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Again, numerous changes.

Browse Source 
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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\newlabel{eq:feat_avg}{{16}{18}{}{}{}}
\newlabel{eq:feat_prop}{{17}{18}{}{}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {} }}{20}{}\protected@file@percent }
\newlabel{}{{9}{20}{}{}{}}
\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{19}{}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {5}Conclusions \& outlook}{19}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {} }}{21}{}\protected@file@percent }
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@@ -713,7 +713,7 @@ the signal for reliable song recognition.
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_full.pdf}
\includegraphics[width=\textwidth]{figures/fig_invariance_full_Omocestus_rufipes.pdf}
\caption{\textbf{Step-wise emergence of intensity invariant song
representation along the model pathway.}
}
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\end{figure}
\FloatBarrier
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_short_Omocestus_rufipes.pdf}
\caption{\textbf{Step-wise emergence of intensity invariant song
representation along the model pathway.}
}
\label{fig:inv_thresh-lp_short}
\end{figure}
\FloatBarrier
The second key mechanism for the emergence of intensity invariance along the
model pathway takes place during the transformation of the kernel responses
$c_i(t)$ over the binary responses $b_i(t)$ into the finalized features
@@ -888,6 +898,15 @@ initiation of one behavior over another is categorical (e.g. approach/stay)
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import numpy as np
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data, save_data
from IPython import embed
# GENERAL SETTINGS:
target_species = ['Pseudochorthippus_parallelus']
mode = ['song', 'noise'][1]
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
search_path = f'../data/inv/field/{mode}/'
save_path = f'../data/inv/field/{mode}/collected/'
# EXECUTION:
for i, species in enumerate(target_species):
print(f'Processing {species}')
# Fetch all species-specific song files:
all_paths = search_files(species, ext='npz', dir=search_path)
if not all_paths:
continue
# Run through files:
for j, path in enumerate(all_paths):
# Load invariance data:
data, config = load_data(path, 'distances', 'measure')
if j == 0:
# Prepare species-specific storage:
species_data = dict(scales=data['distances'])
for stage in stages:
mkey = f'measure_{stage}'
shape = data[mkey].shape + (len(all_paths),)
species_data[mkey] = np.zeros(shape, dtype=float)
# Log species data:
for stage in stages:
mkey = f'measure_{stage}'
species_data[mkey][..., j] = data[mkey]
# Save collected file data:
save_name = save_path + species
save_data(save_name, species_data, config, overwrite=True)
print('Done.')

123
python/condense_inv_data_field.py Normal file
View File

@@ -0,0 +1,123 @@
import numpy as np
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.modeltools import load_data, save_data
from IPython import embed
def sort_files_by_rec(paths, sources=['JJ', 'SLO']):
# Separate by source:
sorted_paths = {}
for source in sources:
# Check for any source-specific song files:
source_paths = [path for path in paths if source in path]
if not source_paths:
continue
# Separate by recording:
sorted_paths[source] = {}
for path, name in zip(source_paths, crop_paths(source_paths)):
# Find global time stamp behind source tag:
ind = name.find(source) + len(source) + 1
time_stamps = name[ind:].split('_')[-1]
global_time = '-'.join(time_stamps.split('-')[:2])
if global_time in sorted_paths[source]:
# Found existing time stamp (known recording):
sorted_paths[source][global_time].append(path)
else:
# Found new time stamp (novel recording):
sorted_paths[source][global_time] = [path]
# Re-sort song files by recording only (discarding source separation):
flat_sorted = []
for source_paths in sorted_paths.values():
for rec_paths in source_paths.values():
flat_sorted.append(rec_paths)
return flat_sorted
# GENERAL SETTINGS:
target_species = ['Pseudochorthippus_parallelus']
mode = ['song', 'noise'][0]
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
search_path = f'../data/inv/field/{mode}/'
save_path = f'../data/inv/field/{mode}/condensed/'
sources = [
'JJ',
'SLO',
]
# ANALYSIS SETTINGS:
normalization = 'none'
if mode == 'song':
normalization = [
'none',
# 'base',
'range'
][-1]
# EXECUTION:
for i, species in enumerate(target_species):
print(f'Processing {species}')
# Fetch all species-specific song files:
all_paths = search_files(species, ext='npz', dir=search_path)
if not all_paths:
continue
# Sort song files by recording (one or more per source):
sorted_paths = sort_files_by_rec(all_paths, sources)
# Condense across song files per recording:
for j, rec_paths in enumerate(sorted_paths):
for k, path in enumerate(rec_paths):
# Load invariance data:
data, config = load_data(path, 'distances', 'measure')
if k == 0:
# Prepare song file-specific storage:
file_data = {}
for stage in stages:
shape = data[f'measure_{stage}'].shape + (len(rec_paths),)
file_data[stage] = np.zeros(shape, dtype=float)
if j == 0:
# Prepare recording-specific storage:
rec_mean, rec_sd = {}, {}
for stage in stages:
shape = data[f'measure_{stage}'].shape + (len(sorted_paths),)
rec_mean[f'mean_{stage}'] = np.zeros(shape, dtype=float)
rec_sd[f'sd_{stage}'] = np.zeros(shape, dtype=float)
# Log song file data:
for stage in stages:
mkey = f'measure_{stage}'
if normalization == 'range':
# Min-max normalization:
min_measure = data[mkey].min(axis=0, keepdims=True)
max_measure = data[mkey].max(axis=0, keepdims=True)
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
file_data[stage][..., k] = data[mkey]
# Get recording statistics:
for stage in stages:
rec_mean[f'mean_{stage}'][..., j] = np.nanmean(file_data[stage], axis=-1)
rec_sd[f'sd_{stage}'][..., j] = np.nanstd(file_data[stage], axis=-1)
# Save condensed recording data:
save_name = save_path + species
if normalization == 'none':
save_name += '_unnormed'
elif normalization == 'base':
save_name += '_norm-base'
elif normalization == 'range':
save_name += '_norm-range'
archive = dict(distances=data['distances'])
archive.update(rec_mean)
archive.update(rec_sd)
save_data(save_name, archive, config, overwrite=True)
print('Done.')

