Made fig_features_cross_species.pdf.
This commit is contained in:
j-hartling
@@ -9,9 +9,9 @@ target_species = [
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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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'Gomphocerippus_rufus',
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'Omocestus_rufipes',
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# 'Pseudochorthippus_parallelus',
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'Pseudochorthippus_parallelus',
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]
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stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
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search_path = '../data/inv/full/'
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425
python/fig_features_cross_species.py
Normal file
425
python/fig_features_cross_species.py
Normal file
@@ -0,0 +1,425 @@
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import plotstyle_plt
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import numpy as np
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import matplotlib.pyplot as plt
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from itertools import product
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from scipy.stats import ttest_ind
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from thunderhopper.modeltools import load_data
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from thunderhopper.filetools import search_files
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from thunderhopper.filtertools import find_kern_specs
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from misc_functions import shorten_species
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from color_functions import load_colors
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from plot_functions import hide_ticks, xlabel, ylabel
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from IPython import embed
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# GENERAL SETTINGS:
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cross_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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]
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n_spec = len(cross_species)
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example_files = {
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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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}
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in_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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save_path = '../figures/fig_features_cross_species.pdf'
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# ANALYSIS SETTINGS:
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thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
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single_spec_file = True # Only use example files for cross-species comparison
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equalize_spec_files = False # Prune to minimum available across species
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n_song = n_spec#None # Limit to n first songs of in-species dataset (None for all)
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color_kern_types = True
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calculate_regression = True
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test_regression = True
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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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spec_grid_kwargs = dict(
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wspace=0.1,
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hspace=0.1,
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left=0.07,
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right=0.93,
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bottom=0.07,
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top=0.93,
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)
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song_grid_kwargs = dict(
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wspace=0.1,
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hspace=0.1,
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left=spec_grid_kwargs['left'],
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right=spec_grid_kwargs['right'],
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bottom=spec_grid_kwargs['bottom'],
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top=spec_grid_kwargs['top'],
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)
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# PLOT SETTINGS:
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kern_colors = load_colors('../data/feat_colors_all.npz')
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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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label_song_prefix = ['song ', ''][0]
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xlab_low_kwargs = dict(
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y=0.01,
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fontsize=fs['lab_norm'],
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ha='center',
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va='bottom',
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fontstyle='italic',
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)
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xlab_up_kwargs = dict(
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y=0.99,
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fontsize=fs['lab_norm'],
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ha='center',
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va='top',
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)
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ylab_low_kwargs = dict(
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x=0.01,
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fontsize=fs['lab_norm'],
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ha='center',
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va='top',
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fontstyle='italic',
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)
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ylab_up_kwargs = dict(
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x=0.99,
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rotation=270,
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fontsize=fs['lab_norm'],
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ha='center',
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va='top',
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)
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loc = 0.5
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dot_spec_kwargs = dict(
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ls='none',
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marker='o',
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mec='w',
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ms=6,
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mew=0.5,
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alpha=1,
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zorder=2,
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)
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dot_song_kwargs = dict(
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ls='none',
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marker='o',
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mec='w',
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ms=6,
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mew=0.5,
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alpha=1,
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zorder=2,
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)
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if not color_kern_types:
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dot_spec_kwargs['mfc'] = 'k'
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dot_song_kwargs['mfc'] = 'k'
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diag_fig_kwargs = dict(
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c='k',
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lw=1,
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ls='-'
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)
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diag_ax_kwargs = dict(
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c='k',
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lw=1,
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ls='-',
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zorder=3,
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)
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text_spec_kwargs = dict(
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x=0.05,
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y=0.95,
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ha='left',
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va='top',
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fontsize=16,
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)
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text_song_kwargs = dict(
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x=0,
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y=0.95,
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ha='left',
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va='top',
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fontsize=16,
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)
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text_spec_prefix = '$\\rho\\,=\\,$'
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text_song_prefix = ['$\\rho\\,=\\,$', ''][0]
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if test_regression:
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test_ax_side = 0.15
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test_ax_bounds = [
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song_grid_kwargs['right'] - test_ax_side,
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spec_grid_kwargs['bottom'],
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test_ax_side,
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test_ax_side
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]
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xlab_test = '$\\rho$'
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ylab_test = '$\\text{PDF}_{\\rho}$'
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xloc_test = 0.5
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yloc_test = 10
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ylab_test_kwargs = dict(
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x=-0.3,
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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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nbins = 10
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spec_color = 'darkorchid'
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song_color = 'goldenrod'
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bar_kwargs = dict(
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width=0.95,
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alpha=0.75,
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)
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mean_kwargs = dict(
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ls='--',
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lw=1,
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)
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leg_kwargs = dict(
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ncols=1,
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loc='lower center',
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bbox_to_anchor=(0, 1.05, 1, 0.5),
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frameon=False,
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prop=dict(
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size=14,
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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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# EXECUTION:
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# Gather cross-species data:
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spec_data, min_files = {}, np.inf
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for species in cross_species:
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# Load species data:
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if single_spec_file:
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path = search_files(example_files[species], dir='../data/inv/full/')[0]
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else:
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path = search_files(species, dir='../data/inv/full/collected/')[0]
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data, config = load_data(path, ['measure_feat', 'thresh_rel'])
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# Reduce to single threshold, last scale, optional kernel subset:
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thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
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kern_inds = slice(None)
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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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config['kernels'] = config['kernels'][:, kern_inds]
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config['k_specs'] = config['k_specs'][kern_inds, :]
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spec_data[species] = data['measure_feat'][-1, kern_inds, thresh_ind, ...]
