def solve(index, parameter, lower, upper, tolerance, ref, model):
modelirrs = []
diffs = []
sol = None
err = 0
def f(x):
spectrum = model({parameter: x})
model_irrad = trapz(spectrum[1], spectrum[0])
modelirrs.append(model_irrad)
diff = model_irrad - ref
diffs.append({x: diff})
return diff
try:
sol = zeroin(lower, upper, f, tolerance)
#sol = rtmcache(model, zeroin, (lower, upper, f, tolerance))
print '%d: %s in %d' % (index, str('%s %g' % (model['time'], sol)).ljust(28),
len(modelirrs))
except BadBoundsError as e:
print 'Bad Bounds at %s: %s; %s' % (model['time'], diffs, e.message)
err = 1
except NoConvergeError as e:
print 'NO CONVERGE'
err = 2
#print 'No convergence at %s: %s; %s\ngiven %s' % (model['time'], diffs, e.message, model)
except _rtm.RTMError as e:
print 'No output at %s: %s; %s' % (model['time'], diffs, e.message)
err = 3
return [model['time'], ref, modelirrs, sol, index, err]
def optimize(self, model, target_irradiance):
self.meta = {
'model': dict(model),
'parameter': self.parameter,
'target_irradiance': target_irradiance,
'iterations': {},
}
def f(x):
model.update({self.parameter: x})
diff = model.irradiance[self.irradiance] - target_irradiance
self.meta['iterations'].update({x: diff})
return diff
result = zeroin(self.bounds[0], self.bounds[1], f, self.tolerance)
self.meta['model'].update({self.parameter: result})
return result
def optimize(self, model, target_irradiance):
self.meta = {
'model': dict(model),
'parameter': self.parameter,
'target_irradiance': target_irradiance,
'iterations': {},
}
def f(x):
model.update({self.parameter: x})
diff = model.irradiance[self.irradiance] - target_irradiance
self.meta['iterations'].update({x: diff})
return diff
result = zeroin(self.bounds[0], self.bounds[1], f, self.tolerance)
self.meta['model'].update({self.parameter: result})
return result
