Holiday syncing :)
This commit is contained in:
j-hartling
@@ -4,9 +4,11 @@ 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.modeltools import load_data
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from misc_functions import get_saturation
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from color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
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plot_line, plot_barcode, strip_zeros, time_bar, super_xlabel
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plot_line, plot_barcode, strip_zeros, time_bar,\
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letter_subplot, letter_subplots
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from IPython import embed
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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@@ -20,52 +22,70 @@ def plot_bi_snippets(axes, time, snippets, **kwargs):
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plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
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return None
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def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
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if measures.ndim == 1:
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ax.plot(scales, measures, **kwargs)[0]
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return measures
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median_measure = np.median(measures, axis=1)
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spread_measure = [np.percentile(measures, 25, axis=1),
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np.percentile(measures, 75, axis=1)]
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ax.plot(scales, median_measure, **kwargs)[0]
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ax.fill_between(scales, *spread_measure, **fill_kwargs)
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return median_measure
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def show_saturation(ax, scales, measures, high=0.95, **kwargs):
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high_ind = get_saturation(measures, high=high)[1]
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return ax.plot(scales[high_ind], 0, transform=ax.get_xaxis_transform(),
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marker='o', ms=10, zorder=6, clip_on=False, **kwargs)
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = glob.glob(f'../data/inv/full/{target}*.npz')
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stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
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ref_path = '../data/inv/full/ref_measures.npz'
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save_path = '../figures/fig_invariance_full.pdf'
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stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
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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_full.pdf'
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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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super_grid_kwargs = dict(
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nrows=1,
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ncols=3,
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nrows=2,
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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=[3, 2]
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)
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subfig_specs = dict(
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snip=(slice(None), slice(0, -1)),
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big=(slice(None), -1),
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snip=(0, 0),
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big=(1, 0),
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)
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snip_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=0.4,
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left=0.15,
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left=0.08,
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right=0.95,
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bottom=0.08,
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top=0.95
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)
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big_grid_kwargs = dict(
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nrows=1,
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ncols=1,
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wspace=0,
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ncols=3,
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wspace=0.2,
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hspace=0,
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left=0.2,
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left=snip_grid_kwargs['left'],
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right=0.96,
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bottom=0.08,
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bottom=0.2,
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top=0.95
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)
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@@ -79,9 +99,7 @@ fs = dict(
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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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colors['raw'] = "#000000"
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lw = dict(
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raw=0.25,
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filt=0.25,
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env=0.25,
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log=0.25,
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@@ -92,25 +110,17 @@ lw = dict(
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big=3
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)
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xlabels = dict(
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snip='time [s]',
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big='scale $\\alpha$',
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)
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ylabels = dict(
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raw='$x$',
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filt='$x_{\\text{filt}}$',
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env='$x_{\\text{env}}$',
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log='$x_{\\text{log}}$',
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log='$x_{\\text{db}}$',
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inv='$x_{\\text{inv}}$',
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conv='$c_i$',
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bi='$b_i$',
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feat='$f_i$',
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big='norm. intensity measure'
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)
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xlab_snip_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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big=['intensity', 'rel. intensity', 'norm. intensity']
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)
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xlab_big_kwargs = dict(
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y=0,
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@@ -126,18 +136,17 @@ ylab_snip_kwargs = dict(
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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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x=-0.12,
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fontsize=fs['lab_norm'],
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ha='center',
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va='top',
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va='bottom',
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)
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yloc = dict(
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raw=500,
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filt=500,
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env=250,
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log=25,
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inv=10,
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conv=1,
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filt=3000,
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env=1000,
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log=50,
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inv=20,
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conv=2,
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feat=1,
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)
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title_kwargs = dict(
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@@ -148,20 +157,18 @@ title_kwargs = dict(
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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=0.02,
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y=1,
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x=0,
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yref=0.5,
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ha='left',
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va='top',
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va='center',
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fontsize=fs['letter'],
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fontweight='bold'
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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='top',
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va='bottom',
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fontsize=fs['letter'],
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fontweight='bold'
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)
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bar_time = 5
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bar_kwargs = dict(
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@@ -181,6 +188,8 @@ bar_kwargs = dict(
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)
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)
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# PREPARATION:
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ref_data = dict(np.load(ref_path))
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# EXECUTION:
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for data_path in data_paths:
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@@ -188,7 +197,7 @@ for data_path in data_paths:
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# Load invariance data:
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data, config = load_data(data_path, **load_kwargs)
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t_full = np.arange(data['snip_raw'].shape[0]) / config['rate']
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t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
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# Adjust grid parameters:
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snip_grid_kwargs['ncols'] = data['example_scales'].size
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@@ -204,78 +213,91 @@ for data_path in data_paths:
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for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
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ax = snip_subfig.add_subplot(snip_grid[i, j])
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ax.set_xlim(t_full[0], t_full[-1])
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
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hide_axis(ax, 'bottom')
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if i == 0:
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title = f'$\\alpha={strip_zeros(data["example_scales"][j])}$'
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title_subplot(ax, title, ref=snip_subfig, **title_kwargs)
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title = title_subplot(ax, f'$\\alpha={strip_zeros(data["example_scales"][j])}$',
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ref=snip_subfig, **title_kwargs)
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if j == 0:
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ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
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else:
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hide_axis(ax, 'left')
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if stages[i] != 'bi':
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
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snip_axes[i, j] = ax
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time_bar(snip_axes[-1, -1], **bar_kwargs)
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letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
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# Prepare single analysis axis:
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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_ax = big_subfig.add_subplot(big_grid[0, 0])
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big_ax.set_xlim(data['scales'].min(), data['scales'].max())
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big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
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big_ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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xlabel(big_ax, xlabels['big'], **xlab_big_kwargs, transform=big_subfig.transSubfigure)
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ylabel(big_ax, ylabels['big'], **ylab_big_kwargs, transform=big_subfig.transSubfigure)
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big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i in range(big_grid.ncols):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(data['scales'][0], data['scales'][-1])
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ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
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ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
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big_axes[i] = ax
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letter_subplots(big_axes, 'bc', **letter_big_kwargs)
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# Plot raw snippets:
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plot_snippets(snip_axes[0, :], t_full, data['snip_raw'],
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c=colors['raw'], lw=lw['raw'])
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# # Plot filtered snippets:
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# plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
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# c=colors['filt'], lw=lw['filt'])
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# Plot filtered snippets:
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plot_snippets(snip_axes[1, :], t_full, data['snip_filt'],
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c=colors['filt'], lw=lw['filt'])
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# # Plot envelope snippets:
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# plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
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# ymin=0, c=colors['env'], lw=lw['env'])
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# Plot envelope snippets:
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plot_snippets(snip_axes[2, :], t_full, data['snip_env'],
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ymin=0, c=colors['env'], lw=lw['env'])
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# # Plot logarithmic snippets:
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# plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
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# c=colors['log'], lw=lw['log'])
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# Plot logarithmic snippets:
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plot_snippets(snip_axes[3, :], t_full, data['snip_log'],
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ymax=0, c=colors['log'], lw=lw['log'])
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# # Plot invariant snippets:
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# plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
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# c=colors['inv'], lw=lw['inv'])
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# Plot invariant snippets:
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plot_snippets(snip_axes[4, :], t_full, data['snip_inv'],
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c=colors['inv'], lw=lw['inv'])
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# # Plot kernel response snippets:
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# plot_snippets(snip_axes[4, :], t_full, data['snip_conv'],
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# c=colors['conv'], lw=lw['conv'])
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# Plot kernel response snippets:
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plot_snippets(snip_axes[5, :], t_full, data['snip_conv'],
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c=colors['conv'], lw=lw['conv'])
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# Plot binary snippets:
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plot_bi_snippets(snip_axes[6, :], t_full, data['snip_bi'],
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color=colors['bi'], lw=lw['bi'])
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# Plot feature snippets:
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plot_snippets(snip_axes[7, :], t_full, data['snip_feat'],
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ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
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# # Plot feature snippets:
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# plot_snippets(snip_axes[5, :], t_full, data['snip_feat'],
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# ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
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# Analysis results:
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scales_rel = data['scales'] - data['scales'][0]
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scales_rel /= scales_rel[-1]
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for stage in stages:
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key = f'measure_{stage}'
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if stage == 'bi':
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continue
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# Min-max normalization:
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base_ind = np.argmin(data['scales'])
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data[key] -= data[key][base_ind, ...]
