mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 14:50:25 +01:00
Merge branch 'develop' of https://github.com/JanNeuendorf/pyerrors into develop
This commit is contained in:
JanNeuendorf
commit
05f5720876
8 changed files with 125 additions and 45 deletions
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -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)
|
||||
> ------------------
|
||||
|
|
|
|||
|
|
@ -19,46 +19,55 @@ 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) or isinstance(item, CObs)) 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
|
||||
|
||||
# data_input in the form [np.array(Obs,NxN)]
|
||||
|
||||
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]
|
||||
# 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()
|
||||
|
|
@ -406,7 +415,7 @@ 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):
|
||||
|
|
@ -416,7 +425,7 @@ 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."""
|
||||
|
|
@ -428,7 +437,7 @@ 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
|
||||
|
|
@ -456,7 +465,7 @@ 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']:
|
||||
|
|
@ -479,7 +488,7 @@ 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 = []
|
||||
|
|
@ -490,7 +499,7 @@ 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.')
|
||||
|
|
|
|||
|
|
@ -8,6 +8,7 @@ import platform
|
|||
import warnings
|
||||
from ..obs import Obs
|
||||
from ..covobs import Covobs
|
||||
from ..correlators import Corr
|
||||
from .. import version as pyerrorsversion
|
||||
|
||||
|
||||
|
|
@ -19,7 +20,7 @@ def create_json_string(ol, description='', indent=1):
|
|||
----------
|
||||
ol : list
|
||||
List of objects that will be exported. At the moments, these objects can be
|
||||
either of: Obs, list, numpy.ndarray.
|
||||
either of: Obs, list, numpy.ndarray, Corr.
|
||||
All Obs inside a structure have to be defined on the same set of configurations.
|
||||
description : str
|
||||
Optional string that describes the contents of the json file.
|
||||
|
|
@ -173,6 +174,18 @@ def create_json_string(ol, description='', indent=1):
|
|||
d['cdata'] = cdata
|
||||
return d
|
||||
|
||||
def write_Corr_to_dict(my_corr):
|
||||
front_padding = next(i for i, j in enumerate(my_corr.content) if np.all(j))
|
||||
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)
|
||||
dat = write_Array_to_dict(np.array(my_corr.content[front_padding:back_padding_start]))
|
||||
dat['type'] = 'Corr'
|
||||
corr_meta_data = str(front_padding) + '|' + str(my_corr.T - back_padding_start) + '|' + str(my_corr.tag)
|
||||
if 'tag' in dat.keys():
|
||||
dat['tag'].append(corr_meta_data)
|
||||
else:
|
||||
dat['tag'] = [corr_meta_data]
|
||||
return dat
|
||||
|
||||
if not isinstance(ol, list):
|
||||
ol = [ol]
|
||||
|
||||
|
|
@ -193,6 +206,10 @@ def create_json_string(ol, description='', indent=1):
|
|||
d['obsdata'].append(write_List_to_dict(io))
|
||||
elif isinstance(io, np.ndarray):
|
||||
d['obsdata'].append(write_Array_to_dict(io))
|
||||
elif isinstance(io, Corr):
|
||||
d['obsdata'].append(write_Corr_to_dict(io))
|
||||
else:
|
||||
raise Exception("Unkown datatype.")
|
||||
|
||||
jsonstring = json.dumps(d, indent=indent, cls=my_encoder, ensure_ascii=False)
|
||||
|
||||
|
|
@ -222,7 +239,7 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True):
|
|||
----------
|
||||
ol : list
|
||||
List of objects that will be exported. At the moments, these objects can be
|
||||
either of: Obs, list, numpy.ndarray.
|
||||
either of: Obs, list, numpy.ndarray, Corr.
|
||||
All Obs inside a structure have to be defined on the same set of configurations.
|
||||
fname : str
|
||||
Filename of the output file.
|
||||
|
|
@ -255,7 +272,7 @@ def dump_to_json(ol, fname, description='', indent=1, gz=True):
|
|||
def import_json_string(json_string, verbose=True, full_output=False):
|
||||
"""Reconstruct a list of Obs or structures containing Obs from a json string.
