python类axes()的实例源码

def _flatten(axes):
    if not com.is_list_like(axes):
        return np.array([axes])
    elif isinstance(axes, (np.ndarray, Index)):
        return axes.ravel()
    return np.array(axes)

def _set_ticks_props(axes, xlabelsize=None, xrot=None,
                     ylabelsize=None, yrot=None):
    import matplotlib.pyplot as plt

    for ax in _flatten(axes):
        if xlabelsize is not None:
            plt.setp(ax.get_xticklabels(), fontsize=xlabelsize)
        if xrot is not None:
            plt.setp(ax.get_xticklabels(), rotation=xrot)
        if ylabelsize is not None:
            plt.setp(ax.get_yticklabels(), fontsize=ylabelsize)
        if yrot is not None:
            plt.setp(ax.get_yticklabels(), rotation=yrot)
    return axes

def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
               **kwds):
        """
        Hexbin plot

        .. versionadded:: 0.17.0

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        C : label or position, optional
            The value at each `(x, y)` point.
        reduce_C_function : callable, optional
            Function of one argument that reduces all the values in a bin to
            a single number (e.g. `mean`, `max`, `sum`, `std`).
        gridsize : int, optional
            Number of bins.
        **kwds : optional
            Keyword arguments to pass on to :py:meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot or np.array of them
        """
        if reduce_C_function is not None:
            kwds['reduce_C_function'] = reduce_C_function
        if gridsize is not None:
            kwds['gridsize'] = gridsize
        return self(kind='hexbin', x=x, y=y, C=C, **kwds)

def rplot_ax(self, obj, *args, **kwargs):
    return obj.__plot__(*args, axes=self, **kwargs)

def rerrorbar_ax(self, obj, *args, **kwargs):
    return obj.__errorbar__(*args, axes=self, **kwargs)

def rtext_ax(self, obj, *args, **kwargs):
    return obj.__text__(*args, axes=self, **kwargs)

def rhist_ax(self, obj, *args, **kwargs):
    return draw_th1.hist(obj, *args, axes=self, **kwargs)

def rcontour_ax(self, obj, *args, **kwargs):
    return draw_th2.contour(obj, *args, axes=self, **kwargs)

def rpcolor_ax(self, obj, *args, **kwargs):
    return draw_th2.pcolor(obj, *args, axes=self, **kwargs)

def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
    """Bootstrap plot.

    Parameters:
    -----------
    series: Time series
    fig: matplotlib figure object, optional
    size: number of data points to consider during each sampling
    samples: number of times the bootstrap procedure is performed
    kwds: optional keyword arguments for plotting commands, must be accepted
        by both hist and plot

    Returns:
    --------
    fig: matplotlib figure
    """
    import random
    import matplotlib.pyplot as plt

    # random.sample(ndarray, int) fails on python 3.3, sigh
    data = list(series.values)
    samplings = [random.sample(data, size) for _ in range(samples)]

    means = np.array([np.mean(sampling) for sampling in samplings])
    medians = np.array([np.median(sampling) for sampling in samplings])
    midranges = np.array([(min(sampling) + max(sampling)) * 0.5
                          for sampling in samplings])
    if fig is None:
        fig = plt.figure()
    x = lrange(samples)
    axes = []
    ax1 = fig.add_subplot(2, 3, 1)
    ax1.set_xlabel("Sample")
    axes.append(ax1)
    ax1.plot(x, means, **kwds)
    ax2 = fig.add_subplot(2, 3, 2)
    ax2.set_xlabel("Sample")
    axes.append(ax2)
    ax2.plot(x, medians, **kwds)
    ax3 = fig.add_subplot(2, 3, 3)
    ax3.set_xlabel("Sample")
    axes.append(ax3)
    ax3.plot(x, midranges, **kwds)
    ax4 = fig.add_subplot(2, 3, 4)
    ax4.set_xlabel("Mean")
    axes.append(ax4)
    ax4.hist(means, **kwds)
    ax5 = fig.add_subplot(2, 3, 5)
    ax5.set_xlabel("Median")
    axes.append(ax5)
    ax5.hist(medians, **kwds)
    ax6 = fig.add_subplot(2, 3, 6)
    ax6.set_xlabel("Midrange")
    axes.append(ax6)
    ax6.hist(midranges, **kwds)
    for axis in axes:
        plt.setp(axis.get_xticklabels(), fontsize=8)
        plt.setp(axis.get_yticklabels(), fontsize=8)
    return fig

def _make_plot(self):
        x, y, c, data = self.x, self.y, self.c, self.data
        ax = self.axes[0]

        c_is_column = com.is_hashable(c) and c in self.data.columns

        # plot a colorbar only if a colormap is provided or necessary
        cb = self.kwds.pop('colorbar', self.colormap or c_is_column)

        # pandas uses colormap, matplotlib uses cmap.
        cmap = self.colormap or 'Greys'
        cmap = self.plt.cm.get_cmap(cmap)
        color = self.kwds.pop("color", None)
        if c is not None and color is not None:
            raise TypeError('Specify exactly one of `c` and `color`')
        elif c is None and color is None:
            c_values = self.plt.rcParams['patch.facecolor']
        elif color is not None:
            c_values = color
        elif c_is_column:
            c_values = self.data[c].values
        else:
            c_values = c

        if self.legend and hasattr(self, 'label'):
            label = self.label
        else:
            label = None
        scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
                             label=label, cmap=cmap, **self.kwds)
        if cb:
            img = ax.collections[0]
            kws = dict(ax=ax)
            if self.mpl_ge_1_3_1():
                kws['label'] = c if c_is_column else ''
            self.fig.colorbar(img, **kws)

        if label is not None:
            self._add_legend_handle(scatter, label)
        else:
            self.legend = False

        errors_x = self._get_errorbars(label=x, index=0, yerr=False)
        errors_y = self._get_errorbars(label=y, index=0, xerr=False)
        if len(errors_x) > 0 or len(errors_y) > 0:
            err_kwds = dict(errors_x, **errors_y)
            err_kwds['ecolor'] = scatter.get_facecolor()[0]
            ax.errorbar(data[x].values, data[y].values,
                        linestyle='none', **err_kwds)