Finished (:D) fig_invariance_thresh_lp_single.pdf.
Added/modified few plot functions. Cleaned up simulation/plotting scripts regarding Thresh-LP.
This commit is contained in:
j-hartling
@@ -1,73 +1,71 @@
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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 thunderhopper.filtertools import find_kern_specs
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from color_functions import load_colors, shade_colors
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from plot_functions import hide_axis, ylimits, xlabel, ylabel, super_ylabel, super_xlabel,\
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plot_line, plot_barcode, strip_zeros, time_bar,\
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letter_subplot, letter_subplots, title_subplot
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from plot_functions import shift_subplot, hide_axis, ylimits, xlabel, ylabel,\
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super_ylabel, plot_line, plot_barcode, strip_zeros,\
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time_bar, letter_subplot, letter_subplots, title_subplot,\
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set_clip_box
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from IPython import embed
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def add_snip_axes(fig, grid_kwargs, snip_specs):
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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((len(snip_specs), grid.ncols), dtype=object)
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for i, specs in enumerate(snip_specs.values()):
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for j in range(grid.ncols):
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axes[i, j] = fig.add_subplot(grid[specs, j])
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[hide_axis(ax, 'left') for ax in axes[:, 1:].flatten()]
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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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if j == 0:
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shift_subplot(axes[i, j], dx=snip_col_shift)
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[hide_axis(ax, 'left') for ax in axes[:, 2:].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,
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, ypad=0.05,
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thresh=None, fill_kwargs={}, **kwargs):
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=ypad)
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handles = []
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for ax, snippet in zip(axes, snippets.T):
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plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
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handles.append(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
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if thresh is not None:
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ax.fill_between(time, thresh, snippet, where=(snippet > thresh), **fill_kwargs)
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return None
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return handles
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def plot_bi_snippets(axes, time, binary, **kwargs):
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for ax, binary in zip(axes, binary.T):
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plot_barcode(ax, time, binary[:, None], **kwargs)
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return None
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def side_distributions(axes, snippets, inset_bounds, thresh, nbins=50,
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ymin=None, ymax=None, fill_kwargs={}, **kwargs):
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limits = np.array([snippets.min(), snippets.max()]) * 1.05
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def side_distributions(axes, snippets, inset_bounds, thresh, nbins=1000,
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fill_kwargs={}, **kwargs):
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limits = np.array([snippets.min(), snippets.max()]) * 1.1
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edges = np.linspace(*limits, nbins + 1)
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centers = edges[:-1] + (edges[1] - edges[0]) / 2
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insets = []
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for ax, snippet in zip(axes, snippets.T):
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pdf, _ = np.histogram(snippet, edges, density=True)
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inset = ax.inset_axes(inset_bounds)
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inset.plot(pdf, centers, **kwargs)
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inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh),
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**fill_kwargs)
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ylimits(centers, inset, minval=ymin, maxval=ymax, pad=0)
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inset.fill_betweenx(centers, pdf.min(), pdf, where=(centers > thresh), **fill_kwargs)
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inset.set_xlim(0, pdf.max())
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inset.set_ylim(ax.get_ylim())
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inset.axis('off')
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return None
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insets.append(inset)
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return insets
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# GENERAL SETTINGS:
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with_noise = True
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target = 'Omocestus_rufipes'
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search_kwargs = dict(
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incl=['subset', 'noise'] if with_noise else 'subset',
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excl=None if with_noise else 'noise',
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dir='../data/inv/thresh_lp/'
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)
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data_paths = search_files(target, **search_kwargs)
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data_paths = search_files(target, incl='noise', dir='../data/inv/thresh_lp/')
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stages = ['conv', 'bi', 'feat']
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load_kwargs = dict(
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files=stages,
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keywords=['scales', 'snip', 'measure', 'thresh']
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)
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save_path = '../figures/fig_invariance_thresh_lp_single.pdf'
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if with_noise and save_path is not None:
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save_path = save_path.replace('.pdf', '_noise.pdf')
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exclude_zero = True
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# GRAPH SETTINGS:
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fig_kwargs = dict(
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@@ -83,19 +81,34 @@ super_grid_kwargs = dict(
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bottom=0,
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top=1
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)
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input_rows = 1
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snip_rows = 2
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subfig_specs = dict(
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snip=(slice(None), slice(super_grid_kwargs['ncols'] - 1)),
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input=(slice(input_rows), slice(-1)),
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snip=[np.array([input_rows, input_rows + snip_rows]), slice(-1)],
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big=(slice(None), -1),
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)
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snip_col_shift = -0.07
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snip_grid_kwargs = dict(
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nrows=len(stages) + 1,
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nrows=len(stages),
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ncols=None,
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wspace=0.3,
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hspace=0,
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left=0.17,
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left=0.25,
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right=0.93,
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bottom=0.15,
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top=0.9
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top=0.95,
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height_ratios=[2, 1, 1]
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)
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input_grid_kwargs = dict(
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nrows=1,
