Added multi-thresh simulation to "full" and "short" (currently running).

Added complete "rect-lp" analysis except figure. Added multiple appendix figs. Overhauled normalization options across all condense scripts. Co-authored-by: Copilot <copilot@github.com>
2026-04-24 16:50:14 +02:00
parent 1a586848e8
commit 5411a309f7
48 changed files with 1549 additions and 300 deletions
--- a/figures/fig_invariance_field.pdf
+++ b/figures/fig_invariance_field.pdf
--- a/figures/fig_invariance_full_Chorthippus_biguttulus.pdf
+++ b/figures/fig_invariance_full_Chorthippus_biguttulus.pdf
--- a/figures/fig_invariance_full_Omocestus_rufipes.pdf
+++ b/figures/fig_invariance_full_Omocestus_rufipes.pdf
--- a/figures/fig_invariance_log-hp_appendix.pdf
+++ b/figures/fig_invariance_log-hp_appendix.pdf
--- a/figures/fig_invariance_log_hp.pdf
+++ b/figures/fig_invariance_log_hp.pdf
--- a/figures/fig_invariance_short_Omocestus_rufipes.pdf
+++ b/figures/fig_invariance_short_Omocestus_rufipes.pdf
--- a/figures/fig_invariance_thresh-lp_noise_appendix.pdf
+++ b/figures/fig_invariance_thresh-lp_noise_appendix.pdf
--- a/figures/fig_invariance_thresh-lp_pure_appendix.pdf
+++ b/figures/fig_invariance_thresh-lp_pure_appendix.pdf
--- a/figures/fig_invariance_thresh_lp_species.pdf
+++ b/figures/fig_invariance_thresh_lp_species.pdf
--- a/figures/fig_kernel_sd_perc_field_appendix.pdf
+++ b/figures/fig_kernel_sd_perc_field_appendix.pdf
--- a/figures/fig_kernel_sd_perc_full_appendix.pdf
+++ b/figures/fig_kernel_sd_perc_full_appendix.pdf
--- a/figures/fig_kernel_sd_perc_short_appendix.pdf
+++ b/figures/fig_kernel_sd_perc_short_appendix.pdf
--- a/figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf
+++ b/figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf
--- a/main.aux
+++ b/main.aux
@@ -256,25 +256,29 @@
 \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf  {Feature representation of different species-specific songs saturates at different points in feature space.} }}{15}{}\protected@file@percent }
 \newlabel{fig:inv_thresh-lp_species}{{6}{15}{}{}{}}
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+\newlabel{fig:inv_full}{{7}{16}{}{}{}}
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-\newlabel{eq:pdf_split}{{15}{18}{}{}{}}
-\newlabel{eq:feat_avg}{{16}{18}{}{}{}}
-\newlabel{eq:feat_prop}{{17}{18}{}{}{}}
+\newlabel{fig:inv_short}{{8}{17}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf  {Step-wise emergence of intensity invariant song representation along the model pathway.} }}{18}{}\protected@file@percent }
+\newlabel{fig:inv_field}{{9}{18}{}{}{}}
+\newlabel{eq:pdf_split}{{15}{19}{}{}{}}
+\newlabel{eq:feat_avg}{{16}{19}{}{}{}}
+\newlabel{eq:feat_prop}{{17}{19}{}{}{}}
 \abx@aux@cite{0}{stumpner1991auditory}
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-\newlabel{}{{9}{21}{}{}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf  {} }}{21}{}\protected@file@percent }
-\newlabel{}{{10}{21}{}{}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{20}{}\protected@file@percent }
+\@writefile{toc}{\contentsline {section}{\numberline {5}Conclusions \& outlook}{20}{}\protected@file@percent }
+\abx@aux@page{73}{20}
+\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf  {} }}{22}{}\protected@file@percent }
+\newlabel{}{{10}{22}{}{}{}}
 \@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces \textbf  {} }}{22}{}\protected@file@percent }
 \newlabel{}{{11}{22}{}{}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces \textbf  {} }}{22}{}\protected@file@percent }
-\newlabel{}{{12}{22}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces \textbf  {} }}{23}{}\protected@file@percent }
+\newlabel{}{{12}{23}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces \textbf  {} }}{23}{}\protected@file@percent }
+\newlabel{}{{13}{23}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces \textbf  {} }}{24}{}\protected@file@percent }
+\newlabel{}{{14}{24}{}{}{}}
 \gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }}
 \abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F}
-\gdef \@abspage@last{22}
+\gdef \@abspage@last{24}
--- a/main.blg
+++ b/main.blg
@@ -1,71 +1,71 @@
 [0] Config.pm:307> INFO - This is Biber 2.19
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-[37] biber:340> INFO - === Mo Apr 20, 2026, 17:47:44
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+[39] biber:340> INFO - === Do Apr 23, 2026, 12:30:17
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+[81] Biber.pm:979> INFO - Found 55 citekeys in bib section 0
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+[126] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
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--- a/main.tex
+++ b/main.tex
@@ -717,7 +717,7 @@ the signal for reliable song recognition.
    \caption{\textbf{Step-wise emergence of intensity invariant song
                     representation along the model pathway.}
                     }
-    \label{fig:inv_thresh-lp_full}
+    \label{fig:inv_full}
 \end{figure}
 \FloatBarrier

@@ -727,7 +727,17 @@ the signal for reliable song recognition.
    \caption{\textbf{Step-wise emergence of intensity invariant song
                     representation along the model pathway.}
                     }
-    \label{fig:inv_thresh-lp_short}
+    \label{fig:inv_short}
+\end{figure}
+\FloatBarrier
+
+\begin{figure}[!ht]
+    \centering
+    \includegraphics[width=\textwidth]{figures/fig_invariance_field.pdf}
+    \caption{\textbf{Step-wise emergence of intensity invariant song
+                     representation along the model pathway.}
+                     }
+    \label{fig:inv_field}
 \end{figure}
 \FloatBarrier

@@ -909,7 +919,16 @@ initiation of one behavior over another is categorical (e.g. approach/stay)

 \begin{figure}[!ht]
    \centering
-    \includegraphics[width=\textwidth]{figures/fig_invariance_thresh-lp_appendix.pdf}
+    \includegraphics[width=\textwidth]{figures/fig_invariance_thresh-lp_pure_appendix.pdf}
+    \caption{\textbf{}
+                     }
+    \label{}
+\end{figure}
+\FloatBarrier
+
+\begin{figure}[!ht]
+    \centering
+    \includegraphics[width=\textwidth]{figures/fig_invariance_thresh-lp_noise_appendix.pdf}
    \caption{\textbf{}
                     }
    \label{}
--- a/python/collect_inv_data_field.py
+++ b/python/collect_inv_data_field.py
@@ -15,7 +15,7 @@ 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)
+    all_paths = search_files(species, excl='merged_noise',ext='npz', dir=search_path)
    if not all_paths:
        continue

--- a/python/collect_inv_data_log-hp.py
+++ b/python/collect_inv_data_log-hp.py
@@ -1,7 +1,6 @@
 import numpy as np
 from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data, save_data
-from misc_functions import sort_files_by_rec
 from IPython import embed

 # GENERAL SETTINGS:
--- a/python/collect_inv_data_rect-lp.py
+++ b/python/collect_inv_data_rect-lp.py
@@ -0,0 +1,51 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from IPython import embed
+
+# GENERAL SETTINGS:
+mode = ['pure', 'noise'][1]
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+stages = ['filt', 'env']
+search_path = '../data/inv/rect_lp/'
+save_path = '../data/inv/rect_lp/collected/'
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Fetch all species-specific song files:
+    all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
+    
+    # Run through files:
+    for j, path in enumerate(all_paths):
+
+        # Load invariance data:
+        data, config = load_data(path, 'scales', 'measure')
+
+        if j == 0:
+            # Prepare species-specific storage:
+            species_data = dict(scales=data['scales'])
+            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 + '_' + mode
+    save_data(save_name, species_data, config, overwrite=True)
+
+print('Done.')
--- a/python/collect_inv_data_short.py
+++ b/python/collect_inv_data_short.py
@@ -13,7 +13,7 @@ target_species = [
    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
 ]
-stages = ['filt', 'env', 'conv', 'feat']
+stages = ['filt', 'env', 'inv', 'conv', 'feat']
 search_path = '../data/inv/short/'
 save_path = '../data/inv/short/collected/'

