Python pyplot.fignum_exists函数代码示例

 def test_nominal(self):
     fig1 = plt.figure()
     fig2 = plt.figure()
     self.assertTrue(plt.fignum_exists(fig1.number))
     self.assertTrue(plt.fignum_exists(fig2.number))
     dcs.close_all()
     self.assertFalse(plt.fignum_exists(fig1.number))
     self.assertFalse(plt.fignum_exists(fig2.number))

	def _Plot_3D_fired(self):

		try:
			plt.fignum_exists()
			xmin, xmax = plt.xlim()
			ymin, ymax = plt.ylim()

			index_wavelength_left=(np.abs(Data.wavelength-xmin)).argmin()
			index_wavelength_right=(np.abs(Data.wavelength-xmax)).argmin()

			index_time_left=(np.abs(Data.time-ymin)).argmin()
			index_time_right=(np.abs(Data.time-ymax)).argmin()
		except:
			index_wavelength_left=0
			index_wavelength_right=Data.wavelength[-1]

			index_time_left=0
			index_time_right=Data.wavelength[-1]

		Data.Three_d = Data.TrA_Data[index_time_left:index_time_right,index_wavelength_left:index_wavelength_right]
		Data.Three_d_wavelength = Data.wavelength[index_wavelength_left:index_wavelength_right]
		Data.Three_d_time = Data.time[index_time_left:index_time_right]

		self.scene.mlab.clf()

		x = np.linspace(Data.Three_d_wavelength[0],Data.Three_d_wavelength[-1],len(Data.Three_d_wavelength))
		y = np.linspace(Data.Three_d_time[0], Data.Three_d_time[-1],len(Data.Three_d_wavelength))
		[xi,yi] = np.meshgrid(x,y)

		for i in range(len(Data.Three_d_wavelength)):
			repeating_wavelength = np.array(np.ones((len(Data.Three_d_time)))*Data.Three_d_wavelength[i])
			vectors = np.array([Data.Three_d_time,repeating_wavelength,Data.Three_d[:,i]])
			if i==0:
				Data.TrA_Data_gridded = vectors
			else:
				Data.TrA_Data_gridded = np.hstack((Data.TrA_Data_gridded, vectors))

		zi = interpolate.griddata((Data.TrA_Data_gridded[1,:],Data.TrA_Data_gridded[0,:]),Data.TrA_Data_gridded[2,:],(xi,yi), method='linear', fill_value=0)

		#Sends 3D plot to mayavi in gui

		#uncomment for plotting actual data matrix
		#self.scene.mlab.surf(Data.time,Data.wavelength,Data.TrA_Data,warp_scale=-self.z_height*100)

		#gridded plot which gives correct view
		self.plot = self.scene.mlab.surf(yi,xi,zi, warp_scale=-self.z_height*100)
		self.scene.mlab.colorbar(orientation="vertical")
		self.scene.mlab.axes(nb_labels=5,)
		self.scene.mlab.ylabel("wavelength (nm)")
		self.scene.mlab.xlabel("time (ps)")

    def plot_process(queue, name, labels=None):
        """Grabs data from the queue and plots it in a named figure
        """
        # Set up the figure and display it
        fig = setup_figure(name)
        plt.show(block=False)
        if labels is not None:
            plt.xlabel(labels[0])
            plt.ylabel(labels[1])

        while True:
            # Get all the data currently on the queue
            data = []
            while not queue.empty():
                data.append(queue.get())

            # If there is no data, no need to plot, instead wait for a
            # while
            if len(data) == 0:
                plt.pause(0.015)
                continue

            # Check if poison pill (None) arrived or the figure was closed
            if None in data or not plt.fignum_exists(fig.number):
                # If yes, leave the process
                break
            else:
                # Plot the data, then wait 15ms for the plot to update
                for datum in data:
                    plt.plot(*datum)
                plt.pause(0.015)

def savefig(filename, **kwargs):
    """
    Save a figure plotted by Matplotlib.

    Note : This function is supposed to be used after the ``plot``
    function. Otherwise it will save a blank image with no plot.

