def testExtractDerivedQuantityPSD(exp, convert_name):
"""
Attempts to extract the PSD from a set of data
"""
fudge_data_1 = pd.DataFrame({
'Time (ms)': [1, 2, 3],
'Photocurrent (nA)': [0.5, 0.6, 0.7]
})
fudge_data_2 = pd.DataFrame({
'Time (ms)': [1, 2, 3],
'Photocurrent (nA)': [1, 1.2, 1.4]
})
name_1 = convert_name('TEST1~wavelength-1~replicate-0')
name_2 = convert_name('TEST1~wavelength-1~replicate-1')
data_dict = {name_1: fudge_data_1, name_2: fudge_data_2}
def powerSpectrum(data, cond):
return power_spectrum(data)
actual_psd_data = exp.derived_quantity(data_dict=data_dict,
quantity_func=powerSpectrum)
desired_psd_data = {
name_1: power_spectrum(fudge_data_1),
name_2: power_spectrum(fudge_data_2)
}
assertDataDictEqual(actual_psd_data, desired_psd_data)
def testExtractDerivedQuantityPSDAverage(exp, convert_name):
fudge_data_1 = pd.DataFrame({
'Time (ms)': [1, 2, 3],
'Photocurrent (nA)': [0.5, 0.6, 0.7]
})
fudge_data_2 = pd.DataFrame({
'Time (ms)': [1, 2, 3],
'Photocurrent (nA)': [1, 1.2, 1.4]
})
name_1 = convert_name('TEST1~wavelength-1~replicate-0')
name_2 = convert_name('TEST1~wavelength-1~replicate-1')
data_dict = {name_1: fudge_data_1, name_2: fudge_data_2}
data_psd_1 = power_spectrum(fudge_data_1)
data_psd_2 = power_spectrum(fudge_data_2)
data_psd_1.iloc[:, 1:] += data_psd_2.iloc[:, 1:]
data_psd_1.iloc[:, 1:] /= 2
def powerSpectrum(data, cond):
return power_spectrum(data)
desired_psd = data_psd_1
desired_data_dict = {convert_name('TEST1~wavelength-1'): desired_psd}
actual_data_dict = exp.derived_quantity(data_dict=data_dict,
quantity_func=powerSpectrum,
average_along='replicate')
assertDataDictEqual(actual_data_dict, desired_data_dict)
def testDoubleSidedSinewaveBoxcar():
"""
Tests teh power spectrum of a sinewave with no hann window.
"""
x = np.arange(-5, 5 + 0.5, 0.5)
raw_data = np.sin(x)
actual_spectrum = power_spectrum(raw_data, window='box', siding='double')
desired_spectrum = np.array([
1.118000149122749e-34,
0.022942929484678257,
0.20704159581664763,
0.018317774296044642,
0.007597511788147477,
0.004508971439847654,
0.0031729976902471545,
0.0024827702154015855,
0.0020984476697393016,
0.0018876552893358684,
0.0017933402731461997,
0.0017933402731461997,
0.0018876552893358684,
0.0020984476697393016,
0.0024827702154015855,
0.0031729976902471545,
0.004508971439847654,
0.007597511788147477,
0.018317774296044642,
0.20704159581664763,
0.022942929484678257,
])
assert_allclose(actual_spectrum, desired_spectrum, atol=1e-15)
total_power = np.sum(desired_spectrum)
assert_equal(total_power, 0.5436879879264715)
def testPowerSpectrumBoxcar():
"""
Tests calculation of the power spectrum with a boxcar window
"""
# Tests the case where we have an even number of samples
raw_data = np.array([1, 2, 3, 4, 5, 0])
actual_spectrum = \
power_spectrum(raw_data, window='box', siding='single')
desired_spectrum = np.array([6.25, 2 * 1, 2 * 1 / 3.0, 1 / 4.0])
assert_allclose(actual_spectrum, desired_spectrum)
# Odd number of samples
raw_data = np.array([1, 2, 3, 4, 5, 0, 1])
actual_spectrum = power_spectrum(raw_data, window='box', siding='single')
desired_spectrum = np.array([
5.224489795918367, 2 * 0.9303959383749711, 2 * 0.21858791491176946,
2 * 0.2387712487540753
])
assert_allclose(actual_spectrum, desired_spectrum)
def testDCHann():
"""
Tests whether the DC component is correct with a Hann Window
"""
desired_dc_component = 0.6349206349206348
x = np.arange(-5, 5 + 0.5, 0.5)
test_data = 1 + np.sin(2 * x)
actual_spectrum = power_spectrum(test_data, window='hann', siding='single')
actual_dc_component = actual_spectrum[0]
assert_equal(actual_dc_component, desired_dc_component)
def testDCBoxcar():
"""
Tests whether the DC component is correct with a boxcar window
windowing.
