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Merge pull request #48 from JanNeuendorf/develop
Changes in the correlator init, GEVP and docstrings & tag format includes dict and prange
This commit is contained in:
Fabian Joswig
GitHub
commit
6cdb9a2042
2 changed files with 95 additions and 23 deletions
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@ -28,8 +28,8 @@ class Corr:
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Parameters
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----------
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data_input : list
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list of Obs or list of arrays of Obs.
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data_input : list or array
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list of Obs or list of arrays of Obs or array of Corrs
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padding : list, optional
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List with two entries where the first labels the padding
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at the front of the correlator and the second the padding
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@ -39,25 +39,56 @@ class Corr:
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region indentified for this correlator.
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"""
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if not isinstance(data_input, list):
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raise TypeError('Corr__init__ expects a list of timeslices.')
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if isinstance(data_input, np.ndarray): # Input is an array of Corrs
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if all([(isinstance(item, Obs) or isinstance(item, CObs)) or item is None for item in data_input]):
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_assert_equal_properties([o for o in data_input if o is not None])
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self.content = [np.asarray([item]) if item is not None else None for item in data_input]
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self.N = 1
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# This only works, if the array fulfills the conditions below
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if not len(data_input.shape) == 2 and data_input.shape[0] == data_input.shape[1]:
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raise Exception("Incompatible array shape")
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if not all([isinstance(item, Corr) for item in data_input.flatten()]):
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raise Exception("If the input is an array, its elements must be of type pe.Corr")
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if not all([item.N == 1 for item in data_input.flatten()]):
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raise Exception("Can only construct matrix correlator from single valued correlators")
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if not len(set([item.T for item in data_input.flatten()])) == 1:
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raise Exception("All input Correlators must be defined over the same timeslices.")
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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]):
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self.content = data_input
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T = data_input[0, 0].T
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N = data_input.shape[0]
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input_as_list = []
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for t in range(T):
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if any([(item.content[t][0] is None) for item in data_input.flatten()]):
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if not all([(item.content[t][0] is None) for item in data_input.flatten()]):
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warnings.warn("Input ill-defined at different timeslices. Conversion leads to data loss!", RuntimeWarning)
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input_as_list.append(None)
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else:
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array_at_timeslace = np.empty([N, N], dtype="object")
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for i in range(N):
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for j in range(N):
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array_at_timeslace[i, j] = data_input[i, j][t]
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input_as_list.append(array_at_timeslace)
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data_input = input_as_list
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noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements
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self.N = noNull[0].shape[0]
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if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
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raise Exception("Smearing matrices are not NxN")
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if (not all([item.shape == noNull[0].shape for item in noNull])):
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raise Exception("Items in data_input are not of identical shape." + str(noNull))
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if isinstance(data_input, list):
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if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]):
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_assert_equal_properties(data_input)
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self.content = [np.asarray([item]) for item in data_input]
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if all([(isinstance(item, Obs) or isinstance(item, CObs)) or item is None for item in data_input]):
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_assert_equal_properties([o for o in data_input if o is not None])
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self.content = [np.asarray([item]) if item is not None else None for item in data_input]
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self.N = 1
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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]):
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self.content = data_input
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noNull = [a for a in self.content if not (a is None)] # To check if the matrices are correct for all undefined elements
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self.N = noNull[0].shape[0]
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if self.N > 1 and noNull[0].shape[0] != noNull[0].shape[1]:
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raise Exception("Smearing matrices are not NxN")
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if (not all([item.shape == noNull[0].shape for item in noNull])):
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raise Exception("Items in data_input are not of identical shape." + str(noNull))
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else:
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raise Exception("data_input contains item of wrong type")
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else:
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raise Exception("data_input contains item of wrong type")
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raise Exception("Data input was not given as list or correct array")
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self.tag = None
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@ -214,8 +245,27 @@ class Corr:
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# There are two ways, the GEVP metod can be called.
