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Kinda finished analysis and figure for Log-HP invariance (WIP).

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j-hartling
2026-03-03 16:23:17 +01:00
parent 4b76478408
commit 933d28b5f8
13 changed files with 484 additions and 349 deletions
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figures/fig_invariance_log_hp.pdf Normal file
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@@ -248,15 +248,17 @@
\newlabel{eq:toy_log}{{12}{11}{}{}{}} \newlabel{eq:toy_log}{{12}{11}{}{}{}}
\newlabel{eq:toy_highpass}{{13}{11}{}{}{}} \newlabel{eq:toy_highpass}{{13}{11}{}{}{}}
\newlabel{eq:toy_snr}{{14}{11}{}{}{}} \newlabel{eq:toy_snr}{{14}{11}{}{}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Thresholding nonlinearity \& temporal averaging}{11}{}\protected@file@percent } \@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation.} \textbf {a}:~Synthetic envelopes resulting from the mixture of song component $s(t)$ with scale $\alpha $ and noise component $\eta (t)$ with fixed scale. \textbf {b}:~Corresponding logarithmically scaled envelopes. \textbf {c}:~Corresponding intensity-adapted envelopes. \textbf {d}:~Absolute SNRs of each representation relative to the representation without song component ($\alpha =0$). \textbf {e}:~Same SNRs as in \textbf {d} but normalized to the maximum SNR for each representation. \textbf {f}:~Relative amplification of normalized SNRs relative to the normalized SNRs of the initial envelope. }}{12}{}\protected@file@percent }
\newlabel{eq:pdf_split}{{15}{12}{}{}{}} \newlabel{fig:inv_log-hp}{{4}{12}{}{}{}}
\newlabel{eq:feat_avg}{{16}{12}{}{}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Thresholding nonlinearity \& temporal averaging}{12}{}\protected@file@percent }
\newlabel{eq:feat_prop}{{17}{12}{}{}{}} \newlabel{eq:pdf_split}{{15}{13}{}{}{}}
\newlabel{eq:feat_avg}{{16}{13}{}{}{}}
\newlabel{eq:feat_prop}{{17}{13}{}{}{}}
\abx@aux@cite{0}{stumpner1991auditory} \abx@aux@cite{0}{stumpner1991auditory}
\abx@aux@segm{0}{0}{stumpner1991auditory} \abx@aux@segm{0}{0}{stumpner1991auditory}
\@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{13}{}\protected@file@percent } \@writefile{toc}{\contentsline {section}{\numberline {4}Discriminating species-specific song\\patterns in feature space}{14}{}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {5}Conclusions \& outlook}{13}{}\protected@file@percent } \@writefile{toc}{\contentsline {section}{\numberline {5}Conclusions \& outlook}{14}{}\protected@file@percent }
\abx@aux@page{73}{13} \abx@aux@page{73}{14}
\gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }} \gdef\svg@ink@ver@settings{{\m@ne }{inkscape}{\m@ne }}
\abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F} \abx@aux@read@bbl@mdfivesum{1380DC8C93D2855FDB132CC5A40AD52F}
\gdef \@abspage@last{14} \gdef \@abspage@last{15}

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[0] Config.pm:307> INFO - This is Biber 2.19 [0] Config.pm:307> INFO - This is Biber 2.19
[0] Config.pm:310> INFO - Logfile is 'main.blg' [0] Config.pm:310> INFO - Logfile is 'main.blg'
[36] biber:340> INFO - === Fr Feb 27, 2026, 16:08:08 [38] biber:340> INFO - === Di Mär 3, 2026, 16:21:37
[42] Biber.pm:419> INFO - Reading 'main.bcf' [45] Biber.pm:419> INFO - Reading 'main.bcf'
[70] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 [75] Biber.pm:979> INFO - Found 55 citekeys in bib section 0
[75] Biber.pm:4419> INFO - Processing section 0 [82] Biber.pm:4419> INFO - Processing section 0
[80] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 [87] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0
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[110] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' [120] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
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[276] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' [303] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
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[307] bbl.pm:763> INFO - Output to main.bbl [337] bbl.pm:763> INFO - Output to main.bbl
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 10, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 10, warning: 6 characters of junk seen at toplevel
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[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 49, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 49, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 58, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 58, warning: 6 characters of junk seen at toplevel
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[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 91, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 91, warning: 6 characters of junk seen at toplevel
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[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 118, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 118, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 127, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 127, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 136, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 136, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 157, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 157, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 178, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 178, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 187, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 187, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 196, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 196, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 207, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 207, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 218, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 218, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 229, warning: 6 characters of junk seen at toplevel [337] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 229, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 240, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 240, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 249, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 249, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 258, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 258, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 269, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 269, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 278, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 278, warning: 6 characters of junk seen at toplevel