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data[key] /= data[key].max(axis=0)
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measure = data[f'measure_{stage}']
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# Condense measure:
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if stage in ['conv', 'feat']:
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data[key] = np.nanmedian(data[key], axis=1)
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# Plot unmodified intensity measures:
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curve = plot_curves(big_axes[0], data['scales'], measure, c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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if stage in ['log', 'inv', 'conv', 'feat']:
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show_saturation(big_axes[0], data['scales'], curve, c=colors[stage])
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# Plot measure over scales:
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big_ax.plot(data['scales'], data[key],
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c=colors[stage], lw=lw['big'])
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# # Relate to pure-noise reference:
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# norm_measure = measure / ref_data[stage]
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# # Plot noise-related intensity measures:
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# big_axes[1].plot(data['scales'], norm_measure, c=colors[stage], lw=lw['big'])
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# Normalize measure to [0, 1]:
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min_measure = measure.min(axis=0)
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max_measure = measure.max(axis=0)
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norm_measure = (measure - min_measure) / (max_measure - min_measure)
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# Plot normalized intensity measures:
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curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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if stage in ['log', 'inv', 'conv', 'feat']:
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show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
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# # Plot over relative scales:
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# plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
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# fill_kwargs=dict(color=colors[stage], alpha=0.25))
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# scales_rel = curve - curve.min()
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# scales_rel /= scales_rel.max()
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if save_path is not None:
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fig.savefig(save_path)
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@@ -4,10 +4,11 @@ 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 misc_functions import shorten_species, get_kde, get_saturation
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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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from plot_functions import hide_axis, ylimits, super_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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letter_subplot, letter_subplots, 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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@@ -26,39 +27,86 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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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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def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
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base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
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if pdf is None:
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pdf, axis = get_kde(data, sigma, axis)
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if base is None:
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base = pdf.min()
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if cap is None:
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cap = pdf.max()
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pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
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if which == 'x':
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transform = ax.get_xaxis_transform()
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elif which == 'y':
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transform = ax.get_yaxis_transform()
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else:
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transform = ax.transData
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rng = np.random.default_rng()
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handles = []
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for value in data:
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ind = np.nonzero(axis == value)[0][0]
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offset = base if not shifted else rng.uniform(base, pdf[ind])
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variables = (offset, value) if which=='y' else (value, offset)
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handles.extend(ax.plot(*variables, transform=transform, **kwargs))
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if add_pdf:
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variables = (pdf, axis) if which=='y' else (axis, pdf)
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pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
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return handles, pdf_handle
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return handles
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def zalpha(handles, background='w', down=1):
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twins = []
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for handle in handles:
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twin = handle.copy()
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twin.set(color=background, alpha=1)
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twin.set_zorder(handle.get_zorder() - down)
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twins.append(twin)
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return twins
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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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ref_path = '../data/inv/log_hp/ref_measures.npz'
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save_path = '../figures/fig_invariance_log_hp.pdf'
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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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'Gomphocerippus_rufus',
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'Pseudochorthippus_parallelus',
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]
|
||||
stages = ['env', 'log', 'inv']
|
||||
load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'snip', 'measure']
|
||||
)
|
||||
save_path = '../figures/fig_invariance_log_hp.pdf'
|
||||
compute_ratios = True
|
||||
show_diag = True
|
||||
show_noise = True
|
||||
show_plateaus = True
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=3,
|
||||
nrows=3,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1
|
||||
top=1,
|
||||
height_ratios=[1, 1, 1]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
pure=(0, slice(0, -1)),
|
||||
noise=(1, slice(0, -1)),
|
||||
big=(slice(None), -1),
|
||||
pure=(0, slice(None)),
|
||||
noise=(1, slice(None)),
