def run(profile,tndiff,tstart,tend,left=None,right=None):
MRTp, MRTs, RFDs, Rept_time, single_mol_exp, pos_time_activated_ori, It,wRFD = get_fast_MRT_RFDs(
nsim, profile, tndiff, kon=kon,
fork_speed=fork_speed, dori=20*5/resolution,single_mol_exp=False,continuous=args.continuous,wholeRFD=True)
#print("check", np.sum(d3p), np.sum(np.ones_like(d3p)*np.sum(d3p)*0.1/len(d3p)))
#print("Start, end",tstart,tend,tndiff)
# Compare to exp data
"""
MRTpearson, MRTstd, MRT = compare(
MRTp[::10//resolution], "MRT", cell, res=10, ch=ch, start=start, end=end, return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs, "OKSeq", cell, res=resolution, ch=ch,
start=start, end=end, return_exp=True, rescale=1/resolution)
"""
if left is not None:
RFDs += 1.5*left
if right is not None:
RFDs += 1.5*right
if cell != "Cerevisae":
MRTpearson, MRTstd, MRT = compare(
MRTp[::10//resolution], "MRT", cell, res=10, ch=ch, start=tstart, end=tend, return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs, "OKSeq", cell, res=resolution, ch=ch,
start=tstart, end=tend, return_exp=True, rescale=1/resolution)
else:
MRTpearson, MRTstd, MRT = compare(
MRTp, "MRT", cell, res=1, ch=ch, start=tstart, end=tend, return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs, "OKSeq", cell, res=1, ch=ch,
start=tstart, end=tend, return_exp=True, rescale=1,nanpolate=True,smoothf=2)
RFDs = smooth(RFDs,2)
if args.RFDo:
#print(RFDstd)
return RFDstd,MRTp,MRT,RFD,RFDs,wRFD
else:
return MRTstd+RFDstd,MRTp,MRT,RFD,RFDs,wRFD
if args.cutholes:
#Get masks and recompute ndiffs:
tot_diff = 0
for i, ((start, end, ch, diff),
(x, d3p, stallp)) in enumerate(zip(list_task, d3pl)):
#To get masks
MRTpearson, MRTstd, MRT, [mask_MRT,
l] = compare(d3p[::mrt_res // resolution],
"MRT",
comp,
res=mrt_res,
ch=ch,
start=start,
end=end,
return_exp=True,
trunc=True,
pad=True,
return_mask=True,
masking=masking,
propagateNan=propagateNan)
RFDpearson, RFDstd, RFD, [mask_RFD,
l] = compare(d3p,
expRFD,
cellseq,
res=resolution,
ch=ch,
start=start,
end=end,
def run(profile, tndiff, tstart, tend, actualProfile=None):
res = args.resolution
if actualProfile is not None:
tmpProfile = actualProfile.copy()
#print(tmpProfile.shape,np.array(profile).shape,start,tstart,tend,tmpProfile[tstart-start:tend-start].shape,tstart-start,tend-start)
weight_t = np.sum(tmpProfile[tstart - start:tend - start])
tmpProfile[tstart - start:tend -
start] = weight_t * np.array(profile) / np.sum(profile)
else:
tmpProfile = profile
MRTp, MRTs, RFDs, Rept_time, single_mol_exp, pos_time_activated_ori, It, wRFD = get_fast_MRT_RFDs(
nsim,
tmpProfile,
tndiff,
kon=kon,
fork_speed=fork_speed,
dori=20 * 5 / resolution,
single_mol_exp=False,
continuous=args.continuous,
wholeRFD=True)
#print("check", np.sum(d3p), np.sum(np.ones_like(d3p)*np.sum(d3p)*0.1/len(d3p)))
#print("Start, end",tstart,tend,tndiff)
# Compare to exp data
"""
MRTpearson, MRTstd, MRT = compare(
MRTp[::10//resolution], "MRT", cell, res=10, ch=ch, start=start, end=end, return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs, "OKSeq", cell, res=resolution, ch=ch,
start=start, end=end, return_exp=True, rescale=1/resolution)
"""
if cell != "Cerevisae":
if actualProfile is not None:
MRTpearsonl, MRTstdl, MRTl = compare(
MRTp[int((tstart - start) / res):int((tend - start) /
res)][::10 // resolution],
"MRT",
cell,
res=10,
ch=ch,
start=tstart,
end=tend,
return_exp=True)
RFDpearsonl, RFDstdl, RFDl = compare(
RFDs[int((tstart - start) / res):int((tend - start) / res)],
"OKSeq",
cell,
res=resolution,
ch=ch,
start=tstart,
end=tend,
return_exp=True,
rescale=1 / resolution)
else:
RFDstdl = 0
MRTpearson, MRTstd, MRT = compare(MRTp[::10 // resolution],
"MRT",
cell,
res=10,
ch=ch,
start=start,
end=end,
return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs,
"OKSeq",
cell,
res=resolution,
ch=ch,
start=start,
end=end,
return_exp=True,
rescale=1 / resolution)
else:
if actualProfile is not None:
MRTpearsonl, MRTstdl, MRTl = compare(
MRTp[int((tstart - start) / res):int((tend - start) / res)],
"MRT",
cell,
res=1,
ch=ch,
start=tstart,
end=tend,
return_exp=True)
RFDpearsonl, RFDstdl, RFDl = compare(
RFDs[int((tstart - start) / res):int((tend - start) / res)],
"OKSeq",
cell,
res=1,
ch=ch,
start=tstart,
end=tend,
return_exp=True,
rescale=1,
nanpolate=True,
smoothf=2)
else:
RFDstdl = 0
MRTpearson, MRTstd, MRT = compare(MRTp,
"MRT",
cell,
res=1,
ch=ch,
start=start,
end=end,
return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs,
"OKSeq",
cell,
res=1,
ch=ch,
start=start,
end=end,
return_exp=True,
rescale=1,
nanpolate=True,
smoothf=2)
RFDs = smooth(RFDs, 2)
if args.RFDo:
print(RFDstd, RFDstdl)
return RFDstd + RFDstdl, MRTp, MRT, RFD, RFDs, wRFD
else:
return MRTstd + RFDstd, MRTp, MRT, RFD, RFDs, wRFD
start=start, end=end,
resolution=5, raw=False)
CNV[CNV == 0] = 2
d3p /= CNV
d3p[np.isnan(d3p)] = 0
print("Simu")
sim = get_fast_MRT_RFDs(
nsim, d3p+np.ones_like(d3p)*np.sum(d3p)*random_activation/len(d3p), ndiff, kon=kon,
fork_speed=fork_speed, dori=dori)
with open(filename, "wb") as f:
pickle.dump(sim, f)
MRTp, MRTs, RFDs, Rep_Time, single_mol_exp, pos_time_activated_ori, It = sim
# Compare to exp data
MRTpearson, MRTstd, MRT = compare(
MRTp[::2], "MRT", compMRT, res=10, ch=ch, start=start, end=end, return_exp=True)
RFDpearson, RFDstd, RFD = compare(RFDs, "OKSeq", compRFD, res=5, ch=ch,
start=start, end=end, return_exp=True, rescale=1/5)
print(MRTpearson, MRTstd, RFDpearson, RFDstd)
M.append(mark)
D.append(ndiff)
R.append(random_activation)
Do.append(dori)
P.append(p)
SM.append(smooth)
MRT_std.append(MRTstd)
RFD_std.append(RFDstd)
MRT_pearson.append(MRTpearson)
RFD_pearson.append(RFDpearson)