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Fetched bunch of species-specific song snippets.

Browse Source 
Worked those into LogHP analysis.
Worked results into fig_invariance_log-hp.pdf.
Put details into new fig_invariance_log-hp_species.pdf (appendix).
This commit is contained in:
j-hartling
2026-04-14 17:30:58 +02:00
parent 0b9264b1e1
commit 36ac504efa
17 changed files with 490 additions and 205 deletions
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@@ -267,6 +267,8 @@
\abx@aux@page{73}{18} \abx@aux@page{73}{18}
\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {} }}{20}{}\protected@file@percent } \@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {} }}{20}{}\protected@file@percent }
\newlabel{}{{8}{20}{}{}{}} \newlabel{}{{8}{20}{}{}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {} }}{20}{}\protected@file@percent }
\newlabel{}{{9}{20}{}{}{}}
\gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }} \gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }}
\abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F} \abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F}
\gdef \@abspage@last{20} \gdef \@abspage@last{20}

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[0] Config.pm:307> INFO - This is Biber 2.19 [0] Config.pm:307> INFO - This is Biber 2.19
[0] Config.pm:310> INFO - Logfile is 'main.blg' [0] Config.pm:310> INFO - Logfile is 'main.blg'
[36] biber:340> INFO - === Mi Mär 25, 2026, 15:53:59 [36] biber:340> INFO - === Di Apr 14, 2026, 17:23:24
[43] Biber.pm:419> INFO - Reading 'main.bcf' [45] Biber.pm:419> INFO - Reading 'main.bcf'
[71] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 [74] Biber.pm:979> INFO - Found 55 citekeys in bib section 0
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[321] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_gWc3/347c261ec4135a5723bef5c751f5078f_62855.utf8, line 667, warning: 6 characters of junk seen at toplevel [324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 667, warning: 6 characters of junk seen at toplevel
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@@ -866,4 +866,13 @@ initiation of one behavior over another is categorical (e.g. approach/stay)
\end{figure} \end{figure}
\FloatBarrier \FloatBarrier
\begin{figure}[!ht]
\centering
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\caption{\textbf{}
}
\label{}
\end{figure}
\FloatBarrier
\end{document} \end{document}

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import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.filetools import search_files, crop_paths
from thunderhopper.modeltools import load_data, save_data
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',
]
sources = [
'BM04',
'BM93',
'DJN',
'GBC',
'FTN'
]
search_path = '../data/inv/log_hp/'
ref_path = '../data/inv/log_hp/ref_measures.npz'
save_path = '../data/inv/log_hp/condensed/'
# ANALYSIS SETTINGS:
compute_ratios = True
plot_overview = True
# PREPARATION:
if compute_ratios:
ref_measure = np.load(ref_path)['inv']
if plot_overview:
fig, axes = plt.subplots(3, len(target_species), figsize=(16, 9),
sharex=True, sharey=True, layout='constrained')
axes[0, 0].set_ylabel('songs')
axes[1, 0].set_ylabel('recordings\n(mean ± SD)')
axes[2, 0].set_ylabel('total\n(mean ± SEM)')
# EXECUTION:
for i, species in enumerate(target_species):
print(f'Processing {species}')
if plot_overview:
axes[0, i].set_title(shorten_species(species))
# Fetch all species-specific song files:
all_paths = search_files(species, incl='noise', ext='npz', dir=search_path)
# Separate by source:
sorted_paths = {}
for source in sources:
# Check for any source-specific song files:
source_paths = [path for path in all_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 numerical ID behind source tag:
id_ind = name.find(source) + len(source) + 1
# Check if ID is followed by sub-ID:
sub_id = name[id_ind:].split('-')[1]
if 's' in sub_id:
# Single (time stamp in next spot):
sorted_paths[source][0].append(path)
continue
sub_id = int(sub_id)
# Multiple (sub-ID in next spot):
if sub_id > len(sorted_paths[source]):
# Open new recording-specific slot:
sorted_paths[source].append([])
sorted_paths[source][sub_id - 1].append(path)
# Re-sort song files only by recording (discarding source separation):
sorted_paths = [path for paths in sorted_paths.values() for path in paths]
# 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, _ = load_data(path, ['scales', 'measure_inv'])
scales, measure = data['scales'], data['measure_inv']
# Relate to noise:
if compute_ratios:
measure /= ref_measure
if k == 0:
# Prepare song file-specific storage:
file_data = np.zeros((scales.size, len(rec_paths)), dtype=float)
if j == 0:
# Prepare recording-specific storage:
rec_mean = np.zeros((scales.size, len(sorted_paths)), dtype=float)
rec_sd = np.zeros((scales.size, len(sorted_paths)), dtype=float)
# Log song file data:
file_data[:, k] = measure
if plot_overview:
axes[0, i].plot(scales, measure, c='k', alpha=0.5)
# Get recording statistics:
rec_mean[:, j] = file_data.mean(axis=1)
rec_sd[:, j] = file_data.std(axis=1)
if plot_overview:
axes[1, i].plot(scales, rec_mean[:, j], c='k')
axes[1, i].fill_between(scales, rec_mean[:, j] - rec_sd[:, j],
rec_mean[:, j] + rec_sd[:, j], color='k', alpha=0.2)
# Save condensed recording data for current species:
np.savez(save_path + species, scales=scales, mean=rec_mean, sd=rec_sd)
if plot_overview:
spec_mean = rec_mean.mean(axis=1)
spec_sd = rec_mean.std(axis=1)
axes[2, i].plot(scales, spec_mean, c='k')
axes[2, i].fill_between(scales, spec_mean - spec_sd, spec_mean + spec_sd,
color='k', alpha=0.2)
print('Done.')
if plot_overview:
axes[0, 0].set_xlim(scales[0], scales[-1])
axes[0, 0].set_xscale('log')
axes[0, 0].set_yscale('log')
plt.show()

