def test_arbitrary_on_empty():
fs = 20000.0 # Hz
dt = 1 / fs # s
time = 0.0 # s
n_scans = int(round(time / dt))
t = dt * np.arange(n_scans)
x = (0.9 * pow(2, 14) * np.sin(2 * math.pi * 10 * t)).astype('int16').reshape(1, n_scans)
# V/whatevers, scale for converting from V to whatever or vice-versa
channel_scale = (np.array(1, ndmin=1)).astype('float64')
adc_coefficients = (np.array([1, 2, 3, 4])).astype('float64').reshape(1, 4) # identity function
xf = x.astype('float64')
y_theoretical = 1.0 + 2.0 * xf + 3.0 * xf * xf + 4.0 * xf * xf * xf
y = ws.scaled_double_analog_data_from_raw(x, channel_scale, adc_coefficients)
assert (y_theoretical == y).all()
def test_arbitrary_on_matrix_zero_coeffs():
fs = 20000.0 # Hz
dt = 1 / fs # s
time = 0.2 # s
n_scans = int(round(time / dt))
n_channels = 0
i_channel = np.arange(n_channels).reshape(n_channels, 1)
t = (dt * np.arange(n_scans)).reshape(1, n_scans)
x = (0.9 * pow(2, 14) * np.sin(2 * math.pi * 10 * t + 2 * math.pi * (i_channel / n_channels))).astype('int16')
channel_scale = 1 / (i_channel + 1)
adc_coefficients = np.zeros((n_channels, 0))
y_theoretical = np.zeros(x.shape)
y = ws.scaled_double_analog_data_from_raw(x, channel_scale, adc_coefficients)
assert (y_theoretical == y).all()
def test_identity_function_on_vector():
fs = 20000.0 # Hz
dt = 1 / fs # s
time = 0.2 # s
n_scans = int(round(time / dt))
t = dt * np.arange(n_scans)
x = (0.9 * pow(2, 14) * np.sin(2 * math.pi * 10 * t)).astype('int16').reshape(1, n_scans)
channel_scale = (np.array(1, ndmin=1)).astype(
'float64') # V/whatevers, scale for converting from V to whatever or vice-versa
adc_coefficients = (np.array([0, 1, 0, 0])).astype('float64').reshape(1, 4) # identity function
y_theoretical = x.astype('float64')
y = ws.scaled_double_analog_data_from_raw(x, channel_scale, adc_coefficients)
# yMex = ws.scaledDoubleAnalogDataFromRawMex(x, channel_scale, adc_coefficients)
assert (y_theoretical == y).all()
def test_arbitrary_on_matrix_one_coeff():
fs = 20000.0 # Hz
dt = 1 / fs # s
time = 0.2 # s
n_scans = int(round(time / dt))
n_channels = 6
i_channel = np.arange(n_channels).reshape(n_channels, 1)
t = (dt * np.arange(n_scans)).reshape(1, n_scans)
x = (0.9 * pow(2, 14) * np.sin(2 * math.pi * 10 * t + 2 * math.pi * (i_channel / n_channels))).astype('int16')
channel_scale = 1 / (i_channel + 1)
# adc_coefficients = np.tile((np.array([1, 2, 3, 4])).astype('float64').reshape(1, 4), (n_channels,1))
adc_coefficients = np.tile(0.001, (n_channels, 1))
# xf = x.astype('float64')
y_theoretical = np.tile(0.001, (n_channels, n_scans)) / channel_scale
y = ws.scaled_double_analog_data_from_raw(x, channel_scale, adc_coefficients)
assert (y_theoretical == y).all()
