Commit 97cbe912 authored by Carlos de Lannoy's avatar Carlos de Lannoy
Browse files

add resimulation script for tandem_10nt experiment

parent a9d2d57d
import os, sys
import pomegranate as pg
import numpy as np
import matplotlib.pyplot as plt
# Resimulate state paths for in vitro experiment 3 (tandem 10nt, 1 ground state, 2 FRET states),
# with separate chains for each FRET chain. This shows that co-occurence of FRET states
# is unlikely.
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
from helper_functions import parse_input_path
def get_mc_sample(tr, st_dict, length):
ps = pg.DiscreteDistribution(st_dict)
cpt = pg.ConditionalProbabilityTable(tr,[ps])
mc = pg.MarkovChain([ps, cpt])
return np.array(mc.sample(length))
# Collect trace durations
trace_durations = []
trace_list = parse_input_path(f'{__location__}/../in_vitro/data/3_tandem_10nt/dats_unlabeled/', pattern='*.dat')
for trace_fn in trace_list:
with open(trace_fn, 'r') as fh: lc = len(fh.readlines())
# Define MC starting properties and transition rates, taken from invitro/eval/3_tandem_10nt_fb/FRETboard_report.html
st_dict = {'g': 1.0, 'f': 0.0}
tr_state2 = [
['g', 'g', 0.9997],
['g', 'f', 0.0003],
['f', 'g', 0.0535],
['f', 'f', 0.9465]]
tr_state3 = [
['g', 'g', 0.9995],
['g', 'f', 0.0005],
['f', 'g', 0.0625],
['f', 'f', 0.9375]]
simultaneous_list = []
for td in trace_durations:
state2_seq = get_mc_sample(tr_state2, st_dict, td)
state3_seq = get_mc_sample(tr_state3, st_dict, td)
simultaneous_list.append(np.sum(np.logical_and(tr_state2 == 'f', tr_state3 == 'f')) / td)
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