26
python/condense_inv_data_full.py
View File

@@ -26,7 +26,11 @@ search_path = '../data/inv/full/'
save_path = '../data/inv/full/condensed/'
# ANALYSIS SETTINGS:
compute_ratios = False
normalization = [
'none',
'base',
'range'
][2]
# EXECUTION:
for i, species in enumerate(target_species):
@@ -64,8 +68,16 @@ for i, species in enumerate(target_species):
# Log song file data:
for stage in stages:
mkey = f'measure_{stage}'
if compute_ratios:
if normalization == 'base':
# Noise baseline normalization:
data[mkey] /= data[mkey][0]
elif normalization == 'range':
# Min-max normalization:
min_measure = data[mkey].min(axis=0, keepdims=True)
max_measure = data[mkey].max(axis=0, keepdims=True)
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
file_data[stage][..., k] = data[mkey]
# Get recording statistics:
@@ -75,10 +87,12 @@ for i, species in enumerate(target_species):
# Save condensed recording data:
save_name = save_path + species
if compute_ratios:
save_name += '_normed'
else:
save_name += '_raw'
if normalization == 'none':
save_name += '_unnormed'
elif normalization == 'base':
save_name += '_norm-base'
elif normalization == 'range':
save_name += '_norm-range'
archive = dict(scales=data['scales'])
archive.update(rec_mean)
archive.update(rec_sd)

26
python/condense_inv_data_short.py
View File

@@ -26,7 +26,11 @@ search_path = '../data/inv/short/'
save_path = '../data/inv/short/condensed/'
# ANALYSIS SETTINGS:
compute_ratios = False
normalization = [
'none',
'base',
'range'
][1]
# EXECUTION:
for i, species in enumerate(target_species):
@@ -64,8 +68,16 @@ for i, species in enumerate(target_species):
# Log song file data:
for stage in stages:
mkey = f'measure_{stage}'
if compute_ratios:
if normalization == 'base':
# Noise baseline normalization:
data[mkey] /= data[mkey][0]
elif normalization == 'range':
# Min-max normalization:
min_measure = data[mkey].min(axis=0, keepdims=True)
max_measure = data[mkey].max(axis=0, keepdims=True)
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
file_data[stage][..., k] = data[mkey]
# Get recording statistics:
@@ -75,10 +87,12 @@ for i, species in enumerate(target_species):
# Save condensed recording data:
save_name = save_path + species
if compute_ratios:
save_name += '_normed'
else:
save_name += '_raw'
if normalization == 'none':
save_name += '_unnormed'
elif normalization == 'base':
save_name += '_norm-base'
elif normalization == 'range':
save_name += '_norm-range'
archive = dict(scales=data['scales'])
archive.update(rec_mean)
archive.update(rec_sd)

4
python/condense_inv_data_thresh-lp.py
View File

@@ -26,8 +26,8 @@ search_path = '../data/inv/thresh_lp/'
save_path = '../data/inv/thresh_lp/condensed/'
# ANALYSIS SETTINGS:
with_noise = True
plot_overview = True
with_noise = False
plot_overview = False
thresh_rel = np.array([0.5, 1, 3])
# PREPARATION:

152
python/fig_invariance_full.py
View File

@@ -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.')

15
python/fig_invariance_log-hp.py
View File

@@ -36,7 +36,7 @@ target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
# 'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
@@ -137,7 +137,7 @@ ylabels = dict(
env='$x_{\\text{env}}$',
log='$x_{\\text{dB}}$',
inv='$x_{\\text{adapt}}$',
big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$',
big='$\\sigma_x\\,/\\,\\sigma_{\\eta}$',
)
xlab_big_kwargs = dict(
y=0,
@@ -354,11 +354,18 @@ big_axes = np.zeros((big_grid.ncols,), dtype=object)
for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
ax = big_subfig.add_subplot(big_grid[0, i])
ax.set_xlim(scales[0], scales[-1])
ax.set_ylim(scales[0], scales[-1])
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
ax.set_aspect(**anchor_kwargs)
if i > 0:
if i in [0, 1]:
ax.set_ylim(scales[0], scales[-1])
pos_equal = ax.get_position().bounds
else:
pos_auto = list(ax.get_position().bounds)
ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
ax.set_ylim(0.9, 30)
if i == 1:
hide_ticks(ax, 'left')
big_axes[i] = ax
ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)

2
python/fig_invariance_log-hp_appendix.py
View File

@@ -11,7 +11,7 @@ target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
# 'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',

