Finished fig_invariance_full.pdf and fig_invariance_short.pdf.
Some renaming shenanigans.
This commit is contained in:
j-hartling
@@ -6,11 +6,11 @@ from thunderhopper.filetools import search_files
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from thunderhopper.modeltools import load_data
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from thunderhopper.filtertools import find_kern_specs
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from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
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divide_by_zero
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divide_by_zero, x_dist, y_dist
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from color_functions import load_colors
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from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
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plot_line, strip_zeros, time_bar, assign_colors,\
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letter_subplot, letter_subplots
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letter_subplot, letter_subplots, hide_ticks
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from IPython import embed
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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@@ -21,15 +21,14 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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ymin=ymin, ymax=ymax, **kwargs))
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return handles
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def plot_curves(ax, scales, measures, fill_kwargs={}, compress=False, **kwargs):
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if not compress or measures.ndim == 1:
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def plot_curves(ax, scales, measures, **kwargs):
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if measures.ndim == 1:
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handles = ax.plot(scales, measures, **kwargs)
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return handles, measures
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median_measure = np.nanmedian(measures, axis=1)
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spread_measure = np.nanpercentile(measures, [25, 75], axis=1)
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line_handle = ax.plot(scales, median_measure, **kwargs)[0]
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fill_handle = ax.fill_between(scales, *spread_measure, **fill_kwargs)
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return [line_handle, fill_handle], median_measure
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return line_handle, median_measure
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# GENERAL SETTINGS:
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target_species = [
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@@ -56,8 +55,8 @@ save_path = '../figures/fig_invariance_full.pdf'
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# ANALYSIS SETTINGS:
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exclude_zero = True
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compress_kernels = True
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thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
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percentiles = np.array([0, 100])
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scale_subset_kwargs = dict(
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combis=[['measure'], stages],
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)
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@@ -67,9 +66,9 @@ kern_subset_kwargs = dict(
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)
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# SUBSET SETTINGS:
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types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
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types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
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# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
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sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
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sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
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# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
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kernels = None
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reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
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@@ -80,18 +79,19 @@ fig_kwargs = dict(
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)
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super_grid_kwargs = dict(
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nrows=2,
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ncols=1,
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ncols=2,
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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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height_ratios=[3, 2]
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height_ratios=[1, 1]
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)
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subfig_specs = dict(
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snip=(0, 0),
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big=(1, 0),
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snip=(0, slice(None)),
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raw=(1, 0),
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base=(1, 1),
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)
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snip_grid_kwargs = dict(
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nrows=len(stages),
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@@ -100,19 +100,31 @@ snip_grid_kwargs = dict(
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hspace=0.4,
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left=0.13,
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right=0.98,
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bottom=0.08,
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bottom=0.05,
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top=0.95
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)
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big_grid_kwargs = dict(
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nrows=1,
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ncols=3,
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wspace=0.4,
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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=0.13,
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top=0.98
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raw_grid_kwargs = dict(
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nrows=2,
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ncols=1,
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wspace=0,
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hspace=0.15,
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left=0.14,
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right=0.9,
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bottom=0.1,
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top=0.95,
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height_ratios=[0.8, 0.2]
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)
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base_grid_kwargs = dict(
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nrows=4,
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ncols=1,
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wspace=0,
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hspace=0.25,
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left=raw_grid_kwargs['left'],
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right=raw_grid_kwargs['right'],
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bottom=raw_grid_kwargs['bottom'],
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top=raw_grid_kwargs['top'],
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)
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inset_bounds = [1.01, 0, 0.95, 1]
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# PLOT SETTINGS:
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fs = dict(
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@@ -125,8 +137,8 @@ fs = dict(
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)
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stage_colors = load_colors('../data/stage_colors.npz')
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kern_colors = dict(
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conv=load_colors('../data/conv_colors_all.npz'),
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feat=load_colors('../data/feat_colors_all.npz')
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conv=load_colors('../data/conv_colors_subset.npz'),
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feat=load_colors('../data/feat_colors_subset.npz')
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)
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lw = dict(
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filt=0.25,
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@@ -135,9 +147,11 @@ lw = dict(
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inv=0.25,
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conv=0.25,
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feat=1,
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big=3,
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single=3,
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swarm=1,
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plateau=1.5,
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legend=5,
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dist=1
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)
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xlabels = dict(
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big='scale $\\alpha$',
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@@ -149,7 +163,8 @@ ylabels = dict(
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inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
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conv='$c_i$\n$[\\text{dB}]$',
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feat='$f_i$',
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big=['measure', 'rel. measure', 'norm. measure']
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raw=['$m$', '$\\mu_{f_i}$'],
