import matplotlib.pyplot as pl import numpy as np import pandas as pd import json from optimization.errfuns import rms from new_optimization.fitter.hodgkinhuxleyfitter import HodgkinHuxleyFitter __author__ = 'caro' save_dir = '../../results/new_optimization/V_18_10_16/' method = 'L-BFGS-B' candidates = pd.read_csv(save_dir + method + '/candidates.csv') best_candidate = candidates.candidate[np.argmin(candidates.fitness)] best_candidate = np.array([float(x) for x in best_candidate.split()]) with open(save_dir + 'optimization_settings.json', 'r') as f: optimization_settings = json.load(f) fitter = HodgkinHuxleyFitter(**optimization_settings['fitter']) v_model, t, i_inj = fitter.simulate_cell(best_candidate) pl.figure() pl.plot(t, fitter.data.v, 'k', label='data') pl.plot(t, v_model, 'r', label='model') pl.legend() pl.show()