def test_bound():
assert helpers.bound(-1, [0, 1]) == 0 and helpers.bound(2, [0, 1]) == 1
def pulse_finder_species_set(spec, species_df, window_len="from_df", plot=False):
""" perform windowed pulse finding (ribbit) on one file for each species in a set
Args:
spec: opensoundscape.Spectrogram object
species_df: a dataframe describing species by their pulsed calls.
columns: species | pulse_rate_low (Hz)| pulse_rate_high (Hz) | low_f (Hz)| high_f (Hz)| reject_low (Hz)| reject_high (Hz) |
window_length (sec) (optional) | reject_low2 (opt) | reject_high2 |
window_len: length of analysis window, in seconds.
Or 'from_df' (default): read from dataframe.
or 'dynamic': adjust window size based on pulse_rate
Returns:
the same dataframe with a "score" (max score) column and "time_of_score" column
"""
species_df = species_df.copy()
species_df = species_df.set_index(species_df.columns[0], drop=True)
species_df["score"] = [[] for i in range(len(species_df))]
species_df["t"] = [[] for i in range(len(species_df))]
species_df["max_score"] = [np.nan for i in range(len(species_df))]
for i, row in species_df.iterrows():
# we can't analyze pulse rates of 0 or NaN
if isNan(row.pulse_rate_low) or row.pulse_rate_low == 0:
# cannot analyze
continue
pulse_rate_range = [row.pulse_rate_low, row.pulse_rate_high]
if window_len == "from_df":
window_len = row.window_length
elif window_len == "dynamic":
# dynamically choose the window length based on the species pulse-rate
# try to capture ~4-10 pulses
min_len = 0.5 # sec
max_len = 10 # sec
target_n_pulses = 5
window_len = bound(
target_n_pulses / pulse_rate_range[0], [min_len, max_len]
)
# otherwise, use the numerical value provided for window length
signal_band = [row.low_f, row.high_f] # changed from low_f, high_f
noise_bands = None
if not isNan(row.reject_low):
noise_bands = [[row.reject_low, row.reject_high]]
if "reject_low2" in species_df.columns and not isNan(row.reject_low2):
noise_bands.append([row.reject_low2, row.reject_high2])
# score this species for each window using ribbit
if plot:
print(f"{row.name}")
pulse_scores, window_start_times = ribbit(
spec, signal_band, pulse_rate_range, window_len, noise_bands, plot
)
# add the scores to the species df
species_df.at[i, "score"] = pulse_scores
species_df.at[i, "t"] = window_start_times
species_df.at[i, "max_score"] = (
max(pulse_scores) if len(pulse_scores) > 0 else np.nan
)
species_df.at[i, "time_of_max_score"] = (
window_start_times[np.argmax(pulse_scores)]
if len(pulse_scores) > 0
else np.nan
)
return species_df