|
||||
big=(2, slice(None)),
|
||||
)
|
||||
block_height = 0.8
|
||||
edge_padding = 0.08
|
||||
@@ -67,7 +115,7 @@ pure_grid_kwargs = dict(
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.15,
|
||||
left=0.16,
|
||||
left=0.11,
|
||||
right=0.95,
|
||||
bottom=1 - block_height - edge_padding,
|
||||
top=1 - edge_padding,
|
||||
@@ -76,23 +124,23 @@ pure_grid_kwargs = dict(
|
||||
noise_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.15,
|
||||
left=0.16,
|
||||
right=0.95,
|
||||
wspace=pure_grid_kwargs['wspace'],
|
||||
hspace=pure_grid_kwargs['hspace'],
|
||||
left=pure_grid_kwargs['left'],
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=edge_padding,
|
||||
top=edge_padding + block_height,
|
||||
height_ratios=[1, 2, 1]
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.3,
|
||||
left=0.19,
|
||||
right=0.96,
|
||||
bottom=0.09,
|
||||
top=0.98
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.3,
|
||||
hspace=0,
|
||||
left=pure_grid_kwargs['left'],
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=0.05,
|
||||
top=1
|
||||
)
|
||||
anchor_kwargs = dict(
|
||||
aspect='equal',
|
||||
@@ -110,8 +158,14 @@ fs = dict(
|
||||
bar=16,
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
lw_snippets = 1
|
||||
lw_big = 3
|
||||
species_colors = load_colors('../data/species_colors.npz')
|
||||
noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)]
|
||||
lw = dict(
|
||||
snip=1,
|
||||
big=4,
|
||||
spec=2,
|
||||
plateau=1,
|
||||
)
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
@@ -135,7 +189,7 @@ ylab_snip_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=0.05,
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
@@ -160,10 +214,10 @@ letter_snip_kwargs = dict(
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
letter_big_kwargs = dict(
|
||||
x=0.05,
|
||||
yref=letter_snip_kwargs['yref'],
|
||||
x=0,
|
||||
y=1,
|
||||
ha='left',
|
||||
va='center',
|
||||
va='bottom',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
|
||||
@@ -204,33 +258,77 @@ bar_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
leg_kwargs = dict(
|
||||
ncols=2,
|
||||
loc='upper right',
|
||||
bbox_to_anchor=(0, 0.6, 1, 0.4),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=12,
|
||||
style='italic',
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=1,
|
||||
columnspacing=1,
|
||||
)
|
||||
diag_kwargs = dict(
|
||||
c=(0.75, 0.75, 0.75),
|
||||
lw=2,
|
||||
ls='--',
|
||||
zorder=1.9,
|
||||
)
|
||||
noise_rel_thresh = 0.95
|
||||
noise_kwargs = dict(
|
||||
fc=(0.9, 0.9, 0.9),
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
first=True,
|
||||
last=True,
|
||||
condense=None,
|
||||
)
|
||||
plateau_rect_kwargs = dict(
|
||||
ec='none',
|
||||
lw=0,
|
||||
zorder=1.5,
|
||||
)
|
||||
plateau_line_kwargs = dict(
|
||||
lw=lw['plateau'],
|
||||
ls='--',
|
||||
zorder=1,
|
||||
)
|
||||
plateau_dot_kwargs = dict(
|
||||
marker='o',
|
||||
markersize=10,
|
||||
markeredgecolor='k',
|
||||
markeredgewidth=1,
|
||||
# alpha=1,
|
||||
zorder=6,
|
||||
clip_on=False,
|
||||
# base=0,
|
||||
# cap=0.15,
|
||||
# add_pdf=True,
|
||||
)
|
||||
kde_kwargs = dict(
|
||||
sigma=0.1,
|
||||
)
|
||||
|
||||
# PREPARATION:
|
||||
if compute_ratios:
|
||||
ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
|
||||
ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
|
||||
ref_measures = dict(np.load(ref_path))
|
||||
|
||||
species_measures = []
|
||||
for species_path in species_paths:
|
||||
species_data, _ = load_data(species_path, **load_kwargs)
|
||||
species_measure = species_data['measure_inv']
|
||||
species_measures = {}
|
||||
thresh_inds = np.zeros((len(target_species),), dtype=int)
|
||||
thresh_scales = np.zeros((len(target_species),), dtype=float)
|
||||
for i, species in enumerate(target_species):
|
||||
path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0]
|
||||
species_data = load_data(path, **load_kwargs)[0]
|
||||
measure = species_data['measure_inv']
|
||||
scales = species_data['scales']
|
||||
if compute_ratios:
|
||||
species_measure /= ref_measures['inv']
|
||||
species_measures.append(species_measure)
|
||||
species_measures = np.array(species_measures).T
|
||||
measure /= ref_measures['inv']
|
||||
species_measures[species] = measure
|
||||
thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]
|
||||
thresh_scales[i] = scales[thresh_inds[i]]
|
||||
thresh_pdf, pdf_axis = get_kde(thresh_scales, axis=scales, **kde_kwargs)
|
||||
|
||||
# EXECUTION:
|
||||
for data_path in data_paths:
|
||||
@@ -273,7 +371,7 @@ for data_path in data_paths:
|
||||
transform=noise_subfig.transSubfigure)
|
||||
for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
|
||||
noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
|
||||
letter_subplot(noise_subfig, 'c', ref=noise_title, **letter_snip_kwargs)
|
||||
letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
|
||||
noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
|
||||
noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
|
||||
noise_inset.tick_params(**inset_tick_kwargs)
|
||||
@@ -282,51 +380,49 @@ for data_path in data_paths:
|
||||
# Prepare analysis axes:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_axes = np.zeros((big_grid.nrows,), dtype=object)
|
||||
for i, scales in enumerate([pure_scales, noise_scales]):
|
||||
ax = big_subfig.add_subplot(big_grid[i, 0])
|
||||
big_axes = np.zeros((big_grid.ncols,), dtype=object)
|
||||
for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
|
||||
ax = big_subfig.add_subplot(big_grid[0, i])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.set_aspect(**anchor_kwargs)
|
||||
ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
if i == 0:
|
||||
hide_ticks(ax, 'bottom')
|
||||
letter_subplot(big_subfig, 'b', ref=pure_title, **letter_big_kwargs)
|
||||
else:
|
||||
xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
|
||||
letter_subplot(big_subfig, 'd', ref=noise_title, **letter_big_kwargs)
|
||||
if i > 0:
|
||||
hide_ticks(ax, 'left')
|
||||
big_axes[i] = ax
|
||||
ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'cde', **letter_big_kwargs)
|
||||
|
||||
# Plot pure-song envelope snippets:
|
||||
handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
|
||||
ymin=0, c=colors['env'], lw=lw_snippets)[0]
|
||||
ymin=0, c=colors['env'], lw=lw['snip'])[0]
|
||||
zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
|
||||
|
||||
# Plot pure-song logarithmic snippets:
|
||||
plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
|
||||
c=colors['log'], lw=lw_snippets)
|
||||
c=colors['log'], lw=lw['snip'])
|
||||
|
||||
# Plot pure-song invariant snippets:
|
||||
plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
|
||||
c=colors['inv'], lw=lw_snippets)
|
||||
c=colors['inv'], lw=lw['snip'])
|
||||
|
||||
# Plot noise-song envelope snippets:
|
||||
ymin, ymax = pure_axes[0, 0].get_ylim()
|
||||
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
|
||||
ymin, ymax, c=colors['env'], lw=lw_snippets)[0]
|
||||
ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
|
||||
zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
|
||||
|
||||
# Plot noise-song logarithmic snippets:
|
||||
ymin, ymax = pure_axes[1, 0].get_ylim()
|
||||
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
|
||||
ymin, ymax, c=colors['log'], lw=lw_snippets)
|
||||
ymin, ymax, c=colors['log'], lw=lw['snip'])
|
||||
|
||||
# Plot noise-song invariant snippets:
|
||||
ymin, ymax = pure_axes[2, 0].get_ylim()
|
||||
plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
|
||||
ymin, ymax, c=colors['inv'], lw=lw_snippets)
|
||||
ymin, ymax, c=colors['inv'], lw=lw['snip'])
|
||||
|
||||
# Indicate time scale:
|
||||
time_bar(noise_axes[-1, -1], **bar_kwargs)
|
||||
@@ -342,34 +438,46 @@ for data_path in data_paths:
|
||||
noise_data['measure_inv'] /= ref_measures['inv']
|
||||
|
||||
# Plot pure-song measures (ideal):
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw_big)
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw_big)
