Overhauled Thresh-LP analysis and figures (WIP).

python/fig_invariance_log-hp.py

@@ -1,31 +1,14 @@
 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, strip_zeros
+from plot_functions import hide_axis, ylimits, xlabel, ylabel,\
+                           plot_line, 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)
@@ -46,8 +29,10 @@ def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
 target = 'Omocestus_rufipes'
 data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
 stages = ['env', 'log', 'inv']
-files = stages + ['scales', 'example_scales', 'limit',
-                  'measure_env', 'measure_log', 'measure_inv']
+load_kwargs = dict(
+    files=stages,
+    keywords=['scales', 'measure']
+)
 save_path = '../figures/fig_invariance_log_hp.pdf'
 
 # GRAPH SETTINGS:
@@ -177,8 +162,8 @@ 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['env'].shape[0]) / config['env_rate']
 
     # Prepare overall graph: