python类ticker()的实例源码

def saveAttention(input_sentence, attentions, outpath):
    # Set up figure with colorbar
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    import matplotlib.ticker as ticker

    fig = plt.figure(figsize=(24,10), )
    ax = fig.add_subplot(111)
    cax = ax.matshow(attentions.cpu().numpy(), cmap='bone')
    fig.colorbar(cax)

    if input_sentence:
        # Set up axes
        ax.set_yticklabels([' '] + list(input_sentence) + [' '])
        # Show label at every tick
        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))

    plt.tight_layout()
    plt.savefig(outpath)
    plt.close('all')

def _remove_labels_from_axis(axis):
    for t in axis.get_majorticklabels():
        t.set_visible(False)

    try:
        # set_visible will not be effective if
        # minor axis has NullLocator and NullFormattor (default)
        import matplotlib.ticker as ticker
        if isinstance(axis.get_minor_locator(), ticker.NullLocator):
            axis.set_minor_locator(ticker.AutoLocator())
        if isinstance(axis.get_minor_formatter(), ticker.NullFormatter):
            axis.set_minor_formatter(ticker.FormatStrFormatter(''))
        for t in axis.get_minorticklabels():
            t.set_visible(False)
    except Exception:   # pragma no cover
        raise
    axis.get_label().set_visible(False)

def visLoc(locs, label="NotSet"):
    axis = lambda i: [loc[i] for loc in locs]
    import matplotlib.ticker as ticker

    fig, ax = plt.subplots()
    #plt.grid(True)
    ax.plot(axis(0), axis(1), 'g^', ms=2)
    ylim = ax.get_ylim()
    xlim = ax.get_xlim()
    ax.set_xlim(min(xlim[0],ylim[0]) ,max(xlim[1],ylim[1]))
    ax.set_ylim(min(xlim[0],ylim[0]) ,max(xlim[1],ylim[1]))

    plt.title("Moving paths from " + label)
    plt.xlabel("West -- East")
    plt.ylabel("South -- North")
    plt.show()

def savePlot(points, outpath):
    plt.figure()
    fig, ax = plt.subplots()
    # this locator puts ticks at regular intervals
    loc = ticker.MultipleLocator(base=0.2)
    ax.yaxis.set_major_locator(loc)
    plt.plot(points)
    plt.savefig(outpath)
    plt.close('all')

######################################################################
# This is a helper function to print time elapsed and estimated time
# remaining given the current time and progress %.
#

def plot_ohlcv(self, df):

        fig, ax = plt.subplots()

        # Plot the candlestick
        candlestick2_ohlc(ax, df['open'], df['high'], df['low'], df['close'],
                          width=1, colorup='g', colordown='r', alpha=0.5)

        # shift y-limits of the candlestick plot so that there is space
        # at the bottom for the volume bar chart
        pad = 0.25
        yl = ax.get_ylim()
        ax.set_ylim(yl[0] - (yl[1] - yl[0]) * pad, yl[1])

        # Add a seconds axis for the volume overlay
        ax2 = ax.twinx()

        ax2.set_position(
            matplotlib.transforms.Bbox([[0.125, 0.1], [0.9, 0.26]]))

        # Plot the volume overlay
        # bc = volume_overlay(ax2, df['open'], df['close'], df['volume'],
        #                     colorup='g', alpha=0.5, width=1)

        ax.xaxis.set_major_locator(ticker.MaxNLocator(6))

        def mydate(x, pos):
            try:
                return df.index[int(x)]
            except IndexError:
                return ''

        ax.xaxis.set_major_formatter(ticker.FuncFormatter(mydate))
        plt.margins(0)
        plt.show()

def __init__(self, mappable, **kw):
        # Ensure the given mappable's norm has appropriate vmin and vmax set
        # even if mappable.draw has not yet been called.
        mappable.autoscale_None()

        self.mappable = mappable
        kw['cmap'] = cmap = mappable.cmap
        kw['norm'] = mappable.norm

        if isinstance(mappable, contour.ContourSet):
            CS = mappable
            kw['alpha'] = mappable.get_alpha()
            kw['boundaries'] = CS._levels
            kw['values'] = CS.cvalues
            kw['extend'] = CS.extend
            #kw['ticks'] = CS._levels
            kw.setdefault('ticks', ticker.FixedLocator(CS.levels, nbins=10))
            kw['filled'] = CS.filled

