diff --git a/PySONIC/core/astim_titrations.log b/PySONIC/core/astim_titrations.log index bfc50a7..dca7ab1 100644 --- a/PySONIC/core/astim_titrations.log +++ b/PySONIC/core/astim_titrations.log @@ -1,24 +1,30 @@ titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.06, 0.05, 100.0, 1.0, 'sonic') 46270.75195312497 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.4, 'sonic') 55572.50976562497 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.6, 'sonic') 38813.78173828122 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.8, 'sonic') 33174.133300781235 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10400000000000001, 0.0, 100.0, 1.0, 'sonic') 40338.13476562497 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.105, 0.0, 100.0, 1.0, 'sonic') 40205.38330078122 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.8, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.6, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 1.0, 'sonic') 19830.322265625 titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 1.0, 0.0, 100.0, 0.2, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 0.2, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 1.0, 0.0, 100.0, 1.0, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, OtsukaSTN), 500000.0, 0.7000000000000001, 0.0, 100.0, 1.0, 'sonic') 19830.322265624985 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 'sonic') 38635.25390624997 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 0.9, 'sonic') 46211.24267578122 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 0.5, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 1.0, 1.0, 'sonic') 38800.04882812497 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 501000.0, 0.1, 0.05, 100.0, 1.0, 1.0, 'sonic') 38800.04882812497 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.13, 1.0, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.11, 1.0, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.1, 0.05, 100.0, 0.34, 1.0, 'sonic') 126544.18945312494 titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.01, 1.0, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.02, 1.0, 'sonic') nan titrate(NeuronalBilayerSonophore(32.0 nm, CorticalFS), 500000.0, 1.0, 0.0, 100.0, 0.03, 1.0, 'sonic') nan +titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10300000000000001, 0.05, 100.0, 1.0, 1.0, 'sonic') 38562.01171874997 +titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.10400000000000001, 0.05, 100.0, 1.0, 1.0, 'sonic') 38488.76953124997 +titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.105, 0.05, 100.0, 1.0, 1.0, 'sonic') 38374.32861328122 +titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.095, 0.05, 100.0, 1.0, 1.0, 'sonic') 39294.43359374997 +titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.065, 0.05, 100.0, 1.0, 1.0, 'sonic') 44567.87109374997 +titrate(NeuronalBilayerSonophore(32.0 nm, CorticalRS), 500000.0, 0.061, 0.05, 100.0, 1.0, 1.0, 'sonic') 45904.54101562497 diff --git a/PySONIC/core/pneuron.py b/PySONIC/core/pneuron.py index b6633fa..3b7be64 100644 --- a/PySONIC/core/pneuron.py +++ b/PySONIC/core/pneuron.py @@ -1,583 +1,577 @@ # -*- 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: 2019-08-14 19:31:06 +# @Last Modified time: 2019-08-22 11:48:37 import abc import inspect import numpy as np import pandas as pd from .batches import Batch from .model import Model from .lookups import SmartLookup from .simulators import PWSimulator -from ..postpro import findPeaks, computeFRProfile +from ..postpro import detectSpikes, computeFRProfile from ..constants import * 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) + 'bounds': (-100, 60) }, 'Vm': { 'desc': 'membrane potential', 'label': 'V_m', 'unit': 'mV', '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' } pltvars['iCap'] = { 'desc': inspect.getdoc(getattr(cls, 'iCap')).splitlines()[0], 'label': 'I_{cap}', 'unit': 'A/m^2', 'factor': 1e-3, 'func': 'iCap({0}t{1}, {0}Vm{1})'.format(wrapleft, wrapright) } 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) + 'func': f'firingRateProfile({wrapleft[:-2]})' } return pltvars @classmethod def iCap(cls, t, Vm): ''' Capacitive current. ''' dVdt = np.insert(np.diff(Vm) / np.diff(t), 0, 0.) return cls.Cm0 * dVdt @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(cls): ''' Dictionary of states derivatives functions ''' raise NotImplementedError @classmethod def getDerStates(cls, Vm, states): ''' Compute states derivatives array given a membrane potential and states dictionary ''' return np.array([cls.derStates()[k](Vm, states) for k in cls.statesNames()]) @classmethod @abc.abstractmethod def steadyStates(cls): ''' Return a dictionary of steady-states functions ''' raise NotImplementedError @classmethod def getSteadyStates(cls, Vm): ''' Compute array of steady-states for a given membrane potential ''' return np.array([cls.steadyStates()[k](Vm) for k in cls.statesNames()]) @classmethod def getDerEffStates(cls, lkp, states): ''' Compute effective states derivatives array given lookups and states dictionaries. ''' return np.array([ cls.derEffStates()[k](lkp, states) for k in cls.statesNames()]) @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 cls.effRates().items()} @classmethod def getLookup(cls): ''' Get lookup of membrane potential rate constants interpolated along the neuron's charge physiological range. ''' 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(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()) + @staticmethod 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 @classmethod def isVoltageGated(cls, state): ''' Determine whether a given state is purely voltage-gated or not.''' return 'alpha{}'.format(state.lower()) in cls.rates @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 += Batch.createQueue(amps, durations, offsets, min(PRFs), 1.0) if np.any(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 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 (-) ''' 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)) @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 = cls.Cm0 Qm, *states = y Vm = Qm / Cm * 1e3 # mV 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.checkTitrate('Astim') @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 = simulator( y0, DT_EFFECTIVE, tstim, toffset, PRF, DC) # Prepend initial conditions (prior to stimulation) t, y, stim = simulator.prependSolution(t, y, stim) # 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] 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[1:2, 't'].values)[0] - ipeaks, *_ = findPeaks( - data['Qm'].values, - SPIKE_MIN_QAMP, - int(np.ceil(SPIKE_MIN_DT / dt)), - SPIKE_MIN_QPROM - ) - return ipeaks.size + return detectSpikes(data)[0].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)) 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/neurons/template.py b/PySONIC/neurons/template.py index 537940d..15adbbe 100644 --- a/PySONIC/neurons/template.py +++ b/PySONIC/neurons/template.py @@ -1,114 +1,114 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2019-06-11 15:58:38 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-08-14 20:05:24 +# @Last Modified time: 2019-08-22 15:19:33 import numpy as np from ..core import PointNeuron class TemplateNeuron(PointNeuron): ''' Template neuron class ''' # Neuron name name = 'template' # ------------------------------ Biophysical parameters ------------------------------ # Resting parameters Cm0 = 1e-2 # Membrane capacitance (F/m2) Vm0 = -71.9 # Membrane potential (mV) # Reversal potentials (mV) ENa = 50.0 # Sodium EK = -90.0 # Potassium ELeak = -70.3 # Non-specific leakage # Maximal channel conductances (S/m2) gNabar = 560.0 # Sodium gKdbar = 60.0 # Delayed-rectifier Potassium gLeak = 0.205 # Non-specific leakage # Additional parameters VT = -56.2 # Spike threshold adjustment parameter (mV) # ------------------------------ States names & descriptions ------------------------------ states = { 'm': 'iNa activation gate', 'h': 'iNa inactivation gate', 'n': 'iKd gate' } # ------------------------------ Gating states kinetics ------------------------------ @classmethod def alpham(cls, Vm): return 0.32 * cls.vtrap(13 - (Vm - cls.VT), 4) * 1e3 # s-1 @classmethod def betam(cls, Vm): return 0.28 * cls.vtrap((Vm - cls.VT) - 40, 5) * 1e3 # s-1 @classmethod def alphah(cls, Vm): return 0.128 * np.exp(-((Vm - cls.VT) - 17) / 18) * 1e3 # s-1 @classmethod def betah(cls, Vm): return 4 / (1 + np.exp(-((Vm - cls.VT) - 40) / 5)) * 1e3 # s-1 @classmethod def alphan(cls, Vm): return 0.032 * cls.vtrap(15 - (Vm - cls.VT), 5) * 1e3 # s-1 @classmethod def betan(cls, Vm): return 0.5 * np.exp(-((Vm - cls.VT) - 10) / 40) * 1e3 # s-1 # ------------------------------ States derivatives ------------------------------ @classmethod def derStates(cls): return { 'm': lambda Vm, x: cls.alpham(Vm) * (1 - x['m']) - cls.betam(Vm) * x['m'], 'h': lambda Vm, x: cls.alphah(Vm) * (1 - x['h']) - cls.betah(Vm) * x['h'], 'n': lambda Vm, x: cls.alphan(Vm) * (1 - x['n']) - cls.betan(Vm) * x['n'] } # ------------------------------ Steady states ------------------------------ @classmethod def steadyStates(cls): return { 'm': lambda Vm: cls.alpham(Vm) / (cls.alpham(Vm) + cls.betam(Vm)), 'h': lambda Vm: cls.alphah(Vm) / (cls.alphah(Vm) + cls.betah(Vm)), 'n': lambda Vm: cls.alphan(Vm) / (cls.alphan(Vm) + cls.betan(Vm)) } # ------------------------------ 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 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), 'iLeak': lambda Vm, _: cls.iLeak(Vm) } diff --git a/PySONIC/plt/actmap.py b/PySONIC/plt/actmap.py index 2564c52..eada876 100644 --- a/PySONIC/plt/actmap.py +++ b/PySONIC/plt/actmap.py @@ -1,293 +1,284 @@ # -*- 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: 2019-08-14 17:47:27 +# @Last Modified time: 2019-08-22 15:28:36 import os import pickle import numpy as np import pandas as pd import matplotlib.pyplot as plt from matplotlib.ticker import FormatStrFormatter from ..core import NeuronalBilayerSonophore from ..utils import logger, si_format, loadData -from ..postpro import findPeaks from ..constants import * from .pltutils import cm2inch, setNormalizer +from ..postpro import detectSpikes class Map: @staticmethod def computeMeshEdges(x, scale): ''' Compute the appropriate edges of a mesh that quads a linear or logarihtmic distribution. :param x: the input vector :param scale: the type of distribution ('lin' for linear, 'log' for logarihtmic) :return: the edges vector ''' if scale == 'log': x = np.log10(x) dx = x[1] - x[0] n = x.size + 1 return {'lin': np.linspace, 'log': np.logspace}[scale](x[0] - dx / 2, x[-1] + dx / 2, n) class ActivationMap(Map): def __init__(self, root, pneuron, a, Fdrive, tstim, PRF, amps, DCs): self.root = root self.pneuron = pneuron self.a = a self.nbls = NeuronalBilayerSonophore(self.a, self.pneuron) self.Fdrive = Fdrive self.tstim = tstim self.PRF = PRF self.amps = amps self.DCs = DCs self.Ascale = self.getAmpScale() self.title = '{} neuron @ {}Hz, {}Hz PRF ({}m sonophore)'.format( self.pneuron.name, *si_format([self.Fdrive, self.PRF, self.a])) out_fname = 'actmap {} {}Hz PRF{}Hz {}s.csv'.format( self.pneuron.name, *si_format([self.Fdrive, self.PRF, self.tstim], space='')) out_fpath = os.path.join(self.root, out_fname) if os.path.isfile(out_fpath): self.data = np.loadtxt(out_fpath, delimiter=',') else: self.data = self.compute() np.savetxt(out_fpath, self.data, delimiter=',') def getAmpScale(self): Amin, Amax, nA = self.amps.min(), self.amps.max(), self.amps.size if np.all(np.isclose(self.amps, np.logspace(np.log10(Amin), np.log10(Amax), nA))): return 'log' elif np.all(np.isclose(self.amps, np.linspace(Amin, Amax, nA))): return 'lin' else: raise ValueError('Unknown distribution type') - def classify(self, df): + def classify(self, data): ''' Classify based on charge temporal profile. ''' - t = df['t'].values - Qm = df['Qm'].values - - # Detect spikes on charge profile during stimulus - dt = t[2] - t[1] - mpd = int(np.ceil(SPIKE_MIN_DT / dt)) - ispikes, *_ = findPeaks( - Qm[t <= self.tstim], - mph=SPIKE_MIN_QAMP, - mpd=mpd, - mpp=SPIKE_MIN_QPROM - ) + # Detect spikes in data + ispikes, _ = detectSpikes(data) # Compute firing metrics if ispikes.size == 0: # if no spike, assign -1 return -1 elif ispikes.size == 1: # if only 1 spike, assign 0 return 0 else: # if more than 1 spike, assign firing rate - FRs = 1 / np.diff(t[ispikes]) - return np.mean(FRs) + t = data['t'].values + sr = 1 / np.diff(t[ispikes]) + return np.mean(sr) @staticmethod def correctAmp(A): return np.round(A * 1e-3, 1) * 1e3 def compute(self): logger.info('Generating activation map for %s neuron', self.pneuron.name) actmap = np.empty((self.amps.size, self.DCs.size)) nfiles = self.DCs.size * self.amps.size for i, A in enumerate(self.amps): for j, DC in enumerate(self.DCs): fname = '{}.pkl'.format(self.nbls.filecode( - self.Fdrive, self.correctAmp(A), self.tstim, 0., self.PRF, DC, 'sonic')) + self.Fdrive, self.correctAmp(A), self.tstim, 0., self.PRF, DC, 1., 'sonic')) fpath = os.path.join(self.root, fname) if not os.path.isfile(fpath): print(fpath) logger.error('"{}" file not found'.format(fname)) actmap[i, j] = np.nan else: # Load data logger.debug('Loading file {}/{}: "{}"'.format( i * self.amps.size + j + 1, nfiles, fname)) df, _ = loadData(fpath) actmap[i, j] = self.classify(df) return actmap @staticmethod def adjustFRbounds(actmap): ''' Check firing rate bounding. ''' minFR, maxFR = (actmap[actmap > 0].min(), actmap.max()) logger.info('FR range: %.0f - %.0f Hz', minFR, maxFR) if FRbounds is None: FRbounds = (minFR, maxFR) else: if minFR < FRbounds[0]: logger.warning( 'Minimal firing rate (%.0f Hz) is below defined lower bound (%.0f Hz)', minFR, FRbounds[0]) if maxFR > FRbounds[1]: logger.warning( 'Maximal firing rate (%.0f Hz) is above defined upper bound (%.0f Hz)', maxFR, FRbounds[1]) @staticmethod def fit2Colormap(actmap, cmap): actmap[actmap == -1] = np.nan actmap[actmap == 0] = 1e-3 cmap.set_bad('silver') cmap.set_under('k') return actmap, cmap def addThresholdCurve(self, ax): Athrs_fname = 'Athrs_{}_{:.0f}nm_{}Hz_PRF{}Hz_{}s.xlsx'.format( self.pneuron.name, self.a * 1e9, *si_format([self.Fdrive, self.PRF, self.tstim], 0, space='')) fpath = os.path.join(self.root, Athrs_fname) if os.path.isfile(fpath): df = pd.read_excel(fpath, sheet_name='Data') DCs = df['Duty factor'].values Athrs = df['Adrive (kPa)'].values iDCs = np.argsort(DCs) DCs = DCs[iDCs] Athrs = Athrs[iDCs] ax.plot(DCs * 1e2, Athrs, '-', color='#F26522', linewidth=2, label='threshold amplitudes') ax.legend(loc='lower center', frameon=False, fontsize=8) else: logger.warning('%s file not found -> cannot draw threshold curve', fpath) def render(self, Ascale='log', FRscale='log', FRbounds=None, fs=8, cmap='viridis', interactive=False, Vbounds=None, trange=None, thresholds=False): # Compute FR normalizer mymap = plt.get_cmap(cmap) norm, sm = setNormalizer(mymap, FRbounds, FRscale) # Compute mesh edges xedges = self.computeMeshEdges(self.DCs, 'lin') yedges = self.computeMeshEdges(self.amps, self.Ascale) # Create figure fig, ax = plt.subplots(figsize=cm2inch(8, 5.8)) fig.subplots_adjust(left=0.15, bottom=0.15, right=0.8, top=0.92) ax.set_title(self.title, fontsize=fs) ax.set_xlabel('Duty cycle (%)', fontsize=fs, labelpad=-0.5) ax.set_ylabel('Amplitude (kPa)', fontsize=fs) ax.set_xlim(np.array([self.DCs.min(), self.DCs.max()]) * 1e2) for item in ax.get_xticklabels() + ax.get_yticklabels(): item.set_fontsize(fs) # Plot activation map with specific color code actmap, cmap = self.fit2Colormap(self.data, plt.get_cmap(cmap)) if Ascale == 'log': ax.set_yscale('log') ax.pcolormesh(xedges * 1e2, yedges * 1e-3, actmap, cmap=mymap, norm=norm) if thresholds: self.addThresholdCurve(ax) # Plot firing rate colorbar pos1 = ax.get_position() # get the map axis position cbarax = fig.add_axes([pos1.x1 + 0.02, pos1.y0, 0.03, pos1.height]) fig.colorbar(sm, cax=cbarax) cbarax.set_ylabel('Firing rate (Hz)', fontsize=fs) for item in cbarax.get_yticklabels(): item.set_fontsize(fs) if interactive: fig.canvas.mpl_connect( 'button_press_event', lambda event: self.onClick(event, (xedges, yedges), trange, Vbounds)) return fig def onClick(self, event, meshedges, trange, Vbounds): ''' Retrieve the specific input parameters of the x and y dimensions when the user clicks on a cell in the 2D map, and define filename from it. ''' # Get DC and A from x and y coordinates x, y = event.xdata, event.ydata DC = self.DCs[np.searchsorted(meshedges[0], x * 1e-2) - 1] Adrive = self.amps[np.searchsorted(meshedges[1], y * 1e3) - 1] # Define filepath fname = '{}.pkl'.format(self.nbls.filecode( - self.Fdrive, self.correctAmp(Adrive), self.tstim, 0., self.PRF, DC, 'sonic')) + self.Fdrive, self.correctAmp(Adrive), self.tstim, 0., self.PRF, DC, 1. , 'sonic')) fpath = os.path.join(self.root, fname) # Plot Q-trace try: self.plotQVeff(fpath, trange=trange, ybounds=Vbounds) plt.show() except FileNotFoundError as err: logger.error(err) def plotQVeff(self, filepath, tonset=10e-3, trange=None, ybounds=None, fs=8, lw=1): ''' Plot superimposed profiles of membrane charge density and effective membrane potential. :param filepath: full path to the data file :param tonset: pre-stimulus onset to add to profiles (s) :param trange: time lower and upper bounds on graph (s) :param ybounds: y-axis bounds (mV / nC/cm2) :return: handle to the generated figure ''' # Check file existence fname = os.path.basename(filepath) if not os.path.isfile(filepath): raise FileNotFoundError('Error: "{}" file does not exist'.format(fname)) # Load data (with optional time restriction) logger.debug('Loading data from "%s"', fname) with open(filepath, 'rb') as fh: frame = pickle.load(fh) df = frame['data'] if trange is not None: tmin, tmax = trange df = df.loc[(df['t'] >= tmin) & (df['t'] <= tmax)] # Load variables, add onset and rescale t, Qm, Vm = [df[k].values for k in ['t', 'Qm', 'Vm']] t = np.hstack((np.array([-tonset, t[0]]), t)) # s Vm = np.hstack((np.array([self.pneuron.Vm0] * 2), Vm)) # mV Qm = np.hstack((np.array([self.pneuron.Qm0()] * 2), Qm)) # C/m2 t *= 1e3 # ms Qm *= 1e5 # nC/cm2 # Determine axes bounds if ybounds is None: ybounds = (min(Vm.min(), Qm.min()), max(Vm.max(), Qm.max())) # Create figure fig, ax = plt.subplots(figsize=cm2inch(7, 3)) fig.canvas.set_window_title(fname) plt.subplots_adjust(left=0.2, bottom=0.2, right=0.95, top=0.95) for key in ['top', 'right']: ax.spines[key].set_visible(False) for key in ['bottom', 'left']: ax.spines[key].set_position(('axes', -0.03)) ax.spines[key].set_linewidth(2) ax.yaxis.set_tick_params(width=2) ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f')) ax.set_xticks([]) ax.set_xlabel('{}s'.format(si_format(np.ptp(t), space=' ')), fontsize=fs) ax.set_ylabel('mV - $\\rm nC/cm^2$', fontsize=fs, labelpad=-15) ax.set_ylim(ybounds) ax.set_yticks(ybounds) for item in ax.get_yticklabels(): item.set_fontsize(fs) # Plot Qm and Vmeff profiles ax.plot(t, Vm, color='darkgrey', linewidth=lw) ax.plot(t, Qm, color='k', linewidth=lw) # fig.tight_layout() return fig diff --git a/PySONIC/plt/phasediagram.py b/PySONIC/plt/phasediagram.py index adcf917..42f7fa0 100644 --- a/PySONIC/plt/phasediagram.py +++ b/PySONIC/plt/phasediagram.py @@ -1,186 +1,191 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2018-10-01 20:40:28 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-29 20:24:36 +# @Last Modified time: 2019-08-22 14:59:13 import numpy as np import matplotlib.pyplot as plt from ..core import getModel from ..utils import * from ..constants import * from .pltutils import * +from ..postpro import detectSpikes, convertPeaksProperties class PhaseDiagram(ComparativePlot): phaseplotvars = { 'Vm': { 'label': 'V_m\ (mV)', 'dlabel': 'dV/dt\ (V/s)', 'factor': 1e0, 'lim': (-80.0, 50.0), 'dfactor': 1e-3, 'dlim': (-300, 700), 'thr_amp': SPIKE_MIN_VAMP, 'thr_prom': SPIKE_MIN_VPROM }, 'Qm': { 'label': 'Q_m\ (nC/cm^2)', 'dlabel': 'I\ (A/m^2)', 'factor': 1e5, 'lim': (-80.0, 50.0), 'dfactor': 1e0, 'dlim': (-2, 5), 'thr_amp': SPIKE_MIN_QAMP, 'thr_prom': SPIKE_MIN_QPROM } } @classmethod def createBackBone(cls, pltvar, tbounds, fs, prettify): # Create figure fig, axes = plt.subplots(1, 2, figsize=(8, 4)) # 1st axis: variable as function of time ax = axes[0] ax.set_xlabel('$\\rm time\ (ms)$', fontsize=fs) ax.set_ylabel('$\\rm {}$'.format(pltvar['label']), fontsize=fs) ax.set_xlim(tbounds) ax.set_ylim(pltvar['lim']) # 2nd axis: phase plot (derivative of variable vs variable) ax = axes[1] ax.set_xlabel('$\\rm {}$'.format(pltvar['label']), fontsize=fs) ax.set_ylabel('$\\rm {}$'.format(pltvar['dlabel']), fontsize=fs) ax.set_xlim(pltvar['lim']) ax.set_ylim(pltvar['dlim']) ax.plot([0, 0], [pltvar['dlim'][0], pltvar['dlim'][1]], '--', color='k', linewidth=1) ax.plot([pltvar['lim'][0], pltvar['lim'][1]], [0, 0], '--', color='k', linewidth=1) if prettify: cls.prettify(axes[0], xticks=tbounds, yticks=pltvar['lim']) cls.prettify(axes[1], xticks=pltvar['lim'], yticks=pltvar['dlim']) for ax in axes: cls.removeSpines(ax) cls.setTickLabelsFontSize(ax, fs) return fig, axes def checkInputs(self, labels): self.checkLabels(labels) @staticmethod def extractSpikesData(t, y, tbounds, rel_tbounds, tspikes): spikes_tvec, spikes_yvec, spikes_dydtvec = [], [], [] for j, (tspike, tbound) in enumerate(zip(tspikes, tbounds)): left_bound = max(tbound[0], rel_tbounds[0] + tspike) right_bound = min(tbound[1], rel_tbounds[1] + tspike) inds = np.where((t > left_bound) & (t < right_bound))[0] spikes_tvec.append(t[inds] - tspike) spikes_yvec.append(y[inds]) dinds = np.hstack(([inds[0] - 1], inds, [inds[-1] + 1])) dydt = np.diff(y[dinds]) / np.diff(t[dinds]) spikes_dydtvec.append((dydt[:-1] + dydt[1:]) / 2) # average of the two return spikes_tvec, spikes_yvec, spikes_dydtvec def addLegend(self, fig, axes, handles, labels, fs): fig.subplots_adjust(top=0.8) if len(self.filepaths) > 1: axes[0].legend(handles, labels, fontsize=fs, frameon=False, loc='upper center', bbox_to_anchor=(1.0, 1.35)) else: fig.suptitle(labels[0], fontsize=fs) def render(self, no_offset=False, no_first=False, labels=None, colors=None, fs=10, lw=2, trange=None, rel_tbounds=None, prettify=False, cmap=None, cscale='lin'): self.checkInputs(labels) if rel_tbounds is None: rel_tbounds = np.array((-1.5e-3, 1.5e-3)) # Check pltvar if self.varname not in self.phaseplotvars: raise KeyError( 'Unknown plot variable: "{}". Possible plot variables are: {}'.format( self.varname, ', '.join(['"{}"'.format(p) for p in self.phaseplotvars.keys()]))) pltvar = self.phaseplotvars[self.varname] fig, axes = self.createBackBone(pltvar, rel_tbounds * 1e3, fs, prettify) # Loop through data files comp_values, full_labels = [], [] handles0, handles1 = [], [] for i, filepath in enumerate(self.filepaths): # Load data data, meta = self.getData(filepath, trange=trange) meta.pop('tcomp') full_labels.append(figtitle(meta)) # Extract model model = getModel(meta) # Check consistency of sim types and check differing inputs comp_values = self.checkConsistency(meta, comp_values) # Extract time and y-variable t = data['t'].values y = data[self.varname].values - # Prominence-based spike detection - tspikes, yspikes, _, _, tbounds = self.getSpikes( + # Detect spikes in signal + ispikes, properties = detectSpikes( data, key=self.varname, mph=pltvar['thr_amp'], mpp=pltvar['thr_prom']) - nspikes = tspikes.size + nspikes = ispikes.size + tspikes = t[ispikes] + yspikes = y[ispikes] + properties = convertPeaksProperties(t, properties) + tbounds = np.array(list(zip(properties['left_bases'], properties['right_bases']))) if nspikes == 0: logger.warning('No spikes detected') else: # Store spikes in dedicated lists spikes_tvec, spikes_yvec, spikes_dydtvec = self.extractSpikesData( t, y, tbounds, rel_tbounds, tspikes) # Plot spikes temporal profiles and phase-plane diagrams lh0, lh1 = [], [] for j in range(nspikes): if colors is None: color = 'C{}'.format(i if len(self.filepaths) > 1 else j % 10) else: color = colors[i] lh0.append(axes[0].plot( spikes_tvec[j] * 1e3, spikes_yvec[j] * pltvar['factor'], linewidth=lw, c=color)[0]) lh1.append(axes[1].plot( spikes_yvec[j] * pltvar['factor'], spikes_dydtvec[j] * pltvar['dfactor'], linewidth=lw, c=color)[0]) handles0.append(lh0) handles1.append(lh1) # Determine labels if self.comp_ref_key is not None: self.comp_info = model.inputs().get(self.comp_ref_key, None) comp_values, comp_labels = self.getCompLabels(comp_values) labels = self.chooseLabels(labels, comp_labels, full_labels) # Post-process figure fig.tight_layout() # Add labels or colorbar legend if cmap is not None: if not self.is_unique_comp: raise ValueError('Colormap mode unavailable for multiple differing parameters') if self.comp_info is None: raise ValueError('Colormap mode unavailable for qualitative comparisons') cmap_handles = [h0 + h1 for h0, h1 in zip(handles0, handles1)] self.addCmap( fig, cmap, cmap_handles, comp_values, self.comp_info, fs, prettify, zscale=cscale) else: leg_handles = [x[0] for x in handles0] self.addLegend(fig, axes, leg_handles, labels, fs) return fig diff --git a/PySONIC/plt/pltutils.py b/PySONIC/plt/pltutils.py index a920d1e..f892bc2 100644 --- a/PySONIC/plt/pltutils.py +++ b/PySONIC/plt/pltutils.py @@ -1,409 +1,389 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2017-08-21 14:33:36 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-08-18 21:00:41 +# @Last Modified time: 2019-08-22 15:09:34 ''' Useful functions to generate plots. ''' import re import numpy as np import pandas as pd +from scipy.signal import peak_widths import matplotlib from matplotlib.patches import Rectangle from matplotlib import cm, colors import matplotlib.pyplot as plt from ..core import getModel from ..utils import logger, isIterable, loadData, rescale, swapFirstLetterCase -from ..postpro import findPeaks from ..constants import SPIKE_MIN_DT, SPIKE_MIN_QAMP, SPIKE_MIN_QPROM # Matplotlib parameters matplotlib.rcParams['pdf.fonttype'] = 42 matplotlib.rcParams['ps.fonttype'] = 42 matplotlib.rcParams['font.family'] = 'arial' def figtitle(meta): ''' Return appropriate title based on simulation metadata. ''' if 'Cm0' in meta: return '{:.0f}nm radius BLS structure: MECH-STIM {:.0f}kHz, {:.2f}kPa, {:.1f}nC/cm2'.format( meta['a'] * 1e9, meta['Fdrive'] * 1e-3, meta['Adrive'] * 1e-3, meta['Qm'] * 1e5) else: if 'DC' in meta: if meta['DC'] < 1: wavetype = 'PW' suffix = ', {:.2f}Hz PRF, {:.0f}% DC'.format(meta['PRF'], meta['DC'] * 1e2) else: wavetype = 'CW' suffix = '' if 'Astim' in meta: return '{} neuron: {} E-STIM {:.2f}mA/m2, {:.0f}ms{}'.format( meta['neuron'], wavetype, meta['Astim'], meta['tstim'] * 1e3, suffix) else: return '{} neuron ({:.1f}nm): {} A-STIM {:.0f}kHz {:.2f}kPa, {:.0f}ms{} - {} model'.format( meta['neuron'], meta['a'] * 1e9, wavetype, meta['Fdrive'] * 1e-3, meta['Adrive'] * 1e-3, meta['tstim'] * 1e3, suffix, meta['method']) else: return '{} neuron: V-CLAMP {:.1f}-{:.1f}mV, {:.0f}ms'.format( meta['neuron'], meta['Vhold'], meta['Vstep'], meta['tstim'] * 1e3) def cm2inch(*tupl): inch = 2.54 if isinstance(tupl[0], tuple): return tuple(i / inch for i in tupl[0]) else: return tuple(i / inch for i in tupl) def extractPltVar(model, pltvar, df, meta=None, nsamples=0, name=''): if 'func' in pltvar: s = 'model.{}'.format(pltvar['func']) try: var = eval(s) except AttributeError: var = eval(s.replace('model', 'model.pneuron')) elif 'key' in pltvar: var = df[pltvar['key']] elif 'constant' in pltvar: var = eval(pltvar['constant']) * np.ones(nsamples) else: var = df[name] if isinstance(var, pd.Series): var = var.values var = var.copy() if var.size == nsamples - 1: var = np.insert(var, 0, var[0]) var *= pltvar.get('factor', 1) return var def setGrid(n, ncolmax=3): ''' Determine number of rows and columns in figure grid, based on number of variables to plot. ''' if n <= ncolmax: return (1, n) else: return ((n - 1) // ncolmax + 1, ncolmax) def setNormalizer(cmap, bounds, scale='lin'): norm = { 'lin': colors.Normalize, 'log': colors.LogNorm }[scale](*bounds) sm = cm.ScalarMappable(norm=norm, cmap=cmap) sm._A = [] return norm, sm class GenericPlot: def __init__(self, filepaths): ''' Constructor. :param filepaths: list of full paths to output data files to be compared ''' if not isIterable(filepaths): filepaths = [filepaths] self.filepaths = filepaths def __call__(self, *args, **kwargs): return self.render(*args, **kwargs) @staticmethod def getData(entry, frequency=1, trange=None): if entry is None: raise ValueError('non-existing data') if isinstance(entry, str): data, meta = loadData(entry, frequency) else: data, meta = entry data = data.iloc[::frequency] if trange is not None: tmin, tmax = trange data = data.loc[(data['t'] >= tmin) & (data['t'] <= tmax)] return data, meta def render(self, *args, **kwargs): return NotImplementedError @staticmethod def getSimType(fname): ''' Get sim type from filename. ''' mo = re.search('(^[A-Z]*)_(.*).pkl', fname) if not mo: raise ValueError('Could not find sim-key in filename: "{}"'.format(fname)) return mo.group(1) @staticmethod def getModel(*args, **kwargs): return getModel(*args, **kwargs) @staticmethod def figtitle(*args, **kwargs): return figtitle(*args, **kwargs) @staticmethod def getTimePltVar(tscale): ''' Return time plot variable for a given temporal scale. ''' return { 'desc': 'time', 'label': 'time', 'unit': tscale, 'factor': {'ms': 1e3, 'us': 1e6}[tscale], 'onset': {'ms': 1e-3, 'us': 1e-6}[tscale] } @staticmethod def createBackBone(*args, **kwargs): return NotImplementedError @staticmethod def prettify(ax, xticks=None, yticks=None, xfmt='{:.0f}', yfmt='{:+.0f}'): try: ticks = ax.get_ticks() ticks = (min(ticks), max(ticks)) ax.set_ticks(ticks) ax.set_ticklabels([xfmt.format(x) for x in ticks]) except AttributeError: if xticks is None: xticks = ax.get_xticks() xticks = (min(xticks), max(xticks)) if yticks is None: yticks = ax.get_yticks() yticks = (min(yticks), max(yticks)) ax.set_xticks(xticks) ax.set_yticks(yticks) if xfmt is not None: ax.set_xticklabels([xfmt.format(x) for x in xticks]) if yfmt is not None: ax.set_yticklabels([yfmt.format(y) for y in yticks]) @staticmethod def addInset(fig, ax, inset): ''' Create inset axis. ''' inset_ax = fig.add_axes(ax.get_position()) inset_ax.set_zorder(1) inset_ax.set_xlim(inset['xlims'][0], inset['xlims'][1]) inset_ax.set_ylim(inset['ylims'][0], inset['ylims'][1]) inset_ax.set_xticks([]) inset_ax.set_yticks([]) inset_ax.add_patch(Rectangle((inset['xlims'][0], inset['ylims'][0]), inset['xlims'][1] - inset['xlims'][0], inset['ylims'][1] - inset['ylims'][0], color='w')) return inset_ax @staticmethod def materializeInset(ax, inset_ax, inset): ''' Materialize inset with zoom boox. ''' # Re-position inset axis axpos = ax.get_position() left, right, = rescale(inset['xcoords'], ax.get_xlim()[0], ax.get_xlim()[1], axpos.x0, axpos.x0 + axpos.width) bottom, top, = rescale(inset['ycoords'], ax.get_ylim()[0], ax.get_ylim()[1], axpos.y0, axpos.y0 + axpos.height) inset_ax.set_position([left, bottom, right - left, top - bottom]) for i in inset_ax.spines.values(): i.set_linewidth(2) # Materialize inset target region with contour frame ax.plot(inset['xlims'], [inset['ylims'][0]] * 2, linestyle='-', color='k') ax.plot(inset['xlims'], [inset['ylims'][1]] * 2, linestyle='-', color='k') ax.plot([inset['xlims'][0]] * 2, inset['ylims'], linestyle='-', color='k') ax.plot([inset['xlims'][1]] * 2, inset['ylims'], linestyle='-', color='k') # Link target and inset with dashed lines if possible if inset['xcoords'][1] < inset['xlims'][0]: ax.plot([inset['xcoords'][1], inset['xlims'][0]], [inset['ycoords'][0], inset['ylims'][0]], linestyle='--', color='k') ax.plot([inset['xcoords'][1], inset['xlims'][0]], [inset['ycoords'][1], inset['ylims'][1]], linestyle='--', color='k') elif inset['xcoords'][0] > inset['xlims'][1]: ax.plot([inset['xcoords'][0], inset['xlims'][1]], [inset['ycoords'][0], inset['ylims'][0]], linestyle='--', color='k') ax.plot([inset['xcoords'][0], inset['xlims'][1]], [inset['ycoords'][1], inset['ylims'][1]], linestyle='--', color='k') else: logger.warning('Inset x-coordinates intersect with those of target region') def postProcess(self, *args, **kwargs): return NotImplementedError @staticmethod def removeSpines(ax): for item in ['top', 'right']: ax.spines[item].set_visible(False) @staticmethod def setXTicks(ax, xticks=None): if xticks is not None: ax.set_xticks(xticks) @staticmethod def setYTicks(ax, yticks=None): if yticks is not None: ax.set_yticks(yticks) @staticmethod def setTickLabelsFontSize(ax, fs): for tick in ax.xaxis.get_major_ticks() + ax.yaxis.get_major_ticks(): tick.label.set_fontsize(fs) @staticmethod def setXLabel(ax, xplt, fs): ax.set_xlabel('$\\rm {}\ ({})$'.format(xplt['label'], xplt['unit']), fontsize=fs) @staticmethod def setYLabel(ax, yplt, fs): ax.set_ylabel('$\\rm {}\ ({})$'.format(yplt['label'], yplt.get('unit', '')), fontsize=fs) @classmethod def addCmap(cls, fig, cmap, handles, comp_values, comp_info, fs, prettify, zscale='lin'): # Create colormap and normalizer try: mymap = plt.get_cmap(cmap) except ValueError: mymap = plt.get_cmap(swapFirstLetterCase(cmap)) norm, sm = setNormalizer(mymap, (comp_values.min(), comp_values.max()), zscale) # Adjust line colors for lh, z in zip(handles, comp_values): if isIterable(lh): for item in lh: item.set_color(sm.to_rgba(z)) else: lh.set_color(sm.to_rgba(z)) # Add colorbar fig.subplots_adjust(left=0.1, right=0.8, bottom=0.15, top=0.95, hspace=0.5) cbarax = fig.add_axes([0.85, 0.15, 0.03, 0.8]) cbar = fig.colorbar(sm, cax=cbarax, orientation='vertical') cbarax.set_ylabel('$\\rm {}\ ({})$'.format( comp_info['desc'].replace(' ', '\ '), comp_info['unit']), fontsize=fs) if prettify: cls.prettify(cbar) for item in cbarax.get_yticklabels(): item.set_fontsize(fs) - @staticmethod - def getSpikes(data, key='Qm', mph=SPIKE_MIN_QAMP, mpp=SPIKE_MIN_QPROM, mpt=SPIKE_MIN_DT): - if key not in data: - raise ValueError('charge profile not avilable in dataframe') - t, y = [data[k].values for k in['t', key]] - dt = t[2] - t[1] - ipeaks, proms, widths, ihalfmaxbounds, ibounds = findPeaks( - data[key].values, mph=mph, mpd=int(np.ceil(mpt / dt)), mpp=mpp) - if ipeaks is None: - return [None] * 4 - widths *= dt - indexes = np.arange(t.size) - thalfmaxbounds = np.array([ - np.interp(ihalfmaxbounds[:, i], indexes, t, left=np.nan, right=np.nan) for i in range(2) - ]).T - tbounds = np.array([ - np.interp(ibounds[:, i], indexes, t, left=np.nan, right=np.nan) for i in range(2) - ]).T - return np.array(t[ipeaks]), np.array(y[ipeaks]), np.array(proms), thalfmaxbounds, tbounds - class ComparativePlot(GenericPlot): def __init__(self, filepaths, varname): ''' Constructor. :param filepaths: list of full paths to output data files to be compared :param varname: name of variable to extract and compare ''' super().