454 lines
14 KiB
Python
454 lines
14 KiB
Python
import plotstyle_plt
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import numpy as np
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import matplotlib.pyplot as plt
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from itertools import product
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from thunderhopper.filetools import search_files
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from thunderhopper.modeltools import load_data
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from thunderhopper.filtertools import find_kern_specs
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from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
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divide_by_zero
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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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from IPython import embed
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def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
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ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
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handles = []
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for i, ax in enumerate(axes):
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handles.append(plot_line(ax, time, snippets[:, ..., i],
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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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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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# GENERAL SETTINGS:
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target_species = [
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'Chorthippus_biguttulus',
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'Chorthippus_mollis',
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'Chrysochraon_dispar',
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'Euchorthippus_declivus',
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'Gomphocerippus_rufus',
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'Omocestus_rufipes',
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'Pseudochorthippus_parallelus',
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][5]
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example_file = {
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'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
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'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
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'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
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'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
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'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
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'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
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'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
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}[target_species]
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stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
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data_path = search_files(example_file, dir='../data/inv/full/')[0]
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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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scale_subset_kwargs = dict(
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combis=[['measure'], stages],
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)
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kern_subset_kwargs = dict(
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combis=[['measure', 'snip'], ['conv', 'feat']],
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keys=['thresh_abs'],
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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 = [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 = [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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# GRAPH SETTINGS:
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fig_kwargs = dict(
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figsize=(32/2.54, 32/2.54),
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)
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super_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,
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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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)
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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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)
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snip_grid_kwargs = dict(
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nrows=len(stages),
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ncols=None,
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wspace=0.1,
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hspace=0.4,
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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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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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)
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# PLOT SETTINGS:
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fs = dict(
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lab_norm=16,
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lab_tex=20,
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letter=22,
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tit_norm=16,
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tit_tex=20,
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bar=16,
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)
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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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)
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lw = dict(
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filt=0.25,
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env=0.25,
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log=0.25,
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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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plateau=1.5,
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legend=5,
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)
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xlabels = dict(
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big='scale $\\alpha$',
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)
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ylabels = dict(
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filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
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env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
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log='$x_{\\text{log}}$\n$[\\text{dB}]$',
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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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)
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xlab_big_kwargs = dict(
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y=0,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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)
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ylab_snip_kwargs = dict(
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x=0.03,
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fontsize=fs['lab_tex'],
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rotation=0,
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ha='center',
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va='center',
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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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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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)
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yloc = dict(
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filt=3000,
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env=1000,
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log=50,
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inv=20,
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conv=1,
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feat=1,
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)
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title_kwargs = dict(
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x=0.5,
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yref=1,
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ha='center',
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va='top',
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fontsize=fs['tit_norm'],
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)
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letter_snip_kwargs = dict(
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x=0,
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yref=0.5,
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ha='left',
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va='center',
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fontsize=fs['letter'],
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)
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letter_big_kwargs = dict(
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x=0,
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y=1,
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ha='left',
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va='bottom',
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fontsize=fs['letter'],
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)
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bar_time = 5
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bar_kwargs = dict(
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dur=bar_time,
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y0=-0.25,
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y1=-0.1,
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xshift=1,
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color='k',
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lw=0,
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clip_on=False,
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text_pos=(-0.1, 0.5),
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text_str=f'${bar_time}\\,\\text{{s}}$',
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text_kwargs=dict(
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fontsize=fs['bar'],
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ha='right',
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va='center',
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)
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)
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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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frameon=False,
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prop=dict(
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size=20,
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),
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borderpad=0,
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borderaxespad=0,
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handlelength=1,
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columnspacing=1,