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base=['$m\\,/\\,m_{\\eta}$', '$\\sigma_{c_i}$', '$\\mu_{f_i}$', '$\\text{PDF}_{\\alpha}$']
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)
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xlab_big_kwargs = dict(
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y=0,
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@@ -166,10 +181,10 @@ ylab_snip_kwargs = dict(
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ma='center'
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)
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ylab_big_kwargs = dict(
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x=-0.2,
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x=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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va='top',
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)
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yloc = dict(
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filt=3000,
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@@ -194,17 +209,17 @@ letter_snip_kwargs = dict(
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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x=0,
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xref=0,
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y=1,
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ha='left',
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va='bottom',
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va='center',
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fontsize=fs['letter'],
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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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y0=-0.3,
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y1=-0.15,
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xshift=1,
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color='k',
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lw=0,
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@@ -220,7 +235,7 @@ bar_kwargs = dict(
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leg_kwargs = dict(
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ncols=1,
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loc='upper left',
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bbox_to_anchor=(0.05, 0.5, 0.5, 0.5),
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bbox_to_anchor=(0.025, 0.5, 0.5, 0.5),
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frameon=False,
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prop=dict(
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size=20,
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@@ -240,6 +255,12 @@ leg_labels = dict(
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conv='$c_i$',
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feat='$f_i$'
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)
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dist_line_kwargs = dict(
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lw=lw['dist'],
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)
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dist_fill_kwargs = dict(
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lw=lw['dist'],
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)
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plateau_settings = dict(
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low=0.05,
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high=0.95,
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@@ -313,23 +334,49 @@ for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
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time_bar(snip_axes[-1, -1], **bar_kwargs)
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letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
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# Prepare analysis axes:
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big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
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big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
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big_axes = np.zeros((big_grid.ncols,), dtype=object)
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for i in range(big_grid.ncols):
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ax = big_subfig.add_subplot(big_grid[0, i])
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# Prepare raw analysis axes:
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raw_subfig = fig.add_subfigure(super_grid[subfig_specs['raw']])
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raw_grid = raw_subfig.add_gridspec(**raw_grid_kwargs)
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raw_axes = np.zeros((raw_grid.nrows,), dtype=object)
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for i in range(raw_grid.nrows):
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ax = raw_subfig.add_subplot(raw_grid[i, 0])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
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# if i < (big_grid.ncols - 1):
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# ax.set_ylim(scales[0], scales[-1])
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# else:
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# ax.set_ylim(0, 1)
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big_axes[i] = ax
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super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
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letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
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ylabel(ax, ylabels['raw'][i], transform=raw_subfig.transSubfigure, **ylab_big_kwargs)
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if i == 0:
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ax.set_yscale('symlog', linthresh=0.001, linscale=0.1)
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hide_ticks(ax, 'bottom')
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else:
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transform = raw_subfig.transSubfigure + ax.transAxes.inverted()
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inset_x1 = transform.transform((inset_bounds[2], 0))[0]
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inset_bounds[2] = inset_x1 - inset_bounds[0]
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raw_inset = ax.inset_axes(inset_bounds)
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raw_inset.axis('off')
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raw_axes[i] = ax
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letter_subplots(raw_axes, 'bc', ref=raw_subfig, **letter_big_kwargs)
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# Prepare base analysis axes:
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base_subfig = fig.add_subfigure(super_grid[subfig_specs['base']])
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base_grid = base_subfig.add_gridspec(**base_grid_kwargs)
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base_axes = np.zeros((base_grid.nrows,), dtype=object)
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base_insets = np.zeros((base_grid.nrows - 1,), dtype=object)
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for i in range(base_grid.nrows):
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ax = base_subfig.add_subplot(base_grid[i, 0])
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ax.set_xlim(scales[0], scales[-1])
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ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
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ylabel(ax, ylabels['base'][i], transform=base_subfig.transSubfigure, **ylab_big_kwargs)
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if i < base_grid_kwargs['nrows'] - 1:
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ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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hide_ticks(ax, 'bottom')
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if i in [1, 2]:
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inset = ax.inset_axes(inset_bounds)
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inset.set_yscale('symlog', linthresh=0.01, linscale=0.1)
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inset.axis('off')
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base_insets[i - 1] = inset
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base_axes[i] = ax
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letter_subplots(base_axes, 'defg', ref=base_subfig, **letter_big_kwargs)
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super_xlabel(xlabels['big'], fig, raw_axes[-1], base_axes[-1],
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left_fig=raw_subfig, right_fig=base_subfig, **xlab_big_kwargs)
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if True:
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# Plot filtered snippets:
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@@ -363,84 +410,114 @@ if True:
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reorder_by_sd(handles, data['snip_feat'][..., i])
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# Plot analysis results:
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crit_inds, crit_scales = {}, {}
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crit_inds, crit_scales_single, crit_scales_swarm = {}, {}, {}
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max_pdf = -np.inf
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leg_handles = []
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for stage in stages:
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mkey = f'measure_{stage}'
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measure = data[mkey]
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color = stage_colors[stage]
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fill_kwargs = dict(color=color, alpha=0.25)
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# Plot raw intensity measure curve(s):
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handles, curve = plot_curves(big_axes[0], scales, measure, fill_kwargs,
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compress_kernels, c=color, lw=lw['big'])
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if not compress_kernels and stage in ['conv', 'feat']:
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assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
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## UNNORMALIZED MEASURE:
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# Plot single raw intensity curve (median where necessary):
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handles, curve = plot_curves(raw_axes[0], scales, measure, c=color, lw=lw['single'])