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[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 300, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 300, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 309, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 309, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 328, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 328, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 337, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 337, warning: 6 characters of junk seen at toplevel
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[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 419, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 419, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 428, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 428, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 437, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 437, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 456, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 456, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 491, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 491, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 526, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 526, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 535, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 535, warning: 6 characters of junk seen at toplevel
[307] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 556, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 556, warning: 6 characters of junk seen at toplevel
[308] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 565, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 565, warning: 6 characters of junk seen at toplevel
[308] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 576, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 576, warning: 6 characters of junk seen at toplevel
[308] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 587, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 587, warning: 6 characters of junk seen at toplevel
[308] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_Q3WE/347c261ec4135a5723bef5c751f5078f_25304.utf8, line 619, warning: 6 characters of junk seen at toplevel [338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_rB4F/347c261ec4135a5723bef5c751f5078f_81147.utf8, line 619, warning: 6 characters of junk seen at toplevel
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main.fdb_latexmk
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5
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@@ -288,6 +288,11 @@ INPUT ./figures/fig_feat_stages.pdf
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INPUT ./figures/fig_feat_stages.pdf INPUT ./figures/fig_feat_stages.pdf
INPUT ./figures/fig_feat_stages.pdf INPUT ./figures/fig_feat_stages.pdf
INPUT ./figures/fig_invariance_log_hp.pdf
INPUT ./figures/fig_invariance_log_hp.pdf
INPUT ./figures/fig_invariance_log_hp.pdf
INPUT ./figures/fig_invariance_log_hp.pdf
INPUT ./figures/fig_invariance_log_hp.pdf
INPUT main.aux INPUT main.aux
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main.tex
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@@ -620,6 +620,27 @@ to recover songs that have already sunken below the noise floor, which
emphasizes the importance of a sufficiently high SNR at the intial reception of emphasizes the importance of a sufficiently high SNR at the intial reception of
the signal for reliable song recognition. the signal for reliable song recognition.
\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/fig_invariance_log_hp.pdf}
\caption{\textbf{Intensity invariance by logarithmic compression and
adaptation.}
\textbf{a}:~Synthetic envelopes resulting from the
mixture of song component $\soc(t)$ with scale $\sca$ and
noise component $\noc(t)$ with fixed scale.
\textbf{b}:~Corresponding logarithmically scaled envelopes.
\textbf{c}:~Corresponding intensity-adapted envelopes.
\textbf{d}:~Absolute SNRs of each representation relative
to the representation without song component ($\sca=0$).
\textbf{e}:~Same SNRs as in \textbf{d} but normalized to
the maximum SNR for each representation.
\textbf{f}:~Relative amplification of normalized SNRs
relative to the normalized SNRs of the initial envelope.
}
\label{fig:inv_log-hp}
\end{figure}
\FloatBarrier
\subsection{Thresholding nonlinearity \& temporal averaging} \subsection{Thresholding nonlinearity \& temporal averaging}
The second key mechanism for the emergence of intensity invariance along the The second key mechanism for the emergence of intensity invariance along the

326
python/fig_invariance_log-hp.py
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@@ -1,133 +1,227 @@
import plotstyle_plt
import glob
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from itertools import product from itertools import product
from thunderhopper.modeltools import load_data
from color_functions import load_colors
from plot_functions import hide_axis, letter_subplots, ylimits, ylabel, super_xlabel, plot_line
from IPython import embed
def prepare_fig(nrows, ncols, width=8, height=None, rheight=2, def strip_zeros(num, right_digits=5):
left=0.01, right=0.95, bottom=0.01, top=0.95, if isinstance(num, int):
wspace=0.4, hspace=0.4): return num
if height is None: num = f'{num:.{right_digits}f}'
height = rheight * nrows left, right = num.split('.')