__init__(filepaths) self.varname = varname self.comp_ref_key = None self.meta_ref = None self.comp_info = None self.is_unique_comp = False def checkColors(self, colors): if colors is None: colors = ['C{}'.format(j) for j in range(len(self.filepaths))] return colors def checkLines(self, lines): if lines is None: lines = ['-'] * len(self.filepaths) return lines def checkLabels(self, labels): if labels is not None: if len(labels) != len(self.filepaths): raise ValueError( 'Invalid labels ({}): not matching number of compared files ({})'.format( len(labels), len(self.filepaths))) if not all(isinstance(x, str) for x in labels): raise TypeError('Invalid labels: must be string typed') def checkSimType(self, meta): ''' Check consistency of sim types across files. ''' if meta['simkey'] != self.meta_ref['simkey']: raise ValueError('Invalid comparison: different simulation types') def checkCompValues(self, meta, comp_values): ''' Check consistency of differing values across files. ''' differing = {k: meta[k] != self.meta_ref[k] for k in meta.keys()} if sum(differing.values()) > 1: logger.warning('More than one differing inputs') self.comp_ref_key = None return [] zkey = (list(differing.keys())[list(differing.values()).index(True)]) if self.comp_ref_key is None: self.comp_ref_key = zkey self.is_unique_comp = True comp_values.append(self.meta_ref[self.comp_ref_key]) comp_values.append(meta[self.comp_ref_key]) else: if zkey != self.comp_ref_key: logger.warning('inconsitent differing inputs') self.comp_ref_key = None return [] else: comp_values.append(meta[self.comp_ref_key]) return comp_values def checkConsistency(self, meta, comp_values): ''' Check consistency of sim types and check differing inputs. ''' if self.meta_ref is None: self.meta_ref = meta else: self.checkSimType(meta) comp_values = self.checkCompValues(meta, comp_values) if self.comp_ref_key is None: self.is_unique_comp = False return comp_values def getCompLabels(self, comp_values): if self.comp_info is not None: comp_values = np.array(comp_values) * self.comp_info.get('factor', 1) comp_labels = [ '$\\rm{} = {}\ {}$'.format(self.comp_info['label'], x, self.comp_info['unit']) for x in comp_values] else: comp_labels = comp_values return comp_values, comp_labels def chooseLabels(self, labels, comp_labels, full_labels): if labels is not None: return labels else: if self.is_unique_comp: return comp_labels else: return full_labels diff --git a/PySONIC/plt/timeseries.py b/PySONIC/plt/timeseries.py index 433ac9a..105ab2c 100644 --- a/PySONIC/plt/timeseries.py +++ b/PySONIC/plt/timeseries.py @@ -1,468 +1,481 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2018-09-25 16:18:45 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-08-18 21:09:37 +# @Last Modified time: 2019-08-22 15:08:01 import numpy as np import matplotlib.pyplot as plt +from ..postpro import detectSpikes, convertPeaksProperties from ..utils import * from .pltutils import * class TimeSeriesPlot(GenericPlot): ''' Generic interface to build a plot displaying temporal profiles of model simulations. ''' @classmethod def setTimeLabel(cls, ax, tplt, fs): return super().setXLabel(ax, tplt, fs) @classmethod def setYLabel(cls, ax, yplt, fs, grouplabel=None): if grouplabel is not None: yplt['label'] = grouplabel return super().setYLabel(ax, yplt, fs) def checkInputs(self, *args, **kwargs): return NotImplementedError @staticmethod def getStimStates(df): try: stimstate = df['stimstate'] except KeyError: stimstate = df['states'] return stimstate.values @classmethod def getStimPulses(cls, t, states): ''' Determine the onset and offset times of pulses from a stimulation vector. :param t: time vector (s). :param states: a vector of stimulation state (ON/OFF) at each instant in time. :return: 3-tuple with number of patches, timing of STIM-ON an STIM-OFF instants. ''' # Compute states derivatives and identify bounds indexes of pulses dstates = np.diff(states) ipulse_on = np.where(dstates > 0.0)[0] + 1 ipulse_off = np.where(dstates < 0.0)[0] + 1 if ipulse_off.size < ipulse_on.size: ioff = t.size - 1 if ipulse_off.size == 0: ipulse_off = np.array([ioff]) else: ipulse_off = np.insert(ipulse_off, ipulse_off.size - 1, ioff) # Get time instants for pulses ON and OFF tpulse_on = t[ipulse_on] tpulse_off = t[ipulse_off] return tpulse_on, tpulse_off @staticmethod def addLegend(ax, handles, labels, fs, color=None, ls=None): lh = ax.legend(handles, labels, loc=1, fontsize=fs, frameon=False) if color is not None: for l in lh.get_lines(): l.set_color(color) if ls: for l in lh.get_lines(): l.set_linestyle(ls) @classmethod def materializeSpikes(cls, ax, data, tplt, yplt, color, mode, add_to_legend=False): - tspikes, Qspikes, Qprominences, thalfmaxbounds, _ = cls.getSpikes(data) - if tspikes is not None: - ax.scatter(tspikes * tplt['factor'], Qspikes * yplt['factor'] + 10, color=color, - label='spikes' if add_to_legend else None, marker='v') + t = data['t'].values + Qm = data['Qm'].values + ispikes, properties = detectSpikes(data) + ileft = properties['left_bases'] + iright = properties['right_bases'] + properties = convertPeaksProperties(t, properties) + if ispikes is not None: + yoffset = 5 + ax.plot(t[ispikes] * tplt['factor'], Qm[ispikes] * yplt['factor'] + yoffset, + 'v', color=color, label='spikes' if add_to_legend else None) if mode == 'details': - Qbottoms = Qspikes - Qprominences - Qmiddles = Qspikes - 0.5 * Qprominences - for i in range(len(tspikes)): - ax.plot(np.array([tspikes[i]] * 2) * tplt['factor'], - np.array([Qspikes[i], Qbottoms[i]]) * yplt['factor'], '--', color=color, - label='prominences' if i == 0 and add_to_legend else '') - ax.plot(thalfmaxbounds[i] * tplt['factor'], - np.array([Qmiddles[i]] * 2) * yplt['factor'], '-.', color=color, - label='widths' if i == 0 and add_to_legend else '') + ax.plot(t[ileft] * tplt['factor'], Qm[ileft] * yplt['factor'] - 5, + '<', color=color, label='left-bases' if add_to_legend else None) + ax.plot(t[iright] * tplt['factor'], Qm[iright] * yplt['factor'] - 10, + '>', color=color, label='right-bases' if add_to_legend else None) + ax.vlines( + x=t[ispikes] * tplt['factor'], + ymin=(Qm[ispikes] - properties['prominences']) * yplt['factor'], + ymax=Qm[ispikes] * yplt['factor'], + color=color, linestyles='dashed', + label='prominences' if add_to_legend else '') + ax.hlines( + y=properties['width_heights'] * yplt['factor'], + xmin=properties['left_ips'] * tplt['factor'], + xmax=properties['right_ips'] * tplt['factor'], + color=color, linestyles='dotted', label='half-widths' if add_to_legend else '') return add_to_legend @staticmethod def prepareTime(t, tplt): if tplt['onset'] > 0.0: t = np.insert(t, 0, -tplt['onset']) return t * tplt['factor'] @staticmethod def addPatches(ax, tpatch_on, tpatch_off, tplt, color='#8A8A8A'): for i in range(tpatch_on.size): ax.axvspan(tpatch_on[i] * tplt['factor'], tpatch_off[i] * tplt['factor'], edgecolor='none', facecolor=color, alpha=0.2) @staticmethod def plotInset(inset_ax, t, y, tplt, yplt, line, color, lw): inset_window = np.logical_and(t > (inset['xlims'][0] / tplt['factor']), t < (inset['xlims'][1] / tplt['factor'])) inset_ax.plot(t[inset_window] * tplt['factor'], y[inset_window] * yplt['factor'], linewidth=lw, linestyle=line, color=color) return inset_ax @staticmethod def addInsetPatches(ax, inset_ax, inset, tpatch_on, tpatch_off, tplt, color): ybottom, ytop = ax.get_ylim() cond_on = np.logical_and(tpatch_on > (inset['xlims'][0] / tfactor), tpatch_on < (inset['xlims'][1] / tfactor)) cond_off = np.logical_and(tpatch_off > (inset['xlims'][0] / tfactor), tpatch_off < (inset['xlims'][1] / tfactor)) cond_glob = np.logical_and(tpatch_on < (inset['xlims'][0] / tfactor), tpatch_off > (inset['xlims'][1] / tfactor)) cond_onoff = np.logical_or(cond_on, cond_off) cond = np.logical_or(cond_onoff, cond_glob) npatches_inset = np.sum(cond) for i in range(npatches_inset): inset_ax.add_patch(Rectangle((tpatch_on[cond][i] * tfactor, ybottom), (tpatch_off[cond][i] - tpatch_on[cond][i]) * tfactor, ytop - ybottom, color=color, alpha=0.1)) class CompTimeSeries(ComparativePlot, TimeSeriesPlot): ''' Interface to build a comparative plot displaying profiles of a specific output variable across different model simulations. ''' def __init__(self, filepaths, varname): ''' Constructor. :param filepaths: list of full paths to output data files to be compared :param varname: name of variable to extract and compare ''' ComparativePlot.__init__(self, filepaths, varname) def checkPatches(self, patches): greypatch = False if patches == 'none': patches = [False] * len(self.filepaths) elif patches == 'all': patches = [True] * len(self.filepaths) elif patches == 'one': patches = [True] + [False] * (len(self.filepaths) - 1) greypatch = True elif isinstance(patches, list): if len(patches) != len(self.filepaths): raise ValueError( 'Invalid patches ({}): not matching number of compared files ({})'.format( len(patches), len(self.filepaths))) if not all(isinstance(p, bool) for p in patches): raise TypeError('Invalid patch sequence: all list items must be boolean typed') else: raise ValueError( 'Invalid patches: must be either "none", all", "one", or a boolean list') return patches, greypatch def checkInputs(self, lines, labels, colors, patches): self.checkLabels(labels) lines = self.checkLines(lines) colors = self.checkColors(colors) patches, greypatch = self.checkPatches(patches) return lines, labels, colors, patches, greypatch @staticmethod def createBackBone(figsize): fig, ax = plt.subplots(figsize=figsize) ax.set_zorder(0) return fig, ax @classmethod def postProcess(cls, ax, tplt, yplt, fs, meta, prettify): cls.removeSpines(ax) if 'bounds' in yplt: ax.set_ylim(*yplt['bounds']) cls.setTimeLabel(ax, tplt, fs) cls.setYLabel(ax, yplt, fs) if prettify: cls.prettify(ax, xticks=(0, meta['tstim'] * tplt['factor'])) cls.setTickLabelsFontSize(ax, fs) def render(self, figsize=(11, 4), fs=10, lw=2, labels=None, colors=None, lines=None, patches='one', inset=None, frequency=1, spikes='none', cmap=None, cscale='lin', trange=None, prettify=False): ''' Render plot. :param figsize: figure size (x, y) :param fs: labels fontsize :param lw: linewidth :param labels: list of labels to use in the legend :param colors: list of colors to use for each curve :param lines: list of linestyles :param patches: string indicating whether/how to mark stimulation periods with rectangular patches :param inset: string indicating whether/how to mark an inset zooming on a particular region of the graph :param frequency: frequency at which to plot samples :param spikes: string indicating how to show spikes ("none", "marks" or "details") :param cmap: color map to use for colobar-based comparison (if not None) :param cscale: color scale to use for colobar-based comparison :param trange: optional lower and upper bounds to time axis :return: figure handle ''' lines, labels, colors, patches, greypatch = self.checkInputs( lines, labels, colors, patches) fig, ax = self.createBackBone(figsize) if inset is not None: inset_ax = self.addInset(fig, ax, inset) # Loop through data files handles, comp_values, full_labels = [], [], [] tmin, tmax = np.inf, -np.inf for j, filepath in enumerate(self.filepaths): # Load data try: data, meta = self.getData(filepath, frequency, trange) except ValueError as err: continue if 'tcomp' in meta: meta.pop('tcomp') full_labels.append(self.figtitle(meta)) # Extract model model = self.getModel(meta) # Check consistency of sim types and check differing inputs comp_values = self.checkConsistency(meta, comp_values) # Extract time and stim pulses t = data['t'].values stimstate = self.getStimStates(data) tpatch_on, tpatch_off = self.getStimPulses(t, stimstate) tplt = self.getTimePltVar(model.tscale) t = self.prepareTime(t, tplt) # Extract y-variable pltvars = model.getPltVars() if self.varname not in pltvars: raise KeyError( 'Unknown plot variable: "{}". Possible plot variables are: {}'.format( self.varname, ', '.join(['"{}"'.format(p) for p in pltvars.keys()]))) yplt = pltvars[self.varname] y = extractPltVar(model, yplt, data, meta, t.size, self.varname) # Plot time series handles.append(ax.plot(t, y, linewidth=lw, linestyle=lines[j], color=colors[j])[0]) # Optional: add spikes if self.varname == 'Qm' and spikes != 'none': self.materializeSpikes(ax, data, tplt, yplt, colors[j], spikes) # Plot optional inset if inset is not None: inset_ax = self.plotInset(inset_ax, t, y, tplt, yplt, lines[j], colors[j], lw) # Add optional STIM-ON patches if patches[j]: ybottom, ytop = ax.get_ylim() color = '#8A8A8A' if greypatch else handles[j].get_color() self.addPatches(ax, tpatch_on, tpatch_off, tplt, color) if inset is not None: self.addInsetPatches(ax, inset_ax, inset, tpatch_on, tpatch_off, tplt, color) tmin, tmax = min(tmin, t.min()), max(tmax, t.max()) # Determine labels if self.comp_ref_key is not None: self.comp_info = model.inputs().get(self.comp_ref_key, None) comp_values, comp_labels = self.getCompLabels(comp_values) labels = self.chooseLabels(labels, comp_labels, full_labels) # Post-process figure self.postProcess(ax, tplt, yplt, fs, meta, prettify) ax.set_xlim(tmin, tmax) fig.tight_layout() if inset is not None: self.materializeInset(ax, inset_ax, inset) # Add labels or colorbar legend if cmap is not None: if not self.is_unique_comp: raise ValueError('Colormap mode unavailable for multiple differing parameters') if self.comp_info is None: raise ValueError('Colormap mode unavailable for qualitative comparisons') self.addCmap( fig, cmap, handles, comp_values, self.comp_info, fs, prettify, zscale=cscale) else: self.addLegend(ax, handles, labels, fs) return fig class GroupedTimeSeries(TimeSeriesPlot): ''' Interface to build a plot displaying profiles of several output variables arranged into specific schemes. ''' def __init__(self, filepaths, pltscheme=None): ''' Constructor. :param filepaths: list of full paths to output data files to be compared :param varname: name of variable to extract and compare ''' super().__init__(filepaths) self.pltscheme = pltscheme @staticmethod def createBackBone(pltscheme): naxes = len(pltscheme) if naxes == 1: fig, ax = plt.subplots(figsize=(11, 4)) axes = [ax] else: fig, axes = plt.subplots(naxes, 1, figsize=(11, min(3 * naxes, 9))) return fig, axes @classmethod def postProcess(cls, axes, tplt, fs, meta, prettify): for ax in axes: cls.removeSpines(ax) # if prettify: # cls.prettify(ax, xticks=(0, meta['tstim'] * tplt['factor']), yfmt=None) cls.setTickLabelsFontSize(ax, fs) for ax in axes[:-1]: ax.set_xticklabels([]) cls.setTimeLabel(axes[-1], tplt, fs) def render(self, fs=10, lw=2, labels=None, colors=None, lines=None, patches='one', save=False, outputdir=None, fig_ext='png', frequency=1, spikes='none', trange=None, prettify=False): ''' Render plot. :param fs: labels fontsize :param lw: linewidth :param labels: list of labels to use in the legend :param colors: list of colors to use for each curve :param lines: list of linestyles :param patches: boolean indicating whether to mark stimulation periods with rectangular patches :param save: boolean indicating whether or not to save the figure(s) :param outputdir: path to output directory in which to save figure(s) :param fig_ext: string indcating figure extension ("png", "pdf", ...) :param frequency: frequency at which to plot samples :param spikes: string indicating how to show spikes ("none", "marks" or "details") :param trange: optional lower and upper bounds to time axis :return: figure handle(s) ''' figs = [] for filepath in self.filepaths: # Load data and extract model try: data, meta = self.getData(filepath, frequency, trange) except ValueError as err: continue model = self.getModel(meta) # Extract time and stim pulses t = data['t'].values stimstate = self.getStimStates(data) tpatch_on, tpatch_off = self.getStimPulses(t, stimstate) tplt = self.getTimePltVar(model.tscale) t = self.prepareTime(t, tplt) # Check plot scheme if provided, otherwise generate it pltvars = model.getPltVars() if self.pltscheme is not None: for key in list(sum(list(self.pltscheme.values()), [])): if key not in pltvars: raise KeyError('Unknown plot variable: "{}"'.format(key)) pltscheme = self.pltscheme else: pltscheme = model.getPltScheme() # Create figure fig, axes = self.createBackBone(pltscheme) # Loop through each subgraph for ax, (grouplabel, keys) in zip(axes, pltscheme.items()): ax_legend_spikes = False # Extract variables to plot nvars = len(keys) ax_pltvars = [pltvars[k] for k in keys] if nvars == 1: ax_pltvars[0]['color'] = 'k' ax_pltvars[0]['ls'] = '-' # Set y-axis unit and bounds self.setYLabel(ax, ax_pltvars[0].copy(), fs, grouplabel=grouplabel) if 'bounds' in ax_pltvars[0]: ax_min = min([ap['bounds'][0] for ap in ax_pltvars]) ax_max = max([ap['bounds'][1] for ap in ax_pltvars]) ax.set_ylim(ax_min, ax_max) # Plot time series icolor = 0 for yplt, name in zip(ax_pltvars, pltscheme[grouplabel]): color = yplt.get('color', 'C{}'.format(icolor)) y = extractPltVar(model, yplt, data, meta, t.size, name) ax.plot(t, y, yplt.get('ls', '-'), c=color, lw=lw, label='$\\rm {}$'.format(yplt['label'])) if 'color' not in yplt: icolor += 1 # Optional: add spikes if name == 'Qm' and spikes != 'none': ax_legend_spikes = self.materializeSpikes( ax, data, tplt, yplt, color, spikes, add_to_legend=True) # Add legend if nvars > 1 or 'gate' in ax_pltvars[0]['desc'] or ax_legend_spikes: ax.legend(fontsize=fs, loc=7, ncol=nvars // 4 + 1, frameon=False) # Set x-limits and add optional patches for ax in axes: ax.set_xlim(t.min(), t.max()) if patches != 'none': self.addPatches(ax, tpatch_on, tpatch_off, tplt) # Post-process figure self.postProcess(axes, tplt, fs, meta, prettify) axes[0].set_title(self.figtitle(meta), fontsize=fs) fig.tight_layout() fig.canvas.set_window_title(model.filecode(meta)) # Save figure if needed (automatic or checked) if save: filecode = model.filecode(meta) if outputdir is None: outputdir = os.path.split(filepath)[0] plt_filename = '{}/{}.{}'.format(outputdir, filecode, fig_ext) plt.savefig(plt_filename) logger.info('Saving figure as "{}"'.format(plt_filename)) plt.close() figs.append(fig) return figs if __name__ == '__main__': # example of use filepaths = OpenFilesDialog('pkl')[0] comp_plot = CompTimeSeries(filepaths, 'Qm') fig = comp_plot.render( lines=['-', '--'], labels=['60 kPa', '80 kPa'], patches='one', colors=['r', 'g'], xticks=[0, 100], yticks=[-80, +50], inset={'xcoords': [5, 40], 'ycoords': [-35, 45], 'xlims': [57.5, 60.5], 'ylims': [10, 35]} ) scheme_plot = GroupedTimeSeries(filepaths) figs = scheme_plot.render() plt.show() diff --git a/PySONIC/postpro.py b/PySONIC/postpro.py index 11a5c27..0e6d837 100644 --- a/PySONIC/postpro.py +++ b/PySONIC/postpro.py @@ -1,535 +1,333 @@ # -*- 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: 2019-08-14 17:46:09 +# @Last Modified time: 2019-08-22 15:29:40 ''' Utility functions to detect spikes on signals and compute spiking metrics. ''' import pickle import numpy as np import pandas as pd from scipy.optimize import brentq +from scipy.signal import find_peaks, peak_prominences from .constants import * -from .utils import logger, debug +from .utils import logger, debug, isIterable, loadData def detectCrossings(x, thr=0.0, edge='both'): ''' Detect crossings of a threshold value in a 1D signal. :param x: 1D array_like data. :param edge: 'rising', 'falling', or 'both' :return: 1D array with the indices preceding the crossings ''' ine, ire, ife = np.array([[], [], []], dtype=int) x_padright = np.hstack((x, x[-1])) x_padleft = np.hstack((x[0], x)) if edge.lower() in ['falling', 'both']: ire = np.where((x_padright <= thr) & (x_padleft > thr))[0] if edge.lower() in ['rising', 'both']: ife = np.where((x_padright >= thr) & (x_padleft < thr))[0] ind = np.unique(np.hstack((ine, ire, ife))) - 1 return ind def getFixedPoints(x, dx, filter='stable', der_func=None): ''' Find fixed points in a 1D plane phase profile. :param x: variable (1D array) :param dx: derivative (1D array) :param filter: string indicating whether to consider only stable/unstable fixed points or both :param: der_func: derivative function :return: array of fixed points values (or None if none is found) ''' fps = [] edge = {'stable': 'falling', 'unstable': 'rising', 'both': 'both'}[filter] izc = detectCrossings(dx, edge=edge) if izc.size > 0: for i in izc: # If derivative function is provided, find root using iterative Brent method if der_func is not None: fps.append(brentq(der_func, x[i], x[i + 1], xtol=1e-16)) # Otherwise, approximate root by linear interpolation else: fps.append(x[i] - dx[i] * (x[i + 1] - x[i]) / (dx[i + 1] - dx[i])) return np.array(fps) else: return np.array([]) def getEqPoint1D(x, dx, x0): ''' Determine equilibrium point in a 1D plane phase profile, for a given starting point. :param x: variable (1D array) :param dx: derivative (1D array) :param x0: abscissa of starting point (float) :return: abscissa of equilibrium point (or np.nan if none is found) ''' # Find stable fixed points in 1D plane phase profile x_SFPs = getFixedPoints(x, dx, filter='stable') if x_SFPs.size == 0: return np.nan # Determine relevant stable fixed point from y0 sign y0 = np.interp(x0, x, dx, left=np.nan, right=np.nan) inds_subset = x_SFPs >= x0 ind_SFP = 0 if y0 < 0: inds_subset = ~inds_subset ind_SFP = -1 x_SFPs = x_SFPs[inds_subset] if len(x_SFPs) == 0: return np.nan return x_SFPs[ind_SFP] -def detectPeaks(x, mph=None, mpd=1, threshold=0, edge='rising', kpsh=False, valley=False, ax=None): - ''' - Detect peaks in data based on their amplitude and other features. - Adapted from Marco Duarte: - http://nbviewer.jupyter.