Added some cmap functions.
Selected species-specific colors. Quite some progress on fig_invariance_thresh_lp_species.pdf.
This commit is contained in:
j-hartling
@@ -1,5 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.colors import ListedColormap, LinearSegmentedColormap
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from tkinter.colorchooser import askcolor
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from IPython import embed
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@@ -81,6 +82,57 @@ def load_colors(path):
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return {k: (c.item() if c.size == 1 else c) for k, c in colors.items()}
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raise ValueError(f'Expected .npy or .npz file extension: {path}')
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# COLORMAPS:
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def create_listed_cmap(colors, name=None, n=None):
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cmap = ListedColormap(colors)
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if n is not None:
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cmap.resampled(n)
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if name is not None:
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cmap.name = name
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plt.colormaps.register(cmap)
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return cmap
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def create_linear_cmap(colors, name=None, n=None):
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cmap = LinearSegmentedColormap.from_list(colors)
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if n is not None:
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cmap.resampled(n)
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if name is not None:
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cmap.name = name
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plt.colormaps.register(cmap)
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return cmap
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def sample_cmap(cmap, n, low=None, high=None, segments=None, alpha=None):
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if isinstance(cmap, str):
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cmap = plt.get_cmap(cmap)
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colors = cmap(np.linspace(0, 1, n))
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if alpha is None:
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colors = colors[:, :3]
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elif 0.0 <= alpha <= 1.0:
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colors[:, 3] = alpha
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if segments is None and (low is not None or high is not None):
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segments = [(0 if low is None else low, 1 if high is None else high)]
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if segments is not None:
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segment_colors = []
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for start, end in segments:
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start, end = int(start * n), int(end * n)
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step = 1 if start <= end else -1
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segment_colors.append(colors[start:end:step, :])
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colors = np.vstack(segment_colors)
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return colors
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def remake_cmap(cmap, n_in, n_out=None, name=None, low=None, high=None, segments=None,
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alpha=None):
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colors = sample_cmap(cmap, n_in, low, high, segments, alpha)
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cmap_type = type(cmap).__name__
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if cmap_type == 'ListedColormap':
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return create_listed_cmap(colors, name, n_out)
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elif cmap_type == 'LinearSegmentedColormap':
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return create_linear_cmap(colors, name, n_out)
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return None
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# ADVANCED FUNCTIONALITY:
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def shade_colors(color, factors, norm=True):
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@@ -30,6 +30,7 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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species_paths = search_files('*', incl='noise', dir='../data/inv/log_hp/')
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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@@ -39,10 +40,6 @@ 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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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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# GRAPH SETTINGS:
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fig_kwargs = dict(
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@@ -221,6 +218,20 @@ noise_kwargs = dict(
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zorder=1.5,
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)
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# PREPARATION:
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if compute_ratios:
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ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
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ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
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species_measures = []
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for species_path in species_paths:
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species_data, _ = load_data(species_path, **load_kwargs)
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species_measure = species_data['measure_inv']