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ncols=None,
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wspace=snip_grid_kwargs['wspace'],
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hspace=0,
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left=snip_grid_kwargs['left'],
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right=snip_grid_kwargs['right'],
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bottom=0.15,
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top=0.75,
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)
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big_grid_kwargs = dict(
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nrows=1,
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@@ -107,12 +120,7 @@ big_grid_kwargs = dict(
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bottom=0.1,
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top=0.99
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)
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snip_specs = dict(
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conv=slice(0, -2),
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bi=-2,
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feat=-1
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)
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inset_bounds = [1.02, 0, 0.2, 1]
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dist_inset_bounds = [1.02, 0, 0.2, 1]
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# PLOT SETTINGS:
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fs = dict(
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@@ -124,29 +132,24 @@ 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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color_factors = [0.2, -0.2]
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shade_factors = [0.2, -0.2]
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lw = dict(
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conv=1,
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inv=1.5,
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conv=1.5,
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bi=0.1,
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feat=3,
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big=4,
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)
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xlabels = dict(
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snip='time [s]',
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big='scale $\\alpha$',
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)
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ylabels = dict(
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inv='$x_{\\text{adapt}}$',
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conv='$c_i$',
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bi='$b_i$',
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feat='$f_i$',
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big='$\\mu_f$',
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)
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xlab_snip_kwargs = dict(
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y=0,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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)
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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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@@ -157,7 +160,7 @@ ylab_snip_kwargs = dict(
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x=0.08,
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fontsize=fs['lab_tex'],
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rotation=0,
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ha='right',
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ha='center',
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va='center',
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)
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ylab_super_kwargs = dict(
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@@ -168,14 +171,20 @@ ylab_super_kwargs = dict(
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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_norm'],
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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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ypad = dict(
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inv=0.05,
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conv=0.05,
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big=0.075
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)
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yloc = dict(
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conv=100,
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bi=1,
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feat=1,
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inv=(2, 200),
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conv=(0.02, 2),
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bi=(1, 1),
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feat=(1, 1),
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big=0.2,
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)
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title_kwargs = dict(
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@@ -200,7 +209,6 @@ letter_big_kwargs = dict(
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fontsize=fs['letter'],
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)
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dist_kwargs = dict(
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nbins=50,
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c='k',
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lw=1,
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)
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@@ -211,8 +219,8 @@ dist_fill_kwargs = dict(
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bar_time = 0.1
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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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y0=-0.5,
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y1=-0.35,
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xshift=1,
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color='k',
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lw=0,
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@@ -225,7 +233,16 @@ bar_kwargs = dict(
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va='center',
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)
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)
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kernel = np.array([
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leg_kwargs = dict(
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ncols=2,
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loc='center',
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bbox_to_anchor=(0, 0.95, 1, 0.05),
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frameon=False,
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fontsize=fs['tit_norm'],
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handlelength=1.5,
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columnspacing=1,
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)
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kern_specs = np.array([
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[1, 0.008],
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[2, 0.004],
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[3, 0.002],
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@@ -238,96 +255,138 @@ for data_path in data_paths:
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print(f'Processing {data_path}')
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# Load invariance data:
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data, config = load_data(data_path, **load_kwargs)
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t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
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noise_data, config = load_data(data_path, **load_kwargs)
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pure_data, _ = load_data(data_path.replace('noise', 'pure'), **load_kwargs)
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# Unpack shared variables:
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scales = noise_data['scales']
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plot_scales = noise_data['example_scales']
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thresh_rel = noise_data['thresh_rel']
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thresh_abs = noise_data['thresh_abs']
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# Reduce to kernel subset and crop to zoom frame:
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t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
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zoom_abs = zoom_rel * t_full[-1]
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zoom_inds = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
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kern_ind = find_kern_specs(config['k_specs'], kerns=kernel)[0]
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# Reduce to kernel subset and crop time to zoom frame:
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data['snip_conv'] = data['snip_conv'][zoom_inds, kern_ind, ...]
