diff --git a/figures/fig_invariance_full.pdf b/figures/fig_invariance_full.pdf index 08a92b1..faa9b76 100644 Binary files a/figures/fig_invariance_full.pdf and b/figures/fig_invariance_full.pdf differ diff --git a/figures/fig_invariance_log_hp.pdf b/figures/fig_invariance_log_hp.pdf index b5705e4..b0d66af 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_noise.pdf b/figures/fig_invariance_thresh_lp_single_noise.pdf index c3d1348..4a7e868 100644 Binary files a/figures/fig_invariance_thresh_lp_single_noise.pdf and b/figures/fig_invariance_thresh_lp_single_noise.pdf differ diff --git a/figures/fig_noise_env_sd_conversion.pdf b/figures/fig_noise_env_sd_conversion.pdf index 5f0c720..59f3c14 100644 Binary files a/figures/fig_noise_env_sd_conversion.pdf and b/figures/fig_noise_env_sd_conversion.pdf differ diff --git a/main.aux b/main.aux index 2e0dea6..b0ae31f 100644 --- a/main.aux +++ b/main.aux @@ -248,7 +248,7 @@ \newlabel{eq:toy_log}{{12}{11}{}{}{}} \newlabel{eq:toy_highpass}{{13}{11}{}{}{}} \newlabel{eq:toy_snr}{{14}{12}{}{}{}} -\@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 } +\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with (\textbf {c}{} and \textbf {d}) or without (\textbf {a}{} and \textbf {b}) additive noise component $\eta (t)$. Input $x_{\text {filt}}(t)$ is transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {dB}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Left}:~$x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ for different scales $\alpha $. \textbf {Right}:~Ratios of the standard deviation of $x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ relative to the respective reference standard deviation for input $x_{\text {filt}}(t)=\eta (t)$. \textbf {a}{} and \textbf {b}:~Ideally, if $x_{\text {filt}}(t)=\alpha \cdot s(t)$, then $x_{\text {adapt}}(t)$ is intensity-invariant across all $\alpha $. \textbf {c}{} and \textbf {d}:~In practice, if $x_{\text {filt}}(t)=\alpha \cdot s(t)+\eta (t)$, the intensity invariance of $x_{\text {adapt}}(t)$ is limited to sufficiently large $\alpha $. Shaded area indicates saturation of $x_{\text {adapt}}(t)$ at $95\,\%$ curve span. }}{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 } \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on the threshold value with regard to variable range but not saturation level.} 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 $100\,$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 } diff --git a/main.blg b/main.blg index ebd6ca6..ccddb83 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' -[38] biber:340> INFO - === Do Mär 19, 2026, 16:33:01 -[45] Biber.pm:419> INFO - Reading 'main.bcf' -[73] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 -[78] Biber.pm:4419> INFO - Processing section 0 -[82] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 -[84] bibtex.pm:1713> INFO - LaTeX decoding ... -[115] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' -[293] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' -[293] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' -[293] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' -[293] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' -[316] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' -[326] bbl.pm:763> INFO - Output to main.bbl -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 10, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 21, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 38, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 49, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 58, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 73, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 82, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 91, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 100, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 109, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 118, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 127, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 136, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 157, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 178, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 187, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 196, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 207, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 218, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 229, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 240, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 249, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 258, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 269, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 278, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 289, warning: 6 characters of junk seen at toplevel -[326] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 300, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 309, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 328, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 337, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 400, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 419, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 428, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 437, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 456, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 491, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 526, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 535, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 556, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 565, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 576, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 587, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 619, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 648, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 658, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 667, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 688, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 709, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 720, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 729, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 749, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 766, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 775, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 800, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_fJKN/347c261ec4135a5723bef5c751f5078f_21083.utf8, line 817, warning: 6 characters of junk seen at toplevel -[327] Biber.pm:133> INFO - WARNINGS: 55 +[36] biber:340> INFO - === Mo Mär 23, 2026, 15:34:22 +[43] Biber.pm:419> INFO - Reading 'main.bcf' +[71] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 +[77] Biber.pm:4419> INFO - Processing section 0 +[81] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 +[83] bibtex.pm:1713> INFO - LaTeX decoding ... +[112] 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' +[307] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' +[317] bbl.pm:763> INFO - Output to main.bbl +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 10, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 21, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 38, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 49, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 58, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 73, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 82, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 91, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 100, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 109, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 118, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 127, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 136, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 157, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 178, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_WUPV/347c261ec4135a5723bef5c751f5078f_55550.utf8, line 187, warning: 6 characters of junk seen at toplevel +[317] Biber.pm:131> WARN - 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[15 @@ -797,7 +797,7 @@ Package pdftex.def Info: figures/fig_invariance_full.pdf used on input line 711 File: figures/fig_noise_env_sd_conversion.