Added loads of units in nearly all graphs.
Overhauled fig_invariance_full.pdf. Added some legends, somewhere.
This commit is contained in:
j-hartling
@@ -4,23 +4,21 @@ 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 thunderhopper.filtertools import find_kern_specs
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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,\
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from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
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plot_line, strip_zeros, time_bar, assign_colors,\
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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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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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handles = []
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for i, ax in enumerate(axes):
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plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
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return None
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def plot_bi_snippets(axes, time, snippets, **kwargs):
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for i, ax in enumerate(axes):
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plot_barcode(ax, time, snippets[:, ..., i], **kwargs)
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return None
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handles.append(plot_line(ax, time, snippets[:, ..., i],
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ymin=ymin, ymax=ymax, **kwargs))
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return handles
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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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@@ -33,10 +31,19 @@ def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
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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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def exclude_zero_scale(data, stages):
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inds = data['scales'] > 0
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data['scales'] = data['scales'][inds]
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for stage in stages:
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data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
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return data
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def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
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for stage in stages:
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key = f'{keyword}_{stage}'
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data[key] = data[key][:, inds, ...]
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return data
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# GENERAL SETTINGS:
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target_species = [
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@@ -57,19 +64,34 @@ example_file = {
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'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
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'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
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}[target_species]
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data_paths = search_files(target_species, dir='../data/inv/full/condensed/')
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snip_paths = search_files(example_file, dir='../data/inv/full/')
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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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raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/full/condensed/')[0]
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base_path = search_files(target_species, incl='base', dir='../data/inv/full/condensed/')[0]
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range_path = search_files(target_species, incl='range', dir='../data/inv/full/condensed/')[0]
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snip_path = search_files(example_file, dir='../data/inv/full/')[0]
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save_path = '../figures/fig_invariance_full.pdf'
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# ANALYSIS SETTINGS:
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exclude_zero = True
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# SUBSET SETTINGS:
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types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
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sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
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# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
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# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
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kernels = np.array([
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[1, 0.002],
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[-1, 0.002],
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[2, 0.004],
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[-2, 0.004],
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[3, 0.032],
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[-3, 0.032]
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])
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kernels = None
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 20/2.54),
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figsize=(32/2.54, 32/2.54),
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)
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super_grid_kwargs = dict(
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nrows=2,
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@@ -91,20 +113,20 @@ snip_grid_kwargs = dict(
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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=0.08,
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right=0.95,
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left=0.11,
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right=0.98,
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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=3,
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wspace=0.2,
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wspace=0.4,
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hspace=0,
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left=snip_grid_kwargs['left'],
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right=0.96,
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bottom=0.2,
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top=0.95
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right=snip_grid_kwargs['right'],
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bottom=0.13,
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top=0.98
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)
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# PLOT SETTINGS:
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@@ -117,15 +139,17 @@ 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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conv_colors = load_colors('../data/conv_colors_all.npz')
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feat_colors = load_colors('../data/feat_colors_all.npz')
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lw = dict(
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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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inv=0.25,
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conv=0.25,
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bi=0,
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feat=1,
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big=3
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big=3,
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plateau=1.5,
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)
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xlabels = dict(
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big='scale $\\alpha$',
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@@ -134,11 +158,10 @@ ylabels = dict(
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filt='$x_{\\text{filt}}$',
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env='$x_{\\text{env}}$',
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log='$x_{\\text{db}}$',
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inv='$x_{\\text{inv}}$',
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inv='$x_{\\text{adapt}}$',
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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=['intensity', 'rel. intensity', 'norm. intensity']
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big=['measure', 'rel. measure', 'norm. measure']
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)
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xlab_big_kwargs = dict(
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y=0,
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@@ -154,7 +177,7 @@ 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=-0.12,
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x=-0.2,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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@@ -164,7 +187,7 @@ yloc = dict(
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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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conv=1,
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feat=1,
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)
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title_kwargs = dict(
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@@ -205,122 +228,199 @@ bar_kwargs = dict(
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va='center',
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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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plateau_settings = dict(
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low=0.05,
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high=0.95,
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first=True,
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last=True,
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condense=None,
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)
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plateau_line_kwargs = dict(
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lw=lw['plateau'],
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ls='--',
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zorder=1,
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)
