inp_sfc -= inp_sfc[0] # reset to 0 at beginning of period
# validation data
seb_dat = np.genfromtxt(
'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Hydro/modelOUT_br1_headings.txt',
skip_header=3)
seb_mb = seb_dat[start_t - 1:end_t, -1]
seb_mb -= seb_mb[0] # reset to 0
# read in measured daily SEB change
mb_dat = np.genfromtxt(
'C:/Users/conwayjp/OneDrive - NIWA/projects/DSC Hydro/mchange.dat')
# note that the measured MB interprets surface height loss in the winter as mass loss, rather than compaction.
mb_dt = make_regular_timeseries(dt.datetime(2010, 10, 26, 00, 00),
dt.datetime(2012, 9, 2, 00, 00), 86400)
ts_mb = plt.cumsum(mb_dat[:, 0])
np.where(np.asarray(mb_dt) == dt.datetime(2011, 5, 13, 00, 00))
ts_mb -= ts_mb[199]
#
init_swe = np.ones(
inp_ta.shape[1:]) * 0 # give initial value of swe as starts in spring
init_d_snow = np.ones(
inp_ta.shape[1:]) * 30 # give initial value of days since snow
# call main function once hourly/sub-hourly temp and precip data available.
st_swe, st_melt, st_acc, st_alb = snow_main_simple(inp_ta,
inp_precip,
inp_doy,
inp_hourdec,
dtstep=1800,
data = np.loadtxt('C:\\Users\\Kristian\\Dropbox\\phd\\Data\\STN',
delimiter=' ')
STNdata = []
tick = []
for n in data:
STNdata.append(n[1])
tick.append(n[0])
data = np.loadtxt('C:\\Users\\Kristian\\Dropbox\\phd\\Data\\GPe',
delimiter=' ')
GPedata = []
for n in data:
GPedata.append(n[1])
time = pylab.cumsum(tick)
time = time * 1000
figure1 = pylab.plot(time, STNdata, label='STN')
figure2 = pylab.plot(time, GPedata, label='GPe')
pylab.legend(loc=4)
pylab.xlabel('time (ms)')
pylab.ylabel('fraction of max rate')
pylab.show()
def FPP(log=Logger.logger(0), N = 10000, dt = 1./24000, distributionParameter = [30], plotAll = True, efield = False):
#check if rate file or rate is present
if len(distributionParameter) == 1:
try:
data = np.loadtxt(distributionParameter[0],delimiter = ' ')
log.info("Rate data loaded")
BGsim = True
STNdata = []
tick = []
for n in data:
STNdata.append(n[1])
tick.append(n[0])
Ratetime = pylab.cumsum(tick)
BGdt = tick[1]
timeSteps = int(Ratetime[-1]/dt)
except:
float(distributionParameter[0])
Ratetime = 1.
timeSteps = int(Ratetime/dt)
BGsim = False
else:
Ratetime = 1.
BGsim = False
timeSteps = int(Ratetime/dt)
maxrate = 1./0.009
times = []
for n in range(timeSteps):
times.append(dt*n)
# check for current file, if none present use impules
try:
It = np.loadtxt('C:\\Users\\Kristian\\Dropbox\\phd\\Data\\apcurrent24k.dat',delimiter = ',')
#/home/uqkweegi/Documents/Data/apcurrent24k.dat',delimiter = ',')
except:
log.error('no current file present')
It = np.array(1)
log.info('Current loaded')
It = np.multiply(np.true_divide(It,It.min()),250e-9) #normalize
currentLength = len(It)
#calculate extracellular effects
epsilon = 8.85e-12 #Permitivity of free space
