ó áp7]c @sÆdZddlZddlmZddlmZmZd„Zd„Z ddde e e de dd „ Z dddd e d e dd „Zddd „Zdddd„Zdddd„ZdS(sCorrelation plot functions.iÿÿÿÿN(tutils(tacftpacfcCsìt|ƒ}|rtnt}|dkr€|jd}tttjdtj |ƒƒƒ|dƒ}tj | |dƒ}nPtj |ƒr¯tj | t|ƒdƒ}n!t}tj |ƒj tjƒ}|jdƒ}|||fS(Nii i(tbooltFalsetTruetNonetshapetmintinttnptceiltlog10tarangetisscalart asanyarraytastypetmax(txtlagstzerot irregulartnobstlimtnlags((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt_prepare_data_corr_plot s   / c Ks~|r,||}|dk r,||}q,n|r[|j|dg|||j|n|jddƒ|jddƒd|krš|jddƒn|jd ƒ|j||||j|ƒ|dk rz|ddkr|d }|d }|d }n|jtj ƒ}|dcd 8<|d cd 7<|j ||dd…df||dd…d f|d dƒndS(Nitmarkertot markersizeitlst linestyleRgš™™™™™©?igà?iÿÿÿÿtalphagÐ?( Rtvlinestaxhlinet setdefaulttmarginstplott set_titleRR tfloatt fill_between( taxttitletacf_xtconfintRRt use_vlinest vlines_kwargstkwargs((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt _plot_corrs0          gš™™™™™©?tAutocorrelationc  KsÑtj|ƒ\} }t|||ƒ\}} } | dkrBin| } d}|dkrt|d| d|d|d|ƒ}n*t|d| d|d|d|ƒ\}}t|||||| || | | S(sh Plot the autocorrelation function Plots lags on the horizontal and the correlations on vertical axis. Parameters ---------- x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : int or array_like, optional int or Array of lag values, used on horizontal axis. Uses np.arange(lags) when lags is an int. If not provided, ``lags=np.arange(len(corr))`` is used. alpha : scalar, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to Bartlett's formula. If None, no confidence intervals are plotted. use_vlines : bool, optional If True, vertical lines and markers are plotted. If False, only markers are plotted. The default marker is 'o'; it can be overridden with a ``marker`` kwarg. unbiased : bool If True, then denominators for autocovariance are n-k, otherwise n fft : bool, optional If True, computes the ACF via FFT. title : str, optional Title to place on plot. Default is 'Autocorrelation' zero : bool, optional Flag indicating whether to include the 0-lag autocorrelation. Default is True. vlines_kwargs : dict, optional Optional dictionary of keyword arguments that are passed to vlines. **kwargs : kwargs, optional Optional keyword arguments that are directly passed on to the Matplotlib ``plot`` and ``axhline`` functions. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- matplotlib.pyplot.xcorr matplotlib.pyplot.acorr Notes ----- Adapted from matplotlib's `xcorr`. Data are plotted as ``plot(lags, corr, **kwargs)`` kwargs is used to pass matplotlib optional arguments to both the line tracing the autocorrelations and for the horizontal line at 0. These options must be valid for a Line2D object. vlines_kwargs is used to pass additional optional arguments to the vertical lines connecting each autocorrelation to the axis. These options must be valid for a LineCollection object. Examples -------- >>> import pandas as pd >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm >>> dta = sm.datasets.sunspots.load_pandas().data >>> dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008')) >>> del dta["YEAR"] >>> sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40) >>> plt.show() .. plot:: plots/graphics_tsa_plot_acf.py RRtffttunbiasedN(Rt create_mpl_axRRRR/(RR(RRR,R2R1R)RR-R.tfigRRR+R*((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pytplot_acf<sQ   t ywunbiasedsPartial Autocorrelationc  KsÅtj|ƒ\} }|dkr'in|}t|||ƒ\}} } d} |dkr{t|d| d|d|ƒ}n$t|d| d|d|ƒ\}} t|||| || ||| | S(sš Plot the partial autocorrelation function Parameters ---------- x : array_like Array of time-series values ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. lags : int or array_like, optional int or Array of lag values, used on horizontal axis. Uses np.arange(lags) when lags is an int. If not provided, ``lags=np.arange(len(corr))`` is used. alpha : float, optional If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to 1/sqrt(len(x)) method : {'ywunbiased', 'ywmle', 'ols'} Specifies which method for the calculations to use: - yw or ywunbiased : yule walker with bias correction in denominator for acovf. Default. - ywm or ywmle : yule walker without bias correction - ols - regression of time series on lags of it and on constant - ld or ldunbiased : Levinson-Durbin recursion with bias correction - ldb or ldbiased : Levinson-Durbin recursion without bias correction use_vlines : bool, optional If True, vertical lines and markers are plotted. If False, only markers are plotted. The default marker is 'o'; it can be overridden with a ``marker`` kwarg. title : str, optional Title to place on plot. Default is 'Partial Autocorrelation' zero : bool, optional Flag indicating whether to include the 0-lag autocorrelation. Default is True. vlines_kwargs : dict, optional Optional dictionary of keyword arguments that are passed to vlines. **kwargs : kwargs, optional Optional keyword arguments