102
python/fig_invariance_log-hp.py
View File

@@ -27,58 +27,29 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)) handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
return handles return handles
def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x', # def zalpha(handles, background='w', down=1):
base=None, cap=None, add_pdf=False, shifted=False, **kwargs): # twins = []
if pdf is None: # for handle in handles:
pdf, axis = get_kde(data, sigma, axis) # twin = handle.copy()
if base is None: # twin.set(color=background, alpha=1)
base = pdf.min() # twin.set_zorder(handle.get_zorder() - down)
if cap is None: # twins.append(twin)
cap = pdf.max() # return twins
pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
if which == 'x':
transform = ax.get_xaxis_transform()
elif which == 'y':
transform = ax.get_yaxis_transform()
else:
transform = ax.transData
rng = np.random.default_rng()
handles = []
for value in data:
ind = np.nonzero(axis == value)[0][0]
offset = base if not shifted else rng.uniform(base, pdf[ind])
variables = (offset, value) if which=='y' else (value, offset)
handles.extend(ax.plot(*variables, transform=transform, **kwargs))
if add_pdf:
variables = (pdf, axis) if which=='y' else (axis, pdf)
pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
return handles, pdf_handle
return handles
def zalpha(handles, background='w', down=1):
twins = []
for handle in handles:
twin = handle.copy()
twin.set(color=background, alpha=1)
twin.set_zorder(handle.get_zorder() - down)
twins.append(twin)
return twins
# GENERAL SETTINGS: # GENERAL SETTINGS:
target = 'Omocestus_rufipes' target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/') data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
ref_path = '../data/inv/log_hp/ref_measures.npz' ref_path = '../data/inv/log_hp/ref_measures.npz'
save_path = '../figures/fig_invariance_log_hp.pdf' save_path = '../figures/fig_invariance_log_hp.pdf'
target_species = [ target_species = [
'Omocestus_rufipes',
'Chorthippus_biguttulus', 'Chorthippus_biguttulus',
'Chorthippus_mollis', 'Chorthippus_mollis',
'Chrysochraon_dispar', 'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus', 'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus', 'Pseudochorthippus_parallelus',
] ]
stages = ['env', 'log', 'inv'] stages = ['env', 'log', 'inv']
load_kwargs = dict( load_kwargs = dict(
files=stages, files=stages,
@@ -159,12 +130,13 @@ fs = dict(
) )
colors = load_colors('../data/stage_colors.npz') colors = load_colors('../data/stage_colors.npz')
species_colors = load_colors('../data/species_colors.npz') species_colors = load_colors('../data/species_colors.npz')
noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)] noise_colors = [(0.6,) * 3, (0.8,) * 3]
lw = dict( lw = dict(
snip=1, snip=1,
big=4, big=4,
spec=2, spec=2,
plateau=1, plateau=1.5,
legend=5,
) )
xlabels = dict( xlabels = dict(
big='scale $\\alpha$', big='scale $\\alpha$',
@@ -273,7 +245,7 @@ leg_kwargs = dict(
columnspacing=1, columnspacing=1,
) )
diag_kwargs = dict( diag_kwargs = dict(
c=(0.75, 0.75, 0.75), c=(0.3,) * 3,
lw=2, lw=2,
ls='--', ls='--',
zorder=1.9, zorder=1.9,
@@ -297,18 +269,9 @@ plateau_line_kwargs = dict(
) )
plateau_dot_kwargs = dict( plateau_dot_kwargs = dict(
marker='o', marker='o',
markersize=10, markersize=8,
