add resimulation script for tandem_10nt experiment

scripts/mc_resimulation_invitro_exp3.py

+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__)))
+sys.path.append(__location__)
+
+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())
+    trace_durations.append(lc)
+
+# 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)
+plt.boxplot(simultaneous_list)
+plt.show()