用频率（kHz）替换imshow的x轴索引

huangapple
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英文:

Replace x-axis index of imshow with frequency (kHz)

问题

以下是您提供的代码的翻译部分：

  1. 我有以下绘图
  3. 导入numpy为np
  4. 导入matplotlib.pyplot为plt
  6. fs = 1000
  7. t = np.arange(0, 0.1, 1/fs)
  8. N = len(t)
  9. f_bin = fs / N
  10. f = np.arange(0, fs, f_bin)
  11. X = [np.fft.fft(np.sin(2 * np.pi * 100 * t)), np.fft.fft(np.sin(2 * np.pi * 200 * t)), np.fft.fft(np.sin(2 * np.pi * 300 * t))]
  12. M = np.absolute(X)
  14. fig, ax = plt.subplots()
  15. im = ax.imshow(M[:,:len(M[0,:]) // 2], cmap='viridis', aspect=1)
  16. clb = fig.colorbar(im, ax=ax)
  17. clb.ax.set_title("Magnitude")
  18. ax.set_xlabel(f"Index (f[{round(100/f_bin)}] = {f[round(100/f_bin)]} Hz)")
  19. ax.set_ylabel("# FFT")
  20. ax.set_title("Built-in FFT")
  21. plt.show()

这段代码产生了以下结果：

用频率（kHz）替换imshow的x轴索引

是否有一种（好的）方法可以使用f_bin和f将“Index”转换为实际的“frequency bin”？

用频率（kHz）替换imshow的x轴索引

我希望我没有忽略在Stack Overflow中已经存在的解决方案。

  2. <details>
  3. <summary>英文:</summary>
  5. I have following plot
  7.     import numpy as np
  8.     import matplotlib.pyplot as plt
  9.     
  10.     fs = 1000
  11.     t = np.arange(0, 0.1, 1/fs)
  12.     N = len(t)
  13.     f_bin = fs / N
  14.     f = np.arange(0, fs, f_bin)
  15.     X = [np.fft.fft(np.sin(2 * np.pi * 100 * t)), np.fft.fft(np.sin(2 * np.pi * 200 * t)), np.fft.fft(np.sin(2 * np.pi * 300 * t))]
  16.     M = np.absolute(X)
  17.     
  18.     fig, ax = plt.subplots()
  19.     im = ax.imshow(M[:,:len(M[0,:]) // 2], cmap=&#39;viridis&#39;, aspect=1)
  20.     clb = fig.colorbar(im, ax=ax)
  21.     clb.ax.set_title(&quot;Magnitude&quot;)
  22.     ax.set_xlabel(f&quot;Index (f[{round(100/f_bin)}] = {f[round(100/f_bin)]} Hz)&quot;)
  23.     ax.set_ylabel(&quot;# FFT&quot;)
  24.     ax.set_title(f&quot;Built-in FFT&quot;)
  25.     plt.show()
  27. which results in
  29. [![enter image description here][2]][2]
  31. Is there a (nice) solution to transform the &quot;Index&quot; into the actual &quot;frequency bin&quot; by using `f_bin` and `f`
  33. [![enter image description here][1]][1]
  35. I hope I haven&#39;t overlooked some already existing solutions in `Stack Overflow`.
  37.   [1]: https://i.stack.imgur.com/cM1ck.png
  38.   [2]: https://i.stack.imgur.com/ZKsSi.png
  40. </details>
  43. # 答案1
  44. **得分**: 1
  46. 你可以使用`ax.set_xticklabels`为你的xtick标签设置新值：
  48. ```python
  49. import numpy as np
  50. import matplotlib.pyplot as plt
  52. fs = 1000
  53. t = np.arange(0, 0.1, 1/fs)
  54. N = len(t)
  55. f_bin = fs / N
  56. f = np.arange(0, fs, f_bin)
  57. X = [np.fft.fft(np.sin(2 * np.pi * 100 * t)), np.fft.fft(np.sin(2 * np.pi * 200 * t)), np.fft.fft(np.sin(2 * np.pi * 300 * t))]
  58. M = np.absolute(X)
  60. fig, ax = plt.subplots()
  61. im = ax.imshow(M[:,:len(M[0,:]) // 2], cmap='viridis', aspect=1)
  62. clb = fig.colorbar(im, ax=ax)
  63. clb.ax.set_title("Magnitude")
  64. ax.set_xlabel(f"Frequency (kHz)")
  65. ax.set_ylabel("# FFT")
  66. ax.set_title(f"Built-in FFT")
  67. ax.set_xticklabels([x/100 for x in ax.get_xticks()])
  68. plt.show()

输出：

用频率（kHz）替换imshow的x轴索引

英文:

