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Again, numerous changes.

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Mostly figure polishing and fixing.
Crucial fix to "short" invariance analysis.
This commit is contained in:
j-hartling
2026-04-21 17:49:30 +02:00
parent 3b4b7f2161
commit 1a586848e8
41 changed files with 1392 additions and 242 deletions
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152
python/fig_invariance_full.py
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@@ -4,10 +4,11 @@ import matplotlib.pyplot as plt
from itertools import product
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data
from thunderhopper.filtertools import find_kern_specs
from misc_functions import get_saturation
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
plot_line, strip_zeros, time_bar,\
plot_line, strip_zeros, time_bar, set_clip_box,\
letter_subplot, letter_subplots
from IPython import embed
@@ -28,10 +29,19 @@ def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
ax.fill_between(scales, *spread_measure, **fill_kwargs)
return median_measure
def show_saturation(ax, scales, measures, high=0.95, **kwargs):
high_ind = get_saturation(measures, high=high)[1]
return ax.plot(scales[high_ind], 0, transform=ax.get_xaxis_transform(),
marker='o', ms=10, zorder=6, clip_on=False, **kwargs)
def exclude_zero_scale(data, stages):
inds = data['scales'] > 0
data['scales'] = data['scales'][inds]
for stage in stages:
data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
return data
def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
for stage in stages:
key = f'{keyword}_{stage}'
data[key] = data[key][:, inds, ...]
return data
# GENERAL SETTINGS:
target_species = [
@@ -52,21 +62,34 @@ example_file = {
'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
}[target_species]
raw_path = search_files(target_species, incl='raw', dir='../data/inv/full/condensed/')[0]
norm_path = search_files(target_species, incl='norm', dir='../data/inv/full/condensed/')[0]
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
trace_path = search_files(target_species, dir='../data/inv/full/collected/')[0]
ref_path = '../data/inv/full/ref_measures.npz'
save_path = '../figures/fig_invariance_full.pdf'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
load_kwargs = dict(
files=stages,
keywords=['scales', 'snip', 'measure']
)
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/full/condensed/')[0]
base_path = search_files(target_species, incl='base', dir='../data/inv/full/condensed/')[0]
range_path = search_files(target_species, incl='range', dir='../data/inv/full/condensed/')[0]
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
save_path = '../figures/fig_invariance_full.pdf'
# ANALYSIS SETTINGS:
exclude_zero = True
# SUBSET SETTINGS:
types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
sigmas = np.array([0.004, 0.008, 0.016, 0.032])
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
kernels = np.array([
[1, 0.002],
[-1, 0.002],
[2, 0.004],
[-2, 0.004],
[3, 0.032],
[-3, 0.032]
])
kernels = None
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 20/2.54),
figsize=(32/2.54, 32/2.54),
)
super_grid_kwargs = dict(
nrows=2,
@@ -222,16 +245,25 @@ plateau_dot_kwargs = dict(
# EXECUTION:
# Load invariance data:
raw_data, config = load_data(raw_path, files='scales', keywords='mean')
norm_data, _ = load_data(norm_path, files='scales', keywords='mean')
scales = raw_data['scales']
# Load raw (unnormed) invariance data:
data, config = load_data(raw_path, files='scales', keywords='mean')
if exclude_zero:
data = exclude_zero_scale(data, stages)
scales = data['scales']
# Load snippet data:
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
snip_scales = snip['example_scales']
# Optional kernel subset:
reduce_kernels = False
if any(var is not None for var in [kernels, types, sigmas]):
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
data = reduce_kernel_set(data, kern_inds, keyword='mean')
snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
reduce_kernels = True
# Adjust grid parameters:
snip_grid_kwargs['ncols'] = snip_scales.size
@@ -270,43 +302,48 @@ for i in range(big_grid.ncols):
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
if i < (big_grid.ncols - 1):
ax.set_ylim(scales[0], scales[-1])
