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utils.py
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Wed, Jul 3, 03:47

utils.py
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# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Email: theo.lemaire@epfl.ch
# @Date: 2018-10-01 20:45:29
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-06-12 23:13:24
import os
import numpy as np
import pandas as pd
from PySONIC.utils import *
from PySONIC.core import NeuronalBilayerSonophore
from PySONIC.neurons import *
from PySONIC.postpro import computeSpikingMetrics
def getCWtitrations_vs_Fdrive(neurons, a, freqs, tstim, toffset, fpath):
fkey = 'Fdrive (kHz)'
freqs = np.array(freqs)
if os.path.isfile(fpath):
df = pd.read_csv(fpath, sep=',', index_col=fkey)
else:
df = pd.DataFrame(index=freqs * 1e-3)
for neuron in neurons:
if neuron not in df:
pneuron = getPointNeuron(neuron)
nbls = NeuronalBilayerSonophore(a, pneuron)
for i, Fdrive in enumerate(freqs):
logger.info('Running CW titration for %s neuron @ %sHz',
neuron, si_format(Fdrive))
Athr = nbls.titrate(Fdrive, tstim, toffset) # Pa
df.loc[Fdrive * 1e-3, neuron] = np.ceil(Athr * 1e-2) / 10
df.sort_index(inplace=True)
df.to_csv(fpath, sep=',', index_label=fkey)
return df
def getCWtitrations_vs_radius(neurons, radii, Fdrive, tstim, toffset, fpath):
akey = 'radius (nm)'
radii = np.array(radii)
if os.path.isfile(fpath):
df = pd.read_csv(fpath, sep=',', index_col=akey)
else:
df = pd.DataFrame(index=radii * 1e9)
for neuron in neurons:
if neuron not in df:
pneuron = getPointNeuron(neuron)
for a in radii:
nbls = NeuronalBilayerSonophore(a, pneuron)
logger.info(
'Running CW titration for %s neuron @ %sHz (%.2f nm sonophore radius)',
neuron, si_format(Fdrive), a * 1e9)
Athr = nbls.titrate(Fdrive, tstim, toffset) # Pa
df.loc[a * 1e9, neuron] = np.ceil(Athr * 1e-2) / 10
df.sort_index(inplace=True)
df.to_csv(fpath, sep=',', index_label=akey)
return df
def getSims(outdir, neuron, a, queue):
fpaths = []
updated_queue = []
pneuron = getPointNeurons(neuron)
nbls = NeuronalBilayerSonophore(a, pneuron)
for i, item in enumerate(queue):
Fdrive, tstim, toffset, PRF, DC, Adrive, method = item
fcode = nbls.filecode(Fdrive, Adrive, tstim, toffset, PRF, DC, method)
fpath = os.path.join(outdir, '{}.pkl'.format(fcode))
if not os.path.isfile(fpath):
print(fpath, 'does not exist')
item.insert(0, outdir)
updated_queue.append(item)
fpaths.append(fpath)
if len(updated_queue) > 0:
print(updated_queue)
# pneuron = getPointNeuron(neuron)
# nbls = NeuronalBilayerSonophore(a, pneuron)
# batch = Batch(nbls.runAndSave, updated_queue)
# batch.run(mpi=True)
return fpaths
def getSpikingMetrics(outdir, neuron, xvar, xkey, data_fpaths, metrics_fpaths):
metrics = {}
for stype in data_fpaths.keys():
if os.path.isfile(metrics_fpaths[stype]):
logger.info('loading spiking metrics from file: "%s"', metrics_fpaths[stype])
metrics[stype] = pd.read_csv(metrics_fpaths[stype], sep=',')
else:
logger.warning('computing %s spiking metrics vs. %s for %s neuron', stype, xkey, neuron)
metrics[stype] = computeSpikingMetrics(data_fpaths[stype])
metrics[stype][xkey] = pd.Series(xvar, index=metrics[stype].index)
metrics[stype].to_csv(metrics_fpaths[stype], sep=',', index=False)
return metrics
def extractCompTimes(filenames):
''' Extract computation times from a list of simulation files. '''
tcomps = np.empty(len(filenames))
for i, fn in enumerate(filenames):
logger.info('Loading data from "%s"', fn)
with open(fn, 'rb') as fh:
frame = pickle.load(fh)
meta = frame['meta']
tcomps[i] = meta['tcomp']
return tcomps
def getCompTimesQuant(outdir, neuron, xvars, xkey, data_fpaths, comptimes_fpath):
if os.path.isfile(comptimes_fpath):
logger.info('reading computation times from file: "%s"', comptimes_fpath)
comptimes = pd.read_csv(comptimes_fpath, sep=',', index_col=xkey)
else:
logger.warning('extracting computation times for %s neuron', neuron)
comptimes = pd.DataFrame(index=xvars)
for stype in data_fpaths.keys():
for i, xvar in enumerate(xvars):
comptimes.loc[xvar, stype] = extractCompTimes([data_fpaths[stype][i]])
comptimes.to_csv(comptimes_fpath, sep=',', index_label=xkey)
return comptimes

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