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if compute_ratios:
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species_measure /= ref_measures['inv']
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species_measures.append(species_measure)
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species_measures = np.array(species_measures).T
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# EXECUTION:
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for data_path in data_paths:
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print(f'Processing {data_path}')
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@@ -340,6 +351,9 @@ for data_path in data_paths:
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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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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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380
python/fig_invariance_log-hp_backup.py
Normal file
380
python/fig_invariance_log-hp_backup.py
Normal file
@@ -0,0 +1,380 @@
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import plotstyle_plt
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import numpy as np
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import matplotlib.pyplot as plt
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from itertools import product
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from thunderhopper.filetools import search_files
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from thunderhopper.modeltools import load_data
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from color_functions import load_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, hide_ticks,\
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plot_line, strip_zeros, time_bar, zoom_inset,\
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letter_subplot, title_subplot
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from IPython import embed
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def add_snip_axes(fig, grid_kwargs):
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grid = fig.add_gridspec(**grid_kwargs)
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axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
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for i, j in product(range(grid.nrows), range(grid.ncols)):
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axes[i, j] = fig.add_subplot(grid[i, j])
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[hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
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[hide_axis(ax, 'bottom') for ax in axes.flatten()]
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return axes
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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handles = []
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for ax, snippet in zip(axes, snippets.T):
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handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
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return handles
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# GENERAL SETTINGS:
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target = 'Omocestus_rufipes'
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data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
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species_paths = search_files('*', incl='noise', dir='../data/inv/log_hp/')
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stages = ['env', 'log', 'inv']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure']
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)
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save_path = '../figures/fig_invariance_log_hp.pdf'
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compute_ratios = True
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show_diag = True
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show_noise = True
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 32/2.54),
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)
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snip_rows = 1
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big_rows = 1
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super_grid_kwargs = dict(
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nrows=2 * snip_rows + big_rows,
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ncols=1,
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wspace=0,
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hspace=0,
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left=0,
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right=1,
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bottom=0,
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top=1
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)
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subfig_specs = dict(
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pure=(slice(0, snip_rows), slice(None)),
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noise=(slice(snip_rows, 2 * snip_rows), slice(None)),
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big=(slice(-big_rows, None), slice(None)),
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)
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block_height = 0.8
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edge_padding = 0.08
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pure_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=0.1,
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hspace=0.15,
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left=0.11,
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right=0.95,
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bottom=1 - block_height - edge_padding,
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top=1 - edge_padding,
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height_ratios=[1, 2, 1]
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)
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noise_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=pure_grid_kwargs['wspace'],