--- a/python/condense_inv_data_field.py
+++ b/python/condense_inv_data_field.py
@@ -42,6 +42,7 @@ target_species = ['Pseudochorthippus_parallelus']
 mode = ['song', 'noise'][0]
 stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
 search_path = f'../data/inv/field/{mode}/'
+ref_path = f'../data/inv/field/ref_measures.npz'
 save_path = f'../data/inv/field/{mode}/condensed/'
 sources = [
    'JJ',
@@ -53,16 +54,27 @@ normalization = 'none'
 if mode == 'song':
    normalization = [
        'none',
-        # 'base',
+        'min',
+        'max',
+        'base',
        'range'
-        ][-1]
+        ][1]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]
+if normalization == 'base':
+    ref_data = dict(np.load(ref_path))

 # 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)
+    all_paths = search_files(species, excl='merged_noise', ext='npz', dir=search_path)
    if not all_paths:
        continue

@@ -94,7 +106,17 @@ for i, species in enumerate(target_species):
            for stage in stages:
                mkey = f'measure_{stage}'

-                if normalization == 'range':
+                if normalization == 'min':
+                    # Minimum normalization:
+                    data[mkey] /= data[mkey].min(axis=0, keepdims=True)
+                elif normalization == 'max':
+                    # Maximum normalization:
+                    data[mkey] /= data[mkey].max(axis=0, keepdims=True)
+                elif normalization == 'base':
+                    # Noise baseline normalization:
+                    data[mkey] /= ref_data[stage]
+                    # 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)
@@ -106,18 +128,15 @@ for i, species in enumerate(target_species):
        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)
+            if len(sorted_paths) == 1:
+                # Prune recording dimension for single recording:
+                rec_mean[f'mean_{stage}'] = rec_mean[f'mean_{stage}'][..., 0]
+                rec_sd[f'sd_{stage}'] = rec_sd[f'sd_{stage}'][..., 0]

    # 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)
+    save_data(save_path + species + suffix, archive, config, overwrite=True)

 print('Done.')         
--- a/python/condense_inv_data_full.py
+++ b/python/condense_inv_data_full.py
@@ -28,9 +28,18 @@ save_path = '../data/inv/full/condensed/'
 # ANALYSIS SETTINGS:
 normalization = [
    'none',
+    'min',
+    'max',
    'base',
-    'range'
+    'range',
    ][2]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]

 # EXECUTION:
 for i, species in enumerate(target_species):
@@ -69,7 +78,13 @@ for i, species in enumerate(target_species):
            for stage in stages:
                mkey = f'measure_{stage}'

-                if normalization == 'base':
+                if normalization == 'min':
+                    # Minimum normalization:
+                    data[mkey] /= data[mkey].min(axis=0, keepdims=True)
+                elif normalization == 'max':
+                    # Maximum normalization:
+                    data[mkey] /= data[mkey].max(axis=0, keepdims=True)
+                elif normalization == 'base':
                    # Noise baseline normalization:
                    data[mkey] /= data[mkey][0]
                elif normalization == 'range':
@@ -86,16 +101,9 @@ for i, species in enumerate(target_species):
            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(scales=data['scales'])
    archive.update(rec_mean)
    archive.update(rec_sd)
-    save_data(save_name, archive, config, overwrite=True)
+    save_data(save_path + species + suffix, archive, config, overwrite=True)

 print('Done.')         
--- a/python/condense_inv_data_log-hp.py
+++ b/python/condense_inv_data_log-hp.py
@@ -26,7 +26,21 @@ search_path = '../data/inv/log_hp/'
 save_path = '../data/inv/log_hp/condensed/'

 # ANALYSIS SETTINGS:
-compute_ratios = True
+mode = 'noise'
+normalization = [
+    'none',
+    'min',
+    'max',
+    'base',
+    'range',
+    ][3]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]
 plot_overview = True

 # PREPARATION:
@@ -44,7 +58,7 @@ for i, species in enumerate(target_species):
        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)
+    all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
    
    # Sort song files by recording (one or more per source):
    sorted_paths = sort_files_by_rec(all_paths, sources)
@@ -57,10 +71,6 @@ for i, species in enumerate(target_species):
            data, config = load_data(path, ['scales', 'measure_inv'])
            scales, measure = data['scales'], data['measure_inv']

-            # Relate to noise:
-            if compute_ratios:
-                measure /= measure[0]
-
            if k == 0:
                # Prepare song file-specific storage:
                file_data = np.zeros((scales.size, len(rec_paths)), dtype=float)
@@ -70,6 +80,21 @@ for i, species in enumerate(target_species):
                    rec_sd = np.zeros((scales.size, len(sorted_paths)), dtype=float)

            # Log song file data:
+            if normalization == 'min':
+                # Minimum normalization:
+                measure /= measure.min(axis=0, keepdims=True)
+            elif normalization == 'max':
+                # Maximum normalization:
+                measure /= measure.max(axis=0, keepdims=True)
+            elif normalization == 'base':
+                # Noise baseline normalization:
+                measure /= measure[0]
+            elif normalization == 'range':
+                # Min-max normalization:
+                min_measure = measure.min(axis=0, keepdims=True)
+                max_measure = measure.max(axis=0, keepdims=True)
+                measure = (measure - min_measure) / (max_measure - min_measure)
+
            file_data[:, k] = measure

            if plot_overview:
@@ -85,8 +110,9 @@ for i, species in enumerate(target_species):
                                    rec_mean[:, j] + rec_sd[:, j], color='k', alpha=0.2)

    # Save condensed recording data for current species:
+    save_name = save_path + species + '_' + mode + suffix
    archive = dict(scales=scales, mean_inv=rec_mean, sd_inv=rec_sd)
-    save_data(save_path + species, archive, config, overwrite=True)
+    save_data(save_name, archive, config, overwrite=True)

    if plot_overview:
        spec_mean = rec_mean.mean(axis=1)
--- a/python/condense_inv_data_rect-lp.py
+++ b/python/condense_inv_data_rect-lp.py
@@ -0,0 +1,109 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from misc_functions import sort_files_by_rec
+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'
+]
+stages = ['filt', 'env']
+search_path = '../data/inv/rect_lp/'
+save_path = '../data/inv/rect_lp/condensed/'
+
+# ANALYSIS SETTINGS:
+mode = ['pure', 'noise'][1]
+normalization = [
+    'none',
+    'min',
+    'max',
+    'base',
+    'range',
+    ][3]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Fetch all species-specific song files:
+    all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
+    
+    # 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, 'scales', '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 == 'min':
+                    # Minimum normalization:
+                    data[mkey] /= data[mkey].min(axis=0, keepdims=True)
+                elif normalization == 'max':
+                    # Maximum normalization:
+                    data[mkey] /= data[mkey].max(axis=0, keepdims=True)
+                elif 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:
+        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:
+    archive = dict(scales=data['scales'])
+    archive.update(rec_mean)
+    archive.update(rec_sd)
+    save_name = save_path + species + '_' + mode + suffix
+    save_data(save_name, archive, config, overwrite=True)
+
+print('Done.')
--- a/python/condense_inv_data_short.py
+++ b/python/condense_inv_data_short.py
@@ -21,16 +21,25 @@ sources = [
    'GBC',
    'FTN'
 ]
-stages = ['filt', 'env', 'conv', 'feat']
+stages = ['filt', 'env', 'inv', 'conv', 'feat']
 search_path = '../data/inv/short/'
 save_path = '../data/inv/short/condensed/'

 # ANALYSIS SETTINGS:
 normalization = [
    'none',
+    'min',
+    'max',
    'base',
-    'range'
-    ][1]
+    'range',
+    ][2]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]

 # EXECUTION:
 for i, species in enumerate(target_species):
@@ -69,7 +78,13 @@ for i, species in enumerate(target_species):
            for stage in stages:
                mkey = f'measure_{stage}'