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if single_spec_file:
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# Ensure consistent shape (features, files=1):
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spec_data[species] = spec_data[species][..., None]
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# Update number of possible comparisons:
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min_files = min(min_files, spec_data[species].shape[-1])
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if equalize_spec_files and not single_spec_file:
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# Equalize number of files across species:
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spec_data = {spec: feat[:, :min_files] for spec, feat in spec_data.items()}
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# Gather in-species data:
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paths = search_files(in_species, dir='../data/inv/full/')
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if n_song is not None:
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paths = paths[:n_song]
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else:
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n_song = len(paths)
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song_data = np.zeros((n_song, config['k_specs'].shape[0]))
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for i, path in enumerate(paths):
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# Load song-specific dataset:
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data, config = load_data(path, ['measure_feat', 'thresh_rel'])
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# Reduce to single threshold, last scale, optional kernel subset:
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thresh_ind = np.nonzero(data['thresh_rel'] == thresh_rel)[0][0]
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kern_inds = slice(None)
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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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config['kernels'] = config['kernels'][:, kern_inds]
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config['k_specs'] = config['k_specs'][kern_inds, :]
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song_data[i] = data['measure_feat'][-1, kern_inds, thresh_ind]
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# Prepare graph:
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fig = plt.figure(**fig_kwargs)
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spec_grid = fig.add_gridspec(nrows=n_spec, ncols=n_spec, **spec_grid_kwargs)
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song_grid = fig.add_gridspec(nrows=n_song, ncols=n_song, **song_grid_kwargs)
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# fig.add_artist(plt.Line2D([0, 1], [1, 0], **diag_fig_kwargs))
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# Prepare cross-species axes:
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spec_labels = [shorten_species(spec) for spec in cross_species]
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spec_axes = np.zeros((n_spec, n_spec), dtype=object)
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for i, j in product(range(n_spec), range(n_spec)):
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if i <= j:
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continue
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# Lower trianglular:
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ax = fig.add_subplot(spec_grid[i, j])
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.yaxis.set_major_locator(plt.MultipleLocator(loc))
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ax.xaxis.set_major_locator(plt.MultipleLocator(loc))
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# Indicate subplot diagonal:
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ax.plot([0, 1], [0, 1], **diag_ax_kwargs)
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# Adjust:
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if j > 0:
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hide_ticks(ax, 'left')
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if i < n_spec - 1:
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hide_ticks(ax, 'bottom')
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if j == 0:
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ylabel(ax, spec_labels[i], transform=fig.transFigure, **ylab_low_kwargs)
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if i == n_spec - 1:
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xlabel(ax, spec_labels[j], transform=fig.transFigure, **xlab_low_kwargs)
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spec_axes[i, j] = ax
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# Prepare in-species axes:
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song_labels = [label_song_prefix + f'{i}' for i in range(1, n_song + 1)]
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song_axes = np.zeros((n_song, n_song), dtype=object)
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for i, j in product(range(n_song), range(n_song)):
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if i >= j:
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continue
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# Upper triangular:
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ax = fig.add_subplot(song_grid[i, j])
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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ax.yaxis.set_major_locator(plt.MultipleLocator(loc))
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ax.xaxis.set_major_locator(plt.MultipleLocator(loc))
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ax.spines[['top', 'right']].set_visible(True)
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ax.spines[['bottom', 'left']].set_visible(False)
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ax.yaxis.set_label_position('right')
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ax.yaxis.tick_right()
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ax.xaxis.set_label_position('top')
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ax.xaxis.tick_top()
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# Indicate subplot diagonal:
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ax.plot([0, 1], [0, 1], **diag_ax_kwargs)
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# Adjust:
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if i > 0:
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hide_ticks(ax, 'top')
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if j < n_song - 1:
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hide_ticks(ax, 'right')
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if i == 0:
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xlabel(ax, song_labels[j], transform=fig.transFigure, **xlab_up_kwargs)
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if j == n_song - 1:
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ylabel(ax, song_labels[i], transform=fig.transFigure, **ylab_up_kwargs)
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song_axes[i, j] = ax
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# Remember coefficients:
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if calculate_regression:
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spec_regs, song_regs = [], []
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# Plot cross-species comparisons:
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for x, y in product(range(n_spec), range(n_spec)):
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if x >= y:
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continue
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# Get axis and data:
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ax = spec_axes[y, x]
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x_spec = spec_data[cross_species[x]]
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y_spec = spec_data[cross_species[y]]
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x_reg, y_reg = [], []
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# Plot features of species 1 against species 2 (all files cross-wise):
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for i, j in product(range(x_spec.shape[-1]), range(y_spec.shape[-1])):
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if color_kern_types:
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for k, (f1, f2) in enumerate(zip(x_spec[:, i], y_spec[:, j])):
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c = kern_colors[str(int(config['k_specs'][k, 0]))]
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ax.plot(f1, f2, mfc=c, **dot_spec_kwargs)
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else:
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ax.plot(x_spec[:, i], y_spec[:, j], **dot_spec_kwargs)
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# Accumulate value pairs:
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if calculate_regression:
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x_reg.append(x_spec[:, i])
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y_reg.append(y_spec[:, j])
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# Get regression coefficient:
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if calculate_regression:
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rho = np.corrcoef(np.concatenate(x_reg), np.concatenate(y_reg))[0, 1]
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ax.text(s=text_spec_prefix + f'{rho:.2f}', transform=ax.transAxes, **text_spec_kwargs)
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spec_regs.append(rho)
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# print('\nAxis position: ', (y, x))
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# print(cross_species[x], '(x) vs.', cross_species[y], '(y)')
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# Plot in-species comparisons:
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for x, y in product(range(n_song), range(n_song)):
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if x <= y:
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continue
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# Get axis and data:
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ax = song_axes[y, x]
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x_song = song_data[x]
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y_song = song_data[y]
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# Plot features of song 1 against song 2:
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if color_kern_types:
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for i, (f1, f2) in enumerate(zip(x_song, y_song)):
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c = kern_colors[str(int(config['k_specs'][i, 0]))]
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ax.plot(f1, f2, mfc=c, **dot_song_kwargs)
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else:
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ax.plot(x_song, y_song, **dot_song_kwargs)
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# Get regression coefficient:
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if calculate_regression:
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rho = np.corrcoef(x_song, y_song)[0, 1]
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ax.text(s=text_song_prefix + f'{rho:.2f}', transform=ax.transAxes, **text_song_kwargs)
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song_regs.append(rho)
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# print('\nAxis position: ', (y, x))
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# print(f'Song {song_labels[x]} (x) vs. Song {song_labels[y]} (y)')
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if test_regression:
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# Add test result subplot:
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test_ax = fig.add_subplot(test_ax_bounds)
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test_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc_test))
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test_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc_test))
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xlabel(test_ax, xlab_test, transform=fig.transFigure, **xlab_low_kwargs)
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ylabel(test_ax, ylab_test, **ylab_test_kwargs)
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# Perform t-test:
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test = ttest_ind(spec_regs, song_regs, equal_var=False)
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t, p = test.pvalue, test.statistic
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print(f'\nT-test result: t={t}, p={p}')
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# Calculate histograms:
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limits = np.array([min(spec_regs + song_regs), max(spec_regs + song_regs)])
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limits += np.array([-1.1, 1.1]) * (limits[1] - limits[0])
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edges = np.linspace(*limits, nbins + 1)
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centers = edges[:-1] + (edges[1] - edges[0]) / 2
|
||||
spec_hist, _ = np.histogram(spec_regs, bins=edges, density=True)
|
||||
song_hist, _ = np.histogram(song_regs, bins=edges, density=True)
|
||||
# Plot histograms:
|
||||
bar_kwargs['width'] *= (centers[1] - centers[0])
|
||||
test_ax.bar(centers, spec_hist, color=spec_color, label='inter-species', **bar_kwargs)
|
||||
test_ax.bar(centers, song_hist, color=song_color, label='intra-species', **bar_kwargs)
|
||||
# Indicate means:
|
||||
test_ax.axvline(np.mean(spec_regs), color=spec_color, **mean_kwargs)
|
||||
test_ax.axvline(np.mean(song_regs), color=song_color, **mean_kwargs)
|
||||
# Add legend:
|
||||
test_ax.legend(**leg_kwargs)
|
||||
# Posthocs:
|
||||
test_ax.set_ylim(0, max(spec_hist.max(), song_hist.max()) * 1.05)
|
||||
test_ax.set_xlim(min(0, max(-1, limits[0])),
|
||||
min(1, limits[1]))
|
||||
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -12,11 +12,11 @@ target_species = [
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
][4]
|
||||
# 'Euchorthippus_declivus',
|
||||
# 'Gomphocerippus_rufus',
|
||||
# 'Omocestus_rufipes',
|
||||
# 'Pseudochorthippus_parallelus',
|
||||
][2]
|
||||
example_file = {
|
||||
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
|
||||
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
|
||||
|
||||
Reference in New Issue
Block a user