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_noise_env_sd_conversion.pdf used on input line 870. +Package pdftex.def Info: figures/fig_noise_env_sd_conversion.pdf used on input line 878. (pdftex.def) Requested size: 483.69687pt x 241.84782pt. [18] [19 <./figures/fig_noise_env_sd_conversion.pdf>] (./main.aux) *********** @@ -809,17 +809,17 @@ Package logreq Info: Writing requests to 'main.run.xml'. ) Here is how much of TeX's memory you used: - 20765 strings out of 474222 - 448444 string characters out of 5748732 + 20769 strings out of 474222 + 448460 string characters out of 5748732 1937975 words of memory out of 5000000 - 42755 multiletter control sequences out of 15000+600000 + 42759 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,1214b,1732s stack positions out of 10000i,1000n,20000p,200000b,200000s + 94i,19n,93p,1478b,1732s stack positions out of 10000i,1000n,20000p,200000b,200000s -Output written on main.pdf (19 pages, 164827394 bytes). +Output written on main.pdf (19 pages, 164788506 bytes). PDF statistics: - 1568 PDF objects out of 1728 (max. 8388607) + 1566 PDF objects out of 1728 (max. 8388607) 861 compressed objects within 9 object streams 0 named destinations out of 1000 (max. 500000) 58 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/main.pdf b/main.pdf index 7763b8b..0cd03e3 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.synctex.gz b/main.synctex.gz index f939193..7a82177 100644 Binary files a/main.synctex.gz and b/main.synctex.gz differ diff --git a/main.tex b/main.tex index 68c57b9..e8bf99b 100644 --- a/main.tex +++ b/main.tex @@ -47,6 +47,12 @@ % \newcommand{\eref}[1]{\mbox{\cref{#1}}} % \newcommand{\eref}[1]{\mbox{Eq.\,\ref{#1}}} +% Subplot lettering: +\newcommand{\figa}{\textbf{a}} +\newcommand{\figb}{\textbf{b}} +\newcommand{\figc}{\textbf{c}} +\newcommand{\figd}{\textbf{d}} + % Math shorthands - Standard symbols: \newcommand{\dec}{\log_{10}} % Logarithm base 10 \newcommand{\infint}{\int_{-\infty}^{+\infty}} % Indefinite integral @@ -625,23 +631,25 @@ 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.} - 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 $\sca$. - \textbf{b}:~In practice, $\env(t)$ also contains - fixed-scale noise component $\noc(t)$, which limits the - effective intensity invariance of $\adapt(t)$ to - sufficiently large $\sca$. - \textbf{c}:~Ratios of the SD of each representation in - \textbf{b} at a given $\sca$ relative to the SD of the - representation for $\sca=0$ (solid lines). The same ratios - for the ideal $\adapt(t)$ in \textbf{a} are shown for - comparison (dashed line). + 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 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_log-hp} \end{figure} diff --git a/python/fig_env_sd_conversion.py b/python/fig_env_sd_conversion.py index 8ce1788..42e843c 100644 --- a/python/fig_env_sd_conversion.py +++ b/python/fig_env_sd_conversion.py @@ -17,7 +17,7 @@ fig_kwargs = dict( gridspec_kw=dict( wspace=0, hspace=0.1, - left=0.065, + left=0.09, right=0.98, bottom=0.08, top=0.95, @@ -92,7 +92,7 @@ fig.suptitle(**title_kwargs) ax1.grid(**grid_line_kwargs) ax1.set_xlim(data['scales'][0], data['scales'][-1]) ax1.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5) -ax1.set_ylim(0.4, 1.2) +ax1.set_ylim(0, 0.1) ylabel(ax1, ylabels['top'], transform=fig.transFigure, **ylab_kwargs) ax2.grid(**grid_line_kwargs) xlabel(ax2, xlabels['bottom'], transform=fig.transFigure, **xlab_kwargs) diff --git a/python/fig_invariance_full.py b/python/fig_invariance_full.py index 4e38e54..944ba69 100644 --- a/python/fig_invariance_full.py +++ b/python/fig_invariance_full.py @@ -70,6 +70,14 @@ big_grid_kwargs = dict( ) # 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'] = "#000000" lw = dict( @@ -100,26 +108,26 @@ ylabels = dict( ) xlab_snip_kwargs = dict( y=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='bottom', ) xlab_big_kwargs = dict( y=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='bottom', ) ylab_snip_kwargs = dict( x=0, - fontsize=20, + fontsize=fs['lab_tex'], rotation=0, ha='left', va='center' ) ylab_big_kwargs = dict( x=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='top', ) @@ -137,14 +145,14 @@ title_kwargs = dict( yref=1, ha='center', va='top', - fontsize=16, + fontsize=fs['tit_norm'], ) letter_snip_kwargs = dict( x=0.02, y=1, ha='left', va='top', - fontsize=22, + fontsize=fs['letter'], fontweight='bold' ) letter_big_kwargs = dict( @@ -152,15 +160,25 @@ letter_big_kwargs = dict( y=1, ha='left', va='top', - fontsize=22, + fontsize=fs['letter'], fontweight='bold' ) bar_time = 5 bar_kwargs = dict( - y0=0.8, - y1=0.9, + dur=bar_time, + y0=-0.25, + y1=-0.1, + xshift=1, color='k', lw=0, + clip_on=False, + text_pos=(-0.1, 