-                if normalization == 'base':
+                if normalization == 'min':
+                    # Minimum normalization:
+                    data[mkey] /= data[mkey].min(axis=0, keepdims=True)
+                elif normalization == 'max':
+                    # Maximum normalization:
+                    data[mkey] /= data[mkey].max(axis=0, keepdims=True)
+                elif normalization == 'base':
                    # Noise baseline normalization:
                    data[mkey] /= data[mkey][0]
                elif normalization == 'range':
@@ -86,16 +101,9 @@ for i, species in enumerate(target_species):
            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(scales=data['scales'])
    archive.update(rec_mean)
    archive.update(rec_sd)
-    save_data(save_name, archive, config)
+    save_data(save_path + species + suffix, archive, config)

 print('Done.')         
--- a/python/condense_inv_data_thresh-lp.py
+++ b/python/condense_inv_data_thresh-lp.py
@@ -26,7 +26,21 @@ search_path = '../data/inv/thresh_lp/'
 save_path = '../data/inv/thresh_lp/condensed/'

 # ANALYSIS SETTINGS:
-with_noise = False
+mode = ['pure', 'noise'][1]
+normalization = [
+    'none',
+    'min',
+    'max',
+    'base',
+    'range',
+    ][0]
+suffix = dict(
+    none='_unnormed',
+    min='_norm-min',
+    max='_norm-max',
+    base='_norm-base',
+    range='_norm-range'
+)[normalization]
 plot_overview = False
 thresh_rel = np.array([0.5, 1, 3])

@@ -53,8 +67,7 @@ for i, species in enumerate(target_species):
            all_axes[thresh][0, i].set_title(shorten_species(species))

    # Fetch all species-specific song files:
-    incl = 'noise' if with_noise else 'pure'
-    all_paths = search_files(species, incl=incl, ext='npz', dir=search_path)
+    all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
    
    # Sort song files by recording (one or more per source):
    sorted_paths = sort_files_by_rec(all_paths, sources)
@@ -78,6 +91,21 @@ for i, species in enumerate(target_species):
                    rec_sd = np.zeros(shape, dtype=float)

            # Log song file data:
+            if normalization == 'min':
+                # Minimum normalization:
+                measure /= measure.min(axis=0, keepdims=True)
+            elif normalization == 'max':
+                # Maximum normalization:
+                measure /= measure.max(axis=0, keepdims=True)
+            elif normalization == 'base':
+                # Noise baseline normalization:
+                measure /= measure[0]
+            elif normalization == 'range':
+                # Min-max normalization:
+                min_measure = measure.min(axis=0, keepdims=True)
+                max_measure = measure.max(axis=0, keepdims=True)
+                measure = (measure - min_measure) / (max_measure - min_measure)
+
            file_data[..., k] = measure

            if plot_overview:
@@ -100,11 +128,7 @@ for i, species in enumerate(target_species):
                    axes[1, i].fill_between(scales, *spread, color=c, alpha=0.2)

    # Save condensed recording data:
-    save_name = save_path + species
-    if with_noise:
-        save_name += '_noise'
-    else:
-        save_name += '_pure'
+    save_name = save_path + species + '_' + mode + suffix
    archive = dict(
        scales=scales,
        mean_feat=rec_mean,
--- a/python/fig_invariance_field.py
+++ b/python/fig_invariance_field.py
@@ -0,0 +1,433 @@
+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, reorder_by_sd, ylimits, super_xlabel,\
+                           ylabel, title_subplot, plot_line, time_bar,\
+                           assign_colors, 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)
+    handles = []
+    for i, ax in enumerate(axes):
+        handles.append(plot_line(ax, time, snippets[:, ..., i],
+                                 ymin=ymin, ymax=ymax, **kwargs))
+    return handles
+
+def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
+    if measures.ndim == 1:
+        ax.plot(scales, measures, **kwargs)[0]
+        return measures
+    median_measure = np.median(measures, axis=1)
+    spread_measure = [np.percentile(measures, 25, axis=1),
+                      np.percentile(measures, 75, axis=1)]
+    ax.plot(scales, median_measure, **kwargs)[0]
+    ax.fill_between(scales, *spread_measure, **fill_kwargs)
+    return median_measure
+
+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
+
+def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']):
+    half_offset = int((nin - nout) / 2)
+    segment = np.arange(half_offset, half_offset + nout)
+    for stage in stages:
+        key = f'snip_{stage}'
+        snippets[key] = snippets[key][segment, ...]
+    return snippets
+
+
+# GENERAL SETTINGS:
+search_target = 'Pseudochorthippus_parallelus'
+stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
+song_example = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
+noise_example = 'merged_noise'
+song_path = '../data/inv/field/song/'
+noise_path = '../data/inv/field/noise/'
+raw_path = search_files(search_target, incl='unnormed', dir=song_path + 'condensed/')[0]
+base_path = search_files(search_target, incl='base', dir=song_path + 'condensed/')[0]
+range_path = search_files(search_target, incl='range', dir=song_path + 'condensed/')[0] 
+song_snip_path = search_files(song_example, dir=song_path)[0]
+noise_snip_path = search_files(noise_example, dir=noise_path)[0]
+save_path = '../figures/fig_invariance_field.pdf'
+
+# ANALYSIS SETTINGS:
+offset_distance = 10 # centimeter
+
+# 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=0.11,
+    right=0.98,
+    bottom=0.08,
+    top=0.95
+)
+big_grid_kwargs = dict(
+    nrows=1,
+    ncols=3,
+    wspace=0.4,
+    hspace=0,
+    left=snip_grid_kwargs['left'],
+    right=snip_grid_kwargs['right'],
+    bottom=0.13,
+    top=0.98
+)
+
+# 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')
+conv_colors = load_colors('../data/conv_colors_all.npz')
+feat_colors = load_colors('../data/feat_colors_all.npz')
+lw = dict(
+    filt=0.25,
+    env=0.25,
+    log=0.25,
+    inv=0.25,
+    conv=0.25,
+    feat=1,
+    big=3,
+    plateau=1.5,
+)
+xlabels = dict(
+    big='distance [cm]',
+)
+ylabels = dict(
+    filt='$x_{\\text{filt}}$',
+    env='$x_{\\text{env}}$',
+    log='$x_{\\text{db}}$',
+    inv='$x_{\\text{adapt}}$',
+    conv='$c_i$',
+    feat='$f_i$',
+    big=['measure', 'rel. measure', 'norm. measure']
+)
+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=-0.2,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='bottom',
+)
+yloc = dict(
+    filt=0.03,
+    env=0.01,
+    log=50,
+    inv=20,
+    conv=1,
+    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'],
+)
+song_bar_time = 1
+song_bar_kwargs = dict(
+    dur=song_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'${song_bar_time}\\,\\text{{s}}$',
+    text_kwargs=dict(
+        fontsize=fs['bar'],
+        ha='right',
+        va='center',
+    )
+)
+noise_bar_time = 0.5
+noise_bar_kwargs = song_bar_kwargs.copy()
+noise_bar_kwargs['dur'] = noise_bar_time
+noise_bar_kwargs['text_str'] = f'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$'
+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='distances', keywords='mean')
+dists = data['distances'] + offset_distance
+
+# Load snippet data:
+song_snip, _ = load_data(song_snip_path, keywords='snip')
+t_song = np.arange(song_snip['snip_filt'].shape[0]) / config['rate']
+noise_snip, _ = load_data(noise_snip_path, keywords='snip')
+noise_snip = crop_noise_snippets(noise_snip, noise_snip['snip_filt'].shape[0], t_song.size)
+t_noise = np.arange(noise_snip['snip_filt'].shape[0]) / config['rate']
+snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists]
+
+# 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')
+    song_snip = reduce_kernel_set(song_snip, kern_inds, keyword='snip')
+    noise_snip = reduce_kernel_set(noise_snip, kern_inds, keyword='snip')
+    config['k_specs'] = config['k_specs'][kern_inds, :]
+    config['kernels'] = config['kernels'][:, kern_inds]
+    reduce_kernels = True
+
+# Adjust grid parameters:
+snip_grid_kwargs['ncols'] = len(snip_dists)
+
+# 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.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
+    hide_axis(ax, 'bottom')
+    if i == 0:
+        title = title_subplot(ax, snip_dists[j], ref=snip_subfig, **title_kwargs)
+    if j == 0:
+        ax.set_xlim(t_noise[0], t_noise[-1])
+        ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
+    else:
+        ax.set_xlim(t_song[0], t_song[-1])
+        hide_axis(ax, 'left')
+    snip_axes[i, j] = ax
+time_bar(snip_axes[-1, -1], **song_bar_kwargs)
+# time_bar(snip_axes[-1, 0], **noise_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(dists[0], 0)
+    # ax.set_xscale('symlog', linthresh=offset_distance, linscale=0.5)
+    ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
+    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
+super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
+letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
+
+if True:
+    # Plot filtered snippets:
+    plot_snippets(snip_axes[0, 1:], t_song, song_snip['snip_filt'],
+                  c=colors['filt'], lw=lw['filt'])
+    plot_line(snip_axes[0, 0], t_noise, noise_snip['snip_filt'][:, 0],
+              *snip_axes[0, 1].get_ylim(), c=colors['filt'], lw=lw['filt'])
+
+    # Plot envelope snippets:
+    plot_snippets(snip_axes[1, 1:], t_song, song_snip['snip_env'],
+                  ymin=0, c=colors['env'], lw=lw['env'])
+    plot_line(snip_axes[1, 0], t_noise, noise_snip['snip_env'][:, 0],
+              *snip_axes[1, 1].get_ylim(), c=colors['env'], lw=lw['env'])
+
+    # Plot logarithmic snippets:
+    plot_snippets(snip_axes[2, 1:], t_song, song_snip['snip_log'],
+                  c=colors['log'], lw=lw['log'])
+    plot_line(snip_axes[2, 0], t_noise, noise_snip['snip_log'][:, 0],
+              *snip_axes[2, 1].get_ylim(), c=colors['log'], lw=lw['log'])
+
+    # Plot invariant snippets:
+    plot_snippets(snip_axes[3, 1:], t_song, song_snip['snip_inv'],
+                  c=colors['inv'], lw=lw['inv'])
+    plot_line(snip_axes[3, 0], t_noise, noise_snip['snip_inv'][:, 0],
+              *snip_axes[3, 1].get_ylim(), c=colors['inv'], lw=lw['inv'])
+
+    # Plot kernel response snippets:
+    all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_snip['snip_conv'],
+                                 c=colors['conv'], lw=lw['conv'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+        reorder_by_sd(handles, song_snip['snip_conv'][..., i])
+    handles = plot_line(snip_axes[4, 0], t_noise, noise_snip['snip_conv'][:, 0],
+                        *snip_axes[4, 1].get_ylim(), c=colors['conv'], lw=lw['conv'])
+    assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+    reorder_by_sd(handles, noise_snip['snip_conv'][:, 0])
+
+    # Plot feature snippets:
+    all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_snip['snip_feat'],
+                                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+        reorder_by_sd(handles, song_snip['snip_feat'][..., i])
+    handles = plot_line(snip_axes[5, 0], t_noise, noise_snip['snip_feat'][:, 0],
+                        ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+    reorder_by_sd(handles, noise_snip['snip_feat'][:, 0])
+del song_snip, noise_snip
+
+# Remember saturation points:
+crit_inds, crit_dists = {}, {}
+
+# Unnormed measures:
+for stage in stages:
+    # Plot average intensity measure across recordings:
+    curve = plot_curves(big_axes[0], dists, data[f'mean_{stage}'],
+                        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]
+    #     dist = dists[ind]
+    #     big_axes[0].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+    #                      transform=big_axes[0].get_xaxis_transform()) 
+    #     big_axes[0].plot(dist, 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(dist, big_axes[0].get_ylim()[0], curve[ind],
+    #                        color=colors[stage], **plateau_line_kwargs)
+    #     # Log saturation point:
+    #     crit_inds[stage] = ind
+    #     crit_dists[stage] = dist
+del data
+
+# Noise baseline-related measures:
+data, _ = load_data(base_path, files='scales', keywords='mean')
+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], dists, data[f'mean_{stage}'],
+                        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, dist = crit_inds[stage], crit_dists[stage]
+    #     big_axes[1].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+    #                      transform=big_axes[1].get_xaxis_transform()) 
+    #     big_axes[1].plot(dist, 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(dist, 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 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], dists, data[f'mean_{stage}'],
+                        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, dist = crit_inds[stage], crit_dists[stage]
+    #     big_axes[2].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+    #                      transform=big_axes[2].get_xaxis_transform()) 
+    #     big_axes[2].plot(dist, 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(dist, 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)
+plt.show()
+
+print('Done.')
+embed()
--- a/python/fig_invariance_full.py
+++ b/python/fig_invariance_full.py
@@ -7,16 +7,18 @@ 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, set_clip_box,\
+from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
+                           plot_line, strip_zeros, time_bar, assign_colors,\
                           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)
+    handles = []
    for i, ax in enumerate(axes):
-        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
-    return None
+        handles.append(plot_line(ax, time, snippets[:, ..., i],
+                       ymin=ymin, ymax=ymax, **kwargs))
+    return handles

