Python pyplot.draw_if_interactive方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def host_axes(*args, axes_class=None, figure=None, **kwargs):
    """
    Create axes that can act as a hosts to parasitic axes.

    Parameters
    ----------
    figure : `matplotlib.figure.Figure`
        Figure to which the axes will be added. Defaults to the current figure
        `pyplot.gcf()`.

    *args, **kwargs :
        Will be passed on to the underlying ``Axes`` object creation.
    """
    import matplotlib.pyplot as plt
    host_axes_class = host_axes_class_factory(axes_class)
    if figure is None:
        figure = plt.gcf()
    ax = host_axes_class(figure, *args, **kwargs)
    figure.add_axes(ax)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def host_subplot(*args, axes_class=None, figure=None, **kwargs):
    """
    Create a subplot that can act as a host to parasitic axes.

    Parameters
    ----------
    figure : `matplotlib.figure.Figure`
        Figure to which the subplot will be added. Defaults to the current
        figure `pyplot.gcf()`.

    *args, **kwargs :
        Will be passed on to the underlying ``Axes`` object creation.
    """
    import matplotlib.pyplot as plt
    host_subplot_class = host_subplot_class_factory(axes_class)
    if figure is None:
        figure = plt.gcf()
    ax = host_subplot_class(figure, *args, **kwargs)
    figure.add_subplot(ax)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def host_axes(*args, axes_class=None, figure=None, **kwargs):
    """
    Create axes that can act as a hosts to parasitic axes.

    Parameters
    ----------
    figure : `matplotlib.figure.Figure`
        Figure to which the axes will be added. Defaults to the current figure
        `pyplot.gcf()`.

    *args, **kwargs
        Will be passed on to the underlying ``Axes`` object creation.
    """
    import matplotlib.pyplot as plt
    host_axes_class = host_axes_class_factory(axes_class)
    if figure is None:
        figure = plt.gcf()
    ax = host_axes_class(figure, *args, **kwargs)
    figure.add_axes(ax)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def host_subplot(*args, axes_class=None, figure=None, **kwargs):
    """
    Create a subplot that can act as a host to parasitic axes.

    Parameters
    ----------
    figure : `matplotlib.figure.Figure`
        Figure to which the subplot will be added. Defaults to the current
        figure `pyplot.gcf()`.

    *args, **kwargs
        Will be passed on to the underlying ``Axes`` object creation.
    """
    import matplotlib.pyplot as plt
    host_subplot_class = host_subplot_class_factory(axes_class)
    if figure is None:
        figure = plt.gcf()
    ax = host_subplot_class(figure, *args, **kwargs)
    figure.add_subplot(ax)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def draw(self):
        self.plt.draw_if_interactive() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def boxplot_frame(self, column=None, by=None, ax=None, fontsize=None, rot=0,
                  grid=True, figsize=None, layout=None,
                  return_type=None, **kwds):
    import matplotlib.pyplot as plt
    _converter._WARN = False
    ax = boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize,
                 grid=grid, rot=rot, figsize=figsize, layout=layout,
                 return_type=return_type, **kwds)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def plot_forecast(self, steps=1, figsize=(10, 10)):
        """
        Plot h-step ahead forecasts against actual realizations of time
        series. Note that forecasts are lined up with their respective
        realizations.

        Parameters
        ----------
        steps :
        """
        import matplotlib.pyplot as plt

        fig, axes = plt.subplots(figsize=figsize, nrows=self.neqs,
                                 sharex=True)

        forc = self.forecast(steps=steps)
        dates = forc.index

        y_overlay = self.y.reindex(dates)

        for i, col in enumerate(forc.columns):
            ax = axes[i]

            y_ts = y_overlay[col]
            forc_ts = forc[col]

            y_handle = ax.plot(dates, y_ts.values, 'k.', ms=2)
            forc_handle = ax.plot(dates, forc_ts.values, 'k-')

        lines = (y_handle[0], forc_handle[0])
        labels = ('Y', 'Forecast')
        fig.legend(lines, labels)
        fig.autofmt_xdate()

        fig.suptitle('Dynamic %d-step forecast' % steps)

        # pretty things up a bit
        plotting.adjust_subplots(bottom=0.15, left=0.10)
        plt.draw_if_interactive() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def __setstate__(self, state):
        version = state.pop('__mpl_version__')
        restore_to_pylab = state.pop('_restore_to_pylab', False)

        if version != _mpl_version:
            import warnings
            warnings.warn("This figure was saved with matplotlib version %s "
                          "and is unlikely to function correctly." %
                          (version, ))

        self.__dict__ = state

        # re-initialise some of the unstored state information
        self._axobservers = []
        self.canvas = None

        if restore_to_pylab:
            # lazy import to avoid circularity
            import matplotlib.pyplot as plt
            import matplotlib._pylab_helpers as pylab_helpers
            allnums = plt.get_fignums()
            num = max(allnums) + 1 if allnums else 1
            mgr = plt._backend_mod.new_figure_manager_given_figure(num, self)

            # XXX The following is a copy and paste from pyplot. Consider
            # factoring to pylab_helpers

            if self.get_label():
                mgr.set_window_title(self.get_label())

            # make this figure current on button press event
            def make_active(event):
                pylab_helpers.Gcf.set_active(mgr)

            mgr._cidgcf = mgr.canvas.mpl_connect('button_press_event',
                                                 make_active)

            pylab_helpers.Gcf.set_active(mgr)
            self.number = num

            plt.draw_if_interactive() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def boxplot(self, column=None, by=None, ax=None, fontsize=None,
            rot=0, grid=True, **kwds):
    """
    Make a box plot from DataFrame column/columns optionally grouped
    (stratified) by one or more columns

