def fitExp(self, src, dest, b=None):
tmp = []
for d in self.data:
if d[src] > 0:
tmp.append((d[1],math.log(d[src]))) # ts, value
if b is None:
ef = function_fit.function_fit(tmp, lin_function.func())
else:
ef = function_fit.function_fit(tmp, parlin_function.func(_m=b))
sys.stderr.write("%s\n"%str(ef.fit()))
[b, a] = ef.f.getParList()
A = math.exp(a)
for d in self.data:
d[dest] = d[src] - A*math.exp(b * d[1])
lead_cols[tmp] = r[:icol]
padding = [NA for x in lead_cols[tmp]]
except ValueError, e:
print "Skipping text values (%s)"%','.join(r)
# fit type
if options.func_name == "lognorm":
func = lognormal_function.func()
elif options.func_name == "gauss":
func = gauss_function.func()
elif options.func_name == "exp":
func = exp_function.func()
elif options.func_name == "gamma":
func = gamma_function.func()
elif options.func_name == "lin":
func = lin_function.func()
else:
func = doubleexp_function.func()
fr = function_fit(d, func, p0)
# fit results
print fr.fit()
# blank line
print
wrt = csv.writer(sys.stdout)
# output is outer join of function and fit
d_dict = dict(d)
f_dict = dict(fr.eval(start=tstart, end=tend, points=points))
for time in sorted(list(set(d_dict.keys() + f_dict.keys()))):