def plot_rsquare(self, ncomp=None, ax=None):
        """
        Box plots of the individual series R-square against the number of PCs

        Parameters
        ----------
        ncomp : int, optional
            Number of components ot include in the plot.  If None, will
            plot the minimum of 10 or the number of computed components
        ax : Matplotlib axes instance, optional
            An axes on which to draw the graph.  If omitted, new a figure
            is created

        Returns
        -------
        fig : figure
            Handle to the figure
        """
        import statsmodels.graphics.utils as gutils

        fig, ax = gutils.create_mpl_ax(ax)

        ncomp = 10 if ncomp is None else ncomp
        ncomp = min(ncomp, self._ncomp)
        # R2s in rows, series in columns
        r2s = 1.0 - self._ess_indiv / self._tss_indiv
        r2s = r2s[1:]
        r2s = r2s[:ncomp]
        ax.boxplot(r2s.T)
        ax.set_title('Individual Input $R^2$')
        ax.set_ylabel('$R^2$')
        ax.set_xlabel('Number of Included Principal Components')

        return fig

def _plot_leverage_resid2(results, influence, alpha=.05, ax=None,
                         **kwargs):

    from scipy.stats import zscore, norm
    fig, ax = utils.create_mpl_ax(ax)

    infl = influence
    leverage = infl.hat_matrix_diag
    resid = zscore(infl.resid)
    ax.plot(resid**2, leverage, 'o', **kwargs)
    ax.set_xlabel("Normalized residuals**2")
    ax.set_ylabel("Leverage")
    ax.set_title("Leverage vs. Normalized residuals squared")

    large_leverage = leverage > _high_leverage(results)
    #norm or t here if standardized?
    cutoff = norm.ppf(1.-alpha/2)
    large_resid = np.abs(resid) > cutoff
    labels = results.model.data.row_labels
    if labels is None:
        labels = lrange(int(results.nobs))
    index = np.where(np.logical_or(large_leverage, large_resid))[0]
    ax = utils.annotate_axes(index, labels, lzip(resid**2, leverage),
                             [(0, 5)]*int(results.nobs), "large",
                             ax=ax, ha="center", va="bottom")
    ax.margins(.075, .075)
    return fig

    def _plot_index(self, y, ylabel, threshold=None, title=None, ax=None,**kwds):
        from statsmodels.graphics import utils
        fig, ax = utils.create_mpl_ax(ax)
        if title is None:
            title = "Index Plot"
        nobs = len(self.endog)
        index = np.arange(nobs)
        ax.scatter(index, y, **kwds)

        if threshold == 'all':
            large_points = np.ones(nobs, np.bool_)
        else:
            large_points = np.abs(y) > threshold
        psize = 3 * np.ones(nobs)
        # add point labels
        labels = self.results.model.data.row_labels
        if labels is None:
            labels = np.arange(nobs)
        ax = utils.annotate_axes(np.where(large_points)[0], labels,
                                 lzip(index, y),
                                 lzip(-psize, psize), "large",
                                 ax)

        font = {"fontsize" : 16, "color" : "black"}
        ax.set_ylabel(ylabel, **font)
        ax.set_xlabel("Observation", **font)
        ax.set_title(title, **font)
        return fig

def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
    """
    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
    """
    fig, ax = utils.create_mpl_ax(ax)
    start = 0
    ticks = []
    for season, df in grouped_x:
        df = df.copy() # or sort balks for series. may be better way
        sort_values(df, inplace=True)
        nobs = len(df)
        x_plot = np.arange(start, start + nobs)
        ticks.append(x_plot.mean())
        ax.plot(x_plot, df.values, 'k')
        ax.hlines(df.values.mean(), x_plot[0], x_plot[-1], colors='k')
        start += nobs

    ax.set_xticks(ticks)
    ax.set_xticklabels(xticklabels)
    ax.set_ylabel(ylabel)
    ax.margins(.1, .05)
    return fig

def plot_ccpr(results, exog_idx, ax=None):
    """Plot CCPR against one regressor.

    Generates a CCPR (component and component-plus-residual) plot.