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data['snip_bi'] = data['snip_bi'][zoom_inds, kern_ind, ...]
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data['snip_feat'] = data['snip_feat'][zoom_inds, kern_ind, ...]
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data['measure_feat'] = data['measure_feat'][:, kern_ind, :]
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t_full = np.arange(data['snip_conv'].shape[0]) / config['env_rate']
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kern_ind = find_kern_specs(config['k_specs'], kerns=kern_specs)[0]
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noise_data['snip_inv'] = noise_data['snip_inv'][zoom_inds, :]
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noise_data['snip_conv'] = noise_data['snip_conv'][zoom_inds, kern_ind, :]
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noise_data['snip_bi'] = noise_data['snip_bi'][zoom_inds, kern_ind, :, :]
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noise_data['snip_feat'] = noise_data['snip_feat'][zoom_inds, kern_ind, :, :]
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noise_data['measure_feat'] = noise_data['measure_feat'][:, kern_ind, :]
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pure_data['measure_feat'] = pure_data['measure_feat'][:, kern_ind, :]
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thresh_abs = thresh_abs[:, kern_ind]
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t_full = np.arange(noise_data['snip_conv'].shape[0]) / config['env_rate']
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if exclude_zero:
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# Reduce to nonzero scales:
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nonzero_inds = scales > 0
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scales = scales[nonzero_inds]
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noise_data['measure_feat'] = noise_data['measure_feat'][nonzero_inds, :]
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pure_data['measure_feat'] = pure_data['measure_feat'][nonzero_inds, :]
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# Get threshold-specific colors:
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factors = np.linspace(*color_factors, data['threshs'].size)
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colors = dict(
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factors = np.linspace(*shade_factors, thresh_rel.size)
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shaded = dict(
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conv=shade_colors(colors['conv'], factors),
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bi=shade_colors(colors['bi'], factors),
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feat=shade_colors(colors['feat'], factors),
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)
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# Adjust grid parameters:
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super_grid_kwargs['nrows'] = data['threshs'].size
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snip_grid_kwargs['ncols'] = data['example_scales'].size
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# Adjust grid parameters to loaded data:
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super_grid_kwargs['nrows'] = snip_rows * thresh_rel.size + 1
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input_grid_kwargs['ncols'] = plot_scales.size
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snip_grid_kwargs['ncols'] = plot_scales.size
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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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# Prepare input snippet axes:
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input_subfig = fig.add_subfigure(super_grid[subfig_specs['input']])
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input_axes = add_snip_axes(input_subfig, input_grid_kwargs).ravel()
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input_axes[0].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][0]))
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input_axes[1].yaxis.set_major_locator(plt.MultipleLocator(yloc['inv'][1]))
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ylabel(input_axes[0], ylabels['inv'], transform=input_subfig.transSubfigure, **ylab_snip_kwargs)
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for ax, scale in zip(input_axes, plot_scales):
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title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=input_subfig, **title_kwargs)
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letter_subplot(input_subfig, 'a', **letter_snip_kwargs)
