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Merge pull request #35 from fjosw/feature/padding

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Feature/padding
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Fabian Joswig 2022-01-18 15:08:37 +00:00 • committed by GitHub
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7 changed files with 49 additions and 44 deletions
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6
examples/03_pcac_example.ipynb
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29
examples/04_fit_example.ipynb
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2
pyerrors/__init__.py
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@ -206,7 +206,7 @@ print(my_corr)
```
In case the correlation functions are not defined on the outermost timeslices, for example because of fixed boundary conditions, a padding can be introduced.
```python
my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding_front=1, padding_back=1)
my_corr = pe.Corr([obs_0, obs_1, obs_2, obs_3], padding=[1, 1])
print(my_corr)
> x0/a Corr(x0/a)
> ------------------

48
pyerrors/correlators.py
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@ -19,46 +19,50 @@ class Corr:
to iterate over all timeslices for every operation. This is especially true, when dealing with smearing matrices.
The correlator can have two types of content: An Obs at every timeslice OR a GEVP
smearing matrix at every timeslice. Other dependency (eg. spacial) are not supported.
smearing matrix at every timeslice. Other dependency (eg. spatial) are not supported.
"""
def __init__(self, data_input, padding_front=0, padding_back=0, prange=None):
# All data_input should be a list of things at different timeslices. This needs to be verified
def __init__(self, data_input, padding=[0, 0], prange=None):
""" Initialize a Corr object.
Parameters
----------
data_input : list
list of Obs or list of arrays of Obs.
padding : list, optional
List with two entries where the first labels the padding
at the front of the correlator and the second the padding
at the back.
prange : list, optional
List containing the first and last timeslice of the plateau
region indentified for this correlator.
"""
if not isinstance(data_input, list):
raise TypeError('Corr__init__ expects a list of timeslices.')
# data_input can have multiple shapes. The simplest one is a list of Obs.
# We check, if this is the case
if all([isinstance(item, Obs) for item in data_input]):
self.content = [np.asarray([item]) for item in data_input]
# Wrapping the Obs in an array ensures that the data structure is consistent with smearing matrices.
self.N = 1 # number of smearings
self.N = 1
# data_input in the form [np.array(Obs,NxN)]
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]
# The checks are now identical to the case above
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: # In case its a list of something else.
else:
raise Exception("data_input contains item of wrong type")
self.tag = None
# 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.
# An undefined timeslice is represented by the None object
self.content = [None] * padding_front + self.content + [None] * padding_back
self.T = len(self.content) # for convenience: will be used a lot
self.content = [None] * padding[0] + self.content + [None] * padding[1]
self.T = len(self.content)
# The attribute "range" [start,end] marks a range of two timeslices.
# This is useful for keeping track of plateaus and fitranges.
# The range can be inherited from other Corrs, if the operation should not alter a chosen range eg. multiplication with a constant.
self.prange = prange
self.gamma_method()
@ -331,7 +335,7 @@ class Corr:
newcontent.append(self.content[t + 1] - self.content[t])
if(all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
return Corr(newcontent, padding_back=1)
return Corr(newcontent, padding=[0, 1])
if symmetric:
newcontent = []
for t in range(1, self.T - 1):
@ -341,7 +345,7 @@ class Corr:
newcontent.append(0.5 * (self.content[t + 1] - self.content[t - 1]))
if(all([x is None for x in newcontent])):
raise Exception('Derivative is undefined at all timeslices')
return Corr(newcontent, padding_back=1, padding_front=1)
return Corr(newcontent, padding=[1, 1])
def second_deriv(self):
"""Return the second derivative of the correlator with respect to x0."""
@ -353,7 +357,7 @@ class Corr:
newcontent.append((self.content[t + 1] - 2 * self.content[t] + self.content[t - 1]))
if(all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
return Corr(newcontent, padding_back=1, padding_front=1)
return Corr(newcontent, padding=[1, 1])
def m_eff(self, variant='log', guess=1.0):
"""Returns the effective mass of the correlator as correlator object
@ -381,7 +385,7 @@ class Corr:
if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding_back=1))
return np.log(Corr(newcontent, padding=[0, 1]))
elif variant in ['periodic', 'cosh', 'sinh']:
if variant in ['periodic', 'cosh']:
@ -404,7 +408,7 @@ class Corr:
if(all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return Corr(newcontent, padding_back=1)
return Corr(newcontent, padding=[0, 1])
elif variant == 'arccosh':
newcontent = []
@ -415,7 +419,7 @@ class Corr:
newcontent.append((self.content[t + 1] + self.content[t - 1]) / (2 * self.content[t]))
if(all([x is None for x in newcontent])):
raise Exception("m_eff is undefined at all timeslices")
return np.arccosh(Corr(newcontent, padding_back=1, padding_front=1))
return np.arccosh(Corr(newcontent, padding=[1, 1]))
else:
raise Exception('Unknown variant.')

2
pyerrors/input/json.py
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@ -402,7 +402,7 @@ def import_json_string(json_string, verbose=True, full_output=False):
if len(tmp_o['tag']) == 0:
del tmp_o['tag']
dat = get_Array_from_dict(tmp_o)
my_corr = Corr(list(dat), padding_front=padding_front, padding_back=padding_back)
my_corr = Corr(list(dat), padding=[padding_front, padding_back])
if corr_tag != 'None':
my_corr.tag = corr_tag
return my_corr

2
tests/correlators_test.py
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@ -115,7 +115,7 @@ def test_plateau():
def test_padded_correlator():
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
my_corr = pe.Corr(my_list, padding_front=7, padding_back=3)
my_corr = pe.Corr(my_list, padding=[7, 3])
my_corr.reweighted
[o for o in my_corr]

4
tests/io_test.py
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@ -101,7 +101,7 @@ def test_json_corr_io():
for fp in [0, 2]:
for bp in [0, 7]:
for corr_tag in [None, 'my_Corr_tag']:
my_corr = pe.Corr(obs_list, padding_front=fp, padding_back=bp)
my_corr = pe.Corr(obs_list, padding=[fp, bp])
my_corr.tag = corr_tag
pe.input.json.dump_to_json(my_corr, 'corr')
recover = pe.input.json.load_json('corr')
@ -116,7 +116,7 @@ def test_json_corr_2d_io():
for tag in [None, "test"]:
obs_list[3][0, 1].tag = tag
for padding in [0, 1]:
my_corr = pe.Corr(obs_list, padding_front=padding, padding_back=padding)
my_corr = pe.Corr(obs_list, padding=[padding, padding])
my_corr.tag = tag
pe.input.json.dump_to_json(my_corr, 'corr')
recover = pe.input.json.load_json('corr')