def run_fit():
t = system.get_t(tmax=500)
sims = odict([
('Turnover', get_sim('full', t=t)),
('No Turnover', get_sim('no-turnover', t=t)),
])
for name, sim in sims.items():
sim.init_outputs(
system.get_outputs(spaces=sim.model.spaces,
select=sim.model.select,
t=sim.t,
names=['prevalence']))
sim.solve()
targets = system.get_targets(
sim.model.spaces,
sim.model.select,
sim.outputs,
t=sim.t,
)
if config.model == 'mort':
sim.model.params['C'].update([25, 10,
5]) # HACK to get low prev > 0
calsim = calibration.CalibrationSim(
name,
sim=sim,
targets=targets,
verbose=True,
)
calsim.optimize(ftol=1e-3, plot='tmp.png')
if config.save:
utils.savejson(fname_fit(name), calsim.fitted_params().todict())
else:
print(dict(calsim.fitted_params().todict()))
def gettypehash(url):
rs = utils.gethtml(url)
soup = BeautifulSoup(rs, "lxml")
# parse main page info
listitems = soup.select('#list_categories_categories_list_items > a')
listinfos = [{'type': item['title'], 'url': item['href']} for item in listitems]
# save json data
utils.savejson(alljsonpath, listinfos)
def getleavelsinfo():
rootpath = './infolist'
lpath = './leaveinfo'
lists = os.listdir(path = rootpath)
for i in range(len(lists)):
p = os.path.join(rootpath, lists[i])
if p.find('all') == -1 and os.path.splitext(p)[1] == '.json':
data = utils.getjsondata(p)
for item in data:
if item['url']:
levlestr = utils.gethtml(item['url'])
vurl = getleavesinfo(levlestr)
print(item['title'], vurl)
if vurl:
item['vurl'] = vurl
utils.savejson(os.path.join(lpath, lists[i]), data)
print(os.path.join(lpath, lists[i]), ' done.')
def savesubinfo():
'''
sub page info
'''
data = utils.getjsondata(alljsonpath)
vinfo = []
for i in data:
purl = i['pageurl']
vtype = i['type']
# initial page num to 1
pagenum = 1
if purl:
while True:
tpurl = purl.replace('pagenum', str(pagenum))
subrs = utils.gethtml(tpurl)
slists = getsubinfo(subrs)
if len(slists) == 0:
break
vinfo.extend(slists)
pagenum += 1
utils.savejson('./infolist/{vtype}.json'.format(vtype = vtype), vinfo)
print('共{count}条'.format(count=len(vinfo)))
vinfo = []
def bAP(Bnum = 34, TTX = False, Atype = False, vec = []):
"""
Bnum: the recording branch
-----------
Outputs:
json: soma and dendritc voltage recording and parameters info
"""
timestr = time.strftime("%H%M")
data = time.strftime("%m_%d")
directory = 'Fig2/'
# directory = 'Data_' + data +'/'
Cell = de.CA229()
###########################################
if (TTX == False and Atype == False):
title = "Control_" + "Bnum_" + str(Bnum) + "_" + timestr
###########################################
# Current injection in soma
###########################################
ic = h.IClamp(Cell.soma[2](0.5))
ic.dur = 1.75
ic.delay = 150
ic.amp = 3
elif (TTX == True):
Cell.TTX_bAP()
Vstim = h.SEClamp(Cell.soma[2](0.5))
Vstim.rs= 0.01
Vstim.dur1 = 1e9
vec.play(Vstim._ref_amp1, h.dt)
title = "TTX_" + "Bnum_" + str(Bnum) + "_" + timestr
else:
Cell = de.CA229(KA_ratio = 0.0)
ic = h.IClamp(Cell.soma[2](0.5))
ic.dur = 1.75
ic.delay = 150
ic.amp = 3
title = "4AP_" + "Bnum_" + str(Bnum) + "_" + timestr
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
dist = []
Loc = []
for seg in Cell.basal[Bnum]:
Loc.append(seg.x)
dist.append(h.distance(seg.x, sec = Cell.basal[Bnum]))
v_vec_dend = []
for loc in Loc:
v_vec_dend.append(h.Vector())
v_vec_dend[-1].record(Cell.basal[Bnum](loc)._ref_v)
###########################################
### Run & Plot
### Be careful, vmax does not have value before run
###########################################
h.celsius = 32 # 32
h.v_init = -73.6927850677
h.init()
h.tstop = 300
