# rr['Vm2'] = rr['Vm2']*v
#
# rr['Vm4'] = rr['Vm4']*v
#
# rr['Vm5'] = rr['Vm5']*v
##
# rr['Vm6'] = rr['Vm6']*v
rr['Vm7'] = rr['Vm7'] * v
# rr['Vm8'] = rr['Vm8']*v
mut_data = rr.simulate(0, sim_time, 5000)
rows, cols = get_dims(len(model_nums))
#plt.subplot(rows, cols, i + 1)
for col in sim_data.colnames[1:]:
if i == 0:
plt.plot(mut_data['time'],
mut_data[col],
color='C' + col[-1],
label=str(num),
linewidth=1)
else:
plt.plot(mut_data['time'],
mut_data[col],
color='C' + col[-1],
label=str(num),
linewidth=1,
linestyle='--')
mRNA_result = mRNA_result.sort_values('chisqr')
time = mRNA_data['time']
t = get_sim_time()
tp = get_num_points()
plt.figure()
print('top regs for ' + mRNA_name + ':')
for i in range(top_plots):
reg = mRNA_result.iloc[i][1]
chisqr = mRNA_result.iloc[i][2]
params = ast.literal_eval(mRNA_result.iloc[i][3])
sub_model = generate_sub_model(mRNA_num, reg)
rr = te.loada(sub_model)
for p in params:
rr[p] = params[p]
sim_data = rr.simulate(0, t, tp)
rows, cols = get_dims(top_plots)
plt.subplot(rows, cols, i + 1)
plt.plot(time,
mRNA_data[mRNA_name],
color='C' + str(mRNA_num),
label='exp')
plt.plot(time, sim_data['[mRNA]'], color='black', label='sim')
plt.title('reg: ' + reg + ', chisqr: ' + str(round(chisqr, 2)))
plt.legend()
print(reg + ', chisqr: ' + str(chisqr))
plt.suptitle('mRNA' + str(mRNA_num))
plt.show()
def p(df):
print(df[['mRNA_num', 'regulators', 'chisqr']])
# {'species': 'P8', 'params':{'Vm4': 2}, 'ylim': (0, 6)}
# ]
results = results.sort_values('chisqr')
#plt.figure()
for idx, model_num in enumerate([0, 1, 2, 3]):
sim_time = 1200
regs = ast.literal_eval(results.iloc[model_num][0])
params = ast.literal_eval(results.iloc[model_num][2])
model = make_model(regs)
rr = te.loada(model)
for p in params:
rr[p] = params[p]
sim_data_wild = rr.simulate(0, sim_time, 5000)
plt.figure('model ' + str(model_num), figsize=(10, 5), dpi=100)
rows, cols = get_dims(len(stress_tests))
# row = 1
# cols = 3
#rows, cols = get_dims(4)
#plt.subplot(rows, cols, idx + 1)
for i, test in enumerate(stress_tests):
rr.resetToOrigin()
for p in params:
rr[p] = params[p]
species = '[' + test['species'] + ']'
title = species + ', '
for p in test['params']:
rr[p] = rr[p] * test['params'][p]
title = title + p + '=' + str(test['params'][p]) + 'x, '
sim_data_mut = rr.simulate(0, sim_time, 5000)
plt.subplot(rows, cols, i + 1)