You can set new values for your xtick labels with ax.set_xticklabels:

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. fs = 1000
  5. t = np.arange(0, 0.1, 1/fs)
  6. N = len(t)
  7. f_bin = fs / N
  8. f = np.arange(0, fs, f_bin)
  9. X = [np.fft.fft(np.sin(2 * np.pi * 100 * t)), np.fft.fft(np.sin(2 * np.pi * 200 * t)), np.fft.fft(np.sin(2 * np.pi * 300 * t))]
  10. M = np.absolute(X)
  12. fig, ax = plt.subplots()
  13. im = ax.imshow(M[:,:len(M[0,:]) // 2], cmap=&#39;viridis&#39;, aspect=1)
  14. clb = fig.colorbar(im, ax=ax)
  15. clb.ax.set_title(&quot;Magnitude&quot;)
  16. ax.set_xlabel(f&quot;Frequency (kHz)&quot;)
  17. ax.set_ylabel(&quot;# FFT&quot;)
  18. ax.set_title(f&quot;Built-in FFT&quot;)
  19. ax.set_xticklabels([x/100 for x in ax.get_xticks()])
  20. plt.show()

Output:

用频率（kHz）替换imshow的x轴索引

答案2

得分: 1

关于显示类似这样的二维数据并设置坐标轴时，你应该使用 pcolormesh。在这种情况下，你的 x 值由 f/1000 设置，y 值由 M 中的行数枚举（在这种情况下为 3）。假设你仍然想要一个宽图，你可以设置纵横比以实现这一点。我还将 y 刻度更改为只显示 y 值。

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. plt.close("all")
  6. fs = 1000
  7. t = np.arange(0, 0.1, 1/fs)
  8. f_bin = fs/len(t)
  9. f = np.arange(0, fs, f_bin)
  10. X = [np.fft.fft(np.sin(2*np.pi*100*t)),
  11.      np.fft.fft(np.sin(2*np.pi*200*t)),
  12.      np.fft.fft(np.sin(2*np.pi*300*t))]
  13. M = np.absolute(X)
  15. fig, ax = plt.subplots()
  17. s = np.s_[:, :len(M[0, :]) // 2]
  18. x = f
    展开收缩
    ]/1000
  19. y = np.arange(M.shape[0])
  21. p = ax.pcolormesh(x, y, M
    展开收缩
    , cmap="viridis")
  22. fig.colorbar(p, ax=ax, label="Magnitude")
  24. ax.set_yticks(y)
  25. ax.set_aspect(10*y.max()/fs)
  26. ax.set_xlabel("Frequency [kHz]")
  27. ax.set_ylabel("# FFT")
  28. ax.set_title("Built-in FFT")
  29. fig.show()

用频率（kHz）替换imshow的x轴索引

  2. [1]: https://i.stack.imgur.com/yKcGU.png
英文:

When it comes to showing 2D data like this while setting the axes, you should use pcolormesh. In this case, your x values are set by f/1000 and your y values are enumerated by the number of rows in M (in this case: 3). Assuming you still want a fat plot, you can set the aspect ratio to achieve that. I also changed the y-ticks to just be the y values.

  1. import numpy as np
  2. import matplotlib.pyplot as plt
  4. plt.close(&quot;all&quot;)
  6. fs = 1000
  7. t = np.arange(0, 0.1, 1/fs)
  8. f_bin = fs/len(t)
  9. f = np.arange(0, fs, f_bin)
  10. X = [np.fft.fft(np.sin(2*np.pi*100*t)),
  11.      np.fft.fft(np.sin(2*np.pi*200*t)),
  12.      np.fft.fft(np.sin(2*np.pi*300*t))]
  13. M = np.absolute(X)
  15. fig, ax = plt.subplots()
  17. s = np.s_[:, :len(M[0, :]) // 2]
  18. x = f
    展开收缩
    ]/1000
  19. y = np.arange(M.shape[0])
  21. p = ax.pcolormesh(x, y, M
    展开收缩
    , cmap=&quot;viridis&quot;)
  22. fig.colorbar(p, ax=ax, label=&quot;Magnitude&quot;)
  24. ax.set_yticks(y)
  25. ax.set_aspect(10*y.max()/fs)
  26. ax.set_xlabel(&quot;Frequency [kHz]&quot;)
  27. ax.set_ylabel(&quot;# FFT&quot;)
  28. ax.set_title(&quot;Built-in FFT&quot;)
  29. fig.show()

用频率（kHz）替换imshow的x轴索引

huangapple
  • 本文由 发表于 2023年8月10日 14:04:01
  • 转载请务必保留本文链接：https://go.coder-hub.com/76872995.html
  • matplotlib
  • numpy
  • python
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