#! /usr/bin/env python3


import numpy as np
from scipy.io import wavfile
from scipy.fft import fft
from scipy.signal import butter, lfilter, decimate
import matplotlib.pyplot as plt
import sys

frequencies = np.array([10 ** ((n + 22) * 0.0225 + 2) for n in range(32)])
widths = np.array([6.25 * 10 ** (n * 0.0225) for n in range(32)])
widths = np.array([6.25 for n in range(32)])

def butter_bandpass(lowcut, highcut, fs, order=5):
    nyquist = 0.5 * fs
    low = lowcut / nyquist
    high = highcut / nyquist
    b, a = butter(order, [low, high], btype='band')
    return b, a

def bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = lfilter(b, a, data)
    return y

def butter_lowpass(cutoff, fs, order=5):
    nyquist = 0.5 * fs
    normal_cutoff = cutoff / nyquist
    b, a = butter(order, normal_cutoff, btype='low', analog=False)
    return b, a

def lowpass_filter(data, cutoff, fs, order=5):
    b, a = butter_lowpass(cutoff, fs, order=order)
    y = lfilter(b, a, data)
    return y

def get_largest_two_indices(numbers, threshold):
    # Check if the list has at least three numbers
    if len(numbers) < 3:
        return None

    # Find the indices of the three largest numbers in the list
    indices = sorted(range(len(numbers)), key=lambda i: numbers[i], reverse=True)
    largest_index = indices[0]
    second_largest_index = indices[1]
    third_largest_index = indices[2]

    # Check if the largest and second largest numbers are at least threshold larger than the third largest
    if numbers[largest_index] - numbers[third_largest_index] >= threshold and \
       numbers[second_largest_index] - numbers[third_largest_index] >= threshold:
        return largest_index, second_largest_index
    else:
        return None

# Step 1: Read the WAV file
sample_rate, data = wavfile.read(sys.argv[1])

# Handle stereo audio by converting to mono if needed
if len(data.shape) == 2:
    data = data.mean(axis=1)

# Define the maximum frequency of interest and Nyquist rate
max_freq = 1600.0  # 2000 Hz
nyquist_rate = 2 * max_freq  # Nyquist rate to prevent aliasing

# If the sample rate is higher than the Nyquist rate, downsample
if sample_rate > nyquist_rate:
    # Apply a lowpass filter before downsampling
    cutoff = max_freq #+ 500  # Lowpass filter cutoff slightly above max_freq to prevent aliasing
    filtered_data = lowpass_filter(data, cutoff, sample_rate)

    # Calculate the downsampling factor
    downsample_factor = int(sample_rate / nyquist_rate)

    # Downsample the filtered signal
    filtered_data = decimate(filtered_data, downsample_factor)
    sample_rate = sample_rate // downsample_factor
else:
    filtered_data = data

# Step 2: Define the increment (0.1 seconds) and segment size
#increment = 0.2  # in seconds
#segment_size = int(sample_rate * increment)
segment_size = 1024
increment = segment_size / sample_rate
print(f"Segment size: {segment_size}")

# Step 3: Process each segment
num_segments = len(filtered_data) // segment_size

# Calculate the frequency resolution
delta_f = sample_rate / segment_size

# Determine the bin range for desired frequency range (100 Hz to 2000 Hz)
high_bin = int(max_freq / delta_f)

# Initialize a 2D array to store DFT results (magnitude spectrum)
# Only store the bins within the desired frequency range
dft_results = np.zeros((num_segments, high_bin))

for i in range(num_segments):
    start = i * segment_size
    end = start + segment_size
    segment = filtered_data[start:end]

    # Step 4: Apply the DFT
    dft_result = fft(segment)

    magnitudes = np.abs(dft_result)
    total_energy = np.sum(magnitudes ** 2)
    normalized_magnitudes = magnitudes / np.sqrt(total_energy)
    normalized_magnitude = np.mean(normalized_magnitudes)

    # Store the magnitude spectrum in the 2D array, only for the desired frequency range
    dft_results[i, :] = normalized_magnitudes[:high_bin]

    scores = [
        10 * np.log10(np.sum(normalized_magnitudes[int((f-w)/delta_f):int((f+w)/delta_f)]))
        for f,w in zip(frequencies, widths)
    ]

    codes = get_largest_two_indices(scores, 3.0)
    if codes:
        print([frequencies[code] for code in codes])


# Step 5: Plot the spectrogram
plt.figure(figsize=(12, 8))
extent = [0, num_segments * increment, 0, max_freq]
plt.imshow(dft_results.T, aspect='auto', origin='lower', extent=extent, cmap='viridis')
plt.colorbar(label='Magnitude')
plt.title('Spectrogram (100 Hz to 2000 Hz)')
plt.xlabel('Time (s)')
plt.ylabel('Frequency (Hz)')


for freq in frequencies:
    plt.axhline(y=freq, color='r', linestyle='--', linewidth=0.5)
plt.show()