diff --git a/figures/fig_invariance_log_hp.pdf b/figures/fig_invariance_log_hp.pdf index 07dc478..116deef 100644 Binary files a/figures/fig_invariance_log_hp.pdf and b/figures/fig_invariance_log_hp.pdf differ diff --git a/figures/fig_invariance_thresh_lp_single.pdf b/figures/fig_invariance_thresh_lp_single.pdf new file mode 100644 index 0000000..7aef2a7 Binary files /dev/null and b/figures/fig_invariance_thresh_lp_single.pdf differ diff --git a/figures/fig_invariance_thresh_lp_single_noise.pdf b/figures/fig_invariance_thresh_lp_single_noise.pdf new file mode 100644 index 0000000..9234c3b Binary files /dev/null and b/figures/fig_invariance_thresh_lp_single_noise.pdf differ diff --git a/main.aux b/main.aux index f9a9721..46b5a29 100644 --- a/main.aux +++ b/main.aux @@ -248,17 +248,21 @@ \newlabel{eq:toy_log}{{12}{11}{}{}{}} \newlabel{eq:toy_highpass}{{13}{11}{}{}{}} \newlabel{eq:toy_snr}{{14}{11}{}{}{}} -\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic envelope $x_{\text {env}}(t)$ is transformed into logarihmically compressed envelope $x_{\text {dB}}(t)$ and further into intensity-adapted envelope $x_{\text {adapt}}(t)$. Indicated time scale is $5\,$s for both \textbf {a} and \textbf {b} (black bars). \textbf {a}:~Ideally, if $x_{\text {env}}(t)$ consists only of song component $s(t)$ rescaled by $\alpha $, then $x_{\text {adapt}}(t)$ is fully intensity-invariant across all $\alpha $. \textbf {b}:~In practice, $x_{\text {env}}(t)$ also contains fixed-scale noise component $\eta (t)$, which limits the effective intensity invariance of $x_{\text {adapt}}(t)$ to sufficiently large $\alpha $. \textbf {c}:~Ratios of the SD of each representation in \textbf {b} at a given $\alpha $ relative to the SD of the representation for $\alpha =0$ (solid lines). The same ratios for the ideal $x_{\text {adapt}}(t)$ in \textbf {a} are shown for comparison (dashed line). }}{12}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Envelope $x_{\text {env}}(t)$ is transformed into logarihmically compressed envelope $x_{\text {dB}}(t)$ and further into intensity-adapted envelope $x_{\text {adapt}}(t)$. Indicated time scale is $5\,$s for both \textbf {a} and \textbf {b} (black bars). \textbf {a}:~Ideally, if $x_{\text {env}}(t)$ consists only of song component $s(t)$ rescaled by $\alpha $, then $x_{\text {adapt}}(t)$ is fully intensity-invariant across all $\alpha $. \textbf {b}:~In practice, $x_{\text {env}}(t)$ also contains fixed-scale noise component $\eta (t)$, which limits the effective intensity invariance of $x_{\text {adapt}}(t)$ to sufficiently large $\alpha $. \textbf {c}:~Ratios of the SD of each representation in \textbf {b} at a given $\alpha $ relative to the SD of the representation for $\alpha =0$ (solid lines). The same ratios for the ideal $x_{\text {adapt}}(t)$ in \textbf {a} are shown for comparison (dashed line). }}{12}{}\protected@file@percent } \newlabel{fig:inv_log-hp}{{4}{12}{}{}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Thresholding nonlinearity \& temporal averaging}{12}{}\protected@file@percent } -\newlabel{eq:pdf_split}{{15}{13}{}{}{}} -\newlabel{eq:feat_avg}{{16}{13}{}{}{}} -\newlabel{eq:feat_prop}{{17}{13}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on the threshold value.} Kernel response $c_i(t)$ is rescaled by $\alpha $ and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $500\,$ms is the same for \textbf {a}-\textbf {c} (black bar). \textbf {a}:~50th percentile. \textbf {b}:~75th percentile. \textbf {c}:~100th percentile. \textbf {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf {a}-\textbf {c}. }}{13}{}\protected@file@percent } +\newlabel{fig:inv_thresh-lp_single}{{5}{13}{}{}{}} +\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on noise.} Kernel response $c_i(t)$ is rescaled by $\alpha $, mixed with fixed-scale noise component $\eta (t)$, and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $500\,$ms is the same for \textbf {a}-\textbf {c} (black bar). \textbf {a}:~50th percentile. \textbf {b}:~75th percentile. \textbf {c}:~100th percentile. \textbf {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf {a}-\textbf {c}. }}{14}{}\protected@file@percent } +\newlabel{fig:inv_thresh-lp_single_noise}{{6}{14}{}{}{}} +\newlabel{eq:pdf_split}{{15}{15}{}{}{}} +\newlabel{eq:feat_avg}{{16}{15}{}{}{}} +\newlabel{eq:feat_prop}{{17}{15}{}{}{}} \abx@aux@cite{0}{stumpner1991auditory} \abx@aux@segm{0}{0}{stumpner1991auditory} -\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{14}{}\protected@file@percent } -\@writefile{toc}{\contentsline {section}{\numberline {5}Conclusions \& outlook}{14}{}\protected@file@percent } -\abx@aux@page{73}{14} +\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{16}{}\protected@file@percent } +\@writefile{toc}{\contentsline {section}{\numberline {5}Conclusions \& outlook}{16}{}\protected@file@percent } +\abx@aux@page{73}{16} \gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }} \abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F} -\gdef \@abspage@last{15} +\gdef \@abspage@last{17} diff --git a/main.bbl-SAVE-ERROR b/main.bbl-SAVE-ERROR new file mode 100644 index 0000000..3536320 --- /dev/null +++ b/main.bbl-SAVE-ERROR @@ -0,0 +1,2207 @@ +% $ biblatex auxiliary file $ +% $ biblatex bbl format version 3.2 $ +% Do not modify the above lines! +% +% This is an auxiliary file used by the 'biblatex' package. +% This file may safely be deleted. 