Python Figure.show方法代码示例

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_correlation(hist, title="Hit correlation", xlabel=None, ylabel=None, filename=None):
    logging.info("Plotting correlations")
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(1, 1, 1)
    cmap = cm.get_cmap('jet')
    extent = [hist[2][0] - 0.5, hist[2][-1] + 0.5, hist[1][-1] + 0.5, hist[1][0] - 0.5]
    ax.set_title(title)
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    im = ax.imshow(hist[0], extent=extent, cmap=cmap, interpolation='nearest')
    ax.invert_yaxis()
    # add colorbar
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    z_max = np.max(hist[0])
    bounds = np.linspace(start=0, stop=z_max, num=255, endpoint=True)
    norm = colors.BoundaryNorm(bounds, cmap.N)
    fig.colorbar(im, boundaries=bounds, cmap=cmap, norm=norm, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True), cax=cax)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_tdc_event(points, filename=None):
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111, projection='3d')
    xs = points[:, 0]
    ys = points[:, 1]
    zs = points[:, 2]
    cs = points[:, 3]

    p = ax.scatter(xs, ys, zs, c=cs, s=points[:, 3] ** (2) / 5., marker='o')

    ax.set_xlabel('x [250 um]')
    ax.set_ylabel('y [50 um]')
    ax.set_zlabel('t [25 ns]')
    ax.title('Track of one TPC event')
    ax.set_xlim(0, 80)
    ax.set_ylim(0, 336)

    c_bar = fig.colorbar(p)
    c_bar.set_label('charge [TOT]')

    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    elif filename:
        fig.savefig(filename)
    return fig

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_scatter(x, y, x_err=None, y_err=None, title=None, legend=None, plot_range=None, plot_range_y=None, x_label=None, y_label=None, marker_style='-o', log_x=False, log_y=False, filename=None):
    logging.info('Plot scatter plot %s', (': ' + title) if title is not None else '')
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    if x_err is not None:
        x_err = [x_err, x_err]
    if y_err is not None:
        y_err = [y_err, y_err]
    if x_err is not None or y_err is not None:
        ax.errorbar(x, y, xerr=x_err, yerr=y_err, fmt=marker_style)
    else:
        ax.plot(x, y, marker_style, markersize=1)
    ax.set_title(title)
    if x_label is not None:
        ax.set_xlabel(x_label)
    if y_label is not None:
        ax.set_ylabel(y_label)
    if log_x:
        ax.set_xscale('log')
    if log_y:
        ax.set_yscale('log')
    if plot_range:
        ax.set_xlim((min(plot_range), max(plot_range)))
    if plot_range_y:
        ax.set_ylim((min(plot_range_y), max(plot_range_y)))
    if legend:
        ax.legend(legend, 0)
    ax.grid(True)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_cluster_tot_size(hist, median=False, z_max=None, filename=None):
    H = hist[0:50, 0:20]
    if z_max is None:
        z_max = np.ma.max(H)
    if z_max < 1 or H.all() is np.ma.masked:
        z_max = 1
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    extent = [-0.5, 20.5, 49.5, -0.5]
    bounds = np.linspace(start=0, stop=z_max, num=255, endpoint=True)
    cmap = cm.get_cmap('jet')
    cmap.set_bad('w')
    norm = colors.BoundaryNorm(bounds, cmap.N)
    im = ax.imshow(H, aspect="auto", interpolation='nearest', cmap=cmap, norm=norm, extent=extent)  # for monitoring
    ax.set_title('Cluster size and cluster ToT (' + str(np.sum(H) / 2) + ' entries)')
    ax.set_xlabel('cluster size')
    ax.set_ylabel('cluster ToT')

    ax.invert_yaxis()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.1)
    cb = fig.colorbar(im, cax=cax, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True))
    cb.set_label("#")
    fig.patch.set_facecolor('white')
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_scatter_time(x, y, yerr=None, title=None, legend=None, plot_range=None, plot_range_y=None, x_label=None, y_label=None, marker_style='-o', log_x=False, log_y=False, filename=None):
    logging.info("Plot time scatter plot %s", (': ' + title) if title is not None else '')
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
    times = []
    for time in x:
        times.append(datetime.fromtimestamp(time))
    if yerr is not None:
        ax.errorbar(times, y, yerr=[yerr, yerr], fmt=marker_style)
    else:
        ax.plot(times, y, marker_style)
    ax.set_title(title)
    if x_label is not None:
        ax.set_xlabel(x_label)
    if y_label is not None:
        ax.set_ylabel(y_label)
    if log_x:
        ax.xscale('log')
    if log_y:
        ax.yscale('log')
    if plot_range:
        ax.set_xlim((min(plot_range), max(plot_range)))
    if plot_range_y:
        ax.set_ylim((min(plot_range_y), max(plot_range_y)))
    if legend:
        ax.legend(legend, 0)
    ax.grid(True)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_1d_hist(hist, yerr=None, title=None, x_axis_title=None, y_axis_title=None, x_ticks=None, color='r', plot_range=None, log_y=False, filename=None, figure_name=None):
    logging.info('Plot 1d histogram%s', (': ' + title) if title is not None else '')
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    if plot_range is None:
        plot_range = range(0, len(hist))
    if not plot_range:
        plot_range = [0]
    if yerr is not None:
        ax.bar(left=plot_range, height=hist[plot_range], color=color, align='center', yerr=yerr)
    else:
        ax.bar(left=plot_range, height=hist[plot_range], color=color, align='center')
    ax.set_xlim((min(plot_range) - 0.5, max(plot_range) + 0.5))
    ax.set_title(title)
    if x_axis_title is not None:
        ax.set_xlabel(x_axis_title)
    if y_axis_title is not None:
        ax.set_ylabel(y_axis_title)
    if x_ticks is not None:
        ax.set_xticks(range(0, len(hist[:])) if plot_range is None else plot_range)
        ax.set_xticklabels(x_ticks)
        ax.tick_params(which='both', labelsize=8)
    if np.allclose(hist, 0.0):
        ax.set_ylim((0, 1))
    else:
        if log_y:
            ax.set_yscale('log')
    ax.grid(True)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
class MatplotlibWidget(FigureCanvas):

    def __init__(self, parent=None):
        super(MatplotlibWidget, self).__init__(Figure())
        #self.hide()
        self.setParent(parent)
        self.figure = Figure()
        self.canvas = FigureCanvas(self.figure)

        self.ax = self.figure.add_subplot(111)
        self.ax.yaxis.grid(color='gray', linestyle='dashed')

    def add_plot_line(self, x, y):
        self.ax.plot(x, y, 'r')

    def add_legend(self, list):
        #list of strings like "X1 = blaghlbah"
        self.figure.legend([x for x in list], loc='upper left')

    def draw_graph(self):
        self.figure.draw()

    def show_graph(self):
        self.figure.show()

    def plot_bar_graph(self):
        pass

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plotThreeWay(hist, title, filename=None, x_axis_title=None, minimum=None, maximum=None, bins=101):  # the famous 3 way plot (enhanced)
    if minimum is None:
        minimum = 0
    elif minimum == 'minimum':
        minimum = np.ma.min(hist)
    if maximum == 'median' or maximum is None:
        median = np.ma.median(hist)
        maximum = median * 2  # round_to_multiple(median * 2, math.floor(math.log10(median * 2)))
    elif maximum == 'maximum':
        maximum = np.ma.max(hist)
        maximum = maximum  # round_to_multiple(maximum, math.floor(math.log10(maximum)))
    if maximum < 1 or hist.all() is np.ma.masked:
        maximum = 1

