Added loads of units in nearly all graphs.
Overhauled fig_invariance_full.pdf. Added some legends, somewhere.
This commit is contained in:
j-hartling
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@@ -244,14 +244,14 @@
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\newlabel{fig:stages_feat}{{3}{10}{}{}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with (\textbf {c}{} and \textbf {d}) or without (\textbf {a}{} and \textbf {b}) additive noise component $\eta (t)$. Input $x_{\text {filt}}(t)$ is transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {dB}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Left}:~$x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ for different scales $\alpha $. \textbf {Right}:~Ratios of the standard deviation of $x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ relative to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text {filt}}(t)=\eta (t)$. \textbf {a}{} and \textbf {b}:~Ideally, if $x_{\text {filt}}(t)=\alpha \cdot s(t)$, then $x_{\text {adapt}}(t)$ is intensity-invariant across all $\alpha $. \textbf {c}{} and \textbf {d}:~In practice, if $x_{\text {filt}}(t)=\alpha \cdot s(t)+\eta (t)$, the intensity invariance of $x_{\text {adapt}}(t)$ is limited to sufficiently large $\alpha $. Shaded area indicates saturation of $x_{\text {adapt}}(t)$ at $95\,\%$ curve span. }}{12}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with (\textbf {c}{} and \textbf {d}) or without (\textbf {a}{} and \textbf {b}) additive noise component $\eta (t)$. Input $x_{\text {filt}}(t)$ is transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {log}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Left}:~$x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$ for different scales $\alpha $. \textbf {Right}:~Ratios of the standard deviation of $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$ relative to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text {filt}}(t)=\eta (t)$. \textbf {a}{} and \textbf {b}:~Ideally, if $x_{\text {filt}}(t)=\alpha \cdot s(t)$, then $x_{\text {adapt}}(t)$ is intensity-invariant across all $\alpha $. \textbf {c}{} and \textbf {d}:~In practice, if $x_{\text {filt}}(t)=\alpha \cdot s(t)+\eta (t)$, the intensity invariance of $x_{\text {adapt}}(t)$ is limited to sufficiently large $\alpha $. Shaded area indicates saturation of $x_{\text {adapt}}(t)$ at $95\,\%$ curve span. }}{12}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with (\textbf {c}{} and \textbf {d}) or without (\textbf {a}{} and \textbf {b}) additive noise component $\eta (t)$. Input $x_{\text {filt}}(t)$ is transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {dB}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Left}:~$x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ for different scales $\alpha $. \textbf {Right}:~Ratios of the standard deviation of $x_{\text {env}}(t)$, $x_{\text {dB}}(t)$, and $x_{\text {adapt}}(t)$ relative to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text {filt}}(t)=\eta (t)$. \textbf {a}{} and \textbf {b}:~Ideally, if $x_{\text {filt}}(t)=\alpha \cdot s(t)$, then $x_{\text {adapt}}(t)$ is intensity-invariant across all $\alpha $. \textbf {c}{} and \textbf {d}:~In practice, if $x_{\text {filt}}(t)=\alpha \cdot s(t)+\eta (t)$, the intensity invariance of $x_{\text {adapt}}(t)$ is limited to sufficiently large $\alpha $. Shaded area indicates saturation of $x_{\text {adapt}}(t)$ at $95\,\%$ curve span. }}{15}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Synthetic input $x_{\text {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with (\textbf {c}{} and \textbf {d}) or without (\textbf {a}{} and \textbf {b}) additive noise component $\eta (t)$. Input $x_{\text {filt}}(t)$ is transformed into envelope $x_{\text {env}}(t)$, logarithmically compressed envelope $x_{\text {log}}(t)$, and intensity-adapted envelope $x_{\text {adapt}}(t)$. \textbf {Left}:~$x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$ for different scales $\alpha $. \textbf {Right}:~Ratios of the standard deviation of $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, and $x_{\text {adapt}}(t)$ relative to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text {filt}}(t)=\eta (t)$. \textbf {a}{} and \textbf {b}:~Ideally, if $x_{\text {filt}}(t)=\alpha \cdot s(t)$, then $x_{\text {adapt}}(t)$ is intensity-invariant across all $\alpha $. \textbf {c}{} and \textbf {d}:~In practice, if $x_{\text {filt}}(t)=\alpha \cdot s(t)+\eta (t)$, the intensity invariance of $x_{\text {adapt}}(t)$ is limited to sufficiently large $\alpha $. Shaded area indicates saturation of $x_{\text {adapt}}(t)$ at $95\,\%$ curve span. }}{15}{}\protected@file@percent }
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\newlabel{fig:inv_log-hp}{{5}{15}{}{}{}}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Thresholding nonlinearity \& temporal averaging}{15}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf {Intensity invariance by thresholding and temporal averaging depends on both the threshold value and the noise floor.} Synthetic input $x_{\text {adapt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with additive noise component $\eta (t)$. Input $x_{\text {adapt}}(t)$ is transformed into kernel response $c_i(t)$, binary response $b_i(t)$, and feature $f_i(t)$. Threshold value $\Theta _i$ is set to multiples of the reference standard deviation $\sigma _{\eta }$ of $c_i(t)$ for input $x_{\text {adapt}}(t)=\eta (t)$. Darker colors correspond to higher $\Theta _i$. \textbf {Left}:~$x_{\text {adapt}}(t)$, $c_i(t)$, $b_i(t)$, and $f_i(t)$ for different scales $\alpha $ and threshold values $\Theta _i$. Left-most column is is the pure-noise reference. \textbf {Right}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$. \textbf {a}:~Input $x_{\text {adapt}}(t)$. \textbf {b}-\textbf {d}:~$c_i(t)$, $b_i(t)$, and $f_i(t)$ for the different $\Theta _i$ based on the same $x_{\text {adapt}}(t)$ from \textbf {a}{}. \textbf {e}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf {b}{}-\textbf {d}. }}{16}{}\protected@file@percent }
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main.tex
@@ -70,7 +70,7 @@
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\newcommand{\raw}{x} % Placeholder input signal
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\newcommand{\filt}{\raw_{\text{filt}}} % Bandpass-filtered signal
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\newcommand{\env}{\raw_{\text{env}}} % Signal envelope
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\newcommand{\db}{\raw_{\text{dB}}} % Logarithmically scaled signal
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\newcommand{\db}{\raw_{\text{log}}} % Logarithmically scaled signal
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\newcommand{\dbref}{\raw_{\text{ref}}} % Decibel reference intensity
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\newcommand{\adapt}{\raw_{\text{adapt}}} % Adapted signal
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@@ -5,13 +5,13 @@ from IPython import embed
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# GENERAL SETTINGS:
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target_species = [
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'Chorthippus_biguttulus',
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'Chorthippus_mollis',
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'Chrysochraon_dispar',
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'Euchorthippus_declivus',
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'Gomphocerippus_rufus',
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# 'Chorthippus_biguttulus',
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# 'Chorthippus_mollis',
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# 'Chrysochraon_dispar',
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# 'Euchorthippus_declivus',
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# 'Gomphocerippus_rufus',
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'Omocestus_rufipes',
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'Pseudochorthippus_parallelus',
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# 'Pseudochorthippus_parallelus',
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]
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stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
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search_path = '../data/inv/full/'
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@@ -30,11 +30,15 @@ for i, species in enumerate(target_species):
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for j, path in enumerate(all_paths):
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# Load invariance data:
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data, config = load_data(path, 'scales', 'measure')