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rehbein1974structure + rehbein1976auditory + eichendorf1980projections + gray1960fine + ronacher1986routes + bauer1987separate + bhavsar2017brain + clemens2011efficient + windmill2008time + malkin2014energy + machens2001discrimination + machens2001representation + suga1960peripheral + gollisch2002energy + romer1976informationsverarbeitung + gollisch2004input + fisch2012channel + hildebrandt2009origin + clemens2010intensity + ronacher1986routes + bauer1987separate + bhavsar2017brain diff --git a/main.blg b/main.blg index 181867a..825ae8a 100644 --- a/main.blg +++ b/main.blg @@ -1,71 +1,71 @@ [0] Config.pm:307> INFO - This is Biber 2.19 [0] Config.pm:310> INFO - Logfile is 'main.blg' -[35] biber:340> INFO - === Fr Mär 6, 2026, 10:37:34 -[43] Biber.pm:419> INFO - Reading 'main.bcf' -[71] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 -[76] Biber.pm:4419> INFO - Processing section 0 -[81] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 -[82] bibtex.pm:1713> INFO - LaTeX decoding ... -[111] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' -[280] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' -[280] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' -[280] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' -[280] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' -[303] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' -[313] bbl.pm:763> INFO - Output to main.bbl -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 10, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 21, warning: 6 characters of junk seen at toplevel -[313] 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-[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 100, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 109, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 118, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 127, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 136, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 157, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 178, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 187, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 196, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 207, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 218, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 229, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 240, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 249, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 258, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 269, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 278, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 289, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 300, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 309, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 328, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 337, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 400, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 419, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 428, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 437, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 456, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 491, warning: 6 characters of junk seen at toplevel -[313] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 526, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 535, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 556, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 565, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 576, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 587, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 619, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 648, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 658, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 667, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 688, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 709, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 720, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 729, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 749, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 766, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 775, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 800, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_YENa/347c261ec4135a5723bef5c751f5078f_27143.utf8, line 817, warning: 6 characters of junk seen at toplevel -[314] Biber.pm:133> INFO - WARNINGS: 55 +[35] biber:340> INFO - === Mi Mär 11, 2026, 15:14:06 +[44] Biber.pm:419> INFO - Reading 'main.bcf' +[72] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 +[77] Biber.pm:4419> INFO - Processing section 0 +[82] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 +[83] bibtex.pm:1713> INFO - LaTeX decoding ... +[113] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' +[284] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' +[284] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' +[284] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' +[284] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' +[308] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' +[318] bbl.pm:763> INFO - Output to main.bbl +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 10, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 21, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 38, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 49, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 58, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 73, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 82, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 91, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 100, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 109, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 118, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 127, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 136, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 157, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 178, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 187, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 196, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 207, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 218, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 229, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 240, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 249, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 258, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 269, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 278, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 289, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 300, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 309, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 328, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 337, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 400, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 419, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 428, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 437, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 456, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 491, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 526, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 535, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 556, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 565, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 576, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 587, warning: 6 characters of junk seen at toplevel +[318] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 619, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 648, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 658, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 667, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 688, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 709, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 720, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 729, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 749, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 766, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 775, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 800, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_yUae/347c261ec4135a5723bef5c751f5078f_41297.utf8, line 817, warning: 6 characters of junk seen at toplevel +[319] Biber.pm:133> INFO - WARNINGS: 55 diff --git a/main.fdb_latexmk b/main.fdb_latexmk index 56ff0f8..3097604 100644 --- a/main.fdb_latexmk +++ b/main.fdb_latexmk @@ -1,14 +1,14 @@ # Fdb version 4 -["biber main"] 1772789854.19015 "main.bcf" "main.bbl" "main" 1772789995.6149 0 +["biber main"] 1773238445.97958 "main.bcf" "main.bbl" "main" 1773238449.24151 0 "cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 "" - "main.bcf" 1772789995.56243 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex" + "main.bcf" 1773238449.18184 