0.5), + text_str=f'${bar_time}\\,\\text{{s}}$', + text_kwargs=dict( + fontsize=fs['bar'], + ha='right', + va='center', + ) ) @@ -197,8 +215,7 @@ for data_path in data_paths: if stages[i] != 'bi': ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]])) snip_axes[i, j] = ax - super_xlabel(xlabels['snip'], snip_subfig, snip_axes[-1, 0], snip_axes[-1, -1], **xlab_snip_kwargs) - time_bar(snip_axes[0, 0], bar_time, **bar_kwargs) + time_bar(snip_axes[-1, -1], **bar_kwargs) # Prepare single analysis axis: big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']]) diff --git a/python/fig_invariance_log-hp.py b/python/fig_invariance_log-hp.py index a522628..3c84d71 100644 --- a/python/fig_invariance_log-hp.py +++ b/python/fig_invariance_log-hp.py @@ -7,7 +7,7 @@ from thunderhopper.modeltools import load_data from color_functions import load_colors from plot_functions import hide_axis, ylimits, xlabel, ylabel, hide_ticks,\ plot_line, strip_zeros, time_bar, zoom_inset,\ - letter_subplot, letter_subplots, title_subplot + letter_subplot, title_subplot from IPython import embed def add_snip_axes(fig, grid_kwargs): @@ -28,7 +28,6 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs): # GENERAL SETTINGS: -compute_ratios = True target = 'Omocestus_rufipes' data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/') stages = ['env', 'log', 'inv'] @@ -37,10 +36,14 @@ load_kwargs = dict( keywords=['scales', 'snip', 'measure'] ) save_path = '../figures/fig_invariance_log_hp.pdf' +compute_ratios = True +show_diag = True +show_noise = True if compute_ratios: ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0] ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')} + # GRAPH SETTINGS: fig_kwargs = dict( figsize=(32/2.54, 16/2.54), @@ -60,22 +63,35 @@ subfig_specs = dict( noise=(1, slice(0, -1)), big=(slice(None), -1), ) -snip_grid_kwargs = dict( +block_height = 0.8 +edge_padding = 0.08 +pure_grid_kwargs = dict( nrows=len(stages), ncols=None, wspace=0.1, hspace=0.15, left=0.16, right=0.95, - bottom=0.1, - top=0.94, + bottom=1 - block_height - edge_padding, + top=1 - edge_padding, + height_ratios=[1, 2, 1] +) +noise_grid_kwargs = dict( + nrows=len(stages), + ncols=None, + wspace=0.1, + hspace=0.15, + left=0.16, + right=0.95, + bottom=edge_padding, + top=edge_padding + block_height, height_ratios=[1, 2, 1] ) big_grid_kwargs = dict( nrows=2, ncols=1, wspace=0, - hspace=0.1, + hspace=0.3, left=0.19, right=0.96, bottom=0.09, @@ -94,9 +110,10 @@ fs = dict( letter=22, tit_norm=16, tit_tex=20, + bar=16, ) colors = load_colors('../data/stage_colors.npz') -lw_snippets = 0.5 +lw_snippets = 1 lw_big = 3 xlabels = dict( big='scale $\\alpha$', @@ -105,7 +122,7 @@ ylabels = dict( env='$x_{\\text{env}}$', log='$x_{\\text{dB}}$', inv='$x_{\\text{adapt}}$', - big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{0}$', + big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$', ) xlab_big_kwargs = dict( y=0, @@ -121,7 +138,7 @@ ylab_snip_kwargs = dict( va='center', ) ylab_big_kwargs = dict( - x=0, + x=0.05, fontsize=fs['lab_tex'], ha='center', va='top', @@ -133,23 +150,23 @@ yloc = dict( ) title_kwargs = dict( x=0.5, - yref=1, + y=1, ha='center', - va='top', + va='bottom', fontsize=fs['tit_norm'], ) letter_snip_kwargs = dict( x=0, - y=1, + yref=0.5, ha='left', - va='top', + va='center', fontsize=fs['letter'], ) letter_big_kwargs = dict( - x=0, - yref=letter_snip_kwargs['y'], + x=0.05, + yref=letter_snip_kwargs['yref'], ha='left', - va='top', + va='center', fontsize=fs['letter'], ) zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6] @@ -164,13 +181,31 @@ zoom_kwargs = dict( lw=1, alpha=1, ) +inset_tick_kwargs = dict( + axis='y', + length=3, + pad=1, + left=False, + labelleft=False, + right=True, + labelright=True, +) bar_time = 5 bar_kwargs = dict( - y0=-0.2, - y1=-0.05, + dur=bar_time, + y0=-0.25, + y1=-0.1, + xshift=1, color='k', lw=0, clip_on=False, + text_pos=(-0.1, 0.5), + text_str=f'${bar_time}\\,\\text{{s}}$', + text_kwargs=dict( + fontsize=fs['bar'], + ha='right', + va='center', + ) ) diag_kwargs = dict( c=(0.75, 0.75, 0.75), @@ -178,6 +213,13 @@ diag_kwargs = dict( ls='--', zorder=1.9, ) +noise_rel_thresh = 0.95 +noise_kwargs = dict( + fc=(0.9, 0.9, 0.9), + ec='none', + lw=0, + zorder=1.5, +) # EXECUTION: for data_path in data_paths: @@ -192,37 +234,39 @@ for data_path in data_paths: # Prepare overall graph: fig = plt.figure(**fig_kwargs) super_grid = fig.add_gridspec(**super_grid_kwargs) + fig.canvas.draw() # Prepare pure-song snippet axes: - snip_grid_kwargs['ncols'] = pure_data['example_scales'].size + 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, snip_grid_kwargs) + pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs) for ax, stage in zip(pure_axes[:, 0], stages): ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage])) ylabel(ax, ylabels[stage], **ylab_snip_kwargs, transform=pure_subfig.transSubfigure) for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']): - title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=pure_subfig, **title_kwargs) + pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs) + letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs) pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds) pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw']) + pure_inset.tick_params(**inset_tick_kwargs) hide_ticks(pure_inset, 'bottom', ticks=False) - hide_ticks(pure_inset, 'left', ticks=False) # Prepare noise-song snippet axes: - snip_grid_kwargs['ncols'] = noise_data['example_scales'].size + 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, snip_grid_kwargs) + noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs) for ax, stage in zip(noise_axes[:, 0], stages): ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage])) ylabel(ax, ylabels[stage], **ylab_snip_kwargs, transform=noise_subfig.transSubfigure) for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']): - title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=noise_subfig, **title_kwargs) - letter_subplots([pure_subfig, noise_subfig], 'ac', **letter_snip_kwargs) + noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs) + letter_subplot(noise_subfig, 'c', ref=noise_title, **letter_snip_kwargs) noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds) noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw']) + noise_inset.tick_params(**inset_tick_kwargs) hide_ticks(noise_inset, 'bottom', ticks=False) - hide_ticks(noise_inset, 'left', ticks=False) # Prepare analysis axes: big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']]) @@ -238,17 +282,17 @@ for data_path in data_paths: ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs) if i == 0: hide_ticks(ax, 'bottom') - letter_subplot(big_subfig, 'b', ref=pure_subfig, **letter_big_kwargs) + letter_subplot(big_subfig, 'b', ref=pure_title, **letter_big_kwargs) else: xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs) - letter_subplot(big_subfig, 'd', ref=noise_subfig, **letter_big_kwargs) + letter_subplot(big_subfig, 'd', ref=noise_title, **letter_big_kwargs) big_axes[i] = ax # Plot pure-song envelope snippets: handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'], ymin=0, c=colors['env'], lw=lw_snippets)[0] zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs) - + # Plot pure-song logarithmic snippets: plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'], c=colors['log'], lw=lw_snippets) @@ -258,20 +302,23 @@ for data_path in data_paths: c=colors['inv'], lw=lw_snippets) # Plot noise-song envelope snippets: + ymin, ymax = pure_axes[0, 0].get_ylim() handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'], - ymin=0, c=colors['env'], lw=lw_snippets)[0] + ymin, ymax, c=colors['env'], lw=lw_snippets)[0] zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs) # Plot noise-song logarithmic snippets: + ymin, ymax = pure_axes[1, 0].get_ylim() plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'], - c=colors['log'], lw=lw_snippets) + ymin, ymax, c=colors['log'], lw=lw_snippets) # Plot noise-song invariant snippets: + ymin, ymax = pure_axes[2, 0].get_ylim() plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'], - c=colors['inv'], lw=lw_snippets) + ymin, ymax, c=colors['inv'], lw=lw_snippets) # Indicate time scale: - time_bar(noise_axes[2, -1], bar_time, **bar_kwargs) + time_bar(noise_axes[-1, -1], **bar_kwargs) if compute_ratios: # Relate pure-song measures to zero scale: @@ -293,12 +340,25 @@ for data_path in data_paths: big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big) big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big) - # Indicate diagonal: - big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs) - big_axes[1].plot(noise_scales, noise_scales, **diag_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) + + if show_noise: + # Indicate noise floor: + if