Made fig_invariance_rect_lp.pdf and corresponding appendix figure.
Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB. Co-authored-by: Copilot <copilot@github.com>
This commit is contained in:
j-hartling
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[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 337, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 400, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 419, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 428, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 437, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 456, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 491, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 526, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 535, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 556, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 565, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 576, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 587, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 619, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 648, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 658, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 667, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 688, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 709, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 720, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 729, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 749, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 766, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 775, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 800, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 817, warning: 6 characters of junk seen at toplevel
|
||||
[360] Biber.pm:133> INFO - WARNINGS: 55
|
||||
[37] biber:340> INFO - === Mo Apr 27, 2026, 18:08:28
|
||||
[46] 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
|
||||
[89] bibtex.pm:1713> INFO - LaTeX decoding ...
|
||||
[118] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
|
||||
[297] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
|
||||
[297] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
|
||||
[297] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
|
||||
[297] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
|
||||
[321] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
|
||||
[331] bbl.pm:763> INFO - Output to main.bbl
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 10, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 21, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 38, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 49, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 58, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 73, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 82, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 91, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 100, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 109, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 118, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 127, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 136, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 157, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 178, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 187, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 196, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 207, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 218, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 229, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 240, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 249, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 258, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 269, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 278, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 289, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 300, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 309, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 328, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 337, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 400, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 419, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 428, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 437, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 456, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 491, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 526, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 535, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 556, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 565, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 576, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 587, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 619, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 648, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 658, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 667, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 688, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 709, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 720, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 729, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 749, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 766, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 775, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 800, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 817, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:133> INFO - WARNINGS: 55
|
||||
|
||||
@@ -1,14 +1,14 @@
|
||||
# Fdb version 4
|
||||
["biber main"] 1776940217.48712 "main.bcf" "main.bbl" "main" 1777031926.86359 0
|
||||
["biber main"] 1777306108.0762 "main.bcf" "main.bbl" "main" 1777306136.88787 0
|
||||
"cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 ""
|
||||
"main.bcf" 1777031926.78659 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
|
||||
"main.bcf" 1777306136.815 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
|
||||
(generated)
|
||||
"main.bbl"
|
||||
"main.blg"
|
||||
(rewritten before read)
|
||||
["pdflatex"] 1777031925.73488 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1777031926.8638 0
|
||||
["pdflatex"] 1777306135.73239 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1777306136.88809 0
|
||||
"/etc/texmf/web2c/texmf.cnf" 1761560044.43676 475 c0e671620eb5563b2130f56340a5fde8 ""
|
||||
"/home/hartling/phd/paper/paper_2025/main.tex" 1777031925.5956 50083 fda38e01bf1bcfd1210581259b2830ea ""
|
||||
"/home/hartling/phd/paper/paper_2025/main.tex" 1777306136.783 52847 5c5d3efa38d66e0332ff351a6d8a115b ""
|
||||
"/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 ""
|
||||
@@ -155,20 +155,26 @@
|
||||
"figures/fig_feat_stages.pdf" 1774002994.98767 11091006 565fe951f1255c121429a060082398f5 ""
|
||||
"figures/fig_invariance_field.pdf" 1776952657.04263 9131898 e9d9acff1d03fdf60ddc9e32b87ae6c2 ""
|
||||
"figures/fig_invariance_full_Omocestus_rufipes.pdf" 1776954098.39044 13431867 08ff6482ff81d2878ddba2a90d032d94 ""
|
||||
"figures/fig_invariance_log-hp_appendix.pdf" 1776779295.50028 534259 6995c3131a22abb1b9aae17e18bc68df ""
|
||||
"figures/fig_invariance_log_hp.pdf" 1776779076.07106 838498 414c5b733797773bd8f0bade77b06814 ""
|
||||
"figures/fig_invariance_log-hp_appendix.pdf" 1777304127.01433 537850 c5902b03c71848b0b51b1d0ffc75105c ""
|
||||
"figures/fig_invariance_log_hp.pdf" 1777301515.60286 854093 c4083041bb1e78e79f11a01194cd9cb9 ""
|
||||
"figures/fig_invariance_rect-lp_appendix.pdf" 1777306033.28882 2034997 57c90e8240f0a58ac5cc522f23d84b37 ""
|
||||
"figures/fig_invariance_rect_lp.pdf" 1777303402.32232 651639 ff457b3d82ab7fefa2b2060a5c7df84c ""
|
||||
"figures/fig_invariance_short_Omocestus_rufipes.pdf" 1776954155.39396 5547735 148c794495092c93fa8b9bf75411280a ""
|
||||
"figures/fig_invariance_thresh-lp_noise_appendix.pdf" 1777031888.645 1484986 be874f4bea496315d46a234002a56649 ""
|
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1143 hyphenation exceptions out of 8191
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||||
94i,18n,93p,1496b,1740s stack positions out of 10000i,1000n,20000p,200000b,200000s
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||||
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|
||||
Output written on main.pdf (24 pages, 47693796 bytes).
