Seriously, no idea. Wild amount of changes. Good luck.

2026-04-17 17:19:30 +02:00
parent 36ac504efa
commit 3b4b7f2161
40 changed files with 2067 additions and 672 deletions
--- a/figures/fig_invariance_full.pdf
+++ b/figures/fig_invariance_full.pdf
--- a/figures/fig_invariance_log-hp_appendix.pdf
+++ b/figures/fig_invariance_log-hp_appendix.pdf
--- a/figures/fig_invariance_log-hp_species.pdf
+++ b/figures/fig_invariance_log-hp_species.pdf
--- a/figures/fig_invariance_log_hp.pdf
+++ b/figures/fig_invariance_log_hp.pdf
--- a/figures/fig_invariance_thresh-lp_appendix.pdf
+++ b/figures/fig_invariance_thresh-lp_appendix.pdf
--- a/figures/fig_invariance_thresh_lp_single.pdf
+++ b/figures/fig_invariance_thresh_lp_single.pdf
--- a/figures/fig_invariance_thresh_lp_species.pdf
+++ b/figures/fig_invariance_thresh_lp_species.pdf
--- a/figures/fig_noise_env_sd_conversion_appendix.pdf
+++ b/figures/fig_noise_env_sd_conversion_appendix.pdf
--- a/main.aux
+++ b/main.aux
@@ -269,6 +269,8 @@
 \newlabel{}{{8}{20}{}{}{}}
 \@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf  {} }}{20}{}\protected@file@percent }
 \newlabel{}{{9}{20}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf  {} }}{21}{}\protected@file@percent }
+\newlabel{}{{10}{21}{}{}{}}
 \gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }}
 \abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F}
-\gdef \@abspage@last{20}
+\gdef \@abspage@last{21}
--- a/main.blg
+++ b/main.blg
@@ -1,71 +1,71 @@
 [0] Config.pm:307> INFO - This is Biber 2.19
 [0] Config.pm:310> INFO - Logfile is 'main.blg'
-[36] biber:340> INFO - === Di Apr 14, 2026, 17:23:24
-[45] Biber.pm:419> INFO - Reading 'main.bcf'
-[74] Biber.pm:979> INFO - Found 55 citekeys in bib section 0
-[79] Biber.pm:4419> INFO - Processing section 0
-[84] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0
-[86] bibtex.pm:1713> INFO - LaTeX decoding ...
-[115] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
-[289] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
-[289] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
-[289] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
-[289] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
-[312] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
-[323] bbl.pm:763> INFO - Output to main.bbl
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 10, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 21, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 38, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 49, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 58, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 73, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 82, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 91, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 100, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 109, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 118, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 127, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 136, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 157, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 178, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 187, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 196, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 207, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 218, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 229, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 240, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 249, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 258, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 269, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 278, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 289, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 300, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 309, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 328, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 337, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 400, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 419, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 428, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 437, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 456, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 491, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 526, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 535, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 556, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 565, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 576, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 587, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 619, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 648, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 658, 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
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 688, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 709, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 720, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 729, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 749, warning: 6 characters of junk seen at toplevel
-[324] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 766, warning: 6 characters of junk seen at toplevel
-[325] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 775, warning: 6 characters of junk seen at toplevel
-[325] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 800, warning: 6 characters of junk seen at toplevel
-[325] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Fmmc/347c261ec4135a5723bef5c751f5078f_87767.utf8, line 817, warning: 6 characters of junk seen at toplevel
-[325] Biber.pm:133> INFO - WARNINGS: 55
+[39] biber:340> INFO - === Fr Apr 17, 2026, 11:47:16
+[47] Biber.pm:419> INFO - Reading 'main.bcf'
+[76] Biber.pm:979> INFO - Found 55 citekeys in bib section 0
+[82] Biber.pm:4419> INFO - Processing section 0
+[87] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0
+[88] bibtex.pm:1713> INFO - LaTeX decoding ...
+[119] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
+[296] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
+[296] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
+[296] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
+[296] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
+[322] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
+[333] bbl.pm:763> INFO - Output to main.bbl
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 10, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 21, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 38, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 49, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 58, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 73, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 82, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 91, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 100, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 109, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 118, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 127, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 136, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 157, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 178, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 187, warning: 6 characters of junk seen at toplevel
+[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 196, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 207, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 218, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 229, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 240, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 249, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 258, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 269, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 278, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 289, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 300, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 309, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 328, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 337, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 400, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 419, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 428, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 437, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 456, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 491, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 526, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 535, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 556, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 565, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 576, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 587, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 619, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 648, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 658, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 667, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 688, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 709, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 720, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 729, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 749, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 766, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 775, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 800, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_0bop/347c261ec4135a5723bef5c751f5078f_47066.utf8, line 817, warning: 6 characters of junk seen at toplevel
+[334] Biber.pm:133> INFO - WARNINGS: 55
--- a/main.fdb_latexmk
+++ b/main.fdb_latexmk
@@ -1,14 +1,14 @@
 # Fdb version 4
-["biber main"] 1776180204.48481 "main.bcf" "main.bbl" "main" 1776180220.70543 0
+["biber main"] 1776419236.06276 "main.bcf" "main.bbl" "main" 1776419708.51683 0
  "cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 ""
-  "main.bcf" 1776180220.65176 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
+  "main.bcf" 1776419708.45482 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
  (generated)
  "main.bbl"
  "main.blg"
  (rewritten before read)
-["pdflatex"] 1776180219.79356 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1776180220.70564 0
+["pdflatex"] 1776419707.50594 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1776419708.51707 0
  "/etc/texmf/web2c/texmf.cnf" 1761560044.43676 475 c0e671620eb5563b2130f56340a5fde8 ""
-  "/home/hartling/phd/paper/paper_2025/main.tex" 1776180218.75477 48226 a69e6ee3ae074d48c75cb0bb31adebb7 ""
+  "/home/hartling/phd/paper/paper_2025/main.tex" 1776419707.40583 49006 9efa08ff012ade2af94d38bf09748eb2 ""
  "/usr/share/texlive/texmf-dist/fonts/map/fontname/texfonts.map" 1577235249 3524 cb3e574dea2d1052e39280babc910dc8 ""
  "/usr/share/texlive/texmf-dist/fonts/tfm/public/amsfonts/cmextra/cmex7.tfm" 1246382020 1004 54797486969f23fa377b128694d548df ""
  "/usr/share/texlive/texmf-dist/fonts/tfm/public/amsfonts/cmextra/cmex8.tfm" 1246382020 988 bdf658c3bfc2d96d3c8b02cfc1c94c20 ""
@@ -154,16 +154,17 @@
  "figures/fig_auditory_pathway.pdf" 1771593904.14638 1153923 3df8539421fd21dc866cc8d320bd9b1d ""
  "figures/fig_feat_stages.pdf" 1774002994.98767 11091006 565fe951f1255c121429a060082398f5 ""
  "figures/fig_invariance_full.pdf" 1775044457.44326 51364 cdc2f94096a4ec98dd58b81315199213 ""
-  "figures/fig_invariance_log-hp_species.pdf" 1776180090.89783 565762 9243d82b7795c23ce67cc60cc79852fe ""
-  "figures/fig_invariance_log_hp.pdf" 1776176630.23299 837853 77e94473c532de76ca2a152c8c1509f4 ""
+  "figures/fig_invariance_log-hp_appendix.pdf" 1776417315.40604 569449 c18c449fd64897296eb276a04e989264 ""
+  "figures/fig_invariance_log_hp.pdf" 1776341186.13598 838755 47a174c0a49618da118b592fecb771d6 ""
+  "figures/fig_invariance_thresh-lp_appendix.pdf" 1776416900.35698 1579513 2f918382c2096f397b128a9d11011674 ""
  "figures/fig_invariance_thresh_lp_single.pdf" 1774448531.93474 921028 cae18b62e262b42f630e219fcaa0ca09 ""
-  "figures/fig_invariance_thresh_lp_species.pdf" 1774942339.01068 397460 09bf9690db00ef77c4a2faf0d4e2c8d7 ""
-  "figures/fig_noise_env_sd_conversion.pdf" 1774256952.42051 45466 671a2b8fbf72b4eba6b970b4421f2521 ""
+  "figures/fig_invariance_thresh_lp_species.pdf" 1776410754.59568 1801023 deef4bae585ba144c48a08300664517f ""
+  "figures/fig_noise_env_sd_conversion_appendix.pdf" 1776328774.43347 45466 c2be20312c1572203bdbeb9c8e32525e ""
  "figures/fig_pre_stages.pdf" 1774002992.74268 449426 5762be15627fe5d8b6d108b7ea18db44 ""
-  "main.aux" 1776180220.64576 15421 c52b01adcd46aa2ef82220b23727550f "pdflatex"
-  "main.bbl" 1776180205.19289 91039 1380dc8c93d2855fdb132cc5a40ad52f "biber main"
-  "main.run.xml" 1776180220.65276 2335 a049bc26a7f032e842ce55de5bc38328 "pdflatex"
-  "main.tex" 1776180218.75477 48226 a69e6ee3ae074d48c75cb0bb31adebb7 ""
+  "main.aux" 1776419708.44782 15566 dacdf566c3a198221a23ecda641af360 "pdflatex"
+  "main.bbl" 1776419236.72776 91039 1380dc8c93d2855fdb132cc5a40ad52f "biber main"
+  "main.run.xml" 1776419708.45582 2335 a049bc26a7f032e842ce55de5bc38328 "pdflatex"
+  "main.tex" 1776419707.40583 49006 9efa08ff012ade2af94d38bf09748eb2 ""
  (generated)
  "main.aux"
  "main.bcf"
--- a/main.fls
+++ b/main.fls
@@ -308,16 +308,21 @@ INPUT ./figures/fig_invariance_full.pdf
 INPUT ./figures/fig_invariance_full.pdf
 INPUT ./figures/fig_invariance_full.pdf
 INPUT ./figures/fig_invariance_full.pdf
-INPUT ./figures/fig_noise_env_sd_conversion.pdf
-INPUT ./figures/fig_noise_env_sd_conversion.pdf
-INPUT ./figures/fig_noise_env_sd_conversion.pdf
-INPUT ./figures/fig_noise_env_sd_conversion.pdf
-INPUT ./figures/fig_noise_env_sd_conversion.pdf
-INPUT ./figures/fig_invariance_log-hp_species.pdf
-INPUT ./figures/fig_invariance_log-hp_species.pdf
-INPUT ./figures/fig_invariance_log-hp_species.pdf
-INPUT ./figures/fig_invariance_log-hp_species.pdf
-INPUT ./figures/fig_invariance_log-hp_species.pdf
+INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf
+INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf
+INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf
+INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf
+INPUT ./figures/fig_noise_env_sd_conversion_appendix.pdf
+INPUT ./figures/fig_invariance_log-hp_appendix.pdf
+INPUT ./figures/fig_invariance_log-hp_appendix.pdf
+INPUT ./figures/fig_invariance_log-hp_appendix.pdf
+INPUT ./figures/fig_invariance_log-hp_appendix.pdf
+INPUT ./figures/fig_invariance_log-hp_appendix.pdf
+INPUT ./figures/fig_invariance_thresh-lp_appendix.pdf
+INPUT ./figures/fig_invariance_thresh-lp_appendix.pdf
+INPUT ./figures/fig_invariance_thresh-lp_appendix.pdf
+INPUT ./figures/fig_invariance_thresh-lp_appendix.pdf
+INPUT ./figures/fig_invariance_thresh-lp_appendix.pdf
 INPUT main.aux
 INPUT main.run.xml
 OUTPUT main.run.xml
--- a/main.log
+++ b/main.log
@@ -1,4 +1,4 @@
-This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28)  14 APR 2026 17:23
+This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28)  17 APR 2026 11:55
 entering extended mode
 restricted \write18 enabled.
 file:line:error style messages enabled.
@@ -595,6 +595,9 @@ Package biblatex Warning: 'babel/polyglossia' detected but 'csquotes' missing.
 \@quotereset=\count435
 (./main.aux

+LaTeX Warning: Label `' multiply defined.
+
+
 LaTeX Warning: Label `' multiply defined.