rho = 10.**5 * 10.**6 #density of neurons in STN m^-3
r = np.power(np.multiply(3./4*N/(np.pi*rho),np.array([random.uniform(0,1) for _ in range(N)])),1./3) #create a power law distribution of neuron radii
r.sort()
if efield:
rijk = [[random.uniform(0,1)-0.5 for _ in range(N)],[random.uniform(0,1)-0.5 for _ in range(N)],[random.uniform(0,1)-0.5 for _ in range(N)]] #create vector direction of field
#if plotAll:
# vi = pylab.plot(rijk[0])
# vj = pylab.plot(rijk[1])
# vk = pylab.plot(rijk[2])
# pylab.show()
R3 = 0.96e3
C3 = 2.22e-6
C2 = 9.38e-9
C3 = 1.56e-6
C2 = 9.38e-9
R4 = 100.e6
R2N = np.multiply(1./(4*np.pi*epsilon),r)
R1 = 2100.;
t_impulse = np.array([dt*n for n in range(100)])
log.info('initialization complete')
Vt = pylab.zeros(len(times))
Vi = Vt
Vj = Vt
Vk = Vt
# start simulation
#-------------------------------------------------------------------------------#
for neuron in range(N):
R2 = R2N[neuron]
ppwave = pylab.zeros(len(times))
if BGsim:
absoluteTimes = np.random.exponential(1./(maxrate*STNdata[0]),1)
else:
if len(distributionParameter) == 1:
absoluteTimes = np.random.exponential(1./(distributionParameter[0]),1)
else:
absoluteTimes = [random.weibullvariate(distributionParameter[0],distributionParameter[1])]
while absoluteTimes[-1] < times[-1]-currentLength*dt:
wave_start = int(absoluteTimes[-1]/dt)
wave_end = wave_start+currentLength
if wave_end > len(times):
break
ppwave[wave_start:wave_end] = np.add(ppwave[wave_start:wave_end],It)
if BGsim:
isi = np.random.exponential(1./(maxrate*STNdata[int(absoluteTimes[-1]/BGdt)]),1)
else:
if len(distributionParameter) == 1:
isi = np.random.exponential(1./(distributionParameter[0]),1)
else:
isi = random.weibullvariate(distributionParameter[0],distributionParameter[1])
absoluteTimes = np.append(absoluteTimes,[absoluteTimes[-1]+isi])
# calculate neuron contribution
#------------------------------------------------------------------------------#
extracellular_impulse_response = np.multiply(np.multiply(np.exp(np.multiply(t_impulse,-20*17*((C2*R1*R2 + C2*R1*R3 + C2*R1*R4 - C3*R1*R3 + C3*R2*R3 + C3*R3*R4))/(2*C2*C3*R1*R3*(R2 + R4)))),(np.add(np.cosh(np.multiply(t_impulse,(C2**2*R1**2*R2**2 + 2*C2**2*R1**2*R2*R3 + 2*C2**2*R1**2*R2*R4 + C2**2*R1**2*R3**2 + 2*C2**2*R1**2*R3*R4 + C2**2*R1**2*R4**2 + 2*C2*C3*R1**2*R2*R3 - 2*C2*C3*R1**2*R3**2 + 2*C2*C3*R1**2*R3*R4 - 2*C2*C3*R1*R2**2*R3 - 2*C2*C3*R1*R2*R3**2 - 4*C2*C3*R1*R2*R3*R4 - 2*C2*C3*R1*R3**2*R4 - 2*C2*C3*R1*R3*R4**2 + C3**2*R1**2*R3**2 - 2*C3**2*R1*R2*R3**2 - 2*C3**2*R1*R3**2*R4 + C3**2*R2**2*R3**2 + 2*C3**2*R2*R3**2*R4 + C3**2*R3**2*R4**2)**(1/2)/(2*C2*C3*R1*R3*(R2 + R4)))),np.divide(np.sinh(np.multiply(t_impulse,(C2**2*R1**2*R2**2 + 2*C2**2*R1**2*R2*R3 + 2*C2**2*R1**2*R2*R4 + C2**2*R1**2*R3**2 + 2*C2**2*R1**2*R3*R4 + C2**2*R1**2*R4**2 + 2*C2*C3*R1**2*R2*R3 - 2*C2*C3*R1**2*R3**2 + 