    Parameters
    ----------
    filename : str
        The name of the image file. Extension must be specified in the
        file name. For example filename with `.png` extension will give a
        rasterized image while `.pdf` extension will give a vectorized
        output.

    kwargs : keyword arguments
        Keyword arguments to be passed to ``savefig`` function of
        ``matplotlib.pyplot``. For example use `bbox_inches='tight'` to
        remove the undesirable whitepace around the image.
    """

    try:
        import matplotlib.pyplot as plt
    except ImportError:
        raise ImportError("Matplotlib required for savefig()")

    if not plt.fignum_exists(1):
        utils.simon("use ``plot`` function to plot the image first and "
                    "then use ``savefig`` to save the figure.")

    plt.savefig(filename, **kwargs)

def make_new_fig(fnum=None, close_old=True, return_fig=False, 
                 use_max_fnum=False, **kwargs):
    """
    Make a new figure, clearing the old one.
    
    Returns the figure after the one you've created.
    """

    if fnum is None:
        fignums = get_fignums()
        max_fnum = max(fignums) if len(fignums) > 0 else 0

        if use_max_fnum:
            fnum = max_fnum+1
        else:
            for fnum in range(0, max_fnum+2):
                if fnum not in fignums:
                    break

    if fignum_exists(fnum) and close_old:
        close(fnum)            # Close the figure if it already exists.

    fig = figure(fnum, **kwargs)
    clf()

    if return_fig:
        return [fig, fnum+1]
    else:
        return fnum+1

def gplot(xval, yval, sym=None, xr=None, yr=None, title=None, xlabel=None, \
              ylabel=None, tsize=None, xsize=None, ysize=None, color=None, \
              fnum=None, fname=None):

# -------- defaults
    if sym==None    : sym='k-'
    if xr==None     : xr = min(xval), max(xval)
    if yr==None     : yr = min(yval), max(yval)
    if title==None  : title=''
    if xlabel==None : xlabel=''
    if ylabel==None : ylabel=''
    if fnum==None   : fnum=0


# -------- make the figure
    if plt.fignum_exists(fnum): plt.close(fnum)

    plt.figure(fnum)
    plt.plot(xval,yval,sym)
    plt.xlim(xr)
    plt.ylim(yr)
    plt.title(title, fontsize=tsize)
    plt.xlabel(xlabel, fontsize=xsize)
    plt.ylabel(ylabel, fontsize=ysize)


# -------- write to output if desired
    if fname!=None:
        if not isinstance(fname,list): fname = [fname]

        for ifile in fname:
            print("GPLOT: Writing output to " + ifile)
            plt.savefig(ifile)
    else:
        plt.show()

def mpl_annotate_interactive(text, ax=None, arrowprops={"facecolor":'black', "shrink":0.05}):
    ax = ax or pyplot.gca()
    fig = ax.figure
    coordinates = [
        None, # xy
        None  # xytext
    ]
    pause = [True,]
    def get_xy(event):
        if coordinates[0] is None:
            coordinates[0] = (event.x, event.y)
            print("xy     : {}".format(coordinates[0]))
        else:
            coordinates[1] = (event.x, event.y)
            print("xytext : {}".format(coordinates[1]))
            pause[0] = False
    cid = fig.canvas.mpl_connect('button_press_event', get_xy)
    while pause[0] and pyplot.fignum_exists(fig.number):
        fig.canvas.get_tk_widget().update()
    fig.canvas.mpl_disconnect(cid)
    ax.annotate(
        text,
        xycoords='figure pixels',
        xy=coordinates[0],
        xytext=coordinates[1],
        arrowprops=arrowprops,
    )

        def closeFigure(event): #remove figure from figs
            for figure in figs:
                if not plt.fignum_exists(figure[0]): #if figure id number doesn't exist, figure doesn't exist
                    print("removing figure",figure[0])
                    figs.remove(figure) #remove from figure list

            if len(figs) == 0:
                raise SystemExit #exit program if all figures have been closed

def mpl_wait_for_key_press(fig=None, key="enter", close=False):
    # I need to use an object. The pointer to the pause list will be captured by
    # closer in key_press_event. When the callback will change the pause
    # content, the outer function will see it. With the simple pause = True
    # code, the inner function would have changed a local copy of pause.
    if fig is None:
        fig = pyplot.gcf()
    pause = [True, ]
    def key_press_event(event):
        if event.key == key:
            pause[0] = False
    cid = fig.canvas.mpl_connect('key_press_event', key_press_event)
    while pause[0] and pyplot.fignum_exists(fig.number):
        fig.canvas.get_tk_widget().update()
    fig.canvas.mpl_disconnect(cid)
    if close and pyplot.fignum_exists(fig.number):
        pyplot.close(fig.number)

 def _redraw(self):
     if plt.isinteractive():
         fig = self.kwargs["fig"]
         if not plt.fignum_exists(fig.number):
             fig.show()
         fig.canvas.draw()
     else:
         print("Redraw() is unsupported in non-interactive plotting mode!")

