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)
# print len([obj for obj in c if obj.wb<obj.vb and obj.isdaughter])*100.0/len([obj for obj in c if obj.isdaughter])
# print len(
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]))
for i1 in range(2):
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)
# % with low birth conc result
del c, obs1
('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 i1 in range(2):
str(stop))
tic = time.clock()
for i0 in xrange(start, stop): # variable number of repeats for each core
c = g.discr_gen(par_vals)
temp = [obj for obj in c if obj.exists]
for i1 in range(X[1]):
# tic = time.clock()
temp0, temp1 = g.starting_popn_seeded_1(temp,
par_vals,
discr_time=True)
if np.mod(i1, save_freq) == 0:
np.save(
'../../Documents/data_storage/March17_dilution_symmetric_3_savedpop_model_{0}_rep_{1}_it_{2}_asymm'
.format(str(par_vals['modeltype']), str(i0),
str(i1)), temp1)
new_c = g.discr_time_1(par_vals, temp0)
mothers = [obj.mother for obj in new_c[0] if obj.exists]
del temp # just to make sure it isn't stored.
temp = [obj for obj in new_c[0] if obj.exists]
for i2 in range(num_cells): # full population included
if i2 <= 1: # in this case we deal with mothers and daughters
temp2 = np.asarray(
[obj.vb for obj in temp if obj.isdaughter == i2])
temp3 = np.asarray(
[obj.wb for obj in temp if obj.isdaughter == i2])
a[i0, i1, i2, 0] = np.mean(temp2)
a[i0, i1, i2, 1] = np.std(temp2)
a[i0, i1, i2, 2] = np.mean(temp3)
a[i0, i1, i2, 3] = np.std(temp3)
a[i0, i1, i2, 4] = np.mean(np.log(temp2))
a[i0, i1, i2, 5] = np.std(np.log(temp2))
# str(par1['modeltype']), str(i0), str(i1)), bbox_inches='tight', dpi=fig.dpi)
for i1 in range(2):
fig = plt.figure(figsize=[14, 6])
for i0 in range(len(r_std)):
ax = plt.subplot(1, 2, 1 + i0)
par1['r_std'] = r_std[i0]
if i0 == 0:
print par1
print 'r_std:', par1['r_std']
temp1 = g.discr_gen(par1)
# This will initialize the subsequent simulations for this model
temp2 = g.starting_popn_seeded([obj for obj in temp1 if obj.exists],
par1)
# initial pop seeded from c
temp3, obs3 = g.discr_time_1(par1, temp2)
plt.xlabel('$V_b$')
plt.ylabel('$V_d$')
# plt.title(plotlab[i0])
x = np.asarray(
[obj.vb for obj in temp3 if obj.exists and obj.isdaughter == i1])
y = np.asarray(
[obj.vd for obj in temp3 if obj.exists and obj.isdaughter == i1])
plt.hexbin(x, y, cmap="Purples", gridsize=40)
plt.ylim(ymin=np.mean(y) - l * np.std(y),
ymax=np.mean(y) + l * np.std(y))
plt.xlim(xmin=np.mean(x) - l * np.std(x),
xmax=np.mean(x) + l * np.std(x))
bin_means, bin_edges, binnumber = scipy.stats.binned_statistic(
x, np.arange(len(x)), statistic='mean', bins=num_bins, range=None)