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# Add stage-specific proxy legend artist:
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leg_handles.append(big_axes[0].plot([], [], c=color, lw=lw['big'],
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label=leg_labels[stage])[0])
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leg_handles.append(raw_axes[0].plot([], [], c=color, label=leg_labels[stage])[0])
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# Plot curve swarm:
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if stage == 'feat':
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# Sync y-limits:
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ylimits(measure, raw_axes[1], minval=0, pad=0.05)
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raw_inset.set_ylim(raw_axes[1].get_ylim())
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# Plot swarm:
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handles = raw_axes[1].plot(scales, measure, lw=lw['swarm'])
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assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
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reorder_by_sd(handles, measure)
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# Plot distribution of saturation levels:
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line_kwargs = dist_line_kwargs | dict(c=color)
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fill_kwargs = dist_fill_kwargs | dict(color=color)
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y_dist(raw_inset, measure[-1], nbins=75, log=False,
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line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
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# Indicate saturation point(s):
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if stage in ['log', 'inv', 'conv', 'feat']:
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ind = get_saturation(curve, **plateau_settings)[1]
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crit_inds[stage] = ind
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if compress_kernels or stage in ['log', 'inv']:
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scale = scales[ind]
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crit_scales[stage] = scale
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big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
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transform=big_axes[0].get_xaxis_transform())
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big_axes[0].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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transform=big_axes[0].get_xaxis_transform())
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big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
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color=color, **plateau_line_kwargs)
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scale = scales[ind]
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crit_scales_single[stage] = scale
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raw_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
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transform=raw_axes[0].get_xaxis_transform())
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raw_axes[0].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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transform=raw_axes[0].get_xaxis_transform())
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raw_axes[0].vlines(scale, raw_axes[0].get_ylim()[0], curve[ind],
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color=color, **plateau_line_kwargs)
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## NORMALIZED MEASURE:
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# Relate to noise baseline:
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measure = divide_by_zero(data[mkey], ref_data[stage])
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# Plot baseline-normalized ntensity measure curve(s):
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handles, curve = plot_curves(big_axes[1], scales, measure, fill_kwargs,
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compress_kernels, c=color, lw=lw['big'])
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if not compress_kernels and stage in ['conv', 'feat']:
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# Plot single baseline-normalized intensity curve (median where necessary):
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handles, curve = plot_curves(base_axes[0], scales, measure, c=color, lw=lw['single'])
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# Plot curve swarm:
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if stage in ['conv', 'feat']:
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i0, i1 = (1, 0) if stage == 'conv' else (2, 1)
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# Sync y-limits:
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ylimits(measure, base_axes[i0], minval=0.9, pad=0.05)
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base_insets[i1].set_ylim(base_axes[i0].get_ylim())
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# Plot swarm:
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handles = base_axes[i0].plot(scales, measure, lw=lw['swarm'])
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assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
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reorder_by_sd(handles, measure)
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# Plot distribution of saturation levels:
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line_kwargs = dist_line_kwargs | dict(c=color)
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fill_kwargs = dist_fill_kwargs | dict(color=color)
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y_dist(base_insets[i1], measure[-1], nbins=100, log=True,
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line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
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# Get and log distribution of saturation points:
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inds = np.array(get_saturation(measure, **plateau_settings)[1])
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if np.isnan(inds).sum():
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inds = inds[~np.isnan(inds)].astype(int)
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crit_scales_swarm[stage] = scales[inds]
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if stage == 'feat':
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# Plot distribution of saturation points on shared bins:
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bin_lims = [0.01, 1.1 * max([s.max() for s in crit_scales_swarm.values()])]
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for temp_stage, crit_scales in crit_scales_swarm.items():
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z = 3 if temp_stage == 'conv' else 2
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line_kwargs = dist_line_kwargs | dict(c=stage_colors[temp_stage], zorder=z)
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fill_kwargs = dist_fill_kwargs | dict(color=stage_colors[temp_stage], alpha=0.25, zorder=z)
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pdf = x_dist(base_axes[-1], crit_scales, nbins=75, limits=bin_lims,
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log=True, line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)[0]
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max_pdf = max(max_pdf, pdf.max())
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base_axes[-1].set_ylim(0, max_pdf * 1.05)
|
||||
# Add single curve saturation point:
|
||||
for temp_stage, crit_scale in crit_scales_single.items():
|
||||
base_axes[-1].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=base_axes[-1].get_xaxis_transform())
|
||||
base_axes[-1].plot(crit_scale, 0, mfc=stage_colors[temp_stage], mec='k', alpha=0.75,
|
||||
zorder=6, **plateau_dot_kwargs,
|
||||
transform=base_axes[-1].get_xaxis_transform())
|
||||
|
||||
# Indicate saturation point(s):
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind = crit_inds[stage]
|
||||
scale = crit_scales[stage]
|
||||
if compress_kernels or stage in ['log', 'inv']:
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
if stage in ['filt', 'env']:
|
||||
continue
|
||||
scale = crit_scales_single[stage]
|
||||
base_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=base_axes[0].get_xaxis_transform())
|
||||
base_axes[0].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=base_axes[0].get_xaxis_transform())
|
||||
base_axes[0].vlines(scale, base_axes[0].get_ylim()[0], curve[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
|
||||
# Relate to curve maximum:
|
||||
measure = data[mkey] / np.nanmax(data[mkey], axis=0)
|
||||
|
||||
# Plot max-normalized ntensity measure curve(s):
|
||||
handles, curve = plot_curves(big_axes[2], scales, measure, fill_kwargs,
|
||||
compress_kernels, c=color, lw=lw['big'])
|
||||
if not compress_kernels and stage in ['conv', 'feat']:
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
|
||||
|
||||
# Indicate saturation point(s):
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind = crit_inds[stage]
|
||||
scale = crit_scales[stage]
|
||||
if compress_kernels or stage in ['log', 'inv']:
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
# Posthoc adjustments:
|
||||
raw_axes[0].set_ylim(bottom=0.001)
|
||||
base_axes[0].set_ylim(1, 100)
|
||||
|
||||
# Add legend to first analysis axis:
|
||||
legend = big_axes[0].legend(handles=leg_handles, **leg_kwargs)
|
||||
legend = raw_axes[0].legend(handles=leg_handles, **leg_kwargs)
|
||||
[handle.set_lw(lw['legend']) for handle in legend.get_lines()]
|
||||
|
||||
# Save graph:
|
||||
|
||||
@@ -1,426 +0,0 @@
|
||||
import plotstyle_plt
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from itertools import product
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from misc_functions import get_saturation
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
|
||||
plot_line, strip_zeros, time_bar, assign_colors,\
|
||||
letter_subplot, letter_subplots
|
||||
from IPython import embed
|
||||
|
||||
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
|
||||
handles = []
|
||||
for i, ax in enumerate(axes):
|
||||
handles.append(plot_line(ax, time, snippets[:, ..., i],
|
||||
ymin=ymin, ymax=ymax, **kwargs))
|
||||
return handles
|
||||
|
||||
def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
|
||||
if measures.ndim == 1:
|
||||
ax.plot(scales, measures, **kwargs)[0]
|
||||
return measures
|
||||
median_measure = np.median(measures, axis=1)
|
||||
spread_measure = [np.percentile(measures, 25, axis=1),
|
||||
np.percentile(measures, 75, axis=1)]
|
||||
ax.plot(scales, median_measure, **kwargs)[0]
|
||||
ax.fill_between(scales, *spread_measure, **fill_kwargs)
|
||||
return median_measure
|
||||
|
||||
def exclude_zero_scale(data, stages):
|
||||
inds = data['scales'] > 0
|
||||
data['scales'] = data['scales'][inds]
|
||||
for stage in stages:
|
||||
data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
|
||||
return data
|
||||
|
||||
def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
|
||||
for stage in stages:
|
||||
key = f'{keyword}_{stage}'
|
||||
data[key] = data[key][:, inds, ...]
|
||||
return data
|
||||
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
][5]
|
||||
example_file = {
|
||||
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
|
||||
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
|
||||
'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
|
||||
'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
|
||||
'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
|
||||
'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
|
||||
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
|
||||
}[target_species]
|
||||
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
|
||||
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/full/condensed/')[0]
|
||||
base_path = search_files(target_species, incl='base', dir='../data/inv/full/condensed/')[0]
|
||||
range_path = search_files(target_species, incl='range', dir='../data/inv/full/condensed/')[0]
|
||||
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
|
||||
save_path = '../figures/fig_invariance_full.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
exclude_zero = True
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
|
||||
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
|
||||
kernels = np.array([
|
||||
[1, 0.002],
|
||||
[-1, 0.002],
|
||||
[2, 0.004],
|
||||
[-2, 0.004],
|
||||
[3, 0.032],
|
||||
[-3, 0.032]
|
||||
])
|
||||
kernels = None
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1,
|
||||
height_ratios=[3, 2]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
snip=(0, 0),
|
||||
big=(1, 0),
|
||||
)
|
||||
snip_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.4,
|
||||
left=0.11,
|
||||
right=0.98,
|
||||
bottom=0.08,
|
||||
top=0.95
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.4,
|
||||
hspace=0,
|
||||
left=snip_grid_kwargs['left'],
|
||||
right=snip_grid_kwargs['right'],
|
||||
bottom=0.13,
|
||||
top=0.98
|
||||
)
|
||||
|
||||
# PLOT SETTINGS:
|
||||
fs = dict(
|
||||
lab_norm=16,
|
||||
lab_tex=20,
|
||||
letter=22,
|
||||
tit_norm=16,
|
||||
tit_tex=20,
|
||||
bar=16,
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
conv_colors = load_colors('../data/conv_colors_all.npz')
|
||||
feat_colors = load_colors('../data/feat_colors_all.npz')
|
||||
lw = dict(
|
||||
filt=0.25,
|
||||
env=0.25,
|
||||
log=0.25,
|
||||
inv=0.25,
|
||||
conv=0.25,
|
||||
feat=1,
|
||||
big=3,
|
||||
plateau=1.5,
|
||||
)
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
log='$x_{\\text{db}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
feat='$f_i$',
|
||||
big=['measure', 'rel. measure', 'norm. measure']
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='left',
|
||||
va='center'
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=-0.2,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
yloc = dict(
|
||||
filt=3000,
|
||||
env=1000,
|
||||
log=50,
|
||||
inv=20,
|
||||
conv=1,
|
||||
feat=1,
|
||||
)
|
||||
title_kwargs = dict(
|
||||
x=0.5,
|
||||
yref=1,
|
||||
ha='center',
|
||||
va='top',
|
||||
fontsize=fs['tit_norm'],
|
||||
)
|
||||
letter_snip_kwargs = dict(
|
||||
x=0,
|
||||
yref=0.5,
|
||||
ha='left',
|
||||
va='center',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
letter_big_kwargs = dict(
|
||||
x=0,
|
||||
y=1,
|
||||
ha='left',
|
||||
va='bottom',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
bar_time = 5
|
||||
bar_kwargs = dict(
|
||||
dur=bar_time,
|
||||
y0=-0.25,
|
||||
y1=-0.1,
|
||||
xshift=1,
|
||||
color='k',
|
||||
lw=0,
|
||||
clip_on=False,
|
||||
text_pos=(-0.1, 0.5),
|
||||
text_str=f'${bar_time}\\,\\text{{s}}$',
|
||||
text_kwargs=dict(
|
||||
fontsize=fs['bar'],
|
||||
ha='right',
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
first=True,
|
||||
last=True,
|
||||
condense=None,
|
||||
)
|
||||
plateau_line_kwargs = dict(
|
||||
lw=lw['plateau'],
|
||||
ls='--',
|
||||
zorder=1,
|
||||
)
|
||||
plateau_dot_kwargs = dict(
|
||||
marker='o',
|
||||
markersize=8,
|
||||
markeredgewidth=1,
|
||||
clip_on=False,
|
||||
)
|
||||
|
||||
# EXECUTION:
|
||||
|
||||
# Load raw (unnormed) invariance data:
|
||||
data, config = load_data(raw_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
scales = data['scales']
|
||||
|
||||
# Load snippet data:
|
||||
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
|
||||
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
|
||||
snip_scales = snip['example_scales']
|
||||
|
||||
# Optional kernel subset:
|
||||
reduce_kernels = False
|
||||
if any(var is not None for var in [kernels, types, sigmas]):
|
||||
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
reduce_kernels = True
|
||||
|
||||
# Adjust grid parameters:
|
||||
snip_grid_kwargs['ncols'] = snip_scales.size
|
||||
|
||||
# Prepare overall graph:
|
||||
fig = plt.figure(**fig_kwargs)
|
||||
super_grid = fig.add_gridspec(**super_grid_kwargs)
|
||||
|
||||
# Prepare stage-specific snippet axes:
|
||||
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
|
||||
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
|
||||
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
|
||||
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
|
||||
ax = snip_subfig.add_subplot(snip_grid[i, j])
|
||||
ax.set_xlim(t_full[0], t_full[-1])
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
|
||||
hide_axis(ax, 'bottom')
|
||||
if i == 0:
|
||||
title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
|
||||
ref=snip_subfig, **title_kwargs)
|
||||
if j == 0:
|
||||
ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
|
||||
else:
|
||||
hide_axis(ax, 'left')
|
||||
snip_axes[i, j] = ax
|
||||
time_bar(snip_axes[-1, -1], **bar_kwargs)
|
||||
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
|
||||
|
||||
# Prepare analysis axes:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_axes = np.zeros((big_grid.ncols,), dtype=object)
|
||||
for i in range(big_grid.ncols):
|
||||
ax = big_subfig.add_subplot(big_grid[0, i])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
|
||||
ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
|
||||
if i < (big_grid.ncols - 1):
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
else:
|
||||
ax.set_ylim(0, 1)
|
||||
big_axes[i] = ax
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
|
||||
|
||||
if True:
|
||||
# Plot filtered snippets:
|
||||
plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
|
||||
c=colors['filt'], lw=lw['filt'])
|
||||
|
||||
# Plot envelope snippets:
|
||||
plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
|
||||
ymin=0, c=colors['env'], lw=lw['env'])
|
||||
|
||||
# Plot logarithmic snippets:
|
||||
plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
|
||||
c=colors['log'], lw=lw['log'])
|
||||
|
||||
# Plot invariant snippets:
|
||||
plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
|
||||
c=colors['inv'], lw=lw['inv'])
|
||||
|
||||
# Plot kernel response snippets:
|
||||
all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
|
||||
c=colors['conv'], lw=lw['conv'])
|
||||
for i, handles in enumerate(all_handles):
|
||||
assign_colors(handles, config['k_specs'][:, 0], conv_colors)
|
||||
reorder_by_sd(handles, snip['snip_conv'][..., i])
|
||||
|
||||
# Plot feature snippets:
|
||||
all_handles = plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
|
||||
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
|
||||
for i, handles in enumerate(all_handles):
|
||||
assign_colors(handles, config['k_specs'][:, 0], feat_colors)
|
||||
reorder_by_sd(handles, snip['snip_feat'][..., i])
|
||||
del snip
|
||||
|
||||
# Remember saturation points:
|
||||
crit_inds, crit_scales = {}, {}
|
||||
|
||||
# Unnormed measures:
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind = get_saturation(curve, **plateau_settings)[1]
|
||||
scale = scales[ind]
|
||||
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
# Log saturation point:
|
||||
crit_inds[stage] = ind
|
||||
crit_scales[stage] = scale
|
||||
del data
|
||||
|
||||
# Noise baseline-related measures:
|
||||
data, _ = load_data(base_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[1], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
# Min-max normalized measures:
|
||||
data, _ = load_data(range_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in ['log', 'inv', 'conv', 'feat']:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
|
||||
# Indicate saturation point:
|
||||
if stage in ['log', 'inv', 'conv', 'feat']:
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
# Save graph:
|
||||
if save_path is not None:
|
||||
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
|
||||
fig.savefig(file_name)
|
||||
plt.show()
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
@@ -5,11 +5,12 @@ from itertools import product
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filtertools import find_kern_specs
|
||||
from misc_functions import get_saturation
|
||||
from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
|
||||
divide_by_zero, x_dist, y_dist
|
||||
from color_functions import load_colors
|
||||
from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, title_subplot,\
|
||||
from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
|
||||
plot_line, strip_zeros, time_bar, assign_colors,\
|
||||
letter_subplot, letter_subplots, reorder_by_sd
|
||||
letter_subplot, letter_subplots, hide_ticks
|
||||
from IPython import embed
|
||||
|
||||
def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
@@ -20,29 +21,13 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
|
||||
ymin=ymin, ymax=ymax, **kwargs))
|
||||
return handles
|
||||
|
||||
def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
|
||||
def plot_curves(ax, scales, measures, **kwargs):
|
||||
if measures.ndim == 1:
|
||||
ax.plot(scales, measures, **kwargs)[0]
|
||||
return measures
|
||||
handles = ax.plot(scales, measures, **kwargs)
|
||||
return handles, measures
|
||||
median_measure = np.nanmedian(measures, axis=1)
|
||||
spread_measure = [np.nanpercentile(measures, 25, axis=1),
|
||||
np.nanpercentile(measures, 75, axis=1)]
|
||||
ax.plot(scales, median_measure, **kwargs)[0]
|
||||
ax.fill_between(scales, *spread_measure, **fill_kwargs)
|
||||
return median_measure
|
||||
|
||||
def exclude_zero_scale(data, stages):
|
||||
inds = data['scales'] > 0
|
||||
data['scales'] = data['scales'][inds]
|
||||
for stage in stages:
|
||||
data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
|
||||
return data
|
||||
|
||||
def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
|
||||
for stage in stages:
|
||||
key = f'{keyword}_{stage}'
|
||||
data[key] = data[key][:, inds, ...]
