I transformed the sampling density to uniform with inverse cdf.

```
def equalize_arr(arr):
"""Equalize array (n, m),
n=number of samples
m=number of parameters
"""
n, *m = arr.shape
ravel = False
if not m:
arr = arr[:, None]
ravel = True
ind = np.indices(arr.shape)
sort_order = np.argsort(arr, axis=0)
sort_back_order = np.argsort(sort_order, axis=0)
sorted_arr = arr[sort_order, ind[1]]
sorted_csum = (sorted_arr - sorted_arr.min(0)).cumsum(0)
sorted_csum /= sorted_csum[-1, :]
equalized_arr = sorted_csum[sort_back_order, ind[1]]
if ravel:
equalized_arr = equalized_arr.ravel()
return equalized_arr
```

One can do other kind of normalizations too, but if the sample space is closer to log than linear it can show all the values concentrating near 0.

Plotting code

```
extract = fit.extract(permuted=False, inc_warmup=False)
ext = extract.reshape(-1, extract.shape[-1], order='F')
divs = np.concatenate([item['divergent__'][fit.sim['warmup']:] for item in fit.get_sampler_params()])
divs_bool = divs.astype(bool)
ext_eq = equalize_arr(ext)
div_samples = ext[divs_bool, :]
nondiv_samples = ext[~divs_bool, :]
div_samples_eq = ext_eq[divs_bool, :]
nondiv_samples_eq = ext_eq[~divs_bool, :]
plt.style.use('ggplot')
plt.figure(figsize=(8,8))
plt.subplot(211)
plt.plot(nondiv_samples.T, color='k', lw=1, alpha=0.02);
plt.plot(div_samples.T, color='lime', lw=1, alpha=0.1);
plt.xticks(np.arange(ext.shape[-1]), fit.sim['fnames_oi'], rotation=90)
plt.grid('off') # due to ggplot style
plt.grid(axis='x')
plt.subplot(212)
plt.plot(nondiv_samples_eq.T, color='k', lw=1, alpha=0.02);
plt.plot(div_samples_eq.T, color='lime', lw=1, alpha=0.1);
plt.xticks(np.arange(ext.shape[-1]), fit.sim['fnames_oi'], rotation=90)
plt.grid('off') # due to ggplot style
plt.grid(axis='x')
plt.tight_layout()
```