def fit_single_mother_and_bud(model,
mothercells_data,
daughtercells_data,
time_data,
cell_id,
parameters_to_fit,
additional_concentrations={},
max_time=400,
params_ini={},
tolerance_factor=1):
data = get_data_dict_for_cell(mothercells_data, daughtercells_data, time_data, cell_id)
data_trunc = model_data.truncate_data(data)
data_trunc['time'] = data_trunc['time'] + abs(min(data_trunc['time']))
data_trunc = model_data.limit_time_in_data(data_trunc, max_time=max_time)
additional_model_parameters = get_additional_model_parameters(data_trunc)
if params_ini=={}:
params_ini = get_initial_parameter_guess(data_trunc)
print(params_ini)
fitted_parameters = fit_data.fit_model_to_data(model,
data_trunc,
parameters_to_fit,
'cmaes',
additional_model_parameters=additional_model_parameters,
additional_concentrations=additional_concentrations,
params_ini=params_ini,
tolerance_factor=tolerance_factor)
msd = fit_data.compute_objective_function(fitted_parameters,
model,
parameters_to_fit,
data_trunc,
additional_model_parameters,
additional_concentrations)
return fitted_parameters, msd
def fit_model_to_all_cells(model,
mothercells_data,
daughtercells_data,
time_data,
parameters_to_fit):
fitting_results = []
rows_and_cols = np.ceil(np.sqrt(len(mothercells_data)))
for mother_pos in range(len(mothercells_data)):
data = {'time': np.array(time_data), 'V_tot_fl': mothercells_data[mother_pos, :]}
data = model_data.truncate_data(data)
data['time'] = data['time'] + abs(min(data['time']))
data = model_data.limit_time_in_data(data, max_time=300)
additional_model_parameters = get_initial_volume_osmotic_from_data(model, data)
fitted_parameters, opt_res = fit_model_to_data(model,
data,
parameters_to_fit,
'cmaes',
additional_model_parameters=additional_model_parameters)
fitting_results.append(fitted_parameters)
plt.subplot(rows_and_cols, rows_and_cols, mother_pos + 1)
plot_fitting_result_and_data(model,
fitted_parameters,
data,
parameters_to_fit,
additional_model_parameters=additional_model_parameters,
subplot=False,
show=False)
plt.show()
return fitting_results
def fit_single_mother_and_bud(model,
mothercells_data,
daughtercells_data,
time_data,
cell_id,
parameters_to_fit,
additional_concentrations={},
additional_model_parameters={},
max_time=400,
params_ini={},
log=True,
bounds={},
tolfun=0.1,
sigma0_log_notlog=(1e-1, 4e-17)):
additional_model_parameters = {}
data = get_data_dict_for_cell(mothercells_data, daughtercells_data,
time_data, cell_id)
data_trunc = model_data.truncate_data(data)
data_trunc['time'] = data_trunc['time'] + abs(min(data_trunc['time']))
data_trunc = model_data.limit_time_in_data(data_trunc, max_time=max_time)
#additional_model_parameters = get_additional_model_parameters(data_trunc)
para_ini_ids = ['budding_start', 'mother_r_os_0']
params_ini = fit_data.get_initial_parameter_from_data(
model, data_trunc, para_ini_ids)
#bud_start_data = get_initial_bud_start_guess(data_trunc)
bud_start_data = params_ini['budding_start']
start_tolerance = 120 * 60
bounds['budding_start'] = [
bud_start_data - start_tolerance, bud_start_data + start_tolerance
]
bounds['mother_r_os_0'] = [
params_ini['mother_r_os_0'] - 0.3, params_ini['mother_r_os_0'] + 0.3
]
#bounds['mother_phi']= [5.e-6, 5.e-1]
#bounds['bud_phi']= [1.e-5, 5.e-1 ]
#bounds['k_nutrient_']= [9.e-16, 9.e-17]
#bounds['k_deg_0']= [1.e-15, 1.e-17]
#bounds['mother_r_os'] = [0.1,2]
fitted_parameters = fit_data.fit_model_to_data(
model,
data_trunc,
parameters_to_fit,
'cmaes',
additional_model_parameters=additional_model_parameters,
additional_concentrations=additional_concentrations,
params_ini=params_ini,
bounds=bounds,
log=log,
tolfun=tolfun,
sigma0_log_notlog=sigma0_log_notlog)
print(fitted_parameters)
log = False
