Nearly finished 1st draft of species-specific Thresh-LP invariance figure (WIP).

main.aux

@@ -251,9 +251,9 @@
 \@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf  {Intensity invariance by logarithmic compression and adaptation is restricted by the noise floor.} Envelope $x_{\text  {env}}(t)$ is transformed into logarihmically compressed envelope $x_{\text  {dB}}(t)$ and further into intensity-adapted envelope $x_{\text  {adapt}}(t)$. Indicated time scale is $5\,$s for both \textbf  {a} and \textbf  {b} (black bars). \textbf  {a}:~Ideally, if $x_{\text  {env}}(t)$ consists only of song component $s(t)$ rescaled by $\alpha $, then $x_{\text  {adapt}}(t)$ is fully intensity-invariant across all $\alpha $. \textbf  {b}:~In practice, $x_{\text  {env}}(t)$ also contains fixed-scale noise component $\eta (t)$, which limits the effective intensity invariance of $x_{\text  {adapt}}(t)$ to sufficiently large $\alpha $. \textbf  {c}:~Ratios of the SD of each representation in \textbf  {b} at a given $\alpha $ relative to the SD of the representation for $\alpha =0$ (solid lines). The same ratios for the ideal $x_{\text  {adapt}}(t)$ in \textbf  {a} are shown for comparison (dashed line). }}{12}{}\protected@file@percent }
 \newlabel{fig:inv_log-hp}{{4}{12}{}{}{}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Thresholding nonlinearity \& temporal averaging}{12}{}\protected@file@percent }
-\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf  {Intensity invariance by thresholding and temporal averaging depends on the threshold value.} Kernel response $c_i(t)$ is rescaled by $\alpha $ and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $500\,$ms is the same for \textbf  {a}-\textbf  {c} (black bar). \textbf  {a}:~50th percentile. \textbf  {b}:~75th percentile. \textbf  {c}:~100th percentile. \textbf  {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf  {a}-\textbf  {c}. }}{13}{}\protected@file@percent }
+\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf  {Intensity invariance by thresholding and temporal averaging depends on the threshold value with regard to variable range but not saturation level.} Kernel response $c_i(t)$ is rescaled by $\alpha $ and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $500\,$ms is the same for \textbf  {a}-\textbf  {c} (black bar). \textbf  {a}:~50th percentile. \textbf  {b}:~75th percentile. \textbf  {c}:~100th percentile. \textbf  {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf  {a}-\textbf  {c}. }}{13}{}\protected@file@percent }
 \newlabel{fig:inv_thresh-lp_single}{{5}{13}{}{}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf  {Intensity invariance by thresholding and temporal averaging depends on noise.} Kernel response $c_i(t)$ is rescaled by $\alpha $, mixed with fixed-scale noise component $\eta (t)$, and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $500\,$ms is the same for \textbf  {a}-\textbf  {c} (black bar). \textbf  {a}:~50th percentile. \textbf  {b}:~75th percentile. \textbf  {c}:~100th percentile. \textbf  {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf  {a}-\textbf  {c}. }}{14}{}\protected@file@percent }
+\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf  {Intensity invariance by thresholding and temporal averaging depends on noise with regard to variable range but not saturation level.} Kernel response $c_i(t)$ is rescaled by $\alpha $, mixed with fixed-scale noise component $\eta (t)$, and transformed into binary response $b_i(t)$ and further into feature $f_i(t)$. Threshold value $\Theta _i$ is set to different percentiles of the the distribution of $c_i(t)$ at $\alpha =1$. Darker colors indicate higher values of $\Theta _i$. Indicated time scale of $500\,$ms is the same for \textbf  {a}-\textbf  {c} (black bar). \textbf  {a}:~50th percentile. \textbf  {b}:~75th percentile. \textbf  {c}:~100th percentile. \textbf  {d}:~Average value of $f_i(t)$ during the song for the different $\Theta _i$ in \textbf  {a}-\textbf  {c}. }}{14}{}\protected@file@percent }
 \newlabel{fig:inv_thresh-lp_single_noise}{{6}{14}{}{}{}}
 \newlabel{eq:pdf_split}{{15}{15}{}{}{}}
 \newlabel{eq:feat_avg}{{16}{15}{}{}{}}

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main.log

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+This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) (preloaded format=pdflatex 2025.10.28)  13 MAR 2026 10:06
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@@ -775,10 +775,10 @@ File: figures/fig_invariance_thresh_lp_single.pdf Graphic file (type pdf)
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-Package pdftex.def Info: figures/fig_invariance_thresh_lp_single_noise.pdf  used on input line 676.
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main.tex

@@ -653,7 +653,8 @@ the signal for reliable song recognition.
     \centering
     \includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_single.pdf}
     \caption{\textbf{Intensity invariance by thresholding and temporal
-                     averaging depends on the threshold value.}
+                     averaging depends on the threshold value with regard to
+                     variable range but not saturation level.}
                      Kernel response $c_i(t)$ is rescaled by $\sca$ and
                      transformed into binary response $b_i(t)$ and further into
                      feature $f_i(t)$. Threshold value $\thr$ is set to
@@ -675,7 +676,8 @@ the signal for reliable song recognition.
     \centering
     \includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_single_noise.pdf}
     \caption{\textbf{Intensity invariance by thresholding and temporal
-                     averaging depends on noise.}
+                     averaging depends on noise with regard to variable range
+                     but not saturation level.}
                      Kernel response $c_i(t)$ is rescaled by $\sca$, mixed with
                      fixed-scale noise component $\noc(t)$, and transformed
                      into binary response $b_i(t)$ and further into feature

python/fig_invariance_full.py

@@ -1,31 +1,13 @@
 import plotstyle_plt
+import glob
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.transforms import BboxTransformTo
 from itertools import product
-from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data
 from color_functions import load_colors
 from plot_functions import hide_axis, ylimits, xlabel, ylabel, plot_line, plot_barcode, strip_zeros
 from IPython import embed
 
-def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, transform=None, **kwargs):
-    t0, t1 = ax.get_xlim()
-    offset = (t1 - t0 - dur) * xshift
-    x0 = t0 + offset
-    x1 = x0 + dur
-    if parent is None:
-        parent = ax
-    if transform is None:
-        transform = BboxTransformTo(parent.bbox)
-    if transform is not ax.transData:
-        trans = ax.transData + transform.inverted()
-        x0 = trans.transform((x0, 0))[0]
-        x1 = trans.transform((x1, 0))[0]
-    parent.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
-                                    transform=transform, **kwargs))
-    return None
-
 def add_snip_axes(fig, grid_kwargs):
     grid = fig.add_gridspec(**grid_kwargs)
     axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
@@ -49,7 +31,7 @@ def plot_bi_snippets(axes, time, binary, **kwargs):
 