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||||
Output written on main.pdf (28 pages, 50733252 bytes).
|
||||
PDF statistics:
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||||
2027 PDF objects out of 2073 (max. 8388607)
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|
||||
|
||||
BIN
main.synctex.gz
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76
main.tex
76
main.tex
@@ -570,6 +570,37 @@ intensity variations. The two mechanisms each comprise a nonlinear signal
|
||||
transformation followed by a linear signal transformation but differ in the
|
||||
specific operations involved, as outlined in the following sections.
|
||||
|
||||
\subsection{Full-wave rectification \& lowpass filtering}
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_rect_lp.pdf}
|
||||
\caption{\textbf{Intensity invariance by logarithmic compression and
|
||||
adaptation is restricted by the noise floor.}
|
||||
Synthetic input $\filt(t)$ consists of song component
|
||||
$\soc(t)$ scaled by $\sca$ with (\figc{} and \figd) or
|
||||
without (\figa{} and \figb) additive noise component
|
||||
$\noc(t)$. Input $\filt(t)$ is transformed into envelope
|
||||
$\env(t)$, logarithmically compressed envelope $\db(t)$,
|
||||
and intensity-adapted envelope $\adapt(t)$.
|
||||
\textbf{Left}:~$\env(t)$, $\db(t)$, and $\adapt(t)$ for
|
||||
different scales $\sca$.
|
||||
\textbf{Right}:~Ratios of the standard deviation of
|
||||
$\env(t)$, $\db(t)$, and $\adapt(t)$ relative to the
|
||||
respective reference standard deviation $\sigma_{\eta}$
|
||||
for input $\filt(t)=\noc(t)$.
|
||||
\figa{} and \figb:~Ideally, if $\filt(t)=\sca\cdot\soc(t)$, then
|
||||
$\adapt(t)$ is intensity-invariant across all $\sca$.
|
||||
\figc{} and \figd:~In practice, if
|
||||
$\filt(t)=\sca\cdot\soc(t)+\noc(t)$, the intensity
|
||||
invariance of $\adapt(t)$ is limited to sufficiently large
|
||||
$\sca$. Shaded area indicates saturation of $\adapt(t)$ at
|
||||
$95\,\%$ curve span.