    Parameters
    ----------
    results : result instance
        A regression results instance.
    exog_idx : int or string
        Exogenous, explanatory variable. If string is given, it should
        be the variable name that you want to use, and you can use arbitrary
        translations as with a formula.
    ax : Matplotlib AxesSubplot instance, optional
        If given, it is used to plot in instead of a new figure being
        created.

    Returns
    -------
    fig : Matplotlib figure instance
        If `ax` is None, the created figure.  Otherwise the figure to which
        `ax` is connected.

    See Also
    --------
    plot_ccpr_grid : Creates CCPR plot for multiple regressors in a plot grid.

    Notes
    -----
    The CCPR plot provides a way to judge the effect of one regressor on the
    response variable by taking into account the effects of the other
    independent variables. The partial residuals plot is defined as
    Residuals + B_i*X_i versus X_i. The component adds the B_i*X_i versus
    X_i to show where the fitted line would lie. Care should be taken if X_i
    is highly correlated with any of the other independent variables. If this
    is the case, the variance evident in the plot will be an underestimate of
    the true variance.

    References
    ----------
    http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm
    """
    fig, ax = utils.create_mpl_ax(ax)

    exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model)

    x1 = results.model.exog[:, exog_idx]
    #namestr = ' for %s' % self.name if self.name else ''
    x1beta = x1*results._results.params[exog_idx]
    ax.plot(x1, x1beta + results.resid, 'o')
    from statsmodels.tools.tools import add_constant
    mod = OLS(x1beta, add_constant(x1)).fit()
    params = mod.params
    fig = abline_plot(*params, **dict(ax=ax))
    #ax.plot(x1, x1beta, '-')
    ax.set_title('Component and component plus residual plot')
    ax.set_ylabel("Residual + %s*beta_%d" % (exog_name, exog_idx))
    ax.set_xlabel("%s" % exog_name)

    return fig

def plot_added_variable(results, focus_exog, resid_type=None,
                        use_glm_weights=True, fit_kwargs=None, ax=None):
    # Docstring attached below

    model = results.model

    fig, ax = utils.create_mpl_ax(ax)

    endog_resid, focus_exog_resid =\
                 added_variable_resids(results, focus_exog,
                                       resid_type=resid_type,
                                       use_glm_weights=use_glm_weights,
                                       fit_kwargs=fit_kwargs)

    ax.plot(focus_exog_resid, endog_resid, 'o', alpha=0.6)

    ax.set_title('Added variable plot', fontsize='large')

    if type(focus_exog) is str:
        xname = focus_exog
    else:
        xname = model.exog_names[focus_exog]
    ax.set_xlabel(xname, size=15)
    ax.set_ylabel(model.endog_names + " residuals", size=15)

    return fig

    def plot_scree(self, ncomp=None, log_scale=True,
                   cumulative=False, ax=None):
        """
        Plot of the ordered eigenvalues

        Parameters
        ----------
        ncomp : int, optional
            Number of components ot include in the plot.  If None, will
            included the same as the number of components computed
        log_scale : boot, optional
            Flag indicating whether ot use a log scale for the y-axis
        cumulative : bool, optional
            Flag indicating whether to plot the eigenvalues or cumulative
            eigenvalues
        ax : Matplotlib axes instance, optional
            An axes on which to draw the graph.  If omitted, new a figure
            is created

        Returns
        -------
        fig : figure
            Handle to the figure
        """
        import statsmodels.graphics.utils as gutils

        fig, ax = gutils.create_mpl_ax(ax)

        ncomp = self._ncomp if ncomp is None else ncomp
        vals = np.asarray(self.eigenvals)
        vals = vals[:self._ncomp]
        if cumulative:
            vals = np.cumsum(vals)

        if log_scale:
            ax.set_yscale('log')
        ax.plot(np.arange(ncomp), vals[: ncomp], 'bo')
        ax.autoscale(tight=True)
        xlim = np.array(ax.get_xlim())
        sp = xlim[1] - xlim[0]
        xlim += 0.02 * np.array([-sp, sp])
        ax.set_xlim(xlim)

        ylim = np.array(ax.get_ylim())
        scale = 0.02
        if log_scale:
            sp = np.log(ylim[1] / ylim[0])
            ylim = np.exp(np.array([np.log(ylim[0]) - scale * sp,
                                    np.log(ylim[1]) + scale * sp]))
        else:
            sp = ylim[1] - ylim[0]
            ylim += scale * np.array([-sp, sp])
        ax.set_ylim(ylim)
        ax.set_title('Scree Plot')
        ax.set_ylabel('Eigenvalue')
        ax.set_xlabel('Component Number')
        fig.tight_layout()

        return fig

def plot_fit(results, exog_idx, y_true=None, ax=None, **kwargs):
    """Plot fit against one regressor.

