IR-edZddgZddlZddlmZddlmZddlm Z dd l m Z dd l m Z dd lmZee ee e ed Zd ZdZddZdZ ddZdS)z Main Lomb-Scargle Implementation. The ``lombscargle`` function here is essentially a sophisticated switch statement for the various implementations available in this submodule lombscargleavailable_methodsN)lombscargle_chi2)lombscargle_cython)lombscargle_fast)lombscargle_fastchi2)lombscargle_scipy)lombscargle_slow)slowfastchi2scipyfastchi2cythoncdgd} ddl}|dn#t$rYnwxYw|S)N)autor rrr rrr)rappend ImportError)methodsrs `lib/python3.11/site-packages/astropy/timeseries/periodograms/lombscargle/implementations/main.pyrrs[DDDG   w       Ns  --ctj|}|jdkrdSt|dkrdStj|}tj|d|S)NrFTr)npasarrayndimlendiffallclose) frequencyrs r _is_regularr *s^ 9%%I~u Y1  twy!!{47D)))Fc4tj|}|jdkrtdt |dkr|d|ddfS|st |std|d|d|dz t |fS)aUtility to get grid parameters from a frequency array. Parameters ---------- frequency : array-like or `~astropy.units.Quantity` ['frequency'] input frequency grid assume_regular_frequency : bool (default = False) if True, then do not check whether frequency is a regular grid Returns ------- f0, df, N : scalar Parameters such that all(frequency == f0 + df * np.arange(N)) rz$frequency grid must be 1 dimensionalrz frequency must be a regular grid)rrr ValueErrorrr )rassume_regular_frequencys r_get_frequency_gridr%6s 9%%I~?@@@ Y1  |Yq\1,,&=+i*@*@=;<<< Q<1 ! 4c)nn DDr!ct}t|dko|pt|}d|vo|duo| }|dkr|dkr|rd}nd}n |rd}n|rd}nd }|tvrt d ||S) z_ Validate the method argument, and if method='auto' choose the appropriate method. rNrrrrr rzinvalid method: )rrr METHODSr#) methoddyfit_meanntermsrr$r prefer_fast prefer_scipys rvalidate_methodr/Ps  !!Gi..3& :K $:$:g%E"*EXL Q;; #  FF  FFF W4F44555 Mr!rstandardTc |j} |}|||f} td||||| d| pi} t|||| ||}|dkrz| drt d|Pt jt j|}t j|d|st d||f} | d r?t| d |\}}}| ||| | d s(| d dkrt dt|| i| }|| 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 )r center_datar+ normalizationr,)r*r+r,rr$rr+z+scipy method does not support fit_mean=TrueNrz3scipy method only supports uniform uncertainties dyr r)f0dfNfrr,rz"*R..))B;r!ub)) X !VWWW1v  )( HH[ ! !#;  B rbR ((( ??6 " " 88H   " "N  &/4 (4 ( (C ;;| $ $$r!)F) NNrFr0TTNr)__doc____all__numpyr chi2_implr cython_implr fast_implr fastchi2_implr scipy_implr slow_implr r(rr r%r/rr7r!rrPs; - .''''''++++++''''''//////))))))''''''    $       * * *EEEE4D  "  x%x%x%x%x%x%r!