    x_axis_title = '' if x_axis_title is None else x_axis_title
    fig = Figure()
    FigureCanvas(fig)
    fig.patch.set_facecolor('white')
    ax1 = fig.add_subplot(311)
    create_2d_pixel_hist(fig, ax1, hist, title=title, x_axis_title="column", y_axis_title="row", z_min=minimum if minimum else 0, z_max=maximum)
    ax2 = fig.add_subplot(312)
    create_1d_hist(fig, ax2, hist, bins=bins, x_axis_title=x_axis_title, y_axis_title="#", x_min=minimum, x_max=maximum)
    ax3 = fig.add_subplot(313)
    create_pixel_scatter_plot(fig, ax3, hist, x_axis_title="channel=row + column*336", y_axis_title=x_axis_title, y_min=minimum, y_max=maximum)
    fig.tight_layout()
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
class PlotFigure(wx.Frame):
    def __init__(self, title='Pyception results'): #, style=wx.DEFAULT_FRAME_STYLE|wx.STAY_ON_TOP):
    #def __init__(self,title='Pyception Lab', pos=None, size=wx.DefaultSize, style=wx.DEFAULT_DIALOG_STYLE|wx.DIALOG_NO_PARENT):
        Frame.__init__(self, None, -1, title, style=wx.STAY_ON_TOP|wx.RESIZE_BORDER) # style=wx.DEFAULT_FRAME_STYLE|wx.STAY_ON_TOP)
        #try:
            #wx.Dialog.__init__(self, None,-1,title)
        #except:
            #global app
            #app = wx.PySimpleApp()
            #wx.Dialog.__init__(self, None,-1,title) 
        #wx.Dialog.__init__(self, None,-1,title="Pyception results")
#style=style|wx.RESIZE_BORDER

        #self.fig = Figure((9,8), 75)
        self.fig = Figure()
        self.canvas = FigCanvas(self, -1, self.fig)
        #self.toolbar = Toolbar(self.canvas)
        #self.toolbar.Realize()

        # On Windows, default frame size behaviour is incorrect
        # you don't need this under Linux
        #tw, th = self.toolbar.GetSizeTuple()
        #fw, fh = self.canvas.GetSizeTuple()
        #self.toolbar.SetSize(Size(fw, th))

        # Create a figure manager to manage things
        self.figmgr = FigureManager(self.canvas, 1, self)
        # Now put all into a sizer
        sizer = BoxSizer(VERTICAL)
        # This way of adding to sizer allows resizing
        sizer.Add(self.canvas, 1, LEFT|TOP|GROW)
        # Best to allow the toolbar to resize!
        #sizer.Add(self.toolbar, 0, GROW)

        btn = wx.Button(self, label='Close')
        btn.Bind( wx.EVT_BUTTON, self.onClose)
        sizer.Add(btn, 0, GROW)

        self.SetSizer(sizer)
        self.Fit()

    def onClose(self, event):
        self.eventLoop.Exit()
        self.Close()

    def ShowModal(self):
        self.MakeModal()
        self.fig.show()
        self.Show()
        self.eventLoop = wx.EventLoop()
        self.eventLoop.Run()

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_scurves(occupancy_hist, scan_parameters, title='S-Curves', ylabel='Occupancy', max_occ=None, scan_parameter_name=None, min_x=None, max_x=None, x_scale=1.0, y_scale=1., filename=None):  # tornado plot
    occ_mask = np.all(occupancy_hist == 0, axis=2)
    if max_occ is None:
        max_occ = 2 * np.median(np.amax(occupancy_hist, axis=2))
        if np.allclose(max_occ, 0.0):
            max_occ = np.amax(occupancy_hist)
        if np.allclose(max_occ, 0.0):
            max_occ = 1
    if len(occupancy_hist.shape) < 3:
        raise ValueError('Found array with shape %s' % str(occupancy_hist.shape))

    n_pixel = occupancy_hist.shape[0] * occupancy_hist.shape[1]

