debugging of fitter, many issues still persist
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ralf
parent
6e7a727b1a
commit
7cf6590f94
108
fitter.py
108
fitter.py
@ -6,7 +6,8 @@ import re
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from scipy.optimize import least_squares
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from model import Model
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from scipy.linalg import svd
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from Model import Model
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from Data import Data
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@ -81,38 +82,38 @@ class Fitter:
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plt.show()
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# exit()
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return res # , measured_fluo - calculated_fluo)
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return res, err # , measured_fluo - calculated_fluo)
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def optimize(self, init_parameters=None):
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t0 = time.time()
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self.iteration = 0
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if init_parameters is None:
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m = self.model()
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K_pc_half = m.K_pc_half
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tau_xfer = m.tau_xfer
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tau_RC = 1.5
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offset = 0
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def optimize(self, init_parameters=None):
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t0 = time.time()
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self.iteration = 0
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if init_parameters is None:
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m = self.model()
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K_pc_half = m.K_pc_half
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tau_xfer = m.tau_xfer
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tau_RC = 1.5
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offset = 0
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d = self.data
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init_parameters = np.array([d.gGaL, d.ECal, K_pc_half, tau_xfer, tau_RC, offset])
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print(init_parameters.tolist())
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d = self.data
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init_parameters = np.array([d.gGaL, d.ECal, K_pc_half, tau_xfer, tau_RC, offset])
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print(init_parameters.tolist())
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bounds = (
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(0.01, 10, 0.1, 0.1, 0.1, -5, -10),
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(10, 100, 100, 1, 100, 10, 10),
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)
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bounds = (
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(0.01, 10, 0.1, 0.1, 0.1, -5, -10),
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(10, 100, 100, 1, 100, 10, 10),
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)
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res = least_squares(self.cost_func, init_parameters, bounds=bounds, xtol=1e-10)
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print()
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print(" Parameters: [gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset]")
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print(" Initial:", init_parameters.tolist())
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print(" Optimized:", res.x.tolist())
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print(" Optim status:", res.status)
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print("Optim message:", res.message)
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res, err = least_squares(self.cost_func, init_parameters, bounds=bounds, xtol=1e-10)
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print()
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print(" Parameters: [gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset]")
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print(" Initial:", init_parameters.tolist())
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print(" Optimized:", res.x.tolist())
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print(" Optim status:", res.status)
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print("Optim message:", res.message)
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gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset = res.x
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gGaL, ECal, K_pc_half, tau_xfer, tau_RC, offset = res.x
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self.fit_results.update({
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self.fit_results.update({
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'gGaL': gGaL,
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'ECal': ECal,
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'K_pc_half': K_pc_half,
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@ -121,35 +122,36 @@ class Fitter:
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'offset': offset,
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'mean_squared_error': err})
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model = self.model()
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model = self.model()
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model.ECaL = ECal
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model.gCaL = gGaL
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model.K_pc_half = K_pc_half
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model.tau_xfer = tau_xfer
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model.ECaL = ECal
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model.gCaL = gGaL
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model.K_pc_half = K_pc_half
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model.tau_xfer = tau_xfer
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model.solve(times=self.time_points)
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model.solve(times=self.time_points)
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_calc_curr = model.calculated_current()
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calculated_current = self.convolve_current(_calc_curr, tau=tau_RC) + offset
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print("Elapsed time:", time.time() - t0)
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_calc_curr = model.calculated_current()
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calculated_current = self.convolve_current(_calc_curr, tau=tau_RC) + offset
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print("Elapsed time:", time.time() - t0)
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fig = plt.figure(figsize=(24, 12))
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ax1 = fig.add_subplot(121)
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ax2 = fig.add_subplot(122)
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ax1.plot(1000 * self.data.current_t, self.data.current, label="Measured")
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ax1.plot(self.time_points, calculated_current, label="Calculated")
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ax1.set_xlabel("time, ms")
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ax1.set_ylabel("current, pA/pF")
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ax1.legend(frameon=False)
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fig = plt.figure(figsize=(24, 12))
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ax1 = fig.add_subplot(121)
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ax2 = fig.add_subplot(122)
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ax1.plot(1000 * self.data.current_t, self.data.current, label="Measured")
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ax1.plot(self.time_points, calculated_current, label="Calculated")
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ax1.set_xlabel("time, ms")
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ax1.set_ylabel("current, pA/pF")
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ax1.legend(frameon=False)
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tp = self.time_points[self.current_time_indecies]
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ax2.plot(tp, self.measured_current, label="Measured")
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ax2.plot(tp, calculated_current[self.current_time_indecies], label="Calculated")
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ax2.set_xlabel("time, ms")
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ax2.set_ylabel("current, pA/pF")
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ax2.legend(frameon=False)
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return res, fig
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tp = self.time_points[self.current_time_indecies]
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ax2.plot(tp, self.measured_current, label="Measured")
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ax2.plot(tp, calculated_current[self.current_time_indecies], label="Calculated")
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ax2.set_xlabel("time, ms")
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ax2.set_ylabel("current, pA/pF")
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ax2.legend(frameon=False)
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return res, fig
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def covcor_from_lsq(res):
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@ -173,8 +175,8 @@ class Fitter:
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plt.ylabel('Variables')
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plt.show()
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if __name__ == "__main__":
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if __name__ == "__main__":
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filename = "ltcc_current.h5"
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eid = "0033635a51b096dc449eb9964e70443a67fc16b9587ae3ff6564eea1fa0e3437_2018.06.18 14:48:40"
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@ -183,6 +185,8 @@ if __name__ == "__main__":
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fit = Fitter(Model, data)
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res, fig = fit.optimize()
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fit_hist = pd.DataFrame.from_dict(fit.fit_results, orient='index').T
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fit_hist.index.name = 'Iterations'
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