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,:])