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Only the changes in the correlator init, GEVP and docstrings

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JanNeuendorf 2022-01-28 10:46:02 +01:00
commit 50b503b838
1 changed files with 70 additions and 18 deletions
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pyerrors/correlators.py
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@ -28,8 +28,8 @@ class Corr:
Parameters
----------
data_input : list
list of Obs or list of arrays of Obs.
data_input : list or array
list of Obs or list of arrays of Obs or array of Corrs
padding : list, optional
List with two entries where the first labels the padding
at the front of the correlator and the second the padding
@ -39,25 +39,53 @@ class Corr:
region indentified for this correlator.
"""
if not isinstance(data_input, list):
raise TypeError('Corr__init__ expects a list of timeslices.')
if isinstance(data_input, np.ndarray): # Input is an array of Corrs
if all([(isinstance(item, Obs) or isinstance(item, CObs)) or item is None for item in data_input]):
_assert_equal_properties([o for o in data_input if o is not None])
self.content = [np.asarray([item]) if item is not None else None for item in data_input]
self.N = 1
# This only works, if the array fulfills the conditions below
if not len(data_input.shape) == 2 and data_input.shape[0] == data_input.shape[1]:
raise Exception("Incompatible array shape")
if not all([isinstance(item, Corr) for item in data_input.flatten()]):
raise Exception("If the input is an array, its elements must be of type pe.Corr")
if not all([item.N == 1 for item in data_input.flatten()]):
raise Exception("Can only construct matrix correlator from single valued correlators")
if not len(set([item.T for item in data_input.flatten()])) == 1:
raise Exception("All input Correlators must be defined over the same timeslices.")
elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]):
self.content = data_input
T = data_input[0, 0].T
N = data_input.shape[0]
input_as_list = []
for t in range(T):
if any([(item.content[t][0] is None) for item in data_input.flatten()]):
if not all([(item.content[t][0] is None) for item in data_input.flatten()]):
warnings.warn("Input ill-defined at different timeslices. Conversion leads to data loss!", RuntimeWarning)
input_as_list.append(None)
else:
array_at_timeslace = np.empty([N, N], dtype="object")
for i in range(N):
for j in range(N):
array_at_timeslace[i, j] = data_input[i, j][t]
input_as_list.append(array_at_timeslace)
data_input = input_as_list
noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements
self.N = noNull[0].shape[0]
if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise Exception("Smearing matrices are not NxN")
if (not all([item.shape == noNull[0].shape for item in noNull])):
raise Exception("Items in data_input are not of identical shape." + str(noNull))
if isinstance(data_input, list):
if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]):
_assert_equal_properties(data_input)
self.content = [np.asarray([item]) for item in data_input]
self.N = 1
elif all([isinstance(item, np.ndarray) or item is None for item in data_input]) and any([isinstance(item, np.ndarray) for item in data_input]):
self.content = data_input
noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements
self.N = noNull[0].shape[0]
if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
raise Exception("Smearing matrices are not NxN")
if (not all([item.shape == noNull[0].shape for item in noNull])):
raise Exception("Items in data_input are not of identical shape." + str(noNull))
else:
raise Exception("data_input contains item of wrong type")
else:
raise Exception("data_input contains item of wrong type")
raise Exception("Data input was not given as list or correct array")
self.tag = None
@ -214,8 +242,30 @@ class Corr:
# There are two ways, the GEVP metod can be called.
# 1. return_list=False will return a single eigenvector, normalized according to V*C(t_0)*V=1
# 2. return_list=True will return a new eigenvector for every timeslice. The time t_s is used to order the vectors according to. arXiv:2004.10472 [hep-lat]
def GEVP(self, t0, ts, state=0, sorting="Eigenvalue", return_list=False):
def GEVP(self, t0, ts=None, state=0, return_list=False, sorting="Eigenvalue"):
"""Solve the general eigenvalue problem on the current correlator
Parameters
----------
t0 : int
The time t0 for G(t)v= lambda G(t_0)v
ts : int
fixed time G(t_s)v= lambda G(t_0)v if return_list=False
If return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
state : int
The state one is interested in ordered by energy. The lowest state is zero.
return_list : bool
If False - The vector $v$ with G(t_s)v= lambda_state G(t_0)v is returned.
If True - The GEVP is solved once per timeslice and a list (len=T) of vectors is returned.
sorting : string
Only matters if return_list=True. Determines how the vectors returned at every timeslice are chosen.
"Eigenvalue" - The eigenvector is chosen according to which einvenvalue it belongs individually on every timeslice.
"Eigenvector" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.
The referense state is identified by its eigenvalue at t=ts
"""
if not return_list:
if (ts is None):
raise Exception("ts is required if return_list=False")
if (self.content[t0] is None) or (self.content[ts] is None):
raise Exception("Corr not defined at t0/ts")
G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
@ -246,6 +296,8 @@ class Corr:
except Exception:
all_vecs.append(None)
if sorting == "Eigenvector":
if (ts is None):
raise Exception("ts is required for the Eigenvector sorting method.")
all_vecs = _sort_vectors(all_vecs, ts)
all_vecs = [a[state] for a in all_vecs]