def fitToParameters(self):
#self.parentFn.updateParamFirst()
#self.parentFn.updateTableFirst()
#self.parentFn.updateParamFirst()
#Populate param for lmfit.
param = Parameters()
#self.def_param.add('A1', value=1.0, min=0,max=1.0, vary=False)
for art in self.param:
if self.param[art]['to_show'] == True:
param.add(art,
value=float(self.param[art]['value']),
min=float(self.param[art]['minv']),
max=float(self.param[art]['maxv']),
vary=self.param[art]['vary'])
#Find the index of the nearest point in the scale.
data = np.array(self.autoNorm).astype(np.float64).reshape(-1)
scale = np.array(self.autotime).astype(np.float64).reshape(-1)
self.indx_L = int(np.argmin(np.abs(scale - self.parentFn.dr.xpos)))
self.indx_R = int(np.argmin(np.abs(scale - self.parentFn.dr1.xpos)))
#Run the fitting.
res = minimize(self.residual,
param,
args=(scale[self.indx_L:self.indx_R + 1],
data[self.indx_L:self.indx_R + 1],
self.parentFn.def_options))
#Repopulate the parameter object.
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
self.param[art]['value'] = param[art].value
self.param[art]['stderr'] = float(param[art].stderr)
#Extra parameters, which are not fit or inherited.
#self.param['N_FCS']['value'] = np.round(1/self.param['GN0']['value'],4)
self.residualVar = res.residual
output = fit_report(param)
print 'residual', res.chisqr
if (res.chisqr > 0.05):
print 'CAUTION DATA DID NOT FIT WELL CHI^2 >0.05', res.chisqr
self.goodFit = False
else:
self.goodFit = True
self.fitted = True
self.chisqr = res.chisqr
self.localTime = time.asctime(time.localtime(time.time()))
#self.parentFn.updateTableFirst();
self.model_autoNorm = equation_(param,
scale[self.indx_L:self.indx_R + 1],
self.parentFn.def_options)
self.model_autotime = scale[self.indx_L:self.indx_R + 1]
def fitToParameters(self):
#self.parentFn.updateParamFirst()
#self.parentFn.updateTableFirst()
#self.parentFn.updateParamFirst()
#Populate param for lmfit.
param = Parameters()
#self.def_param.add('A1', value=1.0, min=0,max=1.0, vary=False)
for art in self.param:
if self.param[art]['to_show'] == True:
param.add(art, value=float(self.param[art]['value']), min=float(self.param[art]['minv']) ,max=float(self.param[art]['maxv']), vary=self.param[art]['vary']);
#Find the index of the nearest point in the scale.
data = np.array(self.autoNorm).astype(np.float64).reshape(-1)
scale = np.array(self.autotime).astype(np.float64).reshape(-1)
self.indx_L = int(np.argmin(np.abs(scale - self.parentFn.dr.xpos)))
self.indx_R = int(np.argmin(np.abs(scale - self.parentFn.dr1.xpos)))
#Run the fitting.
res = minimize(self.residual, param, args=(scale[self.indx_L:self.indx_R+1],data[self.indx_L:self.indx_R+1], self.parentFn.def_options))
#Repopulate the parameter object.
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art]['calc'] == False:
self.param[art]['value'] = param[art].value
self.param[art]['stderr'] = float(param[art].stderr)
#Extra parameters, which are not fit or inherited.
#self.param['N_FCS']['value'] = np.round(1/self.param['GN0']['value'],4)
self.residualVar = res.residual
output = fit_report(param)
print 'residual',res.chisqr
if(res.chisqr>0.05):
print 'CAUTION DATA DID NOT FIT WELL CHI^2 >0.05',res.chisqr
self.goodFit = False
else:
self.goodFit = True
self.fitted = True
self.chisqr = res.chisqr
self.localTime = time.asctime( time.localtime(time.time()) )
#self.parentFn.updateTableFirst();
self.model_autoNorm = equation_(param, scale[self.indx_L:self.indx_R+1],self.parentFn.def_options)
self.model_autotime = scale[self.indx_L:self.indx_R+1]
def residual(self, param, x, data,options): A = equation_(param, x,options) residuals = data-A return residuals
def residual(self, param, x, data, options):
A = equation_(param, x, options)
residuals = data - A
return residuals
