Holiday syncing :)
This commit is contained in:
j-hartling
@@ -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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]
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure']
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)
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save_path = '../figures/fig_invariance_log_hp.pdf'
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compute_ratios = True
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show_diag = True
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show_noise = True
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show_plateaus = True
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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, 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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ncols=3,
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nrows=3,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0,
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right=1,
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bottom=0,
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top=1
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top=1,
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height_ratios=[1, 1, 1]
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)
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subfig_specs = dict(
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pure=(0, slice(0, -1)),
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noise=(1, slice(0, -1)),
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big=(slice(None), -1),
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pure=(0, slice(None)),
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noise=(1, slice(None)),
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big=(2, slice(None)),
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)
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block_height = 0.8
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edge_padding = 0.08
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@@ -67,7 +115,7 @@ pure_grid_kwargs = dict(
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ncols=None,
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wspace=0.1,
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hspace=0.15,
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left=0.16,
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left=0.11,
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right=0.95,
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bottom=1 - block_height - edge_padding,
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top=1 - edge_padding,
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@@ -76,23 +124,23 @@ pure_grid_kwargs = dict(
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noise_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=0.1,
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hspace=0.15,
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left=0.16,
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right=0.95,
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wspace=pure_grid_kwargs['wspace'],
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hspace=pure_grid_kwargs['hspace'],
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=edge_padding,
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top=edge_padding + block_height,
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height_ratios=[1, 2, 1]
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)
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big_grid_kwargs = dict(
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nrows=2,
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ncols=1,
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wspace=0,
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hspace=0.3,
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left=0.19,
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right=0.96,
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bottom=0.09,
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top=0.98
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nrows=1,
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ncols=3,
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wspace=0.3,
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hspace=0,
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=0.05,
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top=1
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)
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anchor_kwargs = dict(
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aspect='equal',
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@@ -110,8 +158,14 @@ 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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lw_snippets = 1
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lw_big = 3
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species_colors = load_colors('../data/species_colors.npz')
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noise_colors = [(0.5, 0.5, 0.5), (0.7, 0.7, 0.7)]
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lw = dict(
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snip=1,
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big=4,
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spec=2,
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plateau=1,
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)
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xlabels = dict(
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big='scale $\\alpha$',
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)
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@@ -135,7 +189,7 @@ 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=0.05,
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x=0,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top',
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@@ -160,10 +214,10 @@ letter_snip_kwargs = dict(
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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x=0.05,
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yref=letter_snip_kwargs['yref'],
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x=0,
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y=1,
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ha='left',
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va='center',
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va='bottom',
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fontsize=fs['letter'],
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)
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zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
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@@ -204,33 +258,77 @@ bar_kwargs = dict(
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va='center',
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)
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)
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leg_kwargs = dict(
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ncols=2,
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loc='upper right',
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bbox_to_anchor=(0, 0.6, 1, 0.4),
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frameon=False,
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prop=dict(
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size=12,
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style='italic',
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),
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borderpad=0,
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borderaxespad=0,
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handlelength=1,
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columnspacing=1,
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)
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diag_kwargs = dict(
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c=(0.75, 0.75, 0.75),
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lw=2,
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ls='--',
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zorder=1.9,
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)
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noise_rel_thresh = 0.95
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noise_kwargs = dict(
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fc=(0.9, 0.9, 0.9),
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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_rect_kwargs = dict(
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ec='none',
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lw=0,
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zorder=1.5,
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)
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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=10,
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markeredgecolor='k',
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markeredgewidth=1,
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# alpha=1,
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zorder=6,
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clip_on=False,
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# base=0,
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# cap=0.15,
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# add_pdf=True,
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)
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kde_kwargs = dict(
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sigma=0.1,
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)
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# PREPARATION:
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if compute_ratios:
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ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
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ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
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ref_measures = dict(np.load(ref_path))
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species_measures = []
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for species_path in species_paths:
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species_data, _ = load_data(species_path, **load_kwargs)
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species_measure = species_data['measure_inv']
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species_measures = {}
