def _convertTmp(self, tmpCacheEntry):
"""
Moves a tmp file to the upload dir, resampling it if necessary, and then deleting the tmp entries.
:param tmpCacheEntry: the cache entry.
:return:
"""
from analyser.common.signal import loadSignalFromWav
tmpCacheEntry['status'] = 'converting'
logger.info("Loading " + tmpCacheEntry['path'])
signal = loadSignalFromWav(tmpCacheEntry['path'])
logger.info("Loaded " + tmpCacheEntry['path'])
if Path(tmpCacheEntry['path']).exists():
logger.info('Deleting ' + tmpCacheEntry['path'])
os.remove(tmpCacheEntry['path'])
else:
logger.warning('Tmp cache file does not exist: ' +
tmpCacheEntry['path'])
self._tmpCache.remove(tmpCacheEntry)
self._conversionCache.append(tmpCacheEntry)
srcFs = signal.fs
completeSamples = signal.samples
outputFileName = os.path.join(self._uploadDir, tmpCacheEntry['name'])
if srcFs > 1024:
self.writeOutput(outputFileName, completeSamples, srcFs, 1000)
else:
self.writeOutput(outputFileName, completeSamples, srcFs, srcFs)
tmpCacheEntry['status'] = 'loaded'
self._conversionCache.remove(tmpCacheEntry)
self._uploadCache.append(self._extractMeta(outputFileName, 'loaded'))
def spec(self):
measurementPath = os.path.join(os.path.dirname(__file__),
'../test/data', 'eot.wav')
measurement = ms.loadSignalFromWav(measurementPath)
# y_1000 = librosa.resample(measurement.samples, measurement.fs, 1000, res_type='kaiser_fast')
# measurement = Signal(y_1000, 1000)
f, Pxx = measurement.peakSpectrum(ref=1 / (2**0.5))
print(str(np.max(Pxx)))
plt.semilogx(f, Pxx)
plt.show()
def test_whenWavIsFullScale_AndNoScalingIsRequired_OutputIsUnchanged():
measurementPath = os.path.join(str(Path(__file__).resolve().parents[2]),
'full_scale_sine.wav')
calibrationPath = os.path.join(str(Path(__file__).resolve().parents[2]),
'full_scale_sine.wav')
measurement = ms.loadSignalFromWav(measurementPath,
calibrationSignalFile=calibrationPath,
calibrationRealWorldValue=1)
assert measurement is not None
assert measurement.fs == 48000
assert np.max(measurement.samples) == pytest.approx(1.0)
def test_whenWavIsHalfScale_AndScalingDownIsRequired_OutputIsCorrect():
measurementPath = os.path.join(str(Path(__file__).resolve().parents[2]),
'half_scale_sine.wav')
calibrationPath = os.path.join(str(Path(__file__).resolve().parents[2]),
'half_scale_sine.wav')
measurement = ms.loadSignalFromWav(measurementPath,
calibrationSignalFile=calibrationPath,
calibrationRealWorldValue=0.02)
assert measurement is not None
assert measurement.fs == 48000
assert np.max(measurement.samples) == pytest.approx(0.02)
def resam(self):
measurementPath = os.path.join(os.path.dirname(__file__),
'../test/data', 'white.wav')
measurement = ms.loadSignalFromWav(measurementPath)
y_1000 = librosa.resample(measurement.samples,
measurement.fs,
1000,
res_type='kaiser_fast')
measurementPath_1000 = os.path.join(os.path.dirname(__file__),
'../test/data', 'white_1000.wav')
maxv = np.iinfo(np.int32).max
librosa.output.write_wav(measurementPath_1000,
(y_1000 * maxv).astype(np.int32), 1000)
# librosa.output.write_wav(measurementPath_1000, y_1000, 1000)
measurement_1000 = ms.loadSignalFromWav(measurementPath_1000)
y_1000 = Signal(y_1000, 1000)
f, Pxx = measurement_1000.spectrum(ref=1.0)
plt.semilogx(f, Pxx)
f, Pxx = y_1000.spectrum(ref=1.0)
plt.semilogx(f, Pxx)
plt.show()
def loadSignal(self, name, start=None, end=None):
"""
Loads the named entry from the upload cache as a signal.
:param name: the name.
:param start: the time to start from in HH:mm:ss.SSS format
:param end: the time to end at in HH:mm:ss.SSS format.
:return: the signal if the named upload exists.
"""
entry = self._getCacheEntry(name)
if entry is not None:
from analyser.common.signal import loadSignalFromWav
return loadSignalFromWav(entry['path'], start=start, end=end)
else:
return None
def showSpectrum(self):
# measurementPath = 'C:\\Users\\\Matt\\OneDrive\\Documents\\eot\\Edge of Tomorrow - Opening.wav'
# measurementPath = os.path.join(os.path.dirname(__file__), '../test/data', 'eot.wav')
# measurementPath = os.path.join(os.path.dirname(__file__), '../test/data', 'white_0_50_1.wav')
measurementPath = os.path.join(os.path.dirname(__file__),
'../test/data', 'PinkNoise_10_50_1.wav')
measurement1 = ms.loadSignalFromWav(measurementPath)
# measurement1 = ms.loadSignalFromWav('C:\\Users\\Matt\\.vibe\\upload\\The Admiral Roaring Currents.wav')
# measurement2 = ms.loadSignalFromWav('C:\\Users\\Matt\\.vibe\\upload\\How to Train Your Dragon - Dragon Crash.wav')
plt.xlim(1, 100)
plt.ylim(-60, 0)
plt.grid()
plt.xlabel('frequency [Hz]')
f, Pxx_spec = measurement1.peakSpectrum(ref=1.0)
plt.semilogx(f, Pxx_spec)
# f, Pxx_spec = measurement2.spectrum(ref=1.0)
# plt.semilogx(f, Pxx_spec)
plt.show()
def showSpectro(self):
# measurementPath = 'C:\\Users\\\Matt\\OneDrive\\Documents\\eot\\Edge of Tomorrow - Opening.wav'
measurementPath = os.path.join(os.path.dirname(__file__), 'data',
'eot.wav')
measurement = ms.loadSignalFromWav(measurementPath)
# t, f, Sxx_spec = measurement.spectrogram()
# plt.pcolormesh(f, t, Sxx_spec)
# plt.ylim(0, 160)
cmap = plt.get_cmap('viridis')
cmap.set_under(color='k', alpha=None)
plt.specgram(measurement.samples,
NFFT=measurement.getSegmentLength(),
Fs=measurement.fs,
detrend=mlab.detrend_none,
mode='magnitude',
noverlap=measurement.getSegmentLength() / 2,
window=mlab.window_hanning,
vmin=-60,
cmap=plt.cm.gist_heat)
plt.ylim(0, 100)
plt.show()
