diff --git a/figures/fig_invariance_field.pdf b/figures/fig_invariance_field.pdf index c76038f..6ad9a22 100644 Binary files a/figures/fig_invariance_field.pdf and b/figures/fig_invariance_field.pdf differ diff --git a/main.aux b/main.aux index f2e9716..800025d 100644 --- a/main.aux +++ b/main.aux @@ -269,43 +269,43 @@ \newlabel{fig:thresh-lp_species}{{7}{21}{}{}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Intensity invariance along the full model pathway}{22}{}\protected@file@percent } \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.1}Including logarithmic compression}{22}{}\protected@file@percent } -\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {Step-wise emergence of intensity-invariant song representation along the full model pathway.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of standard deviation $\sigma _{c_i}$ of each $c_i(t)$. \textbf {f}:~Ratios of $\mu _{f_i}$. \textbf {g}:~Distributions of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $c_i(t)$ and $f_i(t)$. Dots indicate the values from \textbf {b}. }}{24}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {Step-wise emergence of intensity-invariant song representations along the model pathway.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of standard deviation $\sigma _{c_i}$ of each $c_i(t)$. \textbf {f}:~Ratios of $\mu _{f_i}$. \textbf {g}:~Distributions of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $c_i(t)$ and $f_i(t)$. Dots indicate the values from \textbf {b}. }}{24}{}\protected@file@percent } \newlabel{fig:pipeline_full}{{8}{24}{}{}{}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.2}Excluding logarithmic compression}{25}{}\protected@file@percent } -\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway without logarithmic compression.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$, skipping $x_{\text {log}}(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of $\mu _{f_i}$. \textbf {f}:~Distribution of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $f_i(t)$. Dots indicate the value from \textbf {b}. }}{26}{}\protected@file@percent } +\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {Effects of disabling logarithmic compression on intensity invariance along the model pathway.} Input $x_{\text {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$, skipping $x_{\text {log}}(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~Example representations of $x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $f_i(t)$. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf {e}:~Ratios of $\mu _{f_i}$. \textbf {f}:~Distribution of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $f_i(t)$. 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Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). Data is based on the analysis underlying Fig\,\ref {fig:pipeline_full}. \textbf {Lower triangular}:~Interspecific comparisons between single songs of different species. \textbf {Upper triangular}:~Intraspecific comparisons between different songs of a single species (\textit {O. rufipes}). \textbf {Lower right}:~Distribution of correlation coefficients $\rho $ for each interspecific and intraspecific comparison. 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Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf {a}:~$x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ at each $d$. A noise segment from the same recording is shown for reference. \textbf {b}:~Intensity metrics over $d$. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $d$. \textbf {d}:~Ratios of intensity metrics to the respective value obtained from the noise reference. 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Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). Data is based on the analysis underlying Fig\,\ref {fig:pipeline_full}. \textbf {Lower triangular}:~Interspecific comparisons between single songs of different species. \textbf {Upper triangular}:~Intraspecific comparisons between different songs of a single species (\textit {O. rufipes}). \textbf {Lower right}:~Distribution of correlation coefficients $\rho $ for each interspecific and intraspecific comparison. 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BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 278, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 289, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 300, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 309, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 328, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 337, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 400, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 419, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 428, warning: 6 characters of junk seen at toplevel -[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 437, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 456, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 491, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 526, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 535, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 556, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 565, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 576, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 587, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 619, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 648, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 658, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 667, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 688, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 709, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 720, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 729, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 749, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 766, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 775, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 800, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_GU5F/347c261ec4135a5723bef5c751f5078f_310092.utf8, line 