Added some cmap functions.
Selected species-specific colors. Quite some progress on fig_invariance_thresh_lp_species.pdf.
This commit is contained in:
j-hartling
@@ -6,8 +6,8 @@ from itertools import product
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from thunderhopper.filetools import search_files
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from thunderhopper.modeltools import load_data
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from thunderhopper.filtertools import find_kern_specs
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from color_functions import load_colors, shade_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
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from color_functions import load_colors, shade_colors, create_listed_cmap
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from plot_functions import hide_axis, title_subplot, ylimits, xlabel, ylabel, super_ylabel,\
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plot_line, plot_barcode, strip_zeros, time_bar,\
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letter_subplot, letter_subplots, hide_ticks,\
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super_xlabel, super_ylabel, assign_colors
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@@ -125,73 +125,90 @@ def split_subplot(ax, side='right', size=10, pad=10):
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inputs = zip(*force_sequence(side, size, pad, equal_size=True))
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return [div.append_axes(s, f'{n}%', f'{p}%') for s, n, p in inputs]
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def shorten_species(name):
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genus, species = name.split('_')
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return genus[0] + '. ' + species
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# GENERAL SETTINGS:
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targets = [
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target_species = [
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'Omocestus_rufipes',
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'Chorthippus_biguttulus',
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# 'Chorthippus_mollis',
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# 'Chrysochraon_dispar',
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'Chorthippus_mollis',
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'Chrysochraon_dispar',
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'Gomphocerippus_rufus',
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# 'Pseudochorthippus_parallelus',
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'Pseudochorthippus_parallelus',
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]
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pure_paths = search_files(targets, incl='subset', excl='noise', dir='../data/inv/thresh_lp/')
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n_species = len(target_species)
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load_kwargs = dict(
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keywords=['scales', 'measure', 'thresh']
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)
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save_path = '../figures/fig_invariance_thresh_lp_species.pdf'
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exclude_zero = True
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show_noise = True
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# SUBSET SETTINGS:
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thresh_percent = np.array([0.6, 0.75, 0.999])[0]
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kernels = np.array([
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thresh_rel = np.array([0.5, 1, 3])[0]
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kern_specs = np.array([
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[1, 0.008],
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[2, 0.004],
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[3, 0.002],
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])[np.array([0, 1])]
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n_kernels = kern_specs.shape[0]
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 16/2.54),
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figsize=(32/2.54, 20/2.54),
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)
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n_species = len(targets)
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super_grid_kwargs = dict(
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nrows=2,
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ncols=n_species + 2,
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nrows=3,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0,
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right=1,
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bottom=0,
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top=1
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top=1,
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height_ratios=[1, 4, 3]
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)
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subfig_specs = dict(
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spec=(slice(None), slice(0, n_species)),
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big=(slice(None), slice(n_species, None))
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song=(0, 0),
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feat=(1, 0),
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space=(2, 0)
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)
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spec_grid_kwargs = dict(
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feat_grid_kwargs = dict(
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nrows=2,
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ncols=n_species,
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wspace=0.25,
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hspace=0.1,
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left=0.1,
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right=0.97,
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hspace=0.15,
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left=0.06,
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right=0.985,
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bottom=0.1,
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top=0.94
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)
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big_grid_kwargs = dict(
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nrows=2,
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ncols=1,
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wspace=0,
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hspace=0.2,
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left=0,
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right=1,
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bottom=spec_grid_kwargs['bottom'],
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top=spec_grid_kwargs['top']
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song_grid_kwargs = dict(
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nrows=1,
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ncols=n_species,
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wspace=feat_grid_kwargs['wspace'],
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hspace=0,
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left=feat_grid_kwargs['left'],
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right=feat_grid_kwargs['right'],
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bottom=0.1,
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top=0.8
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)
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space_grid_kwargs = dict(
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nrows=1,
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ncols=2,
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wspace=0.2,
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hspace=0,
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left=feat_grid_kwargs['left'],
