419 lines
14 KiB
Python
419 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, 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, 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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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, **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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line_handle = ax.plot(scales, median_measure, **kwargs)[0]
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return line_handle, median_measure
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def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']):
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half_offset = int((nin - nout) / 2)
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segment = np.arange(half_offset, half_offset + nout)
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for stage in stages:
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key = f'snip_{stage}'
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snippets[key] = snippets[key][segment, ...]
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return snippets
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# GENERAL SETTINGS:
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search_target = 'Pseudochorthippus_parallelus'
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stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
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song_example = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
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noise_example = 'merged_noise'
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song_path = '../data/inv/field/song/'
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noise_path = '../data/inv/field/noise/'
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raw_path = search_files(search_target, incl='unnormed', dir=song_path + 'condensed/')[0]
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base_path = search_files(search_target, incl='base', dir=song_path + 'condensed/')[0]
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range_path = search_files(search_target, incl='range', dir=song_path + 'condensed/')[0]
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song_snip_path = search_files(song_example, dir=song_path)[0]
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noise_snip_path = search_files(noise_example, dir=noise_path)[0]
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save_path = '../figures/fig_invariance_field.pdf'
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# ANALYSIS SETTINGS:
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offset_distance = 10 # centimeter
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# SUBSET SETTINGS:
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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])
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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=0.11,
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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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colors = load_colors('../data/stage_colors.npz')
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conv_colors = load_colors('../data/conv_colors_all.npz')
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feat_colors = load_colors('../data/feat_colors_all.npz')
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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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)
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xlabels = dict(
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big='distance [cm]',
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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,
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fontsize=fs['lab_tex'],
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rotation=0,
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ha='left',
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va='center'
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)
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ylab_big_kwargs = dict(
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x=-0.2,
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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=0.03,
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env=0.01,
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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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song_bar_time = 1
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song_bar_kwargs = dict(
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dur=song_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'${song_bar_time}\\,\\text{{s}}$',
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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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noise_bar_time = 0.5
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noise_bar_kwargs = song_bar_kwargs.copy()
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noise_bar_kwargs['dur'] = noise_bar_time
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noise_bar_kwargs['text_str'] = f'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$'
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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 raw (unnormed) invariance data:
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data, config = load_data(raw_path, files='distances', keywords='mean')
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dists = data['distances'] + offset_distance
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# Load snippet data:
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song_snip, _ = load_data(song_snip_path, keywords='snip')
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t_song = np.arange(song_snip['snip_filt'].shape[0]) / config['rate']
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noise_snip, _ = load_data(noise_snip_path, keywords='snip')
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noise_snip = crop_noise_snippets(noise_snip, noise_snip['snip_filt'].shape[0], t_song.size)
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t_noise = np.arange(noise_snip['snip_filt'].shape[0]) / config['rate']
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snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists]
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# Optional kernel subset:
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reduce_kernels = False
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if any(var is not None for var in [kernels, types, sigmas]):
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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, keyword='mean')
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song_snip = reduce_kernel_set(song_snip, kern_inds, keyword='snip')
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noise_snip = reduce_kernel_set(noise_snip, kern_inds, keyword='snip')
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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_kernels = True
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# Adjust grid parameters:
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snip_grid_kwargs['ncols'] = len(snip_dists)
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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.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, snip_dists[j], ref=snip_subfig, **title_kwargs)
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if j == 0:
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ax.set_xlim(t_noise[0], t_noise[-1])
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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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ax.set_xlim(t_song[0], t_song[-1])
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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], **song_bar_kwargs)
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# time_bar(snip_axes[-1, 0], **noise_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(dists[0], 0)
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# ax.set_xscale('symlog', linthresh=offset_distance, 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, 1:], t_song, song_snip['snip_filt'],
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c=colors['filt'], lw=lw['filt'])