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data, config = load_data(path, 'scales', ['measure', 'thresh'])
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if j == 0:
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# Prepare species-specific storage:
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species_data = dict(scales=data['scales'])
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species_data = dict(
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scales=data['scales'],
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thresh_rel=data['thresh_rel'],
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thresh_abs=data['thresh_abs']
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)
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for stage in stages:
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mkey = f'measure_{stage}'
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shape = data[mkey].shape + (len(all_paths),)
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@@ -5,13 +5,13 @@ from IPython import embed
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# GENERAL SETTINGS:
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target_species = [
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'Chorthippus_biguttulus',
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'Chorthippus_mollis',
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'Chrysochraon_dispar',
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'Euchorthippus_declivus',
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'Gomphocerippus_rufus',
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# 'Chorthippus_biguttulus',
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# 'Chorthippus_mollis',
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# 'Chrysochraon_dispar',
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# 'Euchorthippus_declivus',
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# 'Gomphocerippus_rufus',
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'Omocestus_rufipes',
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'Pseudochorthippus_parallelus',
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# 'Pseudochorthippus_parallelus',
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]
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stages = ['filt', 'env', 'inv', 'conv', 'feat']
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search_path = '../data/inv/short/'
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@@ -30,11 +30,15 @@ for i, species in enumerate(target_species):
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for j, path in enumerate(all_paths):
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# Load invariance data:
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data, config = load_data(path, 'scales', 'measure')
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data, config = load_data(path, 'scales', ['measure', 'thresh'])
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if j == 0:
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# Prepare species-specific storage:
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species_data = dict(scales=data['scales'])
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species_data = dict(
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scales=data['scales'],
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thresh_rel=data['thresh_rel'],
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thresh_abs=data['thresh_abs']
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)
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for stage in stages:
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mkey = f'measure_{stage}'
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shape = data[mkey].shape + (len(all_paths),)
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@@ -1,6 +1,7 @@
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import numpy as np
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from misc_functions import divide_by_zero
|
||||
from IPython import embed
|
||||
|
||||
def sort_files_by_rec(paths, sources=['JJ', 'SLO']):
|
||||
@@ -58,7 +59,7 @@ if mode == 'song':
|
||||
'max',
|
||||
'base',
|
||||
'range'
|
||||
][1]
|
||||
][4]
|
||||
suffix = dict(
|
||||
none='_unnormed',
|
||||
min='_norm-min',
|
||||
@@ -108,19 +109,22 @@ for i, species in enumerate(target_species):
|
||||
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].min(axis=0))
|
||||
# data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].max(axis=0))
|
||||
# data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
data[mkey] /= ref_data[stage]
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey][0])
|
||||
# data[mkey] /= data[mkey][0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = data[mkey].min(axis=0, keepdims=True)
|
||||
max_measure = data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
data[mkey] = divide_by_zero(data[mkey] - min_measure, max_measure - min_measure)
|
||||
# data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
|
||||
file_data[stage][..., k] = data[mkey]
|
||||
|
||||
|
||||
@@ -1,18 +1,18 @@
|
||||
import numpy as np
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from misc_functions import sort_files_by_rec
|
||||
from misc_functions import sort_files_by_rec, divide_by_zero
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
# 'Chorthippus_biguttulus',
|
||||
# 'Chorthippus_mollis',
|
||||
# 'Chrysochraon_dispar',
|
||||
# 'Euchorthippus_declivus',
|
||||
# 'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
# 'Pseudochorthippus_parallelus',
|
||||
]
|
||||
sources = [
|
||||
'BM04',
|
||||
@@ -32,7 +32,7 @@ normalization = [
|
||||
'max',
|
||||
'base',
|
||||
'range',
|
||||
][2]
|
||||
][4]
|
||||
suffix = dict(
|
||||
none='_unnormed',
|
||||
min='_norm-min',
|
||||
@@ -58,7 +58,7 @@ for i, species in enumerate(target_species):
|
||||
for k, path in enumerate(rec_paths):
|
||||
|
||||
# Load invariance data:
|
||||
data, config = load_data(path, 'scales', 'measure')
|
||||
data, config = load_data(path, 'scales', ['measure', 'thresh'])
|
||||
|
||||
if k == 0:
|
||||
# Prepare song file-specific storage:
|
||||
@@ -80,18 +80,22 @@ for i, species in enumerate(target_species):
|
||||
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].min(axis=0))
|
||||
# data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].max(axis=0))
|
||||
# data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
data[mkey] /= data[mkey][0]
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey][0])
|
||||
# data[mkey] /= data[mkey][0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = data[mkey].min(axis=0, keepdims=True)
|
||||
max_measure = data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
data[mkey] = divide_by_zero(data[mkey] - min_measure, max_measure - min_measure)
|
||||
# data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
|
||||
file_data[stage][..., k] = data[mkey]
|
||||
|
||||
@@ -101,7 +105,11 @@ for i, species in enumerate(target_species):
|
||||
rec_sd[f'sd_{stage}'][..., j] = np.nanstd(file_data[stage], axis=-1)
|
||||
|
||||
# Save condensed recording data:
|
||||
archive = dict(scales=data['scales'])
|
||||
archive = dict(
|
||||
scales=data['scales'],
|
||||
thresh_rel=data['thresh_rel'],
|
||||
thresh_abs=data['thresh_abs'],
|
||||
)
|
||||
archive.update(rec_mean)
|
||||
archive.update(rec_sd)
|
||||
save_data(save_path + species + suffix, archive, config, overwrite=True)
|
||||
|
||||
@@ -2,7 +2,7 @@ import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from misc_functions import shorten_species, sort_files_by_rec
|
||||
from misc_functions import shorten_species, sort_files_by_rec, divide_by_zero
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
@@ -41,7 +41,7 @@ suffix = dict(
|
||||
base='_norm-base',
|
||||
range='_norm-range'
|
||||
)[normalization]
|
||||
plot_overview = True
|
||||
plot_overview = False
|
||||
|
||||
# PREPARATION:
|
||||
if plot_overview:
|
||||
@@ -59,7 +59,7 @@ for i, species in enumerate(target_species):
|
||||
|
||||
# Fetch all species-specific song files:
|
||||
all_paths = search_files(species, incl=mode, ext='npz', dir=search_path)
|
||||
|
||||
|
||||
# Sort song files by recording (one or more per source):
|
||||
sorted_paths = sort_files_by_rec(all_paths, sources)
|
||||
|
||||
@@ -82,18 +82,22 @@ for i, species in enumerate(target_species):
|
||||
# Log song file data:
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
measure /= measure.min(axis=0, keepdims=True)
|
||||
measure = divide_by_zero(measure, measure.min(axis=0, keepdims=True))
|
||||
# measure /= measure.min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
measure /= measure.max(axis=0, keepdims=True)