"""
desired_dc_component = 1
x = np.arange(-5, 5 + 0.5, 0.5)
test_data = 1 + np.sin(2 * x)
actual_spectrum = power_spectrum(test_data,
window='boxcar',
siding='single')
actual_dc_component = actual_spectrum[0]
assert_equal(actual_dc_component, desired_dc_component)
def noise_current(data, cond):
data_power = power_spectrum(data, window='boxcar')
column_name = cname_from_unit(data_power, ureg.Hz)
if 'filter_cutoff' in cond:
filter_cutoff = cond['filter_cutoff'].to(ureg.Hz).magnitude
else:
filter_cutoff = 200 # Default to 200Hz
filtered_power = data_power[data_power[column_name] >= filter_cutoff]
average_noise_power= \
column_from_unit(filtered_power, ureg.V ** 2).mean()
bin_size = frequency_bin_size(filtered_power)
noise_psd = average_noise_power / bin_size / (cond['gain'])**2
noise_psd = noise_psd.to(ureg.A**2 / ureg.Hz)
return noise_psd
def testPowerSpectrumSinewaveHann():
"""
Tests the power spectrum of a sinewave with a Hann window
"""
x = np.arange(-5, 5 + 0.5, 0.5)
raw_data = np.sin(x)
actual_spectrum = power_spectrum(raw_data, window='hann', siding='single')
desired_spectrum = np.array([
4.367188082510738e-37, 2 * 0.09791637933291152,
2 * 0.13736140613819783, 2 * 0.015585927491469722,
2 * 0.0000995891811181273, 2 * 7.064948406227207e-6,
2 * 8.781078402325934e-7, 2 * 1.1447549848704329e-7,
2 * 7.504694473409487e-9, 2 * 7.232988034990556e-10,
2 * 5.1913320650517704e-9
])
assert_allclose(actual_spectrum, desired_spectrum, atol=1e-15)
total_power = np.sum(desired_spectrum)
assert_equal(total_power, 0.501942746189535)
def test_amplitude_spectrum_double():
raw_data = np.array([1, 2, 3, 4, 5, 0])
times = np.array([1, 2, 3, 4, 5, 6])
desired_frequencies = np.array([0, 1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6],
dtype=np.float64)
desired_amplitudes = np.array([
(2.4999999999999998e-06 + 0j), (-1e-06 + 0j),
(-1.1102230246251565e-22 - 5.773502691896258e-07j),
(4.999999999999999e-07 + 0j),
(-1.1102230246251565e-22 + 5.773502691896258e-07j),
(-9.999999999999997e-07 + 0j)
])
input_data = pd.DataFrame({'Time (s)': times, 'Amplitude (uV)': raw_data})
actual_spectrum = \
power_spectrum(input_data, window='box', siding='double', amplitude=True)
desired_spectrum = pd.DataFrame({
'frequency (Hz)': desired_frequencies,
'voltage (V)': desired_amplitudes
})
assert_frame_equal(actual_spectrum, desired_spectrum, atol=1e-8, rtol=1e-8)
def test_pandas_boxcar():
"""
Tests whether pandas gives the correct sampling frequency,
frequency spacing, and spectral data.
"""
raw_data = np.array([1, 2, 3, 4, 5, 0])
times = np.array([1, 2, 3, 4, 5, 6])
desired_frequencies = np.array([0, 1 / 6, 1 / 3, 1 / 2])
desired_powers = 1e-12 * np.array([6.25, 2 * 1, 2 * 1 / 3.0, 1 / 4.0])
desired_unit = 'Hz'
input_data = pd.DataFrame({'Time (s)': times, 'Amplitude (uV)': raw_data})
actual_spectrum = \
power_spectrum(input_data, window='box', siding='single', amplitude=False)
desired_spectrum = pd.DataFrame({
'frequency (Hz)': desired_frequencies,
'power (V ** 2)': desired_powers
})
assert_frame_equal(actual_spectrum,
desired_spectrum,
atol=1e-15,
rtol=1e-16)
def test_pandas_hann():
"""
Tests whether pandas gives the correct sampling frequency,
frequency spacing, and spectral data.
"""
raw_data = np.array([1, 2, 3, 4, 5, 0])
times = np.array([1, 2, 3, 4, 5, 6])
desired_frequencies = np.array([0, 1 / 6, 1 / 3, 1 / 2])
desired_powers = np.array([
6.805555555555554e-12, 5.739163982499836e-12, 2.0837646694465478e-13,
1.5480025000233596e-15
])
desired_unit = 'Hz'
input_data = pd.DataFrame({'Time (s)': times, 'Amplitude (uV)': raw_data})
actual_spectrum = \
power_spectrum(input_data, window='hann', siding='single', amplitude=False)
desired_spectrum = pd.DataFrame({
'frequency (Hz)': desired_frequencies,
'power (V ** 2)': desired_powers
})
assert_frame_equal(actual_spectrum,
desired_spectrum,
atol=1e-15,
rtol=1e-16)
def psdFunction(data, cond):
return power_spectrum(data, **kwargs)
def powerSpectrum(data, cond):
return power_spectrum(data)