400
python/fig_invariance_short.py Normal file
View File

@@ -0,0 +1,400 @@
import plotstyle_plt
import numpy as np
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,\
letter_subplot, letter_subplots
from IPython import embed
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
for i, ax in enumerate(axes):
plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
return None
def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
if measures.ndim == 1:
ax.plot(scales, measures, **kwargs)[0]
return measures
median_measure = np.nanmedian(measures, axis=1)
spread_measure = [np.nanpercentile(measures, 25, axis=1),
np.nanpercentile(measures, 75, axis=1)]
ax.plot(scales, median_measure, **kwargs)[0]
ax.fill_between(scales, *spread_measure, **fill_kwargs)
return median_measure
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 = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
][5]
example_file = {
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
}[target_species]
stages = ['filt', 'env', 'conv', 'feat']
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/short/condensed/')[0]
base_path = search_files(target_species, incl='base', dir='../data/inv/short/condensed/')[0]
range_path = search_files(target_species, incl='range', dir='../data/inv/short/condensed/')[0]
snip_path = search_files(example_file, dir='../data/inv/short/')[0]
save_path = '../figures/fig_invariance_short.pdf'
# ANALYSIS SETTINGS:
exclude_zero = True
# SUBSET SETTINGS:
types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
sigmas = np.array([0.001, 0.002, 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, 32/2.54),
)
super_grid_kwargs = dict(
nrows=2,
ncols=1,
wspace=0,
hspace=0,
left=0,
right=1,
bottom=0,
top=1,
height_ratios=[3, 2]
)
subfig_specs = dict(
snip=(0, 0),
big=(1, 0),
)
snip_grid_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.1,
hspace=0.4,
left=0.08,
right=0.95,
bottom=0.08,
top=0.95
)
big_grid_kwargs = dict(
nrows=1,
ncols=3,
wspace=0.2,
hspace=0,
left=snip_grid_kwargs['left'],
right=0.96,
bottom=0.2,
top=0.95
)
# PLOT SETTINGS:
fs = dict(
lab_norm=16,
lab_tex=20,
letter=22,
tit_norm=16,
tit_tex=20,
bar=16,
)
colors = load_colors('../data/stage_colors.npz')
lw = dict(
filt=0.25,
env=0.25,
conv=0.25,
feat=1,
big=3,
plateau=1.5,
)
xlabels = dict(
big='scale $\\alpha$',
)
ylabels = dict(
filt='$x_{\\text{filt}}$',
env='$x_{\\text{env}}$',
conv='$c_i$',
feat='$f_i$',
big=['intensity', 'rel. intensity', 'norm. intensity']
)
xlab_big_kwargs = dict(
y=0,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
ylab_snip_kwargs = dict(
x=0,
fontsize=fs['lab_tex'],
rotation=0,
ha='left',
va='center'
)
ylab_big_kwargs = dict(
x=-0.12,
fontsize=fs['lab_norm'],
ha='center',
va='bottom',
)
yloc = dict(
filt=3000,
env=1000,
conv=30,
feat=1,
)
title_kwargs = dict(
x=0.5,
yref=1,
ha='center',
va='top',
fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
x=0,
yref=0.5,
ha='left',
va='center',
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
x=0,
y=1,
ha='left',
va='bottom',
fontsize=fs['letter'],
)
bar_time = 5
bar_kwargs = dict(
dur=bar_time,
y0=-0.25,
y1=-0.1,
xshift=1,
color='k',
lw=0,
clip_on=False,
text_pos=(-0.1, 0.5),
text_str=f'${bar_time}\\,\\text{{s}}$',
text_kwargs=dict(
fontsize=fs['bar'],
ha='right',
va='center',
)
)
plateau_settings = dict(
low=0.05,
high=0.95,
first=True,
last=True,
condense=None,
)
plateau_line_kwargs = dict(
lw=lw['plateau'],
ls='--',
zorder=1,
)
plateau_dot_kwargs = dict(
marker='o',
markersize=8,
markeredgewidth=1,
clip_on=False,
)
# EXECUTION:
# 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
# Prepare overall graph:
fig = plt.figure(**fig_kwargs)
super_grid = fig.add_gridspec(**super_grid_kwargs)
# Prepare stage-specific snippet axes:
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
ax = snip_subfig.add_subplot(snip_grid[i, j])
ax.set_xlim(t_full[0], t_full[-1])
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
hide_axis(ax, 'bottom')
if i == 0:
title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
ref=snip_subfig, **title_kwargs)
if j == 0:
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
else:
hide_axis(ax, 'left')
snip_axes[i, j] = ax
time_bar(snip_axes[-1, -1], **bar_kwargs)
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
# Prepare analysis axes:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
big_axes = np.zeros((big_grid.ncols,), dtype=object)
for i in range(big_grid.ncols):
ax = big_subfig.add_subplot(big_grid[0, i])
ax.set_xlim(scales[0], scales[-1])
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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, 'bcd', **letter_big_kwargs)
if True:
# Plot filtered snippets:
plot_snippets(snip_axes[0, :], t_full, snip['snip_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'])
# Plot kernel response snippets:
plot_snippets(snip_axes[2, :], t_full, snip['snip_conv'],
c=colors['conv'], lw=lw['conv'])
# Plot feature snippets:
plot_snippets(snip_axes[3, :], t_full, snip['snip_feat'],
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
del snip
# Remember saturation points:
crit_inds, crit_scales = {}, {}
# Unnormed measures:
for stage in stages:
# 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 == 'feat':
ind = get_saturation(curve, **plateau_settings)[1]
scale = scales[ind]
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[0].get_xaxis_transform())
big_axes[0].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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
# 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 == '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
# 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 == '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:
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
fig.savefig(file_name)
plt.show()
print('Done.')
embed()

