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>

main.aux

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main.blg

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+[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 218, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.utf8, line 229, 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
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 240, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 249, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 258, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 269, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 278, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 289, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 300, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 309, warning: 6 characters of junk seen at toplevel
-[359] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 328, warning: 6 characters of junk seen at toplevel
-[360] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_UrtA/347c261ec4135a5723bef5c751f5078f_61557.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 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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.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
+[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_eYMb/347c261ec4135a5723bef5c751f5078f_210824.utf8, line 817, warning: 6 characters of junk seen at toplevel
-[360] Biber.pm:133> INFO - WARNINGS: 55
+[332] Biber.pm:133> INFO - WARNINGS: 55

main.fdb_latexmk

@@ -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 @@
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   "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_appendix.pdf" 1777304127.01433 537850 c5902b03c71848b0b51b1d0ffc75105c ""
-  "figures/fig_invariance_log_hp.pdf" 1776779076.07106 838498 414c5b733797773bd8f0bade77b06814 ""
+  "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 ""
   "figures/fig_invariance_thresh-lp_pure_appendix.pdf" 1777031869.54421 1387834 c66ab20b4634e644a5fa0c5034a755d0 ""
-  "figures/fig_invariance_thresh_lp_single.pdf" 1776784894.60717 858048 b9f3ca65b0b098193ad6aaf282d96ee5 ""
+  "figures/fig_invariance_thresh_lp_single.pdf" 1777281305.93103 860673 2f650f14d93dd391a8df39d66a7adbad ""
-  "figures/fig_invariance_thresh_lp_species.pdf" 1776786332.58869 1607791 1f0bca0808347bbf2397b0d6c1523c31 ""
+  "figures/fig_invariance_thresh_lp_species.pdf" 1777032003.01576 1607791 2b46fb396cfa9b9b5d5ab8d56faddbac ""
+  "figures/fig_kernel_sd_perc_field_appendix.pdf" 1777273985.83211 100184 e699513599b5828cd498b1621e1e79ee ""
+  "figures/fig_kernel_sd_perc_full_appendix.pdf" 1777273971.01214 90770 e3ecb7db816fc5046b866a9b27a35193 ""
+  "figures/fig_kernel_sd_perc_short_appendix.pdf" 1777273977.37413 91395 b7d8e68ba3a353986dbde11221d0051f ""
+  "figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf" 1777273964.75216 90770 9cb3f2324eea517a8412177ca3dba932 ""
   "figures/fig_noise_env_sd_conversion_appendix.pdf" 1776328774.43347 45466 c2be20312c1572203bdbeb9c8e32525e ""
   "figures/fig_pre_stages.pdf" 1774002992.74268 449426 5762be15627fe5d8b6d108b7ea18db44 ""
   "figures/fig_saturation_log-hp_appendix.pdf" 1776785249.70227 28579 fadbe904f2452a45ddcbe039438cf4d4 ""
-  "main.aux" 1777031926.77959 16336 edd7f74d92f16bef6aeb665a1e6af779 "pdflatex"
+  "main.aux" 1777306136.809 18671 88ec119ff84df5208f491deee1b53788 "pdflatex"
-  "main.bbl" 1776940218.28593 91039 1380dc8c93d2855fdb132cc5a40ad52f "biber main"
+  "main.bbl" 1777306108.74822 91039 1380dc8c93d2855fdb132cc5a40ad52f "biber main"
-  "main.run.xml" 1777031926.78659 2335 a049bc26a7f032e842ce55de5bc38328 "pdflatex"
+  "main.run.xml" 1777306136.816 2335 a049bc26a7f032e842ce55de5bc38328 "pdflatex"
-  "main.tex" 1777031925.5956 50083 fda38e01bf1bcfd1210581259b2830ea ""
+  "main.tex" 1777306136.783 52847 5c5d3efa38d66e0332ff351a6d8a115b ""
   (generated)
   "main.aux"
   "main.bcf"

main.fls

@@ -288,6 +288,11 @@ INPUT ./figures/fig_feat_stages.pdf
 INPUT ./figures/fig_feat_stages.pdf
 INPUT ./figures/fig_feat_stages.pdf
 INPUT ./figures/fig_feat_stages.pdf
+INPUT ./figures/fig_invariance_rect_lp.pdf
+INPUT ./figures/fig_invariance_rect_lp.pdf
+INPUT ./figures/fig_invariance_rect_lp.pdf
+INPUT ./figures/fig_invariance_rect_lp.pdf
+INPUT ./figures/fig_invariance_rect_lp.pdf
 INPUT ./figures/fig_invariance_log_hp.pdf
 INPUT ./figures/fig_invariance_log_hp.pdf
 INPUT ./figures/fig_invariance_log_hp.pdf
@@ -323,6 +328,11 @@ 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_rect-lp_appendix.pdf
+INPUT ./figures/fig_invariance_rect-lp_appendix.pdf
+INPUT ./figures/fig_invariance_rect-lp_appendix.pdf
+INPUT ./figures/fig_invariance_rect-lp_appendix.pdf
+INPUT ./figures/fig_invariance_rect-lp_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
@@ -343,6 +353,26 @@ INPUT ./figures/fig_invariance_thresh-lp_noise_appendix.pdf
 INPUT ./figures/fig_invariance_thresh-lp_noise_appendix.pdf
 INPUT ./figures/fig_invariance_thresh-lp_noise_appendix.pdf
 INPUT ./figures/fig_invariance_thresh-lp_noise_appendix.pdf
+INPUT ./figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf
+INPUT ./figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf
+INPUT ./figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf
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 INPUT main.run.xml
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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}

python/condense_inv_data_rect-lp.py

@@ -33,7 +33,7 @@ normalization = [
     'max',
     'base',
     'range',
-    ][3]
+    ][0]
 suffix = dict(
     none='_unnormed',
     min='_norm-min',

python/fig_invariance_log-hp.py

@@ -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,\
+                           plot_line, strip_zeros, time_bar, zoom_inset, shift_subplot,\
-                           letter_subplot, letter_subplots, title_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:
+if relate_to_noise:
-    # 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]
@@ -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)
 

python/fig_invariance_log-hp_appendix.py

@@ -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,

python/fig_invariance_rect-lp.py

@@ -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()

python/fig_invariance_rect-lp_appendix.py

@@ -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()  
+
+

python/fig_invariance_thresh-lp_single.py

@@ -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,

python/fig_kernel_sd_perc_appendix.py

@@ -1,8 +1,7 @@
 import plotstyle_plt
 import numpy as np
 import matplotlib.pyplot as plt
-from thunderhopper.modeltools import load_data
+from scipy.stats import norm
-from thunderhopper.filetools import search_files, crop_paths
 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)

python/plot_functions.py

@@ -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:

python/save_inv_data_rect-lp.py

@@ -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])
+example_scales = np.array([0.1, 0.3, 1, 3, 10])
-scales = np.geomspace(0.01, 10000, 1000)
+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':