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hspace=pure_grid_kwargs['hspace'],
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=edge_padding,
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top=edge_padding + block_height,
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height_ratios=[1, 2, 1]
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)
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big_grid_kwargs = dict(
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nrows=1,
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ncols=3,
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wspace=0.3,
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hspace=0,
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left=pure_grid_kwargs['left'],
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right=pure_grid_kwargs['right'],
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bottom=0.05,
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top=1
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)
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anchor_kwargs = dict(
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aspect='equal',
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adjustable='box',
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anchor=(0.5, 0.5)
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)
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# PLOT SETTINGS:
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fs = dict(
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lab_norm=16,
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lab_tex=20,
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letter=22,
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tit_norm=16,
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tit_tex=20,
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bar=16,
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)
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colors = load_colors('../data/stage_colors.npz')
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lw_snippets = 1
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lw_big = 3
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xlabels = dict(
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big='scale $\\alpha$',
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)
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ylabels = dict(
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env='$x_{\\text{env}}$',
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log='$x_{\\text{dB}}$',
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inv='$x_{\\text{adapt}}$',
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big='$\\sigma_{\\alpha}\\,/\\,\\sigma_{\\eta}$',
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)
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xlab_big_kwargs = dict(
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y=0,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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)
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ylab_snip_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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rotation=0,
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ha='left',
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va='center',
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)
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ylab_big_kwargs = dict(
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x=0,
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fontsize=fs['lab_tex'],
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ha='center',
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va='top',
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)
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yloc = dict(
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env=1000,
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log=40,
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inv=20
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)
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title_kwargs = dict(
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x=0.5,
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y=1,
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ha='center',
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va='bottom',
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fontsize=fs['tit_norm'],
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)
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letter_snip_kwargs = dict(
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x=0,
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yref=0.5,
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ha='left',
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va='center',
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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x=0,
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y=1,
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ha='left',
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va='bottom',
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fontsize=fs['letter'],
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)
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zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
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zoom_kwargs = dict(
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x0=0.45,
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x1=0.55,
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y0=0,
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y1=0.0006,
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low_left=True,
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low_right=True,
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ec='k',
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lw=1,
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alpha=1,
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)
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inset_tick_kwargs = dict(
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axis='y',
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length=3,
