datasets in the current working directory (or in datadir if datadir is not None).
The target name can include blanks - these are replaced by “_” automatically before searching for matching file names.
The parameter ident is used to collect star and planet properties from the relevant tables at DACE. If ident is None (default) then the target name is used in place of ident. Set ident=’none’ to disable this feature. See also StarProperties for other options that can be set using id_kws, e.g., id_kws={‘dace’:False} to use SWEET-Cat instead of DACE.
All dates and times in each of the dataset are stored as BJD-2457000 (same as TESS).
- Parameters
target – target name to identify pickled datasets
datadir – directory containing pickled datasets
ident – identifier in star properties table. If None use target. If
‘none’
- Parameters
id_kws – keywords for call to StarProperties.
verbose – print dataset names, etc. if True
The same values of the transit parameters T_0, P, D, W, b, f_c and f_s are used for all the datasets in the combined fit. This also applies to h_1 and h_2 when fitting transits.
User-defined parameters can be specified in one of the following ways:
fixed value, e.g., P=1.234
free parameter with uniform prior interval specified as a 2-tuple, e.g., dfdx=(-1,1). The initial value is taken as the the mid-point of the allowed interval;
free parameter with uniform prior interval and initial value specified as a 3-tuple, e.g., (0.1, 0.2, 1);
free parameter with a Gaussian prior specified as a ufloat, e.g., ufloat(0,1);
as an lmfit Parameter object.
A transit parameter will be fixed in the fit to the combined datasets only if the same parameter was fixed in the last fit to all datasets and the same parameter is not specified as a free parameter in the call to this method.
If no user-defined value is provided then the initial value for each transit parameter is set using the mean value across the individual datasets. For T_0 an integer number of periods are added or subtracted from the individual T_0 values so that the mean T_0 value corresponds to a time of mid-transit near the centre of the datasets.
N.B. The timescale for T_0 in BJD_TDB - 2457000.
Priors on transit parameters are only set if they are specified in the call to the fitting method using either a ufloat, or as an lmfit Parameter object that includes a ufloat in its user_data.
Priors on the derived parameters e, q_1, q_2, logrho, etc. can be specified as a dictionary of ufloat values using the extra_priors keyword, e.g., extra_priors={‘e’:ufloat(0.2,0.01)}. Priors on parameters that apply to individual datasets can also be specified in extra_priors, e.g., extra_priors[‘dfdt_01’] = ufloat(0.0,0.001). Priors listed in extra_priors will supercede priors on parameters saved with the individual datasets.
The noise model assumes that the error bars on each data point have addition white noise with standard deviation log_sigma_w. Optionally, correlated noise can be included using celerite2 with kernel SHOTerm(log_omega0, log_S0, log_Q). The same values of log_sigma_w, log_omega0, log_S0 and log_Q are used for all the datasets in the combined fit.
The fit to the combined datasets will only include a GP if log_omega0 and log_S0 are both specified as arguments in the call to the fitting method. If log_Q is not specified as an argument in the call to the fitting method then it is fixed at the value log_Q=1/sqrt(2).
Gaussian priors on the values of log_omega0, log_S0 and log_Q will only be applied if the user-specified value includes a Gaussian prior, e.g., log_omega0=ufloat(6,1), log_S0=ufloat(-24,2).
N.B. Gaussian priors on log_omega0, log_S0 and log_Q specified in the individual datasets are ignored.
Decorrelation against roll angle (phi) is handled differently in Multivisit to Dataset. The decorrelation against cos(phi), sin(phi), cos(2.phi), sin(2.phi), etc. is done using a combination of the trick from Rodrigo et al. (2017RNAAS…1….7L) and the celerite model by Foremann-Mackey et al. (2017AJ….154..220F). This enables the coefficients of this “linear harmonic instrumental noise model” to be treated as nuisance parameters that are automatically marginalised away by adding a suitable term (CosineTerm) to the covariance matrix. This is all done transparently by setting “unroll=True”. The number of harmonic terms is set by nroll, e.g., setting nroll=3 (default) includes terms up to sin(3.phi) and cos(3.phi). This requires that phi is a linear function of time for each dataset, which is a good approximation for individual CHEOPS visits.
Other decorrelation parameters not derived from the roll angle, e.g. dfdx, dfdy, etc. are included in the fit to individual datasets only if they were free parameters in the last fit to that dataset. The decorrelation is done independently for each dataset. The free parameters are labelled dfdx_ii, dfdy_ii where ii is the number of the dataset to which each decorrelation parameter applies, i.e. ii=01, 02, 03, etc.
Glint correction is done independently for each dataset if the glint correction was included in the last fit to that dataset. The glint scale factor for dataset ii is labelled glint_scale_ii. The glint scaling factor for each dataset can either be a fixed or a free parameter, depending on whether it was a fixed or a free parameter in the last fit to that dataset.