compute_ratios: + span_measure = noise_data['measure_inv'][-1] - ref_measures['inv'] + thresh_measure = ref_measures['inv'] + noise_rel_thresh * span_measure + else: + span_measure = noise_data['measure_inv'][-1] - noise_data['measure_inv'][0] + thresh_measure = noise_data['measure_inv'][0] + noise_rel_thresh * span_measure + thresh_ind = np.nonzero(noise_data['measure_inv'] < thresh_measure)[0][-1] + thresh_scale = noise_scales[thresh_ind] + big_axes[1].axvspan(noise_scales[0], thresh_scale, **noise_kwargs) if save_path is not None: - fig.savefig(save_path) + fig.savefig(save_path, bbox_inches='tight') plt.show() print('Done.') diff --git a/python/fig_invariance_thresh-lp_single.py b/python/fig_invariance_thresh-lp_single.py index 3849f5a..19efb3c 100644 --- a/python/fig_invariance_thresh-lp_single.py +++ b/python/fig_invariance_thresh-lp_single.py @@ -1,7 +1,6 @@ import plotstyle_plt import numpy as np import matplotlib.pyplot as plt -from itertools import product from thunderhopper.filetools import search_files from thunderhopper.modeltools import load_data from thunderhopper.filtertools import find_kern_specs @@ -116,6 +115,14 @@ snip_specs = dict( inset_bounds = [1.02, 0, 0.2, 1] # 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') color_factors = [0.2, -0.2] lw = dict( @@ -136,32 +143,32 @@ ylabels = dict( ) xlab_snip_kwargs = dict( y=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='bottom', ) xlab_big_kwargs = dict( y=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='bottom', ) ylab_snip_kwargs = dict( x=0.08, - fontsize=20, + fontsize=fs['lab_tex'], rotation=0, ha='right', va='center', ) ylab_super_kwargs = dict( x=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='left', va='center', ) ylab_big_kwargs = dict( x=0, - fontsize=20, + fontsize=fs['lab_norm'], ha='center', va='top', ) @@ -176,21 +183,21 @@ title_kwargs = dict( yref=1, ha='center', va='top', - fontsize=16, + fontsize=fs['tit_norm'], ) letter_snip_kwargs = dict( x=0, y=1, ha='left', va='top', - fontsize=22, + fontsize=fs['letter'], ) letter_big_kwargs = dict( x=0, yref=letter_snip_kwargs['y'], ha='left', va='top', - fontsize=22, + fontsize=fs['letter'], ) dist_kwargs = dict( nbins=50, @@ -203,10 +210,20 @@ dist_fill_kwargs = dict( ) bar_time = 0.1 bar_kwargs = dict( - y0=0.3, - y1=0.4, + dur=bar_time, + y0=-0.25, + y1=-0.1, + xshift=1, color='k', lw=0, + clip_on=False, + text_pos=(-0.1, 0.5), + text_str=f'${int(1000 * bar_time)}\\,\\text{{ms}}$', + text_kwargs=dict( + fontsize=fs['bar'], + ha='right', + va='center', + ) ) kernel = np.array([ [1, 0.008], @@ -264,13 +281,12 @@ for data_path in data_paths: ylabel(ax, ylabels[stage], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure) if i == 0: - axes[0, 0].set_xlim(t_full[0], t_full[-1]) - time_bar(axes[0, 0], bar_time, **bar_kwargs) for ax, scale in zip(axes[0, :], data['example_scales']): title = f'$\\alpha={strip_zeros(scale)}$' title_subplot(ax, title, **title_kwargs, ref=fig) - elif i == data['threshs'].size - 1: - super_xlabel(xlabels['snip'], snip_subfig, axes[-1, 0], axes[-1, -1], **xlab_snip_kwargs) + elif i == data['threshs'].size - 1: + axes[-1, -1].set_xlim(t_full[0], t_full[-1]) + time_bar(axes[-1, -1], **bar_kwargs) letter_subplots(snip_axes.keys(), **letter_snip_kwargs) # Prepare analysis axis: diff --git a/python/fig_invariance_thresh-lp_species.py b/python/fig_invariance_thresh-lp_species.py index dd0e4d6..d31a3f4 100644 --- a/python/fig_invariance_thresh-lp_species.py +++ b/python/fig_invariance_thresh-lp_species.py @@ -200,6 +200,14 @@ inset_kwargs = dict( ) # PLOT SETTINGS: +fs = dict( + lab_norm=16, + lab_tex=20, + letter=22, + tit_norm=16, + tit_tex=20, + bar=16, +) base_color = load_colors('../data/stage_colors.npz')['feat'] spec_cmaps = [ 'Reds', @@ -224,31 +232,31 @@ ylabels = dict( ) xlab_spec_kwargs = dict( y=0, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='bottom', ) xlab_big_kwargs = dict( y=0, - fontsize=20, + fontsize=fs['lab_tex'], ha='center', va='bottom', ) ylab_spec_kwargs = dict( x=0, - fontsize=20, + fontsize=fs['lab_tex'], ha='left', va='center', ) ylab_big_kwargs = dict( x=0.03, - fontsize=20, + fontsize=fs['lab_tex'], ha='center', va='center', ) ylab_cbar_kwargs = dict( x=1, - fontsize=16, + fontsize=fs['lab_norm'], ha='center', va='bottom', ) @@ -264,14 +272,14 @@ letter_spec_kwargs = dict( yref=1, ha='center', va='top', - fontsize=22, + fontsize=fs['letter'], ) letter_big_kwargs = dict( x=0, yref=1, ha='center', va='top', - fontsize=22, + fontsize=fs['letter'], ) time_bar_kwargs = dict( dur=0.05, diff --git a/python/plot_functions.py b/python/plot_functions.py index 56b8bb0..e6cf6da 100644 --- a/python/plot_functions.py +++ b/python/plot_functions.py @@ -16,41 +16,56 @@ def hide_axis(ax, side='bottom'): which='both', **params) return None +def get_trans_artist(artist): + artist_type = type(artist).