|
||||
return data
|
||||
line_handle = ax.plot(scales, median_measure, **kwargs)[0]
|
||||
return line_handle, median_measure
|
||||
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
@@ -65,29 +50,28 @@ example_file = {
|
||||
'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
|
||||
}[target_species]
|
||||
stages = ['filt', 'env', 'inv', 'conv', 'feat']
|
||||
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/short/condensed/')[0]
|
||||
base_path = search_files(target_species, incl='base', dir='../data/inv/short/condensed/')[0]
|
||||
range_path = search_files(target_species, incl='range', dir='../data/inv/short/condensed/')[0]
|
||||
snip_path = search_files(example_file, dir='../data/inv/short/')[0]
|
||||
data_path = search_files(example_file, dir='../data/inv/short/')[0]
|
||||
save_path = '../figures/fig_invariance_short.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
exclude_zero = True
|
||||
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
|
||||
percentiles = np.array([0, 100])
|
||||
scale_subset_kwargs = dict(
|
||||
combis=[['measure'], stages],
|
||||
)
|
||||
kern_subset_kwargs = dict(
|
||||
combis=[['measure', 'snip'], ['conv', 'feat']],
|
||||
keys=['thresh_abs'],
|
||||
)
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
|
||||
types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
|
||||
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
|
||||
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
|
||||
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
|
||||
kernels = np.array([
|
||||
[1, 0.002],
|
||||
[-1, 0.002],
|
||||
[2, 0.004],
|
||||
[-2, 0.004],
|
||||
[3, 0.032],
|
||||
[-3, 0.032]
|
||||
])
|
||||
kernels = None
|
||||
reduce_kernels = any(var is not None for var in [kernels, types, sigmas])
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
@@ -95,39 +79,52 @@ fig_kwargs = dict(
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
ncols=2,
|
||||
wspace=0,
|
||||
hspace=0,
|
||||
left=0,
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1,
|
||||
height_ratios=[3, 2]
|
||||
height_ratios=[1, 1]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
snip=(0, 0),
|
||||
big=(1, 0),
|
||||
snip=(0, slice(None)),
|
||||
raw=(1, 0),
|
||||
base=(1, 1),
|
||||
)
|
||||
snip_grid_kwargs = dict(
|
||||
nrows=len(stages),
|
||||
ncols=None,
|
||||
wspace=0.1,
|
||||
hspace=0.4,
|
||||
left=0.11,
|
||||
left=0.13,
|
||||
right=0.98,
|
||||
bottom=0.08,
|
||||
bottom=0.05,
|
||||
top=0.95
|
||||
)
|
||||
big_grid_kwargs = dict(
|
||||
nrows=1,
|
||||
ncols=3,
|
||||
wspace=0.4,
|
||||
hspace=0,
|
||||
left=snip_grid_kwargs['left'],
|
||||
right=snip_grid_kwargs['right'],
|
||||
bottom=0.13,
|
||||
top=0.98
|
||||
raw_grid_kwargs = dict(
|
||||
nrows=2,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.15,
|
||||
left=0.14,
|
||||
right=0.9,
|
||||
bottom=0.1,
|
||||
top=0.95,
|
||||
height_ratios=[0.8, 0.2]
|
||||
)
|
||||
base_grid_kwargs = dict(
|
||||
nrows=3,
|
||||
ncols=1,
|
||||
wspace=0,
|
||||
hspace=0.25,
|
||||
left=raw_grid_kwargs['left'],
|
||||
right=raw_grid_kwargs['right'],
|
||||
bottom=raw_grid_kwargs['bottom'],
|
||||
top=raw_grid_kwargs['top'],
|
||||
)
|
||||
inset_bounds = [1.01, 0, 0.95, 1]
|
||||
|
||||
# PLOT SETTINGS:
|
||||
fs = dict(
|
||||
@@ -138,17 +135,22 @@ fs = dict(
|
||||
tit_tex=20,
|
||||
bar=16,
|
||||
)
|
||||
colors = load_colors('../data/stage_colors.npz')
|
||||
conv_colors = load_colors('../data/conv_colors_all.npz')
|
||||
feat_colors = load_colors('../data/feat_colors_all.npz')
|
||||
stage_colors = load_colors('../data/stage_colors.npz')
|
||||
kern_colors = dict(
|
||||
conv=load_colors('../data/conv_colors_subset.npz'),
|
||||
feat=load_colors('../data/feat_colors_subset.npz')
|
||||
)
|
||||
lw = dict(
|
||||
filt=0.25,
|
||||
env=0.25,
|
||||
conv=0.25,
|
||||
inv=0.25,
|
||||
conv=0.25,
|
||||
feat=1,
|
||||
big=3,
|
||||
single=3,
|
||||
swarm=1,
|
||||
plateau=1.5,
|
||||
legend=5,
|
||||
dist=1
|
||||
)
|
||||
xlabels = dict(
|
||||
big='scale $\\alpha$',
|
||||
@@ -159,7 +161,8 @@ ylabels = dict(
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
feat='$f_i$',
|
||||
big=['measure', 'rel. measure', 'norm. measure']
|
||||
raw=['$m$', '$\\mu_{f_i}$'],
|
||||
base=['$m\\,/\\,m_{\\eta}$', '$\\mu_{f_i}$', '$\\text{PDF}_{\\alpha}$']
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
@@ -168,23 +171,23 @@ xlab_big_kwargs = dict(
|
||||
va='bottom',
|
||||
)
|
||||
ylab_snip_kwargs = dict(
|
||||
x=0,
|
||||
x=0.03,
|
||||
fontsize=fs['lab_tex'],
|
||||
rotation=0,
|
||||
ha='left',
|
||||
va='center'
|
||||
ha='center',
|
||||
va='center',
|
||||
)
|
||||
ylab_big_kwargs = dict(
|
||||
x=-0.2,
|
||||
x=0,
|
||||
fontsize=fs['lab_norm'],
|
||||
ha='center',
|
||||
va='bottom',
|
||||
va='top',
|
||||
)
|
||||
yloc = dict(
|
||||
filt=3000,
|
||||
env=1000,
|
||||
inv=1000,
|
||||
conv=30,
|
||||
inv=500,
|
||||
conv=10,
|
||||
feat=1,
|
||||
)
|
||||
title_kwargs = dict(
|
||||
@@ -202,17 +205,17 @@ letter_snip_kwargs = dict(
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
letter_big_kwargs = dict(
|
||||
x=0,
|
||||
xref=0,
|
||||
y=1,
|
||||
ha='left',
|
||||
va='bottom',
|
||||
va='center',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
bar_time = 5
|
||||
bar_kwargs = dict(
|
||||
dur=bar_time,
|
||||
y0=-0.25,
|
||||
y1=-0.1,
|
||||
y0=-0.3,
|
||||
y1=-0.15,
|
||||
xshift=1,
|
||||
color='k',
|
||||
lw=0,
|
||||
@@ -225,6 +228,34 @@ bar_kwargs = dict(
|
||||
va='center',
|
||||
)
|
||||
)
|
||||
leg_kwargs = dict(
|
||||
ncols=1,
|
||||
loc='upper left',
|
||||
bbox_to_anchor=(0.025, 0.5, 0.5, 0.5),
|
||||
frameon=False,
|
||||
prop=dict(
|
||||
size=20,
|
||||
),
|
||||
borderpad=0,
|
||||
borderaxespad=0,
|
||||
handlelength=1,
|
||||
columnspacing=1,
|
||||
handletextpad=0.5,
|
||||
labelspacing=0.1
|
||||
)
|
||||
leg_labels = dict(
|
||||
filt='$x_{\\text{filt}}$',
|
||||
env='$x_{\\text{env}}$',
|
||||
inv='$x_{\\text{adapt}}$',
|
||||
conv='$c_i$',
|
||||
feat='$f_i$'
|
||||
)
|
||||
dist_line_kwargs = dict(