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# Prepare snippet axes:
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snip_axes = {}
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for i in range(data['threshs'].size):
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subfig_specs['snip'] = (i, subfig_specs['snip'][1])
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snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
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axes = add_snip_axes(snip_subfig, snip_grid_kwargs, snip_specs)
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snip_axes[snip_subfig] = axes
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super_ylabel(f'{strip_zeros(100 * data["thresh_perc"][i])}%',
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snip_subfigs, snip_axes = [], []
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for i in range(thresh_rel.size):
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subfig_spec = subfig_specs['snip'].copy()
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subfig_spec[0] = slice(*(subfig_spec[0] + i * snip_rows))
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snip_subfig = fig.add_subfigure(super_grid[*subfig_spec])
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axes = add_snip_axes(snip_subfig, snip_grid_kwargs)
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[hide_axis(ax, 'left') for ax in axes[1:, 1]]
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super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
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snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
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for ax, stage in zip(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=snip_subfig.transSubfigure)
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if i == 0:
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for ax, scale in zip(axes[0, :], data['example_scales']):
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title = f'$\\alpha={strip_zeros(scale)}$'
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title_subplot(ax, title, **title_kwargs, ref=fig)
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elif i == data['threshs'].size - 1:
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for (ax1, ax2), stage in zip(axes[:, :2], stages):
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ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
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ax2.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][1]))
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ylabel(ax1, ylabels[stage], transform=snip_subfig.transSubfigure, **ylab_snip_kwargs)
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# for ax, scale in zip(axes[0, :], plot_scales):
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# title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', ref=snip_subfig, **title_kwargs)
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if i == thresh_rel.size - 1:
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axes[-1, -1].set_xlim(t_full[0], t_full[-1])
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time_bar(axes[-1, -1], **bar_kwargs)
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letter_subplots(snip_axes.keys(), **letter_snip_kwargs)
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snip_subfigs.append(snip_subfig)
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snip_axes.append(axes)
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letter_subplots(snip_subfigs, 'bcd', **letter_snip_kwargs)
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# Prepare analysis axis:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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big_ax = big_subfig.add_subplot(big_grid[0, 0])
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xlabel(big_ax, xlabels['big'], **xlab_big_kwargs,
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transform=big_subfig.transSubfigure)
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ylabel(big_ax, ylabels['big'], **ylab_big_kwargs,
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transform=big_subfig.transSubfigure)
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big_ax.set_xlim(data['scales'].min(), data['scales'].max())
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big_ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
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ylimits(data['measure_feat'], big_ax, minval=0, pad=0.01)
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big_ax.set_xlim(scales[0], scales[-1])
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big_ax.set_xscale('symlog', linthresh=scales[scales > 0][0], linscale=0.5)
