From 0fc9f78f8c746a1c4a20cb0aa039a76d7fabc1c1 Mon Sep 17 00:00:00 2001 From: fjosw Date: Tue, 15 Feb 2022 13:53:46 +0000 Subject: [PATCH] Documentation updated --- docs/pyerrors/correlators.html | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/docs/pyerrors/correlators.html b/docs/pyerrors/correlators.html index ae4eab15..ed3542f9 100644 --- a/docs/pyerrors/correlators.html +++ b/docs/pyerrors/correlators.html @@ -306,8 +306,6 @@ self.T = len(self.content) self.prange = prange - self.gamma_method() - def __getitem__(self, idx): """Return the content of timeslice idx""" if self.content[idx] is None: @@ -348,8 +346,6 @@ if self.N == 1: raise Exception("Trying to project a Corr, that already has N=1.") - self.gamma_method() - if vector_l is None: vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.]) elif(vector_r is None): @@ -851,7 +847,6 @@ xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] result = least_squares(xs, ys, function, silent=silent, **kwargs) - result.gamma_method() return result def plateau(self, plateau_range=None, method="fit"): @@ -882,7 +877,6 @@ return self.fit(const_func, plateau_range)[0] elif method in ["avg", "average", "mean"]: returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None]) - returnvalue.gamma_method() return returnvalue else: @@ -1397,8 +1391,6 @@ self.T = len(self.content) self.prange = prange - self.gamma_method() - def __getitem__(self, idx): """Return the content of timeslice idx""" if self.content[idx] is None: @@ -1439,8 +1431,6 @@ if self.N == 1: raise Exception("Trying to project a Corr, that already has N=1.") - self.gamma_method() - if vector_l is None: vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.]) elif(vector_r is None): @@ -1942,7 +1932,6 @@ xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] result = least_squares(xs, ys, function, silent=silent, **kwargs) - result.gamma_method() return result def plateau(self, plateau_range=None, method="fit"): @@ -1973,7 +1962,6 @@ return self.fit(const_func, plateau_range)[0] elif method in ["avg", "average", "mean"]: returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None]) - returnvalue.gamma_method() return returnvalue else: @@ -2436,8 +2424,6 @@ matrix at every timeslice. Other dependency (eg. spatial) are not supported.

self.content = [None] * padding[0] + self.content + [None] * padding[1] self.T = len(self.content) self.prange = prange - - self.gamma_method() @@ -2520,8 +2506,6 @@ region indentified for this correlator. if self.N == 1: raise Exception("Trying to project a Corr, that already has N=1.") - self.gamma_method() - if vector_l is None: vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.]) elif(vector_r is None): @@ -3467,7 +3451,6 @@ guess for the root finder, only relevant for the root variant xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] result = least_squares(xs, ys, function, silent=silent, **kwargs) - result.gamma_method() return result @@ -3528,7 +3511,6 @@ Decides whether output is printed to the standard output. return self.fit(const_func, plateau_range)[0] elif method in ["avg", "average", "mean"]: returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None]) - returnvalue.gamma_method() return returnvalue else: