def setup_class(cls):
fpath = directory / 'test_data' / 'simulated_data.csv'
cls.data = txt_to_np(fpath, delimiter=',')
cls.data['end'] += 1 # because this simulated data is in old format of inclusive, inclusive
cls.sequence = 'XXXXTPPRILALSAPLTTMMFSASALAPKIXXXXLVIPWINGDKG'
cls.timepoints = [0.167, 0.5, 1, 5, 10, 30, 100]
cls.start, cls.end = 5, 46 # total span of protein (inc, excl)
cls.nc_start, cls.nc_end = 31, 35 # span of no coverage area (inc, inc)
data = read_dynamx(fpath)
sequence = 'XXXXTPPRILALSAPLTTMMFSASALAPKIXXXXLVIPWINGDKG'
timepoints = [0.167, 0.5, 1, 5, 10, 30, 100]
start, end = 5, 45 # total span of protein (inc, inc)
nc_start, nc_end = 31, 34 # span of no coverage area (inc, inc)
pmt = PeptideMasterTable(data,
drop_first=1,
ignore_prolines=True,
remove_nan=False)
pmt.set_backexchange(0.)
states = pmt.groupby_state()
series = states['state1']
print(series.scores_peptides.T.shape)
print(series.uptake_corrected.shape) ## N_t, N_p
print(series.cov.X.shape)
print(series.cov.X)
print(series.cov.Z)
fmt, hdr = fmt_export(series.cov.data)
np.savetxt('tempfile.txt', series.cov.data, fmt=fmt, header=hdr)
Z = series.cov.Z
print(np.sum(Z, axis=1))
init_arr = txt_to_np(os.path.join(fit_dir, 'fit_simulated_wt_avg.txt'))
from pyhdx.fileIO import read_dynamx, txt_to_np, fmt_export
from pyhdx.models import PeptideMasterTable
from numpy.lib.recfunctions import append_fields
import numpy as np
array = txt_to_np('test_data/simulated_data.csv', delimiter=',')
data = read_dynamx('test_data/simulated_data.csv')
print(array.dtype.names)
print(data.dtype.names)
pmt = PeptideMasterTable(array, drop_first=0, ignore_prolines=False, remove_nan=False)
print(pmt.data.dtype.names)
uptake = pmt.data['ex_residues'] * pmt.data['scores'] / 100
for u, m in zip(uptake, pmt.data['ex_residues']):
print(u, m)
extended = append_fields(pmt.data, ['uptake'], [uptake], usemask=False)
fields = ('start', 'end', 'exposure', 'state', 'sequence', 'ex_residues', 'uptake')
dtype = [(name, extended[name].dtype) for name in fields]
export = np.empty_like(uptake, dtype=dtype)
for name in fields:
export[name] = extended[name]
fmt, hdr = fmt_export(export, delimiter=',', width=0)
np.savetxt('test.txt', export, fmt=fmt, header=hdr)
new_data = read_dynamx('test.txt')