92ee4eda6f
Selected species-specific colors. Quite some progress on fig_invariance_thresh_lp_species.pdf.
381 lines
12 KiB
Python
381 lines
12 KiB
Python
import plotstyle_plt
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import numpy as np
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import matplotlib.pyplot as plt
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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 color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, hide_ticks,\
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plot_line, strip_zeros, time_bar, zoom_inset,\
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letter_subplot, title_subplot
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from IPython import embed
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def add_snip_axes(fig, grid_kwargs):
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grid = fig.add_gridspec(**grid_kwargs)
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axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
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for i, j in product(range(grid.nrows), range(grid.ncols)):
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axes[i, j] = fig.add_subplot(grid[i, j])
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[hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
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[hide_axis(ax, 'bottom') for ax in axes.flatten()]
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return axes
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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handles = []
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for ax, snippet in zip(axes, snippets.T):
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handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
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return handles
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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species_paths = search_files('*', incl='noise', dir='../data/inv/log_hp/')
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure']
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)
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save_path = '../figures/fig_invariance_log_hp.pdf'
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compute_ratios = True
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show_diag = True
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show_noise = True
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 32/2.54),
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)
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snip_rows = 1
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big_rows = 1
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super_grid_kwargs = dict(
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nrows=2 * snip_rows + big_rows,
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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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)
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subfig_specs = dict(
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pure=(slice(0, snip_rows), slice(None)),
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noise=(slice(snip_rows, 2 * snip_rows), slice(None)),
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big=(slice(-big_rows, None), slice(None)),
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)
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block_height = 0.8
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edge_padding = 0.08
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pure_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=0.1,
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hspace=0.15,
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left=0.11,
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right=0.95,
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bottom=1 - block_height - edge_padding,
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top=1 - edge_padding,
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height_ratios=[1, 2, 1]
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)
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noise_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=pure_grid_kwargs['wspace'],
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hspace=pure_grid_kwargs['hspace'],
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=edge_padding,
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top=edge_padding + block_height,
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height_ratios=[1, 2, 1]
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)
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big_grid_kwargs = dict(
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nrows=1,
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ncols=3,
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wspace=0.3,
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hspace=0,
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=0.05,
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top=1
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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=(0.5, 0.5)
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)
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# PLOT SETTINGS:
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fs = dict(
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lab_norm=16,
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lab_tex=20,
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letter=22,
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tit_norm=16,
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tit_tex=20,
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bar=16,
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)
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colors = load_colors('../data/stage_colors.npz')
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lw_snippets = 1
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lw_big = 3
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xlabels = dict(
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big='scale $\\alpha$',
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)
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ylabels = dict(
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env='$x_{\\text{env}}$',
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log='$x_{\\text{dB}}$',
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inv='$x_{\\text{adapt}}$',
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big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$',
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)
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xlab_big_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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ylab_snip_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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rotation=0,
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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,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top',
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)
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yloc = dict(
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env=1000,
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log=40,
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inv=20
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)
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title_kwargs = dict(
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x=0.5,
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y=1,
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ha='center',
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va='bottom',
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fontsize=fs['tit_norm'],
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)
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letter_snip_kwargs = dict(
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x=0,
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yref=0.5,
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ha='left',
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va='center',
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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x=0,
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y=1,
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ha='left',
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va='bottom',
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fontsize=fs['letter'],
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)
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zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
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zoom_kwargs = dict(
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x0=0.45,
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x1=0.55,
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y0=0,
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y1=0.0006,
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low_left=True,
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low_right=True,
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ec='k',
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lw=1,
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alpha=1,
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)
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inset_tick_kwargs = dict(
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axis='y',
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length=3,
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pad=1,
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left=False,
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labelleft=False,
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right=True,
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labelright=True,
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)
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bar_time = 5
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bar_kwargs = dict(
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dur=bar_time,
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y0=-0.25,
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y1=-0.1,
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xshift=1,
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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'${bar_time}\\,\\text{{s}}$',
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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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diag_kwargs = dict(
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c=(0.75, 0.75, 0.75),
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lw=2,
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ls='--',
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zorder=1.9,
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)
