cleared up more errors
This commit is contained in:
ralf
parent
7cf6590f94
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eb32120675
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.gitignore
vendored
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1
.gitignore
vendored
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@ -0,0 +1 @@
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ltcc_current.h5
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43
Model.py
43
Model.py
@ -1,18 +1,13 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.integrate import ode
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# %% Constants
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R = 8.314 # ideal gas constant (J/(mol*K))
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F = 96.485 # coulomb_per_millimole, Faraday constant
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T = 298 # room temperature (K)
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RT = R * T
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# %% model
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class Model:
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def __init__(self):
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@ -20,48 +15,64 @@ class Model:
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self.C_mem = 1.0 # uF/cm2 Specific membrane capacitance
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self.V_myo = 25.84e-6 # uL, Myoplasmic volume
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self.current2flux = self.A_cap * self.C_mem / 2 / F #
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self.period = 1000 # ms, pulse period
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self.V_mem_rest = -80.0 # mV, resting membrane potential
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self.Nai = 11000 # uM, Myoplasmic Na+ concentration
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self.Nao = 150000 # uM, Extracellular Na+ concentration
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self.eta = 0.35 # Controls voltage dependance of Na/Ca2+ exchange
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self.km_Na = 87500 # uM, Na+ half-saturation constant for Na+/Ca2+ exchange
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self.k_sat = (
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0.1 # Na+/Ca2+ exchange saturation factor at very negative potentials
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)
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self.km_Ca = (1380,) # uM, Ca2+ half-saturation constant for Na+/Ca2+ exchange
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self.k_Na_Ca = 292.8 # pA/pF, Scaling factor of Na2+/Ca2+ exchange
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self.I_max_pCa = 1.0 # pA/pF, Maximum Ca2+ pump current
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# self.Cai = 0.1 # uM, Cytoplasmic Ca2+ concentration fixed at this point
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self.LTRPN_tot = (
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70.0 # uM, Total myoplasmic troponin low-affinity site concentration
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)
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self.HTRPN_tot = (
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140.0 # uM, Total myoplasmic troponin high-affinity site concentration
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)
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# self.LTRPNCa = 11.2684 # uM, Concentration Ca2+ bound low-affinity troponin-binding sites
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# self.HTRPNCa = 125.290 # uM, Concentration Ca2+ bound high-affinity troponin-binding sites
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self.k_htrpn_positive = (
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0.00237 # uM^(-1)/ms, Ca2+ on rate const. for troponin high-affinity sites
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)
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self.k_htrpn_negative = (
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3.2e-5 # ms, Ca2+ off rate const. for troponin high-affinity sites
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)
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self.k_ltrpn_positive = (
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0.0327 # uM^(-1)/ms, Ca2+ on rate const. for troponin low-affinity sites
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)
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self.k_ltrpn_negative = (
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0.0196 # ms, Ca2+ off rate const. for troponin low-affinity sites
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)
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self.CMDN_tot = 50 # uM, Total myoplasmic calmodulin concentration
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self.Km_CMDN = 0.238 # uM, Ca2 half-saturation constant for calmodulin
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self.nu_1 = 4.5 # 1/ms, Maximum RyR channel Ca2+ permeability Ca2+ leak rate constant from the NSR
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self.nu_2 = 1.74e-5 # ms^(-1), Ca2+ leak rate const. from the NSR
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self.nu_3 = 0.45 # uM/ms, SR Ca2+ -ATPase maximum pupmp rate
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self.Km_up = 0.5 # uM, Half-saturation constant for SR Ca2+ -ATPase pump
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self.km_p_ca = 0.5 # uM, Ca2+ half-saturation constant for Ca2+ pump current
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# self.Ca_NSR = 1299.50 # uM,NSR Ca2+ concentration
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self.tau_xfer = 8.0 # ms, Time constant for transfer from subspace to myoplasm
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self.K_pc_max = (
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0.23324 # 1/ms, Maximum time constant for Ca2+-induced inactivation
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)
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@ -70,29 +81,37 @@ class Model:
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)
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self.Kpcb = 0.0005 # 1/ms, Voltage-insensitive rate constant for inactivation
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self.km_Ca = 1380 # uM, Ca2+ half-saturation constant for Na+/Ca2+ exchange
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self.Gcab = 0.000367 # mS/uF
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self.Cao = 1130.0 # uM, Ca2+ outside the cell
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self.gCaL = (
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0.1729 # mS/uF, Specific maximum conductivity for L-type Ca2+ channel
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)
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self.ECaL = 43.0 # mV, Reversal potential for L-type Ca2⫹ channel, kas arvutame voi jaab const?