|
||||
lw=lw['dist'],
|
||||
)
|
||||
dist_fill_kwargs = dict(
|
||||
lw=lw['dist'],
|
||||
)
|
||||
plateau_settings = dict(
|
||||
low=0.05,
|
||||
high=0.95,
|
||||
@@ -246,24 +277,30 @@ plateau_dot_kwargs = dict(
|
||||
|
||||
# EXECUTION:
|
||||
|
||||
# Load raw (unnormed) invariance data:
|
||||
data, config = load_data(raw_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
scales = data['scales']
|
||||
# Load invariance data:
|
||||
data, config = load_data(data_path, keywords=['snip', 'scales', 'measure', 'thresh'])
|
||||
t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
|
||||
|
||||
# Load snippet data:
|
||||
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
|
||||
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
|
||||
snip_scales = snip['example_scales']
|
||||
|
||||
# Optional kernel subset:
|
||||
reduce_kernels = False
|
||||
if any(var is not None for var in [kernels, types, sigmas]):
|
||||
# Reduce kernels:
|
||||
if reduce_kernels:
|
||||
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
|
||||
reduce_kernels = True
|
||||
data = reduce_kernel_set(data, kern_inds, **kern_subset_kwargs)
|
||||
config['k_specs'] = config['k_specs'][kern_inds, :]
|
||||
config['kernels'] = config['kernels'][:, kern_inds]
|
||||
|
||||
# Reduce thresholds:
|
||||
thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
|
||||
data['measure_feat'] = data['measure_feat'][:, :, thresh_ind]
|
||||
data['snip_feat'] = data['snip_feat'][:, :, :, thresh_ind]
|
||||
|
||||
# Remember pure-noise reference measures:
|
||||
ref_data = {stage: data[f'measure_{stage}'][0, ...] for stage in stages}
|
||||
|
||||
# Reduce scales:
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, **scale_subset_kwargs)
|
||||
scales = data['scales']
|
||||
snip_scales = data['example_scales']
|
||||
|
||||
# Adjust grid parameters:
|
||||
snip_grid_kwargs['ncols'] = snip_scales.size
|
||||
@@ -292,120 +329,170 @@ for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
|
||||
time_bar(snip_axes[-1, -1], **bar_kwargs)
|
||||
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)
|
||||
|
||||
# Prepare analysis axes:
|
||||
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
|
||||
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
|
||||
big_axes = np.zeros((big_grid.ncols,), dtype=object)
|
||||
for i in range(big_grid.ncols):
|
||||
ax = big_subfig.add_subplot(big_grid[0, i])
|
||||
# Prepare raw analysis axes:
|
||||
raw_subfig = fig.add_subfigure(super_grid[subfig_specs['raw']])
|
||||
raw_grid = raw_subfig.add_gridspec(**raw_grid_kwargs)
|
||||
raw_axes = np.zeros((raw_grid.nrows,), dtype=object)
|
||||
for i in range(raw_grid.nrows):
|
||||
ax = raw_subfig.add_subplot(raw_grid[i, 0])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
|
||||
ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
|
||||
if i < (big_grid.ncols - 1):
|
||||
ax.set_ylim(scales[0], scales[-1])
|
||||
ylabel(ax, ylabels['raw'][i], transform=raw_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
if i == 0:
|
||||
ax.set_yscale('symlog', linthresh=0.0001, linscale=0.1)
|
||||
hide_ticks(ax, 'bottom')
|
||||
else:
|
||||
ax.set_ylim(0, 1)
|
||||
big_axes[i] = ax
|
||||
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
|
||||
letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
|
||||
transform = raw_subfig.transSubfigure + ax.transAxes.inverted()
|
||||
inset_x1 = transform.transform((inset_bounds[2], 0))[0]
|
||||
inset_bounds[2] = inset_x1 - inset_bounds[0]
|
||||
raw_inset = ax.inset_axes(inset_bounds)
|
||||
raw_inset.axis('off')
|
||||
raw_axes[i] = ax
|
||||
letter_subplots(raw_axes, 'bc', ref=raw_subfig, **letter_big_kwargs)
|
||||
|
||||
# Prepare base analysis axes:
|
||||
base_subfig = fig.add_subfigure(super_grid[subfig_specs['base']])
|
||||
base_grid = base_subfig.add_gridspec(**base_grid_kwargs)
|
||||
base_axes = np.zeros((base_grid.nrows,), dtype=object)
|
||||
for i in range(base_grid.nrows):
|
||||
ax = base_subfig.add_subplot(base_grid[i, 0])
|
||||
ax.set_xlim(scales[0], scales[-1])
|
||||
ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
|
||||
ylabel(ax, ylabels['base'][i], transform=base_subfig.transSubfigure, **ylab_big_kwargs)
|
||||
if i < base_grid_kwargs['nrows'] - 1:
|
||||
ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
|
||||
hide_ticks(ax, 'bottom')
|
||||
if i == 1:
|
||||
base_inset = ax.inset_axes(inset_bounds)
|
||||
base_inset.set_yscale('symlog', linthresh=0.01, linscale=0.1)
|
||||
base_inset.axis('off')
|
||||
base_axes[i] = ax
|
||||
letter_subplots(base_axes, 'def', ref=base_subfig, **letter_big_kwargs)
|
||||
super_xlabel(xlabels['big'], fig, raw_axes[-1], base_axes[-1],
|
||||
left_fig=raw_subfig, right_fig=base_subfig, **xlab_big_kwargs)
|
||||
|
||||
if True:
|
||||
# Plot filtered snippets:
|
||||
plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
|
||||
c=colors['filt'], lw=lw['filt'])
|
||||
plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
|
||||
c=stage_colors['filt'], lw=lw['filt'])
|
||||
|
||||
# Plot envelope snippets:
|
||||
plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
|
||||
ymin=0, c=colors['env'], lw=lw['env'])
|
||||
plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
|
||||
ymin=0, c=stage_colors['env'], lw=lw['env'])
|
||||
|
||||
# Plot "adapted" snippets:
|
||||
plot_snippets(snip_axes[2, :], t_full, snip['snip_inv'],
|
||||
c=colors['inv'], lw=lw['inv'])
|
||||
# Plot invariant snippets:
|
||||
plot_snippets(snip_axes[2, :], t_full, data['snip_inv'],
|
||||
c=stage_colors['inv'], lw=lw['inv'])
|
||||
|
||||
# Plot kernel response snippets:
|
||||
all_handles = plot_snippets(snip_axes[3, :], t_full, snip['snip_conv'],
|
||||
c=colors['conv'], lw=lw['conv'])
|
||||
all_handles = plot_snippets(snip_axes[3, :], t_full, data['snip_conv'],
|
||||
c=stage_colors['conv'], lw=lw['conv'])
|
||||
for i, handles in enumerate(all_handles):
|
||||
assign_colors(handles, config['k_specs'][:, 0], conv_colors)
|
||||