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noise_rel_thresh = 0.95
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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=1.5,
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)
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# PREPARATION:
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if compute_ratios:
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ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
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ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
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species_measures = []
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for species_path in species_paths:
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species_measure = load_data(species_path, **load_kwargs)[0]['measure_inv']
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if compute_ratios:
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species_measure /= ref_measures['inv']
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species_measures.append(species_measure)
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species_measures = np.array(species_measures).T
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# EXECUTION:
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for data_path in data_paths:
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print(f'Processing {data_path}')
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# Load invariance data:
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pure_data, config = load_data(data_path, **load_kwargs)
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noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
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pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
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t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
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# Prepare overall graph:
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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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fig.canvas.draw()
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# Prepare pure-song snippet axes:
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pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
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pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
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pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
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for ax, stage in zip(pure_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=pure_subfig.transSubfigure)
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for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
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pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
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pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
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pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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pure_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(pure_inset, 'bottom', ticks=False)
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# Prepare noise-song snippet axes:
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noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
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noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
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noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
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for ax, stage in zip(noise_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
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noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
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noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
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noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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noise_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(noise_inset, 'bottom', ticks=False)
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# Prepare analysis 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((big_grid.ncols,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_aspect(**anchor_kwargs)
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ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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if i == 0:
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hide_ticks(ax, 'bottom')
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letter_subplot(ax, 'c', **letter_big_kwargs)
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else:
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xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
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letter_subplot(ax, 'd', **letter_big_kwargs)
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big_axes[i] = ax
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# Plot pure-song envelope snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
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ymin=0, c=colors['env'], lw=lw_snippets)[0]
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zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot pure-song logarithmic snippets:
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plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
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c=colors['log'], lw=lw_snippets)
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# Plot pure-song invariant snippets:
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plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
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c=colors['inv'], lw=lw_snippets)
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# Plot noise-song envelope snippets:
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ymin, ymax = pure_axes[0, 0].get_ylim()
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handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
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ymin, ymax, c=colors['env'], lw=lw_snippets)[0]
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zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot noise-song logarithmic snippets:
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ymin, ymax = pure_axes[1, 0].get_ylim()
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plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
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ymin, ymax, c=colors['log'], lw=lw_snippets)
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# Plot noise-song invariant snippets:
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ymin, ymax = pure_axes[2, 0].get_ylim()
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plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
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ymin, ymax, c=colors['inv'], lw=lw_snippets)
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# Indicate time scale:
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time_bar(noise_axes[-1, -1], **bar_kwargs)
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if compute_ratios:
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# Relate pure-song measures to zero scale:
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pure_data['measure_env'] /= ref_measures['env']
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pure_data['measure_log'] /= ref_measures['log']
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pure_data['measure_inv'] /= ref_measures['inv']
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# Relate noise-song measures to zero scale:
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noise_data['measure_env'] /= ref_measures['env']
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noise_data['measure_log'] /= ref_measures['log']
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noise_data['measure_inv'] /= ref_measures['inv']
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# Plot pure-song measures (ideal):
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big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw_big)
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big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw_big)
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big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw_big)
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# Plot noise-song measures (limited):
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big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw_big)
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big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big)
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big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big)
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# Plot species measures:
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big_axes[2].plot(noise_scales, species_measures, 'k', lw=lw_big)
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if show_diag:
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# Indicate diagonal:
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big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
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big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
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if show_noise:
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# Indicate noise floor:
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if compute_ratios:
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span_measure = noise_data['measure_inv'][-1] - ref_measures['inv']
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thresh_measure = ref_measures['inv'] + noise_rel_thresh * span_measure
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else:
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span_measure = noise_data['measure_inv'][-1] - noise_data['measure_inv'][0]
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thresh_measure = noise_data['measure_inv'][0] + noise_rel_thresh * span_measure
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thresh_ind = np.nonzero(noise_data['measure_inv'] < thresh_measure)[0][-1]
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thresh_scale = noise_scales[thresh_ind]
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big_axes[1].axvspan(noise_scales[0], thresh_scale, **noise_kwargs)
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if save_path is not None:
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fig.savefig(save_path, bbox_inches='tight')
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plt.show()
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print('Done.')
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embed()
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