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JanNeuendorf
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8 changed files with 125 additions and 45 deletions
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@ -206,7 +206,7 @@ print(my_corr)
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```
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In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.
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```python
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my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding_front=1, padding_back=1)
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my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])
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print(my_corr)
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> x0/a Corr(x0/a)
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> ------------------
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@ -19,46 +19,55 @@ class Corr:
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to iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.
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The correlator can have two types of content: An Obs at every timeslice OR a GEVP
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smearing matrix at every timeslice. Other dependency (eg. spacial) are not supported.
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smearing matrix at every timeslice. Other dependency (eg. spatial) are not supported.
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"""
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def __init__(self, data_input, padding_front=0, padding_back=0, prange=None):
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# All data_input should be a list of things at different timeslices. This needs to be verified
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def __init__(self, data_input, padding=[0, 0], prange=None):
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""" Initialize a Corr object.
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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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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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at the back.
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prange : list, optional
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List containing the first and last timeslice of the plateau
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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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# data_input can have multiple shapes. The simplest one is a list of Obs.
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# We check, if this is the case
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if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]):
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self.content = [np.asarray([item]) for item in data_input]
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# Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices.
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self.N = 1 # number of smearings
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# data_input in the form [np.array(Obs,NxN)]
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self.content = [np.asarray([item]) 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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# The checks are now identical to the case above
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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: # In case its a list of something else.
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else:
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raise Exception("data_input contains item of wrong type")
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self.tag = None
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# We now apply some padding to our list. In case that our list represents a correlator of length T but is not defined at every value.
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# An undefined timeslice is represented by the None object
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self.content = [None] * padding_front + self.content + [None] * padding_back
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self.T = len(self.content) # for convenience: will be used a lot
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self.content = [None] * padding[0] + self.content + [None] * padding[1]
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self.T = len(self.content)
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# The attribute "range" [start,end] marks a range of two timeslices.
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# This is useful for keeping track of plateaus and fitranges.
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# The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant.
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self.prange = prange
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self.gamma_method()
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@ -406,7 +415,7 @@ class Corr:
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newcontent.append(self.content[t + 1] - self.content[t])
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if(all([x is None for x in newcontent])):
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raise Exception("Derivative is undefined at all timeslices")
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return Corr(newcontent, padding_back=1)
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return Corr(newcontent, padding=[0, 1])
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if symmetric:
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newcontent = []
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for t in range(1, self.T - 1):
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@ -416,7 +425,7 @@ class Corr:
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newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
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if(all([x is None for x in newcontent])):
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raise Exception('Derivative is undefined at all timeslices')
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return Corr(newcontent, padding_back=1, padding_front=1)
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return Corr(newcontent, padding=[1, 1])
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def second_deriv(self):
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"""Return the second derivative of the correlator with respect to x0."""
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@ -428,7 +437,7 @@ class Corr:
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newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
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if(all([x is None for x in newcontent])):
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raise Exception("Derivative is undefined at all timeslices")
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return Corr(newcontent, padding_back=1, padding_front=1)
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return Corr(newcontent, padding=[1, 1])
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def m_eff(self, variant='log', guess=1.0):
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"""Returns the effective mass of the correlator as correlator object
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@ -456,7 +465,7 @@ class Corr:
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if(all([x is None for x in newcontent])):
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raise Exception('m_eff is undefined at all timeslices')
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return np.log(Corr(newcontent, padding_back=1))
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return np.log(Corr(newcontent, padding=[0, 1]))
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elif variant in ['periodic', 'cosh', 'sinh']:
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if variant in ['periodic', 'cosh']:
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@ -479,7 +488,7 @@ class Corr:
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if(all([x is None for x in newcontent])):
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raise Exception('m_eff is undefined at all timeslices')
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return Corr(newcontent, padding_back=1)
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return Corr(newcontent, padding=[0, 1])
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elif variant == 'arccosh':
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newcontent = []
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@ -490,7 +499,7 @@ class Corr:
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newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
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if(all([x is None for x in newcontent])):
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raise Exception("m_eff is undefined at all timeslices")
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return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1))
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return np.arccosh(Corr(newcontent, padding=[1, 1]))
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else:
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raise Exception('Unknown variant.')
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@ -8,6 +8,7 @@ import platform
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import warnings
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from ..obs import Obs
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from ..covobs import Covobs
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from ..correlators import Corr
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from .. import version as pyerrorsversion
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@ -19,7 +20,7 @@ def create_json_string(ol, description='', indent=1):
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----------
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ol : list
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List of objects that will be exported. At the moments, these objects can be
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either of: Obs, list, numpy.ndarray.
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either of: Obs, list, numpy.ndarray, Corr.
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All Obs inside a structure have to be defined on the same set of configurations.
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description : str
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Optional string that describes the contents of the json file.