 # GENERAL SETTINGS:
 target = 'Omocestus_rufipes'
-data_paths = search_files(target, excl='noise', dir='../data/inv/full/')
+data_paths = glob.glob(f'../data/processed/{target}*.npz')
 stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
 load_kwargs = dict(
     files=stages,
@@ -62,8 +44,8 @@ fig_kwargs = dict(
     figsize=(32/2.54, 16/2.54),
 )
 super_grid_kwargs = dict(
-    nrows=2,
+    nrows=len(stages),
-    ncols=2,
+    ncols=3,
     wspace=0,
     hspace=0,
     left=0,
@@ -72,31 +54,20 @@ super_grid_kwargs = dict(
     top=1
 )
 subfig_specs = dict(
-    pure=(0, 0),
+    **{stage: (slice(0, -1), i) for i, stage in enumerate(stages)},
-    noise=(1, 0),
+    big=(slice(None), -1)
-    analysis=(slice(None), 1)
 )
-pure_grid_kwargs = dict(
+stage_grid_kwargs = dict(
-    nrows=len(stages),
+    nrows=1,
     ncols=None,
     wspace=0.05,
-    hspace=0.1,
+    hspace=0,
     left=0.07,
     right=0.95,
     bottom=0.15,
     top=0.9
 )
-noise_grid_kwargs = dict(
+big_grid_kwargs = dict(
-    nrows=len(stages),
-    ncols=None,
-    wspace=0.05,
-    hspace=0.1,
-    left=0.07,
-    right=0.95,
-    bottom=0.15,
-    top=0.9   
-)
-analysis_grid_kwargs = dict(
     nrows=1,
     ncols=1,
     wspace=0,
@@ -106,19 +77,20 @@ analysis_grid_kwargs = dict(
     bottom=0.1,
     top=0.95
 )
-snip_specs = dict(
-    conv=(0, slice(None)),
-    bi=(1, slice(None)),
-    feat=(2, slice(None))
-)
 
 # PLOT SETTINGS:
 colors = load_colors('../data/stage_colors.npz')
 lw_snippets = dict(
+    raw=0.25,
+    filt=0.25,
+    env=0.5,
+    log=0.5,
+    inv=0.5,
     conv=0.5,
+    bi=0.01,
     feat=2
 )
-lw_analysis = 3
+lw_big = 3
 xlabels = dict(
     analysis='scale $\\alpha$',
 )
@@ -148,38 +120,38 @@ ylab_analysis_kwargs = dict(
     ha='center',
     va='top',
 )
-xloc = dict(
+# xloc = dict(
-    analysis=10,
+#     analysis=10,
-)
+# )
-letter_snip_kwargs = dict(
+# letter_snip_kwargs = dict(
-    x=0.02,
+#     x=0.02,
-    y=1,
+#     y=1,
-    ha='left',
+#     ha='left',
-    va='top',
+#     va='top',
-    fontsize=22,
+#     fontsize=22,
-    fontweight='bold'
+#     fontweight='bold'
-)
+# )
-letter_analysis_kwargs = dict(
+# letter_analysis_kwargs = dict(
-    x=0,
+#     x=0,
-    y=1,
+#     y=1,
-    ha='left',
+#     ha='left',
-    va='top',
+#     va='top',
-    fontsize=22,
+#     fontsize=22,
-    fontweight='bold'
+#     fontweight='bold'
-)
+# )
-bar_time = 5
+# bar_time = 5
-bar_kwargs = dict(
+# bar_kwargs = dict(
-    y0=0.7,
+#     y0=0.7,
-    y1=0.8,
+#     y1=0.8,
-    color='k',
+#     color='k',
-    lw=0,
+#     lw=0,
-)
+# )
-spread_kwargs = dict(
+# spread_kwargs = dict(
-    alpha=0.3,
+#     alpha=0.3,
-    lw=0,
+#     lw=0,
-    zorder=0
+#     zorder=0
-)
+# )
-kernel_ind = 0
+# kernel_ind = 0
 
 # EXECUTION:
 for data_path in data_paths:

python/fig_invariance_log-hp.py

@@ -124,9 +124,6 @@ ylab_analysis_kwargs = dict(
     ha='center',
     va='top',
 )
-xloc = dict(
-    analysis=200,
-)
 letter_snip_kwargs = dict(
     x=0.02,
     y=0.97,
@@ -189,7 +186,7 @@ for data_path in data_paths:
     analysis_grid = analysis_subfig.add_gridspec(**analysis_grid_kwargs)
     analysis_ax = analysis_subfig.add_subplot(analysis_grid[0, 0])
     analysis_ax.set_xlim(noise_data['scales'].min(), noise_data['scales'].max())
-    analysis_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['analysis']))
+    analysis_ax.set_xscale('symlog', linthresh=pure_data['scales'][1], linscale=0.5)
     xlabel(analysis_ax, xlabels['analysis'], **xlab_analysis_kwargs,
            transform=analysis_subfig.transSubfigure)
     analysis_ax.set_yscale('log')
@@ -240,7 +237,7 @@ for data_path in data_paths:
     analysis_ax.plot(noise_data['scales'], measure_env, c=colors['env'], lw=lw_analysis)
     analysis_ax.plot(noise_data['scales'], measure_log, c=colors['log'], lw=lw_analysis)
     analysis_ax.plot(noise_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis)
-    analysis_ax.set_ylim(0.1, measure_env.max())
+    analysis_ax.set_ylim(0.9, measure_env.max())
 
     if save_path is not None:
         fig.savefig(save_path)

python/fig_invariance_thresh-lp_single.py

@@ -52,7 +52,7 @@ def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
 
 
 # GENERAL SETTINGS:
-with_noise = False
+with_noise = True
 target = 'Omocestus_rufipes'
 search_kwargs = dict(
     incl=['subset', 'noise'] if with_noise else 'subset',
@@ -186,9 +186,10 @@ bar_kwargs = dict(
     lw=0,
 )
 kernel = np.array([
-    [2, 0.008],
+    [1, 0.008],
-    [4, 0.008],
+    [2, 0.004],
-])[:1]
+    [3, 0.002],
+])[np.array([1])]
 zoom_rel = np.array([0.5, 0.525])
 
