assert tester
print('--------------\n\n')
spect_dark = _Spectra()
tester = _lazy5.inspect.valid_dsets(pth=pth,
file=filename,
dset_list=['/Spectra/Dark_3_5ms_2'])
hdf_import_data(pth, filename, '/Spectra/Dark_3_5ms_2', spect_dark)
#hdf_process_attr(rosetta, spect_dark)
print('Shape of dark spectra: {}'.format(spect_dark.shape))
print('Shape of dark spectra.mean(): {}'.format(spect_dark.mean().shape))
print('Dtype of dark spectra: {}'.format(spect_dark._data.dtype))
print('')
img = _Hsi()
hdf_import_data(pth, filename,
'/BCARSImage/mP2_3_5ms_Pos_2_0/mP2_3_5ms_Pos_2_0_small',
img)
print('Shape of img: {}'.format(img.shape))
print('Shape of img.mean(): {}'.format(img.mean().shape))
print('Dtype of img: {}'.format(img._data.dtype))
print('Dtype of img.mean(): {}'.format(img.mean().dtype))
print('--------------\n\n')
pth = 'C:/Users/chc/Documents/Data/2018/OliverJonas/180629/'
filename = 'L1d1_pos0.h5'
dsetname = '/BCARSImage/L1d1_pos0_0/NRB_Post_0'
spect_nrb = _Spectra()
tester = _lazy5.inspect.valid_dsets(pth=pth,
tester = hdf_is_valid_dsets(pth, filename,'fake_dset')
assert tester == False
tester = hdf_is_valid_dsets(pth, filename,['fake_dset1','fake_dset2'])
assert tester == False
tester = hdf_is_valid_dsets(pth, filename,dset)
assert tester == True
dset_list = hdf_dset_list_rep('/Spectra/Dark_3_5ms_',_np.arange(2))
tester = hdf_is_valid_dsets(pth, filename,dset_list)
assert tester == True
print('--------------\n\n')
spect_dark = _Spectra()
tester = hdf_is_valid_dsets(pth, filename,['/Spectra/Dark_3_5ms_2'])
hdf_import_data(pth, filename,'/Spectra/Dark_3_5ms_2',spect_dark)
#hdf_process_attr(rosetta, spect_dark)
print('Shape of dark spectra: {}'.format(spect_dark.shape))
print('Shape of dark spectra.mean(): {}'.format(spect_dark.mean().shape))
print('')
img = _Hsi()
hdf_import_data(pth, filename,'/BCARSImage/mP2_3_5ms_Pos_2_0/mP2_3_5ms_Pos_2_0_small',img)
print('Shape of img: {}'.format(img.shape))
print('Shape of img.mean(): {}'.format(img.mean().shape))
return ret
else:
return None
if __name__ == '__main__':
app = _QApplication(_sys.argv)
app.setStyle('Cleanlooks')
# winDark = DialogDarkOptions.dialogDarkOptions(darkloaded=True)
from crikit.data.hsi import Hsi as _Hsi
temp = _Hsi()
WN = _np.linspace(500,4000,1000)
CARS = _np.zeros((20,20,WN.size))
CARS[:,:,:] = _np.abs(1/(1000-WN-1j*20) + 1/(3000-WN-1j*20) + .055)
temp.data = CARS
temp.freq.data = WN
NRB = 0*WN + .055
winKK = DialogKKOptions.dialogKKOptions(data=[WN, NRB,
temp.get_rand_spectra(10, pt_sz=3, quads=False)])
def _transform(self, cars, nrb):
if issubclass(cars.dtype.type, _np.complex):
success = self._calc(cars, nrb, ret_obj=cars)
return success
else:
return False
if __name__ == '__main__': # pragma: no cover
from crikit.data.spectrum import Spectrum as _Spectrum
from crikit.data.spectra import Spectra as _Spectra
from crikit.data.hsi import Hsi as _Hsi
hsi = _Hsi()
nrb = _Spectra()
WN = _np.linspace(-1386, 3826, 400)
X = .055 + 1 / (1000 - WN - 1j * 20) + 1 / (3000 - WN - 1j * 20)
XNR = 0 * X + 0.055
E = 1 * _np.exp(-(WN - 2000)**2 / (2 * 3000**2))
# Simulated spectrum
CARS = _np.abs(E + X)**2
