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Nearly done with fig_invariance_thresh_lp_species.pdf (WIP).

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j-hartling
2026-03-27 16:09:40 +01:00
parent 92ee4eda6f
commit 411d50ffcf
3 changed files with 187 additions and 93 deletions
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figures/fig_invariance_thresh_lp_species.pdf
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python/fig_invariance_thresh-lp_species.py
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@@ -1,6 +1,7 @@
import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from mpl_toolkits.axes_grid1 import make_axes_locatable
from itertools import product
from thunderhopper.filetools import search_files
@@ -129,6 +130,40 @@ def shorten_species(name):
genus, species = name.split('_')
return genus[0] + '. ' + species
def add_cross_axes(fig, n, long='col', fill='row', **grid_kwargs):
n_axes = n * (n - 1) // 2
nrows = grid_kwargs.get('nrows', None)
ncols = grid_kwargs.get('ncols', None)
if nrows is None or ncols is None:
if nrows is not None:
ncols = int(np.ceil(n_axes / nrows))
elif ncols is not None:
nrows = int(np.ceil(n_axes / ncols))
else:
nrows = int(np.ceil(np.sqrt(n_axes)))
ncols = int(np.ceil(n_axes / nrows))
if long == 'col' and ncols < nrows:
nrows, ncols = ncols, nrows
elif n_axes > nrows * ncols:
msg = f'Cannot place {n_axes} subplots in a {nrows}x{ncols} grid.'
raise ValueError(msg)
row_inds = [i for i in range(n) for j in range(i + 1, n)]
col_inds = [j for i in range(n) for j in range(i + 1, n)]
if fill == 'col':
positions = [(j, i) for i, j in product(range(ncols), range(nrows))]
row_inds, col_inds = col_inds, row_inds
else:
positions = list(product(range(nrows), range(ncols)))
positions = np.array(positions[:n_axes])
grid = fig.add_gridspec(**(grid_kwargs | dict(nrows=nrows, ncols=ncols)))
axes = []
for i, j in positions:
axes.append(fig.add_subplot(grid[i, j]))
return axes, positions, grid, row_inds, col_inds
# GENERAL SETTINGS:
target_species = [
'Omocestus_rufipes',
@@ -152,16 +187,16 @@ kern_specs = np.array([
[1, 0.008],
[2, 0.004],
[3, 0.002],
])[np.array([0, 1])]
])[np.array([0, 1, 2])]
n_kernels = kern_specs.shape[0]
# GRAPH SETTINGS:
fig_kwargs = dict(
figsize=(32/2.54, 20/2.54),
figsize=(32/2.54, 32/2.54),
)
super_grid_kwargs = dict(
nrows=3,
ncols=1,
ncols=2,
wspace=0,
hspace=0,
left=0,
@@ -171,15 +206,16 @@ super_grid_kwargs = dict(
height_ratios=[1, 4, 3]
)
subfig_specs = dict(
song=(0, 0),
feat=(1, 0),
space=(2, 0)
song=(0, slice(None)),
feat=(1, slice(None)),
pure=(2, 0),
noise=(2, 1),
)
feat_grid_kwargs = dict(
nrows=2,
ncols=n_species,
wspace=0.25,
hspace=0.15,
hspace=0.1,
left=0.06,
right=0.985,
bottom=0.1,
@@ -196,19 +232,19 @@ song_grid_kwargs = dict(
top=0.8
)
space_grid_kwargs = dict(
nrows=1,
ncols=2,
wspace=0.2,
hspace=0,
left=feat_grid_kwargs['left'],
right=feat_grid_kwargs['right'],
bottom=0.05,
nrows=None,
ncols=None,
wspace=0.1,
hspace=0.3,
left=0.05,
right=1,
bottom=0.1,
top=0.95
)
anchor_kwargs = dict(
aspect='equal',
adjustable='box',