markeredgecolor='k',
markeredgewidth=1, markeredgewidth=1,
# alpha=1,
zorder=6,
clip_on=False, clip_on=False,
# base=0,
# cap=0.15,
# add_pdf=True,
)
kde_kwargs = dict(
sigma=0.1,
) )
# PREPARATION: # PREPARATION:
@@ -317,18 +280,12 @@ if compute_ratios:
species_measures = {} species_measures = {}
thresh_inds = np.zeros((len(target_species),), dtype=int) thresh_inds = np.zeros((len(target_species),), dtype=int)
thresh_scales = np.zeros((len(target_species),), dtype=float)
for i, species in enumerate(target_species): for i, species in enumerate(target_species):
path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0] spec_path = search_files(species, dir='../data/inv/log_hp/condensed/')[0]
species_data = load_data(path, **load_kwargs)[0] spec_data = dict(np.load(spec_path))
measure = species_data['measure_inv'] measure = spec_data['mean'].mean(axis=1)
scales = species_data['scales']
if compute_ratios:
measure /= ref_measures['inv']
species_measures[species] = measure species_measures[species] = measure
thresh_inds[i] = get_saturation(measure, **plateau_settings)[1] thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]
thresh_scales[i] = scales[thresh_inds[i]]
thresh_pdf, pdf_axis = get_kde(thresh_scales, axis=scales, **kde_kwargs)
# EXECUTION: # EXECUTION:
for data_path in data_paths: for data_path in data_paths:
@@ -460,24 +417,23 @@ for data_path in data_paths:
big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind], big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
fc=noise_colors[1], **plateau_rect_kwargs) fc=noise_colors[1], **plateau_rect_kwargs)
# Plot species-specific noise-song measures: # Plot species-specific noise-song invariance curves:
for i, (species, measure) in enumerate(species_measures.items()): for i, (species, measure) in enumerate(species_measures.items()):
# Plot invariance curve:
color = species_colors[species] color = species_colors[species]
ind, scale = thresh_inds[i], thresh_scales[i]
big_axes[2].plot(noise_scales, measure, label=shorten_species(species), big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
c=color, lw=lw['spec']) c=color, lw=lw['spec'])
# Indicate saturation:
ind = thresh_inds[i]
scale = noise_scales[ind]
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs, big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
transform=big_axes[2].get_xaxis_transform()) transform=big_axes[2].get_xaxis_transform())
handle = big_axes[2].plot(scale, 0, c=color, alpha=0.5, **plateau_dot_kwargs, big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
transform=big_axes[2].get_xaxis_transform()) transform=big_axes[2].get_xaxis_transform())
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind], big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
color=color, **plateau_line_kwargs) color=color, **plateau_line_kwargs)
big_axes[2].legend(**leg_kwargs) legend = big_axes[2].legend(**leg_kwargs)
[h.set_lw(lw['legend']) for h in legend.legend_handles]
# handles = plot_dist_shifted(big_axes[2], species_threshs, axis=pdf_axis,
# pdf=thresh_pdf, **plateau_dot_kwargs)[0]
# [h.set_color(species_colors[s]) for h, s in zip(handles, target_species)]
if save_path is not None: if save_path is not None:
fig.savefig(save_path, bbox_inches='tight') fig.savefig(save_path, bbox_inches='tight')