|
||||
}
|
||||
\label{fig:inv_rect-lp}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\subsection{Logarithmic compression \& spike-frequency adaptation}
|
||||
|
||||
The first notable emergence of intensity invariance along the model pathway
|
||||
@@ -899,6 +930,15 @@ initiation of one behavior over another is categorical (e.g. approach/stay)
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_rect-lp_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\label{}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_log-hp_appendix.pdf}
|
||||
@@ -935,4 +975,40 @@ initiation of one behavior over another is categorical (e.g. approach/stay)
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\label{}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_full_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\label{}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_short_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\label{}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_field_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\label{}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\end{document}
|
||||
@@ -33,7 +33,7 @@ normalization = [
|
||||
'max',
|
||||
'base',
|
||||
'range',
|
||||
][3]
|
||||
][0]
|
||||
suffix = dict(
|
||||
none='_unnormed',
|
||||
min='_norm-min',
|
||||
|
||||
@@ -7,8 +7,8 @@ from thunderhopper.modeltools import load_data
|
||||
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,\
|
||||
letter_subplot, letter_subplots, title_subplot
|
||||
plot_line, strip_zeros, time_bar, zoom_inset, shift_subplot,\
|
||||
letter_subplot, letter_subplots, title_subplot, color_axis
|
||||
from IPython import embed
|
||||
|
||||
def add_snip_axes(fig, grid_kwargs):
|
||||
@@ -27,10 +27,10 @@ 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
|
||||
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
|
||||
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 = [
|
||||
'Chorthippus_biguttulus',
|
||||
@@ -46,7 +46,7 @@ load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'snip', 'measure']
|
||||
)
|
||||
compute_ratios = True
|
||||
relate_to_noise = True
|
||||
exclude_zero = True
|
||||
show_diag = True
|
||||
show_plateaus = True
|
||||
@@ -79,7 +79,7 @@ pure_grid_kwargs = dict(
|
||||
wspace=0.1,
|
||||
hspace=0.15,
|
||||
left=0.11,
|
||||
right=0.95,
|
||||
right=0.98,
|
||||
bottom=1 - block_height - edge_padding,
|
||||
top=1 - edge_padding,
|
||||
height_ratios=[1, 2, 1]
|
||||
@@ -95,14 +95,15 @@ noise_grid_kwargs = dict(
|
||||
top=edge_padding + block_height,
|
||||
height_ratios=[1, 2, 1]
|
||||
)
|
||||
big_col_shift = -0.12
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.3,
|
||||
wspace=0.25,
|
||||
hspace=0,
|
||||
left=pure_grid_kwargs['left'],
|
||||
left=pure_grid_kwargs['left'] - big_col_shift,
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=0.05,
|
||||
bottom=0.03,
|
||||
top=1
|
||||
)
|
||||
anchor_kwargs = dict(
|
||||
@@ -137,7 +138,9 @@ ylabels = dict(
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{dB}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
big='$\\sigma_x\\,/\\,\\sigma_{\\eta}$',
|
||||
big_pure='$\\sigma_x$',
|
||||
big_log='$\\sigma_x\\,[\\text{dB}]$',
|
||||
big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
@@ -145,6 +148,18 @@ xlab_big_kwargs = dict(
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_big_left_kwargs = dict(
|
||||
x=-0.2,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='bottom'
|
||||
)
|
||||
ylab_big_right_kwargs = dict(
|
||||
x=1.2,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top'
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
@@ -152,12 +167,6 @@ ylab_snip_kwargs = dict(
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
yloc = dict(
|
||||
env=1000,
|
||||
log=40,
|
||||
@@ -233,7 +242,7 @@ leg_kwargs = dict(
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=1,
|
||||
handlelength=0.5,
|
||||