    Parameters
    ----------
    data : DataFrame
    column : column names or list of names, or vector
        Can be any valid input to groupby
    by : string or sequence
        Column in the DataFrame to group by
    ax : matplotlib axis object, default None
    fontsize : int or string
    rot : int, default None
        Rotation for ticks
    grid : boolean, default None (matlab style default)
        Axis grid lines

    Returns
    -------
    ax : matplotlib.axes.AxesSubplot
    """
    import pandas.tools.plotting as plots
    import matplotlib.pyplot as plt
    ax = plots.boxplot(self, column=column, by=by, ax=ax,
                       fontsize=fontsize, grid=grid, rot=rot, **kwds)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def host_axes(*args, **kwargs):
    import matplotlib.pyplot as plt
    axes_class = kwargs.pop("axes_class", None)
    host_axes_class = host_axes_class_factory(axes_class)
    fig = plt.gcf()
    ax = host_axes_class(fig, *args, **kwargs)
    fig.add_axes(ax)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def host_subplot(*args, **kwargs):
    import matplotlib.pyplot as plt
    axes_class = kwargs.pop("axes_class", None)
    host_subplot_class = host_subplot_class_factory(axes_class)
    fig = plt.gcf()
    ax = host_subplot_class(fig, *args, **kwargs)
    fig.add_subplot(ax)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def plot_objectives(self, ax=None, title=None, **kwargs):
        """
        Plots the collected objective values at each iteration.

        Parameters
        ----------
        ax : Axes
            Optional axes argument. If not passed, a new figure and axes are
            constructed.
        title : str
            Optional title argument. When not passed, a default is set.
        kwargs : dict
            Optional arguments passed to ``ax.plot``.
        """
        if ax is None:
            _, ax = plt.subplots()

        if title is None:
            title = "Objective value at each iteration"

        # First call is current solution objectives (at each iteration), second
        # call is the best solution found so far (as a running minimum).
        ax.plot(self.statistics.objectives, **kwargs)
        ax.plot(np.minimum.accumulate(self.statistics.objectives), **kwargs)

        ax.set_title(title)
        ax.set_ylabel("Objective value")
        ax.set_xlabel("Iteration (#)")

        ax.legend(["Current", "Best"], loc="upper right")

        plt.draw_if_interactive() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def plot_forecast(self, steps=1, figsize=(10, 10)):
        """
        Plot h-step ahead forecasts against actual realizations of time
        series. Note that forecasts are lined up with their respective
        realizations.

        Parameters
        ----------
        steps :
        """
        import matplotlib.pyplot as plt

        fig, axes = plt.subplots(figsize=figsize, nrows=self.neqs,
                                 sharex=True)

        forc = self.forecast(steps=steps)
        dates = forc.index

        y_overlay = self.y.reindex(dates)

        for i, col in enumerate(forc.columns):
            ax = axes[i]

            y_ts = y_overlay[col]
            forc_ts = forc[col]

            y_handle = ax.plot(dates, y_ts.values, 'k.', ms=2)
            forc_handle = ax.plot(dates, forc_ts.values, 'k-')

        lines = (y_handle[0], forc_handle[0])
        labels =  ('Y', 'Forecast')
        fig.legend(lines,labels)
        fig.autofmt_xdate()

        fig.suptitle('Dynamic %d-step forecast' % steps)

        # pretty things up a bit
        plotting.adjust_subplots(bottom=0.15, left=0.10)
        plt.draw_if_interactive() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def boxplot_frame(self, column=None, by=None, ax=None, fontsize=None, rot=0,
                  grid=True, figsize=None, layout=None,
                  return_type=None, **kwds):
    import matplotlib.pyplot as plt
    _setup()
    ax = boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize,
                 grid=grid, rot=rot, figsize=figsize, layout=layout,
                 return_type=return_type, **kwds)
    plt.draw_if_interactive()
    return ax 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import draw_if_interactive [as 別名]
def __setstate__(self, state):
        version = state.pop('__mpl_version__')
        restore_to_pylab = state.pop('_restore_to_pylab', False)

        if version != _mpl_version:
            import warnings
            warnings.warn("This figure was saved with matplotlib version %s "
                          "and is unlikely to function correctly." %
                          (version, ))

        self.__dict__ = state

        # re-initialise some of the unstored state information
        self._axobservers = []
        self.canvas = None
        self._layoutbox = None

        if restore_to_pylab:
            # lazy import to avoid circularity
            import matplotlib.pyplot as plt
            import matplotlib._pylab_helpers as pylab_helpers
            allnums = plt.get_fignums()
            num = max(allnums) + 1 if allnums else 1
            mgr = plt._backend_mod.new_figure_manager_given_figure(num, self)

            # XXX The following is a copy and paste from pyplot. Consider
            # factoring to pylab_helpers

            if self.get_label():
                mgr.set_window_title(self.get_label())

            # make this figure current on button press event
            def make_active(event):
                pylab_helpers.Gcf.set_active(mgr)

            mgr._cidgcf = mgr.canvas.mpl_connect('button_press_event',
                                                 make_active)

            pylab_helpers.Gcf.set_active(mgr)
            self.number = num

            plt.draw_if_interactive()
        self.stale = True