3 h>@sdZddlmZddlZddlZddlmZm Z ddl Z ddl j ZddlZddlmZddlmZddlmZdd lmZd gZddddd d d dejdddd dfdd ZddZddZddZddZddZddZ ddZ!ddZ"dS) zTimeseries plotting functions.)divisionN)stats interpolate) string_types)utils) algorithms) color_palettetsplotZci_bandDTic- s\d}tj|t|dkr tj}| dkr,i} t|tjr|}|}|dkrftjd|j d}d} d}d}ndol| } |}t ||j }ntt j |}|jdkr|t jddt jf}n"|jdkr|ddddt jf}|j\}}}|dkrt j|}d}t j|||}d}|dkr t j|}n t j |}d}t|d r@|j}d }t jt j|||}|dkrt|}d} ttrd }t|n,t j |}do| } t|d r|j}nd}d }t j|||}|dkrd}n|}d }tjt|j|||d}t|tr|g}n|dkrg}t|ds.|g}dkrftj}t ||krZtd|}n t|d}nfttrfdd||j D}n>yt|}Wn.tk rtjj j!g|}YnXd}xt"|j#|ddD]\}\}}|j$|||}|j%j&j't j(} d|krT| |j&dd}!t j)|j&dd}"|!|"|!|"fg}#|j&n&t*j+|j&| d| dfdd|D}#| |j&dd}$||x|D]}%|%dkrqyt,d|%}&Wn"t-k rtd|%YnX| dk rd|%kr}'t| t |j&t|| |j&|$| d}(x |#D]})|)|(d<|&f|(q$W| dk rd|%kr|'qW|j.d|rndnd |j/d!|rd"nd}*|j.d#|*| r|nd$}+|j0| |$f|+d%|qW|dkrt1d&|j2| j3| j4| d| d},|s|j2| j3|,| j4|,|dk r0|j5||dk rD|j6|| rX|j7d|d'|S)(aPlot one or more timeseries with flexible representation of uncertainty. This function is intended to be used with data where observations are nested within sampling units that were measured at multiple timepoints. It can take data specified either as a long-form (tidy) DataFrame or as an ndarray with dimensions (unit, time) The interpretation of some of the other parameters changes depending on the type of object passed as data. Parameters ---------- data : DataFrame or ndarray Data for the plot. Should either be a "long form" dataframe or an array with dimensions (unit, time, condition). In both cases, the condition field/dimension is optional. The type of this argument determines the interpretation of the next few parameters. When using a DataFrame, the index has to be sequential. time : string or series-like Either the name of the field corresponding to time in the data DataFrame or x values for a plot when data is an array. If a Series, the name will be used to label the x axis. unit : string Field in the data DataFrame identifying the sampling unit (e.g. subject, neuron, etc.). The error representation will collapse over units at each time/condition observation. This has no role when data is an array. value : string Either the name of the field corresponding to the data values in the data DataFrame (i.e. the y coordinate) or a string that forms the y axis label when data is an array. condition : string or Series-like Either the name of the field identifying the condition an observation falls under in the data DataFrame, or a sequence of names with a length equal to the size of the third dimension of data. There will be a separate trace plotted for each condition. If condition is a Series with a name attribute, the name will form the title for the plot legend (unless legend is set to False). err_style : string or list of strings or None Names of ways to plot uncertainty across units from set of {ci_band, ci_bars, boot_traces, boot_kde, unit_traces, unit_points}. Can use one or more than one method. ci : float or list of floats in [0, 100] or "sd" or None Confidence interval size(s). If a list, it will stack the error plots for each confidence interval. If ``"sd"``, show standard deviation of the observations instead of boostrapped confidence intervals. Only relevant for error styles with "ci" in the name. interpolate : boolean Whether to do a linear interpolation between each timepoint when plotting. The value of this parameter also determines the marker used for the main plot traces, unless marker is specified as a keyword argument. color : seaborn palette or matplotlib color name or dictionary Palette or color for the main plots and error representation (unless plotting by unit, which can be separately controlled with err_palette). If a dictionary, should map condition name to color spec. estimator : callable Function to determine central tendency and to pass to bootstrap must take an ``axis`` argument. n_boot : int Number of bootstrap iterations. err_palette : seaborn palette Palette name or list of colors used when plotting data for each unit. err_kws : dict, optional Keyword argument dictionary passed through to matplotlib function generating the error plot, legend : bool, optional If ``True`` and there is a ``condition`` variable, add a legend to the plot. ax : axis object, optional Plot in given axis; if None creates a new figure kwargs : Other keyword arguments are passed to main plot() call Returns ------- ax : matplotlib axis axis with plot data Examples -------- Plot a trace with translucent confidence bands: .. plot:: :context: close-figs >>> import numpy as np; np.random.seed(22) >>> import seaborn as sns; sns.set(color_codes=True) >>> x = np.linspace(0, 15, 31) >>> data = np.sin(x) + np.random.rand(10, 31) + np.random.randn(10, 1) >>> ax = sns.tsplot(data=data) Plot a long-form dataframe with several conditions: .. plot:: :context: close-figs >>> gammas = sns.load_dataset("gammas") >>> ax = sns.tsplot(time="timepoint", value="BOLD signal", ... unit="subject", condition="ROI", ... data=gammas) Use error bars at the positions of the observations: .. plot:: :context: close-figs >>> ax = sns.tsplot(data=data, err_style="ci_bars", color="g") Don't interpolate between the observations: .. plot:: :context: close-figs >>> import matplotlib.pyplot as plt >>> ax = sns.tsplot(data=data, err_style="ci_bars", interpolate=False) Show multiple confidence bands: .. plot:: :context: close-figs >>> ax = sns.tsplot(data=data, ci=[68, 95], color="m") Show the standard deviation of the observations: .. plot:: :context: close-figs >>> ax = sns.tsplot(data=data, ci="sd") Use a different estimator: .. plot:: :context: close-figs >>> ax = sns.tsplot(data=data, estimator=np.median) Show each bootstrap resample: .. plot:: :context: close-figs >>> ax = sns.tsplot(data=data, err_style="boot_traces", n_boot=500) Show the trace from each sampling unit: .. plot:: :context: close-figs >>> ax = sns.tsplot(data=data, err_style="unit_traces") zThe `tsplot` function is deprecated and will be removed in a future release. Please update your code to use the new `lineplot` function.Nr)indexFTunitnametimez.Must have condition names if using color dict.condvalue)rrrr__iter__Zhusl)Zn_colorscsg|] }|qSr).0c)colorr5/tmp/pip-build-riy7u7_k/seaborn/seaborn/timeseries.py sztsplot..)sortsdr)axis)n_bootrfunccsg|]}tj|ddqS)r)r)rci)rv) boot_datarrr4sz_plot_%sz%s is not a valid err_style)axxdatar! central_datarerr_kwsrmarkerols-Z linestyle _nolegend_)rlabelzInvalid input data for tsplot.)loctitle)8warningswarn UserWarningpltZgca isinstancepdZ DataFrameZSeriesr lenuniquenpZasarrayndimZnewaxisshapeZarangerepeathasattrrZtilerangedict ValueErrorZravelrrZget_color_cycler mplcolorsZcolorConverterto_rgb enumerategroupbyZpivotcolumnsvaluesZastypefloatZstdalgoZ bootstrapglobalsKeyError setdefaultpopplot RuntimeErrorZset_xlimminmaxZ set_xlabelZ set_ylabellegend)-r$rr conditionrZ err_stylerrrZ estimatorrZ err_paletter&rQr"kwargsmsgZxlabelZylabelZ legend_nameZn_condZn_unitZn_timeZunitstimesZcondserrZcurrent_paletterArrZdf_cr#ZestrZcisr%styleZ plot_funcZ orig_colorZ plot_kwargsZci_ir*r-Zx_diffr)r!rrr s                            $                cKs6|\}}d|krd|d<|j|||fd|i|dS)z9Plot translucent error bands around the central tendancy.alphag?Z facecolorN)Z fill_between)r"r#rrr&rSlowhighrrr _plot_ci_bandxsr[c KsHxBt|||jD]0\}}\} } |j||g| | gf|dd|qWdS)z#Plot error bars at each data point.round)rZsolid_capstyleN)zipTrM) r"r#r%rrr&rSZx_iZy_irYrZrrr _plot_ci_barssr_cKsN|jdd|jddd|kr.|jd|d<|j||jf|dd|dS)zPlot 250 traces from bootstrap.rXg?Z linewidthZlwr,)rr-N)rKrLrMr^)r"r#r!rr&rSrrr_plot_boot_tracess   r`c Kst|trPd|krd|d<x`t|D]&\}}|j||f||dd|q$Wn,d|kr`d|d<|j||jf|dd|dS)z7Plot a trace for each observation in the original data.rXg?r,)rr-g?N)r4listrCrMr^) r"r#r$rrr&rSiobsrrr_plot_unit_tracess $rdcKslt|trFx\t|D],\}}|j||df||dddd|qWn"|j||jdf|dddd|dS)z)Plot each original data point discretely.r)g?r,)rrXZ markersizer-g?N)r4rarCrMr^)r"r#r$rr&rSrbrcrrr_plot_unit_pointss  rfcKs |jdt||||f|dS)z?Plot the kernal density estimate of the bootstrap distribution.r$N)rL_ts_kde)r"r#r!rrSrrr_plot_boot_kdes rhcKst||||f|dS)z1Plot the kernal density estimate over the sample.N)rg)r"r#r$rrSrrr_plot_unit_kdesricKs g}|j|j}}tj||d}tj||} | tj|j|jd} x(| jD]} tjj | } |j | |qRWtj |}t j jj|} tj|jd|jddf}| |ddddddf<||jdd}d||dk<||dddddf<|j|dd |j|j||fd d d dS) z@Upsample over time and plot a KDE of the bootstrap distribution.drrreN)rZspline16r autolower) interpolationZzorderZextentZaspectorigin)rOrPr8ZlinspacerZinterp1dr^rZkdeZ gaussian_kdeappendZ transposer@rAZColorConverterrBzerosr:Zimshow)r"r#r$rrSZkde_dataZy_minZy_maxZy_valsZ upsamplerZ data_upsampleZpt_dataZpt_kdeZrgbimgrrrrgs$      rg)#__doc__ __future__rZnumpyr8Zpandasr5ZscipyrrZ matplotlibr@Zmatplotlib.pyplotZpyplotr3r0Z external.sixrr(rrrHZpalettesr __all__Zmeanr r[r_r`rdrfrhrirgrrrrs4       b