fig = plt.figure(figsize=(width, height)) right = right.rstrip('0')
grid = fig.add_gridspec(nrows=nrows, ncols=ncols, wspace=wspace, hspace=hspace, if right:
left=left, right=right, top=top, bottom=bottom) return f'{left}.{right}'
axes = np.zeros((nrows, ncols), dtype=object) return left
for i, j in product(range(nrows), range(ncols)):
axes[i, j] = fig.add_subplot(grid[i, j])
axes[i, j].set_facecolor('none')
return fig, axes
def xlimits(ax, time, minval=None, maxval=None, pad=0.05): def plot_snippets(axes, time, snippets, label, scales=None,
limits = [minval, maxval] ymin=None, ymax=None, **kwargs):
if minval is None: ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
limits[0] = time[0] ylabel(axes[0], label, x=-0.7, rotation=0, ha='left', va='center')
if maxval is None: for i, (ax, snippet) in enumerate(zip(axes, snippets.T)):
limits[1] = time[-1] plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
if pad is not None and minval is None: if scales is not None:
limits[0] -= (limits[1] - limits[0]) * pad ax.set_title(f'$\\alpha={strip_zeros(scales[i])}$')
if pad is not None and maxval is None:
limits[1] += (limits[1] - limits[0]) * pad
return ax.set_xlim(limits)
def ylimits(ax, signal, minval=None, maxval=None, pad=0.05):
limits = [minval, maxval]
if minval is None:
limits[0] = signal.min()
if maxval is None:
limits[1] = signal.max()
if pad is not None and minval is None:
limits[0] -= (limits[1] - limits[0]) * pad
if pad is not None and maxval is None:
limits[1] += (limits[1] - limits[0]) * pad
return ax.set_ylim(limits)
def ylabel(ax, label, x=-0.23, fontsize=20):
ax.set_ylabel(label, fontsize=fontsize, rotation=0, ha='left', va='center')
ax.yaxis.set_label_coords(x, 0.5)
return None return None
def super_xlabel(label, fig, high_ax, low_ax, y=0.005, **kwargs): # GENERAL SETTINGS:
x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2 target = 'Omocestus_rufipes'
fig.supxlabel(label, x=x, y=y, **kwargs) data_paths = glob.glob(f'../data/inv/log_hp/{target}*.npz')
return None stages = ['env', 'log', 'inv']
files = stages + ['scales', 'plot_scales', 'snr_env', 'snr_log', 'snr_inv']
save_path = '../figures/fig_invariance_log_hp.pdf'
def super_ylabel(label, fig, high_ax, low_ax, x=0.005, **kwargs): # PLOT SETTINGS:
y = (low_ax.get_position().y0 + high_ax.get_position().y1) / 2 fig_kwargs = dict(
fig.supylabel(label, x=x, y=y, **kwargs) figsize=(32/2.54, 16/2.54),
return None )
grid1_kwargs = dict(
nrows=len(stages),
ncols=None,
wspace=0.05,
hspace=0.3,
left=0.1,
right=0.985,
bottom=0.17,
top=0.9
)
grid2_kwargs = dict(
nrows=1,
ncols=3,
wspace=0.35,
hspace=0,
left=0.1,
right=0.985,
bottom=0.18,
top=0.95
)
colors = load_colors('../data/stage_colors.npz')
lw_snippets = 0.25
lw_analysis = 3
ylabels = dict(
env=r'$x_{\text{env}}$',
log=r'$x_{\text{dB}}$',
inv=r'$x_{\text{adapt}}$',
abs='abs. SNR',#'abs. ' + r'$\text{SNR}$',
norm='norm. SNR',#'norm. ' + r'$\text{SNR}$',
gain='rel. SNR gain'
)
xloc = dict(
abs=5,
)
yloc = dict(
env=100,
log=10,
inv=5,
abs=50,
)
letter_kwargs = dict(
x=0.03,
y=0.99,
ha='left',
va='top'
)
fit_kwargs = dict(
c='darkred',
ls='--',
zorder=1.9
)
fit_lw = dict(
abs=6,
norm=3,
gain=3
)
text_kwargs = dict(
abs=dict(s='$\\alpha^2+1$', fontsize=16, c=fit_kwargs['c'],
x=0.85, y=0.9, ha='right', va='center'),
norm=dict(s='$\\alpha^2+1$', fontsize=16, c=fit_kwargs['c'],
x=0.85, y=0.9, ha='right', va='center'),
gain=dict(s='$\\frac{1}{\\alpha}$', fontsize=24, c=fit_kwargs['c'],
x=0.75, y=0.8, ha='left', va='center')
)
calculated_floor = False
floor_kwargs = dict(