817, warning: 6 characters of junk seen at toplevel -[333] Biber.pm:133> INFO - WARNINGS: 55 +[36] biber:340> INFO - === Fr Mai 8, 2026, 16:13:05 +[44] 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 +[83] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 +[85] bibtex.pm:1713> INFO - LaTeX decoding ... +[115] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' +[289] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' +[289] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' +[289] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' +[289] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' +[312] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' +[322] bbl.pm:763> INFO - Output to main.bbl +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 10, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 21, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 38, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 49, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 58, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 73, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 82, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 91, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 100, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 109, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 118, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 127, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 136, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 157, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 178, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 187, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 196, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 207, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 218, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 229, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 240, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 249, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 258, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 269, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 278, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 289, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 300, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 309, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 328, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 337, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 400, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 419, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 428, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 437, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 456, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 491, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 526, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 535, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 556, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 565, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 576, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 587, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 619, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 648, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 658, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 667, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 688, warning: 6 characters of junk seen at toplevel +[322] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 709, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 720, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 729, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 749, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 766, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 775, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 800, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CkNb/347c261ec4135a5723bef5c751f5078f_133518.utf8, line 817, warning: 6 characters of junk seen at toplevel +[323] Biber.pm:133> INFO - WARNINGS: 55 diff --git a/main.fdb_latexmk b/main.fdb_latexmk index 31be66b..d919351 100644 --- a/main.fdb_latexmk +++ b/main.fdb_latexmk @@ -1,14 +1,14 @@ # Fdb version 4 -["biber main"] 1778167064.11549 "main.bcf" "main.bbl" "main" 1778170419.19465 0 +["biber main"] 1778249584.73083 "main.bcf" "main.bbl" "main" 1778257261.51344 0 "cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 "" - "main.bcf" 1778170419.12045 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex" + "main.bcf" 1778257261.43428 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex" (generated) "main.bbl" "main.blg" (rewritten before read) -["pdflatex"] 1778170418.0584 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1778170419.19487 0 +["pdflatex"] 1778257260.44085 "/home/hartling/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1778257261.51364 0 "/etc/texmf/web2c/texmf.cnf" 1761560044.43676 475 c0e671620eb5563b2130f56340a5fde8 "" - "/home/hartling/phd/paper/paper_2025/main.tex" 1778170417.91947 85421 7d6468de7ebe30036ecfa040f5dc165b "" + "/home/hartling/phd/paper/paper_2025/main.tex" 1778250173.99731 87264 c78c20cbcdfd59418334f0ac9961ac09 "" "/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 "" @@ -157,7 +157,7 @@ "figures/fig_feat_stages.pdf" 1777568594.52063 11308299 aa000e352d557e9395028dd7235cf375 "" "figures/fig_features_cross_species.pdf" 1778167576.33731 206101 cd143cc5c7c8c6d10323b077f6d09274 "" "figures/fig_invariance_cross_species_thresh_appendix.pdf" 1777568946.883 302422 5f33b50142db8b69ae9735c4aa8be688 "" - "figures/fig_invariance_field.pdf" 1776952657.04263 9131898 e9d9acff1d03fdf60ddc9e32b87ae6c2 "" + "figures/fig_invariance_field.pdf" 1778257229.75153 3436718 f30684109d769ce7cb7aaed658bab25e "" "figures/fig_invariance_full_Omocestus_rufipes.pdf" 1777914919.48322 4817854 489437547b91e2c147d390725d05c6bd "" "figures/fig_invariance_log-hp_appendix.pdf" 1777378237.41292 537850 