|
||||
measure = divide_by_zero(measure, measure.max(axis=0, keepdims=True))
|
||||
# measure /= measure.max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
measure /= measure[0]
|
||||
measure = divide_by_zero(measure, measure[0])
|
||||
# measure /= measure[0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = measure.min(axis=0, keepdims=True)
|
||||
max_measure = measure.max(axis=0, keepdims=True)
|
||||
measure = (measure - min_measure) / (max_measure - min_measure)
|
||||
measure = divide_by_zero(measure - min_measure, max_measure - min_measure)
|
||||
# measure = (measure - min_measure) / (max_measure - min_measure)
|
||||
|
||||
file_data[:, k] = measure
|
||||
|
||||
@@ -101,8 +105,8 @@ for i, species in enumerate(target_species):
|
||||
axes[0, i].plot(scales, measure, c='k', alpha=0.5)
|
||||
|
||||
# Get recording statistics:
|
||||
rec_mean[:, j] = file_data.mean(axis=1)
|
||||
rec_sd[:, j] = file_data.std(axis=1)
|
||||
rec_mean[:, j] = np.nanmean(file_data, axis=1)
|
||||
rec_sd[:, j] = np.nanstd(file_data, axis=1)
|
||||
|
||||
if plot_overview:
|
||||
axes[1, i].plot(scales, rec_mean[:, j], c='k')
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import numpy as np
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from misc_functions import sort_files_by_rec
|
||||
from misc_functions import sort_files_by_rec, divide_by_zero
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
@@ -26,14 +26,14 @@ search_path = '../data/inv/rect_lp/'
|
||||
save_path = '../data/inv/rect_lp/condensed/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
mode = ['pure', 'noise'][1]
|
||||
mode = ['pure', 'noise'][0]
|
||||
normalization = [
|
||||
'none',
|
||||
'min',
|
||||
'max',
|
||||
'base',
|
||||
'range',
|
||||
][0]
|
||||
][4]
|
||||
suffix = dict(
|
||||
none='_unnormed',
|
||||
min='_norm-min',
|
||||
@@ -79,18 +79,22 @@ for i, species in enumerate(target_species):
|
||||
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].min(axis=0))
|
||||
# data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].max(axis=0))
|
||||
# data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
data[mkey] /= data[mkey][0]
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey][0])
|
||||
# data[mkey] /= data[mkey][0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = data[mkey].min(axis=0, keepdims=True)
|
||||
max_measure = data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
data[mkey] = divide_by_zero(data[mkey] - min_measure, max_measure - min_measure)
|
||||
# data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
|
||||
file_data[stage][..., k] = data[mkey]
|
||||
|
||||
|
||||
@@ -1,18 +1,18 @@
|
||||
import numpy as np
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from misc_functions import sort_files_by_rec
|
||||
from misc_functions import sort_files_by_rec, divide_by_zero
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
# 'Chorthippus_biguttulus',
|
||||
# 'Chorthippus_mollis',
|
||||
# 'Chrysochraon_dispar',
|
||||
# 'Euchorthippus_declivus',
|
||||
# 'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
# 'Pseudochorthippus_parallelus',
|
||||
]
|
||||
sources = [
|
||||
'BM04',
|
||||
@@ -32,7 +32,7 @@ normalization = [
|
||||
'max',
|
||||
'base',
|
||||
'range',
|
||||
][2]
|
||||
][4]
|
||||
suffix = dict(
|
||||
none='_unnormed',
|
||||
min='_norm-min',
|
||||
@@ -58,7 +58,7 @@ for i, species in enumerate(target_species):
|
||||
for k, path in enumerate(rec_paths):
|
||||
|
||||
# Load invariance data:
|
||||
data, config = load_data(path, 'scales', 'measure')
|
||||
data, config = load_data(path, 'scales', ['measure', 'thresh'])
|
||||
|
||||
if k == 0:
|
||||
# Prepare song file-specific storage:
|
||||
@@ -80,18 +80,22 @@ for i, species in enumerate(target_species):
|
||||
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].min(axis=0))
|
||||
# data[mkey] /= data[mkey].min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey].max(axis=0))
|
||||
# data[mkey] /= data[mkey].max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
data[mkey] /= data[mkey][0]
|
||||
data[mkey] = divide_by_zero(data[mkey], data[mkey][0])
|
||||
# data[mkey] /= data[mkey][0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = data[mkey].min(axis=0, keepdims=True)
|
||||
max_measure = data[mkey].max(axis=0, keepdims=True)
|
||||
data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
data[mkey] = divide_by_zero(data[mkey] - min_measure, max_measure - min_measure)
|
||||
# data[mkey] = (data[mkey] - min_measure) / (max_measure - min_measure)
|
||||
|
||||
file_data[stage][..., k] = data[mkey]
|
||||
|
||||
@@ -101,7 +105,11 @@ for i, species in enumerate(target_species):
|
||||
rec_sd[f'sd_{stage}'][..., j] = np.nanstd(file_data[stage], axis=-1)
|
||||
|
||||
# Save condensed recording data:
|
||||
archive = dict(scales=data['scales'])
|
||||
archive = dict(
|
||||
scales=data['scales'],
|
||||
thresh_rel=data['thresh_rel'],
|
||||
thresh_abs=data['thresh_abs']
|
||||
)
|
||||
archive.update(rec_mean)
|
||||
archive.update(rec_sd)
|
||||
save_data(save_path + species + suffix, archive, config)
|
||||
|
||||
@@ -2,7 +2,7 @@ import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from misc_functions import shorten_species, sort_files_by_rec
|
||||
from misc_functions import shorten_species, sort_files_by_rec, divide_by_zero
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
@@ -91,20 +91,24 @@ for i, species in enumerate(target_species):
|
||||
rec_sd = np.zeros(shape, dtype=float)
|
||||
|
||||
# Log song file data:
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
measure /= measure.min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
measure /= measure.max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
measure /= measure[0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = measure.min(axis=0, keepdims=True)
|
||||
max_measure = measure.max(axis=0, keepdims=True)
|
||||
measure = (measure - min_measure) / (max_measure - min_measure)
|
||||
if normalization == 'min':
|
||||
# Minimum normalization:
|
||||
measure = divide_by_zero(measure, measure.min(axis=0))
|
||||
# measure /= measure.min(axis=0, keepdims=True)
|
||||
elif normalization == 'max':
|
||||
# Maximum normalization:
|
||||
measure = divide_by_zero(measure, measure.max(axis=0))
|
||||
# measure /= measure.max(axis=0, keepdims=True)
|
||||
elif normalization == 'base':
|
||||
# Noise baseline normalization:
|
||||
measure = divide_by_zero(measure, measure[0])
|
||||
# measure /= measure[0]
|
||||
elif normalization == 'range':
|
||||
# Min-max normalization:
|
||||
min_measure = measure.min(axis=0, keepdims=True)
|
||||
max_measure = measure.max(axis=0, keepdims=True)
|
||||
measure = divide_by_zero(measure - min_measure, max_measure - min_measure)
|
||||
# measure = (measure - min_measure) / (max_measure - min_measure)
|
||||
|
||||
file_data[..., k] = measure
|
||||
|
||||
@@ -115,8 +119,8 @@ for i, species in enumerate(target_species):
|
||||
axes[0, i].plot(scales, measure[:, m, l], c=c, alpha=0.5)
|
||||
|
||||
# Get recording statistics:
|
||||
rec_mean[..., j] = file_data.mean(axis=-1)
|
||||
rec_sd[..., j] = file_data.std(axis=-1)
|
||||
rec_mean[..., j] = np.nanmean(file_data, axis=-1)
|
||||
rec_sd[..., j] = np.nanstd(file_data, axis=-1)
|
||||
|
||||
if plot_overview:
|
||||
for l, thresh in enumerate(thresh_rel):
|
||||
|
||||
@@ -144,11 +144,11 @@ xlabels = dict(
|
||||
big='distance [cm]',
|
||||
)
|
||||
ylabels = dict(
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{db}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
|
||||
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
|
||||
log='$x_{\\text{log}}$\n$[\\text{dB}]$',
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
feat='$f_i$',
|
||||
big=['measure', 'rel. measure', 'norm. measure']
|
||||
)
|
||||
|
||||
294
python/fig_invariance_full-short.py
Normal file
294
python/fig_invariance_full-short.py
Normal file
@@ -0,0 +1,294 @@
|
||||
import plotstyle_plt
|
||||
import string
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_ticks, ylabel, super_xlabel, letter_subplots,\
|
||||
ylimits, title_subplot
|
||||
from misc_functions import exclude_zero_scale, reduce_kernel_set
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
][5]
|
||||
modes = [
|
||||
'unnormed',
|
||||
'norm-base',
|
||||
'norm-min',
|
||||
'norm-max',
|
||||
]
|
||||
full_folder = '../data/inv/full/condensed/'
|
||||
short_folder = '../data/inv/short/condensed/'
|
||||
save_path = '../figures/fig_invariance_full_short.pdf'
|
||||
load_kwargs = dict(
|
||||
files=['scales', 'mean_feat', 'sd_feat'],
|
||||
keywords=['thresh'],
|
||||
)
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
exclude_zero = True
|
||||
scale_subset_kwargs = dict(
|
||||
combis=[['mean', 'sd'], ['feat']],
|
||||
)
|
||||
kern_subset_kwargs = dict(
