def match_lines2(s, wvl, ref_line_list):
"""
returns matched_indices, matched_fit_params, matched_distances
matched_indices : indices of lines (from ref_line_list) that are
associated with line features in the given spectra
matched_distances : distances in pixel
"""
# find centroids of s
from libs.find_peak import find_peaks
sol_list = find_peaks(s, sigma=3)
cent_list = np.array([sol[0] for sol in sol_list])
# define transform from lambda to pixel
wvl2pix = interp1d(wvl, np.arange(len(wvl)))
ref_pix_list = wvl2pix(ref_line_list)
# find nearest matches
kdtree = spatial.KDTree(ref_pix_list.reshape([-1,1]))
dists, indices = kdtree.query(cent_list.reshape([-1,1]))
# filter out multiple hits. Only the nearest one remains.
filtered_indices = []
for k, l in itertools.groupby(zip(indices,
sol_list, dists),
operator.itemgetter(0)):
l = list(l)
i = np.argmin([l1[-1] for l1 in l])
filtered_indices.append(l[i])
matched_indices = [s_[0] for s_ in filtered_indices]
matched_fit_params = [s_[1] for s_ in filtered_indices]
matched_distances = [s_[2] for s_ in filtered_indices]
return matched_indices, matched_fit_params, matched_distances
def match_lines1_pix(s, ref_pix_list):
"""
returns matched_indices, matched_fit_params, matched_distances
matched_indices : indices of lines (from ref_line_list) that are
associated with line features in the given spectra
matched_distances : distances in pixel
"""
# find centroids of s
from libs.find_peak import find_peaks
sol_list = find_peaks(s, sigma=3)
cent_list = np.array([sol[0] for sol in sol_list])
cent_list, dists = match_lines1_pixel(cent_list, ref_pix_list)
return cent_list, dists