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thresh_inds = np.zeros((len(target_species),), dtype=int)
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thresh_scales = np.zeros((len(target_species),), dtype=float)
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for i, species in enumerate(target_species):
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path = search_files(species, incl='noise', dir='../data/inv/log_hp/')[0]
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species_data = load_data(path, **load_kwargs)[0]
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measure = species_data['measure_inv']
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scales = species_data['scales']
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if compute_ratios:
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species_measure /= ref_measures['inv']
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species_measures.append(species_measure)
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species_measures = np.array(species_measures).T
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measure /= ref_measures['inv']
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species_measures[species] = measure
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thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]
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thresh_scales[i] = scales[thresh_inds[i]]
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thresh_pdf, pdf_axis = get_kde(thresh_scales, axis=scales, **kde_kwargs)
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# EXECUTION:
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for data_path in data_paths:
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@@ -273,7 +371,7 @@ for data_path in data_paths:
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transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
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noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(noise_subfig, 'c', ref=noise_title, **letter_snip_kwargs)
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letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
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noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
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noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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noise_inset.tick_params(**inset_tick_kwargs)
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@@ -282,51 +380,49 @@ for data_path in data_paths:
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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.nrows,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[i, 0])
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big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_aspect(**anchor_kwargs)
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ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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if i == 0:
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hide_ticks(ax, 'bottom')
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letter_subplot(big_subfig, 'b', ref=pure_title, **letter_big_kwargs)
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else:
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xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
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letter_subplot(big_subfig, 'd', ref=noise_title, **letter_big_kwargs)
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if i > 0:
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hide_ticks(ax, 'left')
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big_axes[i] = ax
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ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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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, 'cde', **letter_big_kwargs)
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# Plot pure-song envelope snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
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ymin=0, c=colors['env'], lw=lw_snippets)[0]
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ymin=0, c=colors['env'], lw=lw['snip'])[0]
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zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot pure-song logarithmic snippets:
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plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
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c=colors['log'], lw=lw_snippets)
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c=colors['log'], lw=lw['snip'])
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# Plot pure-song invariant snippets:
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plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
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c=colors['inv'], lw=lw_snippets)
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c=colors['inv'], lw=lw['snip'])
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# Plot noise-song envelope snippets:
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ymin, ymax = pure_axes[0, 0].get_ylim()
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handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
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ymin, ymax, c=colors['env'], lw=lw_snippets)[0]
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ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
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zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot noise-song logarithmic snippets:
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ymin, ymax = pure_axes[1, 0].get_ylim()
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plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
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ymin, ymax, c=colors['log'], lw=lw_snippets)
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ymin, ymax, c=colors['log'], lw=lw['snip'])
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# Plot noise-song invariant snippets:
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ymin, ymax = pure_axes[2, 0].get_ylim()
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plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
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ymin, ymax, c=colors['inv'], lw=lw_snippets)
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ymin, ymax, c=colors['inv'], lw=lw['snip'])
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# Indicate time scale:
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time_bar(noise_axes[-1, -1], **bar_kwargs)
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@@ -342,34 +438,46 @@ for data_path in data_paths:
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noise_data['measure_inv'] /= ref_measures['inv']
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# Plot pure-song measures (ideal):
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big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw_big)
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big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw_big)
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big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw_big)
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big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'])
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big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'])
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big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'])
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# Plot noise-song measures (limited):
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big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw_big)
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big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw_big)
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big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw_big)
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# Plot species measures:
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big_axes[1].plot(noise_scales, species_measures, 'k', lw=lw_big, zorder=2.1)
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big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
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big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
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big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])
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if show_diag:
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# Indicate diagonal:
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big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
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big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)
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if show_noise:
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# Indicate noise floor:
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if compute_ratios:
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span_measure = noise_data['measure_inv'][-1] - ref_measures['inv']
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thresh_measure = ref_measures['inv'] + noise_rel_thresh * span_measure
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else:
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span_measure = noise_data['measure_inv'][-1] - noise_data['measure_inv'][0]
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thresh_measure = noise_data['measure_inv'][0] + noise_rel_thresh * span_measure
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thresh_ind = np.nonzero(noise_data['measure_inv'] < thresh_measure)[0][-1]
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thresh_scale = noise_scales[thresh_ind]
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big_axes[1].axvspan(noise_scales[0], thresh_scale, **noise_kwargs)
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if show_plateaus:
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# Indicate low and high plateaus of noise invariance curve:
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low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
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big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
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fc=noise_colors[0], **plateau_rect_kwargs)
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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')
|
||||
|
||||
Reference in New Issue
Block a user