 )
@@ -771,7 +774,7 @@ Package pdftex.def Info: figures/fig_feat_stages.pdf  used on input line 542.
 <figures/fig_invariance_log_hp.pdf, id=1141, 890.96089pt x 918.57785pt>
 File: figures/fig_invariance_log_hp.pdf Graphic file (type pdf)
 <use figures/fig_invariance_log_hp.pdf>
-Package pdftex.def Info: figures/fig_invariance_log_hp.pdf  used on input line 632.
+Package pdftex.def Info: figures/fig_invariance_log_hp.pdf  used on input line 645.
 (pdftex.def)             Requested size: 483.69687pt x 498.70178pt.
 [12]

@@ -781,34 +784,39 @@ LaTeX Warning: Text page 13 contains only floats.
 <figures/fig_invariance_thresh_lp_single.pdf, id=1232, 910.48819pt x 455.2441pt>
 File: figures/fig_invariance_thresh_lp_single.pdf Graphic file (type pdf)
 <use figures/fig_invariance_thresh_lp_single.pdf>
-Package pdftex.def Info: figures/fig_invariance_thresh_lp_single.pdf  used on input line 663.
+Package pdftex.def Info: figures/fig_invariance_thresh_lp_single.pdf  used on input line 676.
 (pdftex.def)             Requested size: 483.69687pt x 241.84782pt.
 <figures/fig_invariance_thresh_lp_species.pdf, id=1233, 910.48819pt x 910.48819pt>
 File: figures/fig_invariance_thresh_lp_species.pdf Graphic file (type pdf)
 <use figures/fig_invariance_thresh_lp_species.pdf>
-Package pdftex.def Info: figures/fig_invariance_thresh_lp_species.pdf  used on input line 693.
+Package pdftex.def Info: figures/fig_invariance_thresh_lp_species.pdf  used on input line 706.
 (pdftex.def)             Requested size: 483.69687pt x 483.69566pt.
 [14 <./figures/fig_invariance_thresh_lp_single.pdf>]
 <figures/fig_invariance_full.pdf, id=1305, 910.48819pt x 569.05513pt>
 File: figures/fig_invariance_full.pdf Graphic file (type pdf)
 <use figures/fig_invariance_full.pdf>
-Package pdftex.def Info: figures/fig_invariance_full.pdf  used on input line 703.
+Package pdftex.def Info: figures/fig_invariance_full.pdf  used on input line 716.
 (pdftex.def)             Requested size: 483.69687pt x 302.3098pt.
 [15 <./figures/fig_invariance_thresh_lp_species.pdf>] [16 <./figures/fig_invariance_full.pdf>] [17

 ] [18]
-<figures/fig_noise_env_sd_conversion.pdf, id=1512, 910.48819pt x 455.2441pt>
-File: figures/fig_noise_env_sd_conversion.pdf Graphic file (type pdf)
-<use figures/fig_noise_env_sd_conversion.pdf>
-Package pdftex.def Info: figures/fig_noise_env_sd_conversion.pdf  used on input line 862.
+<figures/fig_noise_env_sd_conversion_appendix.pdf, id=1526, 910.48819pt x 455.2441pt>
+File: figures/fig_noise_env_sd_conversion_appendix.pdf Graphic file (type pdf)
+<use figures/fig_noise_env_sd_conversion_appendix.pdf>
+Package pdftex.def Info: figures/fig_noise_env_sd_conversion_appendix.pdf  used on input line 875.
 (pdftex.def)             Requested size: 483.69687pt x 241.84782pt.
 [19]
-<figures/fig_invariance_log-hp_species.pdf, id=1517, 910.48819pt x 455.2441pt>
-File: figures/fig_invariance_log-hp_species.pdf Graphic file (type pdf)
-<use figures/fig_invariance_log-hp_species.pdf>
-Package pdftex.def Info: figures/fig_invariance_log-hp_species.pdf  used on input line 871.
+<figures/fig_invariance_log-hp_appendix.pdf, id=1531, 910.48819pt x 455.2441pt>
+File: figures/fig_invariance_log-hp_appendix.pdf Graphic file (type pdf)
+<use figures/fig_invariance_log-hp_appendix.pdf>
+Package pdftex.def Info: figures/fig_invariance_log-hp_appendix.pdf  used on input line 884.
 (pdftex.def)             Requested size: 483.69687pt x 241.84782pt.
- [20 <./figures/fig_noise_env_sd_conversion.pdf> <./figures/fig_invariance_log-hp_species.pdf>] (./main.aux)
+<figures/fig_invariance_thresh-lp_appendix.pdf, id=1532, 910.48819pt x 455.2441pt>
+File: figures/fig_invariance_thresh-lp_appendix.pdf Graphic file (type pdf)
+<use figures/fig_invariance_thresh-lp_appendix.pdf>
+Package pdftex.def Info: figures/fig_invariance_thresh-lp_appendix.pdf  used on input line 893.
+(pdftex.def)             Requested size: 483.69687pt x 241.84782pt.
+ [20 <./figures/fig_noise_env_sd_conversion_appendix.pdf> <./figures/fig_invariance_log-hp_appendix.pdf>] [21 <./figures/fig_invariance_thresh-lp_appendix.pdf>] (./main.aux)
 ***********
 LaTeX2e <2023-11-01> patch level 1
 L3 programming layer <2024-01-22>
@@ -822,18 +830,18 @@ Package logreq Info: Writing requests to 'main.run.xml'.

 ) 
 Here is how much of TeX's memory you used:
- 20769 strings out of 474222
- 448376 string characters out of 5748732
+ 20776 strings out of 474222
+ 448833 string characters out of 5748732
 1937975 words of memory out of 5000000
- 42759 multiletter control sequences out of 15000+600000
+ 42766 multiletter control sequences out of 15000+600000
 569394 words of font info for 79 fonts, out of 8000000 for 9000
 1143 hyphenation exceptions out of 8191
- 94i,19n,93p,1496b,1740s stack positions out of 10000i,1000n,20000p,200000b,200000s
+ 94i,18n,93p,1496b,1740s stack positions out of 10000i,1000n,20000p,200000b,200000s
 </usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmbx10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmbx12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmbxti10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmex10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi6.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmmi8.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr17.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr6.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmr8.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy10.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy8.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/cm/cmti12.pfb></usr/share/texlive/texmf-dist/fonts/type1/public/amsfonts/symbols/msbm10.pfb>
-Output written on main.pdf (20 pages, 15648193 bytes).
+Output written on main.pdf (21 pages, 18635824 bytes).
 PDF statistics:
- 1702 PDF objects out of 1728 (max. 8388607)
- 884 compressed objects within 9 object streams
+ 1786 PDF objects out of 2073 (max. 8388607)
+ 905 compressed objects within 10 object streams
 0 named destinations out of 1000 (max. 500000)
- 58 words of extra memory for PDF output out of 10000 (max. 10000000)
+ 63 words of extra memory for PDF output out of 10000 (max. 10000000)

--- a/main.pdf
+++ b/main.pdf
--- a/main.synctex.gz
+++ b/main.synctex.gz
--- a/main.tex
+++ b/main.tex
@@ -393,7 +393,7 @@ signal~(\bcite{machens2001discrimination}), which likely involves a rectifying
 nonlinearity~(\bcite{machens2001representation}). This can be modelled as
 full-wave rectification followed by lowpass filtering
 \begin{equation}
-    \env(t)\,=\,|\filt(t)|\,*\,\lp, \qquad \fc\,=\,500\,\text{Hz}
+    \env(t)\,=\,|\filt(t)|\,*\,\lp, \qquad \fc\,=\,250\,\text{Hz}
    \label{eq:env}
 \end{equation}
 of the tympanal signal $\filt(t)$. Furthermore, the receptors exhibit a
@@ -401,7 +401,7 @@ sigmoidal response curve over logarithmically compressed intensity
 levels~(\bcite{suga1960peripheral}; \bcite{gollisch2002energy}). In the model
 pathway, logarithmic compression is achieved by conversion to decibel scale
 \begin{equation}
-    \db(t)\,=\,10\,\cdot\,\dec \frac{\env(t)}{\dbref}, \qquad \dbref\,=\,\max\big[\env(t)\big]
+    \db(t)\,=\,20\,\cdot\,\dec \frac{\env(t)}{\dbref}, \qquad \dbref\,=\,1
    \label{eq:log}
 \end{equation}
 relative to the maximum intensity $\dbref$ of the signal envelope $\env(t)$.
@@ -586,10 +586,17 @@ and a fixed-scale noise component $\noc(t)$. Both $\soc(t)$ and $\noc(t)$ are
 assumed to have unit variance. By conversion of $\env(t)$ to decibel
 scale~(Eq.\,\ref{eq:log}), $\sca$ turns from a multiplicative scale in linear
 space into an additive term, or offset, in logarithmic space
+% \begin{equation}
+%     \begin{split}
+%         \db(t)\,&=\,\dec \frac{\alpha\,\cdot\,s(t)\,+\,\eta(t)}{\dbref}\\
+%         &=\,\dec \frac{\alpha}{\dbref}\,+\,\dec \left[s(t)\,+\,\frac{\eta(t)}{\alpha}\right], \qquad \sca\,>\,0
+%     \end{split}
+%     \label{eq:toy_log}
+% \end{equation}
 \begin{equation}
    \begin{split}
-        \db(t)\,&=\,\log \frac{\alpha\,\cdot\,s(t)\,+\,\eta(t)}{\dbref}\\
-        &=\,\log \frac{\alpha}{\dbref}\,+\,\log \left[s(t)\,+\,\frac{\eta(t)}{\alpha}\right]
+        \db(t)\,&=\,20\,\cdot\,\dec \left[\,\sca\,\cdot\,s(t)\,+\,\eta(t)\,\right]\\
+        &=\,20\,\cdot\,\left(\dec \sca\,+\,\dec \left[s(t)\,+\,\frac{\eta(t)}{\sca}\right]\right), \qquad \sca\,>\,0
    \end{split}
    \label{eq:toy_log}
 \end{equation}
@@ -598,9 +605,15 @@ $\noc(t)$ by the inverse of $\sca$. The subsequent
 highpass-filtering~(Eq.\,\ref{eq:highpass}) of $\db(t)$ can then be
 approximated as a subtraction of the local offset within a suitable time
 interval $0 \ll \thp < \frac{1}{\fc}$:
+% \begin{equation}
+%     \begin{split}
+%     \adapt(t)\,\approx\,\db(t)\,-\,\dec \frac{\sca}{\dbref}\,=\,\dec\left[s(t)\,+\,\frac{\eta(t)}{\sca}\right], \qquad \sca\,>\,0
+%     \end{split}
+%     \label{eq:toy_highpass}
+% \end{equation}
 \begin{equation}
    \begin{split}
-    \adapt(t)\,\approx\,\db(t)\,-\,\log \frac{\alpha}{\dbref}\,=\,\log\left[s(t)\,+\,\frac{\eta(t)}{\alpha}\right]
+    \adapt(t)\,\approx\,\db(t)\,-\,20\,\cdot\,\dec \sca\,=\,20\,\cdot\,\dec\left[s(t)\,+\,\frac{\eta(t)}{\sca}\right], \qquad \sca\,>\,0
    \end{split}
    \label{eq:toy_highpass}
 \end{equation}
@@ -859,7 +872,7 @@ initiation of one behavior over another is categorical (e.g. approach/stay)

 \begin{figure}[!ht]
    \centering
-    \includegraphics[width=\textwidth]{figures/fig_noise_env_sd_conversion.pdf}
+    \includegraphics[width=\textwidth]{figures/fig_noise_env_sd_conversion_appendix.pdf}
    \caption{\textbf{}
                     }
    \label{}
@@ -868,7 +881,16 @@ initiation of one behavior over another is categorical (e.g. approach/stay)

 \begin{figure}[!ht]
    \centering
-    \includegraphics[width=\textwidth]{figures/fig_invariance_log-hp_species.pdf}
+    \includegraphics[width=\textwidth]{figures/fig_invariance_log-hp_appendix.pdf}
+    \caption{\textbf{}
+                     }
+    \label{}
+\end{figure}
+\FloatBarrier
+
+\begin{figure}[!ht]
+    \centering
+    \includegraphics[width=\textwidth]{figures/fig_invariance_thresh-lp_appendix.pdf}
    \caption{\textbf{}
                     }
    \label{}
--- a/python/collect_inv_data_full.py
+++ b/python/collect_inv_data_full.py
@@ -0,0 +1,52 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
+search_path = '../data/inv/full/'
+save_path = '../data/inv/full/collected/'
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Fetch all species-specific song files:
+    all_paths = search_files(species, ext='npz', dir=search_path)
+    if not all_paths:
+        continue
+
+    # Run through files:
+    for j, path in enumerate(all_paths):
+
+        # Load invariance data:
+        data, config = load_data(path, '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
+    save_data(save_name, species_data, config, overwrite=True)
+
+print('Done.')         
--- a/python/collect_inv_data_log-hp.py
+++ b/python/collect_inv_data_log-hp.py
@@ -0,0 +1,46 @@
+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',
+]
+search_path = '../data/inv/log_hp/'
+save_path = '../data/inv/log_hp/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='noise', 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_inv'])
+        scales, measure = data['scales'], data['measure_inv']
+
+        if j == 0:
+            # Prepare species-specific storage:
+            spec_data = np.zeros((measure.shape + (len(all_paths),)), dtype=float)
+        
+        # Log file data:
+        spec_data[..., j] = measure
+
+    # Save collected file data:
+    save_name = save_path + species    
+    archive = dict(scales=scales, measure_inv=spec_data)
+    save_data(save_name, archive, config, overwrite=True)
+
+print('Done.')
--- a/python/collect_inv_data_short.py
+++ b/python/collect_inv_data_short.py
@@ -0,0 +1,52 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+stages = ['filt', 'env', 'conv', 'feat']
+search_path = '../data/inv/short/'
+save_path = '../data/inv/short/collected/'
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Fetch all species-specific song files:
+    all_paths = search_files(species, ext='npz', dir=search_path)
+    if not all_paths:
+        continue
+
+    # Run through files:
+    for j, path in enumerate(all_paths):
+
+        # Load invariance data:
+        data, config = load_data(path, '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
+    save_data(save_name, species_data, config, overwrite=True)
+
+print('Done.')         
--- a/python/collect_inv_data_thresh-lp.py
+++ b/python/collect_inv_data_thresh-lp.py
@@ -0,0 +1,52 @@
+import numpy as np
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+search_path = '../data/inv/thresh_lp/'
+save_path = '../data/inv/thresh_lp/collected/'
+
+# ANALYSIS SETTINGS:
+with_noise = False
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {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)
+    
+    # Run through files:
+    for j, path in enumerate(all_paths):
+
+        # Load invariance data:
+        data, config = load_data(path, ['scales', 'measure_feat', 'thresh_rel'])
+        measure = data['measure_feat']
+
+        if j == 0:
+            # Prepare species-specific storage:
+            spec_data = np.zeros((measure.shape + (len(all_paths),)), dtype=float)
+
+        # Log file data:
+        spec_data[..., j] = measure
+
+    # Save collected file data:
+    save_name = save_path + species + f'_{incl}'
+    archive = dict(
+        scales=data['scales'],
+        measure_feat=spec_data,
+        thresh_rel=data['thresh_rel'])
+    save_data(save_name, archive, config)
+
+print('Done.')
--- a/python/condense_inv_data_full.py
+++ b/python/condense_inv_data_full.py
@@ -0,0 +1,87 @@
+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', 'log', 'inv', 'conv', 'feat']
+search_path = '../data/inv/full/'
+save_path = '../data/inv/full/condensed/'
+
+# ANALYSIS SETTINGS:
+compute_ratios = False
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Fetch all species-specific song files:
+    all_paths = search_files(species, ext='npz', dir=search_path)
+    if not all_paths:
+        continue
+
+    # Sort song files by recording (one or more per source):
+    sorted_paths = sort_files_by_rec(all_paths, sources)
+    
+    # Condense across song files per recording:
+    for j, rec_paths in enumerate(sorted_paths):
+        for k, path in enumerate(rec_paths):
+
+            # Load invariance data:
+            data, config = load_data(path, '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 compute_ratios:
+                    data[mkey] /= data[mkey][0]
+                file_data[stage][..., k] = data[mkey]
+
+        # Get recording statistics:
+        for stage in stages:
+            rec_mean[f'mean_{stage}'][..., j] = np.nanmean(file_data[stage], axis=-1)
+            rec_sd[f'sd_{stage}'][..., j] = np.nanstd(file_data[stage], axis=-1)
+
+    # Save condensed recording data:
+    save_name = save_path + species
+    if compute_ratios:
+        save_name += '_normed'
+    else:
+        save_name += '_raw'
+    archive = dict(scales=data['scales'])
+    archive.update(rec_mean)
+    archive.update(rec_sd)
+    save_data(save_name, archive, config, overwrite=True)
+
+print('Done.')         
--- a/python/condense_inv_data_log-hp.py
+++ b/python/condense_inv_data_log-hp.py
@@ -1,8 +1,8 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from thunderhopper.filetools import search_files, crop_paths
+from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data, save_data
-from misc_functions import shorten_species
+from misc_functions import shorten_species, sort_files_by_rec
 from IPython import embed