        else:
            if getattr(cmap, 'colorbar_extend', False) is not False:
                kw.setdefault('extend', cmap.colorbar_extend)

            if isinstance(mappable, Artist):
                kw['alpha'] = mappable.get_alpha()

        ticks = kw.pop('ticks', None)
        ticklabels = kw.pop('ticklabels', None)

        self._base = ColorbarBase2(None, **kw)
        if ticks:
            self._base.set_ticks(ticks, update_ticks=False)
        if ticks and ticklabels:
            self._base.set_ticklabels(ticklabels, update_ticks=False)

def setLineSensor(self):
        try:
            self.compressLabel(self.sensorList)

            x1=[]
            x1pos=[]
            labels1=[]

            j = -1
            x1pos.append(0)
            labels1.append('')
           # self.setTestData()
            for i in self.sensorList:
                if i.startX > j:
                    x1.append(i.startX)
                    j = i.startX
                    x1.append(i.stopX)
                    x1pos.append(i.labelPos)
                    labels1.append(i.label)


            self.par1.xaxis.set_major_formatter(ticker.NullFormatter())
            self.par1.xaxis.set_minor_locator(ticker.FixedLocator(x1pos))
            self.par1.xaxis.set_minor_formatter(ticker.FixedFormatter(labels1))
            self.par1.xaxis.set_ticks(x1)

        except Exception as _err:
            print(_err)
            logging.exception(_err)
            pass

def setLineRBW(self):

        self.compressLabel(self.rbwList)

        try:
            x1 = []
            x1pos = []
            labels1 = []
            j = -1
            x1pos.append(0)
            labels1.append('')
           # self.setTestData()
            for i in self.rbwList:
                if i.startX > j:
                    x1.append(i.startX)
                    j = i.startX
                x1.append(i.stopX)
                x1pos.append(i.labelPos)
                labels1.append(i.label)

         #   self.par2.spines["bottom"].set_position(("outward", 50))
         #   self.par2.xaxis.set_ticks_position('bottom')
#            self.par2.set_xscale('log')
            self.par2.xaxis.set_major_formatter(ticker.NullFormatter())
            self.par2.xaxis.set_minor_locator(ticker.FixedLocator(x1pos))
            self.par2.xaxis.set_minor_formatter(ticker.FixedFormatter(labels1))
            self.par2.xaxis.set_ticks(x1)
            self.par2.xaxis.set_tick_params(which='minor',length=1,direction='out', pad=5, labelbottom='on')
            self.par2.xaxis.set_tick_params(which='major',length=10,direction='out', pad=5,labelbottom='on')

        except Exception as _err:
            print(_err)
            logging.exception(_err)

def InteractivePlaneProfile():
    srcLoc = 0.
    orientation = "X"
    nRx = 100

    def foo(Field, Sigma, Scale, Time):

        fig = plt.figure(figsize=(8,4))
        ax1 = plt.subplot(111)

        r = np.linspace(-1000., 0., nRx)
        val_ex, val_hy = PlaneEHfield(r, t=Time, sig=Sigma)

        if Field == "Ex":
            val = val_ex.flatten()
            label = "Ex-field (V/m)"

        elif Field == "Hy":
            val = val_hy.flatten()
            label = "Hy-field (A/m)"

        if Scale == "log":
            val_p, val_n = DisPosNegvalues(val)
            ax1.plot(r, val_p, 'k-', lw=2)
            ax1.plot(r, val_n, 'k--', lw=2)
            ax1.set_yscale(Scale)

        elif Scale == "linear":
            ax1.plot(r, val, 'k-', lw=2)
            ax1.set_yscale(Scale)
            y = ax1.yaxis.get_majorticklocs()
            yticksa = np.linspace(y.min(), y.max(), 3)
            ax1.yaxis.set_ticks(yticksa)
            ax1.yaxis.set_major_formatter(ticker.FormatStrFormatter("%.1e"))

        ax1.set_xlim(0, -1000)
        ax1.set_ylabel(label, color='k')
        ax1.set_xlabel("Z (m)")

        ax1.grid(True)
        plt.show()