039c3b97fa1196f939cb46c7124692c2 "" "figures/fig_invariance_log_hp.pdf" 1777397498.84496 856466 80c2296c4244a5028c5680207c983431 "" @@ -175,10 +175,10 @@ "figures/fig_noise_env_sd_conversion_appendix.pdf" 1776328774.43347 45466 c2be20312c1572203bdbeb9c8e32525e "" "figures/fig_pre_stages.pdf" 1777568592.25966 441645 7231cde61e83c7ce28a9e6cfaceac8d3 "" "figures/fig_saturation_log-hp_appendix.pdf" 1777378621.26288 28579 137855d03bab8dc5f6d31b70d404e082 "" - 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(pdftex.def) Requested size: 483.69687pt x 483.69566pt. [27 - <./figures/fig_invariance_field.pdf>] - +] + +LaTeX Warning: Text page 28 contains only floats. + + +Overfull \vbox (7.05988pt too high) has occurred while \output is active [] + + [28 <./figures/fig_invariance_field.pdf>] + File: figures/fig_features_cross_species.pdf Graphic file (type pdf) -Package pdftex.def Info: figures/fig_features_cross_species.pdf used on input line 1295. +Package pdftex.def Info: figures/fig_features_cross_species.pdf used on input line 1322. 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(pdftex.def) Requested size: 483.69687pt x 483.69566pt. - [36 <./figures/fig_kernel_sd_perc_field_appendix.pdf>] [37 <./figures/fig_invariance_cross_species_thresh_appendix.pdf>] (./main.aux) + [37 <./figures/fig_kernel_sd_perc_field_appendix.pdf>] [38 <./figures/fig_invariance_cross_species_thresh_appendix.pdf>] (./main.aux) *********** LaTeX2e <2023-11-01> patch level 1 L3 programming layer <2024-01-22> @@ -914,12 +921,12 @@ Here is how much of TeX's memory you used: 42869 multiletter control sequences out of 15000+600000 569740 words of font info for 80 fonts, out of 8000000 for 9000 1143 hyphenation exceptions out of 8191 - 94i,18n,93p,1751b,1738s stack positions out of 10000i,1000n,20000p,200000b,200000s + 94i,18n,93p,1747b,1738s stack positions out of 10000i,1000n,20000p,200000b,200000s -Output written on main.pdf (37 pages, 39250039 bytes). +Output written on main.pdf (38 pages, 33556448 bytes). PDF statistics: - 2584 PDF objects out of 2984 (max. 8388607) - 1147 compressed objects within 12 object streams + 2604 PDF objects out of 2984 (max. 8388607) + 1153 compressed objects within 12 object streams 0 named destinations out of 1000 (max. 500000) 123 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/main.pdf b/main.pdf index 8922651..15cd5a4 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.synctex.gz b/main.synctex.gz index 0f307e8..cf1170f 100644 Binary files a/main.synctex.gz and b/main.synctex.gz differ diff --git a/main.tex b/main.tex index 87ebf1b..0503b36 100644 --- a/main.tex +++ b/main.tex @@ -1120,7 +1120,7 @@ in principle, work together towards an intensity-invariant song representation. \centering \includegraphics[width=\textwidth]{figures/fig_invariance_full_Omocestus_rufipes.pdf} \caption{\textbf{Step-wise emergence of intensity-invariant song - representation along the full model pathway.} + representations along the model pathway.} Input $\raw(t)$ consists of song component $\soc(t)$ scaled by $\sca$ with added noise component $\noc(t)$ and is processed up to the feature set $f_i(t)$. Different @@ -1201,9 +1201,8 @@ guaranteed simply by disabling logarithmic compression. \begin{figure}[!ht] \centering \includegraphics[width=\textwidth]{figures/fig_invariance_short_Omocestus_rufipes.pdf} - \caption{\textbf{Step-wise emergence of intensity invariant song - representation along the model pathway without logarithmic - compression.} + \caption{\textbf{Effects of disabling logarithmic compression on intensity + invariance along the model pathway.} Input $\raw(t)$ consists of song component $\soc(t)$ scaled by $\sca$ with added noise component $\noc(t)$ and is processed up to the feature set $f_i(t)$, skipping @@ -1235,13 +1234,41 @@ guaranteed simply by disabling logarithmic compression. \end{figure} \FloatBarrier -\subsubsection{Field data} +\subsubsection{Intensity invariance in a naturalistic setting} + +So far, the analyses on intensity invariance were based on synthetically +generated input signals, since these allow for a systematic manipulation of +the mixture of song component $\soc(t)$ and noise component $\noc(t)$ over +an arbitrary range of scales $\sca$. \begin{figure}[!ht] \centering \includegraphics[width=\textwidth]{figures/fig_invariance_field.pdf} - \caption{\textbf{Step-wise emergence of intensity invariant song - representation along the model pathway.} + \caption{\textbf{Intensity invariance along the model pathway in a + naturalistic setting.} + Input $\raw(t)$ consists of a song of \textit{P. + parallelus} recorded in the field at eight different + distances $d$ and is processed up to the feature set + $f_i(t)$. Different color shades indicate different types + of Gabor kernels with specific lobe number $\kn$ and + either $+$ or $-$ sign, sorted (dark to light) first by + increasing $\kn$ and then by + sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then $-$ for + each $\kn$; five kernel widths $\kw$ of 1, 2, 4, 8, and + $16\,$ms per type; 8 types, 40 kernels in total). + \textbf{a}:~$\filt(t)$, $\env(t)$, $\db(t)$, $\adapt(t)$, + $c_i(t)$, and $f_i(t)$ at each $d$. A noise segment from + the same recording is shown for reference. + \textbf{b}:~Intensity metrics over $d$. For $c_i(t)$ + and $f_i(t)$, the median over kernels is shown. + \textbf{c}:~Average value $\mu_{f_i}$ of each feature + $f_i(t)$ over $d$. + \textbf{d}:~Ratios of intensity metrics to the respective + value obtained from the noise reference. For $c_i(t)$ and + $f_i(t)$, the median over kernel-specific ratios is shown. + \textbf{e}:~Ratios of standard deviation $\sigma_{c_i}$ of + each $c_i(t)$. + \textbf{f}:~Ratios of $\mu_{f_i}$. } \label{fig:pipeline_field} \end{figure} diff --git a/python/fig_invariance_field.py b/python/fig_invariance_field.py index efe91d3..e935a26 100644 --- a/python/fig_invariance_field.py +++ b/python/fig_invariance_field.py @@ -5,11 +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 misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\ - divide_by_zero, x_dist, y_dist +from misc_functions import reduce_kernel_set, divide_by_zero, y_dist from color_functions import load_colors from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\ - plot_line, strip_zeros, time_bar, assign_colors,\ + plot_line, xlabel, time_bar, assign_colors,\ letter_subplot, letter_subplots, hide_ticks from IPython import embed @@ -21,12 +20,12 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs): ymin=ymin, ymax=ymax, **kwargs)) return handles -def plot_curves(ax, scales, measures, **kwargs): +def plot_curves(ax, distances, measures, **kwargs): if measures.ndim == 1: - handles = ax.plot(scales, measures, **kwargs) + handles = ax.plot(distances, measures, **kwargs) return handles, measures median_measure = np.nanmedian(measures, axis=1) - line_handle = ax.plot(scales, median_measure, **kwargs)[0] + line_handle = ax.plot(distances, median_measure, **kwargs)[0] return line_handle, median_measure def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']): @@ -43,17 +42,17 @@ search_target = 'Pseudochorthippus_parallelus' stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat'] song_example = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms' noise_example = 'merged_noise' -song_path = '../data/inv/field/song/' -noise_path = '../data/inv/field/noise/' -raw_path = search_files(search_target, incl='unnormed', dir=song_path + 'condensed/')[0] -base_path = search_files(search_target, incl='base', dir=song_path + 'condensed/')[0] -range_path = search_files(search_target, incl='range', dir=song_path + 'condensed/')[0] -song_snip_path = search_files(song_example, dir=song_path)[0] -noise_snip_path = search_files(noise_example, dir=noise_path)[0] +song_path = search_files(song_example, dir='../data/inv/field/song/')[0] +noise_path = search_files(noise_example, dir='../data/inv/field/noise/')[0] save_path = '../figures/fig_invariance_field.pdf' # ANALYSIS SETTINGS: offset_distance = 10 # centimeter +thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4] +kern_subset_kwargs = dict( + combis=[['measure', 'snip'], ['conv', 'feat']], + keys=['thresh_abs'], +) # SUBSET SETTINGS: types = np.array([1, -1, 2, -2, 3, -3, 4, -4]) @@ -69,39 +68,53 @@ fig_kwargs = dict( ) super_grid_kwargs = dict( nrows=2, - ncols=1, + ncols=2, wspace=0, hspace=0, left=0, right=1, bottom=0, top=1, - height_ratios=[3, 2] + height_ratios=[1, 1] ) subfig_specs = dict( - snip=(0, 0), - big=(1, 0), + snip=(0, slice(None)), + raw=(1, 0), + base=(1, 1), ) snip_grid_kwargs = dict( nrows=len(stages), ncols=None, wspace=0.1, hspace=0.4, - left=0.11, + left=0.13, right=0.98, - bottom=0.08, + bottom=0.05, top=0.95 ) -big_grid_kwargs = dict( - nrows=1, - ncols=3, - wspace=0.4, - hspace=0, - left=snip_grid_kwargs['left'], - right=snip_grid_kwargs['right'], - bottom=0.13, - top=0.98 +raw_grid_kwargs = dict( + nrows=2, + ncols=1, + wspace=0, + hspace=0.15, + left=0.14, + right=0.9, + bottom=0.25, + top=0.95, + height_ratios=[0.8, 0.2] ) +base_grid_kwargs = dict( + nrows=3, + ncols=1, + wspace=0, + hspace=0.25, + left=raw_grid_kwargs['left'], + right=raw_grid_kwargs['right'], + bottom=raw_grid_kwargs['bottom'], + top=raw_grid_kwargs['top'], +) +inset_dist_bounds = [1.01, 0, 0.95, 1] +inset_ax_bounds = [raw_grid_kwargs['left'], 0.1, raw_grid_kwargs['right'] - raw_grid_kwargs['left'], 0.01] # PLOT SETTINGS: fs = dict( @@ -112,9 +125,11 @@ fs = dict( tit_tex=20, bar=16, ) -colors = load_colors('../data/stage_colors.npz') -conv_colors = load_colors('../data/conv_colors_all.npz') -feat_colors = load_colors('../data/feat_colors_all.npz') +stage_colors = load_colors('../data/stage_colors.npz') +kern_colors = dict( + conv=load_colors('../data/conv_colors_subset.npz'), + feat=load_colors('../data/feat_colors_subset.npz') +) lw = dict( filt=0.25, env=0.25, @@ -122,11 +137,14 @@ lw = dict( inv=0.25, conv=0.25, feat=1, - big=3, - plateau=1.5, + single=3, + swarm=1, + legend=5, + dist=1 ) xlabels = dict( - big='distance [cm]', + high='$1\\,/\\,d\\,\\sim\\,\\alpha$ [cm$^{-1}$]', + low='distance $d$ [cm]', ) ylabels = dict( filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$', @@ -135,33 +153,41 @@ ylabels = dict( inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$', conv='$c_i$\n$[\\text{dB}]$', feat='$f_i$', - big=['measure', 'rel. measure', 'norm. measure'] + raw=['$m$', '$\\mu_{f_i}$'], + base=['$m\\,/\\,m_{\\eta}$', '$\\sigma_{c_i}\\,/\\,\\sigma_{\\eta_i}$', '$\\mu_{f_i}\\,/\\,\\mu_{\\eta_i}$'] ) -xlab_big_kwargs = dict( +xlab_high_kwargs = dict( + y=0.15, + fontsize=fs['lab_norm'], + ha='center', + va='bottom', +) +xlab_low_kwargs = dict( y=0, fontsize=fs['lab_norm'], ha='center', va='bottom', ) ylab_snip_kwargs = dict( - x=0, + x=0.03, fontsize=fs['lab_tex'], rotation=0, - ha='left', - va='center' + ha='center', + va='center', + ma='center' ) ylab_big_kwargs = dict( - x=-0.2, + x=0, fontsize=fs['lab_norm'], ha='center', - va='bottom', + va='top', ) yloc = dict( - filt=0.03, - env=0.01, + filt=300, + env=100, log=50, inv=20, - conv=1, + conv=0.5, feat=1, ) title_kwargs = dict( @@ -179,7 +205,7 @@ letter_snip_kwargs = dict( fontsize=fs['letter'], ) letter_big_kwargs = dict( - x=0, + xref=0, y=1, ha='left', va='bottom', @@ -206,49 +232,64 @@ noise_bar_time = 0.5 noise_bar_kwargs = song_bar_kwargs.copy() noise_bar_kwargs['dur'] = noise_bar_time noise_bar_kwargs['text_str'] = f'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$' -plateau_settings = dict( - low=0.05, - high=0.95, - first=True, - last=True, - condense=None, +leg_labels = dict( + filt='$x_{\\text{filt}}$', + env='$x_{\\text{env}}$', + log='$x_{\\text{log}}$', + inv='$x_{\\text{adapt}}$', + conv='$c_i$', + feat='$f_i$' ) -plateau_line_kwargs = dict( - lw=lw['plateau'], - ls='--', - zorder=1, +leg_kwargs = dict( + ncols=3, + loc='upper left', + bbox_to_anchor=(0.025, 0.9, 0.95, 0.1), + frameon=False, + prop=dict( + size=20, + ), + borderpad=0, + borderaxespad=0, + handlelength=1, + columnspacing=1, + handletextpad=0.5, + labelspacing=0.1 ) -plateau_dot_kwargs = dict( - marker='o', - markersize=8, - markeredgewidth=1, - clip_on=False, +dist_line_kwargs = dict( + lw=lw['dist'], +) +dist_fill_kwargs = dict( + lw=lw['dist'], ) # EXECUTION: -# Load raw (unnormed) invariance data: -data, config = load_data(raw_path, files='distances', keywords='mean') -dists = data['distances'] + offset_distance - -# Load snippet data: -song_snip, _ = load_data(song_snip_path, keywords='snip') -t_song = np.arange(song_snip['snip_filt'].shape[0]) / config['rate'] -noise_snip, _ = load_data(noise_snip_path, keywords='snip') -noise_snip = crop_noise_snippets(noise_snip, noise_snip['snip_filt'].shape[0], t_song.size) -t_noise = np.arange(noise_snip['snip_filt'].shape[0]) / config['rate'] +# Load song invariance data: +song_data, config = load_data(song_path, files='distances', keywords=['measure', 'snip', 'thresh']) +t_song = np.arange(song_data['snip_filt'].shape[0]) / config['rate'] +dists = song_data['distances'] + offset_distance +scales = 1 / dists snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists] -# Optional kernel subset: -reduce_kernels = False -if any(var is not None for var in [kernels, types, sigmas]): +# Load noise invariance data: +noise_data, _ = load_data(noise_path, keywords=['measure', 'snip', 'thresh']) +noise_data = crop_noise_snippets(noise_data, noise_data['snip_filt'].shape[0], t_song.size) +t_noise = np.arange(noise_data['snip_filt'].shape[0]) / config['rate'] + +# Reduce kernels: +if reduce_kernels: kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas) - data = reduce_kernel_set(data, kern_inds, keyword='mean') - song_snip = reduce_kernel_set(song_snip, kern_inds, keyword='snip') - noise_snip = reduce_kernel_set(noise_snip, kern_inds, keyword='snip') config['k_specs'] = config['k_specs'][kern_inds, :] config['kernels'] = config['kernels'][:, kern_inds] - reduce_kernels = True + song_data = reduce_kernel_set(song_data, kern_inds, **kern_subset_kwargs) + noise_data = reduce_kernel_set(noise_data, kern_inds, **kern_subset_kwargs) + +# Reduce thresholds: +thresh_ind = np.nonzero(song_data['thresh_rel'] == thresh_rel)[0][0] +song_data['measure_feat'] = song_data['measure_feat'][:, :, thresh_ind] +song_data['snip_feat'] = song_data['snip_feat'][:, :, :, thresh_ind] +noise_data['measure_feat'] = noise_data['measure_feat'][:, :, thresh_ind] +noise_data['snip_feat'] = noise_data['snip_feat'][:, :, :, thresh_ind] # Adjust grid parameters: snip_grid_kwargs['ncols'] = len(snip_dists) @@ -278,136 +319,173 @@ time_bar(snip_axes[-1, -1], **song_bar_kwargs) # time_bar(snip_axes[-1, 0], **noise_bar_kwargs) letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs) -# Prepare analysis axes: -big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']]) -big_grid = big_subfig.add_gridspec(**big_grid_kwargs) -big_axes = np.zeros((big_grid.ncols,), dtype=object) -for i in range(big_grid.ncols): - ax = big_subfig.add_subplot(big_grid[0, i]) - ax.set_xlim(dists[0], 0) - # ax.set_xscale('symlog', linthresh=offset_distance, linscale=0.5) - ax.set_yscale('symlog', linthresh=0.01, linscale=0.1) - ylabel(ax, ylabels['big'][i], **ylab_big_kwargs) - # if i < (big_grid.ncols - 1): - # ax.set_ylim(scales[0], scales[-1]) - # else: - # ax.set_ylim(0, 1) - big_axes[i] = ax -super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs) -letter_subplots(big_axes, 'bcd', **letter_big_kwargs) +# Prepare raw analysis axes: +raw_subfig = fig.add_subfigure(super_grid[subfig_specs['raw']]) +raw_grid = raw_subfig.add_gridspec(**raw_grid_kwargs) +raw_axes = np.zeros((raw_grid.nrows,), dtype=object) +for i in range(raw_grid.nrows): + ax = raw_subfig.add_subplot(raw_grid[i, 0]) + ax.set_xlim(scales[0], scales[-1]) + ax.set_xscale('log') + ylabel(ax, ylabels['raw'][i], transform=raw_subfig.transSubfigure, **ylab_big_kwargs) + if i == 0: + ax.set_yscale('symlog', linthresh=0.00001, linscale=0.1) + hide_ticks(ax, 'bottom') + else: + transform = raw_subfig.transSubfigure + ax.transAxes.inverted() + inset_x1 = transform.transform((inset_dist_bounds[2], 0))[0] + inset_dist_bounds[2] = inset_x1 - inset_dist_bounds[0] + raw_inset = ax.inset_axes(inset_dist_bounds) + raw_inset.axis('off') + raw_axes[i] = ax +letter_subplots(raw_axes, 'bc', ref=raw_subfig, **letter_big_kwargs) +xlabel(raw_axes[-1], xlabels['high'], transform=raw_subfig.transSubfigure, **xlab_high_kwargs) + +# Prepare base analysis axes: +base_subfig = fig.add_subfigure(super_grid[subfig_specs['base']]) +base_grid = base_subfig.add_gridspec(**base_grid_kwargs) +base_axes = np.zeros((base_grid.nrows,), dtype=object) +base_insets = np.zeros((base_grid.nrows - 1,), dtype=object) +for i in range(base_grid.nrows): + ax = base_subfig.add_subplot(base_grid[i, 0]) + ax.set_xlim(scales[0], scales[-1]) + ax.set_xscale('log') + ax.set_yscale('log') + ylabel(ax, ylabels['base'][i], transform=base_subfig.transSubfigure, **ylab_big_kwargs) + if i < base_grid_kwargs['nrows'] - 1: + hide_ticks(ax, 'bottom') + if i > 0: + inset = ax.inset_axes(inset_dist_bounds) + inset.set_yscale('log') + inset.axis('off') + base_insets[i - 1] = inset + base_axes[i] = ax +letter_subplots(base_axes, 'def', ref=base_subfig, **letter_big_kwargs) +xlabel(base_axes[-1], xlabels['high'], transform=base_subfig.transSubfigure, **xlab_high_kwargs) if True: # Plot filtered snippets: - plot_snippets(snip_axes[0, 1:], t_song, song_snip['snip_filt'], - c=colors['filt'], lw=lw['filt']) - plot_line(snip_axes[0, 0], t_noise, noise_snip['snip_filt'][:, 0], - *snip_axes[0, 1].get_ylim(), c=colors['filt'], lw=lw['filt']) + plot_snippets(snip_axes[0, 1:], t_song, song_data['snip_filt'], + c=stage_colors['filt'], lw=lw['filt']) + plot_line(snip_axes[0, 0], t_noise, noise_data['snip_filt'][:, 0], + *snip_axes[0, 1].get_ylim(), c=stage_colors['filt'], lw=lw['filt']) # Plot envelope snippets: - plot_snippets(snip_axes[1, 1:], t_song, song_snip['snip_env'], - ymin=0, c=colors['env'], lw=lw['env']) - plot_line(snip_axes[1, 0], t_noise, noise_snip['snip_env'][:, 0], - *snip_axes[1, 1].get_ylim(), c=colors['env'], lw=lw['env']) + plot_snippets(snip_axes[1, 