msd = fit_data.compute_objective_function(fitted_parameters, model,
parameters_to_fit, data_trunc,
additional_model_parameters,
additional_concentrations, log)
return fitted_parameters, msd
def plot_fitting_for_all(model,
mothercells_data,
daughtercells_data,
time_data,
df_params,
additional_concentrations={},
max_time=400):
assert len(df_params) == len(mothercells_data)
df_params = df_params.copy()
df_params = df_params.drop('MSD', axis=1)
no_cols = 4
no_rows = np.ceil(float(len(mothercells_data))/no_cols)
fig = plt.figure(1, figsize=(10,12))
ax = plt.subplot(no_rows, no_cols, 1)
for cell_id in range(len(mothercells_data)):
data = get_data_dict_for_cell(mothercells_data, daughtercells_data, time_data, cell_id)
data_trunc = model_data.truncate_data(data)
data_trunc['time'] = data_trunc['time'] + abs(min(data_trunc['time']))
data_trunc = model_data.limit_time_in_data(data_trunc, max_time=max_time)
additional_model_parameters = get_additional_model_parameters(data_trunc)
print cell_id
print additional_model_parameters
print
fitted_parameters = df_params.iloc[cell_id]
ax = plt.subplot(no_rows, no_cols, cell_id + 1, sharey=ax)
fit_data.plot_fitting_result_and_data(model,
fitted_parameters,
data_trunc,
parameters_to_fit,
subplot=False,
additional_model_parameters=additional_model_parameters,
additional_concentrations=additional_concentrations,
legend=False)
plt.ylabel('volume, fl')
plt.xlabel('time, min')
plt.title('Cell %s' %cell_id)
plt.legend(bbox_to_anchor=(1.5, 1),
loc=2, borderaxespad=0.,
labels=['bud volume (fitted)',
'bud volume ',
'mother volume (fitted)',
'mother volume '])
plt.tight_layout()
def get_data_trunc(data,max_time):
data_trunc = model_data.truncate_data(data)
data_trunc['time'] = data_trunc['time'] + abs(min(data_trunc['time']))
data_trunc = model_data.limit_time_in_data(data_trunc, max_time=max_time)
return data_trunc
def plot_fitting_for_all(model,
mothercells_data,
daughtercells_data,
time_data,
df_params,
additional_concentrations={},
additional_model_parameters={},
max_time=400,
cols=4,
max_cell_ID=0):
if max_cell_ID == 0:
Cell_IDs = range(len(mothercells_data))
else:
Cell_IDs = range(max_cell_ID)
assert len(df_params) == len(mothercells_data)
df_params = df_params.copy()
df_params.drop('MSD', inplace=True, axis=1)
no_cols = cols
no_rows = np.ceil(float(len(Cell_IDs)) / no_cols)
fig = plt.figure(1, figsize=(8, 8))
ax = plt.subplot(no_rows, no_cols, 1)
for cell_id in Cell_IDs:
data = get_data_dict_for_cell(mothercells_data, daughtercells_data,
time_data, cell_id)
data_trunc = model_data.truncate_data(data)
data_trunc['time'] = data_trunc['time'] + abs(min(data_trunc['time']))
data_trunc = model_data.limit_time_in_data(data_trunc,
max_time=max_time)
#additional_model_parameters = get_additional_model_parameters(data_trunc)
print cell_id
#print additional_model_parameters
fitted_parameters = df_params.iloc[cell_id]
ax = plt.subplot(no_rows, no_cols, cell_id + 1, sharey=ax)
try:
fit_data.plot_fitting_result_and_data(
model,
fitted_parameters,
data_trunc,
parameters_to_fit,
subplot=False,
additional_model_parameters=additional_model_parameters,
additional_concentrations=additional_concentrations,
legend=False,
observables=[])
except:
print('could not simulate for cell Nr. {0}'.format(cell_id))
budstart = fitted_parameters['budding_start'] / 60
nr_of_points = 10
y = np.arange(0, 100, nr_of_points)
x = [budstart] * nr_of_points
plt.plot(x, y)
plt.ylabel('volume, fl', fontsize='x-large')
plt.xlabel('time, min', fontsize='x-large')
plt.title('Cell %s' % cell_id)
plt.legend(bbox_to_anchor=(1.5, 1),
loc=2,
borderaxespad=0.,
labels=[
'bud volume (fitted)', 'bud volume ',
'mother volume (fitted)', 'mother volume '
])
plt.tight_layout()