 # GENERAL SETTINGS:
@@ -23,7 +23,6 @@ sources = [
    '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:
@@ -31,14 +30,12 @@ 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)')
+    axes[2, 0].set_ylabel('total\n(mean ± SD)')

 # EXECUTION:
 for i, species in enumerate(target_species):
@@ -48,49 +45,21 @@ for i, species in enumerate(target_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]
+    # 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, _ = load_data(path, ['scales', 'measure_inv'])
+            data, config = load_data(path, ['scales', 'measure_inv'])
            scales, measure = data['scales'], data['measure_inv']

            # Relate to noise:
            if compute_ratios:
-                measure /= ref_measure
+                measure /= measure[0]

            if k == 0:
                # Prepare song file-specific storage:
@@ -116,7 +85,8 @@ 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:
-    np.savez(save_path + species, scales=scales, mean=rec_mean, sd=rec_sd)
+    archive = dict(scales=scales, mean_inv=rec_mean, sd_inv=rec_sd)
+    save_data(save_path + species, archive, config, overwrite=True)

    if plot_overview:
        spec_mean = rec_mean.mean(axis=1)
@@ -128,9 +98,7 @@ for i, species in enumerate(target_species):
 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()
-
-            
+    axes[0, 0].set_xlim(scales[1], scales[-1])
+plt.show() 
--- a/python/condense_inv_data_short.py
+++ b/python/condense_inv_data_short.py
@@ -0,0 +1,87 @@
+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', 'conv', 'feat']
+search_path = '../data/inv/short/'
+save_path = '../data/inv/short/condensed/'
+
+# ANALYSIS SETTINGS:
+compute_ratios = False
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+
+    # Fetch all species-specific song files:
+    all_paths = search_files(species, ext='npz', dir=search_path)
+    if not all_paths:
+        continue
+
+    # Sort song files by recording (one or more per source):
+    sorted_paths = sort_files_by_rec(all_paths, sources)
+    
+    # Condense across song files per recording:
+    for j, rec_paths in enumerate(sorted_paths):
+        for k, path in enumerate(rec_paths):
+
+            # Load invariance data:
+            data, config = load_data(path, '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 compute_ratios:
+                    data[mkey] /= data[mkey][0]
+                file_data[stage][..., k] = data[mkey]
+
+        # Get recording statistics:
+        for stage in stages:
+            rec_mean[f'mean_{stage}'][..., j] = np.nanmean(file_data[stage], axis=-1)
+            rec_sd[f'sd_{stage}'][..., j] = np.nanstd(file_data[stage], axis=-1)
+
+    # Save condensed recording data:
+    save_name = save_path + species
+    if compute_ratios:
+        save_name += '_normed'
+    else:
+        save_name += '_raw'
+    archive = dict(scales=data['scales'])
+    archive.update(rec_mean)
+    archive.update(rec_sd)
+    save_data(save_name, archive, config)
+
+print('Done.')         
--- a/python/condense_inv_data_thresh-lp.py
+++ b/python/condense_inv_data_thresh-lp.py
@@ -0,0 +1,135 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data, save_data
+from misc_functions import shorten_species, 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'
+]
+search_path = '../data/inv/thresh_lp/'
+save_path = '../data/inv/thresh_lp/condensed/'
+
+# ANALYSIS SETTINGS:
+with_noise = True
+plot_overview = True
+thresh_rel = np.array([0.5, 1, 3])
+
+# PREPARATION:
+if plot_overview:
+    kern_colors = ['r', 'g', 'b']
+    all_figs, all_axes = {}, {}
+    for thresh in thresh_rel:
+        fig, axes = plt.subplots(3, len(target_species), figsize=(16, 9),
+                                 sharex=True, sharey=True, layout='constrained')
+        fig.suptitle(f'rel. thresh: {thresh}')
+        axes[0, 0].set_ylim(0, 1)
+        axes[0, 0].set_ylabel('songs')
+        axes[1, 0].set_ylabel('recordings\n(mean ± SD)')
+        axes[2, 0].set_ylabel('total\n(mean ± SD)')
+        all_figs[thresh] = fig
+        all_axes[thresh] = axes
+
+# EXECUTION:
+for i, species in enumerate(target_species):
+    print(f'Processing {species}')
+    if plot_overview:
+        for thresh in thresh_rel:
+            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)
+    
+    # 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_feat'])
+            scales, measure = data['scales'], data['measure_feat']
+
+            if k == 0:
+                # Prepare song file-specific storage:
+                shape = measure.shape + (len(rec_paths),)
+                file_data = np.zeros(shape, dtype=float)
+                if j == 0:
+                    # Prepare recording-specific storage:
+                    shape = measure.shape + (len(sorted_paths),)
+                    rec_mean = np.zeros(shape, dtype=float)
+                    rec_sd = np.zeros(shape, dtype=float)
+
+            # Log song file data:
+            file_data[..., k] = measure
+
+            if plot_overview:
+                for l, thresh in enumerate(thresh_rel):
+                    axes = all_axes[thresh]
+                    for m, c in enumerate(kern_colors):
+                        axes[0, i].plot(scales, measure[:, m, l], c=c, 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:
+            for l, thresh in enumerate(thresh_rel):
+                axes = all_axes[thresh]
+                for m, c in enumerate(kern_colors):
+                    axes[1, i].plot(scales, rec_mean[:, m, l, j], c=c)
+                    spread = (rec_mean[:, m, l, j] - rec_sd[:, m, l, j],
+                              rec_mean[:, m, l, j] + rec_sd[:, m, l, j])
+                    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'
+    archive = dict(
+        scales=scales,
+        mean_feat=rec_mean,
+        sd_feat=rec_sd,
+        thresh_rel=thresh_rel,)
+    save_data(save_name, archive, config)
+
+    if plot_overview:
+        spec_mean = rec_mean.mean(axis=-1)
+        spec_sd = rec_mean.std(axis=-1)
+        for l, thresh in enumerate(thresh_rel):
+            axes = all_axes[thresh]
+            for m, c in enumerate(kern_colors):
+                axes[2, i].plot(scales, spec_mean[:, m, l], c=c)
+                spread = (spec_mean[:, m, l] - spec_sd[:, m, l],
+                          spec_mean[:, m, l] + spec_sd[:, m, l])
+                axes[2, i].fill_between(scales, *spread, color=c, alpha=0.2)
+
+print('Done.')
+
+if plot_overview:
+    for thresh in thresh_rel:
+        axes = all_axes[thresh]
+        axes[0, 0].set_xscale('log')
+        axes[0, 0].set_xlim(scales[1], scales[-1])
+plt.show()
+
+            
--- a/python/fig_analysis_approach.py
+++ b/python/fig_analysis_approach.py
@@ -0,0 +1,4 @@
+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderhopper.filetools import search_files
--- a/python/fig_env_sd_conversion_appendix.py
+++ b/python/fig_env_sd_conversion_appendix.py
@@ -5,7 +5,7 @@ from plot_functions import xlabel, ylabel, strip_zeros, letter_subplots

 # GENERAL SETTINGS:
 data_path = '../data/inv/noise_env/sd_conversion.npz'
-save_path = '../figures/fig_noise_env_sd_conversion.pdf'
+save_path = '../figures/fig_noise_env_sd_conversion_appendix.pdf'

 # PLOT SETTINGS:
 fig_kwargs = dict(
--- a/python/fig_invariance_full.py
+++ b/python/fig_invariance_full.py
@@ -1,13 +1,13 @@
 import plotstyle_plt
-import glob
 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 misc_functions import get_saturation
 from color_functions import load_colors
 from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
-                           plot_line, plot_barcode, strip_zeros, time_bar,\
+                           plot_line, strip_zeros, time_bar,\
                           letter_subplot, letter_subplots
 from IPython import embed

@@ -17,11 +17,6 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
    return None

-def plot_bi_snippets(axes, time, snippets, **kwargs):
-    for i, ax in enumerate(axes):
-        plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
-    return None
-
 def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
    if measures.ndim == 1:
        ax.plot(scales, measures, **kwargs)[0]
@@ -39,8 +34,28 @@ def show_saturation(ax, scales, measures, high=0.95, **kwargs):
                   marker='o', ms=10, zorder=6, clip_on=False, **kwargs)

 # GENERAL SETTINGS:
-target = 'Omocestus_rufipes'
-data_paths = glob.glob(f'../data/inv/full/{target}*.npz')
+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]
+raw_path = search_files(target_species, incl='raw', dir='../data/inv/full/condensed/')[0]
+norm_path = search_files(target_species, incl='norm', dir='../data/inv/full/condensed/')[0]
+snip_path = search_files(example_file, dir='../data/inv/full/')[0]
+trace_path = search_files(target_species, dir='../data/inv/full/collected/')[0]
 ref_path = '../data/inv/full/ref_measures.npz'
 save_path = '../figures/fig_invariance_full.pdf'
 stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
@@ -105,9 +120,9 @@ lw = dict(
    log=0.25,
    inv=0.25,
    conv=0.25,
-    bi=0,
    feat=1,
-    big=3
+    big=3,
+    plateau=1.5,
 )
 xlabels = dict(
    big='scale $\\alpha$',
@@ -118,7 +133,6 @@ ylabels = dict(
    log='$x_{\\text{db}}$',
    inv='$x_{\\text{inv}}$',
    conv='$c_i$',
-    bi='$b_i$',
    feat='$f_i$',
    big=['intensity', 'rel. intensity', 'norm. intensity']
 )
@@ -187,121 +201,160 @@ bar_kwargs = dict(
        va='center',
    )
 )
-
-# PREPARATION:
-ref_data = dict(np.load(ref_path))
+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:
-for data_path in data_paths:
-    print(f'Processing {data_path}')

-    # Load invariance data:
-    data, config = load_data(data_path, **load_kwargs)
-    t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
+# Load invariance data:
+raw_data, config = load_data(raw_path, files='scales', keywords='mean')
+norm_data, _ = load_data(norm_path, files='scales', keywords='mean')
+scales = raw_data['scales']

-    # Adjust grid parameters:
-    snip_grid_kwargs['ncols'] = data['example_scales'].size
+# Load snippet data:
+snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
+t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
+snip_scales = snip['example_scales']

-    # Prepare overall graph:
-    fig = plt.figure(**fig_kwargs)
-    super_grid = fig.add_gridspec(**super_grid_kwargs)
+# Adjust grid parameters:
+snip_grid_kwargs['ncols'] = snip_scales.size