1:], t_song, song_data['snip_env'], + ymin=0, c=stage_colors['env'], lw=lw['env']) + plot_line(snip_axes[1, 0], t_noise, noise_data['snip_env'][:, 0], + *snip_axes[1, 1].get_ylim(), c=stage_colors['env'], lw=lw['env']) # Plot logarithmic snippets: - plot_snippets(snip_axes[2, 1:], t_song, song_snip['snip_log'], - c=colors['log'], lw=lw['log']) - plot_line(snip_axes[2, 0], t_noise, noise_snip['snip_log'][:, 0], - *snip_axes[2, 1].get_ylim(), c=colors['log'], lw=lw['log']) + plot_snippets(snip_axes[2, 1:], t_song, song_data['snip_log'], + c=stage_colors['log'], lw=lw['log']) + plot_line(snip_axes[2, 0], t_noise, noise_data['snip_log'][:, 0], + *snip_axes[2, 1].get_ylim(), c=stage_colors['log'], lw=lw['log']) # Plot invariant snippets: - plot_snippets(snip_axes[3, 1:], t_song, song_snip['snip_inv'], - c=colors['inv'], lw=lw['inv']) - plot_line(snip_axes[3, 0], t_noise, noise_snip['snip_inv'][:, 0], - *snip_axes[3, 1].get_ylim(), c=colors['inv'], lw=lw['inv']) + plot_snippets(snip_axes[3, 1:], t_song, song_data['snip_inv'], + c=stage_colors['inv'], lw=lw['inv']) + plot_line(snip_axes[3, 0], t_noise, noise_data['snip_inv'][:, 0], + *snip_axes[3, 1].get_ylim(), c=stage_colors['inv'], lw=lw['inv']) # Plot kernel response snippets: - all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_snip['snip_conv'], - c=colors['conv'], lw=lw['conv']) + all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_data['snip_conv'], + c=stage_colors['conv'], lw=lw['conv']) for i, handles in enumerate(all_handles): - assign_colors(handles, config['k_specs'][:, 0], conv_colors) - reorder_by_sd(handles, song_snip['snip_conv'][..., i]) - handles = plot_line(snip_axes[4, 0], t_noise, noise_snip['snip_conv'][:, 0], - *snip_axes[4, 1].get_ylim(), c=colors['conv'], lw=lw['conv']) - assign_colors(handles, config['k_specs'][:, 0], conv_colors) - reorder_by_sd(handles, noise_snip['snip_conv'][:, 0]) + assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv']) + reorder_by_sd(handles, song_data['snip_conv'][..., i]) + handles = plot_line(snip_axes[4, 0], t_noise, noise_data['snip_conv'][:, 0], + *snip_axes[4, 1].get_ylim(), c=stage_colors['conv'], lw=lw['conv']) + assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv']) + reorder_by_sd(handles, noise_data['snip_conv'][:, 0]) # Plot feature snippets: - all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_snip['snip_feat'], - ymin=0, ymax=1, c=colors['feat'], lw=lw['feat']) + all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_data['snip_feat'], + ymin=0, ymax=1, c=stage_colors['feat'], lw=lw['feat']) for i, handles in enumerate(all_handles): - assign_colors(handles, config['k_specs'][:, 0], feat_colors) - reorder_by_sd(handles, song_snip['snip_feat'][..., i]) - handles = plot_line(snip_axes[5, 0], t_noise, noise_snip['snip_feat'][:, 0], - ymin=0, ymax=1, c=colors['feat'], lw=lw['feat']) - assign_colors(handles, config['k_specs'][:, 0], feat_colors) - reorder_by_sd(handles, noise_snip['snip_feat'][:, 0]) -del song_snip, noise_snip + assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat']) + reorder_by_sd(handles, song_data['snip_feat'][..., i]) + handles = plot_line(snip_axes[5, 0], t_noise, noise_data['snip_feat'][:, 0], + ymin=0, ymax=1, c=stage_colors['feat'], lw=lw['feat']) + assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat']) + reorder_by_sd(handles, noise_data['snip_feat'][:, 0]) -# Remember saturation points: -crit_inds, crit_dists = {}, {} - -# Unnormed measures: +# Plot analysis results: +leg_handles = [] for stage in stages: - # Plot average intensity measure across recordings: - curve = plot_curves(big_axes[0], dists, data[f'mean_{stage}'], - c=colors[stage], lw=lw['big'], - fill_kwargs=dict(color=colors[stage], alpha=0.25)) - # # Indicate saturation point: - # if stage in ['log', 'inv', 'conv', 'feat']: - # ind = get_saturation(curve, **plateau_settings)[1] - # dist = dists[ind] - # big_axes[0].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs, - # transform=big_axes[0].get_xaxis_transform()) - # big_axes[0].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs, - # transform=big_axes[0].get_xaxis_transform()) - # big_axes[0].vlines(dist, big_axes[0].get_ylim()[0], curve[ind], - # color=colors[stage], **plateau_line_kwargs) - # # Log saturation point: - # crit_inds[stage] = ind - # crit_dists[stage] = dist -del data + mkey = f'measure_{stage}' + measure = song_data[mkey] + color = stage_colors[stage] -# Noise baseline-related measures: -data, _ = load_data(base_path, files='scales', keywords='mean') -if reduce_kernels: - data = reduce_kernel_set(data, kern_inds, keyword='mean') -for stage in stages: - # Plot average intensity measure across recordings: - curve = plot_curves(big_axes[1], dists, data[f'mean_{stage}'], - c=colors[stage], lw=lw['big'], - fill_kwargs=dict(color=colors[stage], alpha=0.25)) - # Indicate saturation point: - # if stage in ['log', 'inv', 'conv', 'feat']: - # ind, dist = crit_inds[stage], crit_dists[stage] - # big_axes[1].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs, - # transform=big_axes[1].get_xaxis_transform()) - # big_axes[1].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs, - # transform=big_axes[1].get_xaxis_transform()) - # big_axes[1].vlines(dist, big_axes[1].get_ylim()[0], curve[ind], - # color=colors[stage], **plateau_line_kwargs) -del data + ## UNNORMALIZED MEASURE: -# Min-max normalized measures: -data, _ = load_data(range_path, files='scales', keywords='mean') -if reduce_kernels: - data = reduce_kernel_set(data, kern_inds, keyword='mean') -for stage in stages: - # Plot average intensity measure across recordings: - curve = plot_curves(big_axes[2], dists, data[f'mean_{stage}'], - c=colors[stage], lw=lw['big'], - fill_kwargs=dict(color=colors[stage], alpha=0.25)) + # Plot single raw intensity curve (median where necessary): + handles, curve = plot_curves(raw_axes[0], scales, measure, c=color, lw=lw['single']) - # # Indicate saturation point: - # if stage in ['log', 'inv', 'conv', 'feat']: - # ind, dist = crit_inds[stage], crit_dists[stage] - # big_axes[2].