    cmap = cm.get_cmap('jet', 200)
    for index, scan_parameter in enumerate(scan_parameters):
        compressed_data = np.ma.masked_array(occupancy_hist[:, :, index], mask=occ_mask, copy=True).compressed()
        heatmap, xedges, yedges = np.histogram2d(compressed_data, [scan_parameter] * compressed_data.shape[0], range=[[0, max_occ], [scan_parameters[0], scan_parameters[-1]]], bins=(max_occ + 1, len(scan_parameters)))
        if index == 0:
            hist = heatmap
        else:
            hist += heatmap
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    fig.patch.set_facecolor('white')
    if len(scan_parameters) > 1:
        scan_parameter_dist = (np.amax(scan_parameters) - np.amin(scan_parameters)) / (len(scan_parameters) - 1)
    else:
        scan_parameter_dist = 0
    extent = [yedges[0] - scan_parameter_dist / 2, yedges[-1] * x_scale + scan_parameter_dist / 2, xedges[-1] * y_scale + 0.5, xedges[0] - 0.5]
    norm = colors.LogNorm()
    im = ax.imshow(hist, interpolation='nearest', aspect="auto", cmap=cmap, extent=extent, norm=norm)
    ax.invert_yaxis()
    if min_x is not None or max_x is not None:
        ax.set_xlim((min_x if min_x is not None else np.amin(scan_parameters), max_x if max_x is not None else np.amax(scan_parameters)))
    fig.colorbar(im)
    ax.set_title(title + ' for %d pixel(s)' % (n_pixel - np.count_nonzero(occ_mask)))
    if scan_parameter_name is None:
        ax.set_xlabel('Scan parameter')
    else:
        ax.set_xlabel(scan_parameter_name)
    ax.set_ylabel(ylabel)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def subplot_demo():
    f = Figure()
    t = numpy.arange(0.0,3.0,0.01)
    s1 = numpy.sin(2*numpy.pi*t)
    s2 = numpy.zeros(t.shape, numpy.Float)

    a1 = Subplot(211)
    a1.plot(t,s1)
    a1.set_title('And now for something completely different')

    a2 = Subplot(212)
    a2.plot(t,s2)
    a2.set_xlabel('time (s)')

    f.add_axis(a1)
    f.add_axis(a2)
    f.show()

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_profile_histogram(x, y, n_bins=100, title=None, x_label=None, y_label=None, log_y=False, filename=None):
    '''Takes 2D point data (x,y) and creates a profile histogram similar to the TProfile in ROOT. It calculates
    the y mean for every bin at the bin center and gives the y mean error as error bars.

    Parameters
    ----------
    x : array like
        data x positions
    y : array like
        data y positions
    n_bins : int
        the number of bins used to create the histogram
    '''
    if len(x) != len(y):
        raise ValueError('x and y dimensions have to be the same')
    n, bin_edges = np.histogram(x, bins=n_bins)  # needed to calculate the number of points per bin
    sy = np.histogram(x, bins=n_bins, weights=y)[0]  # the sum of the bin values
    sy2 = np.histogram(x, bins=n_bins, weights=y * y)[0]  # the quadratic sum of the bin values
    bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2  # calculate the bin center for all bins
    mean = sy / n  # calculate the mean of all bins
    std = np.sqrt((sy2 / n - mean * mean))  # TODO: no understood, need check if this is really the standard deviation
    #     std_mean = np.sqrt((sy2 - 2 * mean * sy + mean * mean) / (1*(n - 1)))  # this should be the formular ?!
    std_mean = std / np.sqrt((n - 1))
    mean[np.isnan(mean)] = 0.
    std_mean[np.isnan(std_mean)] = 0.

    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    ax.errorbar(bin_centers, mean, yerr=std_mean, fmt='o')
    ax.set_title(title)
    if x_label is not None:
        ax.set_xlabel(x_label)
    if y_label is not None:
        ax.set_ylabel(y_label)
    if log_y:
        ax.yscale('log')
    ax.grid(True)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_three_way(hist, title, filename=None, x_axis_title=None, minimum=None, maximum=None, bins=101, cmap=None):  # the famous 3 way plot (enhanced)
    if cmap is None:
        if maximum == 'median' or maximum is None:
            cmap = cm.get_cmap('coolwarm')
        else:
            cmap = cm.get_cmap('cool')
    # TODO: set color for bad pixels
    # set nan to special value
    # masked_array = np.ma.array (a, mask=np.isnan(a))
    # cmap = matplotlib.cm.jet
    # cmap.set_bad('w',1.)
    # ax.imshow(masked_array, interpolation='nearest', cmap=cmap)
    if minimum is None:
        minimum = 0.0
    elif minimum == 'minimum':
        minimum = np.ma.min(hist)
    if maximum == 'median' or maximum is None:
        maximum = 2 * np.ma.median(hist)
    elif maximum == 'maximum':
        maximum = np.ma.max(hist)
    if maximum < 1 or hist.all() is np.ma.masked:
        maximum = 1.0