columnspacing=1,
|
||||
)
|
||||
diag_kwargs = dict(
|
||||
@@ -287,11 +296,7 @@ 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']
|
||||
|
||||
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]
|
||||
if relate_to_noise:
|
||||
# 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]
|
||||
@@ -354,8 +359,10 @@ 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.xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
|
||||
ax.set_aspect(**anchor_kwargs)
|
||||
if i in [0, 1]:
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
@@ -365,10 +372,19 @@ for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
|
||||
ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
|
||||
ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
|
||||
ax.set_ylim(0.9, 30)
|
||||
if i == 1:
|
||||
hide_ticks(ax, 'left')
|
||||
big_axes[i] = ax
|
||||
ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
shift_subplot(big_axes[0], dx=big_col_shift)
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_left_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_left_kwargs)
|
||||
big_twin = big_axes[0].twinx()
|
||||
hide_axis(big_twin, 'left')
|
||||
big_twin.spines['right'].set_visible(True)
|
||||
big_twin.set_position(big_axes[0].get_position().bounds)
|
||||
big_twin.set_ylim(scales[0], scales[-1])
|
||||
big_twin.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ylabel(big_twin, ylabels['big_log'], transform=big_twin.transAxes, **ylab_big_right_kwargs)
|
||||
color_axis(big_axes[0], colors['env'], side='left')
|
||||
color_axis(big_twin, colors['log'], side='right')
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'cde', **letter_big_kwargs)
|
||||
|
||||
|
||||
@@ -65,7 +65,7 @@ mean_colors = {
|
||||
'Pseudochorthippus_parallelus': (0,) * 3,
|
||||
}
|
||||
xlab = 'scale $\\alpha$'
|
||||
ylab = '$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$'
|
||||
ylab = '$\\sigma_{\\text{adapt}}\\,/\\,\\sigma_{\\eta}$'
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=16,
|
||||
|
||||
424
python/fig_invariance_rect-lp.py
Normal file
424
python/fig_invariance_rect-lp.py
Normal file
@@ -0,0 +1,424 @@
|
||||
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 shorten_species
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_axis, shift_subplot, shift_subplot, ylimits,\
|
||||
super_xlabel, ylabel, hide_ticks,\
|
||||
plot_line, strip_zeros, time_bar,\
|
||||
letter_subplot, letter_subplots, title_subplot
|
||||
from IPython import embed
|
||||
|
||||
def add_snip_axes(fig, grid_kwargs):
|
||||
grid = fig.add_gridspec(**grid_kwargs)
|
||||
axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
|
||||
for i, j in product(range(grid.nrows), range(grid.ncols)):
|
||||
axes[i, j] = fig.add_subplot(grid[i, j])
|
||||
if j == 0:
|
||||
shift_subplot(axes[i, j], dx=snip_col_shift)
|
||||
[hide_axis(ax, 'left') for ax in axes[:, 2:].flatten()]
|
||||
[hide_axis(ax, 'bottom') for ax in axes.flatten()]
|
||||
return axes
|
||||
|
||||
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
|
||||
handles = []
|
||||
for ax, snippet in zip(axes, snippets.T):
|
||||
handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
|
||||
return handles
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
|
||||
data_path = search_files(target, excl='noise', dir='../data/inv/rect_lp/')[0]
|
||||
save_path = '../figures/fig_invariance_rect_lp.pdf'
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
# 'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
stages = ['filt', 'env']
|
||||
load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'cutoff', 'snip', 'measure']
|
||||
)
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
relate_to_noise = True
|
||||
exclude_zero = True
|
||||
show_diag = True
|
||||
snip_cutoff = np.array([np.nan, 2500, 250, 25])[2]
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=3,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1,
|
||||
