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:
|
||||
|
||||
Reference in New Issue
Block a user