Overhauled Thresh-LP analysis and figures (WIP).

python/fig_invariance_thresh-lp.py

@@ -1,33 +1,14 @@
-from pyparsing import alphanums
-
 import plotstyle_plt
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.transforms import BboxTransformTo
 from itertools import product
 from thunderhopper.filetools import search_files
 from thunderhopper.modeltools import load_data
 from color_functions import load_colors
-from plot_functions import hide_axis, ylimits, xlabel, ylabel, plot_line, plot_barcode, strip_zeros
+from plot_functions import hide_axis, ylimits, xlabel, ylabel,\
+                           plot_line, plot_barcode, strip_zeros, time_bar
 from IPython import embed
 
-def time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, transform=None, **kwargs):
-    t0, t1 = ax.get_xlim()
-    offset = (t1 - t0 - dur) * xshift
-    x0 = t0 + offset
-    x1 = x0 + dur
-    if parent is None:
-        parent = ax
-    if transform is None:
-        transform = BboxTransformTo(parent.bbox)
-    if transform is not ax.transData:
-        trans = ax.transData + transform.inverted()
-        x0 = trans.transform((x0, 0))[0]
-        x1 = trans.transform((x1, 0))[0]
-    parent.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
-                                    transform=transform, **kwargs))
-    return None
-
 def add_snip_axes(fig, grid_kwargs):
     grid = fig.add_gridspec(**grid_kwargs)
     axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
@@ -53,8 +34,10 @@ def plot_bi_snippets(axes, time, binary, **kwargs):
 target = 'Omocestus_rufipes'
 data_paths = search_files(target, excl='noise', dir='../data/inv/thresh_lp/')
 stages = ['conv', 'bi', 'feat']
-files = stages + ['scales', 'example_scales', 'measure_conv', 'spread_conv',
-                  'measure_feat', 'spread_feat']
+load_kwargs = dict(
+    files=stages,
+    keywords=['scales', 'measure', 'spread']
+)
 save_path = '../figures/fig_invariance_thresh_lp.pdf'
 
 # GRAPH SETTINGS:
@@ -81,7 +64,7 @@ pure_grid_kwargs = dict(
     ncols=None,
     wspace=0.05,
     hspace=0.1,
-    left=0.13,
+    left=0.07,
     right=0.95,
     bottom=0.15,
     top=0.9
@@ -91,7 +74,7 @@ noise_grid_kwargs = dict(
     ncols=None,
     wspace=0.05,
     hspace=0.1,
-    left=0.13,
+    left=0.07,
     right=0.95,
     bottom=0.15,
     top=0.9   
@@ -114,7 +97,10 @@ snip_specs = dict(
 
 # PLOT SETTINGS:
 colors = load_colors('../data/stage_colors.npz')
-lw_snippets = 0.5
+lw_snippets = dict(
+    conv=0.5,
+    feat=2
+)
 lw_analysis = 3
 xlabels = dict(
     analysis='scale $\\alpha$',
@@ -166,10 +152,10 @@ letter_analysis_kwargs = dict(
 )
 bar_time = 5
 bar_kwargs = dict(
-    y0=0.5,
-    y1=0.6,
+    y0=0.7,
+    y1=0.8,
     color='k',
-    lw = 0,
+    lw=0,
 )
 spread_kwargs = dict(
     alpha=0.3,
@@ -183,8 +169,8 @@ for data_path in data_paths:
     print(f'Processing {data_path}')
 
     # Load invariance data:
-    pure_data, config = load_data(data_path, files)
-    noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), files)
+    pure_data, config = load_data(data_path, **load_kwargs)
+    noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
     t_full = np.arange(pure_data['conv'].shape[0]) / config['env_rate']
 
     # Reduce snippet data to kernel subset:
@@ -237,7 +223,7 @@ for data_path in data_paths:
 
     # Plot pure-song kernel response snippets:
     plot_snippets(pure_axes[snip_specs['conv']], t_full, pure_data['conv'],
-                  ymin=0, c=colors['conv'], lw=lw_snippets)
+                  c=colors['conv'], lw=lw_snippets['conv'])
 
     # Plot pure-song binary snippets:
     plot_bi_snippets(pure_axes[snip_specs['bi']], t_full, pure_data['bi'],
@@ -245,14 +231,14 @@ for data_path in data_paths:
 
     # Plot pure-song feature snippets:
     plot_snippets(pure_axes[snip_specs['feat']], t_full, pure_data['feat'],
-                  c=colors['feat'], lw=lw_snippets)
+                  ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
 
     # Indicate time scale:
     time_bar(pure_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)
 
     # Plot noise-song kernel response snippets:
     plot_snippets(noise_axes[snip_specs['conv']], t_full, noise_data['conv'],
-                  ymin=0, c=colors['conv'], lw=lw_snippets)
+                  c=colors['conv'], lw=lw_snippets['conv'])
 
     # Plot noise-song binary snippets:
     plot_bi_snippets(noise_axes[snip_specs['bi']], t_full, noise_data['bi'],
@@ -260,7 +246,7 @@ for data_path in data_paths:
 
     # Plot noise-song feature snippets:
     plot_snippets(noise_axes[snip_specs['feat']], t_full, noise_data['feat'],
-                  c=colors['feat'], lw=lw_snippets)
+                  ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])
 
     # Indicate time scale:
     time_bar(noise_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)