reorder_by_sd(handles, snip['snip_conv'][..., i])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv'])
|
||||
reorder_by_sd(handles, data['snip_conv'][..., i])
|
||||
|
||||
# Plot feature snippets:
|
||||
all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_feat'],
|
||||
ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
|
||||
all_handles = plot_snippets(snip_axes[4, :], t_full, data['snip_feat'],
|
||||
ymin=0, ymax=1, c=stage_colors['feat'], lw=lw['feat'])
|
||||
for i, handles in enumerate(all_handles):
|
||||
assign_colors(handles, config['k_specs'][:, 0], feat_colors)
|
||||
reorder_by_sd(handles, snip['snip_feat'][..., i])
|
||||
del snip
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat'])
|
||||
reorder_by_sd(handles, data['snip_feat'][..., i])
|
||||
|
||||
# Remember saturation points:
|
||||
crit_inds, crit_scales = {}, {}
|
||||
|
||||
# Unnormed measures:
|
||||
# Plot analysis results:
|
||||
leg_handles = []
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
mkey = f'measure_{stage}'
|
||||
measure = data[mkey]
|
||||
color = stage_colors[stage]
|
||||
|
||||
## UNNORMALIZED MEASURE:
|
||||
|
||||
# Plot single raw intensity curve (median where necessary):
|
||||
handles, curve = plot_curves(raw_axes[0], scales, measure, c=color, lw=lw['single'])
|
||||
|
||||
# Add stage-specific proxy legend artist:
|
||||
leg_handles.append(raw_axes[0].plot([], [], c=color, label=leg_labels[stage])[0])
|
||||
|
||||
# Plot curve swarm:
|
||||
if stage == 'feat':
|
||||
# Sync y-limits:
|
||||
ylimits(measure, raw_axes[1], minval=0, pad=0.05)
|
||||
raw_inset.set_ylim(raw_axes[1].get_ylim())
|
||||
# Plot swarm:
|
||||
handles = raw_axes[1].plot(scales, measure, lw=lw['swarm'])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
|
||||
reorder_by_sd(handles, measure)
|
||||
# Plot distribution of saturation levels:
|
||||
line_kwargs = dist_line_kwargs | dict(c=color)
|
||||
fill_kwargs = dist_fill_kwargs | dict(color=color)
|
||||
y_dist(raw_inset, measure[-1], nbins=75, log=False,
|
||||
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
|
||||
|
||||
# Indicate saturation point:
|
||||
if stage == 'feat':
|
||||
ind = get_saturation(curve, **plateau_settings)[1]
|
||||
scale = scales[ind]
|
||||
big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[0].get_xaxis_transform())
|
||||
big_axes[0].vlines(scale, big_axes[0].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
# Log saturation point:
|
||||
crit_inds[stage] = ind
|
||||
crit_scales[stage] = scale
|
||||
del data
|
||||
crit_ind = get_saturation(curve, **plateau_settings)[1]
|
||||
crit_scale = scales[crit_ind]
|
||||
raw_axes[0].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=raw_axes[0].get_xaxis_transform())
|
||||
raw_axes[0].plot(crit_scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=raw_axes[0].get_xaxis_transform())
|
||||
raw_axes[0].vlines(crit_scale, raw_axes[0].get_ylim()[0], curve[crit_ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
|
||||
# Noise baseline-related measures:
|
||||
data, _ = load_data(base_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in stages:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[1], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Indicate saturation point:
|
||||
## NORMALIZED MEASURE:
|
||||
|
||||
# Relate to noise baseline:
|
||||
measure = divide_by_zero(data[mkey], ref_data[stage])
|
||||
|
||||
# Plot single baseline-normalized intensity curve (median where necessary):
|
||||
handles, curve = plot_curves(base_axes[0], scales, measure, c=color, lw=lw['single'])
|
||||
|
||||
# Plot curve swarm:
|
||||
if stage == 'feat':
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[1].get_xaxis_transform())
|
||||
big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
|
||||
# Min-max normalized measures:
|
||||
data, _ = load_data(range_path, files='scales', keywords='mean')
|
||||
if exclude_zero:
|
||||
data = exclude_zero_scale(data, stages)
|
||||
if reduce_kernels:
|
||||
data = reduce_kernel_set(data, kern_inds, keyword='mean')
|
||||
for stage in ['feat']:
|
||||
# Plot average intensity measure across recordings:
|
||||
curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
|
||||
c=colors[stage], lw=lw['big'],
|
||||
fill_kwargs=dict(color=colors[stage], alpha=0.25))
|
||||
# Sync y-limits:
|
||||
ylimits(measure, base_axes[1], minval=0.9, pad=0.05)
|
||||
base_inset.set_ylim(base_axes[1].get_ylim())
|
||||
# Plot swarm:
|
||||
handles = base_axes[1].plot(scales, measure, lw=lw['swarm'])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
|
||||
reorder_by_sd(handles, measure)
|
||||
# Plot distribution of saturation levels:
|
||||
line_kwargs = dist_line_kwargs | dict(c=color)
|
||||
fill_kwargs = dist_fill_kwargs | dict(color=color)
|
||||
y_dist(base_inset, measure[-1], nbins=100, log=True,
|
||||
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
|
||||
# Plot distribution of saturation points:
|
||||
inds = np.array(get_saturation(measure, **plateau_settings)[1])
|
||||
if np.isnan(inds).sum():
|
||||
inds = inds[~np.isnan(inds)].astype(int)
|
||||
crit_scales = scales[inds]
|
||||
bin_lims = [0.01, 1.1 * crit_scales.max()]
|
||||
line_kwargs = dist_line_kwargs | dict(c=stage_colors['feat'])
|
||||
fill_kwargs = dist_fill_kwargs | dict(color=stage_colors['feat'])
|
||||
x_dist(base_axes[-1], crit_scales, nbins=75, limits=bin_lims, log=True,
|
||||
line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)
|
||||
# Add single curve saturation point:
|
||||
base_axes[-1].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=base_axes[-1].get_xaxis_transform())
|
||||
base_axes[-1].plot(crit_scale, 0, mfc=stage_colors['feat'], mec='k', alpha=0.75,
|
||||
zorder=6, **plateau_dot_kwargs, transform=base_axes[-1].get_xaxis_transform())
|
||||
|
||||
# Indicate saturation point:
|
||||
if stage == 'feat':
|
||||
ind, scale = crit_inds[stage], crit_scales[stage]
|
||||
big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].plot(scale, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=big_axes[2].get_xaxis_transform())