    This creates one graph with the scatterplot of observed values compared to
    fitted values.

    Parameters
    ----------
    results : result instance
        result instance with resid, model.endog and model.exog as attributes
    x_var : int or str
        Name or index of regressor in exog matrix.
    y_true : array_like
        (optional) If this is not None, then the array is added to the plot
    ax : Matplotlib AxesSubplot instance, optional
        If given, this subplot is used to plot in instead of a new figure being
        created.
    kwargs
        The keyword arguments are passed to the plot command for the fitted
        values points.

    Returns
    -------
    fig : Matplotlib figure instance
        If `ax` is None, the created figure.  Otherwise the figure to which
        `ax` is connected.
    """
    fig, ax = utils.create_mpl_ax(ax)

    exog_name, exog_idx = utils.maybe_name_or_idx(exog_idx, results.model)
    results = maybe_unwrap_results(results)

    #maybe add option for wendog, wexog
    y = results.model.endog
    x1 = results.model.exog[:, exog_idx]
    x1_argsort = np.argsort(x1)
    y = y[x1_argsort]
    x1 = x1[x1_argsort]

    ax.plot(x1, y, 'bo', label=results.model.endog_names)
    if not y_true is None:
        ax.plot(x1, y_true[x1_argsort], 'b-', label='True values')
    title = 'Fitted values versus %s' % exog_name

    prstd, iv_l, iv_u = wls_prediction_std(results)
    ax.plot(x1, results.fittedvalues[x1_argsort], 'D', color='r',
            label='fitted', **kwargs)
    ax.vlines(x1, iv_l[x1_argsort], iv_u[x1_argsort], linewidth=1, color='k',
            alpha=.7)
    #ax.fill_between(x1, iv_l[x1_argsort], iv_u[x1_argsort], alpha=0.1,
    #                    color='k')
    ax.set_title(title)
    ax.set_xlabel(exog_name)
    ax.set_ylabel(results.model.endog_names)
    ax.legend(loc='best')

    return fig

def plot_fit(res, exog_idx, exog_name='', y_true=None, ax=None, fontsize='small'):
    """Plot fit against one regressor.

    This creates one graph with the scatterplot of observed values compared to
    fitted values.

    Parameters
    ----------
    res : result instance
        result instance with resid, model.endog and model.exog as attributes
    exog_idx : int
        index of regressor in exog matrix
    y_true : array_like
        (optional) If this is not None, then the array is added to the plot
    ax : Matplotlib AxesSubplot instance, optional
        If given, this subplot is used to plot in instead of a new figure being
        created.

    Returns
    -------
    fig : Matplotlib figure instance
        If `ax` is None, the created figure.  Otherwise the figure to which
        `ax` is connected.

    Notes
    -----
    This is currently very simple, no options or varnames yet.

    """
    fig, ax = utils.create_mpl_ax(ax)

    if exog_name == '':
        exog_name = 'variable %d' % exog_idx

    #maybe add option for wendog, wexog
    y = res.model.endog
    x1 = res.model.exog[:, exog_idx]
    x1_argsort = np.argsort(x1)
    y = y[x1_argsort]
    x1 = x1[x1_argsort]

    ax.plot(x1, y, 'bo', label='observed')
    if not y_true is None:
        ax.plot(x1, y_true[x1_argsort], 'b-', label='true')
        title = 'fitted versus regressor %s' % exog_name
    else:
        title = 'fitted versus regressor %s' % exog_name

    prstd, iv_l, iv_u = wls_prediction_std(res)
    ax.plot(x1, res.fittedvalues[x1_argsort], 'k-', label='fitted') #'k-o')
    #ax.plot(x1, iv_u, 'r--')
    #ax.plot(x1, iv_l, 'r--')
    ax.fill_between(x1, iv_l[x1_argsort], iv_u[x1_argsort], alpha=0.1, color='k')
    ax.set_title(title, fontsize=fontsize)

    return fig

    def plot_partial(self, smooth_index, plot_se=True, cpr=False,
                     include_constant=True, ax=None):
        """plot the contribution of a smooth term to the linear prediction

