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docs: docstring added to Corr.__init__, comments cleaned up
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@ -19,46 +19,50 @@ 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=[0, 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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""" 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) 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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self.N = 1
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# data_input in the form [np.array(Obs,NxN)]
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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[0] + self.content + [None] * padding[1]
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self.T = len(self.content) # for convenience: will be used a lot
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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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