that are directly passed on to the Matplotlib ``plot`` and ``axhline`` functions. Returns ------- fig : Matplotlib figure instance If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- matplotlib.pyplot.xcorr matplotlib.pyplot.acorr Notes ----- Plots lags on the horizontal and the correlations on vertical axis. Adapted from matplotlib's `xcorr`. Data are plotted as ``plot(lags, corr, **kwargs)`` kwargs is used to pass matplotlib optional arguments to both the line tracing the autocorrelations and for the horizontal line at 0. These options must be valid for a Line2D object. vlines_kwargs is used to pass additional optional arguments to the vertical lines connecting each autocorrelation to the axis. These options must be valid for a LineCollection object. Examples -------- >>> import pandas as pd >>> import matplotlib.pyplot as plt >>> import statsmodels.api as sm >>> dta = sm.datasets.sunspots.load_pandas().data >>> dta.index = pd.Index(sm.tsa.datetools.dates_from_range('1700', '2008')) >>> del dta["YEAR"] >>> sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40) >>> plt.show() .. plot:: plots/graphics_tsa_plot_pacf.py RRtmethodN(RR3RRRR/(RR(RRR7R,R)RR-R.R4RRR+R*((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt plot_pacf¡sW !$ c Cstj|ƒ\}}d}g}x¯|D]§\}}|jƒ}|jƒt|ƒ} tj||| ƒ} |j| jƒƒ|j | |j dƒ|j |j jƒ| d| dddddƒ|| 7}q(W|j |ƒ|j |ƒ|j|ƒ|jdd ƒ|S( s@ Consider using one of month_plot or quarter_plot unless you need irregular plotting. Parameters ---------- grouped_x : iterable of DataFrames Should be a GroupBy object (or similar pair of group_names and groups as DataFrames) with a DatetimeIndex or PeriodIndex xticklabels : list of str List of season labels, one for each group. ylabel : str Lable for y axis ax : Matplotlib AxesSubplot instance, optional If given, this subplot is used to plot in instead of a new figure being created. itkiÿÿÿÿtcolorstrt linewidthigš™™™™™¹?gš™™™™™©?(RR3tcopyt sort_indextlenR R tappendtmeanR$tvaluesthlinest set_xtickstset_xticklabelst set_ylabelR#( t grouped_xt xticklabelstylabelR(R4tstartttickstseasontdfRtx_plot((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt seasonal_plots$   )   c Cs²|dkr/ddlm}||ddƒn4ddlm}m}||d||ddƒƒ}ddd d d ddd d d d dg }t|jd„ƒ|d|d|ƒS(sM Seasonal plot of monthly data Parameters ---------- x : array-like Seasonal data to plot. If dates is None, x must be a pandas object with a PeriodIndex or DatetimeIndex with a monthly frequency. dates : array-like, optional If `x` is not a pandas object, then dates must be supplied. ylabel : str, optional The label for the y-axis. Will attempt to use the `name` attribute of the Series. ax : matplotlib.axes, optional Existing axes instance. Returns ------- matplotlib.Figure Examples -------- >>> import statsmodels.api as sm >>> import pandas as pd >>> dta = sm.datasets.elnino.load_pandas().data >>> dta['YEAR'] = dta.YEAR.astype(int).astype(str) >>> dta = dta.set_index('YEAR').T.unstack() >>> dates = pd.to_datetime(list(map(lambda x: '-'.join(x) + '-1', ... dta.index.values))) >>> dta.index = pd.DatetimeIndex(dates, freq='MS') >>> fig = sm.graphics.tsa.month_plot(dta) .. plot:: plots/graphics_tsa_month_plot.py iÿÿÿÿ(t_check_period_indextfreqtM(tSeriest PeriodIndextindextjtftmtatsRtntdcSs|jS(N(tmonth(ty((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt\tRIR(N(Rtstatsmodels.tools.dataRPtpandasRSRTROtgroupby(RtdatesRIR(RPRSRTRH((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt month_plot/s% *cCsš|dkr/ddlm}||ddƒn4ddlm}m}||d||ddƒƒ}ddd d g}t|jd „ƒ|d |d |ƒS(sE Seasonal plot of quarterly data Parameters ---------- x : array-like Seasonal data to plot. If dates is None, x must be a pandas object with a PeriodIndex or DatetimeIndex with a monthly frequency. dates : array-like, optional If `x` is not a pandas object, then dates must be supplied. ylabel : str, optional The label for the y-axis. Will attempt to use the `name` attribute of the Series. ax : matplotlib.axes, optional Existing axes instance. Returns ------- matplotlib.Figure Examples -------- >>> import statsmodels.api as sm >>> import pandas as pd >>> dta = sm.datasets.elnino.load_pandas().data >>> dta['YEAR'] = dta.YEAR.astype(int).astype(str) >>> dta = dta.set_index('YEAR').T.unstack() >>> dates = pd.to_datetime(list(map(lambda x: '-'.join(x) + '-1', ... dta.index.values))) >>> dta.index = dates.to_period('Q') >>> fig = sm.graphics.tsa.quarter_plot(dta) .. plot:: plots/graphics_tsa_quarter_plot.py iÿÿÿÿ(RPRQtQ(RSRTRUtq1tq2tq3tq4cSs|jS(N(tquarter(R^((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyR_R`RIR(N(RRaRPRbRSRTRORc(RRdRIR(RPRSRTRH((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyt quarter_plot`s% (t__doc__tnumpyR tstatsmodels.graphicsRtstatsmodels.tsa.stattoolsRRRR/RRRR5R8ROReRl(((s<lib/python2.7/site-packages/statsmodels/graphics/tsaplots.pyts     c   e'1