2
python/fig_invariance_thresh-lp_appendix.py
View File

@@ -13,7 +13,7 @@ target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
# 'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',

64
python/fig_invariance_thresh-lp_single.py
View File

@@ -39,17 +39,20 @@ def plot_bi_snippets(axes, time, binary, **kwargs):
plot_barcode(ax, time, binary[:, None], **kwargs)
return None
def side_distributions(axes, snippets, inset_bounds, thresh, nbins=1000,
fill_kwargs={}, **kwargs):
limits = np.array([snippets.min(), snippets.max()]) * 1.1
def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
limits=None, fill_kwargs={}, **kwargs):
if limits is None:
limits = np.array([snippets.min(), snippets.max()]) * 1.1
edges = np.linspace(*limits, nbins + 1)
centers = edges[:-1] + (edges[1] - edges[0]) / 2
insets = []
for ax, snippet in zip(axes, snippets.T):
pdf, _ = np.histogram(snippet, edges, density=True)
inset = ax.inset_axes(inset_bounds)
inset.plot(pdf, centers, **kwargs)
inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), **fill_kwargs)
handle = inset.plot(pdf, centers, **kwargs)[0]
set_clip_box(handle, inset, bounds=[[-0.05, 0], [1.05, 1]])
handle = inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), **fill_kwargs)
set_clip_box(handle, inset, bounds=[[-0.05, 0], [1.05, 1]])
inset.set_xlim(0, pdf.max())
inset.set_ylim(ax.get_ylim())
inset.axis('off')
@@ -99,7 +102,7 @@ snip_grid_kwargs = dict(
right=0.93,
bottom=0.15,
top=0.95,
height_ratios=[2, 1, 1]
height_ratios=[4, 1, 2]
)
input_grid_kwargs = dict(
nrows=1,
@@ -115,10 +118,10 @@ big_grid_kwargs = dict(
nrows=2,
ncols=1,
wspace=0,
hspace=0.3,
hspace=0.15,
left=0.17,
right=0.96,
bottom=0.1,
bottom=0.05,
top=0.99
)
dist_inset_bounds = [1.02, 0, 0.2, 1]
@@ -140,6 +143,7 @@ lw = dict(
bi=0.1,
feat=3,
big=4,
thresh=1.5,
kern=2.5,
plateau=1.5,
)
@@ -155,16 +159,16 @@ ylabels = dict(
big='$\\mu_f$',
)
xlab_alpha_kwargs = dict(
y=-0.15,
y=0.5,
fontsize=fs['lab_norm'],
ha='center',
va='top',
va='bottom',
)
xlab_sigma_kwargs = dict(
y=-0.12,
y=0,
fontsize=fs['lab_tex'],
ha=xlab_alpha_kwargs['ha'],
va=xlab_alpha_kwargs['va'],
va='bottom',
)
ylab_snip_kwargs = dict(
x=0.08,
@@ -212,8 +216,8 @@ letter_snip_kwargs = dict(
fontsize=fs['letter'],
)
letter_big_kwargs = dict(
x=0,
yref=letter_snip_kwargs['y'],
xref=0,
y=1,
ha='left',
va='top',
fontsize=fs['letter'],
@@ -230,6 +234,12 @@ dist_fill_kwargs = dict(
color=colors['bi'],
lw=0.1,
)
thresh_kwargs = dict(
color='k',
lw=lw['thresh'],
ls='--',
zorder=3,
)
bar_time = 0.1
bar_kwargs = dict(
dur=bar_time,
@@ -353,9 +363,11 @@ for i in range(thresh_rel.size):
subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
low_box = axes[-1, 0].get_position()
high_box = axes[0, 0].get_position()
[hide_axis(ax, 'left') for ax in axes[1:, 1]]
super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
for (ax1, ax2), stage in zip(axes[:, :2], stages):
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
@@ -376,17 +388,18 @@ alpha_ax.set_xlim(scales[0], scales[-1])
alpha_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
ylimits(pure_data['measure_feat'], alpha_ax, minval=0, pad=ypad['big'])
alpha_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs)
xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs, transform=big_subfig)
ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
letter_subplot(alpha_ax, 'e', ref=big_subfig, **letter_big_kwargs)
sigma_ax = big_subfig.add_subplot(big_grid[1, 0])
sigma_ax.set_xlim(noise_data['measure_inv'].min(), noise_data['measure_inv'].max())
sigma_ax.set_xlim(scales[0], scales[-1])
sigma_ax.set_xlim(1, noise_data['measure_inv'].max())
sigma_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
ylimits(pure_data['measure_feat'], sigma_ax, minval=0, pad=ypad['big'])
sigma_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs)
xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs, transform=big_subfig)
ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
letter_subplot(sigma_ax, 'f', ref=big_subfig, **letter_big_kwargs)
# Plot intensity-adapted snippets:
plot_snippets(input_axes, t_full, noise_data['snip_inv'],
@@ -403,13 +416,18 @@ for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
# Plot kernel response snippets:
plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
ylim_zoom = ylimits(noise_data['snip_conv'][:, 0], axes[0, 0],
pad=ypad['conv'], maxval=thresh_abs[-1])
# Indicate absolute threshold value:
handle = axes[0, 0].axhline(thresh_abs[i], **thresh_kwargs)
set_clip_box(handle, axes[0, 0], bounds=[[0, 0], [1, 1.05]])
# Plot kernel response distributions:
side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
thresh_abs[i], nbins=50, limits=ylim_zoom, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
# Plot binary snippets:
plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
@@ -444,7 +462,7 @@ for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
ax.plot(x[ind], 0, mfc=color, mec='k', alpha=0.75, zorder=6,
**plateau_dot_kwargs, transform=ax.get_xaxis_transform())
ax.vlines(x[ind], ax.get_ylim()[0], noise_data['measure_feat'][ind, i],
color=color, **plateau_line_kwargs)
color=color, **plateau_line_kwargs)
# Add proxy legend:
if ax == alpha_ax:

