Fetched bunch of species-specific song snippets.

Worked those into LogHP analysis.
Worked results into fig_invariance_log-hp.pdf.
Put details into new fig_invariance_log-hp_species.pdf (appendix).

python/plot_functions.py

@@ -2,6 +2,7 @@ import string
 import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox
+from misc_functions import get_kde
 
 def hide_ticks(ax, side='bottom', ticks=True):
     axis = 'x' if side in ['top', 'bottom'] else 'y'
@@ -298,3 +299,33 @@ def zoom_inset(ax, inset, handle, x0=None, x1=None, y0=None, y1=None, ref='x',
 def set_clip_box(artist, ax, bounds=[[0, -0.05], [1, 1.05]]):
     artist.set_clip_box(TransformedBbox(Bbox(bounds), ax.transAxes))
     return None
+
+def plot_dist_shifted(ax, data, axis, pdf=None, sigma=0.1, which='x',
+                      base=None, cap=None, add_pdf=False, shifted=False, **kwargs):
+    if pdf is None:
+        pdf, axis = get_kde(data, sigma, axis)
+    if base is None:
+        base = pdf.min()
+    if cap is None:
+        cap = pdf.max()
+    pdf = (pdf - pdf.min()) / (pdf.max() - pdf.min()) * (cap - base) + base
+
+    if which == 'x':
+        transform = ax.get_xaxis_transform()
+    elif which == 'y':
+        transform = ax.get_yaxis_transform()
+    else:
+        transform = ax.transData
+
+    rng = np.random.default_rng()
+    handles = []
+    for value in data:
+        ind = np.nonzero(axis == value)[0][0]
+        offset = base if not shifted else rng.uniform(base, pdf[ind])
+        variables = (offset, value) if which=='y' else (value, offset)
+        handles.extend(ax.plot(*variables, transform=transform, **kwargs))
+    if add_pdf:
+        variables = (pdf, axis) if which=='y' else (axis, pdf)
+        pdf_handle = ax.plot(*variables, transform=transform, c='k', lw=1)
+        return handles, pdf_handle
+    return handles