 
@@ -207,13 +208,11 @@ for data_path in data_paths:
     data['snip_conv'] = data['snip_conv'][zoom_inds, kern_ind, ...]
     data['snip_bi'] = data['snip_bi'][zoom_inds, kern_ind, ...]
     data['snip_feat'] = data['snip_feat'][zoom_inds, kern_ind, ...]
-    data['measure_conv'] = data['measure_conv'][:, kern_ind, :]
     data['measure_feat'] = data['measure_feat'][:, kern_ind, :]
-    data['threshs'] = data['threshs'][:, kern_ind]
     t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
 
     # Get threshold-specific colors:
-    factors = np.linspace(*color_factors, data['thresh_perc'].size)
+    factors = np.linspace(*color_factors, data['threshs'].size)
     colors = dict(
         conv=shade_colors(colors['conv'], factors),
         bi=shade_colors(colors['bi'], factors),
@@ -221,7 +220,7 @@ for data_path in data_paths:
     )
 
     # Adjust grid parameters:
-    super_grid_kwargs['nrows'] = data['thresh_perc'].size
+    super_grid_kwargs['nrows'] = data['threshs'].size
     snip_grid_kwargs['ncols'] = data['example_scales'].size
 
     # Prepare overall graph:
@@ -230,13 +229,13 @@ for data_path in data_paths:
 
     # Prepare snippet axes:
     snip_axes = {}
-    for i in range(data['thresh_perc'].size):
+    for i in range(data['threshs'].size):
         subfig_specs['snip'] = (i, subfig_specs['snip'][1])
         snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
         axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
         snip_axes[snip_subfig] = axes
-        super_ylabel(f'{data["thresh_perc"][i]}%', snip_subfig,
+        super_ylabel(f'{strip_zeros(100 * data["thresh_perc"][i])}%',
-                     axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
+                     snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
         for ax, stage in zip(axes[:, 0], stages):
             ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
                    transform=snip_subfig.transSubfigure)