54
python/fig_invariance_thresh-lp_species.py
View File

@@ -165,7 +165,7 @@ target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
# 'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
@@ -185,7 +185,7 @@ load_kwargs = dict(
)
save_path = '../figures/fig_invariance_thresh_lp_species.pdf'
exclude_zero = True
show_floor = False
show_floor = True
# SUBSET SETTINGS:
thresh_rel = np.array([0.5, 1, 3])[0]
@@ -267,14 +267,15 @@ fs = dict(
bar=16,
)
species_colors = load_colors('../data/species_colors.npz')
kernel_shades = [0, 0.75]
kernel_shades = [0, 0.5]
scale_shades = [1, 0]
noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)]
lw = dict(
song=0.5,
feat=3,
kern=2.5,
plateau=3,
bar=3,
plateau=1.5,
)
space_kwargs = dict(
s=30,
@@ -411,6 +412,17 @@ plateau_settings = dict(
last=True,
condense='norm',
)
plateau_line_kwargs = dict(
lw=lw['plateau'],
ls='--',
zorder=1,
)
plateau_dot_kwargs = dict(
marker='o',
markersize=8,
markeredgewidth=1,
clip_on=False,
)
# EXECUTION:
@@ -566,6 +578,28 @@ for i, species in enumerate(target_species):
handles = noise_ax.plot(scales, noise_measure, lw=lw['feat'])
[h.set_color(c) for h, c in zip(handles, kern_colors)]
# Indicate saturation points:
for j in range(pure_measure.shape[1]):
color = kern_colors[j]
# Indicate feature-specific saturation points of pure curves:
ind = get_saturation(pure_measure[:, j], **plateau_settings)[1]
scale = scales[ind]
pure_ax.plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=pure_ax.get_xaxis_transform())
pure_ax.plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=pure_ax.get_xaxis_transform())
pure_ax.vlines(scale, pure_ax.get_ylim()[0], pure_measure[ind, j],
color=color, **plateau_line_kwargs)
# Indicate feature-specific saturation points of noise curves:
ind = get_saturation(noise_measure[:, j], **plateau_settings)[1]
scale = scales[ind]
noise_ax.plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=noise_ax.get_xaxis_transform())
noise_ax.plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=noise_ax.get_xaxis_transform())
noise_ax.vlines(scale, noise_ax.get_ylim()[0], noise_measure[ind, j],
color=color, **plateau_line_kwargs)
if i == 0:
# Indicate kernel waveforms:
ylims = ylimits(config['kernels'], pad=0.05)
@@ -604,15 +638,15 @@ for i, species in enumerate(target_species):
noise_bars[0].tick_params(axis='y', which='both', left=True, labelleft=True)
ylabel(noise_bars[0], ylabels['bar'], **ylab_cbar_kwargs)
# Indicate plateaus of pure invariance curves:
# Indicate across-feature saturation points of pure curves:
low_ind, high_ind = get_saturation(pure_measure, **plateau_settings)
pure_bars[i].axhline(scales[low_ind], c=noise_colors[0], lw=lw['plateau'])
pure_bars[i].axhline(scales[high_ind], c=noise_colors[1], lw=lw['plateau'])
pure_bars[i].axhline(scales[low_ind], c=noise_colors[0], lw=lw['bar'])
pure_bars[i].axhline(scales[high_ind], c=noise_colors[1], lw=lw['bar'])
# Indicate plateaus of noise invariance curves:
# Indicate across-feature saturation points of noise curves:
low_ind, high_ind = get_saturation(noise_measure, **plateau_settings)
noise_bars[i].axhline(scales[low_ind], c=noise_colors[0], lw=lw['plateau'])
noise_bars[i].axhline(scales[high_ind], c=noise_colors[1], lw=lw['plateau'])
noise_bars[i].axhline(scales[low_ind], c=noise_colors[0], lw=lw['bar'])
noise_bars[i].axhline(scales[high_ind], c=noise_colors[1], lw=lw['bar'])
# Log start and end of invariance curve:
min_noise_feat[i, :] = noise_measure.min(axis=0)

