83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
import numpy as np
|
|
from scipy.stats import gaussian_kde
|
|
from thunderhopper.filetools import crop_paths
|
|
|
|
def shorten_species(name):
|
|
genus, species = name.split('_')
|
|
return genus[0] + '. ' + species
|
|
|
|
def unsort_unique(array):
|
|
values, inds = np.unique(array, return_index=True)
|
|
return values[np.argsort(inds)]
|
|
|
|
def draw_noise_segment(noise, n):
|
|
rng = np.random.default_rng()
|
|
start = rng.integers(0, noise.shape[0] - n, endpoint=True)
|
|
return np.take(noise, np.arange(start, start + n), axis=0)
|
|
|
|
def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
|
|
# Separate by source:
|
|
sorted_paths = {}
|
|
for source in sources:
|
|
|
|
# Check for any source-specific song files:
|
|
source_paths = [path for path in paths if source in path]
|
|
if not source_paths:
|
|
continue
|
|
|
|
# Separate by recording:
|
|
sorted_paths[source] = [[]]
|
|
for path, name in zip(source_paths, crop_paths(source_paths)):
|
|
|
|
# Find numerical ID behind source tag:
|
|
id_ind = name.find(source) + len(source) + 1
|
|
# Get segment where sub-ID would be:
|
|
sub_id = name[id_ind:].split('-')[1]
|
|
if 's' in sub_id:
|
|
# Found time stamp (single recording):
|
|
sorted_paths[source][0].append(path)
|
|
continue
|
|
sub_id = int(sub_id)
|
|
# Found sub-ID (multiple recordings):
|
|
if sub_id > len(sorted_paths[source]):
|
|
# Open new recording-specific slot:
|
|
sorted_paths[source].append([])
|
|
sorted_paths[source][sub_id - 1].append(path)
|
|
|
|
# Re-sort song files by recording only (discarding source separation):
|
|
sorted_paths = [path for paths in sorted_paths.values() for path in paths]
|
|
return sorted_paths
|
|
|
|
def get_kde(data, sigma, axis=None, n=1000, pad=10):
|
|
if axis is None:
|
|
axis = np.linspace(data.min() - pad * sigma, data.max() + pad * sigma, n)
|
|
pdf = gaussian_kde(data, sigma)(axis)
|
|
return pdf, axis
|
|
|
|
def get_saturation(sigmoid, low=0.05, high=0.95, first=True, last=True,
|
|
condense=None):
|
|
if condense == 'norm' and sigmoid.ndim == 2:
|
|
sigmoid = np.linalg.norm(sigmoid, axis=1)
|
|
|
|
min_value = sigmoid[0] if first else sigmoid.min(axis=0)
|
|
max_value = sigmoid[-1] if last else sigmoid.max(axis=0)
|
|
|
|
span = max_value - min_value
|
|
low_value = min_value + low * span
|
|
high_value = min_value + high * span
|
|
|
|
low_mask = sigmoid <= low_value
|
|
high_mask = sigmoid <= high_value
|
|
if sigmoid.ndim == 1:
|
|
low_ind = np.nonzero(low_mask)[0][-1]
|
|
high_ind = np.nonzero(high_mask)[0][-1]
|
|
elif condense == 'all':
|
|
low_ind = np.nonzero(low_mask.all(axis=1))[0][-1]
|
|
high_ind = np.nonzero(high_mask.all(axis=1))[0][-1]
|
|
else:
|
|
low_ind, high_ind = [], []
|
|
for i in range(sigmoid.shape[1]):
|
|
low_ind.append(np.nonzero(low_mask[:, i])[0][-1])
|
|
high_ind.append(np.nonzero(high_mask[:, i])[0][-1])
|
|
return low_ind, high_ind
|