j_hartling/paper_2025
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Overhauled Thresh-LP analysis and figures (WIP).

Browse Source
This commit is contained in:
j-hartling
2026-03-10 17:48:10 +01:00
parent 0407053c20
commit 4494bc7783
12 changed files with 952 additions and 107 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
python/save_inv_data_log-hp.py
View File

@@ -12,25 +12,29 @@ data_paths = glob.glob(f'../data/processed/{target}*.npz')
save_path = '../data/inv/log_hp/'
# ANALYSIS SETTINGS:
add_noise = False
add_noise = True
single_db_ref = True
# find_saturation = add_noise and False
example_scales = np.array([0, 0.1, 1, 10, 100, 200])
scales = np.geomspace(0.1, 1000, 100)
if not add_noise:
example_scales = example_scales[example_scales > 0]
scales = np.unique(np.concatenate((scales, example_scales)))
# if find_saturation:
# scales = np.append(scales, 10e10)
# EXECUTION:
for data_path, name in zip(data_paths, crop_paths(data_paths)):
print(f'Processing {name}')
# Get normalized song envelope:
# Get song envelope:
data, config = load_data(data_path, files='env')
song, rate = data['env'], config['env_rate']
song /= song.std()
# Get song segment to be analyzed:
time = np.arange(song.shape[0]) / rate
start, end = data['songs_0'].ravel()
segment = (time >= start) & (time <= end)
# Normalize song component:
song /= song[segment].std()
# Rescale song component:
mix = song[:, None] * scales[None, :]
@@ -40,7 +44,7 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
rng = np.random.default_rng()
noise = rng.normal(size=song.shape)
noise = extract_env(noise, rate, config=config)
noise /= noise.std()
noise /= noise[segment].std()
mix += noise[:, None]
# Process mixture:
@@ -49,17 +53,9 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
padtype='constant', padlen=config['padlen'])
# Get "intensity measure" per stage:
measure_env = mix.std(axis=0)
measure_log = mix_log.std(axis=0)
measure_inv = mix_inv.std(axis=0)
# # Find saturation level:
# if find_saturation:
# limit = measure_inv[-1]
# scales = scales[:-1]
# measure_env = measure_env[:-1]
# measure_log = measure_log[:-1]
# measure_inv = measure_inv[:-1]
measure_env = mix[segment, :].std(axis=0)
measure_log = mix_log[segment, :].std(axis=0)
measure_inv = mix_inv[segment, :].std(axis=0)
# Save analysis results:
save_inds = np.nonzero(np.isin(scales, example_scales))[0]
@@ -74,8 +70,6 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
measure_log=measure_log,
measure_inv=measure_inv,
)
# if find_saturation:
# data['limit'] = limit
file_name = save_path + name
if add_noise:
file_name += '_noise'