def test_frequency_input_errors():
"""
Check that errors are properly raised when incorrectly enter frequency information
"""
# Generate a random signal
x = _generate_random_x()
# Check that a bandpass filter cannot be completed without proper frequency limits
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandpass', f_lo=4)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandpass', f_hi=8)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandpass', f_lo=8, f_hi=4)
# Check that a bandstop filter cannot be completed without proper frequency limits
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandstop', f_lo=58)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandstop', f_hi=62)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandstop', f_lo=62, f_hi=58)
# Check that a lowpass filter cannot be completed without proper frequency limits
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'lowpass', f_hi=100)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'lowpass', f_lo=10, f_hi=100)
# Check that a highpass filter cannot be completed without proper frequency limits
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'highpass', f_lo=100)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'highpass', f_lo=10, f_hi=100)
def test_edge_nan():
"""
Confirm that the appropriate amount of edge artifact has been removed for FIR filters
and that edge artifacts are not removed for IIR filters
"""
# Apply a 4-8Hz bandpass filter to random noise
x = _generate_random_x()
x_filt, kernel = neurodsp.filter(x,
1000,
'bandpass',
f_lo=4,
f_hi=8,
return_kernel=True)
# Check if the correct edge artifacts have been removed
N_rmv = int(np.ceil(len(kernel) / 2))
assert all(np.isnan(x_filt[:N_rmv]))
assert all(np.isnan(x_filt[-N_rmv:]))
assert all(~np.isnan(x_filt[N_rmv:-N_rmv]))
# Check that no edge artifacts are removed for IIR filters
x_filt = neurodsp.filter(x,
1000,
'bandpass',
f_lo=4,
f_hi=8,
iir=True,
butterworth_order=3)
assert all(~np.isnan(x_filt))
def test_filter_length():
"""
Check that the output kernel is of the correct length
"""
# Generate a random signal
x = _generate_random_x()
# Specify filter length with number of cycles
Fs = 1000
f_lo = 4
f_hi = 8
N_cycles = 5
x_filt, kernel = neurodsp.filter(x,
Fs,
'bandpass',
f_lo=f_lo,
f_hi=f_hi,
N_cycles=N_cycles,
return_kernel=True)
# Compute how long the kernel should be
force_kernel_length = int(np.ceil(Fs * N_cycles / f_lo))
if force_kernel_length % 2 == 0:
force_kernel_length = force_kernel_length + 1
# Check correct length when defining number of cycles
assert np.allclose(len(kernel), force_kernel_length, atol=.1)
# Specify filter length with number of seconds
Fs = 1000
f_lo = 4
f_hi = 8
N_seconds = .8
x_filt, kernel = neurodsp.filter(x,
Fs,
'bandpass',
f_lo=f_lo,
f_hi=f_hi,
N_seconds=N_seconds,
return_kernel=True)
# Compute how long the kernel should be
force_kernel_length = int(np.ceil(Fs * N_seconds))
if force_kernel_length % 2 == 0:
force_kernel_length = force_kernel_length + 1
# Check correct length when defining number of seconds
assert np.allclose(len(kernel), force_kernel_length, atol=.1)
def test_frequency_input_errors():
"""
Check that errors are properly raised when incorrectly enter frequency information
"""
# Generate a random signal
x = _generate_random_x()
# Check that a bandpass filter cannot be completed without proper frequency limits
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandpass', fc=8)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandpass', fc=(8, 4))
# Check that a bandstop filter cannot be completed without proper frequency limits
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandstop', fc=58)
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'bandstop', fc=(62, 58))
# Check that frequencies cannot be inverted
with pytest.raises(ValueError):
x_filt = neurodsp.filter(x, 1000, 'lowpass', fc=(100, 10))