resampler 0.3.4

#!/usr/bin/env python3
"""
Audio Resampler Quality Testing Suite

This script generates test signals to evaluate audio resampler quality and
visualizes the results with filter frequency response and sweep spectrogram.

Usage:
    1. Generate test files:
       python test_audio_resampler.py generate --input-rate 22050 --output-rate 48000

    2. Run your resampler on the generated files:
       ./resampler test_impulse.wav test_impulse_resampled.wav
       ./resampler test_sweep.wav test_sweep_resampled.wav

    3. Analyze results:
       python test_audio_resampler.py analyze --input-rate 22050 --output-rate 48000
"""

import argparse
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
from scipy import signal
from pathlib import Path


class ResamplerTester:
    def __init__(self, input_rate, output_rate, duration=5.0, channels=2):
        self.input_rate = input_rate
        self.output_rate = output_rate
        self.duration = duration
        self.channels = channels

    def generate_test_signals(self, output_dir="."):
        """Generate test signals as WAV files."""
        output_dir = Path(output_dir)
        output_dir.mkdir(exist_ok=True)

        print(f"Generating test signals at {self.input_rate} Hz...")
        print(f"Target output rate: {self.output_rate} Hz")
        print(f"Duration: {self.duration} seconds")
        print(f"Channels: {self.channels}")
        print()

        # 1. Impulse response test (for filter frequency response)
        impulse = self._generate_impulse()
        self._save_wav(output_dir / "test_impulse.wav", impulse)
        print(f"✓ Generated test_impulse.wav (for filter frequency response)")

        # 2. Sine sweep test (for spectrogram)
        sweep = self._generate_sweep()
        self._save_wav(output_dir / "test_sweep.wav", sweep)
        print(f"✓ Generated test_sweep.wav (for spectrogram)")

        print()
        print("Test signal generation complete!")
        print()
        print("Next steps:")
        print("1. Run your resampler on these files")
        print("2. Use 'analyze' command to visualize results")


    def _generate_impulse(self):
        """Generate impulse signal."""
        n_samples = int(self.duration * self.input_rate)
        impulse = np.zeros((n_samples, self.channels), dtype=np.float32)

        # Place impulse at 0.5 seconds (or middle if duration < 1 second)
        impulse_pos = min(int(0.5 * self.input_rate), n_samples - 1)
        impulse[impulse_pos, :] = 1.0

        return impulse

    def _generate_sweep(self):
        """Generate logarithmic sine sweep from 20 Hz to Nyquist."""
        n_samples = int(self.duration * self.input_rate)
        t = np.linspace(0, self.duration, n_samples)

        # Sweep from 20 Hz to 95% of Nyquist
        f0 = 20
        f1 = self.input_rate / 2 * 0.95

        # Logarithmic sweep
        sweep = signal.chirp(t, f0, self.duration, f1, method='logarithmic')

        # Apply fade in/out to reduce edge effects
        fade_samples = int(0.1 * self.input_rate)
        fade_in = np.linspace(0, 1, fade_samples)
        fade_out = np.linspace(1, 0, fade_samples)
        sweep[:fade_samples] *= fade_in
        sweep[-fade_samples:] *= fade_out

        # Normalize and duplicate for channels
        sweep = sweep * 0.99
        return np.column_stack([sweep] * self.channels).astype(np.float32)


    def _save_wav(self, filename, data):
        """Save audio data as WAV file."""
        # Convert to int16 for WAV
        data_int16 = (data * 32767).astype(np.int16)
        wavfile.write(filename, self.input_rate, data_int16)

    def analyze_results(self, input_dir="."):
        """Analyze resampled output files and generate visualizations."""
        input_dir = Path(input_dir)

        print("Analyzing resampled outputs...")
        print()

        # Create figure with two plots stacked vertically
        fig = plt.figure(figsize=(18, 12))
        fig.suptitle(f'Audio Resampler Quality Analysis: {self.input_rate} Hz → {self.output_rate} Hz',
                     fontsize=16, fontweight='bold')

        # 1. Filter frequency response (from impulse) - top
        self._analyze_filter_frequency_response(fig, input_dir)

        # 2. Sweep spectrogram - bottom
        self._analyze_sweep_spectrogram(fig, input_dir)

        plt.tight_layout()
        output_file = input_dir / "resampler_analysis.png"
        plt.savefig(output_file, dpi=150, bbox_inches='tight')
        print(f"\n✓ Analysis plot saved to: {output_file}")
        plt.show()

    def _analyze_filter_frequency_response(self, fig, input_dir):
        """Analyze impulse response to show filter frequency response."""
        impulse_file = input_dir / "test_impulse_resampled.wav"

        if not impulse_file.exists():
            print(f"⚠ Skipping filter frequency response: {impulse_file} not found")
            return

        rate, data = wavfile.read(impulse_file)

