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\@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 }
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\@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 }
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\@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 }
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\@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 $f_i(t)$. Dots indicate the value from \textbf {b}. }}{26}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces \textbf {Interspecific and intraspecific feature variability.} Average value $\mu _{f_i}$ of each feature $f_i(t)$ against its counterpart from a 2nd feature set based on a different input $x_{\text {raw}}(t)$. Each dot within a subplot represents a single feature $f_i(t)$. 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). 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. Dots indicate single $\rho $ values. }}{29}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf {Intensity invariance along the model pathway in a naturalistic setting.} Input $x_{\text {raw}}(t)$ 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 $n$ 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. 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 $\mu _{f_i}$. }}{28}{}\protected@file@percent }
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[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
|
||||
|
||||
@@ -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 ""
|
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@@ -1120,7 +1120,7 @@ in principle, work together towards an intensity-invariant song representation.
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\centering
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\includegraphics[width=\textwidth]{figures/fig_invariance_full_Omocestus_rufipes.pdf}
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\caption{\textbf{Step-wise emergence of intensity-invariant song
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representation along the full model pathway.}
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representations along the model pathway.}
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Input $\raw(t)$ consists of song component $\soc(t)$
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scaled by $\sca$ with added noise component $\noc(t)$ and
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is processed up to the feature set $f_i(t)$. Different
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\begin{figure}[!ht]
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\centering
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\includegraphics[width=\textwidth]{figures/fig_invariance_short_Omocestus_rufipes.pdf}
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\caption{\textbf{Step-wise emergence of intensity invariant song
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representation along the model pathway without logarithmic
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compression.}
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\caption{\textbf{Effects of disabling logarithmic compression on intensity
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invariance along the model pathway.}
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Input $\raw(t)$ consists of song component $\soc(t)$
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scaled by $\sca$ with added noise component $\noc(t)$ and
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is processed up to the feature set $f_i(t)$, skipping
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\subsubsection{Field data}
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\subsubsection{Intensity invariance in a naturalistic setting}
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So far, the analyses on intensity invariance were based on synthetically
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generated input signals, since these allow for a systematic manipulation of
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the mixture of song component $\soc(t)$ and noise component $\noc(t)$ over
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an arbitrary range of scales $\sca$.
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\begin{figure}[!ht]
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\centering
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\includegraphics[width=\textwidth]{figures/fig_invariance_field.pdf}
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\caption{\textbf{Step-wise emergence of intensity invariant song
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representation along the model pathway.}
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\caption{\textbf{Intensity invariance along the model pathway in a
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naturalistic setting.}
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Input $\raw(t)$ consists of a song of \textit{P.
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parallelus} recorded in the field at eight different
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distances $d$ and is processed up to the feature set
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$f_i(t)$. Different color shades indicate different types
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either $+$ or $-$ sign, sorted (dark to light) first by
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increasing $\kn$ and then by
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sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then $-$ for
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each $\kn$; five kernel widths $\kw$ of 1, 2, 4, 8, and
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$16\,$ms per type; 8 types, 40 kernels in total).
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\textbf{a}:~$\filt(t)$, $\env(t)$, $\db(t)$, $\adapt(t)$,
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$c_i(t)$, and $f_i(t)$ at each $d$. A noise segment from
|
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the same recording is shown for reference.
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\textbf{b}:~Intensity metrics over $d$. For $c_i(t)$
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and $f_i(t)$, the median over kernels is shown.
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\textbf{c}:~Average value $\mu_{f_i}$ of each feature
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$f_i(t)$ over $d$.
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\textbf{d}:~Ratios of intensity metrics to the respective
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value obtained from the noise reference. For $c_i(t)$ and
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$f_i(t)$, the median over kernel-specific ratios is shown.
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\textbf{e}:~Ratios of standard deviation $\sigma_{c_i}$ of
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each $c_i(t)$.
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\textbf{f}:~Ratios of $\mu_{f_i}$.
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}
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\label{fig:pipeline_field}
|
||||
\end{figure}
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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],
|
||||
)
|
||||
|
||||
@@ -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')
|
||||
|
||||
|
||||
@@ -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:
|
||||
|
||||
@@ -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',
|
||||
|
||||
@@ -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)
|
||||
|
||||
28
python/temp_BS.py
Normal file
28
python/temp_BS.py
Normal file
@@ -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()
|
||||
Reference in New Issue
Block a user