        Parameters
        ----------
        smooth_index : int
            index of the smooth term within list of smooth terms
        plot_se : book
            If plot_se is true, then the confidence interval for the linear
            prediction will be added to the plot.
        cpr : bool
            If cpr (component plus residual) is true, the a scatter plot of
            the partial working residuals will be added to the plot.
        include_constant : bool
            If true, then the estimated intercept is added to the prediction
            and its standard errors. This avoids that the confidence interval
            has zero width at the imposed identification constraint, e.g.
            either at a reference point or at the mean.
        ax : None or matplotlib axis instance
           If ax is not None, then the plot will be added to it.

        Returns
        -------
        fig : matplotlib Figure instance

        """
        from statsmodels.graphics.utils import _import_mpl, create_mpl_ax
        _import_mpl()

        variable = smooth_index
        y_est, se = self.partial_values(variable,
                                        include_constant=include_constant)
        smoother = self.model.smoother
        x = smoother.smoothers[variable].x
        sort_index = np.argsort(x)
        x = x[sort_index]
        y_est = y_est[sort_index]
        se = se[sort_index]

        fig, ax = create_mpl_ax(ax)
        ax.plot(x, y_est, c='blue', lw=2)
        if plot_se:
            ax.plot(x, y_est + 1.96 * se, '-', c='blue')
            ax.plot(x, y_est - 1.96 * se, '-', c='blue')
        if cpr:
            # TODO: resid_response doesn't make sense with nonlinear link
            # use resid_working ?
            cpr_ = y_est + self.resid_working
            ax.plot(x, cpr_, '.', lw=2)

        ax.set_xlabel(smoother.smoothers[variable].variable_name)

        return fig

def _influence_plot(results, influence, external=True, alpha=.05,
                    criterion="cooks", size=48, plot_alpha=.75, ax=None,
                    **kwargs):
    infl = influence
    fig, ax = utils.create_mpl_ax(ax)

    if criterion.lower().startswith('coo'):
        psize = infl.cooks_distance[0]
    elif criterion.lower().startswith('dff'):
        psize = np.abs(infl.dffits[0])
    else:
        raise ValueError("Criterion %s not understood" % criterion)

    # scale the variables
    #TODO: what is the correct scaling and the assumption here?
    #we want plots to be comparable across different plots
    #so we would need to use the expected distribution of criterion probably
    old_range = np.ptp(psize)
    new_range = size**2 - 8**2

    psize = (psize - psize.min()) * new_range/old_range + 8**2

    leverage = infl.hat_matrix_diag
    if external:
        resids = infl.resid_studentized_external
    else:
        resids = infl.resid_studentized

    from scipy import stats

    cutoff = stats.t.ppf(1.-alpha/2, results.df_resid)
    large_resid = np.abs(resids) > cutoff
    large_leverage = leverage > _high_leverage(results)
    large_points = np.logical_or(large_resid, large_leverage)

    ax.scatter(leverage, resids, s=psize, alpha=plot_alpha)

    # add point labels
    labels = results.model.data.row_labels
    if labels is None:
        labels = lrange(len(resids))
    ax = utils.annotate_axes(np.where(large_points)[0], labels,
                             lzip(leverage, resids),
                             lzip(-(psize/2)**.5, (psize/2)**.5), "x-large",
                             ax)

    #TODO: make configurable or let people do it ex-post?
    font = {"fontsize" : 16, "color" : "black"}
    ax.set_ylabel("Studentized Residuals", **font)
    ax.set_xlabel("H Leverage", **font)
    ax.set_title("Influence Plot", **font)
    return fig

    def plot_contour(self, mu_low, mu_high, var_low, var_high, mu_step,
                        var_step,
                        levs=[.2, .1, .05, .01, .001]):
        """
        Returns a plot of the confidence region for a univariate
        mean and variance.

