SpringFocus/venv/lib/python3.8/site-packages/seaborn/timeseries.py

"""Timeseries plotting functions."""
from __future__ import division
import numpy as np
import pandas as pd
from scipy import stats, interpolate
import matplotlib as mpl
import matplotlib.pyplot as plt

import warnings

from .external.six import string_types

from . import utils
from . import algorithms as algo
from .palettes import color_palette


__all__ = ["tsplot"]


def tsplot(data, time=None, unit=None, condition=None, value=None,
           err_style="ci_band", ci=68, interpolate=True, color=None,
           estimator=np.mean, n_boot=5000, err_palette=None, err_kws=None,
           legend=True, ax=None, **kwargs):
    """Plot 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")

    """
    msg = (
        "The `tsplot` function is deprecated and will be removed in a future "
        "release. Please update your code to use the new `lineplot` function."
    )
    warnings.warn(msg, UserWarning)

    # Sort out default values for the parameters
    if ax is None:
        ax = plt.gca()

    if err_kws is None:
        err_kws = {}

    # Handle different types of input data
    if isinstance(data, pd.DataFrame):

        xlabel = time
        ylabel = value

        # Condition is optional
        if condition is None:
            condition = pd.Series(1, index=data.index)
            legend = False
            legend_name = None
            n_cond = 1
        else:
            legend = True and legend
            legend_name = condition
            n_cond = len(data[condition].unique())

    else:
        data = np.asarray(data)

        # Data can be a timecourse from a single unit or
        # several observations in one condition
        if data.ndim == 1:
            data = data[np.newaxis, :, np.newaxis]
        elif data.ndim == 2:
            data = data[:, :, np.newaxis]
        n_unit, n_time, n_cond = data.shape

        # Units are experimental observations. Maybe subjects, or neurons
        if unit is None:
            units = np.arange(n_unit)
        unit = "unit"
        units = np.repeat(units, n_time * n_cond)
        ylabel = None

        # Time forms the xaxis of the plot
        if time is None:
            times = np.arange(n_time)
        else:
            times = np.asarray(time)
        xlabel = None
        if hasattr(time, "name"):
            xlabel = time.name
        time = "time"
        times = np.tile(np.repeat(times, n_cond), n_unit)

        # Conditions split the timeseries plots
        if condition is None:
            conds = range(n_cond)
            legend = False
            if isinstance(color, dict):
                err = "Must have condition names if using color dict."
                raise ValueError(err)
        else:
            conds = np.asarray(condition)
            legend = True and legend
            if hasattr(condition, "name"):
                legend_name = condition.name
            else:
                legend_name = None
        condition = "cond"
        conds = np.tile(conds, n_unit * n_time)

        # Value forms the y value in the plot
        if value is None:
            ylabel = None
        else:
            ylabel = value
        value = "value"

        # Convert to long-form DataFrame
        data = pd.DataFrame(dict(value=data.ravel(),
                                 time=times,
                                 unit=units,
                                 cond=conds))

    # Set up the err_style and ci arguments for the loop below
    if isinstance(err_style, string_types):
        err_style = [err_style]
    elif err_style is None:
        err_style = []
    if not hasattr(ci, "__iter__"):
        ci = [ci]

    # Set up the color palette
    if color is None:
        current_palette = utils.get_color_cycle()
        if len(current_palette) < n_cond:
            colors = color_palette("husl", n_cond)
        else:
            colors = color_palette(n_colors=n_cond)
    elif isinstance(color, dict):
        colors = [color[c] for c in data[condition].unique()]
    else:
        try:
            colors = color_palette(color, n_cond)
        except ValueError:
            color = mpl.colors.colorConverter.to_rgb(color)
            colors = [color] * n_cond

    # Do a groupby with condition and plot each trace
    c = None
    for c, (cond, df_c) in enumerate(data.groupby(condition, sort=False)):

        df_c = df_c.pivot(unit, time, value)
        x = df_c.columns.values.astype(np.float)

        # Bootstrap the data for confidence intervals
        if "sd" in ci:
            est = estimator(df_c.values, axis=0)
            sd = np.std(df_c.values, axis=0)
            cis = [(est - sd, est + sd)]
            boot_data = df_c.values
        else:
            boot_data = algo.bootstrap(df_c.values, n_boot=n_boot,
                                       axis=0, func=estimator)
            cis = [utils.ci(boot_data, v, axis=0) for v in ci]
        central_data = estimator(df_c.values, axis=0)

        # Get the color for this condition
        color = colors[c]

        # Use subroutines to plot the uncertainty
        for style in err_style:

            # Allow for null style (only plot central tendency)
            if style is None:
                continue

            # Grab the function from the global environment
            try:
                plot_func = globals()["_plot_%s" % style]
            except KeyError:
                raise ValueError("%s is not a valid err_style" % style)

            # Possibly set up to plot each observation in a different color
            if err_palette is not None and "unit" in style:
                orig_color = color
                color = color_palette(err_palette, len(df_c.values))