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb +def convertTime2SampleCriterion(x, dt, nsamples): + if isIterable(x) and len(x) == 2: + return (convertTime2Sample(x[0], dt, nsamples), convertTime2Sample(x[1], dt, nsamples)) + else: + if isIterable(x) and len(x) == nsamples: + return np.array([convertTime2Sample(item, dt, nsamples) for item in x]) + elif x is None: + return None + else: + return int(np.ceil(x / dt)) - :param x: 1D array_like data. - :param mph: minimum peak height (default = None). - :param mpd: minimum peak distance in indexes (default = 1) - :param threshold : minimum peak prominence (default = 0) - :param edge : for a flat peak, keep only the rising edge ('rising'), only the - falling edge ('falling'), both edges ('both'), or don't detect a flat peak (None). - (default = 'rising') - :param kpsh: keep peaks with same height even if they are closer than `mpd` (default = False). - :param valley: detect valleys (local minima) instead of peaks (default = False). - :param show: plot data in matplotlib figure (default = False). - :param ax: a matplotlib.axes.Axes instance, optional (default = None). - :return: 1D array with the indices of the peaks +def computeTimeStep(t): + ''' Compute time step based on time vector. + + :param t: time vector (s) + :return: average time step (s) ''' - print('min peak height:', mph, ', min peak distance:', mpd, - ', min peak prominence:', threshold) - # Convert input to numpy array - x = np.atleast_1d(x).astype('float64') + # Compute time step vector + dt = np.diff(t) # s - # Revert signal sign for valley detection - if valley: - x = -x + # Raise error if time step vector is not uniform + is_uniform_dt = np.allclose(np.diff(dt), np.zeros(dt.size - 1), atol=1e-5) + if not is_uniform_dt: + raise ValueError(f'non-uniform time vector (variation range = {np.ptp(dt)}') - # Differentiate signal - dx = np.diff(x) + # Return average dt value + return np.mean(dt) # s - # Find indices of all peaks with edge criterion - ine, ire, ife = np.array([[], [], []], dtype=int) - if not edge: - ine = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) > 0))[0] - else: - if edge.lower() in ['rising', 'both']: - ire = np.where((np.hstack((dx, 0)) <= 0) & (np.hstack((0, dx)) > 0))[0] - if edge.lower() in ['falling', 'both']: - ife = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) >= 0))[0] - ind = np.unique(np.hstack((ine, ire, ife))) - - # Remove first and last values of x if they are detected as peaks - if ind.size and ind[0] == 0: - ind = ind[1:] - if ind.size and ind[-1] == x.size - 1: - ind = ind[:-1] - - print('{} raw peaks'.format(ind.size)) - - # Remove peaks < minimum peak height - if ind.size and mph is not None: - ind = ind[x[ind] >= mph] - print('{} height-filtered peaks'.format(ind.size)) - - # Remove peaks - neighbors < threshold - if ind.size and threshold > 0: - dx = np.min(np.vstack([x[ind] - x[ind - 1], x[ind] - x[ind + 1]]), axis=0) - ind = np.delete(ind, np.where(dx < threshold)[0]) - print('{} prominence-filtered peaks'.format(ind.size)) - - # Detect small peaks closer than minimum peak distance - if ind.size and mpd > 1: - ind = ind[np.argsort(x[ind])][::-1] # sort ind by peak height - idel = np.zeros(ind.size, dtype=bool) - for i in range(ind.size): - if not idel[i]: - # keep peaks with the same height if kpsh is True - idel = idel | (ind >= ind[i] - mpd) & (ind <= ind[i] + mpd) \ - & (x[ind[i]] > x[ind] if kpsh else True) - idel[i] = 0 # Keep current peak - # remove the small peaks and sort back the indices by their occurrence - ind = np.sort(ind[~idel]) - print('{} distance-filtered peaks'.format(ind.size)) - - return ind +def find_tpeaks(t, y, **kwargs): + ''' Wrapper around the scipy.signal.find_peaks function that provides a time vector + associated to the signal, and translates time-based selection criteria into + index-based criteria before calling the function. -def detectPeaksTime(t, y, mph, mtd, mpp=0): - ''' Extension of the detectPeaks function to detect peaks in data based on their - amplitude and time difference, with a non-uniform time vector. - - :param t: time vector (not necessarily uniform) - :param y: signal - :param mph: minimal peak height - :param mtd: minimal time difference - :mpp: minmal peak prominence - :return: array of peak indexes + :param t: time vector + :param y: signal vector + :return: 2-tuple with peaks timings and properties dictionary ''' + # Compute index vector + nsamples = t.size + indexes = np.arange(nsamples) - # Determine whether time vector is uniform (threshold in time step variation) - dt = np.diff(t) - if (dt.max() - dt.min()) / dt.min() < 1e-2: - isuniform = True - else: - isuniform = False + # Compute time step + dt = computeTimeStep(t) # s - if isuniform: - print('uniform time vector') - dt = t[2] - t[1] - mpd = int(np.ceil(mtd / dt)) - ipeaks = detectPeaks(y, mph, mpd=mpd, threshold=mpp) - else: - print('non-uniform time vector') - # Detect peaks on signal with no restriction on inter-peak distance - irawpeaks = detectPeaks(y, mph, mpd=1, threshold=mpp) - npeaks = irawpeaks.size - if npeaks > 0: - # Filter relevant peaks with temporal distance - ipeaks = [irawpeaks[0]] - for i in range(1, npeaks): - i1 = ipeaks[-1] - i2 = irawpeaks[i] - if t[i2] - t[i1] < mtd: - if y[i2] > y[i1]: - ipeaks[-1] = i2 - else: - ipeaks.append(i2) - else: - ipeaks = [] - ipeaks = np.array(ipeaks) + # Convert provided time-based input criteria into samples-based criteria + time_based_inputs = ['distance', 'width', 'wlen', 'plateau_size'] + for key in time_based_inputs: + if key in kwargs: + kwargs[key] = convertTime2SampleCriterion(kwargs[key], dt, nsamples) + if 'width' not in kwargs: + kwargs['width'] = 1 - return ipeaks + # Find peaks in the signal and return + return find_peaks(y, **kwargs) -def detectSpikes(t, Qm, min_amp, min_dt): - ''' Detect spikes on a charge density signal, and - return their number, latency and rate. +def convertPeaksProperties(t, properties): + ''' Convert index-based peaks properties into time-based properties. :param t: time vector (s) - :param Qm: charge density vector (C/m2) - :param min_amp: minimal charge amplitude to detect spikes (C/m2) - :param min_dt: minimal time interval between 2 spikes (s) - :return: 3-tuple with number of spikes, latency (s) and spike rate (sp/s) + :param properties: properties dictionary (with index-based information) + :return: properties dictionary (with time-based information) ''' - i_spikes = detectPeaksTime(t, Qm, min_amp, min_dt) - if len(i_spikes) > 0: - latency = t[i_spikes[0]] # s - n_spikes = i_spikes.size - if n_spikes > 1: - first_to_last_spike = t[i_spikes[-1]] - t[i_spikes[0]] # s - spike_rate = (n_spikes - 1) / first_to_last_spike # spikes/s - else: - spike_rate = 'N/A' - else: - latency = 'N/A' - spike_rate = 'N/A' - n_spikes = 0 - return (n_spikes, latency, spike_rate) - - -def findPeaks(y, mph=None, mpd=None, mpp=None): - ''' Detect peaks in a signal based on their height, prominence and/or separating distance. - - :param y: signal vector - :param mph: minimum peak height (in signal units, default = None). - :param mpd: minimum inter-peak distance (in indexes, default = None) - :param mpp: minimum peak prominence (in signal units, default = None) - :return: 4-tuple of arrays with the indexes of peaks occurence, peaks prominence, - peaks width at half-prominence and peaks half-prominence bounds (left and right) - - Adapted from: - - Marco Duarte's detect_peaks function - (http://nbviewer.jupyter.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb) - - MATLAB findpeaks function (https://ch.mathworks.com/help/signal/ref/findpeaks.html) + index_based_outputs = [ + 'left_bases', 'right_bases', + 'left_ips', 'right_ips', + 'left_edges', 'right_edges' + ] + index_distance_based_outputs = ['widths', 'plateau_sizes'] + indexes = np.arange(t.size) + dt = computeTimeStep(t[1:]) + for key in index_based_outputs: + if key in properties: + properties[key] = np.interp(properties[key], indexes, t, left=np.nan, right=np.nan) + for key in index_distance_based_outputs: + if key in properties: + properties[key] = np.array(properties[key]) * dt + return properties + + +def detectSpikes(data, key='Qm', mpt=SPIKE_MIN_DT, mph=SPIKE_MIN_QAMP, mpp=SPIKE_MIN_QPROM, ipad=0): + ''' Detect spikes in simulation output data, by detecting peaks with specific height, prominence + and distance properties on a given signal. + + :param data: simulation output dataframe + :param key: key of signal on which to detect peaks + :param mpt: minimal time interval between two peaks (s) + :param mph: minimal peak height (in signal units) + :param mpp: minimal peak prominence (in signal units) + :return: indexes and properties of detected spikes ''' + if key not in data: + raise ValueError(f'{key} vector not available in dataframe') + + # If first two time samples are equal, remove first row from dataset and call function recursively + if data['t'].values[1] == data['t'].values[0]: + logger.debug('Removing first row from dataframe (reccurent time values)') + data = data.iloc[1:] + return detectSpikes(data, key=key, mpt=mpt, mph=mph, mpp=mpp, ipad=ipad + 1) + + # Detect peaks + ipeaks, properties = find_tpeaks( + data['t'].values, + data[key].values, + height=mph, + distance=mpt, + prominence=mpp + ) + + # Adjust peak prominences and bases with restricted analysis window length + # based on smallest peak width + if len(ipeaks) > 0: + wlen = 5 * min(properties['widths']) + properties['prominences'], properties['left_bases'], properties['right_bases'] = peak_prominences( + data[key].values, ipeaks, wlen=wlen) + + # Correct index of specific outputs + index_based_outputs = [ + 'left_bases', 'right_bases', + 'left_ips', 'right_ips', + 'left_edges', 'right_edges' + ] + ipeaks += ipad + for key in index_based_outputs: + if key in properties: + properties[key] += ipad - # Define empty output - empty = (np.array([]),) * 4 + return ipeaks, properties - # Differentiate signal - dy = np.diff(y) - # Find all peaks and valleys - # s = np.sign(dy) - # ipeaks = np.where(np.diff(s) < 0.0)[0] + 1 - # ivalleys = np.where(np.diff(s) > 0.0)[0] + 1 - ipeaks = np.where((np.hstack((dy, 0)) <= 0) & (np.hstack((0, dy)) > 0))[0] - ivalleys = np.where((np.hstack((dy, 0)) >= 0) & (np.hstack((0, dy)) < 0))[0] +def computeFRProfile(data): + ''' Compute temporal profile of firing rate from simulaton output. - # Return empty output if no peak detected - if ipeaks.size == 0: - return empty + :param data: simulation output dataframe + :return: firing rate profile interpolated along time vector + ''' + # Detect spikes in data + ispikes, _ = detectSpikes(data) - logger.debug('%u peaks found, starting at index %u and ending at index %u', - ipeaks.size, ipeaks[0], ipeaks[-1]) - if ivalleys.size > 0: - logger.debug('%u valleys found, starting at index %u and ending at index %u', - ivalleys.size, ivalleys[0], ivalleys[-1]) - else: - logger.debug('no valleys found') - - # Ensure each peak is bounded by two valleys, adding signal boundaries as valleys if necessary - if ivalleys.size == 0 or ipeaks[0] < ivalleys[0]: - ivalleys = np.insert(ivalleys, 0, -1) - if ipeaks[-1] > ivalleys[-1]: - ivalleys = np.insert(ivalleys, ivalleys.size, y.size - 1) - if ivalleys.size - ipeaks.size != 1: - logger.debug('Cleaning up incongruities') - i = 0 - while i < min(ipeaks.size, ivalleys.size) - 1: - if ipeaks[i] < ivalleys[i]: # 2 peaks between consecutive valleys -> remove lowest - idel = i - 1 if y[ipeaks[i - 1]] < y[ipeaks[i]] else i - logger.debug('Removing abnormal peak at index %u', ipeaks[idel]) - ipeaks = np.delete(ipeaks, idel) - if ipeaks[i] > ivalleys[i + 1]: - idel = i + 1 if y[ivalleys[i]] < y[ivalleys[i + 1]] else i - logger.debug('Removing abnormal valley at index %u', ivalleys[idel]) - ivalleys = np.delete(ivalleys, idel) - else: - i += 1 - logger.debug('Post-cleanup: %u peaks and %u valleys', ipeaks.size, ivalleys.size) - - # Remove peaks < minimum peak height - if mph is not None: - ipeaks = ipeaks[y[ipeaks] >= mph] - if ipeaks.size == 0: - return empty - - # Detect small peaks closer than minimum peak distance - if mpd is not None: - ipeaks = ipeaks[np.argsort(y[ipeaks])][::-1] # sort ipeaks by descending peak height - idel = np.zeros(ipeaks.size, dtype=bool) # initialize boolean deletion array (all false) - for i in range(ipeaks.size): # for each peak - if not idel[i]: # if not marked for deletion - closepeaks = (ipeaks >= ipeaks[i] - mpd) & (ipeaks <= ipeaks[i] + mpd) # close peaks - idel = idel | closepeaks # mark for deletion along with previously marked peaks - # idel = idel | (ipeaks >= ipeaks[i] - mpd) & (ipeaks <= ipeaks[i] + mpd) - idel[i] = 0 # keep current peak - # remove the small peaks and sort back the indices by their occurrence - ipeaks = np.sort(ipeaks[~idel]) - - # Detect smallest valleys between consecutive relevant peaks - ibottomvalleys = [] - if ipeaks[0] > ivalleys[0]: - itrappedvalleys = ivalleys[ivalleys < ipeaks[0]] - ibottomvalleys.append(itrappedvalleys[np.argmin(y[itrappedvalleys])]) - for i, j in zip(ipeaks[:-1], ipeaks[1:]): - itrappedvalleys = ivalleys[np.logical_and(ivalleys > i, ivalleys < j)] - ibottomvalleys.append(itrappedvalleys[np.argmin(y[itrappedvalleys])]) - if ipeaks[-1] < ivalleys[-1]: - itrappedvalleys = ivalleys[ivalleys > ipeaks[-1]] - ibottomvalleys.append(itrappedvalleys[np.argmin(y[itrappedvalleys])]) - ipeaks = ipeaks - ivalleys = np.array(ibottomvalleys, dtype=int) - - # Ensure each peak is bounded by two valleys, adding signal boundaries as valleys if necessary - if ipeaks[0] < ivalleys[0]: - ivalleys = np.insert(ivalleys, 0, 0) - if ipeaks[-1] > ivalleys[-1]: - ivalleys = np.insert(ivalleys, ivalleys.size, y.size - 1) - - # Remove peaks < minimum peak prominence - if mpp is not None: - - # Compute peaks prominences as difference between peaks and their closest valley - prominences = y[ipeaks] - np.amax((y[ivalleys[:-1]], y[ivalleys[1:]]), axis=0) - - # initialize peaks and valleys deletion tables - idelp = np.zeros(ipeaks.size, dtype=bool) - idelv = np.zeros(ivalleys.size, dtype=bool) - - # for each peak (sorted by ascending prominence order) - for ind in np.argsort(prominences): - ipeak = ipeaks[ind] # get peak index - - # get peak bases as first valleys on either side not marked for deletion - indleftbase = ind - indrightbase = ind + 1 - while idelv[indleftbase]: - indleftbase -= 1 - while idelv[indrightbase]: - indrightbase += 1 - ileftbase = ivalleys[indleftbase] - irightbase = ivalleys[indrightbase] - - # Compute peak prominence and mark for deletion if < mpp - indmaxbase = indleftbase if y[ileftbase] > y[irightbase] else indrightbase - if y[ipeak] - y[ivalleys[indmaxbase]] < mpp: - idelp[ind] = True # mark peak for deletion - idelv[indmaxbase] = True # mark highest surrouding valley for deletion - - # remove irrelevant peaks and valleys, and sort back the indices by their occurrence - ipeaks = np.sort(ipeaks[~idelp]) - ivalleys = np.sort(ivalleys[~idelv]) - - if ipeaks.size == 0: - return empty - - # Compute peaks prominences and reference half-prominence levels - prominences = y[ipeaks] - np.amax((y[ivalleys[:-1]], y[ivalleys[1:]]), axis=0) - refheights = y[ipeaks] - prominences / 2 - - # Compute half-prominence bounds - halfmaxbounds = np.empty((ipeaks.size, 2)) - for i in range(ipeaks.size): - - # compute the index of the left-intercept at half max - ileft = ipeaks[i] - while ileft >= ivalleys[i] and y[ileft] > refheights[i]: - ileft -= 1 - if ileft < ivalleys[i]: # intercept exactly on valley - halfmaxbounds[i, 0] = ivalleys[i] - else: # interpolate intercept linearly between signal boundary points - a = (y[ileft + 1] - y[ileft]) / 1 - b = y[ileft] - a * ileft - halfmaxbounds[i, 0] = (refheights[i] - b) / a - - # compute the index of the right-intercept at half max - iright = ipeaks[i] - while iright <= ivalleys[i + 1] and y[iright] > refheights[i]: - iright += 1 - if iright > ivalleys[i + 1]: # intercept exactly on valley - halfmaxbounds[i, 1] = ivalleys[i + 1] - else: # interpolate intercept linearly between signal boundary points - if iright == y.size - 1: # special case: if end of signal is reached, decrement iright - iright -= 1 - a = (y[iright + 1] - y[iright]) / 1 - b = y[iright] - a * iright - halfmaxbounds[i, 1] = (refheights[i] - b) / a - - # Compute peaks widths at half-prominence - widths = np.diff(halfmaxbounds, axis=1) - - # Convert halfmaxbounds to true integers - halfmaxbounds[:, 0] = np.floor(halfmaxbounds[:, 0]) - halfmaxbounds[:, 1] = np.ceil(halfmaxbounds[:, 1]) - halfmaxbounds = halfmaxbounds.astype(int) - - bounds = np.array([ivalleys[:-1], ivalleys[1:]]).T - 1 - bounds[bounds < 0] = 0 - - return ipeaks, prominences, widths, halfmaxbounds, bounds - - -def computeFRProfile(data, t, Qm): - # Prominence-based spike detection - dt = t[2] - t[1] - mpd = int(np.ceil(SPIKE_MIN_DT / dt)) - ispikes, *_ = findPeaks(Qm, mph=SPIKE_MIN_QAMP, mpd=mpd, mpp=SPIKE_MIN_QPROM) - if len(ispikes) <= 1: - return np.full(t.size, np.nan) - - # Compute firing rate as function of spike time and re-interpolate along time vector + # Compute firing rate as function of spike time + t = data['t'].values tspikes = t[ispikes][:-1] sr = 1 / np.diff(t[ispikes]) + + # Interpolate firing rate vector along time vector return np.interp(t, tspikes, sr, left=np.nan, right=np.nan) def computeSpikingMetrics(filenames): ''' Analyze the charge density profile from a list of files and compute for each one of them the following spiking metrics: - latency (ms) - firing rate mean and standard deviation (Hz) - spike amplitude mean and standard deviation (nC/cm2) - spike width mean and standard deviation (ms) :param filenames: list of files to analyze :return: a dataframe with the computed metrics ''' # Initialize metrics dictionaries keys = [ 'latencies (ms)', 'mean firing rates (Hz)', 'std firing rates (Hz)', 'mean spike amplitudes (nC/cm2)', 'std spike amplitudes (nC/cm2)', 'mean spike widths (ms)', 'std spike widths (ms)' ] metrics = {k: [] for k in keys} # Compute spiking metrics for fname in filenames: # Load data from file - logger.debug('loading data from file "{}"'.format(fname)) - with open(fname, 'rb') as fh: - frame = pickle.load(fh) - df = frame['data'] - meta = frame['meta'] + data, meta = loadData(fname) tstim = meta['tstim'] - t = df['t'].values - Qm = df['Qm'].values - dt = t[2] - t[1] - - # Detect spikes on charge profile - mpd = int(np.ceil(SPIKE_MIN_DT / dt)) - ispikes, prominences, widths, *_ = findPeaks(Qm, SPIKE_MIN_QAMP, mpd, SPIKE_MIN_QPROM) - widths *= dt + t = data['t'].values + + # Detect spikes in data + ispikes, properties = detectSpikes(data) + + # Convert index-based outputs into time-based outputs + properties = convertPeaksProperties(t, properties) + widths = properties['widths'] + prominences = properties['prominences'] if ispikes.size > 0: # Compute latency latency = t[ispikes[0]] # Select prior-offset spikes ispikes_prior = ispikes[t[ispikes] < tstim] else: latency = np.nan ispikes_prior = np.array([]) # Compute spikes widths and amplitude if ispikes_prior.size > 0: widths_prior = widths[:ispikes_prior.size] prominences_prior = prominences[:ispikes_prior.size] else: widths_prior = np.array([np.nan]) prominences_prior = np.array([np.nan]) # Compute inter-spike intervals and firing rates if ispikes_prior.size > 1: ISIs_prior = np.diff(t[ispikes_prior]) FRs_prior = 1 / ISIs_prior else: ISIs_prior = np.array([np.nan]) FRs_prior = np.array([np.nan]) # Log spiking metrics logger.debug('%u spikes detected (%u prior to offset)', ispikes.size, ispikes_prior.size) logger.debug('latency: %.2f ms', latency * 1e3) logger.debug('average spike width within stimulus: %.2f +/- %.2f ms', np.nanmean(widths_prior) * 1e3, np.nanstd(widths_prior) * 1e3) logger.debug('average spike amplitude within stimulus: %.2f +/- %.2f nC/cm2', np.nanmean(prominences_prior) * 1e5, np.nanstd(prominences_prior) * 1e5) logger.debug('average ISI within stimulus: %.2f +/- %.2f ms', np.nanmean(ISIs_prior) * 1e3, np.nanstd(ISIs_prior) * 1e3) logger.debug('average FR within stimulus: %.2f +/- %.2f Hz', np.nanmean(FRs_prior), np.nanstd(FRs_prior)) # Complete metrics dictionaries metrics['latencies (ms)'].append(latency * 1e3) metrics['mean firing rates (Hz)'].append(np.mean(FRs_prior)) metrics['std firing rates (Hz)'].append(np.std(FRs_prior)) metrics['mean spike amplitudes (nC/cm2)'].append(np.mean(prominences_prior) * 1e5) metrics['std spike amplitudes (nC/cm2)'].append(np.std(prominences_prior) * 1e5) metrics['mean spike widths (ms)'].append(np.mean(widths_prior) * 1e3) metrics['std spike widths (ms)'].append(np.std(widths_prior) * 1e3) # Return dataframe with metrics return pd.DataFrame(metrics, columns=metrics.keys()) diff --git a/notebooks/STN neuron.ipynb b/notebooks/STN neuron.ipynb index 652dd70..74b8dad 100644 --- a/notebooks/STN neuron.ipynb +++ b/notebooks/STN neuron.ipynb @@ -1,448 +1,447 @@ { "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": 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, Intensity2Pressure\n", "from PySONIC.plt import CompTimeSeries, GroupedTimeSeries\n", - "from PySONIC.postpro import findPeaks\n", "from PySONIC.constants import *\n", "\n", "pneuron = getPointNeuron('STN')\n", "standard_Vm0 = pneuron.Vm0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spontaneous spiking activity" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[
]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "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": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for A in [5., 20., 60.]:\n", " 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": 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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4ceKE6ftz587FP//5T8sV2BEjRqBRo0Z46qmnLNu46qqrUFhYiD597B8pk4rvETEgBD9k6hxnar8yBZmf8JKqucl4A0IQhPTCzJYbIpcsWYLRo0eb7i8YOnQoLr74YowYMcK07FVXXYVnnnkGc+fONX3/6aefBgA8+eSTlrK5Vdr9Kvfp/pFNd/tC8snUECa5d8ONzE94EQNCEITQEYlEsHXr1rj0Y8eOYfDgwfjwww/j3hswYACqVauGTz/9NO69Tp064fHHH8fbb78d956y3+Cll16ylSmR01rCZECIt1QQBEEoLogBIQinMH/88Qe2bdsWl/7CCy+ge/fucceA9u/fHw0bNsQ777yjS3/11VcxYcIEXH755XF1KZuD7QyBX375xY/4AJyV9CBWY4qrASGrhJlPps5xpvYrU5D5CS/igRCEUxAv4QjffPMNNm7cqEvbuHEjunbtiq+//lqXvmTJEgwePBiHDh3SpdevXx8NGjTATz/9pEu/99578emnn+Ldd9/VpSuGw6uvvqpLz83NdZTXb6hFogaC3fthWvUXA0LwQ6bOcab2SxAyhYw3IMKkIAiZz/Hjx+N++DZs2IDPPvtMl3bgwAFMmzZNd8xnQUEB6tSpg5tuukmXd/78+ejatSt+++03NW3Pnj3o0KEDmjRpost74403YtGiRbjooot06Z06dcKECRPwxBNP6NL37t0LAHEGh4LVg8iMfXTzObNTCPy+F8T7bhAPhBBWMnWOM7VfmYLMT3gRD4QgBMjx48exdu1a3Qdr586dmDBhgk5J/v777zFkyBDdUzZXr16NLl26YNWqVbqybdu21a3Q//HHHyhTpgw6d+6sa/vcc89Ft27ddHsHbr75ZvTv3x933nmnmvbVV1/ht99+iwsP+tOf/oRFixbhb3/7m5q2a9cu034qBoEVO3fuNE33+oXj5wvKzgORyBdeIgZEVpa7r0DZRC2EFZljQRC0iAGRRPLy8rBu3brA6y0sLMTChQt1q8rM7OlhUqcChYWF2L59uy5t165d+Pnnn3VpL730Er788kv1+o8//sDIkSPx/fffq2lbt27F7bffrgvBmTlzJs4880ysXr1aTbv66qvRsmVLTJkyRU1r164dBg8erDvms3Xr1njttdd05/337NkTixcvRvfu3dW0ESNGYOXKlbjxxhvVtG+//RZFRUVYsmSJab+1fVYe9DJ79mw1zUlJPXDggGNepzqsvlgSTU+nByKRdjN9E7Uol0JxRe7dcCPzI5ySBkSLFi3Qs2dPbNiwAQBw+PBh3HPPPTrFb+nSpermT7c89thj6NGjh06pvOGGG3D66aerCm5hYSHatWuHYcOGqXk2bNiAW2+9VbdCvWjRIrz//vvq9cGDBzFy5Ej8+OOPatq6deviTq8xhpzs3bsXixcv1n3YN23apHvA1datWzFq1Cjk5+eraa+99hpeeeUV9Xr37t3o168fli1bpqZNnDgRXbt2VQ2kSCSCbt266VbK9+3bhxEjRmDTpk1qWvfu3XHWWWdh8eLFatoZZ5yB+vXrq/J/9dVXGDp0KDp27KjmGT16NMaMGYMWLVqoaX369MHEiRN1m3f//Oc/Y+fOnbj11lvVtAULFgAApk6dqqYpq/jaeS8sLAQAbN68WU1TTvnRjllBQQGMOCmAZivwXr6EtXmt2nJaUbdqz8o7EKQBEcY9EG4prh4IIfPJ1HssU/uVKcj8hBfxQKSAtWvXAgDGjx+PGTNm4Prrr1ffu/baazF8+HDVU/Htt9+idevW+OSTT9Q83333HXbv3q1eT58+HQB059LPmDEDADBt2jQAUcV4xYoVGD9+vJqne/fumDp1Kq6++mo1rWvXrujbt6+qvA4fPhxjxoxBs2bN1Dznn38+br75ZixduhRA1BAoXbo0Bg4cqOY577zz0KVLF3zwwQcAoqvgjRs3RvXq1dU8F198MZ588kldOM2QIUNw1113qefzDxs2DNOnT0f79u3VPLfffjsWLVqkbqhdvXo1PvvsMzz33HNqnnvuuQdPPfUU2rVrp6Z98cUXABC3QReAusn3119/jXtPMfi0KAaVVuFXMFNY/SjxQa1SJ9qOGwMiVR4I4ziGeQ+EHWHaIyUeCMEPmfocCEEQwk3GGxB2P8rKF6/dCTLKewMHDsSuXbtwyy23AIgq4j169ECtWrVctWX3Q67EpZspwQcPHgQA/PDDD5b1bNmyBQDw5ptv6v4DUDfeKmfwK3H82qf7KkaQNuRHQRkjq9h5ADhy5IilbIoBpvRDi1l+pT2zsXSbZle/n7QgZPHbjlP5IGRwW7dd/nQq4pnigRADQhBOIvduuJH5CS/igUgByiC7UULNTtYx4teAsMOLjG6MJa99VV7bhcYodZvl8TsmqTQgnEimB8KLAeGmraAV3SBDmPy07/ReonXLHgihuCNzLAhCOhADAu5+uI15vCqzXhR4pzQ/KKFIbk6d0bZpZxy4yRNkf83qspPLzsORiBxWsjgRpAciVSFMQZIsI0E8EMG0LxRPMnWOM7VfmYLMT3gRD0QKcBM7amVk+FVmg1yR91K3Xw+EG8PDrn2v/U1HCJNT2TCGMKV7D0SYTmFK5Msy2ce4CoLgD1FQw43MT3hJ1dyUSEkrPiCiHAD/D0B9AKUAPAHgBwCTADCA7wHczcy+d5B5UaoT9UB48XYEUc5IogaEG+PErTJmLGcmgx8F3aouP2lO7QZlQHghHXsgUmVAJEIiBkayDQPxQAjJRuZYEIR0EGYPRH8Ae5m5I4DLAYwH8ByAUbE0AtDHqRI/4UlaEglzMqvHDr8r5l5kdONJMNsD4XeM/HoI3Hp4vIYwFec9EEF4IKxIxTGudqTLA+EW8UAIQmoRwyjcyPyEFwlhAmYAeEhzfQJAGwDKwwMWAOhuLOSHVOyBSHcIkxcjw68BYUay90C48YwkSlAeCKf9F0EYEE6k80vfb9th2ESdjLaNiAdC8IPMsSAI6SC0BgQz/8HMh4ioHICZAEYBID75bXkIQAWzskR0BxGtIKIVZg/80rSh5Pecx+seCLtyxra0+N18bZXH7ybqZIZnJVJXKsKaglLsgtyYfSrtgUik3qBIhQdCDAjBD5k6x5nar0xB5ie8iAcCABHVAbAIwFRmfhuAdgm3HID9ZuWY+XVmbsvMbUuXLu2mHcv30h3C5IWgvBRhCGEyw+spT0GduGRGMjZROxHEHgivdReHPRCJEKZjXJNButsXkk+mznGm9itTkPkJL6e8AUFENQB8DOBfzPz/YsmriahL7PXlAJYk0kYig5yqEKagwpPCaEAEsYk6WSvDTh4IOYUpdXsgklnWLeKBEMKKzLEgCFpS9Z0Q2lOYAIwEUAnAQ0Sk7IW4F8ALRFQSwI+Ihjb5JugQpnQbEG5wU49WsfeyjyAIBchvOJRdXYmmFacQJr8yeE0PmnQZEMk+xjXdyl262xeST6bOcab2K1OQ+Qkvp7wBwcz3ImowGOnspZ6glL/icIxrUEaG1/bDuok6mQZEUN6QU8kD4XUPBBElFPrnpnyYNlF7PQZZEAQhXYgBIZzSv1iJKidu0ry05Rcv/fCqMCW6iTpIwyNZIUypMiCCDGGyImiFuDhsok4FqfBASAiT4IdMneNM7ZcgJJtTfg9EKghq70BQbdmRiXsgMs0D4USiq+GZ7IEww+0YezHy/bYhCGElUxXtTO2XIGQKYkDAn6LhV6lMdwhTuvdgmJVza7D4HTuntKAIwoPgBy/1hvVH2SqEKSyIB0IQBOEk8t0SXsQDkQJSaUAEuU8gkTxePRB+Q5iSGTKlpbiHMAVRPogwqHR7IBIxIFLhgQiTMeMF+ZHPfDJ1jjO1X5lCUA9qFYovp7QB4QYrRTjITdSpDE9KhgfEzpPgpm635SSEyZuS74UwGhBuCXMIU7o9EIIgCMKphXggAiJZindxNSDsUFYUzDwQXvHiuXDr8Qi7AZGoAujFgLB6nSoj5FTbA+Flbr0a0n7a8COLkJlk6hxnar8yBZmf8CIGRApIRwhTug2IVHsgkrHnQktxDWEKUlnUGnlB78NIptcjmfUBxc8DkYntC8lH5lgQhHRwShsQCkFtEE7GHggvedyQzD0Qfg2IdG+iTkcIU6IGiN9V7qD2QBhJRgiT2+cipEKB8vKMhjB5IAShuCKGUbiR+Qkv4oFIIekOYbIjqPCkZHoEzMolGsJkRrJCmJzK+vGG+Gk7FZuorbAa93TugUh2SJiXNlJxClMySHf7QvLJ1DmWTbrhJlPvO8E9p7QB4UXxNuJ3VTrMIUzJ8CTY4dVgCfLkK7dyeGnXqf9BejWC3gPhFT8GRLII2w+Z3z1E4oEQ/BC2+18QhPQiHoiASFZ4Uqr2QCRat588Wrwcq+pVcQoihClZm6gTaddNO4mWtxrroDdRFwcPhBcj328bqfBAyCZqQTiJ3LvhRuYnvIgBkQISUbyDCEVKh3chjJuhve650JKKfRFBeSC8yGZmgAURwpSsPRDpJBEZk620p3v8JAwk80n3PSYIwqnJKW1AKKRrE7WVku13Jd9NHq8KUzI8CcY8buVM1yZqM5xkcTIAgvRABLEHQjwQweQzkm4PhJD5ZKoBkan9yhRkfsKLeCBSQCKKt19Pghsl268inmg4T5CGQFifA+E2nx+lPNE9EF7Ka/GyypzOPRDJMiCsSNcxrmHyQKS7fSH5ZOocZ2q/MgWZn/AiBkQKCGrl3q8B4UbJ9hLSE5QBYZYWhk3UZiRrD0QyQpicxsRLCJNfD0RQIUxhMiAS8UC4JRUeCC9HxSajfaF4InMspAO578KLGBAWEFEWEb1KRF8T0edEdLZDfsv3EjEOkhmuE0TdbvK68UAkw5NgV85rCFMqw5q8tJuMECY3eU/FPRCJkIwQJr8eCAlhEoSTZPp3T3FH5kfwZUAQ0WlElB20MC65GkBpZu4AYDiAZ/1W5GW1OygvgRslO1UhTEYlN1EPRJAypfsUpnR7IJzKW3krxAPhrXyylfZ0/8imu30h+cgcC4KgJVQeiNiqfz8imkdEewBsALCLiNYT0TNE1Ci5Yuq4BMCHAMDM3wBom2iF6fZA2KV7WclPNMwoGZ4EO5LtgfCjxJvlS8YpTIluonaTHja8ypmoARFkG7KJWggrxeXz75VM7VemIPMTXkJlQABYBKAhgBEAajJzHWauDqAjgG8APEVE/ZMko5HyAA5oriNEVEKbgYjuIKIVRLTi6NGjlhUFFR6UijCfZO7BMObxW7eW4vQk6qA8EFr8hDA5jYOVgZKOEKZTzQORihCmZJDu9gXBL3LvCkK4KeGcBQDQnZkLjYnMnA9gFoBZRJQTqGTWHARQTnOdxcwnDHK9DuB1AKhRowYfOnTItCIvq+RuPBBu6vFatxdF3G/7xmu3Cr1dfUGGPjGzr9XZRAwILck4hclLXV7yBr1SbjX/fgwIr+2HaUXeryzpfhK1KGGZj8yxkA7kvgsvofJAmBkPfvIExFcArgAAImoP4Du/FQW1uh+0B8KvIh5GD4QddiFMiYQ1eSUoD4QXAyLR8lbzGLQHwm09yfBA+JVFwc2pRpnugRAyn0y9xzK1X5mCzI/g+AtLRJcR0RtE1DJ2fUfyxbLlfQAFRLQUwDgA99tlTjSMwY3C5VeBD9o4STScx6+MfsuZYRe6U5xCmPzUqcXvk6idCCqEKRUkahyGwYPhZfzEAyEIJ5F7VxD8karPjpsQprsADAAwiogqA2iZXJHsYeYiAEOCrDMVHggv5YIIBXKT1005vyFUiRoexhX27OzslB7Z6icsyCn0K1EPhJu8fj0QXvOnwgNRXDdR+/VAhMHgEYofmapoZ2q/MgWZH8FNCFMuM+9n5r8D6AGgXZJlShmJhAclc5XeTZiTGxmt6jTWbZcn0fCoIEKfku2BcFvWbbvJKm9Vp5cQJi/tuW3TbbupNiCC3ETtl3T/yKa7fUHwi9y7guCPVH123BgQ85QXzDwcwJTkiRM+vHgg3NRjVi6oDdLpPsY1SJncyhCmECanlWcvXoWwnMLkljDugXBDpnsgvGziFoonomgL6UDuu/ASGgOCmecYrl9MnjipJajwoGR6IBLxkljVqRBGD4RXGZJlQDi1kYxjXJ1kCkL591pcqyaRAAAgAElEQVRHsgwON6RiD0SmeyCEzCdT77FM7VemIPMjuD3GFQBARG0BPAigXqwsAWBmPi8JsiWdoFYHE1XgU7mJ2qsHwosB5VUms/JKf4PwQLglHR4I2QORmFxBlXVLcfVAyI+8IAjJQL5bwkuq5saTAQFgGoB/IHp0arHwjQd1ClNQ+xTcKBfJ9EDYhckkGmblFIJjVc6pLrsPQ3EKYfKCFw9EOvZABG1AmBEmD0TQ4xpkG0G1LxRPMnWOM7VfmYLMT3gJqwGRy8z/S4okaSBo5TyoMB+/xkm6j3H1K5OT4h3GPRBOZPqTqI3IJuqTBGlICoITco8JgqAlrAbEw0Q0AcCnAI4picw8O1CpUoSX8JxElXwvdfvN46atVHkgEg3rcltXcfJAJPrAt0SVfD91BNGmG8JuQPg1NORJ1EKyydQ5ztR+ZQoyP+ElrAbEAABNAOTgZAgTAyiWBoRCujZRZ4oHwowgw7rC6IFIxjGuXlaurQxBOYXJG5l+CpOQ+WSqIpep/coUZH4ErwbE+czcIimSpIFEPAB+lfyg2vebx065cVo1d0I8EO5DkJzw+yTqdO2BSKQNK1LhgQhaFiPpNiDkR14QBOHUIlXf+26eA6HlGyJqmhRJkkQqlfNkeCCCat8MqxCmIA2BIDwQfvvnllR5IJzqdDsOdq9TtQfCmJ7OPRCJkOkeCDEgMp9MneNM7VemIPMjePVAXALgL0S0DdE9EBlxjKufEKZkeiC81q2QDA9Eot4Nv2PidgxS4YFwixcDIMjjVv16iRLJHyYDIhUeCL/Ij6yQbDL1HsvUfglCsknVZ8erAdErKVKkCTeDnIiR4ZQ3mU+iZmZHBcpK8QzSOLLDzlhIZQiTU9lUhTB5KZ8qD4RbElXWxQMhHghB0CL3briR+RE8GRDM/HOyBEknqdwD4UYxDqpuo0Fh1b4dQYdnuZHbiJcVdif8eCBSFcLkR06v9XrFj8FhZbx69UC4JREPRLKNlHT/yKa7fSH5ZOocZ2q/BCHZpOqz42kPBBFNJqKKmutKRPT/ghcrtQS1eup3n0BQddutYHtp32kl3A1u5DYjLJuondpI9BSlTNsD4abvXr/UiusxrmHyQAiZjyjaQjqQ+y68hNKAAHAeM+9XLph5H4BWwYqUOrwo50FtdE6mB8IuzUv7ZuXC4IEI+ylMWvw8SM6PnFZteak3yHQ7ebx6IMJkQPgl3QaE/MgLxRW5d8ONzI/g1YDIIqJKygURVYb3fRSuIKIKRPQBES0moq+JqEMsvT0RLSOir4joYRf1uGnL8j03CrRfJT/oU5i8rgKnwziyasdvXWEyILx4EMxw8vyEyQPhVrZECNOKfCo8EMkg3e0LyUfmWEgHct+Fl1TNjVfl/1kAXxPRDAAM4HoATwYuVZQHAHzKzP8hosYApgNoDeBVANcC2ApgHhG1ZuZVfhoIKu46FUp2ouFRfkOovIRZmaX59Vy49WZ4VTKDMiASbSfIU5gSldWPHF69CX7LuCFsIUxaxAMhCP6QezfcyPwIrgyI2Or/N8w8hYhWAOiK6BGufZn5hyTJNg7Ro2KBqJwFRFQeQClm/ikm10cAugFwZUAwm28sdioTa8syj5ejVt2US0YokN8QKjf9T1Rut2lBGBBuSdQD4SeEyYsHw6qt4roHIpEQJivCsAciyAMA/CA/8plPps5xpvZLEJJN2DwQfwHwEhFtAvAhgJnMvDsoIYhoEID7DckDmPlbIqoJ4C0A9wEoD+CgJs8hAA1M6rsDwB0AUKFCBTW9qKgI2dnZZu1bypaKMKNUeCDMcKPkh+EY13R4IJxIRgiTFr9PonbCKq9XRbc4eCDckOkeCCHzEUVbSAdy3wmuDAhmHgIARNQEwOUAJhFRBQCLEDUovmLmiF8hmHkigInGdCJqAeAdAH9n5sUxD0Q5TZZyAPYbyzHz6wBeB4AzzjiDDxw4oKQb85nJYuqlcKNk22GnLBd3D4SXvlm14ybNrQFBRJ6UWKcvQj+Gi5863aziO+UNMjTKLr9dPUGtumdluduilUgIUzLwMo/JJt3tC4Jf5N4NNzI/4SVVc+NpEzUzb2DmcczcC9Ewpi8B/BnAsqAFI6KmAGYA6MfMC2LtHwRwnIgaUlQ76AlgiQf5Ta/tQg7cKFBewnzM6kimAm9WpzFPMsKzEvXKuFXCkrWx2k8biYYw+ZUzCAMiE0OY3JDpHgj5kc98ZI4FQdASKgOCiM4moou1acx8FMAfAP7DzG2TINsYAKUBPE9EnxPRnFj6EADTACwHsJqZXRsvbpRqK8UoKCXb6yp9Oo5xTacHwkzx9hrClMhYBbXPwo+h4sUAscobtKLr54soLCFMYdgDkW4DQhCKK2IYhRuZn/CSqrlxuwfiPwBGmqQfib13VWASxWDmPhbp3wBo77NO22vAvQfCLE9QK99+vRteFRe/Sr6bPMnwQASh7KXKA+HHUPFS3k26l7yJfuEUVw9EsvE7rszmT/NOVfuCkG7SfQCBIBRXQuWBAFCfmdcZE5l5BYD6gUqURLwo7EbS5YHwq8BbGUJeFfMgHwjnBq/ejKCVzCC8HEByQpis3k/ml4WfusPyw1/cPBBOHihBEISwIIsTglsDorTNe2WCECQVuPFAuMljTA96ld6vkWEnUzJDmMzwK7ed4h2mEKZEN0EH6YGQY1z9k+w2EjWk09W+EH4ywUNnh9y74UbmJ7yEzQPxLRENNibGjl9dGaxIycONUu0nhMnvKUxBKfBuPBBmeNmDYYdf74adkpyIByKRsTI7+SdVpzBZyWRWZxCr3MlI9yqPHW4VpETaC5MHwqqORJAf+cwl0w0IQRDCjds9EPcBeJ+IbsZJg6EtgJIArkmGYMnAz49p0Eq+V+9CcfBA2CnlyfZAmJFISFgibZj1w2pOkrUHIuhjXN2STg+En7DDVOHXuBPFXzjVkc9AuJH5CS+pmhu3z4H4HcBFRHQpgOax5HnM/FnSJEsCVqvpiXogEl1lDPMxrl6U92R7IIz5iChtx7ga+0qGZ0+47YcXOa3eD7LeRPPblUmF18IryT7G1e/ciAdCcCIMBrJw6iLfLeElVAaEhqUAzgBQD8AlRHQJADDzY0ELlgy8rMobrxPdp2BXzo6gnwTtdaU40faDOD3KKi0ZBoTf/Q5W94dfD4RfxTNopcLtF5Eb2ZPlgUiEZD97Id0eCNmMLRRXREENNzI/glcDYg6iT35eBeBY8OIkFyvjwJgWxEq6mzxelPygQpi0BH0KlF/Dwyn0xy4cyKsCmAwDoqioCFlZWQmHMDnJafV+Mr/Ig/RAeCVR5T5dpzBpSbcBIavUmUumz60oqILgj7B6IM7k6FOoiw1ulGqj4udGEQ5itdhLSFWim6jtjIygToFySnOS0UlOY9ns7OzQeCCs3veiFHrJqx07L4ZJEAaXWbpXz5ab91KxByLTPRBC5pLpBoQQbuQ7SnB7CpPCUiJqkRRJUoCXE4Wsrt2UcZsnqHKp9kC4NcSc6jarz+5J1G49EIkoyW7rM1PgzeTT5nM6btWLUerXgPVKUMaAnzKZoCCJASEI/pDPQLiR+QkvYfVAXALgNiLahmgIEwFgZj4vcMmSgB8PhBtlKFEPhJtN1MZy2dnZtjK68W548UAY33cbguNXmXe7wp8KD4RTPX48KYk+R8KvB8LrF4vb/Il6INJFmDwQVnUIghlyjwiCYEZYDYjLkyJFmnCjrAZ11KpZ/WbtW9XtxoDw6oFwo+RbGTDa5yV4CY/SorznRxn3+3Rqv54guzJ28nkxIKyMArftJ0IiYUDG8qneuBu2ECYt4oEQBH/IZyDcyPwIrgwIIiKO8rNTnuBECx63IUx+PRBeFddIJOKY143Ca7fx2FgmCA+Im2lONDzKbVhTEAaEU1knD4+dseBFsbYKdzLDqt6g90C4ad8ogx8PRCJyhe1rx68hIAaEIJxEPgOC4I9UfXbc7oFYRETDiKiuNpGIShJRVyKaDOAvwYsXLG5CmPx4IBRDwO9JScanHzutZBsND6fVbKe6zZ6+bFePlw3abjDrm9MY2CnYqQ5hcmsIJRrCZNV/P/IbSXRzdaIGRLoQD4RQXMn0eyTT+1fckfkJL2ELYeoFYCCA6UR0FqJHuZYGkA3gYwDjmHlNckQMDrdhPW5Ccbwq4mYKpV/Dw4t3wUxeuzRjut/wKC9jYlbOyViwGzu/bVrV52cTdXELYbIiKIPKLt1NnX6QU5gEofgin4FwI/MjuH0SdQGAlwG8TEQ5AKoCOMrM+5MpXNC4Vby1K/xWiqq2LrM8zOYbjbV1e/ESGNtzUjbc9NUqhMpteI5dOTPDyzgmdt4Vs/F1GzZmZ0CY4Sfsx8nA8RPC5JTXbOyM5fySaB1ewq9SRRj2QKT7SdSCUFyRz4Ag+CNVJxh6PcYVzFzIzLtSZTwQURMiOkBEpWPX7YloGRF9RUQPe6nL7Yq71+NevexBMFO03IQQGct5DXOya9/OOPJinDgZR1bjaiaTU5qdB8LNxm2rNLc/Wm69DUF6IKzq8rIHIijsvFBh8UCkqw3xQAipINPvkUw4xjmTkfkRPBsQqYSIygN4FvqnXr8KoB+iR8peSESt3dbnNvTGTZiRmZfCqwHhxruhvDa255THSlk3k9uI22ccGK/NytnJZDcmbo0KrzgZEE4r/2b53D67wstzIKzmxql9L1/qbhRWt/Wlcw9EIvW6LZvqJ1GHxYsjCOlCFFRB8EdoPRCpgqIj8DqAkQCOxNLKAyjFzD9x9Nf2IwDd7Opx413QKmvGPG5W6d14AOyUYDvjxKqc2xV6bb+s6jZilseNcWQcR2OaG5nsQpjcGD9O+HnwnFtl36vhZcTvKUx+Feggw6DSuXqeiPGT6R6ITF+lFgRBENKD1+dAJAUiGgTgfkPyzwDeYea1GkWgPICDmjyHADQwqe8OAHcAQPny5dV0ZsaJEyd010C8wmymqBrDjJwMiKA8EFYKtZOC7UbJd9O+nXdBQSlv1r5XD4RdObcKdtAhTG73QDgZXk7Gm5MHwkpOv8aU1f3qRuk05nFT3i5Uz+3xucWBIAyz4tp3QQgK8UCEG5mf8FIsPBBEVIKIzieiC4joAr/1MPNEZm6u/QPQGMAgIvocQE1ET3s6CKCcpmg5RE+EMtb3OjO3Zea2ZcqU0aY7KrlG5dxqM7DTJl8vHggjTsqzVQiTXd3Kg+fctG+WR2t4GcspiqFTCJObfRnJ9kA44daAcDJ6nDZRO+0tcTIwrPIGsWnXT2iTm/CbZD3gLxUhTF4wuw+8yiIGhHCqIwqqIISbRD0Q7wFYDqAQAMdeBwIzn628JqLtAHowcwERHSeihgC2AugJ4FEPdTqGmViFBxkVaK8eCAUz48C4MusU0mNs342CrRgQbjwQTqFIRuVGkd+vB8KttyGIPRBmq+CJbqK2G38vir7T/eNmE7XTuLjxYri5j+08cn48EF5xM/9+PClB4NcDIQaEIAjFBTHwwkuq5iZRA2I9Mz8ViCTuGQJgGmLPoGDmZXaZjcqQk3JcVGS+idqYx6sCb7ZK72UTdaIeCDsl34jb1XW7uoP0QNjt8XDrMbB7Lx0hTE4eCC+bqL14ZrwaIW6NATdlvM6LHW5W+cWAEDKZTL9HREENNzI/QqIGRCERLQSQCwDM3C9xkeJh5vqa198AaO+zHtNQHLs9EGYeiEgkYpqnsLBQl2amoDspxkZ53HggrEKxtJgp+V5OYbIzsryU02K3B8LrHo9E8LOJ2q3RYxX6lWgIk18PhFU5q7G1ukcUj5ZTvVoSfdq1FjcGhJt7xO195EVZkxAmQUgcUVAFIdwkakDUZObLApEkSRh/lM2UOK/KuZUHQmtARCIRU+XRSTE2a8uYZjRgnMJlAG8eCDchTG48OWZya7ELq3KzD0TbtvLajzHg5IEwa8NoLFr1w3hP2OFkCGhX/a3m0UsIk5Vx46Y+N4cK2LUN6MfVaJA44UZJD3KfjJe6/HogzA54SBRRwgRBSAby3RJeiksIU1kiuhGxk5GYeX7iIiUPNyFMkUjEVDk0pjnlMdajtHH8+PG4clrFwUwes3LHjh3TXRvriUQipgqamRJubN+sb0Zl0+k0KzceCDMDwmoszfpnLBuJRFCiRAlb5ctpD4SZomgcj+zsbNN5dOOVUjCTUVunmRxaJdsqr5MBoa3Dyrhx46UyKvxuvBZ2e328Yrep3yldi1tF3a8B4cVTlgwDwqthJhQfxEslCEI68WRAEFFZAMrm5o0AFgEoBaBawHIFhnH12OxH2k45t1oRN1PgjcqimZJTUFAQl2aUyaxuo0xmMhrrNlNenAwfKxm1eZjZlZGhlU15rZVJUSi17Sn5ncbALs3YHy0lS5a0TTNTFI33R3Z2tqOB42RAmGFmlGixMiCslHczj4ybOpzkAOKNAbP70YhRFqexsVPagwphcquEeTEE/IYw+S1nhxgQQnFFVrjDjcxPeClRIjVPaHDVChHlAHgGwK0AtiF6/Gt1AOOZeQwRtWLm1ckTMzi0ypHyI61VYK0UeOOKr5nC5MYDYVbOqLAdPXo0Lo9RqTdT8o3lzPphVrdR4daWU8ZIW84oo1ndVuXMlEazvpgZFWaym6Vpx9NIqVKl4tJycnLi0rQfQDOF2izNaWydPAV+PRDauozGqJ0BYSWbG2PAaEC4MTqMyqyZR8nqfSOp3gPhxYAIUwiTGBCZT5Cnm4UJs+9lITzI/ISXUBkQAJ4FUBZAPWY+BKhPhf43Eb0CoBeAs5IjYmIYN3ceOXIk7j2jsmqm6B0+fFiXx81quLYtO2XZqDiaKedGpc5stdtYTiuz0letTFYKt5nybixnZgiYja2xnJPnxMpg8pJm7I/2HlC8Ddo0sy9CbZobYxGIH3+jLFrFUptXwa8HQnu/Go1h4/tarDwAbowB4xeUG6ND+0wWY/teDQijR8wMu/IKWrnt8BLC5EY2M5JhQKTqh0RIH5mqyMm9G24y9b7LBFI1N24/oVcAaMSaXzVmPkhEdwLIA3B5MoQLGmZ2pdSbGQdGZdxoUADAgQMHdGlmecwU+IMHTz5c22h4KHmMdZmtdhvL/fHHH3H9MPbfGGpl1pZZ3U7tW7WnHSOrPIC5Mu7kgSgqKooLUwP0Cp2yWqdVkpVVejNDA4DpOBrnzKofhw4diksD9PecgtO9qV1pNMtrJmt2drapkQWYjydgHhYG6MenbNmyOtmtjA7ta2P4mJOhYqe0mxnHRsyMNCNuDQi7sDi7dr0YAn4NDzvEA5H5mIVlZgKioIYbMfDCS6rmxq3vs4hNftGYOQIgN3a0aigxruppFT+lS/v27VPTIpEI9u7dq14rSqm2nFkeANi/f78uzXgNALm5ubq0EydOxBkexjxm5czy7NmzR5emlVHpq7ZcJBLRyagoG8Y8Tu2b5VHay8vL0+XLz8+PK2fMYyWDNp8yL8ayWgVa6Y+xHADd2Cj1a8tqP4BmMmvvGaVO4zxapRkNUK/t2KVr+2BWh1Ypt0rX3hPadK3Cb/xxN34+FMwMJacybsoGZUC4NQzctGfWrhdDwMxQTRSj10fIPDJJ0dZ+XkRBDTcyP+ElbAbED0R0qzGRiPoD+DFYkYLFGOrz66+/qtfMDGbGzp07dXl++eUX9ToSiSAvL0+nQBjzKHsSdu3apSu3bds23TUAbN++XVdOe62kGcsdO3ZMJ6ObuiORCH766SddvQB05czaAoCtW7fGtf/bb7957tuRI0fw+++/W5azkgkAfv75Z11aUVERduzYoasrLy8vzgNhLAcgbj69pO3fv99UsdPeR0perXxm+ZS0vLw8U4VZe/8oeXfv3h2XZkzXGibGeQKgmwNtHWZGqJ90pU/Gdo1ljMq0lQGjoDW+7N6zUraVOu0ULK0RY4fWk+RkFGjvFy8GhJNB5Rat4WW250fILDLJSNR+zmSTbvjQ/t7Kd0u40P5mpOo7wa2ZcjeA2UQ0EMBKAAygHYAyAK5JkmyBYAwLWLbs5IOri4qKsGrVKt3K/YkTJzBnzhxdnk8++URX57p167BmzRr1OhKJYOHChXH7LWbNmqW73rt3L77++mtduQ8++EBX9/HjxzFv3jxduY8++igu5t0o48qVK+OMjEmTJun6/uuvv+Lbb7/V5Zk9e7au/aNHj+LDDz/U1T1//vy4eHVj+3v37sWSJUt0eebOnRs3JjNnztTliUQimD9/vi5t165dWLFihS5tyZIlcWFD2nJKPm2aYiAuWLBAlwdAXB8B4OOPP47LZ5z7SCSC9evXxynlBQUFWLp0aVydn3/+eVydX3zxha5O5bkaxnsDgGma2X0MAOvXrzfdT7Jy5cq4NABYtWqVafratWtN07/77jvTdGbG+vXr4+QBgI0bN5qmO72nNYTMQjS0Rq6Zkq71MJUvXz7ufSD6WdN6W6xgZp1x6WQUGBcp3GJcgPCL9t6Uoz4zE62RanV/F0eMC05CuNDqSjI/4UI7N6F6DgQz7wRwIRF1BdAMAAFYwMyfJlO4INB6IK688so4g2L06NG6/G+99Ra+//579Xrv3r145plndHkeeOAB3Yfn6NGj+Oc//6nLM3XqVJ2SfeLECQwZMkRnwW/cuBEzZszQlRs5ciS2bNmiXv/2228YNmyYLs+LL76IxYsXq9f79+/Hbbfdpsszbtw4fPXVV+r1sWPHcMstt+jGY+HChTqlGQCGDRumU9527NiBoUOH6vK8+eab+O9//6teHz9+HLfeeqvOS7N9+3Y88cQTunJTpkzBtGnT1OuioiKMHj0amzdvVtMKCwsxcOBA3Tzt378fQ4YM0dW1Y8eOuDH/4Ycf4tpct24dxo4dq15HIhH8+OOPujmNRCLYsWMHHnvsMV3agQMH4to4ceJE3HxEIhGMGTMmLizqm2++iTMijx07hkcffTSu/GuvvaYbd2UT/L///W9dGgDMmTMHGzZsiEs33qfKZvfx48fr0oDoHorJkyfH1XH8+HGd4alV7LXp2vv/888/t1R+33vvPfW1UZl9//33TcsA0BmCZh4G7X1rpiR/9tlntuUB6Aw+u70CGzdujPOW2OX/5puTEZ1uFfiCggKsW7dO14ZfzIxLIbPQLmBlkpFotaghhIPVq08etinzEy6sFgqTirJKm6l/iHpLdH+nnXaa7rp69ercpk0bXdqtt96qu27Tpg3XqVNHva5duzbfdtttDICzs7MZAJ9zzjncpEkTXbkHHnhAd3366afzddddp0vr27cvV65cWb3Oysriv/71r7o8F154Ibdv316XNmzYMN1148aNuVevXrq0hx9+WHdds2ZNvummm3Rpw4cP5xIlSqjXJUuWjOt/p06d+JxzztGlDR48WHdduXJl7t27ty6tS5cu3LRpU13aI488orvOysriO+64Q5dWpUoV7tOnjy6