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plot_line(snip_axes[0, 0], t_noise, noise_snip['snip_filt'][:, 0],
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*snip_axes[0, 1].get_ylim(), c=colors['filt'], lw=lw['filt'])
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# Plot envelope snippets:
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plot_snippets(snip_axes[1, 1:], t_song, song_snip['snip_env'],
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ymin=0, c=colors['env'], lw=lw['env'])
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plot_line(snip_axes[1, 0], t_noise, noise_snip['snip_env'][:, 0],
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*snip_axes[1, 1].get_ylim(), c=colors['env'], lw=lw['env'])
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# Plot logarithmic snippets:
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plot_snippets(snip_axes[2, 1:], t_song, song_snip['snip_log'],
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c=colors['log'], lw=lw['log'])
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plot_line(snip_axes[2, 0], t_noise, noise_snip['snip_log'][:, 0],
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*snip_axes[2, 1].get_ylim(), c=colors['log'], lw=lw['log'])
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# Plot invariant snippets:
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plot_snippets(snip_axes[3, 1:], t_song, song_snip['snip_inv'],
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c=colors['inv'], lw=lw['inv'])
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plot_line(snip_axes[3, 0], t_noise, noise_snip['snip_inv'][:, 0],
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*snip_axes[3, 1].get_ylim(), c=colors['inv'], lw=lw['inv'])
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# Plot kernel response snippets:
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all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_snip['snip_conv'],
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c=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], conv_colors)
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reorder_by_sd(handles, song_snip['snip_conv'][..., i])
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handles = plot_line(snip_axes[4, 0], t_noise, noise_snip['snip_conv'][:, 0],
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*snip_axes[4, 1].get_ylim(), c=colors['conv'], lw=lw['conv'])
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assign_colors(handles, config['k_specs'][:, 0], conv_colors)
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reorder_by_sd(handles, noise_snip['snip_conv'][:, 0])
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# Plot feature snippets:
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all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_snip['snip_feat'],
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ymin=0, ymax=1, c=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], feat_colors)
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reorder_by_sd(handles, song_snip['snip_feat'][..., i])
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handles = plot_line(snip_axes[5, 0], t_noise, noise_snip['snip_feat'][:, 0],
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ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
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assign_colors(handles, config['k_specs'][:, 0], feat_colors)
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reorder_by_sd(handles, noise_snip['snip_feat'][:, 0])
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del song_snip, noise_snip
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# Remember saturation points:
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crit_inds, crit_dists = {}, {}
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# Unnormed measures:
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for stage in stages:
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# Plot average intensity measure across recordings:
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curve = plot_curves(big_axes[0], dists, data[f'mean_{stage}'],
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c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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# # Indicate saturation point:
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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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# dist = dists[ind]
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# big_axes[0].plot(dist, 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(dist, 0, mfc=colors[stage], 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(dist, big_axes[0].get_ylim()[0], curve[ind],
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# color=colors[stage], **plateau_line_kwargs)
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# # Log saturation point:
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# crit_inds[stage] = ind
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# crit_dists[stage] = dist
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del data
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# Noise baseline-related measures:
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data, _ = load_data(base_path, files='scales', keywords='mean')
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if reduce_kernels:
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data = reduce_kernel_set(data, kern_inds, keyword='mean')
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for stage in stages:
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# Plot average intensity measure across recordings:
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curve = plot_curves(big_axes[1], dists, data[f'mean_{stage}'],
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c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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# Indicate saturation point:
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# if stage in ['log', 'inv', 'conv', 'feat']:
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# ind, dist = crit_inds[stage], crit_dists[stage]
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# big_axes[1].plot(dist, 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(dist, 0, mfc=colors[stage], 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(dist, big_axes[1].get_ylim()[0], curve[ind],
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# color=colors[stage], **plateau_line_kwargs)
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del data
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# Min-max normalized measures:
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data, _ = load_data(range_path, files='scales', keywords='mean')
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if reduce_kernels:
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data = reduce_kernel_set(data, kern_inds, keyword='mean')
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for stage in stages:
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# Plot average intensity measure across recordings:
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curve = plot_curves(big_axes[2], dists, data[f'mean_{stage}'],
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c=colors[stage], lw=lw['big'],
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fill_kwargs=dict(color=colors[stage], alpha=0.25))
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# # Indicate saturation point:
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# if stage in ['log', 'inv', 'conv', 'feat']:
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# ind, dist = crit_inds[stage], crit_dists[stage]
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# big_axes[2].plot(dist, 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(dist, 0, mfc=colors[stage], 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(dist, big_axes[2].get_ylim()[0], curve[ind],
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# color=colors[stage], **plateau_line_kwargs)
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del data
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|
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# Save graph:
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if save_path is not None:
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fig.savefig(save_path)
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plt.show()
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|
|
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print('Done.')
|
|
embed()
|