更改 y 对数比例 imshow()

问题描述

我试图根据时间段绘制 y 尺度的频谱图，所以我想要一个反向的对数尺度.

问题是:我找到了使用 pcolormesh()而不是使用 imshow()的方法. imshow()似乎比 pcolormesh()更有效率，也就是说，对我来说，有理由更喜欢它！

我错过了什么吗?

我不知道如何说得更清楚，所以这里有一个可重现的例子:

 将matplotlib.pyplot导入为plt将numpy导入为np大小 = 10数据 = np.arange(size * size).reshape((size, size))x_start = 1x_end = 10y_start = 1y_end = 10范围 = [x_start, x_end, y_start, y_end]图，轴 = plt.subplots(1,4)轴 [0].set_yscale('log')im =axis[0].imshow(data,extent=extent,origin='upper',interpolation='None',cmap='viridis')轴 [1].set_yscale('log')im2 =轴[1] .imshow(数据，范围=范围，原点=下限"，插值=无"，cmap ="viridis")axes [2] .set_yscale('log')im2 = 轴 [2].imshow(数据，范围=范围，原点='下'，插值='无'，cmap='viridis')轴[2].invert_yaxis()y = np.arange(1,11)*0.1x = np.arange(0,10)axes [3] .set_yscale('log')im3 =轴[3] .pcolormesh(x，1/y，数据)axes [0] .set_title(不正确")axes [1] .set_title(不正常")axes [2] .set_title(不正常")轴[3].set_title("OK")plt.tight_layout()plt.show()

您在上一张图像中看到，使用 imshow()不会改变紧坐标，即使我使用 lower 或upper origin.通过 pcolormesh()，我实现了在图的底部获得紧密的坐标.

我希望通过使用 imshow() 获得ok"的数字！

这个问题是相对于这个问题的:

I'm trying to plot spectrogram with y-scale depending on the period, so I want to have a reversed log-scale.

The thing is: I found how to do it using pcolormesh(), not by using imshow(). imshow() seems to be more efficient than pcolormesh(), that is, for me, a great reason to prefer it !

Did I miss something ?

I do not know how to say more clearly so here is a reproducible example:

import matplotlib.pyplot as plt
import numpy as np

size = 10
data = np.arange(size * size).reshape((size, size))

x_start = 1
x_end = 10
y_start = 1
y_end = 10

extent = [x_start, x_end, y_start, y_end]

fig, axes = plt.subplots(1,4)

axes[0].set_yscale('log')
im = axes[0].imshow(data, extent=extent, origin='upper', interpolation='None', cmap='viridis')


axes[1].set_yscale('log')
im2 = axes[1].imshow(data, extent=extent, origin='lower', interpolation='None', cmap='viridis')

axes[2].set_yscale('log')
im2 = axes[2].imshow(data, extent=extent, origin='lower', interpolation='None', cmap='viridis')
axes[2].invert_yaxis()

y = np.arange(1,11)*0.1
x = np.arange(0,10)
axes[3].set_yscale('log')
im3 = axes[3].pcolormesh(x, 1/y , data)

axes[0].set_title("not ok")
axes[1].set_title("not ok")
axes[2].set_title("not ok")
axes[3].set_title("OK")

plt.tight_layout()

plt.show()

You see on the previous image that tight ordinates do not change using imshow(), they are always on the top of the figure even if I use lower or upper origin. With pcolormesh(), I achieved to get tight ordinates on the bottom of the graph.

I would dream to get the "ok" figure by using imshow() !

This question is relative to this one: Aggregate several AxesSubplot after multiprocessing to draw a matplotlib figure

解决方案

Is this what you are trying to get?

axes[1].set_yscale('log')
im2 = axes[1].imshow(data, extent=extent, origin='lower', interpolation='None', cmap='viridis')
axes[1].invert_yaxis()

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