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simutils.py
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# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2017-08-22 14:33:04
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2018-09-19 16:01:35
""" Utility functions used in simulations """
import os
import time
import logging
import pickle
import shutil
import tkinter as tk
from tkinter import filedialog
import numpy as np
import pandas as pd
from openpyxl import load_workbook
import lockfile
import multiprocessing as mp
from ..bls import BilayerSonophore
from .SolverUS import SolverUS
from .SolverElec import SolverElec
from ..constants import *
from ..neurons import *
from ..utils import getNeuronsDict, InputError, PmCompMethod, si_format, getCycleAverage, nDindexes
# Get package logger
logger = logging.getLogger('PySONIC')
class Consumer(mp.Process):
''' Generic consumer process, taking tasks from a queue and outputing results in
another queue.
'''
def __init__(self, task_queue, result_queue):
mp.Process.__init__(self)
self.task_queue = task_queue
self.result_queue = result_queue
print('Starting {}'.format(self.name))
def run(self):
while True:
nextTask = self.task_queue.get()
if nextTask is None:
print('Exiting {}'.format(self.name))
self.task_queue.task_done()
break
print('{}: {}'.format(self.name, nextTask))
answer = nextTask()
self.task_queue.task_done()
self.result_queue.put(answer)
return
class Worker():
''' Generic worker class. '''
def __init__(self, wid, nsims, ncoeffs=0):
''' Class constructor.
:param wid: worker ID
:param nsims: total number or simulations
'''
self.id = wid
self.nsims = nsims
self.ncoeffs = ncoeffs
def __call__(self):
''' worker method. '''
return (self.id, np.random.rand(self.ncoeffs) * 1e9)
def __str__(self):
return 'simulation {}/{}'.format(self.id + 1, self.nsims)
class LookupWorker(Worker):
''' Worker class that computes "effective" coefficients of the HH system for a specific
combination of stimulus frequency, stimulus amplitude and charge density.
A short mechanical simulation is run while imposing the specific charge density,
until periodic stabilization. The HH coefficients are then averaged over the last
acoustic cycle to yield "effective" coefficients.
'''
def __init__(self, wid, bls, neuron, Fdrive, Adrive, Qm, phi, nsims):
''' Class constructor.
:param wid: worker ID
:param bls: BilayerSonophore object
:param neuron: neuron object
:param Fdrive: acoustic drive frequency (Hz)
:param Adrive: acoustic drive amplitude (Pa)
:param Qm: imposed charge density (C/m2)
:param phi: acoustic drive phase (rad)
:param nsims: total number or simulations
'''
super().__init__(wid, nsims)
self.bls = bls
self.neuron = neuron
self.Fdrive = Fdrive
self.Adrive = Adrive
self.Qm = Qm
self.phi = phi
def __call__(self):
''' Method that computes effective coefficients. '''
try:
# Run simulation and retrieve deflection and gas content vectors from last cycle
_, [Z, ng], _ = self.bls.run(self.Fdrive, self.Adrive, self.Qm, self.phi)
Z_last = Z[-NPC_FULL:] # m
# Compute membrane potential vector
Vm = self.Qm / self.bls.v_Capct(Z_last) * 1e3 # mV
# Compute average cycle value for membrane potential and rate constants
Vm_eff = np.mean(Vm) # mV
rates_eff = self.neuron.getEffRates(Vm)
# Take final cycle value for gas content
ng_eff = ng[-1] # mole
return (self.id, [Vm_eff, ng_eff, *rates_eff])
except (Warning, AssertionError) as inst:
logger.warning('Integration error: %s. Continue batch? (y/n)', extra={inst})
user_str = input()
if user_str not in ['y', 'Y']:
return -1
def __str__(self):
return '{} (a = {}m, f = {}Hz, A = {}Pa, Q = {:.2f} nC/cm2)'.format(
super().__str__(),
*si_format([self.bls.a, self.Fdrive, self.Adrive], space=' '), self.Qm * 1e5)
class AStimWorker(Worker):
''' Worker class that runs a single A-STIM simulation a given neuron for specific
stimulation parameters, and save the results in a PKL file. '''
def __init__(self, wid, batch_dir, log_filepath, solver, neuron, Fdrive, Adrive, tstim, toffset,
PRF, DC, int_method, nsims):
''' Class constructor.
:param wid: worker ID
:param solver: SolverUS object
:param neuron: neuron object
:param Fdrive: acoustic drive frequency (Hz)
:param Adrive: acoustic drive amplitude (Pa)
:param tstim: duration of US stimulation (s)
:param toffset: duration of the offset (s)
:param PRF: pulse repetition frequency (Hz)
:param DC: pulse duty cycle (-)
:param int_method: selected integration method
:param nsims: total number or simulations
'''
super().__init__(wid, nsims)
self.batch_dir = batch_dir
self.log_filepath = log_filepath
self.solver = solver
self.neuron = neuron
self.Fdrive = Fdrive
self.Adrive = Adrive
self.tstim = tstim
self.toffset = toffset
self.PRF = PRF
self.DC = DC
self.int_method = int_method
def __call__(self):
''' Method that runs the simulation. '''
simcode = 'ASTIM_{}_{}_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.0f}ms_{}{}'\
.format(self.neuron.name, 'CW' if self.DC == 1 else 'PW', self.solver.a * 1e9,
self.Fdrive * 1e-3, self.Adrive * 1e-3, self.tstim * 1e3,
'PRF{:.2f}Hz_DC{:.2f}%_'.format(self.PRF, self.DC * 1e2) if self.DC < 1. else '',
self.int_method)
try:
# Get date and time info
date_str = time.strftime("%Y.%m.%d")
daytime_str = time.strftime("%H:%M:%S")
# Run simulation
tstart = time.time()
(t, y, states) = self.solver.run(self.neuron, self.Fdrive, self.Adrive, self.tstim,
self.toffset, self.PRF, self.DC, self.int_method)
Z, ng, Qm, Vm, *channels = y
U = np.insert(np.diff(Z) / np.diff(t), 0, 0.0)
tcomp = time.time() - tstart
logger.debug('completed in %ss', si_format(tcomp, 2))
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'U': U, 'Z': Z, 'ng': ng, 'Qm': Qm,
'Vm': Vm})
for j in range(len(self.neuron.states_names)):
df[self.neuron.states_names[j]] = channels[j]
meta = {'neuron': self.neuron.name, 'a': self.solver.a, 'd': self.solver.d,
'Fdrive': self.Fdrive, 'Adrive': self.Adrive, 'phi': np.pi,
'tstim': self.tstim, 'toffset': self.toffset, 'PRF': self.PRF, 'DC': self.DC,
'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(self.batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.debug('simulation data exported to "%s"', output_filepath)
# Detect spikes on Qm signal
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(Qm, SPIKE_MIN_QAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_QPROM)
n_spikes = ipeaks.size
lat = t[ipeaks[0]] if n_spikes > 0 else 'N/A'
sr = np.mean(1 / np.diff(t[ipeaks])) if n_spikes > 1 else 'N/A'
logger.debug('%u spike%s detected', n_spikes, "s" if n_spikes > 1 else "")
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': self.neuron.name,
'D': self.solver.a * 1e9,
'E': self.solver.d * 1e6,
'F': self.Fdrive * 1e-3,
'G': self.Adrive * 1e-3,
'H': self.tstim * 1e3,
'I': self.PRF * 1e-3 if self.DC < 1 else 'N/A',
'J': self.DC,
'K': self.int_method,
'L': t.size,
'M': round(tcomp, 2),
'N': n_spikes,
'O': lat * 1e3 if isinstance(lat, float) else 'N/A',
'P': sr * 1e-3 if isinstance(sr, float) else 'N/A'
}
if xlslog(self.log_filepath, 'Data', log) == 1:
logger.debug('log exported to "%s"', self.log_filepath)
else:
logger.error('log export to "%s" aborted', self.log_filepath)
return output_filepath
except (Warning, AssertionError) as inst:
logger.warning('Integration error: %s. Continue batch? (y/n)', extra={inst})
user_str = input()
if user_str not in ['y', 'Y']:
return -1
def __str__(self):
worker_str = 'A-STIM {} {}: {} neuron, a = {}m, f = {}Hz, A = {}Pa, t = {}s'\
.format(self.int_method, super().__str__(), self.neuron.name,
*si_format([self.solver.a, self.Fdrive], 1, space=' '),
si_format(self.Adrive, 2, space=' '), si_format(self.tstim, 1, space=' '))
if self.DC < 1.0:
worker_str += ', PRF = {}Hz, DC = {:.2f}%'\
.format(si_format(self.PRF, 2, space=' '), self.DC * 1e2)
return worker_str
class EStimWorker(Worker):
''' Worker class that runs a single E-STIM simulation a given neuron for specific
stimulation parameters, and save the results in a PKL file. '''
def __init__(self, wid, batch_dir, log_filepath, solver, neuron, Astim, tstim, toffset,
PRF, DC, nsims):
''' Class constructor.