|
||||
combis=[['mean', 'sd'], ['feat']],
|
||||
keys=['thresh_abs'],
|
||||
)
|
||||
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])
|
||||
percentiles = np.array([
|
||||
[25, 75],
|
||||
# [0, 100],
|
||||
])
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
types = np.array([1, 2, 3])
|
||||
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
|
||||
kernels = None
|
||||
reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=2,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1,
|
||||
)
|
||||
subfig_specs = dict(
|
||||
full=(0, 0),
|
||||
short=(0, 1),
|
||||
)
|
||||
col_width = 0.85
|
||||
col_rest = 1 - col_width
|
||||
full_grid_kwargs = dict(
|
||||
nrows=len(modes),
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.1,
|
||||
left=col_rest,
|
||||
right=1,
|
||||
bottom=0.05,
|
||||
top=0.9
|
||||
)
|
||||
short_grid_kwargs = dict(
|
||||
nrows=len(modes),
|
||||
ncols=1,
|
||||
wspace=full_grid_kwargs['wspace'],
|
||||
hspace=full_grid_kwargs['hspace'],
|
||||
left=col_rest / 2,
|
||||
right=1 - col_rest / 2,
|
||||
bottom=full_grid_kwargs['bottom'],
|
||||
top=full_grid_kwargs['top']
|
||||
)
|
||||
|
||||
# PLOT SETTINGS:
|
||||
fs = dict(
|
||||
lab_norm=16,
|
||||
lab_tex=20,
|
||||
letter=22,
|
||||
tit_norm=16,
|
||||
tit_tex=20,
|
||||
bar=16,
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
feat_colors = load_colors('../data/feat_colors_all.npz')
|
||||
lw = dict(
|
||||
feat=3,
|
||||
plateau=1.5
|
||||
)
|
||||
line_kwargs = dict(
|
||||
lw=lw['feat']
|
||||
)
|
||||
fill_kwargs = dict(
|
||||
alpha=0.15
|
||||
)
|
||||
xlabels = dict(
|
||||
super='scale $\\alpha$',
|
||||
)
|
||||
ylabels = {
|
||||
'unnormed': '$\\mu_{f_i}$',
|
||||
'norm-base': '$\\mu_{f_i}\\,/\\,\\mu_{f_i}\\,[\\,\\eta\\,]$',
|
||||
'norm-min': '$\\mu_{f_i}\\,/\\,\\min\\,[\\,\\mu_{f_i}\\,]$',
|
||||
'norm-max': '$\\mu_{f_i}\\,/\\,\\max\\,[\\,\\mu_{f_i}\\,]$'
|
||||
}
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ylims = {
|
||||
'unnormed': [0, 1],
|
||||
'norm-base': [0, None],
|
||||
'norm-min': [0, None],
|
||||
'norm-max': [0, 1]
|
||||
}
|
||||
yloc = {
|
||||
'unnormed': 0.5,
|
||||
'norm-base': 0.5,
|
||||
'norm-min': 0.5,
|
||||
'norm-max': 0.5
|
||||
}
|
||||
title_kwargs = dict(
|
||||
x=0.5,
|
||||
y=1,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
fontsize=fs['tit_norm'],
|
||||
)
|
||||
titles = dict(
|
||||
full='Including $x_\\text{dB}$',
|
||||
short='Excluding $x_\\text{dB}$',
|
||||
)
|
||||
letter_kwargs = dict(
|
||||
xref=0.01,
|
||||
y=1,
|
||||
ha='left',
|
||||
va='center',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
letters = dict(
|
||||
full=string.ascii_lowercase[0::2],
|
||||
short=string.ascii_lowercase[1::2],
|
||||
)
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
first=True,
|
||||
last=True,
|
||||
condense=None,
|
||||
)
|
||||
plateau_line_kwargs = dict(
|
||||
lw=lw['plateau'],
|
||||
ls='--',
|
||||
zorder=1,
|
||||
)
|
||||
plateau_dot_kwargs = dict(
|
||||
marker='o',
|
||||
markersize=8,
|
||||
markeredgewidth=1,
|
||||
clip_on=False,
|
||||
)
|
||||
|
||||
# EXECUTION:
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
|
||||
# Prepare full analysis axes:
|
||||
full_subfig = fig.add_subfigure(super_grid[*subfig_specs['full']])
|
||||
full_grid = full_subfig.add_gridspec(**full_grid_kwargs)
|
||||
full_axes = np.zeros((len(modes),), dtype=object)
|
||||
for i, mode in enumerate(modes):
|
||||
full_axes[i] = full_subfig.add_subplot(full_grid[i, 0])
|
||||
# full_axes[i].yaxis.set_major_locator(plt.MultipleLocator(yloc[mode]))
|
||||
full_axes[i].set_xscale('symlog', linthresh=0.01, linscale=0.5)
|
||||
ylabel(full_axes[i], ylabels[mode], transform=full_subfig.transSubfigure, **ylab_kwargs)
|
||||
if i == 0:
|
||||
title_subplot(full_axes[i], titles['full'], **title_kwargs)
|
||||
if i < full_grid_kwargs['nrows'] - 1:
|
||||
hide_ticks(full_axes[i], 'bottom')
|
||||
letter_subplots(full_axes, letters['full'], ref=full_subfig, **letter_kwargs)
|
||||
|
||||
# Prepare short analysis axes:
|
||||
short_subfig = fig.add_subfigure(super_grid[*subfig_specs['short']])
|
||||
short_grid = short_subfig.add_gridspec(**short_grid_kwargs)
|
||||
short_axes = np.zeros((len(modes),), dtype=object)
|
||||
for i, mode in enumerate(modes):
|
||||
short_axes[i] = short_subfig.add_subplot(short_grid[i, 0])
|
||||
# short_axes[i].yaxis.set_major_locator(plt.MultipleLocator(yloc[mode]))
|
||||
short_axes[i].set_xscale('symlog', linthresh=0.01, linscale=0.5)
|
||||
hide_ticks(short_axes[i], 'left')
|
||||
if i == 0:
|
||||
title_subplot(short_axes[i], titles['short'], **title_kwargs)
|
||||
if i < short_grid_kwargs['nrows'] - 1:
|
||||
hide_ticks(short_axes[i], 'bottom')
|
||||
letter_subplots(short_axes, letters['short'], ref=short_subfig, **letter_kwargs)
|
||||
|
||||
super_xlabel(xlabels['super'], fig, full_axes[-1], short_axes[-1],
|
||||
left_fig=full_subfig, right_fig=short_subfig, **xlab_kwargs)
|
||||
|
||||
# Run through normalization modes:
|
||||
for mode, full_ax, short_ax in zip(modes, full_axes, short_axes):
|
||||
|
||||
# Load invariance data:
|
||||
full_path = search_files(target_species, incl=mode, dir=full_folder)[0]
|
||||
short_path = search_files(target_species, incl=mode, dir=short_folder)[0]
|
||||
full_data, config = load_data(full_path, **load_kwargs)
|
||||
short_data, _ = load_data(short_path, **load_kwargs)
|
||||
|
||||
# Reduce datasets:
|
||||
if reduce_kernels:
|
||||
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
|
||||
full_data = reduce_kernel_set(full_data, kern_inds, **kern_subset_kwargs)
|
||||
short_data = reduce_kernel_set(short_data, kern_inds, **kern_subset_kwargs)
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
if exclude_zero:
|
||||
full_data = exclude_zero_scale(full_data, **scale_subset_kwargs)
|
||||
short_data = exclude_zero_scale(short_data, **scale_subset_kwargs)
|
||||
|
||||
# Average over recordings:
|
||||
full_measure = full_data['mean_feat'].mean(axis=-1)
|
||||
short_measure = short_data['mean_feat'].mean(axis=-1)
|
||||
|
||||
# Condense over kernels:
|
||||
full_median = np.nanmedian(full_measure, axis=1)
|
||||
full_spread = np.nanpercentile(full_measure, percentiles, axis=1)
|
||||
short_median = np.nanmedian(short_measure, axis=1)
|
||||
short_spread = np.nanpercentile(short_measure, percentiles, axis=1)
|
||||
|
||||
# Determine shared ylims:
|
||||
if None in ylims[mode]:
|
||||
min_val, max_val = ylims[mode]
|
||||
full_limits = ylimits(full_median, minval=min_val, maxval=max_val)
|
||||
short_limits = ylimits(short_median, minval=min_val, maxval=max_val)
|
||||
ylims[mode] = [min(full_limits[0], short_limits[0]),
|
||||
max(full_limits[1], short_limits[1])]
|
||||
if np.inf in ylims[mode]:
|
||||
embed()
|
||||
|
||||
# Plot full analysis results:
|
||||
for i, thresh in enumerate(thresh_rel):
|
||||
full_ax.plot(full_data['scales'], full_median[:, i], lw=lw['feat'])
|
||||
for spread in full_spread[:, :, :, i]:
|
||||
full_ax.fill_between(full_data['scales'], *spread, **fill_kwargs)
|
||||
full_ax.set_xlim(full_data['scales'][0], full_data['scales'][-1])
|
||||
full_ax.set_ylim(ylims[mode])
|
||||
|
||||
# Plot short analysis results:
|
||||
for i, thresh in enumerate(thresh_rel):
|
||||
short_ax.plot(short_data['scales'], short_median[:, i], lw=lw['feat'])
|
||||
for spread in short_spread[:, :, :, i]:
|
||||
short_ax.fill_between(short_data['scales'], *spread, **fill_kwargs)
|
||||
short_ax.set_xlim(short_data['scales'][0], short_data['scales'][-1])
|
||||
short_ax.set_ylim(ylims[mode])
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
@@ -5,7 +5,8 @@ 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
|
||||
from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
|
||||
divide_by_zero
|
||||
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,\
|
||||
@@ -20,30 +21,15 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
ymin=ymin, ymax=ymax, **kwargs))
|
||||
return handles
|
||||
|
||||
def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
|
||||
if measures.ndim == 1:
|
||||
ax.plot(scales, measures, **kwargs)[0]
|
||||
return measures
|
||||
median_measure = np.median(measures, axis=1)
|
||||
spread_measure = [np.percentile(measures, 25, axis=1),
|
||||
np.percentile(measures, 75, axis=1)]
|
||||
ax.plot(scales, median_measure, **kwargs)[0]
|
||||
ax.fill_between(scales, *spread_measure, **fill_kwargs)
|
||||
return median_measure
|
||||
|
||||
def exclude_zero_scale(data, stages):
|
||||
inds = data['scales'] > 0
|
||||
data['scales'] = data['scales'][inds]
|
||||
for stage in stages:
|
||||
data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
|
||||
return data
|
||||
|
||||
def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
|
||||
for stage in stages:
|
||||
key = f'{keyword}_{stage}'
|
||||
data[key] = data[key][:, inds, ...]
