def run(self):
self.br=BGrate.calcBGrate(self.dbname,20,400,T0=600.0)
self.burst=BurstSearch.findBurst(self.br,self.dbname,["All"])
self.burstSeff, self.burstFRET,self.wei,self.H,self.xedges, \
self.yedges=fretAndS.FretAndS(self.dbname,self.burst,(27,27),self.br)
#self.timer.stop()
self.viewbox.canvas.drawHist(self.H,self.xedges,self.yedges)
if self.mainui is not None:
print ("copy burst")
self.mainui.burst=copy.deepcopy(self.burst)
print(self.mainui.burst["SyncResolution"])
def main(comm, dbname, n_states, Sth):
rank = comm.Get_rank()
clsize = comm.Get_size()
#print("============size=====",clsize)
if rank == 0:
br = BGrate.calcBGrate(dbname, 20, 400)
burst = BurstSearch.findBurst(br, dbname, ["All"])
burstSeff, burstFRET, wei, H, xedges, yedges = fretAndS.FretAndS(
dbname, burst, (27, 27), br)
n_burst = len(burst["All"]['chl'])
if n_burst < clsize:
clsize = n_burst
chunkLists = list(chunks(range(n_burst), clsize))
#gsml=GS_MLE(burst,0.891)
#gsml.n_states=2
#gsml.MaxLikehood([0.3,0.7,0.2, 3,3,3, 3,3,3])
#endtime = datetime.datetime.now()
#print (endtime - starttime)
else:
burst = dict()
chunkLists = list()
clsize = comm.bcast(clsize, root=0)
burst = comm.bcast(burst, root=0)
burstIdxRange = comm.scatter(chunkLists, root=0)
#print(burstIdxRange,rank)
gsml = GS_MLE(burst, comm, burstIdxRange, Sth)
gsml.n_states = n_states
params = [0.38, 0.6, 675.0, 325.0, 3, 3, 3, 3, 3]
params = params[:n_states * n_states]
#print(params)
stop = [0]
if rank == 0:
gsml.MaxLikehood(params)
#gsml.lnLikelihood(params,stop)
else:
while stop[0] == 0:
gsml.lnLikelihood(params, stop)
return None
matP = np.empty([n, 1])
ap = 0
for i in range(n):
ap += matK[i, i]
for i in range(n):
matP[i, 0] = matK[i, i] / ap
return matP
if __name__ == '__main__':
import matplotlib
import datetime
starttime = datetime.datetime.now()
dbname = '/home/liuk/sf/oc/data/38.sqlite'
dbname = 'E:/liuk/proj/ptu/data/55.sqlite'
#dbname='E:/sf/oc/data/38.sqlite'
dbname = "E:/dbox/sf/oc/data/1min.sqlite"
br = BGrate.calcBGrate(dbname, 20, 400)
burst = BurstSearch.findBurst(br, dbname, ["All"])
burstSeff, burstFRET, wei, H, xedges, yedges = fretAndS.FretAndS(
dbname, burst, (27, 27), br)
#matplotlib.pyplot.plot(burst["All"].s)
gsml = GS_MLE(burst, 0.891)
gsml.n_states = 3
gsml.MaxLikehood([0.3, 0.7, 0.2, 3, 3, 3, 3, 3, 3])
endtime = datetime.datetime.now()
print(endtime - starttime)
def FretZ(dbname, burst, bins=(25, 25), bgrate=None):
#conn = sqlite3.connect(dbname)
#c = conn.cursor()
lenburst = len(burst["All"]['chl'])
print("lenburst:", lenburst)
burstFRET = array("d") #np.zeros(lenburst)
burstSeff = array("d") #np.zeros(lenburst)
burstZ = array("d")
wei = array("d") #np.zeros(lenburst)
#fw = np.zeros(lenburst)
for i in range(lenburst):
# c.execute("select Dtime,ch from fretData_All where TimeTag>=? and TimeTag<= ?",
# (burst["All"].stag[i],burst["All"].etag[i]))
# data=c.fetchall()
data = burst["All"]['chl'][i]
if bgrate != None:
tt = burst["All"]['timetag'][i]
#print(tt)
backgT = burst["All"]['burstW'][i] / 2 + tt.iloc[0] * bgrate[
"SyncResolution"] #中点时刻
bgAA = BurstSearch.getBGrateAtT(bgrate, "AexAem", backgT)
bgDD = BurstSearch.getBGrateAtT(bgrate, "DexDem", backgT)
bgDA = BurstSearch.getBGrateAtT(bgrate, "DexAem", backgT)
w = len(data)
#print(w)
nda = 0 #ch1
ndd = 0
#ch2
naa = 0
#ch3
nad = 0 #ch4
for d in data:
if d == 1:
nda += 1
elif d == 2:
ndd += 1
elif d == 3:
naa += 1
elif d == 4:
nad += 1
if bgrate != None:
naa = naa - bgAA * burst["All"]['burstW'][i]
ndd = ndd - bgDD * burst["All"]['burstW'][i]
nda = nda - bgDA * burst["All"]['burstW'][i]
if naa < bgAA * burst["All"]['burstW'][i] or ndd < 0 or nda < 0:
continue
wei.append(w)
gamma = 0.31
beta = 1.42
if ndd == 0:
zp = np.log(nda / (gamma * 0.001))
burstZ.append(zp)
burst["All"]['z'][i] = zp
else:
zp = np.log(nda / (gamma * ndd))
burst["All"]['z'][i] = zp
burstZ.append(zp)
if (nda + ndd) == 0:
burstFRET.append(1)
burst["All"]['e'][i] = 1
else:
burstFRET.append((nda) / (nda + gamma * ndd))
burst["All"]['e'][i] = (nda) / (nda + gamma * ndd)
if (nda + ndd + naa) == 0:
burstSeff.append(1)
burst["All"]['s'][i] = 1
else:
burstSeff.append(
(nda + gamma * ndd) / (nda + gamma * ndd + naa / beta))
burst["All"]['s'][i] = (nda + gamma * ndd) / (nda + gamma * ndd +
naa / beta)
H, xedges, yedges = np.histogram2d(burstFRET,
burstSeff,
bins=bins,
weights=wei)
#conn.close()
#fig, ax = plt.subplots()
#plt.subplots_adjust(bottom=0.15)
#im=plt.imshow(H.transpose()[::-1], interpolation='bessel',
# cmap=cm.jet,extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
#plt.colorbar(im)
#rs=RectSect(ax,xedges,yedges)
#toggle_selector(rs.toggle_selectorRS,plt)
#RectBuilder(ax,xedges,yedges,rs.toggle_selectorRS)
#callback = MaxLikehood(ax,burstSeff, burstFRET,burst)
#axMLH = plt.axes([0.1, 0.02, 0.1, 0.05])
#bnMLH = Button(axMLH, 'calc ML')
#bnMLH.on_clicked(callback.calc)
#import seaborn as sns
#g = sns.JointGrid(x=burstFRET, y=burstSeff)
#g.plot_marginals(sns.distplot)
#g.plot_joint(plt.hist2d)
return burstSeff, burstFRET, burstZ, wei, H, xedges, yedges