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex" (generated) "main.bbl" "main.blg" (rewritten before read) -["pdflatex"] 1772789994.76445 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1772789995.6151 0 +["pdflatex"] 1773238448.36992 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1773238449.24172 0 "/etc/texmf/web2c/texmf.cnf" 1761560044.43676 475 c0e671620eb5563b2130f56340a5fde8 "" - "/home/hartling/phd/paper/paper_2025/main.tex" 1772789993.80744 45115 2fcef2652b25ea1193a94494b1d1cf7c "" + "/home/hartling/phd/paper/paper_2025/main.tex" 1773238449.19584 47479 96e40a52b29dbb10a3ed8b868cb9724c "" "/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 "" @@ -153,12 +153,14 @@ "/var/lib/texmf/web2c/pdftex/pdflatex.fmt" 1761648508 8213325 7fd20752ab46ff9aa583e4973d7433df "" "figures/fig_auditory_pathway.pdf" 1771593904.14638 1153923 3df8539421fd21dc866cc8d320bd9b1d "" 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./figures/fig_invariance_thresh_lp_single_noise.pdf +INPUT ./figures/fig_invariance_thresh_lp_single_noise.pdf INPUT main.aux INPUT main.run.xml OUTPUT main.run.xml diff --git a/main.log b/main.log index 92ba3cb..f454512 100644 --- a/main.log +++ b/main.log @@ -1,4 +1,4 @@ -This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 6 MAR 2026 10:39 +This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28) 11 MAR 2026 15:14 entering extended mode restricted \write18 enabled. file:line:error style messages enabled. @@ -768,7 +768,21 @@ File: figures/fig_invariance_log_hp.pdf Graphic file (type pdf) Package pdftex.def Info: figures/fig_invariance_log_hp.pdf used on input line 625. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. - [11] [12 <./figures/fig_invariance_log_hp.pdf>] [13] [14] [15] (./main.aux) + [11] + +File: figures/fig_invariance_thresh_lp_single.pdf Graphic file (type pdf) + +Package pdftex.def Info: figures/fig_invariance_thresh_lp_single.pdf used on input line 654. +(pdftex.def) Requested size: 483.69687pt x 241.84782pt. + [12 <./figures/fig_invariance_log_hp.pdf>] + +File: figures/fig_invariance_thresh_lp_single_noise.pdf Graphic file (type pdf) + +Package pdftex.def Info: figures/fig_invariance_thresh_lp_single_noise.pdf used on input line 676. +(pdftex.def) Requested size: 483.69687pt x 241.84782pt. + [13 <./figures/fig_invariance_thresh_lp_single.pdf>] [14 <./figures/fig_invariance_thresh_lp_single_noise.pdf>] [15 + +] [16] [17] (./main.aux) *********** LaTeX2e <2023-11-01> patch level 1 L3 programming layer <2024-01-22> @@ -778,18 +792,18 @@ Package logreq Info: Writing requests to 'main.run.xml'. ) Here is how much of TeX's memory you used: - 20725 strings out of 474222 - 446531 string characters out of 5748732 + 20741 strings out of 474222 + 447377 string characters out of 5748732 1937975 words of memory out of 5000000 - 42715 multiletter control sequences out of 15000+600000 + 42731 multiletter control sequences out of 15000+600000 569394 words of font info for 79 fonts, out of 8000000 for 9000 1143 hyphenation exceptions out of 8191 - 94i,19n,93p,1224b,1732s stack positions out of 10000i,1000n,20000p,200000b,200000s + 94i,19n,93p,1214b,1732s stack positions out of 10000i,1000n,20000p,200000b,200000s -Output written on main.pdf (15 pages, 10838053 bytes). +Output written on main.pdf (17 pages, 13334435 bytes). PDF statistics: - 1290 PDF objects out of 1440 (max. 8388607) - 764 compressed objects within 8 object streams + 1393 PDF objects out of 1440 (max. 8388607) + 802 compressed objects within 9 object streams 0 named destinations out of 1000 (max. 500000) - 33 words of extra memory for PDF output out of 10000 (max. 10000000) + 43 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/main.pdf b/main.pdf index a77bfc4..9fb31d3 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.synctex.gz b/main.synctex.gz index 476c8b7..514fafa 100644 Binary files a/main.synctex.gz and b/main.synctex.gz differ diff --git a/main.tex b/main.tex index f89ac4f..20c5015 100644 --- a/main.tex +++ b/main.tex @@ -625,11 +625,11 @@ the signal for reliable song recognition. \includegraphics[width=\textwidth]{figures/fig_invariance_log_hp.pdf} \caption{\textbf{Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} - Synthetic envelope $\env(t)$ is transformed into - logarihmically compressed envelope $\db(t)$ and further - into intensity-adapted envelope $\adapt(t)$. Indicated - time scale is $5\,$s for both \textbf{a} and \textbf{b} - (black bars). + Envelope $\env(t)$ is transformed into logarihmically + compressed envelope $\db(t)$ and further into + intensity-adapted envelope $\adapt(t)$. Indicated time + scale is $5\,$s for both \textbf{a} and \textbf{b} (black + bars). \textbf{a}:~Ideally, if $\env(t)$ consists only of song component $\soc(t)$ rescaled by $\sca$, then $\adapt(t)$ is fully intensity-invariant across all $\sca$. @@ -649,6 +649,51 @@ the signal for reliable song recognition. \subsection{Thresholding nonlinearity \& temporal averaging} +\begin{figure}[!ht] + \centering + \includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_single.pdf} + \caption{\textbf{Intensity invariance by thresholding and temporal + averaging depends on the threshold value.