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right=feat_grid_kwargs['right'],
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bottom=0.05,
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top=0.95
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)
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anchor_kwargs = dict(
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aspect='equal',
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adjustable='box',
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anchor=(0.3, 0.5)
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anchor=(0, 0.5)
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)
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inset_kwargs = dict(
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y0=0.7,
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@@ -208,50 +225,56 @@ fs = dict(
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tit_tex=20,
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bar=16,
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)
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base_color = load_colors('../data/stage_colors.npz')['feat']
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spec_cmaps = [
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'Reds',
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'Greens',
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'Blues',
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]
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species_colors = load_colors('../data/species_colors.npz')
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kernel_shades = [0, 0.5]
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# scale_shades = [1, 0]
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lw = dict(
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spec=2,
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song=0.5,
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feat=3,
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kern=3
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)
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zorder = dict(
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Omocestus_rufipes=2,
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Chorthippus_biguttulus=2.5,
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Chorthippus_mollis=2.4,
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Chrysochraon_dispar=2,
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Gomphocerippus_rufus=2,
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Pseudochorthippus_parallelus=2,
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)
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space_kwargs = dict(
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s=30,
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)
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xlabels = dict(
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spec='scale $\\alpha$',
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big='$\\mu_{f_1}$'
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feat='scale $\\alpha$',
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space='$\\mu_{f_1}$'
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)
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ylabels = dict(
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spec='$\\mu_f$',
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big='$\\mu_{f_2}$',
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feat='$\\mu_f$',
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space='$\\mu_{f_2}$',
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bar='scale $\\alpha$',
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)
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xlab_spec_kwargs = dict(
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xlab_feat_kwargs = dict(
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y=0,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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)
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xlab_big_kwargs = dict(
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xlab_space_kwargs = dict(
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y=0,
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fontsize=fs['lab_tex'],
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ha='center',
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va='bottom',
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)
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ylab_spec_kwargs = dict(
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ylab_feat_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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ha='left',
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va='center',
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)
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ylab_big_kwargs = dict(
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x=0.03,
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ylab_space_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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ha='center',
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ha='left',
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va='center',
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)
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ylab_cbar_kwargs = dict(
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@@ -261,28 +284,57 @@ ylab_cbar_kwargs = dict(
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va='bottom',
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)
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xloc = dict(
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big=0.5,
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space=0.5,
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)
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yloc = dict(
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spec=0.5,
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big=0.5
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feat=0.5,
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space=0.5
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)
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letter_spec_kwargs = dict(
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symlog_kwargs = dict(
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linscale=0.5,
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)
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title_kwargs = dict(
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x=0.5,
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yref=1,
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ha='center',
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va='top',
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fontsize=fs['tit_norm'],
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fontstyle='italic'
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)
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letter_feat_kwargs = dict(
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x=0,
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yref=1,
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ha='center',
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va='top',
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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letter_space_kwargs = dict(
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x=0,
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yref=1,
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ha='center',
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va='top',
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fontsize=fs['letter'],
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)
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time_bar_kwargs = dict(
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dur=0.05,
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song_bar_time = 1.0
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song_bar_kwargs = dict(
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dur=song_bar_time,