height_ratios=[1, 1, 1]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
pure=(0, slice(None)),
|
||||
noise=(1, slice(None)),
|
||||
big=(2, slice(None)),
|
||||
)
|
||||
block_height = 0.8
|
||||
edge_padding = 0.08
|
||||
snip_col_shift = -0.05
|
||||
pure_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.15,
|
||||
left=0.08 - snip_col_shift,
|
||||
right=0.95,
|
||||
bottom=1 - block_height - edge_padding,
|
||||
top=1 - edge_padding,
|
||||
height_ratios=[1, 1]
|
||||
)
|
||||
noise_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=pure_grid_kwargs['wspace'],
|
||||
hspace=pure_grid_kwargs['hspace'],
|
||||
left=pure_grid_kwargs['left'],
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=edge_padding,
|
||||
top=edge_padding + block_height,
|
||||
height_ratios=[1, 1]
|
||||
)
|
||||
big_col_shift = -0.05
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.25,
|
||||
hspace=0,
|
||||
left=pure_grid_kwargs['left'] + snip_col_shift - big_col_shift,
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=0.04,
|
||||
top=1
|
||||
)
|
||||
anchor_kwargs = dict(
|
||||
aspect='equal',
|
||||
adjustable='box',
|
||||
anchor=(0.5, 0.5)
|
||||
)
|
||||
|
||||
# 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')
|
||||
colors['raw'] = (0., 0., 0.,)
|
||||
species_colors = load_colors('../data/species_colors.npz')
|
||||
lw = dict(
|
||||
snip=0.5,
|
||||
big=3,
|
||||
spec=2,
|
||||
legend=5,
|
||||
)
|
||||
dash_cycle = 6 # points
|
||||
ls_env = [
|
||||
(0, np.array((0.2, 0.8)) * dash_cycle),
|
||||
(0, np.array((0.6, 0.1, 0.2, 0.1)) * dash_cycle),
|
||||
(0, np.array((0.5, 0.5)) * dash_cycle),
|
||||
'solid',
|
||||
] # [np.nan, 2500, 250, 25]
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
raw='$x$',
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
big_pure='$\\sigma_x$',
|
||||
big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
|
||||
)
|
||||
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_pure_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ylab_noise_kwargs = dict(
|
||||
y=0.5,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ylim_zoom_factor = 0.03
|
||||
yloc = dict(
|
||||
filt=(3, 100),
|
||||
env=(0.5, 30),
|
||||
)
|
||||
ypad = dict(
|
||||
filt=0.05,
|
||||
env=0.05,
|
||||
)
|
||||
title_kwargs = dict(
|
||||
x=0.5,
|
||||
y=1,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
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=-0.2,
|
||||
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',
|
||||
)
|
||||
)
|
||||
cutoff_leg_kwargs = dict(
|
||||
ncols=1,
|
||||
loc='upper left',
|
||||
bbox_to_anchor=(0.05, 0.5, 0.5, 0.5),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=14,
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handletextpad=0.3
|
||||
)
|
||||
cutoff_leg_kwargs['handlelength'] = 2 * dash_cycle * lw['big'] / cutoff_leg_kwargs['prop']['size']
|
||||
spec_leg_kwargs = dict(
|
||||
ncols=2,
|
||||
loc='lower center',
|
||||
bbox_to_anchor=(0, 0, 1, 0.5),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=13,
|
||||
style='italic',
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=0.75,
|
||||
handletextpad=0.5,
|
||||
columnspacing=1,
|
||||
)
|
||||
diag_kwargs = dict(
|
||||
c=(0.3,) * 3,
|
||||
lw=2,
|
||||
ls='--',
|
||||
zorder=1.9,
|
||||
)
|
||||
|
||||
# PREPARATION:
|
||||
species_measures = {}
|
||||
for i, species in enumerate(target_species):
|
||||
spec_path = search_files(species, incl=['noise', 'norm-base'], dir='../data/inv/rect_lp/condensed/')[0]
|
||||
spec_data = dict(np.load(spec_path))
|
||||
measure = spec_data['mean_env'].mean(axis=-1)
|
||||
if exclude_zero:
|
||||
measure = measure[spec_data['scales'] > 0, :]
|
||||
species_measures[species] = measure
|
||||
|
||||
# EXECUTION:
|
||||
print(f'Processing {data_path}')
|
||||
|
||||
# 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']
|
||||
cutoff_ind = np.nonzero(pure_data['cutoffs'] == snip_cutoff)[0][0]