} + Kernel response $c_i(t)$ is rescaled by $\sca$ and + transformed into binary response $b_i(t)$ and further into + feature $f_i(t)$. Threshold value $\thr$ is set to + different percentiles of the the distribution of $c_i(t)$ + at $\sca=1$. Darker colors indicate higher values of + $\thr$. Indicated time scale of $500\,$ms is the same for + \textbf{a}-\textbf{c} (black bar). + \textbf{a}:~50th percentile. + \textbf{b}:~75th percentile. + \textbf{c}:~100th percentile. + \textbf{d}:~Average value of $f_i(t)$ during the song for + the different $\thr$ in \textbf{a}-\textbf{c}. + } + \label{fig:inv_thresh-lp_single} +\end{figure} +\FloatBarrier + +\begin{figure}[!ht] + \centering + \includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_single_noise.pdf} + \caption{\textbf{Intensity invariance by thresholding and temporal + averaging depends on noise.} + Kernel response $c_i(t)$ is rescaled by $\sca$, mixed with + fixed-scale noise component $\noc(t)$, and transformed + into binary response $b_i(t)$ and further into feature + $f_i(t)$. Threshold value $\thr$ is set to different + percentiles of the the distribution of $c_i(t)$ at + $\sca=1$. Darker colors indicate higher values of $\thr$. + Indicated time scale of $500\,$ms is the same for + \textbf{a}-\textbf{c} (black bar). + \textbf{a}:~50th percentile. + \textbf{b}:~75th percentile. + \textbf{c}:~100th percentile. + \textbf{d}:~Average value of $f_i(t)$ during the song for + the different $\thr$ in \textbf{a}-\textbf{c}. + } + \label{fig:inv_thresh-lp_single_noise} +\end{figure} +\FloatBarrier + The second key mechanism for the emergence of intensity invariance along the model pathway takes place during the transformation of the kernel responses $c_i(t)$ over the binary responses $b_i(t)$ into the finalized features diff --git a/python/fig_invariance_log-hp.py b/python/fig_invariance_log-hp.py index 7bc7906..1297134 100644 --- a/python/fig_invariance_log-hp.py +++ b/python/fig_invariance_log-hp.py @@ -6,7 +6,8 @@ from thunderhopper.filetools import search_files from thunderhopper.modeltools import load_data from color_functions import load_colors from plot_functions import hide_axis, ylimits, xlabel, ylabel,\ - plot_line, strip_zeros, time_bar + plot_line, strip_zeros, time_bar,\ + letter_subplot, letter_subplots from IPython import embed def add_snip_axes(fig, grid_kwargs): @@ -128,26 +129,17 @@ xloc = dict( ) letter_snip_kwargs = dict( x=0.02, - y=1, + y=0.97, ha='left', va='top', fontsize=22, - fontweight='bold' ) letter_analysis_kwargs = dict( x=0, - y=1, + yref=letter_snip_kwargs['y'], ha='left', va='top', fontsize=22, - fontweight='bold' -) -indicate_unsaturated = False -unsaturated_proportion = 0.85 -unsaturated_kwargs = dict( - color=3 * (0.85,), - zorder=0, - lw=0 ) bar_time = 5 bar_kwargs = dict( @@ -179,7 +171,6 @@ for data_path in data_paths: transform=pure_subfig.transSubfigure) for ax, scale in zip(pure_axes[snip_specs['env']], pure_data['example_scales']): ax.set_title(f'$\\alpha={strip_zeros(scale)}$') - pure_subfig.text(s='a', **letter_snip_kwargs) # Prepare noise-song snippet axes: noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']]) @@ -191,7 +182,7 @@ for data_path in data_paths: transform=noise_subfig.transSubfigure) for ax, scale in zip(noise_axes[snip_specs['env']], noise_data['example_scales']): ax.set_title(f'$\\alpha={strip_zeros(scale)}$') - noise_subfig.text(s='b', **letter_snip_kwargs) + letter_subplots([pure_subfig, noise_subfig], **letter_snip_kwargs) # Prepare analysis axis: analysis_subfig = fig.add_subfigure(super_grid[subfig_specs['analysis']]) @@ -204,7 +195,7 @@ for data_path in data_paths: analysis_ax.set_yscale('log') ylabel(analysis_ax, ylabels['analysis'], **ylab_analysis_kwargs, transform=analysis_subfig.transSubfigure) - analysis_subfig.text(s='c', **letter_analysis_kwargs) + letter_subplot(analysis_subfig, 'c', **letter_analysis_kwargs, ref=pure_subfig) # Plot pure-song envelope snippets: plot_snippets(pure_axes[snip_specs['env']], t_full, pure_data['env'], @@ -238,19 +229,9 @@ for data_path in data_paths: # Plot pure-song SD ratios (ideal): base_ind = np.argmin(pure_data['scales']) - # measure_env = pure_data['measure_env'] / pure_data['measure_env'][base_ind] - # measure_log = pure_data['measure_log'] / pure_data['measure_log'][base_ind] measure_inv = pure_data['measure_inv'] / pure_data['measure_inv'][base_ind] - # analysis_ax.plot(pure_data['scales'], measure_env, c=colors['env'], lw=lw_analysis, ls='--') - # analysis_ax.plot(pure_data['scales'], measure_log, c=colors['log'], lw=lw_analysis, ls='--') analysis_ax.plot(pure_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis, ls='--') - if