    Q2 = widgets.interactive (foo
                    ,Field=widgets.ToggleButtons(options=['Ex','Hy'], value='Ex')
                    ,Sigma=widgets.FloatText(value=1, continuous_update=False, description='$\sigma$ (S/m)') \
                    ,Scale=widgets.ToggleButtons(options=['log','linear'], value="linear") \
                    ,Time=widgets.FloatSlider(min=0.01, max=1., step=0.01, value=0., description='$t$ (s)')
                    )
    return Q2

def porestreamlines(polygon=None, rx=10., ry=10., Nx=100, Ny=100,
                    maxvalue=None, **fields):
    "streamlines plot of vector field around nanopore"

    # interpolate on regular mesh symmetric w.r.t. center axis
    mesh2D = nanopores.RectangleMesh([-rx-0.1,-ry-0.1], [rx+0.1,ry+0.1], Nx, Ny)
    fields2 = nanopores.convert2D(mesh2D, *(fields.values()))

    # prepare polygon and copy to left half
    settings = dict(closed=True, facecolor="#eeeeee", linewidth=3.,
                    edgecolor="black")
    if polygon is not None:
        polygon = np.array(polygon)
        polygon_m = np.column_stack([-polygon[:,0], polygon[:,1]])

    # prepare plots
    Ny += 1
    Nx += 1
    Y, X = np.mgrid[-ry:ry:Ny*1j, -rx:rx:Nx*1j]
    U = np.zeros((Ny,Nx))
    V = np.zeros((Ny,Nx))
    formt = matplotlib.ticker.FuncFormatter(fmt)
    ticks = [0] + [10**n for n in range(-16, -9)]

    # determine uniform color range from fields (maybe round to nearest 10-power)
    if maxvalue is None:
        maxvalue = max(dolfin.norm(F.vector(), "linf") for F in fields2)
        #maxvalue = 10**int(np.log10(maxvalue))

    for i, F in enumerate(fields2):
        Fstr = fields.keys()[i]
        fig, ax = plt.subplots(figsize=(5, 4.5), num=Fstr)

        # fill array with function values
        for y in range(Ny):
            for x in range(Nx):
                f = F(X[y][x], Y[y][x])
                U[y][x] = f[0]
                V[y][x] = f[1]

        # streamplot with logarithmic scale
        strength = np.sqrt(U*U+V*V)
        norm = matplotlib.colors.SymLogNorm(linthresh=ticks[1], linscale=1.0,
                                            vmin=0., vmax=maxvalue)
        strm = plt.streamplot(X, Y, U, V, arrowsize=1.5, linewidth=1.5, density=1.5,
                              cmap=cm.viridis, color=strength, norm=norm)
        plt.colorbar(strm.lines, ticks=ticks, format=formt)
        plt.xlabel('x [nm]') #, fontsize=20)
        plt.ylabel('z [nm]') #, fontsize=20)

        # plot pore polygon on top
        if polygon is not None:
            patch = patches.Polygon(polygon, **settings)
            patchm = patches.Polygon(polygon_m, **settings)
            patch.set_zorder(10)
            patchm.set_zorder(10)
            ax.add_patch(patch)
            ax.add_patch(patchm)

def __init__(self, data, cbname, cblinthresh, cmap, extent, zlim,
                 figure_size, fontsize, aspect, figure, axes, cax):
        from matplotlib.ticker import ScalarFormatter
        self._draw_colorbar = True
        self._draw_axes = True
        self._fontsize = fontsize
        self._figure_size = figure_size

        # Compute layout
        fontscale = float(fontsize) / 18.0
        if fontscale < 1.0:
            fontscale = np.sqrt(fontscale)

        if iterable(figure_size):
            fsize = figure_size[0]
        else:
            fsize = figure_size
        self._cb_size = 0.0375*fsize
        self._ax_text_size = [1.2*fontscale, 0.9*fontscale]
        self._top_buff_size = 0.30*fontscale
        self._aspect = ((extent[1] - extent[0])/(extent[3] - extent[2])).in_cgs()
        self._unit_aspect = aspect

        size, axrect, caxrect = self._get_best_layout()

        super(WindowPlotMPL, self).__init__(
            size, axrect, caxrect, zlim, figure, axes, cax)

        self._init_image(data, cbname, cblinthresh, cmap, extent, aspect)

        # In matplotlib 2.1 and newer we'll be able to do this using
        # self.image.axes.ticklabel_format
        # See https://github.com/matplotlib/matplotlib/pull/6337
        formatter = ScalarFormatter(useMathText=True)
        formatter.set_scientific(True)
        formatter.set_powerlimits((-2, 3))
        self.image.axes.xaxis.set_major_formatter(formatter)
        self.image.axes.yaxis.set_major_formatter(formatter)
        if cbname == 'linear':
            self.cb.formatter.set_scientific(True)
            self.cb.formatter.set_powerlimits((-2, 3))
            self.cb.update_ticks()

def plot(self, filename):
        r"""Save an image file of the transfer function.