|
||||
return data
|
||||
|
||||
def plot_curves(ax, scales, measures, fill_kwargs={}, compress=False, **kwargs):
|
||||
if not compress or measures.ndim == 1:
|
||||
handles = ax.plot(scales, measures, **kwargs)
|
||||
return handles, measures
|
||||
median_measure = np.nanmedian(measures, axis=1)
|
||||
spread_measure = np.nanpercentile(measures, [25, 75], axis=1)
|
||||
line_handle = ax.plot(scales, median_measure, **kwargs)[0]
|
||||
fill_handle = ax.fill_between(scales, *spread_measure, **fill_kwargs)
|
||||
return [line_handle, fill_handle], median_measure
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
@@ -65,29 +51,28 @@ example_file = {
|
||||
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
|
||||
}[target_species]
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
|
||||
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/full/condensed/')[0]
|
||||
base_path = search_files(target_species, incl='base', dir='../data/inv/full/condensed/')[0]
|
||||
range_path = search_files(target_species, incl='range', dir='../data/inv/full/condensed/')[0]
|
||||
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
|
||||
data_path = search_files(example_file, dir='../data/inv/full/')[0]
|
||||
save_path = '../figures/fig_invariance_full.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
exclude_zero = True
|
||||
compress_kernels = True
|
||||
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
|
||||
scale_subset_kwargs = dict(
|
||||
combis=[['measure'], stages],
|
||||
)
|
||||
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, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
|
||||
kernels = np.array([
|
||||
[1, 0.002],
|
||||
[-1, 0.002],
|
||||
[2, 0.004],
|
||||
[-2, 0.004],
|
||||
[3, 0.032],
|
||||
[-3, 0.032]
|
||||
])
|
||||
kernels = None
|
||||
reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
@@ -113,7 +98,7 @@ snip_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.4,
|
||||
left=0.11,
|
||||
left=0.13,
|
||||
right=0.98,
|
||||
bottom=0.08,
|
||||
top=0.95
|
||||
@@ -138,9 +123,11 @@ 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_all.npz'),
|
||||
feat=load_colors('../data/feat_colors_all.npz')
|
||||
)
|
||||
lw = dict(
|
||||
filt=0.25,
|
||||
env=0.25,
|
||||
@@ -150,16 +137,17 @@ lw = dict(
|
||||
feat=1,
|
||||
big=3,
|
||||
plateau=1.5,
|
||||
legend=5,
|
||||
)
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{db}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
|
||||
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
|
||||
log='$x_{\\text{log}}$\n$[\\text{dB}]$',
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
feat='$f_i$',
|
||||
big=['measure', 'rel. measure', 'norm. measure']
|
||||
)
|
||||
@@ -170,11 +158,12 @@ xlab_big_kwargs = dict(
|
||||
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,
|
||||
@@ -228,6 +217,29 @@ bar_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
leg_kwargs = dict(
|
||||
ncols=1,
|
||||
loc='upper left',
|
||||
bbox_to_anchor=(0.05, 0.5, 0.5, 0.5),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=20,
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=1,
|
||||
columnspacing=1,
|
||||
handletextpad=0.5,
|
||||
labelspacing=0.1
|
||||
)
|
||||
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_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
@@ -249,26 +261,30 @@ plateau_dot_kwargs = dict(
|
||||
|
||||
# EXECUTION:
|
||||
|
||||
# Load raw (unnormed) invariance data:
|
||||
data, config = load_data(raw_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
scales = data['scales']
|
||||
# Load invariance data:
|
||||
data, config = load_data(data_path, keywords=['snip', 'scales', 'measure', 'thresh'])
|
||||
t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
|
||||
|
||||
# Load snippet data:
|
||||
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
|
||||
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
|
||||
snip_scales = snip['example_scales']
|
||||
|
||||
# Optional kernel subset:
|
||||
reduce_kernels = False
|
||||
if any(var is not None for var in [kernels, types, sigmas]):
|
||||
# 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')
|
||||
snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
|
||||
data = reduce_kernel_set(data, kern_inds, **kern_subset_kwargs)
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
reduce_kernels = True
|
||||
|
||||
# Reduce thresholds:
|
||||
thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
|
||||
data['measure_feat'] = data['measure_feat'][:, :, thresh_ind]
|
||||
data['snip_feat'] = data['snip_feat'][:, :, :, thresh_ind]
|
||||
|
||||
# Remember pure-noise reference measures:
|
||||
ref_data = {stage: data[f'measure_{stage}'][0, ...] for stage in stages}
|
||||
|
||||
# Reduce scales:
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, **scale_subset_kwargs)
|
||||
scales = data['scales']
|
||||
snip_scales = data['example_scales']
|
||||
|
||||
# Adjust grid parameters:
|
||||
snip_grid_kwargs['ncols'] = snip_scales.size
|
||||
@@ -307,114 +323,125 @@ for i in range(big_grid.ncols):
|
||||
ax.set_xscale('symlog', linthresh=scales[1], 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)
|
||||
# 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)
|
||||
|
||||
if True:
|
||||
# Plot filtered snippets:
|
||||
plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
|
||||
c=colors['filt'], lw=lw['filt'])
|
||||
plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
|
||||
c=stage_colors['filt'], lw=lw['filt'])
|
||||
|
||||
# Plot envelope snippets:
|
||||
plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
|
||||
ymin=0, c=colors['env'], lw=lw['env'])
|
||||
plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
|
||||
ymin=0, c=stage_colors['env'], lw=lw['env'])
|
||||
|
||||
# Plot logarithmic snippets:
|
||||
plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
|
||||
c=colors['log'], lw=lw['log'])
|
||||
plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
|
||||
c=stage_colors['log'], lw=lw['log'])
|
||||
|
||||
# Plot invariant snippets:
|
||||
plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
|
||||
c=colors['inv'], lw=lw['inv'])
|
||||
plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
|
||||
c=stage_colors['inv'], lw=lw['inv'])
|
||||
|
||||
# Plot kernel response snippets:
|
||||
all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
|
||||
c=colors['conv'], lw=lw['conv'])
|
||||
all_handles = plot_snippets(snip_axes[4, :], t_full, 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, snip['snip_conv'][..., i])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv'])
|
||||
reorder_by_sd(handles, data['snip_conv'][..., i])
|
||||
|
||||
# Plot feature snippets:
|
||||
all_handles = plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
|
||||
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
|
||||
all_handles = plot_snippets(snip_axes[5, :], t_full, 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, snip['snip_feat'][..., i])
|
||||
del snip
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat'])
|
||||
reorder_by_sd(handles, data['snip_feat'][..., i])
|
||||
|
||||
# Remember saturation points:
|
||||
# Plot analysis results:
|
||||
crit_inds, crit_scales = {}, {}
|
||||
|
||||
# Unnormed measures:
|
||||
leg_handles = []
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
mkey = f'measure_{stage}'
|
||||
measure = data[mkey]
|
||||
color = stage_colors[stage]
|
||||
fill_kwargs = dict(color=color, alpha=0.25)
|
||||
|
||||
# Plot raw intensity measure curve(s):
|
||||
handles, curve = plot_curves(big_axes[0], scales, measure, fill_kwargs,
|
||||
compress_kernels, c=color, lw=lw['big'])
|
||||
if not compress_kernels and stage in ['conv', 'feat']:
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
|
||||
|
||||
# Add stage-specific proxy legend artist:
|
||||
leg_handles.append(big_axes[0].plot([], [], c=color, lw=lw['big'],
|
||||
label=leg_labels[stage])[0])
|
||||
|
||||
# Indicate saturation point(s):
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind = get_saturation(curve, **plateau_settings)[1]
|
||||
scale = scales[ind]
|
||||
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].plot(scale, 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(scale, big_axes[0].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
# Log saturation point:
|
||||
crit_inds[stage] = ind
|
||||
crit_scales[stage] = scale
|
||||
del data
|
||||
if compress_kernels or stage in ['log', 'inv']:
|
||||
scale = scales[ind]
|
||||
crit_scales[stage] = scale
|
||||
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
|
||||
# Noise baseline-related measures:
|
||||
data, _ = load_data(base_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
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], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
# Relate to noise baseline:
|
||||
measure = divide_by_zero(data[mkey], ref_data[stage])
|
||||
|
||||
# Plot baseline-normalized ntensity measure curve(s):
|
||||
handles, curve = plot_curves(big_axes[1], scales, measure, fill_kwargs,
|
||||
compress_kernels, c=color, lw=lw['big'])
|
||||
if not compress_kernels and stage in ['conv', 'feat']:
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
|
||||
|
||||
# Indicate saturation point(s):
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 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(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
ind = crit_inds[stage]
|
||||
scale = crit_scales[stage]
|
||||
if compress_kernels or stage in ['log', 'inv']:
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
if stage in ['filt', 'env']:
|
||||
continue
|
||||
|
||||
# Min-max normalized measures:
|
||||
data, _ = load_data(range_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in ['log', 'inv', 'conv', 'feat']:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Relate to curve maximum:
|
||||
measure = data[mkey] / np.nanmax(data[mkey], axis=0)
|
||||
|
||||
# Indicate saturation point:
|
||||
# Plot max-normalized ntensity measure curve(s):
|
||||
handles, curve = plot_curves(big_axes[2], scales, measure, fill_kwargs,
|
||||
compress_kernels, c=color, lw=lw['big'])
|
||||
if not compress_kernels and stage in ['conv', 'feat']:
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
|
||||
|
||||
# Indicate saturation point(s):
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 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(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
ind = crit_inds[stage]
|
||||
scale = crit_scales[stage]
|
||||
if compress_kernels or stage in ['log', 'inv']:
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
|
||||
# Add legend to first analysis axis:
|
||||
legend = big_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:
|
||||
|
||||
@@ -4,23 +4,21 @@ import matplotlib.pyplot as plt
|
||||
from itertools import product
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from misc_functions import get_saturation
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
|
||||
plot_line, plot_barcode, strip_zeros, time_bar,\
|
||||
from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
|
||||
plot_line, strip_zeros, time_bar, assign_colors,\
|
||||
letter_subplot, letter_subplots
|
||||
from IPython import embed
|
||||
|
||||
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
|
||||
handles = []
|
||||
for i, ax in enumerate(axes):
|
||||
plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
|
||||
return None
|
||||
|
||||
def plot_bi_snippets(axes, time, snippets, **kwargs):
|
||||
for i, ax in enumerate(axes):
|
||||
plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
|
||||
return None
|
||||
handles.append(plot_line(ax, time, snippets[:, ..., i],
|
||||
ymin=ymin, ymax=ymax, **kwargs))
|
||||
return handles
|
||||
|
||||
def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
|
||||
if measures.ndim == 1:
|
||||
@@ -33,10 +31,19 @@ def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
|
||||
ax.fill_between(scales, *spread_measure, **fill_kwargs)
|
||||
return median_measure
|
||||
|
||||
def show_saturation(ax, scales, measures, high=0.95, **kwargs):
|
||||
high_ind = get_saturation(measures, high=high)[1]
|
||||
return ax.plot(scales[high_ind], 0, transform=ax.get_xaxis_transform(),
|
||||
marker='o', ms=10, zorder=6, clip_on=False, **kwargs)
|
||||
def exclude_zero_scale(data, stages):
|
||||
inds = data['scales'] > 0
|
||||
data['scales'] = data['scales'][inds]
|
||||
for stage in stages:
|
||||
data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
|
||||
return data
|
||||
|
||||
def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
|
||||
for stage in stages:
|
||||
key = f'{keyword}_{stage}'
|
||||
data[key] = data[key][:, inds, ...]