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pad=1,
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left=False,
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labelleft=False,
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right=True,
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labelright=True,
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)
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bar_time = 5
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bar_kwargs = dict(
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dur=bar_time,
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y0=-0.25,
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y1=-0.1,
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xshift=1,
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color='k',
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lw=0,
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clip_on=False,
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text_pos=(-0.1, 0.5),
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text_str=f'${bar_time}\\,\\text{{s}}$',
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text_kwargs=dict(
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fontsize=fs['bar'],
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ha='right',
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va='center',
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)
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)
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diag_kwargs = dict(
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c=(0.75, 0.75, 0.75),
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lw=2,
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ls='--',
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zorder=1.9,
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)
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noise_rel_thresh = 0.95
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noise_kwargs = dict(
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fc=(0.9, 0.9, 0.9),
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ec='none',
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lw=0,
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zorder=1.5,
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)
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# PREPARATION:
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if compute_ratios:
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ref_data = load_data('../data/processed/white_noise_sd-1.npz', files=stages)[0]
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ref_measures = {k: v.std() for k, v in ref_data.items() if not k.endswith('rate')}
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species_measures = []
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for species_path in species_paths:
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species_measure = load_data(species_path, **load_kwargs)[0]['measure_inv']
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if compute_ratios:
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species_measure /= ref_measures['inv']
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species_measures.append(species_measure)
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species_measures = np.array(species_measures).T
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# EXECUTION:
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for data_path in data_paths:
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print(f'Processing {data_path}')
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# Load invariance data:
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pure_data, config = load_data(data_path, **load_kwargs)
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noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
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pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
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t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
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# Prepare overall graph:
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fig = plt.figure(**fig_kwargs)
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super_grid = fig.add_gridspec(**super_grid_kwargs)
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fig.canvas.draw()
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# Prepare pure-song snippet axes:
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pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
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pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
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pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
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for ax, stage in zip(pure_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=pure_subfig.transSubfigure)
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for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
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pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
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pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
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pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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pure_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(pure_inset, 'bottom', ticks=False)
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# Prepare noise-song snippet axes:
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noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
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noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
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noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
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for ax, stage in zip(noise_axes[:, 0], stages):
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
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ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