__name__ + if artist_type == 'Axes': + return artist.transAxes + elif artist_type == 'Figure': + return artist.transFigure + elif artist_type == 'Subfigure': + return artist.transSubfigure + elif hasattr(artist, 'bbox'): + return BboxTransformTo(artist.bbox) + renderer = artist.get_figure(root=True).canvas.get_renderer() + if hasattr(artist, 'get_window_extent'): + return BboxTransformTo(artist.get_window_extent(renderer)) + elif hasattr(artist, 'get_tightbbox'): + return BboxTransformTo(artist.get_tightbbox(renderer)) + raise ValueError('Artist does not have a bounding box to use as transform.') + def title_subplot(artist, title, x=0.5, y=1.0, xref=None, yref=None, ref=None, ha='center', va='bottom', fontsize=16, fontweight='normal', **kwargs): - - trans_artist = BboxTransformTo(artist.bbox) + trans_artist = get_trans_artist(artist) if xref is not None or yref is not None: - transform = BboxTransformTo(ref.bbox) + trans_artist.inverted() + transform = get_trans_artist(ref) + trans_artist.inverted() if xref is not None: x = transform.transform((xref, 0))[0] if yref is not None: y = transform.transform((0, yref))[1] - artist.text(x, y, title, transform=trans_artist, ha=ha, va=va, - fontsize=fontsize, fontweight=fontweight, **kwargs) - return None + return artist.text(x, y, title, transform=trans_artist, ha=ha, va=va, + fontsize=fontsize, fontweight=fontweight, **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) + trans_artist = get_trans_artist(artist) if x is None or y is None: - transform = BboxTransformTo(ref.bbox) + trans_artist.inverted() + transform = get_trans_artist(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 + return artist.text(x, y, label, transform=trans_artist, ha=ha, va=va, + fontsize=fontsize, fontweight=fontweight, **kwargs) 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 + handles = [] 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 + handles.append(letter_subplot(artist, label, x, y, xref, yref, ref, + ha=ha, va=va, fontsize=fontsize, fontweight=fontweight, **kwargs)) + return handles def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05): limits = [minval, maxval] @@ -83,34 +98,32 @@ def ylimits(signal, ax=None, minval=None, maxval=None, pad=0.05): return limits def xlabel(ax, label, x=None, y=-0.1, fontsize=20, transform=None, **kwargs): - ax.set_xlabel(label, fontsize=fontsize, **kwargs) if x is None: x = 0.5 if transform is not None: x = (ax.transAxes + transform.inverted()).transform((x, 0))[0] ax.xaxis.set_label_coords(x, y, transform=transform) - return None + return ax.set_xlabel(label, fontsize=fontsize, **kwargs) def ylabel(ax, label, x=-0.2, y=None, fontsize=20, transform=None, **kwargs): - ax.set_ylabel(label, fontsize=fontsize, **kwargs) if y is None: y = 0.5 if transform is not None: y = (ax.transAxes + transform.inverted()).transform((0, y))[1] ax.yaxis.set_label_coords(x, y, transform=transform) - return None + return ax.set_ylabel(label, fontsize=fontsize, **kwargs) def super_xlabel(label, fig, left_ax, right_ax, y=0.005, left_fig=None, right_fig=None, **kwargs): left_x = left_ax.get_position().x0 right_x = right_ax.get_position().x1 if left_fig is not None or right_fig is not None: - trans_fig = BboxTransformTo(fig.bbox) + trans_fig = get_trans_artist(fig) if left_fig is not None: - transform = BboxTransformTo(left_fig.bbox) + trans_fig.inverted() + transform = get_trans_artist(left_fig) + trans_fig.inverted() left_x = transform.transform((left_x, 0))[0] if right_fig is not None: - transform = BboxTransformTo(right_fig.bbox) + trans_fig.inverted() + transform = get_trans_artist(right_fig) + trans_fig.inverted() right_x = transform.transform((right_x, 0))[0] return fig.supxlabel(label, x=(left_x + right_x) / 2, y=y, **kwargs) @@ -119,12 +132,12 @@ def super_ylabel(label, fig, low_ax, high_ax, x=0.005, low_y = high_ax.get_position().y0 high_y = low_ax.get_position().y1 if low_fig is not