import time import numpy as np import pylab as plt import pandas as pd import re from scipy.optimize import least_squares from Model import Model from Data import Data class Fitter: def __init__( self, model: Model, data: Data, current_fit_range: tuple = (107, 341) ) -> None: """ current_fit_range : tuple (t0, t1), t0 nad t1 are start and stop times in between current is fitted """ self.model = model self.data = data self.current_fit_range = current_fit_range self.fit_results = {} self.tspan = [0, 1000] self.dt = 1 # 1.0 self.time_points = np.arange(*self.tspan, self.dt) self.iteration = 0 # least squares iteration counter t0, t1 = self.current_fit_range = current_fit_range self.current_time_indecies = (t0 <= self.time_points) & (self.time_points <= t1) self.measured_current = self.data.get_current_slice( self.time_points[self.current_time_indecies] / 1000 ) # calculating in ms but data recorded in sec def convolve_current(self, current: np.ndarray, tau=1.5): if np.abs(tau) < 1e-8: return current k = np.zeros(current.size) k[k.size // 2:] = np.exp(-np.arange(k.size // 2) / np.abs(tau)) k /= k.sum() if tau > 0: return np.convolve(current, k, mode="same") else: return np.convolve(current, k[::-1], mode="same") def cost_func(self, parameters: np.ndarray): model = self.model() gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset = parameters model.ECaL = ECal model.gCaL = gGaL model.K_pc_half = K_pc_half model.tau_xfer = tau_xfer model.solve(times=self.time_points) _calc_curr = model.calculated_current() calculated_current = ( self.convolve_current(_calc_curr, tau=tau_RC)[self.current_time_indecies] + offset ) res = self.measured_current - calculated_current err = np.mean(res**2) # mean squared error self.iteration += 1 print(self.iteration, parameters.tolist(), "err", err) if self.iteration < -0: t = self.time_points[self.current_time_indecies] plt.plot( t, _calc_curr[self.current_time_indecies], label="calculated current" ) plt.plot(t, self.measured_current, label="measured current") plt.plot(t, calculated_current, label="conv calculated current") plt.plot(t, self.measured_current - calculated_current, label="error") plt.xlabel("time, ms") plt.ylabel("current, pA/pF") plt.legend(frameon=False) plt.show() return res # , measured_fluo - calculated_fluo) def optimize(self, init_parameters=None): t0 = time.time() self.iteration = 0 if init_parameters is None: m = self.model() K_pc_half = m.K_pc_half tau_xfer = m.tau_xfer tau_RC = 1.5 offset = 0 d = self.data init_parameters = np.array( [d.gGaL, d.ECal, K_pc_half, tau_xfer, tau_RC, offset] ) print(init_parameters.tolist()) bounds = ( (0.01, 10, 0.1, 0.1, -5, -10), (10, 100, 100, 100, 10, 10), ) res = least_squares(self.cost_func, init_parameters, bounds=bounds, xtol=1e-10) print() print(" Parameters: [gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset]") print(" Initial:", init_parameters.tolist()) print(" Optimized:", res.x.tolist()) print(" Optim status:", res.status) print("Optim message:", res.message) gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset = res.x err = self.cost_func(res.x) self.fit_results.update( { "gGaL": gGaL, "ECal": ECal, "K_pc_half": K_pc_half, "tau_xfer": tau_xfer, "tau_RC": tau_RC, "offset": offset, "mean_squared_error": np.mean((err) ** 2), } ) model = self.model() model.ECaL = ECal model.gCaL = gGaL model.K_pc_half = K_pc_half model.tau_xfer = tau_xfer model.solve(times=self.time_points) _calc_curr = model.calculated_current() calculated_current = self.convolve_current(_calc_curr, tau=tau_RC) + offset print("Elapsed time:", time.time() - t0) fig = plt.figure(figsize=(6, 3)) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) ax1.plot(1000 * self.data.current_t, self.data.current, label="Mõõdetud") ax1.plot(self.time_points, calculated_current, label="Arvutatud") ax1.set_xlabel("Aeg [ms]") ax1.set_ylabel("Vool [pA/pF]") ax1.legend(frameon=False) tp = self.time_points[self.current_time_indecies] ax2.plot(tp, self.measured_current, label="Mõõdetud") ax2.plot(tp, calculated_current[self.current_time_indecies], label="Arvutatud") ax2.set_xlabel("Aeg [ms]") ax2.set_ylabel("Vool [pA/pF]") ax2.legend(frameon=False) return res, fig def covcor_from_lsq(res): _, s, VT = np.linalg.svd(res.jac, full_matrices=False) threshold = np.finfo(float).eps * max(res.jac.shape) * s[0] s = s[s > threshold] VT = VT[: s.size] cov = np.dot(VT.T / s**2, VT) std = np.sqrt(np.diag(cov)) cor = cov / np.outer(std, std) cor[cov == 0] = 0 return cov, cor def plot_correlation_matrix(cor): plt.imshow(cor, cmap="viridis", interpolation="nearest") plt.colorbar(label="Correlation") plt.title("Correlation Matrix") plt.xlabel("Variables") plt.ylabel("Variables") plt.show() if __name__ == "__main__": filename = "ltcc_current.h5" eid = "1c5ca4b12ae2ddffc3960c1fe39a3cce35967ce23dbac57c010f450e796d01fd_2017.11.27 14:07:04" data = Data(filename, eid) fit = Fitter(Model, data) res, fig = fit.optimize() fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient="index").T fit_hist.index.name = "Iterations" res_filename = f"fit_results_{eid}.csv" res_filename = res_filename.replace(" ", "_").replace(":", "-") fit_hist.to_csv(res_filename, index=True) eid_cleaned = re.sub( r"[^\w.-]", "", eid ) # Eemalda kõik eritähed ja jääb alles alphanumbrilised tähed, sidekriipsud ja punktid fig.savefig(f"plot_{eid_cleaned}.png") fig.savefig(f"plot_{eid_cleaned}.pdf") # plot_filename = "fit_plot" # fig.savefig(f"{plot_filename}.png") # fig.savefig(f"{plot_filename}.pdf") fig.savefig("naidis_fit.pdf") plt.show()