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ylimits(noise_data['measure_feat'], big_ax, minval=0, pad=ypad['big'])
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big_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc['big']))
|
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letter_subplot(big_subfig, 'd', **letter_big_kwargs, ref=list(snip_axes.keys())[0])
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xlabel(big_ax, xlabels['big'], transform=big_subfig.transSubfigure, **xlab_big_kwargs)
|
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ylabel(big_ax, ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
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letter_subplot(big_subfig, 'e', **letter_big_kwargs, ref=input_subfig)
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|
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# Plot intensity-adapted snippets:
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plot_snippets(input_axes, t_full, noise_data['snip_inv'],
|
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ypad=ypad['inv'], c=colors['inv'], lw=lw['inv'])
|
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ylimits(noise_data['snip_inv'][:, 0], input_axes[0], pad=ypad['inv'])
|
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|
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# Plot representation snippets per threshold:
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conv_min, conv_max = ylimits(data['snip_conv'], pad=0.02)
|
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for i, (subfig, axes) in enumerate(snip_axes.items()):
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dist_fill_kwargs['color'] = colors['bi'][i]
|
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for i, (subfig, axes) in enumerate(zip(snip_subfigs, snip_axes)):
|
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dist_fill_kwargs['color'] = shaded['bi'][i]
|
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|
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# Plot kernel response snippets:
|
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plot_snippets(axes[0, :], t_full, data['snip_conv'][:, :, i],
|
||||
thresh=data['threshs'][i], ymin=conv_min, ymax=conv_max,
|
||||
fill_kwargs=dist_fill_kwargs, c=colors['conv'][i], lw=lw['conv'])
|
||||
plot_snippets(axes[0, :], t_full, noise_data['snip_conv'], thresh=thresh_abs[i],
|
||||
ypad=ypad['conv'], fill_kwargs=dist_fill_kwargs, c=shaded['conv'][i], lw=lw['conv'])
|
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ylimits(noise_data['snip_conv'][:, 0], axes[0, 0], pad=ypad['conv'])
|
||||
|
||||
# Plot kernel response distributions:
|
||||
side_distributions(axes[0, :1], noise_data['snip_conv'][:, :1], dist_inset_bounds,
|
||||
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
|
||||
side_distributions(axes[0, 1:], noise_data['snip_conv'][:, 1:], dist_inset_bounds,
|
||||
thresh_abs[i], nbins=50, fill_kwargs=dist_fill_kwargs, **dist_kwargs)
|
||||
|
||||
# Plot binary snippets:
|
||||
plot_bi_snippets(axes[1, :], t_full, data['snip_bi'][:, :, i],
|
||||
color=colors['bi'][i], lw=lw['bi'])
|
||||
plot_bi_snippets(axes[1, :], t_full, noise_data['snip_bi'][:, :, i],
|
||||
color=shaded['bi'][i], lw=lw['bi'])
|
||||
|
||||
# Plot feature snippets:
|
||||
plot_snippets(axes[2, :], t_full, data['snip_feat'][:, :, i],
|
||||
ymin=0, ymax=1, c=colors['feat'][i], lw=lw['feat'])
|
||||
handles = plot_snippets(axes[2, :], t_full, noise_data['snip_feat'][:, :, i],
|
||||
ymin=0, ymax=1, c=shaded['feat'][i], lw=lw['feat'])
|
||||
[set_clip_box(h[0], ax, bounds=[[0, -0.05], [1, 1.05]]) for h, ax in zip(handles, axes[2, :])]
|
||||
|
||||
# Plot kernel response distribution:
|
||||
side_distributions(axes[0, :], data['snip_conv'][:, :, i], inset_bounds,
|
||||
data['threshs'][i], ymin=conv_min, ymax=conv_max,
|
||||
fill_kwargs=dist_fill_kwargs, **dist_kwargs)
|
||||
# Plot pure-song analysis results:
|
||||
handles = big_ax.plot(scales, pure_data['measure_feat'], lw=lw['big'], ls='dotted')
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Plot analysis results:
|
||||
handles = big_ax.plot(data['scales'], data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, colors['feat'])]
|
||||
# Plot noise-song analysis results:
|
||||
handles = big_ax.plot(scales, noise_data['measure_feat'], lw=lw['big'])
|
||||
[h.set_color(c) for h, c in zip(handles, shaded['feat'])]
|
||||
|
||||
# Add proxy legend:
|
||||
h1 = big_ax.plot([], [], c='k', lw=lw['big'], label='$\\alpha\\cdot s(t) + \\eta(t)$')[0]
|
||||
h2 = big_ax.plot([], [], c='k', lw=lw['big'], ls='dotted', label='$\\alpha\\cdot s(t)$')[0]
|
||||
big_ax.legend(handles=[h1, h2], **leg_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
Reference in New Issue
Block a user