python, matplotlib: specgram data array values does not match specgram plot

Question

I am using matplotlib.pyplot.specgram and matplotlib.pyplot.pcolormesh to make spectrogram plots of a seismic signal.

Background information -The reason for us

Answer 1

First off, let's show an example of what you're describing so that other folks

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)

# Brownian noise sequence
x = np.random.normal(0, 1, 10000).cumsum()

fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(8, 10))

values, ybins, xbins, im = ax1.specgram(x, cmap='gist_earth')
ax1.set(title='Specgram')
fig.colorbar(im, ax=ax1)

mesh = ax2.pcolormesh(xbins, ybins, values, cmap='gist_earth')
ax2.axis('tight')
ax2.set(title='Raw Plot of Returned Values')
fig.colorbar(mesh, ax=ax2)

plt.show()

Magnitude Differences

You'll immediately notice the difference in magnitude of the plotted values.

By default, plt.specgram doesn't plot the "raw" values it returns. Instead, it scales them to decibels (in other words, it plots the 10 * log10 of the amplitudes). If you'd like it not to scale things, you'll need to specify scale="linear". However, for looking at frequency composition, a log scale is going to make the most sense.

With that in mind, let's mimic what specgram does:

plotted = 10 * np.log10(values)

fig, ax = plt.subplots()
mesh = ax.pcolormesh(xbins, ybins, plotted, cmap='gist_earth')

ax.axis('tight')
ax.set(title='Plot of $10 * log_{10}(values)$')
fig.colorbar(mesh)

plt.show()

Using a Log Color Scale Instead

Alternatively, we could use a log norm on the image and get a similar result, but communicate that the color values are on a log scale more clearly:

from matplotlib.colors import LogNorm

fig, ax = plt.subplots()
mesh = ax.pcolormesh(xbins, ybins, values, cmap='gist_earth', norm=LogNorm())

ax.axis('tight')
ax.set(title='Log Normalized Plot of Values')
fig.colorbar(mesh)

plt.show()

imshow vs pcolormesh

Finally, note that the examples we've shown have had no interpolation applied, while the original specgram plot did. specgram uses imshow, while we've been plotting with pcolormesh. In this case (regular grid spacing) we can use either.

Both imshow and pcolormesh are very good options, in this case. However,imshow will have significantly better performance if you're working with a large array. Therefore, you might consider using it instead, even if you don't want interpolation (e.g. interpolation='nearest' to turn off interpolation).

As an example:

extent = [xbins.min(), xbins.max(), ybins.min(), ybins.max()]

fig, ax = plt.subplots()
mesh = ax.imshow(values, extent=extent, origin='lower', aspect='auto',
                 cmap='gist_earth', norm=LogNorm())

ax.axis('tight')
ax.set(title='Log Normalized Plot of Values')
fig.colorbar(mesh)

plt.show()