python/fig_invariance_thresh-lp_species.py

@@ -0,0 +1,400 @@
+import plotstyle_plt
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+from itertools import product
+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, shade_colors
+from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
+                           plot_line, plot_barcode, strip_zeros, time_bar,\
+                           letter_subplot, letter_subplots, hide_ticks,\
+                           super_xlabel, super_ylabel, assign_colors
+from IPython import embed
+
+def force_sequence(*vars, skip_None=False, equal_size=False):
+    """ Ensures single-loop compatibility of one or several input variables.
+        Uses np.ndim() to separate sequence-likes (tuples, lists, >=1D arrays)
+        and scalar inputs (int, float, bool, 0D arrays, strings, dicts, None).
+        Scalar variables are promoted to 1D sequences by either tuple wrapping
+        or expanding by one array dimension (only 0D arrays). All single-entry
+        sequences can be repeated to match the length of the longest sequence.
+        Input variables that are None can be excluded from these treatments.  
+
+    Parameters
+    ----------
+    *vars : tuple (m,) of inputs (any type)
+        Input variables to be checked, promoted, and equalized as required.
+    skip_None : bool, optional
+        If True, None inputs fall through unmodified. The default is False.
+    equal_size : bool, optional
+        If True, counts the number of elements in each passed or promoted
+        sequence (using len(), meaning that elements are defined as entries
+        along the first sequence axis) and repeats single-element sequences to
+        match the maximum count. Arrays with shape[0] == 1 are not tiled but
+        tuple-wrapped and repeated to avoid deep copies. The default is False.
+
+    Returns
+    -------
+    vars : array-like or None or list (m,) of array-likes or Nones
+        Treated output variables, each either a >=1D sequence-like or None.
+        Single variables are returned without list wrapper.
+
+    Raises
+    ------
+    ValueError
+        Breaks if equal_size is True and a sequence has incompatible length,
+        i.e. any number of elements other than 1, 0 (Nones) or the maximum.
+    """    
+    # Enforce input iterability:
+    vars, sizes = list(vars), []
+    for i, var in enumerate(vars):
+        if skip_None and var is None:
+            # Maintain None:
+            sizes.append(0)
+            continue
+        if np.ndim(var) == 0:
+            # Make each input variable at least 1D sequence-like:
+            vars[i] = var[None] if isinstance(var, np.ndarray) else (var,)
+        # Count sequence elements:
+        sizes.append(len(vars[i]))
+
+    # Check early exits:
+    if len(vars) == 1:
+        return vars[0]
+    target = max(sizes)
+    if not equal_size or target <= 1 or all(n == target for n in sizes):
+        return vars
+
+    # Validate compatibility of element counts:
+    if not all(n in (0, 1, target) for n in sizes):
+        msg = f'Given a maximum sequence length of {target}, all variables '\
+              f'must either have 1 or {target} elements or be None: {sizes}'
+        raise ValueError(msg)
+
+    # Equalize sequence length across input variables:
+    for i, (var, size) in enumerate(zip(vars, sizes)):
+        if size == 1:
+            vars[i] = ((var,) if isinstance(var, np.ndarray) else var) * target
+    return vars
+
+def split_subplot(ax, side='right', size=10, pad=10):
+    """ Divides the given parent subplot into two or more separate subplots.
+        Opens a new axes divider on the area of the parent axes and appends a
+        number of child axes of given size and padding on the specified sides.
+        The parent's size is reduced in the process. Values passed for size and
+        pad are interpreted as percentages of the width (if side is 'left' or
+        'right') or height (if side is 'top' or 'bottom') of the remainder of
+        the parent. Practically, size=100 means that child and parent will be
+        of equal size after the split (regardless of padding) and pad=100 means
+        that the space between child and parent equals the parent's new width
+        or height. Any of side, size, or pad can be 1D sequence-likes of equal
+        length to perform multiple splits using the same divider. Calling this
+        function multiple times on the same parent subplot is possible but will
+        open a new and updated divider each time, making the effects of size
+        and pad values inconsistent between calls.
+
+    Parameters
+    ----------
+    ax : matplotlib axes
+        Parent subplot to be divided.
+    side : str or 1D array-like of str (m,)
+        Sides of the parent subplot where new subplots are to be appended.
+        Options are 'bottom', 'left', 'top', 'right'. The default is 'right'.
+    size : int or float or 1D array-like of ints or floats (m,), optional
+        Horizontal or vertical extent of each child axes as percentage of width
+        or height of the parent axes after splitting. Multiple splits from the
+        same side are possible and performed in given order, with the earliest
+        child axes being positioned closest to the parent. The default is 10.
+    pad : int or float or 1D array-like of ints or floats (m,), optional
+        Padding between each child axes and the parent as percentage of width
+        or height of the parent axes after splitting. The default is 10.
+
+    Returns
+    -------
+    matplotlib axes or list of matplotlib axes (m,)
+        One or multiple newly appended child subplots.
+    """    
+    # Open divider on parent axes:
+    div = make_axes_locatable(ax)
+
+    # Split off one or multiple child axes:
+    if not any(np.ndim(var) for var in (side, size, pad)):
+        return div.append_axes(side, size=f'{size}%', pad=f'{pad}%')
+    inputs = zip(*force_sequence(side, size, pad, equal_size=True))
+    return [div.append_axes(s, f'{n}%', f'{p}%') for s, n, p in inputs]
+
+
+# GENERAL SETTINGS:
+targets = [
+    'Omocestus_rufipes',
+    'Chorthippus_biguttulus',
+    # 'Chorthippus_mollis',
+    # 'Chrysochraon_dispar',
+    'Gomphocerippus_rufus',
+    # 'Pseudochorthippus_parallelus',
+    ]
+pure_paths = search_files(targets, incl='subset', excl='noise', dir='../data/inv/thresh_lp/')
+load_kwargs = dict(
+    keywords=['scales', 'measure', 'thresh']
+)
+save_path = '../figures/fig_invariance_thresh_lp_species.pdf'
+
+# SUBSET SETTINGS:
+thresh_percent = np.array([0.6, 0.75, 0.999])[0]
+kernels = np.array([
+    [1, 0.008],
+    [2, 0.004],
+    [3, 0.002],
+])[np.array([0, 1])]
+
+# GRAPH SETTINGS:
+fig_kwargs = dict(
+    figsize=(32/2.54, 16/2.54),
+)
+n_species = len(targets)
+super_grid_kwargs = dict(
+    nrows=2,
+    ncols=n_species + 2,
+    wspace=0,
+    hspace=0,