None or high_fig is not None: - trans_fig = BboxTransformTo(fig.bbox) + trans_fig = get_trans_artist(fig) if low_fig is not None: - transform = BboxTransformTo(low_fig.bbox) + trans_fig.inverted() + transform = get_trans_artist(low_fig) + trans_fig.inverted() low_y = transform.transform((0, low_y))[1] if high_fig is not None: - transform = BboxTransformTo(high_fig.bbox) + trans_fig.inverted() + transform = get_trans_artist(high_fig) + trans_fig.inverted() high_y = transform.transform((0, high_y))[1] return fig.supylabel(label, x=x, y=(low_y + high_y) / 2, **kwargs) @@ -161,9 +174,8 @@ def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs): transform = low_ax.transData + fig.transFigure.inverted() x0 = transform.transform((zoom_abs[0], 0))[0] x1 = transform.transform((zoom_abs[1], 0))[0] - fig.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0, - transform=fig.transFigure, **kwargs)) - return None + return fig.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0, + transform=fig.transFigure, **kwargs)) def assign_colors(handles, types, colors): for handle, type_id in zip(handles, types): @@ -187,22 +199,30 @@ def strip_zeros(num, right_digits=5): return f'{left}.{right}' return left -def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, **kwargs): +def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, + text_pos=None, text_str=None, text_kwargs={}, **kwargs): if parent is None: parent = ax - trans_parent = BboxTransformTo(parent.bbox) - kwargs['transform'] = trans_parent + trans_parent = get_trans_artist(parent) transform = ax.transData + trans_parent.inverted() t0 = ax.get_xlim()[0] x0 = transform.transform((t0, 0))[0] x1 = transform.transform((t0 + dur, 0))[0] dur = x1 - x0 x0 = (1 - dur) * xshift - parent.add_artist(plt.Rectangle((x0, y0), dur, y1 - y0, **kwargs)) - return None + rect = parent.add_artist(plt.Rectangle((x0, y0), dur, y1 - y0, + transform=trans_parent, **kwargs)) + if text_pos is not None: + trans_bar = get_trans_artist(rect) + text_pos = (trans_bar + trans_parent.inverted()).transform(text_pos) + if text_str is None: + text_str = f'{dur:.2f} s' + t = parent.text(*text_pos, text_str, transform=trans_parent, **text_kwargs) + return rect, t + return rect def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x', - transform = None, + transform=None, low_left=False, up_left=False, low_right=False, up_right=False, props=['c', 'lw', 'ls', 'zorder', 'alpha'], **kwargs): if not kwargs: diff --git a/python/save_env_sd_conversion.py b/python/save_env_sd_conversion.py index 9b864af..9ab914f 100644 --- a/python/save_env_sd_conversion.py +++ b/python/save_env_sd_conversion.py @@ -12,7 +12,7 @@ save_path = '../data/inv/noise_env/' # ANALYSIS SETTINGS: scales = np.geomspace(0.1, 10000, 200) -sd_inputs = np.arange(10.9, 11.1, 0.01) +sd_inputs = np.array([1.0]) n_trials = 10 tol_to_one = 0.1 @@ -32,15 +32,16 @@ signal /= signal[segment].std() signal = signal[:, None] * scales[None, :] # Prepare storage: -current_match = 0 -storage = dict( - scales=scales, - n_trials=n_trials, - sd_factor=np.array([0.]), - trials=np.zeros((scales.size, n_trials), dtype=float), - mean=np.zeros(scales.size, dtype=float), - spread=np.zeros(scales.size, dtype=float), -) +if sd_inputs.size > 1: + current_match = 0 + storage = dict( + scales=scales, + n_trials=n_trials, + sd_factor=np.array([0.]), + trials=np.zeros((scales.size, n_trials), dtype=float), + mean=np.zeros(scales.size, dtype=float), + spread=np.zeros(scales.size, dtype=float), + ) # Analyze piece-wise: rng = np.random.default_rng() @@ -59,7 +60,22 @@ for i, sigma in enumerate(sd_inputs): # Estimate noise SD: sd = mix.std(axis=0) + # Average SD over trials: mean_sd = sd.mean(axis=-1) + + # Log single-run results: + if sd_inputs.size == 1: + storage = dict( + scales=scales, + n_trials=n_trials, + sd_factor=sigma, + trials=sd, + mean=mean_sd, + spread=sd.std(axis=-1), + ) + break + + # Update multi-run results if better than previous: n_match = (np.abs(1 - mean_sd) <= tol_to_one).sum() if n_match > current_match: print(f'Found better SD: {sigma:.3f} with {n_match} matches (previous: {current_match})') @@ -70,13 +86,10 @@ for i, sigma in enumerate(sd_inputs): current_match = n_match del mix del signal + if save_path is not None: np.savez(save_path + 'sd_conversion.npz', **storage) -plt.plot(scales, storage['mean'], 'k') -plt.show() -embed() - print('Done.') embed()