6
python/fig_pathway_stages.py
View File

@@ -136,9 +136,9 @@ zoom_kwargs = dict(
t = [1, -1, 2, -2, 3, -3, 4, -4]
s = [0.004, 0.032]
kernels = np.array([[i, j] for i in t for j in s])
conv_colors = load_colors('../data/conv_colors.npz')
bi_colors = load_colors('../data/bi_colors.npz')
feat_colors = load_colors('../data/feat_colors.npz')
conv_colors = load_colors('../data/conv_colors_subset.npz')
bi_colors = load_colors('../data/bi_colors_subset.npz')
feat_colors = load_colors('../data/feat_colors_subset.npz')
# EXECUTION:
for data_path in data_paths:

130
python/fig_saturation_log-hp_appendix.py Normal file
View File

@@ -0,0 +1,130 @@
import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.filetools import search_files
from plot_functions import xlabel, super_ylabel
from color_functions import load_colors
from misc_functions import shorten_species
from IPython import embed
# GENERAL SETTINGS:
target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
# 'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
]
data_path = '../data/inv/log_hp/saturation/'
save_path = '../figures/fig_saturation_log-hp_appendix.pdf'
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 16/2.54),
nrows=len(target_species),
ncols=1,
sharex=True,
sharey=False,
gridspec_kw=dict(
wspace=0,
hspace=0.3,
left=0.09,
right=0.99,
bottom=0.1,
top=0.95,
)
)
# PLOT SETTINGS:
colors = load_colors('../data/species_colors.npz')
bar_kwargs = dict(
ec='w',
)
mean_kwargs = dict(
c='k',
lw=3,
ls='--'
)
xlab = 'scale $\\alpha$'
ylab = '$\\text{PDF}_{\\alpha}$'
xlab_kwargs = dict(
y=0,
fontsize=16,
ha='center',
va='bottom',
)
ylab_kwargs = dict(
x=0.005,
fontsize=16,
ha='left',
va='center',
)
leg_x = fig_kwargs['gridspec_kw']['left']
leg_y = fig_kwargs['gridspec_kw']['top']
leg_box = [
leg_x,
leg_y,
fig_kwargs['gridspec_kw']['right'] - leg_x,
1 - leg_y
]
leg_kwargs = dict(
ncols=len(target_species),
loc='upper center',
bbox_to_anchor=leg_box,
frameon=False,
prop=dict(
size=15,
style='italic',
),
borderpad=0,
borderaxespad=0,
handlelength=1,
columnspacing=1,
)
text_kwargs = dict(
x=1,
y=1,
fontsize=14,
ha='right',
va='top',
)
# Prepare graph:
fig, axes = plt.subplots(**fig_kwargs)
xlabel(axes[-1], xlab, **xlab_kwargs, transform=fig.transFigure)
super_ylabel(ylab, fig, axes[0], axes[-1], **ylab_kwargs)
# Run through species:
handles = []
for species, ax in zip(target_species, axes):
color = colors[species]
# Load species data:
path = search_files(species, dir=data_path)[0]
data = dict(np.load(path))
hist = data['hist']
bins = data['bins']
n_songs = data['crit_scales'].size
# Plot distribution of saturation points:
handles.append(ax.bar(bins, hist, width=bins[1] - bins[0], fc=color, **bar_kwargs))
ax.set_ylim(0, hist.max() * 1.05)
# Indicate mean of distribution:
ax.axvline(data['crit_scales'].mean(), **mean_kwargs)
# Indicate number of songs:
ax.text(**text_kwargs, s=f'n = {n_songs}', transform=ax.transAxes)
# Posthocs:
labels = [shorten_species(species) for species in target_species]
fig.legend(handles, labels, **leg_kwargs)
ax.set_xlim(0, bins[-1])
# Save graph:
fig.savefig(save_path)
plt.show()

22
python/misc_functions.py
View File

@@ -48,6 +48,28 @@ def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
sorted_paths = [path for paths in sorted_paths.values() for path in paths]
return sorted_paths
def get_histogram(data, edges=None, nbins=50, pad=0.1, shared=True):
if edges is None:
axis = None if shared else 0
min_data, max_data = data.min(axis=axis), data.max(axis=axis)
pad = pad * (max_data - min_data)
if shared or data.ndim == 1:
edges = np.linspace(min_data - pad, max_data + pad, nbins + 1)
else:
edges = np.zeros((nbins + 1, data.shape[1]))
for i, mini, maxi, padi in enumerate(zip(min_data, max_data, pad)):
edges[:, i] = np.linspace(mini - padi, maxi + padi, nbins + 1)
centers = edges[:-1] + np.diff(edges, axis=0) / 2
if data.ndim == 1:
hists, _ = np.histogram(data, bins=edges, density=True)
else:
hists = np.zeros((nbins, data.shape[1]))
for i in range(data.shape[1]):
bins = edges if shared else edges[:, i]
hists[:, i], _ = np.histogram(data[:, i], bins=bins, density=True)
return hists, centers
def get_kde(data, sigma, axis=None, n=1000, pad=10):
if axis is None:
axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)