else:
ax.set_ylim(0, 1)
big_axes[i] = ax
letter_subplots(big_axes, 'bc', **letter_big_kwargs)
letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
if False:
if True:
# Plot filtered snippets:
plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
c=colors['filt'], lw=lw['filt'])
c=colors['filt'], lw=lw['filt'])
# Plot envelope snippets:
plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
ymin=0, c=colors['env'], lw=lw['env'])
ymin=0, c=colors['env'], lw=lw['env'])
# Plot logarithmic snippets:
plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
c=colors['log'], lw=lw['log'])
c=colors['log'], lw=lw['log'])
# Plot invariant snippets:
plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
c=colors['inv'], lw=lw['inv'])
c=colors['inv'], lw=lw['inv'])
# Plot kernel response snippets:
plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
c=colors['conv'], lw=lw['conv'])
c=colors['conv'], lw=lw['conv'])
# Plot feature snippets:
plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
del snip
# Plot analysis results:
# Remember saturation points:
crit_inds, crit_scales = {}, {}
# Unnormed measures:
for stage in stages:
# Get average unnormed measure across recordings:
raw_measure = raw_data[f'mean_{stage}'].mean(axis=-1)
# Plot unmodified intensity measures:
curve = plot_curves(big_axes[0], scales, raw_measure, c=colors[stage], lw=lw['big'],
# Plot average intensity measure across recordings:
curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
# Indicate saturation point:
if stage in ['log', 'inv', 'conv', 'feat']:
ind = get_saturation(curve, **plateau_settings)[1]
@@ -317,43 +354,60 @@ for stage in stages:
transform=big_axes[0].get_xaxis_transform())
big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
color=colors[stage], **plateau_line_kwargs)
# Log saturation point:
crit_inds[stage] = ind
crit_scales[stage] = scale
del data
# Get average noise-related measure across recordings:
norm_measure = norm_data[f'mean_{stage}'].mean(axis=-1)
# Plot noise-related intensity measure:
curve = plot_curves(big_axes[1], scales, norm_measure, c=colors[stage], lw=lw['big'],
# Noise baseline-related measures:
data, _ = load_data(base_path, files='scales', keywords='mean')
if exclude_zero:
data = exclude_zero_scale(data, stages)
if reduce_kernels:
data = reduce_kernel_set(data, kern_inds, keyword='mean')
for stage in stages:
# Plot average intensity measure across recordings:
curve = plot_curves(big_axes[1], scales, data[f'mean_{stage}'].mean(axis=-1),
c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
# Indicate saturation point:
if stage in ['log', 'inv', 'conv', 'feat']:
ind, scale = crit_inds[stage], crit_scales[stage]
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[1].get_xaxis_transform())
big_axes[1].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=big_axes[1].get_xaxis_transform())
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
color=colors[stage], **plateau_line_kwargs)
del data
# Normalize measure to [0, 1]:
min_measure = raw_measure.min(axis=0)
max_measure = raw_measure.max(axis=0)
norm_measure = (raw_measure - min_measure) / (max_measure - min_measure)
# Plot range-normalized intensity measure:
curve = plot_curves(big_axes[2], scales, norm_measure, c=colors[stage], lw=lw['big'],
# Min-max normalized measures:
data, _ = load_data(range_path, files='scales', keywords='mean')
if exclude_zero:
data = exclude_zero_scale(data, stages)
if reduce_kernels:
data = reduce_kernel_set(data, kern_inds, keyword='mean')
for stage in stages:
# Plot average intensity measure across recordings:
curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
c=colors[stage], lw=lw['big'],
fill_kwargs=dict(color=colors[stage], alpha=0.25))
# Indicate saturation point:
if stage in ['log', 'inv', 'conv', 'feat']:
ind, scale = crit_inds[stage], crit_scales[stage]
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[2].get_xaxis_transform())
big_axes[2].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=big_axes[2].get_xaxis_transform())
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
color=colors[stage], **plateau_line_kwargs)
del data
# Save graph:
if save_path is not None:
fig.savefig(save_path)
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
fig.savefig(file_name)
plt.show()
print('Done.')