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw_big)
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])
|
||||
|
||||
# Plot noise-song measures (limited):
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw_big)
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big)
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big)
|
||||
|
||||
# Plot species measures:
|
||||
big_axes[1].plot(noise_scales, species_measures, 'k', lw=lw_big, zorder=2.1)
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])
|
||||
|
||||
if show_diag:
|
||||
# Indicate diagonal:
|
||||
big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
|
||||
big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
|
||||
|
||||
if show_noise:
|
||||
# Indicate noise floor:
|
||||
if compute_ratios:
|
||||
span_measure = noise_data['measure_inv'][-1] - ref_measures['inv']
|
||||
thresh_measure = ref_measures['inv'] + noise_rel_thresh * span_measure
|
||||
else:
|
||||
span_measure = noise_data['measure_inv'][-1] - noise_data['measure_inv'][0]
|
||||
thresh_measure = noise_data['measure_inv'][0] + noise_rel_thresh * span_measure
|
||||
thresh_ind = np.nonzero(noise_data['measure_inv'] < thresh_measure)[0][-1]
|
||||
thresh_scale = noise_scales[thresh_ind]
|
||||
big_axes[1].axvspan(noise_scales[0], thresh_scale, **noise_kwargs)
|
||||
if show_plateaus:
|
||||
# Indicate low and high plateaus of noise invariance curve:
|
||||
low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
|
||||
big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
|
||||
fc=noise_colors[0], **plateau_rect_kwargs)
|
||||
big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
|
||||
fc=noise_colors[1], **plateau_rect_kwargs)
|
||||
|
||||
# Plot species-specific noise-song measures:
|
||||
for i, (species, measure) in enumerate(species_measures.items()):
|
||||
color = species_colors[species]
|
||||
ind, scale = thresh_inds[i], thresh_scales[i]
|
||||
big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
|
||||
c=color, lw=lw['spec'])
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
handle = big_axes[2].plot(scale, 0, c=color, alpha=0.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
big_axes[2].legend(**leg_kwargs)
|
||||
|
||||
# handles = plot_dist_shifted(big_axes[2], species_threshs, axis=pdf_axis,
|
||||
# pdf=thresh_pdf, **plateau_dot_kwargs)[0]
|
||||
# [h.set_color(species_colors[s]) for h, s in zip(handles, target_species)]
|
||||
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path, bbox_inches='tight')
|
||||
|
||||
@@ -1,407 +0,0 @@
|
||||
import plotstyle_plt
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from itertools import product
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from misc_functions import shorten_species, get_saturation
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, hide_ticks,\
|
||||
plot_line, strip_zeros, time_bar, zoom_inset,\
|
||||
letter_subplot, letter_subplots, title_subplot
|
||||
from IPython import embed
|
||||
|
||||
def add_snip_axes(fig, grid_kwargs):
|
||||
grid = fig.add_gridspec(**grid_kwargs)
|
||||
axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
|
||||
for i, j in product(range(grid.nrows), range(grid.ncols)):
|
||||
axes[i, j] = fig.add_subplot(grid[i, j])
|
||||
[hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
|
||||
[hide_axis(ax, 'bottom') for ax in axes.flatten()]
|
||||
return axes
|
||||
|
||||
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
|
||||
handles = []
|
||||
for ax, snippet in zip(axes, snippets.T):
|
||||
handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
|
||||
return handles
|
||||
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
|
||||
target_species = [
|
||||
'Omocestus_rufipes',
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Gomphocerippus_rufus',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
stages = ['env', 'log', 'inv']
|
||||
load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'snip', 'measure']
|
||||
)
|
||||
save_path = '../figures/fig_invariance_log_hp.pdf'
|
||||
compute_ratios = True
|
||||
show_diag = True
|
||||
show_plateaus = True
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
)
|
||||
# snip_rows = 1
|
||||
# big_rows = 1
|
||||
super_grid_kwargs = dict(
|
||||
nrows=3,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1,
|
||||
height_ratios=[1, 1, 1]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
pure=(0, slice(None)),
|
||||
noise=(1, slice(None)),
|
||||
big=(2, slice(None)),
|
||||
)
|
||||
block_height = 0.8
|
||||
edge_padding = 0.08
|
||||
pure_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.15,
|
||||
left=0.11,
|
||||
right=0.95,
|
||||
bottom=1 - block_height - edge_padding,
|
||||
top=1 - edge_padding,
|
||||
height_ratios=[1, 2, 1]
|
||||
)
|
||||
noise_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=pure_grid_kwargs['wspace'],
|
||||
hspace=pure_grid_kwargs['hspace'],
|
||||
left=pure_grid_kwargs['left'],
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=edge_padding,
|
||||
top=edge_padding + block_height,
|
||||
height_ratios=[1, 2, 1]
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.3,
|
||||
hspace=0,
|
||||
left=pure_grid_kwargs['left'],
|
||||
right=pure_grid_kwargs['right'],
|
||||
bottom=0.05,
|
||||
top=1
|
||||
)
|
||||
anchor_kwargs = dict(
|
||||
aspect='equal',
|
||||
adjustable='box',
|
||||
anchor=(0.5, 0.5)
|
||||
)
|
||||
|
||||
# PLOT SETTINGS:
|
||||
fs = dict(
|
||||
lab_norm=16,
|
||||
lab_tex=20,
|
||||
letter=22,
|
||||
tit_norm=16,
|
||||
tit_tex=20,
|
||||
bar=16,
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
species_colors = load_colors('../data/species_colors.npz')
|
||||
noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)]
|
||||
lw_snippets = 1
|
||||
lw_big = 3
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{dB}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$',
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
yloc = dict(
|
||||
env=1000,
|
||||
log=40,
|
||||
inv=20
|
||||
)
|
||||
title_kwargs = dict(
|
||||
x=0.5,
|
||||
y=1,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
fontsize=fs['tit_norm'],
|
||||
)
|
||||
letter_snip_kwargs = dict(
|
||||
x=0,
|
||||
yref=0.5,
|
||||
ha='left',
|
||||
va='center',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
letter_big_kwargs = dict(
|
||||
x=0,
|
||||
y=1,
|
||||
ha='left',
|
||||
va='bottom',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
|
||||
zoom_kwargs = dict(
|
||||
x0=0.45,
|
||||
x1=0.55,
|
||||
y0=0,
|
||||
y1=0.0006,
|
||||
low_left=True,
|
||||
low_right=True,
|
||||
ec='k',
|
||||
lw=1,
|
||||
alpha=1,
|
||||
)
|
||||
inset_tick_kwargs = dict(
|
||||
axis='y',
|
||||
length=3,
|
||||
pad=1,
|
||||
left=False,
|
||||
labelleft=False,
|
||||
right=True,
|
||||
labelright=True,
|
||||
)
|
||||
bar_time = 5
|
||||
bar_kwargs = dict(
|
||||
dur=bar_time,
|
||||
y0=-0.25,
|
||||
y1=-0.1,
|
||||
xshift=1,
|
||||
color='k',
|
||||
lw=0,
|
||||
clip_on=False,
|
||||
text_pos=(-0.1, 0.5),
|
||||
text_str=f'${bar_time}\\,\\text{{s}}$',
|
||||
text_kwargs=dict(
|
||||
fontsize=fs['bar'],
|
||||
ha='right',
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
leg_kwargs = dict(
|
||||
ncols=2,
|
||||
loc='upper right',
|
||||
bbox_to_anchor=(0, 0.6, 1, 0.4),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=12,
|
||||
style='italic',
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=1,
|
||||
columnspacing=1,
|
||||
)
|
||||
diag_kwargs = dict(
|
||||
c=(0.75, 0.75, 0.75),
|
||||
lw=2,
|
||||
ls='--',
|
||||
zorder=1.9,
|
||||
)
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
first=True,
|
||||
last=True,
|
||||
condense=None,
|
||||
)
|
||||
plateau_kwargs = dict(
|
||||
ec='none',
|
||||
lw=0,
|
||||
zorder=1.5,
|
||||
)
|
||||
|
||||
# PREPARATION:
|
||||
if compute_ratios:
|
||||
ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
|
||||
ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
|
||||
|
||||
species_measures = {}
|
||||
for species in target_species:
|
||||
path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0]