-    # Prepare stage-specific snippet axes:
-    snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
-    snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
-    snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
-    for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
-        ax = snip_subfig.add_subplot(snip_grid[i, j])
-        ax.set_xlim(t_full[0], t_full[-1])
-        ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
-        hide_axis(ax, 'bottom')
-        if i == 0:
-            title = title_subplot(ax, f'$\\alpha={strip_zeros(data["example_scales"][j])}$',
-                                  ref=snip_subfig, **title_kwargs)
-        if j == 0:
-            ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
-        else:
-            hide_axis(ax, 'left')
-        snip_axes[i, j] = ax
-    time_bar(snip_axes[-1, -1], **bar_kwargs)
-    letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
+# Prepare overall graph:
+fig = plt.figure(**fig_kwargs)
+super_grid = fig.add_gridspec(**super_grid_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(data['scales'][0], data['scales'][-1])
-        ax.set_xscale('symlog', linthresh=data['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)
-        big_axes[i] = ax
-    letter_subplots(big_axes, 'bc', **letter_big_kwargs)
+# Prepare stage-specific snippet axes:
+snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
+snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
+snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
+for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
+    ax = snip_subfig.add_subplot(snip_grid[i, j])
+    ax.set_xlim(t_full[0], t_full[-1])
+    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
+    hide_axis(ax, 'bottom')
+    if i == 0:
+        title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
+                              ref=snip_subfig, **title_kwargs)
+    if j == 0:
+        ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
+    else:
+        hide_axis(ax, 'left')
+    snip_axes[i, j] = ax
+time_bar(snip_axes[-1, -1], **bar_kwargs)
+letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)

-    # # Plot filtered snippets:
-    # plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
-    #               c=colors['filt'], lw=lw['filt'])
+# Prepare analysis axes:
+big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
+big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
+big_axes = np.zeros((big_grid.ncols,), dtype=object)
+for i in range(big_grid.ncols):
+    ax = big_subfig.add_subplot(big_grid[0, i])
+    ax.set_xlim(scales[0], scales[-1])
+    ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
+    ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
+    xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
+    ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
+    big_axes[i] = ax
+letter_subplots(big_axes, 'bc', **letter_big_kwargs)

-    # # Plot envelope snippets:
-    # plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
-    #               ymin=0, c=colors['env'], lw=lw['env'])
+if False:
+    # Plot filtered snippets:
+    plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
+                c=colors['filt'], lw=lw['filt'])

-    # # Plot logarithmic snippets:
-    # plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
-    #               c=colors['log'], lw=lw['log'])
+    # Plot envelope snippets:
+    plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
+                ymin=0, c=colors['env'], lw=lw['env'])

-    # # Plot invariant snippets:
-    # plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
-    #               c=colors['inv'], lw=lw['inv'])
+    # Plot logarithmic snippets:
+    plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
+                c=colors['log'], lw=lw['log'])

-    # # Plot kernel response snippets:
-    # plot_snippets(snip_axes[4, :], t_full, data['snip_conv'],
-    #               c=colors['conv'], lw=lw['conv'])
+    # Plot invariant snippets:
+    plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
+                c=colors['inv'], lw=lw['inv'])

-    # # Plot feature snippets:
-    # plot_snippets(snip_axes[5, :], t_full, data['snip_feat'],
-    #               ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    # Plot kernel response snippets:
+    plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
+                c=colors['conv'], lw=lw['conv'])

-    # Analysis results:
-    scales_rel = data['scales'] - data['scales'][0]
-    scales_rel /= scales_rel[-1]
-    for stage in stages:
-        measure = data[f'measure_{stage}']
+    # Plot feature snippets:
+    plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
+                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])

-        # Plot unmodified intensity measures:
-        curve = plot_curves(big_axes[0], data['scales'], measure, c=colors[stage], lw=lw['big'],
-                             fill_kwargs=dict(color=colors[stage], alpha=0.25))
-        if stage in ['log', 'inv', 'conv', 'feat']:
-            show_saturation(big_axes[0], data['scales'], curve, c=colors[stage])
+# Plot analysis results:
+for stage in stages:
+    # Get average unnormed measure across recordings:
+    raw_measure = raw_data[f'mean_{stage}'].mean(axis=-1)

-        # # Relate to pure-noise reference:
-        # norm_measure = measure / ref_data[stage]
+    # Plot unmodified intensity measures:
+    curve = plot_curves(big_axes[0], scales, raw_measure, 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]
+        scale = scales[ind]
+        big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+                         transform=big_axes[0].get_xaxis_transform()) 
+        big_axes[0].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
+                         transform=big_axes[0].get_xaxis_transform())
+        big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
+                           color=colors[stage], **plateau_line_kwargs)

-        # # Plot noise-related intensity measures:
-        # big_axes[1].plot(data['scales'], norm_measure, c=colors[stage], lw=lw['big'])
+    # Get average noise-related measure across recordings:
+    norm_measure = norm_data[f'mean_{stage}'].mean(axis=-1)

-        # Normalize measure to [0, 1]:
-        min_measure = measure.min(axis=0)
-        max_measure = measure.max(axis=0)
-        norm_measure = (measure - min_measure) / (max_measure - min_measure)
+    # Plot noise-related intensity measure:
+    curve = plot_curves(big_axes[1], scales, norm_measure, c=colors[stage], lw=lw['big'],
+                        fill_kwargs=dict(color=colors[stage], alpha=0.25))

-        # Plot normalized intensity measures:
-        curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
-                             fill_kwargs=dict(color=colors[stage], alpha=0.25))
-        if stage in ['log', 'inv', 'conv', 'feat']:
-            show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
+    # Indicate saturation point:
+    if stage in ['log', 'inv', 'conv', 'feat']:
+        big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+                         transform=big_axes[1].get_xaxis_transform()) 
+        big_axes[1].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
+                         transform=big_axes[1].get_xaxis_transform())
+        big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
+                           color=colors[stage], **plateau_line_kwargs)

-        # # Plot over relative scales:
-        # plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
-        #             fill_kwargs=dict(color=colors[stage], alpha=0.25))
-        # scales_rel = curve - curve.min()
-        # scales_rel /= scales_rel.max()
+    # Normalize measure to [0, 1]:
+    min_measure = raw_measure.min(axis=0)
+    max_measure = raw_measure.max(axis=0)
+    norm_measure = (raw_measure - min_measure) / (max_measure - min_measure)

-    if save_path is not None:
-        fig.savefig(save_path)
-    plt.show()
+    # Plot range-normalized intensity measure:
+    curve = plot_curves(big_axes[2], scales, norm_measure, 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']:
+        big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+                         transform=big_axes[2].get_xaxis_transform()) 
+        big_axes[2].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
+                         transform=big_axes[2].get_xaxis_transform())
+        big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
+                           color=colors[stage], **plateau_line_kwargs)
+
+if save_path is not None:
+    fig.savefig(save_path)
+plt.show()

 print('Done.')
 embed()
--- a/python/fig_invariance_full_backup.py
+++ b/python/fig_invariance_full_backup.py
@@ -0,0 +1,326 @@
+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 misc_functions import get_saturation
+from color_functions import load_colors
+from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
+                           plot_line, plot_barcode, strip_zeros, time_bar,\
+                           letter_subplot, letter_subplots
+from IPython import embed
+
+def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
+    ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
+    for i, ax in enumerate(axes):
+        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
+    return None
+
+def plot_bi_snippets(axes, time, snippets, **kwargs):
+    for i, ax in enumerate(axes):
+        plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
+    return None
+
+def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
+    if measures.ndim == 1:
+        ax.plot(scales, measures, **kwargs)[0]
+        return measures
+    median_measure = np.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 show_saturation(ax, scales, measures, high=0.95, **kwargs):
+    high_ind = get_saturation(measures, high=high)[1]
+    return ax.plot(scales[high_ind], 0, transform=ax.get_xaxis_transform(),
+                   marker='o', ms=10, zorder=6, clip_on=False, **kwargs)
+
+# 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/inv/full/condensed/')
+snip_paths = search_files(example_file, dir='../data/inv/full/')
+ref_path = '../data/inv/full/ref_measures.npz'
+save_path = '../figures/fig_invariance_full.pdf'
+stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
+load_kwargs = dict(
+    files=stages,
+    keywords=['scales', 'snip', 'measure']
+)
+
+# GRAPH SETTINGS:
+fig_kwargs = dict(
+    figsize=(32/2.54, 20/2.54),
+)
+super_grid_kwargs = dict(
+    nrows=2,
+    ncols=1,
+    wspace=0,
+    hspace=0,
+    left=0,
+    right=1,
+    bottom=0,
+    top=1,
+    height_ratios=[3, 2]
+)
+subfig_specs = dict(
+    snip=(0, 0),
+    big=(1, 0),
+)
+snip_grid_kwargs = dict(
+    nrows=len(stages),
+    ncols=None,
+    wspace=0.1,
+    hspace=0.4,
+    left=0.08,
+    right=0.95,
+    bottom=0.08,
+    top=0.95
+)
+big_grid_kwargs = dict(
+    nrows=1,
+    ncols=3,
+    wspace=0.2,
+    hspace=0,
+    left=snip_grid_kwargs['left'],
+    right=0.96,
+    bottom=0.2,
+    top=0.95
+)
+
+# PLOT SETTINGS:
+fs = dict(
+    lab_norm=16,
+    lab_tex=20,
+    letter=22,
+    tit_norm=16,
+    tit_tex=20,
+    bar=16,
+)
+colors = load_colors('../data/stage_colors.npz')
+lw = dict(
+    filt=0.25,
+    env=0.25,
+    log=0.25,
+    inv=0.25,
+    conv=0.25,
+    bi=0,
+    feat=1,
+    big=3
+)
+xlabels = dict(
+    big='scale $\\alpha$',
+)
+ylabels = dict(
+    filt='$x_{\\text{filt}}$',
+    env='$x_{\\text{env}}$',
+    log='$x_{\\text{db}}$',
+    inv='$x_{\\text{inv}}$',
+    conv='$c_i$',
+    bi='$b_i$',
+    feat='$f_i$',
+    big=['intensity', 'rel. intensity', 'norm. intensity']
+)
+xlab_big_kwargs = dict(
+    y=0,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='bottom',
+)
+ylab_snip_kwargs = dict(
+    x=0,
+    fontsize=fs['lab_tex'],
+    rotation=0,
+    ha='left',
+    va='center'
+)
+ylab_big_kwargs = dict(
+    x=-0.12,
+    fontsize=fs['lab_norm'],
+    ha='center',
+    va='bottom',
+)
+yloc = dict(
+    filt=3000,
+    env=1000,
+    log=50,
+    inv=20,
+    conv=2,
+    feat=1,
+)
+title_kwargs = dict(
+    x=0.5,
+    yref=1,
+    ha='center',
+    va='top',
+    fontsize=fs['tit_norm'],
+)
+letter_snip_kwargs = dict(
+    x=0,
+    yref=0.5,
+    ha='left',
+    va='center',
+    fontsize=fs['letter'],
+)
+letter_big_kwargs = dict(
+    x=0,
+    y=1,
+    ha='left',
+    va='bottom',
+    fontsize=fs['letter'],
+)
+bar_time = 5
+bar_kwargs = dict(
+    dur=bar_time,
+    y0=-0.25,
+    y1=-0.1,
+    xshift=1,
+    color='k',
+    lw=0,
+    clip_on=False,
+    text_pos=(-0.1, 0.5),
+    text_str=f'${bar_time}\\,\\text{{s}}$',
+    text_kwargs=dict(
+        fontsize=fs['bar'],
+        ha='right',
+        va='center',
+    )
+)
+
+# PREPARATION:
+ref_data = dict(np.load(ref_path))
+
+# EXECUTION:
+for data_path in data_paths:
+    print(f'Processing {data_path}')
+
+    # Load invariance data:
+    data, config = load_data(data_path, **load_kwargs)
+    t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
+
+    # Adjust grid parameters:
+    snip_grid_kwargs['ncols'] = data['example_scales'].size
+
+    # Prepare overall graph:
+    fig = plt.figure(**fig_kwargs)
+    super_grid = fig.add_gridspec(**super_grid_kwargs)
+
+    # Prepare stage-specific snippet axes:
+    snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
+    snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
+    snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
+    for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
+        ax = snip_subfig.add_subplot(snip_grid[i, j])
+        ax.set_xlim(t_full[0], t_full[-1])
+        ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
+        hide_axis(ax, 'bottom')
+        if i == 0:
+            title = title_subplot(ax, f'$\\alpha={strip_zeros(data["example_scales"][j])}$',
+                                  ref=snip_subfig, **title_kwargs)
+        if j == 0:
+            ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
+        else:
+            hide_axis(ax, 'left')
+        snip_axes[i, j] = ax
+    time_bar(snip_axes[-1, -1], **bar_kwargs)
+    letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
+
+    # Prepare analysis axes:
+    big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
+    big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
+    big_axes = np.zeros((big_grid.ncols,), dtype=object)
+    for i in range(big_grid.ncols):
+        ax = big_subfig.add_subplot(big_grid[0, i])
+        ax.set_xlim(data['scales'][0], data['scales'][-1])
+        ax.set_xscale('symlog', linthresh=data['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)
+        big_axes[i] = ax
+    letter_subplots(big_axes, 'bc', **letter_big_kwargs)
+    plt.show()
+
+    # # Plot filtered snippets:
+    # plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
+    #               c=colors['filt'], lw=lw['filt'])
+
+    # # Plot envelope snippets:
+    # plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
+    #               ymin=0, c=colors['env'], lw=lw['env'])
+
+    # # Plot logarithmic snippets:
+    # plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
+    #               c=colors['log'], lw=lw['log'])
+
+    # # Plot invariant snippets:
+    # plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
+    #               c=colors['inv'], lw=lw['inv'])
+
+    # # Plot kernel response snippets:
+    # plot_snippets(snip_axes[4, :], t_full, data['snip_conv'],
+    #               c=colors['conv'], lw=lw['conv'])
+
+    # # Plot feature snippets:
+    # plot_snippets(snip_axes[5, :], t_full, data['snip_feat'],
+    #               ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+
+    # Analysis results:
+    scales_rel = data['scales'] - data['scales'][0]
+    scales_rel /= scales_rel[-1]
+    for stage in stages:
+        measure = data[f'measure_{stage}']
+
+        # Plot unmodified intensity measures:
+        curve = plot_curves(big_axes[0], data['scales'], measure, c=colors[stage], lw=lw['big'],
+                             fill_kwargs=dict(color=colors[stage], alpha=0.25))
+        if stage in ['log', 'inv', 'conv', 'feat']:
+            show_saturation(big_axes[0], data['scales'], curve, c=colors[stage])
+
+        # # Relate to pure-noise reference:
+        # norm_measure = measure / ref_data[stage]
+
+        # # Plot noise-related intensity measures:
+        # big_axes[1].plot(data['scales'], norm_measure, c=colors[stage], lw=lw['big'])
+
+        # Normalize measure to [0, 1]:
+        min_measure = measure.min(axis=0)
+        max_measure = measure.max(axis=0)
+        norm_measure = (measure - min_measure) / (max_measure - min_measure)
+
+        # Plot normalized intensity measures:
+        curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
+                             fill_kwargs=dict(color=colors[stage], alpha=0.25))
+        if stage in ['log', 'inv', 'conv', 'feat']:
+            show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
+
+        # # Plot over relative scales:
+        # plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
+        #             fill_kwargs=dict(color=colors[stage], alpha=0.25))
+        # scales_rel = curve - curve.min()
+        # scales_rel /= scales_rel.max()
+
+    if save_path is not None:
+        fig.savefig(save_path)
+    plt.show()
+
+print('Done.')
+embed()
--- a/python/fig_invariance_log-hp.py
+++ b/python/fig_invariance_log-hp.py
@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 from itertools import product
 from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data
-from misc_functions import shorten_species, get_kde, get_saturation
+from misc_functions import shorten_species, get_saturation
 from color_functions import load_colors
 from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, hide_ticks,\
                           plot_line, strip_zeros, time_bar, zoom_inset,\
@@ -27,18 +27,9 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
        handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
    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:
 target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
-data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
+data_path = search_files(target, excl='noise', dir='../data/inv/log_hp/')[0]
 ref_path = '../data/inv/log_hp/ref_measures.npz'
 save_path = '../figures/fig_invariance_log_hp.pdf'
 target_species = [
@@ -56,6 +47,7 @@ load_kwargs = dict(
    keywords=['scales', 'snip', 'measure']
 )
 compute_ratios = True
+exclude_zero = True
 show_diag = True
 show_plateaus = True