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@ -173,6 +174,18 @@ def create_json_string(ol, description='', indent=1):
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d['cdata'] = cdata
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return d
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def write_Corr_to_dict(my_corr):
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front_padding = next(i for i, j in enumerate(my_corr.content) if np.all(j))
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back_padding_start = front_padding + next((i for i, j in enumerate(my_corr.content[front_padding:]) if not np.all(j)), my_corr.T)
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dat = write_Array_to_dict(np.array(my_corr.content[front_padding:back_padding_start]))
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dat['type'] = 'Corr'
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corr_meta_data = str(front_padding) + '|' + str(my_corr.T - back_padding_start) + '|' + str(my_corr.tag)
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if 'tag' in dat.keys():
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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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return dat
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if not isinstance(ol, list):
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ol = [ol]
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@ -193,6 +206,10 @@ def create_json_string(ol, description='', indent=1):
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d['obsdata'].append(write_List_to_dict(io))
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elif isinstance(io, np.ndarray):
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d['obsdata'].append(write_Array_to_dict(io))
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elif isinstance(io, Corr):
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d['obsdata'].append(write_Corr_to_dict(io))
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else:
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raise Exception("Unkown datatype.")
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jsonstring = json.dumps(d, indent=indent, cls=my_encoder, ensure_ascii=False)
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@ -222,7 +239,7 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True):
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----------
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ol : list
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List of objects that will be exported. At the moments, these objects can be
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either of: Obs, list, numpy.ndarray.
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either of: Obs, list, numpy.ndarray, Corr.
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All Obs inside a structure have to be defined on the same set of configurations.
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fname : str
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Filename of the output file.
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@ -255,7 +272,7 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True):
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def import_json_string(json_string, verbose=True, full_output=False):
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"""Reconstruct a list of Obs or structures containing Obs from a json string.
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The following structures are supported: Obs, list, numpy.ndarray
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The following structures are supported: Obs, list, numpy.ndarray, Corr
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If the list contains only one element, it is unpacked from the list.
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Parameters
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@ -374,6 +391,22 @@ def import_json_string(json_string, verbose=True, full_output=False):
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ret[-1].tag = taglist[i]
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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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corr_meta_data = taglist[-1].split('|')
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padding_front = int(corr_meta_data[0])
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padding_back = int(corr_meta_data[1])
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corr_tag = corr_meta_data[2]
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tmp_o = o
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tmp_o['tag'] = taglist[:-1]
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if len(tmp_o['tag']) == 0:
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del tmp_o['tag']
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dat = get_Array_from_dict(tmp_o)
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my_corr = Corr(list(dat), padding=[padding_front, padding_back])
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if corr_tag != 'None':
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my_corr.tag = corr_tag
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return my_corr
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json_dict = json.loads(json_string)
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prog = json_dict.get('program', '')
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@ -400,6 +433,10 @@ def import_json_string(json_string, verbose=True, full_output=False):
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ol.append(get_List_from_dict(io))
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elif io['type'] == 'Array':
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ol.append(get_Array_from_dict(io))
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elif io['type'] == 'Corr':
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ol.append(get_Corr_from_dict(io))
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else:
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raise Exception("Unkown datatype.")
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if full_output:
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retd = {}
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@ -422,7 +459,7 @@ def import_json_string(json_string, verbose=True, full_output=False):
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def load_json(fname, verbose=True, gz=True, full_output=False):
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"""Import a list of Obs or structures containing Obs from a .json.gz file.
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The following structures are supported: Obs, list, numpy.ndarray
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The following structures are supported: Obs, list, numpy.ndarray, Corr
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If the list contains only one element, it is unpacked from the list.
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Parameters
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@ -115,7 +115,7 @@ def test_plateau():
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def test_padded_correlator():
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my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
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my_corr = pe.Corr(my_list, padding_front=7, padding_back=3)
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my_corr = pe.Corr(my_list, padding=[7, 3])
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my_corr.reweighted
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[o for o in my_corr]
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@ -89,3 +89,36 @@ def test_json_string_reconstruction():
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assert reconstructed_string == json_string
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assert my_obs == reconstructed_obs2
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def test_json_corr_io():
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my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
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rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
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for obs_list in [my_list, rw_list]:
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for tag in [None, "test"]:
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obs_list[3].tag = tag
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for fp in [0, 2]:
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for bp in [0, 7]:
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for corr_tag in [None, 'my_Corr_tag']:
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my_corr = pe.Corr(obs_list, padding=[fp, bp])
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my_corr.tag = corr_tag
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
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assert my_corr.tag == recover.tag
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assert my_corr.reweighted == recover.reweighted
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def test_json_corr_2d_io():
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obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(8)]
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for tag in [None, "test"]:
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obs_list[3][0, 1].tag = tag
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for padding in [0, 1]:
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my_corr = pe.Corr(obs_list, padding=[padding, padding])
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my_corr.tag = tag
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
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assert my_corr.tag == recover.tag
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@ -615,7 +615,7 @@ def test_covariance_symmetry():
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cov_ab = pe.covariance(test_obs1, a)
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cov_ba = pe.covariance(a, test_obs1)
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assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
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assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
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assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
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def test_empty_obs():
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