indicate_unsaturated: - # Indicate influence of noise floor: - limit = noise_data['limit'] * unsaturated_proportion - thresh_ind = np.nonzero(noise_data['measure_inv'] <= limit)[0][-1] - analysis_ax.axvspan(0, noise_data['scales'][thresh_ind], **unsaturated_kwargs) - # Plot noise-song SD ratios (limited): base_ind = np.argmin(noise_data['scales']) measure_env = noise_data['measure_env'] / noise_data['measure_env'][base_ind] diff --git a/python/fig_invariance_thresh-lp_single.py b/python/fig_invariance_thresh-lp_single.py index d6f85b2..e7fd52d 100644 --- a/python/fig_invariance_thresh-lp_single.py +++ b/python/fig_invariance_thresh-lp_single.py @@ -5,9 +5,10 @@ from itertools import product from thunderhopper.filetools import search_files from thunderhopper.modeltools import load_data from thunderhopper.filtertools import find_kern_specs -from color_functions import load_colors -from plot_functions import hide_axis, xlimits, ylimits, xlabel, ylabel, super_ylabel,\ - plot_line, plot_barcode, strip_zeros, time_bar +from color_functions import load_colors, shade_colors +from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\ + plot_line, plot_barcode, strip_zeros, time_bar,\ + letter_subplot, letter_subplots from IPython import embed def add_snip_axes(fig, grid_kwargs): @@ -19,10 +20,13 @@ def add_snip_axes(fig, grid_kwargs): [hide_axis(ax, 'bottom') for ax in axes.flatten()] return axes -def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs): +def plot_snippets(axes, time, snippets, ymin=None, ymax=None, + thresh=None, fill_kwargs={}, **kwargs): ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05) for ax, snippet in zip(axes, snippets.T): plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs) + if thresh is not None: + ax.fill_between(time, thresh, snippet, where=(snippet > thresh), **fill_kwargs) return None def plot_bi_snippets(axes, time, binary, **kwargs): @@ -30,27 +34,29 @@ def plot_bi_snippets(axes, time, binary, **kwargs): plot_barcode(ax, time, binary[:, None], **kwargs) return None -def side_distributions(axes, snippets, inset_bounds, thresh, - ymin=None, ymax=None): - bins = np.linspace(snippets.min(), snippets.max(), 50) - centers = bins[:-1] + (bins[1] - bins[0]) / 2 +def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50, + ymin=None, ymax=None, fill_kwargs={}, **kwargs): + limits = np.array([snippets.min(), snippets.max()]) * 1.05 + edges = np.linspace(*limits, nbins + 1) + centers = edges[:-1] + (edges[1] - edges[0]) / 2 for ax, snippet in zip(axes, snippets.T): + pdf, _ = np.histogram(snippet, edges, density=True) inset = ax.inset_axes(inset_bounds) - inset.axis('off') - pdf, _ = np.histogram(snippet, bins, density=True) - inset.plot(pdf, centers, c='k', lw=1) + inset.plot(pdf, centers, **kwargs) inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), - color=colors['bi'], lw=0) - inset.set_xlim(0, pdf.max()) + **fill_kwargs) ylimits(centers, inset, minval=ymin, maxval=ymax, pad=0) + inset.set_xlim(0, pdf.max()) + inset.axis('off') return None # GENERAL SETTINGS: -with_noise = True +with_noise = False target = 'Omocestus_rufipes' search_kwargs = dict( - incl='subset' if not with_noise else 'subset_noise', + incl=['subset', 'noise'] if with_noise else 'subset', + excl=None if with_noise else 'noise', dir='../data/inv/thresh_lp/' ) data_paths = search_files(target, **search_kwargs) @@ -59,9 +65,9 @@ load_kwargs = dict( files=stages, keywords=['scales', 'snip', 'measure', 'thresh'] ) -save_path = None#'../figures/fig_invariance_thresh_lp_single' +save_path = '../figures/fig_invariance_thresh_lp_single.pdf' if with_noise and save_path is not None: - save_path += '_noise' + save_path = save_path.replace('.pdf', '_noise.pdf') # GRAPH SETTINGS: fig_kwargs = dict( @@ -69,7 +75,7 @@ fig_kwargs = dict( ) super_grid_kwargs = dict( nrows=None, - ncols=2, + ncols=3, wspace=0, hspace=0, left=0, @@ -77,14 +83,18 @@ super_grid_kwargs = dict( bottom=0, top=1 ) +subfig_specs = dict( + snip=(slice(None), slice(super_grid_kwargs['ncols'] - 1)), + big=(slice(None), -1), +) snip_grid_kwargs = dict( nrows=len(stages), ncols=None, - wspace=0.11, + wspace=0.3, hspace=0.1, left=0.1, - right=0.95, - bottom=0.01, + right=0.93, + bottom=0.05, top=0.85 ) big_grid_kwargs = dict( @@ -92,20 +102,22 @@ big_grid_kwargs = dict( ncols=1, wspace=0, hspace=0, - left=0.15, + left=0.17, right=0.96, bottom=0.1, top=0.99 ) -inset_bounds = [1, 0, 0.1, 1] +inset_bounds = [1.02, 0, 0.2, 1] # PLOT SETTINGS: colors = load_colors('../data/stage_colors.npz') -# lw_snippets = dict( -# conv=0.5, -# feat=2 -# ) -# lw_analysis = 3 +color_factors = [0.2, -0.2] +lw = dict( + conv=1, + bi=0.1, + feat=3, + big=4, +) xlabels = dict( big='scale $\\alpha$', ) @@ -135,42 +147,49 @@ ylab_super_kwargs = dict( va='center', ) ylab_big_kwargs = dict( - x=0.02, + x=0, fontsize=20, ha='center', va='top', ) -# xloc = dict( -# analysis=10, -# ) -# letter_snip_kwargs = dict( -# x=0.02, -# y=1, -# ha='left', -# va='top', -# fontsize=22, -# fontweight='bold' -# ) -# letter_analysis_kwargs = dict( -# x=0, -# y=1, -# ha='left', -# va='top', -# fontsize=22, -# fontweight='bold' -# ) -# bar_time = 5 -# bar_kwargs = dict( -# y0=0.7, -# y1=0.8, -# color='k', -# lw=0, -# ) +yloc = dict( + big=0.2, +) +letter_snip_kwargs = dict( + x=0.01, + y=0.9, + ha='left', + va='top', + fontsize=22, +) +letter_big_kwargs = dict( + x=0, + yref=letter_snip_kwargs['y'], + ha='left', + va='top', + fontsize=22, +) +dist_kwargs = dict( + nbins=50, + c='k', + lw=1, +) +dist_fill_kwargs = dict( + color=colors['bi'], + lw=0.1, +) +bar_time = 0.5 +bar_kwargs = dict( + y0=0.3, + y1=0.4, + color='k', + lw=0, +) kernel = np.array([ [2, 0.008], [4, 0.008], ])[:1] -zoom_rel = np.array([0.5, 0.55]) +zoom_rel = np.array([0.5, 0.525]) # EXECUTION: @@ -193,6 +212,14 @@ for data_path in data_paths: data['threshs'] = data['threshs'][:, kern_ind] t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate'] + # Get threshold-specific colors: + factors = np.linspace(*color_factors, data['thresh_perc'].size) + colors = dict( + conv=shade_colors(colors['conv'], factors), + bi=shade_colors(colors['bi'], factors), + feat=shade_colors(colors['feat'], factors), + ) + # Adjust grid parameters: super_grid_kwargs['nrows'] = data['thresh_perc'].size snip_grid_kwargs['ncols'] = data['example_scales'].size @@ -201,22 +228,11 @@ for data_path in data_paths: fig = plt.figure(**fig_kwargs) super_grid = fig.add_gridspec(**super_grid_kwargs) - # Prepare analysis axis: - big_subfig = fig.add_subfigure(super_grid[slice(None), 1]) - big_grid = big_subfig.add_gridspec(**big_grid_kwargs) - big_ax = big_subfig.add_subplot(big_grid[0, 0]) - xlabel(big_ax, xlabels['big'], **xlab_big_kwargs, - transform=big_subfig.transSubfigure) - ylabel(big_ax, ylabels['big'], **ylab_big_kwargs, - transform=big_subfig.transSubfigure) - big_ax.set_xlim(data['scales'].min(), data['scales'].max()) - ylimits(data['measure_feat'], big_ax, minval=0, pad=0.05) - big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5) - # Prepare snippet axes: snip_axes = {} for i in range(data['thresh_perc'].size): - snip_subfig = fig.add_subfigure(super_grid[i, 0]) + subfig_specs['snip'] = (i, subfig_specs['snip'][1]) + snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']]) axes = add_snip_axes(snip_subfig, snip_grid_kwargs) snip_axes[snip_subfig] = axes super_ylabel(f'{data["thresh_perc"][i]}%', snip_subfig, @@ -225,95 +241,50 @@ for data_path in data_paths: ylabel(ax, ylabels[stage], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure) if i == 0: + time_bar(axes[0, 0], bar_time, **bar_kwargs) for ax, scale in zip(axes[0, :], data['example_scales']): ax.set_title(f'$\\alpha={strip_zeros(scale)}$') + letter_subplots(snip_axes.keys(), **letter_snip_kwargs) + + # Prepare analysis axis: + big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']]) + big_grid = big_subfig.add_gridspec(**big_grid_kwargs) + big_ax = big_subfig.add_subplot(big_grid[0, 0]) + xlabel(big_ax, xlabels['big'], **xlab_big_kwargs, + transform=big_subfig.transSubfigure) + ylabel(big_ax, ylabels['big'], **ylab_big_kwargs, + transform=big_subfig.transSubfigure) + big_ax.set_xlim(data['scales'].min(), data['scales'].max()) + big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5) + ylimits(data['measure_feat'], big_ax, minval=0, pad=0.01) + big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big'])) + letter_subplot(big_subfig, 'd', **letter_big_kwargs, ref=list(snip_axes.keys())[0]) # Plot representation snippets per threshold: + conv_min, conv_max = ylimits(data['snip_conv'], pad=0.02) for i, (subfig, axes) in enumerate(snip_axes.items()): + dist_fill_kwargs['color'] = colors['bi'][i] + # Plot kernel response snippets: plot_snippets(axes[0, :], t_full, data['snip_conv'][:, :, i], - c=colors['conv'], lw=0.5) + thresh=data['threshs'][i], ymin=conv_min, ymax=conv_max, + fill_kwargs=dist_fill_kwargs, c=colors['conv'][i], lw=lw['conv']) + # Plot binary snippets: plot_bi_snippets(axes[1, :], t_full, data['snip_bi'][:, :, i], - color=colors['bi'], lw=0) + color=colors['bi'][i], lw=lw['bi']) # Plot feature snippets: plot_snippets(axes[2, :], t_full, data['snip_feat'][:, :, i], - ymin=0, ymax=1, c=colors['feat'], lw=2) + ymin=0, ymax=1, c=colors['feat'][i], lw=lw['feat']) # Plot kernel response distribution: - side_distributions(axes[0, :], data['snip_conv'][:, :, i], - inset_bounds, data['threshs'][i]) + side_distributions(axes[0, :], data['snip_conv'][:, :, i], inset_bounds, + data['threshs'][i], ymin=conv_min, ymax=conv_max, + fill_kwargs=dist_fill_kwargs, **dist_kwargs) # Plot analysis results: - big_ax.plot(data['scales'], data['measure_feat'], - c=colors['feat'], lw=3) - - - # # Prepare pure-song snippet axes: - # pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']]) - # pure_grid_kwargs['nrows' if pure_grid_kwargs['nrows'] is None else 'ncols'] = pure_data['example_scales'].size - # pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs) - # for ax, stage in zip(pure_axes[:, 0], stages): - # ylabel(ax, ylabels[stage], **ylab_snip_kwargs, - # transform=pure_subfig.transSubfigure) - # for ax, scale in zip(pure_axes[snip_specs['conv']], pure_data['example_scales']): - # ax.set_title(f'$\\alpha={strip_zeros(scale)}$') - # pure_subfig.text(s='a', **letter_snip_kwargs) - - # # Prepare analysis axis: - # analysis_subfig = fig.add_subfigure(super_grid[subfig_specs['analysis']]) - # analysis_grid = analysis_subfig.add_gridspec(**analysis_grid_kwargs) - # analysis_ax = analysis_subfig.add_subplot(analysis_grid[0, 0]) - # analysis_ax.set_xlim(noise_data['scales'].min(), noise_data['scales'].max()) - # analysis_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['analysis'])) - # xlabel(analysis_ax, xlabels['analysis'], **xlab_analysis_kwargs, - # transform=analysis_subfig.transSubfigure) - # # analysis_ax.set_yscale('log') - # ylabel(analysis_ax, ylabels['analysis'], **ylab_analysis_kwargs, - # transform=analysis_subfig.transSubfigure) - # analysis_subfig.text(s='c', **letter_analysis_kwargs) - - # # Plot pure-song kernel response snippets: - # plot_snippets(pure_axes[snip_specs['conv']], t_full, pure_data['conv'], - # c=colors['conv'], lw=lw_snippets['conv']) - - # # Plot pure-song binary snippets: - # plot_bi_snippets(pure_axes[snip_specs['bi']], t_full, pure_data['bi'], - # color=colors['bi'], lw=0) - - # # Plot pure-song feature snippets: - # plot_snippets(pure_axes[snip_specs['feat']], t_full, pure_data['feat'], - # ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat']) - - # # Indicate time scale: - # time_bar(pure_axes[snip_specs['conv']][0], bar_time, **bar_kwargs) - - # # Plot noise-song kernel response snippets: - # plot_snippets(noise_axes[snip_specs['conv']], t_full, noise_data['conv'], - # c=colors['conv'], lw=lw_snippets['conv']) - - # # Plot noise-song binary snippets: - # plot_bi_snippets(noise_axes[snip_specs['bi']], t_full, noise_data['bi'], - # color=colors['bi'], lw=0) - - # # Plot noise-song feature snippets: - # plot_snippets(noise_axes[snip_specs['feat']], t_full, noise_data['feat'], - # ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat']) - - # # Indicate time scale: - # time_bar(noise_axes[snip_specs['conv']][0], bar_time, **bar_kwargs) - - # # Plot noise-song SD ratios (limited): - # analysis_ax.plot(noise_data['scales'], noise_data['measure_conv'], - # c=colors['conv'], lw=lw_analysis) - # lower, upper = noise_data['spread_conv'] - # analysis_ax.fill_between(noise_data['scales'], lower, upper, - # color=colors['conv'], **spread_kwargs) - # analysis_ax.plot(noise_data['scales'], noise_data['measure_feat'], - # c=colors['feat'], lw=lw_analysis) - # lower, upper = noise_data['spread_feat'] - # analysis_ax.fill_between(noise_data['scales'], lower, upper, - # color=colors['feat'], **spread_kwargs) + handles = big_ax.plot(data['scales'], data['measure_feat'], lw=lw['big']) + [h.set_color(c) for h, c in zip(handles, colors['feat'])] if save_path is not None: fig.savefig(save_path) diff --git a/python/plot_functions.py b/python/plot_functions.py index 4c995ff..9a05cf7 100644 --- a/python/plot_functions.py +++ b/python/plot_functions.py @@ -31,13 +31,27 @@ def hide_axis(ax, side='bottom'): which='both', **params) return None -def letter_subplots(axes, labels=None, x=0.02, y=1, ha='left', va='bottom', - fontsize=16, fontweight='bold', **kwargs): +def letter_subplot(artist, label, x=None, y=None, xref=None, yref=None, ref=None, + ha='left', va='bottom', fontsize=16, fontweight='bold', **kwargs): + trans_artist = BboxTransformTo(artist.bbox) + if x is None or y is None: + trans_ref = BboxTransformTo(ref.bbox) + transform = trans_ref + trans_artist.inverted() + if x is None: + x = transform.transform([xref, 0])[0] + if y is None: + y = transform.transform([0, yref])[1] + artist.text(x, y, label, transform=trans_artist, ha=ha, va=va, + fontsize=fontsize, fontweight=fontweight, **kwargs) + return None + +def letter_subplots(artists, labels=None, x=None, y=None, xref=None, yref=None, ref=None, + ha='left', va='bottom', fontsize=16, fontweight='bold', **kwargs): if labels is None: labels = string.ascii_lowercase - for ax, label in zip(axes, labels): - ax.text(x, y, label, transform=ax.transAxes, ha=ha, va=va, - fontsize=fontsize, fontweight=fontweight, **kwargs) + for artist, label in zip(artists, labels): + letter_subplot(artist, label, x, y, xref, yref, ref=ref, ha=ha, va=va, + fontsize=fontsize, fontweight=fontweight, **kwargs) return None def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):