    x_axis_title = '' if x_axis_title is None else x_axis_title
    fig = Figure()
    FigureCanvas(fig)
    fig.patch.set_facecolor('white')
    ax1 = fig.add_subplot(311)
    create_2d_pixel_hist(fig, ax1, hist, title=title, x_axis_title="column", y_axis_title="row", z_min=minimum if minimum else 0, z_max=maximum, cmap=cmap)
    ax2 = fig.add_subplot(312)
    create_1d_hist(fig, ax2, hist, bins=bins, x_axis_title=x_axis_title, y_axis_title="#", x_min=minimum, x_max=maximum)
    ax3 = fig.add_subplot(313)
    create_pixel_scatter_plot(fig, ax3, hist, x_axis_title="channel=row + column*336", y_axis_title=x_axis_title, y_min=minimum, y_max=maximum)
    fig.tight_layout()
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_occupancy(hist, title='Occupancy', z_max=None, filename=None):
    if z_max == 'median':
        z_max = 2 * np.ma.median(hist)
    elif z_max == 'maximum' or z_max is None:
        z_max = np.ma.max(hist)
    if z_max < 1 or hist.all() is np.ma.masked:
        z_max = 1.0

    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    ax.set_adjustable('box-forced')
    extent = [0.5, 80.5, 336.5, 0.5]
    bounds = np.linspace(start=0, stop=z_max, num=255, endpoint=True)
    if z_max == 'median':
        cmap = cm.get_cmap('coolwarm')
    else:
        cmap = cm.get_cmap('cool')
    cmap.set_bad('w', 0.0)
    norm = colors.BoundaryNorm(bounds, cmap.N)

    im = ax.imshow(hist, interpolation='nearest', aspect='auto', cmap=cmap, norm=norm, extent=extent)  # TODO: use pcolor or pcolormesh
    ax.set_ylim((336.5, 0.5))
    ax.set_xlim((0.5, 80.5))
    ax.set_title(title + r' ($\Sigma$ = %d)' % (0 if hist.all() is np.ma.masked else np.ma.sum(hist)))
    ax.set_xlabel('Column')
    ax.set_ylabel('Row')

    divider = make_axes_locatable(ax)

    cax = divider.append_axes("right", size="5%", pad=0.1)
    cb = fig.colorbar(im, cax=cax, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True))
    cb.set_label("#")

    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)

# 需要导入模块: from matplotlib.figure import Figure [as 别名]
# 或者: from matplotlib.figure.Figure import show [as 别名]
def plot_pixel_matrix(hist, title="Hit correlation", filename=None):
    logging.info("Plotting pixel matrix: %s", title)
    fig = Figure()
    FigureCanvas(fig)
    ax = fig.add_subplot(111)
    ax.set_title(title)
    ax.set_xlabel('Col')
    ax.set_ylabel('Row')
    cmap = cm.get_cmap('jet')
    ax.imshow(hist.T, aspect='auto', cmap=cmap, interpolation='nearest')
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="5%", pad=0.05)
    z_max = np.max(hist)
    bounds = np.linspace(start=0, stop=z_max, num=255, endpoint=True)
    norm = colors.BoundaryNorm(bounds, cmap.N)
    fig.colorbar(boundaries=bounds, cmap=cmap, norm=norm, ticks=np.linspace(start=0, stop=z_max, num=9, endpoint=True), cax=cax)
    if not filename:
        fig.show()
    elif isinstance(filename, PdfPages):
        filename.savefig(fig)
    else:
        fig.savefig(filename)