h.run()
# pdb.set_trace() #Debugging
# print v_vec_soma[-1]
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
# for index, loc in enumerate(Loc):
# plt.plot(t_vec, v_vec_dend[index], label = 'dend-loc'+"{0:.2f}".format(loc))
# plt.ylim([-90, 40])
# plt.xlim([0, 300])
# plt.legend(loc = 'upper right')
# plt.ylabel('mV')
# plt.xlabel('Time (ms)')
# plt.title ("bAP")
# ut.save(title, directory, ext="png", close=True, verbose=True)
#######################
data = ut.Vividict()
data['Bnum'] = Bnum
data['Loc'] = Loc
data['dist'] = dist
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
for index, dist in enumerate(dist):
data['recording']['dend']["{0:.2f}".format(dist)] = list(v_vec_dend[index])
ut.savejson(data, title, directory, ext = "json", verbose = False)
if (TTX == False and Atype == False):
return v_vec_soma
def __init__(self, TTX = False, Pool1_num = 9, Pool2_num = 9, Beta = 0.067,
Cdur = 1, Syn_w1 = 0.01, Syn_w2 = 0.01, Loc = [0.2, 0.6]):
"""
Model the Glumate Stimulation.
Model the Receptors in 2 pools:
Pool 1: AMPA + NMDA (same synaptic weight, represent spine conductance)
Pool 2: NMDA only (represent the extrasyanptic NMDARs)
Parameters:
-----------
TTX: True or False.
True: setting all the sodium channel conductance to 0 to mimic
TTX application in experiments
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Beta: parameter for NMDA Receptors
Cdur: parameter for NMDA Receptors
Syn_w1: the syanptic weight of AMPA/NMDA receptors in pool1
Syn_w2: the syanptic weight of AMPA/NMDA receptors in pool2
Loc: the stimulation location
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
Cell = de.CA229(HVA_ratio = 0.0, LVA_ratio = 0.0)
self.Cell = Cell
# Can adjust channel conductance ratio here:
# eg. Cell = CA229(KA_ratio = 0.5)
###########################################
timestr = time.strftime("%H%M")
data = time.strftime("%m_%d")
# directory = 'Data_' + data +'/'
directory_root = 'Fig3/'
directory = directory_root + 'DMS/Plot/'
# directory = directory_root + 'DMS/Analysis/'
if (TTX == True):
Cell.TTX()
title = "TTX_Pool1_"+ str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_"+str(Cdur)+ "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
else:
title = "Pool1_"+ str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_"+str(Cdur)+ "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
###########################################
# Adding Pool 1
###########################################
##### AMPA
SynAMPA = []
nc_AMPA = []
SynNMDA = []
nc_NMDA = []
self.SynAMPA = SynAMPA
self.nc_AMPA = nc_AMPA
self.SynNMDA = SynNMDA
self.nc_NMDA = nc_NMDA
###########################################
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
###########################################
# Loc and time delay set up
delay1 = random_2(10, 50 + int(Syn_w1*50), Pool1_num)
# delay1 = random_beta(10, 50 + int(Syn_w1*50), Pool1_num)
ns = h.NetStim()
self.ns = ns
ns.interval = 20
ns.number = 1
ns.start = 190
ns.noise = 0
###########################################
for i in range(Pool1_num):
###########################
# Adding AMPA
SynAMPA.append(h.AMPA(Cell.basal[34](loc1[i])))
SynAMPA[-1].gmax = 0.05
nc_AMPA.append(h.NetCon(ns, SynAMPA[i]))
nc_AMPA[-1].delay = delay1[i]
nc_AMPA[-1].weight[0] = Syn_w1
###########################
#Adding NMDA
SynNMDA.append(h.NMDA(Cell.basal[34](loc1[i])))
SynNMDA[-1].gmax = 0.005
SynNMDA[-1].Beta = Beta
SynNMDA[-1].Cdur = Cdur
nc_NMDA.append(h.NetCon(ns, SynNMDA[i]))
nc_NMDA[-1].delay = delay1[i]
nc_NMDA[-1].weight[0] = Syn_w1
###########################################