@@ -275,169 +267,180 @@ plateau_dot_kwargs = dict(
 )

 # PREPARATION:
-if compute_ratios:
-    ref_measures = dict(np.load(ref_path))
-
 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_data = dict(np.load(spec_path))
-    measure = spec_data['mean'].mean(axis=1)
+    measure = spec_data['mean_inv'].mean(axis=-1)
+    if exclude_zero:
+        measure = measure[spec_data['scales'] > 0]
    species_measures[species] = measure
    thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]

 # EXECUTION:
-for data_path in data_paths:
-    print(f'Processing {data_path}')
+print(f'Processing {data_path}')

-    # Load invariance data:
-    pure_data, config = load_data(data_path, **load_kwargs)
-    noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
-    pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
-    t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
+# Load invariance data:
+pure_data, config = load_data(data_path, **load_kwargs)
+noise_data, _ = load_data(data_path.replace('pure', 'noise'), **load_kwargs)
+pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
+t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']

-    # Prepare overall graph:
-    fig = plt.figure(**fig_kwargs)
-    super_grid = fig.add_gridspec(**super_grid_kwargs)
-    fig.canvas.draw()
+if compute_ratios:
+    # Relate pure-song measures to near-zero scale:
+    pure_data['measure_env'] /= pure_data['measure_env'][1]
+    pure_data['measure_log'] /= pure_data['measure_log'][1]
+    pure_data['measure_inv'] /= pure_data['measure_inv'][1]
+    # Relate noise-song measures to zero scale:
+    noise_data['measure_env'] /= noise_data['measure_env'][0]
+    noise_data['measure_log'] /= noise_data['measure_log'][0]
+    noise_data['measure_inv'] /= noise_data['measure_inv'][0]

-    # Prepare pure-song snippet axes:
-    pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
-    pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
-    pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
-    for ax, stage in zip(pure_axes[:, 0], stages):
-        ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
-        ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
-               transform=pure_subfig.transSubfigure)
-    for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
-        pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
-    letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
-    pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
-    pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
-    pure_inset.tick_params(**inset_tick_kwargs)
-    hide_ticks(pure_inset, 'bottom', ticks=False)
+if exclude_zero:
+    # Exclude zero scales:
+    inds = pure_scales > 0
+    pure_scales = pure_scales[inds]
+    pure_data['measure_env'] = pure_data['measure_env'][inds]
+    pure_data['measure_log'] = pure_data['measure_log'][inds]
+    pure_data['measure_inv'] = pure_data['measure_inv'][inds]
+    inds = noise_scales > 0
+    noise_scales = noise_scales[inds]
+    noise_data['measure_env'] = noise_data['measure_env'][inds]
+    noise_data['measure_log'] = noise_data['measure_log'][inds]
+    noise_data['measure_inv'] = noise_data['measure_inv'][inds]

-    # Prepare noise-song snippet axes:
-    noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
-    noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
-    noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
-    for ax, stage in zip(noise_axes[:, 0], stages):
-        ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
-        ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
-               transform=noise_subfig.transSubfigure)
-    for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
-        noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
-    letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
-    noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
-    noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
-    noise_inset.tick_params(**inset_tick_kwargs)
-    hide_ticks(noise_inset, 'bottom', ticks=False)
+# Prepare overall graph:
+fig = plt.figure(**fig_kwargs)
+super_grid = fig.add_gridspec(**super_grid_kwargs)
+fig.canvas.draw()

-    # 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, scales in enumerate([pure_scales, noise_scales, noise_scales]):
-        ax = big_subfig.add_subplot(big_grid[0, i])
-        ax.set_xlim(scales[0], scales[-1])
-        ax.set_ylim(scales[0], scales[-1])
-        ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
-        ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
-        ax.set_aspect(**anchor_kwargs)
-        if i > 0:
-            hide_ticks(ax, 'left')
-        big_axes[i] = ax
-    ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
-    super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
-    letter_subplots(big_axes, 'cde', **letter_big_kwargs)
+# Prepare pure-song snippet axes:
+pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
+pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
+pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
+for ax, stage in zip(pure_axes[:, 0], stages):
+    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
+    ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
+            transform=pure_subfig.transSubfigure)
+for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
+    pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
+letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
+pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
+pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
+pure_inset.tick_params(**inset_tick_kwargs)
+hide_ticks(pure_inset, 'bottom', ticks=False)

-    # Plot pure-song envelope snippets:
-    handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
-                           ymin=0, c=colors['env'], lw=lw['snip'])[0]
-    zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
+# Prepare noise-song snippet axes:
+noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
+noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
+noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
+for ax, stage in zip(noise_axes[:, 0], stages):
+    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
+    ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
+            transform=noise_subfig.transSubfigure)
+for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
+    noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
+letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
+noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
+noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
+noise_inset.tick_params(**inset_tick_kwargs)
+hide_ticks(noise_inset, 'bottom', ticks=False)

-    # Plot pure-song logarithmic snippets:
-    plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
-                  c=colors['log'], lw=lw['snip'])
+# 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, scales in enumerate([pure_scales, noise_scales, noise_scales]):
+    ax = big_subfig.add_subplot(big_grid[0, i])
+    ax.set_xlim(scales[0], scales[-1])
+    ax.set_ylim(scales[0], scales[-1])
+    ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
+    ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
+    ax.set_aspect(**anchor_kwargs)
+    if i > 0:
+        hide_ticks(ax, 'left')
+    big_axes[i] = ax
+ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
+super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
+letter_subplots(big_axes, 'cde', **letter_big_kwargs)

-    # Plot pure-song invariant snippets:
-    plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
-                  c=colors['inv'], lw=lw['snip'])
+# Plot pure-song envelope snippets:
+handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
+                        ymin=0, c=colors['env'], lw=lw['snip'])[0]
+zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)

-    # Plot noise-song envelope snippets:
-    ymin, ymax = pure_axes[0, 0].get_ylim()
-    handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
-                           ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
-    zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
+# Plot pure-song logarithmic snippets:
+plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
+                c=colors['log'], lw=lw['snip'])

-    # Plot noise-song logarithmic snippets:
-    ymin, ymax = pure_axes[1, 0].get_ylim()
-    plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
-                  ymin, ymax, c=colors['log'], lw=lw['snip'])
+# Plot pure-song invariant snippets:
+plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
+                c=colors['inv'], lw=lw['snip'])

-    # Plot noise-song invariant snippets:
-    ymin, ymax = pure_axes[2, 0].get_ylim()
-    plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
-                  ymin, ymax, c=colors['inv'], lw=lw['snip'])
+# Plot noise-song envelope snippets:
+ymin, ymax = pure_axes[0, 0].get_ylim()
+handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
+                        ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
+zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)

-    # Indicate time scale:
-    time_bar(noise_axes[-1, -1], **bar_kwargs)
+# Plot noise-song logarithmic snippets:
+ymin, ymax = pure_axes[1, 0].get_ylim()
+plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
+                ymin, ymax, c=colors['log'], lw=lw['snip'])

-    if compute_ratios:
-        # Relate pure-song measures to zero scale:
-        pure_data['measure_env'] /= ref_measures['env']
-        pure_data['measure_log'] /= ref_measures['log']
-        pure_data['measure_inv'] /= ref_measures['inv']
-        # Relate noise-song measures to zero scale:
-        noise_data['measure_env'] /= ref_measures['env']
-        noise_data['measure_log'] /= ref_measures['log']
-        noise_data['measure_inv'] /= ref_measures['inv']
+# Plot noise-song invariant snippets:
+ymin, ymax = pure_axes[2, 0].get_ylim()
+plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
+                ymin, ymax, c=colors['inv'], lw=lw['snip'])

-    # Plot pure-song measures (ideal):
-    big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
-    big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
-    big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])
+# Indicate time scale:
+time_bar(noise_axes[-1, -1], **bar_kwargs)

-    # Plot noise-song measures (limited):
-    big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
-    big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
-    big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])
+# Plot pure-song measures (ideal):
+big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
+big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
+big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])

-    if show_diag:
-        # Indicate diagonal:
-        big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
-        big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
+# Plot noise-song measures (limited):
+big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
+big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
+big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])

-    if show_plateaus:
-        # Indicate low and high plateaus of noise invariance curve:
-        low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
-        big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
-                            fc=noise_colors[0], **plateau_rect_kwargs)
-        big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
-                            fc=noise_colors[1], **plateau_rect_kwargs)
+if show_diag:
+    # Indicate diagonal:
+    big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
+    big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)

-    # Plot species-specific noise-song invariance curves:
-    for i, (species, measure) in enumerate(species_measures.items()):
-        # Plot invariance curve:
-        color = species_colors[species]
-        big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
-                         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,
-                         transform=big_axes[2].get_xaxis_transform()) 
-        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())
-        big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
-                           color=color, **plateau_line_kwargs)
-    legend = big_axes[2].legend(**leg_kwargs)
-    [h.set_lw(lw['legend']) for h in legend.legend_handles]
+if show_plateaus:
+    # Indicate low and high plateaus of noise invariance curve:
+    low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
+    big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
+                        fc=noise_colors[0], **plateau_rect_kwargs)
+    big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
+                        fc=noise_colors[1], **plateau_rect_kwargs)

-    if save_path is not None:
-        fig.savefig(save_path, bbox_inches='tight')
-    plt.show()
+# Plot species-specific noise-song invariance curves:
+for i, (species, measure) in enumerate(species_measures.items()):
+    # Plot invariance curve:
+    color = species_colors[species]
+    big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
+                        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,
+                        transform=big_axes[2].get_xaxis_transform()) 
+    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())
+    big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
+                        color=color, **plateau_line_kwargs)
+legend = big_axes[2].legend(**leg_kwargs)
+[h.set_lw(lw['legend']) for h in legend.legend_handles]
+
+if save_path is not None:
+    fig.savefig(save_path, bbox_inches='tight')
+plt.show()

 print('Done.')
 embed()
--- a/python/fig_invariance_log-hp_appendix.py
+++ b/python/fig_invariance_log-hp_appendix.py
@@ -2,7 +2,7 @@ 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 plot_functions import ylabel, super_xlabel, title_subplot
 from color_functions import load_colors
 from misc_functions import shorten_species

@@ -17,7 +17,10 @@ target_species = [
    'Pseudochorthippus_parallelus',
 ]
 data_path = '../data/inv/log_hp/condensed/'
-save_path = '../figures/fig_invariance_log-hp_species.pdf'
+save_path = '../figures/fig_invariance_log-hp_appendix.pdf'
+
+# ANALYSIS SETTINGS:
+exclude_zero = True