            # Pass all parameters to the error plotter as keyword args
            plot_kwargs = dict(ax=ax, x=x, data=df_c.values,
                               boot_data=boot_data,
                               central_data=central_data,
                               color=color, err_kws=err_kws)

            # Plot the error representation, possibly for multiple cis
            for ci_i in cis:
                plot_kwargs["ci"] = ci_i
                plot_func(**plot_kwargs)

            if err_palette is not None and "unit" in style:
                color = orig_color

        # Plot the central trace
        kwargs.setdefault("marker", "" if interpolate else "o")
        ls = kwargs.pop("ls", "-" if interpolate else "")
        kwargs.setdefault("linestyle", ls)
        label = cond if legend else "_nolegend_"
        ax.plot(x, central_data, color=color, label=label, **kwargs)

    if c is None:
        raise RuntimeError("Invalid input data for tsplot.")

    # Pad the sides of the plot only when not interpolating
    ax.set_xlim(x.min(), x.max())
    x_diff = x[1] - x[0]
    if not interpolate:
        ax.set_xlim(x.min() - x_diff, x.max() + x_diff)

    # Add the plot labels
    if xlabel is not None:
        ax.set_xlabel(xlabel)
    if ylabel is not None:
        ax.set_ylabel(ylabel)
    if legend:
        ax.legend(loc=0, title=legend_name)

    return ax

# Subroutines for tsplot errorbar plotting
# ----------------------------------------


def _plot_ci_band(ax, x, ci, color, err_kws, **kwargs):
    """Plot translucent error bands around the central tendancy."""
    low, high = ci
    if "alpha" not in err_kws:
        err_kws["alpha"] = 0.2
    ax.fill_between(x, low, high, facecolor=color, **err_kws)


def _plot_ci_bars(ax, x, central_data, ci, color, err_kws, **kwargs):
    """Plot error bars at each data point."""
    for x_i, y_i, (low, high) in zip(x, central_data, ci.T):
        ax.plot([x_i, x_i], [low, high], color=color,
                solid_capstyle="round", **err_kws)


def _plot_boot_traces(ax, x, boot_data, color, err_kws, **kwargs):
    """Plot 250 traces from bootstrap."""
    err_kws.setdefault("alpha", 0.25)
    err_kws.setdefault("linewidth", 0.25)
    if "lw" in err_kws:
        err_kws["linewidth"] = err_kws.pop("lw")
    ax.plot(x, boot_data.T, color=color, label="_nolegend_", **err_kws)


def _plot_unit_traces(ax, x, data, ci, color, err_kws, **kwargs):
    """Plot a trace for each observation in the original data."""
    if isinstance(color, list):
        if "alpha" not in err_kws:
            err_kws["alpha"] = .5
        for i, obs in enumerate(data):
            ax.plot(x, obs, color=color[i], label="_nolegend_", **err_kws)
    else:
        if "alpha" not in err_kws:
            err_kws["alpha"] = .2
        ax.plot(x, data.T, color=color, label="_nolegend_", **err_kws)


def _plot_unit_points(ax, x, data, color, err_kws, **kwargs):
    """Plot each original data point discretely."""
    if isinstance(color, list):
        for i, obs in enumerate(data):
            ax.plot(x, obs, "o", color=color[i], alpha=0.8, markersize=4,
                    label="_nolegend_", **err_kws)
    else:
        ax.plot(x, data.T, "o", color=color, alpha=0.5, markersize=4,
                label="_nolegend_", **err_kws)


def _plot_boot_kde(ax, x, boot_data, color, **kwargs):
    """Plot the kernal density estimate of the bootstrap distribution."""
    kwargs.pop("data")
    _ts_kde(ax, x, boot_data, color, **kwargs)


def _plot_unit_kde(ax, x, data, color, **kwargs):
    """Plot the kernal density estimate over the sample."""
    _ts_kde(ax, x, data, color, **kwargs)


def _ts_kde(ax, x, data, color, **kwargs):
    """Upsample over time and plot a KDE of the bootstrap distribution."""
    kde_data = []
    y_min, y_max = data.min(), data.max()
    y_vals = np.linspace(y_min, y_max, 100)
    upsampler = interpolate.interp1d(x, data)
    data_upsample = upsampler(np.linspace(x.min(), x.max(), 100))
    for pt_data in data_upsample.T:
        pt_kde = stats.kde.gaussian_kde(pt_data)
        kde_data.append(pt_kde(y_vals))
    kde_data = np.transpose(kde_data)
    rgb = mpl.colors.ColorConverter().to_rgb(color)
    img = np.zeros((kde_data.shape[0], kde_data.shape[1], 4))
    img[:, :, :3] = rgb
    kde_data /= kde_data.max(axis=0)
    kde_data[kde_data > 1] = 1
    img[:, :, 3] = kde_data
    ax.imshow(img, interpolation="spline16", zorder=2,
              extent=(x.min(), x.max(), y_min, y_max),
              aspect="auto", origin="lower")