+    left=0,
+    right=1,
+    bottom=0,
+    top=1
+)
+subfig_specs = dict(
+    spec=(slice(None), slice(0, n_species)),
+    big=(slice(None), slice(n_species, None))
+)
+spec_grid_kwargs = dict(
+    nrows=2,
+    ncols=n_species,
+    wspace=0.25,
+    hspace=0.1,
+    left=0.1,
+    right=0.97,
+    bottom=0.1,
+    top=0.94
+)
+big_grid_kwargs = dict(
+    nrows=2,
+    ncols=1,
+    wspace=0,
+    hspace=0.2,
+    left=0,
+    right=1,
+    bottom=spec_grid_kwargs['bottom'],
+    top=spec_grid_kwargs['top']
+)
+anchor_kwargs = dict(
+    aspect='equal',
+    adjustable='box',
+    anchor=(0.3, 0.5)
+)
+inset_kwargs = dict(
+    y0=0.7,
+    w=0.3,
+    h=0.2,
+)
+
+# PLOT SETTINGS:
+base_color = load_colors('../data/stage_colors.npz')['feat']
+spec_cmaps = [
+    'Reds',
+    'Greens',
+    'Blues',
+]
+lw = dict(
+    spec=2,
+    kern=3
+)
+space_kwargs = dict(
+    s=30,
+)
+xlabs = dict(
+    spec='scale $\\alpha$',
+    big='$\\mu_{f_1}$'
+)
+ylabs = dict(
+    spec='$\\mu_f$',
+    big='$\\mu_{f_2}$',
+)
+xlab_spec_kwargs = dict(
+    y=0.005,
+    fontsize=16,
+    ha='center',
+    va='bottom',
+)
+ylab_spec_kwargs = dict(
+    x=0,
+    fontsize=20,
+    ha='left',
+    va='center',
+)
+xlab_big_kwargs = dict(
+    y=0.005,
+    fontsize=20,
+    ha='center',
+    va='bottom',
+)
+ylab_big_kwargs = dict(
+    x=0.03,
+    fontsize=20,
+    ha='center',
+    va='center',
+)
+xloc = dict(
+    big=0.5,
+)
+yloc = dict(
+    spec=0.5,
+    big=0.5
+)
+spec_letter_kwargs = dict(
+    x=0,
+    y=1.03,
+    ha='center',
+    va='bottom',
+    fontsize=22,  
+)
+big_letter_kwargs = dict(
+    x=0,
+    yref=spec_letter_kwargs['y'],
+    ha='center',
+    va='bottom',
+    fontsize=22,
+)
+time_bar_kwargs = dict(
+    dur=0.05,
+    y0=inset_kwargs['y0'],
+    y1=inset_kwargs['y0'] + 0.03,
+    color='k',
+    lw=0
+)
+cbar_bounds = [
+    0.8,
+    big_grid_kwargs['bottom'],
+    0.15,
+    big_grid_kwargs['top'] - big_grid_kwargs['bottom']
+]
+shade_factors = [0.9, -0.9]
+
+# EXECUTION:
+
+# Prepare overall graph:
+fig = plt.figure(**fig_kwargs)
+super_grid = fig.add_gridspec(**super_grid_kwargs)
+
+# Prepare species-specific axes:
+spec_subfig = fig.add_subfigure(super_grid[subfig_specs['spec']])
+spec_grid = spec_subfig.add_gridspec(**spec_grid_kwargs)
+spec_axes = np.zeros((spec_grid_kwargs['nrows'], n_species), dtype=object)
+for i, j in product(range(spec_grid_kwargs['nrows']), range(n_species)):
+    ax = spec_subfig.add_subplot(spec_grid[i, j])
+    ax.set_xscale('symlog', linthresh=0.1, linscale=0.5)
+    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['spec']))
+    ax.set_ylim(0, 1)
+    spec_axes[i, j] = ax
+super_xlabel(xlabs['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[-1, -1], **xlab_spec_kwargs)
+super_ylabel(ylabs['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[0, 0], **ylab_spec_kwargs)
+[hide_ticks(ax, side='bottom') for ax in spec_axes[0, :]]
+[hide_ticks(ax, side='left') for ax in spec_axes[:, 1:].ravel()]
+letter_subplots(spec_axes[0, :], labels='abc', **spec_letter_kwargs)
+
+# Prepare kernel insets:
+x0 = np.linspace(0, 1, kernels.shape[0] + 1)[:-1] + 1 / kernels.shape[0] / 2
+x0 -= inset_kwargs['w'] / 2
+insets = []
+for i in range(kernels.shape[0]):
+    bounds = [x0[i], inset_kwargs['y0'], inset_kwargs['w'], inset_kwargs['h']]
+    inset = spec_axes[0, 0].inset_axes(bounds)
+    inset.set_title(rf'$k_{{{i+1}}}$', fontsize=20)
+    inset.axis('off')
+    insets.append(inset)
+
+# Prepare feature space axes:
+big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
+big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
+big_axes = np.zeros(super_grid_kwargs['nrows'], dtype=object)
+for i in range(big_axes.size):
+    ax = big_subfig.add_subplot(big_grid[i, 0])
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['big']))
+    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
+    ax.set_aspect(**anchor_kwargs)
+    # ax.set_ylabel(ylabs['big'], **ylab_big_kwargs)
+    ylabel(ax, ylabs['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
+    big_axes[i] = ax
+super_xlabel(xlabs['big'], big_subfig, big_axes[1], big_axes[1], **xlab_big_kwargs)
+hide_ticks(big_axes[0], side='bottom')
+letter_subplot(big_axes[0], 'd', ref=spec_axes[0, 0], **big_letter_kwargs)
+
+# Prepare colorbars:
+bar_ax = big_subfig.add_axes(cbar_bounds)
+bar_axes = split_subplot(bar_ax, side=['right', 'right'], size=100, pad=0)
+bar_axes = [bar_ax] + bar_axes
+for ax in bar_axes:
+    ax.spines[['right', 'top']].set_visible(True)
+    hide_ticks(ax, 'bottom', ticks=False)
+    hide_ticks(ax, 'left', ticks=False)
+bar_axes[-1].tick_params(axis='y', which='both', right=True, labelright=True)
+# plt.show()
+
+# Plot results per species:
+for i, pure_path in enumerate(pure_paths):
+    print(f'Processing {pure_path}')
+    noise_path = pure_path.replace('.npz', '_noise.npz')
+
+    # Load invariance data:
+    pure_data, config = load_data(pure_path, **load_kwargs)
+    noise_data, _ = load_data(noise_path, **load_kwargs)
+    scales = pure_data['scales']
+
+    # Reduce to kernel subset and single threshold:
+    thresh_ind = np.nonzero(pure_data['thresh_perc'] == thresh_percent)[0][0]
+    kern_inds = find_kern_specs(config['k_specs'], kerns=kernels)
+    config['k_specs'] = config['k_specs'][kern_inds]
+    config['kernels'] = config['kernels'][:, kern_inds]
+    pure_measure = pure_data['measure_feat'][:, kern_inds, thresh_ind]
+    noise_measure = noise_data['measure_feat'][:, kern_inds, thresh_ind]
+
+    # Plot invariance curves:
+    pure_ax, noise_ax = spec_axes[:, i]
+    pure_ax.plot(scales, pure_measure, c=base_color, lw=lw['spec'])
+    noise_ax.plot(scales, noise_measure, c=base_color, lw=lw['spec'])
+
+    if i == 0:
+        # Indicate kernel waveforms:
+        ylims = ylimits(config['kernels'], pad=0.05)
+        xlims = (config['k_times'][0], config['k_times'][-1])
+        for j, inset in enumerate(insets):
+            inset.plot(config['k_times'], config['kernels'][:, j],
+            c='k', lw=lw['kern'])
+            inset.set_xlim(xlims)
+            inset.set_ylim(ylims)
+        time_bar(insets[0], parent=spec_axes[0, 0], **time_bar_kwargs)
+
+    # Prepare shaded colors:
+    # factors = np.linspace(*shade_factors, scales.size)
+    # shaded_colors = shade_colors(spec_colors[i], factors)
+
+    # Plot pure feature space:
+    handle = big_axes[0].scatter(pure_measure[:, 0], pure_measure[:, 1], 
+                                 c=scales, cmap=spec_cmaps[i], **space_kwargs)
+
+    # Plot noise feature space:
+    big_axes[1].scatter(noise_measure[:, 0], noise_measure[:, 1],
+                        c=scales, cmap=spec_cmaps[i], **space_kwargs)
+
+    # Indicate scale color code:
+    big_subfig.colorbar(handle, cax=bar_axes[i])
+
+if save_path is not None:
+    fig.savefig(save_path)
+plt.show()
+
+print('Done.')
+embed()