Note that the “unroll” method implicitly assumes that the rate of change of roll angle, Omega = d(phi)/dt, is constant. This is a reasonable approximation but can introduce some extra noise in cases where instrumental noise correlated with roll angle is large, e.g., observations of faint stars in crowded fields. In this case it may be better to include the best-fit trends against roll angle from the last fit stored in the .dataset file in the fit to each dataset. This case be done using the keyword argument “unwrap=True”. This option can be combined with the “unroll=True” option, i.e. to use “unroll” as a small correction to the “unwrap” roll-angle decorrelation from the last fit to each data set.
If you only want to store and yield 1-in-thin samples in the chain, set
thin to an integer greater than 1. When this is set, thin*steps will be made and the chains returned with have “steps” values per walker.
The best fit to the light curve in each data set is
f_fit = f_sys x f_fac + f_glint + f_celerite + f_unwrap
- “f_sys” includes all the photometric effects intrinsic to the
star/planet system, i.e. transits and eclipses
- “f_fac” includes all the trends correlated with parameters apart
from spacecraft roll angle
“f_glint” is an optional function of roll angle scaled by the parameter glint_scale used to model internal reflections or other features correlated with roll angle (otherwise f_glint=0).
“f_celerite” is the maximum-likelihood Gaussian process generated for a kernel SHOTerm() + CosineTerm(Omega) + CosineTerm(2*Omega) + …, where the number of CosineTerm() kernels is specified by nroll and SHOTerm() is only included if correlated noise is included in the model.
“f_unwrap” are the trends correlated with spacecraft roll angle removed if the unwrap=True option is specified (otherwise f_unwrap = 0)
For plotting and data output we require the “detrended flux”, i.e.
flux_d = f_sys + f_sho + f_fit - f_obs
where f_obs is the observed flux and f_sho is the maximum-likelihood Gaussian process generated using only the SHOTerm() kernel, i.e. the detrended fluxes include the correlated noise modelled by f_sho. The detrended fluxes for the best fits to each dataset are included in the output lmfit ModelResult object in the attribute fluxes_det.
The fitting routines return lmfit MinimizerResult objects with a few extra attributes. Samples generated by emcee are returned as a python array in the attribute flat_chain instead of a pandas.DataFrame object in the attribute flatchain.
See https://emcee.readthedocs.io/en/stable/tutorials/monitor/ for use of the backend keyword.
- pycheops_analysis.custom_plot_phase(M, result, title=None)¶
- pycheops_analysis.custom_plot_phase_from_model(model, title=None)¶
- pycheops_analysis.mask_data_clipping(x, k, clip_type='median')¶
- pycheops_analysis.computes_bayes_factor(params)¶
- pycheops_analysis.planet_check(dataset, olog=None)¶
- pycheops_analysis.copy_DRP_report(dataset, output_folder, olog=None)¶
- class pycheops_analysis.CustomStarProperties(identifier, force_download=False, dace=False, match_arcsec=None, configFile=None, teff=None, logg=None, metal=None, passband='CHEOPS', verbose=True)¶
Bases:
objectCHEOPS StarProperties object
The observed properties T_eff, log_g and [Fe/H] are obtained from DACE or SWEET-Cat, or can be specified by the user.
Set match_arcsec=None to skip extraction of parameters from SWEET-Cat.
By default properties are obtained from SWEET-Cat.
Set dace=True to obtain parameters from the stellar properties table at DACE.
User-defined properties are specified either as a ufloat or as a 2-tuple (value, error), e.g., teff=(5000,100).
User-defined properties over-write values obtained from SWEET-Cat or DACE.
The stellar density is estimated using an linear relation between log(rho) and log(g) derived using the method of Moya et al. (2018ApJS..237…21M)
Limb darkening parameters in the CHEOPS band are interpolated from Table 2 of Maxted (2018A&A…616A..39M). The error on these parameters is propogated from the errors in Teff, log_g and [Fe/H] plus an additional error of 0.01 for h_1 and 0.05 for h_2, as recommended in Maxted (2018). If [Fe/H] for the star is not specified, the value 0.0 +/- 0.3 is assumed.
If the stellar parameters are outside the range covered by Table 2 of Maxted (2018), then the results from ATLAS model from Table 10 of Claret (2019RNAAS…3…17C) are used instead. For stars cooler than 3500K the PHOENIX models for solar metalicity from Table 5 of Claret (2019) are used. The parameters h_1 and h_2 are both given nominal errors of 0.1 for both ATLAS model, and 0.15 for PHOENIX models.
- __repr__(self)¶
Return repr(self).
- pycheops_analysis.quick_save_params(out_file, params, bjd_ref)¶
- pycheops_analysis.quick_save_params_ultranest(out_file, params, bjd_ref)¶
- pycheops_analysis.quick_save_params_ultra(out_file, star_inputs, planet_inputs, params_lm_loop, results, bjd_ref, mod='median')¶
- pycheops_analysis.u1u2_to_q1q2(u1, u2)¶
- pycheops_analysis.q1q2_to_u1u2(q1, q2)¶
- pycheops_analysis.generate_private_key(path)¶