 def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
    if measures.ndim == 1:
@@ -73,8 +75,8 @@ save_path = '../figures/fig_invariance_full.pdf'
 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 = 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([
@@ -111,20 +113,20 @@ snip_grid_kwargs = dict(
    ncols=None,
    wspace=0.1,
    hspace=0.4,
-    left=0.08,
-    right=0.95,
+    left=0.11,
+    right=0.98,
    bottom=0.08,
    top=0.95
 )
 big_grid_kwargs = dict(
    nrows=1,
    ncols=3,
-    wspace=0.2,
+    wspace=0.4,
    hspace=0,
    left=snip_grid_kwargs['left'],
-    right=0.96,
-    bottom=0.2,
-    top=0.95
+    right=snip_grid_kwargs['right'],
+    bottom=0.13,
+    top=0.98
 )

 # PLOT SETTINGS:
@@ -137,6 +139,8 @@ fs = dict(
    bar=16,
 )
 colors = load_colors('../data/stage_colors.npz')
+conv_colors = load_colors('../data/conv_colors_all.npz')
+feat_colors = load_colors('../data/feat_colors_all.npz')
 lw = dict(
    filt=0.25,
    env=0.25,
@@ -154,10 +158,10 @@ ylabels = dict(
    filt='$x_{\\text{filt}}$',
    env='$x_{\\text{env}}$',
    log='$x_{\\text{db}}$',
-    inv='$x_{\\text{inv}}$',
+    inv='$x_{\\text{adapt}}$',
    conv='$c_i$',
    feat='$f_i$',
-    big=['intensity', 'rel. intensity', 'norm. intensity']
+    big=['measure', 'rel. measure', 'norm. measure']
 )
 xlab_big_kwargs = dict(
    y=0,
@@ -173,7 +177,7 @@ ylab_snip_kwargs = dict(
    va='center'
 )
 ylab_big_kwargs = dict(
-    x=-0.12,
+    x=-0.2,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
@@ -183,7 +187,7 @@ yloc = dict(
    env=1000,
    log=50,
    inv=20,
-    conv=2,
+    conv=1,
    feat=1,
 )
 title_kwargs = dict(
@@ -262,6 +266,8 @@ 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')
+    config['k_specs'] = config['k_specs'][kern_inds, :]
+    config['kernels'] = config['kernels'][:, kern_inds]
    reduce_kernels = True

 # Adjust grid parameters:
@@ -300,13 +306,13 @@ for i in range(big_grid.ncols):
    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
+super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
 letter_subplots(big_axes, 'bcd', **letter_big_kwargs)

 if True:
@@ -327,12 +333,18 @@ if True:
                  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'])
+    all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
+                                c=colors['conv'], lw=lw['conv'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+        reorder_by_sd(handles, snip['snip_conv'][..., i])

    # Plot feature snippets:
-    plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
-                  ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    all_handles = plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
+                                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+        reorder_by_sd(handles, snip['snip_feat'][..., i])
 del snip