|
||||
|
||||
if relate_to_noise:
|
||||
# Relate noise-song measures to zero scale:
|
||||
noise_data['measure_filt'] /= noise_data['measure_filt'][0]
|
||||
noise_data['measure_env'] /= noise_data['measure_env'][0]
|
||||
|
||||
if exclude_zero:
|
||||
# Exclude zero scales:
|
||||
inds = pure_scales > 0
|
||||
pure_scales = pure_scales[inds]
|
||||
pure_data['measure_filt'] = pure_data['measure_filt'][inds]
|
||||
pure_data['measure_env'] = pure_data['measure_env'][inds]
|
||||
inds = noise_scales > 0
|
||||
noise_scales = noise_scales[inds]
|
||||
noise_data['measure_filt'] = noise_data['measure_filt'][inds]
|
||||
noise_data['measure_env'] = noise_data['measure_env'][inds]
|
||||
symlog_kwargs = dict(linthresh=pure_scales[pure_scales > 0][0], linscale=0.5)
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
fig.canvas.draw()
|
||||
|
||||
# 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 (ax1, ax2), stage in zip(pure_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], **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)
|
||||
|
||||
# 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 (ax1, ax2), stage in zip(noise_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], **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)
|
||||
|
||||
# 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', **symlog_kwargs)
|
||||
ax.set_yscale('symlog', **symlog_kwargs)
|
||||
ax.set_aspect(**anchor_kwargs)
|
||||
if i in [0, 1]:
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
pos_equal = ax.get_position().bounds
|
||||
else:
|
||||
pos_auto = list(ax.get_position().bounds)
|
||||
ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
|
||||
ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
|
||||
ax.set_ylim(0.1, 100)
|
||||
big_axes[i] = ax
|
||||
shift_subplot(big_axes[0], dx=big_col_shift)
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_subfig.transSubfigure, **ylab_pure_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_noise_kwargs,
|
||||
x=(big_subfig.transSubfigure + big_axes[0].transAxes.inverted()).transform((ylab_pure_kwargs['x'], 0))[0])
|
||||
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 filtered snippets:
|
||||
handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_filt'],
|
||||
c=colors['filt'], lw=lw['snip'], ypad=ypad['filt'])
|
||||
|
||||
# Plot pure-song envelope snippets:
|
||||
plot_snippets(pure_axes[1, :], t_full, pure_data['snip_env'][..., cutoff_ind],
|
||||
ymin=0, c=colors['env'], lw=lw['snip'], ypad=ypad['env'])
|
||||
|
||||
# Plot noise-song filtered snippets:
|
||||
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_filt'], ypad=ypad['filt'],
|
||||
*pure_axes[0, 0].get_ylim(), c=colors['filt'], lw=lw['snip'])
|
||||
|
||||
# Plot noise-song envelope snippets:
|
||||
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_env'][..., cutoff_ind],
|
||||
*pure_axes[1, 0].get_ylim(), c=colors['env'], lw=lw['snip'])
|
||||
|
||||
# Zoom into first filtered snippet:
|
||||
# ylim_zoom = np.array(noise_axes[0, -1].get_ylim()) * ylim_zoom_factor
|
||||
# noise_axes[0, 0].set_ylim(*ylim_zoom)
|
||||
ylim_zoom = ylimits(noise_data['snip_filt'][:, 0], noise_axes[0, 0], pad=ypad['filt'])
|
||||
pure_axes[0, 0].set_ylim(*ylim_zoom)
|
||||
|
||||
# Zoom into first envelope snippet:
|
||||
# ylim_zoom = np.array(noise_axes[1, -1].get_ylim()) * ylim_zoom_factor
|
||||
# noise_axes[1, 0].set_ylim(*ylim_zoom)
|
||||
ylim_zoom = ylimits(noise_data['snip_env'][:, 0, cutoff_ind], noise_axes[1, 0], minval=0, pad=ypad['env'])
|
||||
pure_axes[1, 0].set_ylim(*ylim_zoom)
|
||||
|
||||
# Indicate time scale:
|
||||
time_bar(noise_axes[-1, -1], **bar_kwargs)
|
||||
|
||||
# Plot pure-song measures (ideal):
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_filt'], c=colors['filt'], lw=lw['big'])
|
||||
handles = big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
|
||||
|
||||