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self.V_ss = 1.485e-9 # uL, Dyadic aka subspace volume
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# self.Ca_JSR = 1299.50 # uM, JSR Ca2+ concentration
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self.F_tot = 25 # uM, total concentration of Fluo-4
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self.k_on = 0.1 # 1/uM * 1/ms, Fluo-4 reaction rate constant
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self.k_off = 0.11 # 1/ms, Fluo-4 reaction rate constant 2
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self.V_NSR = 2.098e-6 # ul, Network SR volume
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self.tau_tr = 20 # ms, Time const for transfer from NSR to JSR
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self.CSQN_tot = 15000.0 # uM, total junctional SR calsequestrin concentration
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self.Km_CSQN = 800.0 # uM, Ca2 half-saturation constant for calsequestrin
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self.k_a_positive = 0.006075 # uM^(-4)/ms, RyR Pc1 - Po1 rate constant
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self.k_a_negative = 0.07125 # 1/ms, RyR Po1 - Pc1 rate constant
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self.k_b_positive = 0.00405 # uM^(-3)/ms, RyR Po1 - Po2 rate constant
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self.k_b_negative = 0.965 # 1/ms, RyR Po2 - Po1 rate constant
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self.k_c_positive = 0.009 # 1/ms, RyR Po1 - Pc2 rate constant
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self.k_c_negative = 0.0008 # 1/ms, RyR Pc2 - Po1 rate constant
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self.n_ryr = 4 # RyR Ca2+ cooperativity parameter Pc1 - Po1
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self.m_ryr = 3 # RyR Ca2+ cooperativity parameter Po1 - Po2
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self.I_CaL_max = 7.0 # pA/pF, normalization constant for L-type Ca2+ current
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self.V_JSR = 0.12e-6 # ul, Junctional SR volume
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@ -180,11 +199,11 @@ class Model:
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Bi = 1 / (
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1 + (self.CMDN_tot * self.Km_CMDN) / (self.Km_CMDN + Cai) ** 2
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) # A6 !!!!!!! korras V8
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)
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Bss = 1 / (
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1 + (self.CMDN_tot * self.Km_CMDN) / (self.Km_CMDN + Cass) ** 2
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) # A7 !!!!!!!!! korras V8
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)
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J_xfer = (Cass - Cai) / self.tau_xfer
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@ -199,7 +218,7 @@ class Model:
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- self.k_htrpn_negative * HTRPNCa
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+ self.k_ltrpn_positive
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* Cai
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* (self.LTRPN_tot - LTRPNCa) # !!!!!! korras v8
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* (self.LTRPN_tot - LTRPNCa)
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- self.k_ltrpn_negative * LTRPNCa
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)
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@ -207,7 +226,7 @@ class Model:
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J_tr = (Ca_NSR - Ca_JSR) / self.tau_tr
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J_leak = self.nu_2 * (Ca_NSR - Cai) # puudu CaNSR diff vorrand
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J_leak = self.nu_2 * (Ca_NSR - Cai)
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dCa_NSRdt = (J_up - J_leak) * (self.V_myo / self.V_NSR)
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-J_tr * (self.V_JSR / self.V_NSR)
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@ -239,7 +258,9 @@ class Model:
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P_C1 = 1 - (P_C2 + P_O1 + P_O2)
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dP_O1dt = self.k_a_positive * (Cass) ** self.n_ryr * P_C1
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dP_O1dt = (
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self.k_a_positive * (Cass) ** self.n_ryr * P_C1
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)
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-self.k_a_negative * P_O1 - self.k_a_positive * (Cass) ** self.m_ryr * P_O1
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+self.k_b_negative * P_O2 - self.k_c_positive * P_O1 + self.k_c_negative * P_C2
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@ -265,7 +286,7 @@ class Model:
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- (I_Cab - 2 * I_NaCa + I_pCa) * ((self.A_cap * self.C_mem)/(2 * self.V_myo * F)))
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"""
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# J_rel_caf = self.nu_1 * (self.Ca_JSR - Cass)
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# J_rel_caf = self.nu_1 * (self.Ca_JSR - Cass) #lisatud ette ennetavalt
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JFCa = self.k_on * (self.F_tot - FCa) * Cai - self.k_off * FCa
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95
fitter.py
95
fitter.py
@ -4,18 +4,19 @@ import pylab as plt
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import pandas as pd
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import re
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from scipy.optimize import least_squares
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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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class Fitter:
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def __init__(self, model: Model, data: Data, current_fit_range: tuple = (107, 341)) -> None:
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def __init__(
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self, model: Model, data: Data, current_fit_range: tuple = (107, 341)
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) -> None:
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"""
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current_fit_range : tuple (t0, t1), t0 nad t1 are start and stop times in between current is fitted
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"""
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self.model = model
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self.data = data
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self.current_fit_range = current_fit_range
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@ -60,19 +61,21 @@ class Fitter:
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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)[self.current_time_indecies] + offset
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calculated_current = (
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self.convolve_current(_calc_curr, tau=tau_RC)[self.current_time_indecies]
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+ offset
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)
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res = self.measured_current - calculated_current
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err = np.mean(res**2) # mean squared error
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self.iteration += 1
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print(self.iteration, parameters.tolist(), "err", err)
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# measured_fluo = self.data.fluo
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# fluo_interplolator = interp1d(self.time_domain, model.calculated_fluo)
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# calculated_fluo = fluo_interplolator(self.data.fluo_time)
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if self.iteration < -0:
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t = self.time_points[self.current_time_indecies]
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plt.plot(t, _calc_curr[self.current_time_indecies], label="calculated current")