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs, - # transform=big_axes[2].get_xaxis_transform()) - # big_axes[2].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs, - # transform=big_axes[2].get_xaxis_transform()) - # big_axes[2].vlines(dist, big_axes[2].get_ylim()[0], curve[ind], - # color=colors[stage], **plateau_line_kwargs) -del data + # Add stage-specific proxy legend artist: + leg_handles.append(raw_axes[0].plot([], [], c=color, label=leg_labels[stage])[0]) + + # Plot curve swarm: + if stage == 'feat': + # Sync y-limits: + ylimits(measure, raw_axes[1], minval=0, pad=0.05) + raw_inset.set_ylim(raw_axes[1].get_ylim()) + # Plot swarm: + handles = raw_axes[1].plot(scales, measure, lw=lw['swarm']) + assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage]) + reorder_by_sd(handles, measure) + # Plot distribution of saturation levels: + line_kwargs = dist_line_kwargs | dict(c=color) + fill_kwargs = dist_fill_kwargs | dict(color=color) + y_dist(raw_inset, measure[-1], nbins=75, log=False, + line_kwargs=line_kwargs, fill_kwargs=fill_kwargs) + + ## NORMALIZED MEASURE: + + # Relate to noise baseline: + measure = divide_by_zero(song_data[mkey], noise_data[mkey].mean(axis=0)) + + # Plot single baseline-normalized intensity curve (median where necessary): + handles, curve = plot_curves(base_axes[0], scales, measure, c=color, lw=lw['single']) + + # Plot curve swarm: + if stage in ['conv', 'feat']: + i0, i1 = (1, 0) if stage == 'conv' else (2, 1) + # Sync y-limits: + ylimits(measure, base_axes[i0], minval=0.9, pad=0.05) + base_insets[i1].set_ylim(base_axes[i0].get_ylim()) + # Plot swarm: + handles = base_axes[i0].plot(scales, measure, lw=lw['swarm']) + assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage]) + reorder_by_sd(handles, measure) + # Plot distribution of saturation levels: + line_kwargs = dist_line_kwargs | dict(c=color) + fill_kwargs = dist_fill_kwargs | dict(color=color) + y_dist(base_insets[i1], measure[-1], nbins=100, log=True, + line_kwargs=line_kwargs, fill_kwargs=fill_kwargs) + +# Posthoc adjustments: +raw_axes[0].set_ylim(top=100) +base_axes[0].set_ylim(1, 100) +base_axes[1].set_ylim(bottom=1) +base_insets[0].set_ylim(bottom=1) +base_axes[2].set_ylim(bottom=1) +base_insets[1].set_ylim(bottom=1) + +# Add secondary x-axes: +for subfig in [raw_subfig, base_subfig]: + dual_ax = subfig.add_subplot(inset_ax_bounds) + dual_ax.set_xlim(scales[0], scales[-1]) + dual_ax.set_xscale('log') + dual_ax.tick_params(axis='x', which='minor', bottom=False) + dual_ax.tick_params(axis='x', which='major', labelrotation=45) + dual_ax.set_xticks(scales, dists) + hide_axis(dual_ax, 'left') + xlabel(dual_ax, xlabels['low'], transform=subfig.transSubfigure, **xlab_low_kwargs) + +# Add legend to first analysis axis: +legend = raw_axes[0].legend(handles=leg_handles, **leg_kwargs) +[handle.set_lw(lw['legend']) for handle in legend.get_lines()] # Save graph: if save_path is not None: diff --git a/python/fig_invariance_full.py b/python/fig_invariance_full.py index 50cd4f4..8f1550f 100644 --- a/python/fig_invariance_full.py +++ b/python/fig_invariance_full.py @@ -56,7 +56,6 @@ save_path = '../figures/fig_invariance_full.pdf' # ANALYSIS SETTINGS: exclude_zero = True thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4] -percentiles = np.array([0, 100]) scale_subset_kwargs = dict( combis=[['measure'], stages], ) diff --git a/python/save_field_data.py b/python/save_field_data.py index 404caf7..c51b24b 100644 --- a/python/save_field_data.py +++ b/python/save_field_data.py @@ -8,11 +8,11 @@ from IPython import embed # General: search_target = '*' -mode = ['song', 'noise'][1] +mode = ['song', 'noise'][0] input_folder = f'../data/field/raw/{mode}/' output_folder = f'../data/field/processed/{mode}/' stages = ['raw', 'norm'] -if True: +if False: # Overwrites edited: stages.append('songs') diff --git a/python/save_inv_data_field.py b/python/save_inv_data_field.py index 460e48b..49ce3e3 100644 --- a/python/save_inv_data_field.py +++ b/python/save_inv_data_field.py @@ -1,7 +1,9 @@ import numpy as np +import matplotlib.pyplot as plt from thunderhopper.modeltools import load_data, save_data from thunderhopper.filetools import search_files, crop_paths from thunderhopper.filtertools import find_kern_specs +from thunderhopper.filters import sosfilter from thunderhopper.model import process_signal from IPython import embed @@ -13,31 +15,24 @@ example_file = dict( )[mode] search_path = f'../data/field/processed/{mode}/' data_paths = search_files('*', ext='npz', dir=search_path) -ref_path = '../data/inv/field/ref_measures.npz' +thresh_path = '../data/inv/field/thresholds.npz' stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat'] +pre_stages = stages[:-1] save_path = f'../data/inv/field/{mode}/' # ANALYSIS SETTINGS: distances = np.load('../data/field/recording_distances.npy')[::-1] -thresh_rel = 0.5 +thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3]) +init_scale = 10000 # SUBSET SETTINGS: -kernels = np.array([ - [1, 0.002], - [-1, 0.002], - [2, 0.004], - [-2, 0.004], - [3, 0.032], - [-3, 0.032] -]) kernels = None -types = None#np.array([-1]) -sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032]) +types = None +sigmas = None # PREPARATION: -if thresh_rel is not None: - # Get threshold values from pure-noise response SD: - thresh_abs = np.load(ref_path)['conv'] * thresh_rel +thresh_data = dict(np.load(thresh_path)) +thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :] # EXECUTION: for data_path, name in zip(data_paths, crop_paths(data_paths)): @@ -48,9 +43,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)): data, config = load_data(data_path, files='raw') song, rate = data['raw'], config['rate'] - if thresh_rel is not None: - # Set kernel-specific thresholds: - config['feat_thresh'] = thresh_abs + # Sort max to min distance: + song = song[:, ::-1] * init_scale # Reduce to kernel subset: if any(var is not None for var in [kernels, types, sigmas]): @@ -58,7 +52,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)): config['kernels'] = config['kernels'][:, kern_inds] config['k_specs'] = config['k_specs'][kern_inds, :] config['k_props'] = [config['k_props'][i] for