Calcium_Model/fitter.py

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)
        
        # measured_fluo = self.data.fluo
        # fluo_interplolator = interp1d(self.time_domain, model.calculated_fluo)
        # calculated_fluo = fluo_interplolator(self.data.fluo_time)

        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()
            # exit()

        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, 0.1, -5, -10),
                (10, 100, 100, 1, 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
    
            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': err })
            
    
            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=(24, 12))
            ax1 = fig.add_subplot(121)
            ax2 = fig.add_subplot(122)
            ax1.plot(1000 * self.data.current_t, self.data.current, label="Measured")
            ax1.plot(self.time_points, calculated_current, label="Calculated")
            ax1.set_xlabel("time, ms")
            ax1.set_ylabel("current, pA/pF")
            ax1.legend(frameon=False)
    
            tp = self.time_points[self.current_time_indecies]
            ax2.plot(tp, self.measured_current, label="Measured")
            ax2.plot(tp, calculated_current[self.current_time_indecies], label="Calculated")
            ax2.set_xlabel("time, ms")
            ax2.set_ylabel("current, pA/pF")
            ax2.legend(frameon=False)
            return res, fig

    def covcor_from_lsq(res):
        
        _, s, VT = 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 = "0033635a51b096dc449eb9964e70443a67fc16b9587ae3ff6564eea1fa0e3437_2018.06.18 14:48:40"

    data = Data(filename, eid)
    
    fit = Fitter(Model, data)
    
    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()