anchor=(0, 0.5)
anchor=(0.5, 0.5)
)
inset_kwargs = dict(
y0=0.7,
@@ -226,8 +262,8 @@ fs = dict(
bar=16,
)
species_colors = load_colors('../data/species_colors.npz')
kernel_shades = [0, 0.5]
# scale_shades = [1, 0]
kernel_shades = [0, 0.75]
scale_shades = [1, 0]
lw = dict(
song=0.5,
feat=3,
@@ -246,11 +282,11 @@ space_kwargs = dict(
)
xlabels = dict(
feat='scale $\\alpha$',
space='$\\mu_{f_1}$'
space=[f'$\\mu_{{f_{i}}}$' for i in range(1, n_kernels + 1)],
)
ylabels = dict(
feat='$\\mu_f$',
space='$\\mu_{f_2}$',
space=[f'$\\mu_{{f_{i}}}$' for i in range(1, n_kernels + 1)],
bar='scale $\\alpha$',
)
xlab_feat_kwargs = dict(
@@ -260,7 +296,7 @@ xlab_feat_kwargs = dict(
va='bottom',
)
xlab_space_kwargs = dict(
y=0,
y=-0.3,
fontsize=fs['lab_tex'],
ha='center',
va='bottom',
@@ -268,14 +304,14 @@ xlab_space_kwargs = dict(
ylab_feat_kwargs = dict(
x=0,
fontsize=fs['lab_tex'],
ha='left',
va='center',
ha='center',
va='top',
)
ylab_space_kwargs = dict(
x=0,
x=-0.2,
fontsize=fs['lab_tex'],
ha='left',
va='center',
ha='center',
va='bottom',
)
ylab_cbar_kwargs = dict(
x=1,
@@ -284,6 +320,7 @@ ylab_cbar_kwargs = dict(
va='bottom',
)
xloc = dict(
feat=(1,),
space=0.5,
)
yloc = dict(
@@ -302,17 +339,24 @@ title_kwargs = dict(
fontstyle='italic'
)
letter_feat_kwargs = dict(
x=0,
yref=1,
ha='center',
va='top',
xref=0,
y=1,
ha='left',
va='center',
fontsize=fs['letter'],
)
letter_song_kwargs = dict(
x=0,
y=1,
ha='left',
va='top',
fontsize=fs['letter'],
)
letter_space_kwargs = dict(
x=0,
yref=1,
ha='center',
va='top',
ha='left',
va='center',
fontsize=fs['letter'],
)
song_bar_time = 1.0
@@ -325,33 +369,29 @@ song_bar_kwargs = dict(
lw=0,
clip_on=False,
# text_pos=(-0.1, 0.5),
text_str=f'${int(1000 * song_bar_time)}\\,\\text{{ms}}$',
text_kwargs=dict(
fontsize=fs['bar'],
ha='right',
va='center',
)
# text_str=f'${int(1000 * song_bar_time)}\\,\\text{{ms}}$',
# text_kwargs=dict(
# fontsize=fs['bar'],
# ha='right',
# va='center',
# )
)
kern_bar_time = 0.05
kern_bar_kwargs = dict(
dur=kern_bar_time,
y0=inset_kwargs['y0'],
y1=inset_kwargs['y0'] + 0.03,
y0=inset_kwargs['y0'] - 0.03,
y1=inset_kwargs['y0'],
color='k',
lw=0
)
cbar_bounds = [
0.05,
space_grid_kwargs['bottom'],
0.15,
space_grid_kwargs['top'] - space_grid_kwargs['bottom']
]
noise_kwargs = dict(
fc=(0.9, 0.9, 0.9),
ec='none',
lw=0,
zorder=0.5,
)
low_rel_thresh = 0.05
high_rel_thresh = 0.95
# EXECUTION:
@@ -368,6 +408,7 @@ for i in range(n_species):
hide_axis(ax, 'bottom')
hide_axis(ax, 'left')
song_axes[i] = ax
letter_subplot(song_subfig, 'a', **letter_song_kwargs)
# Prepare feature invariance axes:
feat_subfig = fig.add_subfigure(super_grid[subfig_specs['feat']])
@@ -377,12 +418,13 @@ for i, j in product(range(feat_grid_kwargs['nrows']), range(n_species)):
ax = feat_subfig.add_subplot(feat_grid[i, j])
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['feat']))
ax.set_ylim(0, 1)
if j == 0:
ylabel(ax, ylabels['feat'], transform=feat_subfig, **ylab_feat_kwargs)
feat_axes[i, j] = ax
super_xlabel(xlabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[-1, -1], **xlab_feat_kwargs)