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transform=noise_subfig.transSubfigure)
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for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
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noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
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letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
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noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
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noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
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noise_inset.tick_params(**inset_tick_kwargs)
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hide_ticks(noise_inset, 'bottom', ticks=False)
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
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ax = big_subfig.add_subplot(big_grid[0, i])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_ylim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_aspect(**anchor_kwargs)
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ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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if i == 0:
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hide_ticks(ax, 'bottom')
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letter_subplot(ax, 'c', **letter_big_kwargs)
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else:
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xlabel(ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
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letter_subplot(ax, 'd', **letter_big_kwargs)
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big_axes[i] = ax
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# Plot pure-song envelope snippets:
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handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
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ymin=0, c=colors['env'], lw=lw_snippets)[0]
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zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)
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# Plot pure-song logarithmic snippets:
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plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
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c=colors['log'], lw=lw_snippets)
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# Plot pure-song invariant snippets:
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plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
|
||||
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)
|
||||
|
||||
# Plot species measures:
|
||||
big_axes[2].plot(noise_scales, species_measures, 'k', 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 save_path is not None:
|
||||
fig.savefig(save_path, bbox_inches='tight')
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
@@ -6,8 +6,8 @@ from itertools import product
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from color_functions import load_colors, shade_colors
|
||||
from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel,\
|
||||
from color_functions import load_colors, shade_colors, create_listed_cmap
|
||||
from plot_functions import hide_axis, title_subplot, ylimits, xlabel, ylabel, super_ylabel,\
|
||||
plot_line, plot_barcode, strip_zeros, time_bar,\
|
||||
letter_subplot, letter_subplots, hide_ticks,\
|
||||
super_xlabel, super_ylabel, assign_colors
|
||||
@@ -125,73 +125,90 @@ def split_subplot(ax, side='right', size=10, pad=10):
|
||||
inputs = zip(*force_sequence(side, size, pad, equal_size=True))
|
||||
return [div.append_axes(s, f'{n}%', f'{p}%') for s, n, p in inputs]
|
||||
|
||||
def shorten_species(name):
|
||||
genus, species = name.split('_')
|
||||
return genus[0] + '. ' + species
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
targets = [
|
||||
target_species = [
|
||||
'Omocestus_rufipes',
|
||||
'Chorthippus_biguttulus',
|
||||
# 'Chorthippus_mollis',
|
||||
# 'Chrysochraon_dispar',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Gomphocerippus_rufus',
|
||||
# 'Pseudochorthippus_parallelus',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
pure_paths = search_files(targets, incl='subset', excl='noise', dir='../data/inv/thresh_lp/')
|
||||
n_species = len(target_species)
|
||||
load_kwargs = dict(
|
||||
keywords=['scales', 'measure', 'thresh']
|
||||
)
|
||||
save_path = '../figures/fig_invariance_thresh_lp_species.pdf'
|
||||
exclude_zero = True
|
||||
show_noise = True
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
thresh_percent = np.array([0.6, 0.75, 0.999])[0]
|
||||
kernels = np.array([
|
||||
thresh_rel = np.array([0.5, 1, 3])[0]
|
||||
kern_specs = np.array([
|
||||
[1, 0.008],
|
||||
[2, 0.004],
|
||||
[3, 0.002],
|
||||
])[np.array([0, 1])]
|
||||
n_kernels = kern_specs.shape[0]
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
figsize=(32/2.54, 20/2.54),
|
||||
)
|
||||
n_species = len(targets)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=n_species + 2,
|
||||
nrows=3,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1
|
||||
top=1,
|
||||
height_ratios=[1, 4, 3]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
spec=(slice(None), slice(0, n_species)),
|
||||
big=(slice(None), slice(n_species, None))
|
||||
song=(0, 0),
|
||||
feat=(1, 0),
|
||||
space=(2, 0)
|
||||
)
|
||||
spec_grid_kwargs = dict(
|
||||
feat_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=n_species,
|
||||
wspace=0.25,
|
||||
hspace=0.1,
|
||||
left=0.1,
|
||||
right=0.97,
|
||||
hspace=0.15,
|
||||
left=0.06,
|
||||
right=0.985,
|
||||
bottom=0.1,
|
||||
top=0.94
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.2,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=spec_grid_kwargs['bottom'],
|
||||
top=spec_grid_kwargs['top']
|
||||
song_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=n_species,
|
||||
wspace=feat_grid_kwargs['wspace'],
|
||||
hspace=0,
|
||||
left=feat_grid_kwargs['left'],
|
||||
right=feat_grid_kwargs['right'],
|
||||
bottom=0.1,
|
||||
top=0.8
|
||||
)
|
||||
space_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=2,
|
||||
wspace=0.2,
|
||||
hspace=0,
|
||||