 # Remember saturation points:
@@ -387,7 +399,7 @@ 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:
+for stage in ['log', 'inv', 'conv', 'feat']:
    # 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'],
--- a/python/fig_invariance_log-hp.py
+++ b/python/fig_invariance_log-hp.py
@@ -270,7 +270,7 @@ plateau_dot_kwargs = dict(
 species_measures = {}
 thresh_inds = np.zeros((len(target_species),), dtype=int)
 for i, species in enumerate(target_species):
-    spec_path = search_files(species, dir='../data/inv/log_hp/condensed/')[0]
+    spec_path = search_files(species, incl=['noise', 'norm-base'], dir='../data/inv/log_hp/condensed/')[0]
    spec_data = dict(np.load(spec_path))
    measure = spec_data['mean_inv'].mean(axis=-1)
    if exclude_zero:
--- a/python/fig_invariance_log-hp_appendix.py
+++ b/python/fig_invariance_log-hp_appendix.py
@@ -108,7 +108,7 @@ for species, ax in zip(target_species, axes):
    color = colors[species]
    
    # Load species data:
-    path = search_files(species, dir=data_path)[0]
+    path = search_files(species, incl=['noise', 'norm-base'], dir=data_path)[0]
    data = dict(np.load(path))
    scales = data['scales']
    means = data['mean_inv']
--- a/python/fig_invariance_short.py
+++ b/python/fig_invariance_short.py
@@ -7,16 +7,18 @@ 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 plot_functions import hide_axis, ylimits, super_xlabel, ylabel, title_subplot,\
+                           plot_line, strip_zeros, time_bar, assign_colors,\
+                           letter_subplot, letter_subplots, reorder_by_sd
 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)
+    handles = []
    for i, ax in enumerate(axes):
-        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
-    return None
+        handles.append(plot_line(ax, time, snippets[:, ..., i],
+                       ymin=ymin, ymax=ymax, **kwargs))
+    return handles

 def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
    if measures.ndim == 1:
@@ -62,7 +64,7 @@ example_file = {
    'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
    'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
 }[target_species]
-stages = ['filt', 'env', 'conv', 'feat']
+stages = ['filt', 'env', 'inv', '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]
@@ -111,20 +113,20 @@ snip_grid_kwargs = dict(
    ncols=None,
    wspace=0.1,
    hspace=0.4,
-    left=0.08,
-    right=0.95,
+    left=0.11,
+    right=0.98,
    bottom=0.08,
    top=0.95
 )
 big_grid_kwargs = dict(
    nrows=1,
    ncols=3,
-    wspace=0.2,
+    wspace=0.4,
    hspace=0,
    left=snip_grid_kwargs['left'],
-    right=0.96,
-    bottom=0.2,
-    top=0.95
+    right=snip_grid_kwargs['right'],
+    bottom=0.13,
+    top=0.98
 )

 # PLOT SETTINGS:
@@ -137,10 +139,13 @@ fs = dict(
    bar=16,
 )
 colors = load_colors('../data/stage_colors.npz')
+conv_colors = load_colors('../data/conv_colors_all.npz')
+feat_colors = load_colors('../data/feat_colors_all.npz')
 lw = dict(
    filt=0.25,
    env=0.25,
    conv=0.25,
+    inv=0.25,
    feat=1,
    big=3,
    plateau=1.5,
@@ -151,9 +156,10 @@ xlabels = dict(
 ylabels = dict(
    filt='$x_{\\text{filt}}$',
    env='$x_{\\text{env}}$',
+    inv='$x_{\\text{adapt}}$',
    conv='$c_i$',
    feat='$f_i$',
-    big=['intensity', 'rel. intensity', 'norm. intensity']
+    big=['measure', 'rel. measure', 'norm. measure']
 )
 xlab_big_kwargs = dict(
    y=0,
@@ -169,7 +175,7 @@ ylab_snip_kwargs = dict(
    va='center'
 )
 ylab_big_kwargs = dict(
-    x=-0.12,
+    x=-0.2,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
@@ -177,6 +183,7 @@ ylab_big_kwargs = dict(
 yloc = dict(
    filt=3000,
    env=1000,
+    inv=1000,
    conv=30,
    feat=1,
 )
@@ -294,13 +301,13 @@ for i in range(big_grid.ncols):
    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
+super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
 letter_subplots(big_axes, 'bcd', **letter_big_kwargs)

 if True:
@@ -312,13 +319,23 @@ if True:
    plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
                  ymin=0, c=colors['env'], lw=lw['env'])

+    # Plot "adapted" snippets:
+    plot_snippets(snip_axes[2, :], t_full, snip['snip_inv'],
+                  c=colors['inv'], lw=lw['inv'])
+
    # Plot kernel response snippets:
-    plot_snippets(snip_axes[2, :], t_full, snip['snip_conv'],
-                  c=colors['conv'], lw=lw['conv'])
+    all_handles = plot_snippets(snip_axes[3, :], t_full, snip['snip_conv'],
+                                c=colors['conv'], lw=lw['conv'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+        reorder_by_sd(handles, snip['snip_conv'][..., i])

    # Plot feature snippets:
-    plot_snippets(snip_axes[3, :], t_full, snip['snip_feat'],
-                  ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_feat'],
+                                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+        reorder_by_sd(handles, snip['snip_feat'][..., i])
 del snip

 # Remember saturation points:
@@ -373,7 +390,7 @@ 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:
+for stage in ['feat']:
    # 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'],
--- a/python/fig_invariance_thresh-lp_appendix.py
+++ b/python/fig_invariance_thresh-lp_appendix.py
@@ -9,6 +9,7 @@ from misc_functions import shorten_species
 from IPython import embed

 # GENERAL SETTINGS:
+mode = ['pure', 'noise'][1]
 target_species = [
    'Chorthippus_biguttulus',
    'Chorthippus_mollis',
@@ -19,7 +20,7 @@ target_species = [
    'Pseudochorthippus_parallelus',
 ]
 data_path = '../data/inv/thresh_lp/condensed/'
-save_path = '../figures/fig_invariance_thresh-lp_appendix.pdf'
+save_path = f'../figures/fig_invariance_thresh-lp_{mode}_appendix.pdf'

 # ANALYSIS SETTINGS:
 exclude_zero = True
@@ -145,7 +146,7 @@ for i, (species, spec_axes) in enumerate(zip(target_species, axes.T)):
    title_subplot(spec_axes[0], shorten_species(species), ref=fig, **title_kwargs)
    
    # Load species data:
-    path = search_files(species, dir=data_path)[0]
+    path = search_files(species, incl=[mode, 'unnormed'], dir=data_path)[0]
    data, config = load_data(path, files=['scales', 'mean_feat', 'sd_feat', 'thresh_rel'])
    scales = data['scales']
    means = data['mean_feat']
--- a/python/fig_invariance_thresh-lp_species.py
+++ b/python/fig_invariance_thresh-lp_species.py
@@ -537,8 +537,8 @@ for i, species in enumerate(target_species):
             text_str=f'${spec_bar_times[species]}\\,\\text{{s}}$')

    # Fetch species-specific invariance files:
-    pure_path = search_files(species, incl='pure', dir='../data/inv/thresh_lp/condensed/')[0]
-    noise_path = search_files(species, incl='noise', dir='../data/inv/thresh_lp/condensed/')[0]
+    pure_path = search_files(species, incl=['pure', 'unnormed'], dir='../data/inv/thresh_lp/condensed/')[0]
+    noise_path = search_files(species, incl=['noise', 'unnormed'], dir='../data/inv/thresh_lp/condensed/')[0]

