Seriously, no idea. Wild amount of changes. Good luck.

python/fig_invariance_thresh-lp_species.py

@@ -162,20 +162,30 @@ def add_cross_axes(fig, n, long='col', fill='row', **grid_kwargs):
 
 # GENERAL SETTINGS:
 target_species = [
-    'Omocestus_rufipes',
     'Chorthippus_biguttulus',
     'Chorthippus_mollis',
     'Chrysochraon_dispar',
+    'Euchorthippus_declivus',
     'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
     'Pseudochorthippus_parallelus',
-    ]
+]
+example_files = {
+    'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
+    'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
+    'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
+    'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
+    'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
+    'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
+    'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
+}
 n_species = len(target_species)
 load_kwargs = dict(
-    keywords=['scales', 'measure', 'thresh']
+    keywords=['scales', 'mean', 'thresh']
 )
 save_path = '../figures/fig_invariance_thresh_lp_species.pdf'
 exclude_zero = True
-show_floor = True
+show_floor = False
 
 # SUBSET SETTINGS:
 thresh_rel = np.array([0.5, 1, 3])[0]
@@ -266,14 +276,6 @@ lw = dict(
     kern=2.5,
     plateau=3,
 )
-zorder = dict(
-    Omocestus_rufipes=2,
-    Chorthippus_biguttulus=2.5,
-    Chorthippus_mollis=2.4,
-    Chrysochraon_dispar=2,
-    Gomphocerippus_rufus=2,
-    Pseudochorthippus_parallelus=2,
-)
 space_kwargs = dict(
     s=30,
 )
@@ -357,21 +359,27 @@ letter_space_kwargs = dict(
     va='center',
     fontsize=fs['letter'],
 )
-song_bar_time = 1.0
+spec_bar_times = dict(
+    Chorthippus_biguttulus=1,
+    Chorthippus_mollis=10,
+    Chrysochraon_dispar=1,
+    Euchorthippus_declivus=0.25,
+    Gomphocerippus_rufus=5,
+    Omocestus_rufipes=5,
+    Pseudochorthippus_parallelus=1,
+)
 song_bar_kwargs = dict(
-    dur=song_bar_time,
     y0=-0.1,
     y1=0,
-    xshift=0,
+    xshift=0.5,
     color='k',
     lw=0,
     clip_on=False,
-    text_pos=(1.25, 0.5),
-    text_str=f'${int(song_bar_time)}\\,\\text{{s}}$',
+    text_pos=(0.5, -0.1),
     text_kwargs=dict(
         fontsize=fs['bar'],
-        ha='left',
-        va='center',
+        ha='center',
+        va='top',
     )
 )
 kern_bar_time = 0.05
@@ -382,7 +390,7 @@ kern_bar_kwargs = dict(
     color='k',
     lw=0,
     clip_on=False,
-    text_pos=(0.6, -1),
+    text_pos=(0.7, -1),
     text_str=f'${int(kern_bar_time * 1000)}\\,\\text{{ms}}$',
     text_kwargs=dict(
         fontsize=fs['bar'],
@@ -502,7 +510,7 @@ for i, species in enumerate(target_species):
     print(f'Processing {species}')
 
     # Fetch species-specific recording file:
-    song_path = search_files(species, dir='../data/processed/')[0]
+    song_path = search_files(example_files[species], dir='../data/processed/')[0]
 
     # Load song data:
     song_data, _ = load_data(song_path, files='filt')
@@ -513,16 +521,18 @@ for i, species in enumerate(target_species):
     time = np.arange(song.shape[0]) / rate
     plot_line(song_ax, time, song, ypad=0.05, c='k', lw=lw['song'])
     title_subplot(song_ax, shorten_species(species), ref=song_subfig, **title_kwargs)
-    time_bar(song_ax, **song_bar_kwargs)
-    song_bar_kwargs['text_pos'] = None
+    time_bar(song_ax, dur=spec_bar_times[species], **song_bar_kwargs,
+             text_str=f'${spec_bar_times[species]}\\,\\text{{s}}$')
 
     # Fetch species-specific invariance files:
-    pure_path = search_files(species, incl='pure', dir='../data/inv/thresh_lp/')[0]
-    noise_path = search_files(species, incl='noise', dir='../data/inv/thresh_lp/')[0]
+    pure_path = search_files(species, incl='pure', dir='../data/inv/thresh_lp/condensed/')[0]
+    noise_path = search_files(species, incl='noise', dir='../data/inv/thresh_lp/condensed/')[0]
 
     # Load invariance data:
     pure_data, config = load_data(pure_path, **load_kwargs)
     noise_data, _ = load_data(noise_path, **load_kwargs)
+    pure_measure = pure_data['mean_feat'].mean(axis=-1)
+    noise_measure = noise_data['mean_feat'].mean(axis=-1)
     scales = pure_data['scales']
 
     # Reduce to kernel subset and a single threshold:
@@ -530,8 +540,8 @@ for i, species in enumerate(target_species):
     kern_inds = find_kern_specs(config['k_specs'], kerns=kern_specs)
     config['k_specs'] = config['k_specs'][kern_inds]
     config['kernels'] = config['kernels'][:, kern_inds]
-    pure_measure = pure_data['measure_feat'][:, kern_inds, thresh_ind]
-    noise_measure = noise_data['measure_feat'][:, kern_inds, thresh_ind]
+    pure_measure = pure_measure[:, kern_inds, thresh_ind]
+    noise_measure = noise_measure[:, kern_inds, thresh_ind]
     if exclude_zero:
         # Reduce to nonzero scales:
         nonzero_inds = scales > 0
@@ -564,7 +574,6 @@ for i, species in enumerate(target_species):
             inset.plot(config['k_times'], kern, c=c, lw=lw['kern'])
             inset.set_xlim(xlims)
             inset.set_ylim(ylims)
-        # time_bar(insets[0], parent=feat_axes[0, 0], **kern_bar_kwargs)
         time_bar(insets[0], **kern_bar_kwargs)
 
     # Plot invariance curves in feature space:
@@ -572,13 +581,11 @@ for i, species in enumerate(target_species):
     for ind, (pure_ax, noise_ax) in enumerate(zip(pure_axes, noise_axes)):
         irow, icol = row_inds[ind], col_inds[ind]
         pure_handle = pure_ax.scatter(pure_measure[:, icol], pure_measure[:, irow],
-                                      c=scales, cmap=scale_cmap, norm=norm,
-                                      zorder=zorder[species], **space_kwargs)
+                                      c=scales, cmap=scale_cmap, norm=norm, **space_kwargs)
         pure_space_handles[pure_ax].append(pure_handle)
 
         noise_handle = noise_ax.scatter(noise_measure[:, icol], noise_measure[:, irow],
-                                        c=scales, cmap=scale_cmap, norm=norm,
-                                        zorder=zorder[species], **space_kwargs)
+                                        c=scales, cmap=scale_cmap, norm=norm, **space_kwargs)
         noise_space_handles[noise_ax].append(noise_handle)
     
     # Indicate scale color code in pure subfigure: