diff --git a/PySONIC/core/batches.py b/PySONIC/core/batches.py index bba4241..4c8d542 100644 --- a/PySONIC/core/batches.py +++ b/PySONIC/core/batches.py @@ -1,146 +1,146 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-08-22 14:33:04 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-26 13:51:06 +# @Last Modified time: 2019-07-01 10:10:45 ''' Utility functions used in simulations ''' import logging import multiprocess as mp import numpy as np from ..utils import logger class Consumer(mp.Process): ''' Generic consumer process, taking tasks from a queue and outputing results in another queue. ''' def __init__(self, queue_in, queue_out): mp.Process.__init__(self) self.queue_in = queue_in self.queue_out = queue_out logger.debug('Starting %s', self.name) def run(self): while True: nextTask = self.queue_in.get() if nextTask is None: logger.debug('Exiting %s', self.name) self.queue_in.task_done() break answer = nextTask() self.queue_in.task_done() self.queue_out.put(answer) return class Worker: ''' Generic worker class calling a specific function with a given set of parameters. ''' def __init__(self, wid, func, params, loglevel): ''' Worker constructor. :param wid: worker ID :param func: function object :param params: list of method parameters :param loglevel: logging level ''' self.id = wid self.func = func self.params = params self.loglevel = loglevel def __call__(self): ''' Caller to the function with specific parameters. ''' logger.setLevel(self.loglevel) return self.id, self.func(*self.params) class Batch: ''' Generic interface to run batches of function calls. ''' def __init__(self, func, queue): ''' Batch constructor. :param func: function object :param queue: list of list of function parameters ''' self.func = func self.queue = queue def __call__(self, *args, **kwargs): ''' Call the internal run method. ''' return self.run(*args, **kwargs) def getNConsumers(self): ''' Determine number of consumers based on queue length and number of available CPUs. ''' return min(mp.cpu_count(), len(self.queue)) def start(self): ''' Create tasks and results queues, and start consumers. ''' mp.freeze_support() self.tasks = mp.JoinableQueue() self.results = mp.Queue() self.consumers = [Consumer(self.tasks, self.results) for i in range(self.getNConsumers())] for c in self.consumers: c.start() def assign(self, loglevel): ''' Assign tasks to workers. ''' for i, params in enumerate(self.queue): worker = Worker(i, self.func, params, loglevel) self.tasks.put(worker, block=False) def join(self): ''' Put all tasks to None and join the queue. ''' for i in range(len(self.consumers)): self.tasks.put(None, block=False) self.tasks.join() def get(self): ''' Extract and re-order results. ''' outputs, idxs = [], [] for i in range(len(self.queue)): wid, out = self.results.get() outputs.append(out) idxs.append(wid) return [x for _, x in sorted(zip(idxs, outputs))] def stop(self): ''' Close tasks and results queues. ''' self.tasks.close() self.results.close() def run(self, mpi=False, loglevel=logging.INFO): ''' Run batch with or without multiprocessing. ''' if mpi: self.start() self.assign(loglevel) self.join() outputs = self.get() self.stop() else: outputs = [self.func(*params) for params in self.queue] return outputs + @staticmethod + def createQueue(*dims): + ''' Create a serialized 2D array of all parameter combinations for a series of individual + parameter sweeps. -def createQueue(*dims): - ''' Create a serialized 2D array of all parameter combinations for a series of individual - parameter sweeps. - - :param dims: list of lists (or 1D arrays) of input parameters - :return: list of parameters (list) for each simulation - ''' - ndims = len(dims) - dims_in = [dims[1], dims[0]] - inds_out = [1, 0] - if ndims > 2: - dims_in += dims[2:] - inds_out += list(range(2, ndims)) - queue = np.stack(np.meshgrid(*dims_in), -1).reshape(-1, ndims) - queue = queue[:, inds_out] - return queue.tolist() + :param dims: list of lists (or 1D arrays) of input parameters + :return: list of parameters (list) for each simulation + ''' + ndims = len(dims) + dims_in = [dims[1], dims[0]] + inds_out = [1, 0] + if ndims > 2: + dims_in += dims[2:] + inds_out += list(range(2, ndims)) + queue = np.stack(np.meshgrid(*dims_in), -1).reshape(-1, ndims) + queue = queue[:, inds_out] + return queue.tolist() diff --git a/PySONIC/core/bls.py b/PySONIC/core/bls.py index 13666f7..9278fab 100644 --- a/PySONIC/core/bls.py +++ b/PySONIC/core/bls.py @@ -1,782 +1,780 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2016-09-29 16:16:19 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 19:58:01 +# @Last Modified time: 2019-07-01 16:13:29 from enum import Enum import os import json import numpy as np import pandas as pd import scipy.integrate as integrate from scipy.optimize import brentq, curve_fit from .model import Model from .simulators import PeriodicSimulator -from ..utils import logger, si_format +from ..utils import logger, si_format, debug from ..constants import * class PmCompMethod(Enum): ''' Enum: types of computation method for the intermolecular pressure ''' direct = 1 predict = 2 def LennardJones(x, beta, alpha, C, m, n): ''' Generic expression of a Lennard-Jones function, adapted for the context of symmetric deflection (distance = 2x). :param x: deflection (i.e. half-distance) :param beta: x-shifting factor :param alpha: x-scaling factor :param C: y-scaling factor :param m: exponent of the repulsion term :param n: exponent of the attraction term :return: Lennard-Jones potential at given distance (2x) ''' return C * (np.power((alpha / (2 * x + beta)), m) - np.power((alpha / (2 * x + beta)), n)) def lookup(func): ''' Load parameters from lookup file, or compute them and sotre them in lookup file. ''' lookup_path = os.path.join(os.path.split(__file__)[0], 'bls_lookups.json') def wrapper(obj): # lookup_path = obj.getLookupsPath() akey = '{:.1f}'.format(obj.a * 1e9) Qkey = '{:.2f}'.format(obj.Qm0 * 1e5) # Open lookup files try: with open(lookup_path, 'r') as fh: lookups = json.load(fh) except FileNotFoundError: lookups = {} # If info not in lookups, compute parameters and add them if akey not in lookups or Qkey not in lookups[akey]: func(obj) if akey not in lookups: lookups[akey] = {Qkey: {'LJ_approx': obj.LJ_approx, 'Delta_eq': obj.Delta}} else: lookups[akey][Qkey] = {'LJ_approx': obj.LJ_approx, 'Delta_eq': obj.Delta} logger.debug('Saving BLS derived parameters to lookup file') with open(lookup_path, 'w') as fh: json.dump(lookups, fh, indent=2) # If lookup exists, load parameters from it else: logger.debug('Loading BLS derived parameters from lookup file') obj.LJ_approx = lookups[akey][Qkey]['LJ_approx'] obj.Delta = lookups[akey][Qkey]['Delta_eq'] return wrapper class BilayerSonophore(Model): ''' Definition of the Bilayer Sonophore Model - geometry - pressure terms - cavitation dynamics ''' # BIOMECHANICAL PARAMETERS T = 309.15 # Temperature (K) delta0 = 2.0e-9 # Thickness of the leaflet (m) Delta_ = 1.4e-9 # Initial gap between the two leaflets on a non-charged membrane at equil. (m) pDelta = 1.0e5 # Attraction/repulsion pressure coefficient (Pa) m = 5.0 # Exponent in the repulsion term (dimensionless) n = 3.3 # Exponent in the attraction term (dimensionless) rhoL = 1075.0 # Density of the surrounding fluid (kg/m^3) muL = 7.0e-4 # Dynamic viscosity of the surrounding fluid (Pa.s) muS = 0.035 # Dynamic viscosity of the leaflet (Pa.s) kA = 0.24 # Area compression modulus of the leaflet (N/m) alpha = 7.56 # Tissue shear loss modulus frequency coefficient (Pa.s) C0 = 0.62 # Initial gas molar concentration in the surrounding fluid (mol/m^3) kH = 1.613e5 # Henry's constant (Pa.m^3/mol) P0 = 1.0e5 # Static pressure in the surrounding fluid (Pa) Dgl = 3.68e-9 # Diffusion coefficient of gas in the fluid (m^2/s) xi = 0.5e-9 # Boundary layer thickness for gas transport across leaflet (m) c = 1515.0 # Speed of sound in medium (m/s) # BIOPHYSICAL PARAMETERS epsilon0 = 8.854e-12 # Vacuum permittivity (F/m) epsilonR = 1.0 # Relative permittivity of intramembrane cavity (dimensionless) tscale = 'us' # relevant temporal scale of the model simkey = 'MECH' # keyword used to characterize simulations made with this model def __init__(self, a, Cm0, Qm0, Fdrive=None, embedding_depth=0.0): ''' Constructor of the class. :param a: in-plane radius of the sonophore structure within the membrane (m) :param Cm0: membrane resting capacitance (F/m2) :param Qm0: membrane resting charge density (C/m2) :param Fdrive: frequency of acoustic perturbation (Hz) :param embedding_depth: depth of the embedding tissue around the membrane (m) ''' # Extract resting constants and geometry self.Cm0 = Cm0 self.Qm0 = Qm0 self.a = a self.d = embedding_depth self.S0 = np.pi * self.a**2 # Derive frequency-dependent tissue elastic modulus if Fdrive is not None: G_tissue = self.alpha * Fdrive # G'' (Pa) self.kA_tissue = 2 * G_tissue * self.d # kA of the tissue layer (N/m) else: self.kA_tissue = 0. # Compute Pm params self.computePMparams() # Compute initial volume and gas content self.V0 = np.pi * self.Delta * self.a**2 self.ng0 = self.gasPa2mol(self.P0, self.V0) def __repr__(self): s = '{}({:.1f} nm'.format(self.__class__.__name__, self.a * 1e9) if self.d > 0.: s += ', d={}m'.format(si_format(self.d, precision=1, space=' ')) return s + ')' @staticmethod def inputs(): return { 'a': { 'desc': 'sonophore radius', 'label': 'a', 'unit': 'nm', 'factor': 1e9, 'precision': 0 }, 'Fdrive': { 'desc': 'US frequency', 'label': 'f', 'unit': 'kHz', 'factor': 1e-3, 'precision': 0 }, 'Adrive': { 'desc': 'US pressure amplitude', 'label': 'A', 'unit': 'kPa', 'factor': 1e-3, 'precision': 2 }, 'Qm': { 'desc': 'membrane charge density', 'label': 'Q_m', 'unit': 'nC/cm^2', 'factor': 1e5, 'precision': 1 } } def filecodes(self, Fdrive, Adrive, Qm): return { 'simkey': self.simkey, 'a': '{:.0f}nm'.format(self.a * 1e9), 'Fdrive': '{:.0f}kHz'.format(Fdrive * 1e-3), 'Adrive': '{:.2f}kPa'.format(Adrive * 1e-3), 'Qm': '{:.1f}nCcm2'.format(Qm * 1e5) } @staticmethod def getPltVars(wrapleft='df["', wrapright='"]'): return { 'Pac': { 'desc': 'acoustic pressure', 'label': 'P_{AC}', 'unit': 'kPa', 'factor': 1e-3, 'func': 'Pacoustic({0}t{1}, meta["Adrive"] * {0}stimstate{1}, meta["Fdrive"])'.format( wrapleft, wrapright) }, 'Z': { 'desc': 'leaflets deflection', 'label': 'Z', 'unit': 'nm', 'factor': 1e9, 'bounds': (-1.0, 10.0) }, 'ng': { 'desc': 'gas content', 'label': 'n_g', 'unit': '10^{-22}\ mol', 'factor': 1e22, 'bounds': (1.0, 15.0) }, 'Pmavg': { 'desc': 'average intermolecular pressure', 'label': 'P_M', 'unit': 'kPa', 'factor': 1e-3, 'func': 'PMavgpred({0}Z{1})'.format(wrapleft, wrapright) }, 'Telastic': { 'desc': 'leaflet elastic tension', 'label': 'T_E', 'unit': 'mN/m', 'factor': 1e3, 'func': 'TEleaflet({0}Z{1})'.format(wrapleft, wrapright) }, 'Cm': { 'desc': 'membrane capacitance', 'label': 'C_m', 'unit': 'uF/cm^2', 'factor': 1e2, 'bounds': (0.0, 1.5), 'func': 'v_capacitance({0}Z{1})'.format(wrapleft, wrapright) } } @staticmethod def getPltScheme(): return { 'P_{AC}': ['Pac'], 'Z': ['Z'], 'n_g': ['ng'] } def curvrad(self, Z): ''' Leaflet curvature radius (signed variable) :param Z: leaflet apex deflection (m) :return: leaflet curvature radius (m) ''' if Z == 0.0: return np.inf else: return (self.a**2 + Z**2) / (2 * Z) def v_curvrad(self, Z): ''' Vectorized curvrad function ''' return np.array(list(map(self.curvrad, Z))) def surface(self, Z): ''' Surface area of the stretched leaflet (spherical cap formula) :param Z: leaflet apex deflection (m) :return: stretched leaflet surface (m^2) ''' return np.pi * (self.a**2 + Z**2) def volume(self, Z): ''' Volume of the inter-leaflet space (cylinder +/- 2 spherical caps) :param Z: leaflet apex deflection (m) :return: bilayer sonophore inner volume (m^3) ''' return np.pi * self.a**2 * self.Delta\ * (1 + (Z / (3 * self.Delta) * (3 + Z**2 / self.a**2))) def arealStrain(self, Z): ''' Areal strain of the stretched leaflet epsilon = (S - S0)/S0 = (Z/a)^2 :param Z: leaflet apex deflection (m) :return: areal strain (dimensionless) ''' return (Z / self.a)**2 def capacitance(self, Z): ''' Membrane capacitance (parallel-plate capacitor evaluated at average inter-layer distance) :param Z: leaflet apex deflection (m) :return: capacitance per unit area (F/m2) ''' if Z == 0.0: return self.Cm0 else: return ((self.Cm0 * self.Delta / self.a**2) * (Z + (self.a**2 - Z**2 - Z * self.Delta) / (2 * Z) * np.log((2 * Z + self.Delta) / self.Delta))) def v_capacitance(self, Z): ''' Vectorized capacitance function ''' return np.array(list(map(self.capacitance, Z))) def derCapacitance(self, Z, U): ''' Evolution of membrane capacitance :param Z: leaflet apex deflection (m) :param U: leaflet apex deflection velocity (m/s) :return: time derivative of capacitance per unit area (F/m2.s) ''' dCmdZ = ((self.Cm0 * self.Delta / self.a**2) * ((Z**2 + self.a**2) / (Z * (2 * Z + self.Delta)) - ((Z**2 + self.a**2) * np.log((2 * Z + self.Delta) / self.Delta)) / (2 * Z**2))) return dCmdZ * U @staticmethod def localDeflection(r, Z, R): ''' Local leaflet deflection at specific radial distance (signed) :param r: in-plane distance from center of the sonophore (m) :param Z: leaflet apex deflection (m) :param R: leaflet curvature radius (m) :return: local transverse leaflet deviation (m) ''' if np.abs(Z) == 0.0: return 0.0 else: return np.sign(Z) * (np.sqrt(R**2 - r**2) - np.abs(R) + np.abs(Z)) @staticmethod def Pacoustic(t, Adrive, Fdrive, phi=np.pi): ''' Time-varying acoustic pressure :param t: time (s) :param Adrive: acoustic drive amplitude (Pa) :param Fdrive: acoustic drive frequency (Hz) :param phi: acoustic drive phase (rad) ''' return Adrive * np.sin(2 * np.pi * Fdrive * t - phi) def PMlocal(self, r, Z, R): ''' Local intermolecular pressure :param r: in-plane distance from center of the sonophore (m) :param Z: leaflet apex deflection (m) :param R: leaflet curvature radius (m) :return: local intermolecular pressure (Pa) ''' z = self.localDeflection(r, Z, R) relgap = (2 * z + self.Delta) / self.Delta_ return self.pDelta * ((1 / relgap)**self.m - (1 / relgap)**self.n) def PMavg(self, Z, R, S): ''' Average intermolecular pressure across the leaflet (computed by quadratic integration) :param Z: leaflet apex outward deflection value (m) :param R: leaflet curvature radius (m) :param S: surface of the stretched leaflet (m^2) :return: averaged intermolecular resultant pressure (Pa) .. warning:: quadratic integration is computationally expensive. ''' # Integrate intermolecular force over an infinitely thin ring of radius r from 0 to a fTotal, _ = integrate.quad(lambda r, Z, R: 2 * np.pi * r * self.PMlocal(r, Z, R), 0, self.a, args=(Z, R)) return fTotal / S def v_PMavg(self, Z, R, S): ''' Vectorized PMavg function ''' return np.array(list(map(self.PMavg, Z, R, S))) def LJfitPMavg(self): ''' Determine optimal parameters of a Lennard-Jones expression approximating the average intermolecular pressure. These parameters are obtained by a nonlinear fit of the Lennard-Jones function for a range of deflection values between predetermined Zmin and Zmax. :return: 3-tuple with optimized LJ parameters for PmAvg prediction (Map) and the standard and max errors of the prediction in the fitting range (in Pascals) ''' # Determine lower bound of deflection range: when Pm = Pmmax PMmax = LJFIT_PM_MAX # Pa Zminlb = -0.49 * self.Delta Zminub = 0.0 Zmin = brentq(lambda Z, Pmmax: self.PMavg(Z, self.curvrad(Z), self.surface(Z)) - PMmax, Zminlb, Zminub, args=(PMmax), xtol=1e-16) # Create vectors for geometric variables Zmax = 2 * self.a Z = np.arange(Zmin, Zmax, 1e-11) Pmavg = self.v_PMavg(Z, self.v_curvrad(Z), self.surface(Z)) # Compute optimal nonlinear fit of custom LJ function with initial guess x0_guess = self.delta0 C_guess = 0.1 * self.pDelta nrep_guess = self.m nattr_guess = self.n pguess = (x0_guess, C_guess, nrep_guess, nattr_guess) popt, _ = curve_fit(lambda x, x0, C, nrep, nattr: LennardJones(x, self.Delta, x0, C, nrep, nattr), Z, Pmavg, p0=pguess, maxfev=10000) (x0_opt, C_opt, nrep_opt, nattr_opt) = popt Pmavg_fit = LennardJones(Z, self.Delta, x0_opt, C_opt, nrep_opt, nattr_opt) # Compute prediction error residuals = Pmavg - Pmavg_fit ss_res = np.sum(residuals**2) N = residuals.size std_err = np.sqrt(ss_res / N) max_err = max(np.abs(residuals)) logger.debug('LJ approx: x0 = %.2f nm, C = %.2f kPa, m = %.2f, n = %.2f', x0_opt * 1e9, C_opt * 1e-3, nrep_opt, nattr_opt) LJ_approx = {"x0": x0_opt, "C": C_opt, "nrep": nrep_opt, "nattr": nattr_opt} return (LJ_approx, std_err, max_err) @lookup def computePMparams(self): # Find Delta that cancels out Pm + Pec at Z = 0 (m) if self.Qm0 == 0.0: D_eq = self.Delta_ else: (D_eq, Pnet_eq) = self.findDeltaEq(self.Qm0) assert Pnet_eq < PNET_EQ_MAX, 'High Pnet at Z = 0 with ∆ = %.2f nm' % (D_eq * 1e9) self.Delta = D_eq # Find optimal Lennard-Jones parameters to approximate PMavg (self.LJ_approx, std_err, _) = self.LJfitPMavg() assert std_err < PMAVG_STD_ERR_MAX, 'High error in PmAvg nonlinear fit:'\ ' std_err = %.2f Pa' % std_err def PMavgpred(self, Z): ''' Approximated average intermolecular pressure (using nonlinearly fitted Lennard-Jones function) :param Z: leaflet apex deflection (m) :return: predicted average intermolecular pressure (Pa) ''' return LennardJones(Z, self.Delta, self.LJ_approx['x0'], self.LJ_approx['C'], self.LJ_approx['nrep'], self.LJ_approx['nattr']) def Pelec(self, Z, Qm): ''' Electrical pressure term :param Z: leaflet apex deflection (m) :param Qm: membrane charge density (C/m2) :return: electrical pressure (Pa) ''' relS = self.S0 / self.surface(Z) abs_perm = self.epsilon0 * self.epsilonR # F/m return - relS * Qm**2 / (2 * abs_perm) # Pa def findDeltaEq(self, Qm): ''' Compute the Delta that cancels out the (Pm + Pec) equation at Z = 0 for a given membrane charge density, using the Brent method to refine the pressure root iteratively. :param Qm: membrane charge density (C/m2) :return: equilibrium value (m) and associated pressure (Pa) ''' def dualPressure(Delta): x = (self.Delta_ / Delta) return (self.pDelta * (x**self.m - x**self.n) + self.Pelec(0.0, Qm)) Delta_eq = brentq(dualPressure, 0.1 * self.Delta_, 2.0 * self.Delta_, xtol=1e-16) logger.debug('∆eq = %.2f nm', Delta_eq * 1e9) return (Delta_eq, dualPressure(Delta_eq)) def gasFlux(self, Z, P): ''' Gas molar flux through the sonophore boundary layers :param Z: leaflet apex deflection (m) :param P: internal gas pressure (Pa) :return: gas molar flux (mol/s) ''' dC = self.C0 - P / self.kH return 2 * self.surface(Z) * self.Dgl * dC / self.xi @classmethod def gasmol2Pa(cls, ng, V): ''' Internal gas pressure for a given molar content :param ng: internal molar content (mol) :param V: sonophore inner volume (m^3) :return: internal gas pressure (Pa) ''' return ng * Rg * cls.T / V @classmethod def gasPa2mol(cls, P, V): ''' Internal gas molar content for a given pressure :param P: internal gas pressure (Pa) :param V: sonophore inner volume (m^3) :return: internal gas molar content (mol) ''' return P * V / (Rg * cls.T) def PtotQS(self, Z, ng, Qm, Pac, Pm_comp_method): ''' Net quasi-steady pressure for a given acoustic pressure (Ptot = Pm + Pg + Pec - P0 - Pac) :param Z: leaflet apex deflection (m) :param ng: internal molar content (mol) :param Qm: membrane charge density (C/m2) :param Pac: acoustic pressure (Pa) :param Pm_comp_method: computation method for average intermolecular pressure :return: total balance pressure (Pa) ''' if Pm_comp_method is PmCompMethod.direct: Pm = self.PMavg(Z, self.curvrad(Z), self.surface(Z)) elif Pm_comp_method is PmCompMethod.predict: Pm = self.PMavgpred(Z) return Pm + self.gasmol2Pa(ng, self.volume(Z)) - self.P0 - Pac + self.Pelec(Z, Qm) def balancedefQS(self, ng, Qm, Pac=0.0, Pm_comp_method=PmCompMethod.predict): ''' Quasi-steady equilibrium deflection for a given acoustic pressure (computed by approximating the root of quasi-steady pressure) :param ng: internal molar content (mol) :param Qm: membrane charge density (C/m2) :param Pac: external acoustic perturbation (Pa) :param Pm_comp_method: computation method for average intermolecular pressure :return: leaflet deflection canceling quasi-steady pressure (m) ''' lb, ub = -0.49 * self.Delta, self.a Plb, Pub = [self.PtotQS(x, ng, Qm, Pac, Pm_comp_method) for x in [lb, ub]] assert (Plb > 0 > Pub), '[{}, {}] is not a sign changing interval for PtotQS'.format(lb, ub) return brentq(self.PtotQS, lb, ub, args=(ng, Qm, Pac, Pm_comp_method), xtol=1e-16) def TEleaflet(self, Z): ''' Elastic tension in leaflet :param Z: leaflet apex deflection (m) :return: circumferential elastic tension (N/m) ''' return self.kA * self.arealStrain(Z) def TEtissue(self, Z): ''' Elastic tension in surrounding viscoelastic layer :param Z: leaflet apex deflection (m) :return: circumferential elastic tension (N/m) ''' return self.kA_tissue * self.arealStrain(Z) def TEtot(self, Z): ''' Total elastic tension (leaflet + surrounding viscoelastic layer) :param Z: leaflet apex deflection (m) :return: circumferential elastic tension (N/m) ''' return self.TEleaflet(Z) + self.TEtissue(Z) def PEtot(self, Z, R): ''' Total elastic tension pressure (leaflet + surrounding viscoelastic layer) :param Z: leaflet apex deflection (m) :param R: leaflet curvature radius (m) :return: elastic tension pressure (Pa) ''' return - self.TEtot(Z) / R @classmethod def PVleaflet(cls, U, R): ''' Viscous stress pressure in leaflet :param U: leaflet apex deflection velocity (m/s) :param R: leaflet curvature radius (m) :return: leaflet viscous stress pressure (Pa) ''' return - 12 * U * cls.delta0 * cls.muS / R**2 @classmethod def PVfluid(cls, U, R): ''' Viscous stress pressure in surrounding medium :param U: leaflet apex deflection velocity (m/s) :param R: leaflet curvature radius (m) :return: fluid viscous stress pressure (Pa) ''' return - 4 * U * cls.muL / np.abs(R) @classmethod def accP(cls, Ptot, R): ''' Leaflet transverse acceleration resulting from pressure imbalance :param Ptot: net pressure (Pa) :param R: leaflet curvature radius (m) :return: pressure-driven acceleration (m/s^2) ''' return Ptot / (cls.rhoL * np.abs(R)) @staticmethod def accNL(U, R): ''' Leaflet transverse nonlinear acceleration :param U: leaflet apex deflection velocity (m/s) :param R: leaflet curvature radius (m) :return: nonlinear acceleration term (m/s^2) .. note:: A simplified version of nonlinear acceleration (neglecting dR/dH) is used here. ''' # return - (3/2 - 2*R/H) * U**2 / R return -(3 * U**2) / (2 * R) @staticmethod def checkInputs(Fdrive, Adrive, Qm, phi): ''' Check validity of stimulation parameters :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param phi: acoustic drive phase (rad) :param Qm: imposed membrane charge density (C/m2) ''' if not all(isinstance(param, float) for param in [Fdrive, Adrive, Qm, phi]): raise TypeError('Invalid stimulation parameters (must be float typed)') if Fdrive <= 0: raise ValueError('Invalid US driving frequency: {} kHz (must be strictly positive)' .format(Fdrive * 1e-3)) if Adrive < 0: raise ValueError('Invalid US pressure amplitude: {} kPa (must be positive or null)' .format(Adrive * 1e-3)) if Qm < CHARGE_RANGE[0] or Qm > CHARGE_RANGE[1]: raise ValueError('Invalid applied charge: {} nC/cm2 (must be within [{}, {}] interval' .format(Qm * 1e5, CHARGE_RANGE[0] * 1e5, CHARGE_RANGE[1] * 1e5)) if phi < 0 or phi >= 2 * np.pi: raise ValueError('Invalid US pressure phase: {:.2f} rad (must be within [0, 2 PI[ rad' .format(phi)) def derivatives(self, t, y, Fdrive, Adrive, Qm, phi, Pm_comp_method=PmCompMethod.predict): ''' Evolution of the mechanical system :param t: time instant (s) :param y: vector of HH system variables at time t :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param Qm: membrane charge density (F/m2) :param phi: acoustic drive phase (rad) :param Pm_comp_method: computation method for average intermolecular pressure :return: vector of mechanical system derivatives at time t ''' # Split input vector explicitly U, Z, ng = y # Correct deflection value is below critical compression if Z < -0.5 * self.Delta: logger.warning('Deflection out of range: Z = %.2f nm', Z * 1e9) Z = -0.49 * self.Delta # Compute curvature radius R = self.curvrad(Z) # Compute total pressure Pg = self.gasmol2Pa(ng, self.volume(Z)) if Pm_comp_method is PmCompMethod.direct: Pm = self.PMavg(Z, self.curvrad(Z), self.surface(Z)) elif Pm_comp_method is PmCompMethod.predict: Pm = self.PMavgpred(Z) Ptot = (Pm + Pg - self.P0 - self.Pacoustic(t, Adrive, Fdrive, phi) + self.PEtot(Z, R) + self.PVleaflet(U, R) + self.PVfluid(U, R) + self.Pelec(Z, Qm)) # Compute derivatives dUdt = self.accP(Ptot, R) + self.accNL(U, R) dZdt = U dngdt = self.gasFlux(Z, Pg) # Return derivatives vector return [dUdt, dZdt, dngdt] + @Model.addMeta def simulate(self, Fdrive, Adrive, Qm, phi=np.pi, Pm_comp_method=PmCompMethod.predict): ''' Simulate until periodic stabilization for a specific set of ultrasound parameters, and return output data in a dataframe. :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param phi: acoustic drive phase (rad) :param Qm: imposed membrane charge density (C/m2) :param Pm_comp_method: type of method used to compute average intermolecular pressure :return: 2-tuple with the output dataframe and computation time. ''' logger.info('%s: simulation @ f = %sHz, A = %sPa, Q = %sC/cm2', self, *si_format([Fdrive, Adrive, Qm * 1e-4], 2, space=' ')) # Check validity of stimulation parameters self.checkInputs(Fdrive, Adrive, Qm, phi) # Determine time step dt = 1 / (NPC_DENSE * Fdrive) # Compute non-zero deflection value for a small perturbation (solving quasi-steady equation) Pac = self.Pacoustic(dt, Adrive, Fdrive, phi) Z0 = self.balancedefQS(self.ng0, Qm, Pac, Pm_comp_method) # Set initial conditions y0 = np.array([0., Z0, self.ng0]) # Initialize simulator and compute solution simulator = PeriodicSimulator( lambda t, y: self.derivatives(t, y, Fdrive, Adrive, Qm, phi, Pm_comp_method), ivars_to_check=[1, 2]) - (t, y, stim), tcomp = simulator(y0, dt, 1. / Fdrive, monitor_time=True) - logger.debug('completed in %ss', si_format(tcomp, 1)) + t, y, stim = simulator(y0, dt, 1. / Fdrive) # Set last stimulation state to zero stim[-1] = 0 - # Store output in dataframe - data = pd.DataFrame({ + # Store output in dataframe and return + return pd.DataFrame({ 't': t, 'stimstate': stim, 'Z': y[:, 1], 'ng': y[:, 2] }) - # Return dataframe and computation time - return data, tcomp - - def meta(self, Fdrive, Adrive, Qm): + def meta(self, Fdrive, Adrive, Qm, Pm_comp_method): return { 'simkey': self.simkey, 'a': self.a, 'd': self.d, 'Cm0': self.Cm0, 'Qm0': self.Qm0, 'Fdrive': Fdrive, 'Adrive': Adrive, - 'Qm': Qm + 'Qm': Qm, + 'Pm_comp_method': Pm_comp_method } def getCycleProfiles(self, Fdrive, Adrive, Qm): ''' Simulate mechanical system and compute pressures over the last acoustic cycle :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param Qm: imposed membrane charge density (C/m2) :return: dataframe with the time, kinematic and pressure profiles over the last cycle. ''' # Run default simulation and retrieve last cycle solution logger.info('Running mechanical simulation (a = %sm, f = %sHz, A = %sPa)', si_format(self.a, 1), si_format(Fdrive, 1), si_format(Adrive, 1)) data = self.simulate( Fdrive, Adrive, Qm, Pm_comp_method=PmCompMethod.direct)[0].iloc[-NPC_DENSE:, :] # Extract relevant variables and de-offset time vector t, Z, ng = [data[key].values for key in ['t', 'Z', 'ng']] dt = (t[-1] - t[0]) / (NPC_DENSE - 1) t -= t[0] # Compute pressure cyclic profiles logger.info('Computing pressure cyclic profiles') R = self.v_curvrad(Z) U = np.diff(Z) / dt U = np.hstack((U, U[-1])) data = { 't': t, 'Z': Z, 'Cm': self.v_capacitance(Z), 'P_M': self.v_PMavg(Z, R, self.surface(Z)), 'P_Q': self.Pelec(Z, Qm), 'P_{VE}': self.PEtot(Z, R) + self.PVleaflet(U, R), 'P_V': self.PVfluid(U, R), 'P_G': self.gasmol2Pa(ng, self.volume(Z)), 'P_0': - np.ones(Z.size) * self.P0 } return pd.DataFrame(data, columns=data.keys()) diff --git a/PySONIC/core/model.py b/PySONIC/core/model.py index 359ba0b..51d6e4b 100644 --- a/PySONIC/core/model.py +++ b/PySONIC/core/model.py @@ -1,165 +1,216 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-08-03 11:53:04 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 20:00:42 +# @Last Modified time: 2019-07-01 16:43:26 import os +from functools import wraps from inspect import signature, getdoc import pickle import abc import inspect import numpy as np -from .batches import createQueue -from ..utils import logger, loadData +from .batches import Batch +from ..utils import logger, loadData, timer, si_format, plural class Model(metaclass=abc.ABCMeta): ''' Generic model interface. ''' @property @abc.abstractmethod def tscale(self): ''' Relevant temporal scale of the model. ''' raise NotImplementedError @property @abc.abstractmethod def simkey(self): ''' Keyword used to characterize simulations made with the model. ''' raise NotImplementedError @property @abc.abstractmethod def __repr__(self): ''' String representation. ''' raise NotImplementedError def params(self): ''' Return a dictionary of all model parameters (class and instance attributes) ''' def toAvoid(p): return (p.startswith('__') and p.endswith('__')) or p.startswith('_abc_') class_attrs = inspect.getmembers(self.__class__, lambda a: not(inspect.isroutine(a))) inst_attrs = inspect.getmembers(self, lambda a: not(inspect.isroutine(a))) class_attrs = [a for a in class_attrs if not toAvoid(a[0])] inst_attrs = [a for a in inst_attrs if not toAvoid(a[0]) and a not in class_attrs] params_dict = {a[0]: a[1] for a in class_attrs + inst_attrs} return params_dict @classmethod def description(cls): return getdoc(cls).split('\n', 1)[0].strip() @staticmethod @abc.abstractmethod def inputs(): ''' Return an informative dictionary on input variables used to simulate the model. ''' raise NotImplementedError @property @abc.abstractmethod def filecodes(self, *args): ''' Return a dictionary of string-encoded inputs used for file naming. ''' raise NotImplementedError def filecode(self, *args): ''' Generate file code given a specific combination of model input parameters. ''' # If meta dictionary was passed, generate inputs list from it if len(args) == 1 and isinstance(args[0], dict): meta = args[0] meta.pop('tcomp', None) nparams = len(signature(self.meta).parameters) args = list(meta.values())[-nparams:] # Create file code by joining string-encoded inputs with underscores codes = self.filecodes(*args).values() return '_'.join([x for x in codes if x is not None]) @classmethod @abc.abstractmethod def getPltVars(self, *args, **kwargs): ''' Return a dictionary with information about all plot variables related to the model. ''' raise NotImplementedError @classmethod @abc.abstractmethod def getPltScheme(self): ''' Return a dictionary model plot variables grouped by context. ''' raise NotImplementedError @staticmethod - def simQueue(*args, outputdir=None): - ''' Create a simulation queue from a combination of simulation parameters. ''' - queue = createQueue(*args) + def checkOutputDir(queue, outputdir): + ''' Check if an outputdir is provided in input arguments, and if so, add it as + the first element of each item in the returned queue. + ''' if outputdir is not None: for item in queue: item.insert(0, outputdir) + else: + if len(queue) > 5: + logger.warning('Running more than 5 simulations without file saving') return queue + @classmethod + def simQueue(cls, *args, outputdir=None): + return cls.checkOutputDir(Batch.createQueue(*args), outputdir) + @staticmethod @abc.abstractmethod def checkInputs(self, *args): ''' Check the validity of simulation input parameters. ''' raise NotImplementedError @property @abc.abstractmethod def derivatives(self, *args, **kwargs): ''' Compute ODE derivatives for a specific set of ODE states and external parameters. ''' raise NotImplementedError @property @abc.abstractmethod def simulate(self, *args, **kwargs): ''' Simulate the model's differential system for specific input parameters and return output data in a dataframe. ''' raise NotImplementedError @classmethod @abc.abstractmethod def meta(self, *args): ''' Return an informative dictionary about model and simulation parameters. ''' raise NotImplementedError - def checkAmplitude(self, args): + @staticmethod + def addMeta(simfunc): + ''' Add an informative dictionary about model and simulation parameters to simulation output ''' + + @wraps(simfunc) + def wrapper(self, *args, **kwargs): + data, tcomp = timer(simfunc)(self, *args, **kwargs) + logger.debug('completed in %ss', si_format(tcomp, 1)) + + # Add keyword arguments from simfunc signature if not provided + bound_args = inspect.signature(simfunc).bind(self, *args, **kwargs) + bound_args.apply_defaults() + target_args = dict(bound_args.arguments) + + # Try to retrieve meta information + try: + meta_params = [target_args[k] for k in inspect.signature(self.meta).parameters.keys()] + meta = self.meta(*meta_params) + except: + meta = {} + + # Add computation time to it + meta['tcomp'] = tcomp + + # Return data with meta dict + return data, meta + + return wrapper + + @staticmethod + def logNSpikes(simfunc): + ''' Log number of detected spikes on charge profile of simulation output. ''' + @wraps(simfunc) + def wrapper(self, *args, **kwargs): + data, meta = simfunc(self, *args, **kwargs) + nspikes = self.getNSpikes(data) + logger.debug('{} spike{} detected'.format(nspikes, plural(nspikes))) + return data, meta + + return wrapper + + @staticmethod + def checkAmplitude(simfunc): ''' If no (None) amplitude provided in the list of input parameters, perform a titration to find the threshold amplitude and add it to the list. ''' - if None in args: - iA = args.index(None) - new_args = [x for x in args if x is not None] - Athr = self.titrate(*new_args) - if np.isnan(Athr): - logger.error('Could not find threshold excitation amplitude') - return None - new_args.insert(iA, Athr) - args = new_args - return args - - def runAndSave(self, outdir, *args): - ''' Simulate the model for specific parameters and save the results - in a specific output directory. ''' - args = self.checkAmplitude(args) - data, tcomp = self.simulate(*args) - meta = self.meta(*args) - meta['tcomp'] = tcomp + @wraps(simfunc) + def wrapper(self, *args, **kwargs): + if None in args: + iA = args.index(None) + new_args = [x for x in args if x is not None] + Athr = self.titrate(*new_args) + if np.isnan(Athr): + logger.error('Could not find threshold excitation amplitude') + return None + new_args.insert(iA, Athr) + args = new_args + return simfunc(self, *args, **kwargs) + + return wrapper + + def simAndSave(self, outdir, *args): + ''' Simulate the model and save the results in a specific output directory. ''' + data, meta = self.simulate(*args) fpath = '{}/{}.pkl'.format(outdir, self.filecode(*args)) with open(fpath, 'wb') as fh: pickle.dump({'meta': meta, 'data': data}, fh) logger.debug('simulation data exported to "%s"', fpath) return fpath def getOutput(self, outdir, *args): ''' Get simulation output data for a specific parameters combination, by looking for an output file into a specific directory. If a corresponding output file is not found in the specified directory, the model is first run and results are saved in the output file. ''' fpath = '{}/{}.pkl'.format(outdir, self.filecode(*args)) if not os.path.isfile(fpath): - self.runAndSave(outdir, *args) + self.simAndSave(outdir, *args) return loadData(fpath) diff --git a/PySONIC/core/nbls.py b/PySONIC/core/nbls.py index 0b072f9..6759df5 100644 --- a/PySONIC/core/nbls.py +++ b/PySONIC/core/nbls.py @@ -1,725 +1,711 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2016-09-29 16:16:19 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 19:11:54 +# @Last Modified time: 2019-07-01 16:38:48 from copy import deepcopy import logging import numpy as np import pandas as pd from scipy.integrate import solve_ivp from .simulators import PWSimulator, HybridSimulator, PeriodicSimulator from .bls import BilayerSonophore from .pneuron import PointNeuron from .model import Model -from .batches import createQueue +from .batches import Batch from ..utils import * from ..constants import * from ..postpro import getFixedPoints from .lookups import SmartLookup NEURONS_LOOKUP_DIR = os.path.abspath(os.path.split(__file__)[0] + "/../neurons/") class NeuronalBilayerSonophore(BilayerSonophore): ''' This class inherits from the BilayerSonophore class and receives an PointNeuron instance at initialization, to define the electro-mechanical NICE model and its SONIC variant. ''' tscale = 'ms' # relevant temporal scale of the model simkey = 'ASTIM' # keyword used to characterize simulations made with this model def __init__(self, a, pneuron, Fdrive=None, embedding_depth=0.0): ''' Constructor of the class. :param a: in-plane radius of the sonophore structure within the membrane (m) :param pneuron: point-neuron model :param Fdrive: frequency of acoustic perturbation (Hz) :param embedding_depth: depth of the embedding tissue around the membrane (m) ''' # Check validity of input parameters if not isinstance(pneuron, PointNeuron): raise ValueError('Invalid neuron type: "{}" (must inherit from PointNeuron class)' .format(pneuron.name)) self.pneuron = pneuron # Initialize BilayerSonophore parent object BilayerSonophore.__init__(self, a, pneuron.Cm0, pneuron.Qm0(), embedding_depth) def __repr__(self): s = '{}({:.1f} nm, {}'.format(self.__class__.__name__, self.a * 1e9, self.pneuron) if self.d > 0.: s += ', d={}m'.format(si_format(self.d, precision=1, space=' ')) return s + ')' def params(self): return {**super().params(), **self.pneuron.params()} def getPltVars(self, wrapleft='df["', wrapright='"]'): return {**super().getPltVars(wrapleft, wrapright), **self.pneuron.getPltVars(wrapleft, wrapright)} def getPltScheme(self): return self.pneuron.getPltScheme() def filecode(self, *args): return Model.filecode(self, *args) @staticmethod def inputs(): # Get parent input vars and supress irrelevant entries bls_vars = BilayerSonophore.inputs() pneuron_vars = PointNeuron.inputs() del bls_vars['Qm'] del pneuron_vars['Astim'] # Fill in current input vars in appropriate order inputvars = bls_vars inputvars.update(pneuron_vars) inputvars['fs'] = { 'desc': 'sonophore membrane coverage fraction', 'label': 'f_s', 'unit': '\%', 'factor': 1e2, 'precision': 0 } inputvars['method'] = None return inputvars def filecodes(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs, method): # Get parent codes and supress irrelevant entries bls_codes = super().filecodes(Fdrive, Adrive, 0.0) pneuron_codes = self.pneuron.filecodes(0.0, tstim, toffset, PRF, DC) for x in [bls_codes, pneuron_codes]: del x['simkey'] del bls_codes['Qm'] del pneuron_codes['Astim'] # Fill in current codes in appropriate order codes = { 'simkey': self.simkey, 'neuron': pneuron_codes.pop('neuron'), 'nature': pneuron_codes.pop('nature') } codes.update(bls_codes) codes.update(pneuron_codes) codes['fs'] = 'fs{:.0f}%'.format(fs * 1e2) if fs < 1 else None codes['method'] = method return codes @staticmethod def interpOnOffVariable(key, Qm, stim, lkp): ''' Interpolate Q-dependent effective variable along ON and OFF periods of a solution. :param key: lookup variable key :param Qm: charge density solution vector :param stim: stimulation state solution vector :param lkp: dictionary of lookups for ON and OFF states :return: interpolated effective variable vector ''' x = np.zeros(stim.size) x[stim == 0] = lkp['OFF'].interpVar(Qm[stim == 0], 'Q', key) x[stim == 1] = lkp['ON'].interpVar(Qm[stim == 1], 'Q', key) return x @staticmethod def spatialAverage(fs, x, x0): ''' fs-modulated spatial averaging. ''' return fs * x + (1 - x) * x0 def computeEffVars(self, Fdrive, Adrive, Qm, fs): ''' Compute "effective" coefficients of the HH system for a specific combination of stimulus frequency, stimulus amplitude and charge density. A short mechanical simulation is run while imposing the specific charge density, until periodic stabilization. The HH coefficients are then averaged over the last acoustic cycle to yield "effective" coefficients. :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param Qm: imposed charge density (C/m2) :param fs: list of sonophore membrane coverage fractions :return: list with computation time and a list of dictionaries of effective variables ''' # Run simulation and retrieve deflection and gas content vectors from last cycle - data, tcomp = BilayerSonophore.simulate(self, Fdrive, Adrive, Qm) + data, meta = BilayerSonophore.simulate(self, Fdrive, Adrive, Qm) Z_last = data.loc[-NPC_DENSE:, 'Z'].values # m Cm_last = self.v_capacitance(Z_last) # F/m2 # For each coverage fraction effvars = [] for x in fs: # Compute membrane capacitance and membrane potential vectors Cm = self.spatialAverage(x, Cm_last, self.Cm0) # F/m2 Vm = Qm / Cm * 1e3 # mV # Compute average cycle value for membrane potential and rate constants effvars.append({**{'V': np.mean(Vm)}, **self.pneuron.getEffRates(Vm)}) # Log process log = '{}: lookups @ {}Hz, {}Pa, {:.2f} nC/cm2'.format( self, *si_format([Fdrive, Adrive], precision=1, space=' '), Qm * 1e5) if len(fs) > 1: log += ', fs = {:.0f} - {:.0f}%'.format(fs.min() * 1e2, fs.max() * 1e2) - log += ', tcomp = {:.3f} s'.format(tcomp) + log += ', tcomp = {:.3f} s'.format(meta['tcomp']) logger.info(log) # Return effective coefficients - return [tcomp, effvars] + return [meta['tcomp'], effvars] def fullDerivatives(self, t, y, Fdrive, Adrive, phi, fs): ''' Compute the full system derivatives. :param t: specific instant in time (s) :param y: vector of state variables :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param phi: acoustic drive phase (rad) :param fs: sonophore membrane coevrage fraction (-) :return: vector of derivatives ''' dydt_mech = BilayerSonophore.derivatives( self, t, y[:3], Fdrive, Adrive, y[3], phi) dydt_elec = self.pneuron.derivatives( t, y[3:], Cm=self.spatialAverage(fs, self.capacitance(y[1]), self.Cm0)) return dydt_mech + dydt_elec def effDerivatives(self, t, y, lkp1d): ''' Compute the derivatives of the n-ODE effective HH system variables, based on 1-dimensional linear interpolation of "effective" coefficients that summarize the system's behaviour over an acoustic cycle. :param t: specific instant in time (s) :param y: vector of HH system variables at time t :param lkp: dictionary of 1D data points of "effective" coefficients over the charge domain, for specific frequency and amplitude values. :return: vector of effective system derivatives at time t ''' Qm, *states = y states_dict = dict(zip(self.pneuron.statesNames(), states)) lkp0d = lkp1d.interpolate1D('Q', Qm) dQmdt = - self.pneuron.iNet(lkp0d['V'], states_dict) * 1e-3 return [dQmdt, *self.pneuron.getDerEffStates(lkp0d, states_dict)] - def _runFull(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs, phi=np.pi): + def _simFull(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs, phi=np.pi): # Determine time step dt = 1 / (NPC_DENSE * Fdrive) # Compute non-zero deflection value for a small perturbation (solving quasi-steady equation) Pac = self.Pacoustic(dt, Adrive, Fdrive, phi) Z0 = self.balancedefQS(self.ng0, self.Qm0, Pac) # Set initial conditions y0 = np.concatenate(( [0., Z0, self.ng0, self.Qm0], self.pneuron.getSteadyStates(self.pneuron.Vm0))) # Initialize simulator and compute solution logger.debug('Computing detailed solution') simulator = PWSimulator( lambda t, y: self.fullDerivatives(t, y, Fdrive, Adrive, phi, fs), lambda t, y: self.fullDerivatives(t, y, 0., 0., 0., fs)) - (t, y, stim), tcomp = simulator( + t, y, stim = simulator( y0, dt, tstim, toffset, PRF, DC, print_progress=logger.getEffectiveLevel() <= logging.INFO, - target_dt=CLASSIC_TARGET_DT, - monitor_time=True) - logger.debug('completed in %ss', si_format(tcomp, 1)) + target_dt=CLASSIC_TARGET_DT) - # Store output in dataframe + # Store output in dataframe and return data = pd.DataFrame({ 't': t, 'stimstate': stim, 'Z': y[:, 1], 'ng': y[:, 2], 'Qm': y[:, 3] }) data['Vm'] = data['Qm'].values / self.spatialAverage( fs, self.v_capacitance(data['Z'].values), self.Cm0) * 1e3 # mV for i in range(len(self.pneuron.states)): data[self.pneuron.statesNames()[i]] = y[:, i + 4] + return data - # Return dataframe and computation time - return data, tcomp - - def _runHybrid(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs, phi=np.pi): + def _simHybrid(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs, phi=np.pi): # Determine time steps dt_dense, dt_sparse = [1. / (n * Fdrive) for n in [NPC_DENSE, NPC_SPARSE]] # Compute non-zero deflection value for a small perturbation (solving quasi-steady equation) Pac = self.Pacoustic(dt_dense, Adrive, Fdrive, phi) Z0 = self.balancedefQS(self.ng0, self.Qm0, Pac) # Set initial conditions y0 = np.concatenate(( [0., Z0, self.ng0, self.Qm0], self.pneuron.getSteadyStates(self.pneuron.Vm0))) is_dense_var = np.array([True] * 3 + [False] * (len(self.pneuron.states) + 1)) # Initialize simulator and compute solution logger.debug('Computing hybrid solution') simulator = HybridSimulator( lambda t, y: self.fullDerivatives(t, y, Fdrive, Adrive, phi, fs), lambda t, y: self.fullDerivatives(t, y, 0., 0., 0., fs), lambda t, y, Cm: self.pneuron.derivatives( t, y, Cm=self.spatialAverage(fs, Cm, self.Cm0)), lambda yref: self.capacitance(yref[1]), is_dense_var, ivars_to_check=[1, 2]) - (t, y, stim), tcomp = simulator( - y0, dt_dense, dt_sparse, 1. / Fdrive, tstim, toffset, PRF, DC, - monitor_time=True) - logger.debug('completed in %ss', si_format(tcomp, 1)) + t, y, stim = simulator(y0, dt_dense, dt_sparse, 1. / Fdrive, tstim, toffset, PRF, DC) - # Store output in dataframe + # Store output in dataframe and return data = pd.DataFrame({ 't': t, 'stimstate': stim, 'Z': y[:, 1], 'ng': y[:, 2], 'Qm': y[:, 3] }) data['Vm'] = data['Qm'].values / self.spatialAverage( fs, self.v_capacitance(data['Z'].values), self.Cm0) * 1e3 # mV for i in range(len(self.pneuron.states)): data[self.pneuron.statesNames()[i]] = y[:, i + 4] + return data - # Return dataframe and computation time - return data, tcomp - - def _runSONIC(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs): + def _simSonic(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs): # Load appropriate 2D lookups lkp2d = self.getLookup2D(Fdrive, fs) # Interpolate 2D lookups at zero and US amplitude logger.debug('Interpolating lookups at A = %.2f kPa and A = 0', Adrive * 1e-3) lkps1d = {'ON': lkp2d.project('A', Adrive), 'OFF': lkp2d.project('A', 0.)} # Set initial conditions y0 = np.concatenate(([self.Qm0], self.pneuron.getSteadyStates(self.pneuron.Vm0))) # Initialize simulator and compute solution logger.debug('Computing effective solution') simulator = PWSimulator( lambda t, y: self.effDerivatives(t, y, lkps1d['ON']), lambda t, y: self.effDerivatives(t, y, lkps1d['OFF'])) - (t, y, stim), tcomp = simulator( - y0, DT_EFFECTIVE, tstim, toffset, PRF, DC, monitor_time=True) - logger.debug('completed in %ss', si_format(tcomp, 1)) + t, y, stim = simulator(y0, DT_EFFECTIVE, tstim, toffset, PRF, DC) - # Store output in dataframe + # Store output in dataframe and return data = pd.DataFrame({ 't': t, 'stimstate': stim, 'Qm': y[:, 0] }) data['Vm'] = self.interpOnOffVariable('V', data['Qm'].values, stim, lkps1d) for key in ['Z', 'ng']: data[key] = np.full(t.size, np.nan) for i in range(len(self.pneuron.states)): data[self.pneuron.statesNames()[i]] = y[:, i + 1] + return data - # Return dataframe and computation time - return data, tcomp - - @staticmethod - def simQueue(freqs, amps, durations, offsets, PRFs, DCs, fs, methods, outputdir=None): + @classmethod + def simQueue(cls, freqs, amps, durations, offsets, PRFs, DCs, fs, methods, outputdir=None): ''' Create a serialized 2D array of all parameter combinations for a series of individual parameter sweeps, while avoiding repetition of CW protocols for a given PRF sweep. :param freqs: list (or 1D-array) of US frequencies :param amps: list (or 1D-array) of acoustic amplitudes :param durations: list (or 1D-array) of stimulus durations :param offsets: list (or 1D-array) of stimulus offsets (paired with durations array) :param PRFs: list (or 1D-array) of pulse-repetition frequencies :param DCs: list (or 1D-array) of duty cycle values :param fs: sonophore membrane coverage fractions (-) :params methods: integration methods :return: list of parameters (list) for each simulation ''' method_ids = list(range(len(methods))) + if ('full' in methods or 'hybrid' in methods) and outputdir is None: + logger.warning('Running cumbersome simulation(s) without file saving') if amps is None: amps = [np.nan] DCs = np.array(DCs) queue = [] if 1.0 in DCs: - queue += createQueue( + queue += Batch.createQueue( freqs, amps, durations, offsets, min(PRFs), 1.0, fs, method_ids) if np.any(DCs != 1.0): - queue += createQueue( + queue += Batch.createQueue( freqs, amps, durations, offsets, PRFs, DCs[DCs != 1.0], fs, method_ids) for item in queue: if np.isnan(item[1]): item[1] = None item[-1] = methods[int(item[-1])] - if outputdir is not None: - for item in queue: - item.insert(0, outputdir) - return queue + return cls.checkOutputDir(queue, outputdir) + @Model.logNSpikes + @Model.checkAmplitude + @Model.addMeta def simulate(self, Fdrive, Adrive, tstim, toffset, PRF=100., DC=1., fs=1., method='sonic'): ''' Simulate the electro-mechanical model for a specific set of US stimulation parameters, and return output data in a dataframe. :param Fdrive: acoustic drive frequency (Hz) :param Adrive: acoustic drive amplitude (Pa) :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :param fs: sonophore membrane coverage fraction (-) :param method: selected integration method :return: 2-tuple with the output dataframe and computation time. ''' logger.info( '%s: simulation @ f = %sHz, A = %sPa, t = %ss (%ss offset)%s%s', self, si_format(Fdrive, 0, space=' '), si_format(Adrive, 2, space=' '), *si_format([tstim, toffset], 1, space=' '), (', PRF = {}Hz, DC = {:.2f}%'.format( si_format(PRF, 2, space=' '), DC * 1e2) if DC < 1.0 else ''), ', fs = {:.2f}%'.format(fs * 1e2) if fs < 1.0 else '') # Check validity of stimulation parameters BilayerSonophore.checkInputs(Fdrive, Adrive, 0.0, 0.0) PointNeuron.checkInputs(Adrive, tstim, toffset, PRF, DC) - # Call appropriate simulation function + # Call appropriate simulation function and return try: simfunc = { - 'full': self._runFull, - 'hybrid': self._runHybrid, - 'sonic': self._runSONIC + 'full': self._simFull, + 'hybrid': self._simHybrid, + 'sonic': self._simSonic }[method] except KeyError: raise ValueError('Invalid integration method: "{}"'.format(method)) - data, tcomp = simfunc(Fdrive, Adrive, tstim, toffset, PRF, DC, fs) - - # Log number of detected spikes - nspikes = self.pneuron.getNSpikes(data) - logger.debug('{} spike{} detected'.format(nspikes, plural(nspikes))) - - # Return dataframe and computation time - return data, tcomp + return simfunc(Fdrive, Adrive, tstim, toffset, PRF, DC, fs) def meta(self, Fdrive, Adrive, tstim, toffset, PRF, DC, fs, method): return { 'simkey': self.simkey, 'neuron': self.pneuron.name, 'a': self.a, 'd': self.d, 'Fdrive': Fdrive, 'Adrive': Adrive, 'tstim': tstim, 'toffset': toffset, 'PRF': PRF, 'DC': DC, 'fs': fs, 'method': method } + @staticmethod + def getNSpikes(data): + return PointNeuron.getNSpikes(data) + @logCache(os.path.join(os.path.split(__file__)[0], 'astim_titrations.log')) def titrate(self, Fdrive, tstim, toffset, PRF=100., DC=1., fs=1., method='sonic', xfunc=None, Arange=None): ''' Use a binary search to determine the threshold amplitude needed to obtain neural excitation for a given frequency, duration, PRF and duty cycle. :param Fdrive: US frequency (Hz) :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :param fs: sonophore membrane coverage fraction (-) :param method: integration method :param xfunc: function determining whether condition is reached from simulation output :param Arange: search interval for Adrive, iteratively refined :return: determined threshold amplitude (Pa) ''' # Default output function if xfunc is None: xfunc = self.pneuron.titrationFunc # Default amplitude interval if Arange is None: Arange = [0., self.getLookup().refs['A'].max()] return binarySearch( lambda x: xfunc(self.simulate(*x)[0]), [Fdrive, tstim, toffset, PRF, DC, fs, method], 1, Arange, THRESHOLD_CONV_RANGE_ASTIM ) def quasiSteadyStates(self, Fdrive, amps=None, charges=None, DCs=1.0, squeeze_output=False): ''' Compute the quasi-steady state values of the neuron's gating variables for a combination of US amplitudes, charge densities and duty cycles, at a specific US frequency. :param Fdrive: US frequency (Hz) :param amps: US amplitudes (Pa) :param charges: membrane charge densities (C/m2) :param DCs: duty cycle value(s) :return: 4-tuple with reference values of US amplitude and charge density, as well as interpolated Vmeff and QSS gating variables ''' # Get DC-averaged lookups interpolated at the appropriate amplitudes and charges lkp = self.getLookup().projectDCs(amps=amps, DCs=DCs).projectN({'a': self.a, 'f': Fdrive}) if charges is not None: lkp = lkp.project('Q', charges) # Specify dimensions with A and DC as the first two axes A_axis = lkp.getAxisIndex('A') lkp.move('A', 0) lkp.move('DC', 1) nA, nDC = lkp.dims()[:2] # Compute QSS states using these lookups QSS = {k: np.empty(lkp.dims()) for k in self.pneuron.statesNames()} for iA in range(nA): for iDC in range(nDC): QSS_1D = self.pneuron.quasiSteadyStates({k: v[iA, iDC] for k, v in lkp.items()}) for k in QSS.keys(): QSS[k][iA, iDC] = QSS_1D[k] QSS = SmartLookup(lkp.refs, QSS) for item in [lkp, QSS]: item.move('A', A_axis) item.move('DC', -1) # Compress outputs if needed if squeeze_output: QSS = QSS.squeeze() lkp = lkp.squeeze() return lkp, QSS def iNetQSS(self, Qm, Fdrive, Adrive, DC): ''' Compute quasi-steady state net membrane current for a given combination of US parameters and a given membrane charge density. :param Qm: membrane charge density (C/m2) :param Fdrive: US frequency (Hz) :param Adrive: US amplitude (Pa) :param DC: duty cycle (-) :return: net membrane current (mA/m2) ''' lkp, QSS = self.quasiSteadyStates( Fdrive, amps=Adrive, charges=Qm, DCs=DC, squeeze_output=True) return self.pneuron.iNet(lkp['V'], QSS) # mA/m2 def evaluateStability(self, Qm0, states0, lkp): ''' Integrate the effective differential system from a given starting point, until clear convergence or clear divergence is found. :param Qm0: initial membrane charge density (C/m2) :param states0: dictionary of initial states values :param lkp: dictionary of 1D data points of "effective" coefficients over the charge domain, for specific frequency and amplitude values. :return: boolean indicating convergence state ''' # Initialize y0 vector t0 = 0. y0 = np.array([Qm0] + list(states0.values())) # Initialize simulator and compute solution # simulator = PeriodicSimulator( # lambda t, y: self.effDerivatives(t, y, lkp), # ivars_to_check=[0]) # simulator.stopfunc = simulator.stopFuncTmp # simulator.refs = [Qm0] # simulator.conv_thr = [QSS_Q_CONV_THR] # simulator.div_thr = [QSS_Q_DIV_THR] # simulator.t_history = QSS_HISTORY_INTERVAL # t, y, stim = simulator.compute( # y0, # QSS_INTEGRATION_INTERVAL, # QSS_INTEGRATION_INTERVAL, # nmax=int(QSS_MAX_INTEGRATION_DURATION // QSS_INTEGRATION_INTERVAL) # ) # conv = simulator.isAsymptoticallyStable(t, y, QSS_INTEGRATION_INTERVAL) != -1 # tf = t[-1] # dQ = [y[-1, 0]] # Initializing empty list to record evolution of charge deviation n = int(QSS_HISTORY_INTERVAL // QSS_INTEGRATION_INTERVAL) # size of history dQ = [] # As long as there is no clear charge convergence or divergence conv, div = False, False tf, yf = t0, y0 while not conv and not div: # Integrate system for small interval and retrieve final charge deviation t0, y0 = tf, yf sol = solve_ivp( lambda t, y: self.effDerivatives(t, y, lkp), [t0, t0 + QSS_INTEGRATION_INTERVAL], y0, method='LSODA' ) tf, yf = sol.t[-1], sol.y[:, -1] dQ.append(yf[0] - Qm0) # logger.debug('{:.0f} ms: dQ = {:.5f} nC/cm2, avg dQ = {:.5f} nC/cm2'.format( # tf * 1e3, dQ[-1] * 1e5, np.mean(dQ[-n:]) * 1e5)) # If last charge deviation is too large -> divergence if np.abs(dQ[-1]) > QSS_Q_DIV_THR: div = True # If last charge deviation or average deviation in recent history # is small enough -> convergence for x in [dQ[-1], np.mean(dQ[-n:])]: if np.abs(x) < QSS_Q_CONV_THR: conv = True # If max integration duration is been reached -> error if tf > QSS_MAX_INTEGRATION_DURATION: logger.warning('too many iterations (dQ = {:.5f} nC/cm2)'.format(dQ[-1] * 1e5)) conv = True logger.debug('{}vergence after {:.0f} ms: dQ = {:.5f} nC/cm2'.format( {True: 'con', False: 'di'}[conv], tf * 1e3, dQ[-1] * 1e5)) return conv def fixedPointsQSS(self, Fdrive, Adrive, DC, lkp, dQdt): ''' Compute QSS fixed points along the charge dimension for a given combination of US parameters, and determine their stability. :param Fdrive: US frequency (Hz) :param Adrive: US amplitude (Pa) :param DC: duty cycle (-) :param lkp: lookup dictionary for effective variables along charge dimension :param dQdt: charge derivative profile along charge dimension :return: 2-tuple with values of stable and unstable fixed points ''' logger.debug('A = {:.2f} kPa, DC = {:.0f}%'.format(Adrive * 1e-3, DC * 1e2)) # Extract stable and unstable fixed points from QSS charge variation profile def dfunc(Qm): return - self.iNetQSS(Qm, Fdrive, Adrive, DC) SFP_candidates = getFixedPoints( lkp.refs['Q'], dQdt, filter='stable', der_func=dfunc).tolist() UFPs = getFixedPoints( lkp.refs['Q'], dQdt, filter='unstable', der_func=dfunc).tolist() SFPs = [] pltvars = self.getPltVars() # For each candidate SFP for i, Qm in enumerate(SFP_candidates): logger.debug('Q-SFP = {:.2f} nC/cm2'.format(Qm * 1e5)) # Re-compute QSS *_, QSS_FP = self.quasiSteadyStates(Fdrive, amps=Adrive, charges=Qm, DCs=DC, squeeze_output=True) # Simulate from unperturbed QSS and evaluate stability if not self.evaluateStability(Qm, QSS_FP, lkp): logger.warning('diverging system at ({:.2f} kPa, {:.2f} nC/cm2)'.format( Adrive * 1e-3, Qm * 1e5)) UFPs.append(Qm) else: # For each state unstable_states = [] for x in self.pneuron.statesNames(): pltvar = pltvars[x] unit_str = pltvar.get('unit', '') factor = pltvar.get('factor', 1) is_stable_direction = [] for sign in [-1, +1]: # Perturb state with small offset QSS_perturbed = deepcopy(QSS_FP) QSS_perturbed[x] *= (1 + sign * QSS_REL_OFFSET) # If gating state, bound within [0., 1.] if self.pneuron.isVoltageGated(x): QSS_perturbed[x] = np.clip(QSS_perturbed[x], 0., 1.) logger.debug('{}: {:.5f} -> {:.5f} {}'.format( x, QSS_FP[x] * factor, QSS_perturbed[x] * factor, unit_str)) # Simulate from perturbed QSS and evaluate stability is_stable_direction.append( self.evaluateStability(Qm, QSS_perturbed, lkp)) # Check if system shows stability upon x-state perturbation # in both directions if not np.all(is_stable_direction): unstable_states.append(x) # Classify fixed point as stable only if all states show stability is_stable_FP = len(unstable_states) == 0 {True: SFPs, False: UFPs}[is_stable_FP].append(Qm) logger.info('{}stable fixed-point at ({:.2f} kPa, {:.2f} nC/cm2){}'.format( '' if is_stable_FP else 'un', Adrive * 1e-3, Qm * 1e5, '' if is_stable_FP else ', caused by {} states'.format(unstable_states))) return SFPs, UFPs def isStableQSS(self, Fdrive, Adrive, DC): lookups, QSS = self.quasiSteadyStates( Fdrive, amps=Adrive, DCs=DC, squeeze_output=True) dQdt = -self.pneuron.iNet(lookups['V'], QSS.tables) # mA/m2 SFPs, _ = self.fixedPointsQSS(Fdrive, Adrive, DC, lookups, dQdt) return len(SFPs) > 0 def titrateQSS(self, Fdrive, DC=1., Arange=None): # Default amplitude interval if Arange is None: Arange = [0., self.getLookup().refs['A'].max()] # Titration function def xfunc(x): if self.pneuron.name == 'STN': return self.isStableQSS(*x) else: return not self.isStableQSS(*x) return binarySearch( xfunc, [Fdrive, DC], 1, Arange, THRESHOLD_CONV_RANGE_ASTIM) def getLookupFileName(self, a=None, Fdrive=None, Adrive=None, fs=False): fname = '{}_lookups'.format(self.pneuron.name) if a is not None: fname += '_{:.0f}nm'.format(a * 1e9) if Fdrive is not None: fname += '_{:.0f}kHz'.format(Fdrive * 1e-3) if Adrive is not None: fname += '_{:.0f}kPa'.format(Adrive * 1e-3) if fs is True: fname += '_fs' return '{}.pkl'.format(fname) def getLookupFilePath(self, *args, **kwargs): return os.path.join(NEURONS_LOOKUP_DIR, self.getLookupFileName(*args, **kwargs)) def getLookup(self, *args, **kwargs): lookup_path = self.getLookupFilePath(*args, **kwargs) if not os.path.isfile(lookup_path): raise FileNotFoundError('Missing lookup file: "{}"'.format(lookup_path)) with open(lookup_path, 'rb') as fh: frame = pickle.load(fh) if 'ng' in frame['lookup']: del frame['lookup']['ng'] refs = frame['input'] # Move fs to last reference dimension keys = list(refs.keys()) if 'fs' in keys and keys.index('fs') < len(keys) - 1: del keys[keys.index('fs')] keys.append('fs') refs = {k: refs[k] for k in keys} return SmartLookup(refs, frame['lookup']) def getLookup2D(self, Fdrive, fs): if fs < 1: lkp2d = self.getLookup(a=self.a, Fdrive=Fdrive, fs=True).project('fs', fs) else: lkp2d = self.getLookup().projectN({'a': self.a, 'f': Fdrive}) return lkp2d diff --git a/PySONIC/core/pneuron.py b/PySONIC/core/pneuron.py index a77f033..e7b088a 100644 --- a/PySONIC/core/pneuron.py +++ b/PySONIC/core/pneuron.py @@ -1,594 +1,597 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-08-03 11:53:04 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 19:44:43 +# @Last Modified time: 2019-07-01 15:48:02 import abc import inspect import numpy as np import pandas as pd -from .batches import createQueue +from .batches import Batch from .model import Model from .lookups import SmartLookup from .simulators import PWSimulator from ..postpro import findPeaks, computeFRProfile from ..constants import * -from ..utils import si_format, logger, plural, binarySearch +from ..utils import * class PointNeuron(Model): ''' Generic point-neuron model interface. ''' tscale = 'ms' # relevant temporal scale of the model simkey = 'ESTIM' # keyword used to characterize simulations made with this model def __repr__(self): return self.__class__.__name__ @property @classmethod @abc.abstractmethod def name(cls): ''' Neuron name. ''' raise NotImplementedError @property @classmethod @abc.abstractmethod def Cm0(cls): ''' Neuron's resting capacitance (F/cm2). ''' raise NotImplementedError @property @classmethod @abc.abstractmethod def Vm0(cls): ''' Neuron's resting membrane potential(mV). ''' raise NotImplementedError @classmethod def Qm0(cls): return cls.Cm0 * cls.Vm0 * 1e-3 # C/cm2 @staticmethod def inputs(): return { 'Astim': { 'desc': 'current density amplitude', 'label': 'A', 'unit': 'mA/m2', 'factor': 1e0, 'precision': 1 }, 'tstim': { 'desc': 'stimulus duration', 'label': 't_{stim}', 'unit': 'ms', 'factor': 1e3, 'precision': 0 }, 'toffset': { 'desc': 'offset duration', 'label': 't_{offset}', 'unit': 'ms', 'factor': 1e3, 'precision': 0 }, 'PRF': { 'desc': 'pulse repetition frequency', 'label': 'PRF', 'unit': 'Hz', 'factor': 1e0, 'precision': 0 }, 'DC': { 'desc': 'duty cycle', 'label': 'DC', 'unit': '%', 'factor': 1e2, 'precision': 2 } } @classmethod def filecodes(cls, Astim, tstim, toffset, PRF, DC): is_CW = DC == 1. return { 'simkey': cls.simkey, 'neuron': cls.name, 'nature': 'CW' if is_CW else 'PW', 'Astim': '{:.1f}mAm2'.format(Astim), 'tstim': '{:.0f}ms'.format(tstim * 1e3), 'toffset': None, 'PRF': 'PRF{:.2f}Hz'.format(PRF) if not is_CW else None, 'DC': 'DC{:.2f}%'.format(DC * 1e2) if not is_CW else None } @classmethod def getPltVars(cls, wrapleft='df["', wrapright='"]'): pltvars = { 'Qm': { 'desc': 'membrane charge density', 'label': 'Q_m', 'unit': 'nC/cm^2', 'factor': 1e5, 'bounds': (-100, 50) }, 'Vm': { 'desc': 'membrane potential', 'label': 'V_m', 'unit': 'mV', 'y0': cls.Vm0, 'bounds': (-150, 70) }, 'ELeak': { 'constant': 'obj.ELeak', 'desc': 'non-specific leakage current resting potential', 'label': 'V_{leak}', 'unit': 'mV', 'ls': '--', 'color': 'k' } } for cname in cls.getCurrentsNames(): cfunc = getattr(cls, cname) cargs = inspect.getargspec(cfunc)[0][1:] pltvars[cname] = { 'desc': inspect.getdoc(cfunc).splitlines()[0], 'label': 'I_{{{}}}'.format(cname[1:]), 'unit': 'A/m^2', 'factor': 1e-3, 'func': '{}({})'.format(cname, ', '.join(['{}{}{}'.format(wrapleft, a, wrapright) for a in cargs])) } for var in cargs: if var != 'Vm': pltvars[var] = { 'desc': cls.states[var], 'label': var, 'bounds': (-0.1, 1.1) } pltvars['iNet'] = { 'desc': inspect.getdoc(getattr(cls, 'iNet')).splitlines()[0], 'label': 'I_{net}', 'unit': 'A/m^2', 'factor': 1e-3, 'func': 'iNet({0}Vm{1}, {2}{3}{4})'.format( wrapleft, wrapright, wrapleft[:-1], cls.statesNames(), wrapright[1:]), 'ls': '--', 'color': 'black' } pltvars['dQdt'] = { 'desc': inspect.getdoc(getattr(cls, 'dQdt')).splitlines()[0], 'label': 'dQ_m/dt', 'unit': 'A/m^2', 'factor': 1e-3, 'func': 'dQdt({0}Vm{1}, {2}{3}{4})'.format( wrapleft, wrapright, wrapleft[:-1], cls.statesNames(), wrapright[1:]), 'ls': '--', 'color': 'black' } for rate in cls.rates: if 'alpha' in rate: prefix, suffix = 'alpha', rate[5:] else: prefix, suffix = 'beta', rate[4:] pltvars['{}'.format(rate)] = { 'label': '\\{}_{{{}}}'.format(prefix, suffix), 'unit': 'ms^{-1}', 'factor': 1e-3 } pltvars['FR'] = { 'desc': 'riring rate', 'label': 'FR', 'unit': 'Hz', 'factor': 1e0, # 'bounds': (0, 1e3), 'func': 'firingRateProfile({0}t{1}.values, {0}Qm{1}.values)'.format(wrapleft, wrapright) } return pltvars @classmethod def getPltScheme(cls): pltscheme = { 'Q_m': ['Qm'], 'V_m': ['Vm'] } pltscheme['I'] = cls.getCurrentsNames() + ['iNet'] for cname in cls.getCurrentsNames(): if 'Leak' not in cname: key = 'i_{{{}}}\ kin.'.format(cname[1:]) cargs = inspect.getargspec(getattr(cls, cname))[0][1:] pltscheme[key] = [var for var in cargs if var not in ['Vm', 'Cai']] return pltscheme @classmethod def statesNames(cls): ''' Return a list of names of all state variables of the model. ''' return list(cls.states.keys()) + @classmethod @abc.abstractmethod - def derStates(self): + def derStates(cls): ''' Dictionary of states derivatives functions ''' raise NotImplementedError - def getDerStates(self, Vm, states): + @classmethod + def getDerStates(cls, Vm, states): ''' Compute states derivatives array given a membrane potential and states dictionary ''' - return np.array([self.derStates()[k](Vm, states) for k in self.statesNames()]) + return np.array([cls.derStates()[k](Vm, states) for k in cls.statesNames()]) + @classmethod @abc.abstractmethod - def steadyStates(self): + def steadyStates(cls): ''' Return a dictionary of steady-states functions ''' raise NotImplementedError - def getSteadyStates(self, Vm): + @classmethod + def getSteadyStates(cls, Vm): ''' Compute array of steady-states for a given membrane potential ''' - return np.array([self.steadyStates()[k](Vm) for k in self.statesNames()]) + return np.array([cls.steadyStates()[k](Vm) for k in cls.statesNames()]) - def getDerEffStates(self, lkp, states): + @classmethod + def getDerEffStates(cls, lkp, states): ''' Compute effective states derivatives array given lookups and states dictionaries. ''' return np.array([ - self.derEffStates()[k](lkp, states) for k in self.statesNames()]) + cls.derEffStates()[k](lkp, states) for k in cls.statesNames()]) - def getEffRates(self, Vm): + @classmethod + def getEffRates(cls, Vm): ''' Compute array of effective rate constants for a given membrane potential vector. ''' - return {k: np.mean(np.vectorize(v)(Vm)) for k, v in self.effRates().items()} + return {k: np.mean(np.vectorize(v)(Vm)) for k, v in cls.effRates().items()} - def getLookup(self): + @classmethod + def getLookup(cls): ''' Get lookup of membrane potential rate constants interpolated along the neuron's charge physiological range. ''' - Qref = np.arange(*self.Qbounds(), 1e-5) # C/m2 - Vref = Qref / self.Cm0 * 1e3 # mV - tables = {k: np.vectorize(v)(Vref) for k, v in self.effRates().items()} + Qref = np.arange(*cls.Qbounds(), 1e-5) # C/m2 + Vref = Qref / cls.Cm0 * 1e3 # mV + tables = {k: np.vectorize(v)(Vref) for k, v in cls.effRates().items()} return SmartLookup({'Q': Qref}, {**{'V': Vref}, **tables}) @classmethod @abc.abstractmethod - def currents(self): + def currents(cls): ''' Dictionary of ionic currents functions (returning current densities in mA/m2) ''' @classmethod def iNet(cls, Vm, states): ''' net membrane current :param Vm: membrane potential (mV) :states: states of ion channels gating and related variables :return: current per unit area (mA/m2) ''' return sum([cfunc(Vm, states) for cfunc in cls.currents().values()]) @classmethod def dQdt(cls, Vm, states): ''' membrane charge density variation rate :param Vm: membrane potential (mV) :states: states of ion channels gating and related variables :return: variation rate (mA/m2) ''' return -cls.iNet(Vm, states) @classmethod def titrationFunc(cls, *args, **kwargs): ''' Default titration function. ''' return cls.isExcited(*args, **kwargs) @staticmethod def currentToConcentrationRate(z_ion, depth): ''' Compute the conversion factor from a specific ionic current (in mA/m2) into a variation rate of submembrane ion concentration (in M/s). :param: z_ion: ion valence :param depth: submembrane depth (m) :return: conversion factor (Mmol.m-1.C-1) ''' return 1e-6 / (z_ion * depth * FARADAY) @staticmethod def nernst(z_ion, Cion_in, Cion_out, T): ''' Nernst potential of a specific ion given its intra and extracellular concentrations. :param z_ion: ion valence :param Cion_in: intracellular ion concentration :param Cion_out: extracellular ion concentration :param T: temperature (K) :return: ion Nernst potential (mV) ''' return (Rg * T) / (z_ion * FARADAY) * np.log(Cion_out / Cion_in) * 1e3 @staticmethod def vtrap(x, y): ''' Generic function used to compute rate constants. ''' return x / (np.exp(x / y) - 1) @staticmethod def efun(x): ''' Generic function used to compute rate constants. ''' return x / (np.exp(x) - 1) @classmethod def ghkDrive(cls, Vm, Z_ion, Cion_in, Cion_out, T): ''' Use the Goldman-Hodgkin-Katz equation to compute the electrochemical driving force of a specific ion species for a given membrane potential. :param Vm: membrane potential (mV) :param Cin: intracellular ion concentration (M) :param Cout: extracellular ion concentration (M) :param T: temperature (K) :return: electrochemical driving force of a single ion particle (mC.m-3) ''' x = Z_ion * FARADAY * Vm / (Rg * T) * 1e-3 # [-] eCin = Cion_in * cls.efun(-x) # M eCout = Cion_out * cls.efun(x) # M return FARADAY * (eCin - eCout) * 1e6 # mC/m3 @classmethod def getCurrentsNames(cls): return list(cls.currents().keys()) def firingRateProfile(*args, **kwargs): return computeFRProfile(*args, **kwargs) @classmethod def Qbounds(cls): ''' Determine bounds of membrane charge physiological range for a given neuron. ''' return np.array([np.round(cls.Vm0 - 25.0), 50.0]) * cls.Cm0 * 1e-3 # C/m2 - def isVoltageGated(self, state): + @classmethod + def isVoltageGated(cls, state): ''' Determine whether a given state is purely voltage-gated or not.''' - return 'alpha{}'.format(state.lower()) in self.rates + return 'alpha{}'.format(state.lower()) in cls.rates @staticmethod def qsStates(lkp, states): ''' Compute a collection of quasi steady states using the standard xinf = ax / (ax + Bx) equation. :param lkp: dictionary of 1D vectors of "effective" coefficients over the charge domain, for specific frequency and amplitude values. :return: dictionary of quasi-steady states ''' return { x: lkp['alpha{}'.format(x)] / (lkp['alpha{}'.format(x)] + lkp['beta{}'.format(x)]) for x in states } @classmethod def quasiSteadyStates(cls, lkp): ''' Compute the quasi-steady states of a neuron for a range of membrane charge densities, based on 1-dimensional lookups interpolated at a given sonophore diameter, US frequency, US amplitude and duty cycle. :param lkp: dictionary of 1D vectors of "effective" coefficients over the charge domain, for specific frequency and amplitude values. :return: dictionary of quasi-steady states ''' return cls.qsStates(lkp, cls.statesNames()) # return {k: func(lkp['Vm']) for k, func in self.steadyStates().items()} - @staticmethod - def simQueue(amps, durations, offsets, PRFs, DCs, outputdir=None): + @classmethod + def simQueue(cls, amps, durations, offsets, PRFs, DCs, outputdir=None): ''' Create a serialized 2D array of all parameter combinations for a series of individual parameter sweeps, while avoiding repetition of CW protocols for a given PRF sweep. :param amps: list (or 1D-array) of acoustic amplitudes :param durations: list (or 1D-array) of stimulus durations :param offsets: list (or 1D-array) of stimulus offsets (paired with durations array) :param PRFs: list (or 1D-array) of pulse-repetition frequencies :param DCs: list (or 1D-array) of duty cycle values :return: list of parameters (list) for each simulation ''' if amps is None: amps = [np.nan] DCs = np.array(DCs) queue = [] if 1.0 in DCs: - queue += createQueue(amps, durations, offsets, min(PRFs), 1.0) + queue += Batch.createQueue(amps, durations, offsets, min(PRFs), 1.0) if np.any(DCs != 1.0): - queue += createQueue(amps, durations, offsets, PRFs, DCs[DCs != 1.0]) + queue += Batch.createQueue(amps, durations, offsets, PRFs, DCs[DCs != 1.0]) for item in queue: if np.isnan(item[0]): item[0] = None - if outputdir is not None: - for item in queue: - item.insert(0, outputdir) - return queue + return cls.checkOutputDir(queue, outputdir) @staticmethod def checkInputs(Astim, tstim, toffset, PRF, DC): ''' Check validity of electrical stimulation parameters. :param Astim: pulse amplitude (mA/m2) :param tstim: pulse duration (s) :param toffset: offset duration (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) ''' # Check validity of stimulation parameters if not all(isinstance(param, float) for param in [Astim, tstim, toffset, DC]): raise TypeError('Invalid stimulation parameters (must be float typed)') if tstim <= 0: raise ValueError('Invalid stimulus duration: {} ms (must be strictly positive)' .format(tstim * 1e3)) if toffset < 0: raise ValueError('Invalid stimulus offset: {} ms (must be positive or null)' .format(toffset * 1e3)) if DC <= 0.0 or DC > 1.0: raise ValueError('Invalid duty cycle: {} (must be within ]0; 1])'.format(DC)) if DC < 1.0: if not isinstance(PRF, float): raise TypeError('Invalid PRF value (must be float typed)') if PRF is None: raise AttributeError('Missing PRF value (must be provided when DC < 1)') if PRF < 1 / tstim: raise ValueError('Invalid PRF: {} Hz (PR interval exceeds stimulus duration)' .format(PRF)) - def derivatives(self, t, y, Cm=None, Iinj=0.): + @classmethod + def derivatives(cls, t, y, Cm=None, Iinj=0.): ''' Compute system derivatives for a given mambrane capacitance and injected current. :param t: specific instant in time (s) :param y: vector of HH system variables at time t :param Cm: membrane capacitance (F/m2) :param Iinj: injected current (mA/m2) :return: vector of system derivatives at time t ''' if Cm is None: - Cm = self.Cm0 + Cm = cls.Cm0 Qm, *states = y Vm = Qm / Cm * 1e3 # mV - states_dict = dict(zip(self.statesNames(), states)) - dQmdt = (Iinj - self.iNet(Vm, states_dict)) * 1e-3 # A/m2 - return [dQmdt, *self.getDerStates(Vm, states_dict)] + states_dict = dict(zip(cls.statesNames(), states)) + dQmdt = (Iinj - cls.iNet(Vm, states_dict)) * 1e-3 # A/m2 + return [dQmdt, *cls.getDerStates(Vm, states_dict)] + @Model.logNSpikes + @Model.checkAmplitude + @Model.addMeta def simulate(self, Astim, tstim, toffset, PRF=100., DC=1.0): ''' Simulate a specific neuron model for a specific set of electrical parameters, and return output data in a dataframe. :param Astim: pulse amplitude (mA/m2) :param tstim: pulse duration (s) :param toffset: offset duration (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :return: 2-tuple with the output dataframe and computation time. ''' logger.info( '%s: simulation @ A = %sA/m2, t = %ss (%ss offset)%s', self, si_format(Astim, 2, space=' '), *si_format([tstim, toffset], 1, space=' '), (', PRF = {}Hz, DC = {:.2f}%'.format( si_format(PRF, 2, space=' '), DC * 1e2) if DC < 1.0 else '')) # Check validity of stimulation parameters self.checkInputs(Astim, tstim, toffset, PRF, DC) # Set initial conditions y0 = np.array((self.Qm0(), *self.getSteadyStates(self.Vm0))) # Initialize simulator and compute solution logger.debug('Computing solution') simulator = PWSimulator( lambda t, y: self.derivatives(t, y, Iinj=Astim), lambda t, y: self.derivatives(t, y, Iinj=0.)) - (t, y, stim), tcomp = simulator( - y0, DT_EFFECTIVE, tstim, toffset, PRF, DC, monitor_time=True) - logger.debug('completed in %ss', si_format(tcomp, 1)) + t, y, stim = simulator( + y0, DT_EFFECTIVE, tstim, toffset, PRF, DC) - # Store output in dataframe + # Store output in dataframe and return data = pd.DataFrame({ 't': t, 'stimstate': stim, 'Qm': y[:, 0], 'Vm': y[:, 0] / self.Cm0 * 1e3, }) for i in range(len(self.states)): data[self.statesNames()[i]] = y[:, i + 1] - - # Log number of detected spikes - nspikes = self.getNSpikes(data) - logger.debug('{} spike{} detected'.format(nspikes, plural(nspikes))) - - # Return dataframe and computation time - return data, tcomp + return data @classmethod def meta(cls, Astim, tstim, toffset, PRF, DC): return { 'simkey': cls.simkey, 'neuron': cls.name, 'Astim': Astim, 'tstim': tstim, 'toffset': toffset, 'PRF': PRF, 'DC': DC } @staticmethod def getNSpikes(data): ''' Compute number of spikes in charge profile of simulation output. :param data: dataframe containing output time series :return: number of detected spikes ''' dt = np.diff(data.ix[:1, 't'].values)[0] ipeaks, *_ = findPeaks( data['Qm'].values, SPIKE_MIN_QAMP, int(np.ceil(SPIKE_MIN_DT / dt)), SPIKE_MIN_QPROM ) return ipeaks.size @staticmethod def getStabilizationValue(data): ''' Determine stabilization value from the charge profile of a simulation output. :param data: dataframe containing output time series :return: charge stabilization value (or np.nan if no stabilization detected) ''' # Extract charge signal posterior to observation window t, Qm = [data[key].values for key in ['t', 'Qm']] if t.max() <= TMIN_STABILIZATION: raise ValueError('solution length is too short to assess stabilization') Qm = Qm[t > TMIN_STABILIZATION] # Compute variation range Qm_range = np.ptp(Qm) logger.debug('%.2f nC/cm2 variation range over the last %.0f ms, Qmf = %.2f nC/cm2', Qm_range * 1e5, TMIN_STABILIZATION * 1e3, Qm[-1] * 1e5) # Return final value only if stabilization is detected if np.ptp(Qm) < QSS_Q_DIV_THR: return Qm[-1] else: return np.nan @classmethod def isExcited(cls, data): ''' Determine if neuron is excited from simulation output. :param data: dataframe containing output time series :return: boolean stating whether neuron is excited or not ''' return cls.getNSpikes(data) > 0 @classmethod def isSilenced(cls, data): ''' Determine if neuron is silenced from simulation output. :param data: dataframe containing output time series :return: boolean stating whether neuron is silenced or not ''' return not np.isnan(cls.getStabilizationValue(data)) + @debug def titrate(self, tstim, toffset, PRF, DC, xfunc=None, Arange=(0., 2 * AMP_UPPER_BOUND_ESTIM)): ''' Use a binary search to determine the threshold amplitude needed to obtain neural excitation for a given duration, PRF and duty cycle. :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :param xfunc: function determining whether condition is reached from simulation output :param Arange: search interval for Astim, iteratively refined :return: excitation threshold amplitude (mA/m2) ''' # Default output function if xfunc is None: xfunc = self.titrationFunc return binarySearch( lambda x: xfunc(self.simulate(*x)[0]), [tstim, toffset, PRF, DC], 0, Arange, THRESHOLD_CONV_RANGE_ESTIM ) diff --git a/PySONIC/core/simulators.py b/PySONIC/core/simulators.py index 28fb10f..ee23403 100644 --- a/PySONIC/core/simulators.py +++ b/PySONIC/core/simulators.py @@ -1,477 +1,469 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2019-05-28 14:45:12 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 20:06:46 +# @Last Modified time: 2019-07-01 13:20:37 import abc import numpy as np from scipy.integrate import ode, odeint, solve_ivp from tqdm import tqdm from ..utils import * from ..constants import * class Simulator(metaclass=abc.ABCMeta): ''' Generic interface to simulator object. ''' @staticmethod def initialize(y0): ''' Initialize global arrays. :param y0: vector of initial conditions :return: 3-tuple with the initialized time vector, solution matrix and state vector ''' t = np.array([0.]) y = np.atleast_2d(y0) stim = np.array([1]) return t, y, stim @staticmethod def appendSolution(t, y, stim, tnew, ynew, is_on): ''' Append to time vector, solution matrix and state vector. :param t: preceding time vector :param y: preceding solution matrix :param stim: preceding stimulation state vector :param tnew: integration time vector for current interval :param ynew: derivative function for current interval :param is_on: stimulation state for current interval :return: 3-tuple with the appended time vector, solution matrix and state vector ''' t = np.concatenate((t, tnew[1:])) y = np.concatenate((y, ynew[1:]), axis=0) stim = np.concatenate((stim, np.ones(tnew.size - 1) * is_on)) return t, y, stim @classmethod def integrate(cls, t, y, stim, tnew, dfunc, is_on, use_adaptive_dt=False): ''' Integrate system for a time interval and append to preceding solution arrays. :param t: preceding time vector :param y: preceding solution matrix :param stim: preceding stimulation state vector :param tnew: integration time vector for current interval :param dfunc: derivative function for current interval :param is_on: stimulation state for current interval :return: 3-tuple with the appended time vector, solution matrix and state vector ''' if tnew.size == 0: return t, y, stim if use_adaptive_dt: ynew = solve_ivp(dfunc, [tnew[0], tnew[-1]], y[-1], t_eval=tnew, method='LSODA').y.T else: ynew = odeint(dfunc, y[-1], tnew, tfirst=True) return cls.appendSolution(t, y, stim, tnew, ynew, is_on) @staticmethod def resample(t, y, stim, target_dt): ''' Resample a solution to a new target time step. :param t: time vector :param y: solution matrix :param stim: stimulation state vector :target_dt: target time step after resampling :return: 3-tuple with the resampled time vector, solution matrix and state vector ''' dt = t[1] - t[0] rf = int(np.round(target_dt / dt)) assert rf >= 1, 'Hyper-sampling not supported' logger.debug( 'Downsampling output arrays by factor %u (Fs = %sHz)', rf, si_format(1 / (dt * rf), 2)) return t[::rf], y[::rf, :], stim[::rf] @property @abc.abstractmethod def compute(self): ''' Abstract compute method. ''' return 'Should never reach here' def __call__(self, *args, **kwargs): - ''' Call and return compute method, with conditional time monitoring. ''' - monitor_time = kwargs.pop('monitor_time') - if monitor_time: - start_time = time.perf_counter() - output = self.compute(*args, **kwargs) - if monitor_time: - end_time = time.perf_counter() - run_time = end_time - start_time - output = output, run_time - return output + ''' Call and return compute method ''' + return self.compute(*args, **kwargs) class PeriodicSimulator(Simulator): def __init__(self, dfunc, stopfunc=None, ivars_to_check=None): ''' Initialize simulator with specific derivative and stop functions :param dfunc: derivative function :param stopfunc: function estimating stopping criterion :param ivars_to_check: solution indexes of variables to check for stability ''' self.dfunc = dfunc self.ivars_to_check = ivars_to_check if stopfunc is not None: self.stopfunc = stopfunc else: self.stopfunc = self.isPeriodicallyStable @staticmethod def getNPerCycle(dt, T): ''' Compute number of samples per cycle given a time step and a specific periodicity. :param dt: integration time step (s) :param T: periodicity (s) :return: number of samples per cycle ''' return int(np.round(T / dt)) + 1 @classmethod def getTimeReference(cls, dt, T): ''' Compute reference integration time vector for a specific periodicity. :param dt: integration time step (s) :param T: periodicity (s) :return: time vector for 1 periodic cycle ''' return np.linspace(0, T, cls.getNPerCycle(dt, T)) def isPeriodicallyStable(self, t, y, T): ''' Assess the periodic stabilization of a solution, by evaluating the deviation of system variables between the last two periods. :param t: time vector :param y: solution matrix :param T: periodicity (s) :return: boolean stating whether the solution is periodically stable or not ''' # Extract the 2 cycles of interest from the solution n = self.getNPerCycle(t[1] - t[0], T) - 1 y_last = y[-n:, :] y_prec = y[-2 * n:-n, :] # For each variable of interest, evaluate the RMSE between the two cycles, the # variation range over the last cycle, and the ratio of these 2 quantities ratios = np.array([rmse(y_last[:, ivar], y_prec[:, ivar]) / np.ptp(y_last[:, ivar]) for ivar in self.ivars_to_check]) # Classify the solution as periodically stable only if all RMSE/PTP ratios # are below critical threshold is_periodically_stable = np.all(ratios < MAX_RMSE_PTP_RATIO) # logger.debug( # 'step %u: ratios = [%s]', icycle, # ', '.join(['{:.2e}'.format(r) for r in ratios])) return is_periodically_stable def isAsymptoticallyStable(self, t, y, T): ''' Assess the asymptotically stabilization of a solution, by evaluating the deviation of system variables from their initial values. :param t: time vector :param y: solution matrix :param T: periodicity (s) :return: boolean stating whether the solution is asymptotically stable or not ''' n_history = int(self.t_history // T) # size of history n = self.getNPerCycle(t[1] - t[0], T) - 1 # For each variable to be checked convs = [None] * len(self.ivars_to_check) for i, ivar in enumerate(self.ivars_to_check): xdev = y[::n, ivar] - self.refs[i] # print(np.array(xdev) * 1e5) # If last deviation is too large -> divergence if np.abs(xdev[-1]) > self.div_thr[i]: return -1 # If last deviation or average deviation in recent history # is small enough -> convergence for x in [xdev[-1], np.mean(xdev[-n_history:])]: if np.abs(x) < self.conv_thr[i]: convs[i] = 1 # print(convs) return np.all(convs == 1) def stopFuncTmp(self, t, y, T): return self.isAsymptoticallyStable(t, y, T) != 0 def compute(self, y0, dt, T, t0=0., nmax=NCYCLES_MAX): ''' Simulate system with a specific periodicity until stopping criterion is met. :param y0: 1D vector of initial conditions :param dt: integration time step (s) :param T: periodicity (s) :param t0: starting time :return: 3-tuple with the time profile, the effective solution matrix and a state vector ''' # If none specified, set all variables to be checked for stability if self.ivars_to_check is None: self.ivars_to_check = range(y0.size) # Get reference time vector tref = self.getTimeReference(dt, T) # Initialize global arrays t, y, stim = self.initialize(y0) # Integrate system for a few cycles until stopping criterion is met icycle = 0 stop = False while not stop and icycle < nmax: t, y, stim = self.integrate(t, y, stim, tref + icycle * T, self.dfunc, True) if icycle > 0: stop = self.stopfunc(t, y, T) icycle += 1 t += t0 # Log stopping criterion t_str = 't = {:.5f} ms'.format(t[-1] * 1e3) if icycle == nmax: logger.warning('%s: criterion not met -> stopping after %u cycles', t_str, icycle) else: logger.debug('%s: stopping criterion met after %u cycles', t_str, icycle) # Return output variables return t, y, stim class PWSimulator(Simulator): def __init__(self, dfunc_on, dfunc_off): ''' Initialize simulator with specific derivative functions :param dfunc_on: derivative function for ON periods :param dfunc_off: derivative function for OFF periods ''' self.dfunc_on = dfunc_on self.dfunc_off = dfunc_off @staticmethod def getTimeReference(dt, tstim, toffset, PRF, DC): ''' Compute reference integration time vectors for a specific stimulus application pattern. :param dt: integration time step (s) :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :return: 3-tuple with time vectors for stimulus ON and OFF periods and stimulus offset ''' # Compute vector sizes T_ON = DC / PRF T_OFF = (1 - DC) / PRF # For high-PRF pulsed protocols: adapt time step to ensure minimal # number of samples during TON or TOFF dt_warning_msg = 'high-PRF protocol: lowering time step to %.2e s to properly integrate %s' for key, T in {'TON': T_ON, 'TOFF': T_OFF}.items(): if T > 0 and T / dt < MIN_SAMPLES_PER_PULSE_INTERVAL: dt = T / MIN_SAMPLES_PER_PULSE_INTERVAL logger.warning(dt_warning_msg, dt, key) # Initializing accurate time vectors pulse ON and OFF periods, as well as offset t_on = np.linspace(0, T_ON, int(np.round(T_ON / dt)) + 1) t_off = np.linspace(T_ON, 1 / PRF, int(np.round(T_OFF / dt))) t_offset = np.linspace(tstim, tstim + toffset, int(np.round(toffset / dt))) return t_on, t_off, t_offset @staticmethod def adjustPRF(tstim, PRF, DC, print_progress): ''' Adjust the PRF in case of continuous wave stimulus, in order to obtain the desired number of integration interval(s) during stimulus. :param tstim: duration of US stimulation (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :param print_progress: boolean specifying whether to show a progress bar :return: adjusted PRF value (Hz) ''' if DC < 1.0: # if PW stimuli, then no change return PRF else: # if CW stimuli, then divide integration according to presence of progress bar return {True: 100., False: 1.