        Parameters
        ----------
        mu_low : float
            Lowest value of the mean to plot

        mu_high : float
            Highest value of the mean to plot

        var_low : float
            Lowest value of the variance to plot

        var_high : float
            Highest value of the variance to plot

        mu_step : float
            Increments to evaluate the mean

        var_step : float
            Increments to evaluate the mean

        levs : list
            Which values of significance the contour lines will be drawn.
            Default is [.2, .1, .05, .01, .001]

        Returns
        -------
        fig : matplotlib figure instance
            The contour plot
        """
        fig, ax = utils.create_mpl_ax()
        ax.set_ylabel('Variance')
        ax.set_xlabel('Mean')
        mu_vect = list(np.arange(mu_low, mu_high, mu_step))
        var_vect = list(np.arange(var_low, var_high, var_step))
        z = []
        for sig0 in var_vect:
            self.sig2_0 = sig0
            for mu0 in mu_vect:
                z.append(self._opt_var(mu0, pval=True))
        z = np.asarray(z).reshape(len(var_vect), len(mu_vect))
        ax.contour(mu_vect, var_vect, z, levels=levs)
        return fig

def plot_leverage_resid2(results, alpha=.05, label_kwargs={}, ax=None,
                         **kwargs):
    """
    Plots leverage statistics vs. normalized residuals squared

    Parameters
    ----------
    results : results instance
        A regression results instance
    alpha : float
        Specifies the cut-off for large-standardized residuals. Residuals
        are assumed to be distributed N(0, 1) with alpha=alpha.
    label_kwargs : dict
        The keywords to pass to annotate for the labels.
    ax : Axes instance
        Matplotlib Axes instance

    Returns
    -------
    fig : matplotlib Figure
        A matplotlib figure instance.
    """
    from scipy.stats import zscore, norm, t
    fig, ax = utils.create_mpl_ax(ax)

    infl = results.get_influence()
    leverage = infl.hat_matrix_diag
    resid = zscore(results.resid)
    ax.plot(resid**2, leverage, 'o', **kwargs)
    ax.set_xlabel("Normalized residuals**2")
    ax.set_ylabel("Leverage")
    ax.set_title("Leverage vs. Normalized residuals squared")

    large_leverage = leverage > _high_leverage(results)
    #norm or t here if standardized?
    cutoff = norm.ppf(1.-alpha/2)
    large_resid = np.abs(resid) > cutoff
    labels = results.model.data.row_labels
    if labels is None:
        labels = range(results.nobs)
    index = np.where(np.logical_or(large_leverage, large_resid))[0]
    ax = utils.annotate_axes(index, labels, zip(resid**2, leverage),
                             [(0, 5)]*int(results.nobs), "large",
                             ax=ax, ha="center", va="bottom")
    ax.margins(.075, .075)
    return fig

def plot_ccpr_ax(res, exog_idx=None, ax=None):
    """Plot CCPR against one regressor.

    Generates a CCPR (component and component-plus-residual) plot.

    Parameters
    ----------
    res : result instance
        uses exog and params of the result instance
    exog_idx : int
        (column) index of the exog used in the plot
    ax : Matplotlib AxesSubplot instance, optional
        If given, this subplot is used to plot in instead of a new figure being
        created.

    Returns
    -------
    fig : Matplotlib figure instance
        If `ax` is None, the created figure.  Otherwise the figure to which
        `ax` is connected.

    See Also
    --------
    plot_ccpr : Creates CCPR plot for multiple regressors in a plot grid.

    References
    ----------
    See http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm

    """
    fig, ax = utils.create_mpl_ax(ax)

    x1 = res.model.exog[:,exog_idx]
    #namestr = ' for %s' % self.name if self.name else ''
    x1beta = x1*res.params[1]
    ax.plot(x1, x1beta + res.resid, 'o')
    ax.plot(x1, x1beta, '-')
    ax.set_title('X_%d beta_%d plus residuals versus exog (CCPR)' % \
                                                (exog_idx, exog_idx))

    return fig

def plot_partregress_ax(endog, exog_i, exog_others, varname='',
                        title_fontsize=None, ax=None):
    """Plot partial regression for a single regressor.