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plateau_dot_kwargs = dict(
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marker='o',
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markersize=8,
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markeredgewidth=1,
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clip_on=False,
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)
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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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data, config = load_data(data_path, **load_kwargs)
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t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
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# Load raw (unnormed) invariance data:
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data, config = load_data(raw_path, files='scales', keywords='mean')
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if exclude_zero:
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data = exclude_zero_scale(data, stages)
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scales = data['scales']
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# Adjust grid parameters:
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snip_grid_kwargs['ncols'] = data['example_scales'].size
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# Load snippet data:
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snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
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t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
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snip_scales = snip['example_scales']
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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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# Optional kernel subset:
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reduce_kernels = False
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if any(var is not None for var in [kernels, types, sigmas]):
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kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
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data = reduce_kernel_set(data, kern_inds, keyword='mean')
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snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
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config['k_specs'] = config['k_specs'][kern_inds, :]
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config['kernels'] = config['kernels'][:, kern_inds]
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reduce_kernels = True
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# Prepare stage-specific snippet axes:
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snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
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snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
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snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
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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 = 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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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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# Adjust grid parameters:
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snip_grid_kwargs['ncols'] = snip_scales.size
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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 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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plt.show()
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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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# # 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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# Prepare stage-specific snippet axes:
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snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
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snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
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snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
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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 = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
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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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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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# # 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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# 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 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(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=0.01, linscale=0.1)
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ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
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if i < (big_grid.ncols - 1):
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ax.set_ylim(scales[0], scales[-1])
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else:
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ax.set_ylim(0, 1)
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big_axes[i] = ax
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super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
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letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
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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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if True:
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# Plot filtered snippets:
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plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
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c=colors['filt'], lw=lw['filt'])
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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 envelope snippets:
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plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
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ymin=0, c=colors['env'], lw=lw['env'])
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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 logarithmic snippets:
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plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
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c=colors['log'], lw=lw['log'])
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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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# Plot invariant snippets:
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plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
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c=colors['inv'], lw=lw['inv'])
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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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measure = data[f'measure_{stage}']
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# Plot kernel response snippets:
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all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
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c=colors['conv'], lw=lw['conv'])
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for i, handles in enumerate(all_handles):
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assign_colors(handles, config['k_specs'][:, 0], conv_colors)
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reorder_by_sd(handles, snip['snip_conv'][..., i])
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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 feature snippets:
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all_handles = plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
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ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
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for i, handles in enumerate(all_handles):
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assign_colors(handles, config['k_specs'][:, 0], feat_colors)
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reorder_by_sd(handles, snip['snip_feat'][..., i])
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del snip
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# # Relate to pure-noise reference:
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# norm_measure = measure / ref_data[stage]
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# Remember saturation points:
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crit_inds, crit_scales = {}, {}
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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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# Unnormed measures:
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for stage in stages:
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# Plot average intensity measure across recordings:
|
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curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind = get_saturation(curve, **plateau_settings)[1]
|
||||
scale = scales[ind]
|
||||
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
# Log saturation point:
|
||||
crit_inds[stage] = ind
|
||||
crit_scales[stage] = scale
|
||||
del data
|
||||
|
||||
# Normalize measure to [0, 1]:
|
||||
min_measure = measure.min(axis=0)
|
||||
max_measure = measure.max(axis=0)
|
||||
norm_measure = (measure - min_measure) / (max_measure - min_measure)
|
||||
# Noise baseline-related measures:
|
||||
data, _ = load_data(base_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[1], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
# Plot normalized intensity measures:
|
||||
curve = plot_curves(big_axes[1], data['scales'], norm_measure, c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
show_saturation(big_axes[1], data['scales'], curve, c=colors[stage])
|
||||
# Min-max normalized measures:
|
||||
data, _ = load_data(range_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in ['log', 'inv', 'conv', 'feat']:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
|
||||
# # Plot over relative scales:
|
||||
# plot_curves(big_axes[2], scales_rel, norm_measure, c=colors[stage], lw=lw['big'],
|
||||
# fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# scales_rel = curve - curve.min()
|
||||
# scales_rel /= scales_rel.max()
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
# Save graph:
|
||||
if save_path is not None:
|
||||
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
|
||||
fig.savefig(file_name)
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
Reference in New Issue
Block a user