        This function loads up matplotlib, plots all of the constituent
        transfer functions and saves.

        Parameters
        ----------
        filename : string
            The file to save out the plot as.

        Examples
        --------

        >>> tf = ColorTransferFunction( (-10.0, -5.0) )
        >>> tf.add_layers(8)
        >>> tf.plot("sample.png")
        """
        from matplotlib import pyplot
        from matplotlib.ticker import FuncFormatter
        pyplot.clf()
        ax = pyplot.axes()
        i_data = np.zeros((self.alpha.x.size, self.funcs[0].y.size, 3))
        i_data[:,:,0] = np.outer(np.ones(self.alpha.x.size), self.funcs[0].y)
        i_data[:,:,1] = np.outer(np.ones(self.alpha.x.size), self.funcs[1].y)
        i_data[:,:,2] = np.outer(np.ones(self.alpha.x.size), self.funcs[2].y)
        ax.imshow(i_data, origin='lower')
        ax.fill_between(np.arange(self.alpha.y.size), self.alpha.x.size * self.alpha.y, y2=self.alpha.x.size, color='white')
        ax.set_xlim(0, self.alpha.x.size)
        xticks = np.arange(np.ceil(self.alpha.x[0]), np.floor(self.alpha.x[-1]) + 1, 1) - self.alpha.x[0]
        xticks *= (self.alpha.x.size-1) / (self.alpha.x[-1] - self.alpha.x[0])
        ax.xaxis.set_ticks(xticks)
        def x_format(x, pos):
            return "%.1f" % (x * (self.alpha.x[-1] - self.alpha.x[0]) / (self.alpha.x.size-1) + self.alpha.x[0])
        ax.xaxis.set_major_formatter(FuncFormatter(x_format))
        yticks = np.linspace(0,1,5) * self.alpha.y.size
        ax.yaxis.set_ticks(yticks)
        def y_format(y, pos):
            return (y / self.alpha.y.size)
        ax.yaxis.set_major_formatter(FuncFormatter(y_format))
        ax.set_ylabel("Transmission")
        ax.set_xlabel("Value")
        pyplot.savefig(filename)

def show(self, ax=None):
        r"""Display an image of the transfer function

        This function loads up matplotlib and displays the current transfer function.

        Parameters
        ----------

        Examples
        --------

        >>> tf = TransferFunction( (-10.0, -5.0) )
        >>> tf.add_gaussian(-9.0, 0.01, 1.0)
        >>> tf.show()
        """
        from matplotlib import pyplot
        from matplotlib.ticker import FuncFormatter
        pyplot.clf()
        ax = pyplot.axes()
        i_data = np.zeros((self.alpha.x.size, self.funcs[0].y.size, 3))
        i_data[:,:,0] = np.outer(np.ones(self.alpha.x.size), self.funcs[0].y)
        i_data[:,:,1] = np.outer(np.ones(self.alpha.x.size), self.funcs[1].y)
        i_data[:,:,2] = np.outer(np.ones(self.alpha.x.size), self.funcs[2].y)
        ax.imshow(i_data, origin='lower')
        ax.fill_between(np.arange(self.alpha.y.size), self.alpha.x.size * self.alpha.y, y2=self.alpha.x.size, color='white')
        ax.set_xlim(0, self.alpha.x.size)
        xticks = np.arange(np.ceil(self.alpha.x[0]), np.floor(self.alpha.x[-1]) + 1, 1) - self.alpha.x[0]
        xticks *= (self.alpha.x.size-1) / (self.alpha.x[-1] - self.alpha.x[0])
        if len(xticks) > 5:
            xticks = xticks[::len(xticks)/5]
        ax.xaxis.set_ticks(xticks)
        def x_format(x, pos):
            return "%.1f" % (x * (self.alpha.x[-1] - self.alpha.x[0]) / (self.alpha.x.size-1) + self.alpha.x[0])
        ax.xaxis.set_major_formatter(FuncFormatter(x_format))
        yticks = np.linspace(0,1,5) * self.alpha.y.size
        ax.yaxis.set_ticks(yticks)
        def y_format(y, pos):
            s = '%0.2f' % ( y )
            return s
        ax.yaxis.set_major_formatter(FuncFormatter(y_format))
        ax.set_ylabel("Opacity")
        ax.set_xlabel("Value")

