def optimizetimescales(self, myExp):
myExp.plotTrainingSet()
myExp.plotTestSet()
myGIF_rect = GIF(0.1)
myGIF_rect.Tref = self.T_ref
myGIF_rect.eta = Filter_Rect_LogSpaced()
myGIF_rect.eta.setMetaParameters(length=500.0,
binsize_lb=2.0,
binsize_ub=100.0,
slope=4.5)
myGIF_rect.fitVoltageReset(myExp, myGIF_rect.Tref, do_plot=False)
myGIF_rect.fitSubthresholdDynamics(myExp,
DT_beforeSpike=self.DT_beforespike)
myGIF_rect.eta.fitSumOfExponentials(3, [1.0, 0.5, 0.1],
self.tau_gamma,
ROI=None,
dt=0.1)
print "Optimal timescales: ", myGIF_rect.eta.tau0
self.tau_opt = [t for t in myGIF_rect.eta.tau0 if t < self.eta_tau_max]
self.fitmodel(self, myExp)
def main(nombreGif):
(folder1, folder2) = Directory.createDirectory()
# read the gif
images = GIF.readGif(nombreGif)
# play with scanned images
(motionPixels, pixeles) = detectMotion(images)
# save files
Directory.movedir(folder1)
saveImage(motionPixels)
Directory.creatGif()
Directory.backDirectory()
Directory.movedir(folder2)
saveImage(pixeles)
Directory.creatGif()
Directory.backDirectory()
def fitmodel(self, myExp):
myGIF = GIF(0.1)
myGIF.Tref = self.T_ref
myGIF.eta = Filter_Exps()
myGIF.eta.setFilter_Timescales(self.tau_opt)
myGIF.gamma = Filter_Exps()
myGIF.gamma.setFilter_Timescales(self.tau_gamma)
myGIF.fit(myExp, DT_beforeSpike=self.DT_beforespike)
# Use the myGIF model to predict the spiking data of the test data set in myExp
myPrediction = myExp.predictSpikes(myGIF, nb_rep=500)
# Compute Md* with a temporal precision of +/- 4ms
Md = myPrediction.computeMD_Kistler(4.0, 0.1)
# Plot data vs model prediction
myPrediction.plotRaster(delta=1000.0)
clamp_period=1.0)
myAEC.p_expFitRange = [3.0, 150.0]
myAEC.p_nbRep = 15
# Assign myAEC to experiment and compensate the voltage recordings
experiment.setAEC(myAEC)
experiment.performAEC()
# Determine the refractory period
#################################################################################################
# FIT STANDARD GIF
#################################################################################################
# Create a new object GIF
GIF_fit = GIF(sampling_time)
# Define parameters
GIF_fit.Tref = 6.0
GIF_fit.eta = Filter_Rect_LogSpaced()
GIF_fit.eta.setMetaParameters(length=2000.0,
binsize_lb=0.5,
binsize_ub=500.0,
slope=10.0)
GIF_fit.gamma = Filter_Rect_LogSpaced()
GIF_fit.gamma.setMetaParameters(length=2000.0,
binsize_lb=2.0,
binsize_ub=500.0,
slope=5.0)
is_E_K_fixed = True
GIF_K_fit.fit(experiment, DT_beforeSpike=5.0, is_E_K_fixed=is_E_K_fixed)
# Plot the model parameters
GIF_K_fit.plotParameters()
# Save the model
GIF_TYPE = 'GIF_K_'
#GIF_K_fit.save(PATH_RESULTS + GIF_TYPE + CELL_NAME + SPECIFICATION + ADDITIONAL_SPECIFIER + '.pck')
#################################################################################################
# FIT standard GIF
#################################################################################################
# Create a new object GIF
GIF_fit = GIF(sampling_time)
# Define parameters
GIF_fit.Tref = 6.0
GIF_fit.eta = Filter_Rect_LogSpaced()
GIF_fit.eta.setMetaParameters(length=2000.0,
binsize_lb=0.5,
binsize_ub=500.0,
slope=10.0)
#5HT GIF_fit.eta.setMetaParameters(length=5000.0, binsize_lb=2.0, binsize_ub=1000.0, slope=4.5)
GIF_fit.gamma = Filter_Rect_LogSpaced()
GIF_fit.gamma.setMetaParameters(length=2000.0,
binsize_lb=2.0,
binsize_ub=500.0,
############################################################################################################ # STEP 2B: TO NOT PERFORM ACTIVE ELECTRODE COMPENSATION DO NOT RUN STEP 2A AND EXECUTE STEP 2B INSTEAD ############################################################################################################ """ myAEC_Dummy = AEC_Dummy() myExp.setAEC(myAEC_Dummy) myExp.performAEC() """ ############################################################################################################ # STEP 3A: FIT GIF WITH RECT BASIS FUNCTIONS TO DATA ############################################################################################################ # Create a new object GIF myGIF_rect = GIF(0.1) # Define parameters myGIF_rect.Tref = 4.0 # Define eta and gamma as a sum of rectangular functions (log-spaced) myGIF_rect.eta = Filter_Rect_LogSpaced() myGIF_rect.eta.setMetaParameters(length=5000.0, binsize_lb=2.0, binsize_ub=1000.0, slope=4.5) myGIF_rect.gamma = Filter_Rect_LogSpaced() myGIF_rect.gamma.setMetaParameters(length=5000.0, binsize_lb=5.0, binsize_ub=1000.0, slope=5.0) # Perform the fit myGIF_rect.fit(myExp, DT_beforeSpike=5.0)