xmin=0,
color=3 * (0.85,),
zorder=0,
lw=0
)
def hide_axis(ax, side='bottom'): # EXECUTION:
ax.spines[side].set_visible(False) for data_path in data_paths:
params = {side: False, 'label' + side: False} print(f'Processing {data_path}')
ax.tick_params(axis='x' if side in ['top', 'bottom'] else 'y',
which='both', **params)
return None
def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None, # Load invariance data:
xpad=None, ypad=0.05, yloc=None, **kwargs): data, config = load_data(data_path, files)
handles = ax.plot(time, signal, **kwargs) t_full = np.arange(data['env'].shape[0]) / config['env_rate']
xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad) nonzero_scales = data['scales'][data['scales'] > 0]
ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad) floor_kwargs['xmax'] = data['floor_scale'] if calculated_floor else 1
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
return handles
def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs): # Prepare overall graph:
if xmin is None: fig = plt.figure(**fig_kwargs)
xmin = time[0] super_grid = fig.add_gridspec(2, 1, wspace=0, hspace=1,
if xmax is None: left=0, right=1, bottom=0, top=1)
xmax = time[-1]
lower, upper, handles = 0, 1, []
for i in range(binary.shape[1]):
h = ax.fill_between(time, lower, upper, where=binary[:, i], **kwargs)
handles.append(h)
if i < binary.shape[1] - 1:
lower += offset + 1
upper += offset + 1
xlimits(ax, time, minval=xmin, maxval=xmax)
ax.set_ylim(0, upper)
hide_axis(ax, 'bottom')
hide_axis(ax, 'left')
return handles
def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs): # Prepare snippet axes:
y0 = low_ax.get_position().y0 subfig1 = fig.add_subfigure(super_grid[0, :])
y1 = high_ax.get_position().y1 grid1_kwargs['ncols'] = data['plot_scales'].size
transform = low_ax.transData + fig.transFigure.inverted() grid1 = subfig1.add_gridspec(**grid1_kwargs)
x0 = transform.transform((zoom_abs[0], 0))[0] axes = np.zeros((grid1.nrows, grid1.ncols), dtype=object)
x1 = transform.transform((zoom_abs[1], 0))[0] for i, j in product(range(grid1.nrows), range(grid1.ncols)):
rect = plt.Rectangle((x0, y0), x1 - x0, y1 - y0, axes[i, j] = subfig1.add_subplot(grid1[i, j])
transform=fig.transFigure, **kwargs) [hide_axis(ax, 'bottom') for ax in axes[:-1, :].flatten()]
fig.add_artist(rect) [hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
return None super_xlabel('time [s]', subfig1, axes[-1, 0], axes[-1, -1], y=0)
letter_subplots(axes[:, 0], labels='abc', **letter_kwargs)
def assign_colors(handles, types, colors): # Prepare analysis axes:
for handle, type_id in zip(handles, types): subfig2 = fig.add_subfigure(super_grid[1, :])
handle.set_color(colors[str(int(type_id))]) grid2 = subfig2.add_gridspec(**grid2_kwargs)
return None symlog_kwargs = dict(linthresh=nonzero_scales.min(), linscale=0.2)
def reorder_traces(handles, signal, zlow=2, zhigh=2.5): ax_abs_snr = subfig2.add_subplot(grid2[:, 0])
inds = np.argsort(signal.std(axis=0)) ax_abs_snr.set_ylabel(ylabels['abs'])
zorders = np.linspace(zlow, zhigh, len(inds))[::-1]
for ind, z in zip(inds, zorders):
handles[ind].set_zorder(z)
return None
def choose_kernels(kern_specs, features, kern_types, per_type=2, thresh=0.01): ax_norm_snr = subfig2.add_subplot(grid2[:, 1])
mean_feat = features.mean(axis=0) ax_norm_snr.set_ylabel(ylabels['norm'])
feat_diff = np.abs(mean_feat[:, None] - mean_feat[None, :]) ax_norm_snr.set_xscale('symlog', **symlog_kwargs)
feat_diff[features.max(axis=0) < thresh, :] = np.nan ax_norm_snr.set_yscale('log')
feat_diff = np.nanmean(feat_diff, axis=0)
