test_ribbit.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul  9 12:14:23 2021

@author: amandabreton
"""
# suppress warnings
import warnings
warnings.simplefilter('ignore')

#import packages
import numpy as np
from glob import glob
import pandas as pd
from matplotlib import pyplot as plt
import argparse
import yaml

#local imports from opensoundscape
from opensoundscape.audio import Audio
from opensoundscape.spectrogram import Spectrogram
from opensoundscape.ribbit import ribbit
from opensoundscape.helpers import run_command

# create big visuals
plt.rcParams['figure.figsize'] = [15, 8]

# download files from box.com to the current directory
#_ = run_command(f"curl -L https://pitt.box.com/shared/static/9mrxib85y1jmf1ybbjvbr0tv171iekvy.gz -o ./great_plains_toad_dataset.tar.gz")# | tar -xz -f")
#_ = run_command(f"tar -xz -f great_plains_toad_dataset.tar.gz")

# this will print `0` if everything went correctly. If it prints 256 or another number, something is wrong (try downloading from the link above)

# using our own data: just put the audio into a folder w/ same name
#parser = argparse.ArgumentParser()
#parser.add_argument('config_filename')
#args = parser.parse_args()
#CONFIG_FILE = args.config_filename

#with open(CONFIG_FILE) as f:
#    configs = yaml.load(f, Loader=yaml.SafeLoader)
#audio_path = configs['path']
# audio_path is the path to a single audio file
# example:
# path: /Users/amandabreton/Documents/GitHub/gnatcatcher/sounds/great_plains_toad_dataset/5D3C4530.WAV'
# audio_path = np.sort(glob('./great_plains_toad_dataset/5D31ED38.WAV'))[0]
audio_path = np.sort(glob('./great_plains_toad_dataset/*'))[0]


# load the audio file into an OpenSoundscape Audio object
audio = Audio.from_file(audio_path)

# create a Spectrogram object
spectrogram = Spectrogram.from_audio(audio)

#%%

# minimum and maximum rate of pulsing (pulses per second) to search for
pulse_rate_range = [10, 20]
# look for a vocalization in the range of 1000-2000 Hz
signal_band = [2000, 2500]
# subtract the amplitude signal from these frequency ranges
noise_bands = [[0, 200], [10000, 10100]]
# divides the signal into segments this many seconds long, analyzes each independently
window_length = 2  # (seconds)
# if True, it will show the power spectrum plot for each audio segment
show_plots = True

# %% this is the part that gives you scoress
#get the audio file path
#audio_path = np.sort(glob('./great_plains_toad_dataset/*'))[1]
#make the spectrogram
spec = Spectrogram.from_audio(audio.from_file(audio_path))

#run RIBBIT
scores, times =  ribbit(
                    spec,
                    pulse_rate_range=pulse_rate_range,
                    signal_band=signal_band,
                    window_len=window_length,
                    noise_bands=noise_bands,
                    plot=False)

#show the spectrogram
spec.plot()

# %% plot the score vs time of each window
times = np.array(times)
times = times/2
plt.scatter(times,scores)
plt.xlabel('window start time (sec)')
plt.ylabel('RIBBIT score')
plt.title('RIBBIT scores')
print('The max score is:')
print(np.max(scores))
maxscore = np.max(scores)