4
python/plot_functions.py
View File

@@ -150,8 +150,8 @@ def super_xlabel(label, fig, left_ax, right_ax, y=0.005,
def super_ylabel(label, fig, low_ax, high_ax, x=0.005,
high_fig=None, low_fig=None, **kwargs):
low_y = high_ax.get_position().y0
high_y = low_ax.get_position().y1
low_y = low_ax.get_position().y0
high_y = high_ax.get_position().y1
if low_fig is not None or high_fig is not None:
trans_fig = get_trans_artist(fig)
if low_fig is not None:

62
python/save_field_data.py Normal file
View File

@@ -0,0 +1,62 @@
import numpy as np
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.model import configuration, process_signal
from thunderhopper.modeltools import load_data
from IPython import embed
## SETTINGS:
# General:
search_target = '*'
mode = ['song', 'noise'][1]
input_folder = f'../data/field/raw/{mode}/'
output_folder = f'../data/field/processed/{mode}/'
stages = ['raw', 'norm']
if False:
# Overwrites edited:
stages.append('songs')
# Interactivity:
reload_saved = False
gui = True
# Processing:
env_rate = 96000.0
feat_rate = 96000.0
sigmas = [0.001, 0.002, 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]
config = configuration(env_rate, feat_rate, types=types, sigmas=sigmas)
config.update({
'channel': None,
'rate_ratio': None,
'env_fcut': 250,
'db_ref': 1,
'inv_fcut': 10,
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
'feat_fcut': 0.5,
'label_channels': np.array([0]),
'label_thresh': 0.5,
})
## PREPARATION:
# Fetch WAV recording files:
input_paths = search_files(search_target, ext='wav', dir=input_folder)
path_names = crop_paths(input_paths)
# PROCESSING:
# Run processing pipeline:
for path, name in zip(input_paths, path_names):
print('Processing:', name)
# Fetch and store representations:
save = None if output_folder is None else output_folder + f'{name}.npz'
process_signal(config, stages, path, save=save, label_edit=gui)
# Cross-control:
if reload_saved:
data, params = load_data(save, stages, ['songs'])
embed()
print('Done.')

87
python/save_inv_data_field.py Normal file
View File

@@ -0,0 +1,87 @@
import numpy as np
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.filtertools import find_kern_specs
from thunderhopper.model import process_signal
from IPython import embed
# GENERAL SETTINGS:
target = '*'
example_file = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
mode = ['song', 'noise'][1]
search_path = f'../data/field/processed/{mode}/'
data_paths = search_files(target, ext='npz', dir=search_path)
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
save_path = f'../data/inv/field/{mode}/'
# ANALYSIS SETTINGS:
distances = np.load('../data/field/recording_distances.npy')
# SUBSET SETTINGS:
kernels = np.array([
[1, 0.002],
[-1, 0.002],
[2, 0.004],
[-2, 0.004],
[3, 0.032],
[-3, 0.032]
])
kernels = None
types = None#np.array([-1])
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
save_detailed = example_file in name
print(f'Processing {name}')
# Get song recording (prior to anything):
data, config = load_data(data_path, files='raw')
song, rate = data['raw'], config['rate']
# Reduce to kernel subset:
if any(var is not None for var in [kernels, types, sigmas]):
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
config['kernels'] = config['kernels'][:, kern_inds]
config['k_specs'] = config['k_specs'][kern_inds, :]
config['k_props'] = [config['k_props'][i] for i in kern_inds]
config['feat_thresh'] = config['feat_thresh'][kern_inds]
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
start, end = data['songs_0'].ravel()
segment = (time >= start) & (time <= end)
# Prepare storage:
measures = {}
if save_detailed:
snippets = {}
# Process snippet:
signals, rates = process_signal(config, returns=stages, signal=song, rate=rate)
# Store results:
for stage in stages:
# Log intensity measures:
mkey = f'measure_{stage}'
if stage == 'feat':
measures[mkey] = signals[stage][segment, ...].mean(axis=0)
else:
measures[mkey] = signals[stage][segment, ...].std(axis=0)
# Log optional snippet data:
if save_detailed:
snippets[f'snip_{stage}'] = signals[stage]
# Save analysis results:
if save_path is not None:
data = dict(
distances=distances,
)
data.update(measures)
if save_detailed:
data.update(snippets)
save_data(save_path + name, data, config, overwrite=True)
print('Done.')
embed()

2
python/save_inv_data_full.py
View File

@@ -16,7 +16,7 @@ target_species = [
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
][0]
][4]
example_file = {
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',

10
python/save_inv_data_short.py
View File

@@ -17,7 +17,7 @@ target_species = [
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
][5]
][6]
example_file = {
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
@@ -31,7 +31,7 @@ data_paths = search_files(target_species, dir='../data/processed/')
noise_path = '../data/processed/white_noise_sd-1.npz'
ref_path = '../data/inv/short/ref_measures.npz'
pre_stages = ['filt', 'env']
stages = pre_stages + ['conv', 'feat']
stages = pre_stages + ['inv', 'conv', 'feat']
save_path = '../data/inv/short/'
# ANALYSIS SETTINGS:
@@ -98,6 +98,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
measures = dict(
measure_filt=np.zeros(shape_low, dtype=float),
measure_env=np.zeros(shape_low, dtype=float),
measure_inv=np.zeros(shape_low, dtype=float),
measure_conv=np.zeros(shape_high, dtype=float),
measure_feat=np.zeros(shape_high, dtype=float)
)
@@ -108,6 +109,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
snippets = dict(
snip_filt=np.zeros(shape_low, dtype=float),
snip_env=np.zeros(shape_low, dtype=float),
snip_inv=np.zeros(shape_low, dtype=float),
snip_conv=np.zeros(shape_high, dtype=float),
snip_feat=np.zeros(shape_high, dtype=float)
)
@@ -124,7 +126,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
signal=scaled, rate=rate)
# Process mixture further:
signals['conv'] = convolve_kernels(signals['env'], config['kernels'], config['k_specs'])
signals['inv'] = sosfilter(signals['env'], rate, config['inv_fcut'], 'hp',
padtype='constant', padlen=config['padlen'])
signals['conv'] = convolve_kernels(signals['inv'], config['kernels'], config['k_specs'])
signals['feat'] = sosfilter((signals['conv'] > config['feat_thresh']).astype(float),
rate, config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])

