def test_save(self):
filename = get_file_name()
df = AudioDataFile()
df.load(filename, formatter=AIFFormatter())
df.save("tmp.h5", formatter=HDF5Formatter())
df2 = AudioDataFile().load("tmp.h5", formatter=HDF5Formatter())
assert df2.duration.seconds > 0
def test_scale():
file_name = get_file_name()
df = AudioDataFile().load(file_name, formatter=AIFFormatter())
p = Scale()
p.parameters["data"] = df
result = p.transform()
assert p.description != ""
assert type(result) is df.__class__
def test_identity():
file_name = get_file_name()
df = AudioDataFile().load(file_name, formatter=AIFFormatter())
f = Identity()
f.parameters["data"] = df
t = f.transform()
np.testing.assert_allclose(df.data.values.ravel(), t.data.values.ravel())
assert f.description != ""
assert t.data.values.ndim == 2
def test_add_remove_datafile(self):
filename = get_file_name()
df = AudioDataFile()
df.load(filename,
formatter=AIFFormatter())
ds = OneDataFileOut()
ds.add_data_file(df)
assert len(ds.datafiles) == 1
ds.remove_data_file(df)
assert len(ds.datafiles) == 0
def test_skewness():
file_name = get_file_name()
df = AudioDataFile().load(file_name, formatter=AIFFormatter())
df.data -= df.data.mean()
f = Skewness()
f.parameters["data"] = df
t = f.transform()
assert t.data.values.shape[1] == 1
assert f.description != ""
assert t.data.values.ndim == 2
def test_sliding_windows():
filename = get_file_name()
p = SlidingWindows()
p.parameters["sliding_window_width"] = "13s"
p.parameters["overlap"] = 0.12
df = AudioDataFile()
df.load(filename, formatter=AIFFormatter())
p.parameters["data"] = df
new_df = p.transform()
new_df.get_windows_data_frame()
assert p.description != ""
def test_spectral_frames():
file_name = get_file_name()
df = AudioDataFile().load(file_name, formatter=AIFFormatter())
df.data -= df.data.mean()
f = SpectralFrames()
f.parameters["sampling_rate"] = df.sampling_rate
f.parameters["data"] = df
t = f.transform()
assert t.data.values.shape[0] == 1
assert f.description != ""
assert t.data.values.ndim == 2
def test_mfcc():
file_name = get_file_name()
df = AudioDataFile().load(file_name, formatter=AIFFormatter())
df.data -= df.data.mean()
f = MFCC()
f.parameters["sampling_rate"] = df.sampling_rate
f.parameters["n_components"] = 25
f.parameters["data"] = df
t = f.transform()
assert t.data.values.shape[1] == 25
assert f.description != ""
assert t.data.values.ndim == 2
def test_load(self):
filename = get_file_name()
df = AudioDataFile()
# No attribute data
assert str(df) == "AudioDataFile"
with pytest.raises(RuntimeError):
df.duration
with pytest.raises(RuntimeError):
df.start_time
with pytest.raises(RuntimeError):
df.end_time
df.load(filename, formatter=AIFFormatter())
assert df.duration.seconds > 0
assert str(df) == "AudioDataFile (0 days 00:14:59.999500)"
def test_read(self):
filename = get_file_name()
formatter = AIFFormatter()
df = formatter.read(filename)
assert type(df) is pd.Series
assert len(df) > 0
assert type(df.index) is pd.DatetimeIndex
new_filename = "same_file_new_name.aif"
rename(filename, "same_file_new_name.aif")
df = formatter.read(new_filename)
assert type(df) is pd.Series
assert len(df) > 0
assert type(df.index) is pd.RangeIndex
def test_sampling_rate(self):
filename = get_file_name()
df = AudioDataFile()
assert df.sampling_rate is None
df.load(filename, formatter=AIFFormatter())
assert df.sampling_rate > 0
def test_read_metadata(self):
filename = get_file_name()
formatter = AIFFormatter()
metadata = formatter.read_metadata(filename)
assert type(metadata) is dict