U d»{jŒã @sždZddgZddlZddlmZddlmZddlm Z dd l m Z dd l m Z dd lmZee ee e ed œZd d„Zdd„Zddd„Zdd„Zddd„ZdS)z± Main Lomb-Scargle Implementation The ``lombscargle`` function here is essentially a sophisticated switch statement for the various implementations available in this submodule Ú lombscargleÚavailable_methodséNé)Úlombscargle_chi2)Úlombscargle_cython)Úlombscargle_fast)Úlombscargle_fastchi2)Úlombscargle_scipy)Úlombscargle_slow)ÚslowÚfastÚchi2ÚscipyÚfastchi2ÚcythoncCs@ddddddg}z ddl}Wntk r0Yn X| d¡|S) NÚautor r rr rrr)rÚ ImportErrorÚappend)Úmethodsr©rú/var/www/html/bwave/python/venv/lib/python3.8/site-packages/astropy/timeseries/periodograms/lombscargle/implementations/main.pyrs  cCsFt |¡}|jdkrdSt|ƒdkr(dSt |¡}t |d|¡SdS)NrFTr)ÚnpÚasarrayÚndimÚlenÚdiffÚallclose)Ú frequencyrrrrÚ _is_regular*s    rFcCsnt |¡}|jdkrtdƒ‚n2t|ƒdkr<|d|ddfS|sPt|ƒsPtdƒ‚|d|d|dt|ƒfS)a¯Utility to get grid parameters from a frequency array Parameters ---------- frequency : array-like or `~astropy.units.Quantity` ['frequency'] input frequency grid assume_regular_frequency : bool (default = False) if True, then do not check whether frequency is a regular grid Returns ------- f0, df, N : scalar Parameters such that all(frequency == f0 + df * np.arange(N)) rz$frequency grid must be 1 dimensionalrz frequency must be a regular grid)rrrÚ ValueErrorrr)rÚassume_regular_frequencyrrrÚ_get_frequency_grid6s     r!c Cs†tƒ}t|ƒdko|pt|ƒ}d|ko2|dko2| }|dkrl|dkrT|rNd}qld}n|r^d}n|rhd}nd }|tkr‚td |›ƒ‚|S) z^ Validate the method argument, and if method='auto' choose the appropriate method éÈrNrrrr r rzinvalid method: )rrrÚMETHODSr) ÚmethodÚdyÚfit_meanÚntermsrr rZ prefer_fastZ prefer_scipyrrrÚvalidate_methodPs$  ÿr(rÚstandardTc Cs|j} | ¡}|||f} tf||||| dœ| p0i—Ž} t|||| ||d}|dkrœ|  d¡rdtdƒ‚|dk r”t t |¡¡}t |d|¡s”tdƒ‚||f} |  d ¡rÌt |  d ¡|ƒ\}}}| j |||d |  d ¡sì|  d ¡dkrìtdƒ‚t || | Ž}| | ¡S)a Compute the Lomb-scargle Periodogram with a given method. Parameters ---------- t : array-like sequence of observation times y : array-like sequence of observations associated with times t dy : float or array-like, optional error or sequence of observational errors associated with times t frequency : array-like frequencies (not angular frequencies) at which to evaluate the periodogram. If not specified, optimal frequencies will be chosen using a heuristic which will attempt to provide sufficient frequency range and sampling so that peaks will not be missed. Note that in order to use method='fast', frequencies must be regularly spaced. method : str, optional specify the lomb scargle implementation to use. Options are: - 'auto': choose the best method based on the input - 'fast': use the O[N log N] fast method. Note that this requires evenly-spaced frequencies: by default this will be checked unless ``assume_regular_frequency`` is set to True. - `slow`: use the O[N^2] pure-python implementation - `chi2`: use the O[N^2] chi2/linear-fitting implementation - `fastchi2`: use the O[N log N] chi2 implementation. Note that this requires evenly-spaced frequencies: by default this will be checked unless `assume_regular_frequency` is set to True. - `scipy`: use ``scipy.signal.lombscargle``, which is an O[N^2] implementation written in C. Note that this does not support heteroskedastic errors. assume_regular_frequency : bool, optional if True, assume that the input frequency is of the form freq = f0 + df * np.arange(N). Only referenced if method is 'auto' or 'fast'. normalization : str, optional Normalization to use for the periodogram. Options are 'standard' or 'psd'. fit_mean : bool, optional if True, include a constant offset as part of the model at each frequency. This can lead to more accurate results, especially in the case of incomplete phase coverage. center_data : bool, optional if True, pre-center the data by subtracting the weighted mean of the input data. This is especially important if `fit_mean = False` method_kwds : dict, optional additional keywords to pass to the lomb-scargle method nterms : int, optional number of Fourier terms to use in the periodogram. Not supported with every method. Returns ------- PLS : array-like Lomb-Scargle power associated with each frequency omega )rÚ center_datar&Ú normalizationr')r%r&r'rr rr&z+scipy method does not support fit_mean=TrueNrz3scipy method only supports uniform uncertainties dyr r)Úf0ÚdfÚNfr r'rzs:      ú   "õ