Added multi-thresh simulation to "full" and "short" (currently running).

Added complete "rect-lp" analysis except figure.
Added multiple appendix figs.
Overhauled normalization options across all condense scripts.

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

python/fig_invariance_full.py

@@ -7,16 +7,18 @@ from thunderhopper.modeltools import load_data
 from thunderhopper.filtertools import find_kern_specs
 from misc_functions import get_saturation
 from color_functions import load_colors
-from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
-                           plot_line, strip_zeros, time_bar, set_clip_box,\
+from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
+                           plot_line, strip_zeros, time_bar, assign_colors,\
                            letter_subplot, letter_subplots
 from IPython import embed
 
 def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
     ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
+    handles = []
     for i, ax in enumerate(axes):
-        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
-    return None
+        handles.append(plot_line(ax, time, snippets[:, ..., i],
+                       ymin=ymin, ymax=ymax, **kwargs))
+    return handles
 
 def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
     if measures.ndim == 1:
@@ -73,8 +75,8 @@ save_path = '../figures/fig_invariance_full.pdf'
 exclude_zero = True
 
 # SUBSET SETTINGS:
-types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
-sigmas = np.array([0.004, 0.008, 0.016, 0.032])
+types = np.array([1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10])
+sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
 # types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
 # sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
 kernels = np.array([
@@ -111,20 +113,20 @@ snip_grid_kwargs = dict(
     ncols=None,
     wspace=0.1,
     hspace=0.4,
-    left=0.08,
-    right=0.95,
+    left=0.11,
+    right=0.98,
     bottom=0.08,
     top=0.95
 )
 big_grid_kwargs = dict(
     nrows=1,
     ncols=3,
-    wspace=0.2,
+    wspace=0.4,
     hspace=0,
     left=snip_grid_kwargs['left'],
-    right=0.96,
-    bottom=0.2,
-    top=0.95
+    right=snip_grid_kwargs['right'],
+    bottom=0.13,
+    top=0.98
 )
 
 # PLOT SETTINGS:
@@ -137,6 +139,8 @@ fs = dict(
     bar=16,
 )
 colors = load_colors('../data/stage_colors.npz')
+conv_colors = load_colors('../data/conv_colors_all.npz')
+feat_colors = load_colors('../data/feat_colors_all.npz')
 lw = dict(
     filt=0.25,
     env=0.25,
@@ -154,10 +158,10 @@ ylabels = dict(
     filt='$x_{\\text{filt}}$',
     env='$x_{\\text{env}}$',
     log='$x_{\\text{db}}$',
-    inv='$x_{\\text{inv}}$',
+    inv='$x_{\\text{adapt}}$',
     conv='$c_i$',
     feat='$f_i$',
-    big=['intensity', 'rel. intensity', 'norm. intensity']
+    big=['measure', 'rel. measure', 'norm. measure']
 )
 xlab_big_kwargs = dict(
     y=0,
@@ -173,7 +177,7 @@ ylab_snip_kwargs = dict(
     va='center'
 )
 ylab_big_kwargs = dict(
-    x=-0.12,
+    x=-0.2,
     fontsize=fs['lab_norm'],
     ha='center',
     va='bottom',
@@ -183,7 +187,7 @@ yloc = dict(
     env=1000,
     log=50,
     inv=20,
-    conv=2,
+    conv=1,
     feat=1,
 )
 title_kwargs = dict(
@@ -262,6 +266,8 @@ if any(var is not None for var in [kernels, types, sigmas]):
     kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
     data = reduce_kernel_set(data, kern_inds, keyword='mean')
     snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
+    config['k_specs'] = config['k_specs'][kern_inds, :]
+    config['kernels'] = config['kernels'][:, kern_inds]
     reduce_kernels = True
 
 # Adjust grid parameters:
@@ -300,13 +306,13 @@ for i in range(big_grid.ncols):
     ax.set_xlim(scales[0], scales[-1])
     ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
     ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
-    xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
     ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
     if i < (big_grid.ncols - 1):
         ax.set_ylim(scales[0], scales[-1])
     else:
         ax.set_ylim(0, 1)
     big_axes[i] = ax
+super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
 letter_subplots(big_axes, 'bcd', **letter_big_kwargs)
 
 if True:
@@ -327,12 +333,18 @@ if True:
                   c=colors['inv'], lw=lw['inv'])
 
     # Plot kernel response snippets:
-    plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
-                  c=colors['conv'], lw=lw['conv'])
+    all_handles = plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
+                                c=colors['conv'], lw=lw['conv'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], conv_colors)
+        reorder_by_sd(handles, snip['snip_conv'][..., i])
 
     # Plot feature snippets:
-    plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
-                  ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    all_handles = plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
+                                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
+    for i, handles in enumerate(all_handles):
+        assign_colors(handles, config['k_specs'][:, 0], feat_colors)
+        reorder_by_sd(handles, snip['snip_feat'][..., i])
 del snip
 
 # Remember saturation points:
@@ -387,7 +399,7 @@ if exclude_zero:
     data = exclude_zero_scale(data, stages)
 if reduce_kernels:
     data = reduce_kernel_set(data, kern_inds, keyword='mean')
-for stage in stages:
+for stage in ['log', 'inv', 'conv', 'feat']:
     # Plot average intensity measure across recordings:
     curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
                         c=colors[stage], lw=lw['big'],