tefPm3K5dO11a9+7duVq1agyAy5UrxwD4+uuvV/tTt25dBqCOS05ODjdu3JgBcI8ePRgAlylThtu2bcsA+NJLL1XzKX1p3bo1V6pUiQHwwIEDGQBXq1ZNHY8HH3yQiYiJSO3rgw8+qN4vStqf/vQnvvPOOxkAn3XWWWofli1bxmXLlmUAfMYZZzAAHjdunNpW1apVGQDXqlWLf/vtN65Ro4Y6TwD46aef5v/973+6NACcm5vLjz/+uG68zj77bGZmvu+++3TpXbt2ZWaOmxslfeHChbr0evXqMTPzsWPHuHnz5rr3PvroI2ZmXr58ufrZAMDNmjVjhcWLF8fdJwoHDhzgM888U32PiFjLmjVrOCsrS33/pptu0r1//PhxPv/889X3y5Urx0aKioq4e/fuOhmsGDBggC7fkSNHLPP+9NNPujl4+OGHLfNqeemll3RtrF271lU5I4cPH+ZWrVqp9bRt29ZXPUK4UX57AHCjRo3SLU4gFBUVqd+/ALh9+/bpFknQUFRUpNMv2rRpk26RhBhFRUV8xRVXqHNz/vnnJ1qlO/3abcbi+qf9UQbAnTt35ueff16XNm7cOO7SpYt6fcEFF/DMmTPV65ycHF6+fDk3aNBATXvxxRd1CjwR8Zdffqn78b7yyit5w4YNurZmz57NDz74oHp9zjnn8KFDh1QlGACPHTuW3333XZ0y/fvvv3Pnzp3VtPvvv5+XL1+uXpcqVYq3bNnCN954o5r2l7/8hb/77jtd+19//TX/61//Uq87duzIkUiES5UqpaZNnjyZx48fr17XrFmT8/LydErZ4MGDec2aNbq6P/30U/773/+uXtetW5fz8vJ0Y3LzzTfz77//riv3wgsv6PpbokQJXrx4Md9zzz26MdiyZQt369ZNTWvQoAHv2bNHVboBcIsWLfjQoUO6/rRq1Yp//vlnXZstWrSISzvrrLN4586durRKlSrxpk2buFatWmpadnY2f/TRR9ypUydd3kceeUQnMwDu0KEDz5o1S5dWsmRJXr58uSpjhQoVGIgarYpiX7p0afX/ggULdHIr97Ey1l27duXTTz9dvXeUe+nRRx9lImIA/PLLLzMArl+/Po8bN07txzPPPKMq8NOnT1cV89GjR6vpP/zwA1evXp0BqIZe7dq1uaioSL1u2LChOh7z58/nvLw8btSoEQNQFfWmTZsyM/OePXtUA0H53HXs2JGZo1+EioF7wQUXqP0uKipi5qhxodSrGFY33HCD7ptPmQPFKC9btmzct+PTTz+tywOAI5FIXL4pU6ao3wFKvoMHD8blU/rVsmVL3Vw/9NBDpnm1fPTRR7r7FQCvWrXKsZyRdevW6cYMALds2dJzPUK4mTBhgm6OGzRokG6REubo0aM8dOhQXb/atWuXbrGEGIWFhTx8+HD5bgkhR44cifvsaBfrfCIGBJsYELt27eK3335bvT7jjDP4l19+0Smmr7zyCs+ePVu97tSpE+/atYvr1aunpv3www987733qteKEqOsZCvK+ubNm9Xrc845h4uKitx5XuUAACAASURBVPihhx5S08aOHcvMzBUrVtQpKFoDZtCgQczM3LVrVzXt+++/5xUrVqjXPXr0YGbmfv36qWkff/wx//jjj3E31YgRI9S0CRMmRO8WzRgVFhaqCicAvvvuu5mZuXXr1joFZ926dep18+bNmZl1XzKPP/543JisW7eO8/Ly1OuaNWtyJBLhGTNmqGl9+/ZlZv0q+ZNPPsnMrHoNAPDMmTOZmXWeoWXLljEz6xS+DRs2cH5+vnpNRLxx40bev3+/rt+rV6/mwsJCXdqCBQuYmXUr4uPGjWNm1hmd1113HUciEZ3M9erV4927d+sMACLid955h5mZy5Qpo6Z369aNCwoK+G9/+5ualp2dzbNmzYozuBo3bsy5ubm6eQTAt9xyCxcVFXHNmjV16WPGjOFt27bp0gDwSy+9FOcJAKKr5p9//rl6rXhzunXrxjt27NDdT0DUeF22bJm6AnLuuedy/fr1GQCfd955vHLlSrXvY8aMUT0WHTp04E8//ZSBqLE5Z84cvuuuuxiIeurWr1+vGkBbtmzhw4cPq/PfokULnjx5MgPgPn36cGFhITOzet/m5OTwZ599xkDUYNMyadIktQ+zZ89WvSTHjx/X5Zs2bZpqUI0fP1419Pbt28dGNm7cyA0bNmQgqtAp98GoUaPi8iqcOHGCH3vsMbWNu+66SzW2V6xYYVnOWMcHH3zAl112mdqn2rVr8zvvvKOOk1D8OX78OM+dO5d79uypzrPy+1O3bt10i+eLgoIC/uqrr3j06NHqd3hOTo66eCEr3OmlqKiI169fz88++6zqbc/KyuJRo0bJd0uayc3N5blz5/IDDzygRiSUKFGCn3zySQbATZo0SbQJV/r1KXkOl/ZUlosuugglSpTQnbHfvn173Yk6LVu2BADdJuVKlSrp4qD79OkDQH+ufLt27XSnAnXo0AFEpGvrwgsvBHDy6Mxy5cqhXLlyujxt2rQBoD9KskmTJrqY6datWwPQx3K3atVKt0m0VatWcXnOOeccaGnRogVKlCihy1OnTh0A+ucQ1KtXT3cS0FlnnRVX9wUXXABAf/xn8+bNdSfotG/fHllZWbpyHTt2jGuvc+fOMNK1a1cA+o13bdu2BXBy83yNGjXQuHFj3Qbs5s2b45xzztFtRGzatClatmypGJ0AgNq1a6Nnz54A9KeDKPsxtPI9/PDDcf34+9//jho1aujy9e7dGzfccAMA/b3yzjvvoFSpUrq8jzzyCPr27avbW1GxYkUsWbIEVatW1eXt168fJk2aBCLSyfDKK69gyJAhuvu5ZMmSmDJlCm644QZ8+eWXanqpUqUwduxYDBs2TJd+4sQJ3HrrrXjppZd0z59Yv349qlatiilTpuCCCy5Q2/3xx+jBbOeeey7mz5+vjj0zY8SIEQCi993MmTPVz8eOHTvUz1B2djbefvttNG3aFNnZ2Thx4gTOPvtstd1q1aph1qxZ6sbtOXPmoEaNGrjoooswd+5ctd+XXHIJgOj916hRI4wdOxaHDh3CoEGDAAD/+c9/cM011yA7OxuRSAQ9e/bEK6+8gsaNG2PSpEkYOHAgmBmjRo3CXXfdpe6XGjZsGF544QVUqlQJQHQvRe/evbF37160bt0ac+fOxYQJEwAAr732Gvr374/GjRtDy44dO3DLLbfgiy++ABFh1KhRePTRR9Xvg5UrV6qfezPy8/Px5ptv4uWXX1Y3lJcpUwaDBg3C448/rn5XuXmYnhAeioqK8Ntvv2HLli346aefsGnTJixfvhzLly9XP3vly5fHs88+i169euH5558PXSx6YWEh8vPzsXfv3rj/v/zyC7Zu3YqtW7diy5Ytut+GZs2a4bXXXkOZMmXw2GOPha5fmYRyfHxubi7y8vKQl5eH3Nxc/P7779iyZQs2b96MDRs26I4Wr1+/Pl5//XXUqlULTzzxhMyPCcrR/IWFhThx4oTta6v3jh07hgMHDmD//v3q//379yM3Nxc///wztm/fHndCYcuWLfHyyy+jcuXKePDBB1M2N6eUAXH66acD0J+R26RJEwD6B3PVqFFDd5LKGWecAeDkj3G5cuUA6BVIRYHXHluZnZ2tU+YaNmyopisoyrnx2iyPVpE01l23bt24flSpUkWnJNevXz9O7po1a+raV661dSv91yq8lSpV0vVVyaMdW0Xp0+YzGlD16tWLk8nMGGnUqBEA/dGfigKn/AhVr15dV4+2nLauFi1axKWdf/75qnzaNOVa+cBWrFgRpUuX1qUBUQPE2I8GDRrEpSnGEXByg2uFChVQtWrVOJl69eoVV75z586oVq1aXPo111yjXmu/PAYPHhxX7xVXXKEaMdr0O+64A/fcc09c+rXXXqtuuNbeA+eeey7WrFmjnpKkleeNN97ALbfcglKlSsWdGPPoo49iwIABqFChgu50IwDo0aMHhg8fjksvvRRAvALcuHFjzJgxA40aNdKd4pSfn68aD8899xwGDRqk20C8ZcsW9O3bV71++OGHce+99wI4ef8sWrQIHTt2RPfu3TF9+nQAwOOPP45Ro0bpxuStt97C4cOHMWvWLMyaNQv9+/fHsWPHcMUVV+Ddd9/F6aefrn4OcnNz0aVLF2zZsgWnnXYaAGD69Om48847ceDAAdSsWROTJ09Gjx49dG389a9/RVFRUdzhAfv378dDDz2EiRMnqgcy1K9fH0OHDsXAgQPVz4RiVP/444+4/vrrMX36dMeHyuXn5+P777/H77//jvz8fBARSpcujZo1a+Kss85CvXr1MvahZUFTWFiIAwcO4ODBgzh48KDu9cGDB5Gfn489e/aof7m5udizZ4/lEc9AVMHu378/Bg4ciOrVq6sLODt37sSwYcPw/PPPx33/+eXEiRPIy8tT5VOUGDPFRpuWn5+vW2xxolmzZrjkkktw/fXXo0uXLsjKylJPgVuzZg3GjBmjLjoEBTOjsLAQBQUFOHr0KAoKCtS/wsJC9RlDdv+Vesz+273ntUxhYSGOHz+u+3NKKygowOHDh23/tIabHbVq1ULnzp1x3XXXoXfv3sjJyVEXiH744Qc8//zz6vdokCiHiBw5ckQnt3J97NgxFBYWqv1WXhuvlddOSruf12bvaeczmZQtWxZt2rRBhw4dcPXVV6N9+/YgIvUAns2bN+Ppp5/Gv/71r6TKEVoDgoiyATwHoC2AUgAeYea5RNQewPMATgD4mJkftalGh3LcnVbJrV69OgD9ufrGVVxFuTNea5XN2rVrA9Ary4C5sq5NU9pXqFGjBgC9AlerVi0A+rP2jfUoSqWxH9q+Knm0dRsNiCpVqsTlUYwD7Uq/cYwUGbVpSnvGMdHmUfpvVk57EpMy5lojyohxnqz6Yyar0kctxrExpmnnQ5kL7ZwodWrbUQwmLcqcG8ubGVKKUWLVJyDeiDXWq10N16afe+65pulambVtXnLJJTqFUnuE7KBBg9TPh9bj169fP9x3332m9Y0ZMwbDhw+HGRdffDGmT5+OM844Qy2jrffaa69FkyZN0KtXL9XzYFSmSpcujZycHIwdOzZOMVfIzc3F9OnTQUR47rnndLJqvVjvv/8+Lr74YvWY3SFDhuDFF19UP2/acdm9ezfGjh2LBx54AEOHDsVbb70FIOq1fOONN9T7HdCfsz569GgMHDgQJUuWBDPjvffew7333qt+Dnv06IGhQ4fiiiuuiDMOtJ/7GTNm4JZbbsFVV12ly6McUf3+++/jww8/jHsmjZGcnBw0b94crVu3Rps2bXD++efj3HPPVY2W4ggz4/jx444Kl9Wf0ThQXts9BNGJ6tWr4+yzz0bDhg3RsGFDtGrVCh06dNDdJ4D+szN+/Hj07t0bl112mas2IpEINm7ciPXr12Pbtm3YunUrtm3bhl9//RV79uzB3r17fStDWVlZqFy5MqpUqaL+V17Xrl0bDRo0UP+UxTirfo0cORK9e/dGs2bNXLV9+PBhfPfdd9i6dSu2b9+On3/+GTt37kR+fj7y8/Oxb98+5Ofnn/KeOSJC+fLlUa1aNVSrVg1Vq1ZVXzdo0ACNGjVCo0aNULt27bgjQbXzc99996Fz585qlIYTzNHj5Ddv3qyupu/YsQO5ubnYu3cv8vLysHfvXt1CYXEjJycHOTk5KFGiBEqUKOH5dcmSJVGhQgVUrFgRFStWVF9XrlwZ9erVQ7169VC1alXTo1q1czN8+HD06NFDjTxxw6FDh7B582Z1QdyJ0BoQAG4BkMPMFxNRbQB/jqW/CuBaAFsBzCOi1sy8yqqSOnXqqKucFStWBGBuQGjP0AX0xoGVAaH9sVe8G8rqkfLFaKbkacspq5IKZqvLiiJqPLPeTOnWKvmAXslS+qr1UihyKygKt1n7xjEyy6P94VTqVr6slWszw8fMqDJT0LUuVSNuDQgzQ06rgBvlsEozM2bMjBJtO1pjwSxNu/qoKGZW5c3GX1uH9sfZymi0MqKs0rWfHWNftPOl/fxolWnjmFr1wUitWrXivHVaWW666SZce+21luX79++PN954A1lZWZar6J06dUKHDh2wdetW3H333XFhc1rvCxB9RkeJEiUwduxY3Hfffbo+Gx9e98QTT2D8+PHIz89H2bJlMW7cOAwePDjuR0D7GcvNzcXEiRPRq1cv3HXXXeqzSy6++GK88sorqifNDKNB8fLLL6sGBDPjrbfewuOPP647Qrls2bJo3rw56tSpg8qVK4OZcfToUezcuRPbtm3Djh07sHr1aqxevRoTJ05UyylhgrVr10aNGjVQo0YNVKpUCWXKlEHZsmXV/yVLlkRWVpblHxD9rohEIuqqnvJam6asHtv9GVeWCwoK1NVL46pmMtz92dnZKF++PCpUqIDy5cvHva5UqRKqV6+O6tWro1q1aurrqlWruvbyGJ82++abb1oaEEePHsWSJUvw8ccf46uvvsLatWttn9RORKhatSpq1KiBatWqoVKlSnEKjVHJqVChAipXrozy5csn5Akx3rtTp07FU089ZZp3165dWLhwIRYuXIhvv/0WmzZtcmX45OTkoHTp0upfmTJlULp0aTWcOSsrS/WWm/1XPrdW/+3e81KmZMmS6l9OTo7u2iwtJycHpUqVwmmnnWb7V7p0ad/PCjDOz7Rp0ywNiGPHjmHp0qX45JNPsGzZMqxZs8Z2EVBL2bJlUbZs2TjZy5Ytqy4GKf03e61cKwp6Ikq929dBeQD9YpybKVOmWBoQkUgEq1atwieffILly5dj7dq16kN93S4ehNmA6AngOyKah+hzJ4YRUXkApZj5JwAgoo8AdANgaUBUr15dNSAUD4SZUm10u9p5ICpXrgzAXpk1U4wVhcvoSTArZ5emtG+miBuNDDMPhDGkSIudB8Iuj6KAa8fEWLeZB8TMA6GkGT0XQHz/tHg1IKxW8I1ymPUBOPkwJ+0Ya13Dijx2IWPGdrTjp5TTyqlV2rXjaya/Nq+VAWGlwFulW8kCWHuHtJ83u1VUs7GxKgeY39tWVKpUSQ09s+LMM8+0VFS01K1bF++99x6++eYb9OzZUw2D1KLtc//+/fHWW28hPz8fF154ISZPnhy3J0LBOIbDhg1DdnY2jh8/jooVK2Ls2LEYNGiQ4w+VUbn88MMPsXHjRmRlZWHw4MHqc2Tq1q2L2267Db1790bLli1tw5wOHTqEtWvXYtWqVVi1ahXWrVuHjRs34vfff49buChO5OTkOCpcVn9WRkKZMmWS/jAn4xy///77OHDgACpUqKCmffnll3jjjTcwc+ZM3cIREPUsnnfeeWjYsCEaNGiAs846C3Xr1kWNGjVQpUqVuPpThZkS9MQTT6jynDhxAu+++y4mTpyIRYsW6fKWKFECTZs2RePGjdXV2jp16qgekMqVK6NSpUryFOUEMDMgxowZo84PM+PLL7/ExIkTMXPmTN3eTSCqvzRt2hT169dH/fr1dfdclSpVULVq1bg9poI7zOZm7Nixut+jlStXYuLEiXj33XfjdNiSJUuqYd9uCIUBQUSDANxvSM4FUADgSgCdALwJoB+Ag5o8hwA0MJQDEd0B4A7g5N4AAOoXq/aLUVEwFRQjQ/sDbVRMlTx2lrSZsqzUY2dAKKvO2rhx4xe5mZfArwFhRKlb+2NjfNKpWZiVomR6NaoU5Vm7uqusnNsZC8Z5s0ozU+LNQtAUo0yL4rHy0q5WkTIzAMwUXa3ibHZPWXkgtONjphxbeSusDAutbFbGlZ0BYXVfaVdU7QwIM++MVTlAr6SbGY9azObXiFMdWlkuvPBCdcOzGdrP3cSJE3H//fejoKBAPTTACiVsr2zZsrj22msxdepURCIR9OvXD88++6ytkaVFO66nn346/vjjD/To0QO5ubk4evQoqlWrhrFjx6J///6uFcVy5crhkksuUUPEgGgY1C+//IJNmzZh9+7dqjFx8OBBHDlyBEeOHMHRo0dx5MgRNb7c6o+Z1dW87OzsuNfKf2X1WFk1dvtXpkwZUyPA6C0qLhiVhYKCAkyfPh1DhgzBt99+i5EjR+KTTz5R32/VqhV69uyJrl27ok2bNq4+E+nA2K9du3Zh/vz5uOqqqzB79mw8+OCD6v6n0qVLo2vXrujRowc6duyIZs2aiXGQZMzmZ968eejTpw+WLVuGESNG6Ay7Fi1a4LLLLkPnzp3RunVr07AoIRiM3+W5ubmYN28err76anz33XcYOXKkulcQiIZEd+/eHR07dkTLli3RuHFjT9+HoTAgmHkigInaNCJ6B8BcjvpSFhPROYgaD9qgyXIA4rRMZn4dwOsA0LZtW1ZOoDHbRK1drQFOKsva+HujAq1ce1WWlS9sOwNCUVqNu+y1KPVoN4kqaUq4kPIl6teAMO530GJnQNjJbWZUKWlmnguvBoTZD6JZPuN8WqUZ7w3A3KjQtmu2EqsNkTCrU6s4O4VFaZVc7X4XM6wMBe2YaNO1sexWHgjt/WRUuJWVJuMPjJ0HwuzzYYbZuHv1QDjhNpbfqS1Ab4CXLFnSdUypQpUqVTB58mT84x//QLly5dQDENyiHZtXX30VI0aMUE/i6t+/P55//vlAFMisrCx1pVdILdo57tu3r6pcz5kzRw13q1ChAu6++24MGDBAd5JZmNF+fyj9GjJkCB577DH1absNGzbEP/7xD9x4442m36lC8tB+Z/fp0wdz5szBPffcgzfeeAPz5s0DEP0uveuuu3DbbbcVm/suE9B+dpo1a4b169fj/vvvx/Tp0zFjxgwwM0477TQMHjwYt912m3p4jF/SG7Blz5cArgAAIjofwA5mPgjgOBE1pKiW2RPAEqeKrrnmGgBQT57RbqBSFG1FgWzfvj0A/ZezotAqP7jt2rUDYB+zrcSdaVdDlDa6deuma0uLsllMu1lWQYkBv/LKKwHo908oN44S56ycOKNd/VUUR+WoU20YhaI8Ke0rm8K1xoOyyq2cDqTtm6JUnXfeeQBOHrFq1jdtnYoHwiwO+cwzz4xLU9B6lhTMlBizVWWzH5xE0rRy7tq16/+3d9/hUZXp/8c/dxJCDUVBsgqI2ECKAgEBKYJEpKi7dixfWF3QFbF8+VlYZXFZbLus2EFWFBfxK/YSVMAoJVQRREQQEVGEoFTpCST374+UTSCBgUxyJsn7dV1zXXOec+aZe57A5HxyznPOIevzhqqC/vJS2ClMOQrbyT7SaSOFHYHIu6OcN4TmnTNR2PyQwvrJ6+AxCvUIxOF24AsKeKHUEur6wt6jIAWd2nawvEcQj0X16tVlZmrevPlRhwcp//dX06ZNtXTpUr322mtasmSJJk6cGLF/fUbo8v7fGTRokLp3766tW7fq448/VuXKlXXvvfdqzZo1euihh0rVTlzez/XXv/5VrVu3Vmpqqr744gvVrVtXzz33nL755hvdfPPNhIcA5P353H///WrRooV++uknTZkyRVWqVNHQoUO1Zs0ajRw5slT9uysL8v5sRowYoaZNm2rt2rV6/fXXFRMTo8GDB2vNmjUaPXp0kcODJAV+o7fCHsq68tKLkuZLWiCpVXZ7u+y2zyU9dKR+Wrdu7atXr/aZM2d6amqqp6am+o8//uiNGzf2q666KrctKSnJe/Xq5UuXLvXU1FTfsGGDDxs2zCdPnpy7zezZs/2pp57yDRs2eGpqqi9cuNATExN9/vz5uXffmDFjht96662+d+/e3LYJEyb4O++8k7u8fft2HzlypK9Zsya3bdGiRf7KK6/kLmdkZPiYMWP8q6++ym1bu3atjxkzJt8NryZPnuwLFy7MXd64caM/+eSTvnPnzty2qVOn+rRp0/K9/7/+9S/fsGFDbltKSoqPHz8+3/s/+eSTvmzZsty2FStW+MiRI/O9/4svvuivv/567vKvv/7qt9xyiy9ZsiS3bdq0ad6vXz/ftWtXbtuIESN8+PDhucu7d+/2Sy65xCdNmpTbtnjxYj/33HM9JSUlt+3NN9/0s88+O9/YjRw50ps0aeKbN2/ObRs4cKA3bdo038+hZ8+e3qJFC9+3b19uW6dOnfy0007L19a1a1evW7eub9++Pbft4osvdinrBn45cm7klHOzOXfPvYP37bffntu2atUql7LuepyXsm8ItXz58ty2nJso3XDDDQVu+9tvv+W25dys7OA7guZs+/LLL+e27du3L7c9788h793E8yqsPW//P/zwQ772nButtWvXLl97ZmZm7ms2btyYb90333yTuy7nbtMFvVfOzffyynuzu8LkrM/7/6+wbcaNG1foNnm3GzJkyGG3c3f/y1/+csTaDvceB4/h0frtt98K/TmhbMj7/+qLL77wffv2+YsvvujPPvusp6amBl3eMVu/fn3u50pNTfXdu3f7hAkTfNKkSfm+uxCMX3/9Nffn89NPP/n27dt99OjRPnr06FL9764syPu9v3jxYt+yZYs//PDDPnLkSF+7du3RdBXafnqoG5bWR+vWrXMDQHE8ELkK2iE92IEDB3LvYpxj7969vmPHjnxte/bs8Z9++ilfW1pami9duvSQ95w3b16+QOLuvmDBAl+3bl2+tu+//95nzJiRr23fvn3+/PPP+9atW/O1Jycn5wsqOXU+8sgj/uOPP+Zrnzdvno8YMcIzMjLytT/77LP+3HPP5Ws7cOCA33XXXfnCo3tWgPzzn//sTzzxhB/snnvu8T/+8Y+H9J+UlOTNmzfPFzpz9OjRw3v06HHIz2Tfvn1+1lln+SWXXHLIa9zdL7vsMq9du7Zv2rTpkHWbNm3ySpUqeceOHQt8rbt7o0aNXJKvX7++0G2qV6+euxN2ODlfzFOmTDnsdu7u48aNK1KA6Nev31G97mC7d+/O7StvEEbZkvMzXrVqVdClhM0vv/yS+7n27NkTdDk4yNatWwv8gxaCl/d7v4j7pyHtX5t7ydz4IigJCQmed9JIuIU6qRHA0cu5bGflypULXL9hw4bcu7cXZNOmTdq2bdshd1zPK+f693lv8leQlStX6vPPP9f1119/xEmAe/fu1bBhw3T55Zerffv2h902r1mzZumZZ57Rc889F/Kk7oK4u6644gpJ0ptvvsmkxTLqD3/4g3755RfNnDmz1E4GP1hGRoa6dOmi6tWr68MPPwy6HBzE3dW1a1fFxMRo+vTpfLdEEHfXBRdcoPT0dM2aNasol5UN6YdKgCgiAgQAAADKiJACRCRPogYAAAAQYQgQAAAAAEJGgAAAAAAQMgIEAAAAgJARIAAAAACEjAABAAAAIGQECAAAAAAhI0AAAAAACBkBAgAAAEDICBAAAAAAQkaAAAAAABAyAgQAAACAkBEgAAAAAIQsYgOEmdUws4/MbJaZfWJm8dnt7cxsgZnNMbPhQdcJAAAAlCcRGyAk9Ze0zN07S5os6e7s9rGSrpXUUdK5ZtYqmPIAAACA8ieSA8QySXHZz6tL2m9m1SVVdPfv3d0lTZV0QVAFAgAAAOVNTNAFSJKZ3STproOaB0m60My+kXScpE7KChI78myzU1KjAvobKGmgJDVo0KA4SgYAAADKpYg4AuHu4929Wd6HpDsk/cPdz5J0oaS3lBUe4vK8NE7S9gL6G+fuCe6eUKdOnZL4CAAAAEC5EBEBohDbJP2W/fxXSdXdfYekdDM71cxMUg9Js4MqEAAAAChvIuIUpkIMk/SCmd0qqYKkAdntt0iaJCla0jR3XxBQfQAAAEC5E7EBwt03SOpVQPt8Se1KviIAAAAAkXwKEwAAAIAIQ4AAAAAAEDICBAAAAICQESAAAAAAhIwAAQAAACBkBAgAAAAAISNAAAAAAAgZAQIAAABAyAgQAAAAAEJGgAAAAAAQMgIEAAAAgJARIAAAAACEjAABAAAAIGQECAAAAAAhI0AAAAAACFnEBAgz+4OZvZpnuZ2ZLTCzOWY2PLstyszGmtk8M5thZqcFVzEAAABQ/sQEXYAkmdmTknpI+jJP81hJl0taI2mKmbWS1FBSJXdvb2btJP1L0qUlXC4AAABQbkXKEYi5kv6cs2Bm1SVVdPfv3d0lTZV0gaSOkj6WJHefLykhgFoBAACAcqtEj0CY2U2S7jqo+Y/uPtnMzs/TVl3SjjzLOyU1ym7/LU97hpnFuPuBg95noKSB2Yu7fve7322RtDkMHwGhqS3Gu6Qx5iWL8S55jHnJYrxLHmNe8hjzQ33s7hcdaaMSDRDuPl7S+BA23SEpLs9ynKTtkqoc1B51cHjIfp9xksblLJvZInfnaEUJYbxLHmNeshjvkseYlyzGu+Qx5iWPMT92kXIKUz7uvkNSupmdamamrPkRsyXNkdRLyppkLWlZcFUCAAAA5U9ETKIuxC2SJkmKljTN3ReY2eeSEs1sriST9McgCwQAAADKm4gJEO4+Q9KMPMvzJbU7aJtMZQWLozXuyJsgjBjvkseYlyzGu+Qx5iWL8S55jHnJY8yPkWVd5AgAAAAAjiwi50AAAAAAiEwECAAAAAAhP2+W4AAAIABJREFUI0AAAAAACBkBAgAAAEDICBAAAAAAQkaAAAAAABAyAgQAAACAkBEgAAAAAISMAAEAAAAgZAQIAAAAACEjQAAAAAAIGQECAAAAQMgIEAAAAABCVuYDhJl9HHQNAAAAQFkRE3QBxa1169Y9Nm7c6MXVf3x8fHF1DQAAAJQkC2WjMn8EAgAAAED4ECAAAAAAhIwAAQAAACBkBAgAAAAAISNAAAAAAAgZASJE3377re677z59//33QZcCAAAABKbMX8Y1HN5++20NGjRIkrRr1y4988wzAVcEAAAABIMAcRjp6ekaMWKExo8fn9s2ffp0HThwQDExDB0AAADKH05hKsSKFSt0+eWXa/z48apQoYIeffRRnXLKKdqxY4cWLVoUdHkAAABAIAgQeezZs0evv/66Lr74YnXr1k2LFi3SiSeeqPfee0/9+vVTYmKipKyjEAAAAEB5RICQtH37dj322GNq1aqV7rjjDi1atEhxcXHq37+/pk6dqpYtW0qSLrzwQknStGnTgiwXAAAACEy5PpF/x44deuGFF/T8889rx44dkqSWLVvqhhtu0KWXXqoqVark275t27aqXr26Vq9erR9++EGnnHJKEGUDAAAAgSmXRyA2btyo0aNH69xzz9U///lP7dixQ506ddJ7772nDz/8UH379j0kPEhShQoV1K1bN0mcxgQAAIDyqdwcgdizZ48++ugjvfHGG5o9e7YyMzMlSe3atdPdd9+tDh06hNRPYmKi3n33XU2fPl0DBw4szpKPirtr/fr1+u6775SRkaEmTZroxBNPlJkFXRoAAADKEHP3oGsoVmeddZa3aNFCSUlJ2r17t6SsIwmJiYnq37+/OnbseFQ72du3b1ezZs1kZlq+fLnOOOOMsNa7ZcsWffvtt1q5cqVWrlypbdu2KSMjo9DHgQMHtG7dOq1evVp79uzJ11dcXJyaNGlyyKNRo0aKjo4Oa90AAAAo9ULaKY6oAGFm50p6zN3PN7PTJE2Q5JK+ljTI3TPNbLik3pIOSLrT3Rcers+TTz7Zf/rpJ0lS69atdeWVV+riiy/Wcccdd8x1XnbZZZo3b57Gjh2rm2+++ahfn5GRobVr1+aGhLyPzZs3H3NdtWvX1umnn67o6GitWLFCW7ZsKXC72NhYNWrUSFWrVlVsbGzu4/jjj1ezZs3UvHlzNW/eXCeffLKiosrlWW4AAADlUUgBImJOYTKzeyTdIGl3dtPjkh5w9xlmNlbSpWb2o6Quks6VVF/SW5LaHK7f4447TpdddpmuuOIKnXrqqWGp9cILL9S8efM0bdq0IwaI9PR0zZw5U7NmzcoNCatWrVJ6enqB21etWlWNGzdW48aNdeaZZ+qEE05QTEyMoqOjcx8HL8fHx+v0009XzZo18/W1adMmrVixIt/jm2++0c8//6yVK1ce8XNWq1YtN1DkDRa1a9cOfbAAAABQpkTMEQgzu1zSV5Imuns7M1svqZ67u5ldKulCSd9KquLuj2a/ZomkC919U2H9JiQkeFJSUlhr/f7779WxY0fVrFlTmzZtOuSu1Js2bdKHH36oDz74QNOmTdPOnTsP6aNevXq5ISEnMDRu3FgnnXRSsc9b2Llzp9auXau0tDSlp6crPT1daWlp2rBhg5YtW6avv/5ay5Yt08aNGwt8fXx8fL5Q0apVKzVv3pyjFQAAAKVb6ToC4e5vmVnDPE3m/003OyXVkFRdUt7zcnLa8wUIMxsoaaAkNWjQIOy1nnrqqWrUqJHWrFmjuXPnqlOnTvr666+VlJSkDz74QPPnz1feYNaiRQtddNFFatGihRo3bqwzzjhDcXFxYa8rVHFxcWrevPkRt9u0aVNumMh5fP3119q4caM2btyY70pU8fHx6tWrl3r16qXOnTurWrVqio2NZa4FAABAGRMxRyAkKTtAvJZ9BOJnd6+X3X6ppERJqyRVcvd/ZLcvkZTo7oVOHCiOIxCS9OCDD+r555+XJDVs2FBr167NXRcbG6tu3bqpT58+6tOnj04++eSwv39QMjMz9eOPP+YLFSkpKVq/fn2B20dFReXOuejdu7f69OmjDh06HHLUBgAAAIErlZOoG+q/AeIDSf/KMwfiM0mrJf1DWWGinqQP3P3sw/VZXAFi7ty5uvzyy3OX69atm7uDnJiYqGrVqoX9PSOVu2vZsmWaMmWKpkyZomXLluWeFlXQv69atWqpZ8+e6tOnjy666CLVqlUrgKoBAABwkFIfIM6Q9G9JsZJWSBrg7hlm9qCknsq6Cd5d7p5yuD6LK0C4u5588knFxsaqT58+SkhIYA5AATIyMrRv3z4tXLhQSUlJSkpK0qpVq3LXR0dH67zzzlOfPn3Uq1cvNWnShHEEAAAIRukLEMWhuAJEjvj4+GLru6xatWqVpkyZoqSkJM2aNUsHDhzIXVe5cmU1adJETZs2VdOmTdWsWTN16NCBoxQAAADFjwAhESAi3W+//aZp06YpKSlJycnJBc6liIqKUtu2bXXRRRepR48eatOmDZOzAQAAwo8AIREgSptt27bpm2++0fLly7V8+XItWbJE8+fP1/79+3O3qVWrlhITE9WjRw/16NFDJ510UoAVAwAAlBkECIkAURbs3LlTn332maZOnaqPP/5Ya9asybe+WbNm6tGjhy666CJ17NhRlSpVCqhSAACAUo0AIREgyqLVq1fnhonPPvtMu3fvzl1XuXJlnX/++erRo4e6d++us846q9hvzAcAAFBGECAkAkRZl5aWpjlz5mjq1KmaOnWqli5dmm993bp11a1bN11wwQXq1q2bTjnllIAqBQAAiHgECIkAUd6kpqZq2rRpmj59upKTk7Vx48Z860855ZTcMNGtWzfVrVs3oEoBAAAiDgFCIkCUZ+6ulStXKjk5WcnJyZoxY4a2b9+eb5tmzZqpe/fu6t69u7p06VKubgAIAABwEAKERIDAf2VkZGjJkiVKTk7Wp59+qtmzZ2vv3r256ytUqKD27dsrMTFRiYmJatOmDTe1AwAA5QkBQiJAoHBpaWmaP3++pk+frk8++USff/65MjMzc9fXq1dPV111la6++mq1adOGydgAAKCsI0BIBAiEbtu2bfrss8/0ySefKCkpSevWrctd17BhQ1155ZXq3r27OnTowKlOAACgLCJASAQIHJvMzEwtWLBAkydP1uuvv67U1NTcddHR0WrdurW6dOmizp07q2PHjqpZs2aA1QIAAIQFAUIiQKDoMjMzlZKSovfff18zZ87U4sWL853qZGY655xz1LlzZ3Xp0kWdOnVS7dq1A6wYAADgmBAgJAIEwm/nzp2aO3euZs6cqVmzZmnhwoXav39/vm0SExM1aNAg9enTR9HR0QFVCgAAcFQIEBIBAsVvz549WrBgQW6gmDt3rtLS0iRJDRo00M0336ybbrqJe04AAIBIR4CQCBAoeVu3btWECRM0ZswYrV69WlLWJWI7dOig8847T+edd546dOjAvAkAABBpCBASAQLByczM1PTp0/Xcc88pKSnpkHkTTZs21QUXXKD+/fvrnHPOCbBSAAAASQSILAQIRILNmzdr7ty5mjNnjubMmaPPP/9c6enpuetbtWqlm266Sddeey1HJgAAQFAIEBIBApFp3759WrRokSZPnqxJkyZp27ZtkqRKlSrp8ssv1/XXX69u3bopNjY24EoBAEA5QoCQCBCIfPv27dM777yj8ePHKzk5Obc9Li5OPXv21O9//3v17NmTIxMAAKC4ESAkAgRKl7Vr12rChAl6++23tWzZstz2mJgYnX/++brmmmt09dVXcydsAABQHAgQEgECpdeaNWv0/vvv67333tOsWbNyJ2FXq1ZNffv21Z/+9Ce1adNGZiH9XwcAADgSAoREgEDZsGXLFr333nt66aWXlJKSktvevHlzDRgwQNdff71q1aoVYIUAAKAMKN0BwswqSHpZUkNJGZIGSDogaYIkl/S1pEHunllIF5IIECh7VqxYofHjx+vll1/W5s2bJUmVK1fWddddp1tvvVUtW7YMuEIAAFBKhRQgooq7iiLoJSnG3TtIGiHpIUmPS3rA3Tsp6wNeGmB9QCCaNGmiUaNGaf369XrjjTfUvXt37d27Vy+88IJatWqlDh066JVXXtG+ffuCLhUAAJRBkRwgVkmKMbMoSdUl7ZfUWtLM7PUfSeoeUG1A4GJjY3XFFVdo+vTpWrlype644w7VqFFD8+bN0w033KC6devqxhtvVHJysjIyMoIuFwAAlBGRHCB2Kev0pZWS/i3pKWWdcpVzztVOSTUKeqGZDTSzRWa2aNOmTSVRKxCoM888U0888YTWr1+vcePGqXXr1tqxY4deeuklde/eXQ0aNNCQIUO0cuXKoEsFAAClXCTPgXhcUpq7DzWz+pI+lVTL3Wtnr79UUqK733a4fpgDgfJq5cqVevXVVzVp0iStWbNGkhQVFaV+/frpwQcfVIMGDQKuEAAARJhSPwdim6Tfsp9vlVRB0hIzOz+7raek2QHUBZQKjRs31ogRI7R69WrNmzdPAwYMUFRUlF566SWdccYZGjJkiLZs2RJ0mQAAoJSJ5AAxWlIrM5utrKMPf5E0SNLfzGyepFhJbwZYH1AqmJnatWuncePGacWKFerbt6/S0tL0+OOPq1GjRho5cqR27doVdJkAAKCUiNhTmMKFU5iAQy1ZskRDhw7V1KlTJUl169bVsGHDNGDAAMXGxgZcHQAACEipP4UJQDFp2bKlPv74Y3366adq27atfvnlF912221q0qSJXnnlFa7aBAAACkWAAMqxrl27av78+Xr77bfVuHFjrVmzRjfccIPOPvtsvfPOOyrrRygBAMDRI0AA5ZyZ6Q9/+IOWLVuml156SSeffLKWL1+uyy67TG3bttW0adMIEgAAIBcBAoAkKSYmRv3799e3336rp59+WnXr1tWiRYvUo0cPde3aVXPmzAm6RAAAEAEIEADyqVixom677TZ9//33evTRR1WrVi3NnDlTHTt21CWXXKJvv/026BIBAECACBAAClS1alXde++9WrNmjR544AFVrVpVH3zwgZo1a6b//d//1bZt24IuEQAABIAAAeCwatasqb///e9avXq1brrpJmVkZGj06NE644wzNHHiROZHAABQzhAgAIQkPj5eL7zwgr744gt17txZmzdv1v/8z/8oMTFR3333XdDlAQCAEkKAAHBUWrZsqRkzZmjChAk6/vjjlZycrObNm2vkyJFKT08PujwAAFDMCBAAjpqZqV+/flq5cqX69euntLQ0DRs2TOecc45SUlKCLg8AABQjAgSAY1a7dm1NmDBBycnJOv3007VixQp17txZ9913H0cjAAAoowgQAIqsW7du+uqrr3T//ffLzPTYY4+pffv2XPIVAIAyiAABICwqVaqkkSNHatasWWrYsKEWL16sVq1a6d///jdXagIAoAwhQAAIq/POO09ffvmlrr/+eu3Zs0cDBw7UZZddps2bNwddGgAACAMCBICwq1GjhiZOnKhJkyapevXqevfdd9WiRQtNnz496NIAAEARhT1AmFlVM4sOd78ASp9rr71WS5cuVceOHZWamqoLL7xQQ4YMUVpaWtClAQCAY1TkAGFmUWZ2rZlNMbNfJa2UlGpmy83sn2Z2etHLBFBaNWzYUDNmzNDIkSMVHR2txx9/XG3bttXy5cuDLg0AAByDcByB+EzSqZKGSop39/rufoKkTpLmS3rUzK4Pw/sAKKWio6N1//33a86cOTr11FP11VdfKSEhQc8++ywTrAEAKGWsqL+8zayCu+8v6jbFJSEhwZOSkoqt//j4+GLrGyiLdu7cqTvvvFMvvviiJKlXr1568cUXVbdu3YArAwCg3LNQNiryEYicYGBmyWbWK18FZuPybgMAcXFxGj9+vN544w3VqlVLH374oVq0aKHPPvss6NIAAEAIwjmJ+hRJ95rZ8DxtCWHsH0AZcsUVV+irr75S165d9euvvyoxMVHPPPMMpzQBABDhwhkgtku6QFJdM/vAzGqEsW8AZVC9evU0ffp03XPPPcrIyNDgwYM1YMAArtIEAEAEC2eAMHc/4O63SnpLUoqkE4rUodlQM5tnZl+Y2U1mdpqZpZjZbDMbY2bcxwIo5aKjo/XYY4/p1VdfVaVKlTR+/Hh17dpVqampQZcGAAAKEM4d8LE5T9x9gqT+kqYda2dmdr6kDpLOk9RFUn1Jj0t6wN07KWuSx6XHXC2AiNK3b1+