def dietrich_baseline(bands, intensities, half_window=16, num_erosions=10):
'''
Fast and precise automatic baseline correction of ... NMR spectra, 1991.
http://www.sciencedirect.com/science/article/pii/002223649190402F
http://www.inmr.net/articles/AutomaticBaseline.html
'''
# Step 1: moving-window smoothing
w = half_window*2+1
window = np.ones(w) / float(w)
Y = intensities.copy()
if Y.ndim == 2:
window = window[None]
Y[...,half_window:-half_window] = convolve(Y, window, mode='valid')
# Step 2: Derivative.
dY = np.diff(Y)**2
# Step 3: Iterative thresholding.
is_baseline = np.ones(Y.shape, dtype=bool)
is_baseline[...,1:] = iterative_threshold(dY)
# Step 3: Binary erosion, to get rid of peak-tops.
mask = np.zeros_like(is_baseline)
mask[...,half_window:-half_window] = True
s = np.ones(3, dtype=bool)
if Y.ndim == 2:
s = s[None]
is_baseline = binary_erosion(is_baseline, structure=s,
iterations=num_erosions, mask=mask)
# Step 4: Reconstruct baseline via interpolation.
if Y.ndim == 2:
return np.row_stack([np.interp(bands, bands[m], y[m])
for y, m in zip(intensities, is_baseline)])
return np.interp(bands, bands[is_baseline], intensities[is_baseline])
def fabc_baseline(intensities, dilation_param=50, smoothness_param=1e3): '''Fully Automatic Baseline Correction, by Carlos Cobas (2006). http://www.sciencedirect.com/science/article/pii/S1090780706002266 ''' cwt = scipy.signal.cwt(intensities, scipy.signal.ricker, (dilation_param,)) dY = cwt.ravel()**2 is_baseline = iterative_threshold(dY) is_baseline[0] = True is_baseline[-1] = True smoother = WhittakerSmoother(intensities, smoothness_param, deriv_order=1) return smoother.smooth(is_baseline)