11
python/save_kernel_colors.py
View File

@@ -3,8 +3,13 @@ from color_functions import load_colors, shade_colors
# Settings:
stages = ['conv', 'bi', 'feat']
kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
shade_factors = np.linspace(-0.6, 0.2, kern_types.size)
mode = ['subset', 'all'][1]
if mode == 'subset':
kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
shade_factors = np.linspace(-0.6, 0.2, kern_types.size)
elif mode == 'all':
kern_types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
shade_factors = np.linspace(-0.6, 0.6, kern_types.size)
# Main colors:
stage_colors = load_colors('../data/stage_colors.npz')
@@ -15,4 +20,4 @@ for stage in stages:
colors = {str(k): c for k, c in zip(kern_types, colors)}
print(f'\n{stage} colors:')
print(colors)
np.savez(f'../data/{stage}_colors.npz', **colors)
np.savez(f'../data/{stage}_colors_{mode}.npz', **colors)

6
python/save_ref_measures.py
View File

@@ -16,7 +16,7 @@ stages = dict(
log_hp=['filt', 'env', 'log', 'inv'],
thresh_lp=['inv', 'conv', 'feat'],
full=['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat'],
short=['raw', 'filt', 'env', 'conv', 'feat']
short=['raw', 'filt', 'env', 'inv', 'conv', 'feat']
)[mode]
# PROCESSING:
@@ -52,7 +52,9 @@ elif mode == 'full':
data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]
elif mode == 'short':
data = process_signal(config, ['raw', 'filt', 'env'], signal=starter, rate=config['rate'])[0]
data['conv'] = convolve_kernels(data['env'], config['kernels'], config['k_specs'])
data['inv'] = sosfilter(data['env'], config['env_rate'], config['inv_fcut'], 'hp',
padtype='constant', padlen=config['padlen'])
data['conv'] = convolve_kernels(data['inv'], config['kernels'], config['k_specs'])
data['feat'] = sosfilter((data['conv'] > config['feat_thresh']).astype(float),
config['env_rate'], config['feat_fcut'], 'lp',
padtype='fixed', padlen=config['padlen'])

79
python/save_saturation_log-hp.py Normal file
View File

@@ -0,0 +1,79 @@
import numpy as np
from thunderhopper.filetools import search_files
from thunderhopper.modeltools import load_data, save_data
from misc_functions import get_saturation
from IPython import embed
# GENERAL SETTINGS:
target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
]
search_path = '../data/inv/log_hp/collected/'
save_path = '../data/inv/log_hp/saturation/'
# ANALYSIS SETTINGS:
plateau_settings = dict(
low=0.05,
high=0.95,
first=True,
last=True,
condense=None,
)
compute_hist = True
bins = 50
pad = 0.05
# PREPARATION:
if compute_hist:
species_scales = []
min_scale, max_scale = [], []
archives = [{} for _ in target_species]
# EXECUTION:
for i, species in enumerate(target_species):
print(f'Processing {species}')
# Load accumulated invariance data:
path = search_files(species, dir=search_path)[0]
data, config = load_data(path, ['scales', 'measure_inv'])
# Find upper saturation point per song file:
crit_inds = np.array(get_saturation(data['measure_inv'], **plateau_settings)[1])
crit_scales = data['scales'][crit_inds]
# Output options:
if not compute_hist:
# Save species data immediately:
archive = dict(crit_inds=crit_inds, crit_scales=crit_scales, scales=data['scales'])
save_data(save_path + species, archive, config, overwrite=True)
continue
# Log but don't save data yet:
archives[i]['crit_inds'] = crit_inds
archives[i]['crit_scales'] = crit_scales
archives[i]['scales'] = data['scales']
min_scale.append(crit_scales.min())
max_scale.append(crit_scales.max())
# Optional histogram:
if compute_hist:
# Generated shared histogram edges:
min_scale, max_scale = min(min_scale), max(max_scale)
pad *= (max_scale - min_scale)
edges = np.linspace(max(0, min_scale - pad), max_scale + pad, bins + 1)
centers = edges[:-1] + np.diff(edges) / 2
# Compute histogram and save species data:
for i, (species, archive) in enumerate(zip(target_species, archives)):
hist = np.histogram(archive['crit_scales'], bins=edges, density=True)[0]
archive['hist'] = hist
archive['bins'] = centers
save_data(save_path + species, archive, config, overwrite=True)
print('Done.')