    # Load invariance data:
    pure_data, config = load_data(pure_path, **load_kwargs)
--- a/python/fig_kernel_sd_perc_appendix.py
+++ b/python/fig_kernel_sd_perc_appendix.py
@@ -0,0 +1,69 @@
+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderhopper.modeltools import load_data
+from thunderhopper.filetools import search_files, crop_paths
+from plot_functions import xlabel, ylabel
+from IPython import embed
+
+# Analysis settings:
+mode = ['thresh_lp', 'full', 'short', 'field'][3]
+thresh_path = f'../data/inv/{mode}/thresholds.npz'
+save_path = f'../figures/fig_kernel_sd_perc_{mode}_appendix.pdf'
+
+# Plot settings:
+fig_kwargs = dict(
+    figsize=(32/2.54, 16/2.54),
+    nrows=1,
+    ncols=1,
+    gridspec_kw=dict(
+        wspace=0,
+        hspace=0,
+        left=0.09,
+        right=0.99,
+        bottom=0.11,
+        top=0.98,
+    )
+)
+line_kwargs = dict(
+    color='black',
+    lw=1,
+    alpha=0.5,
+)
+xlab = '$\\text{multiple of }\\sigma_{k_i}$'
+ylab = '$P\\,(c_i > \\Theta_i)$'
+xlab_kwargs = dict(
+    y=0,
+    fontsize=20,
+    ha='center',
+    va='bottom',
+)
+ylab_kwargs = dict(
+    x=0,
+    fontsize=20,
+    ha='center',
+    va='top',
+)
+
+# Load threshold data:
+data = dict(np.load(thresh_path))
+factors = data['factors']
+perc = data['percs']
+
+# Prepare graph:
+fig, ax = plt.subplots(**fig_kwargs)
+ax.set_xlim(factors[0], factors[-1])
+ax.set_ylim(0, 1)
+ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
+xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
+
+# Plotting:
+ax.plot(factors, perc, **line_kwargs)
+
+# Save figure:
+fig.savefig(save_path)
+
+plt.show()
+print('Done.')
+
+
--- a/python/misc_functions.py
+++ b/python/misc_functions.py
@@ -1,6 +1,7 @@
 import numpy as np
 from scipy.stats import gaussian_kde
 from thunderhopper.filetools import crop_paths
+from IPython import embed

 def shorten_species(name):
    genus, species = name.split('_')
@@ -48,6 +49,40 @@ 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_thresholds(data=None, path=None, perc=None, factor=None,
+                   direct=False, which=None):
+
+    def get_inds(nearest, which):
+        if which == 'floor':
+            nearest[nearest < 0] = np.inf
+            return nearest.argmin(axis=0)
+        elif which == 'ceil':
+            nearest[nearest > 0] = -np.inf
+            return nearest.argmax(axis=0)
+        return np.abs(nearest).argmin(axis=0)
+    
+    if data is None:
+        # Load threshold data:
+        data = dict(np.load(path))
+
+    # From SD scaling factor:
+    if factor is not None:
+        if direct:
+            # Scale SDs directly by factor:
+            return data['sds'] * factor, factor, None
+
+        # Link to supra-thresh proportion:
+        nearest = np.atleast_2d(factor) - data['factors'][:, None]
+        inds = get_inds(nearest, which)
+        factors = data['factors'][inds]
+        return data['sds'] * factors, factors, data['percs'][inds, :]
+
+    # From supra-thresh proportion:
+    nearest = perc - data['percs']
+    inds = get_inds(nearest, which)
+    factors = data['factors'][inds]
+    return data['sds'] * factors, factors, data['percs'][inds, :]
+
 def get_histogram(data, edges=None, nbins=50, pad=0.1, shared=True):
    if edges is None:
        axis = None if shared else 0
--- a/python/save_field_data.py
+++ b/python/save_field_data.py
@@ -12,7 +12,7 @@ mode = ['song', 'noise'][1]
 input_folder = f'../data/field/raw/{mode}/'
 output_folder = f'../data/field/processed/{mode}/'
 stages = ['raw', 'norm']
-if False:
+if True:
    # Overwrites edited:
    stages.append('songs')

--- a/python/save_inv_data_field.py
+++ b/python/save_inv_data_field.py
@@ -6,16 +6,20 @@ 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]
+mode = ['song', 'noise'][0]
+example_file = dict(
+    song='Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms',
+    noise='merged_noise'
+)[mode]
 search_path = f'../data/field/processed/{mode}/'
-data_paths = search_files(target, ext='npz', dir=search_path)
+data_paths = search_files('*', ext='npz', dir=search_path)
+ref_path = '../data/inv/field/ref_measures.npz'
 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')
+distances = np.load('../data/field/recording_distances.npy')[::-1]
+thresh_rel = 0.5

 # SUBSET SETTINGS:
 kernels = np.array([
@@ -30,6 +34,11 @@ kernels = None
 types = None#np.array([-1])
 sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])

+# PREPARATION:
+if thresh_rel is not None:
+    # Get threshold values from pure-noise response SD:
+    thresh_abs = np.load(ref_path)['conv'] * thresh_rel
+
 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
    save_detailed = example_file in name
@@ -39,6 +48,10 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    data, config = load_data(data_path, files='raw')
    song, rate = data['raw'], config['rate']

+    if thresh_rel is not None:
+        # Set kernel-specific thresholds:
+        config['feat_thresh'] = thresh_abs
+
    # 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)
@@ -59,6 +72,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):

    # Process snippet:
    signals, rates = process_signal(config, returns=stages, signal=song, rate=rate)
+    for stage in stages:
+        # Sort largest to smallest distance:
+        signals[stage] = signals[stage][..., ::-1]

    # Store results:
    for stage in stages:
@@ -68,6 +84,10 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
            measures[mkey] = signals[stage][segment, ...].mean(axis=0)
        else:
            measures[mkey] = signals[stage][segment, ...].std(axis=0)
+        
+        if measures[mkey].ndim == 2:
+            # Make shape (distances, kernels):
+            measures[mkey] = np.moveaxis(measures[mkey], 1, 0)

        # Log optional snippet data:
        if save_detailed:
--- a/python/save_inv_data_full.py
+++ b/python/save_inv_data_full.py
@@ -1,9 +1,9 @@
 import numpy as np
-import matplotlib.pyplot as plt
 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 thunderhopper.filters import sosfilter
 from misc_functions import draw_noise_segment
 from IPython import embed

@@ -16,7 +16,7 @@ target_species = [
    'Gomphocerippus_rufus',
    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
-][4]
+][5]
 example_file = {
    'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
    'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
@@ -28,34 +28,26 @@ example_file = {
 }[target_species]
 data_paths = search_files(target_species, dir='../data/processed/')
 noise_path = '../data/processed/white_noise_sd-1.npz'
-ref_path = '../data/inv/full/ref_measures.npz'
+thresh_path = '../data/inv/full/thresholds.npz'
 stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
+pre_stages = stages[:-1]
 save_path = '../data/inv/full/'

 # ANALYSIS SETTINGS:
 example_scales = np.array([0.1, 1, 10, 30, 100, 300])
 scales = np.geomspace(0.01, 10000, 500)
 scales = np.unique(np.concatenate(([0], scales, example_scales)))
-thresh_rel = 0.5
+thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])

 # 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])
+types = None
+sigmas = None

 # PREPARATION:
 pure_noise = np.load(noise_path)['raw']
-if thresh_rel is not None:
-    # Get threshold values from pure-noise response SD:
-    thresh_abs = np.load(ref_path)['conv'] * thresh_rel
+thresh_data = dict(np.load(thresh_path))
+thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :]

 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
@@ -66,17 +58,13 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    data, config = load_data(data_path, files='raw')
    song, rate = data['raw'], config['rate']

-    if thresh_rel is not None:
-        # Set kernel-specific thresholds:
-        config['feat_thresh'] = thresh_abs
-
    # 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]
+        thresh_abs = thresh_abs[:, kern_inds]

    # Get song segment to be analyzed:
    time = np.arange(song.shape[0]) / rate
@@ -99,8 +87,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
        measure_log=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)
-    ) 
+        measure_feat=np.zeros(shape_high + (thresh_rel.size,), dtype=float)
+    )
    if save_detailed:
        # Prepare optional storage:
        shape_low = (song.shape[0], example_scales.size)
@@ -111,7 +99,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
            snip_log=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)
+            snip_feat=np.zeros(shape_high + (thresh_rel.size,), dtype=float)
    )

    # Execute piecewise:
@@ -121,28 +109,40 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
        # Rescale song and add noise:
        scaled = song * scale + noise