# Plot noise-song measures (limited):
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_filt'], c=colors['filt'], lw=lw['big'])
|
||||
handles = big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
|
||||
|
||||
# Add proxy legend:
|
||||
proxy_handles = []
|
||||
for i, cutoff in enumerate(pure_data['cutoffs']):
|
||||
label = '$\\text{unfiltered}$' if np.isnan(cutoff) else f'${int(cutoff)}\\,\\text{{Hz}}$'
|
||||
proxy_handles.append(big_axes[0].plot([], [], c=colors['env'], lw=lw['big'],
|
||||
ls=ls_env[i], label=label)[0])
|
||||
big_axes[0].legend(handles=proxy_handles, **cutoff_leg_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:
|
||||
leg_handles = []
|
||||
for i, (species, measure) in enumerate(species_measures.items()):
|
||||
handles = big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
|
||||
c=species_colors[species], lw=lw['spec'])
|
||||
[handle.set_ls(ls) for handle, ls in zip(handles, ls_env)]
|
||||
leg_handles.append(handles[-1])
|
||||
legend = big_axes[2].legend(handles=leg_handles, **spec_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()
|
||||
159
python/fig_invariance_rect-lp_appendix.py
Normal file
159
python/fig_invariance_rect-lp_appendix.py
Normal file
@@ -0,0 +1,159 @@
|
||||
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, super_xlabel, super_ylabel, title_subplot, time_bar
|
||||
from color_functions import load_colors
|
||||
from misc_functions import shorten_species
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
# 'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
data_path = '../data/inv/rect_lp/condensed/'
|
||||
save_path = '../figures/fig_invariance_rect-lp_appendix.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
relate_to_noise = True
|
||||
exclude_zero = True
|
||||
cutoffs = np.array([np.nan, 2500, 250, 25])
|
||||
search_kwargs = dict(
|
||||
incl=['noise', 'norm-base' if relate_to_noise else 'unnormed'],
|
||||
dir=data_path,
|
||||
)
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
nrows=cutoffs.size,
|
||||
ncols=len(target_species),
|
||||
sharex=True,
|
||||
sharey=True,
|
||||
gridspec_kw=dict(
|
||||
wspace=0.4,
|
||||
hspace=0.2,
|
||||
left=0.12,
|
||||
right=0.98,
|
||||
bottom=0.1,
|
||||
top=0.95,
|
||||
)
|
||||
)
|
||||
|
||||
# PLOT SETTINGS:
|
||||
species_colors = load_colors('../data/species_colors.npz')
|
||||
line_kwargs = dict(
|
||||
lw=2,
|
||||
alpha=0.5,
|
||||
zorder=2,
|
||||
)
|
||||
fill_kwargs = dict(
|
||||
alpha=0.3,
|
||||
zorder=1,
|
||||
)
|
||||
mean_kwargs = dict(
|
||||
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,
|
||||
}
|
||||
xlab = 'scale $\\alpha$'
|
||||
ylabs = ['$\\text{unfiltered}$'] + [f'${int(cutoff)}\\,\\text{{Hz}}$' for cutoff in cutoffs[1:]]
|
||||
super_ylab = '$\\sigma_{\\text{env}}\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else '$\\sigma_{\\text{env}}$'
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=16,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_kwargs = dict(
|
||||
x=0.05,
|
||||
fontsize=16,
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ylab_super_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=20,
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
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)
|
||||
[ylabel(ax, lab, transform=fig.transFigure, **ylab_kwargs) for ax, lab in zip(axes[:, 0], ylabs)]
|
||||
super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
|
||||
super_ylabel(super_ylab, fig, axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
|
||||
|
||||
# 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, **search_kwargs)[0]
|
||||
data, config = load_data(path, files=['scales', 'mean_env', 'sd_env'])
|
||||
scales = data['scales']
|
||||
means = data['mean_env']
|
||||
sds = data['sd_env']
|
||||
|
||||
if exclude_zero:
|
||||
# Exclude zero scale:
|
||||
inds = scales > 0
|
||||
scales = scales[inds]
|
||||
means = means[inds, ...]
|
||||
sds = sds[inds, ...]