|
||||
return data
|
||||
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
@@ -57,19 +64,34 @@ example_file = {
|
||||
'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
|
||||
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
|
||||
}[target_species]
|
||||
data_paths = search_files(target_species, dir='../data/inv/full/condensed/')
|
||||
snip_paths = search_files(example_file, dir='../data/inv/full/')
|
||||
ref_path = '../data/inv/full/ref_measures.npz'
|
||||
save_path = '../figures/fig_invariance_full.pdf'
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
|
||||
load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'snip', 'measure']
|
||||
)
|
||||
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/full/condensed/')[0]
|
||||
base_path = search_files(target_species, incl='base', dir='../data/inv/full/condensed/')[0]
|
||||
range_path = search_files(target_species, incl='range', dir='../data/inv/full/condensed/')[0]
|
||||
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
|
||||
save_path = '../figures/fig_invariance_full.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
exclude_zero = True
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
|
||||
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
|
||||
kernels = np.array([
|
||||
[1, 0.002],
|
||||
[-1, 0.002],
|
||||
[2, 0.004],
|
||||
[-2, 0.004],
|
||||
[3, 0.032],
|
||||
[-3, 0.032]
|
||||
])
|
||||
kernels = None
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 20/2.54),
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
@@ -91,20 +113,20 @@ snip_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.4,
|
||||
left=0.08,
|
||||
right=0.95,
|
||||
left=0.11,
|
||||
right=0.98,
|
||||
bottom=0.08,
|
||||
top=0.95
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.2,
|
||||
wspace=0.4,
|
||||
hspace=0,
|
||||
left=snip_grid_kwargs['left'],
|
||||
right=0.96,
|
||||
bottom=0.2,
|
||||
top=0.95
|
||||
right=snip_grid_kwargs['right'],
|
||||
bottom=0.13,
|
||||
top=0.98
|
||||
)
|
||||
|
||||
# PLOT SETTINGS:
|
||||
@@ -117,15 +139,17 @@ fs = dict(
|
||||
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')
|
||||
lw = dict(
|
||||
filt=0.25,
|
||||
env=0.25,
|
||||
log=0.25,
|
||||
inv=0.25,
|
||||
conv=0.25,
|
||||
bi=0,
|
||||
feat=1,
|
||||
big=3
|
||||
big=3,
|
||||
plateau=1.5,
|
||||
)
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
@@ -134,11 +158,10 @@ ylabels = dict(
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{db}}$',
|
||||
inv='$x_{\\text{inv}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
bi='$b_i$',
|
||||
feat='$f_i$',
|
||||
big=['intensity', 'rel. intensity', 'norm. intensity']
|
||||
big=['measure', 'rel. measure', 'norm. measure']
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
@@ -154,7 +177,7 @@ ylab_snip_kwargs = dict(
|
||||
va='center'
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=-0.12,
|
||||
x=-0.2,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
@@ -164,7 +187,7 @@ yloc = dict(
|
||||
env=1000,
|
||||
log=50,
|
||||
inv=20,
|
||||
conv=2,
|
||||
conv=1,
|
||||
feat=1,
|
||||
)
|
||||
title_kwargs = dict(
|
||||
@@ -205,122 +228,199 @@ bar_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
|
||||
# PREPARATION:
|
||||
ref_data = dict(np.load(ref_path))
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
first=True,
|
||||
last=True,
|
||||
condense=None,
|
||||
)
|
||||
plateau_line_kwargs = dict(
|
||||
lw=lw['plateau'],
|
||||
ls='--',
|
||||
zorder=1,
|
||||
)
|
||||
plateau_dot_kwargs = dict(
|
||||
marker='o',
|
||||
markersize=8,
|
||||
markeredgewidth=1,
|
||||
clip_on=False,
|
||||
)
|
||||
|
||||
# EXECUTION:
|
||||
for data_path in data_paths:
|
||||
print(f'Processing {data_path}')
|
||||
|
||||
# Load invariance data:
|
||||
data, config = load_data(data_path, **load_kwargs)
|
||||
t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
|
||||
# Load raw (unnormed) invariance data:
|
||||
data, config = load_data(raw_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
scales = data['scales']
|
||||
|
||||
# Adjust grid parameters:
|
||||
snip_grid_kwargs['ncols'] = data['example_scales'].size
|
||||
# Load snippet data:
|
||||
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
|
||||
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
|
||||
snip_scales = snip['example_scales']
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
# Optional kernel subset:
|
||||
reduce_kernels = False
|
||||
if any(var is not None for var in [kernels, types, sigmas]):
|
||||
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
reduce_kernels = True
|
||||
|
||||
# Prepare stage-specific snippet axes:
|
||||
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
|
||||
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
|
||||
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
|
||||
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
|
||||
ax = snip_subfig.add_subplot(snip_grid[i, j])
|
||||
ax.set_xlim(t_full[0], t_full[-1])
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
|
||||
hide_axis(ax, 'bottom')
|
||||
if i == 0:
|
||||
title = title_subplot(ax, f'$\\alpha={strip_zeros(data["example_scales"][j])}$',
|
||||
ref=snip_subfig, **title_kwargs)
|
||||
if j == 0:
|
||||
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
|
||||
else:
|
||||
hide_axis(ax, 'left')
|
||||
snip_axes[i, j] = ax
|
||||
time_bar(snip_axes[-1, -1], **bar_kwargs)
|
||||
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
|
||||
# Adjust grid parameters:
|
||||
snip_grid_kwargs['ncols'] = snip_scales.size
|
||||
|
||||
# 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(data['scales'][0], data['scales'][-1])
|
||||
ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
|
||||
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
|
||||
xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
|
||||
ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
|
||||
big_axes[i] = ax
|
||||
letter_subplots(big_axes, 'bc', **letter_big_kwargs)
|
||||
plt.show()
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
|
||||
# # Plot filtered snippets:
|
||||
# plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
|
||||
# c=colors['filt'], lw=lw['filt'])
|
||||
# Prepare stage-specific snippet axes:
|
||||
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
|
||||
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
|
||||
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
|
||||
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
|
||||
ax = snip_subfig.add_subplot(snip_grid[i, j])
|
||||
ax.set_xlim(t_full[0], t_full[-1])
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
|
||||
hide_axis(ax, 'bottom')
|
||||
if i == 0:
|
||||
title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
|
||||
ref=snip_subfig, **title_kwargs)
|
||||
if j == 0:
|
||||
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
|
||||
else:
|
||||
hide_axis(ax, 'left')
|
||||
snip_axes[i, j] = ax
|
||||
time_bar(snip_axes[-1, -1], **bar_kwargs)
|
||||
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
|
||||
|
||||
# # Plot envelope snippets:
|
||||
# plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
|
||||
# ymin=0, c=colors['env'], lw=lw['env'])
|
||||
# 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(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', linthresh=scales[1], 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)
|
||||
|
||||
# # Plot logarithmic snippets:
|
||||
# plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
|
||||
# c=colors['log'], lw=lw['log'])
|
||||
if True:
|
||||
# Plot filtered snippets:
|
||||
plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
|
||||
c=colors['filt'], lw=lw['filt'])
|
||||
|
||||
# # Plot invariant snippets:
|
||||
# plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
|
||||
# c=colors['inv'], lw=lw['inv'])
|
||||
# Plot envelope snippets:
|
||||
plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
|
||||
ymin=0, c=colors['env'], lw=lw['env'])
|
||||
|
||||
# # Plot kernel response snippets:
|
||||
# plot_snippets(snip_axes[4, :], t_full, data['snip_conv'],
|
||||
# c=colors['conv'], lw=lw['conv'])
|
||||
# Plot logarithmic snippets:
|
||||
plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
|
||||
c=colors['log'], lw=lw['log'])
|
||||
|
||||
# # Plot feature snippets:
|
||||
# plot_snippets(snip_axes[5, :], t_full, data['snip_feat'],
|