python/fig_invariance_thresh-lp_subset.py

@@ -0,0 +1,160 @@
+import plotstyle_plt
+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, shade_colors
+from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
+                           plot_line, plot_barcode, strip_zeros, time_bar,\
+                           letter_subplot, letter_subplots, hide_ticks,\
+                           super_xlabel, super_ylabel, assign_colors
+from IPython import embed
+
+# GENERAL SETTINGS:
+target = 'Omocestus_rufipes'
+search_kwargs = dict(
+    incl='subset',
+    excl='noise',
+    dir='../data/inv/thresh_lp/'
+)
+pure_paths = search_files(target, **search_kwargs)
+load_kwargs = dict(
+    keywords=['scales', 'measure', 'thresh']
+)
+save_path = None#'../figures/fig_invariance_thresh_lp_subset.pdf'
+
+# GRAPH SETTINGS:
+fig_kwargs = dict(
+    figsize=(32/2.54, 16/2.54),
+)
+super_grid_kwargs = dict(
+    nrows=1,
+    ncols=1,
+    wspace=0,
+    hspace=0,
+    left=0,
+    right=1,
+    bottom=0,
+    top=1
+)
+grid_kwargs = dict(
+    nrows=2,
+    ncols=1,
+    wspace=0,
+    hspace=0.1,
+    left=0.15,
+    right=0.95,
+    bottom=0.1,
+    top=0.85
+)
+inset_bounds = [0.2, 1.01, 0.6, 0.4]
+
+# PLOT SETTINGS:
+colors = load_colors('../data/stage_colors.npz')
+color_factors = [-0.5, 0.5]
+lw = dict(
+    one=3,
+    kern=3,
+    all=1,
+)
+ax_labels = dict(
+    x='scale $\\alpha$',
+    y='$\\mu_f$',
+)
+xlab_kwargs = dict(
+    y=0.005,
+    fontsize=16,
+    ha='center',
+    va='bottom',
+)
+ylab_kwargs = dict(
+    x=0,
+    fontsize=20,
+    ha='left',
+    va='center',
+)
+yloc = 0.2
+
+# EXECUTION:
+for pure_path in pure_paths:
+    print(f'Processing {pure_path}')
+    noise_path = pure_path.replace('.npz', '_noise.npz')
+
+    # Load kernel invariance data:
+    pure_data, config = load_data(pure_path, **load_kwargs)
+    noise_data, _ = load_data(noise_path, **load_kwargs)
+    scales = pure_data['scales']
+
+    # Adjust grid parameters:
+    n_columns = config['k_specs'].shape[0] + 1
+    super_grid_kwargs['ncols'] = n_columns
+
+    # Prepare overall graph:
+    fig = plt.figure(**fig_kwargs)
+    super_grid = fig.add_gridspec(**super_grid_kwargs)
+
+    # Prepare axes:
+    all_axes = np.zeros((grid_kwargs['nrows'], n_columns), dtype=object)
+    subfigs = []
+    for i in range(n_columns):
+        subfig = fig.add_subfigure(super_grid[0, i])
+        grid = subfig.add_gridspec(**grid_kwargs)
+        subfigs.append(subfig)
+        for j in range(grid_kwargs['nrows']):
+            ax = subfig.add_subplot(grid[j, 0])
+            ax.set_xlim(scales[0], scales[-1])
+            ax.set_ylim(0, 1)
+            ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
+            ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
+            if i > 0:
+                hide_ticks(ax, side='left')
+            all_axes[j, i] = ax
+        hide_ticks(all_axes[0, i], side='bottom')
+    super_xlabel(ax_labels['x'], fig, all_axes[-1, 0], all_axes[-1, -1], **xlab_kwargs)
+    super_ylabel(ax_labels['y'], fig, all_axes[0, 0], all_axes[1, 0], **ylab_kwargs)
+
+    # Plot kernel-specific results:
+    in_min, in_high = ylimits(config['kernels'], pad=0.05)
+    for i in range(config['k_specs'].shape[0]):
+        pure_ax, noise_ax = all_axes[:, i]
+        # Plot results of pure-song analysis:
+        pure_ax.plot(scales, pure_data['measure_feat'][:, i, :],
+                     c=colors['feat'], lw=lw['one'])
+        # Plot results of noise-song analysis:
+        noise_ax.plot(scales, noise_data['measure_feat'][:, i, :],
+                      c=colors['feat'], lw=lw['one'])
+        # Indicate kernel waveform:
+        inset = pure_ax.inset_axes(inset_bounds)
+        inset.plot(config['k_times'], config['kernels'][:, i], c='k', lw=lw['kern'])
+        inset.set_xlim(config['k_times'][0], config['k_times'][-1])
+        inset.set_ylim(in_min, in_high)
+        inset.axis('off')
+
+    # Load population invariance data:
+    pure_data, config = load_data(pure_path.replace('_subset', ''), **load_kwargs)
+    noise_data, _ = load_data(noise_path.replace('_subset', ''), **load_kwargs)
+    scales = pure_data['scales']
+
+    # Get kernel type-specific colors:
+    types, ind = np.unique(config['k_specs'][:, 0], return_index=True)
+    types = types[np.argsort(ind)].astype(int)
+    factors = np.linspace(*color_factors, types.size)
+    kern_colors = shade_colors(colors['feat'], factors)
+    kern_colors = dict(zip(types.astype(str), kern_colors))
+
+    # Plot population-wide results:
+    pure_ax, noise_ax = all_axes[:, -1]
+    handles = pure_ax.plot(scales, pure_data['measure_feat'], c='k', lw=lw['all'])
+    assign_colors(handles, config['k_specs'][:, 0], kern_colors)
+
+    handles = noise_ax.plot(scales, noise_data['measure_feat'], c='k', lw=lw['all'])
+    assign_colors(handles, config['k_specs'][:, 0], kern_colors)
+
+
+    if save_path is not None:
+        fig.savefig(save_path)
+    plt.show()
+
+print('Done.')
+embed()

python/plot_functions.py

@@ -35,8 +35,7 @@ def letter_subplot(artist, label, x=None, y=None, xref=None, yref=None, ref=None
                    ha='left', va='bottom', fontsize=16, fontweight='bold', **kwargs):
     trans_artist = BboxTransformTo(artist.bbox)
     if x is None or y is None:
-        trans_ref = BboxTransformTo(ref.bbox)
+        transform = BboxTransformTo(ref.bbox) + trans_artist.inverted()
-        transform = trans_ref + trans_artist.inverted()
         if x is None:
             x = transform.transform([xref, 0])[0]
         if y is None:
@@ -102,15 +101,33 @@ def ylabel(ax, label, x=-0.2, y=None, fontsize=20, transform=None, **kwargs):
     ax.yaxis.set_label_coords(x, y, transform=transform)
     return None
 
-def super_xlabel(label, fig, high_ax, low_ax, y=0.005, **kwargs):
+def super_xlabel(label, fig, left_ax, right_ax, y=0.005,
-    x = (low_ax.get_position().x0 + high_ax.get_position().x1) / 2
+                 left_fig=None, right_fig=None, **kwargs):
-    fig.supxlabel(label, x=x, y=y, **kwargs)
+    left_x = left_ax.get_position().x0
-    return None
+    right_x = right_ax.get_position().x1
+    if left_fig is not None or right_fig is not None:
+        trans_fig = BboxTransformTo(fig.bbox)
+        if left_fig is not None:
+            transform = BboxTransformTo(left_fig.bbox) + trans_fig.inverted()
+            left_x = transform.transform((left_x, 0))[0]
+        if right_fig is not None:
+            transform = BboxTransformTo(right_fig.bbox) + trans_fig.inverted()
+            right_x = transform.transform((right_x, 0))[0]
+    return fig.supxlabel(label, x=(left_x + right_x) / 2, y=y, **kwargs)
 
-def super_ylabel(label, fig, high_ax, low_ax, x=0.005, **kwargs):
+def super_ylabel(label, fig, low_ax, high_ax, x=0.005,
-    y = (low_ax.get_position().y0 + high_ax.get_position().y1) / 2
+                 high_fig=None, low_fig=None, **kwargs):
-    fig.supylabel(label, x=x, y=y, **kwargs)
+    low_y = high_ax.get_position().y0
-    return None
+    high_y = low_ax.get_position().y1
+    if low_fig is not None or high_fig is not None:
+        trans_fig = BboxTransformTo(fig.bbox)
+        if low_fig is not None:
+            transform = BboxTransformTo(low_fig.bbox) + trans_fig.inverted()
+            low_y = transform.transform((0, low_y))[1]
+        if high_fig is not None:
+            transform = BboxTransformTo(high_fig.bbox) + trans_fig.inverted()
+            high_y = transform.transform((0, high_y))[1]
+    return fig.supylabel(label, x=x, y=(low_y + high_y) / 2, **kwargs)
 
 def plot_line(ax, time, signal, ymin=None, ymax=None, xmin=None, xmax=None,
               xpad=None, ypad=0.05, yloc=None, xloc=None, **kwargs):
@@ -170,18 +187,20 @@ def strip_zeros(num, right_digits=5):
     return left
 