|
||||
|
||||
# Run through cutoffs:
|
||||
for j, ax in enumerate(spec_axes):
|
||||
# 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 cutoff-specific mean trace:
|
||||
ax.plot(scales, means[:, j, :].mean(axis=-1), c=mean_colors[species], **mean_kwargs)
|
||||
|
||||
# Posthocs:
|
||||
sylog_kwargs = dict(linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
axes[0, 0].set_xscale('symlog', **sylog_kwargs)
|
||||
axes[0, 0].set_yscale('symlog', **sylog_kwargs)
|
||||
axes[0, 0].set_xlim(scales[0], scales[-1])
|
||||
axes[0, 0].set_ylim(0.9, scales[-1])
|
||||
axes[0, 0].xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
|
||||
|
||||
# Save graph:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
|
||||
|
||||
@@ -98,7 +98,7 @@ snip_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.3,
|
||||
hspace=0,
|
||||
left=0.25,
|
||||
left=0.2 - snip_col_shift,
|
||||
right=0.93,
|
||||
bottom=0.15,
|
||||
top=0.95,
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
import plotstyle_plt
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
from scipy.stats import norm
|
||||
from plot_functions import xlabel, ylabel
|
||||
from IPython import embed
|
||||
|
||||
@@ -26,10 +25,22 @@ fig_kwargs = dict(
|
||||
)
|
||||
)
|
||||
line_kwargs = dict(
|
||||
color='black',
|
||||
c='black',
|
||||
lw=1,
|
||||
alpha=0.5,
|
||||
)
|
||||
fit_kwargs = dict(
|
||||
c='red',
|
||||
lw=3,
|
||||
ls='--',
|
||||
)
|
||||
grid_line_kwargs = dict(
|
||||
visible=True,
|
||||
which='major',
|
||||
axis='both',
|
||||
color='k',
|
||||
lw=0.5,
|
||||
)
|
||||
xlab = '$\\text{multiple of }\\sigma_{k_i}$'
|
||||
ylab = '$P\\,(c_i > \\Theta_i)$'
|
||||
xlab_kwargs = dict(
|
||||
@@ -50,15 +61,20 @@ data = dict(np.load(thresh_path))
|
||||
factors = data['factors']
|
||||
perc = data['percs']
|
||||
|
||||
# Get Gaussian CDF for reference:
|
||||
fit = norm.cdf(factors, loc=0, scale=1)[::-1]
|
||||
|
||||
# Prepare graph:
|
||||
fig, ax = plt.subplots(**fig_kwargs)
|
||||
ax.grid(**grid_line_kwargs)
|
||||
ax.set_xlim(factors[0], factors[-1])
|
||||
ax.set_ylim(0, 1)
|
||||
ax.set_ylim(-0.01, 1.01)
|
||||
ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
|
||||
xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
|
||||
|
||||
# Plotting:
|
||||
ax.plot(factors, perc, **line_kwargs)
|
||||
ax.plot(factors, fit, **fit_kwargs)
|
||||
|
||||
# Save figure:
|
||||
fig.savefig(save_path)
|
||||
|
||||
@@ -300,6 +300,12 @@ def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
|
||||
artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
|
||||
return None
|
||||
|
||||
def color_axis(ax, color, axis='y', side='left'):
|
||||
ax.spines[side].set_color(color)
|
||||
ax.tick_params(colors=color, axis=axis, which='both')
|
||||
ax.yaxis.label.set_color(color)
|
||||
return None
|
||||
|
||||
def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
|
||||
base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
|
||||
if pdf is None:
|
||||
|
||||
@@ -7,16 +7,17 @@ from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
|
||||
data_paths = search_files('*', excl='noise', dir='../data/processed/')
|
||||
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'
|
||||
save_path = '../data/inv/rect_lp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
mode = ['pure', 'noise'][1]
|
||||
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
|
||||
scales = np.geomspace(0.01, 10000, 1000)
|
||||
example_scales = np.array([0.1, 0.3, 1, 3, 10])
|
||||
scales = np.geomspace(0.01, 100, 1000)
|
||||
scales = np.unique(np.concatenate(([0], scales, example_scales)))
|
||||
cutoffs = np.array([np.nan, 125, 250, 500])
|
||||
cutoffs = np.array([np.nan, 2500, 250, 25])
|
||||
|
||||
# PREPARATION:
|
||||
if mode == 'noise':
|
||||
|
||||
Reference in New Issue
Block a user