重新取樣資料#

降低取樣會降低訊號的取樣率或取樣大小。在本教學中，當透過拖曳和縮放調整圖表時，訊號會降低取樣。

注意

這個範例練習 Matplotlib 的互動功能，這不會出現在靜態文件中。請在您的機器上執行此程式碼以查看互動性。

您可以複製並貼上個別部分，或使用頁面底部的連結下載整個範例。

import matplotlib.pyplot as plt
import numpy as np


# A class that will downsample the data and recompute when zoomed.
class DataDisplayDownsampler:
    def __init__(self, xdata, y1data, y2data):
        self.origY1Data = y1data
        self.origY2Data = y2data
        self.origXData = xdata
        self.max_points = 50
        self.delta = xdata[-1] - xdata[0]

    def plot(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 range
        mask = (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 line
        mask = np.convolve([1, 1, 1], mask, mode='same').astype(bool)
        # sort out how many points to drop
        ratio = max(np.sum(mask) // self.max_points, 1)

        # mask data
        xdata = self.origXData[mask]
        y1data = self.origY1Data[mask]
        y2data = self.origY2Data[mask]

        # downsample data
        xdata = xdata[::ratio]
        y1data = y1data[::ratio]
        y2data = y2data[::ratio]

        print(f"using {len(y1data)} of {np.sum(mask)} visible points")

        return xdata, y1data, y2data

    def update(self, ax):
        # Update the artists
        lims = ax.viewLim
        if abs(lims.width - self.delta) > 1e-8:
            self.delta = lims.width
            xstart, xend = lims.intervalx
            x, 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 signal
xdata = np.linspace(16, 365, (365-16)*4)
y1data = np.sin(2*np.pi*xdata/153) + np.cos(2*np.pi*xdata/127)
y2data = y1data + .2

d = DataDisplayDownsampler(xdata, y1data, y2data)

fig, ax = plt.subplots()

# Hook up the line
d.plot(ax)
ax.set_autoscale_on(False)  # Otherwise, infinite loop

# Connect for changing the view limits
ax.callbacks.connect('xlim_changed', d.update)
ax.set_xlim(16, 365)
plt.show()