left=feat_grid_kwargs['left'],
|
||||
right=feat_grid_kwargs['right'],
|
||||
bottom=0.05,
|
||||
top=0.95
|
||||
)
|
||||
anchor_kwargs = dict(
|
||||
aspect='equal',
|
||||
adjustable='box',
|
||||
anchor=(0.3, 0.5)
|
||||
anchor=(0, 0.5)
|
||||
)
|
||||
inset_kwargs = dict(
|
||||
y0=0.7,
|
||||
@@ -208,50 +225,56 @@ fs = dict(
|
||||
tit_tex=20,
|
||||
bar=16,
|
||||
)
|
||||
base_color = load_colors('../data/stage_colors.npz')['feat']
|
||||
spec_cmaps = [
|
||||
'Reds',
|
||||
'Greens',
|
||||
'Blues',
|
||||
]
|
||||
species_colors = load_colors('../data/species_colors.npz')
|
||||
kernel_shades = [0, 0.5]
|
||||
# scale_shades = [1, 0]
|
||||
lw = dict(
|
||||
spec=2,
|
||||
song=0.5,
|
||||
feat=3,
|
||||
kern=3
|
||||
)
|
||||
zorder = dict(
|
||||
Omocestus_rufipes=2,
|
||||
Chorthippus_biguttulus=2.5,
|
||||
Chorthippus_mollis=2.4,
|
||||
Chrysochraon_dispar=2,
|
||||
Gomphocerippus_rufus=2,
|
||||
Pseudochorthippus_parallelus=2,
|
||||
)
|
||||
space_kwargs = dict(
|
||||
s=30,
|
||||
)
|
||||
xlabels = dict(
|
||||
spec='scale $\\alpha$',
|
||||
big='$\\mu_{f_1}$'
|
||||
feat='scale $\\alpha$',
|
||||
space='$\\mu_{f_1}$'
|
||||
)
|
||||
ylabels = dict(
|
||||
spec='$\\mu_f$',
|
||||
big='$\\mu_{f_2}$',
|
||||
feat='$\\mu_f$',
|
||||
space='$\\mu_{f_2}$',
|
||||
bar='scale $\\alpha$',
|
||||
)
|
||||
xlab_spec_kwargs = dict(
|
||||
xlab_feat_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
xlab_space_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_spec_kwargs = dict(
|
||||
ylab_feat_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=0.03,
|
||||
ylab_space_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
ha='center',
|
||||
ha='left',
|
||||
va='center',
|
||||
)
|
||||
ylab_cbar_kwargs = dict(
|
||||
@@ -261,28 +284,57 @@ ylab_cbar_kwargs = dict(
|
||||
va='bottom',
|
||||
)
|
||||
xloc = dict(
|
||||
big=0.5,
|
||||
space=0.5,
|
||||
)
|
||||
yloc = dict(
|
||||
spec=0.5,
|
||||
big=0.5
|
||||
feat=0.5,
|
||||
space=0.5
|
||||
)
|
||||
letter_spec_kwargs = dict(
|
||||
symlog_kwargs = dict(
|
||||
linscale=0.5,
|
||||
)
|
||||
title_kwargs = dict(
|
||||
x=0.5,
|
||||
yref=1,
|
||||
ha='center',
|
||||
va='top',
|
||||
fontsize=fs['tit_norm'],
|
||||
fontstyle='italic'
|
||||
)
|
||||
letter_feat_kwargs = dict(
|
||||
x=0,
|
||||
yref=1,
|
||||
ha='center',
|
||||
va='top',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
letter_big_kwargs = dict(
|
||||
letter_space_kwargs = dict(
|
||||
x=0,
|
||||
yref=1,
|
||||
ha='center',
|
||||
va='top',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
time_bar_kwargs = dict(
|
||||
dur=0.05,
|
||||
song_bar_time = 1.0
|
||||
song_bar_kwargs = dict(
|
||||
dur=song_bar_time,
|
||||
y0=-0.1,
|
||||
y1=0,
|
||||
xshift=0,
|
||||
color='k',
|
||||
lw=0,
|
||||
clip_on=False,
|
||||
# text_pos=(-0.1, 0.5),
|
||||
text_str=f'${int(1000 * song_bar_time)}\\,\\text{{ms}}$',
|
||||
text_kwargs=dict(
|
||||
fontsize=fs['bar'],
|
||||
ha='right',
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
kern_bar_time = 0.05
|
||||
kern_bar_kwargs = dict(
|
||||
dur=kern_bar_time,
|
||||
y0=inset_kwargs['y0'],
|
||||
y1=inset_kwargs['y0'] + 0.03,
|
||||
color='k',
|
||||
@@ -290,11 +342,16 @@ time_bar_kwargs = dict(
|
||||
)
|
||||
cbar_bounds = [
|
||||
0.05,
|
||||
big_grid_kwargs['bottom'],
|
||||
space_grid_kwargs['bottom'],
|
||||
0.15,
|
||||
big_grid_kwargs['top'] - big_grid_kwargs['bottom']
|
||||
space_grid_kwargs['top'] - space_grid_kwargs['bottom']
|
||||
]
|
||||
shade_factors = [0.9, -0.9]
|
||||
noise_kwargs = dict(
|
||||
fc=(0.9, 0.9, 0.9),
|
||||
ec='none',
|
||||
lw=0,
|
||||
zorder=0.5,
|
||||
)
|
||||
|
||||
# EXECUTION:
|
||||
|
||||
@@ -302,105 +359,165 @@ shade_factors = [0.9, -0.9]
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
|
||||
# Prepare species-specific axes:
|
||||
spec_subfig = fig.add_subfigure(super_grid[subfig_specs['spec']])
|
||||
spec_grid = spec_subfig.add_gridspec(**spec_grid_kwargs)
|
||||
spec_axes = np.zeros((spec_grid_kwargs['nrows'], n_species), dtype=object)
|
||||
for i, j in product(range(spec_grid_kwargs['nrows']), range(n_species)):
|
||||
ax = spec_subfig.add_subplot(spec_grid[i, j])
|
||||
ax.set_xscale('symlog', linthresh=0.1, linscale=0.5)
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['spec']))
|
||||
# Prepare song axes:
|
||||
song_subfig = fig.add_subfigure(super_grid[subfig_specs['song']])
|
||||
song_grid = song_subfig.add_gridspec(**song_grid_kwargs)
|
||||
song_axes = np.zeros((n_species,), dtype=object)
|
||||
for i in range(n_species):
|
||||
ax = song_subfig.add_subplot(song_grid[i])
|
||||
hide_axis(ax, 'bottom')
|
||||
hide_axis(ax, 'left')
|
||||
song_axes[i] = ax
|
||||
|
||||
# Prepare feature invariance axes:
|
||||
feat_subfig = fig.add_subfigure(super_grid[subfig_specs['feat']])
|
||||
feat_grid = feat_subfig.add_gridspec(**feat_grid_kwargs)
|
||||
feat_axes = np.zeros((feat_grid_kwargs['nrows'], n_species), dtype=object)
|
||||
for i, j in product(range(feat_grid_kwargs['nrows']), range(n_species)):
|
||||
ax = feat_subfig.add_subplot(feat_grid[i, j])
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['feat']))
|
||||
ax.set_ylim(0, 1)
|
||||
spec_axes[i, j] = ax
|
||||
super_xlabel(xlabels['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[-1, -1], **xlab_spec_kwargs)
|
||||
super_ylabel(ylabels['spec'], spec_subfig, spec_axes[-1, 0], spec_axes[0, 0], **ylab_spec_kwargs)
|
||||
[hide_ticks(ax, side='bottom') for ax in spec_axes[0, :]]
|
||||
[hide_ticks(ax, side='left') for ax in spec_axes[:, 1:].ravel()]
|
||||
letter_subplots(spec_axes[0, :], labels='abc', ref=spec_subfig, **letter_spec_kwargs)
|
||||
feat_axes[i, j] = ax
|
||||
super_xlabel(xlabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[-1, -1], **xlab_feat_kwargs)
|
||||
super_ylabel(ylabels['feat'], feat_subfig, feat_axes[-1, 0], feat_axes[0, 0], **ylab_feat_kwargs)
|
||||
[hide_ticks(ax, side='bottom') for ax in feat_axes[0, :]]
|
||||
[hide_ticks(ax, side='left') for ax in feat_axes[:, 1:].ravel()]
|
||||
letter_subplots(feat_axes[0, :], labels='abc', ref=feat_subfig, **letter_feat_kwargs)
|
||||
|
||||
# Prepare kernel insets:
|
||||
x0 = np.linspace(0, 1, kernels.shape[0] + 1)[:-1] + 1 / kernels.shape[0] / 2
|
||||
x0 = np.linspace(0, 1, n_kernels + 1)[:-1] + 1 / n_kernels / 2
|
||||
x0 -= inset_kwargs['w'] / 2