# Adding Pool 2
###########################################
ExNMDA = []
nc_ExNMDA = []
self.ExNMDA = ExNMDA
self.nc_ExNMDA = nc_ExNMDA
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
# delay2 = list(np.linspace(5, 10, Pool2_num))
delay2 = random_2(15, 55 + int(Syn_w2*60), Pool2_num)
# delay2 = random_beta(15, 55 + int(Syn_w2*60), Pool2_num)
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
ExNMDA.append(h.NMDA(Cell.basal[34](loc2[i])))
ExNMDA[-1].gmax = 0.005
ExNMDA[-1].Beta = Beta
ExNMDA[-1].Cdur = Cdur
nc_ExNMDA.append(h.NetCon(ns, ExNMDA[i]))
nc_ExNMDA[-1].delay = delay2[i]
nc_ExNMDA[-1].weight[0] = Syn_w2
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_dend1 = h.Vector()
v_vec_dend2 = h.Vector()
v_vec_dend3 = h.Vector()
cai_soma = h.Vector()
cai_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[34](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[34](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[34](0.3)._ref_v)
cai_soma.record(Cell.soma[2](0.5)._ref_cai)
cai_dend.record(Cell.basal[34](0.3)._ref_cai)
###########################################
### Run & Plot
###########################################
h.celsius = 32
h.v_init = -73.6927850677
h.init()
h.tstop = 1000
h.run()
# pdb.set_trace() #Debugging
# print v_vec_soma[-1]
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
# plt.plot(t_vec, v_vec_dend1, label = 'bdend[34](0.8)', color = 'red')
# plt.plot(t_vec, v_vec_dend2, label = 'Basal[34](0.5)', color = 'blue')
# plt.plot(t_vec, v_vec_dend3, label = 'Basal[34](0.3)', color = 'green')
# plt.ylim([-90, 40])
# plt.xlim([0, 800])
# plt.legend(loc = 'best')
# plt.ylabel('mV')
# plt.xlabel('Time (ms)')
# plt.title ("Glumate Receptor Activated Plateau Potential")
#
# save(title, directory, ext="png", close=True, verbose=True)
#######################
# Plot the intracelluar calcium concentration
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, cai_soma, label = 'soma(0.5)', color = 'black')
# #plt.plot(t_vec, cai_dend, label = 'bdend[34](0.3)', color = 'red')
#
# # plt.ylim([-90, 60])
# plt.xlim([0, 800])
# plt.legend(loc = 'best')
# plt.ylabel('mM')
# plt.xlabel('Time (ms)')
# plt.title ("Calcium concentration")
# title1 = "Calcium_" + title
# ut.save(title1, directory, ext="png", close=True, verbose=True)
data = ut.Vividict()
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = loc1
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = loc1
data['SynNMDA']['weight'] = Syn_w1
data['SynNMDA']['Beta'] = Beta
data['SynNMDA']['Cdur'] = Cdur
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = loc2
data['ExNMDA']['weight'] = Syn_w2
data['ExNMDA']['Beta'] = Beta
data['ExNMDA']['Cdur'] = Cdur
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
data['recording']['basal_34']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal_34']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal_34']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['soma']['ica'] = list(cai_soma)
data['recording']['basal_34']['ica_0.3'] = list(cai_dend)
ut.savejson(data, title, directory, ext = "json", verbose = False)
def Glu_Stim(Bnum=34,
TTX=False,
Pool1_num=9,
Pool2_num=9,
Syn_w1=0.01,
Syn_w2=0.01,
Loc=[0.2, 0.6],
DenLoc=0.5):
"""
Model the Glumate Stimulation.
Model the Receptors in 2 pools:
Pool 1: AMPA + NMDA (same synaptic weight, represent spine conductance)
Pool 2: NMDA only (represent the extrasyanptic NMDARs)
Parameters:
-----------
Bnum: the number of basal branch to explore
TTX: True or False.
True: setting all the sodium channel conductance to 0.