:param wid: worker ID
:param solver: SolverElec object
:param neuron: neuron object
:param Astim: stimulus amplitude (mA/m2)
: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 nsims: total number or simulations
'''
super().__init__(wid, nsims)
self.batch_dir = batch_dir
self.log_filepath = log_filepath
self.solver = solver
self.neuron = neuron
self.Astim = Astim
self.tstim = tstim
self.toffset = toffset
self.PRF = PRF
self.DC = DC
def __call__(self):
''' Method that runs the simulation. '''
simcode = 'ESTIM_{}_{}_{:.1f}mA_per_m2_{:.0f}ms{}'\
.format(self.neuron.name, 'CW' if self.DC == 1 else 'PW', self.Astim, self.tstim * 1e3,
'_PRF{:.2f}Hz_DC{:.2f}%'.format(self.PRF, self.DC * 1e2) if self.DC < 1. else '')
try:
# Get date and time info
date_str = time.strftime("%Y.%m.%d")
daytime_str = time.strftime("%H:%M:%S")
# Run simulation
tstart = time.time()
(t, y, states) = self.solver.run(self.neuron, self.Astim, self.tstim, self.toffset,
self.PRF, self.DC)
Vm, *channels = y
tcomp = time.time() - tstart
logger.debug('completed in %ss', si_format(tcomp, 1))
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'Vm': Vm, 'Qm': Vm * self.neuron.Cm0 * 1e-3})
for j in range(len(self.neuron.states_names)):
df[self.neuron.states_names[j]] = channels[j]
meta = {'neuron': self.neuron.name, 'Astim': self.Astim, 'tstim': self.tstim,
'toffset': self.toffset, 'PRF': self.PRF, 'DC': self.DC, 'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(self.batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.debug('simulation data exported to "%s"', output_filepath)
# Detect spikes on Vm signal
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(Vm, SPIKE_MIN_VAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_VPROM)
n_spikes = ipeaks.size
lat = t[ipeaks[0]] if n_spikes > 0 else 'N/A'
sr = np.mean(1 / np.diff(t[ipeaks])) if n_spikes > 1 else 'N/A'
logger.debug('%u spike%s detected', n_spikes, "s" if n_spikes > 1 else "")
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': self.neuron.name,
'D': self.Astim,
'E': self.tstim * 1e3,
'F': self.PRF * 1e-3 if self.DC < 1 else 'N/A',
'G': self.DC,
'H': t.size,
'I': round(tcomp, 2),
'J': n_spikes,
'K': lat * 1e3 if isinstance(lat, float) else 'N/A',
'L': sr * 1e-3 if isinstance(sr, float) else 'N/A'
}
if xlslog(self.log_filepath, 'Data', log) == 1:
logger.debug('log exported to "%s"', self.log_filepath)
else:
logger.error('log export to "%s" aborted', self.log_filepath)
return output_filepath
except (Warning, AssertionError) as inst:
logger.warning('Integration error: %s. Continue batch? (y/n)', extra={inst})
user_str = input()
if user_str not in ['y', 'Y']:
return -1
def __str__(self):
worker_str = 'E-STIM {}: {} neuron, A = {}A/m2, t = {}s'\
.format(super().__str__(), self.neuron.name,
si_format(self.Astim * 1e-3, 2, space=' '),
si_format(self.tstim, 1, space=' '))
if self.DC < 1.0:
worker_str += ', PRF = {}Hz, DC = {:.2f}%'\
.format(si_format(self.PRF, 2, space=' '), self.DC * 1e2)
return worker_str
class MechWorker(Worker):
''' Worker class that runs a single simulation of the mechanical system with specific parameters
and an imposed value of charge density, and save the results in a PKL file. '''
def __init__(self, wid, batch_dir, log_filepath, bls, Fdrive, Adrive, Qm, nsims):
''' Class constructor.
:param wid: worker ID
:param batch_dir: full path to output directory of batch
:param log_filepath: full path log file of batch
:param bls: BilayerSonophore instance
:param Fdrive: acoustic drive frequency (Hz)
:param Adrive: acoustic drive amplitude (Pa)
:param Qm: applided membrane charge density (C/m2)
:param nsims: total number or simulations
'''
super().__init__(wid, nsims)
self.batch_dir = batch_dir
self.log_filepath = log_filepath
self.bls = bls
self.Fdrive = Fdrive
self.Adrive = Adrive
self.Qm = Qm
def __call__(self):
''' Method that runs the simulation. '''
simcode = 'MECH_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.1f}nCcm2'\
.format(self.bls.a * 1e9, self.Fdrive * 1e-3, self.Adrive * 1e-3, self.Qm * 1e5)
try:
# Get date and time info
date_str = time.strftime("%Y.%m.%d")
daytime_str = time.strftime("%H:%M:%S")
# Run simulation
tstart = time.time()
(t, y, states) = self.bls.run(self.Fdrive, self.Adrive, self.Qm)
(Z, ng) = y
U = np.insert(np.diff(Z) / np.diff(t), 0, 0.0)
tcomp = time.time() - tstart
logger.debug('completed in %ss', si_format(tcomp, 1))
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'U': U, 'Z': Z, 'ng': ng})
meta = {'a': self.bls.a, 'd': self.bls.d, 'Cm0': self.bls.Cm0, 'Qm0': self.bls.Qm0,
'Fdrive': self.Fdrive, 'Adrive': self.Adrive, 'phi': np.pi, 'Qm': self.Qm,
'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(self.batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.debug('simulation data exported to "%s"', output_filepath)
# Compute key output metrics
Zmax = np.amax(Z)
Zmin = np.amin(Z)
Zabs_max = np.amax(np.abs([Zmin, Zmax]))
eAmax = self.bls.arealstrain(Zabs_max)
Tmax = self.bls.TEtot(Zabs_max)
Pmmax = self.bls.PMavgpred(Zmin)
ngmax = np.amax(ng)
dUdtmax = np.amax(np.abs(np.diff(U) / np.diff(t)**2))
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': self.bls.a * 1e9,
'D': self.bls.d * 1e6,
'E': self.Fdrive * 1e-3,
'F': self.Adrive * 1e-3,
'G': self.Qm * 1e5,
'H': t.size,
'I': tcomp,
'J': self.bls.kA + self.bls.kA_tissue,
'K': Zmax * 1e9,
'L': eAmax,
'M': Tmax * 1e3,
'N': (ngmax - self.bls.ng0) / self.bls.ng0,
'O': Pmmax * 1e-3,
'P': dUdtmax
}
if xlslog(self.log_filepath, 'Data', log) == 1:
logger.info('log exported to "%s"', self.log_filepath)
else:
logger.error('log export to "%s" aborted', self.log_filepath)
return output_filepath
except (Warning, AssertionError) as inst:
logger.warning('Integration error: %s. Continue batch? (y/n)', extra={inst})
user_str = input()
if user_str not in ['y', 'Y']:
return -1
def __str__(self):
return 'Mechanical {}: a = {}m, d = {}m, f = {}Hz, A = {}Pa, Q = {}C/cm2'\
.format(super().__str__(),
*si_format([self.bls.a, self.bls.d, self.Fdrive], 1, space=' '),
*si_format([self.Adrive, self.Qm * 1e-4], 2, space=' '))
class EStimTitrator(Worker):
''' Worker class that uses a dichotomic recursive search to determine the threshold value
of a specific electric stimulation parameter needed to obtain neural excitation, keeping
all other parameters fixed. The titration parameter can be stimulation amplitude, duration or
any variable for which the number of spikes is a monotonically increasing function.
'''
def __init__(self, wid, solver, neuron, Astim, tstim, toffset, PRF, DC, nsims=1):
''' Class constructor.
:param wid: worker ID
:param solver: SolverElec object
:param neuron: neuron object
:param Astim: injected current density amplitude (mA/m2)
: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 nsims: total number or simulations
'''
super().__init__(wid, nsims)
self.solver = solver
self.neuron = neuron
self.Astim = Astim
self.tstim = tstim
self.toffset = toffset
self.PRF = PRF
self.DC = DC
# Determine titration type
if Astim is None:
self.t_type = 'A'
self.unit = 'A/m2'
self.factor = 1e-3
self.interval = (0., 2 * TITRATION_ESTIM_A_MAX)
self.thr = TITRATION_ESTIM_DA_MAX
self.maxval = TITRATION_ESTIM_A_MAX
elif tstim is None:
self.t_type = 't'
self.unit = 's'
self.factor = 1
self.interval = (0., 2 * TITRATION_T_MAX)
self.thr = TITRATION_DT_THR
self.maxval = TITRATION_T_MAX
elif DC is None:
self.t_type = 'DC'
self.unit = '%'
self.factor = 1e2
self.interval = (0., 2 * TITRATION_DC_MAX)
self.thr = TITRATION_DDC_THR
self.maxval = TITRATION_DC_MAX
else:
print('Error: Invalid titration type')
self.t0 = None
def __call__(self):
''' Method running the titration, called recursively until an accurate threshold is found.
:return: 5-tuple with the determined threshold, time profile,
solution matrix, state vector and response latency
'''
if self.t0 is None:
self.t0 = time.time()
# Define current value
value = (self.interval[0] + self.interval[1]) / 2
# Define stimulation parameters
if self.t_type == 'A':
stim_params = [value, self.tstim, self.toffset, self.PRF, self.DC]
elif self.t_type == 't':
stim_params = [self.Astim, value, self.toffset, self.PRF, self.DC]
elif self.t_type == 'DC':
stim_params = [self.Astim, self.tstim, self.toffset, self.PRF, value]
# Run simulation and detect spikes
(t, y, states) = self.solver.run(self.neuron, *stim_params)
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(y[0, :], SPIKE_MIN_VAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_VPROM)
n_spikes = ipeaks.size
latency = t[ipeaks[0]] if n_spikes > 0 else None
print('{}{} ---> {} spike{} detected'.format(si_format(value * self.factor, 2, space=' '),
self.unit, n_spikes,
"s" if n_spikes > 1 else ""))
# If accurate threshold is found, return simulation results
if (self.interval[1] - self.interval[0]) <= self.thr and n_spikes == 1:
tcomp = time.time() - self.t0
print('completed in {:.2f} s, threshold = {}{}'
.format(tcomp, si_format(value * self.factor, 2, space=' '), self.unit))
return (value, t, y, states, latency, tcomp)
# Otherwise, refine titration interval and iterate recursively
else:
if n_spikes == 0:
# if value too close to max then stop
if (self.maxval - value) <= self.thr:
logger.warning('no spikes detected within titration interval')
return (np.nan, t, y, states, latency)
self.interval = (value, self.interval[1])
else:
self.interval = (self.interval[0], value)
return self.__call__()
def __str__(self):
params = [self.Astim, self.tstim, self.PRF, self.DC]
punits = {'A': 'A/m2', 't': 's', 'PRF': 'Hz', 'DC': '%'}
if self.Astim is not None:
params[0] *= 1e-3
if self.DC is not None:
params[3] *= 1e2
pnames = list(punits.keys())
ittr = params.index(None)
del params[ittr]
del pnames[ittr]
log_str = ', '.join(['{} = {}{}'.format(pname, si_format(param, 2, space=' '), punits[pname])
for pname, param in zip(pnames, params)])
return '{} neuron - E-STIM titration {}/{} ({})'\
.format(self.neuron.name, self.id, self.nsims, log_str)
class AStimTitrator(Worker):
''' Worker class that uses a dichotomic recursive search to determine the threshold value
of a specific acoustic stimulation parameter needed to obtain neural excitation, keeping
all other parameters fixed. The titration parameter can be stimulation amplitude, duration or
any variable for which the number of spikes is a monotonically increasing function.
'''
def __init__(self, wid, solver, neuron, Fdrive, Adrive, tstim, toffset, PRF, DC,
int_method='sonic', nsims=1):
''' Class constructor.
:param wid: worker ID
:param solver: SolverUS object
:param neuron: neuron object
:param Fdrive: acoustic drive frequency (Hz)
:param Adrive: acoustic drive amplitude (Pa)
:param tstim: duration of US stimulation (s)
:param toffset: duration of the offset (s)
:param PRF: pulse repetition frequency (Hz)
:param DC: pulse duty cycle (-)
:param int_method: selected integration method
:param nsims: total number or simulations
'''
super().__init__(wid, nsims)
self.solver = solver
self.neuron = neuron
self.Fdrive = Fdrive
self.Adrive = Adrive
self.tstim = tstim
self.toffset = toffset
self.PRF = PRF
self.DC = DC
self.int_method = int_method
# Determine titration type
if Adrive is None:
self.t_type = 'A'
self.unit = 'Pa'
self.factor = 1
self.interval = (0., 2 * TITRATION_ASTIM_A_MAX)
self.thr = TITRATION_ASTIM_DA_MAX
self.maxval = TITRATION_ASTIM_A_MAX
elif tstim is None:
self.t_type = 't'
self.unit = 's'
self.factor = 1
self.interval = (0., 2 * TITRATION_T_MAX)
self.thr = TITRATION_DT_THR
self.maxval = TITRATION_T_MAX
elif DC is None:
self.t_type = 'DC'
self.unit = '%'
self.factor = 1e2
self.interval = (0., 2 * TITRATION_DC_MAX)
self.thr = TITRATION_DDC_THR
self.maxval = TITRATION_DC_MAX
else:
print('Error: Invalid titration type')
self.t0 = None
def __call__(self):
''' Method running the titration, called recursively until an accurate threshold is found.
:return: 5-tuple with the determined threshold, time profile,
solution matrix, state vector and response latency
'''
if self.t0 is None:
self.t0 = time.time()
# Define current value
value = (self.interval[0] + self.interval[1]) / 2
# Define stimulation parameters
if self.t_type == 'A':
stim_params = [self.Fdrive, value, self.tstim, self.toffset, self.PRF, self.DC]
elif self.t_type == 't':
stim_params = [self.Fdrive, self.Adrive, value, self.toffset, self.PRF, self.DC]
elif self.t_type == 'DC':
stim_params = [self.Fdrive, self.Adrive, self.tstim, self.toffset, self.PRF, value]
# Run simulation and detect spikes
(t, y, states) = self.solver.run(self.neuron, *stim_params, self.int_method)
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(y[2, :], SPIKE_MIN_QAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_QPROM)
n_spikes = ipeaks.size
latency = t[ipeaks[0]] if n_spikes > 0 else None
print('{}{} ---> {} spike{} detected'.format(si_format(value * self.factor, 2, space=' '),
self.unit, n_spikes,
"s" if n_spikes > 1 else ""))
# If accurate threshold is found, return simulation results
if (self.interval[1] - self.interval[0]) <= self.thr and n_spikes == 1:
tcomp = time.time() - self.t0
print('completed in {:.2f} s, threshold = {}{}'
.format(tcomp, si_format(value * self.factor, 2, space=' '), self.unit))
return (value, t, y, states, latency, tcomp)
# Otherwise, refine titration interval and iterate recursively
else:
if n_spikes == 0:
# if value too close to max then stop
if (self.maxval - value) <= self.thr:
logger.warning('no spikes detected within titration interval')
return (np.nan, t, y, states, latency)
self.interval = (value, self.interval[1])
else:
self.interval = (self.interval[0], value)
return self.__call__()
def __str__(self):
params = [self.Fdrive, self.Adrive, self.tstim, self.PRF, self.DC]
punits = {'f': 'Hz', 'A': 'A/m2', 't': 's', 'PRF': 'Hz', 'DC': '%'}
if self.Adrive is not None:
params[1] *= 1e-3
if self.DC is not None:
params[4] *= 1e2
pnames = list(punits.keys())
ittr = params.index(None)
del params[ittr]
del pnames[ittr]
log_str = ', '.join(['{} = {}{}'.format(pname, si_format(param, 2, space=' '), punits[pname])
for pname, param in zip(pnames, params)])
return '{} neuron - A-STIM titration {}/{} ({})'\
.format(self.neuron.name, self.id, self.nsims, log_str)
def setBatchDir():
''' Select batch directory for output files.α
:return: full path to batch directory
'''
root = tk.Tk()
root.withdraw()
batch_dir = filedialog.askdirectory()
if not batch_dir:
raise InputError('No output directory chosen')
return batch_dir
def checkBatchLog(batch_dir, batch_type):
''' Check for appropriate log file in batch directory, and create one if it is absent.
