elif config['target'] == 'spectra_number':
target = spc[config['spectra_number'], :].reshape(1, -1)
else:
target = config['target']
xCS, ints, ind, target = icoshift(target, spc,
inter=intervals,
n=maximum_shift,
coshift_preprocessing=config['coshift_preprocessing'],
coshift_preprocessing_max_shift=config['coshift_preprocessing_max_shift'],
average2_multiplier=config['average2_multiplier'],
fill_with_previous=config['fill_with_previous'],
)
output_data = input_data.copy()
output_data[:] = xCS
# Generate simple result figure (using pathomx libs)
from pathomx.figures import spectra, difference
regions = []
ymin, ymax = np.min(spc.flatten()), np.max(spc.flatten())
for r in config['selected_data_regions']:
x0, y0, x1, y1 = r[1:]
regions.append((x0, ymax, x1, ymin))
View = spectra(output_data, styles=styles, regions=regions)
Difference = difference(input_data, output_data)
spc = None
output_data = pd.DataFrame(np.zeros((input_data.shape[0], number_of_bins)))
for n, d in enumerate(input_data.values):
binned_data = np.histogram(scale, bins=bins, weights=d)
binned_num = np.histogram(scale, bins=bins) # Number of data points that ended up contributing to each bin
output_data.ix[n, :] = binned_data[0] / binned_num[0] # Mean
new_scale = [float(x) for x in binned_data[1][:-1]]
# Binning is low->high only, if the resulting scale is reversed to the source data flip it and the data
original_dir = (scale[0] - scale[1]) / abs((scale[0] - scale[1]))
new_dir = (new_scale[0] - new_scale[1]) / abs((new_scale[0] - new_scale[1]))
if original_dir != new_dir: # Flip horizontal
new_scale = new_scale[::-1]
for n, d in enumerate(output_data.values):
output_data.ix[n, :] = np.fliplr(np.reshape(d, (-1, number_of_bins)))
output_data.columns = pd.Index(new_scale, name='Scales')
output_data.index = input_data.index
output_data.dropna(axis=1, inplace=True)
# Generate simple result figure (using pathomx libs)
from pathomx.figures import spectra, difference
View = spectra(output_data, styles=styles)
Difference = difference(input_data, output_data, styles=styles)