}[print_progress] / tstim @staticmethod def getNPulses(tstim, PRF): ''' Calculate number of pulses from stimulus temporal pattern. :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :return: number of pulses during the stimulus ''' return int(np.round(tstim * PRF)) def compute(self, y0, dt, tstim, toffset, PRF, DC, target_dt=None, print_progress=False): ''' Simulate system for a specific stimulus application pattern. :param y0: 1D vector of initial conditions :param dt: integration time step (s) :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :param target_dt: target time step after resampling :param print_progress: boolean specifying whether to show a progress bar :return: 3-tuple with the time profile, the effective solution matrix and a state vector ''' # Adjust PRF and get number of pulses PRF = self.adjustPRF(tstim, PRF, DC, print_progress) npulses = self.getNPulses(tstim, PRF) # Get reference time vectors t_on, t_off, t_offset = self.getTimeReference(dt, tstim, toffset, PRF, DC) # Initialize global arrays t, y, stim = self.initialize(y0) # Initialize progress bar if print_progress: setHandler(logger, TqdmHandler(my_log_formatter)) ntot = int(npulses * (tstim + toffset) / tstim) pbar = tqdm(total=ntot) # Integrate ON and OFF intervals of each pulse for i in range(npulses): for j, (tref, func) in enumerate(zip([t_on, t_off], [self.dfunc_on, self.dfunc_off])): t, y, stim = self.integrate(t, y, stim, tref + i / PRF, func, j == 0) # Update progress bar if print_progress: pbar.update(i) # Integrate offset interval t, y, stim = self.integrate(t, y, stim, t_offset, self.dfunc_off, False) # Terminate progress bar if print_progress: pbar.update(npulses) pbar.close() # Resample solution if specified if target_dt is not None: t, y, stim = self.resample(t, y, stim, target_dt) # Return output variables return t, y, stim class HybridSimulator(PWSimulator): def __init__(self, dfunc_on, dfunc_off, dfunc_sparse, predfunc, is_dense_var, stopfunc=None, ivars_to_check=None): ''' Initialize simulator with specific derivative functions :param dfunc_on: derivative function for ON periods :param dfunc_off: derivative function for OFF periods :param dfunc_sparse: derivative function for sparse integration :param predfunc: function computing the extra arguments necessary for sparse integration :param is_dense_var: boolean array stating for each variable if it evolves fast or not :param stopfunc: function estimating stopping criterion :param ivars_to_check: solution indexes of variables to check for stability ''' PWSimulator.__init__(self, dfunc_on, dfunc_off) self.sparse_solver = ode(dfunc_sparse) self.sparse_solver.set_integrator('dop853', nsteps=SOLVER_NSTEPS, atol=1e-12) self.predfunc = predfunc self.is_dense_var = is_dense_var self.is_sparse_var = np.invert(is_dense_var) self.stopfunc = stopfunc self.ivars_to_check = ivars_to_check def integrate(self, t, y, stim, tnew, dfunc, is_on): ''' Integrate system for a time interval and append to preceding solution arrays, using a hybrid scheme: - First, the full ODE system is integrated for a few cycles with a dense time granularity until a stopping criterion is met - Second, the profiles of all variables over the last cycle are resampled to a far lower (i.e. sparse) sampling rate - Third, a subset of the ODE system is integrated with a sparse time granularity, for the remaining of the time interval, while the remaining variables are periodically expanded from their last cycle profile. :param t: preceding time vector :param y: preceding solution matrix :param stim: preceding stimulation state vector :param tnew: integration time vector for current interval :param dfunc: derivative function for current interval :param is_on: stimulation state for current interval :return: 3-tuple with the appended time vector, solution matrix and state vector ''' if tnew.size == 0: return t, y, stim # Initialize periodic solver dense_solver = PeriodicSimulator( dfunc, stopfunc=self.stopfunc, ivars_to_check=self.ivars_to_check) dt_dense = tnew[1] - tnew[0] npc_dense = dense_solver.getNPerCycle(dt_dense, self.T) # Until final integration time is reached while t[-1] < tnew[-1]: logger.debug('t = {:.5f} ms: starting new hybrid integration'.format(t[-1] * 1e3)) # Integrate dense system until stopping criterion is met tdense, ydense, stimdense = dense_solver.compute(y[-1], dt_dense, self.T, t0=t[-1]) t, y, stim = self.appendSolution(t, y, stim, tdense, ydense, is_on) # Resample signals over last cycle to match sparse time step tlast, ylast, stimlast = self.resample( tdense[-npc_dense:], ydense[-npc_dense:], stimdense[-npc_dense:], self.dt_sparse) npc_sparse = tlast.size # Integrate until either the rest of the interval or max update interval is reached t0 = tdense[-1] tf = min(tnew[-1], tdense[0] + HYBRID_UPDATE_INTERVAL) nsparse = int(np.round((tf - t0) / self.dt_sparse)) tsparse = np.linspace(t0, tf, nsparse) ysparse = np.empty((nsparse, y.shape[1])) ysparse[0] = y[-1] self.sparse_solver.set_initial_value(y[-1, self.is_sparse_var], t[-1]) for j in range(1, tsparse.size): self.sparse_solver.set_f_params(self.predfunc(ylast[j % npc_sparse])) self.sparse_solver.integrate(tsparse[j]) if not self.sparse_solver.successful(): raise ValueError( 'integration error at t = {:.5f} ms'.format(tsparse[j] * 1e3)) ysparse[j, self.is_dense_var] = ylast[j % npc_sparse, self.is_dense_var] ysparse[j, self.is_sparse_var] = self.sparse_solver.y t, y, stim = self.appendSolution(t, y, stim, tsparse, ysparse, is_on) return t, y, stim def compute(self, y0, dt_dense, dt_sparse, T, tstim, toffset, PRF, DC, print_progress=False): ''' Simulate system for a specific stimulus application pattern. :param y0: 1D vector of initial conditions :param dt_dense: dense integration time step (s) :param dt_sparse: sparse integration time step (s) :param T: periodicity (s) :param tstim: duration of US stimulation (s) :param toffset: duration of the offset (s) :param PRF: pulse repetition frequency (Hz) :param DC: pulse duty cycle (-) :param print_progress: boolean specifying whether to show a progress bar :return: 3-tuple with the time profile, the effective solution matrix and a state vector ''' # Set periodicity and sparse time step self.T = T self.dt_sparse = dt_sparse # Call and return parent compute method return PWSimulator.compute( self, y0, dt_dense, tstim, toffset, PRF, DC, target_dt=None, print_progress=print_progress) diff --git a/PySONIC/core/translators.py b/PySONIC/core/translators.py index 4ba2dfc..da31a5b 100644 --- a/PySONIC/core/translators.py +++ b/PySONIC/core/translators.py @@ -1,192 +1,193 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2019-06-29 11:26:27 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-30 13:01:32 +# @Last Modified time: 2019-06-30 13:14:28 from time import gmtime, strftime import re import inspect import pprint from ..constants import * class Translator: ''' Translate PointNeuron standard methods into methods adapted for SONIC simulations''' lambda_pattern = 'lambda ([a-z_A-Z,0-9\s]*): (.+)' func_pattern = '([a-z_A-Z]*).([a-z_A-Z][a-z_A-Z0-9]*)\(([^\)]*)\)' class_attribute_pattern = '(cls).([a-z_A-Z_][a-z_A-Z0-9_]*)' def __init__(self, verbose=False): self.verbose = verbose @classmethod def getLambdaSource(cls, dict_lambda): ''' Get the source code of a lambda function. ''' # Get lambda source code lambda_source = inspect.getsource(dict_lambda) if lambda_source.count(':') == 2: sep_character = ':' else: sep_character = '=' lambda_source = lambda_source.split(sep_character, 1)[-1] # Clean up source from line break, extra spaces and final comma lambda_source = re.sub(' +', ' ', lambda_source.replace('\n', ' ')).strip() if lambda_source[-1] == ',': lambda_source = lambda_source[:-1] # Match lambda pattern in cleaned-up source, and return match groups m = re.match(cls.lambda_pattern, lambda_source) if m is None: raise ValueError('source does not match lambda pattern: \n {}'.format( lambda_source)) return m.groups() @classmethod def getClassAttributeCalls(cls, s): ''' Find attribute calls in expression. ''' return re.finditer(cls.class_attribute_pattern, s) @classmethod def getFuncCalls(cls, s): ''' Find function calls in expression. ''' return re.finditer(cls.func_pattern, s) @staticmethod def defineConstLambda(const): ''' Define a lambda function that returns a constant. ''' return lambda _: const @staticmethod def getFuncArgs(m): ''' Determine function arguments. ''' fprefix, fname, fargs = m.groups() fcall = '{}({})'.format(fname, fargs) if fprefix: fcall = '{}.{}'.format(fprefix, fcall) fargs = fargs.split(',') + fargs = [x.strip() for x in fargs] return fcall, fname, fargs def parseLambdaDict(self, lambda_dict, translate_func): ''' Parse pclassect function. ''' translated_lambda_str_dict = {} # For each key and lambda function for k, dfunc in lambda_dict.items(): # Get lambda function source code dfunc_args, dfunc_exp = self.getLambdaSource(dfunc) # Assign translated expression translated_lambda_str_dict[k] = translate_func(dfunc_exp) return translated_lambda_str_dict class PointNeuronTranslator(Translator): suffix_pattern = '[A-Za-z0-9_]+' # Define patterns xinf_pattern = re.compile('^({})inf$'.format(suffix_pattern)) taux_pattern = re.compile('^tau({})$'.format(suffix_pattern)) alphax_pattern = re.compile('^alpha({})$'.format(suffix_pattern)) betax_pattern = re.compile('^beta({})$'.format(suffix_pattern)) def __init__(self, pclass, verbose=False): super().__init__(verbose=verbose) self.pclass = pclass class SonicTranslator(PointNeuronTranslator): def __init__(self, pclass, verbose=False): super().__init__(pclass, verbose=verbose) self.eff_rates, self.eff_rates_str = {}, {} def addToEffRates(self, expr): ''' add effective rate(s) corresponding to function expression ''' err_str = 'gating states must be defined via the alphaX-betaX or Xinf-tauX paradigm' # If expression matches alpha or beta rate -> return corresponding # effective rate function for pattern in [self.alphax_pattern, self.betax_pattern]: if pattern.match(expr): try: self.eff_rates[expr] = getattr(self.pclass, expr) self.eff_rates_str[expr] = 'self.{}'.format(expr) except AttributeError: raise ValueError(err_str) # If expression matches xinf or taux -> add corresponding alpha and beta # effective rates functions else: for pattern in [self.taux_pattern, self.xinf_pattern]: m = pattern.match(expr) if m: k = m.group(1) alphax_str, betax_str = ['{}{}'.format(p, k) for p in ['alpha', 'beta']] xinf_str, taux_str = ['{}inf'.format(k), 'tau{}'.format(k)] try: xinf, taux = [getattr(self.pclass, s) for s in [xinf_str, taux_str]] # If taux is a constant, define a lambda function that returns it if not callable(taux): taux = self.defineConstLambda(taux) self.eff_rates.update({ alphax_str: lambda Vm: xinf(Vm) / taux(Vm), betax_str: lambda Vm: (1 - xinf(Vm)) / taux(Vm) }) self.eff_rates_str.update({ alphax_str: 'lambda Vm: cls.{}(Vm) / cls.{}(Vm)'.format( xinf_str, taux_str), betax_str: 'lambda Vm: (1 - cls.{}(Vm)) / cls.{}(Vm)'.format( xinf_str, taux_str) }) except AttributeError: raise ValueError(err_str) def createDerEffStateLambda(self, expr): ''' Create a lambda function that computes an effective state derivative ''' f = eval('lambda cls, lkp, x: {}'.format(expr)) return lambda *args: f(self.pclass, *args) def translateExpr(self, expr): # For each internal function call in the expression matches = self.getFuncCalls(expr) for m in matches: # Determine function arguments fcall, fname, fargs = self.getFuncArgs(m) # Translate function call and replace it in the expression if len(fargs) == 1 and fargs[0] == 'Vm': # If sole argument is Vm replace function call by lookup retrieval self.addToEffRates(fname) new_fcall = "lkp['{}']".format(fname) else: # Otherwise, do not replace anything new_fcall = fcall expr = expr.replace(fcall, new_fcall) return expr def parseDerStates(self): ''' Parse neuron's derStates method to construct adapted derEffStates and effRates methods used for SONIC simulations. ''' # Get dictionary of translated lambda functions expressions for derivative states eff_dstates_str = self.parseLambdaDict(self.pclass.derStates(), self.translateExpr) eff_dstates_str = {k: v.replace('Vm', "lkp['V']") for k, v in eff_dstates_str.items()} if self.verbose: print('---------- derEffStates ----------') pprint.PrettyPrinter(indent=4).pprint({ k: 'lambda lkp, x: {}'.format(v) for k, v in eff_dstates_str.items()}) print('---------- effRates ----------') pprint.PrettyPrinter(indent=4).pprint(self.eff_rates_str) # Return dictionary of evaluated functions return {k: self.createDerEffStateLambda(v) for k, v in eff_dstates_str.items()} diff --git a/PySONIC/neurons/RS_lookups_test.pkl b/PySONIC/neurons/RS_lookups_test.pkl index 1d6e425..a259b4c 100644 Binary files a/PySONIC/neurons/RS_lookups_test.pkl and b/PySONIC/neurons/RS_lookups_test.pkl differ diff --git a/PySONIC/neurons/__init__.py b/PySONIC/neurons/__init__.py index 3088c0a..c800a1d 100644 --- a/PySONIC/neurons/__init__.py +++ b/PySONIC/neurons/__init__.py @@ -1,54 +1,54 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-06-06 13:36:00 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 21:15:23 +# @Last Modified time: 2019-07-01 11:06:23 from types import MethodType import inspect import sys from ..core.translators import SonicTranslator from .template import * from .cortical import * from .thalamic import * from .leech import * from .stn import * from .fh import * def getNeuronsDict(): ''' Construct a dictionary of all the implemented point neuron classes, indexed by name. ''' current_module = sys.modules[__name__] neurons_dict = {} for _, obj in inspect.getmembers(current_module): if inspect.isclass(obj) and hasattr(obj, 'name') and isinstance(obj.name, str): neurons_dict[obj.name] = obj return neurons_dict def getPointNeuron(name): ''' Return a point-neuron instance corresponding to a given name. ''' neuron_classes = getNeuronsDict() try: return neuron_classes[name]() except KeyError: raise ValueError('"{}" neuron not found. Implemented neurons are: {}'.format( name, ', '.join(list(neuron_classes.keys())))) def create_derEffStates(eff_dstates): return lambda self: eff_dstates def create_effRates(eff_rates): - return lambda: eff_rates + return lambda self: eff_rates for pname, pclass in getNeuronsDict().items(): translator = SonicTranslator(pclass) eff_dstates = translator.parseDerStates() pclass.derEffStates = MethodType(create_derEffStates(eff_dstates), pclass) pclass.effRates = MethodType(create_effRates(translator.eff_rates), pclass) pclass.rates = list(translator.eff_rates.keys()) diff --git a/PySONIC/neurons/stn.py b/PySONIC/neurons/stn.py index b2755ea..707321b 100644 --- a/PySONIC/neurons/stn.py +++ b/PySONIC/neurons/stn.py @@ -1,460 +1,465 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2018-11-29 16:56:45 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-30 01:21:01 +# @Last Modified time: 2019-07-01 12:11:05 import numpy as np from scipy.optimize import brentq from ..core import PointNeuron from ..constants import FARADAY, Z_Ca class OtsukaSTN(PointNeuron): ''' Sub-thalamic nucleus neuron References: *Otsuka, T., Abe, T., Tsukagawa, T., and Song, W.-J. (2004). Conductance-Based Model of the Voltage-Dependent Generation of a Plateau Potential in Subthalamic Neurons. Journal of Neurophysiology 92, 255–264.* *Tarnaud, T., Joseph, W., Martens, L., and Tanghe, E. (2018). Computational Modeling of Ultrasonic Subthalamic Nucleus Stimulation. IEEE Trans Biomed Eng.* ''' # Neuron name name = 'STN' # ------------------------------ Biophysical parameters ------------------------------ # Resting parameters Cm0 = 1e-2 # Membrane capacitance (F/m2) Vm0 = -58.0 # Membrane potential (mV) Cai0 = 5e-9 # Intracellular Calcium concentration (M) # Reversal potentials (mV) ENa = 60.0 # Sodium EK = -90.0 # Potassium ELeak = -60.0 # Non-specific leakage # Maximal channel conductances (S/m2) gNabar = 490.0 # Sodium gLeak = 3.5 # Non-specific leakage gKdbar = 570.0 # Delayed-rectifier Potassium gCaTbar = 50.0 # Low-threshold Calcium gCaLbar = 150.0 # High-threshold Calcium gAbar = 50.0 # A-type Potassium gKCabar = 10.0 # Calcium-dependent Potassium # Physical constants T = 306.15 # K (33°C) # Calcium dynamics Cao = 2e-3 # extracellular Calcium concentration (M) taur_Cai = 0.5e-3 # decay time constant for intracellular Ca2+ dissolution (s) # Fast Na current m-gate thetax_m = -40 # mV kx_m = -8 # mV tau0_m = 0.2e-3 # s tau1_m = 3e-3 # s thetaT_m = -53 # mV sigmaT_m = -0.7 # mV # Fast Na current h-gate thetax_h = -45.5 # mV kx_h = 6.4 # mV tau0_h = 0e-3 # s tau1_h = 24.5e-3 # s thetaT1_h = -50 # mV thetaT2_h = -50 # mV sigmaT1_h = -15 # mV sigmaT2_h = 16 # mV # Delayed rectifier K+ current n-gate thetax_n = -41 # mV kx_n = -14 # mV tau0_n = 0e-3 # s tau1_n = 11e-3 # s thetaT1_n = -40 # mV thetaT2_n = -40 # mV sigmaT1_n = -40 # mV sigmaT2_n = 50 # mV # T-type Ca2+ current p-gate thetax_p = -56 # mV kx_p = -6.7 # mV tau0_p = 5e-3 # s tau1_p = 0.33e-3 # s thetaT1_p = -27 # mV thetaT2_p = -102 # mV sigmaT1_p = -10 # mV sigmaT2_p = 15 # mV # T-type Ca2+ current q-gate thetax_q = -85 # mV kx_q = 5.8 # mV tau0_q = 0e-3 # s tau1_q = 400e-3 # s thetaT1_q = -50 # mV thetaT2_q = -50 # mV sigmaT1_q = -15 # mV sigmaT2_q = 16 # mV # L-type Ca2+ current c-gate thetax_c = -30.6 # mV kx_c = -5 # mV tau0_c = 45e-3 # s tau1_c = 10e-3 # s thetaT1_c = -27 # mV thetaT2_c = -50 # mV sigmaT1_c = -20 # mV sigmaT2_c = 15 # mV # L-type Ca2+ current d1-gate thetax_d1 = -60 # mV kx_d1 = 7.5 # mV tau0_d1 = 400e-3 # s tau1_d1 = 500e-3 # s thetaT1_d1 = -40 # mV thetaT2_d1 = -20 # mV sigmaT1_d1 = -15 # mV sigmaT2_d1 = 20 # mV # L-type Ca2+ current d2-gate thetax_d2 = 0.1e-6 # M kx_d2 = 0.02e-6 # M tau_d2 = 130e-3 # s # A-type K+ current a-gate thetax_a = -45 # mV kx_a = -14.7 # mV tau0_a = 1e-3 # s tau1_a = 1e-3 # s thetaT_a = -40 # mV sigmaT_a = -0.5 # mV # A-type K+ current b-gate thetax_b = -90 # mV kx_b = 7.5 # mV tau0_b = 0e-3 # s tau1_b = 200e-3 # s thetaT1_b = -60 # mV thetaT2_b = -40 # mV sigmaT1_b = -30 # mV sigmaT2_b = 10 # mV # Ca2+-activated K+ current r-gate thetax_r = 0.17e-6 # M kx_r = -0.08e-6 # M tau_r = 2e-3 # s # ------------------------------ States names & descriptions ------------------------------ states = { 'm': 'iNa activation gate', 'h': 'iNa inactivation gate', 'n': 'iKd gate', 'a': 'iA activation gate', 'b': 'iA inactivation gate', 'p': 'iCaT activation gate', 'q': 'iCaT inactivation gate', 'c': 'iCaL activation gate', 'd1': 'iCaL inactivation gate 1', 'd2': 'iCaL inactivation gate 2', 'r': 'iCaK gate', 'Cai': 'submembrane Calcium concentration (M)' } def __new__(cls): cls.deff = cls.getEffectiveDepth(cls.Cai0, cls.Vm0) # m cls.iCa_to_Cai_rate = cls.currentToConcentrationRate(Z_Ca, cls.deff) return super(OtsukaSTN, cls).__new__(cls) + def reinit(self, Vm0): + self.Vm0 = Vm0 + self.deff = self.getEffectiveDepth(self.Cai0, self.Vm0) # m + self.iCa_to_Cai_rate = self.currentToConcentrationRate(Z_Ca, self.deff) + @classmethod def getPltScheme(cls): pltscheme = super().getPltScheme() pltscheme['[Ca^{2+}]_i'] = ['Cai'] return pltscheme @classmethod def getPltVars(cls, wrapleft='df["', wrapright='"]'): pltvars = super().getPltVars(wrapleft, wrapright) pltvars['Cai'] = { 'desc': 'submembrane Ca2+ concentration', 'label': '[Ca^{2+}]_i', 'unit': 'uM', 'factor': 1e6 } return pltvars @classmethod def titrationFunc(cls, *args, **kwargs): return cls.isSilenced(*args, **kwargs) @classmethod def getEffectiveDepth(cls, Cai, Vm): ''' Compute effective depth that matches a given membrane potential and intracellular Calcium concentration. :return: effective depth (m) ''' iCaT = cls.iCaT(cls.pinf(Vm), cls.qinf(Vm), Vm, Cai) # mA/m2 iCaL = cls.iCaL(cls.cinf(Vm), cls.d1inf(Vm), cls.d2inf(Cai), Vm, Cai) # mA/m2 return -(iCaT + iCaL) / (Z_Ca * FARADAY * Cai / cls.taur_Cai) * 1e-6 # m # ------------------------------ Gating states kinetics ------------------------------ @staticmethod def _xinf(var, theta, k): ''' Generic function computing the steady-state opening of a particular channel gate at a given voltage or ion concentration. :param var: membrane potential (mV) or ion concentration (mM) :param theta: half-(in)activation voltage or concentration (mV or mM) :param k: slope parameter of (in)activation function (mV or mM) :return: steady-state opening (-) ''' return 1 / (1 + np.exp((var - theta) / k)) @classmethod def ainf(cls, Vm): return cls._xinf(Vm, cls.thetax_a, cls.kx_a) @classmethod def binf(cls, Vm): return cls._xinf(Vm, cls.thetax_b, cls.kx_b) @classmethod def cinf(cls, Vm): return cls._xinf(Vm, cls.thetax_c, cls.kx_c) @classmethod def d1inf(cls, Vm): return cls._xinf(Vm, cls.thetax_d1, cls.kx_d1) @classmethod def d2inf(cls, Cai): return cls._xinf(Cai, cls.thetax_d2, cls.kx_d2) @classmethod def minf(cls, Vm): return cls._xinf(Vm, cls.thetax_m, cls.kx_m) @classmethod def hinf(cls, Vm): return cls._xinf(Vm, cls.thetax_h, cls.kx_h) @classmethod def ninf(cls, Vm): return cls._xinf(Vm, cls.thetax_n, cls.kx_n) @classmethod def pinf(cls, Vm): return cls._xinf(Vm, cls.thetax_p, cls.kx_p) @classmethod def qinf(cls, Vm): return cls._xinf(Vm, cls.thetax_q, cls.kx_q) @classmethod def rinf(cls, Cai): return cls._xinf(Cai, cls.thetax_r, cls.kx_r) @staticmethod def _taux1(Vm, theta, sigma, tau0, tau1): ''' Generic function computing the voltage-dependent, activation/inactivation time constant of a particular ion channel at a given voltage (first variant). :param Vm: membrane potential (mV) :param theta: voltage at which (in)activation time constant is half-maximal (mV) :param sigma: slope parameter of (in)activation time constant function (mV) :param tau0: minimal time constant (s) :param tau1: modulated time constant (s) :return: (in)activation time constant (s) ''' return tau0 + tau1 / (1 + np.exp(-(Vm - theta) / sigma)) @classmethod def taua(cls, Vm): return cls._taux1(Vm, cls.thetaT_a, cls.sigmaT_a, cls.tau0_a, cls.tau1_a) @classmethod def taum(cls, Vm): return cls._taux1(Vm, cls.thetaT_m, cls.sigmaT_m, cls.tau0_m, cls.tau1_m) @staticmethod def _taux2(Vm, theta1, theta2, sigma1, sigma2, tau0, tau1): ''' Generic function computing the voltage-dependent, activation/inactivation time constant of a particular ion channel at a given voltage (second variant). :param Vm: membrane potential (mV) :param theta: voltage at which (in)activation time constant is half-maximal (mV) :param sigma: slope parameter of (in)activation time constant function (mV) :param tau0: minimal time constant (s) :param tau1: modulated time constant (s) :return: (in)activation time constant (s) ''' return tau0 + tau1 / (np.exp(-(Vm - theta1) / sigma1) + np.exp(-(Vm - theta2) / sigma2)) @classmethod def taub(cls, Vm): return cls._taux2(Vm, cls.thetaT1_b, cls.thetaT2_b, cls.sigmaT1_b, cls.sigmaT2_b, cls.tau0_b, cls.tau1_b) @classmethod def tauc(cls, Vm): return cls._taux2(Vm, cls.thetaT1_c, cls.thetaT2_c, cls.sigmaT1_c, cls.sigmaT2_c, cls.tau0_c, cls.tau1_c) @classmethod def taud1(cls, Vm): return cls._taux2(Vm, cls.thetaT1_d1, cls.thetaT2_d1, cls.sigmaT1_d1, cls.sigmaT2_d1, cls.tau0_d1, cls.tau1_d1) @classmethod def tauh(cls, Vm): return cls._taux2(Vm, cls.thetaT1_h, cls.thetaT2_h, cls.sigmaT1_h, cls.sigmaT2_h, cls.tau0_h, cls.tau1_h) @classmethod def taun(cls, Vm): return cls._taux2(Vm, cls.thetaT1_n, cls.thetaT2_n, cls.sigmaT1_n, cls.sigmaT2_n, cls.tau0_n, cls.tau1_n) @classmethod def taup(cls, Vm): return cls._taux2(Vm, cls.thetaT1_p, cls.thetaT2_p, cls.sigmaT1_p, cls.sigmaT2_p, cls.tau0_p, cls.tau1_p) @classmethod def tauq(cls, Vm): return cls._taux2(Vm, cls.thetaT1_q, cls.thetaT2_q, cls.sigmaT1_q, cls.sigmaT2_q, cls.tau0_q, cls.tau1_q) # ------------------------------ States derivatives ------------------------------ @classmethod def derCai(cls, p, q, c, d1, d2, Cai, Vm): return - cls.iCa_to_Cai_rate * ( cls.iCaT(p, q, Vm, Cai) + cls.iCaL(c, d1, d2, Vm, Cai)) - Cai / cls.taur_Cai # M/s @classmethod def derStates(cls): return { 'a': lambda Vm, x: (cls.ainf(Vm) - x['a']) / cls.taua(Vm), 'b': lambda Vm, x: (cls.binf(Vm) - x['b']) / cls.taub(Vm), 'c': lambda Vm, x: (cls.cinf(Vm) - x['c']) / cls.tauc(Vm), 'd1': lambda Vm, x: (cls.d1inf(Vm) - x['d1']) / cls.taud1(Vm), 'd2': lambda Vm, x: (cls.d2inf(x['Cai']) - x['d2']) / cls.tau_d2, 'm': lambda Vm, x: (cls.minf(Vm) - x['m']) / cls.taum(Vm), 'h': lambda Vm, x: (cls.hinf(Vm) - x['h']) / cls.tauh(Vm), 'n': lambda Vm, x: (cls.ninf(Vm) - x['n']) / cls.taun(Vm), 'p': lambda Vm, x: (cls.pinf(Vm) - x['p']) / cls.taup(Vm), 'q': lambda Vm, x: (cls.qinf(Vm) - x['q']) / cls.tauq(Vm), 'r': lambda Vm, x: (cls.rinf(x['Cai']) - x['r']) / cls.tau_r, 'Cai': lambda Vm, x: cls.derCai(x['p'], x['q'], x['c'], x['d1'], x['d2'], x['Cai'], Vm) } # ------------------------------ Steady states ------------------------------ @classmethod def Caiinf(cls, p, q, c, d1, Vm): ''' Steady-state intracellular Calcium concentration ''' if isinstance(Vm, np.ndarray): return np.array([cls.Caiinf(v) for v in Vm]) else: return brentq( lambda x: cls.derCai(p, q, c, d1, cls.d2inf(x), x, Vm), cls.Cai0 * 1e-4, cls.Cai0 * 1e3, xtol=1e-16 ) @classmethod def steadyStates(cls): return { 'a': lambda Vm: cls.ainf(Vm), 'b': lambda Vm: cls.binf(Vm), 'c': lambda Vm: cls.cinf(Vm), 'd1': lambda Vm: cls.d1inf(Vm), 'm': lambda Vm: cls.minf(Vm), 'h': lambda Vm: cls.hinf(Vm), 'n': lambda Vm: cls.ninf(Vm), 'p': lambda Vm: cls.pinf(Vm), 'q': lambda Vm: cls.qinf(Vm), 'Cai': lambda Vm: cls.Cai0, 'd2': lambda Vm: cls.d2inf(cls.Cai0), 'r': lambda Vm: cls.rinf(cls.Cai0) } # def quasiSteadyStates(self, lkp): # qsstates = self.qsStates(lkp, ['a', 'b', 'c', 'd1', 'm', 'h', 'n', 'p', 'q']) # qsstates['Cai'] = self.Caiinf(lkp['V'], qsstates['p'], qsstates['q'], qsstates['c'], # qsstates['d1']) # qsstates['d2'] = self.d2inf(qsstates['Cai']) # qsstates['r'] = self.rinf(qsstates['Cai']) # return qsstates # ------------------------------ Membrane currents ------------------------------ @classmethod def iNa(cls, m, h, Vm): ''' Sodium current ''' return cls.gNabar * m**3 * h * (Vm - cls.ENa) # mA/m2 @classmethod def iKd(cls, n, Vm): ''' delayed-rectifier Potassium current ''' return cls.gKdbar * n**4 * (Vm - cls.EK) # mA/m2 @classmethod def iA(cls, a, b, Vm): ''' A-type Potassium current ''' return cls.gAbar * a**2 * b * (Vm - cls.EK) # mA/m2 @classmethod def iCaT(cls, p, q, Vm, Cai): ''' low-threshold (T-type) Calcium current ''' return cls.gCaTbar * p**2 * q * (Vm - cls.nernst(Z_Ca, Cai, cls.Cao, cls.T)) # mA/m2 @classmethod def iCaL(cls, c, d1, d2, Vm, Cai): ''' high-threshold (L-type) Calcium current ''' return cls.gCaLbar * c**2 * d1 * d2 * (Vm - cls.nernst(Z_Ca, Cai, cls.Cao, cls.T)) # mA/m2 @classmethod def iKCa(cls, r, Vm): ''' Calcium-activated Potassium current ''' return cls.gKCabar * r**2 * (Vm - cls.EK) # mA/m2 @classmethod def iLeak(cls, Vm): ''' non-specific leakage current ''' return cls.gLeak * (Vm - cls.ELeak) # mA/m2 @classmethod def currents(cls): return { 'iNa': lambda Vm, x: cls.iNa(x['m'], x['h'], Vm), 'iKd': lambda Vm, x: cls.iKd(x['n'], Vm), 'iA': lambda Vm, x: cls.iA(x['a'], x['b'], Vm), 'iCaT': lambda Vm, x: cls.iCaT(x['p'], x['q'], Vm, x['Cai']), 'iCaL': lambda Vm, x: cls.iCaL( x['c'], x['d1'], x['d2'], Vm, x['Cai']), 'iKCa': lambda Vm, x: cls.iKCa(x['r'], Vm), 'iLeak': lambda Vm, _: cls.iLeak(Vm) } # ------------------------------ Other methods ------------------------------ @staticmethod def getLowIntensities(): ''' Return an array of acoustic intensities (W/m2) used to study the STN neuron in Tarnaud, T., Joseph, W., Martens, L., and Tanghe, E. (2018). Computational Modeling of Ultrasonic Subthalamic Nucleus Stimulation. IEEE Trans Biomed Eng. ''' return np.hstack(( np.arange(10, 101, 10), np.arange(101, 131, 1), np.array([140]) )) # W/m2 diff --git a/PySONIC/parsers.py b/PySONIC/parsers.py index ab38f47..96af84f 100644 --- a/PySONIC/parsers.py +++ b/PySONIC/parsers.py @@ -1,583 +1,620 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2019-06-04 18:24:29 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 13:55:58 +# @Last Modified time: 2019-07-01 15:37:02 import os import logging import pprint import numpy as np +import matplotlib.pyplot as plt from argparse import ArgumentParser from .utils import Intensity2Pressure, selectDirDialog, OpenFilesDialog, isIterable from .neurons import getPointNeuron, CorticalRS +from .plt import GroupedTimeSeries, CompTimeSeries DEFAULT_OUTPUT_FOLDER = os.path.abspath(os.path.split(__file__)[0] + '../../../../dump') class Parser(ArgumentParser): ''' Generic parser interface. ''' dist_str = '[scale min max n]' def __init__(self): super().__init__() self.pp = pprint.PrettyPrinter(indent=4) self.defaults = {} self.allowed = {} self.factors = {} self.to_parse = {} self.addPlot() self.addVerbose() def pprint(self, args): self.pp.pprint(args) def getDistribution(self, xmin, xmax, nx, scale='lin'): if scale == 'log': xmin, xmax = np.log10(xmin), np.log10(xmax) return {'lin': np.linspace, 'log': np.logspace}[scale](xmin, xmax, nx) def getDistFromList(self, xlist): if not isinstance(xlist, list): raise TypeError('Input must be a list') if len(xlist) != 4: raise ValueError('List must contain exactly 4 arguments ([type, min, max, n])') scale = xlist[0] if scale not in ('log', 'lin'): raise ValueError('Unknown distribution type (must be "lin" or "log")') xmin, xmax = [float(x) for x in xlist[1:-1]] if xmin >= xmax: raise ValueError('Specified minimum higher or equal than specified maximum') nx = int(xlist[-1]) if nx < 2: raise ValueError('Specified number must be at least 2') return self.getDistribution(xmin, xmax, nx, scale=scale) def addVerbose(self): self.add_argument( '-v', '--verbose', default=False, action='store_true', help='Increase verbosity') self.to_parse['loglevel'] = self.parseLogLevel def addPlot(self): self.add_argument( '-p', '--plot', type=str, nargs='+', help='Variables to plot') self.to_parse['pltscheme'] = self.parsePltScheme def addMPI(self): self.add_argument( '--mpi', default=False, action='store_true', help='Use multiprocessing') def addTest(self): self.add_argument( '--test', default=False, action='store_true', help='Run test configuration') def addSave(self): self.add_argument( '-s', '--save', default=False, action='store_true', help='Save output figure(s)') def addFigureExtension(self): self.add_argument( '--figext', type=str, default='png', help='Figure type (extension)') def addCompare(self, desc='Comparative graph'): self.add_argument( '--compare', default=False, action='store_true', help=desc) def addSamplingRate(self): self.add_argument( '--sr', type=int, default=1, help='Sampling rate for plot') def addSpikes(self): self.add_argument( '--spikes', type=str, default='none', help='How to indicate spikes on charge profile ("none", "marks" or "details")') def addNColumns(self): self.add_argument( '--ncol', type=int, default=1, help='Number of columns in figure') def addNLevels(self): self.add_argument( '--nlevels', type=int, default=10, help='Number of levels') def addHideOutput(self): self.add_argument( '--hide', default=False, action='store_true', help='Hide output') def addTimeRange(self, default=None): self.add_argument( '--trange', type=float, nargs=2, default=default, help='Time lower and upper bounds (ms)') self.to_parse['trange'] = self.parseTimeRange def addPotentialBounds(self, default=None): self.add_argument( '--Vbounds', type=float, nargs=2, default=default, help='Membrane potential lower and upper bounds (mV)') def addFiringRateBounds(self, default): self.add_argument( '--FRbounds', type=float, nargs=2, default=default, help='Firing rate lower and upper bounds (Hz)') def addFiringRateScale(self, default='lin'): self.add_argument( '--FRscale', type=str, choices=('lin', 'log'), default=default, help='Firing rate scale for plot ("lin" or "log")') def addCmap(self, default=None): self.add_argument( '--cmap', type=str, default=default, help='Colormap name') def addCscale(self, default='lin'): self.add_argument( '--cscale', type=str, default=default, choices=('lin', 'log'), help='Color scale ("lin" or "log")') def addInputDir(self, dep_key=None): self.inputdir_dep_key = dep_key self.add_argument( '-i', '--inputdir', type=str, help='Input directory') self.to_parse['inputdir'] = self.parseInputDir def addOutputDir(self, dep_key=None): self.outputdir_dep_key = dep_key self.add_argument( '-o', '--outputdir', type=str, help='Output directory') self.to_parse['outputdir'] = self.parseOutputDir def addInputFiles(self, dep_key=None): self.inputfiles_dep_key = dep_key self.add_argument( '-i', '--inputfiles', type=str, help='Input files') self.to_parse['inputfiles'] = self.parseInputFiles def addPatches(self): self.add_argument( '--patches', type=str, default='one', help='Stimulus patching mode ("none", "one", all", or a boolean list)') self.to_parse['patches'] = self.parsePatches def addThresholdCurve(self): self.add_argument( '--threshold', default=False, action='store_true', help='Show threshold amplitudes') def addNeuron(self): self.add_argument( '-n', '--neuron', type=str, nargs='+', help='Neuron name (string)') self.to_parse['neuron'] = self.parseNeuron def parseNeuron(self, args): return [getPointNeuron(n) for n in args['neuron']] def addInteractive(self): self.add_argument( '--interactive', default=False, action='store_true', help='Make interactive') def addLabels(self): self.add_argument( '--labels', type=str, nargs='+', default=None, help='Labels') def addRelativeTimeBounds(self): self.add_argument( '--rel_tbounds', type=float, nargs='+', default=None, help='Relative time lower and upper bounds') def addPretty(self): self.add_argument( '--pretty', default=False, action='store_true', help='Make figure pretty') def addSubset(self, choices): self.add_argument( '--subset', type=str, nargs='+', default=['all'], choices=choices + ['all'], help='Run specific subset(s)') self.subset_choices = choices self.to_parse['subset'] = self.parseSubset def parseSubset(self, args): if args['subset'] == ['all']: return self.subset_choices else: return args['subset'] def parseTimeRange(self, args): if args['trange'] is None: return None return np.array(args['trange']) * 1e-3 def parsePatches(self, args): if args['patches'] not in ('none', 'one', 'all'): return eval(args['patches']) else: return args['patches'] def parseInputFiles(self, args): if self.inputfiles_dep_key is not None and not args[self.inputfiles_dep_key]: return None elif args['inputfiles'] is None: return OpenFilesDialog('pkl')[0] def parseDir(self, key, args, title, dep_key=None): if dep_key is not None and args[dep_key] is False: return None try: if args[key] is not None: return args[key] else: return selectDirDialog(title=title) except ValueError: raise ValueError('No {} selected'.format(key)) def parseInputDir(self, args): return self.parseDir( 'inputdir', args, 'Select input directory', self.inputdir_dep_key) def parseOutputDir(self, args): if hasattr(self, 'outputdir') and self.outputdir is not None: return self.outputdir else: if args['outputdir'] is not None and args['outputdir'] == 'dump': return DEFAULT_OUTPUT_FOLDER else: return self.parseDir( 'outputdir', args, 'Select output directory', self.outputdir_dep_key) def parseLogLevel(self, args): return logging.DEBUG if args.pop('verbose') else logging.INFO def parsePltScheme(self, args): if args['plot'] is None or args['plot'] == ['all']: return None else: return {x: [x] for x in args['plot']} def restrict(self, args, keys): if sum([args[x] is not None for x in keys]) > 1: raise ValueError( 'You must provide only one of the following arguments: {}'.format(', '.join(keys))) def parse2array(self, args, key, factor=1): return np.array(args[key]) * factor def parse(self): args = vars(super().parse_args()) for k, v in self.defaults.items(): if k in args and args[k] is None: args[k] = v if isIterable(v) else [v] for k, parse_method in self.to_parse.items(): args[k] = parse_method(args) return args + @staticmethod + def parsePlot(args, output): + render_args = {} + if 'spikes' in args: + render_args['spikes'] = args['spikes'] + if args['compare']: + if args['plot'] == ['all']: + logger.error('Specific variables must be specified for comparative plots') + return + for pltvar in args['plot']: + comp_plot = CompTimeSeries(output, pltvar) + comp_plot.render(**render_args) + else: + scheme_plot = GroupedTimeSeries(output, pltscheme=args['pltscheme']) + scheme_plot.render(**render_args) + plt.show() + class TestParser(Parser): def __init__(self, valid_subsets): super().