:param batch_dir: full path to batch directory
:param batch_type: type of simulation batch
:return: 2 tuple with full path to log file and boolean stating if log file was created
'''
# Check for directory existence
if not os.path.isdir(batch_dir):
raise InputError('"{}" output directory does not exist'.format(batch_dir))
# Determine log template from batch type
if batch_type == 'MECH':
logfile = 'log_MECH.xlsx'
elif batch_type == 'A-STIM':
logfile = 'log_ASTIM.xlsx'
elif batch_type == 'E-STIM':
logfile = 'log_ESTIM.xlsx'
else:
raise InputError('Unknown batch type', batch_type)
# Get template in package subdirectory
this_dir, _ = os.path.split(__file__)
parent_dir = os.path.abspath(os.path.join(this_dir, os.pardir))
logsrc = parent_dir + '/templates/' + logfile
assert os.path.isfile(logsrc), 'template log file "{}" not found'.format(logsrc)
# Copy template in batch directory if no appropriate log file
logdst = batch_dir + '/' + logfile
is_log = os.path.isfile(logdst)
if not is_log:
shutil.copy2(logsrc, logdst)
return (logdst, not is_log)
def createSimQueue(amps, durations, offsets, PRFs, DCs):
''' 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: 2D-array with (amplitude, duration, offset, PRF, DC) for each stimulation protocol
'''
# Convert input to 1D-arrays
amps = np.array(amps)
durations = np.array(durations)
offsets = np.array(offsets)
PRFs = np.array(PRFs)
DCs = np.array(DCs)
# Create index arrays
iamps = range(len(amps))
idurs = range(len(durations))
# Create empty output matrix
queue = np.empty((1, 5))
# Continuous protocols
if 1.0 in DCs:
nCW = len(amps) * len(durations)
arr1 = np.ones(nCW)
iCW_queue = np.array(np.meshgrid(iamps, idurs)).T.reshape(nCW, 2)
CW_queue = np.vstack((amps[iCW_queue[:, 0]], durations[iCW_queue[:, 1]],
offsets[iCW_queue[:, 1]], PRFs.min() * arr1, arr1)).T
queue = np.vstack((queue, CW_queue))
# Pulsed protocols
if np.any(DCs != 1.0):
pulsed_DCs = DCs[DCs != 1.0]
iPRFs = range(len(PRFs))
ipulsed_DCs = range(len(pulsed_DCs))
nPW = len(amps) * len(durations) * len(PRFs) * len(pulsed_DCs)
iPW_queue = np.array(np.meshgrid(iamps, idurs, iPRFs, ipulsed_DCs)).T.reshape(nPW, 4)
PW_queue = np.vstack((amps[iPW_queue[:, 0]], durations[iPW_queue[:, 1]],
offsets[iPW_queue[:, 1]], PRFs[iPW_queue[:, 2]],
pulsed_DCs[iPW_queue[:, 3]])).T
queue = np.vstack((queue, PW_queue))
# Return
return queue[1:, :]
def xlslog(filename, sheetname, data):
""" Append log data on a new row to specific sheet of excel workbook, using a lockfile
to avoid read/write errors between concurrent processes.
:param filename: absolute or relative path to the Excel workbook
:param sheetname: name of the Excel spreadsheet to which data is appended
:param data: data structure to be added to specific columns on a new row
:return: boolean indicating success (1) or failure (0) of operation
"""
try:
lock = lockfile.FileLock(filename)
lock.acquire()
wb = load_workbook(filename)
ws = wb[sheetname]
keys = data.keys()
i = 1
row_data = {}
for k in keys:
row_data[k] = data[k]
i += 1
ws.append(row_data)
wb.save(filename)
lock.release()
return 1
except PermissionError:
# If file cannot be accessed for writing because already opened
logger.warning('Cannot write to "%s". Close the file and type "Y"', filename)
user_str = input()
if user_str in ['y', 'Y']:
return xlslog(filename, sheetname, data)
else:
return 0
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
: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
'''
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')
# Revert signal sign for valley detection
if valley:
x = -x
# Differentiate signal
dx = np.diff(x)
# 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 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
"""
# 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
if isuniform:
print('uniform time vector')
dt = t[1] - t[0]
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)
return ipeaks
def detectSpikes(t, Qm, min_amp, min_dt):
''' Detect spikes on a charge density signal, and
return their number, latency and rate.
: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)
'''
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)
'''
# Define empty output
empty = (np.array([]),) * 4
# 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]
# Return empty output if no peak detected
if ipeaks.size == 0:
return empty
logger.debug('%u peaks found, starting at index %u and ending at index %u',
ipeaks.size, ipeaks[0], ipeaks[-1])
logger.debug('%u valleys found, starting at index %u and ending at index %u',
ivalleys.size, ivalleys[0], ivalleys[-1])
# 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
ibounds = 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
ibounds[i, 0] = ivalleys[i]
else: # interpolate intercept linearly between signal boundary points
a = (y[ileft + 1] - y[ileft]) / 1
b = y[ileft] - a * ileft
ibounds[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
ibounds[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
ibounds[i, 1] = (refheights[i] - b) / a
# Compute peaks widths at half-prominence
widths = np.diff(ibounds, axis=1)
return (ipeaks - 1, prominences, widths, ibounds)
def runMechBatch(batch_dir, log_filepath, Cm0, Qm0, stim_params, a, d=0.0, multiprocess=False):
''' Run batch simulations of the mechanical system with imposed values of charge density,
for various sonophore spans and stimulation parameters.
:param batch_dir: full path to output directory of batch
:param log_filepath: full path log file of batch
:param Cm0: membrane resting capacitance (F/m2)
:param Qm0: membrane resting charge density (C/m2)
:param stim_params: dictionary containing sweeps for all stimulation parameters
:param a: BLS in-plane diameter (m)
:param d: depth of embedding tissue around plasma membrane (m)
:param multiprocess: boolean statting wether or not to use multiprocessing
'''
# Checking validity of stimulation parameters
mandatory_params = ['freqs', 'amps', 'charges']
for mparam in mandatory_params:
if mparam not in stim_params:
raise InputError('Missing stimulation parameter field: "{}"'.format(mparam))
logger.info("Starting mechanical simulation batch")
# Unpack stimulation parameters
amps = np.array(stim_params['amps'])
charges = np.array(stim_params['charges'])
# Generate simulations queue
nA = len(amps)
nQ = len(charges)
sim_queue = np.array(np.meshgrid(amps, charges)).T.reshape(nA * nQ, 2)
nqueue = sim_queue.shape[0]
nsims = len(stim_params['freqs']) * nqueue
# Initiate multiple processing objects if needed
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = min(mp.cpu_count(), nsims)
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Run simulations
wid = 0
filepaths = []
for Fdrive in stim_params['freqs']:
# Create BilayerSonophore instance (modulus of embedding tissue depends on frequency)
bls = BilayerSonophore(a, Fdrive, Cm0, Qm0, d)
for i in range(nqueue):
Adrive, Qm = sim_queue[i, :]
worker = MechWorker(wid, batch_dir, log_filepath, bls, Fdrive, Adrive, Qm, nsims)
if multiprocess:
tasks.put(worker, block=False)
else:
logger.info('%s', worker)
output_filepath = worker.__call__()
filepaths.append(output_filepath)
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
output_filepath = results.get()
filepaths.append(output_filepath)
# Close tasks and results queues
tasks.close()
results.close()
return filepaths
def runEStimBatch(batch_dir, log_filepath, neurons, stim_params, multiprocess=False):
''' Run batch E-STIM simulations of the system for various neuron types and
stimulation parameters.