|
||||
measure = load_data(path, **load_kwargs)[0]['measure_inv']
|
||||
if compute_ratios:
|
||||
measure /= ref_measures['inv']
|
||||
species_measures[species] = measure
|
||||
|
||||
# EXECUTION:
|
||||
for data_path in data_paths:
|
||||
print(f'Processing {data_path}')
|
||||
|
||||
# Load invariance data:
|
||||
pure_data, config = load_data(data_path, **load_kwargs)
|
||||
noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
|
||||
pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
|
||||
t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
fig.canvas.draw()
|
||||
|
||||
# Prepare pure-song snippet axes:
|
||||
pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
|
||||
pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
|
||||
pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
|
||||
for ax, stage in zip(pure_axes[:, 0], stages):
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
|
||||
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
|
||||
transform=pure_subfig.transSubfigure)
|
||||
for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
|
||||
pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
|
||||
letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
|
||||
pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
|
||||
pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
|
||||
pure_inset.tick_params(**inset_tick_kwargs)
|
||||
hide_ticks(pure_inset, 'bottom', ticks=False)
|
||||
|
||||
# Prepare noise-song snippet axes:
|
||||
noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
|
||||
noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
|
||||
noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
|
||||
for ax, stage in zip(noise_axes[:, 0], stages):
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
|
||||
ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
|
||||
transform=noise_subfig.transSubfigure)
|
||||
for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
|
||||
noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
|
||||
letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
|
||||
noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
|
||||
noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
|
||||
noise_inset.tick_params(**inset_tick_kwargs)
|
||||
hide_ticks(noise_inset, 'bottom', ticks=False)
|
||||
|
||||
# Prepare analysis axes:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_axes = np.zeros((big_grid.ncols,), dtype=object)
|
||||
for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
|
||||
ax = big_subfig.add_subplot(big_grid[0, i])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.set_aspect(**anchor_kwargs)
|
||||
big_axes[i] = ax
|
||||
ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'cde', **letter_big_kwargs)
|
||||
|
||||
# Plot pure-song envelope snippets:
|
||||
handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
|
||||
ymin=0, c=colors['env'], lw=lw_snippets)[0]
|
||||
zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
|
||||
|
||||
# Plot pure-song logarithmic snippets:
|
||||
plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
|
||||
c=colors['log'], lw=lw_snippets)
|
||||
|
||||
# Plot pure-song invariant snippets:
|
||||
plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
|
||||
c=colors['inv'], lw=lw_snippets)
|
||||
|
||||
# Plot noise-song envelope snippets:
|
||||
ymin, ymax = pure_axes[0, 0].get_ylim()
|
||||
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
|
||||
ymin, ymax, c=colors['env'], lw=lw_snippets)[0]
|
||||
zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
|
||||
|
||||
# Plot noise-song logarithmic snippets:
|
||||
ymin, ymax = pure_axes[1, 0].get_ylim()
|
||||
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
|
||||
ymin, ymax, c=colors['log'], lw=lw_snippets)
|
||||
|
||||
# Plot noise-song invariant snippets:
|
||||
ymin, ymax = pure_axes[2, 0].get_ylim()
|
||||
plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
|
||||
ymin, ymax, c=colors['inv'], lw=lw_snippets)
|
||||
|
||||
# Indicate time scale:
|
||||
time_bar(noise_axes[-1, -1], **bar_kwargs)
|
||||
|
||||
if compute_ratios:
|
||||
# Relate pure-song measures to zero scale:
|
||||
pure_data['measure_env'] /= ref_measures['env']
|
||||
pure_data['measure_log'] /= ref_measures['log']
|
||||
pure_data['measure_inv'] /= ref_measures['inv']
|
||||
# Relate noise-song measures to zero scale:
|
||||
noise_data['measure_env'] /= ref_measures['env']
|
||||
noise_data['measure_log'] /= ref_measures['log']
|
||||
noise_data['measure_inv'] /= ref_measures['inv']
|
||||
|
||||
# Plot pure-song measures (ideal):
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw_big)
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw_big)
|
||||
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw_big)
|
||||
|
||||
# Plot noise-song measures (limited):
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw_big)
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big)
|
||||
big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big)
|
||||
|
||||
|
||||
if show_diag:
|
||||
# Indicate diagonal:
|
||||
big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
|
||||
big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
|
||||
|
||||
if show_plateaus:
|
||||
# Indicate low and high plateaus of noise invariance curve:
|
||||
low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
|
||||
big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
|
||||
fc=noise_colors[0], **plateau_kwargs)
|
||||
big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
|
||||
fc=noise_colors[1], **plateau_kwargs)
|
||||
|
||||
# Plot species-specific noise-song measures:
|
||||
for species, measure in species_measures.items():
|
||||
label = shorten_species(species)
|
||||
big_axes[2].plot(noise_scales, measure, label=label,
|
||||
c=species_colors[species], lw=lw_big)
|
||||
big_axes[2].legend(**leg_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
@@ -5,6 +5,7 @@ from itertools import product
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from misc_functions import get_saturation
|
||||
from color_functions import load_colors, shade_colors
|
||||
from plot_functions import shift_subplot, hide_axis, ylimits, xlabel, ylabel,\
|
||||
super_ylabel, plot_line, plot_barcode, strip_zeros,\
|
||||
@@ -64,7 +65,7 @@ load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'snip', 'measure', 'thresh']
|
||||
)
|
||||
save_path = '../figures/fig_invariance_thresh_lp_single.pdf'
|
||||
save_path = None#'../figures/fig_invariance_thresh_lp_single.pdf'
|
||||
exclude_zero = True
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
@@ -79,7 +80,7 @@ super_grid_kwargs = dict(
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1
|
||||
top=1,
|
||||
)
|
||||
input_rows = 1
|
||||
snip_rows = 2
|
||||
@@ -111,10 +112,10 @@ input_grid_kwargs = dict(
|
||||
top=0.75,
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
hspace=0.3,
|
||||
left=0.17,
|
||||
right=0.96,
|
||||
bottom=0.1,
|
||||
@@ -141,7 +142,8 @@ lw = dict(
|
||||
big=4,
|
||||
)
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
alpha='scale $\\alpha$',
|
||||
sigma='$\\sigma_{\\text{adapt}}$',
|
||||
)
|
||||
ylabels = dict(
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
@@ -150,11 +152,17 @@ ylabels = dict(
|
||||
feat='$f_i$',
|
||||
big='$\\mu_f$',
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
xlab_alpha_kwargs = dict(
|
||||
y=-0.15,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
va='top',
|
||||
)
|
||||
xlab_sigma_kwargs = dict(
|
||||
y=-0.12,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha=xlab_alpha_kwargs['ha'],
|
||||
va=xlab_alpha_kwargs['va'],
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0.08,
|
||||
@@ -178,7 +186,7 @@ ylab_big_kwargs = dict(
|
||||
ypad = dict(
|
||||
inv=0.05,
|
||||
conv=0.05,
|
||||
big=0.075
|
||||
big=0.1
|
||||
)
|
||||
yloc = dict(
|
||||
inv=(2, 200),
|
||||
@@ -242,6 +250,13 @@ leg_kwargs = dict(
|
||||
handlelength=1.5,
|
||||
columnspacing=1,
|
||||
)