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Syn_w1: the syanptic weight of AMPA/NMDA receptors in pool1
Syn_w2: the syanptic weight of AMPA/NMDA receptors in pool2
Loc: the stimulation location
DenLoc: the targeted recording location on dendrite
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
Cell = de.CA229()
timestr = time.strftime("%Y%m%d-%H%M")
data = time.strftime("%m_%d")
directory_root = "Fig5/Major/"
L1 = "{:.2f}".format(Loc[0])
L2 = "{:.2f}".format(Loc[1])
if (TTX == True):
Cell.TTX()
directory = directory_root + "B" + str(
Bnum) + "/Loc" + L1 + "_" + L2 + "/TTX/"
title = "TTX_Pool1_"+ \
str(Pool1_num) + "_Pool2_" + str(Pool2_num) + \
"_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
else:
directory = directory_root + "B" + str(
Bnum) + "/Loc" + L1 + "_" + L2 + "/N/"
title = "Pool1_"+ \
str(Pool1_num) + "_Pool2_" + str(Pool2_num) + \
"_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
###########################################
# Adding Pool 1
###########################################
##### AMPA
SynAMPA = []
nc_AMPA = []
SynNMDA = []
nc_NMDA = []
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
###########################################
delay1 = random_2(10, 20 + int(Syn_w1 * 50), Pool1_num)
ns = h.NetStim()
ns.interval = 20
ns.number = 1
ns.start = 190
ns.noise = 0
for i in range(Pool1_num):
###########################
# Adding AMPA
SynAMPA.append(h.AMPA(Cell.basal[Bnum](loc1[i])))
SynAMPA[-1].gmax = 0.05
nc_AMPA.append(h.NetCon(ns, SynAMPA[i]))
nc_AMPA[-1].delay = delay1[i]
nc_AMPA[-1].weight[0] = Syn_w1
###########################
for i in range(Pool1_num):
tempNMDA = h.nmda(Cell.basal[Bnum](loc1[i]))
tempNMDA.gmax = 0.005 * Syn_w1
tempNMDA.onset = delay1[i] + ns.start
SynNMDA.append(tempNMDA)
###########################################
# Adding Pool 2
###########################################
ExNMDA = []
nc_ExNMDA = []
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
delay2 = random_2(15, 25 + int(Syn_w2 * 60), Pool2_num)
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
tempNMDA2 = h.nmda(Cell.basal[Bnum](loc2[i]))
tempNMDA2.gmax = 0.005 * Syn_w2
tempNMDA2.onset = delay2[i] + ns.start
ExNMDA.append(tempNMDA2)
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_dend1 = h.Vector()
v_vec_dend2 = h.Vector()
v_vec_dend3 = h.Vector()
v_vec_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[Bnum](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[Bnum](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[Bnum](0.3)._ref_v)
v_vec_dend.record(Cell.basal[Bnum](DenLoc)._ref_v)
###########################################
### Run & Plot
###########################################
h.celsius = 32
h.v_init = -73.6927850677
h.init()
h.tstop = 1000
h.run()
# pdb.set_trace() #Debugging
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
# plt.plot(t_vec, v_vec_dend1, label = 'Basal['+str(Bnum)+'](0.8)', color = 'red')
# plt.plot(t_vec, v_vec_dend2, label = 'Basal['+str(Bnum)+'](0.5)', color = 'blue')
# plt.plot(t_vec, v_vec_dend3, label = 'Basal['+str(Bnum)+'](0.3)', color = 'green')
# plt.ylim([-90, 40])
# plt.xlim([0, 700])
# plt.legend(loc = 'best')
# plt.ylabel('mV')
# plt.xlabel('Time (ms)')
# plt.title ("Glumate Receptor Activated Plateau Potential")
# save(title, directory, ext="png", close=True, verbose=True)
data = ut.Vividict()
data['TTX'] = TTX
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = Loc
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = Loc
data['SynNMDA']['weight'] = Syn_w1
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = Loc
data['ExNMDA']['weight'] = Syn_w2
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
data['recording']['basal']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['basal']['voltage_input'] = list(v_vec_dend)
ut.savejson(data, title, directory, ext="json", verbose=False)