def test_datasets(self):
# this routine tests:
# dataset input into setup
# the function fdc_quantize_data
# the flux calculations of function fdc_flux_and_wind
# expected values were calculated using matlab test code for a cutoff frequency
# of 1/12.0
# the first row are the uw, vw, wT flux values for the 1st dataset, etc.
xpctd_fluxes = np.array([[-0.350371566926, 0.115496951668, -0.008274161328],
[-0.410571629711, 0.092164914869, -0.006687641111],
[-0.080418722438, -0.001432879153, -0.004455583513]])
# the agreement is good for datasets 1 and 2, not so good for 3 because when
# goodcompass calculates to 1 (True), filtfilt is executed with the ahi2 and
# bhi2 filter coeffs, the matlab v. scipy results of which barely agree (the
# calculation is not robust).
reltol = np.array([1.e-9, 1.e-9, 0.0, 0.0])
abstol = np.array([0.0, 0.0, 1.e-5, 1.e-5])
### NOTE that the variables parsed out from 'array' are not column vectors;
### they are python 1D arrays which is what these DPAs expect from CI.
for ii in range(4):
array = np.copy(self.Ldata[ii])
timestamps = array[:, 0]
windX = array[:, 1]
windY = array[:, 2]
windZ = array[:, 3]
sound = array[:, 4] # (temperature proxy)
rateX = array[:, 5]
rateY = array[:, 6]
rateZ = array[:, 7]
accelX = array[:, 8]
accelY = array[:, 9]
accelZ = array[:, 10]
roll = array[:, 11] # (not used by DPA)
pitch = array[:, 12] # (not used by DPA)
heading = array[:, 13]
lat = array[:, 14]
if ii < 3: # single datasets
calc_fluxes, _ = fd.fdc_flux_and_wind(timestamps,
windX, windY, windZ,
sound, heading,
rateX, rateY, rateZ,
accelX, accelY, accelZ, lat)
calc_fluxes = np.asarray(calc_fluxes).flatten()
np.testing.assert_allclose(calc_fluxes, xpctd_fluxes[ii, :],
rtol=reltol[ii], atol=abstol[ii])
if ii == 3: # multiple
calc_fluxes, _ = fd.fdc_flux_and_wind(timestamps,
windX, windY, windZ,
sound, heading,
rateX, rateY, rateZ,
accelX, accelY, accelZ, lat)
calc_fluxes = np.asarray(calc_fluxes).transpose()
np.testing.assert_allclose(calc_fluxes, xpctd_fluxes,
rtol=reltol[ii], atol=abstol[ii])
def test_extrapolation_fault(self):
# this routine tests whether nans are output for the flux products, as occurs when
# running the matlab test code, when the interpolation routine in the despikesimple
# function encounters an extrapolation case. the original python coding was changed
# to accommodate this behavior to avoid a series of runtime execution errors.
for ii in (5, 8):
array = np.copy(self.Ldata[0])
array[0, ii] = 99999.9
timestamps = array[:, 0]
windX = array[:, 1]
windY = array[:, 2]
windZ = array[:, 3]
sound = array[:, 4] # (temperature proxy)
rateX = array[:, 5]
rateY = array[:, 6]
rateZ = array[:, 7]
accelX = array[:, 8]
accelY = array[:, 9]
accelZ = array[:, 10]
roll = array[:, 11] # (not used by DPA)
pitch = array[:, 12] # (not used by DPA)
heading = array[:, 13]
lat = array[:, 14]
calc_fluxes, _ = fd.fdc_flux_and_wind(timestamps, windX, windY,
windZ, sound, heading, rateX,
rateY, rateZ, accelX, accelY,
accelZ, lat)
calc_fluxes = np.asarray(calc_fluxes).flatten()
np.testing.assert_allclose(calc_fluxes,
np.array([np.nan, np.nan, np.nan]))
def test_extrapolation_fault(self):
# this routine tests whether nans are output for the flux products, as occurs when
# running the matlab test code, when the interpolation routine in the despikesimple
# function encounters an extrapolation case. the original python coding was changed
# to accommodate this behavior to avoid a series of runtime execution errors.
for ii in (5, 8):
array = np.copy(self.Ldata[0])
array[0, ii] = 99999.9
timestamps = array[:, 0]
windX = array[:, 1]
windY = array[:, 2]
windZ = array[:, 3]
sound = array[:, 4] # (temperature proxy)
rateX = array[:, 5]
rateY = array[:, 6]
rateZ = array[:, 7]
accelX = array[:, 8]
accelY = array[:, 9]
accelZ = array[:, 10]
roll = array[:, 11] # (not used by DPA)
pitch = array[:, 12] # (not used by DPA)
heading = array[:, 13]
lat = array[:, 14]
calc_fluxes, _ = fd.fdc_flux_and_wind(timestamps,
windX, windY, windZ,
sound, heading,
rateX, rateY, rateZ,
accelX, accelY, accelZ, lat)
calc_fluxes = np.asarray(calc_fluxes).flatten()
np.testing.assert_allclose(calc_fluxes, np.array([np.nan, np.nan, np.nan]))
def test_datasets(self):
# this routine tests:
# dataset input into setup
# the function fdc_quantize_data
# the flux calculations of function fdc_flux_and_wind
# expected values were calculated using matlab test code for a cutoff frequency
# of 1/12.0
# the first row are the uw, vw, wT flux values for the 1st dataset, etc.
xpctd_fluxes = np.array(
[[-0.350371566926, 0.115496951668, -0.008274161328],
[-0.410571629711, 0.092164914869, -0.006687641111],
[-0.080418722438, -0.001432879153, -0.004455583513]])
# the agreement is good for datasets 1 and 2, not so good for 3 because when
# goodcompass calculates to 1 (True), filtfilt is executed with the ahi2 and
# bhi2 filter coeffs, the matlab v. scipy results of which barely agree (the
# calculation is not robust).
reltol = np.array([1.e-4, 1.e-4, 0.0, 0.0])
abstol = np.array([0.0, 0.0, 1.e-4, 1.e-4])
### NOTE that the variables parsed out from 'array' are not column vectors;
### they are python 1D arrays which is what these DPAs expect from CI.
for ii in range(4):
array = np.copy(self.Ldata[ii])
timestamps = array[:, 0]
windX = array[:, 1]
windY = array[:, 2]
windZ = array[:, 3]
sound = array[:, 4] # (temperature proxy)
rateX = array[:, 5]
rateY = array[:, 6]
rateZ = array[:, 7]
accelX = array[:, 8]
accelY = array[:, 9]
accelZ = array[:, 10]
roll = array[:, 11] # (not used by DPA)
pitch = array[:, 12] # (not used by DPA)
heading = array[:, 13]
lat = array[:, 14]
if ii < 3: # single datasets
calc_fluxes, _ = fd.fdc_flux_and_wind(timestamps, windX, windY,
windZ, sound, heading,
rateX, rateY, rateZ,
accelX, accelY, accelZ,
lat)
calc_fluxes = np.asarray(calc_fluxes).flatten()
np.testing.assert_allclose(calc_fluxes,
xpctd_fluxes[ii, :],
rtol=reltol[ii],
atol=abstol[ii])
if ii == 3: # multiple
calc_fluxes, _ = fd.fdc_flux_and_wind(timestamps, windX, windY,
windZ, sound, heading,
rateX, rateY, rateZ,
accelX, accelY, accelZ,
lat)
calc_fluxes = np.asarray(calc_fluxes).transpose()
np.testing.assert_allclose(calc_fluxes,
xpctd_fluxes,
rtol=reltol[ii],
atol=abstol[ii])