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plt.plot(
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t, _calc_curr[self.current_time_indecies], label="calculated current"
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)
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plt.plot(t, self.measured_current, label="measured current")
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plt.plot(t, calculated_current, label="conv calculated current")
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plt.plot(t, self.measured_current - calculated_current, label="error")
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@ -80,9 +83,8 @@ class Fitter:
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plt.ylabel("current, pA/pF")
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plt.legend(frameon=False)
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plt.show()
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# exit()
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return res, err # , measured_fluo - calculated_fluo)
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return res # , 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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@ -95,15 +97,17 @@ class Fitter:
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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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init_parameters = np.array(
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[d.gGaL, d.ECal, K_pc_half, tau_xfer, tau_RC, offset]
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)
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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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(0.01, 10, 0.1, 0.1, -5, -10),
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(10, 100, 100, 100, 10, 10),
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)
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res, err = least_squares(self.cost_func, init_parameters, bounds=bounds, xtol=1e-10)
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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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@ -113,14 +117,19 @@ class Fitter:
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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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'gGaL': gGaL,
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'ECal': ECal,
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'K_pc_half': K_pc_half,
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'tau_xfer': tau_xfer,
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'tau_RC': tau_RC,
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'offset': offset,
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'mean_squared_error': err})
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err = self.cost_func(res.x)
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self.fit_results.update(
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{
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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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"tau_xfer": tau_xfer,
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"tau_RC": tau_RC,
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"offset": offset,
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"mean_squared_error": np.mean((err) ** 2),
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}
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)
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model = self.model()
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@ -135,27 +144,26 @@ class Fitter:
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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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fig = plt.figure(figsize=(6, 3))
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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.plot(1000 * self.data.current_t, self.data.current, label="Mõõdetud")
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ax1.plot(self.time_points, calculated_current, label="Arvutatud")
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ax1.set_xlabel("Aeg [ms]")
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ax1.set_ylabel("Vool [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.plot(tp, self.measured_current, label="Mõõdetud")
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ax2.plot(tp, calculated_current[self.current_time_indecies], label="Arvutatud")
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ax2.set_xlabel("Aeg [ms]")
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ax2.set_ylabel("Vool [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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_, s, VT = svd(res.jac, full_matrices=False)
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_, s, VT = np.linalg.svd(res.jac, full_matrices=False)
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threshold = np.finfo(float).eps * max(res.jac.shape) * s[0]
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s = s[s > threshold]
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VT = VT[: s.size]
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@ -168,18 +176,18 @@ class Fitter:
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return cov, cor
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def plot_correlation_matrix(cor):
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plt.imshow(cor, cmap='viridis', interpolation='nearest')
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plt.colorbar(label='Correlation')
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plt.title('Correlation Matrix')
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plt.xlabel('Variables')
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plt.ylabel('Variables')
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plt.imshow(cor, cmap="viridis", interpolation="nearest")
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plt.colorbar(label="Correlation")
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plt.title("Correlation Matrix")
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plt.xlabel("Variables")
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plt.ylabel("Variables")
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plt.show()
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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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eid = "1c5ca4b12ae2ddffc3960c1fe39a3cce35967ce23dbac57c010f450e796d01fd_2017.11.27 14:07:04"
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data = Data(filename, eid)
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@ -187,14 +195,16 @@ if __name__ == "__main__":
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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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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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res_filename = f"fit_results_{eid}.csv"
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res_filename = res_filename.replace(" ", "_").replace(":", "-")
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fit_hist.to_csv(res_filename, index=True)
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eid_cleaned = re.sub(r'[^\w.-]', '', eid) # Eemalda kõik eritähed ja jääb alles alphanumbrilised tähed, sidekriipsud ja punktid
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eid_cleaned = re.sub(
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r"[^\w.-]", "", eid
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) # Eemalda kõik eritähed ja jääb alles alphanumbrilised tähed, sidekriipsud ja punktid
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fig.savefig(f"plot_{eid_cleaned}.png")
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fig.savefig(f"plot_{eid_cleaned}.pdf")
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@ -202,5 +212,4 @@ if __name__ == "__main__":
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# fig.savefig(f"{plot_filename}.png")
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# fig.savefig(f"{plot_filename}.pdf")
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fig.savefig("naidis_fit.pdf")
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plt.show()
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