NRB = _np.abs(E + XNR)**2
nrb.data = NRB
# Copies of spectrum
temp = _np.dot(_np.ones((30, 30, 1)), CARS[None, :])
dialog.ui.spinBoxPadFactor.value())
else:
return (None, None, None, None, None)
if __name__ == '__main__':
app = _QApplication(_sys.argv)
app.setStyle('Cleanlooks')
winDark = DialogDarkOptions.dialogDarkOptions(darkloaded=True)
from crikit.data.hsi import Hsi as _Hsi
temp = _Hsi()
WN = _np.linspace(500,4000,1000)
CARS = _np.zeros((20,20,WN.size))
CARS[:,:,:] = _np.abs(1/(1000-WN-1j*20) + 1/(3000-WN-1j*20) + .055)
temp.data = CARS
temp.freq.data = WN
NRB = 0*WN + .055
winKK = DialogKKOptions.dialogKKOptions(data=[WN, NRB,
temp.get_rand_spectra(10, pt_sz=3, quads=False)])
#
return None
if __name__ == '__main__': # pragma: no cover
from crikit.data.spectrum import Spectrum as _Spectrum
from crikit.data.spectra import Spectra as _Spectra
from crikit.data.hsi import Hsi as _Hsi
x = _np.linspace(0, 100, 10)
y = _np.linspace(0, 100, 10)
freq = _np.arange(20)
data = _np.ones((10, 10, 20))
hs = _Hsi(data=_copy.deepcopy(data), freq=freq, x=x, y=y)
spa = _Spectra(data=_copy.deepcopy(data), freq=freq)
sp = _Spectrum(data=_copy.deepcopy(data)[0, 0, :], freq=freq)
mean_sub = SubtractMeanOverRange([5, 8])
print('\n---------TRANSFORM TEST----------\n')
print('\n3D----------')
print('Initial mean: {}'.format(hs.data.mean()))
out = mean_sub.transform(hs.data)
print('Success?: {}'.format(out))
print('Final mean: {}\n'.format(hs.data.mean()))
print('2D----------')
print('Initial mean: {}'.format(spa.data.mean()))
out = mean_sub.transform(spa.data)
# Expand dark dimensionality to match data.ndim
self.dark = _expand_1d_to_ndim(self.dark, data.ndim)
ret_obj -= self.dark
return True
if __name__ == '__main__': # pragma: no cover
x = _np.linspace(0,100,10)
y = _np.linspace(0,100,10)
freq = _np.arange(20)
data = _np.ones((10,10,20))
# OVERWRITE TEST
hs = _Hsi(data=_copy.deepcopy(data), freq=freq, x=x, y=y)
spa = _Spectra(data=_copy.deepcopy(data)[0,:,:], freq=freq)
sp = _Spectrum(data=_copy.deepcopy(data)[0,0,:], freq=freq)
dark=0.5 * _copy.deepcopy(data)
dark_sub = SubtractDark(dark)
print('\n---------TRANSFORM TEST----------\n')
print('\n3D----------')
print('Initial mean: {}'.format(hs.data.mean()))
out = dark_sub.transform(hs.data)
print('Success?: {}'.format(out))
print('Final mean: {}\n'.format(hs.data.mean()))
print('2D----------')
print('Initial mean: {}'.format(spa.data.mean()))
def _transform(self, cars, nrb):
if issubclass(cars.dtype.type, _np.complex):
success = self._calc(cars, nrb, ret_obj=cars)
return success
else:
return False
if __name__ == '__main__': # pragma: no cover
from crikit.data.spectrum import Spectrum as _Spectrum
from crikit.data.spectra import Spectra as _Spectra
from crikit.data.hsi import Hsi as _Hsi
hsi = _Hsi()
nrb = _Spectra()
WN = _np.linspace(-1386,3826,400)
X = .055 + 1/(1000-WN-1j*20) + 1/(3000-WN-1j*20)
XNR = 0*X + 0.055
E = 1*_np.exp(-(WN-2000)**2/(2*3000**2))
# Simulated spectrum
CARS = _np.abs(E+X)**2
NRB = _np.abs(E+XNR)**2
nrb.data = NRB
# Copies of spectrum
temp = _np.dot(_np.ones((30,30,1)),CARS[None,:])