lpKSofv36mjdvnhISErRw4cKgywIAAAcJW4Bw9+cPWv7C3W8sQpc9JC2T9I6kDyQlSWotaWb2+o8kdS9C/wAiTOvWrbVo0SJ17NhRGzZsUOfOnfWf//wn6LIAAEAeMUXtwMyellTorEd3v/0Yu64t6WRJfZQ1Qft9SVH+3xmWOyUVOM/CzAZKGihJDRo0OMa3BxCEE044QcnJybrjjjs0duxY9evXT19++aX+8Y9/KCamyF9ZAACgiMJxBGKRpC+yH5fkeZ7zOFZbJE1193R3/1bSPuUPDHHKmrh9CHcf5+4J7p5Qp06dIpQAIAixsbEaM2aMxowZo5iYGI0ePVo9e/bU1q1bgy4NAIByLxz3gXg55yFpW97l7LZjlSLpIstyoqSqkpKz50ZIUk9Js4tWPYBIdsstt+jTTz9VnTp19Mknn6hNmzb6+uuvgy4LAIByLdxXMQrbBdzdPUnSEkkLlTUHYpCkIZL+ZmbzJMVKejNc7wcgMnXq1EmLFi1Sq1attGbNGrVr107vvPNO0GUBAFBuRfRlUN39Hndv4+6t3X2qu69y9y7u3t7db3T3jKBrBFD8GjRooNmzZ6tv377avXu3LrvsMv3tb3/jpnMAAASgyAHCzHaa2Q4z2yGpRc7znPYw1AgAqlKliiZNmqR//OMfMjM9+OCDuuaaa7R3796gSwMAoFwJxxyIOHevnv2IyfM8zt2rh6NIAJAkM9Pdd9+tpKQkxcXF6fXXX1eXLl20YcOGoEsDAKDcCMcRCAvHNgAQql69emnevHk65ZRT9Pnnn6tt27ZavHhx0GUBAFAuhGMOxGdmNtjM8t1wwcxizaybmb0sqV8Y3gcAcjVt2lQLFixQx44dtX79enXs2FFvvfVW0GUBAFDmhSNAXCQpQ9L/mdkGM/vGzNZI+k5SX0mj3X1CGN4HAPLJubxr//79tXfvXl1xxRV66KGHmFwNAEAxsnD+ojWzCsq6g/Redy/wJm8lLSEhwZOSkoqt//j4+GLrG0Bo3F2jRo3SvffeK3fXddddpxdeeEGVKlUKujQAAEqTkKYdhPUyru6+391TIyU8ACgfciZXv/vuu6pataomTZqkrl276pdffgm6NAAAypyIvg8EAByNSy65RHPnzlWDBg00f/58tWnTRkuXLg26LAAAyhQCBIAypUWLFlq4cKHat2+vdevWqWPHjvr444+DLgsAgDKj2AKEmUWb2XXF1T8AFKZu3br69NNPde2112rXrl3q06ePXnjhhaDLAgCgTAjHfSCqm9lQM3vGzC60LIMlrZF0VdFLBICjV6lSJU2cOFFDhw5VRkaGBgwYoGHDhnGFJgAAiigcRyAmSjpT0jJJf5I0TdIVki5190vD0D8AHJOoqCg9/PDDGjt2rKKiojRy5Ej169dP6enpQZcGAECpFROGPhq5e3NJMrMXJG2W1MDdd4ahbwAosptvvln169fXVVddpYkTJ2r9+vV66623VLNmzaBLAwCg1AnHEYj9OU/cPUPSD4QHAJGmV69emjlzpuLj4/Xpp5+qY8eOWrduXdBlAQBQ6oQjQJxtZjuyHzsltch5bmY7wtA/AIRF69atNW/ePDVp0kTLly9Xu3bt9OWXXwZdFgAApUo4AkQjd6+e/Yhz95g8z6uHoX8ACJuGDRtqzpw56tKlizZs2KBOnTpp6tSpQZcFAECpEY4A8U7OEzN7Kwz9AUCxqlWrlqZOnaq+fftq165d6t27t8aPHx90WQAAlArhCBCW53mjMPQHAMWuYsWKeuWVV3TfffcpIyNDf/rTn/TXv/6Vy7wCAHAE4QgQXshzAIhoUVFReuSRRzRmzBhFRUXp73//O5d5BQDgCMI5iTrvBGomUQMoNW655Ra99957qlKliiZOnKgePXpo69atQZcFAEBEKnKAcPfoAiZQM4kaQKnSp08fzZo1S/Hx8ZoxY4bat2+v1atXB10WAAARJxxHIACgTGjdurUWLlyoFi1aaNWqVWrXrp1SUlKCLgsAgIhCgACAPOrXr6+UlBT17NlTW7Zs0QUXXKBXX3016LIAAIgYER8gzOwEM1tnZo3N7DQzSzGz2WY2xswivn4ApU9cXJzef/99DRo0SOnp6bruuus0YsQIrtAEAIAiPECYWQVJz0vam930uKQH3L2Tsi4fe2lQtQEo22JiYvT000/riSeekJlp+PDh6tevn9LS0oIuDQCAQEV0gJA0StJYSRuyl1tLmpn9/CNJ3Qt6kZkNNLNFZrZo06ZNxV8lgDLJzHTHHXfo3Xffzb1C04UXXqgtW7YEXRoAAIGJ2ABhZv0lbXL3qXmb/b/nEOyUVKOg17r7OHdPcPeEOnXqFHOlAMq6Sy65RLNnz9aJJ56oWbNmqX379vruu++CLgsAgEBEbICQdKOkRDObIekcSf+RdEKe9XGStgdQF4ByqFWrVlqwYIHOOeccfffdd2rXrp1mz54ddFkAAJS4iA0Q7t7Z3bu4+/mSvpT0P5I+MrPzszfpKYnf3gBKTL169TR79mz17t1bW7du1QUXXKBXXnkl6LIAAChRERsgCjFE0t/MbJ6kWElvBlwPgHKmWrVqeu+993T77bdr//79uuGGG/Tggw9yhSYAQLlhZf2XXkJCgiclJRVb//Hx8cXWN4DI9vTTT+vOO+9UZmamrrvuOo0fP14VK1YMuiwAAI6VhbJRaTsCAQARY/DgwXr//fdVrVo1TZo0Sd27d9fmzZuDLgsAgGJFgACAIujdu7dSUlJ00kknKSUlRe3atdOqVauCLgsAgGJDgACAIjr77LO1YMECtWzZUt9//73atWunmTNnHvmFAACUQgQIAAiDk046SbNmzdLFF1+sbdu2KTExUS+//HLQZQEAEHYECAAIk2rVqumdd97RXXfdpf3796t///569tlngy4LAICwIkAAQBhFR0fr8ccf15NPPilJuu222zR27NiAqwIAIHwIEABQDG6//XY99dRTkqQ///nPGjduXMAVAQAQHgQIACgmgwcP1ujRoyVJN998s8aPHx9wRQAAFB0BAgCK0Z133qlRo0ZJkgYMGKAJEyYEWxAAAEVEgACAYjZkyBA99thjcnfdeOONmjhxYtAlAQBwzAgQAFAC7rnnHj388MNyd/Xv31+vvvpq0CUBAHBMCBAAUEKGDh2qv//978rMzNQNN9yg1157LeiSAAA4agQIAChBDzzwgIYPH67MzExdf/31euONN4IuCQCAo0KAAIASNnz4cD3wwAPKyMhQ37599dZbbwVdEgAAISNAAEAJMzONGDFCQ4cOVUZGhq655hq9++67QZcFAEBICBAAEAAz00MPPaR77rlHBw4c0FVXXaUPPvgg6LIAADgiAgQABMTM9Oijj2rIkCHav3+/Lr/8ck2ZMiXosgAAOCwCBAAEyMz0z3/+U3fccYf279+vvn37KjU1NeiyAAAoFAECAAJmZho9erR69+6tnTt36r777gu6JAAACkWAAIAIYGZ68sknFRsbq//85z+aO3du0CUBAFAgAgQARIhTTz1Vd999tyRp8ODBysjICLgiAAAORYAAgAgydOhQ1a9fX4sXL9b48eODLgcAgENEbIAwswpmNtHMZpvZQjO7xMxOM7OU7LYxZhax9QPAsahatapGjRolSfrLX/6irVu3BlwRAAD5RfIO+PWStrh7J0k9JT0j6XFJD2S3maRLA6wPAIrFlVdeqa5du2rLli0aNmxY0OUAAJBPJAeINyTl/c15QFJrSTOzlz+S1L2kiwKA4mZmeuqppxQdHa2xY8dq6dKlQZcEAECuiA0Q7r7L3XeaWZykNyU9IMnc3bM32SmpRkGvNbOBZrbIzBZt2rSphCoGgPBp1qyZbrvtNmVmZuq2227Tf7/6AAAIVsQGCEkys/qSPpM00d1flZSZZ3WcpO0Fvc7dx7l7grsn1KlTpwQqBYDwe/DBB1WnTh2lpKTo1VdfDbocAAAkRXCAMLO6kqZJutfdX8xuXmJm52c/7ylpdhC1AUBJqFmzph599FFJ0t13362dO3cGXBEAABEcICT9RVItScPMbIaZzVDWaUx/M7N5kmKVdWoTAJRZ/fv3V9u2bZWamqqRI0cGXQ4AALKyfl5tQkKCJyUlFVv/8fHxxdY3AEjS559/rnPPPVcxMTFatmyZzjzzzKBLAgCUTRbKRpF8BAIAIKlNmza68cYbtX//ft1xxx1MqAYABIoAAQClwMMPP6waNWpo6tSpev/994MuBwBQjhEgAKAUOOGEEzRixAhJ0l133aW9e/cGXBEAoLwiQABAKXHrrbeqWbNm+uGHHzRq1KigywEAlFMECAAoJWJiYvT0009Lkh555BH9+OOPAVcEACiPCBAAUIqcf/75uvrqq7V3714NGTIk6HIAAOUQAQIASplRo0apSpUqeuutt5ScnBx0OQCAcoYAAQClTL169XT//fdLkgYPHqz9+/cHXBEAoDwhQABAKTRkyBCddtppWrFihZ555pmgywEAlCMECAAohSpWrKgnnnhCkjR8+HBt3Lgx4IoAAOUFAQIASqnevXurd+/e2rlzp+67776gy0EJOXDggLZu3aoffvhBP//8szIyMoIuCUA5Y+4edA3FKiEhwZOSkoqt//j4+GLrGwCOZPXq1WratKnS09M1d+5ctW/fPuiScBgHDhzQb7/9VqTH7t278/UZHR2tE088UfXRrzMcAAALTUlEQVTr1899NGjQIN9ynTp1ZGYBfWoApUhIXxQxxV0FAKD4nHbaaRoyZIgeeeQRDR48WAsWLFB0dHTQZZVJ6enpIe/k79ixo8D2PXv2FLmOqKgoVa9eXTVq1NC+ffv0yy+/aN26dVq3bl2hr6lYsaLq1at3SLDIGzhq1KhR5NoAlA8cgSgijkAACNru3bvVuHFj/fzzz3r++ec1cODAoEuKOGlpaUX+y/++ffuKXEdUVJRq1KhRpEe1atXyHU1IS0vT+vXrc0PEunXr9NNPP+Vb3rZt2xFri4uLKzRc5DyvXLlykccAQEQL6QgEAaKICBAAIsHkyZN1zTXX6Pjjj9eqVat03HHHBV1S2Ozbt6/IO/9paWlFriM6OrrIO/9Vq1YN5FSiXbt26eeffz4kWOR9HHxqVEGOP/74Qk+Tql+/vk466SRVqFChBD4RgGJCgJAIEADKB3dXt27dNGPGDA0aNCgiLu3q7tq7d2+RT/tJT08vci0xMTFF3vmvUqVKmZ1H4O7atm1bgcEiJ3T8/PPPR7zniJnpd7/73WGPZNStW1dRUVzDBYhQBAiJAAGg/Fi2bJlatmwpd9fixYt19tlnH3Nf7q49e/YU+S//Bw4cKPLnqlChQpF3/itXrlxmd/5LSmZmpn799ddCT5Nat26dUlNTlZmZedh+KlSooJNOOumwRzKOO+64iPt5Fba/VNrbj+U1FStWVEwM02jLKAKERIAAUL7cfvvtevrpp9WpUyf93//9X5F2/sNxedDY2Ngi7/xXqlQp4nYmUbD9+/crNTW10FOlfvrpJ23evPmI/URHRxf4My+JnWkc2eTJk3XVVVcFXQaKB1dhAoDyZsSIEXrttdc0e/Zs1atXr0h9VapUqcAd+pwrAIW684/yo0KFCmrQoIEaNGhQ6DZ79+7Vzz//fNhJ3zt27CjBqouusIBbWtqP9jVc6Q0ECAAoQ2rWrKlnn31Wt9xyS5H++l+9enVVrFgx6I+DMqhy5co6/fTTdfrppxe6zeHmWgS5kw0gC6cwFRGnMAEAAKCMCClBcxkEAAAAACErdQHCzKLMbKyZzTOzGWZ2WtA1AQAAAOVFqQsQkn4vqZK7t5d0n6R/BVwPAAAAUG6UxgDRUdLHkuTu8yUlBFsOAAAAUH6UxgBRXdJveZYzzCzf1aTMbKCZLTKzRZs2bSrZ6gAAAIAyrDQGiB2S4vIsR7l7vluduvs4d09w94Q6deqUbHUAAABAGVbqLuNqZpdLutjd+5tZO0nD3b3nYbbfJGm3pCPf+hLhUluMd0ljzEsW413yGPOSxXiXPMa85DHmh9rs7hcdaaPSGCCiJD0nqYWyrlX7R3dfeYTXLHJ35kqUEMa75DHmJYvxLnmMeclivEseY17yGPNjV+ruRO3umZJuCboOAAAAoDwqjXMgAAAAAASkvASIcUEXUM4w3iWPMS9ZjHfJY8xLFuNd8hjzkseYH6NSNwcCAAAAQHDKyxEIAAAAAGFAgAAAAAAQsjIbIMwsyszGmtk8M5thZqcFXVNZYWYVzGyimc02s4VmdomZnWZmKdltY7IvtyszG569zVwzaxt07aWdmZ1gZuvMrDFjXrzMbGj298cXZnYT4128sr9XXs0ex9n8Gy9eZnaumc3Ifh7yOBe2LQ7voPE+J3v8ZpjZVDOrm90+wMwWmdl8M+uT3VbbzKZlbz/ZzKoE+DFKlbxjnqftWjObl2eZMT9GZfk//u8lVXL39pLuk/SvgOspS66XtMXdO0nqKekZSY9LeiC7zSRdamatJHWRdK6kayQ9G1C9ZYKZVZD0vKS92U2MeTExs/MldZB0nrLGs74Y7+LWS1KMu3eQNELSQ2LMi4WZ3SPpBUmVspuOZpwP2bYkay+NChjvJyUNdvfzJb0t6V4zi5d0u7K+c3pIesTMKkr6q6RXs8d7iaSbS7j8UqmAMZeZnSPpJmX9uxVjXjRlOUB0lPSxJLn7fEncKCR83pA0LM/yAUmtJc3MXv5IUndl/QymeZafJMWYWZ0SrbRsGSVprKQN2cuMefHpIWmZpHckfSApSYx3cVulrPGLklRd0n4x5sXle0mX5Vk+mnEuaFsc3sHjfY27f5n9PEbSPkltJc1x9zR3/03SamXdMDd3X0aM99HIN+ZmdrykRyXdmWcbxrwIynKAqC7ptzzLGWZW6m6cF4ncfZe77zSzOElvSnpAWVf0yrmk105JNXTozyCnHUfJzPpL2uTuU/M2M+bFpray/uhwpbJuXDlJUhTjXax2SWooaaWkf0t6SvwbLxbu/payAlqOoxnngrbFYRw83u6eKklm1kHSbZJGq/DxztvOeIco75ibWbSk8ZLuUtYY5mDMi6AsB4gdkuLyLEe5+4GgiilrzKy+pM8kTXT3VyVl5lkdJ2m7Dv0Z5LTj6N0oKTH7fM5zJP1H0gl51jPm4bVF0lR3T3f3b5X1F8K8v0QY7/C7S1ljfoaksyW9LCk2z3rGvPgczfd3QdviKJnZ1co6otzb3Tep8PHO2854H5vWkk6XNEbSa5LOMrMnxJgXSVkOEHOUdU6tzKydsk5HQBhkT/iaJuled38xu3lJ9nnjUta8iNnK+hn0sKwJ7Q2UFeI2l3jBZYC7d3b3LtnnzH4p6X8kfcSYF5sUSRdZlhMlVZWUzHgXq23671/9tkqqIL5XSsrRjHNB2+IomNn1yjrycL67r8luXiipk5lVMrMakppI+lp59mXEeB8Td1/o7k2zf39eI+kbd79TjHmRlOVTet5R1l9s5yprwswfA66nLPmLpFqShplZzlyIOyQ9ZWaxklZIetPdM8xstqR5ygqrgwKptuwaIunfjHn4uXuSmXVW1i+YnHH8QYx3cRot6cXs8YxV1vfMIjHmJeFovksO2TaIgkur7NNpnpL0k6S3zUySZrr7cDN7Slk7q1GS7nf3fWY2UtLLZjZA0mZJ1wZUepnj7hsZ82PHnagBAAAAhKwsn8IEAAAAIMwIEAAAAABCRoAAAAAAEDICBAAAAICQESAAAAAAhIwAAQAAACBkBAgAAAAAISNAAAAOYWY1zezWPMtzi+l9KpvZzOwbbBWln1gzm2VmZfkGqQAQEQgQAICC1JSUGyDcvUMxvc+Nkt5294yidOLu6ZKSJV0dlqoAAIUiQAAACvKopFPN7Esz+6eZ7ZIkM2toZivN7AUz+9rMJplZdzObY2bfmVnbnA7M7HozW5jdx/OFHGW4TtJ7R9O3mVU1sylmtjR7u5zQ8G52fwCAYkSAAAAU5D5J37v7Oe5+90HrTpP0pKQWkhpLulZSR0n/T9JfJMnMmijraMB57n6OpAwdtHNvZrGSGrn72qPpW9JFkja4+9nu3kzSx9ntX0tqU7SPDQA4EgIEAOBo/eDuy9w9U9JyScnu7pKWSWqYvc0FklpL+tzMvsxebnRQP7UlbT+GvpdJ6m5mj5lZJ3f/TZKyT4NKN7O4MH5WAMBBmGwGADhaaXmeZ+ZZztR/f6+YpJfdfehh+tkrqdLR9u3uq8ystaRekh4xs2nuPiJ7u4qS9h3FZwEAHCWOQAAACrJTUlH+kp8s6QozO0GSzOw4Mzs57wbuvk1StJkdHCIOy8xOlLTH3V+RNEpSq+z24yVtcvf9RagbAHAEHIEAABzC3bdkT17+WtJHx/D6b8zsAUnTzCxK0n5JgyT9eNCm05Q1x+GTo+i+uaR/mllmdr9/zm7vKunDo60VAHB0LOvUUgAASp6ZtZT0v+5+Qxj6elvSUHf/tuiVAQAKwylMAIDAuPsSSZ+F40Zykt4lPABA8eMIBAAAAICQcQQCAAAAQMgIEAAAAABCRoAAAAAAEDICBAAAAICQESAAAAAAhIwAAQAAACBk/x/ZGaCG5gdbiwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "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": 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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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(pneuron.Vm0)\n", "for A in [5., -20., -60.]:\n", " 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": 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", 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\n", 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\n", 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LX/wi77p+9rOfseeee/Lcc8/ltP2KFSvYe++96d+/f9bbujtjx45l2rRpWW1XU1PDk08+SU1NTeRtFi9enHeiJCIiIiLNp1UmEH/7298A+Pvf/16U+jdu3Jh3HfHEIdcYP/roo5z3/Z///If77ruPiy66KKvtzj//fI455hgmTJgQqby7s+eee7LnnntGKj9r1iz2339/3njjjYxlH3jgAV544YVI9YqIiIhIdGWbQJhZWzO7zcyeMbMXzOyHZra9mf0rWHa9meUUf1VVcQ/b3UteVz7HmGsCdNdddwFw9913Ryrf0NDQ+DnKcR577LH897//ZcyYMWnLLV68mNGjR/OjH/0obbnXX3+dQw45hOeffz5tXRdccAEff/xxxvhEREREWoOyTSCAY4HP3X0/4BBgKnAVcF6wzID0LcQUzKxgQSZT6QlEdXV1s24HUF9fH7nshg0b0q6POu9j1KhRLFiwgJ/85Ccpyxx//PFMnz6dk046Ken6devW8f3vf58//OEPSdfX1tby8MMPs3LlypT7KOT1IiIiIlJs5ZxA/A04P/R9IzAAmBN8nwl8L5eKW0MPRD5JUnMlEOFjyyaByHROoh77F198kbHMokWLAHjllVeSrr/nnnt49dVXmTx5ctL11157LSeeeCLHHHNM0vVjx47lu9/9bsakSERERKRclG0C4e5r3X2NmXUC7gHOA8y/bD2uAbok29bMRpnZfDObv3Tp0ibrKymByFU+CUSu56dNmzZZlS9WAhE1kcnmOFPtM1Msjz32GEDKJ3Pdd999vP/++wV5cpeIiIhIcyjbBALAzPoAs4Hb3P12oCG0uhOQdFyIu09394HuPrB79+7NEGmT/Ze8rlIMYcp2n+Fjy+Y4C9UDkU3CE56vEZbpmKOek3JIOkVERESiKNsEwsx6Ao8BE9z9pmDxf8xscPD5EOCZHOvOP8A0UjU2c1GKIUzFPj9x4WPL5pxlOidRG+3ZJEqp9pnpXEWNpZDXjIiIiEgxZTfmpHmdA3QDzjez+FyI3wDXmlk74A1iQ5uyVklDmErRA5FrApHPPkuRQBTiOshUR9RzqQRCREREKkXZJhDu/htiCUOiA/KtuzX0QOTTOM5122zPa7F6IAr5+zWztPsrVA+EhjCJiIhIpSjbIUzF1Bp6IMIN2+ZqnOaTQJTiKUxRroNMdeW7Pk49ECIiIlIplEAUQbklENk2TnM9P801ibpQcRQigdAQJhEREWltWmUC0RqGMOXTOM+196LS5kBEuQ7y7WHQECYRERFpaVplAlFs5dADkev8gny2zScxK9YciHzmL0D+PQzqgRAREZGWplUmEK1hCFM+CURYMROIYk2ijlpvlOsg3/c8qAdCREREWholEEVQbo3Bcu2BKHUCUYihbIXqgSi3a0ZEREQklVaZQBR7DkS59UBkW0e5JxCFKluIORCaRC0iIiKtTatMICqpB6LUQ5iKOYm6OXog0j0etpzmQKgHQkRERCpFq0wgit0DUUiFaFiW6xyIXPeTTQKRrmw5vQdCCYSIiIhUCiUQZV5vro3/SngKU6nnQDRHAhG1V0ZDmERERKRSKIEo83pLPYQpm22bawhTNoo9B0IJhIiIiLQ2SiDKvN5KmkSdrWJNom7OORB6jKuIiIi0NkogCqgYk7ML0YBvriFMzdUD0RKfwqQEQkRERCpFq0wgivUUpnLtgSjXpzCFtfZJ1BrCJCIiIpVCCUQBtZQEotInUYeVeg6EeiBERESkpam4BMLMqsxsmpk9b2ZPmdn2OdRRjNDKKoEISzcPINM+s9m2XBKIQr6JOt85DkogREREpKXJKYEws83NrLrQwUT0Y6CDu38XOAv4XbYVtLYEIp86ijkHItf9FOpFcoUYwpQpLk2iFhERkZYmUusmuOs/3MweMrMlwCLgEzN7zcyuNLMdihvmJvYFHgFw97nAwGwraA0JRKGGMGWz/3Lsgch3DkSmMpmSFc2BEBERkZYm6i3j2UBf4Gygl7v3cfcewH7AXOAyMzu2SDEm6gysCn2vN7M24QJmNsrM5pvZ/KVLlzapoJISiFI8hanQ+y/0fsppDkSmJEjvgRAREZGWpk3mIgB8z93rEhe6+3LgXuBeM2tb0MhSWw10Cn2vcveNCXFNB6YDDBw4sElrs1iTqMvpMa6lmERdLo9xLeQciHwTCM2BEBERkZYmUosvWfKQS5kCeRYYBmBmg4BXsq2gkt4DUeohTJX4FKao9RZ6DkQ+CYR6IERERKRSZGxBmdn3zewGM9st+D6q+GGldT9QY2bPAVcD47OtoJKGMOUqnzdRh5VjD0ShJlGrB0JEREQke1GGMI0BRgLnmdlXgd2KG1J67t4AjM6njtbwHoiwljaEKZtzkq5sOc2BEBEREakUUVo3S919pbufARwE7FHkmIquknogKulN1PkcfynmQDTHU5g0iVpERERamiitm4fiH9z9LODW4oVT2VpiAlGOcyAKVW+hEwjNgRAREZHWIGMLyt0fSPj+h+KF0zxaw1OY8qmj3CdRZyPferMZwpTPi+SUQIiIiEiliPoYVwDMbCBwLvCNYFsD3N13KUJsRVNJQ5gK8RjXbHsxyjWBMLOsj6U5J1Hn8yI5TaIWERGRSpFVAgH8FTiT2KNTK/aWaSUlEBrC9KWqqqq0CUGyevN9E3VzPcZVCYSIiIhUimwTiKXu/o+iRNKM9B6I4mybz/FH7YGIojnfA6HHuIqIiEhrk20CcYGZ/RmYBWyIL3T3+woaVZFV0mNcC6Fc50Ck2me+9Tfnm6jDlECIiIhIa5BtAjES2Aloy5dDmByoqASiXBv6yZRiEnWu2+YzhCnK0KRcfm/FTiAK9R4ITaIWERGRSpFtArGru/cvSiTNSHMgirNtsV8kl8vd/HwnUef7Hoh0+2iOp1CJiIiIFFq2Y3nmmtnORYmkGbWGORCFqqOcJlHnkkDkO4k63/dApNs+2x4YERERkXKQbQ/EvsAJZvYusTkQFfkY10qaA1GIx7jm0wNRTm+ijvp7K+Sd/WIOYVIPhIiIiFSibBOIg4sSRTPTEKbibJtPD0QhJ1GH5fsUpkwKdZ5vu+02Zs6cyS233ELv3r3zjktERESkWLJqQbn7+8l+ihVcsehN1MXZtlwmURfyMa75DmGKOgfi008/5dVXX+Xyyy/PGJOIiIhIKWXV4jWzW8ysa+h7NzO7qfBhFVcl9UDkqtJ6IErxHojmeApTePvEXpZkvS6rVq3KGJOIiIhIKWV7y3wXd18Z/+LuK4DvFDak4qukBKIQQ5iyrUMJxJfyfQpTtse4YcOGjGVERERESinbBKLKzLrFv5jZV8l+HkUkZtbFzP5pZnPM7Hkz+26wfJCZzTOzZ83sghzrLmywRay3FHMgct1/sROIXIaINeeL5JKdq3THmKx8bW1t5P2JiIiIlEK2jf/fAc+b2d+IvUDuKGBSwaOKOQ2Y5e6/N7N+wB3A7sA04AjgHeAhM9vd3V/KpuLWkECE5TOEKZvHi5bLJOpymgMRVl9fT9u2bZNuG6cEQkRERMpdpAQiuPs/191vNbP5wIHEHuH6E3d/vUixXU3sUbEQi7PGzDoD7d397SCuR4EhQItNIErxGNdc918uQ5jCiv0Y17BshzAlSyA0hElERETKXdQeiBOA68zsLeAR4B53/7RQQZjZicD4hMUj3f1FM+sF/AUYB3QGVofKrAG2S1LfKGAUwNZbb91kf63hKUyVkECEleIpTInb5BJ/pn1pCJOIiIi0NJESCHcfDWBmOwGHADPMrAswm1hC8ay75/wqXXe/EbgxcbmZ9QfuBM5w9zlBD0SnUJFOwMrE7dx9OjAdYODAgU1aaZXUA1HqIUzZ7D/bBKrUk6jDGhoaqK6ubrK8TZv0f0WySSA2btyYMY4VK1bknMyIiIiINIes5kC4+yJgEXC1mXUE/g/4KXAVMLCQgZnZzsDfgKPdfWGw/9VmVmtmfYnNgRgKXJRD3YUMtaj16kVyXyr0JOrEuR7JEojwnIUodaRbn5hAJDvmJUuWMHPmTHbZZReqqqqorq5m48aNbNy4kbq6Ourr66mrq0u6LPxnfX09DQ0NuDvu3vgZSLo82U9iuUSJv+/w91SfC1EuVRxR/ky1Lpe6ovyZrN5C1pXr8Tfn8WZ7/JWupRxHS9ESfh8t4RhAx1FOevXqlXcdUedAbA/0dPdn48vcfb2ZrQV+7+6/zjuSpi4FOgDXBL+sVe7+I2A08FegGnjM3edlW3GxhjCV00VVCUOYso0xWQM/U73p7vpH2X82CUSy4Ufh9VHnN5x44omRyomIiIhkqxCjW6L2QPweOCfJ8nXBuh/kHUmCIFlItnwuMCifuiupB6IUb6Iu1/dAZGrMJ6u3rq4ur/2HhzAlG1qUTQKRuD7xL7CZ0a9fPxYtWkSPHj2oqqpqfHJTdXU1bdu2pU2bNil/wuurqqqoqqpqvOMb/wkvi3+O7zvZusSfVLFH7U1KVS6b+pKtS/wz2fpUZTP9mWlf2fxZjDqj1leK402sL2rZSqXjKC8t4ThawjG0JC3h91GoY4iaQGzj7i8nCWK+mW1TkEiakRKI9EqRQESZRF3MBKK2tpbNNtssbb11dXW0a9cubR2Z9pFqHUDnzp154oknWLduHZ06dUJERESkHEUdy9MhzbqOhQikOYWHwhSigR5XjKFRhZgDke2TfQqRQESJO9v9ZJrQnEy6Y48yvChTMpKpjnTrE89Ru3btqK6uVvIgIiIiZS1qi/dFMzs5cWHw+NV/Fzak5pXuDnW2Eu9OF0IhHuOa7TFGnUOQbrsoSUtzDGGKmjxFSSCS1RWOO98eiPbt20eKVURERKSUot7SHQfcb2bH8GXCMBBoBxxejMCKKbFRV6iGW9QGbjayacCnku3LyXLtvcj2vGa7n6g9EFHrDZerqalJWiacIGRKxDIdQ+L6xKRJCYSIiIhUgqjvgfgM2NvM/g/4drD4IXd/smiRNZNCvrgrlyE2meQaXz49EOGGbTbJR+JwnUxDcbJ9QlH4/DY0NKQcMpZLApHrEKZM5ypdLIkJRIcO6UYKioiIiJSHbFu8zwG9gW8A+5rZvgDufnGhAyumfBrX6YTnVmzcuLEgCUUpEoiwfBKIbMqn6gFIVb6uri7SHftsJlEnk6kHIptJ1JnmQKgHQkRERCpBti3cB4i9+fklILtxMWWkWAlEYmOyEAlEIeLLdghTpnH9qeSTQGQbY7oEopA9EJl6GMLrkyVB4X2sXr065ToozhwaERERkULLtoW7lbsfXJRISqSQQ5iyfSxoFKXogci1YZ/PJOpsE46o5yVquSg9IOvXr08bU2KCkLh++fLlm6xLHMKkBEJEREQqQbbPHX3OzPoXJZISKWQCUch6470XpXiMa6HmQGRTPtshTPn2LETdf/hcrFu3Lu36lStXpt3HihUrUm6bLgYRERGRcpJtD8S+wAgze5fYECYD3N13KXhkRZRP47q56m3btm1eT2Aq1HsgyjWBSFc+07CibOoLl8nUA5FtApGYHCauFxERESlH2SYQhxQlimZWrDkQuc4dSKZdu3ZJG6xRFSqByPU4ij2EKWoCke4chuv74osvMtaVrAciUwIR3j7TEKYtt9wyZawiIiIi5SJSAmFm5jHvZypTuNCaRyF7ILJ5Z0AmUR9ZmkpLHMIUtWchlwQiWXIQpUw2PRDLli1Lue7ggw/mvPPOSxmriIiISLmI2iqdbWa/NrOtwwvNrJ2ZHWhmtwAnFD684ijWEKZcG97J5POEosRY8jnG5kogsi2fLjEoZAKRzRyITEOQUiUQffr04eabb6Zv375ptxcREREpB1GHMB0M/AK4w8y2JfYo1w5ANfAYcLW7LyhOiIUXbvQV4k3Pxag3sTejY8eOOW9fCY9xzbYHImpiUMgeiExzIJINg0rXAxE/nmx7l0RERERKKeqbqGuAPwJ/NLO2wBbAendvOmajAtTX1zd+LtdJ1Pn2IOSzfSkmUUeZ7xH+vRViCFOm3oXEGDP1QGzYsIG6ujratm2bdP3KlSupra1tfFxrfJ2ZpYxRREREpNxkfevT3evc/ZPmSh7MbCczW2VmHYLvg8xsnpk9a2YX5FJnIecqFKvefJOcqI3tZJoUt2o4AAAgAElEQVRrDkRYKeZAhM9RISZRA6xdu3aT74k9UZ9//nmTbdUDISIiIpWkrFsuZtYZ+B2bvvV6GjCc2CNl9zKz3bOttznmQBSyByKXZKQUj3HNNvHIdj/FTCAK0QMBTROI8D4AVq1a1WRb9UCIiIhIJSnbBMJirarpwDnAumBZZ6C9u78dPPHpUWBItnWHG3Xl+hjXQg5hyrY3INfkI9t9hu/OF3IORCETiKh1xSUed2ICET5OJRAiIiJSibJ9D0RRmNmJwPiExe8Dd7r7wlADqzOwOlRmDbBdkvpGAaMAtt5668TVFTGEKd+68klAct0222FX4fL19fVN5g+kiyubF8SlegxuIXogEocoZfoePi/x46murk66bxEREZFylFcPhJm1MbNdzWxPM9sz13rc/UZ3/3b4B+gHnGhmTwG9iD3taTXQKbRpJ2JPhEqsb7q7D3T3gd27d2+yv0qYRJ1vjFEb28lk+3SkuGx7FBLvzmdKlHIZwpSubJSeqPAxRUkgEutJjCXcQxEvmy5pEhERESk3+fZA3A28ANQBHnwuCHffPv7ZzN4DDnL3GjOrNbO+wDvAUOCiHOpu/FyOPRDuXtIeiFyTl3yGMMX3tdlmm0WKK9sEIlm94fpSxRs+98mGMEXtgejQoQM1NTWbxB1fF35poIiIiEi5y7fl8pq7X1aQSKIbDfyV4B0U7j4v2wqK1QNRqHoTn+yTbw9Etu+kCO8vm+Ql27kl2fZARE38EtelOv5C9EAkbpdq3x07dqSmpmaT9eqBEBERkUqUbwJRZ2aPA0sB3H14/iE15e7bhD7PBQblU18hJzuHFWpydmLDOpc3UeeTzIQbzcVMINLND8hUPl1SlKlRHxcl3vDyXOZAxK+1jh07smLFik3qUw+EiIiIVKJ8Wy693P37BYmkGRXrKUy5NrwTJSYQubzVOllDNZdtmzOByLRNOMFIl2wkrouSQKRK0jIlLdkMYUqMTT0QIiIiUonyTSA2M7OfETwZyd0fzj+k4suncZ1OrkN/0tWTa13hbfLtgXD3SI8azTaBSkyUMsUZXp/u95ZLAhGlByJZfJmSoPg28TkY6oEQERGRSpdVy8XMNgPik5vfBGYD7YGmjzoqY4Vq6CfK9c59osS74bnUFT5Gd6e+vj7y40LD+4tvG6WRG54gXAk9EOF4U9WXqQcicbvEMvGJ1126dGkSS/z33K5du6T7FhERESlHkRIIM2sLXAkcD7xL7PGvPYCp7n6pmX3H3f9TvDALK9mjNAuhXBOIeB1RE4hkDftiJBCJT1JKt019ff0mPQaFmAMRfqpSqgQi0zElzotILBPfR+fOnZus/+KLLwDYfPPNk+5bREREpBxFfQ/E74CvAN9w9wHu/h3gm8C2ZnY9cF+xAiyGqENhslWoxCQxgch3DkS28eSawIS3ixJz4mNRs+lVSFc2avzh/ScrU1dXl/F3Gk8gunbtmrRMPAFJ1gMRTyC+8pWvpDgSERERkfITdQjTMGAHDz1H091Xm9kpwDLgkGIEVyxRh8Jkw90LlkBk01hOJbFxnk088YZttttmapAnynT3Pl1M6RKUqPEn9kAkzvVYu3YtEJvkXFdXlzaB6NKlCytXrmwy/Cu+vlOnTo37SaxfPRAiIiJSSaL2QDR44ssJAHevB5YGj1atGPGGGxSuB2LdunV5vXshLLHxn0tdq1ev3uR7NglE+PxE3ba+vj7rBCoxgUiXKCUeT7qyq1at2uR7slhqa2upqamhurq6cWhX4nmOn4dUvQsAa9asAaBbt25NynzxxRfU1tbSsWPHxgQivD5+TEogREREpJJETSBeN7PjExea2bHAG4UNqbjq6uo2uUNdqDkQ2TRwM1mxYsUm33OJMZ8EIt4ozmbbFStWbPKityhJTzbHmZgUpKt/5cqVwJdPN0pW77JlywD42te+Rvv27YGmv7PPP/8cgC222AKIJUmJb7lesmQJAF//+teb7Cu+jy222KLxUa3hfXz66acA9OrVK+WxiIiIiJSbqAnEWGCsmT1lZr8zsylmNgc4FRhTvPAKb/ny5Zt8L1QCEW8sxuXTA5FYVy4xxhu2udTxySefZL3t0qVLN/keJYGKN6A7duyYcZuPPvoI+PJOf7qy8Xq33HJLIHn8n332GQDdu3dvfApSYp0ffvghAH369EmajHzxxResWLGCtm3b0rNnzyZ1fPDBB0AsQYjvI3xdxOvv3bt3ymMRERERKTeREgh3/8jd9wIuBt4DPgAudvc93f2jIsZXcG+//fYm3wuVQLzzzjubfM+nB+J///vfJt+zjdHdG48zfnc9ah319fW8++67QPqhO4neeust4Mu76ZkSKHdn0aJFAGy/fezJwOnOWbz+eNlU9a9bt4733nuPqqoq+vXrl7LsK6+8AsCOO+7Y2DuQeJyvvfYaANttt11jmXBdCxcuBKBfv36N5zm8/oUXXgDgm9/8ZpMeiI0bNzbGsNNOO6U8bhEREZFyE7UHAgB3f9Ld/+Du17r7rGIFVUzPPPMMANtssw1QuDkQTz/9NPBlYzDXet2dp556CoDddtsNyD6BePnll1m2bBk9evRg2223zaqOF154gTVr1rDVVls13hmPsu2jjz4KwKBBgyJts3DhQpYsWcJXv/rVxoZ+qm3cnYcfjr2jcJ999klb9qGHHqK+vp7vfOc7jU83SkxM3J0HHnigMd5470B4Dkd9fT3/+Mc/GveZLMm4777Yw8f23nvvxh6K+L7WrVvHTTfdBMBhhx3WZPsnn3ySL774gr59+2oIk4iIiFSUrBKISvfuu+82NuqOOOIIoDBPYVq4cCH33HMPAIcffnhe9d511128+uqrdOvWjSFDhgDZJRC1tbVccMEFAPzwhz9MeXc9mXXr1jVue+SRRya9657MnDlz+Pvf/051dTVHHXVUxv2tW7eOs846C4Cjjz465RCiuBtuuIHXXnuNHj16MHTo0JT1L1myhEsvvRSAn//85ynjv//++3nuuefo1KkTP/zhDxv3H67zj3/8I++88w59+vRh8ODBTc7jq6++yp133klVVRXDhw9vUsett97KihUrGDBgAPvuu2+T7WfMmAHAsccem/I8iYiIiJSjVpNAPPLIIxx66KGsXr2aoUOHcsABBwD59UC4O//85z/56U9/Sm1tLccddxw777xzTvU2NDRw/fXXc8YZZwBw3nnnNd5Bj5pALFmyhKOPPpp58+bRs2dPTj311KSN42Q+/vhjjjjiCF555RW+8Y1vMHr06KQTfxM9+OCDjBgxAnfn17/+NX379k27v5UrV/Kzn/2MhQsXstVWW/Gb3/wmbYwzZszgwgsvBGDSpEkpz8ny5cs57rjj+OSTTxg4cCBHH3100vhffPFFTjvtNADOPfdcunbt2qRxP2/ePC6//HIALr30Utq0abNJmYaGBiZMmEB9fT0jR46kX79+m8yRWL9+PX/84x8BGD9+PGa2SSxvv/02s2fPpn379o0Jl4iIiEilaPEJhLtz1llnMXLkSFasWMEBBxzA1KlTs7ozn2jjxo3cf//9DBkyhFGjRrFmzRqGDRvGJZdcknW97s6cOXMYNmwYF198MfX19Zx++ukMHz48cg/Axo0bufHGG9l3332ZO3cuPXv25LbbbqN79+5pn0QU3//tt9/O4MGDWbBgAVtvvTV/+ctf6NKlS9r919TUcNZZZ3HyySdTU1PD8OHDOfPMMxv3l2ybl19+maFDh/Liiy/Su3dv7rjjjk32E27oNzQ0cPnll3P22Wfj7px33nkcdthhSZONzz77jCOOOIKXX36Zb3zjG9x8882bNPrjsbz99tuccMIJbNiwgeOPP57jj489WCzcA7J8+XLGjBlDfX09Y8aMaewFCp/HW2+9lZdeeolevXoxYcKETeqoq6vjjjvuYOnSpfTv358DDzywyfpwL9hXv/rVpL8XERERkXIV9UVyFeuNN97gpZdeol27dpx77rmcdNJJVFVV5ZRAbNiwgbvvvpvrrruO999/H4hNGj711FMZMWLEJneao9T70ksvMXnyZJ599lkAevbsyRVXXMFBBx0EEKkHYP78+Zx99tm8+uqrABx44IFMmTKl8QlE6eJZvHgxZ555JnPmzAFg6NChXHnllXTv3j3t/hcsWMD48eNZtGgR7dq148ILL2w8/mTDkRoaGrjlllu46KKL2LBhA/379+emm25iq622AmiSFKxYsYJTTz2VJ554gqqqKq688kqGDx+e9HgWL17M0UcfzbvvvssOO+zAXXfd1fjY1fC8hI8++oijjz6aFStWMGTIECZNmtT40rjwHIhx48bx8ccfM3DgwMZhVuEyH374IZMmTQJg4sSJje93iMcV7n0YN25c4z7i65cvX86sWbHpQyeddFKT34mIiIhIuSvbBMLMqoGrgIFAe+BCd3/QzAYB1wAbgcfc/aJ09axfv55u3bpx5513sssuuzQuz3RnPmzt2rXcdttt/OlPf2p8/Oc222zD2LFj+elPf9r4BJ6o9b755ptcdtllPPLII0DsLca/+tWv+MUvfsFmm23WWC5dD8AHH3zAZZddxv333w/AVlttxSWXXMLQoUM3eZtysjrWrl3LtGnTuO6666ipqaFbt25MnDiRww8/PO22H3/8MVOnTuWWW26hoaGB7bbbjmnTptG/f/8mxx/f5rXXXmPChAn8+9//BuC4447j4osvpkOHDk32U1dXx8KFCzn55JNZvHgx3bp1Y+rUqY138RPLvvXWWxx11FF89tlnfPvb3+aOO+5oTB7gy0b/2rVrOe644/joo4/YY489mD59emOc4TpnzJjB448/TteuXbn++usbl4eP64orrmDt2rUMHTqUQw89tEkdDzzwACtXrqRv374cfPDBTWL517/+BcQmb3/zm99EREREpNKUbQIBHAe0dfd9zOzrwE+D5dOAI4B3gIfMbHd3fylVJVtvvTX33Xdf44u+4qIMD/r888+5+eabufHGGxtfTrbzzjvz61//msMOO2yTRmhcuvH8r732GlOnTuUf//gHDQ0NdOzYkZNOOokxY8Y0PjI1WYy1tbU0NDTw9ttv8+abb/L444/z97//ndraWtq3b88vf/lLfvOb32ySfCTWsWHDBh5++GFmzpzJzJkzG1+m9+Mf/5iLL764sdch2bbLli1j/Pjx3HvvvdTV1VFVVcUpp5zCGWec0WSf4Qb+zTffzG9/+1s2btxIz549mThxIocddljK/bzwwgv84Q9/oKamhl133ZUbbriBPn36JC27Zs0aRowYwWeffcbee+/NzTffTOfOnTcpG//9XHPNNdTW1rLddttx6623Nok5/ju79957AZg8eXJj70himRdeeAEz45xzzkkaV/w6Ofnkk6mq+nKEYOK1Ep/ELyIiIlJpyjmBGAq8YmYPAQb82sw6A+3d/W0AM3sUGAKkTCC6d+/eJHmATRvn7733Hh988AE77bQTX/va11i4cCG333479957LzU1NQDssccenHrqqQwZMmSTu/SJ4g3F9evXc+WVVzJ//nzWr1/P8uXLG9/N0KZNG44//njGjRvX+AKyZOIxLlq0iL333rtx2FRw7Bx55JFMmDChSWM3WR3jxo3b5A3TgwYNYsKECY2PXU237fjx4wGoqqrihz/8IePGjUt59zw8lCfeyB45ciRnn31243CfRPHGeXwo1eGHH87VV1+9Sc9OYv21tbWNw5Zuu+22pMlT4nCqSy+9NG2iBrHH8P74xz9uUiacAAwePJgdd9wx6b4Aqqur+cEPfpByfZs2bRg2bFiTfYiIiIhUgrJIIMzsRGB8wuKlQA1wGLA/cDMwHFgdKrMG2C5JfaOAURDrgUgm3mj8+OOP2Xfffamvr49vi7s3lhsyZAi/+tWv0ja0w+INxbfeeourrrpqk3WdO3fmqKOOYvTo0UmTmkTxBvTrr78OxN7CPGDAAPr3789RRx3V+C6LKPGsWbOGDh06cMYZZ3DQQQexww47ZNw23LBu164djz32WOM7G9JtU11d3Xg+R44cyeTJkyPvp2PHjlxxxRVJk4fEsvH6kyUPiWV79+7Nvvvum7RcuHH/gx/8IGmCGE5+kvWihCdD77nnnk0mR3fp0qXx86BBgzR5WkRERCpWWSQQ7n4jcGN4mZndCTzosdb8HDPbkVjyEL6N3QlYmaS+6cB0gIEDB3rieti0QVdfX89WW23FqlWrWLNmDX369OGggw5ixIgRjW8+jiqxYXjOOeewxx570KFDB3beeedNGquZfO1rX2v83KZNG2bPnp22xyKZ8B33k046ibFjx+a07Y9+9KOMyQPEErBOnTo1DuWJ8p6D8DkZNmxY46Nak0lMIBLv9IeFh2X9+Mc/3mRIUVh4eXheQ1g4gYhPck+1r2Trw0Ox4k92EhEREalEZZFApPAvYBhwr5ntCnzg7qvNrNbM+hKbAzEUSDuJOpWOHTtu8n3OnDl07NiRjRs3NmmkZqNbt26Nn7faaivGjh2bsuGaSXhC8KGHHpp18gCbNmyPPPLIrLbt0aNH4+f4I0+jiPc+xJOmTMLHFSXGr3zlK6xdu5b+/ftvco4SxZ9EBSQdlhQXTtQShybFxXuMtt1226T77NOnT2PPS/xld2FdunRhr7324r///W/jywZFREREKlE5JxA3ANeb2VxicyBGB8tHA38Fqok9hWlerjvYcsst+eSTT/jJT37SOAwmn+QBYuPfe/fuzccff8wJJ5yQc/IAsYZ1+/bt2bBhAyNHjsypjp122gmINYxTNY5T2W233QDYZZddGDBgQOTtDj/8cG699VYuueSSSOV33313OnTowLbbbst+++2XsfwJJ5zAtGnTGt+ancqAAQPo27cv/fr149vf/nbKciNGjGDBggWceuqpKee3/PKXv2TFihUpE6lu3brxl7/8hbq6OrbddtukZe699142btyYcniWiIiISCWw8Hj/lmjgwIH+4IMPJl139913c9ddd3HttddGmpMQ1dNPP82zzz7LuHHjmvR0ZOuZZ57h888/T3sHPR13Z/bs2fTv3z/pk5Yybbtw4UL69u2bcgJ0MnV1dbzxxhv0798/7YTzsOXLl9OhQ4eU8xkS41q/fn2ksiIiIiLypV69ekVrnKXRqhMIEREREZHWpBAJRO7ja0REREREpNVRAiEiIiIiIpEpgRARERERkciUQIiIiIiISGRKIEREREREJDIlECIiIiIiEpkSCBERERERiUwJhIiIiIiIRKYEQkREREREIlMCISIiIiIikSmBEBERERGRyJRAiIiIiIhIZEogREREREQkMiUQIiIiIiISWdkmEGbWxcxmmtnTZvaEmfUKlg8ys3lm9qyZXVDqOEVEREREWpOyTSCAEcAr7r4/cBdwZrB8GjAc2BfYy8x2L014IiIiIiKtTzknEK8AnYLPnYE6M+sMtHf3t93dgUeBIaUKUERERESktWlT6gAAzOxEYHzC4rHAQWb2OvBVYD9iicTqUJk1wHZJ6hsFjALYeuutixGyiIiIiEirVBY9EO5+o7t/O/wD/Aa4wt13Bg4C7iWWPHQKbdoJWJmkvunuPtDdB3bv3r05DkFEREREpFUoiwQihRXAquDzEqCzu68Gas2sr5kZMBR4plQBioiIiIi0NmUxhCmF84E/m9kYoC1wcrB8NPBXoBp4zN3nlSg+EREREZFWp2wTCHf/GBiWZPlcYFDzRyQiIiIiIuU8hElERERERMqMEggREREREYlMCYSIiIiIiESmBEJERERERCJTAiEiIiIiIpEpgRARERERkciUQIiIiIiISGRKIEREREREJDIlECIiIiIiEpkSCBERERERiUwJhIiIiIiIRKYEQkREREREIlMCISIiIiIikSmBEBERERGRyJRAiIiIiIhIZGWTQJjZ4WZ2e+j7IDObZ2bPmtkFwbIqM5tmZs+b2VNmtn3pIhYRERERaX3alDoAADO7BhgKLAgtngYcAbwDPGRmuwPbAB3c/btmNgj4HfCjZg5XRERERKTVKpceiOeAU+JfzKwz0N7d33Z3Bx4FhgD7Ao8AuPtcYGAJYhURERERabWatQfCzE4ExicsHunud5nZ4NCyzsDq0Pc1wHbB8lWh5fVm1sbdNybsZxQwKvi6dsstt/wcWFaAQ5DWZQt03Uj2dN1ILnTdSC503UguXnX3b+dTQbMmEO5+I3BjhKKrgU6h752AlcBmCcurEpOHYD/Tgenx72Y2393VWyFZ0XUjudB1I7nQdSO50HUjuTCz+fnWUS5DmDbh7quBWjPra2ZGbH7EM8CzwDCITbIGXildlCIiIiIirU9ZTKJOYTTwV6AaeMzd55nZi8D3zew5wICRpQxQRERERKS1KZsEwt2fAp4KfZ8LDEoo00AsscjW9MxFRJrQdSO50HUjudB1I7nQdSO5yPu6sdhDjkRERERERDIryzkQIiIiIiJSnpRAiIiIiIhIZEogREREREQkMiUQIiIiIiISmRIIERERERGJTAmEiIiIiIhEpgRCREREREQiUwIhIiIiIiKRKYEQEREREZHIlECIiIiIiEhkSiBERERERCQyJRAiIiIiIhKZEggREREREYmsxScQZvZIqWMQEREREWkp2pQ6gGIbMGDA0E8//dRLHYeIiIiISKn16tXL8q2jxfdAiIiIiIhI4SiBEBERERGRyJRAiIiIiIhIZEogREREREQkMiUQIkXW0NBQ6hBERERECkYJhEgRffjhhwwZMoS5c+eWOhQRERGRglACIVIkK1asYPjw4SxatIirr74adz1NWERERCqfEgiRIli3bh0nnHAC//3vf+nXrx9/+tOfMMv7scsiIiIiJacEQqTANm7cyJgxY3jxxRfp3bs3t99+O127di11WCIiIiIFoQRCpIDcnQkTJvDoo4/StWtXbr/9dnr37l3qsEREREQKRgmESAFNmTKF22+/nQ4dOnDrrbfSr1+/UockIiIiUlBKIEQK5JZbbuGqq66iqqqKadOmsccee5Q6JBEREZGCUwIhUgAPPfQQZ599NgBXXHEFQ4cOLXFEIiIiIsWhBEIkT3PnzmXs2LG4O2eeeSbHHHNMqUMSERERKRolECJ5WL16NaNHj2bDhg0cf/zxjB8/vtQhiYiIiBRV2SYQZtbezG43s7lm9piZ7WBmg8xsnpk9a2YXlDpGkSuuuILPPvuMgQMHMnnyZL3rQURERFq8NqUOII2TgbXuPsjM+gFTgZ7AEcA7wENmtru7v1TKIKX1WrBgATfddBPV1dVcfvnlVFdXlzokERERkaIr2x4IYGdgJoC7vwnsAbR397fd3YFHgSHJNjSzUWY238zmL126tNkCltajvr6e//f//h/uzqhRo9h5551LHZKIiIhIsyjnBGIBcJjFDAK6AGtD69cEy5pw9+nuPtDdB3bv3r0ZQpXWZsaMGbzyyiv07t2b008/vdThiIiIiDSbck4gbgJWA7OBHwALgc1D6zsBK0sQl7Ryn3zyCZdddhkAkyZNYvPNN8+whYiIiEjLUc4JxB7Av9x9MHA/8BZQa2Z9LTZTdSjwTAnjk1bqggsuYO3atQwdOpSDDz641OGIiIiINKtynkT9X+ASMzuDWE/DicDWwF+BauAxd59XwvikFZo1axb//Oc/6dixIxMnTix1OCIiIiLNrmwTCHdfBnwvYfHHwKAShCPCunXrOOeccwA488wz2WqrrUockYiIiEjzK+chTCJl5brrruODDz5g55135qSTTip1OCIiIiIloQRCJIIPPviA6667DoDJkyfTtm3bEkckIiIiUhpKIEQiuOiii9iwYQOHH344e+21V6nDERERESkZJRAiGTz99NM8/PDDbLbZZpx//vmlDkdERESkpJRAiKRRV1fXmDSMGzeOLbfcssQRiYiIiJSWEgiRNG6++WbeeustttlmG0aNGlXqcERERERKTgmESArLli1jypQpAFx88cW0b9++xBGJiIiIlJ4SCJEULr30UtasWcOBBx7I976X+EoSERERkdZJCYRIEgsWLOCOO+6gbdu2XHTRRZhZqUMSERERKQtKIEQSNDQ0cN555+HunHzyyWy//falDklERESkbCiBEElwzz338O9//5sePXowbty4UocjIiIiUlaUQIiErFmzhkmTJgFw7rnn0qlTpxJHJCIiIlJelECIhPz+979nyZIlDBgwgCOPPLLU4YiIiIiUHSUQIoH//e9/3HDDDZgZEydOpKpKfz1EREREEqmFJAK4O7/97W+pq6vj5z//ObvttlupQxIREREpS0ogRIDHH3+c2bNn07lzZ84+++xShyMiIiJStpRASKtXU1PDb3/7WwDOOOMMtthiixJHJCIiIlK+2pQ6gFTMrC1wC7ANUA+cDGwEZgAOvAqMdfeGEoUoLcT06dN5//332XHHHRkxYkSpwxEREREpa+XcAzEMaOPuewMXA5OAq4Dz3H0/wIAflTA+aQE++eQTfv/73wNwySWX0LZt2xJHJCIiIlLeyjmBeAtoY2ZVQGegDhgAzAnWzwS+V6LYpIU499xzWb9+PYcccgj7779/qcMRERERKXtlO4QJWEts+NIiYAvgMGB/d/dg/RqgS7INzWwUMApg6623LnqgUpkefPBBZs6cyeabb84ll1xS6nBEREREKkI590CMBx519x2BXYnNh2gXWt8JWJlsQ3ef7u4D3X1g9+7dix+pVJyVK1dyzjnnAHDeeefx9a9/vcQRiYiIiFSGck4gVgCrgs/LgbbAf8xscLDsEOCZEsQlLcBFF13E0qVL2XPPPTn++ONLHY6IiIhIxSjnIUxXAzeZ2TPEeh7OAeYDN5hZO+AN4J4SxicV6umnn+bOO++kXbt2TJkyRW+cFhEREclC2SYQ7r4WOCrJqgOaOxZpOdatW8eZZ54JwGmnncYOO+xQ4ohEREREKotuvUqrcsUVV/DBB0JjIWMAABrJSURBVB/wrW99izFjxpQ6HBEREZGKowRCWo3//Oc/3HDDDVRVVTFlyhS980FEREQkB0ogpFWora3ltNNOo6GhgdGjR7PbbruVOiQRERGRiqQEQlqFqVOnsmjRIrbZZhtOP/30UocjIiIiUrGUQEiL99Zbb3HNNdcAMGXKFDbbbLMSRyQiIiJSuZRASItWX1/P6aefTm1tLcOHD2efffYpdUgiIiIiFU0JhLRoM2bMYP78+fTs2ZPf/va3pQ5HREREpOIpgZAWa/HixUyePBmASy+9lC5dupQ4IhEREZHKpwRCWqSGhgbOPPNM1q1bx2GHHcYhhxxS6pBEREREWgQlENIi/fnPf2bOnDl069aNSZMmlTocERERkRZDCYS0OK+99lpj0nDVVVfRo0ePEkckIiIi0nIogZAWZf369YwZM4ba2lqOO+44Dj744FKHJCIiItKiKIGQFuWSSy7hrbfeom/fvlx44YWlDkdERESkxVECIS3G448/zs0330zbtm25/vrr9cI4ERERkSJQAiEtwpIlSxg/fjwAZ599Nv379y9xRCIiIiItkxIIqXgNDQ2MGzeOzz//nP32249f/vKXpQ5JREREpMVSAiEV76abbmL27Nl069aNa665hqoqXdYiIiIixVLWLS0zG2FmTwU/c82sxswGmdk8M3vWzC4odYxSWm+88QYTJ04EYMqUKWy55ZYljkhERESkZSvrBMLdZ7j7YHcfDPwbOBWYBgwH9gX2MrPdSxiilND69es55ZRT2LBhA8cccwzDhg0rdUgiIiIiLV5ZJxBxZjYQ+BZwJ9De3d92dwceBYaUNDgpmUmTJvHmm2/St29fLr744lKHIyIiItIqNFsCYWabm1l1jpufA1wEdAZWh5avAbok2dcoM5tvZvOXLl2a4y6lnM2aNYsbb7yRtm3bct111+mRrSIiIiLNpGgJhJlVmdlwM3vIzJYAi4BPzOw1M7vSzHaIWE9XYCd3n00seegUWt0JWJm4jbtPd/eB7j6we/fuBTgaKSdLly5l3LhxAEyYMIFdd921xBGJiIiItB7F7IGYDfQFzgZ6uXsfd+8B7AfMBS4zs2Mj1LM/8ASAu68Gas2sr5kZMBR4pijRS1lyd8aPH8+yZcvYZ599OOWUU0odkoiIiEir0qaIdX/P3esSF7r7cuBe4F4zaxuhnn7AO6Hvo4G/AtXAY+4+rxDBSmWYOnUqs2bNomvXrlx77bV6ZKuIiIhIMytaAhFPHsxsFvA7d384vs7Mprv7qGQJRpJ6rkz4PhcYVOh4pfw9/fTTXHbZZQBcc8019O7du8QRiYiIiLQ+zXH7dltgQsI7GwY2w36lBVm8eDGjR4+moaGB8ePHc9D/b+/Oo6wq73SPf39gVUO5VCSIQ6JxSDvFjrlS2N6kVaI0xDa56jW2RkFswAmiRLkyiSllKaAMLoEoIKXBMRpso6RVXBrniYuKUa/BtNM1IlmFFwRBoIp67x/noNVAUQOc2qeqvp+1XOvsffbZ+yl9l6ee2vvdu0+frCNJkiS1Sy1RIFaSu9XqnhExPyK2uGuStC3r1q1j8ODBrFixghNOOIHhw4dnHUmSJKndaokCESmlmpTSEHJzH54HurfAcdUGpJQYM2YMf/rTn9hvv/2YMWMGHTs2927AkiRJ2l6FnES9ycxNL1JKv4mIN4GhLXBctQF33XUX9957L506daKyspLdd98960iSJEntWsELREpp1mbLrwIDC31ctX6vvfYaV155JQCTJk3iiCOOyDiRJEmSClYgImI6kOp7P6V0aaGOrdZv+fLlDB48mOrqagYOHMjPfvazrCNJkiSJwp6BWFTn9TVARX0bSnXV1NRw4YUX8umnn9KzZ08qKhw6kiRJxaKQz4GYu+l1RPyy7rK0LePHj+fFF1+ke/fuzJ49m9LS0qwjSZIkKa+lHuNb76VMUl0PP/wwt9xyCzvttBOzZs1ir732yjqSJEmS6mipAiE1aMmSJVx22WUAVFRUcMwxPnBckiSp2BRyEvVqvj7zUBYRqza9BaSU0q6FOrZan1WrVjFo0CDWrl3LaaedxqBBg7KOJEmSpK0o5ByIXQq1b7UttbW1XHrppbz33nscfvjhTJ48mYjIOpYkSZK2omCXMEUjfgNszDZq+6ZPn86CBQvYbbfdqKyspKysLOtIkiRJqkch50A8FRGXRMR+dVdGRGlEnBARc4EBBTy+WoH58+dz/fXXAzBjxgz233//bANJkiRpmwr5HIgfk3vi9L0RcQCwEugEdAQeB25MKS0u4PFV5J555hmGDh1KSonRo0fTu3fvrCNJkiSpAYWcA7EOuBm4OSJKgG7AlymllYU6plqP1157jYEDB1JdXc0FF1zAJZdcknUkSZIkNUIhz0B8JaVUDXzaEsdS8VuyZAn9+vVj7dq1nHHGGVRUVDhpWpIkqZUo6udARMToiHgpIl6NiEER8Z2IeD4inouIWyKiqPNrSx9//DFnnXUWK1asoE+fPkyZMoUOHfzPKEmS1FoU7W9uEdEL+AHwQ+B4YF9gKjA2pXQsuedJnJJZQDXZ8uXLOeuss1i2bBnHHHMMM2fOpKSkJOtYkiRJaoIWLxAR0TEizmnEpn2BN4EHgfnAH4AewDP59x8FnHXbSqxevZqf//znvP/++xxxxBHMnTuXzp07Zx1LkiRJTVTI50Dsmr8EaUZE9ImcS4D3gX9txC66AeXAGcBFwN1Ah5TSpqdbrwZ2q+fYF0TEoohYVFVVtf0/jLbLunXrGDBgAG+99RYHHHAA99xzD7vu6oPIJUmSWqNCTqK+E1gBvAQMBq4ASoFTGnn71s+AP6eUNgBLImIducuYNtmF3K1ht5BSmg3MBigvL09b20Yto6amhosuuoiXXnqJvfbai/vuu4899tgj61iSJElqpkIWiANTSv8AEBFzgOXAfiml1Y38/PPAsIiYCuwN7Aw8GRG9UkpPAycBT+342NpRamtrGT58OAsWLKBLly7ce++97Lvvvg1/UJIkSUWrkAWietOLlNLGiPigCeWBlNIfIuI4YCG5S62GAh8At0ZEKfAOMG8HZ9YOklJi3Lhx3H///XTu3Jk777yTQw89NOtYkiRJ2k6FLBBHRsSq/OsAOueXA0gppQYvgk8pjdjK6uN3YEYVyIwZM5g1axYlJSVUVlZSXl6edSRJkiTtAIW+hOmjAu5fRequu+5i/PjxRATTp0/nRz/6UdaRJEmStIMU8jauD256EREPFPA4KiIPPfQQI0eOBGDChAmccoqP6pAkSWpLClkgos7rAwt4HBWJ2267jYsvvpja2lpGjBjBgAEDso4kSZKkHayQlzClel6rjamtrWXChAnMmDEDgJEjRzJs2LCMU0mSJKkQWmISdd0J1NCESdQqfhs2bODyyy/ngQceYKeddmLy5MmceeaZWceSJElSgRSsQKSUOhZq3yoOq1evZvDgwTz77LOUlZUxZ84cJ0xLkiS1cYU8A6E2bNmyZfTr14+3336bPfbYgzvvvJMjjzwy61iSJEkqMAuEmuzdd9/l7LPP5pNPPuGggw7innvuYb/99ss6liRJklpAIe/CpDZo4cKFnHLKKXzyySf06NGDhx56yPIgSZLUjlgg1GiPPPIIZ555JitXrqRv377cf//9fOMb38g6liRJklqQBUKNcvvttzN48GDWrVtH//79mTNnDmVlZVnHkiRJUgtzDoS2KaXE+PHjt3jGQ0Q08ElJkiS1RRYI1WvDhg0MHz6cefPm0bFjR6ZMmeIzHiRJkto5C4S2qqqqisGDB7Nw4ULKysq49dZbOeGEE7KOJUmSpIxZILSF119/nUGDBvHpp5+y9957c/vtt/uMB0mSJAFOotZmfvvb33Laaafx6aefcvTRR/PYY49ZHiRJkvQVC4QAqK6uZsyYMVx22WWsX7+e8847j9/97nd0794962iSJEkqIl7CJKqqqjj//PN55ZVXKC0tZcKECZx99tlZx5IkSVIRKvoCERGvA5/nFz8AZgE3ATXA4ymla7LK1hYsXryYQYMGsXTpUvbaay8qKys56qijso4lSZKkIlXUBSIiOgGklHrVWbcYOB14H/iPiDgqpfRaNglbt/vuu4+RI0eyfv16evbsyZw5c7xkSZIkSdtU7HMgjgTKIuLxiPhjRBwH/F1K6b2UUgIWACdu/qGIuCAiFkXEoqqqqpbOXPSqq6sZO3Ysv/zlL1m/fj0DBgxg3rx5lgdJkiQ1qKjPQABrgcnAHODvgUeBlXXeXw0cuPmHUkqzgdkA5eXlqfAxW4/ly5dz/vnn8/LLL1NaWsr48eM555xzso4lSZKkVqLYC8S7wH/mzza8GxGfA13rvL8L/7VQaBvqznfYc889qayspEePHlnHkiRJUitS7JcwDQSmAETEPkAZsCYiDoqIAPoCz2WYr9W4//77OfXUU1m6dCk9e/ZkwYIFlgdJkiQ1WbGfgagEfhMRzwOJXKGoBe4GOpK7C9MrGeYretXV1VxzzTVUVlYC0L9/f6699lpKS0szTiZJkqTWqKgLREppA7C1BxIc09JZWqMPP/yQIUOG8Prrr1NSUsL48ePp169f1rEkSZLUihV1gVDzpJSYN28eo0ePZs2aNeyzzz7MmjWL8vLyrKNJkiSplbNAtDGrVq1i1KhRPPjggwD89Kc/5YYbbqBLly4ZJ5MkSVJbYIFoQxYtWsSQIUP4+OOPKSsr47rrruPMM88kN99ckiRJ2n4WiDZg48aN3HTTTUydOpWNGzfyve99j5tvvpmDDjoo62iSJElqYywQrdxf//pXfvGLX/DKK7mbUQ0ZMoSRI0d6lyVJkiQVhAWiFZs/fz5XXHEFn3/+OXvuuSfTpk3juOOOyzqWJEmS2jALRCu0Zs0arrrqKu69914A+vTpw5QpU+jWrVvGySRJktTWWSBamTfeeIOhQ4fy3nvv0alTJ66++mrOPfdcJ0pLkiSpRVggWomNGzcya9YsJk6cSHV1NYcddhi33HILhxxySNbRJEmS1I5YIFqBjz76iGHDhn01UXrQoEGMHTuWTp06ZZxMkiRJ7Y0FooillLj77rupqKhg7dq1dO/enSlTptC7d++so0mSJKmdskAUqb/97W8MHz6cJ598Esg9UXrixIl07do142SSJElqzywQRejhhx9m1KhRrFixgt12240JEyZw6qmnOlFakiRJmbNAFJEVK1Zw5ZVX8uCDDwLQq1cvpk6dyt57751xMkmSJCnHAlEknnrqKS6//HKWLVtG586dufrqq+nfv79nHSRJklRULBAZW7NmDePGjeOOO+4AoGfPntx0000ccMABGSeTJEmStmSByNDChQsZNmwYH374IaWlpYwYMYKLLrqIjh07Zh1NkiRJ2qqiLxAR0R14FfhnoAb4DZCAt4ChKaXa7NI1z/r165k8eTI333wztbW1HH744UyfPp3DDz8862iSJEnSNnXIOsC2REQJMAv4Mr9qKjA2pXQsEMApWWVrrrfffpuTTjqJGTNmAHDppZfy6KOPWh4kSZLUKhR1gQAmAzOBpfnlHsAz+dePAq3miWo1NTVMmzaNk046iXfeeYcDDjiAhx56iNGjR1NaWpp1PEmSJKlRirZARMR5QFVKaUHd1SmllH+9Gtitns9eEBGLImJRVVVVgZM27P333+fUU09lwoQJVFdXc9555/HEE09QXl6edTRJkiSpSYp5DsRAIEVEb+D7wB1A9zrv7wKs3NoHU0qzgdkA5eXlaWvbtISUEnPnzmXcuHF8+eWX7L333kydOpVevXplFUmSJEnaLkVbIFJKx216HRFPAxcBkyKiV0rpaeAk4Kls0jVs6dKlXHbZZTz77LMAnH766Vx77bV06dIl42SSJElS8xVtgajHcODWiCgF3gHmZZxnCykl7rvvPioqKli1ahW77747N9xwAz/5yU+yjiZJkiRtt1ZRIFJKveosHp9VjoYsWbKEUaNG8fLLLwPQp08fJk2aRPfu3Rv4pCRJktQ6tIoCUezWrl3LjTfeyMyZM6mpqaFbt25UVFRw+umnExFZx5MkSZJ2GAvEdnriiScYM2YMH3/8MRHBueeey+jRo53rIEmSpDbJAtFMS5cu5aqrruKRRx4B4Lvf/S7XX389PXr0yDiZJEmSVDgWiCaqqamhsrKSSZMmsWbNGnbeeWdGjBjBwIED2Wkn/3VKkiSpbfM33iZ49dVXGTlyJG+//TYAJ598MuPGjWOfffbJOJkkSZLUMiwQjbBy5UrGjx/PXXfdRUqJfffdlwkTJnDiiSdmHU2SJElqURaIbUgp8cADD3D11Vfz2WefUVJSwsUXX8ywYcMoKyvLOp4kSZLU4iwQ9fjLX/7C6NGjeeGFFwA45phjmDhxIoccckjGySRJkqTsWCA28+WXXzJt2jR+/etfU11dTdeuXamoqOCMM87wmQ6SJElq9ywQdfzxj39kzJgxfPTRRwCcc845jBkzhq5du2acTJIkSSoOFghg2bJl/OpXv2L+/PkAHHbYYUycOJGjjz4642SSJElScWnXBWLjxo3cfvvtXH/99XzxxRd07tyZK664gsGDB1NSUpJ1PEmSJKnotNsCsXjxYkaMGMGbb74JQN++fbn22mv51re+lXEySZIkqXi1ywIxdepUJk+eTEqJb37zm1x33XX07ds361iSJElS0WuXBeLggw+mY8eOXHjhhVx++eU+00GSJElqpHZZIE4++WSef/55vv3tb2cdRZIkSWpVOmQdIAsRYXmQJEmSmqFoz0BEREfgVuAQYCPwb0AAvwES8BYwNKVUm1VGSZIkqb0p5jMQPwVIKf0Q+BUwNf/P2JTSseTKxCnZxZMkSZLan6ItECml3wMX5Be/DfwN6AE8k1/3KNA7g2iSJElSu1W0BQIgpVQTEXOB6cA8IFJKKf/2amC3rX0uIi6IiEURsaiqqqqF0kqSJEltX1EXCICU0gDgYHLzITrXeWsXYGU9n5mdUipPKZXvscceLZBSkiRJah/i6z/oF5eI6A98K6U0ISJ2Bd4A/hO4LqX0dETMBJ5KKd3XwH6qgDXA8oKHVlvTDceNms5xo+Zw3Kg5HDdqjk4ppSO2ZwfFXCB2Bm4H9gJKgInAO+TORJTmX5+fUtrYiH0tSimVFzCu2iDHjZrDcaPmcNyoORw3ao4dMW6K9jauKaU1wL9u5a3jWzqLJEmSpJyinwMhSZIkqXi0lwIxO+sAapUcN2oOx42aw3Gj5nDcqDm2e9wU7RwISZIkScWnvZyBkCRJkrQDWCAkSZIkNVqbLRAR0SEiZkbESxHxdER8J+tMKi4RURIRd0bEcxGxMCL+R0R8JyKez6+7JSI65LetyG/zYkQcnXV2ZS8iukfExxFxqONGjRERo/PfSa9GxCDHjRqS/566Jz8WnvP/N2pIRPxjRDydf93osVLftvVpswUCOJXcgzL+OzAKmJJxHhWffsBnKaVjgZOAGcBUYGx+XQCnRMRR5G4f/I/AWcCvM8qrIhERJcAs4Mv8KseNtikiegE/AH5Iblzsi+NGDfsXYKeU0g+AccB1OG5Uj4gYAcwBOuVXNWWsbLHtto7VlgvEPwGPAaSUXgZ80Io29zvgqjrLNUAP4Jn88qNAb3Jj6fGU83+BnSJijxZNqmIzGZgJLM0vO27UkL7Am8CDwHzgDzhu1LB3yY2BDsCuQDWOG9XvPeB/1lluyljZ2rb1assFYlfg8zrLGyOiaB+cp5aXUvoipbQ6InYB5gFjyd2ZbNOtyVYDu7HlWNq0Xu1QRJwHVKWUFtRd7bhRA7qR+0PWGcBFwN1AB8eNGvAFsD/wZ+BWYBr+/0b1SCk9QK5kbtKUsbK1bevVlgvEKmCXOssdUko1WYVRcYqIfYGngDtTSvcAtXXe3gVYyZZjadN6tU8DgX/OX2P6feAOoHud9x032prPgAUppQ0ppSXAOv7rF7TjRltzGblxczBwJDAXKK3zvuNG29KU32m2tm292nKBeIHctYNExDHkTh1LX4mIPYHHgZEppdvyq1/PX6sMuXkRz5EbS33zE/P3I1dGl7d4YBWFlNJxKaXjU0q9gMXAucCjjhs14Hngx5GzD7Az8KTjRg1Ywdd/Lf5/QAl+T6nxmjJWtrZtvdryJT0Pkvsr4YvkJoP8W8Z5VHzGALsDV0XEprkQw4BpEVEKvAPMSyltjIjngJfIle6hmaRVMRsO3Oq4UX1SSn+IiOOAhXw9Hj7AcaNtuxG4LT8mSsl9by3CcaPGacp30xbbbmvHPolakiRJUqO15UuYJEmSJO1gFghJkiRJjWaBkCRJktRoFghJkiRJjWaBkCRJktRoFghJkiRJjWaBkCRJktRoFghJEhHRJSKG1Fl+sUDH6RwRz0REx+3cT2lEPBsRbfmBqJJUlCwQkiSALsBXBSKl9IMCHWcg8O8ppY3bs5OU0gbgSeDMHZJKktRoFghJEsBE4KCIWBwRkyLiC4CI2D8i/hwRcyLirYi4OyJ6R8QLEfGXiDh60w4iol9ELMzvY1Y9ZxnOAR5qyr4jYueI+I+IeCO/3abS8Pv8/iRJLcgCIUkCGAW8l1L6fkrpis3e+w5wE/A94FDgbOCfgP8FjAGIiMPInQ34YUrp+8BGNvvlPiJKgQNTSh82Zd/Aj4GlKaUjU0pHAI/l178F9Ny+H1uS1FQWCElSQz5IKb2ZUqoF3gaeTCkl4E1g//w2JwI9gP8dEYvzywdutp9uwMpm7PtNoHdEXB8Rx6aUPgfIXwa1ISJ22YE/qySpAU4+kyQ1ZH2d17V1lmv5+nskgLkppdHb2M+XQKem7jul9G5E9AD+BZgQEY+nlMblt/s7YF0TfhZJ0nbyDIQkCWA1sD1/yX8S+FlEdAeIiK4R8e26G6SUVgAdI2LzErFNEbEPsDaldBcwGTgqv/4bQFVKqXo7ckuSmsgzEJIkUkqf5ScvvwU82ozP/5+IGAs8HhEdgGpgKPDRZps+Tm6OwxNN2P0/AJMioja/34vz638EPNLUrJKk7RO5S00lSSq8iPhvwOUppf47YF//DoxOKS3Z/mSSpMbyEiZJUotJKb0OPLUjHiQH/N7yIEktzzMQkiRJkhrNMxCSJEmSGs0CIUmSJKnRLBCSJEmSGs0CIUmSJKnRLBCSJEmSGs0CIUmSJKnR/j/zMEwCwjqYWQAAAABJRU5ErkJggg==\n", 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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(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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/scripts/plot_activation_map.py b/scripts/plot_activation_map.py index 533be3a..8d41a60 100644 --- a/scripts/plot_activation_map.py +++ b/scripts/plot_activation_map.py @@ -1,62 +1,60 @@ # -*- coding: utf-8 -*- # @Author: Theo Lemaire # @Email: theo.lemaire@epfl.ch # @Date: 2018-09-26 09:51:43 # @Last Modified by: Theo Lemaire -# @Last Modified time: 2019-06-17 18:51:26 +# @Last Modified time: 2019-08-22 15:26:17 ''' Plot (duty-cycle x amplitude) US activation map of a neuron at a given frequency and PRF. ''' import numpy as np import matplotlib.pyplot as plt from PySONIC.utils import logger from PySONIC.plt import ActivationMap from PySONIC.parsers import AStimParser def main(): # Parse command line arguments parser = AStimParser() parser.defaults['amp'] = np.logspace(np.log10(10), np.log10(600), 30) # kPa parser.defaults['DC'] = np.arange(1, 101) # % parser.defaults['tstim'] = 1000. # ms parser.defaults['toffset'] = 0. # ms parser.addInputDir() parser.addThresholdCurve() parser.addInteractive() - parser.addSave() parser.addAscale() parser.addTimeRange(default=(0., 240.)) - parser.addCmap(default='viridis') parser.addFiringRateBounds((1e0, 1e3)) parser.addFiringRateScale() parser.addPotentialBounds(default=(-150, 50)) parser.outputdir_dep_key = 'save' args = parser.parse() logger.setLevel(args['loglevel']) for pneuron in args['neuron']: for a in args['radius']: for Fdrive in args['freq']: for tstim in args['tstim']: for PRF in args['PRF']: actmap = ActivationMap(args['inputdir'], pneuron, a, Fdrive, tstim, PRF, args['amp'], args['DC']) actmap.render( cmap=args['cmap'], Ascale=args['Ascale'], FRscale=args['FRscale'], FRbounds=args['FRbounds'], interactive=args['interactive'], Vbounds=args['Vbounds'], trange=args['trange'], thresholds=args['threshold'], ) plt.show() if __name__ == '__main__': main()