 # GRAPH SETTINGS:
 fig_kwargs = dict(
@@ -45,6 +48,22 @@ fill_kwargs = dict(
    alpha=0.3,
    zorder=1,
 )
+mean_kwargs = dict(
+    # c=(0.5,) * 3,
+    lw=2,
+    alpha=1,
+    zorder=3,
+    ls='--'
+)
+mean_colors = {
+    'Chorthippus_biguttulus': (1,) * 3,
+    'Chorthippus_mollis': (0,) * 3,
+    'Chrysochraon_dispar': (0,) * 3,
+    'Euchorthippus_declivus': (0,) * 3,
+    'Gomphocerippus_rufus': (0,) * 3,
+    'Omocestus_rufipes': (0,) * 3,
+    'Pseudochorthippus_parallelus': (0,) * 3,
+}
 xlab = 'scale $\\alpha$'
 ylab = '$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$'
 xlab_kwargs = dict(
@@ -82,7 +101,6 @@ 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):
@@ -93,14 +111,24 @@ for species, ax in zip(target_species, axes):
    path = search_files(species, dir=data_path)[0]
    data = dict(np.load(path))
    scales = data['scales']
-    means = data['mean']
-    sds = data['sd']
+    means = data['mean_inv']
+    sds = data['sd_inv']
+
+    if exclude_zero:
+        # Exclude zero scale:
+        inds = scales > 0
+        scales = scales[inds]
+        means = means[inds, :]
+        sds = sds[inds, :]

    # 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)

+    # Plot species mean trace:
+    ax.plot(scales, means.mean(axis=-1), c=mean_colors[species], **mean_kwargs)
+
 # Save graph:
 fig.savefig(save_path)
 plt.show()  
--- a/python/fig_invariance_thresh-lp_appendix.py
+++ b/python/fig_invariance_thresh-lp_appendix.py
@@ -0,0 +1,191 @@
+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from thunderhopper.filetools import search_files
+from thunderhopper.modeltools import load_data
+from plot_functions import ylabel, ylimits, super_xlabel, title_subplot, time_bar
+from color_functions import load_colors, shade_colors
+from misc_functions import shorten_species
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+data_path = '../data/inv/thresh_lp/condensed/'
+save_path = '../figures/fig_invariance_thresh-lp_appendix.pdf'
+
+# ANALYSIS SETTINGS:
+exclude_zero = True
+
+# SUBSET SETTINGS:
+thresh_rel = np.array([0.5, 1, 3])[0]
+kern_specs = np.array([
+    [1, 0.008],
+    [2, 0.004],
+    [3, 0.002],
+])[np.array([0, 1, 2])]
+n_kernels = kern_specs.shape[0]
+
+# GRAPH SETTINGS:
+fig_kwargs = dict(
+    figsize=(32/2.54, 16/2.54),
+    nrows=n_kernels,
+    ncols=len(target_species),
+    sharex=True,
+    sharey=True,
+    gridspec_kw=dict(
+        wspace=0.4,
+        hspace=0.2,
+        left=0.07,
+        right=0.98,
+        bottom=0.1,
+        top=0.95,
+    )
+)
+
+# PLOT SETTINGS:
+species_colors = load_colors('../data/species_colors.npz')
+kern_shades = [0, 0.75]
+kern_colors = shade_colors((0., 0., 0.), np.linspace(*kern_shades, n_kernels))
+line_kwargs = dict(
+    lw=2,
+    alpha=0.5,
+    zorder=2,
+)
+fill_kwargs = dict(
+    alpha=0.3,
+    zorder=1,
+)
+mean_kwargs = dict(
+    # c=(0.5,) * 3,
+    lw=2,
+    alpha=1,
+    zorder=3,
+    ls='--'
+)
+mean_colors = {
+    'Chorthippus_biguttulus': (1,) * 3,
+    'Chorthippus_mollis': (0,) * 3,
+    'Chrysochraon_dispar': (0,) * 3,
+    'Euchorthippus_declivus': (0,) * 3,
+    'Gomphocerippus_rufus': (0,) * 3,
+    'Omocestus_rufipes': (0,) * 3,
+    'Pseudochorthippus_parallelus': (1,) * 3,
+}
+kern_kwargs = dict(
+    lw=2,
+)
+inset_bounds = [0.05, 0.6, 0.3, 0.25]
+kern_bar_time = 0.05
+kern_bar_kwargs = dict(
+    dur=kern_bar_time,
+    y0=0.1,
+    y1=0.2,
+    color='k',
+    lw=0,
+    clip_on=False,
+    text_pos=(0.5, -1),
+    text_str=f'${int(kern_bar_time * 1000)}\\,\\text{{ms}}$',
+    text_kwargs=dict(
+        fontsize=12,
+        ha='center',
+        va='top',
+    )
+)
+xlab = 'scale $\\alpha$'
+ylabs = [f'$\\mu_{{f_{i}}}$' for i in range(1, n_kernels + 1)]
+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, 0].set_xscale('log')
+axes[0, 0].set_ylim(0, 1)
+axes[0, 0].yaxis.set_major_locator(plt.MultipleLocator(0.5))
+super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
+insets = []
+for ax, ylab in zip(axes[:, 0], ylabs):
+    ylabel(ax, ylab, **ylab_kwargs, transform=fig.transFigure)
+    insets.append(ax.inset_axes(inset_bounds))
+
+# Run through species:
+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]
+    data, config = load_data(path, files=['scales', 'mean_feat', 'sd_feat', 'thresh_rel'])
+    scales = data['scales']
+    means = data['mean_feat']
+    sds = data['sd_feat']
+
+    # Reduce to single threshold:
+    ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
+    means = means[:, :, ind, :]
+    sds = sds[:, :, ind, :]
+
+    if exclude_zero:
+        # Exclude zero scale:
+        inds = scales > 0
+        scales = scales[inds]
+        means = means[inds, :, :]
+        sds = sds[inds, :, :]
+
+    # Run through kernels:
+    for j, (ax, inset) in enumerate(zip(spec_axes, insets)):
+        if i == 0:
+            # Indicate kernel waveform:
+            inset.plot(config['k_times'], config['kernels'][:, j],
+                       c=kern_colors[j], **kern_kwargs)
+            inset.set_xlim(config['k_times'][[0, -1]])
+            ylimits(config['kernels'], inset, pad=0.05)
+            inset.set_title(rf'$k_{{{j+1}}}$', fontsize=15)
+            if j == 0:
+                time_bar(inset, **kern_bar_kwargs)
+            inset.axis('off')
+
+        # Plot recording-specific traces:
+        for k in range(means.shape[-1]):
+            ax.plot(scales, means[:, j, k], c=species_colors[species], **line_kwargs)
+            spread = (means[:, j, k] - sds[:, j, k], means[:, j, k] + sds[:, j, k])
+            ax.fill_between(scales, *spread, color=species_colors[species], **fill_kwargs)
+        # Plot kernel-specific mean trace:
+        ax.plot(scales, means[:, j, :].mean(axis=-1), c=mean_colors[species], **mean_kwargs)
+
+# Save graph:
+fig.savefig(save_path)
+plt.show()  
+
+
--- a/python/fig_invariance_thresh-lp_single.py
+++ b/python/fig_invariance_thresh-lp_single.py
@@ -58,19 +58,19 @@ def side_distributions(axes, snippets, inset_bounds, thresh, nbins=1000,


 # GENERAL SETTINGS:
-target = 'Omocestus_rufipes'
-data_paths = search_files(target, incl='noise', dir='../data/inv/thresh_lp/')
+example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
+data_path = search_files(example_file, incl='noise', dir='../data/inv/thresh_lp/')[0]
 stages = ['conv', 'bi', 'feat']
 load_kwargs = dict(
    files=stages,
    keywords=['scales', 'snip', 'measure', 'thresh']
 )
-save_path = None#'../figures/fig_invariance_thresh_lp_single.pdf'
+save_path = '../figures/fig_invariance_thresh_lp_single.pdf'
 exclude_zero = True

 # GRAPH SETTINGS:
 fig_kwargs = dict(
-    figsize=(32/2.54, 16/2.54),
+    figsize=(32/2.54, 32/2.54),
 )
 super_grid_kwargs = dict(
    nrows=None,
@@ -140,6 +140,8 @@ lw = dict(
    bi=0.1,
    feat=3,
    big=4,
+    kern=2.5,
+    plateau=1.5,
 )
 xlabels = dict(
    alpha='scale $\\alpha$',
@@ -216,6 +218,10 @@ letter_big_kwargs = dict(
    va='top',
    fontsize=fs['letter'],
 )
+kern_kwargs = dict(
+    c='k',
+    lw=lw['kern'],
+)
 dist_kwargs = dict(
    c='k',
    lw=1,
@@ -257,171 +263,198 @@ plateau_settings = dict(
    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,
+)
+zoom_rel = np.array([0.5, 0.515])
+
+# SUBSET SETTINGS:
 kern_specs = np.array([
    [1, 0.008],
    [2, 0.004],
    [3, 0.002],
 ])[np.array([1])]
-zoom_rel = np.array([0.5, 0.515])
-

 # EXECUTION:
-for data_path in data_paths:
-    print(f'Processing {data_path}')
+print(f'Processing {data_path}')

-    # Load invariance data:
-    noise_data, config = load_data(data_path, **load_kwargs)
-    pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)
+# Load invariance data:
+noise_data, config = load_data(data_path, **load_kwargs)
+pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)

-    # Unpack shared variables:
-    scales = noise_data['scales']
-    plot_scales = noise_data['example_scales']
-    thresh_rel = noise_data['thresh_rel']
-    thresh_abs = noise_data['thresh_abs']
+# Unpack shared variables:
+scales = noise_data['scales']
+plot_scales = noise_data['example_scales']
+thresh_rel = noise_data['thresh_rel']
+thresh_abs = noise_data['thresh_abs']

-    # Reduce to kernel subset and crop to zoom frame:
-    t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
-    zoom_abs = zoom_rel * t_full[-1]
-    zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
-    kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
-    noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
-    noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
-    noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
-    noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
-    noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
-    pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
-    thresh_abs = thresh_abs[:, kern_ind]
-    t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
-    if exclude_zero:
-        # Reduce to nonzero scales:
-        nonzero_inds = scales > 0
-        scales = scales[nonzero_inds]
-        noise_data['measure_inv'] = noise_data['measure_inv'][nonzero_inds]
-        noise_data['measure_feat'] = noise_data['measure_feat'][nonzero_inds, :]
-        pure_data['measure_feat'] = pure_data['measure_feat'][nonzero_inds, :]
+# Reduce to kernel subset and crop to zoom frame:
+t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
+zoom_abs = zoom_rel * t_full[-1]
+zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
+kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
+noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
+noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
+noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
+noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
+noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
+pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
+config['kernels'] = config['kernels'][:, kern_ind]
+thresh_abs = thresh_abs[:, kern_ind]
+t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']

-    # Get threshold-specific colors:
-    factors = np.linspace(*shade_factors, thresh_rel.size)
-    shaded = dict(
-        conv=shade_colors(colors['conv'], factors),
-        bi=shade_colors(colors['bi'], factors),
-        feat=shade_colors(colors['feat'], factors),
-    )
+if exclude_zero:
+    # Exclude zero scale:
+    inds = scales > 0
+    scales = scales[inds]
+    noise_data['measure_inv'] = noise_data['measure_inv'][inds]
+    noise_data['measure_feat'] = noise_data['measure_feat'][inds, :]
+    pure_data['measure_feat'] = pure_data['measure_feat'][inds, :]

-    # Adjust grid parameters to loaded data:
-    super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + input_rows
-    input_grid_kwargs['ncols'] = plot_scales.size
-    snip_grid_kwargs['ncols'] = plot_scales.size
+# Get threshold-specific colors:
+factors = np.linspace(*shade_factors, thresh_rel.size)
+shaded = dict(
+    conv=shade_colors(colors['conv'], factors),
+    bi=shade_colors(colors['bi'], factors),
+    feat=shade_colors(colors['feat'], factors),
+)

-    # Prepare overall graph:
-    fig = plt.figure(**fig_kwargs)
-    super_grid = fig.add_gridspec(**super_grid_kwargs)
+# Adjust grid parameters to loaded data:
+super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + input_rows
+input_grid_kwargs['ncols'] = plot_scales.size
+snip_grid_kwargs['ncols'] = plot_scales.size

-    # Prepare input snippet axes:
-    input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
-    input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
-    input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
-    input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
-    ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
-    for ax, scale in zip(input_axes, plot_scales):
-        title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
-    letter_subplot(input_subfig, 'a', **letter_snip_kwargs)
+# Prepare overall graph:
+fig = plt.figure(**fig_kwargs)
+super_grid = fig.add_gridspec(**super_grid_kwargs)