:param batch_dir: full path to output directory of batch
:param log_filepath: full path log file of batch
:param neurons: list of neurons names
:param stim_params: dictionary containing sweeps for all stimulation parameters
:param multiprocess: boolean statting wether or not to use multiprocessing
:return: list of full paths to the output files
'''
mandatory_params = ['amps', 'durations', 'offsets', 'PRFs', 'DCs']
for mparam in mandatory_params:
if mparam not in stim_params:
raise InputError('Missing stimulation parameter field: "{}"'.format(mparam))
logger.info("Starting E-STIM simulation batch")
# Generate simulations queue
sim_queue = createSimQueue(stim_params['amps'], stim_params['durations'],
stim_params['offsets'], stim_params['PRFs'], stim_params['DCs'])
nqueue = sim_queue.shape[0]
nsims = len(neurons) * nqueue
# Initialize solver
solver = SolverElec()
# Initiate multiple processing objects if needed
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = min(mp.cpu_count(), nsims)
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Run simulations
wid = 0
filepaths = []
for nname in neurons:
neuron = getNeuronsDict()[nname]()
# Run simulations in queue
for i in range(nqueue):
Astim, tstim, toffset, PRF, DC = sim_queue[i, :]
worker = EStimWorker(wid, batch_dir, log_filepath, solver, neuron, Astim,
tstim, toffset, PRF, DC, nsims)
if multiprocess:
tasks.put(worker, block=False)
else:
logger.info('%s', worker)
output_filepath = worker.__call__()
filepaths.append(output_filepath)
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
output_filepath = results.get()
filepaths.append(output_filepath)
# Close tasks and results queues
tasks.close()
results.close()
return filepaths
def titrateEStimBatch(batch_dir, log_filepath, neurons, stim_params, multiprocess=False):
''' Run batch electrical titrations of the system for various neuron types and
stimulation parameters, to determine the threshold of a specific stimulus parameter
for neural excitation.
:param batch_dir: full path to output directory of batch
:param log_filepath: full path log file of batch
:param neurons: list of neurons names
:param stim_params: dictionary containing sweeps for all stimulation parameters
:param multiprocess: boolean statting wether or not to use multiprocessing
:return: list of full paths to the output files
'''
logger.info("Starting E-STIM titration batch")
# Determine titration parameter and titrations list
if 'durations' not in stim_params:
t_type = 't'
sim_queue = createSimQueue(stim_params['amps'], [None], [TITRATION_T_OFFSET],
stim_params['PRFs'], stim_params['DCs'])
elif 'amps' not in stim_params:
t_type = 'A'
sim_queue = createSimQueue([None], stim_params['durations'],
[TITRATION_T_OFFSET] * len(stim_params['durations']),
stim_params['PRFs'], stim_params['DCs'])
elif 'DC' not in stim_params:
t_type = 'DC'
sim_queue = createSimQueue(stim_params['amps'], stim_params['durations'],
[TITRATION_T_OFFSET] * len(stim_params['durations']),
stim_params['PRFs'], [None])
nqueue = sim_queue.shape[0]
# Create SolverElec instance
solver = SolverElec()
# Initiate multiple processing objects if needed
nsims = len(neurons) * nqueue
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = min(mp.cpu_count(), nsims)
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Run titrations
wid = 0
filepaths = []
for nname in neurons:
neuron = getNeuronsDict()[nname]()
for i in range(nqueue):
# Extract parameters
Astim, tstim, toffset, PRF, DC = sim_queue[i, :]
# Get date and time info
date_str = time.strftime("%Y.%m.%d")
daytime_str = time.strftime("%H:%M:%S")
try:
# Run titration
titrator = EStimTitrator(wid, solver, neuron, *sim_queue[i, :], nsims)
if multiprocess:
tasks.put(titrator, block=False)
else:
logger.info('%s', titrator)
(output_thr, t, y, states, lat, tcomp) = titrator.__call__()
Vm, *channels = y
# Determine output variable
if t_type == 'A':
Astim = output_thr
elif t_type == 't':
tstim = output_thr
elif t_type == 'DC':
DC = output_thr
# Define output naming
simcode = 'ESTIM_{}_{}_{:.1f}mA_per_m2_{:.0f}ms{}'\
.format(neuron.name, 'CW' if DC == 1. else 'PW', Astim, tstim * 1e3,
'_PRF{:.2f}Hz_DC{:.2f}%'.format(PRF, DC * 1e2) if DC < 1. else '')
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'Vm': Vm})
for j in range(len(neuron.states_names)):
df[neuron.states_names[j]] = channels[j]
meta = {'neuron': neuron.name, 'Astim': Astim, 'tstim': tstim, 'toffset': toffset,
'PRF': PRF, 'DC': DC, 'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.info('simulation data exported to "%s"', output_filepath)
filepaths.append(output_filepath)
# Detect spikes on Qm signal
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(Vm, SPIKE_MIN_VAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_VPROM)
n_spikes = ipeaks.size
lat = t[ipeaks[0]] if n_spikes > 0 else 'N/A'
sr = np.mean(1 / np.diff(t[ipeaks])) if n_spikes > 1 else 'N/A'
logger.info('%u spike%s detected', n_spikes, "s" if n_spikes > 1 else "")
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': neuron.name,
'D': Astim,
'E': tstim * 1e3,
'F': PRF * 1e-3 if DC < 1 else 'N/A',
'G': DC,
'H': t.size,
'I': round(tcomp, 2),
'J': n_spikes,
'K': lat * 1e3 if isinstance(lat, float) else 'N/A',
'L': sr * 1e-3 if isinstance(sr, float) else 'N/A'
}
if xlslog(log_filepath, 'Data', log) == 1:
logger.info('log exported to "%s"', log_filepath)
else:
logger.error('log export to "%s" aborted', log_filepath)
except (Warning, AssertionError) as inst:
logger.warning('Integration error: %s. Continue batch? (y/n)', extra={inst})
user_str = input()
if user_str not in ['y', 'Y']:
return filepaths
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
(output_thr, t, y, states, lat, tcomp) = results.get()
Vm, *channels = y
# Determine output variable
if t_type == 'A':
Astim = output_thr
elif t_type == 't':
tstim = output_thr
elif t_type == 'DC':
DC = output_thr
# Define output naming
simcode = 'ESTIM_{}_{}_{:.1f}mA_per_m2_{:.0f}ms{}'\
.format(neuron.name, 'CW' if DC == 1. else 'PW', Astim, tstim * 1e3,
'_PRF{:.2f}Hz_DC{:.2f}%'.format(PRF, DC * 1e2) if DC < 1. else '')
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'Vm': Vm})
for j in range(len(neuron.states_names)):
df[neuron.states_names[j]] = channels[j]
meta = {'neuron': neuron.name, 'Astim': Astim, 'tstim': tstim, 'toffset': toffset,
'PRF': PRF, 'DC': DC, 'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.info('simulation data exported to "%s"', output_filepath)
filepaths.append(output_filepath)
# Detect spikes on Qm signal
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(Vm, SPIKE_MIN_VAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_VPROM)
n_spikes = ipeaks.size
lat = t[ipeaks[0]] if n_spikes > 0 else 'N/A'
sr = np.mean(1 / np.diff(t[ipeaks])) if n_spikes > 1 else 'N/A'
logger.info('%u spike%s detected', n_spikes, "s" if n_spikes > 1 else "")
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': neuron.name,
'D': Astim,
'E': tstim * 1e3,
'F': PRF * 1e-3 if DC < 1 else 'N/A',
'G': DC,
'H': t.size,
'I': round(tcomp, 2),
'J': n_spikes,
'K': lat * 1e3 if isinstance(lat, float) else 'N/A',
'L': sr * 1e-3 if isinstance(sr, float) else 'N/A'
}
if xlslog(log_filepath, 'Data', log) == 1:
logger.info('log exported to "%s"', log_filepath)
else:
logger.error('log export to "%s" aborted', log_filepath)
# Close tasks and results queues
tasks.close()
results.close()
return filepaths
def runAStimBatch(batch_dir, log_filepath, neurons, stim_params, a, int_method='sonic',
multiprocess=False):
''' Run batch simulations of the system for various neuron types, sonophore and
stimulation parameters.
