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