        # Convert to float and take first channel
        if data.dtype == np.int16:
            data = data.astype(np.float32) / 32768.0

        if len(data.shape) > 1:
            data = data[:, 0]

        # Find the impulse peak
        peak_idx = np.argmax(np.abs(data))

        # Extract window around impulse
        window_size = int(0.1 * rate)  # 100ms window
        start = max(0, peak_idx - window_size // 2)
        end = min(len(data), start + window_size)
        impulse_response = data[start:end]

        # Plot frequency domain
        ax = plt.subplot(2, 1, 1)

        # FFT of impulse response
        n_fft = 8192
        freq_response = np.fft.rfft(impulse_response, n=n_fft)
        freqs = np.fft.rfftfreq(n_fft, 1/rate)

        # Convert to dB
        magnitude_db = 20 * np.log10(np.abs(freq_response) + 1e-10)

        ax.plot(freqs / 1000, magnitude_db, linewidth=0.8)
        ax.set_xlabel('Frequency (kHz)', fontsize=11)
        ax.set_ylabel('Magnitude (dB)', fontsize=11)
        ax.set_title('Filter Frequency Response', fontsize=13, fontweight='bold')
        ax.grid(True, alpha=0.3)
        ax.set_ylim(-120, 10)

        # Mark passband and stopband
        nyquist_in = self.input_rate / 2
        ax.axvline(nyquist_in / 1000, color='r', linestyle='--',
                   alpha=0.5, linewidth=2, label=f'Input Nyquist ({nyquist_in:.0f} Hz)')

        # Add transition band shading
        cutoff = nyquist_in * 0.95  # Approximate passband edge
        ax.axvspan(0, cutoff / 1000, alpha=0.1, color='green', label='Passband')
        ax.axvspan(nyquist_in / 1000, rate / 2000, alpha=0.1, color='red', label='Stopband')

        ax.legend(loc='upper right', fontsize=9)

        # Detailed filter analysis
        print(f"✓ Analyzed filter frequency response:")
        print(f"  Input rate: {self.input_rate} Hz, Output rate: {self.output_rate} Hz")
        print(f"  Input Nyquist: {nyquist_in:.0f} Hz, Output Nyquist: {rate/2:.0f} Hz")
        print()

        # Define passband (DC to 90% of input Nyquist or output Nyquist, whichever is lower)
        min_nyquist = min(nyquist_in, rate / 2)
        passband_end_freq = min_nyquist * 0.9
        passband_end_idx = np.argmax(freqs > passband_end_freq)

        # Define stopband (starts above input Nyquist or output Nyquist)
        stopband_start_freq = min_nyquist * 1.1
        stopband_start_idx = np.argmax(freqs > stopband_start_freq)

        # Find DC level (reference for measurements)
        dc_level = magnitude_db[1]  # Skip DC bin at 0

        # Passband analysis (DC to 90% Nyquist)
        if passband_end_idx > 10:
            passband_magnitudes = magnitude_db[1:passband_end_idx]
            passband_max = np.max(passband_magnitudes)
            passband_min = np.min(passband_magnitudes)
            passband_ripple = passband_max - passband_min
            passband_mean = np.mean(passband_magnitudes)

            print(f"  PASSBAND (DC to {passband_end_freq:.0f} Hz):")
            print(f"    - Peak level: {passband_max:.2f} dB")
            print(f"    - Min level: {passband_min:.2f} dB")
            print(f"    - Ripple: {passband_ripple:.2f} dB (±{passband_ripple/2:.2f} dB)")
            print(f"    - Mean level: {passband_mean:.2f} dB")

            # Find -3dB cutoff
            cutoff_3db_idx = np.argmax(magnitude_db < passband_max - 3.0)
            if cutoff_3db_idx > 0:
                cutoff_3db_freq = freqs[cutoff_3db_idx]
                print(f"    - -3dB cutoff: {cutoff_3db_freq:.0f} Hz ({cutoff_3db_freq/min_nyquist:.2f} × Nyquist)")

        # Stopband analysis (above 110% Nyquist)
        if stopband_start_idx > 0 and stopband_start_idx < len(magnitude_db) - 10:
            stopband_magnitudes = magnitude_db[stopband_start_idx:]
            stopband_max = np.max(stopband_magnitudes)
            stopband_min = np.min(stopband_magnitudes)
            stopband_ripple = stopband_max - stopband_min
            stopband_mean = np.mean(stopband_magnitudes)