|
||||
insets = []
|
||||
for i in range(kernels.shape[0]):
|
||||
for i in range(n_kernels):
|
||||
bounds = [x0[i], inset_kwargs['y0'], inset_kwargs['w'], inset_kwargs['h']]
|
||||
inset = spec_axes[0, 0].inset_axes(bounds)
|
||||
inset = feat_axes[0, 0].inset_axes(bounds)
|
||||
inset.set_title(rf'$k_{{{i+1}}}$', fontsize=20)
|
||||
inset.axis('off')
|
||||
insets.append(inset)
|
||||
|
||||
# Prepare feature space axes:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_axes = np.zeros(super_grid_kwargs['nrows'], dtype=object)
|
||||
for i in range(big_axes.size):
|
||||
ax = big_subfig.add_subplot(big_grid[i, 0])
|
||||
space_subfig = fig.add_subfigure(super_grid[subfig_specs['space']])
|
||||
space_grid = space_subfig.add_gridspec(**space_grid_kwargs)
|
||||
space_axes = np.zeros(space_grid_kwargs['ncols'], dtype=object)
|
||||
for i in range(space_axes.size):
|
||||
ax = space_subfig.add_subplot(space_grid[i])
|
||||
ax.set_xlim(0, 1)
|
||||
ax.set_ylim(0, 1)
|
||||
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['big']))
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
|
||||
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['space']))
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['space']))
|
||||
ax.set_aspect(**anchor_kwargs)
|
||||
# ax.set_ylabel(ylabels['big'], **ylab_big_kwargs)
|
||||
ylabel(ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
big_axes[i] = ax
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[1], big_axes[1], **xlab_big_kwargs)
|
||||
hide_ticks(big_axes[0], side='bottom')
|
||||
letter_subplot(big_axes[0], 'd', ref=big_subfig, **letter_big_kwargs)
|
||||
# ax.set_ylabel(ylabels['space'], **ylab_space_kwargs)
|
||||
ylabel(ax, ylabels['space'], transform=space_subfig.transSubfigure, **ylab_space_kwargs)
|
||||
space_axes[i] = ax
|
||||
super_xlabel(xlabels['space'], space_subfig, space_axes[1], space_axes[1], **xlab_space_kwargs)
|
||||
hide_ticks(space_axes[0], side='bottom')
|
||||
letter_subplot(space_axes[0], 'd', ref=space_subfig, **letter_space_kwargs)
|
||||
|
||||
# Prepare colorbars:
|
||||
cbar_bounds[0] += big_axes[-1].get_position().x1
|
||||
bar_axes = [big_subfig.add_axes(cbar_bounds)]
|
||||
bar_axes.extend(split_subplot(bar_axes[0], side=['right', 'right'], size=100, pad=0))
|
||||
cbar_bounds[0] += space_axes[-1].get_position().x1
|
||||
bar_axes = [space_subfig.add_axes(cbar_bounds)]
|
||||
bar_axes.extend(split_subplot(bar_axes[0], side=['right'] * (n_species - 1),
|
||||
size=100, pad=0))
|
||||
|
||||
# Prepare kernel-specific color shading:
|
||||
kern_factors = np.linspace(*kernel_shades, n_kernels)
|
||||
kern_colors_bw = shade_colors((0., 0., 0.), kern_factors)
|
||||
|
||||
# Plot results per species:
|
||||
for i, pure_path in enumerate(pure_paths):
|
||||
print(f'Processing {pure_path}')
|
||||
noise_path = pure_path.replace('.npz', '_noise.npz')
|
||||
min_feat = np.zeros((n_species, n_kernels), dtype=float)
|
||||
for i, species in enumerate(target_species):
|
||||
print(f'Processing {species}')
|
||||
|
||||
# Fetch species-specific recording file:
|
||||
song_path = search_files(species, dir='../data/processed/')[0]
|
||||
|
||||
# Load song data:
|
||||
song_data, _ = load_data(song_path, files='filt')
|
||||
song, rate = song_data['filt'], song_data['filt_rate']
|
||||
|
||||
# Plot species snippet:
|
||||
song_ax = song_axes[i]
|
||||
time = np.arange(song.shape[0]) / rate
|
||||
plot_line(song_ax, time, song, ypad=0.05, c='k', lw=lw['song'])
|
||||
title_subplot(song_ax, shorten_species(species), ref=song_subfig, **title_kwargs)
|
||||
time_bar(song_ax, **song_bar_kwargs)
|
||||
|
||||
# Fetch species-specific invariance files:
|
||||
pure_path = search_files(species, incl='pure', dir='../data/inv/thresh_lp/')[0]
|
||||
noise_path = search_files(species, incl='noise', dir='../data/inv/thresh_lp/')[0]
|
||||
|
||||
# Load invariance data:
|
||||
pure_data, config = load_data(pure_path, **load_kwargs)
|
||||
noise_data, _ = load_data(noise_path, **load_kwargs)
|
||||
scales = pure_data['scales']
|
||||
|
||||
# Reduce to kernel subset and single threshold:
|
||||
thresh_ind = np.nonzero(pure_data['thresh_perc'] == thresh_percent)[0][0]
|
||||
kern_inds = find_kern_specs(config['k_specs'], kerns=kernels)
|
||||
# Reduce to kernel subset and a single threshold:
|
||||
thresh_ind = np.nonzero(pure_data['thresh_rel'] == thresh_rel)[0][0]
|
||||
kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
|
||||
config['k_specs'] = config['k_specs'][kern_inds]
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
pure_measure = pure_data['measure_feat'][:, kern_inds, thresh_ind]
|
||||
noise_measure = noise_data['measure_feat'][:, kern_inds, thresh_ind]
|
||||
if exclude_zero:
|
||||
# Reduce to nonzero scales:
|
||||
nonzero_inds = scales > 0
|
||||
scales = scales[nonzero_inds]
|
||||
pure_measure = pure_measure[nonzero_inds, :]
|
||||
noise_measure = noise_measure[nonzero_inds, :]
|
||||
min_feat[i, :] = noise_measure.min(axis=0)
|
||||
|
||||
# Plot invariance curves:
|
||||
pure_ax, noise_ax = spec_axes[:, i]
|
||||
pure_ax.plot(scales, pure_measure, c=base_color, lw=lw['spec'])
|
||||
noise_ax.plot(scales, noise_measure, c=base_color, lw=lw['spec'])
|
||||
# Prepare species-specific colors:
|
||||
base_color = species_colors[species]
|
||||
kern_colors = shade_colors(base_color, kern_factors)
|
||||
scale_factors = np.linspace(1, 0, scales.size)
|
||||
scale_cmap = create_listed_cmap(shade_colors(base_color, scale_factors))
|
||||
scale_cmap_bw = create_listed_cmap(shade_colors((0., 0., 0.), scale_factors))
|
||||
|
||||
# Plot feature invariance curves:
|
||||
pure_ax, noise_ax = feat_axes[:, i]
|
||||
symlog_kwargs['linthresh'] = scales[scales > 0][0]
|
||||
[ax.set_xscale('symlog', **symlog_kwargs) for ax in feat_axes[:, i]]
|
||||
pure_ax.set_xscale('symlog', **symlog_kwargs)
|
||||
noise_ax.set_xscale('symlog', **symlog_kwargs)
|
||||
handles = pure_ax.plot(scales, pure_measure, lw=lw['feat'])
|
||||
[h.set_color(c) for h, c in zip(handles, kern_colors)]
|
||||
handles = noise_ax.plot(scales, noise_measure, lw=lw['feat'])
|
||||
[h.set_color(c) for h, c in zip(handles, kern_colors)]
|
||||
|
||||
if i == 0:
|
||||
# Indicate kernel waveforms:
|
||||
ylims = ylimits(config['kernels'], pad=0.05)
|
||||
xlims = (config['k_times'][0], config['k_times'][-1])