||||
# ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
|
||||
# Plot invariant snippets:
|
||||
plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
|
||||
c=colors['inv'], lw=lw['inv'])
|
||||
|
||||
# Analysis results:
|
||||
scales_rel = data['scales'] - data['scales'][0]
|
||||
scales_rel /= scales_rel[-1]
|
||||
for stage in stages:
|
||||
measure = data[f'measure_{stage}']
|
||||
# Plot kernel response snippets:
|
||||
all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
|
||||
c=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, snip['snip_conv'][..., i])
|
||||
|
||||
# Plot unmodified intensity measures:
|
||||
curve = plot_curves(big_axes[0], data['scales'], measure, c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
show_saturation(big_axes[0], data['scales'], curve, c=colors[stage])
|
||||
# Plot feature snippets:
|
||||
all_handles = plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
|
||||
ymin=0, ymax=1, c=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, snip['snip_feat'][..., i])
|
||||
del snip
|
||||
|
||||
# # Relate to pure-noise reference:
|
||||
# norm_measure = measure / ref_data[stage]
|
||||
# Remember saturation points:
|
||||
crit_inds, crit_scales = {}, {}
|
||||
|
||||
# # Plot noise-related intensity measures:
|
||||
# big_axes[1].plot(data['scales'], norm_measure, c=colors[stage], lw=lw['big'])
|
||||
# Unnormed measures:
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
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]
|
||||
scale = scales[ind]
|
||||
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].plot(scale, 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(scale, big_axes[0].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
# Log saturation point:
|
||||
crit_inds[stage] = ind
|
||||
crit_scales[stage] = scale
|
||||
del data
|
||||
|
||||
# Normalize measure to [0, 1]:
|
||||
min_measure = measure.min(axis=0)
|
||||
max_measure = measure.max(axis=0)
|
||||
norm_measure = (measure - min_measure) / (max_measure - min_measure)
|
||||
# Noise baseline-related measures:
|
||||
data, _ = load_data(base_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
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], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
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, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 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(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
# Plot normalized intensity measures:
|
||||
curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
|
||||
# Min-max normalized measures:
|
||||
data, _ = load_data(range_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in ['log', 'inv', 'conv', 'feat']:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
|
||||
# # Plot over relative scales:
|
||||
# plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
|
||||
# fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# scales_rel = curve - curve.min()
|
||||
# scales_rel /= scales_rel.max()
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 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(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
# Save graph:
|
||||
if save_path is not None:
|
||||
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
|
||||
fig.savefig(file_name)
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
@@ -95,7 +95,7 @@ noise_grid_kwargs = dict(
|
||||
top=edge_padding + block_height,
|
||||
height_ratios=[1, 2, 1]
|
||||
)
|
||||
big_col_shift = -0.12
|
||||
big_col_shift = -0.04
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
@@ -135,11 +135,10 @@ xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{dB}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
|
||||
log='$x_{\\text{log}}$\n$[\\text{dB}]$',
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
big_pure='$\\sigma_x$',
|
||||
big_log='$\\sigma_x\\,[\\text{dB}]$',
|
||||
big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
@@ -148,23 +147,17 @@ xlab_big_kwargs = dict(
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_big_left_kwargs = dict(
|
||||
ylab_big_kwargs = dict(
|
||||
x=-0.2,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='bottom'
|
||||
)
|
||||
ylab_big_right_kwargs = dict(
|
||||
x=1.2,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top'
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0,
|
||||
x=0.03,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='left',
|
||||
ha='center',
|
||||
va='center',
|
||||
)
|
||||
yloc = dict(
|
||||
@@ -231,19 +224,41 @@ bar_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
leg_kwargs = dict(
|
||||
stage_leg_kwargs = dict(
|
||||
ncols=1,
|
||||
loc='upper left',
|
||||
bbox_to_anchor=(0.05, 0.5, 0.5, 0.5),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=20,
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=1,
|
||||
columnspacing=1,
|
||||
handletextpad=0.5,
|
||||
labelspacing=0
|
||||
)
|
||||
stage_leg_labels = dict(
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{log}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
)
|
||||
spec_leg_kwargs = dict(
|
||||
ncols=2,
|
||||
loc='upper right',
|
||||
bbox_to_anchor=(0, 0.6, 1, 0.4),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=12,
|
||||
size=13.5,
|
||||
style='italic',
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=0.5,
|
||||
columnspacing=1,
|
||||
handletextpad=0.5,
|
||||
labelspacing=0.25,
|
||||
)
|
||||
diag_kwargs = dict(
|
||||
c=(0.3,) * 3,
|
||||
@@ -374,17 +389,8 @@ for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
|
||||
ax.set_ylim(0.9, 30)
|
||||
big_axes[i] = ax
|
||||
shift_subplot(big_axes[0], dx=big_col_shift)
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_left_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_left_kwargs)
|
||||
big_twin = big_axes[0].twinx()
|
||||
hide_axis(big_twin, 'left')
|
||||
big_twin.spines['right'].set_visible(True)
|
||||
big_twin.set_position(big_axes[0].get_position().bounds)
|
||||
big_twin.set_ylim(scales[0], scales[-1])
|
||||
big_twin.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ylabel(big_twin, ylabels['big_log'], transform=big_twin.transAxes, **ylab_big_right_kwargs)
|
||||
color_axis(big_axes[0], colors['env'], side='left')
|
||||
color_axis(big_twin, colors['log'], side='right')
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_kwargs)
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'cde', **letter_big_kwargs)
|
||||
|
||||
@@ -421,9 +427,11 @@ plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
|
||||
time_bar(noise_axes[-1, -1], **bar_kwargs)
|
||||
|
||||
# Plot pure-song measures (ideal):
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'], label=stage_leg_labels['env'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'], label=stage_leg_labels['log'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'], label=stage_leg_labels['inv'])
|
||||
legend = big_axes[0].legend(**stage_leg_kwargs)
|
||||
[h.set_lw(lw['legend']) for h in legend.legend_handles]
|
||||
|
||||
# Plot noise-song measures (limited):
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
@@ -458,7 +466,7 @@ for i, (species, measure) in enumerate(species_measures.items()):
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
legend = big_axes[2].legend(**leg_kwargs)
|
||||
legend = big_axes[2].legend(**spec_leg_kwargs)
|
||||
[h.set_lw(lw['legend']) for h in legend.legend_handles]
|
||||
|
||||
if save_path is not None:
|
||||
|
||||
@@ -83,8 +83,8 @@ pure_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.15,
|
||||
left=0.08 - snip_col_shift,
|
||||
right=0.95,
|
||||
left=0.1 - snip_col_shift,
|
||||
right=0.98,
|
||||
bottom=1 - block_height - edge_padding,
|
||||
top=1 - edge_padding,
|
||||
height_ratios=[1, 1]
|
||||
@@ -132,23 +132,22 @@ species_colors = load_colors('../data/species_colors.npz')
|
||||
lw = dict(
|
||||
snip=0.5,
|
||||
big=3,
|
||||
spec=2,
|
||||
spec=1.5,
|
||||
legend=5,
|
||||
)
|
||||
dash_cycle = 6 # points
|
||||
ls_env = [
|
||||
(0, np.array((0.2, 0.8)) * dash_cycle),
|
||||
(0, np.array((0.1,) * 10) * dash_cycle),
|
||||
(0, np.array((0.6, 0.1, 0.2, 0.1)) * dash_cycle),
|
||||
(0, np.array((0.5, 0.5)) * dash_cycle),
|
||||
(0, np.array((0.3, 0.2, 0.3, 0.2)) * dash_cycle),
|
||||
'solid',
|
||||
] # [np.nan, 2500, 250, 25]
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
