ó šßÈ[c @ s+dZddlmZmZddgZddlZddlZddlZddl m Z ddl m Z dd l mZdd lmZdd lmZdd lmZie d 6e d6ed6ed6ed6ed6Zd„Zd„Zed„Zd„Zdddedeeddd„ ZdS(s± Main Lomb-Scargle Implementation The ``lombscargle`` function here is essentially a sophisticated switch statement for the various implementations available in this submodule iÿÿÿÿ(tprint_functiontdivisiont lombscargletavailable_methodsNi(tlombscargle_slow(tlombscargle_fast(tlombscargle_scipy(tlombscargle_chi2(tlombscargle_fastchi2(tlombscargle_cythontslowtfasttchi2tscipytfastchi2tcythoncC spddddg}ttjdƒr:|jddgƒnytjdƒ|jdƒWntk rknX|S( NtautoR R RtatR RR (thasattrtnptufunctextendtimpt find_moduletappendt ImportError(tmethods((sMlib/python2.7/site-packages/astropy/stats/lombscargle/implementations/main.pyR s  cC s_tj|ƒ}|jdkr"tSt|ƒdkr8tStj|ƒ}tj|d|ƒSdS(Nii(RtasarraytndimtFalsetlentTruetdifftallclose(t frequencyR ((sMlib/python2.7/site-packages/astropy/stats/lombscargle/implementations/main.pyt _is_regular0scC s˜tj|ƒ}|jdkr-tdƒ‚nHt|ƒdkrT|d|ddfS|pct|ƒsutdƒ‚n|d|d|dt|ƒfS(s‘Utility to get grid parameters from a frequency array Parameters ---------- frequency : array_like or Quantity 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 : scalars Parameters such that all(frequency == f0 + df * np.arange(N)) is$frequency grid must be 1 dimensionalis frequency must be a regular grid(RRRt ValueErrorRR#(R"tassume_regular_frequency((sMlib/python2.7/site-packages/astropy/stats/lombscargle/implementations/main.pyt_get_frequency_grid<sc C sótƒ}d|k}|o<t|ƒdko<|p<t|ƒ}d|ko[|d ko[| } |dkrË|s€tjdƒn|dkr¤|r›d}qÈd}qË|r³d}qË| rÂd}qËd }n|tkrïtd j|ƒƒ‚n|S( s^ Validate the method argument, and if method='auto' choose the appropriate method R iÈR Rs[Fast Lomb-Scargle methods require numpy version 1.8 or newer. Using slower methods instead.iRR Rsinvalid method: {0}N( RRR#tNonetwarningstwarntMETHODSR$tformat( tmethodtdytfit_meantntermsR"R%Rtfast_method_okt prefer_fastt prefer_scipy((sMlib/python2.7/site-packages/astropy/stats/lombscargle/implementations/main.pytvalidate_methodVs(          Rtstandardc  C s¨|j} |jƒ}|||f} td|d|d|d|d| | pNi} t|d|d|d| d|d|ƒ}|dkr| jdƒr¨td ƒ‚n|dk rôtjtj|ƒƒ}tj |d |ƒsôtd ƒ‚qôn||f} n|j d ƒrRt | jdƒ|ƒ\}}}| j d |d|d|ƒn|j dƒsˆ| jdƒdkrˆtdƒ‚qˆnt|| | Ž}|j| ƒS(sÀ 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 : string (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 : string (optional, default='standard') Normalization to use for the periodogram. Options are 'standard' or 'psd'. fit_mean : bool (optional, default=True) 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, default=True) 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 (default=1) 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"t center_dataR.t normalizationR/R-R%R s+scipy method does not support fit_mean=Trueis3scipy method only supports uniform uncertainties dyR tf0tdftNfR is<nterms != 1 only supported with 'chi2' or 'fastchi2' methodsN(tshapetraveltdictR3tpopR$R'RRR!t startswithR&tupdatetendswithR*treshape(tttyR-R"R,R%R6R.R5t method_kwdsR/t output_shapetargstkwdsR7R8R9tPLS((sMlib/python2.7/site-packages/astropy/stats/lombscargle/implementations/main.pyRzs:B      (t__doc__t __future__RRt__all__R(RtnumpyRt slow_implRt fast_implRt scipy_implRt chi2_implRt fastchi2_implRt cython_implR R*RR#RR&R3R'RR(((sMlib/python2.7/site-packages/astropy/stats/lombscargle/implementations/main.pyts8         $