 def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, transform=None, **kwargs):
-    t0, t1 = ax.get_xlim()
+    t_lims = ax.get_xlim()
-    offset = (t1 - t0 - dur) * xshift
+    span = t_lims[1] - t_lims[0]
-    x0 = t0 + offset
+    if parent is not None or transform is not None:
-    x1 = x0 + dur
+        if transform is None:
-    if parent is None:
+            transform = BboxTransformTo(parent.bbox)
+        kwargs['transform'] = transform
+        transform = ax.transData + transform.inverted()
+        x0 = transform.transform((t_lims[0], 0))[0]
+        x1 = transform.transform((t_lims[0] + dur, 0))[0]
+        dur = x1 - x0
+        span = 1
+    elif parent is None:
         parent = ax
-    if transform is None:
+    x0 = (span - dur) * xshift
-        transform = BboxTransformTo(parent.bbox)
+    x1 = x0 + dur
-    if transform is not ax.transData:
+    parent.add_artist(plt.Rectangle((x0, y0), dur, y1 - y0, **kwargs))
-        trans = ax.transData + transform.inverted()
-        x0 = trans.transform((x0, 0))[0]
-        x1 = trans.transform((x1, 0))[0]
-    parent.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
-                                    transform=transform, **kwargs))
     return None

python/save_inv_data_full.py

@@ -14,28 +14,30 @@ save_path = '../data/inv/full/'
 
 # ANALYSIS SETTINGS:
 example_scales = np.array([0, 0.5, 1, 5, 10])
-scales = np.linspace(0, 10, 100)
+scales = np.geomspace(0.01, 10, 100)
 scales = np.unique(np.concatenate((scales, example_scales)))
 
 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
     print(f'Processing {name}')
 
-    # Get normalized song recording:
+    # Get song recording:
     data, config = load_data(data_path, files='raw')
     song, rate = data['raw'], config['rate']
-    song /= song.std(axis=0)
-
-    # Get normalized noise:
-    rng = np.random.default_rng()
-    noise = rng.normal(size=song.shape[0])
-    noise /= noise.std()
 
     # Get song segment to be analyzed:
     time = np.arange(song.shape[0]) / rate
     start, end = data['songs_0'].ravel()
     segment = (time >= start) & (time <= end)
 
+    # Normalize song component:
+    song /= song[segment].std(axis=0)
+
+    # Get normalized noise:
+    rng = np.random.default_rng()
+    noise = rng.normal(size=song.shape[0])
+    noise /= noise[segment].std()
+
     # Prepare snippet storage:
     shape_low = (song.shape[0], example_scales.size)
     shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
@@ -82,25 +84,11 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                 scale_ind = np.nonzero(example_scales == scale)[0][0]
                 snippets[stage][:, ..., scale_ind] = signals[stage]
 
-            # Log "intensity measure" per stage:
+            # Log intensity measure per stage (excluding binary):
             if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
                 measures[key][i] = signals[stage][segment, ...].std(axis=0)
             elif stage == 'feat':
-                measures[key][i] = signals[stage][segment, :].mean(axis=0) / signals[stage][segment, :].std(axis=0)
+                measures[key][i] = signals[stage][segment, :].mean(axis=0)
-
-    # Relate to smallest scale:
-    base_ind = np.argmin(scales)
-    for stage in stages:
-        if stage == 'bi':
-            continue
-        key = f'measure_{stage}'
-        measures[key] /= measures[key][base_ind, ...]
-        if stage in ['conv', 'feat']:
-            spread = np.zeros((2, scales.size))
-            spread[0] = np.percentile(measures[key], 25, axis=1)
-            spread[1] = np.percentile(measures[key], 75, axis=1)
-            measures[f'spread_{stage}'] = spread
-            measures[key] = np.median(measures[key], axis=1)
 
     # Save analysis results:
     if save_path is not None:

python/save_inv_data_thresh-lp.py

@@ -1,6 +1,5 @@
 import glob
 import numpy as np
-import matplotlib.pyplot as plt
 from thunderhopper.modeltools import load_data, save_data
 from thunderhopper.filetools import crop_paths
 from thunderhopper.filters import sosfilter
@@ -14,10 +13,9 @@ save_path = '../data/inv/thresh_lp/'
 # ANALYSIS SETTINGS:
 add_noise = False
 thresh_percent = 90
-example_scales = np.array([0, 0.5, 1, 10, 50])
+example_scales = np.array([0, 1, 10, 50])
-scales = np.geomspace(0.01, 50, 100)
+scales = np.geomspace(0.01, 100, 100)
 scales = np.unique(np.concatenate((scales, example_scales)))
-plot_results = True
 
 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
@@ -48,20 +46,14 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
         # Reuse threshold from previous noise run:
         threshold = np.load(save_name + '_noise.npz')['thresh']
 
-    # Prepare snippet storage:
-    shape = song.shape + (example_scales.size,)
-    conv = np.zeros(shape, dtype=float)
-    bi = np.zeros(shape, dtype=float)
-    feat = np.zeros(shape, dtype=float)
-
     # Prepare measure storage:
     shape = (scales.size, song.shape[1])
-    measure_conv = np.zeros(shape, dtype=float)
+    # measure_conv = np.zeros(shape, dtype=float)
     measure_feat = np.zeros(shape, dtype=float)
 
     # Execute piecewise:
     for i, scale in enumerate(scales):
-        print('Simulating scale ', scale)
+        print('Simulating scale', scale)
 
         # Rescale song component:
         scaled_conv = song * scale
@@ -74,53 +66,16 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
         scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
                                 padtype='fixed', padlen=config['padlen'])
 
-        # Log snippet data:
+        # Get intensity measure per stage:
-        if scale in example_scales:
+        # measure_conv[i] = scaled_conv[segment, :].std(axis=0)
-            scale_ind = np.nonzero(example_scales == scale)[0][0]
-            conv[:, :, scale_ind] = scaled_conv
-            bi[:, :, scale_ind] = scaled_bi
-            feat[:, :, scale_ind] = scaled_feat
-
-        # Get "intensity measure" per stage:
-        measure_conv[i] = scaled_conv[segment, :].std(axis=0)
         measure_feat[i] = scaled_feat[segment, :].mean(axis=0)
 
