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))
tester = _lazy5.inspect.valid_dsets(pth=pth, file='fake.h5', dset_list=['fake_dset1', 'fake_dset2']) assert not tester print('Path: {}'.format(pth)) tester = _lazy5.inspect.valid_dsets(pth=pth, file=filename, dset_list=dset, verbose=True) 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))
attr['RasterScanParams.FixedAxis'] = 'Y' elif ax3 == 2: attr['RasterScanParams.FixedAxis'] = 'Z' # Figure out fixed positions later except: pass else: output_cls_instance.data = data output_cls_instance.meta = attr return True except: print('Something failed in import') if __name__ == '__main__': #from crikit.data.spectra import Spectra as _Spectra sp = _Spectra() pth = '../../../Young_150617/' filename_header = 'SH-03.h' filename_data = 'base061715_152213_60ms.txt' csv_nist_import_data(pth, filename_header, filename_data, output_cls_instance=sp) print(sp.__dict__)
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, :]) # Create an HSData class instance
if ax3 == 0: attr['RasterScanParams.FixedAxis'] = 'X' elif ax3 == 1: attr['RasterScanParams.FixedAxis'] = 'Y' elif ax3 == 2: attr['RasterScanParams.FixedAxis'] = 'Z' # Figure out fixed positions later except: pass else: output_cls_instance.data = data output_cls_instance.meta = attr return True except: print('Something failed in import') if __name__ == '__main__': #from crikit.data.spectra import Spectra as _Spectra sp = _Spectra() pth = '../../../Young_150617/' filename_header = 'SH-03.h' filename_data = 'base061715_152213_60ms.txt' csv_nist_import_data(pth, filename_header, filename_data, output_cls_instance=sp) print(sp.__dict__)
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) print('Success?: {}'.format(out))
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())) out = dark_sub.transform(spa.data)
out = sub_baseline_als.transform(sp.data) _plt.plot(sp.data, label='Detrended') _plt.title('Spectrum') _plt.legend(loc='best') _plt.show() sp.data = _np.exp(-(_np.arange(1000)-500)**2/100**2) _plt.plot(sp.data, label='Original') sub_baseline_als.redux_factor = 10 out = sub_baseline_als.transform(sp.data) _plt.plot(sp.data, label='Detrended (Redux)') _plt.title('Spectrum') _plt.legend(loc='best') _plt.show() # spa = _Spectra() sub_baseline_als = SubtractBaselineALS(smoothness_param=1e2, asym_param=1e-4) spa.data = _np.dot(_np.ones((2,1)),_np.exp(-(_np.arange(1000)-500)**2/100**2)[None,:]) _plt.plot(spa.data.T, label='Original') out = sub_baseline_als.transform(spa.data) _plt.plot(spa.data.T, label='Detrended') _plt.title('Spectra') _plt.legend(loc='upper right') _plt.show() hsi = _Hsi() sub_baseline_als.redux_factor = 10 hsi.data = _np.dot(_np.ones((1,1,1)),_np.exp(-(_np.arange(1000)-500)**2/100**2)[None,:]) _plt.plot(hsi.data.reshape((-1,1000)).T, label='Original') out = sub_baseline_als.calculate(hsi.data)
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,:]) # Create an HSData class instance