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handletextpad=0.5,
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labelspacing=0.1
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)
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leg_labels = dict(
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filt='$x_{\\text{filt}}$',
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env='$x_{\\text{env}}$',
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log='$x_{\\text{log}}$',
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inv='$x_{\\text{adapt}}$',
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conv='$c_i$',
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feat='$f_i$'
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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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first=True,
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last=True,
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condense=None,
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)
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plateau_line_kwargs = dict(
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lw=lw['plateau'],
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ls='--',
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zorder=1,
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)
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plateau_dot_kwargs = dict(
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marker='o',
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markersize=8,
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markeredgewidth=1,
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clip_on=False,
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)
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# EXECUTION:
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# Load invariance data:
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data, config = load_data(data_path, keywords=['snip', 'scales', 'measure', 'thresh'])
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t_full = np.arange(data['snip_filt'].shape[0]) / config['rate']
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# Reduce kernels:
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if reduce_kernels:
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kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
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data = reduce_kernel_set(data, kern_inds, **kern_subset_kwargs)
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config['k_specs'] = config['k_specs'][kern_inds, :]
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config['kernels'] = config['kernels'][:, kern_inds]
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# Reduce thresholds:
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thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
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data['measure_feat'] = data['measure_feat'][:, :, thresh_ind]
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data['snip_feat'] = data['snip_feat'][:, :, :, thresh_ind]
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# Remember pure-noise reference measures:
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ref_data = {stage: data[f'measure_{stage}'][0, ...] for stage in stages}
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# Reduce scales:
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if exclude_zero:
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data = exclude_zero_scale(data, **scale_subset_kwargs)
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scales = data['scales']
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snip_scales = data['example_scales']
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# Adjust grid parameters:
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snip_grid_kwargs['ncols'] = snip_scales.size
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# Prepare overall graph:
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fig = plt.figure(**fig_kwargs)
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super_grid = fig.add_gridspec(**super_grid_kwargs)
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# Prepare stage-specific snippet axes:
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snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
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snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
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snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
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for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
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ax = snip_subfig.add_subplot(snip_grid[i, j])
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ax.set_xlim(t_full[0], t_full[-1])
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ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
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hide_axis(ax, 'bottom')
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if i == 0:
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title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
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ref=snip_subfig, **title_kwargs)
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if j == 0:
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ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
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else:
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hide_axis(ax, 'left')
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snip_axes[i, j] = ax
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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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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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if True:
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# Plot filtered snippets:
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plot_snippets(snip_axes[0, :], t_full, data['snip_filt'],
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c=stage_colors['filt'], lw=lw['filt'])
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# Plot envelope snippets:
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plot_snippets(snip_axes[1, :], t_full, data['snip_env'],
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ymin=0, c=stage_colors['env'], lw=lw['env'])
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# Plot logarithmic snippets:
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plot_snippets(snip_axes[2, :], t_full, data['snip_log'],
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c=stage_colors['log'], lw=lw['log'])
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# Plot invariant snippets:
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plot_snippets(snip_axes[3, :], t_full, data['snip_inv'],
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c=stage_colors['inv'], lw=lw['inv'])
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# Plot kernel response snippets:
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all_handles = plot_snippets(snip_axes[4, :], t_full, data['snip_conv'],
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c=stage_colors['conv'], lw=lw['conv'])
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for i, handles in enumerate(all_handles):
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assign_colors(handles, config['k_specs'][:, 0], kern_colors['conv'])
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reorder_by_sd(handles, data['snip_conv'][..., i])
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# Plot feature snippets:
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all_handles = plot_snippets(snip_axes[5, :], t_full, data['snip_feat'],
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ymin=0, ymax=1, c=stage_colors['feat'], lw=lw['feat'])
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for i, handles in enumerate(all_handles):
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assign_colors(handles, config['k_specs'][:, 0], kern_colors['feat'])
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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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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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# 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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# 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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# 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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assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
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# Indicate saturation point(s):
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if stage in ['log', 'inv', 'conv', 'feat']:
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ind = crit_inds[stage]
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scale = crit_scales[stage]
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if compress_kernels or stage in ['log', 'inv']:
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big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
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transform=big_axes[1].get_xaxis_transform())
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big_axes[1].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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transform=big_axes[1].get_xaxis_transform())
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big_axes[1].vlines(scale, big_axes[1].get_ylim()[0], curve[ind],
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color=color, **plateau_line_kwargs)
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if stage in ['filt', 'env']:
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continue
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# Relate to curve maximum:
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measure = data[mkey] / np.nanmax(data[mkey], axis=0)
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# Plot max-normalized ntensity measure curve(s):
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handles, curve = plot_curves(big_axes[2], 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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# Indicate saturation point(s):
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if stage in ['log', 'inv', 'conv', 'feat']:
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ind = crit_inds[stage]
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scale = crit_scales[stage]
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if compress_kernels or stage in ['log', 'inv']:
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big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
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transform=big_axes[2].get_xaxis_transform())
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|
big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], curve[ind],
|
|
color=color, **plateau_line_kwargs)
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|
|
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# Add legend to first analysis axis:
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|
legend = big_axes[0].legend(handles=leg_handles, **leg_kwargs)
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|
[handle.set_lw(lw['legend']) for handle in legend.get_lines()]
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|
|
|
# Save graph:
|
|
if save_path is not None:
|
|
file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
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|
fig.savefig(file_name)
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|
plt.show()
|
|
|
|
print('Done.')
|
|
embed()
|