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# 1. return_list=False will return a single eigenvector, normalized according to V*C(t_0)*V=1
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# 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]
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def GEVP(self, t0, ts, state=0, sorting="Eigenvalue", return_list=False):
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if not return_list:
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def GEVP(self, t0, ts=None, state=0, sorted_list=None):
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"""Solve the general eigenvalue problem on the current correlator
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Parameters
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----------
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t0 : int
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The time t0 for G(t)v= lambda G(t_0)v
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ts : int
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fixed time G(t_s)v= lambda G(t_0)v if return_list=False
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If return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
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state : int
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The state one is interested in ordered by energy. The lowest state is zero.
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sorted list : string
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if this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.
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"Eigenvalue" - The eigenvector is chosen according to which einvenvalue it belongs individually on every timeslice.
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"Eigenvector" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.
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The referense state is identified by its eigenvalue at t=ts
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"""
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if sorted_list is None:
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if (ts is None):
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raise Exception("ts is required if return_list=False")
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if (self.content[t0] is None) or (self.content[ts] is None):
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raise Exception("Corr not defined at t0/ts")
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G0, Gt = np.empty([self.N, self.N], dtype="double"), np.empty([self.N, self.N], dtype="double")
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@ -227,7 +277,8 @@ class Corr:
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sp_vecs = _GEVP_solver(Gt, G0)
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sp_vec = sp_vecs[state]
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return sp_vec
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if return_list:
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else:
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all_vecs = []
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for t in range(self.T):
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try:
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@ -238,14 +289,16 @@ class Corr:
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Gt[i, j] = self.content[t][i, j].value
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sp_vecs = _GEVP_solver(Gt, G0)
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if sorting == "Eigenvalue":
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if sorted_list == "Eigenvalue":
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sp_vec = sp_vecs[state]
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all_vecs.append(sp_vec)
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else:
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all_vecs.append(sp_vecs)
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except Exception:
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all_vecs.append(None)
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if sorting == "Eigenvector":
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if sorted_list == "Eigenvector":
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if (ts is None):
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raise Exception("ts is required for the Eigenvector sorting method.")
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all_vecs = _sort_vectors(all_vecs, ts)
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all_vecs = [a[state] for a in all_vecs]
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@ -186,6 +186,11 @@ def create_json_string(ol, description='', indent=1):
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dat['tag'].append(corr_meta_data)
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else:
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dat['tag'] = [corr_meta_data]
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taglist = dat['tag']
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dat['tag'] = {} # tag is now a dictionary, that contains the previous taglist in the key "tag"
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dat['tag']['tag'] = taglist
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if my_corr.prange is not None:
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dat['tag']['prange'] = my_corr.prange
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return dat
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if not isinstance(ol, list):
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@ -395,7 +400,19 @@ def import_json_string(json_string, verbose=True, full_output=False):
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return np.reshape(ret, layout)
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def get_Corr_from_dict(o):
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taglist = o.get('tag')
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if isinstance(o.get('tag'), list): # supports the old way
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taglist = o.get('tag') # This had to be modified to get the taglist from the dictionary
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temp_prange = None
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elif isinstance(o.get('tag'), dict):
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tagdic = o.get('tag')
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taglist = tagdic['tag']
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if 'prange' in tagdic:
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temp_prange = tagdic['prange']
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else:
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temp_prange = None
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else:
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raise Exception("The tag is not a list or dict")
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corr_tag = taglist[-1]
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tmp_o = o
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tmp_o['tag'] = taglist[:-1]
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@ -405,6 +422,8 @@ def import_json_string(json_string, verbose=True, full_output=False):
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my_corr = Corr([None if np.isnan(o.ravel()[0].value) else o for o in list(dat)])
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if corr_tag != 'None':
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my_corr.tag = corr_tag
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my_corr.prange = temp_prange
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return my_corr
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json_dict = json.loads(json_string)
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