:param batch_dir: full path to output directory of batch
:param log_filepath: full path log file of batch
:param neurons: list of neurons names
:param stim_params: dictionary containing sweeps for all stimulation parameters
:param a: BLS structure diameter (m)
:param int_method: selected integration method
:param multiprocess: boolean statting wether or not to use multiprocessing
:return: list of full paths to the output files
'''
mandatory_params = ['freqs', 'amps', 'durations', 'offsets', 'PRFs', 'DCs']
for mparam in mandatory_params:
if mparam not in stim_params:
raise InputError('Missing stimulation parameter field: "{}"'.format(mparam))
logger.info("Starting A-STIM simulation batch")
# Generate simulations queue
sim_queue = createSimQueue(stim_params['amps'], stim_params['durations'],
stim_params['offsets'], stim_params['PRFs'], stim_params['DCs'])
nqueue = sim_queue.shape[0]
nsims = len(neurons) * len(stim_params['freqs']) * nqueue
# Initiate multiple processing objects if needed
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = min(mp.cpu_count(), nsims)
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Run simulations
wid = 0
filepaths = []
for nname in neurons:
neuron = getNeuronsDict()[nname]()
for Fdrive in stim_params['freqs']:
# Initialize SolverUS
solver = SolverUS(a, neuron, Fdrive)
# Run simulations in queue
for i in range(nqueue):
Adrive, tstim, toffset, PRF, DC = sim_queue[i, :]
worker = AStimWorker(wid, batch_dir, log_filepath, solver, neuron, Fdrive, Adrive,
tstim, toffset, PRF, DC, int_method, nsims)
if multiprocess:
tasks.put(worker, block=False)
else:
logger.info('%s', worker)
output_filepath = worker.__call__()
filepaths.append(output_filepath)
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
output_filepath = results.get()
filepaths.append(output_filepath)
# Close tasks and results queues
tasks.close()
results.close()
return filepaths
def titrateAStimBatch(batch_dir, log_filepath, neurons, stim_params, a, int_method='sonic',
multiprocess=False):
''' Run batch acoustic titrations of the system for various neuron types, sonophore and
stimulation parameters, to determine the threshold of a specific stimulus parameter
for neural excitation.
:param batch_dir: full path to output directory of batch
:param log_filepath: full path log file of batch
:param neurons: list of neurons names
:param stim_params: dictionary containing sweeps for all stimulation parameters
:param a: BLS structure diameter (m)
:param int_method: selected integration method
:param multiprocess: boolean statting wether or not to use multiprocessing
:return: list of full paths to the output files
'''
logger.info("Starting A-STIM titration batch")
# Determine titration parameter and titrations list
if 'durations' not in stim_params:
t_type = 't'
sim_queue = createSimQueue(stim_params['amps'], [None], [TITRATION_T_OFFSET],
stim_params['PRFs'], stim_params['DCs'])
elif 'amps' not in stim_params:
t_type = 'A'
sim_queue = createSimQueue([None], stim_params['durations'],
[TITRATION_T_OFFSET] * len(stim_params['durations']),
stim_params['PRFs'], stim_params['DCs'])
elif 'DC' not in stim_params:
t_type = 'DC'
sim_queue = createSimQueue(stim_params['amps'], stim_params['durations'],
[TITRATION_T_OFFSET] * len(stim_params['durations']),
stim_params['PRFs'], [None])
print(sim_queue)
nqueue = sim_queue.shape[0]
nsims = len(neurons) * len(stim_params['freqs']) * nqueue
# Initialize multiprocessing pobjects if needed
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = min(mp.cpu_count(), nsims)
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Run titrations
wid = 0
filepaths = []
for nname in neurons:
neuron = getNeuronsDict()[nname]()
for Fdrive in stim_params['freqs']:
# Create SolverUS instance (modulus of embedding tissue depends on frequency)
solver = SolverUS(a, neuron, Fdrive)
for i in range(nqueue):
# Extract parameters
Adrive, tstim, toffset, PRF, DC = sim_queue[i, :]
# Get date and time info
date_str = time.strftime("%Y.%m.%d")
daytime_str = time.strftime("%H:%M:%S")
# Run titration
try:
titrator = AStimTitrator(wid, solver, neuron, Fdrive, *sim_queue[i, :],
int_method, nsims)
if multiprocess:
tasks.put(titrator, block=False)
else:
logger.info('%s', titrator)
(output_thr, t, y, states, lat, tcomp) = titrator.__call__()
Z, ng, Qm, Vm, *channels = y
U = np.insert(np.diff(Z) / np.diff(t), 0, 0.0)
# Determine output variable
if t_type == 'A':
Adrive = output_thr
elif t_type == 't':
tstim = output_thr
elif t_type == 'DC':
DC = output_thr
# Define output naming
simcode = 'ASTIM_{}_{}_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.0f}ms_{}{}'\
.format(neuron.name, 'CW' if DC == 1 else 'PW', solver.a * 1e9,
Fdrive * 1e-3, Adrive * 1e-3, tstim * 1e3,
'PRF{:.2f}Hz_DC{:.2f}%_'.format(PRF, DC * 1e2) if DC < 1. else '',
int_method)
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'U': U, 'Z': Z, 'ng': ng,
'Qm': Qm, 'Vm': Vm})
for j in range(len(neuron.states_names)):
df[neuron.states_names[j]] = channels[j]
meta = {'neuron': neuron.name, 'a': solver.a, 'd': solver.d, 'Fdrive': Fdrive,
'Adrive': Adrive, 'phi': np.pi, 'tstim': tstim, 'toffset': toffset,
'PRF': PRF, 'DC': DC, 'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.debug('simulation data exported to "%s"', output_filepath)
filepaths.append(output_filepath)
# Detect spikes on Qm signal
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(Qm, SPIKE_MIN_QAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_QPROM)
n_spikes = ipeaks.size
lat = t[ipeaks[0]] if n_spikes > 0 else 'N/A'
sr = np.mean(1 / np.diff(t[ipeaks])) if n_spikes > 1 else 'N/A'
logger.info('%u spike%s detected', n_spikes, "s" if n_spikes > 1 else "")
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': neuron.name,
'D': solver.a * 1e9,
'E': solver.d * 1e6,
'F': Fdrive * 1e-3,
'G': Adrive * 1e-3,
'H': tstim * 1e3,
'I': PRF * 1e-3 if DC < 1 else 'N/A',
'J': DC,
'K': int_method,
'L': t.size,
'M': round(tcomp, 2),
'N': n_spikes,
'O': lat * 1e3 if isinstance(lat, float) else 'N/A',
'P': sr * 1e-3 if isinstance(sr, float) else 'N/A'
}
if xlslog(log_filepath, 'Data', log) == 1:
logger.info('log exported to "%s"', log_filepath)
else:
logger.error('log export to "%s" aborted', log_filepath)
except (Warning, AssertionError) as inst:
logger.warning('Integration error: %s. Continue batch? (y/n)', extra={inst})
user_str = input()
if user_str not in ['y', 'Y']:
return filepaths
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
(output_thr, t, y, states, lat, tcomp) = results.get()
Z, ng, Qm, Vm, *channels = y
U = np.insert(np.diff(Z) / np.diff(t), 0, 0.0)
# Determine output variable
if t_type == 'A':
Adrive = output_thr
elif t_type == 't':
tstim = output_thr
elif t_type == 'DC':
DC = output_thr
# Define output naming
simcode = 'ASTIM_{}_{}_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.0f}ms_{}{}'\
.format(neuron.name, 'CW' if DC == 1 else 'PW', solver.a * 1e9,
Fdrive * 1e-3, Adrive * 1e-3, tstim * 1e3,
'PRF{:.2f}Hz_DC{:.2f}%_'.format(PRF, DC * 1e2) if DC < 1. else '',
int_method)
# Store dataframe and metadata
df = pd.DataFrame({'t': t, 'states': states, 'U': U, 'Z': Z, 'ng': ng,
'Qm': Qm, 'Vm': Vm})
for j in range(len(neuron.states_names)):
df[neuron.states_names[j]] = channels[j]
meta = {'neuron': neuron.name, 'a': solver.a, 'd': solver.d, 'Fdrive': Fdrive,
'Adrive': Adrive, 'phi': np.pi, 'tstim': tstim, 'toffset': toffset,
'PRF': PRF, 'DC': DC, 'tcomp': tcomp}
# Export into to PKL file
output_filepath = '{}/{}.pkl'.format(batch_dir, simcode)
with open(output_filepath, 'wb') as fh:
pickle.dump({'meta': meta, 'data': df}, fh)
logger.debug('simulation data exported to "%s"', output_filepath)
filepaths.append(output_filepath)
# Detect spikes on Qm signal
dt = t[1] - t[0]
ipeaks, *_ = findPeaks(Qm, SPIKE_MIN_QAMP, int(np.ceil(SPIKE_MIN_DT / dt)),
SPIKE_MIN_QPROM)
n_spikes = ipeaks.size
lat = t[ipeaks[0]] if n_spikes > 0 else 'N/A'
sr = np.mean(1 / np.diff(t[ipeaks])) if n_spikes > 1 else 'N/A'
logger.info('%u spike%s detected', n_spikes, "s" if n_spikes > 1 else "")
# Export key metrics to log file
log = {
'A': date_str,
'B': daytime_str,
'C': neuron.name,
'D': solver.a * 1e9,
'E': solver.d * 1e6,
'F': Fdrive * 1e-3,
'G': Adrive * 1e-3,
'H': tstim * 1e3,
'I': PRF * 1e-3 if DC < 1 else 'N/A',
'J': DC,
'K': int_method,
'L': t.size,
'M': round(tcomp, 2),
'N': n_spikes,
'O': lat * 1e3 if isinstance(lat, float) else 'N/A',
'P': sr * 1e-3 if isinstance(sr, float) else 'N/A'
}
if xlslog(log_filepath, 'Data', log) == 1:
logger.info('log exported to "%s"', log_filepath)
else:
logger.error('log export to "%s" aborted', log_filepath)
return filepaths
def computeSpikeMetrics(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']
tstim = meta['tstim']
t = df['t'].values
Qm = df['Qm'].values
dt = t[1] - t[0]
# 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
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())
def getCycleProfiles(a, f, A, Cm0, Qm0, Qm):
''' Run a mechanical simulation until periodic stabilization, and compute pressure profiles
over the last acoustic cycle.