super_ylabel(ylabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[0, 0], **ylab_feat_kwargs)
[hide_ticks(ax, side='bottom') for ax in feat_axes[0, :]]
[hide_ticks(ax, side='left') for ax in feat_axes[:, 1:].ravel()]
letter_subplots(feat_axes[0, :], labels='abc', ref=feat_subfig, **letter_feat_kwargs)
super_xlabel(xlabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[-1, -1], **xlab_feat_kwargs)
letter_subplots(feat_axes[:, 0], labels='bc', ref=feat_subfig, **letter_feat_kwargs)
# Prepare kernel insets:
x0 = np.linspace(0, 1, n_kernels + 1)[:-1] + 1 / n_kernels / 2
@@ -395,36 +437,49 @@ for i in range(n_kernels):
inset.axis('off')
insets.append(inset)
# Prepare feature space axes:
space_subfig = fig.add_subfigure(super_grid[subfig_specs['space']])
space_grid = space_subfig.add_gridspec(**space_grid_kwargs)
space_axes = np.zeros(space_grid_kwargs['ncols'], dtype=object)
for i in range(space_axes.size):
ax = space_subfig.add_subplot(space_grid[i])
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['space']))
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['space']))
ax.set_aspect(**anchor_kwargs)
# ax.set_ylabel(ylabels['space'], **ylab_space_kwargs)
ylabel(ax, ylabels['space'], transform=space_subfig.transSubfigure, **ylab_space_kwargs)
space_axes[i] = ax
super_xlabel(xlabels['space'], space_subfig, space_axes[1], space_axes[1], **xlab_space_kwargs)
hide_ticks(space_axes[0], side='bottom')
letter_subplot(space_axes[0], 'd', ref=space_subfig, **letter_space_kwargs)
# Prepare pure feature space axes:
pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
outputs = add_cross_axes(pure_subfig, n_kernels, **space_grid_kwargs)
pure_axes, space_pos, space_grid, row_inds, col_inds = outputs
letter_subplot(pure_subfig, 'd', ref=pure_axes[0], **letter_space_kwargs)
# Prepare colorbars:
cbar_bounds[0] += space_axes[-1].get_position().x1
bar_axes = [space_subfig.add_axes(cbar_bounds)]
bar_axes.extend(split_subplot(bar_axes[0], side=['right'] * (n_species - 1),
size=100, pad=0))
# Prepare noise feature space axes:
noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
noise_axes = add_cross_axes(noise_subfig, n_kernels, **space_grid_kwargs)[0]
letter_subplot(noise_subfig, 'e', ref=noise_axes[0], **letter_space_kwargs)
# Format feature space axes:
for ind, axes in enumerate(zip(pure_axes, noise_axes)):
for ax in axes:
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['space']))
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['space']))
ax.set_aspect(**anchor_kwargs)
xlabel(ax, xlabels['space'][col_inds[ind]], **xlab_space_kwargs)
ylabel(ax, ylabels['space'][row_inds[ind]], **ylab_space_kwargs)
# Determine area to place colorbars:
rightmost = pure_axes[np.argmax(space_pos[:, 1])].get_position()
downmost = pure_axes[np.argmax(space_pos[:, 0])].get_position()
bar_bounds = [rightmost.x0, downmost.y0, rightmost.width, downmost.height]
# Prepare pure colorbars:
pure_bars = [pure_subfig.add_axes(bar_bounds)]
pure_bars.extend(split_subplot(pure_bars[0], side=['right'] * (n_species - 1),
size=100, pad=0))