2*C2*C3*R1**2*R3*R4 - 2*C2*C3*R1*R2**2*R3 - 2*C2*C3*R1*R2*R3**2 - 4*C2*C3*R1*R2*R3*R4 - 2*C2*C3*R1*R3**2*R4 - 2*C2*C3*R1*R3*R4**2 + C3**2*R1**2*R3**2 - 2*C3**2*R1*R2*R3**2 - 2*C3**2*R1*R3**2*R4 + C3**2*R2**2*R3**2 + 2*C3**2*R2*R3**2*R4 + C3**2*R3**2*R4**2)**(1/2)/(2*C2*C3*R1*R3*(R2 + R4))))*(C2*R1*R2 - C2*R1*R3 + C2*R1*R4 + C3*R1*R3 - C3*R2*R3 - C3*R3*R4),(C2**2*R1**2*R2**2 + 2*C2**2*R1**2*R2*R3 + 2*C2**2*R1**2*R2*R4 + C2**2*R1**2*R3**2 + 2*C2**2*R1**2*R3*R4 + C2**2*R1**2*R4**2 + 2*C2*C3*R1**2*R2*R3 - 2*C2*C3*R1**2*R3**2 + 2*C2*C3*R1**2*R3*R4 - 2*C2*C3*R1*R2**2*R3 - 2*C2*C3*R1*R2*R3**2 - 4*C2*C3*R1*R2*R3*R4 - 2*C2*C3*R1*R3**2*R4 - 2*C2*C3*R1*R3*R4**2 + C3**2*R1**2*R3**2 - 2*C3**2*R1*R2*R3**2 - 2*C3**2*R1*R3**2*R4 + C3**2*R2**2*R3**2 + 2*C3**2*R2*R3**2*R4 + C3**2*R3**2*R4**2)**(1/2))))),-R4/(C2*(R2 + R4)));
electrode_ppwave = np.convolve(ppwave,extracellular_impulse_response,'same');
if efield: #add fields
amp = 1/np.sqrt((np.square(rijk[0][neuron])+np.square(rijk[1][neuron])+np.square(rijk[2][neuron])))
rijk[0][neuron] = rijk[0][neuron]*amp
rijk[1][neuron] = rijk[1][neuron]*amp
rijk[2][neuron] = rijk[2][neuron]*amp
Vi = np.add(Vi,np.multiply(electrode_ppwave,rijk[0][neuron]))
Vj = np.add(Vj,np.multiply(electrode_ppwave,rijk[1][neuron]))
Vk = np.add(Vk,np.multiply(electrode_ppwave,rijk[2][neuron]))
else: #add scalar
Vt = np.add(Vt,electrode_ppwave)
if np.mod(neuron,1000) == 999:
log.info(str(neuron+1)+" neurons calculated")
#------------------------------------------------------------------------------#
# end simulation
log.info('neuron contribution to MER complete')
#remove bias
if efield:
Vt = np.sqrt(np.add(np.square(Vi),np.square(Vj),np.square(Vk)))
Vt = np.subtract(Vt,np.mean(Vt))
#apply hardware filters
flow = 5500*2.
fhigh = 500.
b,a = signal.butter(18,flow*dt,'low')
Vt = signal.lfilter(b, a, Vt)
b,a = signal.butter(1,fhigh*dt,'high')
Vt = signal.lfilter(b, a, Vt)
#produce plots
if plotAll:
volts = pylab.plot(times,Vt)
if BGsim:
stnrate = pylab.plot(Ratetime,np.multiply(STNdata,200))
pylab.show()
nfft=2**int(math.log(len(Vt),2))+1
sr = 1/dt
Pxi,freqs=pylab.psd(x=Vt,Fs=sr,NFFT=nfft/10,window=pylab.window_none, noverlap=100)
pylab.show()
return freqs, Pxi
psd = pylab.loglog(freqs, Pxi)
pylab.show()
return Vt, times
import numpy as np
from matplotlib import pylab
data = np.loadtxt('C:\\Users\\Kristian\\Dropbox\\phd\\Data\\STN',delimiter=' ')
STNdata=[]
tick=[]
for n in data:
STNdata.append(n[1])
tick.append(n[0])
data = np.loadtxt('C:\\Users\\Kristian\\Dropbox\\phd\\Data\\GPe',delimiter=' ')
GPedata=[]
for n in data:
GPedata.append(n[1])
time=pylab.cumsum(tick)
time=time*1000
figure1=pylab.plot(time, STNdata,label='STN')
figure2=pylab.plot(time, GPedata,label='GPe')
pylab.legend(loc=4)
pylab.xlabel('time (ms)')
pylab.ylabel('fraction of max rate')
pylab.show()