|
||||
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
|
||||
color=colors[stage], **plateau_line_kwargs)
|
||||
del data
|
||||
base_axes[0].plot(crit_scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
|
||||
transform=base_axes[0].get_xaxis_transform())
|
||||
base_axes[0].plot(crit_scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
|
||||
transform=base_axes[0].get_xaxis_transform())
|
||||
base_axes[0].vlines(crit_scale, base_axes[0].get_ylim()[0], curve[crit_ind],
|
||||
color=color, **plateau_line_kwargs)
|
||||
|
||||
# Posthoc adjustments:
|
||||
raw_axes[0].set_ylim(bottom=0.0001)
|
||||
base_axes[0].set_ylim(1, 100)
|
||||
|
||||
# Add legend to first analysis axis:
|
||||
legend = raw_axes[0].legend(handles=leg_handles, **leg_kwargs)
|
||||
[handle.set_lw(lw['legend']) for handle in legend.get_lines()]
|
||||
|
||||
# Save graph:
|
||||
if save_path is not None:
|
||||
|
||||
@@ -153,10 +153,10 @@ xlabels = dict(
|
||||
)
|
||||
ylabels = dict(
|
||||
inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
bi='$b_i$',
|
||||
feat='$f_i$',
|
||||
big='$\\mu_{f_i}$',
|
||||
conv='$c$\n$[\\text{dB}]$',
|
||||
bi='$b$',
|
||||
feat='$f$',
|
||||
big='$\\mu_f$',
|
||||
)
|
||||
xlab_alpha_kwargs = dict(
|
||||
y=0.5,
|
||||
@@ -366,7 +366,7 @@ for i in range(thresh_rel.size):
|
||||
low_box = axes[-1, 0].get_position()
|
||||
high_box = axes[0, 0].get_position()
|
||||
[hide_axis(ax, 'left') for ax in axes[1:, 1]]
|
||||
super_ylabel(f'$\\Theta_i={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
|
||||
super_ylabel(f'$\\Theta={strip_zeros(thresh_rel[i])}\\cdot\\sigma_{{\\eta}}$',
|
||||
snip_subfig, axes[-1, 0], axes[0, 0], **ylab_super_kwargs)
|
||||
for (ax1, ax2), stage in zip(axes[:, :2], stages):
|
||||
ax1.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage][0]))
|
||||
|
||||
@@ -112,6 +112,46 @@ def get_thresholds(data=None, path=None, perc=None, factor=None,
|
||||
factors = data['factors'][inds]
|
||||
return data['sds'] * factors, factors, data['percs'][inds, :]
|
||||
|
||||
def y_dist(ax, values, nbins=50, limits=None, log=False, cap=0.01, density=True,
|
||||
line_kwargs={}, fill_kwargs={}):
|
||||
# Get distribution:
|
||||
if limits is None:
|
||||
limits = np.array([np.nanmin(values), np.nanmax(values)])
|
||||
limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
|
||||
if log:
|
||||
limits[0] = max(limits[0], cap)
|
||||
edges = np.geomspace(*limits, nbins + 1)
|
||||
else:
|
||||
edges = np.linspace(*limits, nbins + 1)
|
||||
centers = edges[:-1] + np.diff(edges) / 2
|
||||
pdf, _ = np.histogram(values, bins=edges, density=density)
|
||||
|
||||
# Plot distribution:
|
||||
fill_handle = ax.fill_betweenx(centers, pdf.min(), pdf, **fill_kwargs)
|
||||
line_handle = ax.plot(pdf, centers, **line_kwargs)[0]
|
||||
ax.set_xlim(0, pdf.max() * 1.05)
|
||||
return pdf, centers, line_handle, fill_handle
|
||||
|
||||
def x_dist(ax, values, nbins=50, limits=None, log=False, cap=0.01, density=True,
|
||||
line_kwargs={}, fill_kwargs={}):
|
||||
# Get distribution:
|
||||
if limits is None:
|
||||
limits = np.array([np.nanmin(values), np.nanmax(values)])
|
||||
limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
|
||||
if log:
|
||||
limits[0] = max(limits[0], cap)
|
||||
edges = np.geomspace(*limits, nbins + 1)
|
||||
else:
|
||||
edges = np.linspace(*limits, nbins + 1)
|
||||
centers = edges[:-1] + np.diff(edges) / 2
|
||||
pdf, _ = np.histogram(values, bins=edges, density=density)
|
||||
|
||||
# Plot distribution:
|
||||
fill_handle = ax.fill_between(centers, pdf.min(), pdf, **fill_kwargs)
|
||||
line_handle = ax.plot(centers, pdf, **line_kwargs)[0]
|
||||
ax.set_ylim(0, pdf.max() * 1.05)
|
||||
return pdf, centers,line_handle, fill_handle
|
||||
|
||||
def get_histogram(data, edges=None, nbins=50, pad=0.1, shared=True):
|
||||
if edges is None:
|
||||
axis = None if shared else 0
|
||||
@@ -142,12 +182,15 @@ def get_kde(data, sigma, axis=None, n=1000, pad=10):
|
||||
|
||||
def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
|
||||
condense=None):
|
||||
|
||||
unpack_inds = lambda inds: np.nan if inds.size == 0 else inds[-1]
|
||||
|
||||
if condense == 'norm' and sigmoid.ndim == 2:
|
||||
sigmoid = np.linalg.norm(sigmoid, axis=1)
|
||||
|
||||
min_value = sigmoid[0] if first else sigmoid.min(axis=0)
|
||||
max_value = sigmoid[-1] if last else sigmoid.max(axis=0)
|
||||
|
||||
min_value = sigmoid[0] if first else np.nanmin(sigmoid, axis=0)
|
||||
max_value = sigmoid[-1] if last else np.nanmax(sigmoid, axis=0)
|
||||
|
||||
span = max_value - min_value
|
||||
low_value = min_value + low * span
|
||||
high_value = min_value + high * span
|
||||
@@ -155,14 +198,14 @@ def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
|
||||
low_mask = sigmoid <= low_value
|
||||
high_mask = sigmoid <= high_value
|
||||
if sigmoid.ndim == 1:
|
||||
low_ind = np.nonzero(low_mask)[0][-1]
|
||||
high_ind = np.nonzero(high_mask)[0][-1]
|
||||
low_ind = unpack_inds(np.nonzero(low_mask)[0])
|
||||
high_ind = unpack_inds(np.nonzero(high_mask)[0])
|
||||
elif condense == 'all':
|
||||
low_ind = np.nonzero(low_mask.all(axis=1))[0][-1]
|
||||
high_ind = np.nonzero(high_mask.all(axis=1))[0][-1]
|
||||
low_ind = unpack_inds(np.nonzero(low_mask.all(axis=1))[0])
|
||||
high_ind = unpack_inds(np.nonzero(high_mask.all(axis=1))[0])
|
||||
else:
|
||||
low_ind, high_ind = [], []
|
||||
for i in range(sigmoid.shape[1]):
|
||||
low_ind.append(np.nonzero(low_mask[:, i])[0][-1])
|
||||
high_ind.append(np.nonzero(high_mask[:, i])[0][-1])
|
||||
low_ind.append(unpack_inds(np.nonzero(low_mask[:, i])[0]))
|
||||
high_ind.append(unpack_inds(np.nonzero(high_mask[:, i])[0]))
|
||||
return low_ind, high_ind
|
||||
|
||||
@@ -16,7 +16,7 @@ target_species = [
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
][6]
|
||||
][4]
|
||||
example_file = {
|
||||
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
|
||||
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
|
||||
|
||||
Reference in New Issue
Block a user