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Lots of stuff. Syncing to home.

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j-hartling
2026-03-20 16:45:54 +01:00
parent 1516fe6090
commit a276883454
28 changed files with 1106 additions and 562 deletions
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59
python/save_inv_data_full.py
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@@ -3,6 +3,7 @@ import numpy as np
import matplotlib.pyplot as plt
from thunderhopper.modeltools import load_data, save_data
from thunderhopper.filetools import crop_paths
from thunderhopper.filtertools import find_kern_specs
from thunderhopper.model import process_signal
from IPython import embed
@@ -13,9 +14,20 @@ stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
save_path = '../data/inv/full/'
# ANALYSIS SETTINGS:
example_scales = np.array([0, 0.5, 1, 5, 10])
scales = np.geomspace(0.01, 10, 100)
example_scales = np.array([0, 1, 10, 50])
scales = np.geomspace(0.01, 100, 100)
scales = np.unique(np.concatenate((scales, example_scales)))
kernels = np.array([
[1, 0.002],
[-1, 0.002],
[2, 0.004],
[-2, 0.004],
[3, 0.032],
[-3, 0.032]
])
kernels = None
types = None#np.array([-1])
sigmas = None#np.array([0.001, 0.002, 0.004, 0.008, 0.016, 0.032])
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
@@ -25,6 +37,13 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
data, config = load_data(data_path, files='raw')
song, rate = data['raw'], config['rate']
# Reduce to kernel subset:
kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
config['kernels'] = config['kernels'][:, kern_inds]
config['k_specs'] = config['k_specs'][kern_inds, :]
config['k_props'] = [config['k_props'][i] for i in kern_inds]
config['feat_thresh'] = config['feat_thresh'][kern_inds]
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
start, end = data['songs_0'].ravel()
@@ -42,14 +61,14 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
shape_low = (song.shape[0], example_scales.size)
shape_high = (song.shape[0], config['k_specs'].shape[0], example_scales.size)
snippets = dict(
raw=np.zeros(shape_low, dtype=float),
filt=np.zeros(shape_low, dtype=float),
env=np.zeros(shape_low, dtype=float),
log=np.zeros(shape_low, dtype=float),
inv=np.zeros(shape_low, dtype=float),
conv=np.zeros(shape_high, dtype=float),
bi=np.zeros(shape_high, dtype=float),
feat=np.zeros(shape_high, dtype=float)
snip_raw=np.zeros(shape_low, dtype=float),
snip_filt=np.zeros(shape_low, dtype=float),
snip_env=np.zeros(shape_low, dtype=float),
snip_log=np.zeros(shape_low, dtype=float),
snip_inv=np.zeros(shape_low, dtype=float),
snip_conv=np.zeros(shape_high, dtype=float),
snip_bi=np.zeros(shape_high, dtype=float),
snip_feat=np.zeros(shape_high, dtype=float)
)
# Prepare measure storage:
@@ -82,13 +101,31 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
# Log snippet data:
if scale in example_scales:
scale_ind = np.nonzero(example_scales == scale)[0][0]
snippets[stage][:, ..., scale_ind] = signals[stage]
snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]
# Log intensity measure per stage (excluding binary):
if stage in ['raw', 'filt', 'env', 'log', 'inv', 'conv']:
measures[key][i] = signals[stage][segment, ...].std(axis=0)
elif stage == 'feat':
measures[key][i] = signals[stage][segment, :].mean(axis=0)
# thresh_y = np.percentile(measures['measure_feat'], 99, axis=0)
# kern_types = np.unique()
# thresh_x = np.zeros(thresh_y.shape, dtype=float)
# for i, thresh in enumerate(thresh_y):
# if thresh < 0.1:
# thresh_x[i] = scales[-1]
# continue
# mask = (measures['measure_feat'][:, i] < thresh)
# thresh_x[i] = scales[np.nonzero(mask)[0][-1]]
# inds = np.argsort(thresh_x)
# print(config['k_specs'][inds, :])
# fig, axes = plt.subplots(1, 2)
# axes[0].plot(snippets['snip_feat'][:, inds, -1])
# axes[1].plot(scales, measures['measure_feat'][:, inds])
# plt.show()
# embed()
# Save analysis results:
if save_path is not None: