Wrote results for pipeline_full, pipeline_short, and feat_cross_species.

python/fig_features_cross_species.py

@@ -2,7 +2,7 @@ import plotstyle_plt
 import numpy as np
 import matplotlib.pyplot as plt
 from itertools import product
-from scipy.stats import ttest_ind
+from scipy.stats import ttest_ind, mannwhitneyu
 from thunderhopper.modeltools import load_data
 from thunderhopper.filetools import search_files
 from thunderhopper.filtertools import find_kern_specs
@@ -15,7 +15,7 @@ from IPython import embed
 # GENERAL SETTINGS:
 cross_species = [
     'Chorthippus_biguttulus',
-    # 'Chorthippus_mollis',
+    'Chorthippus_mollis',
     'Chrysochraon_dispar',
     # 'Euchorthippus_declivus',
     'Gomphocerippus_rufus',
@@ -410,11 +410,17 @@ for x, y in product(range(n_song), range(n_song)):
     # print('\nAxis position: ', (y, x))
     # print(f'Song {song_labels[x]} (x) vs. Song {song_labels[y]} (y)')
 
+print('\nMedian correlation coefficients:')
+print(f'Intraspecies: {np.median(song_regs)}')
+print(f'Interspecies: {np.median(spec_regs)}')
+
 if test_regression:
+    song_regs, spec_regs = np.array(song_regs), np.array(spec_regs)
+
     # Add test result subplot:
     test_ax = fig.add_subplot(test_ax_bounds)
     test_ax.set_xlim(-0.6, 1.6)
-    test_ax.set_ylim(0, 1)
+    test_ax.set_ylim(-0.15, 1)
     test_ax.yaxis.set_major_locator(plt.MultipleLocator(yloc_test))
     ylabel(test_ax, ylab_test, **ylab_test_kwargs)
 
@@ -425,15 +431,22 @@ if test_regression:
     test_ax.plot(np.zeros(len(spec_regs)), spec_regs, **boxplot_dot_kwargs)
     test_ax.plot(np.ones(len(song_regs)), song_regs, **boxplot_dot_kwargs)
 
+    # CAREFUL - PSEUDO-REPLICATION:
+
     # Perform t-test:
     test = ttest_ind(spec_regs, song_regs, equal_var=False)
-    t, p = test.pvalue, test.statistic
+    p, t = test.pvalue, test.statistic
     print(f'\nT-test result: t={t}, p={p}')
 
+    # Perform Wilcoxon rank-sum test:
+    test = mannwhitneyu(spec_regs, song_regs, alternative='two-sided')
+    p, u = test.pvalue, test.statistic
+    print(f'\nMWU rank test result: U={u}, p={p}')
+
 if save_path is not None:
     fig.savefig(save_path)
 plt.show()
-
+embed()
 
 
 

python/fig_invariance_thresh-lp_single.py

@@ -149,7 +149,7 @@ lw = dict(
 )
 xlabels = dict(
     alpha='scale $\\alpha$',
-    sigma='$\\sigma_{\\text{adapt}}$',
+    sigma='$\\sigma_{\\text{adapt}}[\\text{dB}]$',
 )
 ylabels = dict(
     inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',

python/save_inv_data_full.py

@@ -13,12 +13,12 @@ from IPython import embed
 target_species = [
     'Chorthippus_biguttulus',
     'Chorthippus_mollis',
-    # 'Chrysochraon_dispar',
-    # 'Euchorthippus_declivus',
-    # 'Gomphocerippus_rufus',
-    # 'Omocestus_rufipes',
-    # 'Pseudochorthippus_parallelus',
-][1]
+    'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+][0]
 example_file = {
     'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
     'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
@@ -28,7 +28,7 @@ example_file = {
     'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
     'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
 }[target_species]
-data_paths = search_files(target_species, incl='GBC', dir='../data/processed/')
+data_paths = search_files(target_species, incl='DJN', dir='../data/processed/')
 noise_path = '../data/processed/white_noise_sd-1.npz'
 thresh_path = '../data/inv/full/thresholds.npz'
 stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
@@ -43,25 +43,22 @@ thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])
 
 # SUBSET SETTINGS:
 kernels = None
-types = None
-sigmas = None
+types = None#np.array([1, -1, 2, -2, 3, -3, 4, -4])
+sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016])
 
 # PREPARATION:
 pure_noise = np.load(noise_path)['raw']
-thresh_data = dict(np.load(thresh_path))
-thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :]
+thresh_data = np.load(thresh_path)['sds']
+thresh_abs = thresh_rel[:, None] * thresh_data[None, :]
 
 # EXECUTION:
 for data_path, name in zip(data_paths, crop_paths(data_paths)):
     save_detailed = example_file in name
     print(f'Processing {name}')
-    if 'BM04' in name:
-        continue
 
     # Get song recording (prior to anything):
     data, config = load_data(data_path, files='raw')
-    song, rate = data['raw'], config['rate']
-    print(song.shape, song.size)
+    song, rate = copy.deepcopy(data['raw']), config['rate']
 
     # Reduce to kernel subset:
     if any(var is not None for var in [kernels, types, sigmas]):
@@ -73,15 +70,19 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
 
     # Get song segment to be analyzed:
     time = np.arange(song.shape[0]) / rate
-    start, end = data['songs_0'].ravel()
+    start, end = copy.deepcopy(data['songs_0'].ravel())
     segment = (time >= start) & (time <= end)
+    del data, time
+    gc.collect()
 
     # Normalize song component:
     song /= song[segment].std(axis=0)
 
     # Get normalized noise component:
-    noise = draw_noise_segment(pure_noise, song.shape[0])
+    noise = copy.deepcopy(draw_noise_segment(pure_noise, song.shape[0]))
     noise /= noise[segment].std()
+    del pure_noise
+    gc.collect()
 
     # Prepare storage:
     shape_low = (scales.size,)
@@ -128,6 +129,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
                 snippets[f'snip_{stage}'][:, ..., scale_ind] = copy.deepcopy(signals[stage])
         
         conv = copy.deepcopy(signals['conv'])
+        for stage in pre_stages:
+            del signals[stage]
         del scaled, signals
         gc.collect()
 
@@ -161,7 +164,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
             archive.update(snippets)
         save_data(save_path + name, archive, config, overwrite=True)
         del archive
-    del measures, data, config, conv
+    del measures, config, conv
     if save_detailed:
         del snippets
     gc.collect()

python/save_inv_data_short.py

@@ -10,14 +10,14 @@ from IPython import embed
 
 # GENERAL SETTINGS:
 target_species = [
-    'Chorthippus_biguttulus',
+    # 'Chorthippus_biguttulus',
     'Chorthippus_mollis',
     'Chrysochraon_dispar',
     'Euchorthippus_declivus',
     'Gomphocerippus_rufus',
-    'Omocestus_rufipes',
-    'Pseudochorthippus_parallelus',
-][6]
+    # 'Omocestus_rufipes',
+    # 'Pseudochorthippus_parallelus',
+][1]
 example_file = {
     'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
     'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',