i in kern_inds] - config['feat_thresh'] = config['feat_thresh'][kern_inds] + thresh_abs = thresh_abs[:, kern_inds] # Get song segment to be analyzed: time = np.arange(song.shape[0]) / rate @@ -66,37 +60,81 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)): segment = (time >= start) & (time <= end) # Prepare storage: - measures = {} + shape = (distances.size, config['k_specs'].shape[0], thresh_rel.size) + measures = dict(measure_feat=np.zeros(shape, dtype=float)) if save_detailed: - snippets = {} + shape = (song.shape[0], config['k_specs'].shape[0], distances.size, thresh_rel.size) + snippets = dict(snip_feat=np.zeros(shape, dtype=float)) - # Process snippet: - signals, rates = process_signal(config, returns=stages, signal=song, rate=rate) - for stage in stages: - # Sort largest to smallest distance: - signals[stage] = signals[stage][..., ::-1] + # Process snippet (excluding features): + signals, rates = process_signal(config, returns=pre_stages, signal=song, rate=rate) - # Store results: - for stage in stages: - # Log intensity measures: + # Store non-feature results: + for stage in pre_stages: mkey = f'measure_{stage}' - if stage == 'feat': - measures[mkey] = signals[stage][segment, ...].mean(axis=0) - else: - measures[mkey] = signals[stage][segment, ...].std(axis=0) - - if measures[mkey].ndim == 2: - # Make shape (distances, kernels): + + # Log intensity measures: + measures[mkey] = signals[stage][segment, ...].std(axis=0) + if stage == 'conv': + # Make shape (distances, kernels) for consistency: measures[mkey] = np.moveaxis(measures[mkey], 1, 0) - # Log optional snippet data: if save_detailed: + # Log optional snippet data: snippets[f'snip_{stage}'] = signals[stage] + conv = signals['conv'] + + # Execute piecewise per threshold: + for i, thresholds in enumerate(thresh_abs): + # Execute piecewise per distance: + for j in range(conv.shape[-1]): + feat = sosfilter((conv[:, :, j] > thresholds).astype(float), + rate, config['feat_fcut'], 'lp', + padtype='fixed', padlen=config['padlen']) + + # Log intensity measure: + measure = feat[segment, ...].mean(axis=0) + measures['measure_feat'][j, :, i] = measure + if save_detailed: + # Log optional snippet data: + snippets['snip_feat'][:, :, j, i] = feat + + + + # # Log intensity measure, ensuring shape (distances, kernels, thresholds): + # measures['measure_feat'][:, :, i] = np.moveaxis(feat[segment, ...].mean(axis=0), 1, 0) + + # if save_detailed: + # # Log optional snippet data: + # snippets['snip_feat'][:, :, :, i] = feat + + # thresholds = thresholds[None, :, None] + # embed() + + # # Finalize processing: + # feat = sosfilter((signals['conv'] > thresholds).astype(float), + # rate, config['feat_fcut'], 'lp', + # padtype='fixed', padlen=config['padlen']) + # if i == thresholds.shape[0] - 1: + # fig, axes = plt.subplots(1, 8, sharex=True, sharey=True, figsize=(16, 9)) + # for j, ax in enumerate(axes): + # ax.plot(time, feat[..., j]) + # plt.show() + # embed() + + # # Log intensity measure, ensuring shape (distances, kernels, thresholds): + # measures['measure_feat'][:, :, i] = np.moveaxis(feat[segment, ...].mean(axis=0), 1, 0) + + # if save_detailed: + # # Log optional snippet data: + # snippets['snip_feat'][:, :, :, i] = feat # Save analysis results: if save_path is not None: data = dict( distances=distances, + thresh_rel=thresh_rel, + thresh_abs=thresh_abs, ) data.update(measures) if save_detailed: diff --git a/python/save_inv_data_short.py b/python/save_inv_data_short.py index b2b08a6..2fe93ae 100644 --- a/python/save_inv_data_short.py +++ b/python/save_inv_data_short.py @@ -12,8 +12,8 @@ from IPython import embed target_species = [ # 'Chorthippus_biguttulus', 'Chorthippus_mollis', - 'Chrysochraon_dispar', - 'Euchorthippus_declivus', + # 'Chrysochraon_dispar', + # 'Euchorthippus_declivus', 'Gomphocerippus_rufus', # 'Omocestus_rufipes', # 'Pseudochorthippus_parallelus', diff --git a/python/save_thresholds.py b/python/save_thresholds.py index c39d380..5e40190 100644 --- a/python/save_thresholds.py +++ b/python/save_thresholds.py @@ -1,4 +1,5 @@ import numpy as np +import matplotlib.pyplot as plt from thunderhopper.filters import sosfilter from thunderhopper.model import convolve_kernels, process_signal from thunderhopper.modeltools import load_data @@ -54,7 +55,12 @@ elif mode == 'short': conv = convolve_kernels(inv, config['kernels'], config['k_specs']) elif mode == 'field': starter = starter[:, channels].ravel(order='F') - conv = process_signal(config, 'conv', signal=starter, rate=config['rate'])[0]['conv'] + conv = process_signal(config, 'conv', signal=starter, rate=config['rate'])[0]['conv'] + # fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + # ax1.plot(starter) + # ax2.plot(conv) + # plt.show() + # embed() # Get baseline kernel response SDs: sds = conv[segment, :].std(axis=0) diff --git a/python/temp_BS.py b/python/temp_BS.py new file mode 100644 index 0000000..6acf8e5 --- /dev/null +++ b/python/temp_BS.py @@ -0,0 +1,28 @@ +import numpy as np +import matplotlib.pyplot as plt +from thunderhopper.modeltools import load_data +from thunderhopper.filetools import search_files +from thunderhopper.model import process_signal + +paths = search_files('Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms', dir='../data/field/processed/song/') +thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[-1] +thresh_abs = np.load('../data/inv/field/thresholds.npz')['sds'] * thresh_rel + +for path in paths: + print(f'Processing {path}') + + data, config = load_data(path, files='raw') + config['feat_thresh'] = thresh_abs + + song, rate = data['raw'], config['rate'] + time = np.arange(song.shape[0]) / rate + start, end = data['songs_0'].ravel() + segment = (time >= start) & (time <= end) + + signals, rates = process_signal(config, 'feat', signal=song, rate=rate) + feat = signals['feat'] + + fig, axes = plt.subplots(1, 8, sharex=True, sharey=True, figsize=(16, 9)) + for i, ax in enumerate(axes): + ax.plot(feat[..., i]) + plt.show() \ No newline at end of file