def loadsimdat(name=None, getactmap=True):
# load simulation data
global totalDur, tstepPerAction
name = getsimname(name)
print('loading data from', name)
conf.dconf = conf.readconf('backupcfg/' + name + 'sim.json')
simConfig = pickle.load(open('data/' + name + 'simConfig.pkl', 'rb'))
dstartidx = {
p: simConfig['net']['pops'][p]['cellGids'][0]
for p in simConfig['net']['pops'].keys()
} # starting indices for each population
dendidx = {
p: simConfig['net']['pops'][p]['cellGids'][-1]
for p in simConfig['net']['pops'].keys()
} # ending indices for each population
pdf = readinweights(name)
actreward = pd.DataFrame(
np.loadtxt('data/' + name + 'ActionsRewards.txt'),
columns=['time', 'action', 'reward', 'proposed', 'hit'])
dnumc = {
p: dendidx[p] - dstartidx[p] + 1
for p in simConfig['net']['pops'].keys()
}
spkID = np.array(simConfig['simData']['spkid'])
spkT = np.array(simConfig['simData']['spkt'])
dspkID, dspkT = {}, {}
for pop in simConfig['net']['pops'].keys():
dspkID[pop] = spkID[(spkID >= dstartidx[pop])
& (spkID <= dendidx[pop])]
dspkT[pop] = spkT[(spkID >= dstartidx[pop]) & (spkID <= dendidx[pop])]
InputImages = loadInputImages(dconf['sim']['name'])
ldflow = loadMotionFields(dconf['sim']['name'])
totalDur = int(dconf['sim']['duration'])
tstepPerAction = dconf['sim'][
'tstepPerAction'] # time step per action (in ms)
dact = None
lpop = ['ER', 'EV1', 'EV4', 'EMT', 'IR', 'IV1', 'IV4', 'IMT',\
'EV1DW','EV1DNW', 'EV1DN', 'EV1DNE','EV1DE','EV1DSW', 'EV1DS', 'EV1DSE',\
'EMDOWN','EMUP']
if getactmap: dact = getdActMap(totalDur, tstepPerAction, lpop)
return simConfig, pdf, actreward, dstartidx, dendidx, dnumc, dspkID, dspkT, InputImages, ldflow, dact
import fileio as fio import paramrw as paramrw from paramrw import usingOngoingInputs import plotfn as plotfn import specfn as specfn import pickle from dipolefn import Dipole from conf import readconf from L5_pyramidal import L5Pyr from L2_pyramidal import L2Pyr from L2_basket import L2Basket from L5_basket import L5Basket from lfp import LFPElectrode from morphology import shapeplot, getshapecoords dconf = readconf() # data directory - ./data dproj = dconf['datdir'] # fio.return_data_dir(dconf['datdir']) debug = dconf['debug'] pc = h.ParallelContext() pcID = int(pc.id()) f_psim = '' ntrial = 1 testLFP = dconf['testlfp'] testlaminarLFP = dconf['testlaminarlfp'] lelec = [] # list of LFP electrodes # reads the specified param file foundprm = False for i in range(len(sys.argv)):
from elasticsearch import Elasticsearch
import json
import sys
from datetime import datetime
from dateutil import tz
from dateutil import parser
import conf
conf.readconf()
ip = conf.settings.ip
port = int(conf.settings.port)
folder = conf.settings.folder
indexname = sys.argv[1]
starttime = sys.argv[2]
endtime = sys.argv[3]
from_zone = tz.tzutc()
to_zone = tz.tzlocal()
starttime = '2018-09-16T00:28:18.097Z'
endtime = '2018-09-16T23:54:18.097Z'
start = datetime.strptime(starttime, '%Y-%m-%dT%H:%M:%S.%fZ')
end = datetime.strptime(endtime, '%Y-%m-%dT%H:%M:%S.%fZ')
start = start.replace(tzinfo=to_zone)
end = end.replace(tzinfo=to_zone)
es = Elasticsearch([{'host': ip, 'port': port}])
score = 0
# TODO: Make the path in the function to lowercase so it wouldnt matter..