def compile(self):
name = self.name()
default = self.evalStr('default')
mode = self['sequence']
if mode == 'off':
seq = []
elif mode == 'range':
start = self.evalStr('start')
stop = self.evalStr('stop')
nPts = self['steps']
if self['log spacing']:
seq = fn.logSpace(start, stop, nPts)
else:
seq = np.linspace(start, stop, nPts)
elif mode == 'list':
seq = list(self.evalStr('list'))
elif mode == 'eval':
seq = self.evalStr('expression')
else:
raise Exception('Unknown sequence mode %s' % mode)
if self['randomize']:
np.random.shuffle(seq)
## sanity check
try:
len(seq)
except:
raise Exception("Parameter %s generated invalid sequence: %s" %
(name, str(seq)))
return default, seq
def main():
global fits, err, psp, data, xVals
## generate table of PSP shapes
nReps = 20
amps = fn.logSpace(2e-12, 300e-12, 20)
psp = np.empty(
(len(amps), nReps, 4)) ## last axis is [amp, xoff, rise, fall]
psp[:, :, 0] = amps[:, np.newaxis]
psp[..., 1] = 1e-3
psp[..., 2] = 0.1e-3
psp[..., 3] = 0.6e-3
## generate table of traces
data, xVals = mkDataSet(psp, downsample=40)
## fit all traces
guess = np.array([50e-10, 0.1e-3, 0.5e-3, 3e-3])
bounds = np.array([(0, 5e-9), (0, 2e-3), (50e-6, 5e-3), (200e-6, 20e-3)])
testFit("opt.leastsq",
psp,
data,
xVals,
fitPsp,
guess=guess,
bounds=bounds)
def compile(self):
name = self.name()
default = self.evalStr('default')
mode = self['sequence']
if mode == 'off':
seq = []
elif mode == 'range':
start = self.evalStr('start')
stop = self.evalStr('stop')
nPts = self['steps']
if self['log spacing']:
seq = fn.logSpace(start, stop, nPts)
else:
seq = np.linspace(start, stop, nPts)
elif mode == 'list':
seq = list(self.evalStr('list'))
elif mode == 'eval':
seq = self.evalStr('expression')
else:
raise Exception('Unknown sequence mode %s' % mode)
if self['randomize']:
np.random.shuffle(seq)
## sanity check
try:
len(seq)
except:
raise Exception("Parameter %s generated invalid sequence: %s" % (name, str(seq)))
return default, seq
def main():
global fits, err, psp, data, xVals
## generate table of PSP shapes
nReps = 20
amps = fn.logSpace(2e-12, 300e-12, 20)
psp = np.empty((len(amps), nReps, 4)) ## last axis is [amp, xoff, rise, fall]
psp[:,:,0] = amps[:,np.newaxis]
psp[...,1] = 1e-3
psp[...,2] = 0.1e-3
psp[...,3] = 0.6e-3
## generate table of traces
data, xVals = mkDataSet(psp, downsample=40)
## fit all traces
guess = np.array([50e-10, 0.1e-3, 0.5e-3, 3e-3])
bounds = np.array([(0, 5e-9), (0, 2e-3), (50e-6, 5e-3), (200e-6, 20e-3)])
testFit("opt.leastsq", psp, data, xVals, fitPsp, guess=guess, bounds=bounds)
