Bug report

Bug summary

SymLog scale is exactly what I need to display various reinforcement learning related metrics as they can be both positive and negative, interesting mainly in the -10 to 10 range, and frequently explode to very large values only to come back "to sanity" later.

Unfortunately, if things go well and metrics don't go insane, current SymLog graphs have too few ticks and is not possible to understand what am I looking at. Here is a synthetic example that illustrates the issue quite well:

Code for reproduction

import matplotlib.pyplot as plt
import numpy as np

st = np.random.standard_cauchy(size=25)
en = np.random.standard_cauchy(size=25)

fig, axen = plt.subplots(5, 5, figsize=(20, 20))
fig.tight_layout()
for ax, s, e in zip(np.ravel(axen), st, en):
    ax.plot(np.linspace(s, e, 5))
    ax.set_yscale('symlog')

Actual outcome

Expected outcome

It would be awesome to get more ticks and nicer labels!

Matplotlib version

matplotlib 3.1.1

I think the versions of all the remaining stuff are irrelevant, as this is an issue in the SymmetricalLogLocator class, which is built on an assumption that does not hold in my case:

        # b) has a tick at 0 and only 0 (we assume t is a small
        # number, and the linear segment is just an implementation
        # detail and not interesting.)