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y0=-0.1,
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y1=0,
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xshift=0,
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color='k',
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lw=0,
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clip_on=False,
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# text_pos=(-0.1, 0.5),
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text_str=f'${int(1000 * song_bar_time)}\\,\\text{{ms}}$',
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text_kwargs=dict(
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fontsize=fs['bar'],
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ha='right',
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va='center',
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)
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)
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kern_bar_time = 0.05
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kern_bar_kwargs = dict(
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dur=kern_bar_time,
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y0=inset_kwargs['y0'],
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y1=inset_kwargs['y0'] + 0.03,
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color='k',
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@@ -290,11 +342,16 @@ time_bar_kwargs = dict(
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)
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cbar_bounds = [
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0.05,
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big_grid_kwargs['bottom'],
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space_grid_kwargs['bottom'],
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0.15,
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big_grid_kwargs['top'] - big_grid_kwargs['bottom']
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space_grid_kwargs['top'] - space_grid_kwargs['bottom']
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]
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shade_factors = [0.9, -0.9]
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noise_kwargs = dict(
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fc=(0.9, 0.9, 0.9),
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ec='none',
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lw=0,
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zorder=0.5,
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)
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# EXECUTION:
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@@ -302,105 +359,165 @@ shade_factors = [0.9, -0.9]
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fig = plt.figure(**fig_kwargs)
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super_grid = fig.add_gridspec(**super_grid_kwargs)
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# Prepare species-specific axes:
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spec_subfig = fig.add_subfigure(super_grid[subfig_specs['spec']])
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spec_grid = spec_subfig.add_gridspec(**spec_grid_kwargs)
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spec_axes = np.zeros((spec_grid_kwargs['nrows'], n_species), dtype=object)
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for i, j in product(range(spec_grid_kwargs['nrows']), range(n_species)):
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ax = spec_subfig.add_subplot(spec_grid[i, j])
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ax.set_xscale('symlog', linthresh=0.1, linscale=0.5)
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['spec']))
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# Prepare song axes:
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song_subfig = fig.add_subfigure(super_grid[subfig_specs['song']])
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song_grid = song_subfig.add_gridspec(**song_grid_kwargs)
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song_axes = np.zeros((n_species,), dtype=object)
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for i in range(n_species):
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ax = song_subfig.add_subplot(song_grid[i])
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hide_axis(ax, 'bottom')
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hide_axis(ax, 'left')
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song_axes[i] = ax
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# Prepare feature invariance axes:
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feat_subfig = fig.add_subfigure(super_grid[subfig_specs['feat']])
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feat_grid = feat_subfig.add_gridspec(**feat_grid_kwargs)
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feat_axes = np.zeros((feat_grid_kwargs['nrows'], n_species), dtype=object)
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for i, j in product(range(feat_grid_kwargs['nrows']), range(n_species)):
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ax = feat_subfig.add_subplot(feat_grid[i, j])
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['feat']))
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ax.set_ylim(0, 1)
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spec_axes[i, j] = ax
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super_xlabel(xlabels['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[-1, -1], **xlab_spec_kwargs)
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super_ylabel(ylabels['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[0, 0], **ylab_spec_kwargs)
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[hide_ticks(ax, side='bottom') for ax in spec_axes[0, :]]
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[hide_ticks(ax, side='left') for ax in spec_axes[:, 1:].ravel()]
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letter_subplots(spec_axes[0, :], labels='abc', ref=spec_subfig, **letter_spec_kwargs)
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feat_axes[i, j] = ax
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super_xlabel(xlabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[-1, -1], **xlab_feat_kwargs)
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super_ylabel(ylabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[0, 0], **ylab_feat_kwargs)
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[hide_ticks(ax, side='bottom') for ax in feat_axes[0, :]]
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[hide_ticks(ax, side='left') for ax in feat_axes[:, 1:].ravel()]
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letter_subplots(feat_axes[0, :], labels='abc', ref=feat_subfig, **letter_feat_kwargs)
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# Prepare kernel insets:
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x0 = np.linspace(0, 1, kernels.shape[0] + 1)[:-1] + 1 / kernels.shape[0] / 2
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x0 = np.linspace(0, 1, n_kernels + 1)[:-1] + 1 / n_kernels / 2
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x0 -= inset_kwargs['w'] / 2
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insets = []
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for i in range(kernels.shape[0]):
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for i in range(n_kernels):
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bounds = [x0[i], inset_kwargs['y0'], inset_kwargs['w'], inset_kwargs['h']]
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inset = spec_axes[0, 0].inset_axes(bounds)
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inset = feat_axes[0, 0].inset_axes(bounds)
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inset.set_title(rf'$k_{{{i+1}}}$', fontsize=20)
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inset.axis('off')
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insets.append(inset)
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# Prepare feature space axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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big_axes = np.zeros(super_grid_kwargs['nrows'], dtype=object)
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for i in range(big_axes.size):