|
||||
for j, inset in enumerate(insets):
|
||||
inset.plot(config['k_times'], config['kernels'][:, j],
|
||||
c='k', lw=lw['kern'])
|
||||
for kern, inset, c in zip(config['kernels'].T, insets, kern_colors_bw):
|
||||
inset.plot(config['k_times'], kern, c=c, lw=lw['kern'])
|
||||
inset.set_xlim(xlims)
|
||||
inset.set_ylim(ylims)
|
||||
time_bar(insets[0], parent=spec_axes[0, 0], **time_bar_kwargs)
|
||||
time_bar(insets[0], parent=feat_axes[0, 0], **kern_bar_kwargs)
|
||||
|
||||
# Plot pure feature space:
|
||||
handle = big_axes[0].scatter(pure_measure[:, 0], pure_measure[:, 1],
|
||||
c=scales, cmap=spec_cmaps[i], **space_kwargs)
|
||||
from matplotlib.colors import LogNorm
|
||||
norm = LogNorm(vmin=scales[scales > 0][0], vmax=scales[-1])
|
||||
handle = space_axes[0].scatter(pure_measure[:, 0], pure_measure[:, 1],
|
||||
c=scales, cmap=scale_cmap, norm=norm,
|
||||
zorder=zorder[species], **space_kwargs)
|
||||
|
||||
# Plot noise feature space:
|
||||
big_axes[1].scatter(noise_measure[:, 0], noise_measure[:, 1],
|
||||
c=scales, cmap=spec_cmaps[i], **space_kwargs)
|
||||
space_axes[1].scatter(noise_measure[:, 0], noise_measure[:, 1],
|
||||
c=scales, cmap=scale_cmap, norm=norm,
|
||||
zorder=zorder[species], **space_kwargs)
|
||||
|
||||
# Indicate scale color code:
|
||||
big_subfig.colorbar(handle, cax=bar_axes[i])
|
||||
bar_axes[i].set_yscale('symlog', linthresh=scales[1], linscale=0.2)
|
||||
if i < len(pure_paths) - 1:
|
||||
space_subfig.colorbar(handle, cax=bar_axes[i])
|
||||
bar_axes[i].set_yscale('symlog', **symlog_kwargs)
|
||||
if i < n_species - 1:
|
||||
hide_ticks(bar_axes[i], 'right', ticks=False)
|
||||
else:
|
||||
ylabel(bar_axes[i], ylabels['bar'], transform=big_subfig.transSubfigure, **ylab_cbar_kwargs)
|
||||
ylabel(bar_axes[i], ylabels['bar'], transform=space_subfig.transSubfigure, **ylab_cbar_kwargs)
|
||||
|
||||
if show_noise:
|
||||
# Indicate feature noise floor:
|
||||
min_feat = min_feat.mean(axis=0)
|
||||
space_axes[-1].add_patch(plt.Rectangle((0, 0), min_feat[0], min_feat[1], **noise_kwargs))
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
@@ -1,19 +1,19 @@
|
||||
import glob
|
||||
import numpy as np
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from thunderhopper.filetools import crop_paths
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
from thunderhopper.filters import decibel, sosfilter
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_paths = glob.glob(f'../data/processed/{target}*.npz')
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, excl='noise', dir='../data/processed/')
|
||||
save_path = '../data/inv/log_hp/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
add_noise = False
|
||||
save_snippets = target == 'Omocestus_rufipes'
|
||||
example_scales = np.array([0.1, 1, 10, 30, 100, 300])
|
||||
scales = np.geomspace(0.1, 10000, 1000)
|
||||
scales = np.geomspace(0.1, 10000, 500)
|
||||
scales = np.unique(np.concatenate((scales, example_scales)))
|
||||
|
||||
# EXECUTION:
|
||||
@@ -60,13 +60,16 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
data = dict(
|
||||
scales=scales,
|
||||
example_scales=example_scales,
|
||||
snip_env=mix[:, save_inds],
|
||||
snip_log=mix_log[:, save_inds],
|
||||
snip_inv=mix_inv[:, save_inds],
|
||||
measure_env=measure_env,
|
||||
measure_log=measure_log,
|
||||
measure_inv=measure_inv,
|
||||
)
|
||||
if save_snippets:
|
||||
data.update(
|
||||
snip_env=mix[:, save_inds],
|
||||
snip_log=mix_log[:, save_inds],
|
||||
snip_inv=mix_inv[:, save_inds],
|
||||
)
|
||||
file_name = save_path + name
|
||||
if add_noise:
|
||||
file_name += '_noise'
|
||||
|
||||
@@ -8,14 +8,14 @@ from thunderhopper.model import convolve_kernels
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = ['Omocestus_rufipes', '*'][0]
|
||||
data_paths = search_files(target, dir='../data/processed/')
|
||||
target = ['Omocestus_rufipes', '*'][1]
|
||||
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
|
||||
save_snippets = add_noise and True
|
||||
save_snippets = add_noise and (target == 'Omocestus_rufipes')
|
||||
plot_results = False
|
||||
example_scales = np.array([0, 1, 10, 30, 100])
|
||||
scales = np.geomspace(0.01, 10000, 100)
|
||||
@@ -50,11 +50,11 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['k_props'] = [config['k_props'][i] for i in kern_inds]
|
||||
|
||||
# Normalize song component:
|
||||
song /= song[segment].std()
|
||||
|
||||
# Get normalized noise component:
|
||||
noise = pure_noise[:song.shape[0]]
|
||||
|
||||
# Normalize both components:
|
||||
song /= song[segment].std()
|
||||
noise /= noise[segment].std()
|
||||
|
||||
# Define kernel-specific threshold values based on pure-noise response SD:
|
||||
|
||||
@@ -9,7 +9,7 @@ from IPython import embed
|
||||
save_path = '../data/processed/white_noise'
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
sds = [1]
|
||||
dur = 10
|
||||
dur = 60
|
||||
|
||||
# Interactivity:
|
||||
reload_saved = False
|
||||
|
||||
39
python/save_species_colors.py
Normal file
39
python/save_species_colors.py
Normal file
@@ -0,0 +1,39 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from color_functions import load_colors, sample_cmap, color_selector
|
||||
from IPython import embed
|
||||
|
||||
|
||||
# Settings:
|
||||
species = [
|
||||
'Omocestus_rufipes',
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Gomphocerippus_rufus',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
file_name = '../data/species_colors.npz'
|
||||
sample_kwargs = dict(
|
||||
cmap='turbo',
|
||||
n=len(species),
|
||||
low=None,
|
||||
high=None,
|
||||
segments=None,
|
||||
)
|
||||
select_kwargs = dict(
|
||||
n=len(species),
|
||||
save=file_name,
|
||||
labels=species,
|
||||
)
|
||||
new_start = True
|
||||
|
||||
|
||||
# Execution:
|
||||
if new_start:
|
||||
colors = sample_cmap(**sample_kwargs)
|
||||
else:
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
colors = color_selector(colors=colors, **select_kwargs)
|
||||
plt.show()
|
||||
embed()
|
||||
@@ -1,5 +1,5 @@
|
||||
import matplotlib.pyplot as plt
|
||||
from color_functions import load_colors, color_selector, hex_to_rgb
|
||||
from color_functions import load_colors, color_selector
|
||||
from IPython import embed
|
||||
|
||||
# Settings:
|
||||
|
||||
Reference in New Issue
Block a user