108
python/fig_invariance_log-hp_species.py Normal file
View File

@@ -0,0 +1,108 @@
import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.filetools import search_files
from plot_functions import ylabel, super_xlabel, letter_subplots, title_subplot
from color_functions import load_colors
from misc_functions import shorten_species
# GENERAL SETTINGS:
target_species = [
'Chorthippus_biguttulus',
'Chorthippus_mollis',
'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus',
]
data_path = '../data/inv/log_hp/condensed/'
save_path = '../figures/fig_invariance_log-hp_species.pdf'
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 16/2.54),
nrows=1,
ncols=len(target_species),
sharex=True,
sharey=True,
gridspec_kw=dict(
wspace=0.4,
hspace=0,
left=0.07,
right=0.98,
bottom=0.1,
top=0.95,
)
)
# PLOT SETTINGS:
colors = load_colors('../data/species_colors.npz')
line_kwargs = dict(
lw=2,
)
fill_kwargs = dict(
alpha=0.3,
zorder=1,
)
xlab = 'scale $\\alpha$'
ylab = '$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$'
xlab_kwargs = dict(
y=0,
fontsize=16,
ha='center',
va='bottom',
)
ylab_kwargs = dict(
x=0,
fontsize=20,
ha='center',
va='top',
)
title_kwargs = dict(
x=0.5,
yref=0.99,
ha='center',
va='top',
fontsize=16,
fontstyle='italic',
)
letter_kwargs = dict(
x=0.005,
y=0.99,
fontsize=22,
ha='left',
va='top',
)
# Prepare graph:
fig, axes = plt.subplots(**fig_kwargs)
axes[0].set_ylim(0.9, 20)
axes[0].set_xscale('log')
axes[0].set_yscale('log')
super_xlabel(xlab, fig, axes[0], axes[-1], **xlab_kwargs)
ylabel(axes[0], ylab, **ylab_kwargs, transform=fig.transFigure)
# letter_subplots(axes, **letter_kwargs)
# Run through species:
for species, ax in zip(target_species, axes):
title_subplot(ax, shorten_species(species), ref=fig, **title_kwargs)
color = colors[species]
# Load species data:
path = search_files(species, dir=data_path)[0]
data = dict(np.load(path))
scales = data['scales']
means = data['mean']
sds = data['sd']
# Plot recording-specific traces:
for mean, sd in zip(means.T, sds.T):
ax.plot(scales, mean, c=color, **line_kwargs)
ax.fill_between(scales, mean - sd, mean + sd, color=color, **fill_kwargs)
# Save graph:
fig.savefig(save_path)
plt.show()

31
python/plot_functions.py
View File

@@ -2,6 +2,7 @@ import string
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox
from misc_functions import get_kde
def hide_ticks(ax, side='bottom', ticks=True): def hide_ticks(ax, side='bottom', ticks=True):
axis = 'x' if side in ['top', 'bottom'] else 'y' axis = 'x' if side in ['top', 'bottom'] else 'y'
@@ -298,3 +299,33 @@ def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]): def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes)) artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
return None return None
def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
if pdf is None:
pdf, axis = get_kde(data, sigma, axis)
if base is None:
base = pdf.min()
if cap is None:
cap = pdf.max()
pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
if which == 'x':
transform = ax.get_xaxis_transform()
elif which == 'y':
transform = ax.get_yaxis_transform()
else:
transform = ax.transData
rng = np.random.default_rng()
handles = []
for value in data:
ind = np.nonzero(axis == value)[0][0]
offset = base if not shifted else rng.uniform(base, pdf[ind])
variables = (offset, value) if which=='y' else (value, offset)
handles.extend(ax.plot(*variables, transform=transform, **kwargs))
if add_pdf:
variables = (pdf, axis) if which=='y' else (axis, pdf)
pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
return handles, pdf_handle
return handles