        def test_filter_applyFilter(self):
            ppmm = self.runBefore()

            fake_event = matplotlib.backend_bases.MouseEvent('button_press_event', ppmm.figfilter.canvas, 636, 823)
            ppmm.applyFilter(fake_event)

            # check the figure has been closed
            self.assertFalse(py.fignum_exists(ppmm.figfilter.number))

 def _redraw(self):
     if plt.isinteractive():
         fig = self.kwargs.get('fig',False)
         if not fig: return
         if not plt.fignum_exists(fig.number):
             fig.show()
         fig.canvas.draw()
     else:
         print('Redraw() is unsupported in non-interactive plotting mode!')

 def updateTimeUnit(self,event):
     #Verify that the data frame is not empty
     if self.df.empty:
          messagebox.showinfo('Selection Error', 'Error: Please collect data before selecting time units.')
     else:
         self.unit = self.timevar.get()
         self.unit_conv = self.choices[self.unit]
         
         #Change units on timestamp plots if they exist
         self.df['timestamp']=self.df['orig_timestamp'] * self.unit_conv
         
         #Figure two is the EvT plot:
         if plt.fignum_exists(2):
             self.EvT_plot()
         
         #Figure one is the Time Histogram
         if plt.fignum_exists(1):
             self.THist_plot()

 def display(self, image):
     if self.imsh is None or not plt.fignum_exists(self.imsh.figure.number):
         self.imsh = plt.imshow(image, interpolation='nearest')
         self.imsh.axes.axis('off')
         self.imsh.axes.set_position((0, 0, 1, 1))
         self.imsh.figure.canvas.draw()
     else:
         self.imsh.set_data(image)
     plt.pause(1e-4)

def animation():
    for i1 in xrange(1, nanim):
        # remove previous highlights
        icontainer.ax0_hs.remove()
        icontainer.ax1_hs.get_sizes()[0] = 20
        icontainer.ax1_hs.get_facecolor()[0,0] = 0
        icontainer.ax1_hs.get_facecolor()[0,1] = 0.5
        icontainer.ax1_hs.get_facecolor()[0,3] = 0.2
        icontainer.ax1_hs.get_edgecolor()[0,1] = 0.5
        icontainer.ax1_hs.get_edgecolor()[0,3] = 0.2
        # remove previous predictive distribution
        axes[1].lines.remove(icontainer.prev_line)
        # show next sample
        icontainer.ax0_hs = axes[0].scatter(mu[i1], sigma[i1], 40, 'r')
        icontainer.ax1_hs = axes[1].scatter(
            ynew[i1], (0.02 + 0.02*np.random.rand())*np.max(ynewdists), 40, 'r'
        )
        icontainer.prev_line, = axes[1].plot(
            xynew, ynewdists[i1], 'b', linewidth=1.5
        )
        # update figure
        fig.canvas.draw()
        # wait animation delay time or until animation is cancelled
        stop_anim.wait(anim_delay)
        if stop_anim.isSet():
            # animation cancelled
            break
    # skip the rest if the figure does not exist anymore
    if not plt.fignum_exists(fig.number):
        return    
    # advance stage
    icontainer.stage += 1
    # remove highlights
    icontainer.ax0_hs.remove()
    axes[1].lines.remove(icontainer.prev_line)
    icontainer.ax1_hs.get_sizes()[0] = 20
    icontainer.ax1_hs.get_facecolor()[0,0] = 0
    icontainer.ax1_hs.get_facecolor()[0,1] = 0.5
    icontainer.ax1_hs.get_facecolor()[0,3] = 0.2
    icontainer.ax1_hs.get_edgecolor()[0,1] = 0.5
    icontainer.ax1_hs.get_edgecolor()[0,3] = 0.2
    # modify helper text
    htext.set_text('press any key to continue')
    # plot the rest of the samples
    i1 += 1
    icontainer.ax1_hs = axes[1].scatter(
        ynew[i1:], (0.02 + 0.015*np.random.rand(nsamp-i1))*np.max(ynewdists), 10,
        color=[0,0.5,0], alpha=0.2
    )
    # update legend
    axes[1].legend(
        (icontainer.ax1_hs,),
        ('samples from the predictive distribution',),
        loc='upper center'
    )
    fig.canvas.draw()