-        # Process mixture:
-        signals, rates = process_signal(config, returns=stages,
+        # Process mixture (excluding features):
+        signals, rates = process_signal(config, returns=pre_stages,
                                        signal=scaled, rate=rate)
-        # Store results:
-        for stage in stages:
+        # Store non-feature results:
+        for stage in pre_stages:
            # Log intensity measures:
-            mkey = f'measure_{stage}'
-            if stage == 'feat':
-                measures[mkey][i] = signals[stage][segment, :].mean(axis=0)
-            else:
-                measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
+            measures[f'measure_{stage}'][i] = signals[stage][segment, ...].std(axis=0)

            # Log optional snippet data:
            if save_detailed and scale in example_scales:
                scale_ind = np.nonzero(example_scales == scale)[0][0]
                snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]

+        # Execute piecewise again:
+        for j, thresholds in enumerate(thresh_abs):
+            # Finalize processing:
+            feat = sosfilter((signals['conv'] > thresholds).astype(float),
+                             rate, config['feat_fcut'], 'lp',
+                             padtype='fixed', padlen=config['padlen'])
+
+            # Log intensity measure:
+            measures['measure_feat'][i, :, j] = feat[segment, :].mean(axis=0)
+
+            # Log optional snippet data:
+            if save_detailed and scale in example_scales:
+                snippets['snip_feat'][:, :, scale_ind, j] = feat
+
    # Save analysis results:
    if save_path is not None:
        data = dict(
            scales=scales,
            example_scales=example_scales,
+            thresh_rel=thresh_rel,
+            thresh_abs=thresh_abs,
            )
        data.update(measures)
        if save_detailed:
--- a/python/save_inv_data_full_backup.py
+++ b/python/save_inv_data_full_backup.py
@@ -0,0 +1,152 @@
+import numpy as np
+import matplotlib.pyplot as plt
+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 misc_functions import draw_noise_segment
+from IPython import embed
+
+# 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]
+data_paths = search_files(target_species, dir='../data/processed/')
+noise_path = '../data/processed/white_noise_sd-1.npz'
+thresh_path = '../data/inv/full/thresholds.npz'
+stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
+save_path = '../data/inv/full/'
+
+# ANALYSIS SETTINGS:
+example_scales = np.array([0.1, 1, 10, 30, 100, 300])
+scales = np.geomspace(0.01, 10000, 500)
+scales = np.unique(np.concatenate(([0], scales, example_scales)))
+thresh_rel = 0.5
+
+# 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])
+
+# PREPARATION:
+pure_noise = np.load(noise_path)['raw']
+if thresh_rel is not None:
+    # Get threshold values from pure-noise response SD:
+    thresh_abs = np.load(ref_path)['conv'] * thresh_rel
+
+# 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']
+
+    if thresh_rel is not None:
+        # Set kernel-specific thresholds:
+        config['feat_thresh'] = thresh_abs
+
+    # 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)
+
+    # Normalize song component:
+    song /= song[segment].std(axis=0)
+
+    # Get normalized noise component:
+    noise = draw_noise_segment(pure_noise, song.shape[0])
+    noise /= noise[segment].std()
+
+    # Prepare storage:
+    shape_low = (scales.size,)
+    shape_high = (scales.size, config['k_specs'].shape[0])
+    measures = dict(
+        measure_filt=np.zeros(shape_low, dtype=float),
+        measure_env=np.zeros(shape_low, dtype=float),
+        measure_log=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)
+    ) 
+    if save_detailed:
+        # Prepare optional storage:
+        shape_low = (song.shape[0], example_scales.size)
+        shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
+        snippets = dict(
+            snip_filt=np.zeros(shape_low, dtype=float),
+            snip_env=np.zeros(shape_low, dtype=float),
+            snip_log=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)
+    )
+
+    # Execute piecewise:
+    for i, scale in enumerate(scales):
+        print('Simulating scale ', scale)
+
+        # Rescale song and add noise:
+        scaled = song * scale + noise
+
+        # Process mixture:
+        signals, rates = process_signal(config, returns=stages,
+                                        signal=scaled, rate=rate)
+        # Store results:
+        for stage in stages:
+            # Log intensity measures:
+            mkey = f'measure_{stage}'
+            if stage == 'feat':
+                measures[mkey][i] = signals[stage][segment, :].mean(axis=0)
+            else:
+                measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
+
+            # Log optional snippet data:
+            if save_detailed and scale in example_scales:
+                scale_ind = np.nonzero(example_scales == scale)[0][0]
+                snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]
+
+    # Save analysis results:
+    if save_path is not None:
+        data = dict(
+            scales=scales,
+            example_scales=example_scales,
+            )
+        data.update(measures)
+        if save_detailed:
+            data.update(snippets)
+        save_data(save_path + name, data, config, overwrite=True)
+print('Done.')
+embed()
--- a/python/save_inv_data_rect-lp.py
+++ b/python/save_inv_data_rect-lp.py
@@ -0,0 +1,108 @@
+import numpy as np
+from thunderhopper.modeltools import load_data, save_data
+from thunderhopper.filetools import search_files, crop_paths
+from thunderhopper.filters import sosfilter
+from misc_functions import draw_noise_segment
+from IPython import embed
+
+# GENERAL SETTINGS:
+example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
+data_paths = search_files('*', excl='noise', dir='../data/processed/')
+noise_path = '../data/processed/white_noise_sd-1.npz'
+save_path = '../data/inv/rect_lp/'
+
+# ANALYSIS SETTINGS:
+mode = ['pure', 'noise'][1]
+example_scales = np.array([0.1, 1, 10, 30, 100, 300])
+scales = np.geomspace(0.01, 10000, 1000)
+scales = np.unique(np.concatenate(([0], scales, example_scales)))
+cutoffs = np.array([np.nan, 125, 250, 500])
+
+# PREPARATION:
+if mode == 'noise':
+    pure_noise = np.load(noise_path)['raw']
+
+# EXECUTION:
+for data_path, name in zip(data_paths, crop_paths(data_paths)):
+    save_detailed = example_file in name
+    print(f'Processing {name}')
+
+    # Get filtered song (prior to envelope extraction):
+    data, config = load_data(data_path, files='raw')
+    song, rate = data['raw'], config['rate']
+
+    # Get song segment to be analyzed:
+    time = np.arange(song.shape[0]) / rate
+    start, end = data['songs_0'].ravel()
+    segment = (time >= start) & (time <= end)
+
+    # Normalize song component:
+    song /= song[segment].std()
+    if mode == 'noise':
+        # Get normalized noise component:
+        noise = draw_noise_segment(pure_noise, song.shape[0])
+        noise /= noise[segment].std()
+
+    # Prepare storage:
+    measure_filt = np.zeros_like(scales)
+    measure_env = np.zeros((scales.size, len(cutoffs)), dtype=float)
+    if save_detailed:
+        # Prepare optional storage:
+        shape = (song.shape[0], example_scales.size)
+        snip_raw = np.zeros(shape)
+        snip_filt = np.zeros(shape)
+        snip_env = np.zeros(shape + (len(cutoffs),))
+
+    # Execute piecewise:
+    for i, scale in enumerate(scales):
+
+        # Get scaled mixture:
+        mix = song * scale
+        if mode == 'noise':
+            mix += noise
+
+        # Process mixture:
+        mix = sosfilter(mix, rate, config['bp_fcut'], 'bp',
+                        padtype='fixed', padlen=config['padlen'])
+        mix_rect = np.abs(mix)
+
+        # Store non-envelope results:
+        measure_filt[i] = mix[segment].std()
+        if save_detailed and scale in example_scales:
+            scale_ind = np.nonzero(example_scales == scale)[0][0]
+            snip_raw[:, scale_ind] = mix
+            snip_filt[:, scale_ind] = mix
+
+        # Process piecewise again:
+        for j, cutoff in enumerate(cutoffs):
+            if np.isnan(cutoff):
+                mix_env = mix_rect
+            else:
+                mix_env = sosfilter(mix_rect, rate, cutoff, 'lp',
+                                    padtype='even', padlen=config['padlen'])
+
+            # Store envelope results:
+            measure_env[i, j] = mix_env[segment].std()
+            if save_detailed and scale in example_scales:
+                snip_env[:, scale_ind, j] = mix_env
+
+    # Save analysis results:
+    if save_path is not None:
+        archive = dict(
+            scales=scales,
+            example_scales=example_scales,
+            cutoffs=cutoffs,
+            measure_filt=measure_filt,
+            measure_env=measure_env,
+        )
+        if save_detailed:
+            archive.update(
+                snip_raw=snip_raw,
+                snip_filt=snip_filt,
+                snip_env=snip_env,
+            )
+        save_name = save_path + name + '_' + mode
+        save_data(save_name, archive, config, overwrite=True)
+
+print('Done.')
+embed()
--- a/python/save_inv_data_short.py
+++ b/python/save_inv_data_short.py
@@ -17,7 +17,7 @@ target_species = [
    'Gomphocerippus_rufus',
    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
-][6]
+][5]
 example_file = {
    'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
    'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
@@ -29,7 +29,7 @@ example_file = {
 }[target_species]
 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'
+thresh_path = '../data/inv/short/thresholds.npz'
 pre_stages = ['filt', 'env']
 stages = pre_stages + ['inv', 'conv', 'feat']
 save_path = '../data/inv/short/'
@@ -38,26 +38,17 @@ save_path = '../data/inv/short/'
 example_scales = np.array([0.1, 1, 10, 30, 100, 300])
 scales = np.geomspace(0.01, 10000, 500)
 scales = np.unique(np.concatenate(([0], scales, example_scales)))
-thresh_rel = 0.5
+thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])