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
first=True,
|
||||
last=True,
|
||||
condense=None,
|
||||
)
|
||||
kern_specs = np.array([
|
||||
[1, 0.008],
|
||||
[2, 0.004],
|
||||
@@ -281,6 +296,7 @@ for data_path in data_paths:
|
||||
# Reduce to nonzero scales:
|
||||
nonzero_inds = scales > 0
|
||||
scales = scales[nonzero_inds]
|
||||
noise_data['measure_inv'] = noise_data['measure_inv'][nonzero_inds]
|
||||
noise_data['measure_feat'] = noise_data['measure_feat'][nonzero_inds, :]
|
||||
pure_data['measure_feat'] = pure_data['measure_feat'][nonzero_inds, :]
|
||||
|
||||
@@ -293,7 +309,7 @@ for data_path in data_paths:
|
||||
)
|
||||
|
||||
# Adjust grid parameters to loaded data:
|
||||
super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + 1
|
||||
super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + input_rows
|
||||
input_grid_kwargs['ncols'] = plot_scales.size
|
||||
snip_grid_kwargs['ncols'] = plot_scales.size
|
||||
|
||||
@@ -325,8 +341,6 @@ for data_path in data_paths:
|
||||
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
|
||||
ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
|
||||
ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
|
||||
# for ax, scale in zip(axes[0, :], plot_scales):
|
||||
# title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=snip_subfig, **title_kwargs)
|
||||
if i == thresh_rel.size - 1:
|
||||
axes[-1, -1].set_xlim(t_full[0], t_full[-1])
|
||||
time_bar(axes[-1, -1], **bar_kwargs)
|
||||
@@ -334,17 +348,27 @@ for data_path in data_paths:
|
||||
snip_axes.append(axes)
|
||||
letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
|
||||
|
||||
# Prepare analysis axis:
|
||||
# Prepare analysis axes:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_ax = big_subfig.add_subplot(big_grid[0, 0])
|
||||
big_ax.set_xlim(scales[0], scales[-1])
|
||||
big_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
ylimits(noise_data['measure_feat'], big_ax, minval=0, pad=ypad['big'])
|
||||
big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
|
||||
xlabel(big_ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
|
||||
ylabel(big_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
letter_subplot(big_subfig, 'e', **letter_big_kwargs, ref=input_subfig)
|
||||
|
||||
alpha_ax = big_subfig.add_subplot(big_grid[0, 0])
|
||||
alpha_ax.set_xlim(scales[0], scales[-1])
|
||||
alpha_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
ylimits(pure_data['measure_feat'], alpha_ax, minval=0, pad=ypad['big'])
|
||||
alpha_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
|
||||
xlabel(alpha_ax, xlabels['alpha'], **xlab_alpha_kwargs)
|
||||
ylabel(alpha_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
|
||||
sigma_ax = big_subfig.add_subplot(big_grid[1, 0])
|
||||
sigma_ax.set_xlim(noise_data['measure_inv'].min(), noise_data['measure_inv'].max())
|
||||
# sigma_ax.set_xscale('log')
|
||||
sigma_ax.set_xlim(scales[0], scales[-1])
|
||||
sigma_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
ylimits(pure_data['measure_feat'], sigma_ax, minval=0, pad=ypad['big'])
|
||||
sigma_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
|
||||
xlabel(sigma_ax, xlabels['sigma'], **xlab_sigma_kwargs)
|
||||
ylabel(sigma_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
|
||||
# Plot intensity-adapted snippets:
|
||||
plot_snippets(input_axes, t_full, noise_data['snip_inv'],
|
||||
@@ -375,18 +399,25 @@ for data_path in data_paths:
|
||||
ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
|
||||
[set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]
|
||||
|
||||
# Plot pure-song analysis results:
|
||||
handles = big_ax.plot(scales, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
# Get threshold-specific saturation:
|
||||
for i in range(thresh_rel.size):
|
||||
ind = get_saturation(noise_data['measure_feat'][:, i], **plateau_settings)[1]
|
||||
|
||||
# Plot noise-song analysis results:
|
||||
handles = big_ax.plot(scales, noise_data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
# Plot analysis results:
|
||||
for ax, x in zip([alpha_ax, sigma_ax], [scales, noise_data['measure_inv']]):
|
||||
# Plot pure-song analysis results:
|
||||
handles = ax.plot(x, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Add proxy legend:
|
||||
h1 = big_ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
|
||||
h2 = big_ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
|
||||
big_ax.legend(handles=[h1, h2], **leg_kwargs)
|
||||
# Plot noise-song analysis results:
|
||||
handles = ax.plot(x, noise_data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Add proxy legend:
|
||||
if ax == alpha_ax:
|
||||
h1 = ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
|
||||
h2 = ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
|
||||
ax.legend(handles=[h1, h2], **leg_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
131
python/fig_temp.py
Normal file
131
python/fig_temp.py
Normal file
@@ -0,0 +1,131 @@
|
||||
import glob
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from misc_functions import unsort_unique
|
||||
from color_functions import sample_cmap
|
||||
from IPython import embed
|
||||
|
||||
# Settings:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_path = glob.glob(f'../data/inv/full/{target}*.npz')[0]
|
||||
stages = ['conv', 'feat']
|
||||
load_kwargs = dict(
|
||||
files=stages,
|
||||
keywords=['scales', 'measure']
|
||||
)
|
||||
|
||||
# Subset settings:
|
||||
all_types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
|
||||
6, -6, 7, -7, 8, -8, 9, -9, 10, -10]).astype(float)
|
||||
all_sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
kerns = None
|
||||
types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
|
||||
6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
|
||||
sigmas = np.array([0.008, 0.016])
|
||||
|
||||
# Plot settings:
|
||||
line_kwargs = dict(
|
||||
linewidth=2,
|
||||
)
|
||||
median_kwargs = dict(
|
||||
linewidth=4,
|
||||
c='k',
|
||||
linestyle='--'
|
||||
)
|
||||
|
||||
# Load invariance data:
|
||||
data, config = load_data(data_path, **load_kwargs)
|
||||
scales = data['scales']
|
||||
|
||||
# Reduce to kernel subset:
|
||||
if any(var is not None for var in [kerns, types, sigmas]):
|
||||
subset_inds = find_kern_specs(config['k_specs'], kerns, types, sigmas)
|
||||
data['measure_conv'] = data['measure_conv'][:, subset_inds]
|
||||
data['measure_feat'] = data['measure_feat'][:, subset_inds]
|
||||
config['kernels'] = config['kernels'][:, subset_inds]
|
||||
config['k_specs'] = config['k_specs'][subset_inds, :]
|
||||
kern_types = unsort_unique(config['k_specs'][:, 0])
|
||||
kern_sigmas = unsort_unique(config['k_specs'][:, 1])
|
||||
|
||||
# Prepare colors:
|
||||
type_colors = {t: c for t, c in zip(all_types, sample_cmap('turbo', all_types.size))}
|
||||
sigma_colors = {s: c for s, c in zip(all_sigmas, sample_cmap('turbo', all_sigmas.size))}
|
||||
|
||||
# Prepare graph:
|
||||
fig, axes = plt.subplots(2, 4, figsize=(16, 16), layout='constrained', sharex=True)
|
||||
axes[0, 0].set_xlim(scales[0], scales[-1])
|
||||
axes[0, 0].set_xscale('log')
|
||||
axes[0, 0].set_ylabel('conv')
|
||||
axes[1, 0].set_ylabel('feat')
|
||||
|
||||
# Condense across kernels:
|
||||
median_conv = np.median(data['measure_conv'], axis=1)
|
||||
median_feat = np.median(data['measure_feat'], axis=1)
|
||||
|
||||
# Coded by type:
|
||||
leg_handles = []
|
||||
for kern_type in kern_types:
|
||||
color = type_colors[kern_type]
|
||||
inds = find_kern_specs(config['k_specs'], types=kern_type)
|
||||
leg_handles.append(axes[0, 0].plot(scales, data['measure_conv'][:, inds],
|
||||
c=color, label=f'{kern_type}', **line_kwargs)[0])
|
||||
axes[0, 0].plot(scales, median_conv, **median_kwargs)
|
||||
axes[1, 0].plot(scales, data['measure_feat'][:, inds], c=color, **line_kwargs)
|
||||
axes[1, 0].plot(scales, median_feat, **median_kwargs)
|
||||
axes[0, 0].legend(handles=leg_handles, loc='upper left')
|
||||
axes[0, 0].set_title('Coded by type')
|
||||
|
||||
# Coded by sigma:
|
||||
leg_handles = []