def multi_bull_eyes(multi_data, cbar=None, cmaps=None, normalisations=None,
                    global_title=None, canvas_title='title', titles=None, units=None, raidal_subdivisions=(2, 8, 8, 11),
                    centered=(True, False, False, True), add_nomenclatures=(True, True, True, True),
                    pfi_where_to_save=None, show=True):
    plt.clf()
    n_fig = len(multi_data)
    if cbar is None:
        cbar = [True] * n_fig
    if cmaps is None:
        cmaps = [mpl.cm.viridis] * n_fig
    if normalisations is None:
        normalisations = [mpl.colors.Normalize(vmin=np.min(multi_data[i]), vmax=np.max(multi_data[i]))
                          for i in range(n_fig)]
    if titles is None:
        titles = ['Title {}'.format(i) for i in range(n_fig)]

    h_space = 0.15 / n_fig
    h_dim_fig = .8
    w_dim_fig = .8 / n_fig

    def fmt(x, pos):
        # a, b = '{:.2e}'.format(x).split('e')
        # b = int(b)
        # return r'${} \times 10^{{{}}}$'.format(a, b)
        return r"${:.4g}$".format(x)

    # Make a figure and axes with dimensions as desired.
    fig = plt.figure(figsize=(3 * n_fig, 4))
    fig.canvas.set_window_title(canvas_title)
    if global_title is not None:
        plt.suptitle(global_title)

    for n in range(n_fig):
        origin_fig = (h_space * (n + 1) + w_dim_fig * n, 0.15)
        ax = fig.add_axes([origin_fig[0], origin_fig[1], w_dim_fig, h_dim_fig], polar=True)
        bulls_eye(ax, multi_data[n], cmap=cmaps[n], norm=normalisations[n], raidal_subdivisions=raidal_subdivisions,
                  centered=centered, add_nomenclatures=add_nomenclatures[n])
        ax.set_title(titles[n], size=10)

        if cbar[n]:
            origin_cbar = (h_space * (n + 1) + w_dim_fig * n, .15)
            axl = fig.add_axes([origin_cbar[0], origin_cbar[1], w_dim_fig, .05])
            cb1 = mpl.colorbar.ColorbarBase(axl, cmap=cmaps[n], norm=normalisations[n], orientation='horizontal',
                                            format=ticker.FuncFormatter(fmt))
            cb1.ax.tick_params(labelsize=8)
            if units is not None:
                cb1.set_label(units[n])

    if pfi_where_to_save is not None:
        plt.savefig(pfi_where_to_save, format='pdf', dpi=330)
    if show:
        plt.show()

def setLineAMP(self):

        self.compressLabel(self.ampList)

        try:
            x1 = []
            x1pos = []
            labels1 = []
            j = -1
            x1pos.append(0)
            labels1.append('')
           # self.setTestData()
            for i in self.ampList:
                if i.startX > j:
                    x1.append(i.startX)
                    j = i.startX
                x1.append(i.stopX)
                x1pos.append(i.labelPos)
                labels1.append(i.label)


            if not self.line2TextFlag:
                # self.par3.text(-0.05,-0.33,"amp",horizontalalignment='left',transform=self.host.transAxes)
                # self.par3.text(-0.05,-0.40,"att",horizontalalignment='left',transform=self.host.transAxes)
               #  self.par3.spines["bottom"].set_position(("outward", 90))
                 self.par3.xaxis.set_ticks_position('bottom')
                 self.par3.xaxis.set_major_formatter(ticker.NullFormatter())
                 self.par3.xaxis.set_minor_formatter(ticker.NullFormatter())
                 self.par3.xaxis.set_tick_params(which='minor',length=1,direction='in', pad=-10, labelbottom='on')
                 self.par3.xaxis.set_tick_params(which='major',length=10,direction='in', pad=-20,labelbottom='on')
                 self.line2TextFlag = True

            self.par3.xaxis.set_minor_locator(ticker.FixedLocator(x1pos))
            self.par3.xaxis.set_minor_formatter(ticker.FixedFormatter(labels1))
            self.par3.xaxis.set_ticks(x1)

