Made fig_invariance_rect_lp.pdf and corresponding appendix figure.

Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB.

Co-authored-by: Copilot <copilot@github.com>

python/fig_invariance_log-hp.py

@@ -7,8 +7,8 @@ from thunderhopper.modeltools import load_data
 from misc_functions import shorten_species, get_saturation
 from color_functions import load_colors
 from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, hide_ticks,\
-                           plot_line, strip_zeros, time_bar, zoom_inset,\
-                           letter_subplot, letter_subplots, title_subplot
+                           plot_line, strip_zeros, time_bar, zoom_inset, shift_subplot,\
+                           letter_subplot, letter_subplots, title_subplot, color_axis
 from IPython import embed
 
 def add_snip_axes(fig, grid_kwargs):
@@ -27,10 +27,10 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
         handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
     return handles
 
+
 # GENERAL SETTINGS:
 target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
 data_path = search_files(target, excl='noise', dir='../data/inv/log_hp/')[0]
-ref_path = '../data/inv/log_hp/ref_measures.npz'
 save_path = '../figures/fig_invariance_log_hp.pdf'
 target_species = [
     'Chorthippus_biguttulus',
@@ -46,7 +46,7 @@ load_kwargs = dict(
     files=stages,
     keywords=['scales', 'snip', 'measure']
 )
-compute_ratios = True
+relate_to_noise = True
 exclude_zero = True
 show_diag = True
 show_plateaus = True
@@ -79,7 +79,7 @@ pure_grid_kwargs = dict(
     wspace=0.1,
     hspace=0.15,
     left=0.11,
-    right=0.95,
+    right=0.98,
     bottom=1 - block_height - edge_padding,
     top=1 - edge_padding,
     height_ratios=[1, 2, 1]
@@ -95,14 +95,15 @@ noise_grid_kwargs = dict(
     top=edge_padding + block_height,
     height_ratios=[1, 2, 1]
 )
+big_col_shift = -0.12
 big_grid_kwargs = dict(
     nrows=1,
     ncols=3,
-    wspace=0.3,
+    wspace=0.25,
     hspace=0,
-    left=pure_grid_kwargs['left'],
+    left=pure_grid_kwargs['left'] - big_col_shift,
     right=pure_grid_kwargs['right'],
-    bottom=0.05,
+    bottom=0.03,
     top=1
 )
 anchor_kwargs = dict(
@@ -137,7 +138,9 @@ ylabels = dict(
     env='$x_{\\text{env}}$',
     log='$x_{\\text{dB}}$',
     inv='$x_{\\text{adapt}}$',
-    big='$\\sigma_x\\,/\\,\\sigma_{\\eta}$',
+    big_pure='$\\sigma_x$',
+    big_log='$\\sigma_x\\,[\\text{dB}]$',
+    big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
 )
 xlab_big_kwargs = dict(
     y=0,
@@ -145,6 +148,18 @@ xlab_big_kwargs = dict(
     ha='center',
     va='bottom',
 )
+ylab_big_left_kwargs = dict(
+    x=-0.2,
+    fontsize=fs['lab_tex'],
+    ha='center',
+    va='bottom'
+)
+ylab_big_right_kwargs = dict(
+    x=1.2,
+    fontsize=fs['lab_tex'],
+    ha='center',
+    va='top'   
+)
 ylab_snip_kwargs = dict(
     x=0,
     fontsize=fs['lab_tex'],
@@ -152,12 +167,6 @@ ylab_snip_kwargs = dict(
     ha='left',
     va='center',
 )
-ylab_big_kwargs = dict(
-    x=0,
-    fontsize=fs['lab_tex'],
-    ha='center',
-    va='top',
-)
 yloc = dict(
     env=1000,
     log=40,
@@ -233,7 +242,7 @@ leg_kwargs = dict(
     ),
     borderpad=0,
     borderaxespad=0,
-    handlelength=1,
+    handlelength=0.5,
     columnspacing=1,
 )
 diag_kwargs = dict(
@@ -287,11 +296,7 @@ noise_data, _ = load_data(data_path.replace('pure', 'noise'), **load_kwargs)
 pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
 t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']
 
-if compute_ratios:
-    # Relate pure-song measures to near-zero scale:
-    pure_data['measure_env'] /= pure_data['measure_env'][1]
-    pure_data['measure_log'] /= pure_data['measure_log'][1]
-    pure_data['measure_inv'] /= pure_data['measure_inv'][1]
+if relate_to_noise:
     # Relate noise-song measures to zero scale:
     noise_data['measure_env'] /= noise_data['measure_env'][0]
     noise_data['measure_log'] /= noise_data['measure_log'][0]
@@ -354,8 +359,10 @@ big_axes = np.zeros((big_grid.ncols,), dtype=object)
 for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
     ax = big_subfig.add_subplot(big_grid[0, i])
     ax.set_xlim(scales[0], scales[-1])
+    ax.set_ylim(scales[0], scales[-1])
     ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
     ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
+    # ax.xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
     ax.set_aspect(**anchor_kwargs)
     if i in [0, 1]:
         ax.set_ylim(scales[0], scales[-1])
@@ -365,10 +372,19 @@ for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
         ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
         ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
         ax.set_ylim(0.9, 30)
-    if i == 1:
-        hide_ticks(ax, 'left')
     big_axes[i] = ax
-ylabel(big_axes[0], ylabels['big'], transform=big_subfig.transSubfigure, **ylab_big_kwargs)
+shift_subplot(big_axes[0], dx=big_col_shift)
+ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_left_kwargs)
+ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_left_kwargs)
+big_twin = big_axes[0].twinx()
+hide_axis(big_twin, 'left')
+big_twin.spines['right'].set_visible(True)
+big_twin.set_position(big_axes[0].get_position().bounds)
+big_twin.set_ylim(scales[0], scales[-1])
+big_twin.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
+ylabel(big_twin, ylabels['big_log'], transform=big_twin.transAxes, **ylab_big_right_kwargs)
+color_axis(big_axes[0], colors['env'], side='left')
+color_axis(big_twin, colors['log'], side='right')
 super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
 letter_subplots(big_axes, 'cde', **letter_big_kwargs)