|
||||
for kern_sigma in kern_sigmas:
|
||||
color = sigma_colors[kern_sigma]
|
||||
inds = find_kern_specs(config['k_specs'], sigmas=kern_sigma)
|
||||
leg_handles.append(axes[0, 1].plot(scales, data['measure_conv'][:, inds],
|
||||
c=color, label=f'{kern_sigma}', **line_kwargs)[0])
|
||||
axes[0, 1].plot(scales, median_conv, **median_kwargs)
|
||||
axes[1, 1].plot(scales, data['measure_feat'][:, inds], c=color, **line_kwargs)
|
||||
axes[1, 1].plot(scales, median_feat, **median_kwargs)
|
||||
axes[0, 1].legend(handles=leg_handles, loc='upper left')
|
||||
axes[0, 1].set_title('Coded by sigma')
|
||||
|
||||
# Normalize measures:
|
||||
data['measure_conv'] -= data['measure_conv'].min(axis=0)
|
||||
data['measure_conv'] /= data['measure_conv'].max(axis=0)
|
||||
data['measure_feat'] -= data['measure_feat'].min(axis=0)
|
||||
data['measure_feat'] /= data['measure_feat'].max(axis=0)
|
||||
|
||||
# Condense across kernels:
|
||||
median_conv = np.median(data['measure_conv'], axis=1)
|
||||
median_feat = np.median(data['measure_feat'], axis=1)
|
||||
|
||||
# Coded by type:
|
||||
leg_handles = []
|
||||
for kern_type in kern_types:
|
||||
color = type_colors[kern_type]
|
||||
inds = find_kern_specs(config['k_specs'], types=kern_type)
|
||||
leg_handles.append(axes[0, 2].plot(scales, data['measure_conv'][:, inds],
|
||||
c=color, label=f'{kern_type}', **line_kwargs)[0])
|
||||
axes[0, 2].plot(scales, median_conv, **median_kwargs)
|
||||
axes[1, 2].plot(scales, data['measure_feat'][:, inds], c=color, **line_kwargs)
|
||||
axes[1, 2].plot(scales, median_feat, **median_kwargs)
|
||||
axes[0, 2].legend(handles=leg_handles, loc='upper left')
|
||||
axes[0, 2].set_title('Coded by type')
|
||||
|
||||
|
||||
# Coded by sigma:
|
||||
leg_handles = []
|
||||
for kern_sigma in kern_sigmas:
|
||||
color = sigma_colors[kern_sigma]
|
||||
inds = find_kern_specs(config['k_specs'], sigmas=kern_sigma)
|
||||
leg_handles.append(axes[0, 3].plot(scales, data['measure_conv'][:, inds],
|
||||
c=color, label=f'{kern_sigma}', **line_kwargs)[0])
|
||||
axes[0, 3].plot(scales, median_conv, **median_kwargs)
|
||||
axes[1, 3].plot(scales, data['measure_feat'][:, inds], c=color, **line_kwargs)
|
||||
axes[1, 3].plot(scales, median_feat, **median_kwargs)
|
||||
axes[0, 3].legend(handles=leg_handles, loc='upper left')
|
||||
axes[0, 3].set_title('Coded by sigma')
|
||||
|
||||
plt.show()
|
||||
embed()
|
||||
@@ -1,9 +1,20 @@
|
||||
import numpy as np
|
||||
from scipy.stats import gaussian_kde
|
||||
|
||||
def shorten_species(name):
|
||||
genus, species = name.split('_')
|
||||
return genus[0] + '. ' + species
|
||||
|
||||
def unsort_unique(array):
|
||||
values, inds = np.unique(array, return_index=True)
|
||||
return values[np.argsort(inds)]
|
||||
|
||||
def get_kde(data, sigma, axis=None, n=1000, pad=10):
|
||||
if axis is None:
|
||||
axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)
|
||||
pdf = gaussian_kde(data, sigma)(axis)
|
||||
return pdf, axis
|
||||
|
||||
def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
|
||||
condense=None):
|
||||
if condense == 'norm' and sigmoid.ndim == 2:
|
||||
@@ -16,17 +27,17 @@ def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
|
||||
low_value = min_value + low * span
|
||||
high_value = min_value + high * span
|
||||
|
||||
low_mask = sigmoid >= low_value
|
||||
high_mask = sigmoid >= high_value
|
||||
low_mask = sigmoid <= low_value
|
||||
high_mask = sigmoid <= high_value
|
||||
if sigmoid.ndim == 1:
|
||||
low_ind = np.nonzero(low_mask)[0][0]
|
||||
high_ind = np.nonzero(high_mask)[0][0]
|
||||
low_ind = np.nonzero(low_mask)[0][-1]
|
||||
high_ind = np.nonzero(high_mask)[0][-1]
|
||||
elif condense == 'all':
|
||||
low_ind = np.nonzero(low_mask.all(axis=1))[0][0]
|
||||
high_ind = np.nonzero(high_mask.all(axis=1))[0][0]
|
||||
low_ind = np.nonzero(low_mask.all(axis=1))[0][-1]
|
||||
high_ind = np.nonzero(high_mask.all(axis=1))[0][-1]
|
||||
else:
|
||||
low_ind, high_ind = [], []
|
||||
for i in range(sigmoid.shape[1]):
|
||||
low_ind.append(np.nonzero(low_mask[:, i])[0][0])
|
||||
high_ind.append(np.nonzero(high_mask[:, i])[0][0])
|
||||
return low_ind, high_ind
|
||||
low_ind.append(np.nonzero(low_mask[:, i])[0][-1])
|
||||
high_ind.append(np.nonzero(high_mask[:, i])[0][-1])
|
||||
return low_ind, high_ind
|
||||
|
||||
@@ -4,19 +4,23 @@ import matplotlib.pyplot as plt
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from thunderhopper.filetools import crop_paths
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from thunderhopper.model import process_signal
|
||||
from thunderhopper.model import process_signal, convolve_kernels
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_paths = glob.glob(f'../data/processed/{target}*.npz')
|
||||
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
|
||||
save_path = '../data/inv/full/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
example_scales = np.array([0, 1, 10, 50])
|
||||
scales = np.geomspace(0.01, 100, 100)
|
||||
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
|
||||
scales = np.geomspace(0.01, 10000, 100)
|
||||
scales = np.unique(np.concatenate((scales, example_scales)))
|
||||
thresh_rel = 3
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
kernels = np.array([
|
||||
[1, 0.002],
|
||||
[-1, 0.002],
|
||||
@@ -29,20 +33,29 @@ kernels = None
|
||||
types = None#np.array([-1])
|
||||
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
|
||||
# PREPARATION:
|
||||
noise_data = np.load(noise_path)
|
||||
pure_noise = noise_data['raw']
|
||||
|
||||
# EXECUTION:
|
||||
for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
print(f'Processing {name}')
|
||||
|
||||
# Get song recording:
|
||||
# Get song recording (prior to anything):
|
||||
data, config = load_data(data_path, files='raw')
|
||||
song, rate = data['raw'], config['rate']
|
||||
|
||||
if thresh_rel is not None:
|
||||
# Get noise-bound kernel-specific thresholds:
|
||||
config['feat_thresh'] = noise_data['conv'].std(axis=0) * thresh_rel
|
||||
|
||||
# Reduce to kernel subset:
|
||||
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['k_props'] = [config['k_props'][i] for i in kern_inds]
|
||||
config['feat_thresh'] = config['feat_thresh'][kern_inds]
|
||||
if any(var is not None for var in [kernels, types, sigmas]):
|
||||
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['k_props'] = [config['k_props'][i] for i in kern_inds]
|
||||
config['feat_thresh'] = config['feat_thresh'][kern_inds]
|
||||
|
||||
# Get song segment to be analyzed:
|
||||
time = np.arange(song.shape[0]) / rate
|
||||
@@ -52,22 +65,19 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
# Normalize song component:
|
||||
song /= song[segment].std(axis=0)
|
||||
|
||||
# Get normalized noise:
|
||||
rng = np.random.default_rng()
|
||||
noise = rng.normal(size=song.shape[0])
|
||||
# Get normalized noise component:
|
||||
noise = pure_noise[: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)
|
||||
snippets = dict(
|
||||
snip_raw=np.zeros(shape_low, dtype=float),
|
||||
snip_filt=np.zeros(shape_low, dtype=float),
|
||||
snip_env=np.zeros(shape_low, dtype=float),
|
||||
snip_log=np.zeros(shape_low, dtype=float),
|
||||
snip_inv=np.zeros(shape_low, dtype=float),
|
||||
snip_conv=np.zeros(shape_high, dtype=float),
|
||||
snip_bi=np.zeros(shape_high, dtype=float),
|
||||
snip_feat=np.zeros(shape_high, dtype=float)
|
||||
)
|
||||
|
||||
@@ -75,7 +85,6 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
shape_low = (scales.size,)
|
||||
shape_high = (scales.size, config['k_specs'].shape[0])
|
||||
measures = dict(
|
||||
measure_raw=np.zeros(shape_low, dtype=float),
|
||||
measure_filt=np.zeros(shape_low, dtype=float),
|
||||
measure_env=np.zeros(shape_low, dtype=float),
|
||||
measure_log=np.zeros(shape_low, dtype=float),
|
||||
@@ -96,36 +105,18 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
signal=scaled, rate=rate)
|
||||
# Store results:
|
||||
for stage in stages:
|
||||
key = f'measure_{stage}'
|
||||
mkey, skey = f'measure_{stage}', f'snip_{stage}'