            # Calculate attenuation relative to passband
            attenuation = passband_max - stopband_max if passband_end_idx > 10 else -stopband_max

            print(f"\n  STOPBAND ({stopband_start_freq:.0f} Hz to {rate/2:.0f} Hz)")
            print(f"    - Peak level: {stopband_max:.2f} dB")
            print(f"    - Min level: {stopband_min:.2f} dB")
            print(f"    - Ripple: {stopband_ripple:.2f} dB")
            print(f"    - Mean level: {stopband_mean:.2f} dB")
            print(f"    - Attenuation: {attenuation:.2f} dB")

            # Check for ripple pattern (should see oscillations in stopband)
            # Calculate standard deviation as measure of ripple
            stopband_std = np.std(stopband_magnitudes)
            print(f"    - Ripple std dev: {stopband_std:.2f} dB")

        print()

    def _analyze_sweep_spectrogram(self, fig, input_dir):
        """Analyze frequency response from sweep."""
        sweep_file = input_dir / "test_sweep_resampled.wav"

        if not sweep_file.exists():
            print(f"⚠ Skipping frequency response: {sweep_file} not found")
            return

        rate, data = wavfile.read(sweep_file)

        # Convert to float
        if data.dtype == np.int16:
            data = data.astype(np.float32) / 32768.0

        if len(data.shape) > 1:
            data = data[:, 0]

        # Compute spectrogram
        ax = plt.subplot(2, 1, 2)

        nperseg = 4096
        f, t, Sxx = signal.spectrogram(data, rate, nperseg=nperseg,
                                       noverlap=nperseg//2)

        # Plot spectrogram
        Sxx_db = 10 * np.log10(Sxx + 1e-10)
        im = ax.pcolormesh(t, f / 1000, Sxx_db, shading='gouraud',
                           cmap='turbo', vmin=-80, vmax=0)

        ax.set_xlabel('Time (s)', fontsize=11)
        ax.set_ylabel('Frequency (kHz)', fontsize=11)
        ax.set_title('Sweep Spectrogram', fontsize=13, fontweight='bold')

        # Mark input Nyquist
        nyquist_in = self.input_rate / 2
        ax.axhline(nyquist_in / 1000, color='r', linestyle='--',
                   alpha=0.7, linewidth=2, label=f'Input Nyquist ({nyquist_in:.0f} Hz)')

        # Add passband/stopband regions
        ax.axhspan(0, nyquist_in / 1000, alpha=0.05, color='green', zorder=0)
        ax.axhspan(nyquist_in / 1000, rate / 2000, alpha=0.1, color='red', zorder=0)

        ax.legend(loc='upper right', fontsize=9)

        # Add colorbar
        cbar = plt.colorbar(im, ax=ax, label='Magnitude (dB)')
        cbar.set_label('Magnitude (dB)', fontsize=10)

        print(f"✓ Analyzed sweep spectrogram")


def main():
    parser = argparse.ArgumentParser(
        description='Audio Resampler Quality Testing Suite',
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__
    )

    subparsers = parser.add_subparsers(dest='command', help='Command to run')

    # Generate command
    gen_parser = subparsers.add_parser('generate', help='Generate test signals')
    gen_parser.add_argument('--input-rate', type=int, required=True,
                           help='Input sample rate (Hz)')
    gen_parser.add_argument('--output-rate', type=int, required=True,
                           help='Target output sample rate (Hz)')
    gen_parser.add_argument('--duration', type=float, default=5.0,
                           help='Duration of test signals (seconds, default: 5.0)')
    gen_parser.add_argument('--channels', type=int, default=2,
                           help='Number of channels (default: 2)')
    gen_parser.add_argument('--output-dir', type=str, default='.',
                           help='Output directory (default: current directory)')

    # Analyze command
    analyze_parser = subparsers.add_parser('analyze', help='Analyze resampled outputs')
    analyze_parser.add_argument('--input-rate', type=int, required=True,
                               help='Original input sample rate (Hz)')
    analyze_parser.add_argument('--output-rate', type=int, required=True,
                               help='Target output sample rate (Hz)')
    analyze_parser.add_argument('--input-dir', type=str, default='.',
                               help='Directory with resampled files (default: current directory)')

    args = parser.parse_args()

    if args.command is None:
        parser.print_help()
        return

    if args.command == 'generate':
        tester = ResamplerTester(
            input_rate=args.input_rate,
            output_rate=args.output_rate,
            duration=args.duration,
            channels=args.channels
        )
        tester.generate_test_signals(args.output_dir)

    elif args.command == 'analyze':
        tester = ResamplerTester(
            input_rate=args.input_rate,
            output_rate=args.output_rate
        )
        tester.analyze_results(args.input_dir)


if __name__ == '__main__':
    main()