:param a: in-plane diameter of the sonophore structure within the membrane (m)
:param f: acoustic drive frequency (Hz)
:param A: acoustic drive amplitude (Pa)
:param Cm0: membrane resting capacitance (F/m2)
:param Qm0: membrane resting charge density (C/m2)
:param Qm: imposed membrane charge density (C/m2)
:return: a dataframe with the time, kinematic and pressure profiles over the last cycle.
'''
# Create sonophore object
bls = BilayerSonophore(a, f, Cm0, Qm0)
# Run default simulation and compute relevant profiles
logger.info('Running mechanical simulation (a = %sm, f = %sHz, A = %sPa)',
si_format(a, 1), si_format(f, 1), si_format(A, 1))
t, y, _ = bls.run(f, A, Qm, Pm_comp_method=PmCompMethod.direct)
dt = (t[-1] - t[0]) / (t.size - 1)
Z, ng = y[:, -NPC_FULL:]
t = t[-NPC_FULL:]
t -= t[0]
logger.info('Computing pressure cyclic profiles')
R = bls.v_curvrad(Z)
U = np.diff(Z) / dt
U = np.hstack((U, U[-1]))
data = {
't': t,
'Z': Z,
'Cm': bls.v_Capct(Z),
'P_M': bls.v_PMavg(Z, R, bls.surface(Z)),
'P_Q': bls.Pelec(Z, Qm),
'P_{VE}': bls.PEtot(Z, R) + bls.PVleaflet(U, R),
'P_V': bls.PVfluid(U, R),
'P_G': bls.gasmol2Pa(ng, bls.volume(Z)),
'P_0': - np.ones(Z.size) * bls.P0
}
return pd.DataFrame(data, columns=data.keys())
def runSweepSA(bls, f, A, Qm, params, rel_sweep):
''' Run mechanical simulations while varying multiple model parameters around their default value,
and compute the relative changes in cycle-averaged sonophore membrane potential over the last
acoustic period upon periodic stabilization.
:param bls: BilayerSonophore object
:param f: acoustic drive frequency (Hz)
:param A: acoustic drive amplitude (Pa)
:param Qm: imposed membrane charge density (C/m2)
:param params: list of model parameters to explore
:param rel_sweep: array of relative parameter changes
:return: a dataframe with the cycle-averaged sonophore membrane potentials for
the parameter variations, for each parameter.
'''
nsweep = len(rel_sweep)
logger.info('Starting sensitivity analysis (%u parameters, sweep size = %u)',
len(params), nsweep)
t0 = time.time()
# Run default simulation and compute cycle-averaged membrane potential
_, y, _ = bls.run(f, A, Qm, Pm_comp_method=PmCompMethod.direct)
Z = y[0, -NPC_FULL:]
Cm = bls.v_Capct(Z) # F/m2
Vmavg_default = np.mean(Qm / Cm) * 1e3 # mV
# Create data dictionary for computed output changes
data = {'relative input change': rel_sweep - 1}
nsims = len(params) * nsweep
for j, p in enumerate(params):
default = getattr(bls, p)
sweep = rel_sweep * default
Vmavg = np.empty(nsweep)
logger.info('Computing system\'s sentitivty to %s (default = %.2e)', p, default)
for i, val in enumerate(sweep):
# Re-initialize BLS object with modififed attribute
setattr(bls, p, val)
bls.reinit()
# Run simulation and compute cycle-averaged membrane potential
_, y, _ = bls.run(f, A, Qm, Pm_comp_method=PmCompMethod.direct)
Z = y[0, -NPC_FULL:]
Cm = bls.v_Capct(Z) # F/m2
Vmavg[i] = np.mean(Qm / Cm) * 1e3 # mV
logger.info('simulation %u/%u: %s = %.2e (%+.1f %%) --> |Vm| = %.1f mV (%+.3f %%)',
j * nsweep + i + 1, nsims, p, val, (val - default) / default * 1e2,
Vmavg[i], (Vmavg[i] - Vmavg_default) / Vmavg_default * 1e2)
# Fill in data dictionary
data[p] = Vmavg
# Set parameter back to default
setattr(bls, p, default)
tcomp = time.time() - t0
logger.info('Sensitivity analysis susccessfully completed in %.0f s', tcomp)
# return pandas dataframe
return pd.DataFrame(data, columns=data.keys())
def getActivationMap(root, neuron, a, f, tstim, toffset, PRF, amps, DCs):
''' Compute the activation map of a neuron at a given frequency and PRF, by computing
the spiking metrics of simulation results over a 2D space (amplitude x duty cycle).
:param root: directory containing the input data files
:param neuron: neuron name
:param a: sonophore diameter
:param f: acoustic drive frequency (Hz)
:param tstim: duration of US stimulation (s)
:param toffset: duration of the offset (s)
:param PRF: pulse repetition frequency (Hz)
:param amps: vector of acoustic amplitudes (Pa)
:param DCs: vector of duty cycles (-)
:return the activation matrix
'''
# Initialize activation map
actmap = np.empty((amps.size, DCs.size))
# Loop through amplitudes and duty cycles
nfiles = DCs.size * amps.size
for i, A in enumerate(amps):
for j, DC in enumerate(DCs):
# Define filename
PW_str = '_PRF{:.2f}Hz_DC{:.2f}%'.format(PRF, DC * 1e2) if DC < 1 else ''
fname = ('ASTIM_{}_{}W_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.0f}ms{}_effective.pkl'
.format(neuron, 'P' if DC < 1 else 'C', a * 1e9, f * 1e-3, A * 1e-3, tstim * 1e3,
PW_str))
# Extract charge profile from data file
fpath = os.path.join(root, fname)
if os.path.isfile(fpath):
logger.debug('Loading file {}/{}: "{}"'.format(i * amps.size + j + 1, nfiles, fname))
with open(fpath, 'rb') as fh:
frame = pickle.load(fh)
df = frame['data']
meta = frame['meta']
tstim = meta['tstim']
t = df['t'].values
Qm = df['Qm'].values
dt = t[1] - t[0]
# Detect spikes on charge profile during stimulus
mpd = int(np.ceil(SPIKE_MIN_DT / dt))
ispikes, *_ = findPeaks(Qm[t <= tstim], SPIKE_MIN_QAMP, mpd, SPIKE_MIN_QPROM)
# Compute firing metrics
if ispikes.size == 0: # if no spike, assign -1
actmap[i, j] = -1
elif ispikes.size == 1: # if only 1 spike, assign 0
actmap[i, j] = 0
else: # if more than 1 spike, assign firing rate
FRs = 1 / np.diff(t[ispikes])
actmap[i, j] = np.mean(FRs)
else:
logger.error('"{}" file not found'.format(fname))
actmap[i, j] = np.nan
return actmap
def getMaxMap(key, root, neuron, a, f, tstim, toffset, PRF, amps, DCs, mode='max', cavg=False):
''' Compute the max. value map of a neuron's specific variable at a given frequency and PRF
over a 2D space (amplitude x duty cycle).