|
||||
|
||||
# Log snippet data:
|
||||
if scale in example_scales:
|
||||
scale_ind = np.nonzero(example_scales == scale)[0][0]
|
||||
snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]
|
||||
snippets[skey][:, ..., scale_ind] = signals[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)
|
||||
measures[mkey][i] = signals[stage][segment, ...].std(axis=0)
|
||||
elif stage == 'feat':
|
||||
measures[key][i] = signals[stage][segment, :].mean(axis=0)
|
||||
|
||||
# thresh_y = np.percentile(measures['measure_feat'], 99, axis=0)
|
||||
# kern_types = np.unique()
|
||||
# thresh_x = np.zeros(thresh_y.shape, dtype=float)
|
||||
# for i, thresh in enumerate(thresh_y):
|
||||
# if thresh < 0.1:
|
||||
# thresh_x[i] = scales[-1]
|
||||
# continue
|
||||
# mask = (measures['measure_feat'][:, i] < thresh)
|
||||
# thresh_x[i] = scales[np.nonzero(mask)[0][-1]]
|
||||
# inds = np.argsort(thresh_x)
|
||||
# print(config['k_specs'][inds, :])
|
||||
|
||||
# fig, axes = plt.subplots(1, 2)
|
||||
# axes[0].plot(snippets['snip_feat'][:, inds, -1])
|
||||
# axes[1].plot(scales, measures['measure_feat'][:, inds])
|
||||
# plt.show()
|
||||
# embed()
|
||||
measures[mkey][i] = signals[stage][segment, :].mean(axis=0)
|
||||
|
||||
# Save analysis results:
|
||||
if save_path is not None:
|
||||
|
||||
@@ -7,15 +7,19 @@ from IPython import embed
|
||||
# GENERAL SETTINGS:
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, excl='noise', dir='../data/processed/')
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
save_path = '../data/inv/log_hp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
add_noise = False
|
||||
add_noise = target == '*' or False
|
||||
save_snippets = target == 'Omocestus_rufipes'
|
||||
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
|
||||
scales = np.geomspace(0.1, 10000, 500)
|
||||
scales = np.unique(np.concatenate((scales, example_scales)))
|
||||
|
||||
# PREPARATION:
|
||||
pure_noise = np.load(noise_path)['filt']
|
||||
|
||||
# EXECUTION:
|
||||
for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
print(f'Processing {name}')
|
||||
@@ -36,9 +40,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
mix = song[:, None] * scales[None, :]
|
||||
|
||||
if add_noise:
|
||||
# Add normalized envelopenoise:
|
||||
rng = np.random.default_rng()
|
||||
noise = rng.normal(scale=1, size=song.shape)
|
||||
# Add normalized noise component:
|
||||
noise = pure_noise[:song.shape[0]]
|
||||
noise /= noise[segment].std()
|
||||
mix += noise[:, None]
|
||||
|
||||
|
||||
@@ -8,13 +8,13 @@ from thunderhopper.model import convolve_kernels
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = ['Omocestus_rufipes', '*'][1]
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, excl='noise', dir='../data/processed/')
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
save_path = '../data/inv/thresh_lp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
add_noise = True
|
||||
add_noise = False
|
||||
save_snippets = add_noise and (target == 'Omocestus_rufipes')
|
||||
plot_results = False
|
||||
example_scales = np.array([0, 1, 10, 30, 100])
|
||||
@@ -62,8 +62,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
thresh_abs = ref_conv[segment, :].std(axis=0, keepdims=True) * thresh_rel[:, None]
|
||||
|
||||
# Prepare measure storage:
|
||||
shape = (scales.size, kern_specs.shape[0], thresh_rel.size)
|
||||
measure_feat = np.zeros(shape, dtype=float)
|
||||
measure_inv = np.zeros((scales.size,), dtype=float)
|
||||
measure_feat = np.zeros((scales.size, kern_specs.shape[0], thresh_rel.size), dtype=float)
|
||||
if save_snippets:
|
||||
# Prepare snippet storage:
|
||||
snip_inv = np.zeros((song.size, example_scales.size), dtype=float)
|
||||
@@ -81,6 +81,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
if add_noise:
|
||||
# Add noise:
|
||||
scaled_song += noise
|
||||
|
||||
# Log input intensity measure:
|
||||
measure_inv[i] = scaled_song[segment].std()
|
||||
|
||||
# Process mixture:
|
||||
scaled_conv = convolve_kernels(scaled_song, config['kernels'], config['k_specs'])
|
||||
@@ -130,6 +133,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
data = dict(
|
||||
scales=scales,
|
||||
example_scales=example_scales,
|
||||
measure_inv=measure_inv,
|
||||
measure_feat=measure_feat,
|
||||
thresh_rel=thresh_rel,
|
||||
thresh_abs=thresh_abs,
|
||||
|
||||
@@ -7,7 +7,7 @@ from IPython import embed
|
||||
|
||||
# General:
|
||||
save_path = '../data/processed/white_noise'
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
sds = [1]
|
||||
dur = 60
|
||||
|
||||
@@ -23,9 +23,9 @@ types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
|
||||
6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
|
||||
config = configuration(env_rate, feat_rate, types=types, sigmas=sigmas)
|
||||
config.update({
|
||||
'bp_fcut': None,
|
||||
'rate_ratio': None,
|
||||
'env_fcut': 250,
|
||||
'db_ref': 1,
|
||||
'inv_fcut': 5,
|
||||
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
|
||||
'feat_fcut': 0.5,
|
||||
|
||||
65
python/save_ref_measures.py
Normal file
65
python/save_ref_measures.py
Normal file
@@ -0,0 +1,65 @@
|
||||
import numpy as np
|
||||
from thunderhopper.filters import decibel, sosfilter
|
||||
from thunderhopper.model import convolve_kernels, process_signal
|
||||
from thunderhopper.modeltools import load_data
|
||||
from IPython import embed
|
||||
|
||||
## SETTINGS:
|
||||
|
||||
# General:
|
||||
mode = ['log_hp', 'thresh_lp', 'full'][2]
|
||||
noise_path = '../data/processed/white_noise_sd-1.npz'
|
||||
save_path = '../data/inv/'
|
||||
pad = np.array([0.1, 0.9])
|
||||
|
||||
stages = dict(
|
||||
log_hp=['filt', 'env', 'log', 'inv'],
|
||||
thresh_lp=['inv', 'conv', 'feat'],
|
||||
full=['raw', 'filt', 'env', 'log', 'inv', 'conv', 'feat']
|
||||
)[mode]
|
||||
|
||||
# PROCESSING:
|
||||
|
||||
print(f'Fetching references for {mode} invariance...')
|
||||
|
||||
# Load pure-noise starter representation:
|
||||
noise_data, config = load_data(noise_path, stages[0])
|
||||
starter = noise_data[stages[0]]
|
||||
|
||||
# Prepare buffered measurement segment:
|
||||
pad = (pad * starter.shape[0]).astype(int)
|
||||
segment = np.arange(starter.shape[0])[pad[0]:pad[1]]
|
||||
|
||||
# Normalize starter:
|
||||
starter /= starter[segment].std()
|
||||
|
||||
# Run pipeline:
|
||||
if mode == 'log_hp':
|
||||
data = {'filt': starter}
|
||||
data['env'] = sosfilter(np.abs(data['filt']), config['rate'], config['env_fcut'],
|
||||
'lp', padtype='even', padlen=config['padlen'])
|
||||
data['log'] = decibel(data['env'], ref=1)
|
||||
data['inv'] = sosfilter(data['log'], config['env_rate'], config['inv_fcut'],
|
||||
'hp', padtype='constant', padlen=config['padlen'])
|
||||
elif mode == 'thresh_lp':
|
||||
data = {'inv': starter}
|
||||
data['conv'] = convolve_kernels(data['inv'], config['kernels'], config['k_specs'])
|
||||
data['feat'] = sosfilter((data['conv'] > config['feat_thresh']).astype(float),
|
||||
config['env_rate'], config['feat_fcut'], 'lp',
|
||||
padtype='fixed', padlen=config['padlen'])
|
||||
elif mode == 'full':
|
||||
data = process_signal(config, stages, signal=starter, rate=config['rate'])[0]
|
||||
|
||||
# Get measures:
|
||||
measures = {}
|
||||
for stage in stages:
|
||||
if stage == 'feat':
|
||||
measures[stage] = data[stage][segment, :].mean(axis=0)
|
||||
else:
|
||||
measures[stage] = data[stage][segment, ...].std(axis=0)
|
||||
|
||||
# Save results:
|
||||
np.savez(save_path + f'{mode}/ref_measures.npz', **measures)
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
@@ -16,7 +16,7 @@ if False:
|
||||
|
||||
# Interactivity:
|
||||
reload_saved = False
|
||||
gui = False
|
||||
gui = True
|
||||
|
||||
# Processing:
|
||||
env_rate = 44100.0
|
||||
@@ -29,6 +29,7 @@ config.update({
|
||||
'channel': 0,
|
||||
'rate_ratio': None,
|
||||
'env_fcut': 250,
|
||||
'db_ref': 1,
|
||||
'inv_fcut': 5,
|
||||
'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
|
||||
'feat_fcut': 0.5,
|
||||
|
||||
Reference in New Issue
Block a user