-    # Prepare snippet axes:
-    snip_subfigs, snip_axes = [], []
-    for i in range(thresh_rel.size):
-        subfig_spec = subfig_specs['snip'].copy()
-        subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
-        snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
-        axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
-        [hide_axis(ax, 'left') for ax in axes[1:, 1]]
-        super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
-                     snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
-        for (ax1, ax2), stage in zip(axes[:, :2], stages):
-            ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
-            ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
-            ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
-        if i == thresh_rel.size - 1:
-            axes[-1, -1].set_xlim(t_full[0], t_full[-1])
-            time_bar(axes[-1, -1], **bar_kwargs)
-        snip_subfigs.append(snip_subfig)
-        snip_axes.append(axes)
-    letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
+# Prepare input snippet axes:
+input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
+input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
+input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
+input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
+ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
+for ax, scale in zip(input_axes, plot_scales):
+    title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
+letter_subplot(input_subfig, 'a', **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)
+# Prepare snippet axes:
+snip_subfigs, snip_axes = [], []
+for i in range(thresh_rel.size):
+    subfig_spec = subfig_specs['snip'].copy()
+    subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
+    snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
+    axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
+    [hide_axis(ax, 'left') for ax in axes[1:, 1]]
+    super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
+                    snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
+    for (ax1, ax2), stage in zip(axes[:, :2], stages):
+        ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
+        ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
+        ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
+    if i == thresh_rel.size - 1:
+        axes[-1, -1].set_xlim(t_full[0], t_full[-1])
+        time_bar(axes[-1, -1], **bar_kwargs)
+    snip_subfigs.append(snip_subfig)
+    snip_axes.append(axes)
+letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)

-    alpha_ax = big_subfig.add_subplot(big_grid[0, 0])
-    alpha_ax.set_xlim(scales[0], scales[-1])
-    alpha_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
-    ylimits(pure_data['measure_feat'], alpha_ax, minval=0, pad=ypad['big'])
-    alpha_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
-    xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs)
-    ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
+# Prepare analysis axes:
+big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
+big_grid = big_subfig.add_gridspec(**big_grid_kwargs)

-    sigma_ax = big_subfig.add_subplot(big_grid[1, 0])
-    sigma_ax.set_xlim(noise_data['measure_inv'].min(), noise_data['measure_inv'].max())
-    # sigma_ax.set_xscale('log')
-    sigma_ax.set_xlim(scales[0], scales[-1])
-    sigma_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
-    ylimits(pure_data['measure_feat'], sigma_ax, minval=0, pad=ypad['big'])
-    sigma_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
-    xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs)
-    ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
+alpha_ax = big_subfig.add_subplot(big_grid[0, 0])
+alpha_ax.set_xlim(scales[0], scales[-1])
+alpha_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
+ylimits(pure_data['measure_feat'], alpha_ax, minval=0, pad=ypad['big'])
+alpha_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
+xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs)
+ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)

-    # Plot intensity-adapted snippets:
-    plot_snippets(input_axes, t_full, noise_data['snip_inv'],
-                  ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
-    ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])
+sigma_ax = big_subfig.add_subplot(big_grid[1, 0])
+sigma_ax.set_xlim(noise_data['measure_inv'].min(), noise_data['measure_inv'].max())
+sigma_ax.set_xlim(scales[0], scales[-1])
+sigma_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
+ylimits(pure_data['measure_feat'], sigma_ax, minval=0, pad=ypad['big'])
+sigma_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
+xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs)
+ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)

-    # Plot representation snippets per threshold:
-    for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
-        dist_fill_kwargs['color'] = shaded['bi'][i]
+# Plot intensity-adapted snippets:
+plot_snippets(input_axes, t_full, noise_data['snip_inv'],
+                ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
+ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])

-        # Plot kernel response snippets:
-        plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
-                      ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
-        ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
+# Indicate kernel waveform over 1st intensity-adapted snippet:
+input_axes[0].plot(config['k_times'] + 0.5 * t_full[-1], config['kernels'], **kern_kwargs)

-        # Plot kernel response distributions:
-        side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
-                           thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
-        side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
-                           thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
+# Plot representation snippets per threshold:
+for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
+    dist_fill_kwargs['color'] = shaded['bi'][i]

-        # Plot binary snippets:
-        plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
-                         color=shaded['bi'][i], lw=lw['bi'])
+    # Plot kernel response snippets:
+    plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
+                    ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
+    ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])

-        # Plot feature snippets:
-        handles = plot_snippets(axes[2, :], t_full, noise_data['snip_feat'][:, :, i],
-                                ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
-        [set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]
+    # Plot kernel response distributions:
+    side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
+                        thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
+    side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
+                        thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)

-    # Get threshold-specific saturation:
-    for i in range(thresh_rel.size):
-        ind = get_saturation(noise_data['measure_feat'][:, i], **plateau_settings)[1]
+    # Plot binary snippets:
+    plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
+                        color=shaded['bi'][i], lw=lw['bi'])

-    # Plot analysis results:
-    for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
-        # Plot pure-song analysis results:
-        handles = ax.plot(x, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
-        [h.set_color(c) for h, c in zip(handles, shaded['feat'])]
+    # Plot feature snippets:
+    handles = plot_snippets(axes[2, :], t_full, noise_data['snip_feat'][:, :, i],
+                            ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
+    [set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]

-        # Plot noise-song analysis results:
-        handles = ax.plot(x, noise_data['measure_feat'], lw=lw['big'])
-        [h.set_color(c) for h, c in zip(handles, shaded['feat'])]
+# Get saturation:
+saturation_inds = []
+for i in range(thresh_rel.size):
+    ind = get_saturation(noise_data['measure_feat'][:, i], **plateau_settings)[1]
+    saturation_inds.append(ind)

-        # Add proxy legend:
-        if ax == alpha_ax:
-            h1 = ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
-            h2 = ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
-            ax.legend(handles=[h1, h2], **leg_kwargs)
+# Plot analysis results:
+for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
+    # Plot pure-song analysis results:
+    handles = ax.plot(x, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
+    [h.set_color(c) for h, c in zip(handles, shaded['feat'])]

-    if save_path is not None:
-        fig.savefig(save_path)
-    plt.show()
+    # Plot noise-song analysis results:
+    handles = ax.plot(x, noise_data['measure_feat'], lw=lw['big'])
+    [h.set_color(c) for h, c in zip(handles, shaded['feat'])]
+
+    # Indicate threshold-specific saturation:
+    for i, ind in enumerate(saturation_inds):
+        color = shaded['feat'][i]
+        ax.plot(x[ind], 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
+                transform=ax.get_xaxis_transform()) 
+        ax.plot(x[ind], 0, mfc=color, mec='k', alpha=0.75, zorder=6,
+                **plateau_dot_kwargs, transform=ax.get_xaxis_transform())
+        ax.vlines(x[ind], ax.get_ylim()[0], noise_data['measure_feat'][ind, i],
+                    color=color, **plateau_line_kwargs)
+
+    # Add proxy legend:
+    if ax == alpha_ax:
+        h1 = ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
+        h2 = ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
+        ax.legend(handles=[h1, h2], **leg_kwargs)
+
+if save_path is not None:
+    fig.savefig(save_path)
+plt.show()

 print('Done.')
 embed()
--- a/python/fig_invariance_thresh-lp_species.py
+++ b/python/fig_invariance_thresh-lp_species.py
@@ -162,20 +162,30 @@ def add_cross_axes(fig, n, long='col', fill='row', **grid_kwargs):

 # GENERAL SETTINGS:
 target_species = [
-    'Omocestus_rufipes',
    'Chorthippus_biguttulus',
    'Chorthippus_mollis',
    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
-    ]
+]
+example_files = {
+    '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'
+}
 n_species = len(target_species)
 load_kwargs = dict(
-    keywords=['scales', 'measure', 'thresh']
+    keywords=['scales', 'mean', 'thresh']
 )
 save_path = '../figures/fig_invariance_thresh_lp_species.pdf'
 exclude_zero = True
-show_floor = True
+show_floor = False

 # SUBSET SETTINGS:
 thresh_rel = np.array([0.5, 1, 3])[0]
@@ -266,14 +276,6 @@ lw = dict(
    kern=2.5,
    plateau=3,
 )
-zorder = dict(
-    Omocestus_rufipes=2,
-    Chorthippus_biguttulus=2.5,
-    Chorthippus_mollis=2.4,
-    Chrysochraon_dispar=2,
-    Gomphocerippus_rufus=2,
-    Pseudochorthippus_parallelus=2,
-)
 space_kwargs = dict(
    s=30,
 )
@@ -357,21 +359,27 @@ letter_space_kwargs = dict(
    va='center',
    fontsize=fs['letter'],
 )
-song_bar_time = 1.0
+spec_bar_times = dict(
+    Chorthippus_biguttulus=1,
+    Chorthippus_mollis=10,
+    Chrysochraon_dispar=1,
+    Euchorthippus_declivus=0.25,
+    Gomphocerippus_rufus=5,
+    Omocestus_rufipes=5,
+    Pseudochorthippus_parallelus=1,
+)
 song_bar_kwargs = dict(
-    dur=song_bar_time,
    y0=-0.1,
    y1=0,
-    xshift=0,
+    xshift=0.5,
    color='k',
    lw=0,
    clip_on=False,
-    text_pos=(1.25, 0.5),
-    text_str=f'${int(song_bar_time)}\\,\\text{{s}}$',
+    text_pos=(0.5, -0.1),
    text_kwargs=dict(
        fontsize=fs['bar'],
-        ha='left',
-        va='center',
+        ha='center',
+        va='top',
    )
 )
 kern_bar_time = 0.05
@@ -382,7 +390,7 @@ kern_bar_kwargs = dict(
    color='k',
    lw=0,
    clip_on=False,
-    text_pos=(0.6, -1),
+    text_pos=(0.7, -1),
    text_str=f'${int(kern_bar_time * 1000)}\\,\\text{{ms}}$',
    text_kwargs=dict(
        fontsize=fs['bar'],
@@ -502,7 +510,7 @@ for i, species in enumerate(target_species):
    print(f'Processing {species}')

    # Fetch species-specific recording file:
-    song_path = search_files(species, dir='../data/processed/')[0]
+    song_path = search_files(example_files[species], dir='../data/processed/')[0]

    # Load song data:
    song_data, _ = load_data(song_path, files='filt')
@@ -513,16 +521,18 @@ for i, species in enumerate(target_species):
    time = np.arange(song.shape[0]) / rate
    plot_line(song_ax, time, song, ypad=0.05, c='k', lw=lw['song'])
    title_subplot(song_ax, shorten_species(species), ref=song_subfig, **title_kwargs)
-    time_bar(song_ax, **song_bar_kwargs)
-    song_bar_kwargs['text_pos'] = None
+    time_bar(song_ax, dur=spec_bar_times[species], **song_bar_kwargs,
+             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/')[0]
-    noise_path = search_files(species, incl='noise', dir='../data/inv/thresh_lp/')[0]
+    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]

    # Load invariance data:
    pure_data, config = load_data(pure_path, **load_kwargs)
    noise_data, _ = load_data(noise_path, **load_kwargs)
+    pure_measure = pure_data['mean_feat'].mean(axis=-1)
+    noise_measure = noise_data['mean_feat'].mean(axis=-1)
    scales = pure_data['scales']

    # Reduce to kernel subset and a single threshold:
@@ -530,8 +540,8 @@ for i, species in enumerate(target_species):
    kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
    config['k_specs'] = config['k_specs'][kern_inds]
    config['kernels'] = config['kernels'][:, kern_inds]
-    pure_measure = pure_data['measure_feat'][:, kern_inds, thresh_ind]
-    noise_measure = noise_data['measure_feat'][:, kern_inds, thresh_ind]
+    pure_measure = pure_measure[:, kern_inds, thresh_ind]
+    noise_measure = noise_measure[:, kern_inds, thresh_ind]
    if exclude_zero:
        # Reduce to nonzero scales:
        nonzero_inds = scales > 0
@@ -564,7 +574,6 @@ for i, species in enumerate(target_species):
            inset.plot(config['k_times'], kern, c=c, lw=lw['kern'])
            inset.set_xlim(xlims)
            inset.set_ylim(ylims)
-        # time_bar(insets[0], parent=feat_axes[0, 0], **kern_bar_kwargs)
        time_bar(insets[0], **kern_bar_kwargs)

    # Plot invariance curves in feature space:
@@ -572,13 +581,11 @@ for i, species in enumerate(target_species):
    for ind, (pure_ax, noise_ax) in enumerate(zip(pure_axes, noise_axes)):
        irow, icol = row_inds[ind], col_inds[ind]
        pure_handle = pure_ax.scatter(pure_measure[:, icol], pure_measure[:, irow],
-                                      c=scales, cmap=scale_cmap, norm=norm,
-                                      zorder=zorder[species], **space_kwargs)
+                                      c=scales, cmap=scale_cmap, norm=norm, **space_kwargs)
        pure_space_handles[pure_ax].append(pure_handle)

        noise_handle = noise_ax.scatter(noise_measure[:, icol], noise_measure[:, irow],
-                                        c=scales, cmap=scale_cmap, norm=norm,
-                                        zorder=zorder[species], **space_kwargs)
+                                        c=scales, cmap=scale_cmap, norm=norm, **space_kwargs)
        noise_space_handles[noise_ax].append(noise_handle)
    
    # Indicate scale color code in pure subfigure:
--- a/python/misc_functions.py
+++ b/python/misc_functions.py
@@ -1,5 +1,6 @@
 import numpy as np
 from scipy.stats import gaussian_kde
+from thunderhopper.filetools import crop_paths

 def shorten_species(name):
    genus, species = name.split('_')
@@ -9,6 +10,44 @@ def unsort_unique(array):
    values, inds = np.unique(array, return_index=True)
    return values[np.argsort(inds)]