:param key: the variable name to find in the simulations dataframes
:param root: directory containing the input data files
:param neuron: neuron name
:param a: sonophore diameter
:param f: acoustic drive frequency (Hz)
:param tstim: duration of US stimulation (s)
:param toffset: duration of the offset (s)
:param PRF: pulse repetition frequency (Hz)
:param amps: vector of acoustic amplitudes (Pa)
:param DCs: vector of duty cycles (-)
:param mode: string indicating whether to search for maximum, minimum or absolute maximum
:return the maximum matrix
'''
# Initialize max map
maxmap = np.empty((amps.size, DCs.size))
# Loop through amplitudes and duty cycles
nfiles = DCs.size * amps.size
for i, A in enumerate(amps):
for j, DC in enumerate(DCs):
# Define filename
PW_str = '_PRF{:.2f}Hz_DC{:.2f}%'.format(PRF, DC * 1e2) if DC < 1 else ''
fname = ('ASTIM_{}_{}W_{:.0f}nm_{:.0f}kHz_{:.1f}kPa_{:.0f}ms{}_effective.pkl'
.format(neuron, 'P' if DC < 1 else 'C', a * 1e9, f * 1e-3, A * 1e-3, tstim * 1e3,
PW_str))
# Extract charge profile from data file
fpath = os.path.join(root, fname)
if os.path.isfile(fpath):
logger.debug('Loading file {}/{}: "{}"'.format(i * amps.size + j + 1, nfiles, fname))
with open(fpath, 'rb') as fh:
frame = pickle.load(fh)
df = frame['data']
t = df['t'].values
if key in df:
x = df[key].values
else:
x = eval(key)
if cavg:
x = getCycleAverage(t, x, 1 / PRF)
if mode == 'min':
maxmap[i, j] = x.min()
elif mode == 'max':
maxmap[i, j] = x.max()
elif mode == 'absmax':
maxmap[i, j] = np.abs(x).max()
else:
maxmap[i, j] = np.nan
return maxmap
def computeAStimLookups(neuron, aref, fref, Aref, phi=np.pi, multiprocess=False):
''' Run simulations of the mechanical system for a multiple combinations of
imposed US frequencies, acoustic amplitudes and charge densities, compute
effective coefficients and store them in a dictionary of 3D arrays.
:param neuron: neuron object
:param aref: array of sonophore diameters (m)
:param fref: array of acoustic drive frequencies (Hz)
:param Aref: array of acoustic drive amplitudes (Pa)
:param phi: acoustic drive phase (rad)
:param multiprocess: boolean statting wether or not to use multiprocessing
:return: lookups dictionary
'''
# Check validity of input parameters
if not isinstance(neuron, BaseMech):
raise InputError('Invalid neuron type: "{}" (must inherit from BaseMech class)'
.format(neuron.name))
for key, values in {'diameters': aref, 'frequencies': fref, 'amplitudes': Aref}.items():
if not (isinstance(values, list) or isinstance(values, np.ndarray)):
raise InputError('Invalid {} (must be provided as list or numpy array)'.format(key))
if not all(isinstance(x, float) for x in values):
raise InputError('Invalid {} (must all be float typed)'.format(key))
if len(values) == 0:
raise InputError('Empty {} array'.format(key))
if key in ('diameters', 'frequencies') and min(values) <= 0:
raise InputError('Invalid {} (must all be strictly positive)'.format(key))
if key is 'amplitudes' and min(values) < 0:
raise InputError('Invalid {} (must all be positive or null)'.format(key))
logger.info('Starting batch lookup creation for %s neuron', neuron.name)
t0 = time.time()
# Get input dimensions
na, nf, nA = len(aref), len(fref), len(Aref)
# Create neuron-specific charge vector
Qref = np.arange(neuron.Qbounds[0], neuron.Qbounds[1] + 1e-5, 1e-5) # C/m2
nQ = Qref.size
dims = (na, nf, nA, nQ)
# Initialize lookup dictionary of 3D array to store effective coefficients
coeffs_names = ['V', 'ng', *neuron.coeff_names]
ncoeffs = len(coeffs_names)
lookup_dict = {cn: np.empty(dims) for cn in coeffs_names}
# Initiate multipleprocessing objects if needed
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = mp.cpu_count()
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Loop through all (a, f, A, Q) combinations and launch workers
nsims = np.prod(np.array(dims))
wid = 0
for ia, a in enumerate(aref):
# Initialize BLS object
bls = BilayerSonophore(a, 0.0, neuron.Cm0, neuron.Cm0 * neuron.Vm0 * 1e-3)
for iF, f in enumerate(fref):
for iA, A in enumerate(Aref):
for iQ, Q in enumerate(Qref):
worker = LookupWorker(wid, bls, neuron, f, A, Q, phi, nsims)
if multiprocess:
tasks.put(worker, block=False)
else:
logger.info('%s', worker)
_, Qcoeffs = worker.__call__()
for icoeff in range(ncoeffs):
lookup_dict[coeffs_names[icoeff]][ia, iF, iA, iQ] = Qcoeffs[icoeff]
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
wid, Qcoeffs = results.get()
ia, iF, iA, iQ = nDindexes(dims, wid)
for icoeff in range(ncoeffs):
lookup_dict[coeffs_names[icoeff]][ia, iF, iA, iQ] = Qcoeffs[icoeff]
# Close tasks and results queues
tasks.close()
results.close()
# Add input vectors to lookup dictionary
lookup_dict['a'] = aref # nm
lookup_dict['f'] = fref # Hz
lookup_dict['A'] = Aref # Pa
lookup_dict['Q'] = Qref # C/m2
logger.info('%s lookups computed in %.0f s', neuron.name, time.time() - t0)
return lookup_dict
def computeTestLookups(neuron, aref, fref, Aref, phi=np.pi, multiprocess=False):
''' Run simulations of the mechanical system for a multiple combinations of
imposed US frequencies, acoustic amplitudes and charge densities, compute
effective coefficients and store them in a dictionary of 3D arrays.
:param neuron: neuron object
:param aref: array of sonophore diameters (m)
:param fref: array of acoustic drive frequencies (Hz)
:param Aref: array of acoustic drive amplitudes (Pa)
:param phi: acoustic drive phase (rad)
:param multiprocess: boolean statting wether or not to use multiprocessing
:return: lookups dictionary
'''
logger.info('Starting dummy lookup creation for %s neuron', neuron.name)
t0 = time.time()
# Get input dimensions
na, nf, nA = len(aref), len(fref), len(Aref)
# Create neuron-specific charge vector
Qref = np.arange(neuron.Qbounds[0], neuron.Qbounds[1] + 1e-5, 1e-5) # C/m2
nQ = Qref.size
dims = (na, nf, nA, nQ)
# Initialize lookup dictionary of 3D array to store effective coefficients
coeffs_names = ['V', 'ng', *neuron.coeff_names]
ncoeffs = len(coeffs_names)
lookup_dict = {cn: np.empty(dims) for cn in coeffs_names}
# Initiate multipleprocessing objects if needed
if multiprocess:
mp.freeze_support()
# Create tasks and results queues
tasks = mp.JoinableQueue()
results = mp.Queue()
# Create and start consumer processes
nconsumers = mp.cpu_count()
consumers = [Consumer(tasks, results) for i in range(nconsumers)]
for w in consumers:
w.start()
# Loop through all (a, f, A, Q) combinations and launch workers
nsims = np.prod(np.array(dims))
wid = 0
for ia, a in enumerate(aref):
for iF, f in enumerate(fref):
for iA, A in enumerate(Aref):
for iQ, Q in enumerate(Qref):
worker = Worker(wid, nsims, ncoeffs)
if multiprocess:
tasks.put(worker, block=False)
else:
logger.info('%s', worker)
_, Qcoeffs = worker.__call__()
for icoeff in range(ncoeffs):
lookup_dict[coeffs_names[icoeff]][ia, iF, iA, iQ] = Qcoeffs[icoeff]
wid += 1
if multiprocess:
# Stop processes
for i in range(nconsumers):
tasks.put(None, block=False)
tasks.join()
# Retrieve workers output
for x in range(nsims):
wid, Qcoeffs = results.get()
ia, iF, iA, iQ = nDindexes(dims, wid)
for icoeff in range(ncoeffs):
lookup_dict[coeffs_names[icoeff]][ia, iF, iA, iQ] = Qcoeffs[icoeff]
# Close tasks and results queues
tasks.close()
results.close()
# Add input vectors to lookup dictionary
lookup_dict['a'] = aref # nm
lookup_dict['f'] = fref # Hz
lookup_dict['A'] = Aref # Pa
lookup_dict['Q'] = Qref # C/m2
logger.info('%s lookups computed in %.0f s', neuron.name, time.time() - t0)
return lookup_dict

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