# Prepare noise colorbars:
noise_bars = [noise_subfig.add_axes(bar_bounds)]
noise_bars.extend(split_subplot(noise_bars[0], side=['right'] * (n_species - 1),
size=100, pad=0))
# Prepare kernel-specific color shading:
kern_factors = np.linspace(*kernel_shades, n_kernels)
kern_colors_bw = shade_colors((0., 0., 0.), kern_factors)
# Plot results per species:
min_feat = np.zeros((n_species, n_kernels), dtype=float)
noise_feat = np.zeros((n_species, n_kernels), dtype=float)
for i, species in enumerate(target_species):
print(f'Processing {species}')
@@ -464,21 +519,19 @@ for i, species in enumerate(target_species):
scales = scales[nonzero_inds]
pure_measure = pure_measure[nonzero_inds, :]
noise_measure = noise_measure[nonzero_inds, :]
min_feat[i, :] = noise_measure.min(axis=0)
# Prepare species-specific colors:
base_color = species_colors[species]
kern_colors = shade_colors(base_color, kern_factors)
scale_factors = np.linspace(1, 0, scales.size)
scale_factors = np.linspace(*scale_shades, scales.size)
scale_cmap = create_listed_cmap(shade_colors(base_color, scale_factors))
scale_cmap_bw = create_listed_cmap(shade_colors((0., 0., 0.), scale_factors))
# Plot feature invariance curves:
pure_ax, noise_ax = feat_axes[:, i]
symlog_kwargs['linthresh'] = scales[scales > 0][0]
[ax.set_xscale('symlog', **symlog_kwargs) for ax in feat_axes[:, i]]
pure_ax.set_xscale('symlog', **symlog_kwargs)
noise_ax.set_xscale('symlog', **symlog_kwargs)
[ax.xaxis.set_major_locator(plt.LogLocator(base=10, subs=xloc['feat'])) for ax in feat_axes[:, i]]
pure_ax, noise_ax = feat_axes[:, i]
handles = pure_ax.plot(scales, pure_measure, lw=lw['feat'])
[h.set_color(c) for h, c in zip(handles, kern_colors)]
handles = noise_ax.plot(scales, noise_measure, lw=lw['feat'])
@@ -494,30 +547,67 @@ for i, species in enumerate(target_species):
inset.set_ylim(ylims)
time_bar(insets[0], parent=feat_axes[0, 0], **kern_bar_kwargs)
# Plot pure feature space:
from matplotlib.colors import LogNorm
# Plot invariance curves in feature space:
norm = LogNorm(vmin=scales[scales > 0][0], vmax=scales[-1])
handle = space_axes[0].scatter(pure_measure[:, 0], pure_measure[:, 1],
c=scales, cmap=scale_cmap, norm=norm,
zorder=zorder[species], **space_kwargs)
for ind, (pure_ax, noise_ax) in enumerate(zip(pure_axes, noise_axes)):
irow, icol = row_inds[ind], col_inds[ind]
pure_handle = pure_ax.scatter(pure_measure[:, icol], pure_measure[:, irow],
c=scales, cmap=scale_cmap, norm=norm,
zorder=zorder[species], **space_kwargs)
# Plot noise feature space:
space_axes[1].scatter(noise_measure[:, 0], noise_measure[:, 1],
c=scales, cmap=scale_cmap, norm=norm,
zorder=zorder[species], **space_kwargs)
# Indicate scale color code:
space_subfig.colorbar(handle, cax=bar_axes[i])
bar_axes[i].set_yscale('symlog', **symlog_kwargs)
noise_handle = noise_ax.scatter(noise_measure[:, icol], noise_measure[:, irow],
c=scales, cmap=scale_cmap, norm=norm,
zorder=zorder[species], **space_kwargs)
# Indicate scale color code in pure subfigure:
pure_subfig.colorbar(pure_handle, cax=pure_bars[i])
pure_bars[i].set_yscale('symlog', **symlog_kwargs)