ranking = np.argsort(feat_diff) ax_gain = subfig2.add_subplot(grid2[:, 2])
kern_inds = [] ax_gain.set_ylabel(ylabels['gain'])
for type_id in kern_types: ax_gain.set_xscale('symlog', **symlog_kwargs)
type_inds = np.nonzero(kern_specs[:, 0] == type_id)[0] ax_gain.set_yscale('log')
rank_inds = np.nonzero(np.isin(ranking, type_inds))[0][-per_type:] super_xlabel('song scale $\\alpha$', subfig2, ax_abs_snr, ax_gain)
kern_inds.extend(ranking[rank_inds]) letter_subplots([ax_abs_snr, ax_norm_snr, ax_gain], labels='def', **letter_kwargs)
return np.array(kern_inds)
def letter_subplots(axes, labels='abcd', x=0.02, y=1, ha='left', va='bottom', # Plot envelope snippets:
fontsize=16, fontweight='bold', **kwargs): plot_snippets(axes[0, :], t_full, data['env'], ylabels['env'], ymin=0,
for ax, label in zip(axes, labels): scales=data['plot_scales'], c=colors['env'], lw=lw_snippets)
ax.text(x, y, label, transform=ax.transAxes, ha=ha, va=va, axes[0, 0].yaxis.set_major_locator(plt.MultipleLocator(yloc['env']))
fontsize=fontsize, fontweight=fontweight, **kwargs)
return None # Plot logarithmic snippets:
plot_snippets(axes[1, :], t_full, data['log'], ylabels['log'], ymax=0,
c=colors['log'], lw=lw_snippets)
axes[1, 0].yaxis.set_major_locator(plt.MultipleLocator(yloc['log']))
# Plot invariant snippets:
plot_snippets(axes[2, :], t_full, data['inv'], ylabels['inv'],
c=colors['inv'], lw=lw_snippets)
axes[2, 0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv']))
# Plot in-representation SNRs (absolute):
ax_abs_snr.plot(data['scales'], data['snr_env'], c=colors['env'], lw=lw_analysis)
ax_abs_snr.plot(data['scales'], data['snr_log'], c=colors['log'], lw=lw_analysis)
ax_abs_snr.plot(data['scales'], data['snr_inv'], c=colors['inv'], lw=lw_analysis)
ax_abs_snr.axvspan(**floor_kwargs)
ax_abs_snr.xaxis.set_major_locator(plt.MultipleLocator(xloc['abs']))
ax_abs_snr.yaxis.set_major_locator(plt.MultipleLocator(yloc['abs']))
ax_abs_snr.set_ylim(0, data['snr_env'].max())
# Plot envelope SNR fit:
ax_abs_snr.plot(data['scales'], data['scales'] ** 2 + 1,
lw=fit_lw['abs'], **fit_kwargs)
ax_abs_snr.text(transform=ax_abs_snr.transAxes, **text_kwargs['abs'])
# Get normalized SNRs:
norm_snr_env = data['snr_env'] / data['snr_env'].max()
norm_snr_log = data['snr_log'] / data['snr_log'].max()
norm_snr_inv = data['snr_inv'] / data['snr_inv'].max()
# Plot in-representation SNRs (normalized):
ax_norm_snr.plot(data['scales'], norm_snr_env, c=colors['env'], lw=lw_analysis)
ax_norm_snr.plot(data['scales'], norm_snr_log, c=colors['log'], lw=lw_analysis)
ax_norm_snr.plot(data['scales'], norm_snr_inv, c=colors['inv'], lw=lw_analysis)
ax_norm_snr.axvspan(**floor_kwargs)
ax_norm_snr.set_ylim(norm_snr_env.min(), 1)
# # Plot envelope SNR fit:
# ax_norm_snr.plot(nonzero_scales, nonzero_scales / nonzero_scales.max(),
# lw=fit_lw['norm'], **fit_kwargs)
# ax_norm_snr.text(transform=ax_norm_snr.transAxes, **text_kwargs['norm'])
# Get relative SNR gain:
gain_log = norm_snr_log / norm_snr_env
gain_inv = norm_snr_inv / norm_snr_env
# Plot across-representation gain:
ax_gain.plot(data['scales'], gain_log, c=colors['log'], lw=lw_analysis)
ax_gain.plot(data['scales'], gain_inv, c=colors['inv'], lw=lw_analysis)
ax_gain.axvspan(**floor_kwargs)
ax_gain.set_ylim(1, 10)
# Plot amplification fit:
ax_gain.plot(nonzero_scales, nonzero_scales.max() / nonzero_scales,
lw=fit_lw['gain'], **fit_kwargs)
ax_gain.text(transform=ax_gain.transAxes, **text_kwargs['gain'])
if save_path is not None:
fig.savefig(save_path)
plt.show()
print('Done.')
