def create_deamid_mat(tripeps, D, t):
trps_data = np.array(
[tuple(['NA', 'NA', trp, 0, 0, trp]) for trp in tripeps],
dtype=[('prot_id', '<U60'), ('prot_name', '<U10'), ('tripep', 'U3'),
('position', 'i4'), ('corr_pos', 'i4'), ('string', '<U45')])
counts = None
rel_ints = None
Ydata = {'times': t}
sim_mat = deamidationMatrix(D=D,
Ydata=Ydata,
counts=counts,
rel_ints=rel_ints,
trps_data=trps_data)
return sim_mat
type = 'cat'
reader = evidenceBatchReader(datapath,
byPos=True,
qt=False,
sf_exp=sf_exp,
exclude=exclude_data)
data = reader.readBatch()
sampleTripeps = data.get_sampleTripeps(sampleInfo,
protsInfo,
norm_type='simple',
filter=None)
deamid_mat = deamidationMatrix(sampleTripeps, sampleInfo, header)
# Filter out non-helical positions
deamid_mat = deamid_mat.filter_tripeps(deamid_mat.trps_data['position'] < 1193)
# Remove glue samples
samples_filter = deamid_mat.Ydata[key] != 'Glue'
deamid_mat = deamid_mat.filter_samples(samples_filter)
# Correct positions
deamid_mat.correct_pos(protsInfo)
# Merge by position
deamid_mat = deamid_mat.merge_by_pos(corr=True)
# Apply period
n_period = deamid_mat.stagger_pos(l_period=234)
# Sort by corr_pos