"""For the log eq model we should remove edge cultures from the
objective fuction. Read in the saved data, add "obj_fun_internals"
and resave.
"""
import numpy as np
import json
from cans2.cans_funcs import dict_to_json
from cans2.process import find_best_fits, remove_edges
log_path = "results/log_eq_fit_*.json"
# Remove the edge cultures from the obj_fun and resave files.
all_fit_paths = np.array(find_best_fits(log_path, num=None, key="obj_fun"))[:, 0]
for path in all_fit_paths:
with open(path, "r") as f:
data = json.load(f)
obj_fun_internals = np.sum(remove_edges(data["culture_obj_funs"],
data["rows"], data["cols"]))
data["obj_fun_internals"] = obj_fun_internals
with open(path, "w") as f:
json.dump(dict_to_json(data), f, sort_keys=True, indent=4)
with open(error_file, 'a') as f:
f.write(error_log)
continue
t1 = time.time()
# Set out dir/files for data and plots.
outdir = "results/p15_fits/full_plate/CompModelBC/"
datafile = (outdir + "argv_{0}_b_guess_{1}.json").format(
sys.argv[1], b_guess)
sbmlfile = (outdir + "sbml/argv_{0}_b_guess_{1}.xml").format(
sys.argv[1], b_guess)
plotfile = (outdir + "plots/argv_{0}_b_guess_{1}.pdf").format(
sys.argv[1], b_guess)
# Cannot serialize Plate and Model objects as json
guess_kwargs = dict_to_json(guess_kwargs)
json_guess_kwargs = {}
for k, v in guess_kwargs.items():
try:
json.dumps(v)
except TypeError:
pass
else:
json_guess_kwargs[k] = v
data = {
'source_data': 'p15',
'rows': full_plate.rows,
'cols': full_plate.cols,
'c_meas': full_plate.c_meas,
'times': full_plate.times,
'argv': int(sys.argv[1]),
'kn': 0.1,
}
custom_params['kn'] = 0.2
model = CompModel()
times = np.linspace(0, 5, 21)
# Generate sets of simulated amounts
for i in range(1):
plate1 = Plate(rows, cols)
plate1.times = times
plate1.set_sim_data(model, r_mean=mean, r_var=var,
custom_params=custom_params)
data = {
'sim_params': plate1.sim_params,
'sim_amounts': plate1.sim_amounts,
'c_meas': plate1.c_meas,
'times': plate1.times,
'r_mean': mean,
'r_var': var,
'rows': plate1.rows,
'cols': plate1.cols,
'model': model.name,
'model_params': model.params,
}
data = dict_to_json(data)
with open(file_name, 'w') as f:
json.dump(data, f, sort_keys=True, indent=4)
