importmatplotlib.pyplotaspltimportnumpyasnp# A class that will downsample the data and recompute when zoomed.classDataDisplayDownsampler:def__init__(self,xdata,y1data,y2data):self.origY1Data=y1dataself.origY2Data=y2dataself.origXData=xdataself.max_points=50self.delta=xdata[-1]-xdata[0]defplot(self,ax):x,y1,y2=self._downsample(self.origXData.min(),self.origXData.max())(self.line,)=ax.plot(x,y1,'o-')self.poly_collection=ax.fill_between(x,y1,y2,step="pre",color="r")def_downsample(self,xstart,xend):# get the points in the view rangemask=(self.origXData>xstart)&(self.origXData<xend)# dilate the mask by one to catch the points just outside# of the view range to not truncate the linemask=np.convolve([1,1,1],mask,mode='same').astype(bool)# sort out how many points to dropratio=max(np.sum(mask)//self.max_points,1)# mask dataxdata=self.origXData[mask]y1data=self.origY1Data[mask]y2data=self.origY2Data[mask]# downsample dataxdata=xdata[::ratio]y1data=y1data[::ratio]y2data=y2data[::ratio]print(f"using{len(y1data)} of{np.sum(mask)} visible points")returnxdata,y1data,y2datadefupdate(self,ax):# Update the artistslims=ax.viewLimifabs(lims.width-self.delta)>1e-8:self.delta=lims.widthxstart,xend=lims.intervalxx,y1,y2=self._downsample(xstart,xend)self.line.set_data(x,y1)self.poly_collection.set_data(x,y1,y2,step="pre")ax.figure.canvas.draw_idle()# Create a signalxdata=np.linspace(16,365,(365-16)*4)y1data=np.sin(2*np.pi*xdata/153)+np.cos(2*np.pi*xdata/127)y2data=y1data+.2d=DataDisplayDownsampler(xdata,y1data,y2data)fig,ax=plt.subplots()# Hook up the lined.plot(ax)ax.set_autoscale_on(False)# Otherwise, infinite loop# Connect for changing the view limitsax.callbacks.connect('xlim_changed',d.update)ax.set_xlim(16,365)plt.show()

using 52 of 1396 visible points

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