Plots a pretty figure of {0}.{1}

        Matplotlib plot arguments can be passed in inside the kwargs, plus

        Parameters
        -----------
        show_censors: bool
            place markers at censorship events. Default: False
        censor_styles: bool
            If show_censors, this dictionary will be passed into the plot call.
        ci_alpha: bool
            the transparency level of the confidence interval. Default: 0.3
        ci_force_lines: bool
            force the confidence intervals to be line plots (versus default shaded areas). Default: False
        ci_show: bool
            show confidence intervals. Default: True
        ci_legend: bool
            if ci_force_lines is True, this is a boolean flag to add the lines' labels to the legend. Default: False
        at_risk_counts: bool
            show group sizes at time points. See function ``add_at_risk_counts`` for details. Default: False
        loc: slice
            specify a time-based subsection of the curves to plot, ex:

            >>> model.plot(loc=slice(0.,10.))

            will plot the time values between t=0. and t=10.
        iloc: slice
            specify a location-based subsection of the curves to plot, ex:

            >>> model.plot(iloc=slice(0,10))

            will plot the first 10 time points.
        invert_y_axis: bool
            boolean to invert the y-axis, useful to show cumulative graphs instead of survival graphs. (Deprecated, use ``plot_cumulative_density()``)

        Returns
        -------
        ax:
            a pyplot axis object

def plot_cumulative_density(self, **kwargs):
        """
        Plots a pretty figure of {0}.{1}

        Matplotlib plot arguments can be passed in inside the kwargs, plus

        Parameters
        -----------
        show_censors: bool
            place markers at censorship events. Default: False
        censor_styles: bool
            If show_censors, this dictionary will be passed into the plot call.
        ci_alpha: bool
            the transparency level of the confidence interval. Default: 0.3
        ci_force_lines: bool
            force the confidence intervals to be line plots (versus default shaded areas). Default: False
        ci_show: bool
            show confidence intervals. Default: True
        ci_legend: bool
            if ci_force_lines is True, this is a boolean flag to add the lines' labels to the legend. Default: False
        at_risk_counts: bool
            show group sizes at time points. See function ``add_at_risk_counts`` for details. Default: False
        loc: slice
            specify a time-based subsection of the curves to plot, ex:

            >>> model.plot(loc=slice(0.,10.))

            will plot the time values between t=0. and t=10.
        iloc: slice
            specify a location-based subsection of the curves to plot, ex:

            >>> model.plot(iloc=slice(0,10))

            will plot the first 10 time points.
        invert_y_axis: bool
            boolean to invert the y-axis, useful to show cumulative graphs instead of survival graphs. (Deprecated, use ``plot_cumulative_density()``)

        Returns
        -------
        ax:
            a pyplot axis object
        """
        return _plot_estimate(
            self,
            estimate=self.cumulative_density_,
            confidence_intervals=self.confidence_interval_cumulative_density_,
            **kwargs
        )