# Define metaparametres myAEC.K_opt.setMetaParameters(length=150.0, binsize_lb=myExp.dt, binsize_ub=2.0, slope=30.0, clamp_period=1.0) myAEC.p_expFitRange = [3.0,150.0] myAEC.p_nbRep = 15 # Assign myAEC to myExp and compensate the voltage recordings myExp.setAEC(myAEC) myExp.performAEC() ############################################################################################################ # STEP 3A: FIT GIF MODEL TO DATA ############################################################################################################ # Create a new object GIF myGIF = GIF(0.1) # Define parameters myGIF.Tref = 4.0 myGIF.eta = Filter_Rect_LogSpaced() myGIF.eta.setMetaParameters(length=500.0, binsize_lb=2.0, binsize_ub=1000.0, slope=4.5) myGIF.gamma = Filter_Rect_LogSpaced() myGIF.gamma.setMetaParameters(length=500.0, binsize_lb=5.0, binsize_ub=1000.0, slope=5.0) # Perform the fit myGIF.fit(myExp, DT_beforeSpike=5.0) # Plot the model parameters myGIF.printParameters()
# STEP 2B: PLOT TRAINING SET TRACES INDIVIDUALLY
#################################################################################################
# If you would like to see how the raw data looks like, you can uncomment this the following lines:
"""
for tr in experiment.trainingset_traces :
tr.plot()
"""
#################################################################################################
# STEP 3A: FIT GIF MODEL (Pozzorini et al. 2015)
#################################################################################################
# More details on how to fit a simple GIF model to data can be found here: Main_TestGIF.py
GIF_fit = GIF(experiment.dt)
GIF_fit.Tref = 4.0
GIF_fit.eta = Filter_Rect_LogSpaced()
GIF_fit.eta.setMetaParameters(length=4000.0, binsize_lb=1.0, binsize_ub=1000.0, slope=7.0)
GIF_fit.gamma = Filter_Rect_LogSpaced()
GIF_fit.gamma.setMetaParameters(length=4000.0, binsize_lb=5.0, binsize_ub=1000.0, slope=7.0)
GIF_fit.fit(experiment, DT_beforeSpike = 5.0)
#GIF_fit.plotParameters()
#################################################################################################
myExp.performAEC()
# Plot AEC filters (Kopt and Ke)
myAEC.plotKopt()
myAEC.plotKe()
# Plot training and test set
myExp.plotTrainingSet()
myExp.plotTestSet()
############################################################################################################
# STEP 3: FIT GIF MODEL TO DATA
############################################################################################################
# Create a new object GIF
myGIF = GIF(0.1)
# Define parameters
myGIF.Tref = 4.0
myGIF.eta = Filter_Rect_LogSpaced()
myGIF.eta.setMetaParameters(length=500.0,
binsize_lb=2.0,
binsize_ub=1000.0,
slope=4.5)
myGIF.gamma = Filter_Rect_LogSpaced()
myGIF.gamma.setMetaParameters(length=500.0,
binsize_lb=5.0,
binsize_ub=1000.0,
slope=5.0)
temp = GIF_Ca_fit.fit(experiment, DT_beforeSpike=5.0, is_E_Ca_fixed=is_E_Ca_fixed) # Plot the model parameters GIF_Ca_fit.plotParameters() # Save the model GIF_TYPE = 'GIF_Ca_' #GIF_Ca_fit.save(PATH_RESULTS + GIF_TYPE + CELL_NAME + SPECIFICATION + ADDITIONAL_SPECIFIER + '.pck') ################################################################################################# # FIT standard GIF ################################################################################################# # Create a new object GIF GIF_fit = GIF(sampling_time) # Define parameters GIF_fit.Tref = 6.0 GIF_fit.eta = Filter_Rect_LogSpaced() GIF_fit.eta.setMetaParameters(length=2000.0, binsize_lb=0.5, binsize_ub=500.0, slope=10.0) #5HT GIF_fit.eta.setMetaParameters(length=5000.0, binsize_lb=2.0, binsize_ub=1000.0, slope=4.5) GIF_fit.gamma = Filter_Rect_LogSpaced() GIF_fit.gamma.setMetaParameters(length=2000.0, binsize_lb=2.0, binsize_ub=500.0, slope=5.0) #5HT GIF_fit.gamma.setMetaParameters(length=5000.0, binsize_lb=5.0, binsize_ub=1000.0, slope=5.0) GIF_fit.fit(experiment, DT_beforeSpike=5.0) # Plot the model parameters
f.close()
return h
def identify(filename):
h = read_headers(filename)
if h == None:
return None
# This is the GIF header
if h[:6] in ('GIF87a', 'GIF89a'):
return ((ord(h[7])<<8 | ord(h[6])),(ord(h[9])<<8 | ord(h[8])))
return None
if __name__ == "__main__":
import sys
import GIF
if len(sys.argv) < 2:
print 'Usage: %s files...\n' % sys.argv[0]
sys.exit(0)
for filename in sys.argv[1:]:
info=GIF.identify(filename)
if info != None:
print "%s (size = %d,%d)" % (filename,info[0],info[1])
else:
print "%s is not a GIF file" % filename