embed()

136
python/fig_pathway_stages.py
View File

@@ -9,142 +9,11 @@ from plot_functions import prepare_fig, hide_axis, letter_subplots,\
indicate_zoom, assign_colors, reorder_traces indicate_zoom, assign_colors, reorder_traces
from IPython import embed from IPython import embed
# def prepare_fig(nrows, ncols, width=8, height=None, rheight=2,
# left=0.01, right=0.95, bottom=0.01, top=0.95,
# wspace=0.4, hspace=0.4):
# if height is None:
# height = rheight * nrows
# fig = plt.figure(figsize=(width, height))
# grid = fig.add_gridspec(nrows=nrows, ncols=ncols, wspace=wspace, hspace=hspace,
# left=left, right=right, top=top, bottom=bottom)
# axes = np.zeros((nrows, ncols), dtype=object)
# for i, j in product(range(nrows), range(ncols)):
# axes[i, j] = fig.add_subplot(grid[i, j])
# axes[i, j].set_facecolor('none')
# return fig, axes
# def xlimits(ax, time, minval=None, maxval=None, pad=0.05):
# limits = [minval, maxval]
# if minval is None:
# limits[0] = time[0]
# if maxval is None:
# limits[1] = time[-1]
# if pad is not None and minval is None:
# limits[0] -= (limits[1] - limits[0]) * pad
# if pad is not None and maxval is None:
# limits[1] += (limits[1] - limits[0]) * pad
# return ax.set_xlim(limits)
# def ylimits(ax, signal, minval=None, maxval=None, pad=0.05):
# limits = [minval, maxval]
# if minval is None:
# limits[0] = signal.min()
# if maxval is None:
# limits[1] = signal.max()
# if pad is not None and minval is None:
# limits[0] -= (limits[1] - limits[0]) * pad
# if pad is not None and maxval is None:
# limits[1] += (limits[1] - limits[0]) * pad
# return ax.set_ylim(limits)
# def ylabel(ax, label, x=-0.23, fontsize=20):
# ax.set_ylabel(label, fontsize=fontsize, rotation=0, ha='left', va='center')
# ax.yaxis.set_label_coords(x, 0.5)
# return None
# def super_xlabel(label, fig, high_ax, low_ax, y=0.005, **kwargs):
# x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2
# fig.supxlabel(label, x=x, y=y, **kwargs)
# return None
# def super_ylabel(label, fig, high_ax, low_ax, x=0.005, **kwargs):
# y = (low_ax.get_position().y0 + high_ax.get_position().y1) / 2
# fig.supylabel(label, x=x, y=y, **kwargs)
# return None
# def hide_axis(ax, side='bottom'):
# ax.spines[side].set_visible(False)
# params = {side: False, 'label' + side: False}
# ax.tick_params(axis='x' if side in ['top', 'bottom'] else 'y',
# which='both', **params)
# return None
# def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None,
# xpad=None, ypad=0.05, yloc=None, **kwargs):
# handles = ax.plot(time, signal, **kwargs)
# xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad)
# ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad)
# ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
# return handles
# def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs):
# if xmin is None:
# xmin = time[0]
# if xmax is None:
# xmax = time[-1]
# lower, upper, handles = 0, 1, []
# for i in range(binary.shape[1]):
# h = ax.fill_between(time, lower, upper, where=binary[:, i], **kwargs)
# handles.append(h)
# if i < binary.shape[1] - 1:
# lower += offset + 1
# upper += offset + 1
# xlimits(ax, time, minval=xmin, maxval=xmax)
# ax.set_ylim(0, upper)
# hide_axis(ax, 'bottom')
# hide_axis(ax, 'left')
# return handles
# def indicate_zoom(fig, high_ax, low_ax, zoom_abs, **kwargs):
# y0 = low_ax.get_position().y0
# y1 = high_ax.get_position().y1
# transform = low_ax.transData + fig.transFigure.inverted()
# x0 = transform.transform((zoom_abs[0], 0))[0]
# x1 = transform.transform((zoom_abs[1], 0))[0]
# rect = plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
# transform=fig.transFigure, **kwargs)
# fig.add_artist(rect)
# return None
# def assign_colors(handles, types, colors):
# for handle, type_id in zip(handles, types):
# handle.set_color(colors[str(int(type_id))])
# return None
# def reorder_traces(handles, signal, zlow=2, zhigh=2.5):
# inds = np.argsort(signal.std(axis=0))
# zorders = np.linspace(zlow, zhigh, len(inds))[::-1]
# for ind, z in zip(inds, zorders):
# handles[ind].set_zorder(z)
# return None
# def choose_kernels(kern_specs, features, kern_types, per_type=2, thresh=0.01):
# mean_feat = features.mean(axis=0)
# feat_diff = np.abs(mean_feat[:, None] - mean_feat[None, :])
# feat_diff[features.max(axis=0) < thresh, :] = np.nan
# feat_diff = np.nanmean(feat_diff, axis=0)
# ranking = np.argsort(feat_diff)
# kern_inds = []
# for type_id in kern_types:
# type_inds = np.nonzero(kern_specs[:, 0] == type_id)[0]
# rank_inds = np.nonzero(np.isin(ranking, type_inds))[0][-per_type:]
# kern_inds.extend(ranking[rank_inds])
# return np.array(kern_inds)
# def letter_subplots(axes, labels='abcd', x=0.02, y=1, ha='left', va='bottom',
# fontsize=16, fontweight='bold', **kwargs):
# for ax, label in zip(axes, labels):
# ax.text(x, y, label, transform=ax.transAxes, ha=ha, va=va,
# fontsize=fontsize, fontweight=fontweight, **kwargs)
# return None
# GENERAL SETTINGS: # GENERAL SETTINGS:
target = 'Omocestus_rufipes' target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz') data_paths = glob.glob(f'../data/processed/{target}*.npz')
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat'] stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
save_path = None#'../figures/' save_path = '../figures/'
# PLOT SETTINGS: # PLOT SETTINGS:
fig_kwargs = dict( fig_kwargs = dict(
@@ -348,3 +217,6 @@ for data_path in data_paths:
if save_path is not None: if save_path is not None:
fig.savefig(f'{save_path}fig_feat_stages.pdf') fig.savefig(f'{save_path}fig_feat_stages.pdf')
plt.show() plt.show()
print('Done.')