85
python/save_inv_data_log-hp.py
View File

@@ -5,16 +5,17 @@ from thunderhopper.filters import decibel, sosfilter
from IPython import embed from IPython import embed
# GENERAL SETTINGS: # GENERAL SETTINGS:
target = ['Omocestus_rufipes', '*'][0] example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
data_paths = search_files(target, excl='noise', dir='../data/processed/') search_target = ['*', example_file][1]
data_paths = search_files(search_target, excl='noise', dir='../data/processed/')
noise_path = '../data/processed/white_noise_sd-1.npz' noise_path = '../data/processed/white_noise_sd-1.npz'
save_path = '../data/inv/log_hp/' save_path = '../data/inv/log_hp/'
# ANALYSIS SETTINGS: # ANALYSIS SETTINGS:
add_noise = target == '*' or False add_noise = search_target == '*' or False
save_snippets = target == 'Omocestus_rufipes' save_detailed = search_target == example_file
example_scales = np.array([0.1, 1, 10, 30, 100, 300]) example_scales = np.array([0.1, 1, 10, 30, 100, 300])
scales = np.geomspace(0.1, 10000, 500) scales = np.geomspace(0.01, 10000, 1000)
scales = np.unique(np.concatenate((scales, example_scales))) scales = np.unique(np.concatenate((scales, example_scales)))
# PREPARATION: # PREPARATION:
@@ -35,47 +36,67 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
# Normalize song component: # Normalize song component:
song /= song[segment].std() song /= song[segment].std()
# Rescale song component:
mix = song[:, None] * scales[None, :]
if add_noise: if add_noise:
# Add normalized noise component: # Get normalized noise component:
noise = pure_noise[:song.shape[0]] noise = pure_noise[:song.shape[0]]
noise /= noise[segment].std() noise /= noise[segment].std()
mix += noise[:, None]
# Process mixture: # Prepare storage:
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp', measure_inv = np.zeros_like(scales)
padtype='even', padlen=config['padlen']) if save_detailed:
mix_log = decibel(mix, ref=1) # Prepare optional storage:
mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp', measure_env = np.zeros_like(scales)
padtype='constant', padlen=config['padlen']) measure_log = np.zeros_like(scales)
snip_env = np.zeros((song.shape[0], example_scales.size))
snip_log = np.zeros((song.shape[0], example_scales.size))
snip_inv = np.zeros((song.shape[0], example_scales.size))
# Get intensity measure per stage: # Execute piecewise:
measure_env = mix[segment, :].std(axis=0) for i, scale in enumerate(scales):
measure_log = mix_log[segment, :].std(axis=0)
measure_inv = mix_inv[segment, :].std(axis=0) # Get scaled mixture:
mix = song * scale
if add_noise:
mix += noise
# Process mixture:
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
padtype='even', padlen=config['padlen'])
mix_log = decibel(mix, ref=1)
mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
padtype='constant', padlen=config['padlen'])
# Log intensity measures:
measure_inv[i] = mix_inv[segment].std()
if save_detailed:
measure_env[i] = mix[segment].std()
measure_log[i] = mix_log[segment].std()
if scale in example_scales:
# Log snippet data:
save_ind = np.nonzero(example_scales == scale)[0][0]
snip_env[:, save_ind] = mix
snip_log[:, save_ind] = mix_log
snip_inv[:, save_ind] = mix_inv
# Save analysis results: # Save analysis results:
save_inds = np.nonzero(np.isin(scales, example_scales))[0]
if save_path is not None: if save_path is not None:
data = dict( archive = dict(
scales=scales, scales=scales,
example_scales=example_scales, example_scales=example_scales,
measure_env=measure_env,
measure_log=measure_log,
measure_inv=measure_inv, measure_inv=measure_inv,
) )
if save_snippets: if save_detailed:
data.update( archive.update(
snip_env=mix[:, save_inds], measure_env=measure_env,
snip_log=mix_log[:, save_inds], measure_log=measure_log,
snip_inv=mix_inv[:, save_inds], snip_env=snip_env,
snip_log=snip_log,
snip_inv=snip_inv,
) )
file_name = save_path + name file_name = save_path + name
if add_noise: if add_noise:
file_name += '_noise' file_name += '_noise'
save_data(file_name, data, config, overwrite=True) save_data(file_name, archive, config, overwrite=True)
print('Done.') print('Done.')
embed() embed()

7
python/save_snippet_data.py
View File

@@ -7,9 +7,10 @@ from IPython import embed
## SETTINGS: ## SETTINGS:
# General: # General:
search_target = '*'
input_folder = '../data/raw/' input_folder = '../data/raw/'
output_folder = '../data/processed/' output_folder = '../data/processed/'
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat', 'norm'] stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
if False: if False:
# Overwrites edited: # Overwrites edited:
stages.append('songs') stages.append('songs')
@@ -30,7 +31,7 @@ config.update({
'rate_ratio': None, 'rate_ratio': None,
'env_fcut': 250, 'env_fcut': 250,
'db_ref': 1, 'db_ref': 1,
'inv_fcut': 5, 'inv_fcut': 10,
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2, 'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
'feat_fcut': 0.5, 'feat_fcut': 0.5,
'label_channels': 0, 'label_channels': 0,
@@ -40,7 +41,7 @@ config.update({
## PREPARATION: ## PREPARATION:
# Fetch WAV recording files: # Fetch WAV recording files:
input_paths = search_files(ext='wav', dir=input_folder) input_paths = search_files(search_target, ext='wav', dir=input_folder)
path_names = crop_paths(input_paths) path_names = crop_paths(input_paths)
# PROCESSING: # PROCESSING:

3
python/save_species_colors.py
View File

@@ -6,11 +6,12 @@ from IPython import embed
# Settings: # Settings:
species = [ species = [
'Omocestus_rufipes',
'Chorthippus_biguttulus', 'Chorthippus_biguttulus',
'Chorthippus_mollis', 'Chorthippus_mollis',
'Chrysochraon_dispar', 'Chrysochraon_dispar',
'Euchorthippus_declivus',
'Gomphocerippus_rufus', 'Gomphocerippus_rufus',
'Omocestus_rufipes',
'Pseudochorthippus_parallelus', 'Pseudochorthippus_parallelus',
] ]
file_name = '../data/species_colors.npz' file_name = '../data/species_colors.npz'