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ax = big_subfig.add_subplot(big_grid[i, 0])
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space_subfig = fig.add_subfigure(super_grid[subfig_specs['space']])
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space_grid = space_subfig.add_gridspec(**space_grid_kwargs)
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space_axes = np.zeros(space_grid_kwargs['ncols'], dtype=object)
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for i in range(space_axes.size):
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ax = space_subfig.add_subplot(space_grid[i])
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['big']))
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
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ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['space']))
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['space']))
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ax.set_aspect(**anchor_kwargs)
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# ax.set_ylabel(ylabels['big'], **ylab_big_kwargs)
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ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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big_axes[i] = ax
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super_xlabel(xlabels['big'], big_subfig, big_axes[1], big_axes[1], **xlab_big_kwargs)
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hide_ticks(big_axes[0], side='bottom')
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letter_subplot(big_axes[0], 'd', ref=big_subfig, **letter_big_kwargs)
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# ax.set_ylabel(ylabels['space'], **ylab_space_kwargs)
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ylabel(ax, ylabels['space'], transform=space_subfig.transSubfigure, **ylab_space_kwargs)
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space_axes[i] = ax
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super_xlabel(xlabels['space'], space_subfig, space_axes[1], space_axes[1], **xlab_space_kwargs)
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hide_ticks(space_axes[0], side='bottom')
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letter_subplot(space_axes[0], 'd', ref=space_subfig, **letter_space_kwargs)
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# Prepare colorbars:
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cbar_bounds[0] += big_axes[-1].get_position().x1
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bar_axes = [big_subfig.add_axes(cbar_bounds)]
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bar_axes.extend(split_subplot(bar_axes[0], side=['right', 'right'], size=100, pad=0))
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cbar_bounds[0] += space_axes[-1].get_position().x1
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bar_axes = [space_subfig.add_axes(cbar_bounds)]
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bar_axes.extend(split_subplot(bar_axes[0], side=['right'] * (n_species - 1),
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size=100, pad=0))
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# Prepare kernel-specific color shading:
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kern_factors = np.linspace(*kernel_shades, n_kernels)
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kern_colors_bw = shade_colors((0., 0., 0.), kern_factors)
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# Plot results per species:
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for i, pure_path in enumerate(pure_paths):
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print(f'Processing {pure_path}')
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noise_path = pure_path.replace('.npz', '_noise.npz')
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min_feat = np.zeros((n_species, n_kernels), dtype=float)
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for i, species in enumerate(target_species):
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print(f'Processing {species}')
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# Fetch species-specific recording file:
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song_path = search_files(species, dir='../data/processed/')[0]
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# Load song data:
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song_data, _ = load_data(song_path, files='filt')
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song, rate = song_data['filt'], song_data['filt_rate']
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# Plot species snippet:
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song_ax = song_axes[i]
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time = np.arange(song.shape[0]) / rate
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plot_line(song_ax, time, song, ypad=0.05, c='k', lw=lw['song'])
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title_subplot(song_ax, shorten_species(species), ref=song_subfig, **title_kwargs)
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time_bar(song_ax, **song_bar_kwargs)
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# Fetch species-specific invariance files:
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pure_path = search_files(species, incl='pure', dir='../data/inv/thresh_lp/')[0]
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noise_path = search_files(species, incl='noise', dir='../data/inv/thresh_lp/')[0]
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# Load invariance data:
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pure_data, config = load_data(pure_path, **load_kwargs)
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noise_data, _ = load_data(noise_path, **load_kwargs)
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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)
|
||||
# Reduce to kernel subset and a single threshold:
|
||||
thresh_ind = np.nonzero(pure_data['thresh_rel'] == thresh_rel)[0][0]
|
||||
kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
|
||||
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]
|
||||
if exclude_zero:
|
||||
# Reduce to nonzero scales:
|
||||
nonzero_inds = scales > 0
|
||||
scales = scales[nonzero_inds]
|
||||
pure_measure = pure_measure[nonzero_inds, :]
|
||||
noise_measure = noise_measure[nonzero_inds, :]
|
||||
min_feat[i, :] = noise_measure.min(axis=0)
|
||||
|
||||
# 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'])
|
||||
# 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_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)
|
||||
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'])
|
||||
[h.set_color(c) for h, c in zip(handles, kern_colors)]
|
||||
|
||||
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'])
|
||||
for kern, inset, c in zip(config['kernels'].T, insets, kern_colors_bw):
|
||||
inset.plot(config['k_times'], kern, c=c, lw=lw['kern'])
|
||||
inset.set_xlim(xlims)
|
||||
inset.set_ylim(ylims)
|
||||
time_bar(insets[0], parent=spec_axes[0, 0], **time_bar_kwargs)
|
||||
time_bar(insets[0], parent=feat_axes[0, 0], **kern_bar_kwargs)
|
||||
|
||||
# Plot pure feature space:
|
||||
handle = big_axes[0].scatter(pure_measure[:, 0], pure_measure[:, 1],
|
||||
c=scales, cmap=spec_cmaps[i], **space_kwargs)
|
||||
from matplotlib.colors import LogNorm
|
||||
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)
|
||||
|
||||
# Plot noise feature space:
|
||||
big_axes[1].scatter(noise_measure[:, 0], noise_measure[:, 1],
|
||||
c=scales, cmap=spec_cmaps[i], **space_kwargs)
|
||||
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:
|
||||
big_subfig.colorbar(handle, cax=bar_axes[i])
|
||||
bar_axes[i].set_yscale('symlog', linthresh=scales[1], linscale=0.2)
|
||||
if i < len(pure_paths) - 1:
|
||||
space_subfig.colorbar(handle, cax=bar_axes[i])
|
||||
bar_axes[i].set_yscale('symlog', **symlog_kwargs)
|
||||
if i < n_species - 1:
|
||||
hide_ticks(bar_axes[i], 'right', ticks=False)
|
||||
else:
|
||||
ylabel(bar_axes[i], ylabels['bar'], transform=big_subfig.transSubfigure, **ylab_cbar_kwargs)
|
||||
ylabel(bar_axes[i], ylabels['bar'], transform=space_subfig.transSubfigure, **ylab_cbar_kwargs)
|
||||
|
||||
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))
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
Reference in New Issue
Block a user