            #set color for autorange indication
            _Ret = self.par3.xaxis.get_minorticklabels()
            n = 0
            for i in _Ret:
                if self.ampList[n].color =='r':
                    i._color = 'r' #sorry, found no other access
                if n < len(self.ampList) - 1:
                    n += 1
        except Exception as _err:
            print(_err)
            logging.exception(_err)
        pass

def setLineATT(self):

        self.compressLabel(self.attList)

        try:
            x1 = []
            x1pos = []
            labels1 = []
            j = -1
            x1pos.append(0)
            labels1.append('')
           # self.setTestData()
            for i in self.attList:
                if i.startX > j:
                    x1.append(i.startX)
                    j = i.startX
                x1.append(i.stopX)
                x1pos.append(i.labelPos)
                labels1.append(i.label)


       #     self.par4.spines["bottom"].set_position(("outward", 90))
       #     self.par4.xaxis.set_ticks_position('bottom')
           # # self.par1.set_xlim(1e3,1e9)
           #  self.par4.set_xscale('log')
            self.par4.xaxis.set_major_formatter(ticker.NullFormatter())
            self.par4.xaxis.set_minor_locator(ticker.FixedLocator(x1pos))
            self.par4.xaxis.set_minor_formatter(ticker.FixedFormatter(labels1))
            self.par4.xaxis.set_ticks(x1)
            self.par4.xaxis.set_tick_params(which='minor',length=1,direction='out', pad=5, labelbottom='on')
            self.par4.xaxis.set_tick_params(which='major',length=10,direction='out', pad=5,labelbottom='on')

            _Ret = self.par4.xaxis.get_minorticklabels()
            n = 0
            for i in _Ret:
                if self.attList[n].color =='r':
                    i._color = 'red'
                if n < len(self.attList) - 1:
                    n += 1

          #  self.signalGraphUpdate.emit()
        except Exception as _err:
            print(_err)
            logging.exception(_err)
        pass

def onNewPlot(self, data):
     #   print ('Graph: new Plot')
        assert isinstance(data,DB_Handler_TPL3.Tpl3Plot)
        self.host.set_xlim(data.x1,data.x2)
        self.host.set_ylim(data.y1,data.y2)
        self.par1.set_xlim(data.x1,data.x2)
        self.par2.set_xlim(data.x1,data.x2)
        self.par3.set_xlim(data.x1,data.x2)
        self.par4.set_xlim(data.x1,data.x2)
        self.par1.set_xscale('log')
        self.par1.xaxis.set_major_formatter(ticker.NullFormatter())
        self.par1.xaxis.set_minor_formatter(ticker.NullFormatter())
        self.par2.set_xscale('log')
        self.par2.xaxis.set_major_formatter(ticker.NullFormatter())
        self.par2.xaxis.set_minor_formatter(ticker.NullFormatter())
        self.par3.set_xscale('log')
        self.par3.xaxis.set_major_formatter(ticker.NullFormatter())
        self.par3.xaxis.set_minor_formatter(ticker.NullFormatter())
        self.par4.set_xscale('log')
        self.par4.xaxis.set_major_formatter(ticker.NullFormatter())
        self.par4.xaxis.set_minor_formatter(ticker.NullFormatter())
        #self.setWindowTitle(data.plot_title)
        self.signalShowTitle.emit(data.plot_title)
        #self.figure_canvas.draw()
        descriptionData = []
        descriptionData.append(data.eut)
        descriptionData.append(data.test_no)
        descriptionData.append(data.serial_no)
        descriptionData.append(data.model_no)
        descriptionData.append(data.model_name)
        descriptionData.append(data.date_time)
        descriptionData.append(data.company)
        descriptionData.append(data.technician)
        descriptionData.append(data.plan_title)
        descriptionData.append(data.routines)
        descriptionData.append(data.plot_no)
        descriptionData.append(data.annotations)
        sList = data.sources.split(',')
        if len(sList) > 0:
            for s in sList:
                descriptionData.append(s)
        self.setText(descriptionData)
        self.signalGraphUpdate.emit()

        self.currentStaticPlotFlag = False
        pass