raw='$x$',
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
|
||||
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
|
||||
big_pure='$\\sigma_x$',
|
||||
big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
|
||||
)
|
||||
@@ -159,23 +158,17 @@ xlab_big_kwargs = dict(
|
||||
va='bottom',
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0,
|
||||
x=0.025,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='left',
|
||||
ha='center',
|
||||
va='center',
|
||||
)
|
||||
ylab_pure_kwargs = dict(
|
||||
x=0,
|
||||
ylab_big_kwargs = dict(
|
||||
x=-0.2,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
ylab_noise_kwargs = dict(
|
||||
y=0.5,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
va='bottom',
|
||||
)
|
||||
ylim_zoom_factor = 0.03
|
||||
yloc = dict(
|
||||
@@ -346,9 +339,8 @@ for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
|
||||
ax.set_ylim(0.1, 100)
|
||||
big_axes[i] = ax
|
||||
shift_subplot(big_axes[0], dx=big_col_shift)
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_subfig.transSubfigure, **ylab_pure_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_noise_kwargs,
|
||||
x=(big_subfig.transSubfigure + big_axes[0].transAxes.inverted()).transform((ylab_pure_kwargs['x'], 0))[0])
|
||||
ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_kwargs)
|
||||
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_kwargs)
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'cde', **letter_big_kwargs)
|
||||
|
||||
|
||||
@@ -154,10 +154,10 @@ xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
|
||||
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
feat='$f_i$',
|
||||
big=['measure', 'rel. measure', 'norm. measure']
|
||||
)
|
||||
|
||||
@@ -9,7 +9,7 @@ from misc_functions import shorten_species
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
mode = ['pure', 'noise'][1]
|
||||
mode = ['pure', 'noise'][0]
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
|
||||
@@ -98,7 +98,7 @@ snip_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.3,
|
||||
hspace=0,
|
||||
left=0.2 - snip_col_shift,
|
||||
left=0.23 - snip_col_shift,
|
||||
right=0.93,
|
||||
bottom=0.15,
|
||||
top=0.95,
|
||||
@@ -119,7 +119,7 @@ big_grid_kwargs = dict(
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.15,
|
||||
left=0.17,
|
||||
left=0.2,
|
||||
right=0.96,
|
||||
bottom=0.05,
|
||||
top=0.99
|
||||
@@ -152,11 +152,11 @@ xlabels = dict(
|
||||
sigma='$\\sigma_{\\text{adapt}}$',
|
||||
)
|
||||
ylabels = dict(
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
bi='$b_i$',
|
||||
feat='$f_i$',
|
||||
big='$\\mu_f$',
|
||||
big='$\\mu_{f_i}$',
|
||||
)
|
||||
xlab_alpha_kwargs = dict(
|
||||
y=0.5,
|
||||
@@ -171,7 +171,7 @@ xlab_sigma_kwargs = dict(
|
||||
va='bottom',
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0.08,
|
||||
x=0.1,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='center',
|
||||
@@ -179,7 +179,7 @@ ylab_snip_kwargs = dict(
|
||||
)
|
||||
ylab_super_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
@@ -366,7 +366,7 @@ for i in range(thresh_rel.size):
|
||||
low_box = axes[-1, 0].get_position()
|
||||
high_box = axes[0, 0].get_position()
|
||||
[hide_axis(ax, 'left') for ax in axes[1:, 1]]
|
||||
super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
|
||||
super_ylabel(f'$\\Theta_i={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
|
||||
snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
|
||||
for (ax1, ax2), stage in zip(axes[:, :2], stages):
|
||||
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
|
||||
|
||||
@@ -2,6 +2,7 @@ import plotstyle_plt
|
||||
import glob
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_axis, letter_subplots,\
|
||||
@@ -10,8 +11,8 @@ from plot_functions import hide_axis, letter_subplots,\
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_paths = glob.glob(f'../data/processed/{target}*.npz')
|
||||
example_file = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
|
||||
data_paths = search_files(example_file, dir='../data/processed/')
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
save_path = '../figures/'
|
||||
|
||||
@@ -25,7 +26,7 @@ fig_kwargs = dict(
|
||||
gridspec_kw=dict(
|
||||
wspace=0.15,
|
||||
hspace=0.3,
|
||||
left=0.1,
|
||||
left=0.12,
|
||||
right=0.99,
|
||||
bottom=0.08,
|
||||
top=0.95
|
||||
@@ -61,13 +62,13 @@ xlabels = dict(
|
||||
super='time [s]',
|
||||
)
|
||||
ylabels = dict(
|
||||
filt=r'$x_{\text{filt}}$',
|
||||
env=r'$x_{\text{env}}$',
|
||||
log=r'$x_{\text{dB}}$',
|
||||
inv=r'$x_{\text{adapt}}$',
|
||||
conv=r'$c_i$',
|
||||
bi=r'$b_i$',
|
||||
feat=r'$f_i$'
|
||||
filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
|
||||
env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
|
||||
log='$x_{\\text{log}}$\n$[\\text{dB}]$',
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
bi='$b_i$',
|
||||
feat='$f_i$',
|
||||
)
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
@@ -76,9 +77,9 @@ xlab_kwargs = dict(
|
||||
fontsize=fs['lab_norm'],
|
||||
)
|
||||
ylab_kwargs = dict(
|
||||
x=0,
|
||||
x=0.03,
|
||||
rotation=0,
|
||||
ha='left',
|
||||
ha='center',
|
||||
va='center',
|
||||
fontsize=fs['lab_tex'],
|
||||
)
|
||||
@@ -89,16 +90,16 @@ xloc = dict(
|
||||
yloc_full = dict(
|
||||
filt=0.2,
|
||||
env=0.1,
|
||||
log=40,
|
||||
log=50,
|
||||
inv=20,
|
||||
conv=2,
|
||||
conv=1,
|
||||
feat=1
|
||||
)
|
||||
yloc_zoom = dict(
|
||||
filt=0.1,
|
||||
env=0.02,
|
||||
log=40,
|
||||
inv=20,
|
||||
log=50,
|
||||
inv=10,
|
||||
conv=0.5,
|
||||
feat=1
|
||||
)
|
||||
@@ -202,7 +203,10 @@ for data_path in data_paths:
|
||||
|
||||
# Update parameters:
|
||||
fig_kwargs['gridspec_kw'].update(
|
||||
left=0.06,
|
||||
left=0.09,
|
||||
)
|
||||
ylab_kwargs.update(
|
||||
x=0.02,
|
||||
)
|
||||
|
||||
# PART II: FEATURE EXTRACTION STAGE:
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
import numpy as np
|
||||
from scipy.stats import gaussian_kde
|
||||
from itertools import product
|
||||
from thunderhopper.filetools import crop_paths
|
||||
from IPython import embed
|
||||
|
||||
@@ -16,6 +17,34 @@ def draw_noise_segment(noise, n):
|
||||
start = rng.integers(0, noise.shape[0] - n, endpoint=True)
|
||||
return np.take(noise, np.arange(start, start + n), axis=0)
|
||||
|
||||
def divide_by_zero(num, denom, replace=np.nan):
|
||||
with np.errstate(divide='ignore', invalid='ignore'):
|
||||
result = np.true_divide(num, denom)
|
||||
result[~np.isfinite(result)] = replace
|
||||
return result
|
||||
|
||||
def exclude_zero_scale(data, keys=None, combis=None):
|
||||
inds = np.nonzero(data['scales'] > 0)[0]
|
||||
data['scales'] = data['scales'][inds]
|
||||
if keys is not None:
|
||||
for key in keys:
|
||||
data[key] = data[key][inds, ...]
|
||||
if combis is not None:
|
||||
for key1, key2 in product(*combis):
|
||||
key = f'{key1}_{key2}'
|
||||
data[key] = data[key][inds, ...]
|
||||
return data
|
||||
|
||||
def reduce_kernel_set(data, inds, keys=None, combis=None):
|
||||
if keys is not None:
|
||||
for key in keys:
|
||||
data[key] = data[key][:, inds, ...]
|
||||
if combis is not None:
|
||||
for key1, key2 in product(*combis):
|
||||
key = f'{key1}_{key2}'
|
||||
data[key] = data[key][:, inds, ...]
|
||||
return data
|
||||
|
||||
def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
|
||||
# Separate by source:
|
||||
sorted_paths = {}
|
||||
|
||||
@@ -104,9 +104,9 @@ def ylimits(signal, ax=None, minval=None, maxval=None, pad=0.05):
|
||||
return [minval, maxval]
|
||||
limits = [minval, maxval]
|
||||
if minval is None:
|
||||
limits[0] = signal.min()
|
||||
limits[0] = np.nanmin(signal)
|
||||
if maxval is None:
|
||||
limits[1] = signal.max()
|
||||
limits[1] = np.nanmax(signal)
|
||||
span = limits[1] - limits[0]
|
||||
if pad and minval is None:
|
||||
limits[0] -= span * pad
|
||||
|
||||
@@ -16,7 +16,7 @@ target_species = [
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
][5]
|
||||
][6]
|
||||
example_file = {
|
||||
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
|
||||
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
|
||||
|
||||
@@ -17,7 +17,7 @@ target_species = [
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
][5]
|
||||
][6]
|
||||
example_file = {
|
||||
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
|
||||
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
|
||||
|
||||
Reference in New Issue
Block a user