embed()

24
python/plot_functions.py
View File

@@ -17,6 +17,12 @@ def prepare_fig(nrows, ncols, width=8, height=None, rheight=2, unit=1/2.54,
axes[i, j].set_facecolor('none') axes[i, j].set_facecolor('none')
return fig, axes return fig, axes
def hide_ticks(ax, side='bottom', ticks=True):
axis = 'x' if side in ['top', 'bottom'] else 'y'
params = {side: ticks, 'label' + side: False}
ax.tick_params(axis=axis, which='both', **params)
return None
def hide_axis(ax, side='bottom'): def hide_axis(ax, side='bottom'):
ax.spines[side].set_visible(False) ax.spines[side].set_visible(False)
params = {side: False, 'label' + side: False} params = {side: False, 'label' + side: False}
@@ -33,7 +39,7 @@ def letter_subplots(axes, labels=None, x=0.02, y=1, ha='left', va='bottom',
fontsize=fontsize, fontweight=fontweight, **kwargs) fontsize=fontsize, fontweight=fontweight, **kwargs)
return None return None
def xlimits(ax, time, minval=None, maxval=None, pad=0.05): def xlimits(time, ax=None, minval=None, maxval=None, pad=0.05):
limits = [minval, maxval] limits = [minval, maxval]
if minval is None: if minval is None:
limits[0] = time[0] limits[0] = time[0]
@@ -44,9 +50,11 @@ def xlimits(ax, time, minval=None, maxval=None, pad=0.05):
limits[0] -= span * pad limits[0] -= span * pad
if pad and maxval is None: if pad and maxval is None:
limits[1] += span * pad limits[1] += span * pad
return ax.set_xlim(limits) if ax is not None:
return ax.set_xlim(limits)
return limits
def ylimits(ax, signal, minval=None, maxval=None, pad=0.05): def ylimits(signal, ax=None, minval=None, maxval=None, pad=0.05):
limits = [minval, maxval] limits = [minval, maxval]
if minval is None: if minval is None:
limits[0] = signal.min() limits[0] = signal.min()
@@ -57,7 +65,9 @@ def ylimits(ax, signal, minval=None, maxval=None, pad=0.05):
limits[0] -= span * pad limits[0] -= span * pad
if pad and maxval is None: if pad and maxval is None:
limits[1] += span * pad limits[1] += span * pad
return ax.set_ylim(limits) if ax is not None:
return ax.set_ylim(limits)
return limits
def xlabel(ax, label, y=-0.1, fontsize=20, **kwargs): def xlabel(ax, label, y=-0.1, fontsize=20, **kwargs):
ax.set_xlabel(label, fontsize=fontsize, **kwargs) ax.set_xlabel(label, fontsize=fontsize, **kwargs)
@@ -82,8 +92,8 @@ def super_ylabel(label, fig, high_ax, low_ax, x=0.005, **kwargs):
def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None, def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None,
xpad=None, ypad=0.05, yloc=None, xloc=None, **kwargs): xpad=None, ypad=0.05, yloc=None, xloc=None, **kwargs):
handles = ax.plot(time, signal, **kwargs) handles = ax.plot(time, signal, **kwargs)
xlimits(ax, time, minval=xmin, maxval=xmax, pad=xpad) xlimits(time, ax=ax, minval=xmin, maxval=xmax, pad=xpad)
ylimits(ax, signal, minval=ymin, maxval=ymax, pad=ypad) ylimits(signal, ax=ax, minval=ymin, maxval=ymax, pad=ypad)
if xloc is not None: if xloc is not None:
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc)) ax.xaxis.set_major_locator(plt.MultipleLocator(xloc))
if yloc is not None: if yloc is not None:
@@ -98,7 +108,7 @@ def plot_barcode(ax, time, binary, offset=0.5, xmin=None, xmax=None, **kwargs):
if i < binary.shape[1] - 1: if i < binary.shape[1] - 1:
lower += offset + 1 lower += offset + 1
upper += offset + 1 upper += offset + 1
xlimits(ax, time, minval=xmin, maxval=xmax, pad=0) xlimits(time, ax=ax, minval=xmin, maxval=xmax, pad=0)