+def draw_noise_segment(noise, n):
+    rng = np.random.default_rng()
+    start = rng.integers(0, noise.shape[0] - n, endpoint=True)
+    return np.take(noise, np.arange(start, start + n), axis=0)
+
+def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
+    # Separate by source:
+    sorted_paths = {}
+    for source in sources:
+
+        # Check for any source-specific song files:
+        source_paths = [path for path in paths if source in path]
+        if not source_paths:
+            continue
+
+        # Separate by recording:
+        sorted_paths[source] = [[]]
+        for path, name in zip(source_paths, crop_paths(source_paths)):
+
+            # Find numerical ID behind source tag:
+            id_ind = name.find(source) + len(source) + 1
+            # Get segment where sub-ID would be:
+            sub_id = name[id_ind:].split('-')[1]
+            if 's' in sub_id:
+                # Found time stamp (single recording):
+                sorted_paths[source][0].append(path)
+                continue
+            sub_id = int(sub_id)
+            # Found sub-ID (multiple recordings):
+            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 by recording only (discarding source separation):
+    sorted_paths = [path for paths in sorted_paths.values() for path in paths]
+    return sorted_paths
+
 def get_kde(data, sigma, axis=None, n=1000, pad=10):
    if axis is None:
        axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)
--- a/python/save_inv_data_full.py
+++ b/python/save_inv_data_full.py
@@ -1,24 +1,42 @@
-import glob
 import numpy as np
 import matplotlib.pyplot as plt
 from thunderhopper.modeltools import load_data, save_data
-from thunderhopper.filetools import crop_paths
+from thunderhopper.filetools import search_files, crop_paths
 from thunderhopper.filtertools import find_kern_specs
-from thunderhopper.model import process_signal, convolve_kernels
+from thunderhopper.model import process_signal
+from misc_functions import draw_noise_segment
 from IPython import embed

 # GENERAL SETTINGS:
-target = 'Omocestus_rufipes'
-data_paths = glob.glob(f'../data/processed/{target}*.npz')
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+][0]
+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'
+ref_path = '../data/inv/full/ref_measures.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, 100)
-scales = np.unique(np.concatenate((scales, example_scales)))
-thresh_rel = 3
+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([
@@ -34,11 +52,14 @@ types = None#np.array([-1])
 sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])

 # PREPARATION:
-noise_data = np.load(noise_path)
-pure_noise = noise_data['raw']
+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):
@@ -46,8 +67,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    song, rate = data['raw'], config['rate']

    if thresh_rel is not None:
-        # Get noise-bound kernel-specific thresholds:
-        config['feat_thresh'] = noise_data['conv'].std(axis=0) * thresh_rel
+        # 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]):
@@ -66,22 +87,10 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    song /= song[segment].std(axis=0)

    # Get normalized noise component:
-    noise = pure_noise[:song.shape[0]]
+    noise = draw_noise_segment(pure_noise, song.shape[0])
    noise /= noise[segment].std()

-    # Prepare snippet 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)
-    )
-
-    # Prepare measure storage:
+    # Prepare storage:
    shape_low = (scales.size,)
    shape_high = (scales.size, config['k_specs'].shape[0])
    measures = dict(
@@ -91,6 +100,18 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
        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:
@@ -105,18 +126,17 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                                        signal=scaled, rate=rate)
        # Store results:
        for stage in stages:
-            mkey, skey = f'measure_{stage}', f'snip_{stage}'
-
-            # Log snippet data:
-            if scale in example_scales:
-                scale_ind = np.nonzero(example_scales == scale)[0][0]
-                snippets[skey][:, ..., scale_ind] = signals[stage]
-
-            # Log intensity measure per stage (excluding binary):
-            if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
-                measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
-            elif stage == 'feat':
+            # 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:
@@ -124,8 +144,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
            scales=scales,
            example_scales=example_scales,
            )
-        data.update(snippets)
        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_log-hp.py
+++ b/python/save_inv_data_log-hp.py
@@ -2,6 +2,7 @@ import numpy as np
 from thunderhopper.modeltools import load_data, save_data
 from thunderhopper.filetools import search_files, crop_paths
 from thunderhopper.filters import decibel, sosfilter
+from misc_functions import draw_noise_segment
 from IPython import embed

 # GENERAL SETTINGS:
@@ -12,17 +13,18 @@ noise_path = '../data/processed/white_noise_sd-1.npz'
 save_path = '../data/inv/log_hp/'

 # ANALYSIS SETTINGS:
-add_noise = search_target == '*' or False
-save_detailed = search_target == example_file
+add_noise = search_target == '*'
 example_scales = np.array([0.1, 1, 10, 30, 100, 300])
 scales = np.geomspace(0.01, 10000, 1000)
-scales = np.unique(np.concatenate((scales, example_scales)))
+scales = np.unique(np.concatenate(([0], scales, example_scales)))

 # PREPARATION:
-pure_noise = np.load(noise_path)['filt']
+if add_noise:
+    pure_noise = np.load(noise_path)['filt']

 # 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):
@@ -38,7 +40,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    song /= song[segment].std()
    if add_noise:
        # Get normalized noise component:
-        noise = pure_noise[:song.shape[0]]
+        noise = draw_noise_segment(pure_noise, song.shape[0])
        noise /= noise[segment].std()

    # Prepare storage:
@@ -93,10 +95,12 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                snip_log=snip_log,
                snip_inv=snip_inv,
            )
-        file_name = save_path + name
+        save_name = save_path + name
        if add_noise:
-            file_name += '_noise'
-        save_data(file_name, archive, config, overwrite=True)
+            save_name += '_noise'
+        else:
+            save_name += '_pure'
+        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
@@ -0,0 +1,157 @@
+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.filters import sosfilter
+from thunderhopper.model import convolve_kernels, 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'
+ref_path = '../data/inv/short/ref_measures.npz'
+pre_stages = ['filt', 'env']
+stages = pre_stages + ['conv', 'feat']
+save_path = '../data/inv/short/'
+
+# 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_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_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=pre_stages,
+                                        signal=scaled, rate=rate)
+
+        # Process mixture further:
+        signals['conv'] = convolve_kernels(signals['env'], 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:
+            # 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_thresh-lp.py
+++ b/python/save_inv_data_thresh-lp.py
@@ -5,21 +5,23 @@ from thunderhopper.filetools import search_files, crop_paths
 from thunderhopper.filters import sosfilter
 from thunderhopper.filtertools import find_kern_specs
 from thunderhopper.model import convolve_kernels
+from misc_functions import draw_noise_segment
 from IPython import embed

 # GENERAL SETTINGS:
-target = ['Omocestus_rufipes', '*'][0]
-data_paths = search_files(target, excl='noise', dir='../data/processed/')
+example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
+search_target = ['*', example_file][0]
+data_paths = search_files(search_target, excl='noise', dir='../data/processed/')
 noise_path = '../data/processed/white_noise_sd-1.npz'
+ref_path = '../data/inv/thresh_lp/ref_measures.npz'
 save_path = '../data/inv/thresh_lp/'

 # ANALYSIS SETTINGS:
 add_noise = False
-save_snippets = add_noise and (target == 'Omocestus_rufipes')
 plot_results = False
 example_scales = np.array([0, 1, 10, 30, 100])
-scales = np.geomspace(0.01, 10000, 100)
-scales = np.unique(np.concatenate((scales, example_scales)))
+scales = np.geomspace(0.01, 10000, 1000)
+scales = np.unique(np.concatenate(([0], scales, example_scales)))
 thresh_rel = np.array([0.5, 1, 3])
 kern_specs = np.array([
    [1, 0.008],
@@ -28,12 +30,15 @@ kern_specs = np.array([
 ])

 # PREPARATION:
-pure_noise = np.load(noise_path)['inv']
+if add_noise:
+    pure_noise = np.load(noise_path)['inv']
+# Define kernel-specific threshold values based on pure-noise response SD:
+thresh_abs = np.load(ref_path)['conv'][None, :] * thresh_rel[:, None]

 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
+    save_detailed = example_file in name
    print(f'Processing {name}')
-    save_name = save_path + name

    # Get adapted envelope (prior to convolution):
    data, config = load_data(data_path, files='inv')
@@ -44,28 +49,25 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
    start, end = data['songs_0'].ravel()
    segment = (time >= start) & (time <= end)

+    # Normalize song component:
+    song /= song[segment].std()
+
    # Reduce to kernel subset:
    kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
    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]

-    # Get normalized noise component:
-    noise = pure_noise[:song.shape[0]]
+    if add_noise:
+        # Get normalized noise component:
+        noise = draw_noise_segment(pure_noise, song.shape[0])
+        noise /= noise[segment].std()

-    # Normalize both components:
-    song /= song[segment].std()
-    noise /= noise[segment].std()
-
-    # Define kernel-specific threshold values based on pure-noise response SD:
-    ref_conv = convolve_kernels(noise, config['kernels'], config['k_specs'])
-    thresh_abs = ref_conv[segment, :].std(axis=0, keepdims=True) * thresh_rel[:, None]
-
-    # Prepare measure storage:
-    measure_inv = np.zeros((scales.size,), dtype=float)
+    # Prepare storage:
    measure_feat = np.zeros((scales.size, kern_specs.shape[0], thresh_rel.size), dtype=float)
-    if save_snippets:
-        # Prepare snippet storage:
+    if save_detailed:
+        # Prepare optional storage:
+        measure_inv = np.zeros((scales.size,), dtype=float)
        snip_inv = np.zeros((song.size, example_scales.size), dtype=float)
        shape = (song.size, kern_specs.shape[0], example_scales.size, thresh_rel.size)
        snip_conv = np.zeros(shape[:-1], dtype=float)
@@ -82,20 +84,21 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
            # Add noise:
            scaled_song += noise
        
-        # Log input intensity measure:
-        measure_inv[i] = scaled_song[segment].std()
+        if save_detailed:
+            # Log input intensity measure:
+            measure_inv[i] = scaled_song[segment].std()

        # Process mixture:
        scaled_conv = convolve_kernels(scaled_song, config['kernels'], config['k_specs'])
    
        # Log threshold-independent snippet data:
-        if save_snippets and scale in example_scales:
+        if save_detailed and scale in example_scales:
            save_ind = np.nonzero(example_scales == scale)[0][0]
            snip_inv[:, save_ind] = scaled_song
            snip_conv[:, :, save_ind] = scaled_conv

        # Execute piecewise again:
-        for j, thresholds in enumerate(thresh_abs):
+        for j, thresholds in enumerate(thresh_abs[:, kern_inds]):

            # Process mixture further:
            scaled_bi = (scaled_conv > thresholds).astype(float)
@@ -103,11 +106,11 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                                    padtype='fixed', padlen=config['padlen'])

            # Log threshold-dependent snippet data:
-            if save_snippets and scale in example_scales:
+            if save_detailed and scale in example_scales:
                snip_bi[:, :, save_ind, j] = scaled_bi
                snip_feat[:, :, save_ind, j] = scaled_feat

-            # Log intensity measure:
+            # Log output intensity measure:
            measure_feat[i, :, j] = scaled_feat[segment, :].mean(axis=0)

    # Overview plot:
@@ -133,18 +136,19 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
        data = dict(
            scales=scales,
            example_scales=example_scales,
-            measure_inv=measure_inv,
            measure_feat=measure_feat,
            thresh_rel=thresh_rel,
            thresh_abs=thresh_abs,
            )
-        if save_snippets:
+        if save_detailed:
            data.update(dict(
+                measure_inv=measure_inv,
                snip_inv=snip_inv,
                snip_conv=snip_conv,
                snip_bi=snip_bi,
                snip_feat=snip_feat,
            ))
+        save_name = save_path + name
        if add_noise:
            save_name += '_noise'
        else:
--- a/python/save_noise_data.py
+++ b/python/save_noise_data.py
@@ -9,7 +9,7 @@ from IPython import embed
 save_path = '../data/processed/white_noise'
 stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
 sds = [1]
-dur = 60
+dur = 180

 # Interactivity:
 reload_saved = False
@@ -45,6 +45,7 @@ for sd in sds:

    # Generate white noise signal:
    noise = rng.normal(loc=0, scale=sd, size=n_samples)
+    print('Got your no(i)se!')

    # Fetch and store representations:
    save = None if save_path is None else save_path + f'_sd-{sd}.npz'
--- a/python/save_ref_measures.py
+++ b/python/save_ref_measures.py
@@ -7,7 +7,7 @@ from IPython import embed
 ## SETTINGS:

 # General:
-mode = ['log_hp', 'thresh_lp', 'full'][2]
+mode = ['log_hp', 'thresh_lp', 'full', 'short'][3]
 noise_path = '../data/processed/white_noise_sd-1.npz'
 save_path = '../data/inv/'
 pad = np.array([0.1, 0.9])
@@ -15,7 +15,8 @@ pad = np.array([0.1, 0.9])
 stages = dict(
    log_hp=['filt', 'env', 'log', 'inv'],
    thresh_lp=['inv', 'conv', 'feat'],
-    full=['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
+    full=['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat'],
+    short=['raw', 'filt', 'env', 'conv', 'feat']
 )[mode]

 # PROCESSING:
@@ -49,6 +50,12 @@ elif mode == 'thresh_lp':
                             padtype='fixed', padlen=config['padlen'])
 elif mode == 'full':
    data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]
+elif mode == 'short':
+    data = process_signal(config, ['raw', 'filt', 'env'], signal=starter, rate=config['rate'])[0]
+    data['conv'] = convolve_kernels(data['env'], config['kernels'], config['k_specs'])
+    data['feat'] = sosfilter((data['conv'] > config['feat_thresh']).astype(float),
+                             config['env_rate'], config['feat_fcut'], 'lp',
+                             padtype='fixed', padlen=config['padlen'])

 # Get measures:
 measures = {}