-    # # Relate to smallest scale:
-    # base_ind = np.argmin(scales)
-    # measure_conv /= measure_conv[base_ind, :]
-
-    if plot_results:
-        fig, (ax1, ax2) = plt.subplots(2, 1)
-        ax1.plot(scales, measure_conv)
-        ax1.plot(scales, measure_conv.mean(axis=1), c='k')
-        ax1.plot(scales, np.median(measure_conv, axis=1), c='k', ls='--')
-        ax2.plot(scales, measure_feat)
-        ax2.plot(scales, np.nanmean(measure_feat, axis=1), c='k')
-        ax2.plot(scales, np.nanmedian(measure_feat, axis=1), c='k', ls='--')
-        plt.show()
-
-    # Condense measures across kernels:
-    spread_conv = np.zeros((2, scales.size))
-    spread_conv[0] = np.nanpercentile(measure_conv, 25, axis=1)
-    spread_conv[1] = np.nanpercentile(measure_conv, 75, axis=1)
-    measure_conv = np.nanmedian(measure_conv, axis=1)
-    spread_feat = np.zeros((2, scales.size))
-    spread_feat[0] = np.nanpercentile(measure_feat, 25, axis=1)
-    spread_feat[1] = np.nanpercentile(measure_feat, 75, axis=1)
-    measure_feat = np.nanmedian(measure_feat, axis=1)
-
     # Save analysis results:
     if save_path is not None:
         data = dict(
             scales=scales,
-            example_scales=example_scales,
+            # measure_conv=measure_conv,
-            conv=conv,
-            bi=bi,
-            feat=feat,
-            measure_conv=measure_conv,
-            spread_conv=spread_conv,
             measure_feat=measure_feat,
-            spread_feat=spread_feat,
             thresh=threshold,
             thresh_perc=thresh_percent,
             )

python/save_inv_data_thresh-lp_subset.py

@@ -1,27 +1,29 @@
-import glob
 import numpy as np
 import matplotlib.pyplot as plt
 from thunderhopper.modeltools import load_data, save_data
-from thunderhopper.filetools import crop_paths
+from thunderhopper.filetools import search_files, crop_paths
 from thunderhopper.filters import sosfilter
-from thunderhopper.filtertools import find_kern_specs
+from thunderhopper.filtertools import find_kern_specs, pdf_proportion
 from IPython import embed
 
 # GENERAL SETTINGS:
-target = 'Omocestus_rufipes'
+target = ['Omocestus_rufipes', '*'][0]
-data_paths = glob.glob(f'../data/processed/{target}*.npz')
+data_paths = search_files(target, dir='../data/processed/')
 save_path = '../data/inv/thresh_lp/'
 
 # ANALYSIS SETTINGS:
-add_noise = True
+add_noise = False
-thresh_percent = np.array([50, 75, 100])
+save_snippets = True
 example_scales = np.array([0, 1, 10, 50])
-scales = np.geomspace(0.01, 100, 100)
+scales = np.geomspace(0.01, 1000, 100)
 scales = np.unique(np.concatenate((scales, example_scales)))
+thresh_percent = np.array([0.6, 0.75, 0.999])
+thresholds = pdf_proportion(thresh_percent, sd=1, mu=0)
 plot_results = False
 kernels = np.array([
-    [2, 0.008],
+    [1, 0.008],
-    [4, 0.008],
+    [2, 0.004],
+    [3, 0.002],
 ])
 
 # EXECUTION:
@@ -54,22 +56,20 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
         noise = rng.normal(size=(conv.shape[0], 1))
         noise /= noise[segment].std()
 
-    # Prepare snippet storage:
+    if save_snippets:
-    shape = conv.shape + (example_scales.size, thresh_percent.size)
+        # Prepare snippet storage:
-    snip_conv = np.zeros(shape, dtype=float)
+        shape = conv.shape + (example_scales.size, thresh_percent.size)
-    snip_bi = np.zeros(shape, dtype=float)
+        snip_conv = np.zeros(shape, dtype=float)
-    snip_feat = np.zeros(shape, dtype=float)
+        snip_bi = np.zeros(shape, dtype=float)
+        snip_feat = np.zeros(shape, dtype=float)
 
     # Prepare measure storage:
     shape = (scales.size, conv.shape[1], thresh_percent.size)
-    measure_conv = np.zeros(shape, dtype=float)
+    # measure_conv = np.zeros(shape, dtype=float)
     measure_feat = np.zeros(shape, dtype=float)
 
-    # Compute kernel-specific thresholds (thresholds, kernels):
-    thresholds = np.percentile(conv, thresh_percent, axis=0)
-    
     # Execute piecewise analysis:
-    for i, threshs in enumerate(thresholds):
+    for i, thresh in enumerate(thresholds):
         print('\nSimulating threshold ', thresh_percent[i])
 
         for j, scale in enumerate(scales):
@@ -82,20 +82,20 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                 scaled_conv += noise
 
             # Process mixture:
-            scaled_bi = (scaled_conv > threshs).astype(float)
+            scaled_bi = (scaled_conv > thresh).astype(float)
             scaled_feat = sosfilter(scaled_bi, rate, config['feat_fcut'], 'lp',
                                     padtype='fixed', padlen=config['padlen'])
 
             # Log snippet data:
-            if scale in example_scales:
+            if save_snippets and scale in example_scales:
                 scale_ind = np.nonzero(example_scales == scale)[0][0]
                 snip_conv[:, :, scale_ind, i] = scaled_conv
                 snip_bi[:, :, scale_ind, i] = scaled_bi
                 snip_feat[:, :, scale_ind, i] = scaled_feat
 
             # Get intensity measure per stage:
-            measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
             measure_feat[j, :, i] = scaled_feat[segment, :].mean(axis=0)
+            # measure_conv[j, :, i] = scaled_conv[segment, :].std(axis=0)
 
     if plot_results:
         fig, axes = plt.subplots(thresh_percent.size, kernels.shape[0], 
@@ -103,6 +103,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                                  sharex=True, sharey=True, squeeze=True)
         axes[0, 0].set_xscale('symlog', linthresh=scales[scales>0].min(),
                               linscale=0.25)
+        axes[0, 0].set_ylim(0, 1)
         
         for i, thresh in enumerate(thresh_percent):
             for j, kernel in enumerate(kernels):
@@ -111,7 +112,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                 if i == 0:
                     ax.set_title(f'Kernel {kernel}')
                 if j == 0:
-                    ax.set_ylabel(f'{thresh}%')
+                    ax.set_ylabel(f'{100 * thresh}%')
         plt.show()
 
     # Save analysis results:
@@ -119,14 +120,17 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
         data = dict(
             scales=scales,
             example_scales=example_scales,
-            snip_conv=snip_conv,
+            # measure_conv=measure_conv,
-            snip_bi=snip_bi,
-            snip_feat=snip_feat,
-            measure_conv=measure_conv,
             measure_feat=measure_feat,
             thresh_perc=thresh_percent,
             threshs=thresholds,
             )
+        if save_snippets:
+            data.update(dict(
+                snip_conv=snip_conv,
+                snip_bi=snip_bi,
+                snip_feat=snip_feat,
+            ))
         if add_noise:
             save_name += '_noise'
         save_data(save_name, data, config, overwrite=True)