ax.set_ylim(0, upper) ax.set_ylim(0, upper)
hide_axis(ax, 'bottom') hide_axis(ax, 'bottom')
hide_axis(ax, 'left') hide_axis(ax, 'left')

124
python/save_inv_data_log-hp.py Normal file
View File

@@ -0,0 +1,124 @@
import plotstyle_plt
import glob
import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.filters import decibel, sosfilter
from thunderhopper.model import extract_env
from IPython import embed
# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
save_path = '../data/inv/log_hp/'
# ANALYSIS SETTINGS:
scales = np.arange(0, 10.1, 0.1)
save_scales = np.array([0, 0.5, 1, 2, 5, 10])
floor_percents = dict(song=100, noise=99)
single_db_ref = True
plot_redundant = False
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
# Load song envelope:
data, config = load_data(data_path, files='env')
song, rate = data['env'], config['env_rate']
t_full = np.arange(song.shape[0]) / rate
# Generate noise envelope:
rng = np.random.default_rng()
noise = rng.normal(size=song.shape)
noise = extract_env(noise, rate, config=config)
# Normalize each:
song /= song.std()
noise /= noise.std()
# Mix song component and noise component:
scaled = song[:, None] * scales[None, :]
mix = scaled + noise[:, None]
# Find noise floor (experimental):
song_percent = np.percentile(scaled, floor_percents['song'], axis=0)
noise_percent = np.percentile(noise, floor_percents['noise'])
floor_scale = scales[np.nonzero(song_percent <= noise_percent)[0][-1]]
# Process mixture:
mix_log = decibel(mix, axis=None if single_db_ref else 0)
mix_inv = sosfilter(mix_log, rate, config['inv_fcut'], 'hp',
padtype='constant', padlen=config['padlen'])
# Get variances per stage:
var_env = mix.var(axis=0)
var_log = mix_log.var(axis=0)
var_inv = mix_inv.var(axis=0)
# Get SNRs against pure noise per stage:
base_ind = np.nonzero(scales == 0)[0][0]
snr_env = var_env / var_env[base_ind]
snr_log = var_log / var_log[base_ind]
snr_inv = var_inv / var_inv[base_ind]
if plot_redundant:
# Normalize SNRs:
norm_snr_env = snr_env / snr_env.max()
norm_snr_log = snr_log / snr_log.max()
norm_snr_inv = snr_inv / snr_inv.max()
# Get SNR gain against env:
gain_log = snr_log / snr_env
gain_inv = snr_inv / snr_env
# Plot results:
fig, axes = plt.subplots(6, 1, sharex=True, layout='constrained')
axes[0].set_title('variance')
axes[0].plot(scales, var_env)
axes[0].plot(scales, var_log)
axes[0].plot(scales, var_inv)
axes[1].set_title('normalized variance')
axes[1].plot(scales, var_env / var_env.max())
axes[1].plot(scales, var_log / var_log.max())
axes[1].plot(scales, var_inv / var_inv.max())
axes[2].set_title('SNR')
axes[2].plot(scales, snr_env)
axes[2].plot(scales, snr_log)
axes[2].plot(scales, snr_inv)
axes[3].set_title('normalized SNR')
axes[3].plot(scales, norm_snr_env)
axes[3].plot(scales, norm_snr_log)
axes[3].plot(scales, norm_snr_inv)
axes[4].set_title('gain (absolute SNR)')
axes[4].plot(scales, gain_log)
axes[4].plot(scales, gain_inv)
axes[5].set_title('gain (normalized SNR)')
axes[5].plot(scales, norm_snr_log / norm_snr_env)
axes[5].plot(scales, norm_snr_inv / norm_snr_env)
plt.show()
# Save analysis results:
save_inds = np.isin(scales, save_scales)
if save_path is not None:
data = dict(
scales=scales,
plot_scales=scales[save_inds],
floor_scale=floor_scale,
env=mix[:, save_inds],
log=mix_log[:, save_inds],
inv=mix_inv[:, save_inds],
snr_env=snr_env,
snr_log=snr_log,
snr_inv=snr_inv,
)
save_data(save_path + name, data, config, overwrite=True)
print('Done.')
embed()