if i < n_species - 1:
hide_ticks(bar_axes[i], 'right', ticks=False)
hide_ticks(pure_bars[i], 'right', ticks=False)
else:
ylabel(bar_axes[i], ylabels['bar'], transform=space_subfig.transSubfigure, **ylab_cbar_kwargs)
ylabel(pure_bars[i], ylabels['bar'], transform=pure_subfig.transSubfigure, **ylab_cbar_kwargs)
# Indicate scale color code in noise subfigure:
noise_subfig.colorbar(noise_handle, cax=noise_bars[i])
noise_bars[i].set_yscale('symlog', **symlog_kwargs)
if i < n_species - 1:
hide_ticks(noise_bars[i], 'right', ticks=False)
else:
ylabel(noise_bars[i], ylabels['bar'], transform=noise_subfig.transSubfigure, **ylab_cbar_kwargs)
# Log feature noise floor:
noise_feat[i, :] = noise_measure.min(axis=0)
# Indicate low and high plateaus:
min_feat = pure_measure.min(axis=0)
span_feat = pure_measure.max(axis=0) - min_feat
low_thresh = min_feat + low_rel_thresh * span_feat
low_ind = np.nonzero((pure_measure >= low_thresh).all(axis=1))[0][0]
pure_bars[i].axhline(scales[low_ind], c='k', lw=3)
high_thresh = min_feat + high_rel_thresh * span_feat
high_ind = np.nonzero((pure_measure >= high_thresh).any(axis=1))[0][0]
pure_bars[i].axhline(scales[high_ind], c='w', lw=3)
# Indicate low and high plateaus:
min_feat = noise_measure.min(axis=0)
span_feat = noise_measure.max(axis=0) - min_feat
low_thresh = min_feat + low_rel_thresh * span_feat
low_ind = np.nonzero((noise_measure >= low_thresh).all(axis=1))[0][0]
noise_bars[i].axhline(scales[low_ind], c='k', lw=3)
high_thresh = min_feat + high_rel_thresh * span_feat
high_ind = np.nonzero((noise_measure >= high_thresh).any(axis=1))[0][0]
noise_bars[i].axhline(scales[high_ind], c='w', lw=3)
if show_noise:
# Indicate feature noise floor:
min_feat = min_feat.mean(axis=0)
space_axes[-1].add_patch(plt.Rectangle((0, 0), min_feat[0], min_feat[1], **noise_kwargs))
noise_feat = noise_feat.mean(axis=0)
for ind, ax in enumerate(noise_axes):
irow, icol = row_inds[ind], col_inds[ind]
ax.add_patch(plt.Rectangle((0, 0), noise_feat[icol], noise_feat[irow], **noise_kwargs))
if save_path is not None:
fig.savefig(save_path)

4
python/plot_functions.py
View File

@@ -18,6 +18,8 @@ def hide_axis(ax, side='bottom'):
def get_trans_artist(artist):
artist_type = type(artist).__name__
if 'Transform' in artist_type:
return artist
if artist_type == 'Axes':
return artist.transAxes
elif artist_type == 'Figure':
@@ -117,6 +119,7 @@ def xlabel(ax, label, x=None, y=-0.1, fontsize=20, transform=None, **kwargs):
if x is None:
x = 0.5
if transform is not None:
transform = get_trans_artist(transform)
x = (ax.transAxes + transform.inverted()).transform((x, 0))[0]
ax.xaxis.set_label_coords(x, y, transform=transform)
return ax.set_xlabel(label, fontsize=fontsize, **kwargs)
@@ -125,6 +128,7 @@ def ylabel(ax, label, x=-0.2, y=None, fontsize=20, transform=None, **kwargs):
if y is None:
y = 0.5
if transform is not None:
transform = get_trans_artist(transform)
y = (ax.transAxes + transform.inverted()).transform((0, y))[1]
ax.yaxis.set_label_coords(x, y, transform=transform)
return ax.set_ylabel(label, fontsize=fontsize, **kwargs)