    Parameters
    ----------
    endog : ndarray
       endogenous or response variable
    exog_i : ndarray
        exogenous, explanatory variable
    exog_others : ndarray
        other exogenous, explanatory variables, the effect of these variables
        will be removed by OLS regression
    varname : str
        name of the variable used in the title
    ax : Matplotlib AxesSubplot instance, optional
        If given, this subplot is used to plot in instead of a new figure being
        created.

    Returns
    -------
    fig : Matplotlib figure instance
        If `ax` is None, the created figure.  Otherwise the figure to which
        `ax` is connected.

    See Also
    --------
    plot_partregress : Plot partial regression for a set of regressors.

    """
    fig, ax = utils.create_mpl_ax(ax)

    res1a = OLS(endog, exog_others).fit()
    res1b = OLS(exog_i, exog_others).fit()
    ax.plot(res1b.resid, res1a.resid, 'o')
    res1c = OLS(res1a.resid, res1b.resid).fit()
    ax.plot(res1b.resid, res1c.fittedvalues, '-', color='k')
    ax.set_title('Partial Regression plot %s' % varname,
                 fontsize=title_fontsize)# + namestr)

    return fig

def plot_ceres_residuals(results, focus_exog, frac=0.66, cond_means=None,
               ax=None):
    # Docstring attached below

    model = results.model

    focus_exog, focus_col = utils.maybe_name_or_idx(focus_exog, model)

    presid = ceres_resids(results, focus_exog, frac=frac,
                          cond_means=cond_means)

    focus_exog_vals = model.exog[:, focus_col]

    fig, ax = utils.create_mpl_ax(ax)
    ax.plot(focus_exog_vals, presid, 'o', alpha=0.6)

    ax.set_title('CERES residuals plot', fontsize='large')

    ax.set_xlabel(focus_exog, size=15)
    ax.set_ylabel("Component plus residual", size=15)

    return fig

def plot_partial_residuals(results, focus_exog, ax=None):
    # Docstring attached below

    model = results.model

    focus_exog, focus_col = utils.maybe_name_or_idx(focus_exog, model)

    pr = partial_resids(results, focus_exog)
    focus_exog_vals = results.model.exog[:, focus_col]

    fig, ax = utils.create_mpl_ax(ax)
    ax.plot(focus_exog_vals, pr, 'o', alpha=0.6)

    ax.set_title('Partial residuals plot', fontsize='large')

    if type(focus_exog) is str:
        xname = focus_exog
    else:
        xname = model.exog_names[focus_exog]
    ax.set_xlabel(xname, size=15)
    ax.set_ylabel("Component plus residual", size=15)

    return fig

def seasonal_plot(grouped_x, xticklabels, ylabel=None, ax=None):
    """
    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.
    """
    fig, ax = utils.create_mpl_ax(ax)
    start = 0
    ticks = []
    for season, df in grouped_x:
        df = df.copy()  # or sort balks for series. may be better way
        df.sort_index()
        nobs = len(df)
        x_plot = np.arange(start, start + nobs)
        ticks.append(x_plot.mean())
        ax.plot(x_plot, df.values, 'k')
        ax.hlines(df.values.mean(), x_plot[0], x_plot[-1], colors='r',
                  linewidth=3)
        start += nobs

    ax.set_xticks(ticks)
    ax.set_xticklabels(xticklabels)
    ax.set_ylabel(ylabel)
    ax.margins(.1, .05)
    return fig

def plot_pacf(x, ax=None, lags=None, alpha=.05, method='ywm', use_vlines=True,
              title='Partial Autocorrelation', zero=True, **kwargs):
    """Plot the partial 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
        1/sqrt(len(x))
    method : 'ywunbiased' (default) or 'ywmle' or 'ols'
        specifies which method for the calculations to use:

        - yw or ywunbiased : yule walker with bias correction in denominator
          for acovf
        - 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.
    **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
    mpl_examples/pylab_examples/xcorr_demo.py

    Notes
    -----
    Adapted from matplotlib's `xcorr`.

