acoustic_reflection_analysis/fft_analysis/tests/test_io_utils.py

76 lines
2.2 KiB
Python

import json
import os
import tempfile
import numpy as np
import pytest
from pathlib import Path
from fft_analysis import io_utils
def test_load_audio_file_wav(monkeypatch):
# Mock scipy.io.wavfile.read to return synthetic data
import scipy.io.wavfile as wavfile
sample_rate = 44100
data = np.array([0, 32767, -32768], dtype=np.int16)
def mock_read(file_path):
assert isinstance(file_path, (str, Path))
return sample_rate, data
monkeypatch.setattr(wavfile, 'read', mock_read)
arr, sr = io_utils.load_audio_file("dummy.wav")
assert isinstance(arr, np.ndarray)
assert sr == sample_rate
assert np.allclose(arr, data.astype(np.float32) / np.max(np.abs(data)))
def test_load_audio_file_invalid_path():
with pytest.raises(FileNotFoundError):
io_utils.load_audio_file("nonexistent_path.wav")
def test_save_spectrum_to_json_and_verify(tmp_path):
spectrum = np.array([0.1, 0.5, 0.9, 0.2])
output_file = tmp_path / "spectrum.json"
io_utils.save_spectrum_to_json(spectrum, str(output_file))
assert output_file.exists()
with open(output_file, "r", encoding="utf-8") as f:
data = json.load(f)
assert isinstance(data, dict)
assert 'frequencies' in data and 'magnitudes' in data
assert isinstance(data['frequencies'], list)
assert isinstance(data['magnitudes'], list)
assert len(data['frequencies']) == len(data['magnitudes']) == len(spectrum)
def test_save_spectrum_to_json_invalid_path():
spectrum = np.array([0.1, 0.2, 0.3])
invalid_path = "/invalid_directory/output.json"
with pytest.raises((OSError, IOError)):
io_utils.save_spectrum_to_json(spectrum, invalid_path)
def test_load_audio_file_normalization(monkeypatch):
import scipy.io.wavfile as wavfile
sample_rate = 48000
data = np.array([0, 1000, -1000], dtype=np.int16)
def mock_read(file_path):
return sample_rate, data
monkeypatch.setattr(wavfile, 'read', mock_read)
arr, sr = io_utils.load_audio_file("file.wav")
assert sr == sample_rate
assert np.isclose(np.max(arr), 1.0, atol=0.01) or np.isclose(np.max(arr), 0.0)
assert np.isclose(np.min(arr), -1.0, atol=0.01) or np.isclose(np.min(arr), 0.0)