Documentation extended, periodic effective mass changed

This commit is contained in:
Fabian Joswig 2021-09-29 12:55:19 +01:00
parent d38bc656df
commit b48326244e
2 changed files with 19 additions and 18 deletions

View file

@ -204,8 +204,7 @@ class Corr:
Parameters
----------
variant -- log: uses the standard effective mass log(C(t) / C(t+1))
periodic : uses arccosh((C(t+1)+C(t-1)) / (2C(t))
root : Solves C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m
periodic : Solves C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m. See, e.g., arXiv:1205.5380
guess -- guess for the root finder, only relevant for the root variant
"""
if self.N != 1:
@ -222,17 +221,7 @@ class Corr:
return np.log(Corr(newcontent, padding_back=1))
elif variant is 'periodic': # This is usually not very stable.
newcontent = []
for t in range(1, self.T - 1):
if (self.content[t] is None) or (self.content[t + 1] is None)or (self.content[t - 1] is None):
newcontent.append(None)
else:
newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1))
elif variant is 'root':
elif variant is 'periodic':
newcontent = []
for t in range(self.T - 1):
if (self.content[t] is None) or (self.content[t + 1] is None):
@ -248,7 +237,7 @@ class Corr:
raise Exception('Unkown variant.')
#We want to apply a pe.standard_fit directly to the Corr using an arbitrary function and range.
def fit(self, function, fitrange=None, silent=False):
def fit(self, function, fitrange=None, silent=False, **kwargs):
if self.N != 1:
raise Exception("Correlator must be projected before fitting")
@ -257,8 +246,13 @@ class Corr:
xs = [x for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1]) if not self.content[x] is None]
result = standard_fit(xs, ys, function, silent=silent)
[item.gamma_method() for item in result if isinstance(item,Obs)]
result = standard_fit(xs, ys, function, silent=silent, **kwargs)
if isinstance(result, list):
[item.gamma_method() for item in result if isinstance(item,Obs)]
elif isinstance(result, dict):
[item.gamma_method() for item in result['fit_parameters'] if isinstance(item,Obs)]
else:
raise Exception('Unexpected fit result.')
return result
#we want to quickly get a plateau
@ -282,7 +276,7 @@ class Corr:
#quick and dirty plotting function to view Correlator inside Jupyter
#If one would not want to import pyplot, this could easily be replaced by a call to pe.plot_corrs
#This might be a bit more flexible later
def show(self, x_range=None, comp=None, logscale=False, plateau=None, save=None):
def show(self, x_range=None, comp=None, logscale=False, plateau=None, fit_res=None, save=None):
"""Plots the correlator, uses tag as label if available.
Parameters
@ -325,6 +319,12 @@ class Corr:
else:
raise Exception('plateau must be an Obs')
if fit_res:
x_samples = np.arange(x_range[0], x_range[1] + 1, 0.05)
ax1.plot(x_samples,
fit_res['fit_function']([o.value for o in fit_res['fit_parameters']], x_samples)
, ls='-', marker=',', lw=2)
ax1.set_xlabel(r'$x_0 / a$')
ax1.set_xlim([x_range[0] - 0.5, x_range[1] + 0.5])

View file

@ -11,11 +11,12 @@ def find_root(d, func, guess=1.0, **kwargs):
Parameters
-----------------
d -- Obs passed to the function.
func -- Function to be minimized.
func -- Function to be minimized. Any numpy functions have to use the autograd.numpy wrapper
guess -- Initial guess for the minimization.
"""
root = scipy.optimize.fsolve(func, guess, d.value)
# Error propagation as detailed in arXiv:1809.01289
dx = jacobian(func)(root[0], d.value)
da = jacobian(lambda u, v : func(v, u))(d.value, root[0])
deriv = - da / dx