    Data are plotted as ``plot(lags, corr, **kwargs)``

    """
    fig, ax = utils.create_mpl_ax(ax)

    lags, nlags, irregular = _prepare_data_corr_plot(x, lags, zero)

    confint = None
    if alpha is None:
        acf_x = pacf(x, nlags=nlags, alpha=alpha, method=method)
    else:
        acf_x, confint = pacf(x, nlags=nlags, alpha=alpha, method=method)

    _plot_corr(ax, title, acf_x, confint, lags, irregular, use_vlines, **kwargs)

    return fig

#.........这里部分代码省略.........
        Michael Friendly, York University, Psychology Department
        Journal of the american statistical association
        March 1994, Vol. 89, No. 425, Theory and Methods

    Examples
    ----------
    The most simple use case is to take a dictionary and plot the result

    >>> data = {'a': 10, 'b': 15, 'c': 16}
    >>> mosaic(data, title='basic dictionary')
    >>> pylab.show()

    A more useful example is given by a dictionary with multiple indices.
    In this case we use a wider gap to a better visual separation of the
    resulting plot

    >>> data = {('a', 'b'): 1, ('a', 'c'): 2, ('d', 'b'): 3, ('d', 'c'): 4}
    >>> mosaic(data, gap=0.05, title='complete dictionary')
    >>> pylab.show()

    The same data can be given as a simple or hierarchical indexed Series

    >>> rand = np.random.random
    >>> from itertools import product
    >>>
    >>> tuples = list(product(['bar', 'baz', 'foo', 'qux'], ['one', 'two']))
    >>> index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
    >>> data = pd.Series(rand(8), index=index)
    >>> mosaic(data, title='hierarchical index series')
    >>> pylab.show()

    The third accepted data structureis the np array, for which a
    very simple index will be created.

    >>> rand = np.random.random
    >>> data = 1+rand((2,2))
    >>> mosaic(data, title='random non-labeled array')
    >>> pylab.show()

    If you need to modify the labeling and the coloring you can give
    a function tocreate the labels and one with the graphical properties
    starting from the key tuple

    >>> data = {'a': 10, 'b': 15, 'c': 16}
    >>> props = lambda key: {'color': 'r' if 'a' in key else 'gray'}
    >>> labelizer = lambda k: {('a',): 'first', ('b',): 'second',
                               ('c',): 'third'}[k]
    >>> mosaic(data, title='colored dictionary',
                properties=props, labelizer=labelizer)
    >>> pylab.show()

    Using a DataFrame as source, specifying the name of the columns of interest
    >>> gender = ['male', 'male', 'male', 'female', 'female', 'female']
    >>> pet = ['cat', 'dog', 'dog', 'cat', 'dog', 'cat']
    >>> data = pandas.DataFrame({'gender': gender, 'pet': pet})
    >>> mosaic(data, ['pet', 'gender'])
    >>> pylab.show()
    """
    from pylab import Rectangle
    fig, ax = utils.create_mpl_ax(ax)
    # normalize the data to a dict with tuple of strings as keys
    data = _normalize_data(data, index)
    # split the graph into different areas
    rects = _hierarchical_split(data, horizontal=horizontal, gap=gap)
    # if there is no specified way to create the labels
    # create a default one
    if labelizer is None:
        labelizer = lambda k: "\n".join(k)
    if statistic:
        default_props = _statistical_coloring(data)
    else:
        default_props = _create_default_properties(data)
    if isinstance(properties, dict):
        color_dict = properties
        properties = lambda key: color_dict.get(key, None)
    for k, v in list(rects.items()):
        # create each rectangle and put a label on it
        x, y, w, h = v
        conf = properties(k)
        props = conf if conf else default_props[k]
        text = labelizer(k)
        Rect = Rectangle((x, y), w, h, label=text, **props)
        ax.add_patch(Rect)
        ax.text(x + w / 2, y + h / 2, text, ha='center',
                 va='center', size='smaller')
    #creating the labels on the axis
    #o clearing it
    if axes_label:
        if np.iterable(label_rotation):
            rotation = label_rotation
        else:
            rotation = [label_rotation] * 4
        labels = _create_labels(rects, horizontal, ax, rotation)
    else:
        ax.set_xticks([])
        ax.set_xticklabels([])
        ax.set_yticks([])
        ax.set_yticklabels([])
    ax.set_title(title)
    return fig, rects