 # 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])
+types = None
+sigmas = None

 # PREPARATION:
 pure_noise = np.load(noise_path)['raw']
-if thresh_rel is not None:
-    # Get threshold values from pure-noise response SD:
-    thresh_abs = np.load(ref_path)['conv'] * thresh_rel
+thresh_data = dict(np.load(thresh_path))
+thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :]

 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
@@ -68,17 +59,13 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    data, config = load_data(data_path, files='raw')
    song, rate = data['raw'], config['rate']

-    if thresh_rel is not None:
-        # Set kernel-specific thresholds:
-        config['feat_thresh'] = thresh_abs
-
    # 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]
+        thresh_abs = thresh_abs[:, kern_inds]

    # Get song segment to be analyzed:
    time = np.arange(song.shape[0]) / rate
@@ -100,7 +87,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
        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)
+        measure_feat=np.zeros(shape_high + (thresh_rel.size,), dtype=float)
    ) 
    if save_detailed:
        # Prepare optional storage:
@@ -111,7 +98,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
            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)
+            snip_feat=np.zeros(shape_high + (thresh_rel.size,), dtype=float)
    )

    # Execute piecewise:
@@ -129,29 +116,38 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
        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'])

-        # Store results:
-        for stage in stages:
+        # Store non-feature results:
+        for stage in stages[:-1]:
            # Log intensity measures:
-            mkey = f'measure_{stage}'
-            if stage == 'feat':
-                measures[mkey][i] = signals[stage][segment, :].mean(axis=0)
-            else:
-                measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
+            measures[f'measure_{stage}'][i] = signals[stage][segment, ...].std(axis=0)

            # Log optional snippet data:
            if save_detailed and scale in example_scales:
                scale_ind = np.nonzero(example_scales == scale)[0][0]
                snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]

+        # Execute piecewise again:
+        for j, thresholds in enumerate(thresh_abs):
+            # Finalize processing:
+            feat = sosfilter((signals['conv'] > thresholds).astype(float),
+                             rate, config['feat_fcut'], 'lp',
+                             padtype='fixed', padlen=config['padlen'])
+
+            # Log intensity measure:
+            measures['measure_feat'][i, :, j] = feat[segment, :].mean(axis=0)
+
+            # Log optional snippet data:
+            if save_detailed and scale in example_scales:
+                snippets['snip_feat'][:, :, scale_ind, j] = feat
+
    # Save analysis results:
    if save_path is not None:
        data = dict(
            scales=scales,
            example_scales=example_scales,
+            thresh_rel=thresh_rel,
+            thresh_abs=thresh_abs,
            )
        data.update(measures)
        if save_detailed:
--- a/python/save_ref_measures_field.py
+++ b/python/save_ref_measures_field.py
@@ -0,0 +1,42 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.model import process_signal
+from thunderhopper.modeltools import load_data
+from IPython import embed
+
+## SETTINGS:
+
+# General:
+stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
+noise_path = search_files('merged_noise', dir='../data/field/processed/noise/')[0]
+save_path = '../data/inv/field/ref_measures.npz'
+channels = np.array([0, 1, 2, 3, 4, 5, 6, 7])
+
+# PROCESSING:
+
+# Load pure-noise starter representation:
+noise_data, config = load_data(noise_path, stages[0])
+# Accumulate channels in time-major order:
+starter = noise_data[stages[0]][:, channels].ravel(order='F')
+
+# Get song segment to be analyzed:
+time = np.arange(starter.shape[0]) / config['rate']
+start, end = noise_data['songs_0'].ravel()
+segment = (time >= start) & (time <= end)
+
+# Run pipeline:
+data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]
+
+# Get measures:
+measures = {}
+for stage in stages:
+    if stage == 'feat':
+        measures[stage] = data[stage][segment, :].mean(axis=0)
+    else:
+        measures[stage] = data[stage][segment, ...].std(axis=0)
+
+# Save results:
+np.savez(save_path, **measures)
+
+print('Done.')
+embed()
--- a/python/save_thresholds.py
+++ b/python/save_thresholds.py
@@ -0,0 +1,72 @@
+import numpy as np
+from thunderhopper.filters import sosfilter
+from thunderhopper.model import convolve_kernels, process_signal
+from thunderhopper.modeltools import load_data
+from IPython import embed
+
+## SETTINGS:
+
+# General:
+mode = ['thresh_lp', 'full', 'short', 'field'][3]
+if mode == 'field':
+    noise_path = '../data/field/processed/noise/merged_noise.npz'
+    channels = np.array([0, 1, 2, 3, 4, 5, 6, 7])
+else:
+    noise_path = '../data/processed/white_noise_sd-1.npz'
+save_path = '../data/inv/'
+start_stage = dict(
+    thresh_lp='inv',
+    full='raw',
+    short='raw',
+    field='raw'
+)[mode]
+
+# Analysis:
+factors = np.concatenate([np.arange(-4, 0, 0.01), np.arange(0, 4.01, 0.01)])
+pad = np.array([0.1, 0.9])
+
+# PROCESSING:
+
+print(f'Fetching threshold data in {mode} mode...')
+
+# Load pure-noise starter representation:
+noise_data, config = load_data(noise_path, start_stage)
+starter = noise_data[start_stage]
+
+# Prepare buffered measurement segment:
+pad = (pad * starter.shape[0]).astype(int)
+segment = np.arange(starter.shape[0])[pad[0]:pad[1]]
+
+if mode != 'field':
+    # Normalize starter:
+    starter /= starter[segment].std()
+
+# Run (partial) pipeline:
+print('Running pipeline...')
+if mode == 'thresh_lp':
+    conv = convolve_kernels(starter, config['kernels'], config['k_specs'])
+elif mode == 'full':
+    conv = process_signal(config, 'conv', signal=starter, rate=config['rate'])[0]['conv']
+elif mode == 'short':
+    env = process_signal(config, 'env', signal=starter, rate=config['rate'])[0]['env']
+    inv = sosfilter(env, config['env_rate'], config['inv_fcut'], 'hp',
+                    padtype='constant', padlen=config['padlen'])
+    conv = convolve_kernels(inv, config['kernels'], config['k_specs'])
+elif mode == 'field':
+    starter = starter[:, channels].ravel(order='F')
+    conv = process_signal(config, 'conv', signal=starter, rate=config['rate'])[0]['conv']
+
+# Get baseline kernel response SDs:
+sds = conv[segment, :].std(axis=0)
+
+# Get corresponding supra-threshold proportions:
+percs = np.zeros((len(factors), conv.shape[1]))
+for i, factor in enumerate(factors):
+    print(f'Processing factor {i + 1} / {factors.size}...')
+    percs[i] = (conv > (factor * sds)).sum(axis=0) / conv.shape[0]
+
+# Save results:
+np.savez(save_path + f'{mode}/thresholds.npz', factors=factors, sds=sds, percs=percs)
+
+print('Done.')
+embed()