par1[vals[1]] = pars[1][i1]
par1['K'] = delta / par1['CD'] # Keep the value of Delta fixed throughout
for i2 in range(X[2]):
par1[vals[2]] = pars[2][i2]
for i3 in range(X[3]):
par1[vals[3]] = pars[3][i3]
for i4 in range(X[4]):
par1[vals[4]] = pars[4][i4]
for i5 in range(X[5]):
par1[vals[5]] = pars[5][i5]
for i6 in range(X[6]):
par1[vals[6]] = pars[6][i6]
for i7 in range(X[7]):
c = g.discr_gen(par1)
# This will initialize the subsequent simulations for this model
temp = g.starting_popn_seeded([obj for obj in c if obj.exists], par1)
# initial pop seeded from c
c1, obs1 = g.discr_time_1(par1, temp)
for i8 in range(X[8]):
x1 = [obj.vb for obj in c if obj.isdaughter == i8]
y1 = [obj.vd for obj in c if obj.isdaughter == i8]
z1 = np.mean([obj.t_grow - obj.t_bud for obj in c if obj.isdaughter == i8])
u1 = [obj for obj in c if obj.mother and obj.mother.isdaughter == i8 and obj.wb < obj.vb]
uu1 = []
for obj in u1: # this step is necessary just to avoid duplicates
if obj.mother in uu1:
continue
uu1.append(obj.mother)
# print len(uu1), len(u1)
w1 = len(uu1) * 100.0 / len(x1)
# print len(u1) * 100.0 / len(x1)
delta = 10.0
par_vals = dict([('dt', 0.01), ('td', 10.0), ('num_s1', 500), ('nstep', 500),
('num_gen', 10), ('modeltype', 27), ('delta', 10.0),
('l_std', 0.2), ('r', 0.67),
('d_std', 0.02)]) # r picked to give same as 'cd'=0.6
celltype = ['Mothers', 'Daughters']
path = './check_correlations_1_output'
full_title = ['Mothers $t_d$ z-score', 'Daughters $t_d$ z-score']
sub_title = [r'Growth rate $\sigma={0}$'.format(par_vals['l_std'])]
# while not os.path.isfile(path+'_td_celltype+{0}'.format(0)+'.npy'): # ensures that this dataset is there
temp = g.discr_gen(par_vals)
temp1 = g.starting_popn_seeded([obj for obj in temp if obj.exists], par_vals)
temp2 = g.discr_time_1(par_vals, temp1)
td = []
td.append(
np.asarray([
max(obj.t_grow, 0.0) for obj in temp2[0]
if obj.exists and obj.isdaughter == 1
]))
# print len(td[-1])
td.append(
np.asarray([
max(obj.mother.nextgen.t_grow, 0.0) for obj in temp2[0]
if obj.exists and obj.isdaughter == 1
]))
# print len(td[-1])
nan_inds = ~(np.isnan(td[0]) + np.isnan(td[1]))
('K', delta / td), ('CD', 1.0), ('r', 1.0), ('r_std', 0.0),
('g2_std', 0.2), ('d_std', 0.1), ('k_std', None),
('g1_thresh_std', 0.1)])
cd = [0.5, 1.0]
vb = [[[], []], [[], []]]
vd = [[[], []], [[], []]]
# for i0 in range(len(cd)):
i0 = 1
par1 = par_vals
par1['CD'] = cd[i0]
c = g.discr_gen(par_vals)
temp = [obj for obj in c if obj.exists]
# del c
for i1 in range(100000):
val = g.starting_popn_seeded(temp, par1)
c = g.discr_time_1(par1, val)
temp = [obj for obj in c[0] if obj.exists]
for i2 in range(2):
temp1 = np.asarray(
[obj.vb for obj in temp if obj.isdaughter == i2 and obj.exists])
temp2 = np.asarray(
[obj.vd for obj in temp if obj.isdaughter == i2 and obj.exists])
np.save('../../Documents/data_storage/celltype_{0}'.format(i2) +
'_vb_it_{0}'.format(i1), temp1) # saving data
np.save('../../Documents/data_storage/celltype_{0}'.format(i2) +
'_vd_it_{0}'.format(i1), temp2) # saving data
vb[i0][i2].append(temp1)
vd[i0][i2].append(temp2)
# for i0 in range(len(cd)):
for i0 in xrange(start, stop): # varying the different growth conditions
temp = np.unravel_index(i0, tuple(Z)) # gives the indices of this in phase space as implied by model_vars
par1 = par_vals
for i1 in range(len(temp)): # set the values for each of the different indices in this range.
par1[fv[model_vars[model_num][i1]]] = fp[model_vars[model_num][i1]][temp[i1]]
if model_num == 0:
par1['K'] = delta/par1['CD']
for i1 in range(num_rep):
# tic1 = time.clock()
c = []
# print par1
temp1 = g.discr_gen(par1)
c.append(temp1)
del temp1
# This will initialize the subsequent simulations for this model
temp2 = g.starting_popn_seeded([obj for obj in c[0] if obj.exists], par1)
# initial pop seeded from c
temp1, obs1 = g.discr_time_1(par1, temp2)
c.append(temp1)
del temp1, temp2
for i2 in range(num_celltype): # celltype
for i3 in range(num_sims): # sim number
x1 = [obj.vb for obj in c[i3][1000:] if obj.isdaughter == i2]
x2 = [obj.vb for obj in c[i3][1000:] if obj.isdaughter == i2 and obj.wd / obj.vd < 1.0]
x3 = [obj.vb for obj in c[i3][1000:] if obj.isdaughter == i2 and obj.wb / obj.vb < 1.0]
y1 = [obj.vd for obj in c[i3][1000:] if obj.isdaughter == i2]
val1 = scipy.stats.linregress(x1, y1)
a[temp[0], temp[1], temp[2], temp[3], temp[4], i1, i2, i3, 0] = val1[0] # slope result
a[temp[0], temp[1], temp[2], temp[3], temp[4], i1, i2, i3, 1] = len(x2) * 100.0 / len(x1)
# % with low div conc result
a[temp[0], temp[1], temp[2], temp[3], temp[4], i1, i2, i3, 2] = len(x3) * 100.0 / len(x1)
for i0 in xrange(start, stop):
# Reset par1 for each new model, with settings appropriate for that model
par1 = par_set
for temp in range(len(par1_vals[i0])):
par1[par1_vals[i0][temp][0]] = par1_vals[i0][temp][1]
c = g.discr_gen(
par1
) # This will initialize the subsequent simulations for this model
for i1 in range(X1):
par1[vals[1]] = pars[1][i1]
for i2 in range(X2):
par1[vals[2]] = pars[2][i2]
par1['nstep'] = int(pars[2][i2] * 1.0 / par1['dt'])
for i3 in range(X3):
temp = g.starting_popn_seeded(
[obj for obj in c if obj.exists],
par1) # initial pop seeded from c
c1, obs1 = g.discr_time_1(par1, temp)
del temp
temp = g.starting_popn_seeded(
[obj for obj in c if obj.exists],
par1) # initial pop seeded from c
c2 = g.discr_gen_1(par1, temp)
del temp
for i4 in range(X4):
x1 = [obj.vb for obj in c1 if obj.isdaughter == i4]
y1 = [obj.vd for obj in c1 if obj.isdaughter == i4]
x2 = [obj.vb for obj in c2 if obj.isdaughter == i4]
y2 = [obj.vd for obj in c2 if obj.isdaughter == i4]
val1 = scipy.stats.linregress(x1, y1)
val2 = scipy.stats.linregress(x2, y2)