__init__() self.addProfiling() self.addSubset(valid_subsets) def addProfiling(self): self.add_argument( '--profile', default=False, action='store_true', help='Run with profiling') class FigureParser(Parser): def __init__(self, valid_subsets): super().__init__() self.addSubset(valid_subsets) self.addSave() self.addOutputDir(dep_key='save') class PlotParser(Parser): def __init__(self): super().__init__() self.addHideOutput() self.addInputFiles() self.addOutputDir(dep_key='save') self.addSave() self.addFigureExtension() self.addCmap() self.addPretty() self.addTimeRange() self.addCscale() self.addLabels() class TimeSeriesParser(PlotParser): def __init__(self): super().__init__() self.addSpikes() self.addSamplingRate() self.addCompare() self.addPatches() class SimParser(Parser): ''' Generic simulation parser interface. ''' def __init__(self, outputdir=None): super().__init__() self.outputdir = outputdir self.addMPI() - self.addOutputDir() + self.addOutputDir(dep_key='save') + self.addSave() + self.addCompare() def parse(self): args = super().parse() return args class MechSimParser(SimParser): ''' Parser to run mechanical simulations from the command line. ''' def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.defaults.update({ 'radius': 32.0, # nm 'embedding': 0., # um 'Cm0': CorticalRS.Cm0 * 1e2, # uF/m2 'Qm0': CorticalRS.Qm0() * 1e5, # nC/m2 'freq': 500.0, # kHz 'amp': 100.0, # kPa 'charge': 0., # nC/cm2 'fs': 100. # % }) self.factors.update({ 'radius': 1e-9, 'embedding': 1e-6, 'Cm0': 1e-2, 'Qm0': 1e-5, 'freq': 1e3, 'amp': 1e3, 'charge': 1e-5, 'fs': 1e-2 }) self.addRadius() self.addEmbedding() self.addCm0() self.addQm0() self.addFdrive() self.addAdrive() self.addCharge() self.addFs() def addRadius(self): self.add_argument( '-a', '--radius', nargs='+', type=float, help='Sonophore radius (nm)') def addEmbedding(self): self.add_argument( '--embedding', nargs='+', type=float, help='Embedding depth (um)') def addCm0(self): self.add_argument( '--Cm0', type=float, help='Resting membrane capacitance (uF/cm2)') def addQm0(self): self.add_argument( '--Qm0', type=float, help='Resting membrane charge density (nC/cm2)') def addFdrive(self): self.add_argument( '-f', '--freq', nargs='+', type=float, help='US frequency (kHz)') def addAdrive(self): self.add_argument( '-A', '--amp', nargs='+', type=float, help='Acoustic pressure amplitude (kPa)') self.add_argument( '--Arange', type=str, nargs='+', help='Amplitude range {} (kPa)'.format(self.dist_str)) self.add_argument( '-I', '--intensity', nargs='+', type=float, help='Acoustic intensity (W/cm2)') self.add_argument( '--Irange', type=str, nargs='+', help='Intensity range {} (W/cm2)'.format(self.dist_str)) self.to_parse['amp'] = self.parseAmp def addAscale(self, default='lin'): self.add_argument( '--Ascale', type=str, choices=('lin', 'log'), default=default, help='Amplitude scale for plot ("lin" or "log")') def addCharge(self): self.add_argument( '-Q', '--charge', nargs='+', type=float, help='Membrane charge density (nC/cm2)') def addFs(self): self.add_argument( '--fs', nargs='+', type=float, help='Sonophore coverage fraction (%%)') self.add_argument( '--spanFs', default=False, action='store_true', help='Span Fs from 1 to 100%%') self.to_parse['fs'] = self.parseFs def parseAmp(self, args): params = ['Irange', 'Arange', 'intensity', 'amp'] self.restrict(args, params[:-1]) Irange, Arange, Int, Adrive = [args.pop(k) for k in params] if Irange is not None: amps = Intensity2Pressure(self.getDistFromList(Irange) * 1e4) # Pa elif Int is not None: amps = Intensity2Pressure(np.array(Int) * 1e4) # Pa elif Arange is not None: amps = self.getDistFromList(Arange) * self.factors['amp'] # Pa else: amps = np.array(Adrive) * self.factors['amp'] # Pa return amps def parseFs(self, args): if args.pop('spanFs', False): return np.arange(1, 101) * self.factors['fs'] # (-) else: return np.array(args['fs']) * self.factors['fs'] # (-) def parse(self): args = super().parse() for key in ['radius', 'embedding', 'Cm0', 'Qm0', 'freq', 'charge']: args[key] = self.parse2array(args, key, factor=self.factors[key]) return args + @staticmethod + def parseSimInputs(args): + return [args[k] for k in ['freq', 'amp', 'charge']] + class PWSimParser(SimParser): ''' Generic parser interface to run PW patterned simulations from the command line. ''' def __init__(self): super().__init__() self.defaults.update({ 'neuron': 'RS', 'tstim': 100.0, # ms 'toffset': 50., # ms 'PRF': 100.0, # Hz 'DC': 100.0 # % }) self.factors.update({ 'tstim': 1e-3, 'toffset': 1e-3, 'PRF': 1., 'DC': 1e-2 }) self.allowed.update({ 'DC': range(101) }) self.addNeuron() self.addTstim() self.addToffset() self.addPRF() self.addDC() self.addTitrate() self.addSpikes() def addTstim(self): self.add_argument( '-t', '--tstim', nargs='+', type=float, help='Stimulus duration (ms)') def addToffset(self): self.add_argument( '--toffset', nargs='+', type=float, help='Offset duration (ms)') def addPRF(self): self.add_argument( '--PRF', nargs='+', type=float, help='PRF (Hz)') def addDC(self): self.add_argument( '--DC', nargs='+', type=float, help='Duty cycle (%%)') self.add_argument( '--spanDC', default=False, action='store_true', help='Span DC from 1 to 100%%') self.to_parse['DC'] = self.parseDC def addTitrate(self): self.add_argument( '--titrate', default=False, action='store_true', help='Perform titration') def parseAmp(self, args): return NotImplementedError def parseDC(self, args): if args.pop('spanDC'): return np.arange(1, 101) * self.factors['DC'] # (-) else: return np.array(args['DC']) * self.factors['DC'] # (-) def parse(self, args=None): if args is None: args = super().parse() for key in ['tstim', 'toffset', 'PRF']: args[key] = self.parse2array(args, key, factor=self.factors[key]) return args + @staticmethod + def parseSimInputs(args): + return [args[k] for k in ['amp', 'tstim', 'toffset', 'PRF', 'DC']] + class EStimParser(PWSimParser): ''' Parser to run E-STIM simulations from the command line. ''' def __init__(self): super().__init__() self.defaults.update({'amp': 10.0}) # mA/m2 self.factors.update({'amp': 1.}) self.addAstim() def addAstim(self): self.add_argument( '-A', '--amp', nargs='+', type=float, help='Amplitude of injected current density (mA/m2)') self.add_argument( '--Arange', type=str, nargs='+', help='Amplitude range {} (mA/m2)'.format(self.dist_str)) self.to_parse['amp'] = self.parseAmp def addVext(self): self.add_argument( '--Vext', nargs='+', type=float, help='Extracellular potential (mV)') def parseAmp(self, args): if args.pop('titrate'): return None Arange, Astim = [args.pop(k) for k in ['Arange', 'amp']] if Arange is not None: amps = self.getDistFromList(Arange) * self.factors['amp'] # mA/m2 else: amps = np.array(Astim) * self.factors['amp'] # mA/m2 return amps class AStimParser(PWSimParser, MechSimParser): ''' Parser to run A-STIM simulations from the command line. ''' def __init__(self): MechSimParser.__init__(self) PWSimParser.__init__(self) self.defaults.update({'method': 'sonic'}) self.allowed.update({'method': ['full', 'hybrid', 'sonic']}) self.addMethod() def addMethod(self): self.add_argument( '-m', '--method', nargs='+', type=str, help='Numerical integration method ({})'.format(', '.join(self.allowed['method']))) self.to_parse['method'] = self.parseMethod def parseMethod(self, args): for item in args['method']: if item not in self.allowed['method']: raise ValueError('Unknown method type: "{}"'.format(item)) return args['method'] def parseAmp(self, args): if args.pop('titrate'): return None return MechSimParser.parseAmp(self, args) def parse(self): args = PWSimParser.parse(self, args=MechSimParser.parse(self)) for k in ['Cm0', 'Qm0', 'embedding', 'charge']: del args[k] return args + + @staticmethod + def parseSimInputs(args): + return [args['freq']] + PWSimParser.parseSimInputs(args) + [args[k] for k in ['fs', 'method']] + + + + diff --git a/README.md b/README.md index 4fb54d4..08747ae 100644 --- a/README.md +++ b/README.md @@ -1,197 +1,190 @@ # Description `PySONIC` is a Python implementation of the **multi-Scale Optimized Neuronal Intramembrane Cavitation (SONIC) model [1]**, a computationally efficient and interpretable model of neuronal intramembrane cavitation. It allows to simulate the responses of various neuron types to ultrasonic (and electrical) stimuli. ## Content of repository ### Models The package contains four model classes: - `Model` defines the generic interface of a model, including mandatory attributes and methods for simulating it. - `BilayerSonophore` defines the underlying **biomechanical model of intramembrane cavitation**. - `PointNeuron` defines an abstract generic interface to **conductance-based point-neuron electrical models**. It is inherited by classes defining the different neuron types with specific membrane dynamics. - `NeuronalBilayerSonophore` defines the **full electromechanical model for any given neuron type**. To do so, it inherits from `BilayerSonophore` and receives a specific `PointNeuron` object at initialization. All model classes contain a `simulate` method to simulate the underlying model's behavior for a given set of stimulation and physiological parameters. The `NeuronalBilayerSonophore.simulate` method contains an additional `method` argument defining whether to perform a detailed (`full`), coarse-grained (`sonic`) or hybrid (`hybrid`) integration of the differential system. ### Simulators Numerical integration routines are implemented outside the models, in separate `Simulator` classes: - `PeriodicSimulator` integrates a differential system periodically until a stable periodic behavior is detected. - `PWSimulator` integrates a differential system given a specific temporal stimulation pattern (pulse repetition frequency, stimulus duty cycle and post-stimulus offset), using different derivative functions for "ON" (with stimulus) and "OFF" (without stimulus) periods - `HybridSimulator` inherits from both `PeriodicSimulator`and `PWSimulator`. It integrates a differential system using a hybrid scheme inside each "ON" or "OFF" period: 1. The full ODE system is integrated for a few cycles with a dense time granularity until a periodic stabilization detection 2. The profiles of all variables over the last cycle are resampled to a far lower (i.e. sparse) sampling rate 3. A subset of the ODE system is integrated with a sparse time granularity, while the remaining variables are periodically expanded from their last cycle profile, until the end of the period or that of an predefined update interval. 4. The process is repeated from step 1 ### Neurons Several conductance-based point-neuron models are implemented that inherit from the `PointNeuron` generic interface: - `CorticalRS`: cortical regular spiking (`RS`) neuron - `CorticalFS`: cortical fast spiking (`FS`) neuron - `CorticalLTS`: cortical low-threshold spiking (`LTS`) neuron - `CorticalIB`: cortical intrinsically bursting (`IB`) neuron - `ThalamicRE`: thalamic reticular (`RE`) neuron - `ThalamoCortical`: thalamo-cortical (`TC`) neuron - `OstukaSTN`: subthalamic nucleus (`STN`) neuron ### Other modules - `batches`: a generic interface to run simulation batches with or without multiprocessing - `parsers`: command line parsing utilities - `plt`: graphing utilities - `postpro`: post-processing utilities (mostly signal features detection) - `constants`: algorithmic constants used across modules and classes - `utils`: generic utilities # Requirements - Python 3.6+ # Installation - Open a terminal. - Activate a Python3 environment if needed, e.g. on the tnesrv5 machine: ```$ source /opt/apps/anaconda3/bin activate``` - Check that the appropriate version of pip is activated: ```$ pip --version``` - Go to the package directory (where the setup.py file is located): ```$ cd ``` - Insall the package and all its dependencies: ```$ pip install -e .``` # Usage ## Python scripts You can easily run simulations of any implemented point-neuron model under both electrical and ultrasonic stimuli, and visualize the simulation results, in just a few lines of code: ```python import logging import matplotlib.pyplot as plt from PySONIC.core import NeuronalBilayerSonophore from PySONIC.neurons import getPointNeuron from PySONIC.utils import logger from PySONIC.plt import GroupedTimeSeries logger.setLevel(logging.INFO) # Stimulation parameters a = 32e-9 # m Fdrive = 500e3 # Hz Adrive = 100e3 # Pa Astim = 10. # mA/m2 tstim = 250e-3 # s toffset = 50e-3 # s PRF = 100. # Hz DC = 0.5 # - # Point-neuron model and corresponding neuronal intramembrane cavitation model pneuron = getPointNeuron('RS') nbls = NeuronalBilayerSonophore(a, pneuron) # Run simulation upon electrical stimulation, and plot results -elec_args = (Astim, tstim, toffset, PRF, DC) -data, tcomp = pneuron.simulate(*elec_args) -logger.info('completed in %.0f ms', tcomp * 1e3) -scheme_plot = GroupedTimeSeries([(data, pneuron.meta(*elec_args))]) -fig1 = scheme_plot.render() +data, meta = pneuron.simulate(Astim, tstim, toffset, PRF, DC) +fig1 = GroupedTimeSeries([(data, meta)]).render() # Run simulation upon ultrasonic stimulation, and plot results -US_int_method = 'sonic' # Integration method ('sonic', 'full' or 'hybrid') -US_args = (Fdrive, Adrive, tstim, toffset, PRF, DC, US_int_method) -data, tcomp = nbls.simulate(*US_args) -logger.info('completed in %.0f ms', tcomp * 1e3) -scheme_plot = GroupedTimeSeries([(data, nbls.meta(*US_args))]) -fig2 = scheme_plot.render() +data, meta = nbls.simulate(Fdrive, Adrive, tstim, toffset, PRF, DC) +fig2 = GroupedTimeSeries([(data, meta)]).render() plt.show() ``` ## From the command line You can easily run simulations of all 3 model types using the dedicated command line scripts. To do so, open a terminal in the `scripts` directory. - Use `run_mech.py` for simulations of the **mechanical model** upon **ultrasonic stimulation**. For instance, for a 32 nm radius bilayer sonophore sonicated at 500 kHz and 100 kPa: ```$ python run_mech.py -a 32 -f 500 -A 100 -p Z``` - Use `run_estim.py` for simulations of **point-neuron models** upon **intracellular electrical stimulation**. For instance, a regular-spiking (RS) neuron injected with 10 mA/m2 intracellular current for 30 ms: ```$ python run_estim.py -n RS -A 10 --tstim 30 -p Vm``` - Use `run_astim.py` for simulations of **point-neuron models** upon **ultrasonic stimulation**. For instance, for a coarse-grained simulation of a 32 nm radius bilayer sonophore within a regular-spiking (RS) neuron membrane, sonicated at 500 kHz and 100 kPa for 150 ms: ```$ python run_astim.py -n RS -a 32 -f 500 -A 100 --tstim 150 --method sonic -p Qm``` The simulation results are saved in `.pkl` files. To view these results directly upon simulation completion, you can use the `-p [xxx]` option, where `[xxx]` can be `all` or a given variable name (e.g. `Z` for membrane deflection, `Vm` for membrane potential, `Qm` for membrane charge density). You can also easily run batches of simulations by specifying more than one value for any given stimulation parameter (e.g. `-A 100 200` for sonication with 100 and 200 kPa respectively). These batches can be parallelized using multiprocessing to optimize performance, with the extra argument `--mpi`. Several more options are available. To view them, type in: ```$ python -h``` # Extend the package ## Add other neuron types You can easily add other neuron types into the package, providing their ion channel populations and underlying voltage-gated dynamics equations are known. To add a new point-neuron model, follow this procedure: 1. Create a new file, and save it in the `neurons` sub-folder, with an explicit name (e.g. `my_neuron.py`). 2. Copy-paste the content of the `template.py` file (also located in the `neurons` sub-folder) into your file. 3. In your file, change the **class name** from `TemplateNeuron` to something more explicit (e.g. `MyNeuron`), and change the **neuron name** accordingly (e.g. `myneuron`). This name is a keyword used to refer to the model from outside the class. 4. Modify/add **biophysical parameters** of your model (resting parameters, reversal potentials, channel conductances, ionic concentrations, temperatures, diffusion constants, etc...) as class attributes. 5. Specify a **dictionary of names:descriptions of your different differential states** (i.e. all the differential variables of your model, except for the membrane potential). 6. Modify/add **gating states kinetics** (`alphax` and `betax` methods) that define the voltage-dependent activation and inactivation rates of the different ion channnels gates of your model. Those methods take the membrane potential `Vm` as input and return a rate in `s-1`. Alternatively, your can use steady-state open-probabilties (`xinf`) and adaptation time constants (`taux`) methods. 7. Modify the `derStates` method defining a dictionary of lambda functions that take the membrane potential `Vm` and a states vector `x` as inputs, and return a dictionary of lambda function returning the derivatives of your different state variables (in `/s`). **This method is automatically parsed to generate the equivalent `derEffStates` method used in coarse-grained US simulations. Hence, make sure that all internal calls to functions that depend solely on `Vm` appear directly in these lambda expressions and are not hidden inside nested function calls.** 8. Modify the `steadyStates` method defining a dictionary of lambda functions that take a membrane potential value `Vm` as input, and return the steady-state values of your different state variables (in ``). 9. Modify/add **membrane currents** (`iXX` methods) of your model. Those methods take relevant gating states and the membrane potential `Vm` as inputs, and must return a current density in `mA/m2`. **You also need to modify the docstring accordingly, as this information is used by the package**. 10. Modify the `currents` method defining a dictionary of lambda functions that take a membrane potential value Vm and a states vector `x` as inputs, and return the different membrane currents of your model (in `mA/m2`). 11. Add the neuron class to the package, by importing it in the `__init__.py` file of the `neurons` sub-folder: ```from .my_neuron import MyNeuron``` 12. Verify your point-neuron model by running simulations under various electrical stimuli and comparing the output to the neurons's expected behavior. Implemented required corrections if any. 13. Pre-compute lookup tables required to run coarse-grained simulations of the neuron model upon ultrasonic stimulation. To do so, go to the `scripts` directory and run the `run_lookups.py` script with the neuron's name as command line argument, e.g.: ```$ python run_lookups.py -n myneuron --mpi``` If possible, use the `--mpi` argument to enable multiprocessing, as lookups pre-computation greatly benefits from parallelization. That's it! You can now run simulations of your point-neuron model upon ultrasonic stimulation. ## Future developments Here is a list of future developments: - [x] Integration within the [NEURON simulation environment](https://www.neuron.yale.edu/neuron/) - [x] Spatial expansion into nanoscale multicompartmental model - [ ] Spatial expansion into morphological realistic fiber models - [ ] Model validation against experimental data (leech neurons) # Authors Code written and maintained by Theo Lemaire (theo.lemaire@epfl.ch). # License This project is licensed under the MIT License - see the LICENSE file for details. # References [1] Lemaire, T., Neufeld, E., Kuster, N., and Micera, S. (2019). Understanding ultrasound neuromodulation using a computationally efficient and interpretable model of intramembrane cavitation. J. Neural Eng. diff --git a/notebooks/BLS model - balance deflection.ipynb b/notebooks/BLS model - balance deflection.ipynb index 1f91879..c3cd34c 100644 --- a/notebooks/BLS model - balance deflection.ipynb +++ b/notebooks/BLS model - balance deflection.ipynb @@ -1,129 +1,129 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bilayer Sonophore model: computation of balance quasi-static deflection" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from PySONIC.core import BilayerSonophore" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def plotZeq(bls, ng_range, Q_range, fs=15):\n", - " fig, ax = plt.subplots(figsize=(5, 4))\n", + " fig, ax = plt.subplots(figsize=(15, 4))\n", " ax.set_xlabel('$Q_m\\ (nC/cm^2)$', fontsize=fs)\n", " ax.set_ylabel('$Z_{eq}\\ (nm)$', fontsize=fs)\n", " for ng in ng_range:\n", " ZeqQS = np.array([bls.balancedefQS(ng, Q) for Q in Q_range])\n", - " ax.plot(Q_range * 1e5, ZeqQS * 1e9, label='ng = {:.2f}e-22 mole'.format(ng * 1e22))\n", + " ax.plot(Q_range * 1e5, ZeqQS * 1e9, label=f'ng = {(ng * 1e22):.2f}e-22 mole')\n", " for item in ax.get_xticklabels() + ax.get_yticklabels():\n", " item.set_fontsize(fs)\n", " for key in ['top', 'right']:\n", " ax.spines[key].set_visible(False)\n", " ax.legend(fontsize=fs, loc='center right', bbox_to_anchor=(1.8, 0.5), frameon=False)\n", " fig.tight_layout()\n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "a = 32e-9 # in-plane radius (m)\n", "Cm0 = 1e-2\n", "Qm0 = -71.9e-5\n", "bls = BilayerSonophore(a, Cm0, Qm0)\n", "charges = np.linspace(-80, 40, 200) * 1e-5\n", "gas = np.linspace(0.5 * bls.ng0, 2.0 * bls.ng0, 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Balance deflections" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plotZeq(bls, gas, charges)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/notebooks/BLS model - intermolecular pressure.ipynb b/notebooks/BLS model - intermolecular pressure.ipynb index a6f3b46..47a4b27 100644 --- a/notebooks/BLS model - intermolecular pressure.ipynb +++ b/notebooks/BLS model - intermolecular pressure.ipynb @@ -1,208 +1,214 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bilayer Sonophore model: computation of intermolecular pressure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import logging\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from PySONIC.utils import logger, rmse, rsquared\n", "from PySONIC.neurons import getPointNeuron\n", "from PySONIC.core import BilayerSonophore, PmCompMethod\n", "from PySONIC.constants import *\n", "\n", "# Set logging level\n", "logger.setLevel(logging.INFO)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def plotPmavg(bls, Z, fs=15):\n", " fig, ax = plt.subplots(figsize=(5, 3))\n", " for skey in ['right', 'top']:\n", " ax.spines[skey].set_visible(False)\n", " ax.set_xlabel('Z (nm)', fontsize=fs)\n", " ax.set_ylabel('Pressure (kPa)', fontsize=fs)\n", " ax.set_xticks([0, bls.a * 1e9])\n", " ax.set_xticklabels(['0', 'a'])\n", " ax.set_yticks([-10, 0, 40])\n", " ax.set_ylim([-10, 50])\n", " for item in ax.get_xticklabels() + ax.get_yticklabels():\n", " item.set_fontsize(fs)\n", " ax.plot(Z * 1e9, bls.v_PMavg(Z, bls.v_curvrad(Z), bls.surface(Z)) * 1e-3, label='$P_m$')\n", " ax.plot(Z * 1e9, bls.PMavgpred(Z) * 1e-3, label='$P_{m,approx}$')\n", " ax.axhline(y=0, color='k')\n", " ax.legend(fontsize=fs, frameon=False)\n", " fig.tight_layout()\n", "\n", "def plotZprofiles(bls, f, A, Q, fs=15):\n", - " # Run simulation with integrated intermolecular pressure\n", - " data, _ = bls.simulate(f, A, Qm, Pm_comp_method=PmCompMethod.direct)\n", - " Z1 = data.loc[-NPC_DENSE:, 'Z'].values * 1e9 # nm\n", - "\n", - " # Run simulation with predicted intermolecular pressure\n", - " data, _ = bls.simulate(f, A, Qm, Pm_comp_method=PmCompMethod.predict)\n", - " Z2 = data.loc[-NPC_DENSE:, 'Z'].values * 1e9 # nm\n", + " # Run simulations with integrated and predicted intermolecular pressure\n", + " data1, _ = bls.simulate(f, A, Qm, Pm_comp_method=PmCompMethod.direct)\n", + " Z1 = data1['Z'].values[-NPC_DENSE:] * 1e9 # nm\n", + " data2, _ = bls.simulate(f, A, Qm, Pm_comp_method=PmCompMethod.predict)\n", + " Z2 = data2['Z'].values[-NPC_DENSE:] * 1e9 # nm\n", " \n", " # Plot figure \n", " t = np.linspace(0, 1 / f, NPC_DENSE) * 1e6 # us\n", " fig, ax = plt.subplots(figsize=(5, 3))\n", " for skey in ['right', 'top']:\n", " ax.spines[skey].set_visible(False)\n", " ax.set_xlabel('time (us)', fontsize=fs)\n", " ax.set_ylabel('Deflection (nm)', fontsize=fs)\n", " ax.set_xticks([t[0], t[-1]])\n", " for item in ax.get_xticklabels() + ax.get_yticklabels():\n", " item.set_fontsize(fs)\n", " \n", " ax.plot(t, Z1, label='$P_m$')\n", " ax.plot(t, Z2, label='$P_{m,approx}$')\n", " ax.axhline(y=0, color='k')\n", " ax.legend(fontsize=fs, frameon=False)\n", " fig.tight_layout()\n", " \n", " return fig, Z1, Z2 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pneuron = getPointNeuron('RS')\n", "bls = BilayerSonophore(32e-9, pneuron.Cm0, pneuron.Qm0())\n", "f = 500e3\n", "A = 100e3\n", "Qm = pneuron.Qm0()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Profiles comparison over deflection range " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Z = np.linspace(-0.4 * bls.Delta_, bls.a, 1000)\n", "fig = plotPmavg(bls, Z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Error quantification over a typical acoustic cycle" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 01/07/2019 16:33:31: BilayerSonophore(32.0 nm): simulation @ f = 500.00 kHz, A = 100.00 kPa, Q = -71.90 nC/cm2\n", + " 01/07/2019 16:33:34: BilayerSonophore(32.0 nm): simulation @ f = 500.00 kHz, A = 100.00 kPa, Q = -71.90 nC/cm2\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "Z-error: R2 = 0.9996, RMSE = 0.0454 nm (0.8212% dZ)\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, Z1, Z2 = plotZprofiles(bls, f, A, Qm)\n", "error_Z = rmse(Z1, Z2)\n", "r2_Z = rsquared(Z1, Z2)\n", - "print('Z-error: R2 = {:.4f}, RMSE = {:.4f} nm ({:.4f}% dZ)'.format(\n", - " r2_Z, error_Z, error_Z / (Z1.max() - Z1.min()) * 1e2))" + "err_pct = error_Z / (Z1.max() - Z1.min()) * 1e2\n", + "print(f'Z-error: R2 = {r2_Z:.4f}, RMSE = {error_Z:.4f} nm ({err_pct:.4f}% dZ)')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/notebooks/BLS model - static forces.ipynb b/notebooks/BLS model - static forces.ipynb index 9504909..68c3055 100644 --- a/notebooks/BLS model - static forces.ipynb +++ b/notebooks/BLS model - static forces.ipynb @@ -1,438 +1,438 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bilayer Sonophore model: analysis of static pressure forces\n", "\n", "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import ticker\n", "\n", "from PySONIC.core import BilayerSonophore, PmCompMethod" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "a = 32e-9 # in-plane radius (m)\n", "Cm0 = 1e-2 # membrane resting capacitance (F/m2)\n", "Qm0 = -71.9e-5 # membrane resting charge density (C/m2)\n", "bls = BilayerSonophore(a, Cm0, Qm0)\n", "Z = np.linspace(-0.45 * bls.Delta, 2 * bls.a, 3000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def prepareAxis(ax, xlabel, fs):\n", " if xlabel == 'Leaflet deflection (nm)':\n", " ax.set_xticks([-0.5 * bls.Delta * 1e9, bls.a * 1e9])\n", " ax.set_xticklabels(['$-\\Delta / 2$', 'a'])\n", " ax.axvline(-0.5 * bls.Delta * 1e9, linestyle='--', color='dimgrey')\n", " ax.axvline(bls.a * 1e9, linestyle='--', color='dimgrey')\n", " ax.yaxis.set_major_locator(ticker.MaxNLocator(2))\n", " ax.axhline(0.0, color='k', linewidth=1)\n", " else:\n", " ax.set_yticks([-0.5 * bls.Delta * 1e9, 0])\n", " ax.set_yticklabels(['$-\\Delta / 2$', '0'])\n", " ax.axhline(-0.5 * bls.Delta * 1e9, linestyle='--', color='dimgrey')\n", " ax.axhline(0, linestyle='--', color='dimgrey')\n", " ax.xaxis.set_major_locator(ticker.MaxNLocator(2))\n", " ax.axvline(0.0, color='k', linewidth=1)\n", " ax.set_xlabel(xlabel, fontsize=fs)\n", " for key in ['top', 'right']:\n", " ax.spines[key].set_visible(False)\n", " \n", "\n", "def plotVars(yvars, labels, limits=None, sharex=False, unit=None, xvar=Z * 1e9, xlabel='Leaflet deflection (nm)', fs=12, lw=2):\n", " if not sharex:\n", " naxes = len(yvars)\n", - " fig, axes = plt.subplots(1, naxes, figsize=(5 * naxes, 3))\n", + " fig, axes = plt.subplots(1, naxes, figsize=(7 * naxes, 3))\n", " if naxes == 1:\n", " axes = [axes]\n", " for i, ax, in enumerate(axes):\n", " prepareAxis(ax, xlabel, fs)\n", " ax.set_ylabel(labels[i], fontsize=fs)\n", " ax.plot(xvar, yvars[i], linewidth=lw)\n", " if limits is not None:\n", " ax.set_ylim(limits[i])\n", " else:\n", " naxes = len(yvars)\n", - " fig, ax = plt.subplots(1, 1, figsize=(5, 3))\n", + " fig, ax = plt.subplots(1, 1, figsize=(7, 3))\n", " prepareAxis(ax, xlabel, fs)\n", " ax.set_ylabel(unit, fontsize=fs)\n", " for yvar, label in zip(yvars, labels):\n", " ax.plot(xvar, yvar, linewidth=lw, label=label)\n", " if limits is not None:\n", " ax.set_ylim(limits)\n", " ax.legend(fontsize=fs, loc='center right', bbox_to_anchor=(1.5, 0.5), frameon=False)\n", " fig.tight_layout()\n", " return fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Geometrical variables" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "R = bls.v_curvrad(Z)\n", "S = bls.surface(Z)\n", "V = bls.volume(Z)\n", "\n", "fig = plotVars(\n", " [1 / R * 1e-9, S * 1e18, V * 1e27],\n", " ['Curvature ($nm^{-1}$)', 'Surface ($nm^2$)', 'Volume ($nm^3$)']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The **leaflet curvature** is negative for a compressed sonophore (Z < 0) and positive for a expanded sonophore (Z > 0). It reaches a maximum when the deflection equals the in-plane diameter of the sonophore\n", "- The **leaflet surface** and **sonophore volume** both increase with the deflection magnitude. Note that the relative surface increases is less pronounced than the relative volume increase" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Intermolecular pressure" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Pm_apex = np.array([bls.PMlocal(0.0, z, r) for z, r in zip(Z, R)])\n", "Pm_avg = bls.v_PMavg(Z, R, S)\n", "Pm_avg_predict = bls.PMavgpred(Z)\n", "\n", "fig = plotVars(\n", " [Pm_apex * 1e-3, Pm_avg * 1e-3, Pm_avg_predict * 1e-3],\n", " ['$P_{M, apex}$', '$\\overline{P_{M}}$', 'approximated $\\overline{P_{M}}$'],\n", " limits=(-20, 20),\n", " sharex=True,\n", " unit='Pressure (kPa)'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The **intermolecular pressure at the leaflet apex** follows a marked *Lennard-Jones* profile\n", "- The **average leaflet intermolecular pressure** profile is similar in nature but less marked than that of the apex pressure\n", "- The ***Lennard-Jones* approximation of the average leaflet intermolecular pressure** is quite accurate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Electrical pressure" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "PQ_Qm0 = bls.Pelec(Z, bls.Qm0)\n", "PQ_0 = bls.Pelec(Z, 0)\n", "PQ_AP = bls.Pelec(Z, 30.0e-5)\n", "\n", "fig = plotVars(\n", " [PQ_Qm0 * 1e-3, PQ_0 * 1e-3, PQ_AP * 1e-3],\n", " ['$P_{Q, -71.9\\ nC/cm^2}$', '$P_{Q, 0\\ nC/cm^2}$', '$P_{Q, +30\\ nC/cm^2}$'],\n", " sharex=True,\n", " unit='Pressure (kPa)'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Electrical pressure** is null when the membrane is not charged, and then increases in magnitude with the square of charge density and decreases as the inter-leaflet distance increases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gas pressure" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Pg_ng0 = bls.gasmol2Pa(bls.ng0, V)\n", "Pg_sparse = bls.gasmol2Pa(0.1 * bls.ng0, V)\n", "Pg_dense = bls.gasmol2Pa(10 * bls.ng0, V)\n", "\n", "fig = plotVars(\n", " np.array([Pg_ng0, Pg_sparse, Pg_dense]) * 1e-3,\n", " ['$P_{G, ng0}$', '$P_{G, 0.1 \\cdot ng0}$', '$P_{G, 10 \\cdot ng0}$'],\n", " sharex=True,\n", " unit='Pressure (kPa)',\n", " limits=(-10, 150)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In th absence of gas transport, **gas pressure** increases here when the sonophore is compressed. However, in reality gas transport keeps the pressure around $P_0$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Elastic tension pressure" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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5TopLHGYwslMD7h/Yko4Na1ZlqSLVVfDWAzezTsBPgAwA59yXzrnR4RTewVRYWEhhYaHXZYh44kz9v01qImOv78rURwZwY8+mRPl8fLlyF5f+fRZ3vrGQ5RlaS1ykLM4Y4GZ2OzADeAJYbmbDKr0qEQl7TevE8ZerOzPj8YHc2TeNGlERTF67hyufn81try9gyTatSy5yOmXZA38Q6OSc6wVcDvy6cksSkXNJ/Zqx/O6yDsx6YiD39m9JXHQE09ZlcfULc7jltfks3rrf6xJFqqUyHUJ3zu0M/JwL1KvUikTknFQnIYZfX9Lu+Lrk8dERzNywl2tenMuoV+exIF1BLlJaWeaBnzjKTZdVOoNOnTp5XYKIZyra/5Pjo3l8RDvu7teC12al88bsLczeuI/ZG+fSp0UdHhrSmt4t6gSpWpHQVZbVyJaUnip24uNwomlkItXPwfxCXpudzuuz08k94t9/6JmWzK8Gt6ZPyzr4J8iIhJWgLSdaBOSXeiou8NjwX9Al6WwrrG4U4CLV18HDhbwxewuvzdpMTiDIezSvzUOD23BRKwW5hJWgBXiz073unNtajqKqtWAF+NixYwEYPXp0hdsSCTWV3f9zjhTy79lbeHVWOgcP+6erXdCsNg8Obn18TXKREBeceeDOua2BkG5w7H7gcSbweAWLFBEpl6TYKH45uDWznhjIY8PbUjsuisVbD3DrvxbwkxfmMH19FmW9QJVIKCvPamTvm1lHADPrASwHGlVKVSIiZ5AYG8UvBrZi5hODeGJEO5Ljo1mWkc2t/1rAtS/NZfbGvQpyCWvlCfBRwHgzG4P/Wuh/ds5dVTlliYiUTUJMJPcNaMmsJwbym0vaHd8jH/XqfK5/eR7zNu/zukSRSlHmAHfOzQLuAu4DrnDOvVlpVYmIlFNcdCT39G/JzCcG8djwttSsEcWC9P3c8PI8Rr06j0VbNI9cwssZ54Gb2Up+OBe8CPjMzHYDhNNyosHSrVtYzrITKROv+39CTCS/GNiKW/o0443ZW3hl5ubj88j7tanH6CGtOb9pbU9rFAmGsoxC73+6151z04NakYc0jUwk/BzML+S1WZv51+wt5B31Tz8b1C6F0UPa0LmxVj+Tailo08iGOOcmnWGbYc65b8tRXLUUrAAvKCgAIDo6usJtiYSa6tr/Dxwq4JWZm3ljzhbyC4oBGNohlV8Naa1lTKW6CdpyokPMbLyZjTSz44fczSzazIab2ReAVigr5fnnn+f555/3ugwRT1TX/l87cInWmY8P5J5+LYiN8jFxdSaX/n0W9729mHW7c70uUaRcyjIP/NfAX4BfAgfMbL2ZbQT2AQ/hH43+aOWWKSISHHUSYvjNyPbHlzGNifTx9ardjHh2Bg+8u4SNe/K8LlGkTMqymAnOufnAJWZWE2iFf1DbJufcwcosTkSksqQk+pcxvbtfC16YupH/LMjgixW7+GrlLq7s2ogHB7cmrW6812WKnFJ55oHjnDvonFvsnFui8BaRcJCaFMsfruzEtMcGMKpXUyJ8xidLdzDkmek8+uFyMvbnn7kREQ+UK8BFRMJVw1o1+PNPOjPlkQHc0KMJAOMWb2fQX6fxu/GryMw54nGFIj90xlHo55JgjUKfO3cuAH369KlwWyKhJlz6/9Z9h3h20gY+WbYD5yAm0setFzbn3v4tSY6vXiPsJewEZxrZuUTzwEXkRBsyc3lm4nq+XrUbgPjoCO7sm8bP+7UgKTbK4+okTFVegJvZtcAnzrnicr+5GgtWgOfl+UexJiQkVLgtkVATrv1/5faD/HXiOqatywKgZo0o7unfgtsubE5cdJnGA4uUVdDmgZ/MQ8fC28wizOzJs2wnLL3yyiu88sorXpch4olw7f+dG9fkjdt7Mu7ePvRKS+bg4ULGfLOOfmOm8frsdI4WhdX+jISAsw3wEjPzAQSCvGfwShIRqb66N0/mvbt789adPTmvSS325h3lD5+vZuBT03hvwTYKi0u8LlHOEWcb4C8An5vZYDMbBsQGsSYRkWrNzLi4dT3G338hr/ysO+3qJ7Lz4BF+/fFKhj4znU+X7aCkROOLpHKVK8DNrBWAc+594L+A3sBQ4PbglyYiUr2ZGUM7pPLVgxfz9xvPJ61uPFv25fPQe8u45NmZTPhuNxooLJWlvHvgQwDMbAbw28D7ZwFHg1yXiEjI8PmMK85ryMTR/RhzTRca1arBusxc7nlrMVc+P5vp67MU5BJ0ZzsK/RbgK6BL4NbJOXdXkGurcsEahX6sje7du1e4LZFQo/4PR4uKeX9hBv+YspGsXP/+Tc/myTw6vC0905I9rk5CQPCnkZlZU/x73f9yzg0q9fwzzrmHy11iNaN54CISTIcLinlz7hZenL6J7PxCAPq1qcejw9rQpXEtb4uT6qxSAnwAcAdwObAEWAesBX7unOtS/hqrl2AF+P79+wFITtZf2nLuUf//sdwjhbw2K51XZ6aTd7QIgOEdU3l4aFva1k/0uDqphir1Qi6XA1/gX5msA7DeObem3A1VM8EK8LFjxwIwevToCrclEmrU/0/twKECXpqxiX/P2cKRwhLM4CddGzF6aBuaJMd5XZ5UH5V6IZcFzm+Dc+7TcAhvEZHKVjs+mt9c4l+L/NY+zYj0GR8v3cGgv07jvz9dxR4tmCLlcLYBPh7AzEaa2VQzGxrEmkREwlpKon8J0ymPDOCabo0pKnG8OXcr/Z6aypPfrOVg4Hy5yOmcbYAfu2bgzcBVwB+DU46IyLmjSXIcf73uPCb8qh/DO6ZypLCEF6dt4uIxU3h+6kbyC4q8LlGqsbMN8M1m9g9gi3PuIKBeJiJyltqkJvLPW7oz/hcXcVGrOuQcKeKpCevo/9Q03py7hYIiXZ5VfuxsB7FFAj2ARUAk/mllN1aoELOfAD91zt0UeHw18BSQEdjk/wIz8V/G9Tz8F4/5uXNuo5n1Bp7F/4fEt865PwSu1f6jbU9XQ7AGsa1YsQKALl1CfmC+SLmp/1fc7I17GfPNWpZvPwhAk+QajB7Shiu7NiLCV6bxTRLaKnUU+oXAtcA2YCWwwjmXVe6Gvm/vWWA4sMw5d0PguT8BS51zH5Xa7mrgCufcbYHQ/o1z7kozWwZcA2wGvsR/lbjmJ9v2dHVoHriIVBfOOSZ8l8nT365j4x7/Eq1tUhN4dFhbhnZIxUxBHsYqNcBXAz8DXsE/H7yjc653uRv6vr3rgT3APaUC/GugBEgCFgBPAGPwj4B/L7DNDqA9MN851z7w3ENANNDgxG2dc41OV0ewAjwzMxOA1NTUCrclEmrU/4OruMTxydIdjJ24nh3ZhwE4v2ktHhvelgtb1vW4OqkklTqNLNM5twg44Jy7E1hdporM7jSzVSfcegQWRznxL4mJwC+BfkACcC/+MD9YapviwHM5pZ7LBWqebNvAof8Ta7rbzBaZ2aKsrLM+iPAD7777Lu+++25Q2hIJNer/wRXhM669oDFTHu3P7y/vQN2EaJZuy+amV+Zzy2vzWbE92+sSxSNnG+AzzSwO2B5YTrRMFz12zr3mnOt0wm3hKTb/l3Nus/MfIvgUOB9/UJe+bJHvJM8lAtkn29Y596PBds65l51z3Z1z3evVq1eWryEiUuViIiO47aI0pj82kEeHtSExJpKZG/ZyxXOzue/txWzck+t1iVLFyrucaDczi3HO/bdzLh94BLgM+HMwizL/yZ0VZtY48NRgYDEwGxgZ2KY3sNI5lwMUmFnLwPuG4x/s9qNtg1mjiIgX4mMieWBQa2Y+MZB7+rcgJtLH16t2M2zsDB77cDnbD+R7XaJUkR8dUj6Dm4EOZhYLZOIPxUnAimAW5ZxzZvZz4GMzO4z/EP0r+A+ZDzWzOfjPERxbh/xe4B0gAv8o9PlmtvAU24qIhLxacf6rut1xURp/n7yB9xdm8OHi7Xy6bCc39WrKA4NaUTchxusypRKVK8BLrzhmZvWAzoHb5UCFlhN1zk0DppV6/C3w7Uk2vfck750H9D7huZKTbSsiEk5Sk2L58086c9fFLRg7aT2fLd/JG3O28MGiDO7sm8Zd/VqQFBvldZlSCc52FHp/4DH855p/BtRzzmUGubYqF6xR6GvXrgWgXbt2FW5LJNSo/3trza4cnp6wjslr9wBQKy6K+/q35NYLmxMbFeFxdVJGlTqNbAH+89KfOucGmdk3zrkR5W6omtE8cBEJF4u27GfMhHUsSPcv75qaFMODg1tzXfcmREWc7fhlqSKVOo0s1zlXeshj9Fm2E5YyMjLIyMg484YiYUj9v3ro3jyZ9+/uzRu396BjwyQyc47yfz5ZxdBnpvPpsh2UlJR/502ql7MN8NfN7B2gVuCqbDrBUsq4ceMYN26c12WIeEL9v/owMwa0TeHzB/ry3E3n06JuPFv25fPQe8u49B+zmLI2k7M5CivVwxkHsZlZQyDaObfl2HPOubfNbCnwk8DtjkqrUEREKsTnMy7r0pARHevz0ZLt/G3SBtbsyuGONxbRvVltHh/Rjp5pyV6XKeV02j3wwGVJtwObzGy+mR2/NqJz7jvn3J+cc4855zZUdqEiIlIxkRE+ru/RlKmPDuC3l7andlwUi7Ye4Lp/zuX21xewemfOmRuRauNMh9Afw7/edyNgFVr3W0Qk5MVGRfDzi1sw4/GBPDS4NfHREUxdl8XIv8/kofeWsnXfIa9LlDI4U4DXds595pzbjf+qawOroCYREakCibFRjB7ahhmPD+SOi9KIjvDx6bKdDP7rdH47fiV7co54XaKcxmmnkZlZjnMuqdTjvc65sF3+JljTyDZt2gRAy5YtK9yWSKhR/w9d2w/k87dJG/h4yXZKHMRG+bjjojTu6d+SmjU0VrkKVXweuJmV4F/mcyH+JT0fA9o653YFo8LqRvPARURgQ2YuT3+7jgnf+a/PVbNGFPcNaMmtfZpTI1oXg6kCQQnwWkDXwO38wM/2QB7wHbDKOXdfhUutJrQHLlJx6v/hY8m2A4z5Zi3zNvsvBpOSGMNDQ3QxmCpQOVdiM7NooBOBQHfO/bL8tVVPwQrwsWPHAjB69OgKtyUSatT/w4tzjpkb9jJmwlpW7fCPUm9eJ46Hh7Xlss4N8PnKlDVSPmX6Ry3vamQ45wqAJYGbiIiEMTOjX5t69G1Vl69X7ebpb9eRvvcQD/5nKS9N28TjI9rSv009/Ks5S1XSMRARETkjn8+4tEsDvh3dj79c3ZnUpBhW78rhttcXcsPL81i89YDXJZ5zFOAiIlJmURE+buzZlOmPDeS/RrajZo0o5qfv55oX53DXm4tYn5l75kYkKBTgIiJSbrFREdzdryUzHh/IAwNbUSMqgomrMxn+txk8/MEyMvbne11i2Dur5UTDVbAGsR1bialJkyYVbksk1Kj/n5v25B7huSkbeXf+NopKHFERxqhezXhgUCvqJsR4XV6oqbz1wMOV5oGLiFTMtn35jJ20nvHLduAcxEVH8PO+adzVrwWJsboYTBkpwMsrWAG+du1aANq1a1fhtkRCjfq/AKzZlcPTE9Yxee0eAGrHRfGLga24uXczYqN0MZgzKFOA6xx4Jfj666/5+uuvvS5DxBPq/wLQvkESr93Wgw/v7UOP5rU5kF/In75cw8Cnp/HBwgyKiku8LjHkKcBFRKTS9GiezAf39OH123rQrn4iuw4e4fGPVjD8bzP4euUudBT47CnARUSkUpkZA9ul8NWDF/PsDV1pmhzHpqxD3PfOEq56fjazN+71usSQpAAXEZEq4fMZV3ZtxKSH+/PHKztSLzGG5dsPMurV+dz86nyWZ2R7XWJIUYCLiEiVio70cUuf5kx/bACPDW9LYmwkszbu5crnZ3Pf24vZuCfP6xJDgkahlxKsUeiZmf4l+FJTUyvclkioUf+X8srOL+DF6Zt4Y/YWjhaV4DP46QVNeGhIaxrWquF1eV7QNLLy0jxwERHv7D54hL9P2cD7CzMoLnFER/r4We9m3D+wFcnx0V6XV5UU4OUVrABfsWIFAF26dKlwWyKhRv1fKmpzVh7PTFzPFyt2AZAQE8nd/VpwZ9804mPKvYhmKNI8cK9MnjyZyZMne12GiCfU/6WiWtRL4LmbuvHFL/vSr0098o4W8czE9fR/aiqEtO0qAAALb0lEQVRvzE7naFGx1yVWCwpwERGpljo1qsmbd/TkP3f15vymtdibV8DvP1/NoKen89Hi7RSXnNtHkBXgIiJSrfVpWYeP77uQl2+5gNYpCezIPswjHy7nkmdnMHF15jl7MRgFuIiIVHtmxrCO9fnmV/14+qfn0ahWDdZn5nHXm4u45sU5zNu8z+sSq5wCXEREQkaEz7j2gsZMebQ///fyDtSJj2bJtmxueHket/5rAat2HPS6xCqjUeilBGsU+v79+wFITk6ucFsioUb9X6pS3tEiXpuZziszN5N3tAiAy89ryCND29C8brzH1Z01TSMrL80DFxEJTfvyjvLCtE28NXcrBcUlRPqM63s04cHBrUlNivW6vPJSgJdXsAL8WBvdu3evcFsioUb9X7y0I/swz05az7jF2ylxEBvl47YL07ivf0tqxkV5XV5ZaR64V2bOnMnMmTO9LkPEE+r/4qVGtWow5trz+HZ0P0Z0rM+RwhJemr6Ji8dM4YVpGzlcED5zyBXgIiISdlqlJPLSLRcw/hcXcWHLOuQcKWLMN+vo99RU3pq3lcLiEq9LrDAFuIiIhK2uTWrx7l29efvOXnRuVJOs3KP8bvwqhjwznU+X7aAkhC8GowAXEZGw17d1XT574CJeGNWNFnXj2bovn4feW8al/5jF1HV7QvJiMApwERE5J5gZIzs34NvR/Xjyms7UT4plza4cbn99Idf/cx6Ltuz3usRy0Sj0UoI1Cj0vz78YfUJCQoXbEgk16v8SKo4UFvPW3K08P20j2fmFAAxpn8Kjw9vSrn6Sl6VpGll5aR64iMi5J+dIIa/M2MyrM9M5XFiMGVzVtREPD21Dk+Q4L0pSgJdXsAJ87ty5APTp06fCbYmEGvV/CVVZuUd5fupG3pm/lcJiR1SEcVPPpjwwqDX1EmOqshTNA/fKvHnzmDdvntdliHhC/V9CVb3EGH5/RUemPDKAq89vRFGJ499zt9L/qan89dt15Bwp9LrEH1CAi4iIlNIkOY5nru/K1w9dzJD2KeQXFPOPKRvpN2YqL8/YxJHC6nExGAW4iIjISbSrn8Srt/bgo/v60LN5Mtn5hfzPV2sZ8NQ03luwjSKPLwajABcRETmNC5ol8/49vXn99h60b5DE7pwj/PrjlQz72wy+WrnLsznkCnAREZEzMDMGtk3hy1/25dkbutKsThybsw5x/ztLuOK52czckFXlQa5R6KUEaxR6QUEBANHR0RVuSyTUqP/LuaCwuIT3F2bw7OQNZOUeBeDLB/vSsWHNYDRfplHokcH4JPkh/eKSc5n6v5wLoiJ83Ny7GVd3a8Qbc7awbndusMK7zBTglWD69OkA9O/f3+NKRKqe+r+cS+KiI7l/QCtPPlvnwCvBkiVLWLJkiddliHhC/V+kaijARUREQpACXEREJAQpwEVEREKQAlxERCQEaR54KWaWBWwNUnN1gb1Baksk1Kj/i5y9vc65EWfaSAFeScxskXOuu9d1iHhB/V+k8ukQuoiISAhSgIuIiIQgBXjlednrAkQ8pP4vUsl0DlxERCQEaQ9cREQkBCnARUREQpACXEREJAQpwEVEREKQAjzIzOwnZubMrN1JXvunmfU3s6lmtsbMvjOzh7yoU6QymFmSmX1gZt+a2RIzu8/rmkTClUahB5mZfQCkAV86535/wmvLgEuBVOfcEjNLBBYDVznnVld5sSJBZmbdgObOuY/NrCEw3TnX2uu6RMKR9sCDyMwSgP7AncCNJ7zWHljvnNvhnFsC4JzLBdYAjaq6VpFKshu4yszeBn4LRHlcj0jYUoAH11XAJOfcCuBQYG/kmEuAb0pvbGbNgfOB+VVVoEglexSY65y7GfgQMI/rEQlbkV4XEErMbBJQ/yQv/R/n3Kf497qPXYHqg8DjJYHHw4HbS7WVAHwE/Mo5l1NpRYtUrc+BF81sFLAPKDKzGOfcUY/rEgk7OgceJGZWB1gLNHbOHTWzNGA60AyoAUx1zvUKbBsFfAFMcM4941XNIiISurQHHjzXAl8d29NwzqWb2W6gL5AETAUwMwNeA9YovEVE5GxpDzxIzGwa0AUofTi8DvA2UAyMc85NM7O+wExgJVAS2O6/nHNfVWG5IiIS4hTgVcDMlgC9nHOFXtciIiLhQQEuIiISgjSNTEREJAQpwEVEREKQAlxERCQEKcBFRERCkAJcREQkBCnARSqZmTU3s7wgt/mimaWb2Z/NbIuZdT/D9mlm9lEZ215lZgPOsE2EmX1qZuvN7IHAErp1y/EVSrfVw8xeCtzvbmbjzqad07R/iZn9OUhtNTGzj81MvzvFc7oSm0hougdo6pzbHrju+Jk0A9oG8fMb4b++f7xzrtjM/lGBtjoCjQGcc4vwX9UwKAJL9o4BegWjPedcRmBZ4PuB54LRpsjZ0l+RIh4ys2gzG2tmS8xsuZm9YWZJgdcuM7M5ZrbIzLaZ2R8Dz8/Ev8rX12Z28QntXW5m881sqZnNNrM+ZhYBvAq0NLMJJ6mhg5nNM7MVgfXs40u9dqGZzQzUtzBQUyL+lfWigMVm1vKE9u40s8WBGiaZWbvA8wlm9npgr321mf2PmTUB/h9wceC1AWa2KrB9TTN7O3BEYKWZjTGzyMBrR8zs94F/n3Qzu+8U/8S/wL/mQP7p3mdmt5nZ52b2ZeDzvjGza8xsqpntMLNHSrX5KvAbM4s+839hkUrknNNNN90q8QY0B/JO8dp/A0/x/UWV/gd4AX9ATwVaB55vCBQBdQOPXan7W4DuQGv8l+itE3i+I7ALfyAPAFadooalwJ2B+xfhv8TvAKA2sA5oXqqGDKDpid/pWD1Af2AGEBd4fhj+6/4DPAP8B4gAovEv9jMAuA34IrDN8TqBfwPPBv4tYoAJwK9Lfd4DgfsXAEeA2JN8t0XAgBPq/NH7AjVkA03w79h8h385VB9wHnAY8JVqZwUw0Ou+pdu5fdMhdBFvXQbUAob617khGtjjnHNmdjlwmZndBLTHH2TxwN5TtDUUaABMDrQF/jBudaoPD6yi1wV4E8A5N/vYHjDQJ9De+FLtucD2qzi5SwOfN6fUe2qbWTIwBHjYOVeMf32A/oEabjtFW5cAFznnHHA0cJ78V8D/Bl7/NPBzCf6Aj8cfyKW1Azae8NzJ3gew0DmXEagpHfjWOVdiZpvwh3wccGwsQzr+UxJTT1G7SKVTgIt4KwJ4yDn3NRxfJz7WzOLx7xl/gn/xm38BV+EP8dO1Ndk5d/2xJwKHqHcCF5/yXYFNS90vKtXeGhdYBjfQXkMgC/858FPV8JZz7onA9j78e+4HAu0ev3ZzoLb809TkK7194HFUqceHAQJ/7Jz4HY4p4cenCk/1vhPXLD/d2gWF+P8IEfGMzoGLeGsC8EDgXLgPeAX4C/7D4UnAb51zn+M/tByDPyBPZTIwrNQ555H4D/XWwB+eUSe+wTm3D1gM/Dzwnm5A58DL84DWZtYv8FpXYAOnDu9j3+dGM2sQeHxvoC6AScCtZuYzsxhgHP698JPWxvf/NhbY/m5g4mk++2TWAy3PuFX5pQFrK6FdkTJTgItUjXgzyzvh1hn4I/5z2EuB1fj3Bh/BH7xfAGvNbA1weeD1Ux4Od86txh9y75nZ8kDbVzjn8gLvPWJmC6zUse2AG4EbzGwl8DtgTaC9LOAa4KlAe28Btzjntpymhm+BJ4GJZrYCuAm4OnAY/A9AAbA88H2/cs59jP8PhRZm9vEJzT0IpOA/r78S//n48k4HGweMKOd7TsvMUgN1zQ5muyLlpdXIRCRsBUb0zwO6u8BI9CC0+Xsgyzn3fDDaEzlb2gMXkbDlnMsBfgP8NhjtBc7bdwNeCkZ7IhWhPXAREZEQpD1wERGREKQAFxERCUEKcBERkRCkABcREQlBCnAREZEQ9P8BqYrCRcDRNxEAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Pel = bls.PEtot(Z, R)\n", "fig = plotVars(\n", " np.array([Pel]) * 1e-3,\n", " ['$P_{elastic}$ (kPa)']\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Elastic tension pressure** increases with the relative stretch of the sonpophore leaflets. Therefore, it is null at Z = 0 and increases during both sonophore compressions and expansions. Its effect is very limited during compressions, however it becomes quite significant during expansions phases." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pressure balance" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "P0 = np.ones(len(Z)) * bls.P0\n", "\n", "fig = plotVars(\n", " np.array([Pg_ng0, Pm_avg, Pel, PQ_Qm0, -P0]) * 1e-3,\n", " ['$P_{G, ng0}$', '$\\overline{P_{M}}$', '$P_{elastic}$', '$P_{Q, -71.9\\ nC/cm^2}$', '$P_0$'],\n", " limits=(-150, 150),\n", " sharex=True,\n", " unit='Pressure (kPa)'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, the **intermolecular pressure dominates during compressions**, while the **elastic tension pressure dominates for siginficant expansions**. **In between, the static and gas pressure dominate** together." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Balance quasi-steady deflection" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Pac = np.linspace(-1e5, 1e5, 100)\n", "Zqs = np.array([bls.balancedefQS(bls.ng0, bls.Qm0, pac, PmCompMethod.direct) for pac in Pac])\n", "\n", "fig = plotVars(\n", " [Zqs * 1e9],\n", " ['Balance deflection (nm)'],\n", " xvar=Pac * 1e-3,\n", " xlabel='Acoustic pressure (kPa)'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, a negative acoustic pressure drives the quasi-steady system towards a positive deflection, whereas a positive acoustic pressure drives the system towards comprression." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/notebooks/STN neuron.ipynb b/notebooks/STN neuron.ipynb index 3038b99..652dd70 100644 --- a/notebooks/STN neuron.ipynb +++ b/notebooks/STN neuron.ipynb @@ -1,540 +1,448 @@ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Subthalamic Nucleus neuron\n", "## Validation of the model implementation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from PySONIC.core import NeuronalBilayerSonophore\n", "from PySONIC.neurons import getPointNeuron\n", - "from PySONIC.utils import si_format, pow10_format, getStimPulses, Intensity2Pressure\n", + "from PySONIC.utils import si_format, pow10_format, Intensity2Pressure\n", + "from PySONIC.plt import CompTimeSeries, GroupedTimeSeries\n", "from PySONIC.postpro import findPeaks\n", "from PySONIC.constants import *\n", "\n", - "pneuron = getPointNeuron('STN')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def runAndPlot(pneuron, a, f, A, tstim, toffset, PRF=1e2, DC=1., fs=12, sr_interval='all', Vm0=None):\n", - " ''' Run ESTIM/ASTIM simulation of point-neuron model and plot resulting charge profile. '''\n", - " \n", - " # If different Vm0 provided: re-intialize neuron at different membrane potential\n", - " Vm0ref = pneuron.Vm0\n", - " if Vm0 is not None:\n", - " pneuron.Vm0 = Vm0\n", - " pneuron.__init__()\n", - " \n", - " # Run ESTIM or ASTIM simulation\n", - " tstart = time.time() \n", - " if a is not None:\n", - " nbls = NeuronalBilayerSonophore(a, pneuron)\n", - " data, _ = nbls.simulate(f, A, tstim, toffset, PRF, DC, method='sonic')\n", - " t, Qm, stimon = [data[key].values for key in ['t', 'Qm', 'stimstate']]\n", - " title = '{}, A = {}Pa'.format(nbls.pprint(), si_format(A, space=' '))\n", - " else:\n", - " data, _ = pneuron.simulate(A, tstim, toffset, PRF, DC)\n", - " t, Qm, stimon = [data[key].values for key in ['t', 'Qm', 'stimstate']]\n", - " title = '{} (Vm0 = {:.1f} mV), A = {}A/m2'.format(\n", - " pneuron.pprint(), pneuron.Vm0, si_format(A * 1e-3, space=' '))\n", - " tcomp = time.time() - tstart\n", - " \n", - " # Reset neuron membrane potential to its standard resting value \n", - " pneuron.Vm0 = Vm0ref\n", - " pneuron.__init__()\n", - " \n", - " # Detect spikes on Qm signal\n", - " dt = t[1] - t[0]\n", - " ipeaks, *_ = findPeaks(\n", - " Qm,\n", - " mph=SPIKE_MIN_QAMP,\n", - " mpd=int(np.ceil(SPIKE_MIN_DT / dt)),\n", - " mpp=SPIKE_MIN_QPROM\n", - " )\n", - " nspikes = len(ipeaks)\n", - " if nspikes > 0:\n", - " tspikes = t[ipeaks]\n", - " \n", - " # Restrict spikes analysis if needed\n", - " if sr_interval is 'ON':\n", - " tspikes = tspikes[tspikes <= tstim]\n", - " elif sr_interval is 'OFF':\n", - " tspikes = tspikes[tspikes > tstim]\n", - " \n", - " # Compute spike rate (Hz)\n", - " if len(tspikes) > 1:\n", - " sr = np.mean(1 / np.diff(tspikes))\n", - " title += ' (sr = {:.1f} Hz)'.format(sr)\n", - " elif len(tspikes == 1):\n", - " title += ' (1 spike)'\n", - " else:\n", - " title += ' (no spike)'\n", - " else:\n", - " title += ' (no spike)'\n", - " \n", - " # Rescale vectors to appropriate units\n", - " t *= 1e3\n", - " Qm *= 1e5\n", - " \n", - " # Get pulses timing\n", - " npatches, tpatch_on, tpatch_off = getStimPulses(t, stimon)\n", - " \n", - " # Add onset to signals\n", - " t0 = -100.0\n", - " t = np.hstack((np.array([t0, 0.]), t))\n", - " Qm = np.hstack((np.ones(2) * Qm[0], Qm))\n", - " \n", - " # Create figure and plot charge density profile\n", - " fig, ax = plt.subplots(figsize=(8, 3))\n", - " ax.set_title(title, fontsize=fs)\n", - " ax.set_xlabel('time (ms)', fontsize=fs)\n", - " ax.set_ylabel('Qm (nC/cm2)', fontsize=fs)\n", - " for key in ['top', 'right']:\n", - " ax.spines[key].set_visible(False)\n", - " for item in ax.get_xticklabels() + ax.get_yticklabels():\n", - " item.set_fontsize(fs)\n", - " ax.plot(t, Qm)\n", - " if A != 0.0:\n", - " for i in range(npatches):\n", - " ax.axvspan(tpatch_on[i], tpatch_off[i], edgecolor='none',\n", - " facecolor='#8A8A8A', alpha=0.2)\n", - " ax.set_xlim(t0, (tstim + toffset) * 1e3)\n", - " ax.set_ylim(-80, 70)\n", - " ax.set_xlabel('time (ms)', fontsize=fs)\n", - " \n", - " return fig\n" + "pneuron = getPointNeuron('STN')\n", + "standard_Vm0 = pneuron.Vm0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spontaneous spiking activity" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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HKAMGg8FgMPgcowwYDAaDweBzjDJgMBgMBoPPMcqAwWAwGAw+xygDBoPBYDD4nJQqAyJylojsdfy/VUSWiMgKEblNROL6ViaDwWAwGAz7kzJlQEQGAvdhv4ZRRE4FzgNGAsOBMcC5qZLPYDAYDAa/kBJlQEQKgOeBGxyXxwIvqmqpqlYATwHjUiGfwWAwGAx+IlWWgUftzzzHtZ7Aesf/DUCPxgIQkfEiMkNEZmzfvj0xUhoMBoPB4AOSrgyIyLVAjao+GUIWdToFahsLR1UfU9VRqjqqY8eOCZDUYDAYDAZ/kArLwKXAaBGZA0wCmtu/NwDdHO662dc8x0cLt7CjpDLVYhh8yva90ZW9ZVv38ur365t26GDBxiI+XbQ1Kj8Bf3PX74nanyE6dpdWsTMJbVBFdS17K6qj8vP5km0s2lScIImSR12dRl3X0pmkKwOqeqiqDlfVEcCpQLn9+y3gQhEpFJE8LKXh7WTL1xQllTVc9dxMLnlyesR+3pq9gX63vk9lTaOGjgZMnLOR/3thVsThV9fW8c2KHRG7D1BcUU1tnTbtMASrd5Ty5TIzPRMLp/z7K576ZnVUfr5fs4vRf/+Ud+duitjPif/6kpvfmNe0QwenP/Q1Vz47Iyo/AX9n/uebqP0ZouPgv37CyL99mvDnnPLvrzjwto+j8nPZ099z6oNfJUii0FRU1/L18ujbwHBM+GIlo//+Ket3lcU1XK/imXMGVPVd4E1gOrAAmAk8m1KhQlBba3We0RSQuz9YQp3C7tLINOxfvzyH9+dvjjj8f36yjAsfn8aMNbsi9lNRXctBt33Mbe8sjNiPkzH3TeHiKBSiePHCtLUs3FSU9OcmgsWbi7n93UVR+Vm40Yp7NHmdTlTV1FFTW5dqMTKWaJX/1TtKEyRJQ1SVx79a5dra8ZeJCxn3xDSWbd3btOMImbJ0GwCbiyriFqaXSakyoKprVLWF4/+dqnqAqg5U1RtV1d2wNQl4SbCV20oA2FFSFbGfymqrwZ04Z2NCZEoUf3hrAac9+HWqxUg5Xip/8WTQHz/g7AlT4xZeRXUtfW55nwlTVsYtzHRl2da99P/9JD5auCXVouzHvA1F/O39xfz2tbmu/K/YbrWBxeXRTWlEgoe7objiGctA2hDDMUia8CY8+vD9UcwN6cS8DfGz/OytqAHgia9XxS3MdGWOvZbjExdrQRJNtW0NCuSXW+LZnkksjX0aYpQBt0RR6hJdqFyd0+ivcp4xmEM5DbHi5YGu21F4ImuFh5MrrhhlIINwVY/8UtINvsbLHWCy8LIaGS8d1+Sze4wy4BYv1yyDwWBohMRPV7rHrWSJNJj5pak3yoBbvFufDD7AjIAMhuTgl6pmlIEoiUUDTVQD7mZNgpl6Tk9MvkWHSS+Da3xWdowy4BIvaotmyUDmYywCBrd4e/FpfGRLxDZAv9Q5owxkAG7quJebBYMh3vikPY8MDyeG247Xb9sAE4FRBpKAlxVyvxyokSl4uSwZvI2Xi07cdhPEJxhfktOUAxE5CBgLDMZ6i+AS4HVVXZpg2TyJlyuUwT94eUW4FzH1dh8ZWXISkMF+KzONWgZEpIOIvAa8BLTDemfAd0Bb4DUReUVEOidHzMwg4ecPZmQtNzjxWwMVL0zVSA+8mE9+UbzDWQaeAu5R1ZCvnxKR44AngNMTIJfn8ZJ53dWaAWNvTku8U+rSA1PK0wMv5pPfmshwysCZqtro68NUdYqIfJkAmQwucaPBms7FYPAHgc7NSwOZ/YhRNi9Hzes0Ok2gqnUicpyIXBI8HSAilwTcuH2wiIwTkbkiMkdEporIKPv6rSKyRERWiMhtkgFD2ERHwNU5AwmQw5B4TL4Z3JL+LWnjBKLmF5N+Igi3ZuAG4FHgPGCxiIxx3P51LA8VkcHAvcDJqjoC+Bvwpoicaj9vJDAcGAOcG8uz4k0G6CaGDMCMgAxuycSiY5rl2Am3tfByYLSqngb8HHhFRA6078Wa9JXAlaq62f4/A+iC1fG/qKqlqlqBtW5hXIzPSgiZWKEMhkzF06ZxQ/wGWYnIZp8UnXDKQLWqFgOo6ofAjcA7ItKeGJNHVdeo6vsA9jTAP4F3gK7AeofTDUCPUGGIyHgRmSEiM7Zv3x6LOFHhZQXUTXtn2sj0xIyEDJmIaY5SRzhlYLuIXCYi+QCq+izwJjAJaB2Ph4tIIfAqMAC40pbHWR4E62yD/VDVx1R1lKqO6tixYzzEiYhYCmvCRiemY/AdRokzRIuXT+nzrmT+IZwycA3WVMHPAhdU9bfAl0DvWB8sIr2AqVid/RhV3QOsA7o5nHXDsg6kNclaZ+Du3QSmV0krjEkgKswan/3JREUyEYqOl5WnRBBuN8FKVT1aVZ8Jun4TMSoDItISmAK8qarnq2q5fWsicKGIFIpIHnAp8HYszzKEJgPbA19h8s8QLX7Qi8ySAfdEchxxF6xOuV3QrZtjeO51WArFWBEZ67j+I6ypiOlALpZy8GwMz0kYmahdGwwGQypx/aKiRBxH7APlyUmTygDWwr4NwMp4PVRV7wLuauT2nfbHECE+K7MGTJ4bMou4bSYwgzTXRKIM5Krq2QmXJE2IZRFgoguqG9lM5UlPTLYZ3OLlsmPWMKWOSF5hPFNEhidcEoNrzCIp/xBLTvt5r71/Y26IFb9Um0gsA98Ac0RkM1AduKiq/RImlSHh+LljMBj8TCbWfbNmIHYiUQZuwjqBMG5rBjIBY84ypBuq/mvgDOlBvLbxmXbZPZEoA3tU9dWES2JICabqGDIZo/ukF653E5icjplIlIHJInIf8AbWOwUAUNVZCZPKw5jO0+AFXB09HX8xDGmEl9cWmd0EqScSZeDn9vc5jmsKmDUDEZLoOujdKm6INx5uzw1pgukvo8MvUw9NKgOq2ldEWqhqif2eglaqui0JsnmaaDRQL2qrHhTJkGCshWNGm/ArJuejw29TD01uLRSR84DZ9t9ewAIROSOhUhkMBkMc8aJCnjI8nBZeOoEwgF/KTiTnDPwBGAOgqsuAkcDtiRQq00iWaddVofVJQc88TMYZDMGYWuGeSJSBbFWtf3Ogqq6P0F9GEouWmLA3GPvLmuVrYjFdmobS4HViLaOZeIZCsoikU98mIleJSI6IZIvI5cDWRAuWSSTNMuCiKvllcUymYPIrOkxq7SPQDmViGQrslIhnzPw2yIpEGbgaGA+UAxX272sSJZCInCYi80RkqYi8JiKtEvWsWIim0CVaWfVZmTW4xAya/E0mL4hLZMz8Um0aVQZEZChY6wRUdSTQCWinqoer6qpECCMiHYGngHNUdTCwCrg7Ec+KmTQvIaZjSE9imyYwmW7wZt338ijcL1MP4SwDd4jITBG5R0SOUtXdqro3wfKcCHyvqsvt/xOAC8XLp2VEQHpLb/AiPmmf4oZfGvR0J+Z8imM2p3m3EzWNKgOqei5wBDAFuMQ23T8uIqeLSF6C5OkJrHf83wC0Alom6HnRE8sCwgSPzFydSmfayLQilvbJj3ltlID0INYpDJ/12wkh7JoBVa1S1UmqOl5VDwKeAH6I9SbDRMkTqvbWBl8QkfEiMkNEZmzfvj1B4jSOl0yuftNgDQZD9PihmUhEu+ydlj6xhFUGRKS9iHRyXGoO3K+qoxIkzzqgm+N/d2C3qpYGO1TVx1R1lKqO6tixY4LEaZxkDDiiHdWY8+r9g7ECGdySieUgoOfEM271ulMGplcowi0gPABYAhzluHw2ME9EBidIno+Bw0VkoP3/amBigp6VNAImsGgLakIrrU8KeKZR3+iZDDRESSYbBuq3FsZ1zYD17Ze6Fu7dBHcDv1bVtwIXVPU6EZkB3AOcGW9hVHWbiFwGvC4iucBK4OJ4PyceRFM89hWqxD3D4A9iWjPgwxKlQd8Gb5YDP0xheJ1wykAvVX0x+KKqPi0iNyZKIFWdBExKVPix4qYi7TNhJaYSmnpkiIRMNA8bMgtTRlNHuDUD+y3ac1AVb0HSjWSsUo56zUASnmHwBmZ9SJT4OvIWXh59xyrbvumz+JGIdQheJpwysFVERgRfFJGDgf0W9BniT8RlMDAN4aLU+qScZwz1609SLEe6YdJrH17u3NxOYSRC0fHbLq1w0wR/BSaKyO3AVCzF4Qjgz8CVSZDN00S3ZsBdAx5ppXWzR9eL84aGCIjpnAH/5bkPoxwG7yqS8ToqOZ5l3FgGbFR1KnARMA6YjqUQ/BS4UFU/SY543iWaAuK2UEXbYbubJnDhyZAyYmmgTFb7m3QY6Lpvj7yr6KQL4SwDqOqXwPFJkiUtcFVYE1wJ06GSG+KLqzdU+ril9KNVpDG8mBRud1wF+08EHkyuhBBWGQAQkc5Y+/3b4TyHQfVXCZTLgItK65dSazBEiakazjGJ91LDi+MZvw2ymlQGgBewFgzOxoulKK1I7NZCsw4g85FYhlA+LB6mTuwjHRbExWrBSYTVwy9WpUiUge6qOjThkmQwrtcMRLqA0EUd90n5NjgwHaMBvFn3Y54mqP+ViM2F/iDsuwls1opIYcIlyWDcauRRLyD0YCU3eAc/lw8/xz2At7s289bCVBOJZWAzMEdEpgDlgYt+XTPgxTbF7D33D4k4XMUPGKvIPjydEjEKl5h3E/iDSJSBNfbHECMJO2fAaMW+ob6BMgdMRYYvIx2aWMpO4olNpnidUxAKTyZXAmhUGRCRjqq6XVVvD+Omk6puS4xomYP7cwaidO+TQutnYnpRkY8LiI+jXk86DBq8lE1e3n2RCMKtGXhSRG4QkbbBN0SklYjcBDydMMkyCLevwoy08U7lqzb93MEYDOmIl2tszLsJ4iQHpIfyFE/CKQNnAtnAAhGZLCKPicjj9tqBpfY9V68xFpFxIjJXROaIyFQRGeW4d6uILBGRFSJym6TDfpgmSKQJK/CEaIlXpTG6QGowOwujw89xD1C/tsjDiRHroUNejpvXaXSaQFXrgHtF5GGsUwiHYOXVW8Cnqlrp5oEiMhi4FzhEVTeLyKnAm0Av+/d5wEistyZ+BCwCXnXzrHQnHaYJTN1LLrEolr5uKP0c9wBpP6xqnERaR/1Sb5pcQKiq5cD79iceVAJXqupm+/8MoIuI5AJjgRdVtRRARJ7CejeCZ5SBWMxYiT5nIBVl1kqPDG5lMgg/rqj3X4ybxotpEmuHmwjra+Itut4iknMGXCEip4pITfAHOEZV37fdCPBP4B1VrQJ6AusdwWwAeiRKxmTh2oQV8VsLU4cXG5ZMxphD3eFHRSiYfQuZvZsWXhLNbC2ME6o6KVz49kFGT2MpACfbl7NomPaCNV0Qyv94YDxAr169YhfYg0TdgKWgJnmp8voJV8nu47wy5TQ9iFVpM/nsnoRZBsIhIr2wXolcC4xR1T32rXVAN4fTbljWgf1Q1cdUdZSqjurYsWNC5Y2VwBrIRI1O3B1HHB9ZzIgrfXD3imuTv5lCOqzFdl3cEvnWQp9UgUjeWtgFuBTrrYX1qOrNbh4oIi2BKcAzIc4wmAj8RUQeA2rs5z7t5jleJFFrBurdR+c8LvilomQCbvJKNb23WJnyuY80zsaIMVsL3RPJNME7WKPzlXF65nVAb2CsiIx1XP+Rqr4rIgcC04FcLOXg2Tg9Ny4ks22J9FnpsGXIEB/qrUxJyuxMKVKZEo944E4p1IRaFmLNn0Ssh9h3zLs/Sk8kykCuqp4drweq6l3AXWHu3wncGa/neQH3by2M8tAhs2bAEAa/NGqhMNMdsW2/S5aFyG02JURR8dli3UjWDMwUkeEJlySDcVtOI7cMGPxGNO1TLDsQMqUTzYxYxEZMZ1TEUQ6DN4nEMvAN1lsLNwPVgYuq2i9hUmUoXhqZxe0EQg/FyQ+4ac4FK7/9eGqhKZ/742X9LlOUz3QkEmXgJuDnxG/NgG8xCwgNsVJvZUpSupv8zRxif8lV4m2QXtpM4LfXhUeiDOxRVc+cAJhq3DSObg+viHRUs29RWZQPiAN+qSiZgJ9HXT6O+n64mi6KvxgNw/dw/vil3kSiDEwWkfuAN7COEgZAVWclTKoMw/VcXVpYBvxRUbyG20VgyXiOwZvsG+kmp+ykO+lwLkM8iUQZ+Ln9fY7jmgJmzUCEuF3tH/ECwlTuJkj6EzPAwCZMAAAgAElEQVQDt3nlZhupiLhuzdO9E0h3+eNKLNMESarpseZXPPPbX6pAZC8q6psMQfyAl9qleFUa09gmF58NVmLGFM/9cXv4VDIwlqjUEVYZEJEWwNXAUVjbEL8F/gucCWxU1ckJlzCDSPQCwpSQRBnNlERsmOTzN+nwFj4vllEvypQIwr1IqB1W578Y+MS+fDzWK4f3AmMSLp0HcaO5uq2CES8gTGElN5p8cnFzgJWf54qNArk/XkyRWNuRhJw55H3dKa6EswzcDjyuqvc6rv1HRF4HqlW1OLGiZRAu94NF2o6l8rW2pq1NDUmbw/Vk1xE5pnzuI5ZtqemSjvEsr7Eo0elIOGXgOGCE84JtLRgKNEugTBlLoipUKgutP6qJd4htr3j85DCkL64sRElTPg2pItxxxHWqWht0bS/WroLyxImUuUR/zkBkpNYyYKqvG5KZbG7PuYD0VyDSXX6vkLQFhCa/UkbYdxOISCvnf1WtBrYkVKIMJlEvKnJDvDT9ZNZd01AAbrYW1vtxMyI0ZBqZeOhQQk4gTOFhbqkgnDLwIvCYiOQFLohIPvAI8Hw8Hi4iZ4nI3qBrt4rIEhFZISK3iddOfkjq6vnkPcst6SCjwT3pbvnxy3xvJMSSlckrB97JL7dvm01XwikD99nfq0RkoohMBFYBtY57rhGRgXY44rh2KnAeMBIYjrVj4dxYnxVPYikXiapQ9RpsQkIPj2ls0wc/5pRfGvJo8OMLq1wRw/RaOtKoMqCqtap6PtaZAp/bnzNV9UKNsVcTkQIs68INQbfGAi+qaqmqVgBPAeNieVa8cRVz21OiClVKNVi/1BSP4Gr+P5ZXGEfvxVOku/yJwMvWHi+dQOg3IjmBcAbW2QJRYY/y3wlx63LgBOBRYF7QvZ7AZ47/G4AejYQ/HhgP0KtXr2jFc00sI+GEHTpU30GY3QTpgtt0i03x8985A4b4kLwTCN2RyNlkb81TJ46wCwhjQVUnqWpO8AdoAdSo6pONyOMsD4I1LREq/MdUdZSqjurYsWP8I9AIyWwcE9q5p+FxxJnULyVzdBZTY5bmie7lUXCqSJIeGV3wGvj2UH55SJRkEMmLiuLNpUCBiMwBcoHm9u9TgXVAN4fbbljWAc8Q05qBBB06FAg2mpMI41XOk2mN8FRDESMpmdHJnOSLGB9GuVECddWLb68MhO+19eJ+ImGWgcZQ1UNVdbiqjsBSAMpVdYSqbgImAheKSKG9i+FS4O1kyxiOmDqkaKcJonQXTT2qi1PPkMwOpi6DWna36S8xHCPnbuFYeie6HxWgRgkMGlz0t4lOR/PitNSTCstAo6jquyJyIDAdy2owEXg2tVI1JJZFWIkqpwEFJZo6HrfKF59gInxW5tR0t+nv6t0EsSwgTPskT/sIxI2AMu1m7J2scwayPGgY8IuxIqXKgKquwVpD4Lx2J3BnSgRKMIk+dCiaQpuOTWT6d0yxE7AoGHOqIVpiMcUneopun6XMO+Xab81N0qcJ0h0vLphzI1NdnGzuyZzHzyRlwG1c6lyMoAJ+3J1Jn95kUpmJlVgsA4km1myK4R1MBhujDERJTFsLE7SAcJ9OnfxqnjG7K5KM27jUTwlFNScU8Ov+eelKeksfX2LJy0SnY52bcu0gIF8iphn8YoQzykCUxHakZ9Q+ogo3qmmCNGwl01HmxnBrmNk3uos8swMNrS8PHUr3CMQRN+1EsN9E4Wbdk5Nau2Jk+aXnTgBGGYgSN6vA6/fQxlmW+vBT+EpSL06bpANuR2luRlDx2jliSG+SadWMOvwYg4/VshAuTL9glIEoqXUxpHNvEo7OXTQLg6pr46QMJLGLrqmtS9qzEk2Ny/QPNFDRjIBiWjOQTGUvAQ9zU18zlUD1cbX4NMHJuG8tjLvePBHlNFBH/WJtMMpAlLjpRAOFKtrGLvIFhIFCG3nY1XHqWJPZWVRlkDJQXecuLvWjlSSZepOp7CWi4w6Umdxs09RV1bhPi0hzxm27ss/M78p7QspOvNrIdMHUkCipcdGIBxqkaItrpI13RbUVfk4UlTzQMMRKMsdd8bJmeAG3loFKO6/zcpJUdZOY5InI3/oOMFnplWSiGWAEOjc3aRHpY8qrQ54e3yQVNZa/vGbZrvwH4hbPwUlNnfu1NulIZtaQBFJcXgNAi7zIj2gor7IrSIIKVX0FjKLUllZZ8ciJcfltMlebl1TUJO1ZiWavy7iUVFr+mrtsNBNJrGVhb0V1nCTZR6Cc5zfLzKYumiQPpEUiFcmKKnfKQFmlrQy4lC1QL+JpySqtzJz2JhIys4YkkG17KwBoV5gbsZ+txRWunhVpwQ6EH001CPjp3Co/WrEaNPrJVJo37SlP4tMSyxaXZWJLkeWvZX6zqP1G2nE4z6CIJn+Lyvd15m4Ug81F7tIkbJh7rDA7toy+nHuVPWVV9b+jSeVA2enQIi8i9w3LQWRP2uQyDzcXWXXbTXsE7utTJGFm0pbmcBhlIErmbSgCoFe7gojcV9fWRbWAK6BsQGSNt6oyf2NRRLI4mbve8tOjbfOo/a7YVuJ4ftTeXfPNih0A9GwXvcxeY+76PQB0bxNdXBZuKgYiXzW9q9TZcUSWWUu37t3nJ4r8nW3HKVp/9f7X7Y7eUxN8v2YXAF1bZ44yMHdD9PUd9pWdSI2BK7ZHX8/dtEWwL06tm0ev5NbU1rF2ZxkQv/aosqY27mF6HU+9m8DrFJVX8+7cTQBkR1ijPlu8rf53JIXqzVkbo3L/7aqdlNmmuUgL7a7SKt6eYz2nWZSLiVSV/05Z6bwSlX831NTW8cmirTw/bS0AhbnpXWxLK2t45fv1ALTMjzwu63eVMd3u3CLN6zdmRv/Sz5emr6v/HakCoao89c0ah7/oKKms4ZlvrfyNV8c9a91u3p+3GfDmqXtuUFWembqmwf9IYretuIKvl++I6lmvO8pOJPlZW6e8apfraJTc9bvK+Gjhlqhkc/KS/UyIT8etqjz42fK4hpkOpHermkTW7izlptfmsae8miyJrHJs2lPOX99bVP+/qUI1c+3uhoWwiad8u3Inv3llDoW52ZRW1UZkml26ZS+/fW0OZZW1FORmR9XYz16/hwlTVvLJoq3071jIyu2lritKbZ2yu6yKXaVNf9bvLmNvRQ1DurSkRV6O6/n2VFNXp0xfs4u7Ji1mU1E5bQoiHwXtKKnkuhdnkZeTRVlVbZP5pqp8tHAL93+y1HEt/DOKyqr5z5QVPPvtWnKzsyLevbG5qJy7P1jCl8u2064wt4E1oinKq2r5bMlWHvh0Oet2ldGjbXPXiyuLK6pZtmUvS7bs5duVO/lo4RY6t8rPmP3ia3aUcv8ny5i8ZButmzejqLw6otpbXFHNdS/NrtcZmvJTW6e8NH0dT3y9uv5aU23L1uIK7nhvEfM3FkW8QFFV+W7VLm55cx55OVmoRmeQX7FtL89/t45nv11Dn/YFrNlZ5mpoUlunrN9VxpIte1m6ZS8fL9rCwk3FHNqnXb3y7QeMMoBVKFWtSlJn/y6rqmHR5mIWbCxiytLtfLdqJ3k52TzwsxE88fVqdpZUNhpebZ3yxswN3PPREiqr67hz7IH8/q35bGxkzruksoZ/f7qMJ79ZQ4+2zbnih33588SF7CgJ3ahu2F3GnZMWM2n+Frq3ac7jl4zmgv99x/rdjc+pz1izi0e+WMmni7fRKj+HRy46hP98vpJ1u8rCpsu8DUV8uHAL78/bzLpdZTRvls0tpwyhe5vm/PKl2dQ20Uhs21vBvPVFLNhUxJodpazbVca6XeXsLK1stHNqmZ9Du8Jc2hXm0q1NPiN6teGo/h048YDO/PLF2RQnYKFZvKitU3aVVrGjpJIdJZVsLqpg5fYSVm4rYfa6PewsraJDi1wmXDiSt2ZvYI7DtB6K0soanv12Lf/7ahWllTU8dMHBjH9uJmt3NJ5v8zbs4e/vL2ba6l0M6dKSK37Yl5ten9dgTt9JRXUtz367hv98vpLiimrOHdmDA3u05s8TF9bvXghGVZmxdjdPf7OGDxduQVX57QmDALj/k2XUqZLdyIh1T1kVU5Zu59PFW5m8ZBtlVbX0alfAU5eO5v15m3l33qawaaKqrNtVxux1e1i61WrAl27Z26B+tSvM5aIjevOr4wdy8ZPTI1JsVJXdZdWs3lHK5qJyistrKK6oprZOycvJokVeDt3bNqdXuwK6t2ke1e6dSFFVdpZWsXlPBZuLytmwu5yFm4qZv3EPy7aW0CxbuOGEQWQJ3PfxsrCKTmllDc9/Z5WdPWXV3H/eD/jdG/NYumVvo36mrdrJ7e8uYtHmYg7v146TD+jCbe8uorQy9MLA4opqHv1iJU98vZraOqsMrNtVxmszN1BXp2SFsKBW1tTy3tzNPDV1NQs2FtO1dT5PX3YoN7w6J+y6IFVl0eZiPl64lQ8WbGbZ1hJE4PzRvbj0yD6c9MCXTe5mKKuqYeba3SzaVMyyrSUs37aXZVv31u/IAjigWyvuPvtAjuzfgWPu/TyiHWSqypbiCtbuLGNPWRV7yqqprKkjJ1vIz8mmS+t8urdpTrc2zT27syUlyoD9muKHgNZALXCVqs60790KXGLL9jxwuzahli7aXMwPbv+4XrNUpcHvuoDGqdZouy7oflP061jItccN4OIjetOpVT6/fGk2YJkhD+nVtoHbqSt28PdJi1m4qZiDe7Xh7rMPqt/28pd3FnLJkX3q3aoqHyzYwh3vLmJLcQUXHNqTm08aUq+NXvLkdNbcfVq9+4rqWh75YiUTpqxEBG44YRDjj+lHhV0Bnvh6NX84dWiDCvj50m08PHkFM9fupm1BM37z40FcfERv2hbmcvnTMwCYvGQrxw/pXO+nqKyal75fx/PfrWXD7nKys4Qj+7fnl8cP4OThXWiZ36ze/PrLF2fzyQ3HNkiDzUXlvDR9PR8v3MISu+HJEujWxmpIfzSkE51b59Pe7vDbFebStiCX9i2s73CVZf3uMpZtLeHblTs5on/7/e6v3lHK9NU7mbehiHW7ythcVEFZZQ3l1bUoUNAsm/zcbFrk5dAyP4dW+c1omZ9DS8d3K/sbrDUfgU9lTR17K2rYW1FDSWX1vt8VNRSVV7OjpJJdZVX7lanc7Cz6dijkmEEdOW5wR04Y1pmC3Bzu+mAxW4srmThnI2eO6N7Az9biCp6euoYXvltLcUUNxw7qyC2nDGFo11YAfLhwC0Vl1bR2WBc2F5Vzz4dLeWv2RtoX5vLXs4ZzweiefGWbhy8OKk+qyttzNnLfR8vYuKe8wTNetqcKrntpFu/98uh6P3V1ysS5G3n8q9Us3FRMq/wcrvhhXy46vDc92xVw1weLAfjHB0v44+nDGjxrytLtPP/dWqYs205tndKhRR5njujG6Qd147C+7cjJzuLhySsoq6oNmSYLNhbx+swNfLJoa33H3yxb6N+xBSN7t+Xnh/ViaNeWDO7Sim6t8+sP1wnMY6/YtpcBnVo2CHN3aRXvzd/M1BU7+H7NrkYV8GBys7Po06GAAZ1a0L9ji/rvLq3zyRYhK0uoqqmjrKqGsqpayqpqKKmsre8odju+d5dVs6esit1lVWwtrtxv22+HFrkM796acw7pwU9GdKNr6+b848MlADzw6XJ+d/KQBu5LKmt4ZuoaHv9qFbvLqjl6YAd+e+JgRvRsw69fnsO01bvYVlxBJ8divY17yrlz0mLen7eZbq3zeeiCgzn9oK71UwW/fnk2H15/TL376to6nvt2LQ9OXs6esmrOHNGN354wmF7tCzj9oa8AePKb1Vx5dL96P3srqnny6zU8991adpRUMqBTC+4ceyBjD+5O89xs1u4sY+3OMhZtKmZYt1b1/orKqnn5+3W8MG0d63aVIQKje7fjtjOGccqBXencKp/19qDmT28v4KLDezdIj70V1bw7dzNvz9nIrLW767cMdmqZx6DOLbnwsN4M7tySwV1aMrBzCwrsachAmL97Yz4/G91rvzKwdMtePlywhakrd7BgYxGlEeykyM4SerZtTp8OhfTtUEi/DoX07dCC3u0LyMvJQkRQlIqqOkqraiitrKG0qpbSSquN2VNWzZ7yKorKqrn7nIOafF40JF0ZEJEC4GPgClWdJCJnAi8AQ0TkVOA8YCSWkvARsAh4NVyYbZo346wR3RARRKxz261vyMqyT3G3r2cJDd2IdV+C7uflZDO4S0sO6NaK9o2svp2yZFu9MrBpTzl/ensBny3ZRvc2zXnwgoM546CuiAgLHItqiiuqaZXfjJ0lldz0+jwmL9nGsK6t+O+4Q+rDcurSqoqIMG/DHq5/eQ6rdpRy2kFd+f2pQ+vn5Zyjt1U7ShnQqQU7Syr53Rvz+XTxVnq0bc5tZwzjvNE96wu6k8lLtnH8kM6oKq/N3MBdkxazu6yaw/u14zc/HsSPhnaiTUHD3RMBbXm5YzFhcUU193y4hJenr6dOlVF92nHrKUM4pHdbDujWKuSzoyWwCOrxr1bVKwO1dcrbszfy9NQ19Q1/q/wc+nQoZGCnFhTm5dRvxauorqWs2qpceytq2FZcYnfq1RFVZrA6oJb5zeoVihZ5OfRqX8AhvdvQoUWe45NL51b59GgbehQZWKD0+swN9R1fSWUND09ewZNfr6a6ro6ThnXhqmP7cXCQ0gkwZ8Mejh3Ukbo65clvVvPPT5ZRU6dce1x/rjmu/74dB44CFRitrdtZxg2vzmHG2t0M796Ke396EEcO6FDvLqBgLthYXH9tyZZibnptHvM3FjGocwv+PnY4Yw/u3iBfd9md6eNfr65XBpZt3cvv35zPjLW76dgyj18c3Y+Th3fhoO6t9xs5Bjr5hyavqE+T1TtK+dt7i/hsyTZyc7I4blBHrjq2H6P7tKN/xxYRj7Q+XrS1XhnYUlTBvz5ZxpuzN1Bdq/Ro25xjBnZkWLdW9O1QSI+2BbQpsBTELBGqausoLq9mw+5y1u0qs609pSzebHUIbs68aZmfQ9uCXNoWNKNtQS79OhTSuVU+XVvn06V1c7q1yadr6+Z0aJG736mBu+2pmAlTVtYrA7V11nqChyYvZ3dZNWMGd+RXPxoYsuzMWrebk4d3rV/rce9HS1GU6388kKuO6U/zXKu+VNqKyRKHNWHBxiKuf2UOK7aVcNSA9tx6ylCGd29df3+nXQae/25tvTLwwfzN/GniQnaUVDJmcEcuO6ovRw/sEPI0xO/X7GJYt1aoKi9OX8c9Hy6lqLyaw/q249rj+vPjYZ3D7ojYtreCTi3zqa1TnvpmNQ9/voI9ZdUM6NSCXxzTj8P7tecHPVrv16YF4xRtT1lVvfvpq3dxz4dLmLF2NyKWJeGckT0Y2KkFfToU0r4wjzYFzcjNyaKmVimvrmVzUTkbd5ezdmcZq3eWsnp7KdNW7XJ1LkNudlaDQUC8SIVl4ERgpapOsv+/AwQmp8YCL6pqKYCIPAWMowlloFub5tx+5vAEids4K3eUAjBl6TZ+/fIcqmvr+N3JQ7jsqD7kO/aBOwvVwo3FdGmdz7jHp7GjpJI/nT6MS47o3ajJMdDwXPP8LDq0yOO5Kw7l6IEdGzpyhL9gYxG52Vn8/PHv2L63kltPGcLlP+wbdqHgmh1l1NUpf5y4gBenrWN0n7b85YwDGlTwYModHeeOkkrq6pRzH/2W9bvKuPCw3ow/ph89I9xx4YbVO620X7+rjOtenMXcDUUM6dKSP50+jGMHdaR/x8Koj12tqa2jxFYSiiuqEYTcHCEnK4tmOVnkZmfRMj+nXoOPF6u2W3FZvLmYq5+fydqdZZxzSA9+9aMB9G5f2Ki/BRuLOLxfO3790hw+XLiFHw/txF/OOCBsuq/aUUpReTWXP/09qso/zjmQc0f23K9TLnM0UkXl1cxat5trn59FYV4OD/xsBGfayncwTn91dcqni7fy65fnUJCbzZ1jD+TcUT3ClsXAFNA6W1H6aOEWbnhlDllZws0nD+bCQ3u7bggD6fzJoq3c8OocKqvruODQXpw/uleDkWgo8ptl0yq/GT3aFnB4v4YWqcDK85XbSthu14VahdxsoSA3h4LcbArycijMzaZNQTPaFOTSpnmzmKYZyqoapnNpVQ1XPDOD6at3cfTADtx44mB+0LNNo/7nbiji5OFduf/jZTz8+QqOH9KJ23+yf9kpq9q3PmdPWRUz1uzm2hdn0a4gl8cvHsWPhnbarxwEOrg1dh4+8OkyHvh0OQf1aM0Tl4wKKxfAqu0l1NTWcdPr83hr9kYO79eOP542LGx75BRh/oYiDu2bzTXPz+LrFTs4emAHfnPCIA7u2cZ1vV2w0Zo2ufuDJTz+9Wq6tMrnj6cN5ayDu0e0VbNvh/3rsaqybW8lq7aXsn5XGVW1dbYFWynIzaEwL7v+uzAvhzbNc2ndvBn5zeLb/gRImDJgj/LfCXHrDmCLiDwB/ADYA9xs3+sJfOZwuwHo0Uj444HxAL167W/CSQZLNhfz+ZJtjH9uBgM7teS/Fx5CnxCZ7nzD3OdLtzFp/mbKq2t57eojOKjH/hWj0mEmfGHaOp77di2DOrfk6ctGN2qlCPD1ih08OHk5JZU1vHrVEU1WPLBGXvd+vJQXp63jqmP78buThoSc63PiHEV/tXw7j36xiu17K3nlqiMY3addk8+MlbU7y1i3s4yzJ0ylsqaWf58/gp/8IHQHFSk52VlWQ93EiCHebNxTzpItxZz/2HfkN8vi1auO4NC+Tafh3PV7uPG1eXy4cAt/OHUoVx7dN2T8nZajt2dv5Lnv1tK2oBnPXH5oo8qG8/CY579by0OTlzOgUwuevGR0A/NyOH8vf7+e295ZyNBurfjfRSPD+gsQWBxaVVvHxwu38H8vzGJ499ZMGHcIXVvHtqV06Za9fLRwC9e+MIsDurXioQsODqtsRUpeTjaDOrdkUOeWTTuOE84R5aLNxdzx7iJmrdvNfef+gHMO6d5kPZi3YQ8PT17Ow5+v4IJDe3Hn2OEh/ZRX7Ss7T36zhke+WMnQLi158tLG2yKnojJhykoe+HQ55xzSg7vOPjAiK87CTcXc/u4i3pq9kRtOGMQvjx8QVb2esXY3j365illrd/OPcw7kvFE9XbULTj/zNxbx3rxNvPz9ei4+oje3nDIkZkuniNC5VT6dW+WHnPJMNglTBuyR/37hi8gfgFOBMao6zZ4mmCQivbHOPXAa3ARruiBU+I8BjwGMGjUqJcuFV24v5bKnv2d491a8cOXhje6RdZbDx75cRX6zLF4ZH1oRgIba+GNfrqJ7m+Y8cemoRiufM/zXZ26gWbbw/BWHRaQIgNUZTZiykgsO7cUtJw+JqOKUO2T8zStzaZYtPHXpoUlRBMAyiZ7x8NcAvHnNkQxMYkOcCE5+4Cs6t8rj9auPjNii8vGirQDcfPJgfnFMv0bdVTg6joc/X0G7wlyeu+KwsM9xNuj3frSUnu2a88xlhzapjDo7qd+/NZ/+HQt55rLRrhSs8c/NZGjXVjx7xaG0cnHIUjDzNxZx1XMzGdGzDS9ceRiFUZwi6jWclrnTH/qaLIEHLziY0w/qFpH/b1bs5JsVOzn74O78/azQigBAWfW+ev7gZ8vp076Apy87lLZhDl1zrnn4x4dL+PHQTtzz04Mi3o49Y+1uZqzdzVXH9uNXPxoYkR+n/BPsrc//Pn/EfutOosEpbWCNxnVjBnDjSYNdh+llUlEbNgGLVXUagKpOFJHHgX7AOsBZmrthWQc8SW52Fu0Kc/nfxaPCHpbhfOFFhxZ53HHmAWE7aufK3SFdWnL/eT+gU5gT1JyFtle7An79o4Ec1i9yTbNr63yGdm3FHWceELEG7ewsmmUL/z7/YH44sEMYH/GnqLyal8cfnvaKQLNsITtLePLS0VFNrRTkZnPmiO5cc2z/sO6cedW1dT7/+tmIJp8TMPdnCXRt3ZxHxo1sUhFwPqsw1zJtPnrRSFeKQIcWuYDw2EUj46IIBOjUMo/HLh6Z1ooA7DtaOMBdZx8YsSIA1pqaHw/tzD0/PSisFTCgdDTLFnq0LeCRcSPDKgJOhnZtRcu8HP75sxERKwJgHTz0wwEduPmkIU07tgkO/boxA2JSBGD/g71OO6grvz1xUExheplU1IgPgPtFZKSqzhSRY7CsAauBicBfROQxoAa4FHg6BTJGxJSbjqtfgR6OQAN5aN92vHrVEU2GG7AMXHtcf24+uekK4ezAv7x5TJPug5l6y/FRm9ECUxl/OHUo543u6erksFh44crDaJGXE7H1w8t8dP0x5DXLjvo0wgW3ndTkdA7sK0+XH9WXP58xrAnXFoFO4N6f/oBzRoacqQvr77Wrj2RIl5YRyReKL24aQ3aWNFh7EysfXn80nVrmR3WUuFcJpPOLvziM9oV5DO4SnUI8588nRlh2rOf87azhIVfUh+O9X/4wKiVgn2wnRN0eBbZYdm2dz6RfHR2xwhKOwK6gDi1yeWTcSEbEsOYgHUi6MqCqW0TkLOC/IlIIVAJnq2oF8K697XA6kIulHDybbBkjpVuEjXeg42wfYQENvL0t2g62i8tzvd0U8IDpOb9ZVtIVAYCjBiTXCpFI+nVs4cpfpB1tYI99i7zIO9aAn4Lc6DrjgBWsMC/btSJg+Y9/0zSkS/hFgulEoE3p2rp5yMVpTRFp3gReDdwiL/o67kYRALftkZUerfKbxUURsMK02rgWeTmMStL0ZypJia1MVb8EDmvk3p3AncmVKDruO/cHUb3r+oh+7bno8N7835gBEbm//Id92V5SyYVB+2UbozA3m6uP7c+ZIyI3EwI8Mm5kg/nkaLj+x4Morqjh7EMiHzXGg0fGjWSl48z0dOZ/F49yFZebTrL2jEfKRYf3ZvX2Uq44uvF1BcH86bRhtMjN4fihnaKSbcK4kTz/3Vp6tnW3k+SFKw9j2qqdrvw2xt/OGh7xuxzShQnjDuHpb9ZE/I6UAH8+fRgDO64tPYQAAAwHSURBVEeufN56yhBaN2/GCcM6N+3Y5sVfHMaMNdG/Z+LvY4e7nhLq16GQS4/sw8VHRNZmRkLv9oWcfUh3xodZj5NJSDJfQZsoRo0apTNmzEi1GAaDwWAwJIu4qrjePBfRYDAYDAZD0jDKgMFgMBgMPscoAwaDwWAw+ByjDBgMBoPB4HOMMmAwGAwGg88xyoDBYDAYDD7HKAMGg8FgMPgcowwYDAaDweBzjDJgMBgMBoPPMcqAwWAwGAw+xygDBoPBYDD4nJQoAyIyVkTmicgcEZksIv3t69ki8oCILBGRFSJydSrkMxgMBoPBTyRdGRCR5sDzWK8tHgG8Czxo374KGAQMB0YD14vIocmW0WAwGAwGP5EKy0A21tuWWtv/WwAV9u+xwFOqWqOqu4GXgXHJF9FgMBgMBv+Qk6iAReRU4J0Qty4HrgamishOLOXgKPteT2C9w+0G4KBEyWgwGAwGgyGByoCqTgoVvogcCLwFDFPVlSLyK+ANERmBZalQp3OgNlT4IjIeGG//LRGRpfGUH+gA7IhzmKnGxCk9MHFKHzIxXiZO6cECVR0er8ASpgyE4STgG1Vdaf//D/AvoD2wDujmcNsNyzqwH6r6GPBYooQUkRmqOipR4acCE6f0wMQpfcjEeJk4pQciMiOe4aVizcAs4FgR6Wz/PwtYrao7gInA5SKSIyJtgPOBt1Mgo8FgMBgMviHplgFVnSwi9wJTRKQK2AWcad+eAPQH5gK5wKOq+kWyZTQYDAaDwU+kYpoAVf0P1vRA8PUa4PrkSxSShE1BpBATp/TAxCl9yMR4mTilB3GNk6hq064MBoPBYDBkLOY4YoPBYDAYfI5RBoIQkdPso5KXishrItIq1TJFgoiME5G59hHPU0VklH19pogssq/PEZGb7OsFIvKiiCy243pWamMQGhG5X0TWOeR/xb5+q+PY6ttEROzrHUXkAzvOC0TkyNTGoCEicrEjLnNEZLWIVItIZxHZEXTvQtuPJ+MkFs+IyI32/0aPExeRgSLypR2H6SIyxHHvcvv6chGZICLNUhEfW5bgODUXkSftdF9o/25u3ztQREqC8mywfe8wEfnerl+fiUjXVMUpVLzsa1GXNy+1jyHy6vWg+BSJyDv2vTNEZFfQ/ZZeipM03oZH3da5ipOqmo/9AToC24CB9v9/AP9NtVwRyD0Y2Ax0tf+firVNsxDYAzQL4ece4DH7dy9gI9Aj1XEJIee3wJFB104FZtvxywe+AM6z770K/N7+PcKOV0Gq49FI3JrZ8bvKzsNljbjzXJyAocBkoBS40b52LRA4X6QtsAQ41L43Hfi5/fsUYAHWOSLDsQ4a64g1OHkJuNlDcfob8KwtW7Yt3x32vasCdSgonFw7TkfZ/68BJnksr6Iub15qH0PFKej+aGAt0NP+f1cgTkHuPBEnGm/Do27r3MYpJYXTqx/gQuB9x/8+QBH22gqvfmw5T3P87wRUYZ3psAH4DJiPdZ5Dc9vNcmC0w8/TwA2pjktQvPKwjqp+25b/DSzF5X/ATQ53l2KddpkDlAEdHfemYL0HI+XxCRG/PwIT7d+XAYuBr4B5wJ+xOh9Pxgl4GPi5XW4CHcwnwLkON7dhvXekO1AMZDnurQEOAf4A/Mdx/ThgnofidCIwyOHmZuAZ+/czdn7NwlJ2zravHwUsdPjJBSqB9h6KV9TlDQ+1j6HiFJTei4CxjmtfAJ9i7VT7CjjGvu6JONF4G/4UUbZ1buOUkt0EHibUccitgJZYjZknUdU1WI0rtgnpn1gFJg/4HPg1VsF5AUtDvp7Qce2RLJkjpBuW9v9HYCFwI9ZZFNuwFJwAAdk7YHU420Pc8xQi0gH4LTDSvpSD1VjdgmUxeB+rzL2MB+OkqtcBiMiJjsuNHSfeE9ikqnVB93rY99aEuJ50QsVJVT8O/BaR3lh1J3DyaSmWpeBRrBesfSEi6whKB1WtEpHtWErRzgRHYz8aySs35a0Aj7SPjcQpwBVY5e0tx7WdwItYA4qjgIki8gM80uaHacO7Ah8FyddUW+cqn4wy0JDg45ADhDwS2WuISCGWptwTOFlV9+B4P4SI3Am8idWgRXz0c6pQ1dVYZjIAROQ+4E9Yx4qGkj1U/nkuXjbjsawCqwBU9X/OmyLyT+BXWKbAdIlTY2UqXL54vhwCiMhIrGPUH1bV9wBU9VqHk8VirWc5A1iKx/PMZXlLl/bxN+xT2ABQ1bMdf78WkanACXgsTsFtOPvnh5s6FSBsnMwCwoYEH4fcHditqqUpkidiRKQXMBUrw8eo6h570cwxTmdAtf074qOfU4WIHCQiFwVfxpoLDCX7NsubtAtxz2v8DMsECICIXCQizpdyBfIqneLUWJlaB3QNLHwKcc/r5fB8rCmQW1T1Tvtatoj8IbAILeAUK88axEmsBZHtseZ0PYHL8ub59lFEDsYa5H7huNZGRH4fVP5C5hUpjFOoNjyEfJG0da7iZJSBhnwMHC4iA+3/V2OZpT2N3SBNAd5U1fNVtdy+1QO4T6wV0dnADcAr9r2J2NqziPTA0kLfS6rgTVMHPCgife3/12DNb04ELhSRQhHJw5pHe1utQ6veZ1+8DgKGYaWNZxCRtsAArIofYDhwh93JNAeuA15JlzjZhDxOXFU3ACuwFCBE5CSsvJ2PZbn6iYh0shvr8XjoCHIROQNr3cOJqvpi4Lqq1gI/YV++9AbOwTJDTwPaO1Z3Xw58azfuXsFNeUuH9vFYYLLak+U2e4H/w5pPDygMhwIf4pE4hWnD3bR17uKU7IUfXv9gmaXnYi2ueQ9ol2qZIpD5Vixtck7Qpz1wrx2X5VgnVuXZfloAz2HNxS8BxqU6Ho3EbRzWyvPFWKOzXvb139uyLwfuY98BWp2Bd20/87Ea8ZTHIyhOo4EVQdcKgCexFj4tB+5MhzjRcFFaDvCAI19udLgbaDdUC4AZwCGOe5fZ15dirdzP91CclgLbg+rVf+x7A7Dm3efb+fYzRxiHYi0qXAh8CfTxWF65Km9eax8JWkCIdbLtH0O4G4W1c2eBnYdjvBQnwrfhUbd1buJkTiA0GAwGg8HnmGkCg8FgMBh8jlEGDAaDwWDwOUYZMBgMBoPB5xhlwGAwGAwGn2OUAYPBYDAYfI5RBgyGDEZEPraPPkZEJonIsAQ+6xoRGd+0yybDyRaR90SkUzzkMhgMTWO2FhoMGYyIKNbLTHYk+Dm9sY5OPVzj0KjYJ2f+SlV/GrNwBoOhSYxlwGDIUEQkcNzx5yLSU0TWiMgoETlORL4VkVfsd6d/Yx9d/YmIrBORfznCOENEpon8f3t3zxpVFEVh+F0OFqKo6A8wAQuJRVIoaiOpAopEEPUPiI2ClY2VtrY2o7WNomkECUJSKBJQlEEQu3xUEcQghjQm6rbYJ3CJ0TDODMjc9UAgIefuczNF2Nw5s5daZd2JP2x3A7gfESFpQNKcpHuS3pQ9xiU9lTRb9t1WJhU2lbnrb5W567sAIuIFMCRppLevkpmBnwyY9bXqkwFJC8B5cvrkFBlh3ZI0Cewh44N3A4tk7OlOMthqNCKWJB0u1x2MypzzMkb4U6m3IGkAmAfORsQTSU1y3PUwGcs6V+6jQU7FHCpNxG0yvGmm1L1DzlS/2avXx8ySUwvN6mk+Ilrl+1nga0SsAp8lLQP7gJNkhOp0JePlJzmG912l1n5gb2QM67o1clTqev2ZiFgGkLRY6r8kR7C+kvQMmIiI19V7BI514W81sy34bQKzevq24ee1TdY0gOmIGFn/Ao6Ts9CrgnxAUP1/srrh7MBv9SODe4aB62RT8FDSlQ3X/G/xuGZ9yc2AWX/7AWz/x2ungTFJhwAknSZTI3dUF0XEEvAFONBOcUlnyh4zEXGLDCk6WlkySIZomVmP+W0Cs/72CHgu6Vy7F0bEh/JRwQflXMB3YDwiVjZZPkGeC2i2scUkcAp4L2mFbCguV34/Blxs977NrH0+QGhmHZM0CDwGjnTpo4WjwNWIuNBpLTPbmpsBM+sKSdfIswJ3O6zTIA8fXoqIj125OTP7KzcDZmZmNecDhGZmZjXnZsDMzKzm3AyYmZnVnJsBMzOzmnMzYGZmVnNuBszMzGruF69cID00N9+XAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "[
]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PfVRqOlIjuaAEQkREWoSSLckmDYYzpzJLT31UcEogUii0RlZo8YZNnYRki+pScCqrzKhfD05lJdKylEC0Euo8M6OBTGZUvyQb1O4kV9RHBaNykmxRApGCGpmIBKFBseSKtkPBqaxS0zUQwak/D04JhIiISB7SYC89DYwzp3JKTwlEcEogWgl1CplRJyHS8tTuJFe0DRRpWUogpE3SQEZEpPVQApGajtRILiiBaCXUKUguqX6lpmQ0OJVVZtTu0lP5ZE5llp76qOCUQKSgRta6qZOQbFFdCk5lJRIujW0kW5RAtBLqFDKjgUxmVL8kG9TuMqN2F5zKKjWVjeSCEogU1OBEJAgNiiVXtB1KT+f2Z07llJ768+CUQLQS6hQkl1S/JBu0cZZcUR8VjMopPfVRwRVcAmFm7czsl2a2wMzmmtmQXKyn0BpZocUbNnUSIi1P7S4z6teDU1kFo3KSbDmsBMLMOptZUbaDCehTQKm7nwH8F/DjkOLIK+oUMqOBTGZUv1JTXRIJh05hCkblFJz68+ACJRDRvf6Xm9nfzOwD4E1gk5ktN7MfmdkxuQ2zgbHA0wDuvhAYlYuVqJGJfEjtITVtcETCpz4qGJWTZEvQIxBzgMHATUCFuw9w9z7AOGAh8H0zuyJHMSbrCuxMeF1nZsWJM5jZNDNbYmZLqqqqDmslhdbICi3esGnQlxnVr9RUlyRX1O6CU1kFo3KSbCluehYAPu7uB5Inuvs24E/An8ysJKuRpbYLKEt43c7dDybFNR2YDjBixIjDai1qZCIi2aVkKzPaDgWnskpNpzAFpz4quEBHIBpLHg5nnix5CZgMYGanA/9oofWKtFna6Eg2aOMsuaI+SrJBfVRwTSYQZvYJM7vfzE6Kvp6W+7DSegKoNbOXgbuAG3KxkkLrjAot3rCpk8iM6ldqqksi4VMfJdKygpzC9FVgKnCrmfUATsptSOm5ez1wbQusJ9eryKpCi1dE2h4lW5lRvy7ZpjqVnvqo4IKcwrTF3Xe4+38A5wCn5jgmkZxTJ5EZbXRSU10KTmUl2aR+KRhdAxGc+qjggiQQf4s9cff/An6Xu3DyhxqZiAShDY5I+LTNDkblJNnSZALh7k8mvf5p7sKRw6VOITMa9Im0PLU7yRVtA4NROaWnPiq4oLdxBcDMRgG3AAOjnzXA3X14DmKTDKhTyIw6icyofom0PLW79HRqTjAqp+A0NgguowQCeBj4BpFbp9ZnP5z8oUYm8iG1B8kGbZwzo3YXnMoqGJVTeuqjgss0gdji7n/OSSR5Ro2sdVMnIdmiuiQSPm2zRVpWpgnE7Wb2a+A5YF9sorvPzGpUkjF1npnRoC8zql+SDWp3mVG7S0+n5mRO5ZSe+qjgMk0gpgJDgRI+PIXJgVaXQBRaIyu0eKWwqH6lpg1OcCqrzKjdBaeyCkbllJ76qOAyTSBGuPuJOYkkz6iRiUgQ2uAEp7KSXNE2OxiVk2RLkN+BSLTQzIblJBJpFnUKmdFAJjOqXyKSb3QKk0h4Mj0CMRa40szeIXINRKu9jWuhdUaFFm/YlEBkRvVLpOWp3Uk2KNEKTmOD4DJNID6ZkyjykBqZiAShDY5I+LTNDkblJNmSUQLh7u/mKpB8o0bWumnQlxm1B5GWp3Yn0rI0Ngguo2sgzOxBM+uW8Lq7mf0m+2FJprShkVxS/UpNG5zgVFaZUbtLT6fmBKNyCk59VHCZXkQ93N13xF64+3bg5OyGlB8KrZEVWrwirYU2OMGprCRXtA0MRuUk2ZJpAtHOzLrHXphZDzK/jiIQMys3s7+Y2TwzW2BmZ0Snn25mi8zsJTO7PRfrBjUykURqD6lpUBycyiozanfBqayCUTmlpz4quEwH/z8GFpjZY0R+QO4y4HtZjyriRuA5d7/bzI4DHgFOAX4JXAK8DfzNzE5x91dzFEPBUKeQGXUSIiKFTafmZE7llJ7GBsEFSiCie/8XuvvvzGwJMJHILVw/7e4rchTbXURuFQuROGvNrCvQwd3XROOqBM4G2nwCIZlRJyHS8tTuJFc0ME5NiVZw6qOCC3oE4krgZ2b2FvA08Li7v5+tIMzsauCGpMlT3f0VM6sAfg9cD3QFdiXMUw0c3cjypgHTAPr163dYMRVaIyu0eKWwqH6lpg1OcCoryRX1UcGonCRbAiUQ7n4tgJkNBc4Ffmtm5cAcIgnFS+5ed7hBuPsDwAPJ083sROCPwH+4+7zoEYiyhFnKgB3Jn3P36cB0gBEjRqi1yCE0kMmMNjqSDWp3mVG7S0971jOnckpPfVRwGV1E7e5vuvtd7v5JIqcxzQcuBRZlOzAzGwY8Blzu7rOi698F7DezwRb5L08CXsz2uqPrysViJU+ok8iM2kNqqksi4VMflZrKJjj158EFSiDMbIiZjUmc5u57gRrgbncflYPY7gRKgXvMbK6ZPRmdfi3wMLAYWObuWU9eoPAaXKHFKyIi6alfD05lFYzKSbIl6DUQdwM3NzJ9T/S9C7IWUZS7X5Ri+kLg9GyvT9oW7WXIjDY6kg1qd5lRu0tP5SPZpj4quKCnMB3l7q8nT3T3JcBRWY0oTxRax1Ro8UphUf1KTRsckfCpjwpG5ZSe+vPggiYQpWne65iNQPJNoTWyQos3bOokRFqe2p1Iy9PF5pILQROIV8zsmuSJ0duvLs1uSPmh0BpZocUr0lpoUBycyioz6tfT08A4cyonyZag10BcDzxhZp/nw4RhFNAeuDgXgYnkkgYymdFGR7JB7U5yRX1UMConyZagvwOxGfiYmU0ATohO/pu7P5+zyEKmRta6aSAj2aK6JLmi7VBwKqvUdKRGciHoEYiYl4EjgIHAWDMbC+Du/53twMJWaI2s0OKVwqL6lZoSCMkVtbvgVFapqWyCU38eXKYJxJNEfvn5VWBf9sMRaRnqJDKjDZBkg9qdZJP2rAejcgpOfVRwmSYQ/aO/Qt3qFVojK7R4RUQkPfXrwamsUlMCEZwSiOCC3oUp5mUzOzEnkUizqFOQXFL9Sk0bHMkVtbvgVFapKYGQXMj0CMRY4Etm9g6RU5gMcHcfnvXIJCPqFEQk3ynZklzRNjAYlVN66qOCyzSBODcnUeQhNTIRCUIbnOBUViKSz9RHBRcogTAz84h3m5one6GFqxV9FZFmU3tITRscyRW1u+BUVqnpFCbJhaDXQMwxs6+Z2ZGJE82svZlNNLMHgSuzH56I5ANtdFJTAiESDg2Mg1E5Baf+PLigpzB9ErgKeMTMBhG5lWspUAQ8A9zl7q/lJsRwqJGJiIhIodN4JjglEMEF/SXqWuDnwM/NrAToBex19x25DC5MhdbgCi1eKSyqX5INqkeSK6pbqekIhORCprdxxd0PuPumlkoezGyome00s9Lo69PNbJGZvWRmt+dqvYXWyAot3rCpvDKj8kpNe6wkV1S30tPAOBiVU3Bqc8FlnEC0JDPrCvyYhr96/UvgciK3lB1tZqfkaN25WGzOqFOQXFL9kmxQPZJs0sA4GJVTcIU29gtT3iYQFvkvTgduBvZEp3UFOrj7mugdnyqBs8OLMn+oU5BcUqcq0vLU7tKrr6+PP9c2MDUlEMGpfILL9HcgcsLMrgZuSJr8LvBHd/97QifaFdiVME81cHQjy5sGTAPo16/f4cZ0WJ8LS6HFK4VF9Ss1lY1IODQwDkblJLnQrCMQZlZsZiPM7DQzO+1wl+PuD7j7CYkP4DjgajObC1QQudvTLqAs4aNlRO4Ilby86e4+yt1H9ezZ87BiKrRBQaHFK9JaaIMcnMoqM+rX09MRiGCUQASn8gmuuUcgHgUWAwcAjz7PCncfEntuZmuBc9y91sz2m9lg4G1gEvDtbK2zkLVrl7dno0kroIFMatrgiIRDA2PJNtWj4JqbQCx39+9nJZLgrgUeJvobFO6+KBcr0YBc5ENKIFLTBkckHGp7mVOZpafyCa65CcQBM5sNbAFw98ubH9Kh3P2ohOcLgdNzsR4RkUxpgyO5osQ9PR2BCEZlE5zKKrjmJhAV7v6JrEQizaINTWbUSWRG9Ss11SWRcOgaiGCUaEkuNDeB6GRmnyV6ZyR3f6r5IeWHQhswFVq8Iq2FNsgi4VACEYwSiOBUPsFllECYWScgdnHzKmAO0AHoneW4QldoA/JCi1ektdAGR3JF/Xp6anvBKNEKTuUTXKAEwsxKgB8BXwTeIXL71z7Afe5+p5md7O7LchemNEUbmszU1dWFHUJBUf1KTRuc4BIHMtI0tbv0NDAO5uDBg/HnKqf0VD7BBT0C8WOgEzDQ3ash/qvQ/8/MfgF8EhiUmxDDUWh3YdKGJjOJHao0TfUrNQ2Kgztw4EDYIRSU4uK8+K3XvKUEIhglEMGpfIIL2jtNBo7xhJJ1911m9hVgK3BuLoILU/v27cMOISOFFm/YdAQiM6pfqSkZDU7tLjMlJSVhh5DX9u3bF3+ugV9qStyDU1kFF3Q3e7030jrdvQ7YEr21aqtSaAOmDh06hB1CQdGgLzMayKS2f//+sEMoGNo4Ny0xySoqKgoxkvyX2PaUQKSWWKdUTumpjwouaAKxwsy+mDzRzK4AVmY3pPxQaAOmQkt4wrZ3796wQygohdYeWlJtbW3YIRQMJVtN01714FRWwSRu71RO6SXWKUkv6ClM/wrMNLOrgKWAA6cCHYGLcxRbi0tsWGVlZSFGEkzixrhTp04hRlJ4duzYEXYIeS+xPfTo0SPESPKb6lJw27dvDzuEvFdVVRV/rsFeeiqrYBLbncopvW3btoUdQsEIlEC4+wZgtJlNBD4KGDDL3Z/LZXAt7f33348/L4QB+bvvvht/XmgXfYeprq6OmpoaADp27BhyNPlry5Yt8eedO3cOMZL8deDAgfgGuWfPniFHk9/cnbfffjvsMPJeYr8u6a1duzb+XAPj1BLbncopNXdX+8tARrd4cPfngedzFEvonn/+w69WCI3s2WefDTuEgrRo0aL48+7du4cYSX6bPXt2/HkhtIcwzJ8/P/68a9euIUaS/9544434qRSFsIMmLIn9utpdes8916r2YeaM6lQwK1asYOfOnYCuKw1Cu62jtm/fzj333BN/ne+N7J133uGnP/1p/HW+x5svampquOOOO8IOI++tW7eOH/3oR/HXql+H2rlzJ9/5znfir1VGqe3Zs4dbbrkl7DDy3uuvv86DDz4Yf606ldrDDz/MK6+8En+tsmrcjBkzGpSTNG7Pnj3cfPPNYYdRUNr8Tabr6+uZM2cOt99+O+vWrYtPz9fOaO3atcycOZNf/epX7Nq1K+xwCsbu3bt56qmnuOuuu3jnnXcoKiqirq4ub//PYXB3Vq9ezZ///Gfuv/9+du7cSbt27fQ7B0m2bdvGrFmzuOeee1i3bl28jFSXDrV161ZmzZrFz3/+c9auXUuXLl3ipw9KhLuzZs0aHnvsMe6///4GF+WrTjVUV1fH4sWLeeihh3jiiScavKey+lBdXR2vvfYav/3tb3n88ccbvKdyamj79u1UVlZy3333sWbNGrp168aOHTtUTgG0uQSipqaGtWvX8vrrr7Ns2TKef/55Nm7cCMDQoUP56le/ynXXXRdK5dmzZw9VVVVs27atwWPz5s2sWrWKlStXsmHDhvj8kyZN4oILLuDf/u3f2nxlP3jwILt27aK6upr333+fDRs2sHHjRt577z3+/ve/s3Llyvjt2Y499li+//3v8+lPf7pVlVt9fT379+8P9Ni+fTtbtmxhy5YtbN26lbfffps333yzwQXBZ599NlOnTuWKK65oVeWUTn19PbW1tWzbto2tW7fGHxs3bmT16tW89dZbrFy5Mp5UnXDCCXzrW9/isssuazNldPDgQWpqaqipqWH37t3x51u2bGHz5s1s2rSJDRs2sHz58gY7ZY499ljuu+8+zjnnnDZTVu7O3r1743371q1b4+1u48aNrFq1ilWrVsW3QQBTpkxh2LBh3H777W2mnA4cOBCvT7FHrD5t3ryZ999/n1WrVrFixYr4aXDt27fnlltuYfny5Tz66KNtqqx27drFrl272LlzJ9XV1XzwwQesX7+e9evXs3btWl577bV4ot6+fXtuuukmVq5cyaOPPhpy9C3D3dm/fz81NTXs2bOH3bt3s21GkJezAAAgAElEQVTbtnid2rJlC++++y4rVqzg7bffjtedwYMH86tf/YqPf/zjbaY+NUerTyA2bNjA1KlT442rsTum9OvXj6lTp3LVVVexbNkyABYsWMCLL77IuHHjDmu99fX1bNu2jaqqqnhSkPg89joxUQhyO8iuXbsyYcIEPv/5zzN27Nj4eeqzZ8/m1Vdf5ZRTTjmseGPcndraWvbu3Rt/HDhwgLq6Ourr6+OP2OvYXvzE9xp7uPshy4h9LnF6bW0t+/btiz+SX8em7dmzJ9557ty5kz179qT9XmbGqaeeyqWXXspnP/tZtm7dCkQunH/yySe56KKLmlVuELlVXqxT37NnT4PYa2trD/kuQQf76R4HDhyILysbv23Ro0cPzj77bC699FLGjh3LkiVLAFiyZAmLFi1i9OjRzV4HROpZctnU1tam/H4HDhxI+/2T30+c3tT7idODlGFJSQljxozhsssu41Of+lQ8qX/vvfeYNWsW557b/N/VbGxAFSufgwcPxr9DusfBgwcPeR77bOLrVJ9p7HOxeh1UaWkpo0eP5tOf/jQXX3xxPInfu3cvjz/+OJ/5zGeaXVYQ6W/37NkTHyzs27ePgwcPNloGdXV1h3y3dGWTrkxif/ft29dg/bG/e/fuDTQQ6d69OxMnTuTKK6/k1FNPjZ/G9NBDD/HlL3+ZwYMHN7uMDhw40KA+7dmzJ15Oid+7sXqUrlwyKafEv7W1tfFYMrm976BBg5g8eTJXXnklAwYM4Prrrwfgxhtv5IILLqBLly7NLqvYjTb27NnD/v37G/TXyX13YvmkK8fY43A+E/tcTU1N4FuQDxgwgPPOO48vfvGLDBo0iBtuuKFBOWXjOqT9+/c3qFN79+49pK409bqpv0Hnj40LYrEE3R4WFxfzsY99jIsuuohLL7003l4PHDjAjBkzmDJlSrPLKTa2iY2tEv8mfr+6ujoOHjwY/5v4PLHfij2P1YvG6kpiXUucFtvuvvXWW83+XnmbQJhZEfATYBTQAbjD3f9qZqcD9wAHgWfc/dvpllNVVcXTTz8df11aWkr//v356Ec/ykknncRpp53GySefjJkd8tmrrrqKf/7zn2nj3L17N8uXL2f58uWsXLmS9957L56sZHo/4Q4dOtCzZ0969OjR4NGzZ0+OPfZYjjvuOAYNGkRx8Yf/tsS7L11zzTUsXbo07Tp27drF66+/zurVq1mzZg3r16+nqqoqvie6UE8vaNeuHWVlZZSVldGnTx/69evHEUccQb9+/fjoRz/K8OHDG2xYEv/f1157Leedd16Dck1WX1/P2rVrWbFiBW+99RYbN25k48aNbNq0iaqqKnbt2pUX949u3749JSUltG/fng4dOjT6vH379nTr1o3evXvTu3dvevXqRf/+/Rk2bBgf+chHGpRN4vMrrriiyfbg7qxdu5ZVq1bx7rvvsm7dOjZt2sT27dvjj507d7Jv37683cNTWloab3u9evWiV69efOQjH2HIkCEcc8wxDB06tMFdqRJ/7Ouqq65i/fr1aX8ArL6+ntWrV/PPf/6TNWvWsHbtWj744AM++OADtm7dyrZt2/KiLqXSrl07OnfuTJcuXeJ/u3TpQs+ePamoqIg/hg4dypAhQxq0q8SN+te+9jXOPffctHf4cnfWr1/foKxifdWWLVvYvn17PLnKV6WlpXTq1Inu3bvTq1eveLvr06dPvF8/6qijGtSZxDL75je/echpKMn27dvHypUrWbNmDWvWrGHdunV88MEH8b59x44def0bHEVFRfH61KlTJzp37hxvd3369Im3v2HDhqW9pfSMGTO4+uqr065r9+7dvPnmm6xcuZL169ezadMmNm3axJYtW+J79vN5O1hUVETXrl0bPHr06EH//v3p378/AwYMYMSIEfTp06fB52Jjhfr6en73u99x7bXXpl1PdXU1K1asiNepjRs3NjhqXV1dndc/ulZSUkKXLl3i9am8vJw+ffrEH0cccQTDhg3jmGOOaXDBdOJ3uv766zn//POb7KNiR1zffffd+Bgwcefwzp07W+WpwHmbQABfAErcfYyZ9QMujU7/JXAJ8DbwNzM7xd1fTbWQI444gjvuuCPesHr27NloshCT+F5NTQ2bNm2ib9++DeZZt24dM2fO5Pnnn2fp0qUNfuUxUXl5Ob169YonAcmP5CShY8eOaWNrTOJGJ9bAe/fuHZ/m7ixfvpwnn3ySOXPmsGLFiiYHbqWlpXTs2DH+KCkpoaioCDOjqKiIdu3aHfKITTezRl8nzpc8LTZfUVERHTp0oLS0lNLSUjp06BB/JL/u1KkT5eXllJWVUV5eTufOnTMqu+Tb3r7yyiucccYZDabt2LGDv/zlL8yZM4eXXnqpyWtO2rdvT3l5OV27dqVz584N4k6MP/a8pKQk5SC/secdOnSIJwipnmdaf5qS3B7Wr19P//79G8zz/vvvU1lZyezZs1m6dGng30UoKSmJ/68Tyykx0Un8jsnTg76fybzFxcUZl2Fy4rl48eJD6lJVVRVPPfUUs2fP5pVXXmmyjJIHVF26dKG0tDT+HYqLi+PxxqYlT49Ni01v7P1M5mvfvj2dOnU6rH4qJvkHCefMmcP555/fYFpNTQ2VlZU888wzLF68uMHttdPp1KlT/FFaWkpxcfEh3zM2rbHpqcohyPvt27dvMPiN/e3YseNh/Zp0Yp166aWX2Ldv3yF3hVmzZg1PPvkk8+bN4+9//3uTSWdRURGdO3duEF+sLTRVbw63XNLVp44dO8bjad++/WHXqerq6vjz2bNnH5JAuDtLlizh6aefZt68eSxfvjzQcmODz1j/Gvub+Dz2v09uj8nft6l5EttwcnkmzhOL6XDKKvEofWVl5SEJhLvz6quv8uc//5mXX36ZFStWNDnoTe6nOnbsSIcOHZrsR9LVk0zrUexvhw4d4vUpVqcOR3LZzp07l/POO6/BtF27djF79mwqKytZuHBhg1uepxIbV8W2d7Hnsf99UVERxcXFFBUVxb9T4rRYvxV7HqtXyXUmeVpj82Tr9vX5nEBMAv5hZn8j8rsTXzOzrkAHd18DYGaVwNlAygSiV69eXHDBBYFXmlx5nn32Wb7whS8AkQ77jjvu4LnnnosPwouKijjhhBM44YQTOP744xk0aBADBgygf//+WTmU2pTkDXJlZSVXXHEFELnF3U9+8hNeffXVBvOfeOKJHHfccQwePJiBAwfG97L26tWLsrKyw9rgFZrkQd+zzz4bH/Rt3bqV//mf/2HmzJkNNsoVFRUMGzaMoUOHcuSRR9K3b1+OOOIIevXqRXl5OaWlpVkfwIct+fvMnj2bqVOnArBy5UruuusunnrqqQZJdO/evTnhhBM46qijOPLII+nXrx/du3ene/fudOvWjfLy8sMeWOWj5Lr01FNPxevSmjVruPfee5k5c2aDve99+/Zl2LBhDBkyhEGDBtG3b9/40aDD3ZlQCJL/50899VQ8gdi4cSM/+9nP+MMf/tDgiEL37t0ZNmwYRx99NEcffTQVFRXx/qpHjx7x5Ko1/RZOcp2aP38+Z599NgDz5s3j7rvvZuHChQ3mOeaYY+L9+lFHHRU/0tG7d2+6devWKvsnoMGOnQULFlBTU0OXLl1wd/7617/yox/9qMGR05KSkviRxKOPPpq+ffvSt29f+vTpE9+j36VLl1bTP8Xs3r07/nzRokXxnY11dXU89thj8YuIY4qLiznhhBMYMmQIgwcPZsCAAQ3qVHl5ebMSv3yV/H2eeeaZeAKxefNm7r77bmbMmNHgVLJu3boxfPhwBg8ezJFHHhnfWR3bQdytW7e0ZzgUqrz4RmZ2NXBD0uQtQC1wPnAm8L/A5UDibuBq4OhGljcNmAaR6xsykbwRevbZZ5kyZQo///nPufvuu+N7giZPnsx5553HuHHjQr3/e2ODl/PPP5+bb745fpeK7t27c9FFF3HeeecxcuRI/Xgah5bb7NmzufXWW3n88ce5/fbb2b59O2bGWWedxfnnn89ZZ53FgAEDQoo2PMnt4ZlnnuGKK67gvvvu46677uLAgQMUFxczadIkzj33XMaOHcsRRxzR6jYq6STXpVmzZnH77bczffp0fvCDH7B//36KiooYP348F154IWPHjqV///5tqoxiGttBs2/fPv70pz/xrW99Kz7IOe2007jwwgsZN24cQ4YMaVXJQRCNJVojR47klltuYebMmUBkD/m5557L5MmTGT16dJv9TZvEo3n79+/nhRde4OSTT+amm26isrISgD59+nDxxRdz9tlnc+qpp1JaWhpWuKFJPALh7lRWVnLGGWfw9a9/PX7qc0VFBRdeeCHnnHMOJ598cpv8vZbGxoB1dXX83//9H7feemu8vp1xxhlMnjyZiRMnMmjQoDbZn+dFAuHuDwAPJE4zsz8Cf/XIrv55ZnYskeShLGG2MuCQcwHcfTowHWDEiBEZnWidfNhr9uzZnHnmmfFfJ5wyZQq33XZb3vzqbPLgZc6cOYwdO5aqqio6duzIN77xDa688so22RGkk1xu//znP5kwYQKrVq0CYNy4cdx5551ZuXixkCWfNjF37lwmTpzI6tWrAfjCF77A9ddfzxFHHBFGeHkhuS5t2LCB0aNHx++sc9lll3HjjTcycODAMMLLa9XV1YwZMyZ+IfqkSZP45je/yfHHHx9yZOFKvl7hj3/8I8899xybN2+mY8eOXH/99UydOpWysrIUS2g7ki8qvu2226iurqa6upqysjJuueUWLr/88kOO1rc1iUcgAL773e/Gb2LRt29fbrrpJi6++OJWuac8E8mJwLZt2zjrrLPiR2cmTJjAt771LYYOHRpGeHkln3frzAcmA5jZCOA9d98F7DezwRb5L08CXszmShM7mU996lPxnzYfOHAgM2bM4O67786b5AEaVvYLL7wQiJxvPXLkSJ599lm+8pWvKHloROL/efLkyQCsWrWKbt26xQ9RtvXkARqW0znnnAPA6tWr6devH48++ig//OEP23TyAA0TiFhd2rhxI3369OH3v/8999xzj5KHRsTucLdhwwbKy8v52c9+xv/+7/+2+eQBGu4tPvnkk6mvr2fz5s3xfv26665T8hCVeJppeXk5GzdupLq6mkmTJjFv3jyuvPLKNp88AA0uDO/WrRs7d+6ktraWSy65hDlz5nDppZe2+eQh2dixY4HIqahdunThJz/5CQ8//LCSh6h8ri33A78ws4VEroGIXfFzLfAwUETkLkyLsrnSxAuM77rrLk4//XSKioq45JJL8vLUn8TO8+6772bs2LF06dKFCy64QJ1BGollc+utt/Lxj3+cbdu2MWXKFHr16hViZPkl8VSKX/ziF/zud78DIkce0t2Zoi1JrEt33HEHEyZMoKamhssuuyztHWPauq985St85jOfYePGjUyZMuWQm1W0ZYl71X//+9/z0EMP0bdvXy655JJWd25+cyVuA5988kmefPJJRo0axYQJE9rkaSWpJCalf/7zn3niiSc49dRTmTBhQohR5bdrr72WKVOmsH79ej772c9SUVERdkh5JW9HmO6+D7iqkekLgdNztd7Ei0FLS0u58sorc7WqrEi8MLNjx47xC74lvcQNS8eOHfnc5z4XYjT5K7E9dOrUqclb/7VFiQO6srKy+E0MJL3S0lIuu+yysMPIez169ODrX/962GHkrcTTjo877jj+8z//M8Ro8lfiEatjjjlG5RRAhw4dsvZbNa1RPp/CFIqhQ4cyaNAgLr744rBDCWTUqFH069dPG+LDMGbMGE488cQGt72VhgYOHMiQIUOYNGlS2KHkLTPjtNNO44QTTgj1hgqF4vzzz+eoo47ipJNOCjuUvPWpT32Kbt268dWvfjXsUPLeD37wAzp27Mj9998fdih57Xvf+x5dunThN7/5Tdih5L2LL76Yo48+mpEjR4YdSl6zfP0xp2wZMWKEx+7EEJS7F9Shz/r6+jZ3l5JsqK+vx8wK6n8dBtWvpqkuBefuuLvqVBPq6up0ulJA6qOCKbSxTVjaQh9VUVHR7IqQt6cwhanQGlhrruS5pHILRuXUNJVRcEq0glHyEJzaXzBqd8GojwpGrU5ERERERAJTAiEiIiIiIoEpgRARERERkcCUQIiIiIiISGBKIEREREREJDAlECIiIiIiEpgSCBERERERCUwJhIiIiIiIBKYEQkREREREAlMCISIiIiIigSmBEBERERGRwJRAiIiIiIhIYHmbQJhZuZnNMrMXzOxZM6uITj/dzBaZ2UtmdnvYcYqIiIiItCV5m0AAXwL+4e5nAjOAb0Sn/xK4HBgLjDazU8IJT0RERESk7cnnBOIfQFn0eVfggJl1BTq4+xp3d6ASODusAEVERERE2prisAMAMLOrgRuSJv8rcI6ZrQB6AOOIJBK7EuapBo5uZHnTgGkA/fr1y0XIIiIiIiJtUl4cgXD3B9z9hMQH8HXgh+4+DDgH+BOR5KEs4aNlwI5Gljfd3Ue5+6iePXu2xFcQEREREWkT8iKBSGE7sDP6/AOgq7vvAvab2WAzM2AS8GJYAYqIiIiItDV5cQpTCrcBvzazrwIlwDXR6dcCDwNFwDPuviik+ERERERE2py8TSDcfSMwuZHpC4HTWz4iERERERHJ51OYREREREQkzyiBEBERERGRwJRAiIiIiIhIYEogREREREQkMCUQIiIiIiISmBIIEREREREJTAmEiIiIiIgEpgRCREREREQCUwIhIiIiIiKBKYEQEREREZHAlECIiIiIiEhgSiBERERERCQwJRAiIiIiIhKYEggREREREQlMCYSIiIiIiASWNwmEmV1sZn9IeH26mS0ys5fM7PbotHZm9kszW2Bmc81sSHgRi4iIiIi0PcVhBwBgZvcAk4DXEib/ErgEeBv4m5mdAhwFlLr7GWZ2OvBj4KIWDldEREREpM3KlyMQLwNfib0ws65AB3df4+4OVAJnA2OBpwHcfSEwKoRYRURERETarBY9AmFmVwM3JE2e6u4zzGx8wrSuwK6E19XA0dHpOxOm15lZsbsfTFrPNGBa9OW+vn37vpGN+CWlXsDWsINo5VTGuacyzi2Vb+6pjHNPZZx7KuPce8PdT2jOAlo0gXD3B4AHAsy6CyhLeF0G7AA6JU1vl5w8RNczHZgOYGZL3F1HKnJIZZx7KuPcUxnnlso391TGuacyzj2Vce6Z2ZLmLiNfTmFqwN13AfvNbLCZGZHrI14EXgImQ+Qia+Af4UUpIiIiItL25MVF1ClcCzwMFAHPuPsiM3sF+ISZvQwYMDXMAEVERERE2pq8SSDcfS4wN+H1QuD0pHnqiSQWmZje3NikSSrj3FMZ557KOLdUvrmnMs49lXHuqYxzr9llbJGbHImIiIiIiDQtL6+BEBERERGR/KQEQkREREREAlMCISIiIiIigSmBEBERERGRwJRAiIiIiIhIYEogREREREQkMCUQIiIiIiISmBIIEREREREJTAmEiIiIiIgEpgRCREREREQCUwIhIiIiIiKBKYEQEREREZHAlECIiIiIiEhgSiBERERERCSw4rADyLUJEyb4I488EnYYIiIiIiKhq6iosOYuo9Ufgdi2bVvYIYiIiIiItBqtPoEQEREREZHsUQIhIiIiIiKBKYEQEREREZHAlECIiIiIiEhgSiCS/P73v+czn/kMK1euDDsUEREREZG8owQiydKlS3nppZd47rnnwg5FRERERCTv5F0CYWYlZvYHM3vZzF40s6FJ719gZq+Y2QIzuybb658wYQIAc+bMyfaiRUREREQKXt4lEMBkoNjdPwb8N/C92BtmVgLcBZwDnAVMM7OKbK583LhxtGvXjsWLF1NTU5PNRYuIiIiIFLx8TCDeAorNrB3QFTiQ8N7xwGp33+7u+4H5wLhsrrx79+6ccsopHDx4kPnz52dz0SIiIiIiBS8fE4ga4CjgTeB+4N6E97oCOxNeVwPlyQsws2lmtsTMllRVVWUcgE5jEhERERFpXD4mEDcAle5+LDACeNDMSqPv7QLKEuYtA3YkL8Ddp7v7KHcf1bNnz4wDSEwg3D3jz4uIiIiItFb5mEBs58OjDNuAEqAo+nolcIyZ9TCz9sCZwIJsBzB8+HC6d+/OunXrWLNmTbYXLyIiIiJSsPIxgbgLOMXMXgSeB24GLjKzae5+ALgRqCSSOPzG3TdkO4CioiLGjx8P6DQmEREREZFExWEHkMzda4DL0rz/F+AvuY5jwoQJPPHEE8yZM4drrsn63WJFRERERApSPh6ByAuxIxALFixg79694QYjIiIiIpInlECk0Lt3b0488URqa2tZuHBh2OGIiIiIiOQFJRBp6HauIiIiIiINKYFIQwmEiIiIiEhDSiDSGDlyJGVlZaxevZp169aFHY6IiIiISOiUQKRRUlLCuHHjAB2FEBEREREBJRBN0mlMIiIiIiIfUgLRhNjtXOfPn8/+/fvDDUZEREREJGRKIJrQv39/jj32WGpqaliyZEnY4YiIiIiIhEoJRAA6jUlEREREJEIJRACxBGLu3LnhBiIiIiIiEjIlEAGMHj2a0tJS3njjDTZv3hx2OCIiIiIioVECEUBpaSljxowBYN68eSFHIyIiIiISHiUQAek6CBERERERKA47gGRm9iXgS9GXpcBJQIW774i+fy8wBqiOznORu+/MdVyxBGLevHnU1dVRVFSU61WKiIiIiOSdvDsC4e6/dffx7j4eWApcF0seok4BJsXmaYnkAWDQoEEMHDiQ7du38/e//70lVikiIiIiknfyLoGIMbNRwEfdfXrCtHbAMcB0M3vJzK5qwXh0GpOIiIiItHl5m0AANwPfTprWGfgpcAXwSeCrZjY8+YNmNs3MlpjZkqqqqqwFpARCRERERNq6vEwgzKwbMNTdk0fqe4B73H2Pu1cDzwMjkj/v7tPdfZS7j+rZs2fW4hozZgwlJSUsW7aMbdu2ZW25IiIiIiKFIi8TCOBM4NlGph8LzDezIjMrAcYCr7ZUUJ07d2b06NHU19fz4osvttRqRURERETyRr4mEMcBb8demNmNZnahu68EHgYWAvOA37n78pYMTKcxiYiIiEhbZu4edgw5NWLECK+srMza8lauXMnEiRPp06cPr732GmaWtWWLiIiIiORSRUVFswev+XoEIm8NHTqUiooKPvjgA1asWBF2OCIiIiIiLUoJRIZ0O1cRERERacuUQBwGJRAiIiIi0lYpgTgM48aNo127dixevJiampqwwxERERERaTFKIA5Dt27dGDlyJAcPHmT+/PlhhyMiIiIi0mKUQBym8ePHAzqNSURERETaFiUQh2nixIlAJIFo7bfCFRERERGJUQJxmIYPH06PHj1Yt24da9asCTscEREREZEWoQTiMLVr146zzjoL0GlMIiIiItJ2KIFoBt3OVURERETaGiUQzRC7kHrBggXs3bs33GBERERERFqAEohm6N27NyeeeCK1tbUsXLgw7HBERERERHJOCUQz6TQmEREREWlLlEA0kxIIEREREWlL8i6BMLMvmdnc6GOhmdWaWbeE968xsyXR984PM1aAkSNHUlZWxurVq1m3bl3Y4YiIiIiI5FTeJRDu/lt3H+/u44GlwHXuvgPAzCqA64AxwCTgTjPrEFqwQElJCePGjQN0FEJEREREWr+8SyBizGwU8FF3n54w+TTgJXff5+47gdXA8FACTKDTmERERESkrcjbBAK4Gfh20rSuwM6E19VAefIHzWxa9DSnJVVVVTkMMSJ2O9f58+ezf//+nK9PRERERCQseZlARK95GOruybv0dwFlCa/LgB3Jn3f36e4+yt1H9ezZM4eRRvTv359jjz2WmpoalixZkvP1iYiIiIiEJS8TCOBM4NlGpi8GxplZqZmVA8cDb7RoZCnoNCYRERERaQvyNYE4Dng79sLMbjSzC939feBe4EXgeeAWd68NKcYGlECIiIiISFtg7h52DDk1YsQIr6yszPl6amtrOf7446mtrWXZsmVUVFTkfJ0iIiIiIpmoqKiw5i4jX49AFJzS0lLGjBkDwLx580KORkREREQkN5RAZJFOYxIRERGR1k4JRBbFEoh58+ZRV1cXcjQiIiIiItmnBCKLBg0axMCBA9mxYwevvfZa2OGIiIiIiGSdEogsMjMmTpwI6DQmEREREWmdlEBkma6DEBEREZHWTAlEln3sYx+jffv2LFu2jG3btoUdjoiIiIhIVimByLLOnTszevRo3J0XXngh7HBERERERLJKCUQO6DQmEREREWmtcppAmFlnMyvK5TryUWICUV9fH3I0IiIiIiLZk9UEwszamdnlZvY3M/sAeBPYZGbLzexHZnZMNteXr4477jj69u3Lli1bWLFiRdjhiIiIiIhkTbaPQMwBBgM3ARXuPsDd+wDjgIXA983siiyvM++YmU5jEhEREZFWKdsJxMfd/Tvu/rq7x8/dcfdt7v4nd78EmJHldeYlJRAiIiIi0hplNYFw9wMAZvacmU1OfM/MpifO09qNGzeOoqIiXnnlFaqrq8MOR0REREQkK3J1EfUg4JtmdnvCtFFBP2xmN5nZAjNbamZXJ713Y/SairnRx3HZCjqbysvLOeWUUzh48CDz588POxwRERERkazIVQKxAzgb+IiZ/cXMyoN+0MzGAx8DxgBnAQOSZjkF+KK7j48+VmUp5qzTaUwiIiIi0trkKoEwdz/o7l8F/gTMB/oE/Owk4B/AE8BfgL8mvT8SuMnM5pvZTdkKOBcSEwh3DzkaEREREZHmy1UC8cvYE3f/LfAl4JmAn+1F5HSnS4FrgYfNzBLe/2N0+kRgrJmdn7wAM5tmZkvMbElVVdVhfYFsGD58OD169GD9+vWsXr06tDhERERERLIlJwmEu/8q6fVSd78q4MergEp33x89PakW6A0QTSTudvet7r4f+BtwciPrn5IIBeYAABsdSURBVO7uo9x9VM+ePZv1XZqjXbt2jB8/HtBpTCIiIiLSOmT7h+R+amb3pnoEXMx84JMWcQTQmUhSAdAVeMPMukSTiYnA0mx+h2zTdRAiIiIi0poUZ3l5SxKefxu4PdWMqbj7X83sTGAxkQTnX4EpZtbF3aeb2c1EfrBuH/Ccuz+VhbhzJnYEYuHChezdu5eOHTuGG5CIiIiISDNkNYFw9wdjz83s+sTXGS7nP9O89xDw0OEsNwy9evVi+PDhvP766yxYsICJEyeGHZKIiIiIyGHL1UXUALrtUJROYxIRERGR1iKXCYREKYEQERERkdYiq6cwmVk1Hx556GRmu2JvAe7uXbO5vkIxcuRIunbtypo1a3jvvfc48sgjww5JREREROSwZPUIhLuXuXvX6KM44XlZW00eAIqLixk3bhygoxAiIiIiUtiyfRtXy8Y8rZFOYxIRERGR1iDb10DMMbOvmVmDc3TMrL2ZTTSzB4Ers7zOghC7nev8+fPZv39/uMGIiIiIiBymbCcQnwTqgEfMbKOZrTCzt4F/Ap8D7nL332Z5nQWhX79+HHfccezevZslS5Y0/QERERERkTyU7Wsgat395+4+BhgInA2c4u4D3f0ad38tm+srNDqNSUREREQKXc5u4+ruB9x9k7vvyNU6Co0SCBEREREpdPodiBZ02mmn0bFjR5YvX877778fdjgiIiIiIhlTAtGCSktLGTNmDADz5s0LORoRERERkcy1SAJhZkVm9vmWWFe+02lMIiIiIlLIsv07EF3N7CYzu8/MzrGIrwFvA5dlc12FKpZAzJs37/+3d/fRcdz1vcffXz2sZD1ZkRwTi1DbskkCBTs4jvHxExYPx7Tlwr29FDgpvYALaQoX7ilt05K2h3Pv6Tnc24YCoffcYEqghORAH0iB8GCHWPJDYhM7tblJjHEiObaPY4MtR5Ys2Vqt9O0fOytWq11pJe1qZqXP65w9OzszO/ruz+PZ+cz8Zpbh4eGQqxERERERmZpCn4F4ELgZeAb4MLALeDfwLnd/V4H/Vklavnw5y5Yto6enh6NH5/VNqURERESkBBU6QLS6+wfd/Uskf/dhLfCO+X771kzqxiQiIiIiparQAWIoNeDuw8BJd++b6kKCblAHzOxpM/v9jGn/ycwOBdM/UoCaZ50ChIiIiIiUqooCL2+1mfUGwwYsCF4b4O7eMNkCzGwrsAHYCNQAf5I2rRL4HHA70A88YWbfc/eSuifqhg0biMViHDlyhEuXLtHU1BR2SSIiIiIieSlGF6aG4FHv7hVpw5OGh8A2ktdQPAJ8D3g0bdprgBfc/WV3jwP7gc2ZCzCzO83ssJkd7u7unuFHKrza2lre+MY34u7s3bs37HJERERERPJW6ADxSGrAzP51mstYRPLaid8B7gIeMjMLpjUAl9Pm7QMWZi7A3Xe4+1p3X9vc3DzNMopL3ZhEREREpBQVOkBY2nDrNJfRDex097i7/xy4BlwfTOsF6tPmrQd6pvl3QpUeIEZGRkKuRkREREQkP4UOEJ5jeCr2A28PfkOiBaglGSoAfga82syazCwGbAEOTLvaEN18880sWbKECxcucOzYsbDLERERERHJS6EDxGoz6zWzPmBVMNxrZn1pF1dPyN0fBY4AT5G8BuJjwHvN7E53HwI+CewkGRwecPezBf4Ms8LM1I1JREREREpOQe/C5O7lBVrO3RNM+x7JYFHy2traePjhh2lvb+fjH/942OWIiIiIiEyq0GcgZAo2b95MeXk5hw4doq9vyj+XISIiIiIy6xQgQrRw4ULWrFlDIpFg//79YZcjIiIiIjIpBYiQpa6D2L17d8iViIiIiIhMTgEiZOkXUrtP98ZVIiIiIiKzQwEiZKtWraKpqYmzZ8/y/PPPh12OiIiIiMiEFCBCVlZWxtatWwHdzlVEREREok8BIgL0exAiIiIiUioUICIgdQbi4MGDDAwMhFuMiIiIiMgEFCAiYNGiRaxatYrBwUEOHDgQdjkiIiIiIjkpQESEujGJiIiISClQgIiIN7/5zQB0dHSEW4iIiIiIyAQUICJizZo1NDQ00NnZyalTp8IuR0REREQkKwWIiKioqGDLli2AzkKIiIiISHQpQESIroMQERERkairCLuAbMzsCHA5eHnS3T+UNu0+YCPQF4x6l7tfZg5I3c513759xONxYrFYuAWJiIiIiGSIXIAws2oAd9+aY5Y1wDZ3vzhrRc2SlpYWbrnlFo4fP86hQ4fYuHFj2CWJiIiIiIwRxS5Mq4EaM9tlZrvNbH1qgpmVAa8GdpjZE2a2PdsCzOxOMztsZoe7u7tnqezCUDcmEREREYmyKAaIAeBeYBtwF/CQmaXOlNQCXwTeD7wd+KiZrcpcgLvvcPe17r62ubl5lsouDAUIEREREYmyKAaIE8A3POkE0A0sCaYNAF9w9wF37wN2kzxjMWesW7eOBQsWcOzYMc6fPx92OSIiIiIiY0QxQGwHPgtgZi1AA3AumHYTsN/Mys2sEtgE/HsoVRZJVVUVmzZtAnQ7VxERERGJnigGiK8AjWa2H/gWyUDxCTN7p7v/DHgIOAjsAb7u7s+FV2pxqBuTiIiIiERV5O7C5O5x4I6M0U+mTf8b4G9mtahZlgoQe/fuJZFIUFERuX8mEREREZmnongGYt5btmwZy5cvp6enh6NHj4ZdjoiIiIjIKAWIiFI3JhERERGJIgWIiFKAEBEREZEoUoCIqA0bNhCLxTh69Cil9mN4IiIiIjJ3KUBEVE1NDevXr8fd2bt3b9jliIiIiIgAChCRpm5MIiIiIhI1ChARlgoQHR0djIyMhFyNiIiIiIgCRKTddNNNtLS0cOHCBZ57bs79Xp6IiIiIlCAFiAgzM7Zu3QqoG5OIiIiIRIMCRMTpOggRERERiRIFiIjbvHkz5eXlHD58mN7e3rDLEREREZF5TgEi4hYuXMhtt91GIpFg//79YZcjIiIiIvOcAkQJUDcmEREREYkKBYgSkB4g3D3kakRERERkPotkgDCzI2bWETy+mjHtI2Z22MwOmtk7wqpxNr3+9a+nubmZs2fP8vzzz4ddjoiIiIjMY5ELEGZWDeDuW4PHh9Km3QB8AtgIbAM+Y2ZV4VQ6e8rKynQ7VxERERGJhMgFCGA1UGNmu8xst5mtT5u2DnjC3Qfd/TLwArAqlCpnma6DEBEREZEoiGKAGADuJXmG4S7gITOrCKY1AJfT5u0DFmYuwMzuDLo5He7u7i52vbPiTW96E2bGwYMHGRgYCLscEREREZmnohggTgDf8KQTQDewJJjWC9SnzVsP9GQuwN13uPtad1/b3Nxc9IJnw6JFi1i1ahWDg4McOHAg7HJEREREZJ6KYoDYDnwWwMxaSJ51OBdMewrYbGbVZrYQeA3wbChVhkDdmEREREQkbFEMEF8BGs1sP/AtkoHiE2b2Tnc/D9wH7AN2A3/h7tfCK3V2pQJER0dHuIWIiIiIyLxVMfkss8vd48AdGaOfTJv+ZeDLs1pURKxZs4aGhgY6Ozs5deoUS5cuDbskEREREZlnIhcgJLeKigq2bNnCo48+SkdHBx/4wAfCLklmgbszNDREPB5ncHCQwcHB0eF4PD5meKJp8Xica9eujQ5PNC19fDweJ5FI0NDQwHXXXUdTUxNNTU1cd911Yx6pcU1NTTQ2NlJZWRl204mIiEgRKECUmLa2Nh599FHa29sVIIrE3UkkEhPuVKfvyE+0Mz/RPFOZFgW//OUvpzR/fX19zqCROT71uqampkjVy2xLJBJcuXJlzKOvr4/+/n76+/spLy+npqaGBQsWUFNTM/pIf11ZWYmZhf1RREQkgwJEiUn9oNy+ffuIx+PEYrFwCyoRIyMjnD17lq6uLrq6uujs7KSzs5MzZ85w9erVcUfl3T3skseoqKggFotRVVVFVVUVsVhs9HX6cD7T0sdPNC19uLy8nMuXL3Pp0iVefvnl0Ueu1z09PfT19dHX18epU6fy/pzV1dXjQsVk4aOhoYGysihezlV6hoaGxu3wX7lyhf7+/tHhbI9s8127NvPL01IhI1u4SA1nPk8WSjIDikRPvgdxMoeHh4eJxWKj28vUc2VlZV6P1LZORCanAFFiWlpauOWWWzh+/DiHDh1i48aNYZcUKZcuXaKzs3M0JKSeX3zxxSnt0KTvsE91Z7vQO/lVVVWR2EFesmTJ5DMFRkZGuHz58rhwkRk4MkPItWvXOHfuHOfOnZv8jwTKy8tpbGycMGTM5S5W8Xh89Mh+tp381LT04VzBoJBnu8yM+vp66urqxj1qa2tJJBIMDAxw9epVrl69ysDAwOjr1HAikRgNosVQWVlZ8FCS/rqiojS+Yt09ry6Q+Q7P5P1hH8Qxs3HhYyaBJP292cbNZHnp743Cd4TML6WxdZMx2traOH78OO3t7fMyQAwMDPDiiy/S1dXFCy+8MHpWoauri5dffjnn+17xilfQ2tpKa2srK1asYMWKFSxdupSamppxO+w6CjUzZWVlozvu+XJ3rl69OmHQyDa+r6+P7u5upvqjkWF1sXJ3BgcHJzyan77DP9l88Xh8xjWllJeXZ93hr6uro76+ntra2tHhiearq6tjwYIFM+5+FI/Hc4aL9OFs0/J539DQEENDQ/T29haoBceKxWLjgsZUQ8nIyEjOneyp7LBPNi1qysvLJzxQk+tAS+rMRSKRGP33TV1Dlj4tfZ70can/n4ODg/T394fdDHkrLy/PK3w88sgjVFdXh12uzAEWta4ahbZ69WrfuXNn2GUU1L59+3jPe97Da1/7Wh5//PGwyymK4eFhzpw5M3oWIfV44YUXeOmll3K+r66ubkxASAWG1tZW6uvrc75PSlc8Hqenp2fM2YzJAkhPTw8jIyNT/lsTdbGqq6tjYGAgr2AwNDRUsM9fUVExbue+trZ2dEc+fXiiHf7a2tqC7PSXitRR96mEkmzPE71vOutYWFI7mbl22vM58zrT4fTnsA7iDA8PE4/Hx4SPiR65gklqGdnGTeUxWeBJPefr9OnTc+bsq0zfDTfcMOMNvc5AlKB169axYMECjh07xvnz57nhhhvCLmla3J2LFy+O6WqUGj516lTOjWJlZSVLly4dExBSgeH666+fNztAkhSLxVi8eDGLFy/O+z0jIyP09vbmDB25Ash0ulhlU1lZOe7Ifrad/ImCQepRVVWldX4azGy022BjY2PBl586kp1P0JgolJSVlU14BL4QO/axWExdYALl5eWjZ4pKReqakXwCSal0q5Po05pUgqqqqti0aROPPfYYHR0dvO997wu7pAn19/ePOZOQPjxR14GWlpZxAaG1tZVXvepV2gjKjJSVldHY2EhjYyPLly/P6z3pXayyXcNx5coVamtr8+rmU1VVVeRPKGEzM6qrq0fPWokUi5mNdlUSmS3aCytRbW1tPPbYY7S3t0ciQAwNDXH69OlxZxK6uro4f/58zvc1NDSMCQfpZxV0S0+JEjMb7Z9+4403hl2OiIhIaBQgSlRbWxsAe/bsIZFIzMoReXfnF7/4xbiAkOpylEgksr4vFouxfPnyrNcmNDc3q/uFiIiISAlRgChRy5YtY/ny5Zw8eZIjR45w++23F2zZvb29484kpJ4HBgayvsfMuPHGG2ltbWXlypVjuh698pWv1F2NREREROYIBYgS1tbWxsmTJ2lvb59ygBgcHBy9FWrmj6tdvHgx5/uamprG3d1o5cqVLF26tKQuOhMRERGR6VGAKGFtbW088MADtLe3c/fdd4+bPjIywksvvTTmFqip4TNnzuS8xWB1dfWYMwjpz7oYUERERGR+U4AoYRs2bCAWi/HTn/6UPXv2jF6fkH5WIdevL5eVlY25FWr685IlS3RLPxERERHJKpIBwswWA08Db3P342njPwn8PnAhGPUH7v7zEEqMhJqaGtavX8/evXtz3onp+uuvz3rx8tKlS3UrSRERERGZssgFCDOrBL4EXM0yeQ3w39z96dmtKrq2b9/OiRMnWLRo0biLl1tbW2loaAi7RBERERGZQyIXIIB7gfuBT2WZdhvwKTO7Afi+u39mViuLoG3btrFt27awyxARERGReSJSHd3N7IPABXffmWOWbwJ3AW8GNpnZO3Is504zO2xmh7u7u4tTrIiIiIjIPBSpAAFsB95mZh3ArcDXg7MNWPLXxj7v7hfdPQ58H3hDtoW4+w53X+vua5ubm2epdBERERGRuS9SXZjcfUtqOAgRd7n7+WBUA/Csmb0G6Cd5FuKBWS9SRERERGQei1SAyMbM7gDq3H2Hmd0DtAODwOPu/oNwqxMRERERmV8iGyDcfWsweDxt3IPAg6EUJCIiIiIikbsGQkREREREIszcPewaisrM+oB5+2Nzs2QRcDHsIuY4tXHxqY2LS+1bfGrj4lMbF5/auPiq3f11M1lAZLswFdDP3X1t2EXMZWZ2WG1cXGrj4lMbF5fat/jUxsWnNi4+tXHxmdnhmS5DXZhERERERCRvChAiIiIiIpK3+RAgdoRdwDygNi4+tXHxqY2LS+1bfGrj4lMbF5/auPhm3MZz/iJqEREREREpnPlwBkJERERERApEAUJERERERPI2ZwOEmZWZ2f1mdsDMOsxsZdg1lSozqzSzB81sn5k9ZWbvNLM1ZnY2aNsOM3tvMO+ng3meNLN1YddeSszsSFp7ftXM1pvZT8zsCTP7dDCP1utpMrMPprXvQTO7Zma/bWadaePfpDaeHjN7o5l1BMMrzWx/sM34f2ZWFowft33INa+MldG+twbt1WFmO83sFcH4+8zs6bT1eaGZLTKzXcH83zKzmlA/SIRltHHe33Fah/OX0cbfTGvfF83sm8H47wbfex1m9sNgnNp4Ejn21Yq3LXb3OfkAfhv4WjC8HvhO2DWV6gP4EPD5YLgZOA18GPjjjPnWALsBA34NOBR27aXyAKqBIxnjjgIrgvb8QdC+Wq8L097/F7gT+Gvgv2ZMUxtPvT3vBp4BDgavvwtsDYbvB/5Lru1DtnnD/jxRe2Rp3z3ArcHwHwB/FwzvBxZlvPc+4IPB8J8DfxT254niI0sb5/0dp3V4em2cNv664PtuSfD6GME1umnzqI0nb99s+2pF2xbP5QS3CfgRgLsfBPSjJNP3z8Bfpb1OALcBv2Vme83sK2ZWT7LNd3nSaaDCzK4Pod5StBqoCY4U7jazLUCVu3d68n/zTuAtaL2eMTNbC/y6u+8guR5vD464fNbMKlAbT0cnyeCVchvJnVyAHwJvJff2Idu8MlZm+77P3Y8GwxXAteBo4auBHcHR2+3B9NH1GbXvRLKtw/l+x2kdzk9mG6f8T+CL7n4uOJvWCHwvOBr+jmAetfHkcu2rFWVbPJcDRANwOe31cLBzIFPk7lfcvS/YgP4L8JfAU8CfuvsWoAv4NOPbvA9YONv1lqgB4F5gG3AX8NVgXEqqLbVez9w9JL+wAB4DPg5sAepItr3aeIrc/V+BobRRFgRfyL3upsZnm1fSZLavu58DMLMNwH8HPgfUAl8E3g+8Hfioma1ibLurfXPIsg5P5TtO63AesrQxZraY5MGxrwWjYsBngf9MMmx8LphHbTyJHPtqRdsWz+UA0QvUp70uc/dEWMWUOjN7FdAOPOjuDwOPuPvTweRHgDcwvs3rgZ5ZLbR0nQC+ERwROEHyP3dT2vRUW2q9ngEzawRucff2YNQD7t4VbDS/Q/b1WG08dSNpw7nW3dT4bPPKJII++fcDv+XuF0gecPiCuw+4ex/JLgqrGdvuat/8TeU7Tuvw9L0beNjdh4PX54H73T3h7r8EjgA3ozbOS5Z9taJti+dygHgC+E0AM1tPst+dTENwSnEX8Gfu/kAweqf96iLptwBPk2zzbcFFqL9Gcsfr4uxXXJK2kzzqgpm1ADVAv5mtMDMjeWZiH1qvZ2oL8GOAoF3/v5ndGExLX4/VxjNzxMy2BsO/wa/W3Wzbh2zzygTM7P0kzzxsdfeuYPRNwH4zKzezSpLdFP6dtPUZte9UTOU7Tuvw9L2VZHeZ9Nf/BGBmdcDrgJ+hNp5Ujn21om2L5/Jp+UeAt5nZkyQvFPlQyPWUsntIXuT0V2aW6l/3SeDzZhYnecTgTnfvNbN9wAGS4fRjoVRbmr4CfM3M9gNOMlCMAA8B5ST7K/7EzA6h9XombibZHQF3dzP7MPBtM7tK8sK9LwPDqI1n6o+BL5tZjOSX/7+4+3CO7cO4ecMouFSYWTnJC6NPk1x3Afa4+6fN7CHgIMluIl939+fM7K+BfzSzjwAXgTtCKr3U/CHw93l+x2kdnr7RbTKAu//QzLaZ2UGS34H3uPtFM1MbTy7bvtr/AO4rxrZYv0QtIiIiIiJ5m8tdmEREREREpMAUIEREREREJG8KECIiIiIikjcFCBERERERyZsChIiIiIiI5E0BQkRERERE8qYAISIiIiIieVOAEBERzKzRzD6a9vrJIv2dBWa2J/hBtJksJ2Zme81sLv8gqohIJClAiIgIQCMwGiDcfUOR/s524NvuPjyThbh7HHgceG9BqhIRkbwpQIiICMD/BlaY2VEz+1szuwJgZsvM7LiZ/YOZPWtmD5nZW83sCTN73szWpRZgZu83s6eCZXwpx1mG3wW+M5Vlm1mtmX3fzH4azJcKDf8WLE9ERGaRAoSIiAD8OdDp7re6+59mTFsJfAFYBdwC3AFsAv4EuAfAzF5D8mzARne/FRgmY+fezGJAq7u/OJVlA28HXnL31e7+OuBHwfhngdtn9rFFRGSqFCBERGQyJ939GXcfAZ4DHnd3B54BlgXzvAW4DThkZkeD160Zy1kE9Exj2c8AbzWz/2Nmm939MkDQDSpuZvUF/KwiIjIJXXwmIiKTGUwbHkl7PcKvvkcM+Ed3/9QEy7kKVE912e5+wsxuA34T+IyZ7XL3/xXMVwVcm8JnERGRGdIZCBERAegDZnIk/3Hg3Wa2GMDMmsxsafoM7v4yUG5mmSFiQmbWAgy4+zeAe4E1wfhm4IK7D82gbhERmSKdgRAREdy9O7h4+Vngh9N4/zEz+0tgl5mVAUPAx4BTGbPuInmNw4+nsPjXA39rZiPBcv8wGN8G/GCqtYqIyMxYsqupiIhI8ZnZG4BPuvvvFWBZ3wY+5e4/n3llIiKSL3VhEhGRWePuR4D2QvyQHPBvCg8iIrNPZyBERERERCRvOgMhIiIiIiJ5U4AQEREREZG8KUCIiIiIiEjeFCBERERERCRvChAiIiIiIpI3BQgREREREcnbfwBzFm+Wcj+NVgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ - "fig = runAndPlot(pneuron, None, None, 0., 2., 0.)" + "data, meta = pneuron.simulate(0., 2., 0.)\n", + "GroupedTimeSeries([(data, meta)], pltscheme={'Q_m': ['Qm'], 'FR': ['FR']}).render()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe a variable spontenaous firing rate between 5 and 20 Hz, with an average of about 13-14 Hz. This is slightly higher than that reported by Otsuka (approx. 10 Hz), but within the same order of magnitude. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Response to depolarizing current pulses" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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16lQAwOLFizFp0iSMGDECAPDKK69g4sSJOPzwwwEAzzzzDAYMGIBJkyYBAJ544glUV1fjnXfeAQB8/fXXePHFF61j++GHH7gFiVu3buWcD9HwsbfJF198gS+//NJKr1ixgpvitXev+2c4tmzZ4vr173g8zskRsevAGOOMLxGvfTt27OCcqx9++IEztOzGDWOMq3fbtm2e/cNtX11dHebPn88dn9dX0FevXm0Zm1u2bMHMmTOxY8cOAImv477xxhtW3vnz5+OTTz6x6pk2bRp3fuxG61dffYVXX33VSr/11luYP3++ld6+fbt1DLW1tbjyyiut91N/+eWXePjhh63zO3/+fLz22mvWcV933XXWtL4dO3Zg+vTpqK2tBQB88MEHGDNmDL755hsAwPPPP4/zzjvPao8pU6Zg8uTJlqy77roLb7/9NgAgFovh17/+Nd58800AwPfff4+pU6fi559/ttrqmWeesdrziSeewC233AIgcT5PPPFEXHbZZZbsp59+Gl999RUAYOnSpRg8eLBV1xNPPIEuXbrgo48+AgDcfPPN6NOnj2Vc//Wvf8U111xjnb/TTz8dV199NYDEHP5jjz0WDz74oNW23bp1s74n8Oijj6JTp05Wm1155ZXo2rUrPv30UwDAMcccg169emHdunUAgAkTJuC3v/2tdV66deuGU045BUDifepHH300Hn74YQCJAYlDDz0Ul19+OQDg9ttvx6GHHor77rsPAHDRRRehpqbG6jeDBg3CEUccgeXLlwMAunfvjv33398a8Ljjjjvwt7/9zeoHgwYNQp8+fQAkroHx48dbUzkfe+wxHHPMMVZ09frrr8dJJ52Em266CQBwySWX4PDDD8eLL74IABg1ahT69+9vDYZkGmPoGQzZwTjl7hTUfSllOBXqHwAGgN1xxx0sFouxWCzGUtsaN26ctg1A2razzz6bxWIxNnPmTNc8p59+OovFYqxnz55cniuuuIJLP/7441z6mWee4dL33Xcfl7744ou59AEHHMClU7/bt2/PpR977DEuvXr1arZw4UIGgA0ePJjFYjE2depUBoDddNNNLBaLscmTJ7PKykr2wQcfsNraWq6eGTNmMADs4IMPZrFYjN10000MAHv11VdZLBZj5557Lhs6dCj77rvv2IIFCxgAdtRRR7FYLMaeeOIJVl1dbeWtqalhANgXX3zBvvvuO3bppZeyxx9/nMViMfb000+zqqoqNmPGDBaLxdiRRx7JALAlS5aw7777jo0fP57df//9LBaLsenTpzMA7P/+7/9YLBZjjzzyCDvrrLNYbW0t+/rrrxkA1rFjRxaLxdi0adMYADZu3DgWi8XY0KFDGQD24osvsu+++4717duXHXjggSwWi7HZs2czAOyMM86wztERRxzBFi5cyGKxGDvllFNY165d2Zo1a1htbS075phj2JQpU7jzdeqpp7JYLMYmTpzISkpK2KJFi6yyqX0vv/wyA8AOOOAAFovF2JAhQzgdU+3/yiuvsBUrVnDnY8qUKVz6tttuYwDYgw8+yJV98cUX2cqVK7m87733HgPARo4cyWKxGOvduzcDwLp168aVvf3227n0kiVL2PPPP8/JmjhxIgPAKisrubwHHXQQl77tttu49IoVK9L6/siRI7l0nz59GAB27LHHcmWnTp3KpefOncvmzJnDlU2d71S6a9eujtdNv379uPQ111zDpRcvXszeeecdruxVV10ldU22aNGCS48ePZpLP/DAA1x61apVVp9IyZo0aZJUXal7WSp93HHHcekbb7yRS8+ZMyetX/zzn//k0pdffjmX3n///R3rbtiwIZc+7LDDuPTEiROt+yVjjD377LPWPpHU9ksuuSRt30MPPeRaLsXOnTutPHv27HHNZ2e//fbzlWvQz9q1a/Oq3VO6fvzxx9lWJee56KKL8urcZoJUe7z22mvZViVFaPvaRCAUyupYoKc7LeqY4rPPPuPS27Zts6ZVzZ07FwCsEdbrrrsOAHDnnXdi06ZNePDBB1FXV8eVT41mf/DBB1yZKVOmAAAeeughzJkzBytXrrRGXt99910AwJgxY1BbW4uzzjoLQCLKkvr/1ltvYerUqRg7diwA4Mwzz8SGDRvwm9/8BgDw3nvvAQDeeecdLFy4EPfffz/Gjx/P6X/99dcDAM4++2w89thjeOGFF6wR8NSUitQI7aOPPgoAmDNnDoDEiO6ePXuwdOlSa6Q0lSc1HW306NGYP3++NXL+0ksvYeXKlVi6dClmz56NN99802qHJ598EgCsUdh7770Xu3fvxsMPPwzGGF566SVrX+p8pKIKqShSanuKzz//HJs3b+a2pUaUU1x11VXc/xRffvllWrTn2WefBZCIbqXkA7BG6+312tmyZUva12NTbSa+WUecJiOWi8fjaXnsLzUAgGXLlgFIvHbZjvgl4lTExo7YPm4vNRCvGzHtND1Q3BZ0tM2vbpW6/O5lTnWJkTFRtjiVKWjdQQgq015OVoYZLTWoEEV/NxjykXrjQDhhvxF4PURUHIhceRg5GQyyzojTPlnjwameFKLBwhjznI8t5hUNTLd6fv75ZyX9ZfOKRjwAaz64X9lUXV71OuVzkyvbzxhjKCriL/OgssLqoUtWEL10Gfk6ZTsR1XHoqEtX3Zm6RxpDz2Aw5BqFdF+qNw6E30kL+1BzczIy9VDW2SnDyFItmwnjVSWvl/4yI8ayqDgQMkZskLyqslScLb9j0C0rKKoj92FkR1mXqqwo6/JLB73nReV45MqgjyE/KCQDMCrMNeVOIfUf40AkkR05jpqgxp6I7giEbD1h8+qYJqF6vmSnPYQZjU7NGYy6HhlZfrLdyno5PTJl7cTj8ayOrrsR5JgLJQKhO38YgjjCYrlCelAXIsbILFzMuXWnkO5L9caB8CNTEYhMEfQB7FRWdbQ8isiASllVZyno1KKwbZypCEo2+mQQxyXMNKNcNLx1RE6iqiubdefaFCZj7BgMBoM6xoGQQOciar/9URpCQWV5vYZUhbBTc6JyNvx0CiLXqZ6gEYiwIxY6pzDpKhtlREkFp3U5YjrKqGBUdanq4iQ76BQzHeiQWUgjfYVIvjpupl/5k6/nNhMUUv8pzrYCbhBRQwAPAegEoBTATQA+A/AIAAZgKYCLGWOBrVv7iZQZoZa5KKI2ADI1dSXMgl9ZVKfEhDFooohAOJGpCITOdsvUOpRMylLd70Ymoxs66wpyXWYyihOEqKYwZfu4DIZCw1xT7hSSA5HLEYixAH5ijA0CMALAvQDuAnBdchsBOFlWmNNJi2Ihoe6HsC6DIcwIvq52ytV1GUHnTas6NbKywzplKukwsgB9o+9RlU0RVQQqjOwo7j+y+6M8t377VR0BVX1UMcaOwWDIFMaByAzPArjelt4D4GAA7ybTswAco6sy2RFqnXJ1ygtiEEQVgYhqUa6K/kGjFVGtgQhbj67RYJXFy2HOl58sp3pzIZoRZdur1u1UV1RTmDI5fSqoA6GKiUDkD/lkVOWTrobco5D6T846EIyxrYyxLUTUFMBzAK4DQOyX1t8CoNKpLBFdQESLiWixTZ5THfYyvjpF5WR41RGlwaYiKxNGJqBnnrWqYyIbgQg7ghw00gGEm8oSpt6geuie7uWX1rXOQwadevudG511qZR3qjsT0SYvoprCZMgO+Xqu8knXbGGccncKqW1y1oEAACKqBjAHwL8YY08AsK93aApgo1M5xth0xlgNY6zGts0pn16FNRDVgtewo+VuhM2bqTUQUejkhI429VvY61WPTL1hIhCyemRy+o4Tssfo94KAKJ2TqCM8fvmDTC3KFCYCUb/IxWexITjmmnKnkPp6zjoQRNQawBsArmSMPZTc/BERDUn+HgFgnsb6XPelTriOiyKXpkPIlnea9uJVT5A6UkRRj5/coBEIJ7myUSpVYzFovxHzhe0XUa7JCWOIR3kdZWpxcTajAEGOM4o1SWHKRCHDEB35GoEwGMJQSH09Z9/CBOAaAFUAriei1FqISwH8lYhKACxHYmpTYHSeSDcnI+y84kxNYQiaV6WsbuNVx7oMp7JRLLjNVPs7yQr6oNZp0DoRpeEdtGyUU6+c9odxKlWIcvqUKkHlmlHNwiWfjKp80jVbmGu1fpCzDgRj7FIkHAaRowLKk9rmRZCLIpsRBz90GNVh5Drl1bUwWuc0Ha99QaM6Ko6K0/5MTGVT1UulbKanZemULaLrmnaaTpWpUf+g948g99Wgi6ijWgNhjJ3skK+GeL7qnUnMNeVOIfWfnJ3CpBsdxo+OaU6qF5auOdEqRrWTrEyvlwhDGONJ9Vhl61LJK1OvbF5xW9hR7igXUUexFkNVtsx+XXoHOS6djqNqBCJTgyFu+kQlwxg72aeQjCqDwYtC6uv1xoHIFFE/ZKOYXuMnT3XaS1Rz6lXaNhMRiDBOmcq6EpVRcb81DyrGd6ajBjrRaXhHtbA5jFOpWpdf/iinMImyM2WwGwcitwn6jDEY8plC6uv1xoHwG4HLlUXUUY36qRhCKkaUboNUF2EWN4epxwtdD8ywEaFsRCCiHl3P5kJmnWsi/NB5nKqRqSicchWimsJkyD75dK7ySddsYZzy+kG9diBUkbkosnXhZGvuu25DOIp1DX46yJbVPZqvczqKG6KhmKkPyckYy7kQgRDRPfXPL61rellY5yTXjKJM6mOMnexgnL3CxVxT9YN640A4oRqBCILuCEMm5pyL8nQajn466SCs0acj2qK7HtnzEaUj6YQuY9pLlo4IRCadlRSqjl+YCITqeQ/SD3Q58EHPhWo5M4UpfzAORGFhril3Cqmv12sHwk7Y6IJbpwhr2OicsqDT2HPb5/VhLt3GbBTTVsJEL7x00jmtI6rpT371hHUk/Zwgnc5YlNOKolrnJHMcOh/Mfv0+Kl1UpyKpYEa1DVFj+pXBkKDeOBA6DLZcmMIka6DG4/FQBlum1gXoiAw45ZWtJ+x8+kxEdZzKyuSTLZutRdSyBCkb1VqNsA6mV54opzD55dfp0PnVFVWZKGQYMoM5V4WFiUC4U0h93TgQSXRNYdI9RSkbo7Q65+ur5s1GBMKObgPRLW+myjrJysaUJSeHNqhsv7rCyBYjaIzp++CajmlF2Vx/kckIhI5yZgpTbmOiRYb6SCH19XrjQOggqnUSQev0Qsbw8SsvUzZTToBqBELFWRKPIcwaCNl6wjhlqm2hMwKhc5Q6quhF2GiVX91RjczrlC3iNbXQSRcg+GCG3/kKOoVJtZxxIPKHfDKq8klXQ+5RSP2nXjoQTg+MbEUgdC2idho5VdVNpayOvKoGqYr+YUZuo5jSEzayEdTJidpI1alH0LaLcqqVzH6d60L8ZOt0XsS6i4r4x0GmplNFJcOQP5jzXVgYp7x+UC8dCCfCdni3G2A2LyS/EW8dRlWmpjBFNUc7bGQgigiETL1B0e3wBa07akM8FyIQqlOWZPpTVPeTIFOYgmKmMBmA/J3ClE+6ZgtzTdUP6qUDEdWDSEZ2WIMgzIiviqygBpmXXJm8Kg6EG2GcGpW3SIVB1VEJ4+RE6biEkZULEYhMOhAy6Fzz5Ldfl0OnWnfQKUyqdRsHIn8wRnlhYa4pdwqpr9cbB8J+0qKYwpTKE3ZKUlQjkGFGj8NO6/HKG8W6BpnpXEF00h2BCErURqxX2TBGv87j95Ot0/iNKnKiw4jXGQ2J6l4U5FxnchqUMXYMKhSSAWjIPIXUf+qlAxEU+4Mm6MMpyikIYjqowaZz1FoVHWsIxPx+TkGmjlX2fMiMHocx3MMshtU5B1/XolwZ2V55VYg6cpKpiKS4LYizGPS4c20KkyE75OsUJoM/ximvH9RLB0I1AuEkQ9WocUPXqJ/MCKNf3V5lda03CCNXlwHjRRjDXkUn3U6AG7r6qRu5sJg7TF/3kyVui1q2Xx+XXeisw3mJ6vwYB8Igkk/nKp90zRbGgXCnkPpP3jkQRFRERP8gov8R0TtE1C2AjNB6uBktuqcg6Vq0Go/HlQydoKPlmTLAVQg7LSmIXKe82XACdBqKYUf2xf4oEsYZ8evruhxvnZETHdOI3IjacfTSRUfdQfUNMqptjJ3sU0hGlcHgRSH19UAOBBGVE1ED3cpIcgqjf3mzAAAgAElEQVSAMsbYAABXAfiLTCHdo9KZijj4lXfTR/eorIphH1VkQ6WtVIx1XVOLVFCtR/Z4Mj29JxMRCNXRdJ2oODoiQaZHqZYP0w+COnSqBIlAhLlfeclwwjgQ2SefjKp80tWQexRS/5FyIJKj/mOI6DUiWgfgcwAxIlpGRHcQUfdo1eQYCODfAMAYWwCgRlWA6hQmGQdCNgKhqluYEUjV6RH2vF5pv3rDOCZeOoUZfY3KAZItK+aPKgKRaUdSpa2C9kc/2eI2J9ni1B8vPVVlBzVA/SJ9mYxIyPRHXcdtpjAZgGDRolwgn3TNFsYprx/IRiDmAOgK4GoAbRhj1YyxVgAGAVgA4DYiGhuRjiIVADbZ0nuJqNiegYguIKLFRLTYSUAUU5hkCRph8EPnyLMoL6opQEA4o1HWWPebvhX0QaZ6rG51+u2TMb69ZPkdX1TnVhWd8/mjlB1V5CQej0fqZIXNn4kBiLAEuZaNsWMw6MVcU/WDYv8sAIBjGGN14kbG2M8AngfwPBE11KqZO5sBNLWlixhjewS9pgOYDgBExJLbAleYKus3WpisT0m2rlG/IMeXKUPfK28mdAgzYu+3T+X8hHHKwujotz/MzT4TU5j8ZDvt1yU7yshJmPMcRd26oh1+mClMBpF8GtXPJ10NuUch9R+pCIST8xAkjybeA3ACABDR4QCWyBSyn7RsTmHK1MMqjOEjM2rttk/nCK0fmTDWvQjrlEYVffEqq9oP/MqqEDQSIiNXl2ydDphM3xL1Vj2OMA6calQrkxGITDoQhuwQNPKbbfJJ12xhnHJ3Cqn/+DoQRHQsET1ARAcm0xdEr5YnLwLYSUTzAdwN4DKZQrpPmq7RLb+Hss7XNmZquk0YvHS079M5nUbVkPLKq8Mp89PR62vZfmV1n59ciEDodnRU6oqqbJD8uusKOhgS1QNSNXJRSA/qQiefzlU+6ZotjANRP5CZwjQBwDkAriOi5gAOjFYlbxhjcQAX6ZYrE4Hwejil0m6LNVXq8cqfibnvYn7VKTNOOrvJ0Hk8bvt0RUV0GnRhdArr0GXCCZAZ8fbTyw2ZkXydo+VR6S3TRn4DCGHqDtpmQepSlavDSNM1yGOIHmOUFxbmmnKnkPq6zBSm9YyxjYyxyQCOA3BIxDpFjuoUpjCy/Yx0cWQ5qgsvrMEaxTSXsNNBdC2iVpHrl1eHUyZTVteiYJW8YaMofnpEZbB6yVZtr0xGN3Qa9aq6OKHLKTNrIAwi+WRU5ZOuhtyjkPqPjAPxWuoHY+wqAI9Fp0506D5psqPCKhEH3aPFukYYVR0blXqyPa3KyYlTqVPneoKo5pRHOdKcC1OYVPRSla3zOgrirOjC77iA6NZr6ToOM4WpsMjXc5VPuhoMUeLrQDDGXhbSf4tOneiwX/RBF1F73fBSab+HsJ8MXQ9t0RAOO9rvRtgpHVEs6JUx+oLK9UqrEFX0Reeotk4nIIghnonpeqrnVLdRL+qtq41kzp2Y9ltjpGsNhJnCZDAUNuaaqh/IvsYVAEBENQCuBdAxWZYAMMZYvwh0iwwdU5hkIw4qU5iifGhHaaz76eFVNorR3DAOUFhnIxNTvVSMKxVnSlWXsA6UaLCKuK2diTIC4VeXjOxc1Fs1f5iBAT+CyjFTmAqXfBrVzyddDblHIfUfJQcCwAwAf0Di1alqr4LJMjpGi2UiECp6BDVO/OSm0mGMvTBlw0xh0hVVkNU/zGi1CplyPnSOoIeJQMj0R1Xn2UvPoOdG5hhVR9RlHQgdEZ4w/UD1fMgSxuH1IqopTMaByA66BiMyTT7pmi3MNeVOIfUfVQdiPWPslUg0ySBRRiBU5IYZlZaRJab93hClQ68wBqqqTrmQN4wDpEJQ4y7sufQzMnWhc8Q7rHOmKjsofka7zHFENTUrTN1++B2jkz5BKKQHdaFjzlVhYRyI+oGqA3EDET0I4C0Au1IbGWMvaNUqxwgymphC1YHwK69zdFzW2PUj6Ehy2FFn2bx+i8CjGnn12xfFolWZkX9d08ZycZQ6JUvndaPafrqiQzK6hTl3Xs5g1IMZQeqJapDHkH3y6Vzlk67ZwjgQ7hRS/1F1IM4BsB+AhvhlChMDUC8cCJnRV78LJ6ppA171OKUBtdFLt+hFWMNDdc2IW71+eskSJlrhd+x++utwcnS2S5jReJm2CuN4+skKShDDOoyhrXo/iHIRteq9SMdghlveoBGpIP3AGDvZIapoZtTkk66G3KOQ+o+qA3EAY2z/SDSJGPtJi2IKUyod1CB2Q+cIo8oxBjVYgz743cp61aMjWhGmnVQM1zAj7mHLholA+Bm4QcmVCISMLDsyrzTWFSVU/eK4Kn6OkkgmXnigk0J6UBsM+YRxyusHcpPif2EBEfWORJOI8XMgZMu6bZN9WKmMboYxxp3qjWL+tGhUOhlYuhYHZ8NYV5EbNgKhUq+ukWfVesPooWtkX2cEIopISZROf5RrIMIMOKjULXOudN77/DDGTvbJJ2cvn3Q1GKJENQIxEMA4IlqFxBoIAsBYnr3G1Qld4fEwzknY0XA/2SJRjcpHMS0prPEaRV5xm6rhmokF2GJZp3pU6tXVP3X3db/9Oh0dXeh0qqKoW0SsO+hLGWT26XAgZM+VcSCyTz4Z5fmka7Yw15Q7hdR/VB2I4ZFokQHCRCBU5Ps98L0e2k4ff9M14qj6NWkvWX55M7EoVzUqIisrzOi1ypQTlfOhs53C3LxUZKlGQsIa/bpkOe2PKiogU5cfOiMQKvl11a37YRrkWjbGTnYIet81GPKZQurrSg4EY2x1VIpkEtU1EFGd8CAjvDqdAF2LdnUZpSpOQdioiI68KmXDntswD1u/spmaDqVrtF1GL50Lm73q8tJTpi6/dKYWUTttizL64aWH23ZxeqefPoX0oC508ulc5ZOu2cI45e4UUv9RWgNBRI8SUTNbuoqIHtKvVm4iY1iEiUDonsIQ1OD2kuO3L4whqDKCr8sp0G0U65gqFaRsFG0sllcZIVcdbQ+qk9t+XcZvkIXMUU6B0znNK6yjlOkIhGpbmQhE/pBPRlU+6ZotzDVVP1BdRN2PMbYxlWCMbQDQX69K0WC/6DO5iNpvVFnVQNP1hiDV0XK/umTzupXzK6uaV8V5ktVZpR1kdBLJVARCl/Oh6gQF7Y+qsp2IUk+dUULVc6VzDUomBhxk8wa91sJcJ4bMYs6VoT5SSH1d1YEoIqKqVIKImkN9HYUURFRJRDOJ6F0i+h8RDUhuP5yI3iei94joBll5YQyMVFmZG55KWN3PKA6zbsGr3lQ6qhH8MFNzdOX10tFrn+raBLG8W1nVaXNeOoYZYQ8bYQkacZGRrXMqUJjRcl0j5jJlwzorunSRqVtcNK1j+pRuB0I1D2BGSw1qFJIBGBXmmqofqBr/fwHwPyJ6FgADMBrAzdq1SvB7AG8xxu4hop4AngRwEIB/ABgF4GsArxHRQYyxD/2E6Rqh9JMX1HhOpXVFILx0lNkX9CaZ7ZHL1L4wjklQHcJGIHTUE6Rs0OiMrvOV2h+03zhNE8xmVMANpwiOyn4VVPtMkH5SKFOYDNknn85VPulqyD0Kqf9IORDJ0f8FjLHHiGgxgKOReIXrrxljn0Wk291IvCoWSOi5k4gqAJQyxlYm9foPgGEAfB0IO1FMYZKty29EV8wrM4VBRr8wo8d+ecNEBmRH8EW9VPWXRXVUPcyx62ynKM6PkywxHXRkP4xhHsTxlkVGT5GoHArZ6z8IQc5HFIQZqPDbbiIQuY2ua9aQe5hrqn4gG4EYB+DvRLQCwL8BPMcY+16XEkR0HoDLhM3nMMYWEVEbAI8DmASgAsBmW54tALo4yLsAwAX2bfYbVNDpJH7GvoycTN40ZfSVLRvGMM7GYucw+svuC6uTiKwTEOYLxWEiEFFGUVQNVq/20B0ZEdN+egZ1QGX26Vzz4Jc/qmlyOu6BOiMQxtjJPsaBMNQXCqmvSzkQjLGLAICI9gMwAsAjRFQJYA4SDsV7jLG9QZVgjP0TwD/F7US0P4CnAExmjL2bjEA0tWVpCmCjWI4xNh3A9KSM0GdLdoQ/WR+33c84Czsq6xWBCDrCqGIY6zYqM+1s6GwXnQ6QF6qj9WJdQSMfKrKc8oUd2VfRM8xIvY5rUrUe2bpEdK1BEZ0wneuv/PSQ3a56nymkB3Whk0/nKp90NeQehdR/lBZRM8Y+Z4zdzRgbjsQ0pv8COA3A+7oVI6LeAJ4FMIYxNitZ/2YAu4moKyWebscDmBdAttS2FKojh0HlBjEyozB2xfxROgUqxruKTmGcgjDGZph6wpyPMI5LGINXFtURcT9ZqiP3Knp51aXaP1T0CtsXw+gSpH9GuQZCta2cthfSg7rQyfVzZfqVGiaq504h9R8pB4KIuhHRkfZtjLEdALYCuIcxVhOBbrcCKAMwlYjeIaKXk9svAjADwEIAHzHGpJwX+0nTYQDoGlGOarTTz0BTeaiHyRvGmPPLG9W0pKgM6jAPoTBGbJiRftWogaweYRcMq+oVJpoh7tcVFXCqS7V9xTcjydblJz9MG8roIqODisygeQzZI1+N8nzS1WCIEtk1EPcAuMZh+/bkvpO0aZSEMXayy/YFAA4PIC+MLr4y3Pb5LaL2khHGgXDSL6gRqhJFEPGary/q5DVtQsW4Evf5TccIKtepbBSRgDAGclgHKawx7aZL2Pbw0zPoaLnMVCtdo2sy17/fq1OjWkQdVrZXWmafzLWmqocbZrTUoIJxIAyGBLJTmDoxxj4VNzLGFgPopFWjDBB0CpOMAaS65sFOPB7XOioT1PgLE0VwOgZdiy/FvFE4BWH09dNfV7vodAJUCCtL1YFQkaXzGMV0VI6P034/I15X3wwyeCESRQRQpoxsOWPo5Q+5fq5MvzIY0pF1IMo89jXSoUgmCfoWJjtBHQg7fg5DmId4GKMqjCMTZvRRl1MTxrBXyRf2y8wimVrHoNMo1TUCHqS8l15B5DjpFWQkXGeExw+d06WicsK89getx0Qg8p98NcrzSVeDIUpkHYhFRHS+uJESr1/9QK9KuYdMBCKF35QlFYNB5nWduTaFSbUelYdIULmyMsPK1enUiHnDnDudUYMwxrNf+4RZpBu0fWQI6+SHkS2iK5qnGpEIU3cmIxCqeQy5QT6dq3zSNVsYp7x+ILsGYhKAF4noN/jFYagBUALg1CgU0439og8agZB52KtOYVI1vqMyIPz0lJUTNqoQxbSqsJEBlbwqN05d89lVyoYx1MVtqsazV193cpZ1Lu7WNa0oakPbr01k6woSgRDz6+yfbgS93owDUVjk07nKJ10NuUch9R/Z70D8AOAIIhoKoG9y82uMsbcj0yxCdDwIZSMQdlTfPCPzEA86uq9q/KlMlVI1goIie+x+OsgaxWGPNYyRm08RCFnjTlc0I4iRL4uMIR6VLPE86xxQUHUonAjq7MvUE3QAJMi5MaOl2SfXjaow91CDoVCRjUCkmA+gHYCOAAYS0UAAYIz9SbdiuvG76HVFIPzKhH1oB0WUrfLGI783KXmlVXRSMd5VHBOVyIaqXK+0aj26pnqJ5cI4kroMcxWjUpUwRn4mR+qd9PAzrsM4jmHTuRaBUC1nDL3cJl/PT77qnUmMU+5OIfUfVQfiZSS+/PwhgF361YkO+0mLYgqTjJxMPsR1jsqGNVyieFuLilOg61h1j0brbNMoIhJ+x+tUb6YiYip9W/WGrSpb59oN1Ws2zHmPsi4/WTL7vPIaB6KwyKdzlU+6GgxRoupAtGeJr1DnHTpGOGUeuCqLqJ3qieqhrvp6VRUjU8Uw8Ssro08QubqcjTBGsEpfEMv5tVNQxzWMzlEa/UDwaXNh+pXf/jBOo9PxqBr1Yd565ZdWPbeFMoXJkH1y/VyZfmUwpCP7FqYU84lo/0g0yTOC3ERUHQQZI0vWOAozKhh0lFC1Hr8PvolldSzODitXlwPkV6+fcafDmJOpV5cjE8YQDyJb12LvMNeGTL1R1SWuTXGqyz6tTqWP+RHGgQtTTla2mW6RHfLVKM8nXQ2GKFGNQAwEcDYRrUJiChMBYIyxfto104z9IaE6hSl1w5C54QU1VNzSUY74qhh7Qev1W2vhlQ6aV0TXF65ldIpiPYFM3my8ZlenY6nq5IVxxGX3udXl11+CtotOB0LM6+dA+MnP1CCC13bV69QYevlDPp2rfNLVYIgSVQdiRCRa5DhODoRbHq/tMsaI10NSnIakgorhqNO4lTHQZHRSzRsVOg28TBy7n46qi8b99NA5hSuqtQReiGWd3pymsx+qGPVhjHg/WVE6Lyp6BJGhKlvERCCyT64b5UGvQ4NBpJD6j5QDQUTEEqz2y6NPtegIuojajuyDT+WhLGOsqOgX5qaXqXrFaRMqeXW89jSss6RSTxgDza9Nw6wXCGr0q76iV5eR7yRXl+GdSdl+shjjpxWFeUOYTF2q5yfotzV0IFNHnjyO6i35apTnk67Zwjjl9QPZNRBziOgSIupg30hEJUR0NBE9CmCcfvWiQYezIGsA+j2UVR7aOm9cOo1qnYZxVK8yjUKHsMgej1ObhpmPrtKOXg6Dkx6yjozT/ky+UchPvltap+Mjsy7Bry7ZgQxxm8xghi7nRaVve21XbXdj6BmiwPQrQxgKqf/ITmEaDuBcAE8SUWckXuVaBqABgDcA3M0Y+zgaFTOD6hoI2TxeI+x+BoPTm5Nk0TnyDET3CkfRMPEyQFUiEGI6qlfJhnGAZM+tk6HplEdWZzEd9LsFYZy4MCPcfnqonBfVY5S5JnUu2I7SYQ3rvLjh51TqcsCciFK2ITpy/VyZfmUwpCP7JeqdAO4DcB8RNQSwD4AdjLGNUSoXFUFvAGFvIn6GDuD9teowI6lhRgVV8qo6PSoGeNBR0DDHGlU7qTo5OvpsKh3U8Jc53kwsyHaSG8ZZzqZsv7r8pvi51R3klbG6HAg/ZKYtqtw3wuQx5Ab5dK7ySddsYaYw1Q9UX+MKxlgdYyyWKeeBiPYjok1EVJZMH05E7xPRe0R0g4Ic63cYQ9xtW0q+l2EkYyD4pYPqrDoqq2Ls+R2TjjcgiXn9XvkqTr9QiWy4yRH3qzoBYt6gBloY5yNs2TDGsy4DVdXYVXXsMmlYZ7MucZ94HfjVreK8yOrhtT0qh8YYO9khqvMZNfmkq8EQJcoORCYhogoAfwH/1et/ABiDxCtlDyOigzTV5bovdcPwuuG5lfd7KKs+tIMamn7GiZ8cN71Uj8GrrJ9ToOJshFmcLXus4jxxP4Parx5ZQ8bLqREpKiqKzLh2IipHxoswDoSMbDEd1KmXqUv1fuCG0zQiVWcl6P3CTxcdgyIyjocx9PKHXD9Xpl8ZDOnkrANBiafOdADXANie3FYBoJQxtpIlruL/ABimKlvVMHeKLrg5EH4Gs7jP76Ed5mGrYuiLxyI7EuokN0wEwsswULmJq+jgt07FS25U9TiV9yrrdS79ZIWJmmRqMbdK2TBOkrhN5hoNQybrUl2PFfTeE1UEQnXqk6zOJgKRfXLdKDcOhEEXhdR/VL8DEQlEdB6Ay4TNqwE8xRj7xHaDrwCw2ZZnC4AuDvIuAHCBig5hHQgZuU6GXianMO3du9dTlxQqDoR4DGJaNDL9js/rGGXzqjpLsm3uJ1fc76SXjE5OOnrVI0ZC/MqKOska2KpOj1deGVkqa0KiMrxljPqgBmjYqICfbBHVNvOrK+hxB72nyZSLKjpk0E8+nR/TrwyGdEI5EERUDKAPgFIAYIwtDCKHMfZPAP8UZH8F4Lykc9EGibc9nQigqS1bUyTeCCXKm45E9AJEJHW1yzgQMvn9HAQxr6qBGhQVA8HJqBZleeWVXVOgYoA3aNBAycCRles0xUd0tLz0t+f1+tCfzLn1Oh+iLBVnMGi9Mv1VRMWBUpn+5aWX08J92eumqIgPwvr1XxmnXqVfqqZlUV1E7SQ/yqlafnKd9I/KgTARiOyQT0Z5PumaC5hrqn4QNgLxDICFAOoAsORvLTDGuqV+E9E3AI5jjO0kot1E1BXA1wCOB3BjANlS21KkjIwgD3O/h7LqGglZioqK0oxM2VF5p7Jux+5kgIuj4/b9QXXyM4TtNGjQwLOd/eS6tbmfXD8HKGj0xUlHWQcCSG9zr3PgV6+XswW4OxAyfdvrdZ92ioqKfI188RhVFvzqigI64WW0+zlkOutWdWj9+pgdv9e4yhyT6NjJlgvqcBkyTz4Z5aZfqSG2l3EofqGQ+k9YB2IZY+w2LZrIcxGAGUh+g4Ix9r6qANUT6La+QSaPl8HlZ4D6GeNeNGjQwNdQVDE69+zZ41jOqR4/h8Irr9uN2s/Qt+NkYHrl9TOw3XTwOx47RKR07kWCOl5OOso6EEVFRWnn3c9xUXGg7LIBSDsjfn1bxcFq0KBB2jaV60bVsFYx0p0iKyIq9zK/e5GXXD/nRgUZYyyTEYhCeqDnE/nkQOSTrrmGcSAKl7AORB0RzQawHgAYY2PCq5QOY6yT7fcCAIeryvB7jauMc+B1E5H5UJWqwyDzEPfS2c8w9np4yzoBfsZ6GAdCZSqJHVUHQnbamIxclVFzcfqTVyQg6LkMG4lSdT68jH4/Z0R0IMS01zGJzoiX3qIsUU8xr6i3rGwRp77l53zrHPn0qtvJWfEzmtyOW8eH5DLpQAR1hAzhyCejPJ90zQV03rcKAXv/KSRnKqwD0YYxdqwWTTJI0JE8t1F4QN6BkJ0aBDi/JjToiK+TLrJGJ+B+7DKRAXtaxQANGkFRme7kNwXLT67KFCa/viAbgRDTfs6gaCjK1isa/U71yhpfoh4pXbxkhYmMeO0XZdlxim54tbWT46fi+PjpHbSNnOr2igA5pf3uFyrn3q2c13UpYhyIwsIrApZrGINYDdNePPnU11UI60A0JqIzkHwzEmPs9fAqRU/QCIRXJ3CbwuRnIPgZr34jrWI6RZiRZ78Rb7Eev1FTN6cgtd+trEpeO+K0lLCj7F55w4zme5UV+4GsQSzqDMhPR3PSOahhLhqNThEIv5F8laiRX3/1cgpF2X5Gfl1dnWddbm1SXFzsa7T7pb0ce6/jatCgQZreYhuK+/3qlnXKTATC4EQ+jernk665gLmmeIwDAYCIGgNILW7+AsAcJN7A1FKzXpHidAK9OnwYB8Lvoa1qrHhN1bAjM5IaZqTdXo/flCVxBNz+e/fu3VzarR7GWFpeN4PbadRdZXqQmw5BpvTYy6qcD/ux+uX1cj7CjGKr6iyWFae6qcpSmQ6lEpGw42Ro+zlNYRwIv7J+dfmtG0nRsGFDX9l+bRa07uJi/rHSsGFDLi1jjMm8RcoJswYif8gnI9M4EGrIDlLVF+q1A0FEDQHcAeAsAKuQ+ABdKwD3MsZuJaL+jLGPolMzPDrWQHhdFKlRN/tDV0zLPKS90oD7Q1ucIiPmjcfj0qPHxcXF0qPWRUVF2LVrF7dPNH7dogpOee37xTrFeuz7RbliXi9D12/k1Z5X1NfPIPSqJ4wD4XV+xGP3m/6kMlXIzRB3Kuuns9fUqtQ2J2ScY5UImpfjEiQC4RUV9HMQ/PqibARSdFYA/l7kdD8Q6xb7oIrzIuripofbfTeTb2HKJ0O2kMgnoypIv6rP5NO5zQSF2h6yEYi/AGgMoCNjbAtgfRX6TiK6H8BwAJ2jUVEP9mktTg9dr4dI6gHo5UCUlJQASHcg/B7SO3fu5NKi8SdroIrTI4B0Q3LHjh1WOh6Pc/vtchs2bJiml2hMpCgqKvI9BnvaT65bXjFaIeb1a1cvQ0vMK6ZTlJSUeB6bUzqFTDvZj8/PUXGbDlRcXOyrk1c72pFZ+OzWrn5RAj+H1imdwmk9hd/ouko0Q8WId5KtEoHwawMvRxJwdyBKSkp8nQ+/NRFBo5/iAIToDLjdS+w4RSBkHsIyzomIcSCyQz6N6heqARgVJgLBU6j9R9aBOAFAd2Y7csbYZiIaD+BHACOiUE4n9pPmZDDZjTuR0tJSAN6dIOVA2B+OYlo04kVdnAwGL0PSTnFxMbZv385ts9flZBjb0+I+saxdtv3YGzduLG1El5SUpMn1Kivm9TJ8Rf3Ec2yXZT+PJSUlvseeoqyszDevm05OxyPuT1FaWppmCInHt23bNkcdS0tLPY9dlBWPx7ljsrdxw4YN08rK9otGjRr55hWPQcyfkic6bkVFRWmy/Opyu75l9BT3ezmrdsSooJMD4Xf9e0UFAO8IhNf14tT//OoW7z1ujqfTcXrV4yZDxO2+oppHxDgQ2SGfHIgg/ao+YxwIHtk39eUb6XFiZ+LMoRcwxvYCWM8Sr1bNaezqOxnhohFgv7mlQvJbt27l5Imj9k6yRSPJy9B1MqrEOrds2eJ4fE2bNk3L62cI2Y/Zz4jyMhRljduysjJfuV46eRmcKsapaDj5tZOX/l4Gtp+B7NampaWlaefZrx/Zy/q1sVebi/X49UcxnaJRo0bKZd1klZeXe14HTmW96rbTuHFjbN682VO2mN60aRMnw01206ZNubYuLi7myjrJ9tNFrCv1oC4tLeXuR2VlZcrHpVp3qt+Ir312Ol923BwIcWBCxO26dNLJK4+ImyNkiJYg5ypb5JOuuYBxIHiCREbzAVkH4jMiOkvcSERjASzXq1I02E9a6kFrH+USIwNORoH4gBXTALBhwwauzMaNG7k89v2MMaxfv55L//jjj1z6p59+4tJ2efaLtGnTpvj55589Zf/www+ussV969ats9LxeJxL242gsrIyz7J2HZs3b47vv//eU6dU/oqKirS8drliWtwnHrs9LdZp3yfutxtR5eXlnu0iyhL3qbSTvR/E4/G0fmCXJZb1qscpnXo4lpWVcfrH43HPdhT7kN0Qq6io8O3L9v4KgMtvP6YmTZp4lpW5bux91m7UN2rUKE2WPR2Pxz33i9ekfeBBvCZFWQA8ZQNIOw7x/mGvy76vqKjIV9dEmtAAACAASURBVG9RlspxinXb75clJSVpetv/O903Af4e7LQGIioHws0BNESL20BTLmIcCDW8BhDqI+JgTKEgO4XpYgAvENG5AD4AwAAcAqARgFMj0i0S7CevVatW1rbUg79Zs2YAgFgsllbWvo0xhtraWi4dj8exdu1arszXX3/N5RHTK1eu5NIrVqzg0p9//rmV3rFjB1atWmWl7XkbNmyIpUuXcmWXLFnCpZctW+Z4LFVVVWll7eldu3Zh+fJf/MSPP/6YO8ZPP/3U+h2Px7m0vc7i4uI0neyy7G3RpEkTTo6YNxaLWeesadOmXF5Rxw0bNnDnSpT7ySefWOndu3dzx2rfJ5YV07FYzOpfFRUV3LHG43GuLcS0vc6GDRum6bhw4UJOxy+++MJKf/bZZ9bv4uJiTq54LuPxOFfXmjVrrN9+/UDsQ9u3b8eXX35ppe19tUGDBr6y7OkdO3bgq6++stL2vt2oUSMlvcS23bhxo2UsN2zYkGsvIkora98PIC0ttoH9mrS3R6NGjdLOhV2WqKe4X0zv2LED33zzjZVevXq19buiokJJltgPnPbb0zt37uTuXd9++631u6qqyrMuxhhmzZqFCRMm4KWXXsJ3333H7UshOr4ifvsBpDm4MtgdI0PmcHNIcxEnh9jgjjgQUt8RI8+FgpQDwRhbC+AwIjoaQB8ABGAWY+ytKJXTSeqk2R/2qTC5/WFYWVkJgDccU2U/+oh/0dT777/P5Vm2bJkVni8rK8OPP/7IGYJbt27Fe++9Z6U3btzIpWtra/HBBx9Y6UWLFnEG3ksvvWT9btCgAZ5++mkrvXfvXjz11FNW+ueff+b0e+eddzgDfdq0adyxPPDAA9bv1atXc8bvs88+y40a2vOuX7+eq/fll1/m2vPBBx+0fm/evBmPP/64lf7Pf/7DGY333Xef9Xvbtm145JFHrPS8efM4o9met66uDg8//LCV/vjjjzkD5rHHHuOmd9h1WrVqFafD448/zl3s9nbauHEjZsyYYaXffvttzmi+//77rd+MMU7/2tpari+88MIL3EPJ3qa7du3idPzwww+5si+99BL38P3HP/7BtYVY77x586z0rFmzOOfR3m579+7FE088YaU3b96M+fPnW+mlS5fiww8/5I7BPjJnP7dif9y4cSMna9myZdw1NnPmTC4yYNeDMcb19Z07d+LFF1+00t9++y13jHPnzuWM1Geffdb63aBBAy7NGMMzzzzDyX755ZetdCwWw7vvvmul33//fc6If+mll7gHwnPPPWf9jsfjXF1ietu2bXj11Vet9Nq1a7n7wYIFCzjH95VXXuH68fPPP8/JttfNGOPSdXV1eOGFF6z0pk2b8MYbb1jpVatWYdGiRVb6vffe49rw5Zdf5o7TLks8LrFuxhjOPfdcAMCVV14JO3aZdqff6SErOkhO2K9HmQf13r17uWeCIXPYne1cx/6MKCQDMCrEgdD6jt12Kaj2YIwV9B8S0RJ28cUXs1gsxqZMmcJS27p27cpisRi7/fbbrW0dOnRgsViMnXDCCda2k08+mc2bN89KA2DPPfcc69u3r5UeMWIEO/vss610WVkZu/baa7ky9v0A2LBhw7j0fvvtx6VbtGjBpYuLi7l0aWkplzZ/5s/8+f+J15HOv7KysshkExGXbtq0KZdu0KCBNtliWjyuqqoqLm2/F5WUlHD7GjVqZP1u27Ytt2/x4sUsxamnnmpt/81vfsPs1NbWcudtyZIlTGTPnj2sW7duVp4ZM2ak5RF59dVXrfzNmzf3zW/QQzweZ3369LHa/vXXX8+2Sp6cdNJJlq533nlnttXJab799ltWVFRktdeWLVuyrVLWOeOMM6z2+NOf/pRtdVKEtq9l10DkPX//+9+xatUqbpQ0hX2klDGG77//nhudY4xxI8JAYkTfPo1i1apVnJydO3dyo+QAuJFhAHjrLT6A8/nnn6OoqMh6o9NPP/2Epk2bWuk9e/agZ8+eVv5du3ahX79+nIz+/ftbv4kIAwYMsNLt2rVDt27drPTQoUO5sscee6z1u7S0lNvft29ftG7d2kofffTRXD3HH3+8le7atStXj30fAAwfPtz63b59e/Tt29dKn3TSSVxee9lWrVpxxyfmtetfUVGBww8/3EoPGTLEakeAP/aSkhIu3b9/f7Rt29ZRLgAcd9xx1u+2bdty50A81iFDhli/GzZsiMGDB1vpnj17Yt9997XS9n2ijo0bN8ZRRx1lpXv16oWqqirHegBg4MCBXL2HHHKIle7cuTPatWtnpe1yAXB5AXDtWFFRgT59+ljpvn37cu/9t/c3ADjooIO4dE1NDXdMnTp1stJ9+vRBo0aNrLT9XPulS0pKuH60zz77oEWLFo567NmzB127duVkHXjggVza3n+Li4ux3377WenmzZtzsvv27Wu9dnTnzp3o1asXJ0tM22UBQJcuXbjjsKerqqqsaZWMMa7sli1buOPYu3cv154A0KNHD9fjAhLXX0p2aWkpl66qqkJ5ebl1XPayGzZsQIcOHaz0rl27rL6cisKm0va1C+LUUJYcjVu4cCEXVUptT/3+wx/+4Lsw89prr1UaKV69ejUmTJggnd+gj0cffVQqopQLzJw5EzNnzrTSuaxrttmzZw8uvvjivHrDVtTMnj2bi6AXUnvUGwcCAI444gjuAfPjjz/iueee46Ym1dbW4rzzzuMeVq+88goeffRRAEDLlomPbt97770AgAMOOABAwvjfuXMnZ0Bu2LABgwcPRufOv3wi49hjj8Whhx5qpUeNGoUjjjjCSp9//vno3bu3lb7iiiusOgFgypQp3DFdf/311u/27dvjnHPOsdK/+tWvOKNq/Pjx3NtNLr/8cut327ZtMXr0aCt98skncwbCJZdcwhkCF198sfV70KBBXD3nnXced5Fccskl1u/+/fvjsMMOs9JnnXUWZ9hfdNFF1u+ePXtyRvSYMWM4/e1527dvj5NPPtlKjx49mjOwJ06caBk2paWlGDdunLXvxBNPRHV1tZW+8MILuQXB9noOP/xwzhg955xzOJ1+97vfWb/btGmD3/zmN1Z65MiRnIE3YcIE7n33kyZNsn53796d60unnXYa1w8uvfRSro/a6+3atSvXFqNGjeL64CWXXMK1+eTJk63fLVu2xIUXXmilhwwZwjkQ559/Pmc8X3nlldwbJuyyOnTowLXz8ccfzzkQEyZMsNYhAYm+bpd1zTXXWL9bt26N3/72t1Z6wIABOOaYY6z02LFjubadPHkyKioqrPQf//hH63dpaSmuu+46K73vvvty180RRxyBESN+eTP12LFjOeP5sssu4/rWtddey/X3G264wfpdVVWF3//+91a6R48eXJ8YNmwY58CNGzeOcyAuv/xyri673gDwpz/9yfpdXl6OK664wkpXV1fjggsusNI1NTU49dRflqyddNJJnMN3/vnnc214xRVXWM6LeFwAcPPNN1u/y8rKON1atmyJSy+91Ep36NDBcg47d+5sDYQwlngTXWqKk92BSTFjxgw89dRTaNy4McrKytL2A4npg7fffjsaNGhgXSdeD+qnn34a/fv3x5o1azhdDNGyYcMGXHvttTjvvPO47bnW9owxLF++HJMmTeKumdQ+A8+WLVvw1FNP4bDDDsPLL7+Mpk2bWvvqY3vF43F88sknuOSSSzBixIiCbYN65UCkSBkmmzZtsoxbu/H84YcfoqysDFdddRVX7rLLLuNGE0855RRuBOvAAw/EnXfeaaVbtWqF6dOnc3O77777bk7m9ddfz81rnjhxIvd2ijFjxnALs+3GRr9+/Tgjf9iwYdYDFkgYf/YP6A0aNIhbiGg/lpqaGut7FwBw2GGHcWUPPvhgax1EkyZNrLUiqbL2t6YcfPDB3EijfZT9oIMOSpNrX/hoN9QOPvjgtLz2txnYnZb+/ftzb9WqqanhRkEOPvhg63fv3r05A7qmpoarp6amxloEVl5ejubNm1v7DjnkEK6eQw89lHubjV2nmpoarp4BAwZw9QwYMIBbL5IynlL77M7FAQccwLXxoEGDrLZo2rQp18YDBw7kogJHHXUUV+9RRx3FLcC1R7WOPPJILgowbNgwruywYcO4dQ+DBg2yfnfp0oXTY+jQoVx/HD58OCdr+PDhnFM6ZMgQyynad999OWN26NChnKM2YsQIrj1OOOEETvZJJ53EXTd2p7WmpoZr6+HDh3PHfPLJJ6fJsp+LU045hXvrkxgZsvfhY445hrtWTjnlFO7cnHbaaVxdo0aN4vrtqFGjuLeR2SN/PXv25Prbsccey0UJTz75ZK79R48ezdU1ZswYLn366adzr1g97bTTuHUQw4YNs3736NGDi9oMHTqUO/cnn3wy58CdffbZePzxx3H11Vfj7bffthyTgQMHYuTIkVi2bBm6d++OG2+8EQDw5JNP4rXXXsPSpUsxfvx4AMBf//pXdO/eHQAwffp066H86quvYuLEidb2VIRQfAECkFgbNmbMGJxxxhnYsGEDRowYgVmzZqXlM4SHMYZYLIZ///vfuPXWWzFixAi0bt0at9xyC+LxOKZMmYITTjjBypsN/TZu3Ijly5fj7bffxowZM3DDDTfg17/+Nbp06YLevXtj6tSp2Lt3L6666iprwK1QjUEnGEu8zrm2thZLlizBvHnzMHPmTEyfPh3XXHMNxowZg/79+6NZs2Y488wz8eGHH6JDhw6YPXu25UQUWnvt3bsXP/30E7766issXrwYs2fPxlNPPYU///nPGD9+PI4//ni0bNkSBx54IO69917E43H84Q9/sAZYCqk9ZN/CVBCMGTMGxx9/PLp164ZbbrnF2n7UUUdh4sSJ1mLKzp07Y9q0adzD88Ybb8QFF1zATWe59957MXv2bCt93nnncaOFgwcPRmVlpWW0l5SUoEWLFpxh07p1a86o32effTiDwW40tWnThjNkqqurOSOqQ4cOXLpLly7cm1M6d+7M1WU3Hjp16sSVra6u5hYi243oyspKzoiurq7mXg1aXV1tOU2NGjXi6mnTpg1Xtn379pwB7pW3urqay2s3xKqqqriy7du3594EYTekRP07dOjALb60T+9p1aoV1y4dO3bknJiOHTtyxqRd7j777MPp1LFjR27am92IbdOmDZe3c+fOXL0tWrTgDHf7+ejYsSOXt3Pnzmmy7P20bdu21k2sZcuWXF4x3bFjR24BfYcOHbh2tTudoh6tW7dO65/2/ij2fbuz1aFDB64tu3fvzunVvXt3bqF/9+7duahR8+bNLWPY3lZA4pzaZfXo0YPTs127dpxT27NnT+6c77PPPtYi9kaNGnHXZKdOnTjZ3bp1S9PbvgC+urqaW5jes2dPro83a9bMcrKqqqq4uvbdd19Ob7GN9ttvv7Q2XLx4MVe33WHo0qULdx2Xl5db/aRt27Zc3e3atePq6tmzJ1dXr1690nQ54ogj8Otf/xrAL6/w3L17N9544w2UlJTg0Ucf5RzqE088EU2aNMHWrVtx5pln4txzz8U999wDIDEldeDAgejSpQtOP/10xONx/PGPf8S5556LOXPmAADuvPNOHH744Rg1ahQA4I033sC4cePw/fffo3HjxrjrrrtwwQUXWOdj48aNuOyyy9IGeVKsX78eixYtQm1tLbZt24ZmzZqhU6dOOPjggzknsZBhLPGNlZ9++gk//fQTfvzxR+v3unXr8O2332LNmjVYs2YNamtr076vVFRUhOOOOw5//OMfceSRR+LEE08EkIh+ffPNN9z9RJW9e/di3bp1WL9+PaeX0+8ff/wRsVgs7fXtdlq0aIGTTjoJl112Gfr162dF96688kqcc845XEQ4KHV1ddi0aRO2bt2KXbt2cX+7d+/mftfV1aGurk7qt658W7dulfoIWnFxMQ455BCMHTsWZ599Npo0aWINhNx9991pMyfCsHPnTmzduhU7d+7Erl27sHPnTu7Pvs3enmI6yLadO3e6fotLZN9998WvfvUrTJgwAQcccIAVwZ0yZQouvPBCtGnTRkt7pM7Ttm3brHbZvXs395c6n/Y/e3Q6KDnrQBBRAwB3AagBUApgCmPsVSI6HMBUAHsAvMEYu1FW5p/+9CeUl5dzb1FJnUz7g2vs2LHYf//9OeMmNdInGrv2h2Tbtm25B7p4g0kZMuJ7x+0GGgDXDmof0QMSI8/2h3azZs04fZo1a8bdwO0GWuPGjdPy2mVVVlZyZe0jtBUVFdxxVlZWcgaXXU/RiKysrORGWCsrK7n2EPOKaXtb2Q0aUSfxOxJ2Kisr047droNdbpMmTbi8lZWVXIRITIs6iGW96rG3f1VVFddOLVq08PxgmXg8Ytquo1e7OfUDe37xGOyIxys6dc2aNeNkNWvWzLOvi2VFPcX9steNKLt58+ZpetsR20/cZ0c8F059za6n3UEAEo6u23GIRmpFRUXaubKnxT7jdD7Ea0+2bqfjstctOm32aW8A/za8QYMG4eqrr8aAAQO4dRBA4l5ZU1ODBx98EETE3X/HjRtnfThx3LhxlpFi72OXX345Ro0ahX/84x/W3OwjjzwSjzzyiBUpsvere+65B5MnT7aiKak3k02bNo17Q5ZI3759MXjwYAwePBiDBg3iBiFyCcYS333ZtGkT97d582bXbSnjO2V4i19D96JZs2bo168fDjzwQNTU1GD48OHcczF1XX3//fd47rnnuOl9TuzcuRNLlizB8uXLsXz5cnz55Zf49ttvsXbtWsRiMeUv/paXl6Nt27Zo164d2rZti06dOmH//ffH/vvvjz59+nB93M5f/vIX3HbbbZ6yt23bhqVLl+LTTz/F119/zem5ceNGbNq0ydOByRXKy8vRrFkz7q9Vq1bo1KkTOnXqhK5du6J///5pH39MndvU4KvfNbF161YsW7YMn332GdasWYO1a9fi22+/xfr167Fp0yarzVT6XxQQESorK1FVVWWtUauqqkJ1dTU6d+6Mzp07Y//990fHjh25e5H9PnP99ddzb150oq6uDsuXL8eKFSvwxRdfYPXq1Vi3bh1++OEHrFu3Dhs2bMDWrVsdP4wsQ0E7EAD+PwANGWNHEtG+AE5Lbv8HgFEAvgbwGhEdxBj70E1ISUmJ1eFSHVw0HoD0kWMxX+oBKhoS9nItW7bkOox9HiCQMBIBfwfCrbyTPC9jxcvYE8s6GWyyxp1oRPsZs/YRbPsxOekg1iN+Bdi+Tyzr1q5O7eRmIDoZhPbpP40aNbIcLfHjV071uLVpkyZN0pwP+7lr3ry5Z1mxHvtN1qve8vLytOtBdNrErynL9qmqqqo0A9fezsXFxdYD1H7dOMly6nOic+zWN1LXXQonx9v+IG/WrBl3zA0aNPBsP1G2Xx+2RzZFIx5Iv8e4HYff9S8eh+gMhqnbqQ1FB9jeZqJTZq9n7ty51m+7jFGjRqFXr16YPHmyde+2R/tSI2knnngiHnjggbQ+BCQWSo8cOdJaBHvttdfixhtv5NrJHskEEq/9nTRpEj7//HOcccYZ1lSosrIyHHrooejRowfKy8uxYcMGLF++HJ988gmWLl2KpUuXWi/O6NatGwYNGoRu3bqhXbt2aNWqFZo0aYLy8nKUl5ejcePGKC4uRlFRkfVHRCgqKgJjLG3E0J7esWMHtm3bZo04pn67pUWnIKjBkaKsrMx6SUGLFi2s3/vssw/at2+PDh06oLq6GtXV1WnOu4j9fL7wwguODsSyZcvw7LPP4q233sLChQs9Dch99tkHrVu3TtPLSdc2bdqkPVO9sE89mTVrVpoDwRjDokWL8MILL2D27Nn46KOPfKerFBUVobKyEk2aNEFZWRlKS0tRWlqKkpIS63cq3bBhQ+t/Jn43bNgQTZo0Sbs+gvDqq6+mGax79uzBm2++iddffx1vvvkm910aL1J6NWrUCGVlZY5/qXaz/9a1rWnTpo4fuvTDfm974YUXMH369LR71ooVK/DMM8/gzTffxMKFC6UczAYNGqBp06bcvcXeX5z+dJxTILcdiOMBLCGi15D47sQlRFQBoJQxthIAiOg/AIYBcHUgmjVrZoXlUyfL3nipkTFx5A5AmgEDpBv/XqNu4s0z9RBOjZKkwrWped9i+NatfAqxI4sRBz+DVRwBFw1HN4PMydB3MypLS0vTjBz7ty3sdToZRPbpHvYoiJ/hVlFRwU1p8dPfrZ2cHAgvA9qvrJejIhrbdv29IhBOzof9GxOiMyKWVYleAO5f7nWSZV/o7dXOfm0njvQ7taWsbCfHzt7WTv1Z9pyLDpmTUa/rOJz6sWjE26OoYt9t0KBB2nG5OcPi9eYUhRSdR/vDT7w3uh2TXf977rmHm+YH8F87HzNmDLp3746rr76au6eLxzRz5kwQEaZNm4bzzz8/TQfxYfr000+jd+/eGD16NDZt2oSOHTvi+uuvx+mnn56mL5Bos8WLF2Pu3LmYO3cu5s+fj6+++op7YUcuUVpaisrKSlRUVKCyspL7E7dVVFSgefPmnBEujjSHwd7XZ82ahe3bt6Nx48aIx+N4/vnncfvtt3PfRiIi9O7dG3379kWvXr3Qs2dPVFdXY99990W7du1CTYHyw/48/PTTT/Hdd99Zo+qvv/46rrrqKm5aZcOGDbHffvuhX79+6NGjB6qrq9G+fXu0bdsWVVVVqKysRHl5uaPjW2jYHYjdu3fj7rvvxtSpU7m1kqn26tu3Lzp37oz27dujffv2aNWqFZo1a2b1ybKysrxvs59//hmffvopDjjgADDG8Prrr+PWW29Ni3J269YNvXv3Ro8ePdC5c2e0adMGrVq1QuvWrdG8eXM0adIEJSUlWWuPnHAgiOg8AJcJm9cD2AngRACDATwMYAwA+7DyFgBdhHIgogsAXACkP6AAZ6Pf/hBJRRvEETbgF2M/tc+exy3i4LZfvBGLD2mnEUc7TqOy9hHHxo0buxqsTga4GD3xMqq9RsvtODkQbnnLy8vT5No/pGVHbLuKigrOeSotLU2bf2vPKxo/ssa5n1Mm1mPHq01Fw7NZs2bcFCy/8yGWFZ00leOTjZo49SHRobUbsI0bN3aNCjn1dfu1VVFRwenh1OdkZTsZ9fYpeGFH5r2mbTlF64I6K2J7i1OYKisrudHmRo0apQ0KeE2L80o73XtE58V+7boZnaJcp3uuEy1btuQ+6mjHfoxHH300amtrceutt1prIUTsdQKJD/il3oB26qmn4l//+ldaX7dTVlaGgQMHYuDAgbjmmmuwZ88efPLJJ/jf//6H2tpafPfdd1i3bh22b99uRQa2b9+OeDzu+AeAGykURw7Lysq4aIb9t5hOvfDi/2/vzuOjru79j78+k5UlICQgiyxZ2EGqQusCSkULxVaU2ysK1qVarNfWqm1tte5Vr95WW5dWitW6V1sVoV4oLj8jaHGhBWUNSTAQxMsSloQ12/n9MYuTISETMpOZzLyfj0cezHxn8p3PHCYz3/ec7zknOBhE8yC7pYJffwcOHOCNN95g9OjRXH755YEFHLt168a0adOYOnUq48aNO6w3q62Erq68YMECzj33XK677jr+8pe/AN4ekBkzZjBlyhTGjx8f0bDVnr311lscOHCATz/9lCuvvDIwFnDw4MFMnz6ds88+m6997WsNTrNONKHvtXPnzqVPnz5cddVVgVM3u3Tpwvnnnx94rUdinE00xUWAcM49ATwRvM3MXgRed94+wHfNbDDe8BD8iZMF7CaEc24OMAegR48e7ki9Bv4PhuAA0diHRVMH98HnqYe+MYe+eYReDz0gCP6GHRoO/G3s/o192xm8uqeZHfGgM3RfoefyR6IHokOHDocdVDZVU+g3C1lZWUfcb2hNwQeBwULflELDRlpaWtjjC47Ui9DYAV5om4b7OKEHzEf6vwx9HTR2cB3uQWnoqUFHOie/sddQ6OlQwaufm1mTB/mN/X2Fvm6CB/g2dipVS3ogQsNJ6EF9uL0CzYWo0J68zp07H9YGTf3fNPcFQ+jfYTiBNfR6uK+LlvZ+ZGVlNeiBaOobstD9Br+fHumg/UjhIri9Q9faaUxwbWPGjAkMNL/lllu48847W3y6QmpqKieddFKDmd+kcaGvx2uvvZadO3eyb98+evbsye233873vve9wz4LYyH0b+WWW27h5z//OTt37qRjx4786le/4oc//GFCHwQfrQMHDnDGGWewbNkynHPk5eXx+9//nkmTJrX73oRwhb7WH3jgAf7whz+wfft2srKyuO222/jBD37Q6Jfe8SouAkQT3gOmAK+Y2Whgk3Ou0syqzSwf7xiIScARB1E3dg5icFjw/2cFf/j5tzU1OBa+/HBr6ltuODwANHdA0FyAaCxQNHe6SUvOpw5d5OlIYzNCP+ibGmyWkZFx2LeKTZ3DGvr8Qw9CgoW2RZcuXRocrIbWELrf4J4aODxk+IWe6nWkc2YbO1ANHi8BR/7Gt7nxHk39fzQ2IL4132oHn5cM4b+GGutFCX3TDHe8T3M9EHD4c2rqVMDGnmPo6XuhbR36nJvad3N1d+3atcHfVWMDlVvSvsEaO4gPDlmN9W6E2wMRToAIfk/MyspqMID1SD2AwULbr6nB/qGO9C10U198hOP++++nc+fOdOnS5bAF/yTygj8LMjIyAr1WF1xwAb///e8D4xHjQfDpc3379g3Mpnj22Wfzxz/+scFaO/KlgoICSkpK+Pjjj0lJSeEnP/kJt99+e9L1zgQfcwwcOJCysjL27t3LhAkTeOqppxgwYEAMqzs68bwOxOOAmdkHeHsT/Kt5/QB4HvgIWO6c+/BIO2nsQyj4Q9f/oRz8oe9/YR9psFbowW5jQj/w/Ytm+ee3968S6184yj/Tk39Rs9BVpoMXmPP/fmjw8S9s5/9gvuyyywD47ne/CxBYFMe/irN/tpF+/foFVhj2H5xfcsklDf711zl58mS6d+9OVlZWnB5yOQAAIABJREFUYBrL73znOwCBhcP8pwCEznPfpUsXZsyYAXw5r/xFF10E0GAxL/DO4OSf9tG/+J5/obLghezAO0Wlf9E5/7/+ueH99/W3ydixYwNz2PtPV/MvnuZf4Mj/fzRp0iSysrLIysoKjA/wTz3oX2zM/3j+5+GXm5sb+D/zt8H06dOBL9cd8Z9DO3HixMN6wfwrLftrvPDCCwHvLGH+/fvbJj09PfAYnTp1CqxM7X/O11/vPUPQ///vX7sgeB0H8E4nG3re+WmnnQYQeH34X2P+dvDPzT9s2LDDwnnom6J/X/59nHfeecDhK2mPGjXqsNeNf1/+x/C3j386PP++/XPL+6+Hro6dm5vb4O83JSWlwcrj4F2EEb6cate/joR/jQH/DD7+5+FXUFBwWIAI7RkIXrsFvAsMwpfrcfj/9beJ/3kHr/kA3gUDQ3tpQh8reF2I4Lr9Qci/gJt/FXT/AZt/8UD/6zf0vSf0YKlbt24NQr2ZhTVNYWiAaCrQhDrStKnB46Zayj9QWuGhbXXu3Jl33nmH6667jvnz5/PSSy/FVXiAhscDixcv5s4772TevHksWrRI4eEIvv/97/Pcc8/xs5/9jOXLl3P//fcnXXgAGnwZWlhYyM0338yzzz7L22+/3S7DA4Al0qIWjRk+fLirrq7mqquuChxE19fXBw6cN2/eHDhQ6devH7W1tZSXl5OamkpFRQVf/epXGT9+PE899RQAf/3rX7nlllt44YUXGDNmDPv37+eb3/wmEydODKx2e99997Fq1Sr+/Oc/k5aWxuuvv87zzz/PH/7wB7p168by5ct56qmnuPXWW8nJyWHNmjU8++yz/PznP+eYY45h3bp1vPbaa/z4xz+mQ4cOrF69msWLF3PVVVfh8XhYvnw5ZWVlgYPBJUuWkJGRETjIfvPNNxk6dGhgnvfFixdzyimnBGYOWbFiBRMmTMDM2LhxIwcPHgwctKxevZrevXvTvXt3amtrWbFiBccffzzp6elUVlayefPmwMFEeXk5HTt2JDs7G+cc69atIy8vL/DtY1lZWeCDeOvWrZhZIESVlJQwYMAA0tLSqK2tZcOGDQwaNAgzY8eOHdTU1AQO6j777DN69+5NZmYmNTU1bNmyJfAHt2PHDg4cOBAIXRs2bKBXr1507NiR2tpaVq9ezahRo/B4POzcuZOKiorAAW9ZWVlgOra6ujpWrVoVuG9lZSUbN25k1KhRgefq8Xjo27cvzjmWL1/O0KFD6dixI/v372fNmjWceOKJeDweysvL2bt3b2ChvhUrVnDccceRk5NDTU0NH3/8MSeeeCKZmZlUVFRQVFQUWI28uLgYj8dDfn4+zjmWLFnCsGHD6NGjB/v37+f9999n/PjxZGZm8vnnn7N+/fpAgFm9ejXOucCB5jvvvMOoUaPIyclh//79LF68OBBUPv/8cz799FMmT56MmbFixQqqq6sDr6G3336b3Nxc8vLyqK6uZsGCBZx++ul0796dHTt2UFhYyLnnnkt6ejrFxcWUl5cHFjlbvHgxHTp0YOzYsTjneO211xg9ejR5eXns37+fuXPnMnnyZLKzs9m6dStLlizhvPPOIzU1NTBtnX8l7ffeew+Px8Opp56Kc45XXnmFIUOGMGrUKA4dOsTf/vY3Jk6cSO/evdmxYwfz58/nggsuoHPnzhQXF/Phhx8yc+ZMzIylS5eyffv2wAH7/Pnzyc7O5rTTTsM5xzPPPMPYsWMZPnw4Bw4c4Pnnn2fq1Kn06NGD7du38+qrr3LRRRfRpUsXSkpKWLp0KTNnzsTj8fDhhx/y2WefBULewoULSUlJCQSO5557jiFDhjB27Fiqq6t54oknOPPMMxkyZAiVlZU899xzTJ8+PbBOzNy5c7n00kvJyspi7dq1LF26lEsvvZSUlBTef/99SkpKAmH99ddfxzkX+FLgmWeeITc3l/Hjx1NXV8fs2bMZN24co0ePZu/evcyZM4fzzjsvsO7D888/z8UXX0yPHj3YsGEDr732GldddRWdOnUKnMt/xRVXkJaWRmFhIaWlpYGgPW/ePGpqagJfIDzzzDP06dOHs846C+ccjz32GCeddFIggPlDxfXXX8/vfvc7nnzyyQZfHKxZsyYQVBv7bOrcuTP79u3jtttuCyw6F2rs2LEsW7aMvn37Npie+0j8XzT961//arDSvESXv9179OjRoPcsHn3jG98IrKeT6MdNkeD/v33ooYe49tprY1xN7F100UW8+OKLQNy8flp97ljCB4jRo0e7RYsWHbZ9/fr1eDyeBqvGVlRU4Jxr8M3Hvn37DjuP3zmXNOftiYhEij9A7Nu3j6KiokYP1t966y0GDx58WE8NwKpVq3j11Vf5xS9+0eRph2vXruW2227jV7/6Vdg9CbNmzaKoqIi33377sEHVEj0vvvgiV155JfPnz2+wyno8Wrp0KZMmTeJ3v/tdoOdOmnbNNdfw6quv8sknnwS+OExmK1euZNy4cdx+++3ccMMNsS4HFCCa11SAEBGRthWp1VclcdTX1x/VvPqx0J5qjTXnHM45tVeQurq6JseLxkCrA4S+ahEREZGYaE8HmO2p1lgzM52pESKOwkNE6K9BRERERETCpgAhIiIiIiJhU4AQEREREZGwKUCIiIiIiEjYFCBERERERCRsChAiIiIiIhI2BQgREREREQmbAoSIiIiIiIRNAUJERERERMKmACEiIiIiImFTgBARERERkbApQIiIiIiISNjiNkCYWVczW2hmi83sLTPr5dt+spl9aGbvm9ntsa5TRERERCSZxG2AAC4DVjrnTgdeAn7m2z4bmAGMA75mZifGpjwRERERkeQTzwFiJZDlu9wFqDGzLkCGc67UOeeARcDEWBUoIiIiIpJsUmNdAICZXQFcH7L5GuAbZrYG6A6MxxskKoPuUwXkNbK/WcAsgL59+0ajZBERERGRpBQXPRDOuSeccyODf4AfA//jnBsOfAN4BW94yAr61SxgdyP7m+OcG+OcG5Odnd0WT0FEREREJCnERYBowi5gj+/yNqCLc64SqDazfDMzYBKwJFYFioiIiIgkm7g4hakJtwJ/MrP/AtKA7/u2/wB4HkgB3nDOfRij+kREREREkk7cBgjn3BZgSiPbPwBObvuKREREREQknk9hEhERERGROKMAISIiIiIiYVOAEBERERGRsClAiIiIiIhI2BQgREREREQkbAoQIiIiIiISNgUIEREREREJmwKEiIiIiIiETQFCRERERETCpgAhIiIiIiJhU4AQEREREZGwKUCIiIiIiEjYFCBERERERCRsChAiIiIiIhI2BQgREREREQlb3AQIMzvfzF4Iun6ymX1oZu+b2e2+bR4zm21mS82s0MwKYlexiIiIiEjySY11AQBm9hAwCVgRtHk28B/ABuB/zexEYCCQ6Zw7xcxOBh4AprZxuSIiIiIiSSteeiD+CVztv2JmXYAM51ypc84Bi4CJwDjgHwDOuQ+AMTGoVUREREQkabVpD4SZXQFcH7L5cufcS2Y2IWhbF6Ay6HoVkOfbvidoe52ZpTrnakMeZxYwy3f1UO/evVdFon5pUg6wI9ZFJDi1cfSpjaNL7Rt9auPoUxtHn9o4+lY550a2ZgdtGiCcc08AT4Rx10ogK+h6FrAb6Biy3RMaHnyPMweYA2Bmy5xz6qmIIrVx9KmNo09tHF1q3+hTG0ef2jj61MbRZ2bLWruPeDmFqQHnXCVQbWb5ZmZ4x0csAd4HpoB3kDWwMnZVioiIiIgkn7gYRN2EHwDPAynAG865D83sY+BsM/snYMDlsSxQRERERCTZxE2AcM4VAoVB1z8ATg65Tz3eYNESc1pbmzRLbRx9auPoUxtHl9o3+tTG0ac2jj61cfS1uo3NO8mRiIiIiIhI8+JyDISIiIiIiMQnBQgREREREQmbAoSIiIiIiIRNAUJERERERMKmACEiIiIiImFTgBARERERkbApQIiIiIiISNgUIEREREREJGwKECIiIiIiEjYFCBERERERCZsChIiIiIiIhE0BQkREREREwqYAISIiIiIiYYurAGFmXzOzQt/lAjN7z8yWmNljZubxbb/dzD4ys3+a2VdjWrCIiIiISJJJjXUBfmZ2I/BdYJ9v04PALc65QjObDUw1s43AGcDXgH7AK8DYI+3361//uvvLX/4SvcJFRCQsvXr1inUJIiIC1todxFMPRCkwLej6ScC7vssLgbOAccAbzmsTkGpmPY600507d0ajVhERERGRpBQ3AcI59wpQE7TJnHPOd7kK6Ap0AfYE3ce/vQEzm2Vmy8xsWUVFRbRKFhERERFJOnETIBpRH3Q5C9gNVPouh25vwDk3xzk3xjk3Jjs7O7pVioiIiIgkkXgOEMvNbILv8jeBJcD7wCQz85hZf8DjnNsRqwJFRERERJJN3AyibsRPgMfNLB1YC7zsnKszsyXAUrzh55pYFigiIiIikmzsy2EGiWn06NFu0aJFsS4jKVVVVbFmzRrWrl3LmjVrqKysJCMjg/T0dDIzMxk4cCDDhg1j+PDhpKWlsXbtWlavXs2aNWvYs2cP6enpZGRkkJGRwYABAxgxYgRDhw4lMzOToqIi1qxZQ1FREVlZWQwfPpwRI0bQv39/PJ547lgTSV6ahUlEJC60ehameO6BkBjYsWMHVVVVVFdXc+jQIfbu3cv69etZs2YN69at4/PPP6euro66ujrq6+sDP01djwYzo6ng27lzZ7p27YrH4yElJQWPxxP4aex67969GTZsGMOGDaN79+4UFRWxbt061q5dS2pqKiNHjmTUqFGMGjWK/Px8UlJSDntM/3NOTdWfk4iIiCQ+9UAIe/bsYd68ebz00kv8+9//jth+MzIyGDx4MCNGjGDYsGH06NGD6upqDh48yP79+ykpKQkEk7q6OgYNGhToSTj22GMD9z1w4AAlJSWsXbuWtWvXUlNTQ0FBQeDAv7KyktWrV7N69Wq2bdsWsfpDdejQIRAo+vbtG6hp3bp11NbWMmjQoECPynHHHUdKSgpmFgguTV3v1KkTBQUFdOzYMWq1i8QD9UCIiMSFVvdAKEAkMOccb775JoWFhdTU1FBXV0dtbW2gB6G2tpb9+/ezdOlSDh48CHgPknv06BE4fahDhw7k5+czfPhwhg8fzoABA0hLSzvsYPhIB8jN8fdWpKWlNXtff+9GU/etqKhg//79jfaKhG6rra1l48aNgWCyc+dOBg8ezPDhwxk6dCi1tbV8+umnrFy5kpUrV/L555+37D+gBcyMAQMGMHToUPr06UNKSgopKSmkpqY2+DcjI4Pc3FyGDh1K//79cc6xYcMGioqKKCkpIScnh5EjRzJkyBAFEok7ChAiInFBAaI5yRogVqxYwV133cXSpUvDuv+4ceOYPn06U6ZM0YFnEyoqKli1ahUrV67kiy++ID8/n2HDhjF06FAyMjIC4zLWrFnD9u3bA4HFORcILsEBxn959+7dbNiwgdra2hbVk5mZSX19PdXV1Yfd5vF4yMvLY+TIkYwYMYIRI0bQvXt3iouLKSoqYv369dTX1zNkyJDAT9++fZvtLQknEIo0RQFCRCQuKEA0J9EDhHOOVatWUVxczKFDhzh06BAfffQRc+fOBaBbt25ceeWV5OTkBL7F9n+j7b8+cuRI+vXrF+Nnktyqq6spLS1l3bp1VFRUNOglqq2tDfSY7Nu3j9LSUoqKitiyZQsA/fr1Y+jQoRQUFLBt2zZWr15NcXExdXV1Ea/THyr8PSFDhgxh8ODB9O/fn9TU1COON/FfTk1NpX///vTo0UOBJMkoQIiIxAUFiOYkaoAoLS3ltddeY+7cuZSWlh52e3p6OldeeSXXXnstXbsetli3JIDKyko8Hg+dO3c+7LaDBw+yfv36wNiQ1atXs2vXLgoKCgI9Dh6PJzBo3B9cjjQgPtLvFccccwyDBg0iNzeX9PT0BoHD39sRfD34X//llJQU+vbty6BBgxg0aBAdOnTgs88+o7i4mJKSEurr6xk0aBCDBw8mNzc3rNPkJHoUIERE4oICRHMSKUBs2bKF+fPnM3fuXD799NPA9pycHE499VQ6duxIRkYGXbt2ZebMmfTv3z+G1Uqicc4FAsW+ffsoKSlh/fr1FBUV8cUXXxxxvEnw9UOHDlFWVsaePXsiXmNKSkqTPS+pqakce+yxgTE8wWNL/Jd79uxJQUEBBQUF9OzZk7KyMoqLiykuLmbv3r3k5eUxePBgBg0axMCBAxv0uoQTfIJv69y5c6M9MP6exC5dukS8fWJNAUJEJC4oQDSnvQeIXbt28frrrzN37lw++OCDwLfAWVlZTJkyhfPPP5/TTjtNU4hKu+KcY9u2bRQXF7Np0yZqa2txzgVCSvDPkbbX1NSwadOmQI/DwYMH6devX6BHwuPxUFxczPr16ykvL494L0prdOrUifz8fAoKCsjOzqasrIySkhI2bdpEXV0dOTk5DBo0iIKCAgYMGHBYWPGvdxK6LTTEeDweevToQUFBQeC0sbq6OsrLyykpKWHjxo2YWYOJEwYOHEheXl7Ex0MpQIiIxAUFiOa0xwDhnOPtt9/mmWeeCcygBN5pUc866yzOP/98Jk6cSGZmZowrFYkf/gHlTf1d7N+/nx07dgTGl4TOSlZdXc2WLVsoKSmhpKSEbdu2MXDgwMBBfFZWFqWlpaxfv57i4mI2b94cCDLNBZ7QbbW1tRw4cKDROj0eD+np6YGZ0SIpKyuLnj17snnzZg4dOtTs/fv27Uu/fv3C/oKiW7du3HvvveTk5DR6uwKEiEhc0EJyiaS+vp4FCxbw0EMPsWrVKsB7MHHGGWcwbdo0Jk+enJCnNYhEgsfjOWKo7tixY6tP6zvllFNa9fvBKioqKC0tpaSkhJ07dzJw4EAKCgoYOHAg6enpbNmyJdCz4l/AsaneGKDJnpva2lq2bNlCaWkpe/bsoaqqCoDevXsHHi8lJSWw7srevXvZsGEDZWVlfP755y2evnjChAnMmDEjYu0kIiLxRwEiDtTV1TF//nweeughioqKAOjZsyc/+MEP+M53vkOPHj1iXKGIRFp2djbZ2dl89atfbfT24447juOOO46vf/3rEXk85xwVFRVs376dfv36NTr4Ppj/9LAvvvgirFO/nn32Wf7+979HZWyLiIjEFwWIGKqpqeHVV1/l4YcfZsOGDQD06dOHH/7wh1x44YV06NAhxhWKSKIwM3Jycpo8vShUWloa+fn55Ofnh3X/jz/+WAFCRCRJKEDEQHV1NS+++CKPPvoo5eXlAPTv358f/ehHXHDBBaSnp8e4QhGRlvFPF11ZWRnjSkREJNoUINpYWVkZs2bNYuXKlQDk5+dz7bXXMm3aNM2kJCLtln98lnogREQSn45Y29Drr7/ODTfcQFVVFf379+fmm2/mW9/6FikpKbEuTUSkVfwBwj9IW0REEpcCRBs4dOgQd911F08++SQAU6ZM4cEHH9QK0SKSMNQDISKSPBQgomzp0qXceOONlJSUkJaWxm233cYVV1zR6Aq0IiLtlcZAiIgkDwWIKNm1axd33303L7zwAuAd6/DII49wwgknxLgyEZHI8/dAKECIiCQ+BYgoeP311/nFL35BRUUF6enpXHvttfzwhz8kIyMj1qWJiESFvwdCpzCJiCQ+BYgIqq2t5d577+Wxxx4D4NRTT+X++++noKAgxpWJiERXp06d8Hg87Nu3j9raWs0qJyKSwPQOHyE7d+7k6quvZvHixaSmpnL77bdrrIOIJA2Px0NWVhZ79uyhqqqKbt26xbokERGJEgWICFi9ejWXX3455eXlZGdn8/jjj3PKKafEuiwRkTbVpUsX9uzZQ2VlpQKEiEgC88S6gPbu3//+N9OmTaO8vJzRo0ezaNEihQcRSUoaByEikhwUIFrho48+Yvr06VRWVnLOOefw2muv0bdv31iXJSISE5qJSUQkOShAHKWlS5dy0UUXsXfvXs477zxmz55NZmZmrMsSEYkZLSYnIpIcFCCOwvvvv8/MmTPZv38/06ZN45FHHtGMIyKS9LSYnIhIcojbo14zSwOeBgYCdcD3gVrgKcABq4BrnHP1bVnXhx9+yMUXX8zBgweZPn06DzzwACkpKW1ZgohIXMrKygIUIEREEl0890BMAVKdc6cCdwH3AA8CtzjnxgMGTG3LgtavX89ll13GwYMHueiii3jwwQcVHkREfNQDISKSHOI5QKwHUs3MA3QBaoCTgHd9ty8EzmrsF81slpktM7NlFRUVESnm//7v/5gxYwa7d+9m0qRJ/PrXv8bjiefmExFpWxoDISKSHOL5CHgv3tOX1gGPAw8D5pxzvturgK6N/aJzbo5zboxzbkx2dnarC6mqqmLmzJl8/vnnjBkzhj/84Q/qeRARCaEeCBGR5BDPAeJ6YJFzbjAwGu94iPSg27OA3dEuorq6miuuuII1a9aQn5/P008/TceOHaP9sCIi7Y6mcRURSQ7xHCB2Af5+8J1AGrDczCb4tn0TWBLNAurr67n++utZsmQJPXr04IUXXqB79+7RfEgRkXZLAUJEJDnE7SxMwG+BJ81sCd6eh5uBZcDjZpYOrAVejmYB//3f/82rr75Kx44defbZZ+nfv380H05EpF1TgBARSQ5xGyCcc3uBCxq56Yy2ePw///nPPProo6SmpvL4448zevTotnhYEZF2yz8GQoOoRUQSWzyfwhQzCxYs4Je//CUAv/nNbzjzzDNjXJGISPxTD4SISHJQgAjx0Ucfcc011+Cc48Ybb2T69OmxLklEpF3wLyRXVVVFfX2brvEpIiJtSAEiSHFxMZdeeikHDx7ku9/9Ltddd12sSxIRaTdSUlLIysrCOUdVVVWsyxERkShRgPDZunVrYKG4b3zjG9x7772YWazLEhFpV/y9EDqNSUQkcSlA4O1uv/jii9m8eTMnnngijz32GKmpcTu+XEQkbmkxORGRxJf0AaK6uporr7ySVatWkZeXp4XiRERawT+QWjMxiYgkrqQOEM45fvrTn7J48WJycnJ44YUXyMnJiXVZIiLtlnogREQSX1IHiPvuu4+//e1vgYXiBgwYEOuSRETaNU3lKiKS+JI2QDz99NM8/PDDpKSkMGfOHL7yla/EuiQRkXZPi8mJiCS+pAwQ//jHP7j55psB70JxEydOjHFFIiKJIXgtCBERSUxJFyCWLVvG1VdfTX19PT/72c+48MILY12SiEjCUA+EiEjiS6oAUVJSwiWXXMLBgweZOXMm119/faxLEhFJKBoDISKS+JImQGzbto0ZM2awa9cuzj77bO677z4tFCciEmGaxlVEJPElRYDYu3cvF198MeXl5ZxwwgnMnj1bC8WJiESBpnEVEUl8CR8gnHN8//vfZ+XKleTm5vLMM89ooTgRkSjxD6JWD4SISOJK+K/hN2/ezMqVK8nOztZCcSIiUebvgdAsTCIiiSvheyAOHjxIhw4deO655xg4cGCsyxERSWgaRC0ikvgSPkDk5+fz17/+VQvFiYi0geBB1M65GFcjIiLRkPABwuPxMGbMmFiXISKSFNLS0ujYsSP19fXs27cv1uWIiEgUJHyAEBGRtqXF5EREEpsChIiIRJR/JiYNpBYRSUwKECIiElHqgRARSWwKECIiElGaiUlEJLEpQIiISEQFz8QkIiKJRwFCREQiyn8Kk3ogREQSU1wHCDO7ycyWmtm/zOwKMysws/fMbImZPWZmcV2/iEgyUg+EiEhii9sDcDObAJwKnAacAfQDHgRucc6NBwyYGrMCRUSkUf4AoVmYREQSU9wGCGASsBKYC/wdeB04CXjXd/tC4KzYlCYiIk1RD4SISGJLjfQOzawTcNA5V9fKXeUAA4BvAbnAfMDjnHO+26uArk3UMAuYBdC3b99WliEiIi2hMRAiIomt1T0QZuYxsxlm9r9mtg1YB3xhZqvN7NdmNugod10BLHLOVTvnioCDNAwMWcDuxn7ROTfHOTfGOTcmOzv7KB9eRESOhqZxFRFJbJE4hekdIB+4CejlnOvnnOsJjAc+AO4zs4uPYr/vAZPNqw/QCXjbNzYC4JvAklZXLyIiEaWF5EREElskTmE6yzlXE7rRObcTeAV4xczSWrpT59zrZnY68BHeoHMN8BnwuJmlA2uBl1tVuYiIRFxWVhagHggRkUTV6gDhDw9m9jbwgHNugf82M5vjnJvVWMAIc983NrL5jKOrVERE2oLGQIiIJLZIzsKUC/zczG4P2jYmgvsXEZF2ILgH4st5L0REJFFEMkDsBiYCx5rZ382s0RmSREQksWVmZpKZmUlNTQ0HDhyIdTkiIhJhkQwQ5pyrdc79F96xD+8BPSO4fxERaScam4mpurqa9957j/vvv5/FixfHqjQREWmlSK4DMdt/wTn3lJmtxDvwWUREkkyXLl3Ytm0b77zzDtu3b+f9999n2bJl7N+/P3D7xo0bOeaYY2JcqYiItFTEAoRz7o8h1/8FfC9S+xcRkfbD3wNxww03NNg+fPhwqqurKSkp4eGHH+a2226LRXkiItIKrQ4QZvYI0OQoOefcta19DBERaV/Gjh3Lv//9b3Jzcxk3bhynnXYa5513HsceeyzvvvsuEyZM4Le//S3XXXddIGyIiEj7YK2dIcPMLg26eicQPAsTzrmnW/UArTR69Gi3aNGiWJYgIpJ0nHPs27ePzp07B7b16tUrcPmMM85g8eLF3Hvvvdx0002xKFFEJFlZq3cQySn2zGy5c+6EiO0wAhQgRETiQ3CAeOuttzj77LPJzs6mrKysQdAQEZGoanWAiOQsTHCEU5lERET8Jk6cyMknn0xFRQWzZ89u/hdERCRuRDpAiIiINMvMAgOof/Ob3wRmZxIRkfjX6gBhZlVmVmlmlcDx/sv+7RGoUUREEtDkyZMZM2YMW7du5fHHH491OSIiEqZWBwjDEkwhAAAWPElEQVTnXJZzrovvJzXocpZzTlNriIhIo8yMW265BYD/+Z//4eDBgzGuSEREwhGJHohmB2KEcx8REUk+5557LqNHj2bLli08+eSTsS5HRETCEIkxEO+Y2Y/MrH/wRjNLN7Mzzexp4NImfldERJJYcC/EfffdR3V1dYwrEhGR5kQiQEwG6oC/mNkWM1tjZhuAYuAi4LfOuaci8DgiIpKApk2bxvDhwykvL+eZZ56JdTkiItKMSK8DkQbkAAecc7sjtuNW0DoQIiLxIXgdiFB/+ctfmDFjBrm5uRQVFZGWltaGlYmIJJX4WgfCOVfjnPsiXsKDiIi0DxdccAGDBw/ms88+44UXXoh1OSIicgRaB0JERGIuJSWFm2++GYB77rmHurq6GFckIiJNUYAQEZG4MGPGDPLy8iguLuall16KdTkiItKEqAUIM0sxs5nR2r+IiCSWtLQ0brrpJgDuuusuamtrY1yRiIg0JhLrQHQxs5vM7FEz+4Z5/QjYAFzQ+hJFRCRZXHrppeTl5VFUVMRzzz0X63JERKQRkeiBeBYYAqwErgTeAL4DTHXOTY3A/kVEJEmkpaVxxx13AHDHHXdw6NCh2BYkIiKHiUSAyHPOXeac+yPedR/GAN9yzq2IwL5FRCTJzJgxg+HDh7Nx40b+9Kc/xbocEREJEYkAUeO/4JyrAz5zzlVFYL8iIpKEUlJSuPvuuwH41a9+xb59+2JckYiIBItEgBhtZpW+nyrgeP9lM6uMwP5FRCTJnHfeeYwZM4atW7fy6KOPxrocEREJEqlTmLr4frKcc6lBl7tEYP8iIpJkzIx77rkHgPvvv589e/bEuCIREfGLRICY679gZq9EYH8NmFlPMys3s6FmVmBm75nZEjN7zMy0joWISII6++yzOf3009m1axcPPPBArMsRERGfSByAW9DlvAjs78sdm6UBfwQO+DY9CNzinBvve1zN8iQikqCCeyF++9vfsn379hhXJCIiEJkA4Zq4HAm/AWYDW3zXTwLe9V1eCJwV4ccTEZE4Mm7cOKZMmcLevXu57777Yl2OiIgQ2UHUwQOoWz2I2swuA7Y75xYFb3bO+UNKFdC1id+dZWbLzGxZRUXF0ZYgIiJxwD8j0+9//3s2b94c42pERKTVAcI5l9LIAOpIDKL+HnC2mRUCXwGeAXoG3Z4F7G6ipjnOuTHOuTHZ2dmtKEFERGLthBNO4D//8z85dOhQIEyIiEjsxO0gZOfc6c65M5xzE4AVwCXAQjOb4LvLN4ElMSpPRETa0F133YXH4+GJJ56gpKQk1uWIiCS1uA0QTfgJcKeZLQXSgZdjXI+IiLSBoUOHcskll1BbW8sdd9wR63JERJKafTmkIDGNHj3aLVq0qPk7iohIVPXq1atVv19WVsbgwYOpra3l008/ZeTIkRGqTEQkqVjzdzmy9tYDISIiSWrgwIHMmjUL5xy33nprrMsREUlaChAiItJu/PKXv6RDhw689tprfPzxx7EuR0QkKSlAiIhIu9G7d2+uvfZawBsmRESk7SlAiIhIu3LjjTfSpUsX3nzzTQoLC2NdjohI0lGAEBGRdqV79+789Kc/Bby9EIk+GYiISLxRgBARkXbnuuuuIycnh3/+858sWLAg1uWIiCQVBQgREWl3srKyuOmmmwBvL0R9fX2MKxIRSR4KECIi0i5dffXV9O3bl08++YSXX9a6oiIibUUBQkRE2qUOHTpw2223AXDrrbdSW1sb44pERJKDAoSIiLRbl19+Ofn5+axfv55nn3021uWIiCQFBQgREWm30tLSuPPOOwG44447OHToUIwrEhFJfAoQIiLSrl144YWMGDGCTZs2MWfOnFiXIyKS8BQgRESkXUtJSeHuu+8G4J577mHfvn0xrkhEJLEpQIiISLs3depUxo4dy9atW3nkkUdiXY6ISEJTgBARkXbPzLjnnnsA+PWvf011dXWMKxIRSVwKECIikhDOOusshg8fzs6dO1m8eHGsyxERSVgKECIikhDMjKlTpwIwb968GFcjIpK4FCBERCRh+APE/Pnzcc7FuBoRkcSkACEiIglj7Nix9O7dm02bNvHJJ5/EuhwRkYSkACEiIgnD4/Hw7W9/G9BpTCIi0aIAISIiCUXjIEREoksBQkREEsqZZ55Jp06dWL58OZs2bYp1OSIiCUcBQkREEkpmZiaTJk0CvIOpRUQkshQgREQk4QTPxiQiIpGlACEiIgnnnHPOISUlhcLCQvbs2RPrckREEooChIiIJJzs7GzGjRtHTU0NCxcujHU5IiIJJW4DhJmlmdmzZrbEzD4ys3PNrMDM3vNte8zM4rZ+ERGJLc3GJCISHfF8AH4xUOGcGw98E3gUeBC4xbfNgKkxrE9EROLYueeeC8CCBQuorq6OcTUiIokjngPE34Bbg67XAicB7/quLwTOauuiRESkfcjPz2fEiBFUVlby7rvvNv8LIiISlrgNEM65vc65KjPLAl4GbgHMOed8d6kCujb2u2Y2y8yWmdmyioqKNqpYRETijWZjEhGJvLgNEABm1g94B3jWOfcCUB90cxawu7Hfc87Ncc6Ncc6Nyc7OboNKRUQkHgWPg/jy+ycREWmNuA0QZnYs8Abwc+fck77Ny81sgu/yN4ElsahNRETahzFjxtC7d2/Ky8tZsWJFrMsREUkIcRsggJuBbsCtZlZoZoV4T2O608yWAul4T20SERFplMfjCQym1mxMIiKREbcBwjn3Y+dcL+fchKCfT5xzZzjnTnHOfc85VxfrOkVEJL4pQIiIRFbcBggREZFIOPPMM+nUqRMrVqxg48aNsS5HRKTdU4AQEZGElpmZyeTJkwHNxiQiEgkKECIikvA0nauISOQoQIiISMI755xzSElJobCwkN27G50BXEREwqQAISIiCa979+6MHz+e2tpaFi5cGOtyRETaNQUIERFJCpqNSUQkMhQgREQkKfjHQSxcuJDq6uoYVyMi0n4pQIiISFLIy8tj5MiRVFZWUlhYGOtyRETaLQUIERFJGpqNSUSk9RQgREQkaQQHCOdcjKsREWmfFCBERCRpnHTSSfTp04fy8nKWL18e63JERNolBQgREUkaHo+Hb3/724BmYxIROVoKECIiklT8pzEpQIiIHJ3UWBcgIiLSls4880w6d+7MJ598QllZGQMHDox1SQnvwIEDbN++nR07djT4qaqq4rjjjiM/P5/8/Hx69eqFmcW6XBFphgKEiIgklYyMDCZPnszLL7/M3//+d370ox/FuqR2paamhoqKisPCwI4dOxoNCTt27GD//v1h7btDhw7k5eUFAkXw5QEDBpCRkRHlZyci4VCAEBGRpDN16lRefvll5s2bl9QBor6+nt27d4cdBHbs2MHu3btb/Djp6en06NGDHj16kJOTE/jp2LEjmzZtYsOGDZSWllJRUcHq1atZvXr1YfswM/r169douMjPz+eYY46JRJOISBgs0aexGz16tFu0aFGsyxARSXq9evWKdQkBO3fupGfPnpgZ27dvT4iDT+cc+/btCzsIbN++nYqKCurr61v0OB6Ph+zs7MPCQPBP6G2dOnUK69SkPXv2UFpaGggUwZc3bdp0xFq7devWZLjo27cvHo+GfYr4tPo8QQUIERFpE/EUIAC+/vWvU1hYyPPPP8+MGTNiXc5hDh06FDhVqLkg4L986NChFj9O165dGz3obyoMHHPMMTE5GK+urmbjxo0NwkVwwDjSaVLp6enk5uY2GjByc3Pp0KFDGz4TkZhrdYDQKUwiIpKUpk6dSmFhIfPmzYt6gKirq2PXrl0tCgNVVVUtfpzMzMxGTxVqKgxkZ2eTlpYWhWcceenp6QwaNIhBgwYddptzjq1btzYZLrZu3UpRURFFRUWN7rtPnz6Nhou8vDxycnLibmC3c476+nrq6uqoq6trcPlI21py39buMy8vLzDjmSQe9UCIiEibiLceiM8++4y8vDyysrLYvn172AN0nXNUVVWFHQR27NjBzp07W7zydUpKSot6BvxjCuRwe/fuZcOGDY2eGlVWVkZtbW2Tv5uVlRUIFJ06dYqLg/WWnnYWC9/+9reZP39+rMuQxqkHQkRE5Gjk5uYyatQoVq5cySuvvMKwYcPCHkxcU1PT4sfr1q1bi8YNdO3aNe6++W6vOnfuzPHHH8/xxx9/2G21tbWUl5c3Gi5KS0uprKxkxYoVrFixIgaVN83MSElJwePxkJKS0uCnsW1tfd+RI0fGuokkihQgREQkaU2dOpWVK1cyc+bMFv1ep06dWhQGunfvTmqqPnLjUWpqKrm5ueTm5jJx4sQGtznnqKioCPReHDhwIG4O0hUuJZZ0CpOIiLSJeDuFCaC0tJSJEydSXV3donEDGnQrIu2YTmESERE5Wvn5+ZSVlcW6DBGRdkWTIouIiIiISNgUIEREREREJGztLkCYmcfMZpvZUjMrNLOCWNckIiIiIpIs2l2AAM4DMp1zpwC/AB6IcT0iIiIiIkmjPQaIccA/AJxzHwBjQu9gZrPMbJmZLauoqGjr+kREREREElZ7DBBdgD1B1+vMrMFsUs65Oc65Mc65MdnZ2W1bnYiIiIhIAmuP07hWAllB1z3OuSbXoP/000/39u7duyj6ZSW1HGBHrItIcGrj6FMbR5faN/rUxtGnNo4+tXH0rXLOtWqp8PYYIN4Hvg381cxOBlY2c/8i59xhpzlJ5JjZMrVxdKmNo09tHF1q3+hTG0ef2jj61MbRZ2bLWruP9hgg5gJnm9k/8a6kd3mM6xERERERSRrtLkA45+qBH8S6DhERERGRZNQeB1G31JxYF5AE1MbRpzaOPrVxdKl9o09tHH1q4+hTG0dfq9vYnHORKERERERERJJAMvRAiIiIiIhIhChAiIiIiIhI2BI2QJiZx8xmm9lSMys0s4JY19RemVmamT1rZkvM7CMzO9fMTjSzz31tW2hm0333vd13n3+a2VdjXXt7YmbLg9rzz2Z2spl9aGbvm9ntvvvodX2UzOyyoPb9wMwOmtk0MysN2n6G2vjomNnXzKzQd7nAzN7zvWc8ZmYe3/bD3h+auq80FNK+X/G1V6GZLTKzY33bHzazfwW9nruaWY6ZveG7/0tm1jGmTySOhbRx2J9xeg2HL6SNXwxq3zIze9G3fb7vc6/QzBb6tqmNm9HEsVr03oudcwn5A0wDnvJdPhmYF+ua2usP3qlyf+e7nA1sAq4EfhJyvxOB/4d3et3+wMexrr29/ACZwPKQbSuAfF97LvC1r17XkWnv3wOzgLuB/wi5TW3c8va8Ee+aPB/4rs8HJvguzwbOb+r9obH7xvr5xNtPI+37LvAV3+WrgAd9l98DckJ+92HgMt/lXwDXx/r5xONPI20c9mecXsNH18ZB27v5Pu96+66vwTdGN+g+auPm27exY7WovRcncoIbB/wDwDn3AaBFSY7e34Bbg67XAicB55jZYjN7wsyy8Lb5G85rE5BqZj1iUG97NBro6Pum8P+Z2elAhnOu1Hn/mhcBE9HrutXMbAwwwjk3B+/r+Hu+b1weMLNU1MZHoxRv8PI7Ce9BLsBC4Cyafn9o7L7SUGj7XuicW+G7nAoc9H1bOAiY4/v29nu+2wOvZ9S+R9LYazjczzi9hsMT2sZ+dwKPOOe+8PWmHQP83fdt+Ld891EbN6+pY7WovBcncoDoAuwJul7nOziQFnLO7XXOVfneQF8GbgE+An7mnDsd2ADczuFtXgV0bet626n9wG+ASXjXOfmzb5ufvy31um69m/F+YAG8CfwIOB3ojLft1cYt5Jx7BagJ2mS+4AtNv3b92xu7rwQJbV/n3BcAZnYq8EPgt0An4BHgYmAy8F9mdjwN213t24RGXsMt+YzTazgMjbQxZtYT75djT/k2pQMPAOfhDRu/9d1HbdyMJo7VovZenMgBohLICrrucc7VxqqY9s7M+gHvAM86514A5jrn/uW7eS5wAoe3eRawu00Lbb/WA8/5vhFYj/ePu3vQ7f621Ou6FczsGGCoc+4d36YnnXMbfG+a82j8daw2brn6oMtNvXb92xu7rzTDd07+bOAc59x2vF84POSc2++cq8J7isJoGra72jd8LfmM02v46H0HeME5V+e7/n/AbOdcrXNuG7AcGILaOCyNHKtF7b04kQPE+8AUADM7Ge95d3IUfF2KbwA/d8496du8yL4cJD0R+BfeNp/kG4TaH++B1462r7hd+h7eb10wsz5AR2CfmeWbmeHtmViCXtetdTrwFoCvXT81s+N8twW/jtXGrbPczCb4Ln+TL1+7jb0/NHZfOQIzuxhvz8ME59wG3+bBwHtmlmJmaXhPU/g3Qa9n1L4t0ZLPOL2Gj95ZeE+XCb7+VwAz6wyMBNaiNm5WE8dqUXsvTuRu+bnA2Wb2T7wDRS6PcT3t2c14Bzndamb+8+tuAH5nZtV4vzGY5ZyrNLMlwFK84fSamFTbPj0BPGVm7wEOb6CoB54HUvCer/ihmX2MXtetMQTv6Qg455yZXQm8amYH8A7cexyoQ23cWj8BHjezdLwf/i875+qaeH847L6xKLi9MLMUvAOjN+F97QK865y73cyeBz7Ae5rIM8651WZ2N/C0mX0f2AHMiFHp7c3VwKNhfsbpNXz0Au/JAM65hWY2ycw+wPsZeLNzboeZqY2b19ix2o+Bh6PxXqyVqEVEREREJGyJfAqTiIiIiIhEmAKEiIiIiIiETQFCRERERETCpgAhIiIiIiJhU4AQEREREZGwKUCIiIiIiEjYFCBERERERCRsChAiIoKZHWNm/xV0/Z9RepwOZvaub0G01uwn3cwWm1kiL4gqIhKXFCBERATgGCAQIJxzp0bpcb4HvOqcq2vNTpxz1cDbwPSIVCUiImFTgBAREYD7gHwzW2FmvzazvQBmNtDM1pnZn8xslZk9b2Znmdn7ZlZsZl/178DMLjazj3z7+GMTvQwzgXkt2beZdTKz/zWzT3z384eG13z7ExGRNqQAISIiAL8ASp1zX3HO/SzktgLgIeB4YCgwAxgH/BS4GcDMhuHtDTjNOfcVoI6Qg3szSwfynHNlLdk3MBnY4pwb7ZwbCfzDt30VMLZ1T1tERFpKAUJERJrzmXNupXOuHlgNvO2cc8BKYKDvPhOBk4CPzWyF73peyH5ygN1Hse+VwFlmdr+ZjXfO7QHwnQZVbWZZEXyuIiLSDA0+ExGR5hwKulwfdL2eLz9HDHjaOXfTEfZzAMhs6b6dc+vN7CRgCvDfZvaGc+4u3/0ygIMteC4iItJK6oEQERGAKqA13+S/DXzHzHoCmFl3MxsQfAfn3C4gxcxCQ8QRmVkfYL9z7jngN8CJvu3ZwHbnXE0r6hYRkRZSD4SIiOCcq/ANXl4FLDyK319jZrcAb5iZB6gBrgE2htz1DbxjHN5qwe5HAb82s3rffq/2bf86sKCltYqISOuY91RTERGR6DOzE4AbnHPfjcC+XgVucs4Vtb4yEREJl05hEhGRNuOcWw68E4mF5IDXFB5ERNqeeiBERERERCRs6oEQEREREZGwKUCIiIiIiEjYFCBERERERCRsChAiIiIiIhI2BQgREREREQmbAoSIiIiIiITt/wM41c6LtcNzPwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for A in [5., 20., 60.]:\n", - " fig = runAndPlot(pneuron, None, None, A, 1., 1., sr_interval='ON')" + " data, meta = pneuron.simulate(A, 1., 1.)\n", + " GroupedTimeSeries([(data, meta)], pltscheme={'Q_m': ['Qm'], 'FR': ['FR']}).render()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected, injection of depolarizing current induces an increase in the neuron's firing rate. Furthermore, the detected firing rates at 5, 20 and 60 mA/m2 current injection correspond to those indicated by Otsuka." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generation of plateau potentials" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we simply re-initialize the STN neuron at a membrane potential lower than its resting potential, in order to mimick the effect of a hyperpolarizing current that would drive the membrane to a hyperpolarized state and hence suppress the neuron's spontaneous activity.\n", "\n", "Then, we inject a short depolarizing pulse (50 ms, similarly as in Otsuka 2004), in order to elicit the plateau potential burst of spikes. \n", "\n", "Due to the current implementation, no hyperpolarizing current can be injected after the depolarizing pulse, hence the neuron's spontaneous activity can re-occur after the burst. " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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qUsyhwLoePCG/FPOYKxaKXkDIIBQEQYeysjLs3LnTcdv8+fPRr18/LFu2zHH7iBEj0Lp1a/zzn/90rX/8+PHYsGEDpk6d6prn22+/DdbpAMi1UAhLMY8dERCCEI6iFxCCIFQt3AyCZDKJBx98EHPnzs3YVlZWhjp16mD//fd3LNuzZ0+8+eabOPvssx23Dxs2DABwxx13hO4fEM5Qy6cBJMZW1aSkpHhNBZlEHU+KWbQWC8V7VRAEodJTUVHh6BWYM2cOrr322ow42WQyie7du+Occ87JKPPKK6/gpptuQrdu3TK2ff/99wCAH374wbEfW7duBQD8/PPPnv0thIGt22YxG4FCbilmY05EcTwp5jFXLMgdRRCEyFm2bBm2bdtmSdu2bRuuvfZafPrpp5b0HTt24KqrrsLkyZMt6YlEAo0bN0bbtm0z6u/evTsee+wxjBgxIqPduXPn4rXXXsso8+OPP7r2V9e49jM2dIyRqD0QukRRtxhbVZNiNuZkTAtCOERACEIVwG7MAylj23jybsDMWLBgQcZT/3//+9845ZRTUF5ebsnbr18/XHvttZa88+fPR7t27dCxY0dL+siRI/HYY4+hV69elvTnnnsOTz31FE477TRL+qZNm7Blyxb88ssvrvu1fPlyy7qXoRN2m0oUAsKLuIcwCVWTYvZeSQhTPCnmMVcsFP0ZKuYnJ0LVgJnx+++/W9KSySQef/xxfPPNN5b0ZcuW4bPPPrOkvfzyy6hbty4ef/xxS3q7du3Qvn17i1h45JFHcNBBB+HSSy+15L388ssxZcoU83WOAPDLL7/gzTffxGOPPWbJO23aNACZxv3KlSsd92/dunWO6Tq/3SDGcxTXAj9jI1sPRC5vmuKBEMIi91Eh38iYiz9FLyAEIQqYGbNnz7bE3FdUVGD8+PFYtWqVJd8rr7yCFStWmGnl5eXo0aMH7r77bkudF154Ia688kpL2rBhw9C9e3fLk/6hQ4eiWbNmli9yvvrqq7jmmmtw4IEHWsq3a9cOhx9+uOWp/V//+lcAwDXXXOO4b+rrPceMGQMg9cYgJ4y5AF64XfijSlexG7TigcgtIiCqJsVszMmYFoRwiIAQKiVOH//Zvn27ZX3jxo343//+Z0m78cYbMXr0aHN9y5Yt6N69Ox566CEzbdu2bRg0aBBmzpxppo0fPx49e/bE0UcfbaY99thjuPDCC3HwwQebaa+99hrOO+88S9z+xIkT8eWXX+If//iHmVZeXo7x48fj6aeftvRv+PDhmDNnDiZNmmSm3XvvvQCAu+66y0wzvgHghipggtz8o8ibTwERpEw+BES+6giDhAQIYSnmsSMCIp4Us2gtFor3qiDEjp07d+KPP/7wzDNv3jyMGDHCFAjMjL59++Lmm28280yfPh01atTAI488YqYNGTIEtWrVwvz58820hg0bolWrVli/fj2AVHjPyJEjMXjwYDPPv//9b8yZMwdDhgwx00aNGoWxY8daxMLEiRMBAF9//bWZZkwGVsOLvvrqq4x9SiSCfzDdqYx6o/O7uBYyb1X2QOjgFQaVSw+EhDAJYSlmY07GtCCEQwSEEIqdO3daLryLFy/GqFGjTMN/69atGDBgAN577z0zT5cuXdCkSRMzFn7VqlXYd999MXbsWDNP586dcccdd5hpixcvxttvv235QNff/vY3y38ApgdB9SQYGB//cvoSr9MrQtWQJAOnG6humtPTuyCGuleaTvm4Cwg38jkHIlsBEdUT2riHMAlCsSG/n3hSzKK1WBABIWD79u1YvXq1ub58+XLcf//9Zrz7r7/+iv79+5tP3CsqKtCsWTMceuihZpkOHTrghhtuMGPoH3roIbz44ovo06ePmWfBggUAgI8++ggAcPfdd2Pp0qUYNGhQRp+MtwM5Xdy9Lixehnc2IkBXGDgRRiw4PaVW06K8uBZaQGQjFKLyQGT7Gtcg50Ne4ypUNsSYE/KNjLn4U/QCQgZhKt6+rKzMXB81ahSuv/56c71Tp07YbbfdzLfmdOnSBbfccguGDh0KABg8eDAmTJhgvn7zxx9/xPr16/Hll19mtLVw4UIAwNq1a3375RXKYWxzMuy8jL2gBo7u+MjGs+CUpvZT19tQ1UKY/I6RU3qhQpgKeZ3JZwiTUDUp5rEjojieFPOYKxaKXkBUBRKJhEUgfPnll5g9e7a53qhRI9SpU8eMq7/hhhvw8MMPmxNtly5dCgD4+OOPAcCcMzB37lwAwJo1ayzt6XgAss1jCIigT/6dRInRXjZhJrn0VHj12S/NjbCiIEi9bvuVCw+Em9jMdwhTFOXFAyEI8UHGdDwRARF/REBUQhYvXoyxY8eaRtVBBx2EOnXqmCFHPXr0QM+ePc35CMYrQVWRAWS+tchupLkZ+vkQEF5Gf1xCmLLxXsTJA6FS2T0QXngJubh4IOI+iVqomhTzW5iEeCLXq/gT26sCEVUnoheI6BMi+pKITieidkQ0K502hohi2/9sKS8vNw2DlStX4qabbjJfSdqhQwcMGjQIL7/8MgDg22+/BfBn+JCB/VWndkPDb90tjChbcWCg40kIargXWkCEERp+8x2c0uIQwhS2jjBEYSTH5YZUmZ94Vua+C+GJy28nF8iYFoRwxNkAHwBgHTMfAeBkAKMBjAJwezqNAJxRwP5FCjObnoI1a9agdu3aOO200wAAZ5xxBh588EH07dvXUsb+FWK70enmUQiaXzwQ0Xsl1H7qhjAFwc+gd3u6H9ZQ0BE3hZ4D4UVlnkQtr3EVhPDImI4nxSxai4U4C4jXAdyhrO8E0AXAzPT6ewCOy3enoiKZTOLzzz/Htm3bAKREQu3atbF69Wq8++67AGD+nzdvHoA/5ySodagE9TD4CYpCCgivtnItIHTJRlSECWHK1VuYwno23NJzceGPyxyIXAsIQYgjYswJ+UbGXPyJrYBg5i3MvJmI6gF4A8DtAIj/vPtuBtDAqSwRXUVEc4lorvFUPw6MHj0a/fv3RyKRwDPPPIPDDjsMp5xyCgDgnXfeAQBMmjRJ64ms07r942NRCYqoQ5iC5HFCZ/K1brls5jHoEsYo9zMysw1LCuuByGYuQj48EF5lcvkEXjwQQjFTzMacjOl4UsxjrliIrYAAACLaA8DHAF5g5pcAqNZhPQAbnMox85PM3JWZu9auXTsPPf2T1atXY9y4cdi+fTu2bt2Ks846CxMmTADw5+tQp0yZgldffRUAMGPGDHvfM344buEVQUOWdD0OdgoZwlQMcyD8+hlmEnWQ+nM1ByKoCAma7tauLkEERLY3K5lELQiVExEQ8USuV/GnWqE74AYRNQcwDcA1zPxROnkeER3NzDOQmhfxcaH6pzJmzBj8+uuvuPvuu9GrVy8sW7YMP/74I+rWrYu33noLb731luUiVVZW5vnUVddgdxIAXrH1fhdKN8ERtYDQqSdIHj8KLSDCTKJ2ImyYkJ8HotAhTPnyQOji9BvMtk7dOuJ+0xRjq2oS93GZDTKmBSEccfZA3AagEYA7iGgGEc1AKozpLiL6DEANpEKbtFm0aBHuuusubN68OevO7dy501z+61//iuHDh2PlypVYtmwZgNQ3FTZu3OhY1s9A0Q0ZcjKe7Iahl0Glux5EQOjgNUHar22/9qO4GUTtbYhqDkQ+vAphPRDZTKLWQXcOhJeA1h03XmMoihAmL8QDIQj5RQREPJHrVfyJrQeCma8DcJ3DpqPC1tm7d28AwI4dO3DPPfcgkUigtLTUaC9jwCaTSSxZsgTt27dHeXk5xo0bhz59+qBmzZro3LkzLrroIjz33HNmfnW+RTKZdDWQE4mEp1dB12D3MzITiUQgj4TbeiHnQBTDJGonKquA0KnDjcoyByKMgAhC1AZLPg0gMbaqJmLMCflGxlz8ibMHImcsXboUo0aNQqtWrbBkyRKMGzcO++67L7777jtMnToVXbp0wfz58/HAAw/g2GOPxYgRI/DQQw9h+PDhOPbYY/Haa68BAJ5//nlLvXZjPYhIUOsIKyDsHoiwHgf7eiHnQERZTreuqL0NUU2iDjsHwm97vuZAuJHNHIggxr5uv7xCyqLwQBQqhEluyEJYinnsiCgWhHDE1gORS3bu3IkHHngAAPDoo4/izTffBAAMHz4c06dPBwAMHDjQ/HDbmDFjcOSRRwLwn79gEFQkeG3TFRD2kCWnda/y+Qph0qnHaNtupEbVtlFX1MJAN83rvITtT9i8QdrPZg5ELjwQQcpEEcLkRlQCIgz5DGESY0soNmRMx5NiFq3FQpX0QKgGk5shp4Y32dGZ1Oxn7LqFN2UjIOxpQT0SbgR54h+VB8IJr69Tu/VHTXM61/meMO3Uz0KEMEXtgcj1HAi3seolisMaBrmeAxG1ByKfAiKM2BUqP2LMCYJgp+gFhJ/xpOIlANyW3eoN6mXw2hbVJOqoPBA6dUYVwuRkEMY5hMmJQocw5XsORFQhTGG8NF5l4uKBiJp8CgihalLMY0c8EPGkmMdcsVD0AsIJHWOB2fo2JHU5WwGRrzkQUX2p2uuHHEZAGAQVEDpGpO7T4ygERC5DmPxuamFEiU77UcyBcCMbz0QUcyB0yUWdunXE/aYpxpZQbMiYjidxvxYKVVRAqLgZUnYBoQ5mr/AjlUJ4IOzrufZAZCM6CjmJOmgIkxNReSoKEcIURd6wddjJZg6E1xjW/T361alDPuYFuSEeCCHXyNgRBMFOlRQQuk9R3YSCrsHj9Z2DQoQwBXkqrdO+Wx/8yujU61R/kLqd6simrqiFRpiQmbBPyop5DkQQb4HuOM71pPZCeSBkErUQlmIWEDKm40kxj7liQQSEZsy0zmDWnURdTB6IMMZWWE9Ctp4L3T7ENYQpV16FKCbG6rQXND1bkZVPD0QQohYQYgAJuaaYjTl5MUA8KeYxVyyIgPBYztUkaq+PzDmV0w2Z8hMMVXUStdM2r5tG1N4GP3IRMhPE2xR1CFNQgopz3XbjOom6UAa/eCCEsIgxJwiCnaIXEEGe/trTsxUQQUSC17awHgi/EKawAiKqMmE9EF54PbnXFSPZpDkRJoRJN82NXHkrdMR3Pj0QQUKYdHEq53dcChnCJEa9IIRHfj/xRERr/Cl6AeGErgcil5Oodfrmh58Hws8joVt/ZfdABA2HinpeRJgQomxDmPzaj9oDEZc5EGFDmPzESpQCImp0rxnigRDCIsackG9kzMUfERCaBkcYD4QXXiLEvi2bEKYoPBBe5MoD4VR/kHJR1xW1VyJbD0Su5kCEfZIeVtx45c82rCiIgPAqp1O2MnggREAIYSlmY07GdDwp5jFXLIiA8HiyGfRJahwnUbv1T2e7W/v59EDo9M+vnFda1MLAiVyEMGUrIKL2QOjchOPsgVCpbJMqxQAShPDI7yf+yDmKJ1VeQLile4UwZTsHIuiH5NzIlwci7KRlvzL5DmFyEj6FeguTSpgPyfmRTSicbt6gIiSbORCFnkQdZw+ELuKBEMIiT4OFQiLXnXhS5QWE17KbaNAJKfLzMuiGJem257RuFxBxeQuTQVgPRBSCJgoPRDZpYcJzwoYJBXmRQCHmQAQNZfNKzzbsya1clAaUhDAJlY1iFhAypuOPnKN4UukEBBGVENFYIvqMiGYQUTuf/J71hXkLkxu6AsKrPidxESSEyd6HIB4It/qDGOxRhTA51R+lByJXAsKJOIYwRZE3bBiUnWzmQHiN4SAhTOKB0ENu5EKxIWM6nkTxMEjILaEEBBHVJaLSqDujyZkAajHzYQBuATAyaAVuT19146ezDWHy805kM9lTpRAeCB2yFQJhy6noeDP8CCMqvPrily9sqFExz4EIUkYmURfvU2QhtxTz2BHjNP7IOYonWgIi/dT/fCJ6l4h+A/AdgF+J6FsieoCI9sltNy30AjAVAJj5cwBdg1agIxrsAsBt2ater8nWOnX4tafjgfCqOwoBkSsPhJPXIFujP2oPhBO6BniuPRBBCDtpOKgIycccCN2HAF71hLlZFbOBpSI38qpJMY9vGdPxRDwQ8UfXA/ExgL0B3AqgBTPvwczNABwB4HMA9xHRgBz10U59ABuV9QQRVVMzENFVRDSXiOZu27YtowK3geklANwmVHvVG9bLYN8mbtjcAAAgAElEQVQWJIRJxWkSdZiQJq++ZvMWJi+Chh151R3UAxG1qAgjFvzINiwpag9EXOZAuPVJtx2ncjplxQMhCJUTMU7jj5yjeFLNPwsA4DhmrrAnMvMfACYAmEBE1SPtmTubANRT1kuYeaetX08CeBIAWrRowZs3b4Ztu9ZyNpOovbwM9vrsdejOgXAy3u198Ft367+6XkgPhEpl+BK1zpyb0tJS36fruQ5hiiJvVB6IoG255dEtE6TdykA++1vZjo0QDSI+hUIi1514ouWBcBIPYfJExKcA+gAAER0K4JugFegYQXYBEPUciCDzI8LGbgf1OIQRA5XpS9S6xmXUAsKp3VyHMAXxQETxFqZsLvDZeCCClCnWSdT59EDIjbxqUswCQsZ0PJEQpvjjKyCI6HgieoqIDk6vX5X7bnnyFoByIpoN4CEA1wetQDeExs3IVy+muiFQKl7fgXAyyIOEMHk92a7sHoh8hTDpEpWxVwgBETavG9mEMOn0K4xwidIDkS8BEQYJYRKE8IhxGn/kHMUTnRCmvwK4FMDtRNQYwMG57ZI3zJwEMFA3f5D4b68QJjdvhJeACPMWJr9+uOUz1u1l/db96gOCeSB0COuByPY7EIUKYXLah0KHMAUNP/Irl00Ik45hW+hJ1FEa317n0SvsMQ7IjbxqIuJTKCRy3YknOner35l5AzPfCOAEAN1y3KecoysgVHTnPajpQSZKu7Xr99Vqv7J+HgkvitkDkU8BEaZ/fkR5Qw8iAnU8AVEJiGw9EFFOovYjqvMR9xAmoWpSzGNHjNN4IiFM8UdHQLxrLDDzLQCez1138o9qLHgJADdvRBgB4RfeZK8zVyFMufBABPESBI19j3ISda7ewlQZ50BE7YEISpRzIMJ6IPzainMIUz6pzH0XwlPMAkKIP3LdiSe+AoKZJ9rWH8tdd/KDjhEUZhK1XYyEmQPhFGYU1gPhF8LkNwfCIIgHQod8eCB0DXTdG2NU+aIKYZI5EN7pTtuyCWHyo6pMohaqJsU8dsQ4jSfigYg/uq9xBQAQUVcAQwG0TpclAMzMB+agbzlDN4TJTTTozIEAvMOedGOds/VABBEMuh4InTqjCmFyOj9hDdF8eCC82rcvB00LQhAPRNi3MAX1YuQ7hCnsMcy1B6JQRNFHuZELxYaM6fgT9mOnQm4JJCAAvAjgJqRenVppz6jul6iznUQdJoTJSQRk44Hwq1slio/C5VJABAmPCvuEn4iy8jboGuC5DmEKQlhvRdA6ZBK1e/3ZtJNPD4QYW1WTyiCQwyJjOv7IOYonQQXE78w8KSc9yRFBnr7qhg7pCAg/w193DoRfXnu9fh4IP8PaXh9QWA9ElOV0jHY3ARHVHAinY677dCXKsKSg3gO/cl7CwqizskyizrUHIuqboQgIQQiPjOl4IiFM8SeogPgHET0N4CMA241EZn4z0l7lGB1jxGsOhO4k6rAfklPxEiI6Zb22hw1hyraMQdhJ1GE9EDppJSUlkYcw+fUvTAhTlPMaongLk44Qz8aA1b2BROGBcDoe4oEI1pZQXIgHQigkco7iSVABcSmA/QBUx58hTAyg0goIXYPDzeDXfYuTvf1chDDZ03Q8En71AcE8EG5lnMrn2wORTf1ReSDyJSDC9Emn3qAeCFWIx2kOhN9vMChRGVi5FBCCEJZiFhBC/JFrXDwJKiAOYuZOOelJHtEJYbLjFraUSCQc67Ib/rphSdkICCcPhJegKJQHIkgep/4G9Vx41aXr4chmXoSKbghTthfMKMOddPoVVFh41e8neLzSddsOKiDyFcKUS0NNPBCCkImM6XgiIUzxJ+hnTz8nog456Uke8TJ23LwObmFLbgLCr03dEKYgho7fdx6CfkjOzaDW8WLkchK1F7rlnEKYgl6kcumB8KvTDz9R4HZ8whrCOga+zjh2E/S6k6i9tumK58omIOTmKuSaYvZAyO8n/sg5iidBPRC9AFxMRMuRmgNBAJiL6DWuJSUlpijQefOSKiD8wofUbUE8EF774dem13oUHohsQpi8COuB8AoH8jMSveqPal6EV7tuaUSUkcc+Hr1Enh9R5NV5s1lQz5FOuhdev51kMonS0lLttqIUEIVCPBCCIFRG5LoTT4IKiJNy0oscEiT+226IuS2reIUweQkILw+EvVwQb4WKzluadOrzMk7DiI6wQiDK17h6TayOOoTJqQ2//gUNK/I6R7maA6GTruPd8Po9Bm03rJchn+7yQnkgKoPIEeJJMY8dMU7jST6vyUI4AgkIZv4pVx3JJ24D0z7fwG3Zzfizi4SwHgj7ehBvha4XxK0tJ4J4IIJ4F3IxB6LQHgg/gvbFrx6d8kHmZUQxiTqbC39QgeJVf9htYd7CVMjXuOaTytx3QRAqJ3LdiSeB5kAQ0XNE1FBZb0RE/46+W7nFy9hxC1tyK+/mgfDyBgQVBV774WWsRSUoCumBCCsgdA3sbDwQusZ2tmIhiAch2/JhnzTqGPj5mANRKA9EZZgDISFMQljEAyHkG/FAxJ+gk6gPZOYNxgozrwdwSLRdyj1eAiKqSdR+HoggIUzZeCBUopoDEUSEBKnXr54gIUxO5fwM0qCGXZhwn1yEMGX7FqcgbbmNnzjOgYjSOxFnA0purkKuifP4z5Yg38ERCoNc4+JJUAFRQkSNjBUiaozg8yi0IKIGRPQOEc0kos+I6LB0+qFE9AURfUpE/whTt5cR7XahjFpABHmzUpBwJ3tZP0Hhhc6P1q8PTu3k0gPh1I8oPBBO5NID4Wd4B6kzyrcwBTXw8z0Hwq3tINvEA5F9W0JxUcwCQsZ0/JFzFE+CGv8jAXxGRK8DYABnA7gn8l6lGALgI2Z+mIjaA3gZQGcAYwH0A/AjgHeJqDMzfx2kYp2v5gLZTaL2Mtb9BIRXn7zy+r11KeiH5LLxQOgYf15vEgpr4Hu1r2t0ZxPCFEa46IoKN8IIiCjaCrqcCw+El4CNahtQHK9xLWYjUMgtMnaEfKNjQwiFRUtApJ/+f87MzxPRXADHIvUK17OYeVGO+vYQUq+KBVL9LCei+gBqMvOydL/eB9AbgKuAyMVbmNwMaN23MPm9WcleLux8CScjSdeIUteDhAzpGMhuefwERJj+AN5hQ7r1hwlh0v3WRJgQJt19cSMbI9+vPp10nb5k64GIcluUAiIf9eSqbrmRC4KQb+S6E090PRAXA3iciL4HMBXAG8y8OqpOENHlAK63JV/KzHOIqAWA8QD+BqA+gE1Kns0A9nKo7yoAVwFAw4YN7ZsteM1N0JlQHSaEyas+J89FkBCmIB6HXHggnNCZJ+Elqvz645QnjAciiknUum0EMfZzEcLk1CedvG7tBp0DoQpGnfGUz0nUYWKiq4oHQm7kVRPxQAiFRK478URLQDDzQAAgov0AnAxgHBE1APAxUoLiU2ZOeFThV/8zAJ6xpxNRJwCvALiRmWemPRD1lCz1AGywl2PmJwE8CQCtWrXiDRs22Le7Lrt5HcLMgVDJZhK1G36CwakPcfZA+OUJ8iVqFa8n/LqGfDaGXRgxE+SC6XesonwLk44nQOecuQmIyuqBCEJlvhlW5r4LghMypuNJkIdsQmEINImamb9j5oeY+SSkwphmAfgLgC+i7hgRdQDwOoDzmfm9dPubAOwgor0pdUc/EcAnQev2MlJ0PBBqmZ07d5rLUU2i9uqT7r4Ybdq3exl3bj/SbOdA6Hog/OqJ0gPh9XE5v74aBJmc7FVfrkOYgoiCsB6IoMIiF/l1y4TZJnMghKqMjB2hkMibsuKJloAgonZE1FNNY+YyAFsAPMzMXXPQt3sB1ALwCBHNIKKJ6fSBAF4E8CWAecwcWLyE8UBEGcIUJCwpaN5sBEOUHgidPEG8IUBuJlGHERoGuk/2gxj7TmlhvAJhBIhOW27ohDC5tZ+NB0K3T/Y6vLbl2gMRNfkUEPIkUBCEfCAeiPijOwfiYQC3OaRvS287LbIepWHmM1zSPwdwaDZ16xo7OpOoo5gDEcTot++HV14nIykbQeGUXggPhBdedYfxDniJqCBzC3TacCMboaLTzyAX52wMf51l3bkOfum52uZGIT0Q+URu5FWTuI/LbJAxHX/kHMUT3RCmNsy8wJ7IzHMBtIm0R3nAy5DRCVtSRYObgPAy5nWf/BvLuh4AJ4HgtR7WAxHUcxDGA+HVVtDXgQb1NugKOKf2ovJyBPEghBElbnl1J7Lr9kdnUnSUwkJ3bAbZBsT7LUz59EAIQrEhxmn8kXMUT3QFRC2PbbWj6Eiu8DOusvU6hP2QnNc2lWxf4+q1r1F8SE7HsAvjXfDyJAR9jauuYMnGENctm4sQpjBzIILkdSunU19Qz0FU9WSzrZBvYQpDPm+uciOvmoj4FPKNzvVfKCy6AmIOEV1pT0y/fvWraLuUe7yMlHwICK/QI6dy2bzGVSWfHoiw4kCnnqBzIHS/AxHGuA/ablTCxa/OICFMYSeoBfUQRLWs26e4eiAKJSDECBTCUsxjR4zT+CPnKJ7ozoH4G4C3iOgC/CkYugKoAaBvLjqWS8J4IFRUYynMa1y96gsqIIJ6IHQ8A27rfn13K5ONB8Kpv7n2QOiIACKKXGi4ldX1CuTbA5GN4Z+LeuzEdRK1rmfE/kAjW4rZCBQEoXgRARFPdL8DsQbA4UR0DICO6eR3mXl6znqWJ+w37KAfj4s6hMmpf0Fe+erngbCXD7LulF5ZPRBBDXndJ/7ZtOGWlu1rXP3IpYBwE9U66W7t6lIZPRD2Y1FaWppVfYIQBcUsPuX3E0/C3s+E/KHrgTCYDaAlgNYAehFRLwBg5ruj7lgu8fIUeH3gzal8RUWFa71hBERQcaHiF7Lk99amXHkgshEiTmlRTKIOKhacDDtdUaHbhhth3sIURoC45fVrV3fZrR6d+gs9iTpf5LIfxWwECkIQ4vJ7F/SQcxRPggqIiUh9+flrANuj705+0HkSCgSfA6Eu2+ty81Q49cHevyAhTF4eCT9x4mZUBZkD4ZQeVZhTtiFMfvUH9Szo1heVV8INI6/bW8Dy8RrXQs2B0H2a71ef374U4kvUUefLZh/sIXtC1aLYxKcIiPgj5yj+BBUQrTj1FepKjZeRojOJWvU6eAkDtYz9i9VBvAz5CmHy6oNbmajmNwTJE+Ukaqd90TX4/dKCeiXc0nLxJeogeXXK5VNYuNXjV6ZYPRBycxVyTbEJCBX5/cQfOUfxRPctTAaziahTTnqSR7yMFDWESV12M4p0BYSXl8Feh32b7sU7V5Oo7f2JyrsQNk+Uk6ij9kDolg1iKGY7idqPfIUwef0enNLd2tVJd6pLV1zn+mal6zWJk4AoZgNS8KfYzn9cHhgIesg5iidBPRC9AFxCRMuRCmEiAMzMB0besxziZUjovDJV9UDYPQturn5doeFk9If1QDgZ+7pPYdV1exm3UBmvMn55wtTjhK7HIJt8Qcs69U8lTAiTbn+ClAeCTRaO0nPgV7bQcyAKMYlaBIQQF4rt/IuAiD9yjuJPUAFxck56kWe8jJFs3sJkeAvcntyr7btts/9QvDwQfqFQfk/1w3ogdASETjtBDURjOWgIk1d7Tk+e/YRB0DCkbIWGbgiT21N0v3MShYDQaTuqZRVdY9yvvmwFRBCiFhCCIIRHfmfxRK6F8UdLQBARcYqf/PJE17X84/W03+3NS3YPRElJiaORqDsHwmmbbsiO31uX7B4JPw+EbhtOeb3acTJ4CxXC5NSHMB4IXVGk65UIYtTrHHOv+v28FTp1uLWX6zkQuv3zq68yC4hKftkVKgHF7IEIe/0TcosIiPijOwfiYyIaTER7qolEVIOIjiWi5wBcHH33sifIwPMyKlQBoC47eSAM3AzkZDLpOY9CV0D4CQC1XmO7jvHv1Y5X39W8Op6RoPWE9UAEDWHy6pe9jL2s7j4EERphXuPqd1MM4q1wIxvDP2j7bulhvpLuty3XBoUICEEoLGKcxp98zksTwqEbwnQSgMsAvExEbZF6lWstAKUApgF4iJn/m5suxgM30WD3LKh4veJVzatbhx0/j4NXqJTRD6d1r68shwlh0hUmTtuc8tjDaNzCauzl/MSCV1oYQaLbl6g8EEEuuGEvztnMgciFsPDCb66R7jYg2iewYV+KkG192SA38KpNsXkgVGRsxxPxEsUf3S9RlwP4F4B/EVF1ALsCKGPmDbnsXL7R9UC4hTDZDXQvL4OXCLEbsG4ft7P3kZkzRIufwHCipKTEdV6GrijRaSfsZGx7mpNRq2vw+4kFY/90xYJuu0776ka2r3H1azOsmNCpQ0c86izr3kDCehn8jkG+BEQY0VTMxp0g5ALxQMQfOUfxJ+hrXMHMFcz8a77EAxHtR0QbiahWev1QIvqCiD4lon9kW7/XIHWb9+DmWfAyrnfs2GHJ5yYunJ7w64Yw2YWJUzs6RpRdsNjr8DL83Z7Y+z39twsRJ9Fk5PeadB7UA+F0vsLMi/AKQ9KdqO3UZzeC9Mdp/Hidw6Dtei27GebZTLpWyUUIkwiITOQGXrUpNpEqxmn8kXMUfwILiHxCRPUBjIT1q9djAZyP1CtlexBRZ686gg48txANtzkQdg+Em5GkChA/D4SbQHHCboj5Gftehr3O02InD4lTXq85HzqTqI3zoOs1sOMkTnTr8ivrJWTC9NepbBC82gaiFRBO7dqX3fIEFQ1u9ajY983Lk5CNgPAjKgMrrjfNOPVFEKJExnY8ieu1UPiT2AoISt2RnwRwG4Bt6bT6AGoy8zJOjaj3AfSOqk0vD4TXxGk1v5sHQhUQToa+WoeXF0HFHmrkVNbeVz9vhxNeosMucAxDyqsdpyf9bkLJzyB3u7B4fXfDra6gHginfvgJkmy8Ek7992sH8A+B8vLo6PZBZ1mnnaChUPZ90/VORS0g/NCtM64eCKFqU2xjTIzT+CPnKP4E/Q5ETiCiywFcb0v+CcArzDxfuXjVB7BJybMZwF4O9V0F4CoAqF+/vlN7rmEKbh4IVQB4zYFwMwLtIUxe35Lw8hqo+AkIp7q8BIWTsWxf9xMdTgLCLWRLpx6nPF7iwKmclxdBN7TKzwOhm2bUo1vWDV3vBxDeA8Gc+VrjoKFKUYUt6d5AdLxjTvX5ibewXhrd+p3yyU1TEHKP/M7iiVwL409WHggiqkZEBxFRdyLqHrYeZn6GmTuqfwDaA7iciGYAaIHU2542AainFK2H1Buh7PU9ycxdmblrnTp1nPqt5jWXS0tLXW/0biFH5eXllvw6Hgi7Ie+1zW6w2/dD7Yvds6EjGJyEjJ9xpeM50PF0eBlVTkIkWw9EmBCmoB4Ip3Z1RVCQ0DW//Qs7B8Lvoq0jDtyEkE662+9H14DXDWEKIi7c0oKgM2Z1+uFEsT0dFuJHtgI6bohxGn90r5lC4cjWA/EagC8BVADg9HIkMHM7Y5mIVgA4gZnLiWgHEe0N4EcAJwK4y6eejDT1Y2/2dBXVa+A2iXr79j+nZ9jrVNftIkHdZhchuiFMRJS1B8LpFbJeoVx2keTWt6AeCJ1QKL9QHbf2vTweuv3SDRnS7a9u/9zIVkDoGOqJRCLjN+HWho4gcDtvOgJCN4RJV0B4jfEwAiLI9jDhTF6IgBByjf33UdnHnAiI+CPnKP5kKyC+Zeb7IumJPgMBvIj0NyiY+YugFbhd/OweCLewJXVZFRBer3FVxQgzu4oQJ6Pfy7ixeyCyFRRO2MO0dAxdHQ9E0FAoL6+BV/tGHj+x5NUv+3F2a8PJkA4TwuTngXDaF7fjEsQD4Xds1eOlMy8oGwHhdMz98BIdYcVFkPbd0H2aJk/dhDhSbAIijHdTyC8iIOJPtgKigog+APA7ADDz+dl3KRNmbqMsfw7g0GzqUy9+6nJJSYnFmFeXVS+BuuzmgbB7B8J6ILwMdifBYDdgg4Y0GfWq+IVYqYSdA6FTj5Px7XZhcarbyYuk65XwK5tNCJOT0PAzJMN6MIybfxAB4SYCdD6uGNTToOPJ8CKI2Fbxu1n5GRl+23Vvhl4flXSjshtzQvyx/0a9vk1UGQj74gghf4iAiD/ZCogWzHx8JD3JEU4DT71Jq5SUlFg8Bao4KCsrc1x280DY5yd4GeF2EeL1YTm70eblcdARDDpPve3Gs31fVAxjxq+f9np16tEJYfIKfXKqy8kI9vNAOIkA3dCkqDwQfsfT7QbpJCD8DHs3geFm7LoJgqAhTzo3eb9wOxX7m9DcyoUREH7nS9ezEMbrEvWXrQXBTrF5xkRAxB8REPEnWwFRh4jORfrNSMw8Jfsu5R43I6i0tBTbtm0z13UEhN17YFCzZk3XMslk0rWORCLh6ZGwG912g8NuENvFid3I1JkDYc+jiiydSdRuZbzqMZ5wOV3odZ7SOhnjTga2kwfJSRQ6CQ0/EeAlNPw8MkEMVj9BZn8DWElJiWtep3S3vEFDmIKm65znIELAqz4vMetUl50gIWdedYUxbHQFhNuDkyCIt6NqUmwGd7HtTzEi5yj+BBIQRFQHgDG5eQmAjwHUBNA04n7lDdVYZObIPBB2AbF161ZzOZlMuoZKJZNJT0Gh1plIJDKMQ7tQ8RMnbkagit24srep1uFk+Du167TfdjHn1DZgFVz2C0v16tUd6wacDUgnEeNU1k98GP3QFSl+hrqfN8LpvLmVt+9jtWrVXMWb03h2yxvUAxGVsFAxxolOGS8j3ss74dW+U7tO+AkUnT66oWvU+/UxyraE4kI8EEK+8bsmC4VHS0AQUXUADwC4CMBypF7/2gzAaGa+l4gOYeZ5uetmePwGnmrY25/Yb9r05ycn3ASEW56SkhKLN2Pz5s2WPnl5J+zGttrHLVu2uG6z1+tkuNu3q320Gxg1atQAkGmY+okWAJZ67YLJzbB2EgZOdXuJIMOgtO+n03449cGe5uSBcOuHW3/VNKf9MtpwKgtYDXgDPzHk5YHwyut0PNzyuolPN2PZTfi55de5yVerZr2EeYnLbASEn0fI7+m+2zH0qkf3pqkbjy4eCCEsxWZwF9v+FCP2F88I8UPXAzESQB0ArZl5M2B+FfpBIhoD4CQAbXPTxewIMvBUYxxwFwqqEb9hwwbHdLth7bVt/fr15rKT0e8mIBKJRIZnw2642w1V+7q9vHphNQSEKn786nQTEH4eCHs9xnmz1wNkGom6hrxTXX4Cwqk+4xj5CR7dfF5t2Ptj4LQvboa4n1chSF43z4Zah5sodXvhgI5wcjO6DY+TgVtIIeAueHTacjoHKoX0QNiPgRtReCBq1qyZdR1C5aPYDO5i259iRDwQ8UdXQPQBsA8rZ5GZNxHRIABrAZyci85FQZCBpxofuvlU43/dunXmcjKZtGz7448/LNs2btzoWEcymbTUk0gkLKLBbsx7eSQSiYRF+CSTyYzy9vxOhqlaRyKRsIgmu2gxLsz2eu3rQKaoUuvxMsjt+XSNcft+2Oty8gQYafbjZs+nKwy8+hfEA+F3PNUbpNM5DWLku/VLPSZu7anpbt4Bt/xO+2gvaw9h8vJA2MP5VPwMfL9rg9/TfbfwMq96vK5d6jYREEKucQrXrMzI0+34o/vQRSgcugIiyQ6/MmZOENHv6VerxpJcXxxWrVrluJxMJrFmzRpz/X//+59l2+rVq831X3/91VxOJBL4/fffzfU//vjDcvFW60kkEpY2EomEpV57H5zyq+v2/MaP1i5+7B6T3377LaOMug/JZDJjHQDWrl3rW4+ax61utX+GkWQXYQAsos2rfqc0VTQ5tWHksx8bIPP42evzakO9iBoeIQAZwtDetnrBdTo+Tn1y65dTecBZVAGZ832c+qz+Lt3yu9VvF54qbl4ce/v2bW5iyMDPA6GKICe8+qWi7pvXtSvM0zm/fdBBHYNC1cHp4U5lxh76K8QP3YcuQuHQfZnzIiK6yJ5IRAMALI62S9GS64Gn3vBVA66srAzLly8317/99ltzedWqVRZjZtGiReZyIpHAkiVLzPXFi62HV92WSCSwdOlSy/oPP/zgup5MJj3Xncpv27YNv/zyi6XM999/b1n/8ccfLWWYOSNt2bJlnnmSyaTleBmGob2ebdu2ZYgkp3IrVqzISFPzGePip59+ykj7+eefM/pqb4OZLfmMm5CaZrS7cuXKjDR7G275VFGqxp+reY3ydiEKpJ60OQkd+zE09kFNN+pwEpVu7Xmlq3WrN22nNgGrUHRLtwsIVYTaf/tO4s7ATSQZfTXaqVu3LpxQrwNOOAlaJ1SR62XYOIlNP9S6g6D2o3bt2qHqECo3xWZwF9v+FCPq9UoERDzR9UBcDeBNIroMwFcAGEA3ALUB9M1R3yJBVbH5xD7g1ad/qpgAgAULFpjL33zzjWXbG2+8YVl//fXXzeXy8nK888475vr69esxZ84cy/qnn35qrm/btg2ffPKJub5z5068+eab5noymcxYf+WVVywX2DVr1mDmzJmeZSZMmJBhML399tuWPJ999lmGIThp0iRLmQ0bNljaSiQSmDp1asZbftRjYBj3kydPtpRbv349Pv74Y0u+lStXYuHChZa0b775JsNbNHv27Azjc/r06Zb+GyLDLug2btxoOSc7d+5EWVkZ3n//fUsbACz9M/ZxxowZGfnWrVuX0W8A+M9//pOR9sUXXzi+FWn27NkZbX333XeOXpovv/wyIy8AfP311xl5E4mEZTyrk9VVMayKFvX3oNbvlm4X3CqquPXaZhcedmGtooq4WrVqwc6mTZvMkAi3cCIncZhNPrsn0g9mtgjBIKhixWn/heJHfShQDAa3PVpAiB/2aAkhfmgJCGb+H4AeRHQsgAMAEID3mPmjXHYuCtxikxs0aGAaS6WlpeYArVGjhmPMeVh23XVXS0hMUOyx16pxpl4EAeCDDz6wrE+ZYv0sx6uvvmpZf+uttyzrc+fOxVdffWWuJxIJXHfddZY8F110kaVPL730kuVp+i+//N0LPxcAACAASURBVIKBAwdayjz66KMW4bJ+/XpceeWVljxvvvkmXnzxRXO9oqICAwcOtISW/PLLLxg1apSl3IcffoinnnrK0ueRI0di3rx5lrRLL73UYhxXVFTgyiuvzIjRt/drw4YNuOaaayxpa9euxVVXXWVJ27lzJ66++mpLWjKZxC233JIRy3/TTTdleEO++uorvPzyy5aymzdvxvDhwy35AODOO+/MeAvTnDlzLCLNmIx/4403WvqUSCTw6aef4qOPPrKUB4D7778/I++GDRssx1cVJqo4Nfr26quvZoSmAcALL7zgOGdk8uTJjjeK//3vf5g2bVpGOpAac/b+AClh9eGHHzqW+e677ywCxj5nw60cALz33nuO7RlMnz49I82OKgTdboa//fabReR4GWqfffaZb30qixcvNseM7pwJA1VARvEmJ6FysXnzZosHuRgExH//+19zuRj2pxhRH1CJgIgpzFzUf0TESHlMLH+nnHKKudy9e3dz+YQTTjCXd9llF27SpInjtgsuuMBcHjJkiLk8ePBgc7lt27b8//7f/zPXr7vuOnP5yCOPtPThX//6l7m8zz778NVXX22uX3nlldywYUNz/Z///Ke5XFJSwrfffru5Xq9ePR4xYoS53rBhQ77rrrvM9bp16/Ldd99trhMRDx8+3HJs7rnnHsv6Oeecw7169TLXGzVqxAMHDrTk+fvf/25ZP/HEE/n000+3pN14442W9f32289yTAHwxRdfbFmvV68e9+3b15J25JFHcqtWrSxpl1xyieWYAOBLL73UkqdBgwZ8/vnnW9KaNGnCvXv3ZgBcp04dBsB77LEHH3rooWb7APjAAw/kgw8+2NwOgHv06MEdOnRgANyvXz8GwI0bN+ZOnTqZxw0A16hRg/fff38GwGeffTYD4OrVq/N5551n7nObNm0YgNlGr169+Nxzz2UA3LFjR7O/EyZMYABcWlpqHoPnn3+e9913XwZgpv3lL3/hyy67jAHwnnvuydWrV2cAvHDhQm7Xrp05NgDwwIED+c0332QAXLNmTbOt77//ni+66CJznADgbt268datW819NP6GDRvGq1ev5qZNm1rSJ06cyKtWreJGjRpZ0r/++mveuHEj77XXXpb0999/n5PJJPfv3z9jTDIzz5gxw5Lepk0bNrj88sst2x5++GFmZk4mk9ynTx/LtqlTp5rl1N8PAB49erS5bdOmTea5MY6ZSkVFBR9yyCGWsWdn6dKlXLt2bTNP//79M/Iwc8Zv6IcffnDMV15ezvvtt5+Zr1u3bo75VK688krPPrqRTCb5+OOPN8u2bdtWu6xQHIwZM8YyLjds2FDoLmXFli1bLPePQYMGFbpLgo25c+daxtyECRMK3aWqhpZ9XXADP9d/6iAsLS11NDjPOOMMx3TDKFNv1MayKgxeeuklc1kVAqeffjr/9a9/NdefffZZc3nw4MF86qmnmuvz5883lwcMGGAxtmfOnMmqEPr555/N5cMOO4zfeecdc/3cc8/lN954w1y/6qqr+OWXX7bsn5q/T58+lrb32msvTiaTlv1es2YNH3XUUeb6c889xyNHjjTXTzrpJF61apXFQFmxYgWfddZZlov0vHnzzPUaNWrwDz/8YBreAPjMM8/kxYsXZ1w4hg4daq43b96c16xZw/vss4+Zdvzxx1vaB8BDhw617KdR17BhwyzjYdq0aRahUbNmTf7888/5tNNOM9Pq1q3L3333Hffs2dOStnTpUu7cubOZRkT87rvvWsoaY8IwxI2/Bx980HIMAXDXrl35ww8/tKTVrVvXckwMA3/kyJEZ7XTq1Imfe+45S1rt2rX566+/5t13352BlJgw8j7yyCMMpIxww8AdNWqUeWwNAVirVi1+/vnnGQC3a9eOTzrpJAZSQtcwes855xzz93LssceaY3vMmDHm7+akk04yj9f06dPNPIcccggfc8wxDICnTJliCuS6deuaImv48OG8cuVKbtmyJQOwiCNmNsd4zZo1TYN31KhRzMw8atQoBsD169c3jf13332XmZmnT59uXheMvj366KPMnDKejfNmiIgGDRpYrrLXX389A7AYJMlk0ty+detW8wGFce769u2bcbWeNGkSl5SUWH7nS5cuzciXSCTMfTf+OnfunJFPzX/rrbcyAFNE2vvoRkVFheX6pR5voWowa9Ys3mWXXSxjYP369YXuVmjWrVtnXr+Mv4EDBxa6W4LCokWLuG3btpZz9NprrxW6W1UNLftadxJ1UdCgQQNzeddddzWXW7ZsaS43a9bMcblx48aWCZpqOEarVq0cl5s1a2b5yFPr1q3N5d12281SX506dSzb1NdT7r333oYYwu677255E0qHDh0seQ8++GDL+iGHHGLpQ7du3SzrXbp0seQ/7LDDLJN1995774z96N27t6XMySefbFk/8MAD0bp1a0vaOeecY1k/4ogjsPfee1vSBg0aZAmvaNOmDfr27WvJc9lll6FZs2aWtOHDh1uOSYMGDXD77bdb+nz44YfjrLPOsqRdfPHFOP744y1pN910E3r06GGp/4477kD79u0t+f7xj3+gXbt2lg+Z3XbbbejTp48l3yWXXIJBgwZZ6jvqqKNw/fXXW9J22203TJw40TJJtaSkBBMmTMB+++1n5t2+fTuuuOIKXH/99ZZ2WrVqhSlTpmCXXXaByjPPPINDDjnELP/zzz+jVatWmDx5stnWihUrUFZWhssvvxx/+9vfzHonTZqE0tJSjB8/HgcccACA1FyBqVOnokmTJnjrrbdQv359AKnQpe+//x6dOnXCCy+8YJ7HQYMGYc6cOWjdujWeffZZM/3YY4/F5MmT0ahRI7z88stmX/r06YO///3vAICnnnoK7dq1M89Bhw4dsGrVKhxxxBG47bbbzP3p27cvBgwYAAB44IEH0KlTJwDAuHHjMHDgQNxwww0AgGeffdb8rU+cOBFPPPEE+vXrh0QigZtvvhm9evUCkAr3+fXXXzFs2DA8//zzqFWrlhlet3PnTjNE64knnsBDDz2E6tWr45VXXjGPuRHmtHPnTpx33nn48ssvseeee+Lxxx8HkOmO//TTT3HOOecgmUya40qtxyCRSOCyyy7Dv//9b9SqVQtjx451rM9g27ZtOOecc3DvvfeitLQUY8eONceBX0jAl19+ie7du+Nf//oXatSogSeeeEKrnBtbtmzB3Llz8eKLL+LOO+/E1VdfjfPPPx/nnnsurrjiCowYMQLvv/9+pOGjQjiYGZ9//jmuuOIKHHXUUdiyZQvOO+88NGzYEEDlCycxQhSvu+46tG3bFlOnTkWjRo0wePBgADDvrULh2Lx5M6ZOnYqLL74YBx10EJYvX45u3brhlFNOAVD5xlxVQXcSdVFQp04d820rjRs3NtN32203c1kVFqqAaNq0qWWCo2r82w1Bg0aNGlkm2jZp0sSST327kb0OddJa8+bNLctqXvt6q1atMkSL+gq+PfbYI6Mtt/4DKQEBWN9S07JlS0sbBx54oKWOgw46CID1rUGHHHKI5fh16dIlI0/nzp0t8wW6desGIrK0te+++wKwvlawc+fOlrfgdO/eHbVq1bL06ZhjjgFgPc4nnHACAOuXfPv06ZOR1rdv34yy55xzTkbaxRdfnJE2aNCgjLRrrrkGJSUllrSBAweiZcuWlmN04okn4sQTTwQAyxydxx57DERkKT9q1Ci0atUKc+fONdNOOeUUnHfeeQCsxujUqVOx5557Wvbx1FNPxZNPPplxvF977TWcddZZlpjh5s2bY9asWWjXrp1lAnuvXr0wZcoU1KtXL0MwvfTSS2jRooVFcHXp0gXPPvss2rdvb8nfuHFjPPTQQzjvvPNw1113membN2/Gcccdh1deecXyUgJj7sfQoUNxzTXX4OabbwaQmi9kzBm67777cNZZZ+G5554DADz55JNm+TPOOAP33HMPbrrpJgDAmDFjMGbMGACp8fnSSy+ZomTr1q1o2rQpOnbsaE5kHz16NHr27GnOpapfvz6GDBmChQsXYtKkSWjUqBGmTp1qTrCfNGkS/vKXv+Dpp5/GvHnzcOqpp5oCbtiwYaYYOfPMM/Hqq6+iU6dOKC8vx4UXXog33ngDderUwTvvvIOmTZsCAObPn49nnnkGl19+ublPq1evxumnn445c+agfv36eP3113HCCSdg0KBBSCQSmDRpEvr27ZvxZektW7bg1ltvxeOPPw5mxp577onx48dbrgMbNmwwjUk3ysvL8cEHH+CDDz7ArFmzMH/+fK3Jqo0bN8bAgQNx0003+bYhZAczY+3atVi+fDlWrFiBJUuWYM6cOfjiiy/M+1tpaSluuOEG3Hfffab4jss8GGbG5s2bsX79evz+++9YvXo1Vq9ejTVr1mD16tX46aef8O2332L58uUWkdC7d2889dRT5ossREDklvLycqxbtw5r1661/C1fvhw//PADli5diu+++85yfbjiiiswcuRIcz6lCIg/YWbs3LnT8ldRUeG7rJvPmH+qQ5USEOrTXfXmpBroarpdQBivUK1Zs6aWgGjcuLFl0rP6dL1ly5aWOlSjrWXLlpZyqtHVpEkTS1770/gWLVpYJjk3b97c8qrSZs2aWV4h26JFC0t9LVq0gIohetSJsXYj0y4ojDrUdurXr2/pp3EzUvPsuuuulr4bT2Kdjq8qsKpXr25pf7/99gOQeUztae3bt8+ov0OHDuY+GhieI3US9p577gnA+v0Ao7/qxc4QU2ob3bp1y+hL9+7dM9IOPfRQ2DnkkEPMN+Goeffff/+Mdo477jhzWRWrhjdBzdu/f3+zPvW1qmeeeWZG3rPOOsvcV7UPQ4YMQb169TLyP/nkk+bxV4/r5MmTzbGi1jNz5kx07Ngxo56RI0diyJAhGX1s0aIFPvnkE8fxctppp+Hmm29Gz549M7Z169YN/fr1w5AhQ1BaWprxUbqWLVti9OjR6Nu3b8bLDBYuXIiSkhKMHDnSnFCvfpxwxIgRAFLesMmTJ2P//fe3vJL4jTfewKxZs7Bu3TpUVFTgwgsvxNixY0FE5u998eLFOProozFlyhT8/e9/x3/+8x/Ur18fkydPxhFHHGF5G9UVV1yBxo0bo2/fvliwYAFOPfVUrFy5Em3atMHkyZPNc270sV+/fhg/fjwuuOACy3G/9NJLsXz5clSrVg1DhgzBsGHDULduXfNaVVZWhnbt2mHBggUWzy2QurHNmjULY8aMwTvvvGMR9dWqVUOHDh2w3377Yd9990WLFi3QqFEjlJaWYtOmTViyZAk++OADLFy4EP/3f/+HsWPH4q677sLAgQMt1z8hE2bG9u3bsXnzZmzatAmbNm0yDWu70ab+/frrr5YHNiq77747+vfvj0GDBpnXSeM8nHbaaZg2bVpkAq+iogK//fab+bdu3Tps2LAB69evx4YNGyzLatqGDRu0RGm1atXQsWNHnHjiiejXr595/TWuRU8++SSOOeYYnHvuuZHsj7pfZWVlKCsrQ3l5OcrLy81l4/Xg9j/jLYJhRU2YcslkEhUVFaioqMCOHTuwY8cOx2V7WllZGbZu3YqtW7di27Zt5rJ9XcerWFpaih49euD444/HJZdcYj6wMK7JAwYMQJcuXcx7e1Qwc0bf1b/y8vKMY2A/HmpaUMM9jPGfDzFV6QUEEZUCGAWgK4CaAO5k5slEdCiARwDsBDCNme/yqMaCGiakhjOpQsFLQKjLTh89A6xipHHjxhZDR70R7rbbbuaTnFq1alkMKHt4k0qDBg08PRAtWrSwvBWpRYsWFuOxWbNmlpuGU3kVwyNjf5OUWsYe4mQcK/s+2IUPYH0/PmA9RkYeJ+FhvJ7XEIX2Y2JPM/bLqS71abYRkqO+utL4+q56Lg3swgqwemuMsqrhbIggtS977LGHa/9U1BA5p7zqMW7Tpk1G+d13391cVvMaxjfw5/cSSktLzTxOAs3eB7UO9SmlU92Adaypr1s2RJy9/mOPPdYxvWvXrpY21G3333+/Ka7s/Zo9e7ZlvKn7+MEHH6B3797meVPF/2GHHYbBgwfjoIMOsvRVpWfPnmjcuDHuu+8+M49doBjj6brrrsPIkSPN7aqo/uOPP0whufvuu2PKlCk48MADHesbPHgwli1bhjvvvBNbt27FYYcdhrfffttyHVNvPnfccQf69++PRCKBW2+9FY8++iiAVBjkuHHjTPFrb2vdunV47LHHcO+995ppM2fOxA033GB5i1uXLl1w+umn48gjj0T37t0t1183Zs+ejdtuuw0zZ87E4MGDMWbMGIwaNcr0xBUDzOxpfG3dutUUAaog8FoO+7ryhg0bom3btmjTpg322msvdOnSxfw92b1TxsOSOXPmYPTo0bj99tu12jC+FbRgwQL8+OOP5t9PP/2ENWvWWK4JQdlll13QsGFDNG3aFC1atEDz5s3N/61atUKHDh3Qrl07xw8gqvt33nnn4fTTT9cao0Dqt/vNN9+Y3puffvoJv/76K/744w/88ccfWL9+ve+3YaoK1atXx6677oomTZpg1113NZdbt26NvffeG+3atUP79u0dv7GjXneuvPJKy9sc/di2bRsWL16MFStWmH+rVq0yBfS6deuwbt262HjUglBaWopq1aqhevXqqFatWsZy2G3Gsi6xFRAALgRQnZl7EtHuAP6STh8LoB+AHwG8S0Sdmflrt0r22GMPlJWVYe3atWjVqhXmz58PIBVeZLDXXnth//33x+LFi7HPPvugYcOG2LBhA4488ki0aNECq1evxvHHH48FCxZg2bJlOO6447Bo0SL88ssvaN26tSVMST34dgFh90AYGE/iDHbbbTfXi2qDBg0yDHG1zebNm1sERNOmTTPy2wWGeiF180AYRrbx9Ft9qtCgQQOLKDEMFruAUPfREBmqsW3P4yUg7P1zEidqmiEq1ButIY5UEWMcC/WcGqheDwO7AAIyxRZgNZyNm5lfn1XRau8zYA3jMsaz0z6rqGF0fm2paU7nzl6HKk7U46LmsZ9vA/WYqfndvGNO3iWnvtpD8tT27RdKtdzuu+9u+V2o2/bff38zNMyJ008/HRMnTsxIV+t49tlnUaNGDbRv394M5zNQxesRRxyBWbNm4eSTT8YTTzzhKiCB1KtvjTCsCy64AE8//bTndxuWL1+OCy64APPnz8cPP/yAatWqYejQoRg6dGjGq17tbY0ZMwa33HILAODaa6/F888/DyA1NgYOHIjLLrvMUcD6cfjhh+Pjjz/GxIkTceONN2LRokU46aSTcMIJJ2DAgAHo0aMH2rZtG/hVtCrq017jL5FIIJlMYseOHebTYuNv+/btGWnqn9sTTK/0qMNmqlevjvr165t/9erVQ4MGDdC0aVPTaLP/NW/ePJAXQTWIX3jhBQwdOjRDZAApkf7FF19g2rRpmDFjBubNm2fx1NopKSkx+9OsWTPsuuuuaNiwIRo1auT5v0GDBlmNA/vv/+2338b555+fkY+ZsWjRIkybNg0fffQR5s6dq/VNldLSUtSpUwe1atVC7dq1UatWLdSqVQs1a9ZEtWrVQESWv5KSkoy0MAQtR0SoXr06atSoYf53W1b/165dG3Xq1EHdunUtf/a0mjVrRrIvs2bNwu+//+54rwJSc/k++OADzJgxA19//TW+//57LQ9VzZo1scsuu2TsR926dVG7dm3X/benGQa4n3GerYFfWloa+nhGTZwFxIkAviGid5H67sRgIqoPoCYzLwMAInofQG8ArgKiWbNmGDhwIEaPHo0777wTrVu3xtKlS9GxY0fTzd6sWTO89NJL2LJlC1q0aIEZM2Zg1apVOOCAAzBlyhTMmzcPJ598Mjp37oxJkybhiiuuwJo1a/DII49gxIgR5lNw40lnu3bt8MMPP6Bjx47o1KkT1qxZg06dOlkGszoHo3HjxhlPqFUjQsXJA6Eap40aNbKE2lSvXt1i3NetWzfDyFSNPTcPhIFhgKqGmD2kyc274CQOdDwQqodAPW5qf5zad/JAOBmquiLAQL3pGoLE8DS41efkUXIaD26GulOaehMzxo+TJ0ZFPZ9+balPrt3EhvoyAVWUux0/N2HsJNgAq0veTdDYBYQqrFRPI+AuYOx12oWHin0M2rH/ZgzUY9ixY0d07drVMZ/qgfjPf/6DiooKR0NJ7e8DDzyAhQsX4ueff8all16KAQMGeN5k6tati23btmHChAlmf5577jl07tzZMb9dQGzcuBFnn302lixZgp9++gm1a9fGrbfeihv+f3v3HR5lmb59/HuFAAFF6dK7S1NqJFJEXUQB2ZXVtSygsiLo2l11V3fXtio/RV3FtoCoqIC9giK+FkCphl5FQFF6B+kkud4/MjM7CQlMSDIzSc7PcczBPPfc88w1d8LkPudpd9wR8be4uTEz+vTpQ8+ePXnmmWd46KGH+Pzzz7NcFyQ4eYH//Y7ktEtIMBhkv8VaUlLSUSdd4UEg+/2c2sI/f6JhxYoVzJgxg06dOoXali5dysiRIxk7duwR//9r1qxJmzZtOPXUU2nUqFFoq0eNGjWoUqXKEb9f0ZB9q8To0aOzBIhdu3bx4osv8sorr2TZXRAyt1S3bt2aJk2a0KBBA+rXr0+tWrWoUqUKlStXpnLlylSoUCFuJnpFVfbgOXbsWG677bbQ8vbt23nttdcYPXp06MvhoFKlStGyZUsaN25MgwYNaNCgAbVr186yFaRKlSq6OGY+xEWAMLOBwO3ZmrcAB4DeQFfgFaAvsDusz69AoxzWNxgYDJn7qvfu3ZvevXsDmZvtg8L/gIdPnE855ZTQ5KtmzZqhyUT9+vVDZ25o0KABTz31VOh5GzduDE0sx48fz+bNm2nWrBkjRozgP//5D3fffTc1a9bknHPOoWXLlpgZ9913H//+97954IEHsvwSn3jiibRt25Zly5ZlmZRB5uQ1+2Q5/BiHUqVKHTFJy/5hHv6f8oQTTsh1txLI+o01/G/ydLRdmoITveDkMhgKcprkZ681PEAE15PTRDkoOD45vX5O38bnFMxymlTmNqGFIyelkHVSmVOAONbrBuvPaYzChU9Oc9qlKi9bIMK3xuT0TWQkYSP85xf+s8ktQASPJcg+Ic6tf05jBEffApHTbmVBOf1sgsLDUE4/46Ds/yeyyy1AHCuwZRc8niS3b1mzH7uT/aKBR3PppZfSt29f3n77bdq2bcu1116b424eOb3WDTfcwKhRo0IT+vbt2zN27NjQvvIFpWzZstx1110MGDCAN998k88++4wlS5bw888/h/YtP17BLz2Cu+kFb2XKlAl9U5zbrWzZslnu5/Qt7NHCQfny5WMyYS5oo0aNolOnTqxevZoHHniAMWPGhMLcqaeeSo8ePTjvvPPo0KHDEX9X4kH23/cvvviC1atXU6tWLV544QWGDBkS+ryoWrUqPXv2pHv37nTu3JmGDRsqHERB9r/NL730Erfeeit79+5l2LBhDB06NPSFy0knnUS3bt0477zzSElJoWXLlgoHhSwuAoS7vwS8FN5mZm8CEzzzE2mKmf2GzPBQIaxbBeCImZm7jwRGAiQnJ0flFAvhk7VmzZqF9hNv1KgRzz33XOixr7/+OnT//vvvZ+DAgaEDcidOnBiafAwdOpTy5cuHTlf58MMPM2bMGP7yl79QpkwZGjduzIEDB6hatWroD3dwwhF+uljgiG+nwr+hhaz7RWcPLLltgcg+8T/axDenyWn2SViwT/gf1mCfo20uDj4v/PWDNYdveQme3jR860xQTpPK4OQ6p/0yc5pchrcFxzP8m9jwb5WDcgoux5pkhv98cgoQ4Vtrcqo9t6CT0x/D8J9bTuMLOY8nHP2b/uzrhiO/aQrKbUvc0bZA5DTWkdQVvpXoaJODo4ULOPL/UFB4zbn1CXes3UvC15dbaMlN5cqV6d69O927d4+of/hr9ejRg6uuuoq33nqLVq1a0a9fv3ztSnIs1apV4+abbw59eZORkcH+/ftDn2Phu3tk3yUke0AIbv7X5O/41a5dmw0bNvDqq6+yfft2PvnkE9LS0ihdujTXXHMNgwYNol27dnE/xuG/s7169eLTTz+ld+/e7N27N7Sbb5cuXbjzzjvp1atXof6OS87C5xmnnHIKixcv5uKLL2b69Omhz+vf/va33HDDDfTu3TvqW+JKurgIELn4FugFvGdmrYGf3X23mR0ys8ZkHgNxARDxQdTxJiEhIRQeIPMPc1Dt2rV58cUXQ8vBfZODFi9ezOHDhzEzGjduzMyZM0P7R/fo0YOnn36arl27AplnMJg1axaXXHIJkHl6z9/+9rehM+zUqVOHdu3a0bx589BE8bzzzmP69Ol07NgRyLzGwZAhQ0Lfcg4aNIhx48aFJiClSpUiKSmJAwcOhMJUv379GDt2LJdddhmQ9QM7eL979+78v//3/0KnWQ3vEwwQwclRTt+QBrcihU9wg8/L6SC28DNxBVWvXp3NmzfnOAnLaRKf0+4tOU0qg6echZwntTlNjnM6oDtc+KQyeBxKMDjmts5w4cHtWJP88PeU1wARrC23A7Jymxxnf8+5hYGjbYE4WoAIHjCX0x+ao211Cpf9WhvZ5RYwwnfHOtY6jraeoPwEiGOtO7vwn2PFihVJSUkhJSUlT+soKAkJCaFv8yX6OnToQPPmzRkyZAgfffQRCQkJXHXVVTzwwAM0bNgw1uVFLPwzZPjw4aSkpLBs2TIgc5e+//u//+PCCy+M+yBUnIUHiGeffZbLLrssdNrulJQUhgwZkuXkGhJlkV5xLto3Ms+89DIwE5gFtAu0nxlo+w545Fjrad++vW/YsKHQbsVF9ivTpqWl+cGDB0PLhw8f9vXr12fpn5qamqXPokWLfN68eaHlrVu3+rhx4zwtLS3UNm7cOJ8yZUpo+ccff/T777/fd+/eHWobNmyYP//886HlHTt29UE1pQAAIABJREFU+DXXXOPfffddqG38+PHeu3dv37ZtW6jtpptu8muvvTb0Xvbu3etnnnmmP/roo6E+y5Yt81NPPTXLlS0nTpzo1atX90mTJoXaXnrpJS9btqxPnTo11PbYY495YmKiz5kzJ0sb4F9++WWoLXgl5Y8++ijUFrxC9NNPPx1q+/LLLx3wQYMGhdo2bdrkiYmJ3rBhQw9Xv359B/zHH38MtfXt2zd0leaguXPnOuCJiYlZnl+tWrXQFaCD3n77bQe8Zs2aWfoGr5g8atSoUNu6detCVwUNd/fddzvgFStWzNLeqlUrh8yri4cLXrH5jjvuyNIevFJ2x44ds7Rff/31Ob7uoUOHQu3Z/x9eeOGFOT7H3bNctTu7M888M9fnhT/3vffeO+rjr7/+eo6PT5gw4ajrz76eLl26HLXf5s2bQ323b99+1L7Z1x3+exiJ8KvTL1iwIE/PleIh+PO/+uqrPSMjw8ePH+9PP/20L1++PNalHZexY8dm+f+4efNmf/HFF33ixIlZ/mZJ7FSqVCnLz+ibb77xf//73/7ZZ58dMWeRAhXZPD3SjkX1pgAhkcjpwyj7H5GMjAw/dOjQEf327NlzRL+ff/75iLbvv//+iNdZuHDhEc9fvHixr1u3LkvbmjVrfP78+Vna9u7d6x988MERdX7yySe+evXqLG1Lly71119/Pcvrp6en+7PPPutLly7N0nf+/Pn+5JNPenp6epb6hw0b5u+++26Wvrt37/ZbbrnliNpWrlzpAwYMOOJ9fPvtt96nTx9fu3ZtlvbZs2d7z549j6h7/fr13qVLF//44489u4EDB3rfvn2PGNMlS5Z43bp1/c033zziOe+8845XqFAhSzAMrw3wkSNHHvGYu3vnzp1zDCxBHTt2dMBXrVqV4+M//PCDA16hQoUcHw/q06ePA/7f//73qP1+/fXX0B/XSCc8wf6ffPJJRP1zem54iJWSI/jzf/jhh2NdSoEYM2ZMRIFeYqd69eoOeJUqVWJdSkkT0fza3Iv3VRiTk5N9woQJhbb+eDw4TESOT3p6euh0itnt37+fPXv25HoA9M6dO9m1a9cRxyCFmzdvHtWrV89yytvsNmzYwFdffcXll19+zHNyf/TRR5QtWzbL7o9HM3HiRKZPn86DDz6YZReOSDz44INs2rSJ559/Xrt1lEAPPvggL7zwAqmpqaHr1hRlW7du5fTTT6d///48/vjjsS5HcvDFF19w6aWX8sYbb0T8GScFIqIPeAWIfFKAEBGRksDdi1V4LG7vR6SARPSfIm9fQYmIiEiJVNwm28Xt/YhEkwKEiIiIiIhETAFCREREREQipgAhIiIiIiIRU4AQEREREZGIKUCIiIiIiEjEFCBERERERCRiChAiIiIiIhIxBQgREREREYmYAoSIiIiIiERMAUJERERERCKmACEiIiIiIhFTgBARERERkYjFbYAws5PNbKKZTTWzL8ysRqD9TDObZWbTzOz+WNcpIiIiIlKSxG2AAAYAi9y9K/AWcFegfTjQF+gCpJhZu9iUJyIiIiJS8sRzgFgEVAjcPwk4bGYnAWXdfZW7OzAJ6BarAkVERERESprEWBcAYGYDgduzNd8InG9mS4HKwFlkBondYX1+BRrlsL7BwGCAevXqFUbJIiIiIiIlUlxsgXD3l9z9tPAbcCsw1N1bAOcD75EZHiqEPbUCsDOH9Y1092R3T65WrVo03oKIiIiISIkQFwEiFzuAXYH7m4GT3H03cMjMGpuZARcA38SqQBERERGRkiYudmHKxb3AKDO7ASgNDAq0Xw+MBUoBn7v7rBjVJyIiIiJS4sRtgHD39UCvHNpnAmdGvyIREREREYnnXZhERERERCTOKECIiIiIiEjEFCBERERERCRiChAiIiIiIhIxBQgREREREYmYAoSIiIiIiERMAUJERERERCKmACEiIiIiIhFTgBARERERkYgpQIiIiIiISMQUIEREREREJGIKECIiIiIiEjEFCBERERERiZgChIiIiIiIREwBQkREREREIhY3AcLM/mBm48KWzzSzWWY2zczuD7QlmNlwM5thZpPNrEnsKhYRERERKXkSY10AgJkNAy4A5oc1DwcuAVYDn5hZO6ABkOTuHc3sTOBJ4KIolysiIiIiUmLFyxaI6cBfggtmdhJQ1t1XubsDk4BuQBfgMwB3nwkkx6BWEREREZESK6pbIMxsIHB7tuY/u/tbZnZOWNtJwO6w5V+BRoH2XWHt6WaW6O5p2V5nMDA4sLinZs2a24CtBfAWJDJV0XhHm8Y8ujTe0acxjy6Nd/RpzKNPY36kz9y9x7E6RTVAuPtLwEsRdN0NVAhbrgDsBMpna0/IHh4CrzMSGBlcNrNUd9fWiijReEefxjy6NN7RpzGPLo139GnMo09jfvziZRemLNx9N3DIzBqbmZF5fMQ3wDSgF2QeZA0sil2VIiIiIiIlT1wcRJ2L64GxQCngc3efZWbfAd3NbDpgwJ9jWaCIiIiISEkTNwHC3ScDk8OWZwJnZuuTQWawyKuRx+4iBUjjHX0a8+jSeEefxjy6NN7RpzGPPo35cbLMkxyJiIiIiIgcW1weAyEiIiIiIvFJAUJERERERCKmACEiIiIiIhFTgBARERERkYgpQIiIiIiISMQUIEREREREJGIKECIiIiIiEjEFCBERERERiZgChIiIiIiIREwBQkREREREIqYAISIiIiIiEVOAEBERERGRiClAiIiIiIhIxBQgREREREQkYomxLqCw9ejRw0ePHl1o669Ro0ahrVtEREREJIoskk5xFSDMLAV4zN3PMbM3geDsvAEw092vMLOPgSrAYWC/u/c82jq3bt1amCWLiIiIiJQocRMgzOxvwJXAXgB3vyLQXgn4Grg90LUJ0NLdPRZ1ioiIiIiUZPF0DMQq4OIc2h8EnnX3DWZ2ClARGG9m35pZ76hWKCIiIiJSwsVNgHD398jcLSnEzKoD3YDRgaYywJNAHzLDxlOBPmR73mAzSzWz1C1bthRq3SIiIiIiJUncBIhc/BEY5+7pgeWNwHB3T3P3zcA8oGn2J7n7SHdPdvfkatWqRbFcEREREZHiLd4DxHnAxGzLbwOY2YnAacCyo63g119/5brrruPll18utCJFREREREqKeA8QTYHVwQV3nwj8YGYzgc+Bf7j7UU+zlJaWxscff8zXX39duJWKiIiIiJQAcXMWJgB3/wk4M2y5ZQ59bsvLOsuXLw/A/PnzcXfMIjq9rYiIiIiI5CDet0DkW9myZalYsSJbt25l/fr1sS5HRERERKRIK/YBAqBVq1ZA5lYIERERERE5fiUiQLRp0waABQsWxLgSEREREZGirUQEiNatWwMKECIiIiIi+VUiAkRwC8TChQtx9xhXIyIiIiJSdJWIAFGzZk2qVavGzp07WbNmTazLEREREREpskpEgDCz0FYIHUgtIiIiInL8SkSAAB0HISIiIiJSEEpMgNAWCBERERGR/CsxASJ4LYhFixaRnp4e42pERERERIqmEhMgqlWrRq1atdi7dy+rVq2KdTkiIiIiIkVSiQkQ8L/dmFJTU2NciYiIiIhI0VSiAkTnzp0BeOihh1i+fHmMqxERERERKXpKVIC46qqruOCCC9i5cydXXHGFrgkhIiIiIpJHJSpAJCYmMnz4cDp37symTZu4/PLL2bRpU6zLEhEREREpMuIqQJhZiplNDtxvZ2brzGxy4HZ5oP1+M5ttZtPNrENeXyMpKYlXXnmFVq1asWbNGs4//3wmTZpUwO9ERERERKR4Sox1AUFm9jfgSmBvoKkd8B93fzKsTzvgbCAFqAu8B5yR19eqUKECY8eOZeDAgcyePZsBAwbwhz/8gS5dupCUlESFChXo3Lkz5cuXz/f7EhEREREpTszdY10DAGZ2CbAQeN3dzzSz/wJNyQw5PwC3AX8Gyrv7o4HnzAPOd/ctua03OTnZJ0yYkONjGRkZvPzyyzzyyCMcOHAgy2N16tTh0UcfpVu3bketu0aNGpG+RRERERGReGYRdYqXAAFgZg2ANwMB4s/AQnefY2b/BCoBO4Ft7v7fQP+pwDXuvjLbegYDgwHq1avXftasWUd93Z9++olXX32VnTt3cuDAAZYtW8b3338PwB/+8AcuvPBCSpcuTVJSEmeccQblypULPVcBQkRERESKiSIfICq6+85AewvgWeAjIMndhwba5wHd3X1rbus82haI3KSlpTFy5Egef/zxI7ZMXHrppTzzzDOhZQUIERERESkmIgoQcXUQdTaTwg6S7gbMAaYBF5hZgpnVAxKOFh6OV2JiIjfccAOTJ0+mf//+9OjRg/POO4+EhAQ+/PBDtmzJdY8pEREREZFiLW4Oos7BX4DnzOwQsBEY7O67zewbYAaZ4efGwiygfv36PP7446HlAQMGMGnSJMaMGcPtt99emC8tIiIiIhKX4moXpsJwPLsw5WbKlClcccUV1KpVi1mzZpGYmKhdmERERESkuCjyuzDFnbPOOovGjRuzfv16XTtCREREREokBYg8SEhI4OqrrwZg9OjRsS1GRERERCQGFCDy6LLLLqNcuXJ8++23rFixItbliIiIiIhEVTwfRB2XTj75ZC655BLGjBnD3//+d84991zKli1L7dq16devX5ZrRIiIiIiIFDc6iPo4LF26NMcrVDdo0IAnnniCiy++GLOIjkEREREREYkXRe9CcoWhMAIEwNdff833339PmTJl2L9/Px999BGLFi0C4JxzzuHZZ5/ltNNOK/DXFREREREpJAoQUHgBIih4Gte0tDRGjRrFv/71L7Zt20aZMmV48sknufHGG7U1QkRERESKAp3GNZoSExO5/vrrWbFiBYMGDeLQoUPcfPPNXHzxxWzfvj3W5YmIiIiIFAgFiAJWuXJlRo4cydtvv81JJ53Ehx9+SOvWrXnttddIT0+PdXkiIiIiIvmiAFFILr30UubPn0+HDh1Yu3YtV199Na1bt+bjjz+muO82JiIiIiLFlwJEIWrYsCHTp09n9OjR1KtXjyVLlnDRRRdx/fXXk5aWFuvyRERERETyTAGikJUqVYqrr76aFStW8NRTT5GUlMTIkSO5+OKL2bdvX6zLExERERHJEwWIKClbtiy33XYbX3zxBZUqVWL8+PF069aNrVu3xro0EREREZGIKUBEWefOnZk2bRr16tVj5syZnH766QwdOpTdu3fHujQRERERkWNSgIiB5s2bM2PGDFJSUti4cSN///vfqVevHvfccw+bN2+OdXkiIiIiIrlSgIiRWrVqMWPGDCZOnMjZZ5/Nrl27ePTRR2nWrBljxozRmZpEREREJC7FTYAwsxQzmxy438bMvjGzyWY2ycxOCbQ/Y2ZzAu2TzezkmBadT2ZGjx49mDx5MjNnzqR79+7s2LGDK6+8kt///vesW7cu1iWKiIiIiGQRFwHCzP4GjAKSAk3DgJvd/RzgfeDvgfZ2wAXufk7gtivqxRaSlJQUJk2axCuvvMLJJ5/MhAkTaNmyJePGjYt1aSIiIiIiIXERIIBVwMVhy1e4+/zA/UTggJklAKcCI81smpldk9vKzGywmaWaWeqWLVsKr+oCZmYMGDCApUuX0rt3b3bt2kW/fv0YNGiQTvkqIiIiInEhLgKEu78HHA5b3gBgZp2Am4CngBOAZ4H+QA/gBjNrlcv6Rrp7srsnV6tWrbDLL3C1atXi448/ZsSIESQlJTFq1ChSUlJYtmxZrEsTERERkRIuLgJETszscmA4cKG7bwH2AcPcfZ+7/wp8BbSOZY2FycwYPHgws2bNomnTpixevJi2bdty991365SvIiIiIhIzcRkgzKw/mVseznH31YHm3wDfmlkpMysNdAHmxqrGaGnVqhWpqalcc801HDx4kMcee4wmTZowYsQI0tLSYl2eiIiIiJQwcRcgzKwU8AxQAXg/cLalB919GTAWmAlMAV5z9yUxLDVqTjzxRF566SVmzZpF586d2bJlC9dffz1t2rRh0qRJsS5PREREREoQK+7XG0hOTvYJEyYU2vpr1KhRaOvOibvz3nvv8be//Y0ff/wRgJ49e/LEE0/QokWLqNYiIiIiIsWKRdIp7rZAyNGZGX/84x9ZunQpQ4cO5aSTTmLixIm0atWKG264gaJ01ikRERERKXoUIIqopKQk7rrrLlauXMkNN9wAwH//+1+aNGnCU089xeHDh4+xBhERERGRvFOAKOKqVavG888/z8KFC+nZsye7d+/mr3/9K61bt+bpp59m7dq1sS5RRERERIoRBYhiokWLFnz66aeMHz+exo0bs2zZMm6//Xbq1q1L586defrpp9m4cWOsyxQRERGRIk4Bopjp3bs3S5Ys4c033+SSSy4hKSmJ6dOnc/vtt1OvXj369evHrFmzYl2miIiIiBRROgtTPkX7LEx5tWfPHj755BPeeOMNxo8fT0ZGBgAdOnTglltu4dJLL6VMmTIxrlJERERE4kBEZ2FSgMineA8Q4dasWcMLL7zAiy++yI4dO4DM+q+77jquv/76IvVeRERERKTAKUCAAkRO9u3bx7hx4xg2bBiLFy8GoFSpUnTu3JkLL7yQCy+8kBYtWmAW0e+QiIiIiBQPChCgAHE07s6UKVMYNmwY48ePJz09PfRY/fr16d27N/379yclJUVhQkRERKT4U4AABYhI7dy5k88//5xPPvmEiRMnZrkgXdOmTRkwYABXXnkltWvXjmGVIiIiIlKIFCBAAeJ4pKenk5qayttvv83YsWPZtGkTAAkJCXTv3p0BAwZw0UUXUa5cuRhXKiIiIiIFSAECFCDy6/Dhw0yaNIlXX32Vjz/+mEOHDgFw8sknc8kll9CtWze6du1KnTp1YlypiIiIiOSTAgQoQBSkbdu28eabbzJ69GhSU1OzPNaoUSO6du3K2WefTdeuXWnYsKGOmxAREREpWmITIMzsBOCAu6cfs3MUKEAUjiVLlvDJJ58wdepUvvnmG3bv3p3l8Tp16tC1a9dQqGjatKkChYiIiEh8i06AMLME4AqgH3AGcBAoC2wBPgVGuvsP+XqRfFCAKHzp6eksXLiQKVOmMGXKFKZOncr27duz9KlevXqWLRSnnXYaCQm6ELqIiIhIHIlagJgCfAF8BCx294xAe2XgXKAv8IG7j4lgXSnAY+5+jpk1AUYDDiwGbnT3DDO7H7gQSANuc/fZR1unAkT0ZWRksHTpUqZOnRoKFcEDsYMqVapEt27d6NmzJz169KBWrVoxqlZEREREAqIWIEq7++EC6PM34Epgr7ufaWYfA/9x98lmNhyYBKwBngC6AXWB99z9jKOtVwEi9tydH374IUug+OWXX7L0OfXUU0lJSaFDhw6kpKTQunVrypYtG6OKRUREREqk6B4DYWZfAk+6+6dhbSPdfXCEz78EWAi8HggQ64A67u5mdhFwPvA9UN7dHw08Zx5wvrtvyW29ChDxafXq1Xz22WdMnDiRr776in379mV5vEyZMrRt2zYUKFJSUmjcuLGOoxAREREpPFEPEKuBX4Cv3P3BQNtcd2+Xh3U0AN4MBIj17l4r0P5b4BpgObDN3f8baJ8KXOPuK7OtZzAwGKBevXrtZ82ald+3lysFiPw7fPgwixYtYtasWcyaNYvZs2ezbNmyI/pVrlw5FCg6dOhAhw4dqFq1agwqFhERESmWoh4g5gIdgGfI3L2oP/B1PgLEWnevE2i/COgOrACS3H1ooH0e0N3dt+a2Tm2BKJp27drFd999x+zZs0PBIvtxFACNGzcmJSWFs846i7POOovmzZvr4GwRERGR4xP1ADHP3dsG7g8A7gAqBUNAhOtowP8CxHgyd4kKHgPxNbASGEpmmKgDjHf31kdbpwJE8eDu/PLLL6EtFLNmzWLOnDlH7PpUunRpateuTd26dalTpw516tShfv36tGnThjZt2nDCCSfE6B2IiIiIxL2IAkRiAb7g8OAddx9tZouAG/OxvjuAF82sDLAMeNfd083sG2AGkJDP9UsRYmbUq1ePevXqcemllwKQlpbG4sWLmTFjBlOnTuXbb79l7dq1/PTTT/z0009HrCMhIYHmzZuTnJxMcnIynTp1olWrViQmFuR/AxEREZHiTVeizidtgYgv+/fvZ+3ataxdu5ZffvmFtWvXsnLlSubOncvixYtJT896fcMTTzyRlJQUzjzzTBo0aEDt2rWpVasWtWvXpkqVKjpoW0REREqSqJ3G9Vkyr9WQI3e/JV8vkE8KEBK0f/9+Fi5cyJw5c5g1axbTpk1j1apVufYvU6YM9evXp3Xr1rRp0yb0b+3atRUsREREpDiKWoC4OmzxQeD+8Mfd/dV8vUA+KUDI0WzcuJHp06czZ84c1q1bx/r160O3HTt25PicqlWrcsYZZ4TOBNWuXTtOOeUUhQoREREp6qJ7EDVkPZA6XihAyPHat28fP/zwAwsWLGD+/PnMnz+fBQsWsH379iP6li5dmho1alCzZk1q1qxJnTp1aN26NcnJybRs2ZIyZcrE4B2IiIiI5ElMAkServsQDQoQUpDcnTVr1jB79uzQbdGiRezcuTPX55QtW5bWrVvTrl076tevT40aNbLctPVCRERE4oQCBChASHTs37+fDRs2hG4//fQTc+fOZc6cOaxYseKoz61YsSLt27cnOTk59G+DBg0UKkRERCTaonYMxK/87yDq8kDwxPwGuLuflK8XyCcFCIm1Xbt2MXfuXBYsWMD69evZuHFj6LZu3bocd4mqXLly6HSzwVBRt25dhQoREREpTNHfAhGPFCAknrk769evJzU1ldTUVObMmcN3333H1q1HXly9SpUqnHrqqTRp0iT0b/BWuXLlGFQvIiIixUzUtkCYH2MlkfQpLAoQUtQEr7odDBTBcJHTloqgSpUqZQkUwZBx+umnc+KJJ0axehERESnCohYgJgPvAR+5+89h7WWALsDVwNfuPjpfL3ScFCCkOAhuqVi5cmWOtz179uT4PDOjadOmtGvXjvbt29OuXTvatm3LySefHOV3ICIiIkVA1AJEEnAN0A9oCOwEkoBSwOfA8+4+P18vkg8KEFLcuTubN28+IlQsX76cJUuWcPjw4SOe07hx41CgCN6qVKkSg+pFREQkjsTkLEylgarAfnfP/byWUaQAISXZwYMHWbx4ceiMUHPnzmXhwoUcPHjwiL7169enXbt2oQO327dvT9WqVWNQtYiIiMSIDqIGBQiR7A4fPsyyZctCgWLu3LnMnz+fffv2HdG3du3atGnTJnRr27YtjRo10tmgREREiicFCFCAEIlEeno633//feig7Tlz5jB//nz27t17RN+aNWvStWtXzj77bM4++2yaN2+uQCEiIlI8KECAAoTI8crIyGDVqlXMnz8/dPvuu+/YsmVLln5Vq1ala9eudO7cmY4dO9K2bVuSkpJiVLWIiIjkQ2wDhJmVAq5w97GF8gIRUoAQKTjuzvLly5kyZUrotmHDhix9SpcuTdu2benYsSNdunThvPPOo2LFijGqWERERPIgamdhOgm4EagNfAz8P+Am4E5gvrtflI91DwAGBBaTgDZAX+Bx4JdA+/3uPiW3dShAiBQed2fVqlVMmTKFmTNnMnPmTJYsWUL450qpUqXo1KkTvXr1olevXpx++una5UlERCQ+RS1AfATsAGYA3YBKQBng1oI8fauZPQ8sAOoB89z9vUiepwAhEl27d+9m9uzZzJgxg6+++opvv/2WtLS00OO1a9cOhYlu3bpRoUKFGFYrIiIiYaIWIBa5++mB+6WArUA9d/81XyvO+hrJwBPufo6ZTQQygJOA2cDf3T0tt+cqQIjE1q5du/jiiy/49NNP+fTTT9m4cWPosdKlS9O1a1d69uxJr169aNasmbZOiIiIxE7UAsRcd2+X23JBMLP3gWfd/Wsz+yvwIfAjMBxY5O7PZes/GBgMUK9evfazZs0qyHKyUIAQiZy7M3/+fD799FMmTpzIjBkzyMjICD3eoEGD0NaJc889l/Lly8ewWhERkRInagEiHQie69GAcsC+wH1395Pyuf6KwHR3bxFcDl6kzsx6AZe4+8Dcnq8tECLxa/v27Xz++eehQLF169bQY2XLluXcc8+lV69e9OzZkyZNmsSwUhERkRIhagGivruvyddKjr7+3wPnufstlrlvwxqgk7uvNbMngVXu/kJuz1eAECkaMjIySE1NDe3qlJqamuVg7GbNmtG3b1/69etHo0aNYlipiIhIsRX9XZjM7D13vyRfKzxy/XcBh9396cDy+cDDwH5gKXCLux/O7fkKECJF0+bNm5k0aRKffvopkyZNYseOHaHHOnXqRP/+/bnsssuoUqVKDKsUEREpVqIWIOa5e9vs9+OFAoRI0ZeWlsZXX33FmDFjeP/990NXyE5MTKRnz57079+f3/3ud5QrVy7GlYqIiBRpEQWIhAJ4Ic/lvohIgUhMTOT888/ntddeY9OmTYwdO5YePXqQkZHB+PHjufzyy6lRowYDBw5k8uTJWQ7MFhERkYJVkAdRhx9ADQV0EHV+aQuESPG1adMm3nrrLV5//XVSU1ND7XXr1qV///5cd9111K9fP4YVioiIFCnR2YUp3ilAiJQMy5cvZ8yYMYwZM4Y1azLP65CQkECfPn245ZZb6Nq1q64xISIicnRR24VJRCTmmjVrxsMPP8zq1auZMmUK/fr1o1SpUrz//vucc845tG3blpdffpkDBw7EulQREZEiTQFCRIqVhIQEunbtGtoScd9991G9enUWLFjAwIEDqVu3Lv/85z9Zt25drEsVEREpkhQgRKTYqlmzJg8++CA///wzr776Ku3atWPr1q0MGTKEBg0acMUVVzBjxgyK+66cIiIiBUkBQkSKvbJly3LVVVeRmprKt99+y2WXXYa789Zbb9GpUyc6dOjAG2+8QVpaWqxLFRERiXtkVLp2AAATp0lEQVQKECJSYpgZnTt35q233uLHH3/knnvuoUqVKqSmptK3b1+aNGnCsGHD2LNnT6xLFRERiVsKECJSItWtW5chQ4bwyy+/MGLECH7zm9+wZs0abrvtNurVq8c///lPNm7cGOsyRURE4o4ChIiUaOXKlWPw4MEsW7aMDz74gE6dOrFjxw6GDBlC/fr1GTRoEMuXL491mSIiInFDAUJEhP9dM2LatGlMmzaNPn36cPjwYUaNGkWLFi247LLLWLhwYazLFBERiTkFCBGRbDp16sQHH3zAsmXLGDx4MKVLl+add96hdevW/OEPf2Du3LmxLlFERCRmFCBERHLRtGlTRowYwerVq7n11ltJSkriww8/pH379vz+979XkBARkRJJAUJE5Bhq167N008/zY8//sidd95J+fLlGT9+PO3bt6dPnz7Mnz8/1iWKiIhEjQKEiEiEatSoweOPP86PP/7IHXfcQbly5fjoo49o27Ytl1xyCYsWLYp1iSIiIoVOAUJEJI+qV6/OE088werVq7n99ttJSkri/fffp1WrVlx66aUsXrw41iWKiIgUmrgPEGY2z8wmB26vmNmZZjbLzKaZ2f2xrk9ESq4aNWrwn//8h9WrV3PLLbdQtmxZ3n33XVq1asXll1/O0qVLY12iiIhIgYvrAGFmSQDufk7g9mdgONAX6AKkmFm7WNYoIlKzZk2GDRvGqlWruOmmmyhdujRvv/02p512Gn379tV1JEREpFiJ6wABtAbKm9nnZvaVmXUFyrr7Knd3YBLQLbYliohkql27Ns8++yyrVq3ihhtuIDExkTfeeIOWLVty5ZVXsmLFiliXKCIikm/xHiD2AU8AFwDXA68E2oJ+BU7O/iQzG2xmqWaWumXLlqgUKiISVKdOHZ5//nlWrlzJddddR6lSpRgzZgzNmzfn6quvZuXKlbEuUURE5LjFe4BYAYzxTCuAXUDlsMcrADuzP8ndR7p7srsnV6tWLUqliohkVa9ePYYPH84PP/zAoEGDSEhI4LXXXqNZs2b8+c9/ZvXq1bEuUUREJM/iPUBcAzwJYGa1gPLAXjNrbGZG5paJb2JYn4jIMdWvX5+RI0eyYsUKrrnmGgBGjx7Nb37zG6699lp++umn2BYoIiKSB/EeIF4CKprZt8BbZAaKa4GxwGxgnrvPimF9IiIRa9iwIS+99BLff/89AwYMAOCll17i1FNPZfDgwaxZsya2BYqIiETAMo9FLr6Sk5N9woQJhbb+GjVqFNq6RaR4W7lyJQ899BBjxowhIyOD0qVLc8011/CPf/yDevXqxbo8EREpeSySTvG+BUJEpNhq0qQJr776KkuXLqVfv36kp6czYsQImjRpwo033sjatWtjXaKIiMgRFCBERGKsadOmjBkzhiVLlvCnP/2JtLQ0XnjhBRo3bszNN9/M+vXrY12iiIhIiAKEiEicaNasGePGjWPx4sVcdtllHD58mOeee45GjRpx6623smHDhliXKCIiogAhIhJvWrRowVtvvcXChQv54x//yMGDB3nmmWdo1KgRf/3rX9m0aVOsSxQRkRJMAUJEJE6ddtppvPPOOyxYsICLL76YAwcO8NRTT9GwYUPuvPNONm/eHOsSRUSkBFKAEBGJc61ateK9995j3rx5XHTRRezfv58nn3yShg0b8re//Y0tW7bEukQRESlBFCBERIqINm3a8OGHHzJnzhx+97vfsW/fPh5//HEaNmzIPffcw7Zt22JdooiIlAAKECIiRUy7du34+OOPmT17Nr169WLv3r08+uijNGjQgH/+859s37491iWKiEgxpgAhIlJEnXHGGXzyySfMnDmTHj16sGfPHoYMGUKDBg144IEH+PXXX2NdooiIFEMKECIiRVxKSgoTJ05k+vTpnH/++fz66688+OCDNG7cmOeee45Dhw7FukQRESlGFCBERIqJjh07MmnSJKZOnUrHjh3ZsmULN998M82bN+eNN94gIyMj1iWKiEgxoAAhIlLMnHXWWUybNo0PPviAZs2asXr1avr27UtycjKLFy+OdXkiIlLEKUCIiBRDZkafPn1YtGgRo0aNonbt2sybN48//elPpKWlxbo8EREpwhQgRESKscTERAYOHMj3339Po0aNWLx4McOHD491WSIiUoQpQIiIlAAnnHACTz75JAD33XcfW7dujXFFIiJSVMVtgDCz0mb2upl9Y2azzez3ZtbOzNaZ2eTA7fJY1ykiUlRcdNFFnHfeeezYsYN777031uWIiEgRFbcBAugPbHP3s4CewHNAO+A/7n5O4PZWTCsUESlCzIxhw4ZRqlQpRo4cyYIFC2JdkoiIFEHxHCDeAcK/IksD2gMXmtlUM3vJzCrEpjQRkaKpRYsW3HTTTWRkZHDLLbfg7rEuSUREipi4DRDuvsfdfw2EhHeBfwGzgbvcvSuwGrg/p+ea2WAzSzWz1C1btkSvaBGRIuCBBx6gatWqTJ06lXfeeSfW5YiISBETtwECwMzqAl8Dr7v7OOADd58TePgDoG1Oz3P3ke6e7O7J1apVi1K1IiJFQ8WKFXnkkUcAuPPOO9m3b1+MKxIRkaIkbgOEmZ0CfA783d1fDjRPMrMOgfvdgDk5PllERI5q4MCBtG3bll9++YXHHnss1uWIiEgRErcBAvgHUAm4N3jWJeCvwNOB+52Bh2NXnohI0VWqVCmeeeYZAIYOHcqaNWtiXJGIiBQVcRsg3P1Wd68Rdsalc9x9mrt3Cty/wt13x7pOEZGiqkuXLvzpT3/iwIED3HnnnbEuR0REioi4DRAiIlL4hg4dSvny5Xn33Xf5+uuvY12OiIgUAQoQIiIlWJ06dbjnnnsAuOWWW0hLS4txRSIiEu8UIERESrg777yThg0bsnjxYkaMGBHrckREJM4pQIiIlHBJSUk8+eSTANx7771s27YtxhWJiEg8U4AQERH69OlDt27d2LFjB/fee2+syxERkTimACEiIpgZw4YNo1SpUowYMYIFCxbEuiQREYlTChAiIgJAy5YtufHGG8nIyODWW2/F3WNdkoiIxCEFCBERCXnggQeoUqUKU6ZM4d133411OSIiEocUIEREJKRSpUo88sgjANxxxx3s27cvxhWJiEi8SYx1ASIiEl+uvfZahg8fzvz58xk6dCgPPPBArEuSPEhLS2P//v3s37+fffv2HfFvTm2R/Hvw4EEqVqxI1apVj3mrUKECZhbroRCRQmLFfR/X5ORknzBhQqGtv0aNGoW2bhGRWPnmm2/o2rUrSUlJLF++nPr168e6pCItPT39uCbtx/Pv4cOHY/12KV26NFWrVqVatWoRBY6qVatSrly5WJctIhBR8tcWCBEROcJZZ53FFVdcwZtvvsldd93F22+/HeuSClxwUh+Nif2hQ4ei9r4SEhIoV64c5cuXL9B/y5Qpw65du9i6desxb3v27GHDhg1s2LAh4rrLly9/1ICRPYxUqVKF0qVLF+JIikhutAUin7QFQkSKq19++YWmTZuyf/9+vvrqK84999xCf82MjIxCmdTn1BbNSb2Z5XsSn5eJfqx3H9q/fz/btm2LKGxs3bqVLVu2HNfP4+STT454C0fVqlWpVKkSpUqVKoR3LFJsRPThoQCRTwoQIlKcPfTQQ9x3332cfvrpfPnllxw4cKBQv60/ePBgVN9fQU3aj/Vv2bJlYz6pj2fuzt69e3MNFzm1b9u2jfT09Dy9jplRuXLlUKAYM2YMDRo0KJw3JVI0KUCAAoSISH7s37+fFi1a8NNPP0XtNcuVK1cou+Bk/zcpKUmT+iIsIyMj4l2qgrft27dnWce6deuoVatWjN6BSFzSMRAiIpI/5cqVY/jw4fTr14+MjIyoTOoTEnSGcTm2hIQEKlWqRKVKlTj11FMjek5aWhrbt28PBYrq1asXcpUixVORCxBmlgC8ALQGDgLXuvvK2FYlIlJ8XXDBBWzdujXWZYjkW2JiItWrV1dwEMmnovg1Tx8gyd07AncDT8a4HhERERGREqMoBoguwGcA7j4TSM7ewcwGm1mqmaVu2bIl2vWJiIiIiBRbRTFAnATsCltON7Msu2K5+0h3T3b35GrVqkW3OhERERGRYqwoBojdQIWw5QR3T4tVMSIiIiIiJUlRDBDTgF4AZnYmsCi25YiIiIiIlBxF7ixMwAdAdzObTua5av8c43pEREREREqMYn8hOTPbAuwFdA7C6KmKxjvaNObRpfGOPo15dGm8o09jHn0a8yNtdfcex+pU7AMEgJmluvsRZ2uSwqHxjj6NeXRpvKNPYx5dGu/o05hHn8b8+BXFYyBERERERCRGFCBERERERCRiJSVAjIx1ASWMxjv6NObRpfGOPo15dGm8o09jHn0a8+NUIo6BEBERERGRglFStkCIiIiIiEgBUIAQEREREZGIFdsAYWYJZjbczGaY2WQzaxLrmooLMyttZq+b2TdmNtvMfm9mTczs20Dbf80sIdD3/kCf6WbWIda1F3VmVt3MfjGzZhrzwmVm9wQ+P+aY2UCNd+EKfK6MC4zjN/odL1xmlmJmkwP3Ix7n3PrK0WUb7zaB8ZtsZpPM7JRA+yAzSzWzmWbWO9BW1cw+D/R/y8zKx/BtFCnhYx7W1tfMZoQta8yPU3H+j98HSHL3jsDdwJMxrqc46Q9sc/ezgJ7Ac8B/gH8F2gy4yMzaAWcDKcAVwPMxqrdYMLPSwAhgf6BJY15IzOwcoBPQmczxrIvGu7D1AhLdvRPwb+ARNOaFwsz+BowCkgJNeRnnI/pGs/aiKIfxHgbc7O7nAO8DfzezGsAtZH7mXAD8n5mVBe4DxgXGex5wXZTLL5JyGHPMrA0wkMzfWzTm+VOcA0QX4DMAd58J6EIhBecd4N6w5TSgPTAlsDwROI/Mn8HnnulnINHMqkW10uLlCWA4sD6wrDEvPBcAi4APgPHABDTehW0FmeOXAJwEHEZjXlhWAReHLedlnHPqK0eXfbyvcPf5gfuJwAGgAzDN3Q+6+y5gJdCKsLkMGu+8yDLmZlYFeBS4LayPxjwfinOAOAnYFbacbmaJsSqmOHH3Pe7+q5lVAN4F/kXmGb2Cp/T6FTiZI38GwXbJIzMbAGxx90nhzRrzQlOVzC8dLgWuB8YCCRrvQrUHaAAsB14EnkG/44XC3d8jM6AF5WWcc+orR5F9vN19A4CZdQJuAp4i9/EOb9d4Ryh8zM2sFPAScDuZYxikMc+H4hwgdgMVwpYT3D0tVsUUN2ZWF/gaeN3dxwEZYQ9XAHZy5M8g2C55dw3QPbA/ZxvgNaB62OMa84K1DZjk7ofc/XsyvyEM/yOi8S54t5M55r8BWgOvAmXCHteYF568fH7n1FfyyMwuJ3OL8oXuvoXcxzu8XeN9fNoDpwL/Bd4EWpjZ02jM86U4B4hpZO5Ti5mdSebuCFIAAgd8fQ783d1fDjTPC+w3DpnHRXxD5s/gAss8oL0emSFua9QLLgbcvau7nx3YZ3Y+cBUwUWNeaL4FelimWsAJwJca70K1g/9967cdKI0+V6IlL+OcU1/JAzPrT+aWh3PcfXWgeTZwlpklmdnJQHNgMWFzGTTex8XdZ7t7y8DfzyuApe5+GxrzfCnOu/R8QOY3ttPJPGDmzzGupzj5B1AJuNfMgsdC3Ao8Y2ZlgGXAu+6ebmbfADPIDKs3xqTa4usO4EWNecFz9wlm1pXMPzDBcfwRjXdhegp4OTCeZcj8nElFYx4NefksOaJvLAouqgK70zwD/Ay8b2YAU9z9fjN7hszJagLwT3c/YGYPA6+a2SBgK9A3RqUXO+6+UWN+/HQlahERERERiVhx3oVJREREREQKmAKEiIiIiIhETAFCREREREQipgAhIiIiIiIRU4AQEREREZGIKUCIiIiIiEjEFCBERERERCRiChAiInIEM6toZjeELU8vpNcpZ2ZTAhfYys96ypjZVDMrzhdIFRGJCwoQIiKSk4pAKEC4e6dCep1rgPfdPT0/K3H3Q8CXwOUFUpWIiORKAUJERHLyKNDYzOab2eNmtgfAzBqY2XIzG2Vmi81srJmdZ2bTzOwHM+sQXIGZ9Tez2YF1jMhlK0M/4KO8rNvMTjCzT8xsQaBfMDR8GFifiIgUIgUIERHJyd3AKndv4+53ZXusCTAMaAU0A/oCXYA7gX8AmFlzMrcGdHb3NkA62Sb3ZlYGaOTuP+Vl3UAPYL27t3b304DPAu2LgTPy97ZFRORYFCBERCSvfnT3Re6eASwBvnR3BxYBDQJ9ugHtge/MbH5guVG29VQFdh7HuhcB55nZY2Z2lrvvAgjsBnXIzCoU4HsVEZFsdLCZiIjk1cGw+xlhyxn87++KAa+6+z1HWc9+ICmv63b3FWbWHugF/J+Zfe7u/w70KwscyMN7ERGRPNIWCBERycmvQH6+yf8S+KOZVQcws8pmVj+8g7vvAEqZWfYQcVRmVgvY5+5jgCeAdoH2KsAWdz+cj7pFROQYtAVCRESO4O7bAgcvLwYmHsfzl5rZv4DPzSwBOAzcCKzJ1vVzMo9x+CIPqz8deNzMMgLr/Uug/Vzg07zWKiIieWOZu5aKiIhEn5m1Bf7q7lcWwLreB+5x9+/zX5mIiORGuzCJiEjMuPs84OuCuJAc8KHCg4hI4dMWCBERERERiZi2QIiIiIiISMQUIEREREREJGIKECIiIiIiEjEFCBERERERiZgChIiIiIiIREwBQkREREREIvb/AebCMmpDVh9fAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ - "for Vm0 in [-58, -60, -65, -70., -75., -80.0]:\n", - " fig = runAndPlot(pneuron, None, None, 50., 0.05, 1.5, Vm0=Vm0)" + "for Vm0 in [-58., -60., -65., -70., -75., -80.]:\n", + " pneuron.Vm0 = Vm0\n", + " data, meta = pneuron.simulate(50., 0.05, 1.5)\n", + " fig = GroupedTimeSeries([(data, meta)], pltscheme={'Q_m': ['Qm'], 'FR': ['FR']}).render()[0]\n", + " title = fig.get_axes()[0].get_title()\n", + " fig.get_axes()[0].set_title(f'{title} - Vm0 = {pneuron.Vm0} mV')\n", + "pneuron.Vm0 = standard_Vm0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Despite the slight difference of implementation, we still observe the presence of a burst of spikes that start s at the onset of the depolarizing pulse and then outlasts the stimulus duration.\n", "\n", "We also notice that the duration of this burst seems to effectively depend on the initial value of membrane potential, with an optimum of approx. -70 mV at which the burst duration is maximal. This corroborates with the voltage-dependency of plateau potential generation observed in Otsuka 2004." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rebound bursting after hyperpolarizing pulses" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-58.0\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ + "print(pneuron.Vm0)\n", "for A in [5., -20., -60.]:\n", - " fig = runAndPlot(pneuron, None, None, A, 0.2, 1.8)" + " data, meta = pneuron.simulate(A, 0.2, 1.8)\n", + " fig = GroupedTimeSeries([(data, meta)], pltscheme={'Q_m': ['Qm'], 'FR': ['FR']}).render()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also observe the generation of rebound bursts at the offset of short hyperpolarizing pulses. Again, the burst spike rate and duration is dependent on the intensity of the hyperpolarizing pulse." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Typical response to US CW stimulation" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = runAndPlot(pneuron, 32e-9, 500e3, 20e3, 0.15, 0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I = 10 W/m2 (A = 5.58 kPa)\n", "I = 110 W/m2 (A = 18.51 kPa)\n", "I = 115 W/m2 (A = 18.93 kPa)\n", "I = 127 W/m2 (A = 19.89 kPa)\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ + "a = 32e-9 # m\n", + "Fdrive = 500e3 # Hz\n", + "nbls = NeuronalBilayerSonophore(a, pneuron)\n", "for I in [10, 110, 115, 127]:\n", " A = Intensity2Pressure(I, rho=1028.0)\n", - " print('I = {:.0f} W/m2 (A = {:.2f} kPa)'.format(I, A * 1e-3))\n", - " fig = runAndPlot(pneuron, 32e-9, 500e3, A, 1.0, 0.0)" + " print(f'I = {I:.0f} W/m2 (A = {(A * 1e-3):.2f} kPa)')\n", + " data, meta = nbls.simulate(Fdrive, A, 1.0, 0.0)\n", + " fig = GroupedTimeSeries([(data, meta)], pltscheme={'Q_m': ['Qm'], 'FR': ['FR']}).render()[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We observe a transition between different spiking regimes as a function of acoustic intensity:\n", "- for small intensities (10 to 110 W/m2), the acoustic simulus simply excites the neuron at a constant firing rate, higher than its spontaneous physiological counterpart. Within this regime, increasing acoustic intensity simply raises the obtained firing rate.\n", "- for intermediate intensities (115 W/m2), the acoustic stimulus triggers a neural response in which the firing pattern evolves in time, with spikes increasing in frequency and decrease in amplitude.\n", "- for high intensities (127 W/m2), the acoustic stimulus triggers a few spikes and then a slienced plateau potential.\n", "\n", "All those observations are qualitatively similar to those of Tarnaud 2018 for the same intensities (Fig 1)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/paper figures/utils.py b/paper figures/utils.py index 7c7b82f..db062f3 100644 --- a/paper figures/utils.py +++ b/paper figures/utils.py @@ -1,121 +1,121 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2018-10-01 20:45:29 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-12 23:13:24 +# @Last Modified time: 2019-07-01 09:38:52 import os import numpy as np import pandas as pd from PySONIC.utils import * from PySONIC.core import NeuronalBilayerSonophore from PySONIC.neurons import * from PySONIC.postpro import computeSpikingMetrics def getCWtitrations_vs_Fdrive(neurons, a, freqs, tstim, toffset, fpath): fkey = 'Fdrive (kHz)' freqs = np.array(freqs) if os.path.isfile(fpath): df = pd.read_csv(fpath, sep=',', index_col=fkey) else: df = pd.DataFrame(index=freqs * 1e-3) for neuron in neurons: if neuron not in df: pneuron = getPointNeuron(neuron) nbls = NeuronalBilayerSonophore(a, pneuron) for i, Fdrive in enumerate(freqs): logger.info('Running CW titration for %s neuron @ %sHz', neuron, si_format(Fdrive)) Athr = nbls.titrate(Fdrive, tstim, toffset) # Pa df.loc[Fdrive * 1e-3, neuron] = np.ceil(Athr * 1e-2) / 10 df.sort_index(inplace=True) df.to_csv(fpath, sep=',', index_label=fkey) return df def getCWtitrations_vs_radius(neurons, radii, Fdrive, tstim, toffset, fpath): akey = 'radius (nm)' radii = np.array(radii) if os.path.isfile(fpath): df = pd.read_csv(fpath, sep=',', index_col=akey) else: df = pd.DataFrame(index=radii * 1e9) for neuron in neurons: if neuron not in df: pneuron = getPointNeuron(neuron) for a in radii: nbls = NeuronalBilayerSonophore(a, pneuron) logger.info( 'Running CW titration for %s neuron @ %sHz (%.2f nm sonophore radius)', neuron, si_format(Fdrive), a * 1e9) Athr = nbls.titrate(Fdrive, tstim, toffset) # Pa df.loc[a * 1e9, neuron] = np.ceil(Athr * 1e-2) / 10 df.sort_index(inplace=True) df.to_csv(fpath, sep=',', index_label=akey) return df def getSims(outdir, neuron, a, queue): fpaths = [] updated_queue = [] pneuron = getPointNeurons(neuron) nbls = NeuronalBilayerSonophore(a, pneuron) for i, item in enumerate(queue): Fdrive, tstim, toffset, PRF, DC, Adrive, method = item fcode = nbls.filecode(Fdrive, Adrive, tstim, toffset, PRF, DC, method) fpath = os.path.join(outdir, '{}.pkl'.format(fcode)) if not os.path.isfile(fpath): print(fpath, 'does not exist') item.insert(0, outdir) updated_queue.append(item) fpaths.append(fpath) if len(updated_queue) > 0: print(updated_queue) # pneuron = getPointNeuron(neuron) # nbls = NeuronalBilayerSonophore(a, pneuron) - # batch = Batch(nbls.runAndSave, updated_queue) + # batch = Batch(nbls.run, updated_queue) # batch.run(mpi=True) return fpaths def getSpikingMetrics(outdir, neuron, xvar, xkey, data_fpaths, metrics_fpaths): metrics = {} for stype in data_fpaths.keys(): if os.path.isfile(metrics_fpaths[stype]): logger.info('loading spiking metrics from file: "%s"', metrics_fpaths[stype]) metrics[stype] = pd.read_csv(metrics_fpaths[stype], sep=',') else: logger.warning('computing %s spiking metrics vs. %s for %s neuron', stype, xkey, neuron) metrics[stype] = computeSpikingMetrics(data_fpaths[stype]) metrics[stype][xkey] = pd.Series(xvar, index=metrics[stype].index) metrics[stype].to_csv(metrics_fpaths[stype], sep=',', index=False) return metrics def extractCompTimes(filenames): ''' Extract computation times from a list of simulation files. ''' tcomps = np.empty(len(filenames)) for i, fn in enumerate(filenames): logger.info('Loading data from "%s"', fn) with open(fn, 'rb') as fh: frame = pickle.load(fh) meta = frame['meta'] tcomps[i] = meta['tcomp'] return tcomps def getCompTimesQuant(outdir, neuron, xvars, xkey, data_fpaths, comptimes_fpath): if os.path.isfile(comptimes_fpath): logger.info('reading computation times from file: "%s"', comptimes_fpath) comptimes = pd.read_csv(comptimes_fpath, sep=',', index_col=xkey) else: logger.warning('extracting computation times for %s neuron', neuron) comptimes = pd.DataFrame(index=xvars) for stype in data_fpaths.keys(): for i, xvar in enumerate(xvars): comptimes.loc[xvar, stype] = extractCompTimes([data_fpaths[stype][i]]) comptimes.to_csv(comptimes_fpath, sep=',', index_label=xkey) return comptimes diff --git a/scripts/run_astim.py b/scripts/run_astim.py index 0098130..e85ea86 100644 --- a/scripts/run_astim.py +++ b/scripts/run_astim.py @@ -1,44 +1,37 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-02-13 18:16:09 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 13:59:52 +# @Last Modified time: 2019-07-01 15:50:03 ''' Run A-STIM simulations of a specific point-neuron. ''' -import matplotlib.pyplot as plt - from PySONIC.core import NeuronalBilayerSonophore, Batch from PySONIC.utils import logger -from PySONIC.plt import GroupedTimeSeries from PySONIC.parsers import AStimParser def main(): # Parse command line arguments parser = AStimParser() args = parser.parse() logger.setLevel(args['loglevel']) # Run A-STIM batch logger.info("Starting A-STIM simulation batch") - pkl_filepaths = [] - inputs = [args[k] for k in ['freq', 'amp', 'tstim', 'toffset', 'PRF', 'DC', 'fs', 'method']] - queue = NeuronalBilayerSonophore.simQueue(*inputs, outputdir=args['outputdir']) - + output = [] + queue = NeuronalBilayerSonophore.simQueue(*parser.parseSimInputs(args), + outputdir=args['outputdir']) for a in args['radius']: for pneuron in args['neuron']: nbls = NeuronalBilayerSonophore(a, pneuron) - batch = Batch(nbls.runAndSave, queue) - pkl_filepaths += batch(mpi=args['mpi'], loglevel=args['loglevel']) + batch = Batch(nbls.simAndSave if args['save'] else nbls.simulate, queue) + output += batch(mpi=args['mpi'], loglevel=args['loglevel']) # Plot resulting profiles if args['plot'] is not None: - scheme_plot = GroupedTimeSeries(pkl_filepaths, pltscheme=args['pltscheme']) - scheme_plot.render(spikes=args['spikes']) - plt.show() - + parser.parsePlot(args, output) if __name__ == '__main__': main() diff --git a/scripts/run_estim.py b/scripts/run_estim.py index 46a66aa..497a5bc 100644 --- a/scripts/run_estim.py +++ b/scripts/run_estim.py @@ -1,41 +1,35 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-08-24 11:55:07 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-28 15:52:30 +# @Last Modified time: 2019-07-01 15:50:27 ''' Run E-STIM simulations of a specific point-neuron. ''' -import matplotlib.pyplot as plt - from PySONIC.core import Batch, PointNeuron from PySONIC.utils import logger -from PySONIC.plt import GroupedTimeSeries from PySONIC.parsers import EStimParser def main(): # Parse command line arguments parser = EStimParser() args = parser.parse() logger.setLevel(args['loglevel']) # Run E-STIM batch logger.info("Starting E-STIM simulation batch") - pkl_filepaths = [] - inputs = [args[k] for k in ['amp', 'tstim', 'toffset', 'PRF', 'DC']] - queue = PointNeuron.simQueue(*inputs, outputdir=args['outputdir']) + queue = PointNeuron.simQueue(*parser.parseSimInputs(args), outputdir=args['outputdir']) + output = [] for pneuron in args['neuron']: - batch = Batch(pneuron.runAndSave, queue) - pkl_filepaths += batch(mpi=args['mpi'], loglevel=args['loglevel']) + batch = Batch(pneuron.simAndSave if args['save'] else pneuron.simulate, queue) + output += batch(mpi=args['mpi'], loglevel=args['loglevel']) # Plot resulting profiles if args['plot'] is not None: - scheme_plot = GroupedTimeSeries(pkl_filepaths, pltscheme=args['pltscheme']) - scheme_plot.render(spikes=args['spikes']) - plt.show() + parser.parsePlot(args, output) if __name__ == '__main__': main() diff --git a/scripts/run_lookups.py b/scripts/run_lookups.py index da4db00..2736766 100644 --- a/scripts/run_lookups.py +++ b/scripts/run_lookups.py @@ -1,173 +1,173 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-06-02 17:50:10 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-27 14:20:05 +# @Last Modified time: 2019-07-01 10:01:50 ''' Create lookup table for specific neuron. ''' import os import itertools import pickle import logging import numpy as np from PySONIC.utils import logger, isIterable -from PySONIC.core import NeuronalBilayerSonophore, createQueue, Batch +from PySONIC.core import NeuronalBilayerSonophore, Batch from PySONIC.parsers import MechSimParser def computeAStimLookups(pneuron, aref, fref, Aref, Qref, fsref=None, mpi=False, loglevel=logging.INFO): ''' Run simulations of the mechanical system for a multiple combinations of imposed sonophore radius, US frequencies, acoustic amplitudes charge densities and (spatially-averaged) sonophore membrane coverage fractions, compute effective coefficients and store them in a dictionary of n-dimensional arrays. :param pneuron: point-neuron model :param aref: array of sonophore radii (m) :param fref: array of acoustic drive frequencies (Hz) :param Aref: array of acoustic drive amplitudes (Pa) :param Qref: array of membrane charge densities (C/m2) :param fsref: acoustic drive phase (rad) :param mpi: boolean statting wether or not to use multiprocessing :param loglevel: logging level :return: lookups dictionary ''' descs = { 'a': 'sonophore radii', 'f': 'US frequencies', 'A': 'US amplitudes', 'fs': 'sonophore membrane coverage fractions' } # Populate inputs dictionary inputs = { 'a': aref, # nm 'f': fref, # Hz 'A': Aref, # Pa 'Q': Qref # C/m2 } # Check inputs compatibility err_fs = 'cannot span {} for more than 1 {}' if fsref.size > 1 or fsref[0] != 1.: for x in ['a', 'f']: assert inputs[x].size == 1, err_fs.format(descs['fs'], descs[x]) inputs['fs'] = fsref # Check validity of input parameters for key, values in inputs.items(): if not isIterable(values): raise TypeError( 'Invalid {} (must be provided as list or numpy array)'.format(descs[key])) if not all(isinstance(x, float) for x in values): raise TypeError('Invalid {} (must all be float typed)'.format(descs[key])) if len(values) == 0: raise ValueError('Empty {} array'.format(key)) if key in ('a', 'f') and min(values) <= 0: raise ValueError('Invalid {} (must all be strictly positive)'.format(descs[key])) if key in ('A', 'fs') and min(values) < 0: raise ValueError('Invalid {} (must all be positive or null)'.format(descs[key])) # Get inputs dimensions dims = np.array([x.size for x in inputs.values()]) ncombs = dims.prod() # Create simulation queue per radius - queue = createQueue(fref, Aref, Qref) + queue = Batch.createQueue(fref, Aref, Qref) for i in range(len(queue)): queue[i].append(inputs['fs']) # Run simulations and populate outputs (list of lists) logger.info('Starting simulation batch for %s neuron', pneuron.name) outputs = [] for a in aref: nbls = NeuronalBilayerSonophore(a, pneuron) batch = Batch(nbls.computeEffVars, queue) outputs += batch(mpi=mpi, loglevel=loglevel) # Split comp times and effvars from outputs tcomps, effvars = [list(x) for x in zip(*outputs)] effvars = list(itertools.chain.from_iterable(effvars)) # Reshape effvars into nD arrays and add them to lookups dictionary logger.info('Reshaping output into lookup tables') varkeys = list(effvars[0].keys()) nout = len(effvars) assert nout == ncombs, 'number of outputs does not match number of combinations' lookups = {} for key in varkeys: effvar = [effvars[i][key] for i in range(nout)] lookups[key] = np.array(effvar).reshape(dims) tcomps = np.array(tcomps).reshape(dims[:-1]) # Store inputs, lookup data and comp times in dictionary return { 'input': inputs, 'lookup': lookups, 'tcomp': tcomps } def main(): parser = MechSimParser(outputdir='.') parser.addNeuron() parser.addTest() parser.defaults['neuron'] = 'RS' parser.defaults['radius'] = np.array([16.0, 32.0, 64.0]) # nm parser.defaults['freq'] = np.array([20., 100., 500., 1e3, 2e3, 3e3, 4e3]) # kHz parser.defaults['amp'] = np.insert( np.logspace(np.log10(0.1), np.log10(600), num=50), 0, 0.0) # kPa parser.defaults['charge'] = np.nan args = parser.parse() logger.setLevel(args['loglevel']) for pneuron in args['neuron']: # Determine charge vector charges = args['charge'] if charges.size == 1 and np.isnan(charges[0]): charges = np.arange( pneuron.Qbounds()[0], pneuron.Qbounds()[1] + 1e-5, 1e-5) # C/m2 # Determine output filename f = NeuronalBilayerSonophore(32e-9, pneuron).getLookupFilePath if args['fs'].size == 1 and args['fs'][0] == 1.: lookup_fpath = f() else: lookup_fpath = f(a=args['radius'][0], Fdrive=args['freq'][0], fs=True) # Combine inputs into single list inputs = [args[x] for x in ['radius', 'freq', 'amp']] + [charges, args['fs']] # Adapt inputs and output filename if test case if args['test']: for i, x in enumerate(inputs): if x is not None and x.size > 1: inputs[i] = np.array([x.min(), x.max()]) lookup_fpath = '{}_test{}'.format(*os.path.splitext(lookup_fpath)) # Check if lookup file already exists if os.path.isfile(lookup_fpath): logger.warning('"%s" file already exists and will be overwritten. ' + 'Continue? (y/n)', lookup_fpath) user_str = input() if user_str not in ['y', 'Y']: logger.error('%s Lookup creation canceled', pneuron.name) return # Compute lookups df = computeAStimLookups(pneuron, *inputs, mpi=args['mpi'], loglevel=args['loglevel']) # Save dictionary in lookup file logger.info('Saving %s neuron lookup table in file: "%s"', pneuron.name, lookup_fpath) with open(lookup_fpath, 'wb') as fh: pickle.dump(df, fh) if __name__ == '__main__': main() diff --git a/scripts/run_mech.py b/scripts/run_mech.py index 1d61057..f5d8fe9 100644 --- a/scripts/run_mech.py +++ b/scripts/run_mech.py @@ -1,44 +1,39 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2016-11-21 10:46:56 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-28 15:53:14 +# @Last Modified time: 2019-07-01 15:49:33 ''' Run simulations of the NICE mechanical model. ''' -import matplotlib.pyplot as plt - from PySONIC.core import BilayerSonophore, Batch from PySONIC.utils import logger -from PySONIC.plt import GroupedTimeSeries from PySONIC.parsers import MechSimParser def main(): # Parse command line arguments parser = MechSimParser() args = parser.parse() logger.setLevel(args['loglevel']) # Run MECH batch logger.info("Starting mechanical simulation batch") - pkl_filepaths = [] - inputs = [args[k] for k in ['freq', 'amp', 'charge']] - queue = BilayerSonophore.simQueue(*inputs, outputdir=args['outputdir']) + queue = BilayerSonophore.simQueue(*parser.parseSimInputs(args), outputdir=args['outputdir']) + output = [] for a in args['radius']: for d in args['embedding']: for Cm0 in args['Cm0']: for Qm0 in args['Qm0']: bls = BilayerSonophore(a, Cm0, Qm0, embedding_depth=d) - batch = Batch(bls.runAndSave, queue) - pkl_filepaths += batch(mpi=args['mpi'], loglevel=args['loglevel']) + batch = Batch(bls.simAndSave if args['save'] else bls.simulate, queue) + output += batch(mpi=args['mpi'], loglevel=args['loglevel']) # Plot resulting profiles if args['plot'] is not None: - GroupedTimeSeries(pkl_filepaths, pltscheme=args['pltscheme'])() - plt.show() + parser.parsePlot(args, output) if __name__ == '__main__': main()