|
||||
|
||||
The following structures are supported: Obs, list, numpy.ndarray
|
||||
The following structures are supported: Obs, list, numpy.ndarray, Corr
|
||||
If the list contains only one element, it is unpacked from the list.
|
||||
|
||||
Parameters
|
||||
|
|
@ -374,6 +391,22 @@ def import_json_string(json_string, verbose=True, full_output=False):
|
|||
ret[-1].tag = taglist[i]
|
||||
return np.reshape(ret, layout)
|
||||
|
||||
def get_Corr_from_dict(o):
|
||||
taglist = o.get('tag')
|
||||
corr_meta_data = taglist[-1].split('|')
|
||||
padding_front = int(corr_meta_data[0])
|
||||
padding_back = int(corr_meta_data[1])
|
||||
corr_tag = corr_meta_data[2]
|
||||
tmp_o = o
|
||||
tmp_o['tag'] = taglist[:-1]
|
||||
if len(tmp_o['tag']) == 0:
|
||||
del tmp_o['tag']
|
||||
dat = get_Array_from_dict(tmp_o)
|
||||
my_corr = Corr(list(dat), padding=[padding_front, padding_back])
|
||||
if corr_tag != 'None':
|
||||
my_corr.tag = corr_tag
|
||||
return my_corr
|
||||
|
||||
json_dict = json.loads(json_string)
|
||||
|
||||
prog = json_dict.get('program', '')
|
||||
|
|
@ -400,6 +433,10 @@ def import_json_string(json_string, verbose=True, full_output=False):
|
|||
ol.append(get_List_from_dict(io))
|
||||
elif io['type'] == 'Array':
|
||||
ol.append(get_Array_from_dict(io))
|
||||
elif io['type'] == 'Corr':
|
||||
ol.append(get_Corr_from_dict(io))
|
||||
else:
|
||||
raise Exception("Unkown datatype.")
|
||||
|
||||
if full_output:
|
||||
retd = {}
|
||||
|
|
@ -422,7 +459,7 @@ def import_json_string(json_string, verbose=True, full_output=False):
|
|||
def load_json(fname, verbose=True, gz=True, full_output=False):
|
||||
"""Import a list of Obs or structures containing Obs from a .json.gz file.
|
||||
|
||||
The following structures are supported: Obs, list, numpy.ndarray
|
||||
The following structures are supported: Obs, list, numpy.ndarray, Corr
|
||||
If the list contains only one element, it is unpacked from the list.
|
||||
|
||||
Parameters
|
||||
|
|
|
|||
|
|
@ -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]
|
||||
|
||||
|
|
|
|||
|
|
@ -89,3 +89,36 @@ def test_json_string_reconstruction():
|
|||
|
||||
assert reconstructed_string == json_string
|
||||
assert my_obs == reconstructed_obs2
|
||||
|
||||
|
||||
def test_json_corr_io():
|
||||
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
|
||||
rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
|
||||
|
||||
for obs_list in [my_list, rw_list]:
|
||||
for tag in [None, "test"]:
|
||||
obs_list[3].tag = tag
|
||||
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=[fp, bp])
|
||||
my_corr.tag = corr_tag
|
||||
pe.input.json.dump_to_json(my_corr, 'corr')
|
||||
recover = pe.input.json.load_json('corr')
|
||||
assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
|
||||
assert my_corr.tag == recover.tag
|
||||
assert my_corr.reweighted == recover.reweighted
|
||||
|
||||
|
||||
def test_json_corr_2d_io():
|
||||
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)]
|
||||
|
||||
for tag in [None, "test"]:
|
||||
obs_list[3][0, 1].tag = tag
|
||||
for padding in [0, 1]:
|
||||
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')
|
||||
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]])
|
||||
assert my_corr.tag == recover.tag
|
||||
|
|
|
|||
|
|
@ -615,7 +615,7 @@ def test_covariance_symmetry():
|
|||
cov_ab = pe.covariance(test_obs1, a)
|
||||
cov_ba = pe.covariance(a, test_obs1)
|
||||
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
|
||||
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
|
||||
assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
|
||||
|
||||
|
||||
def test_empty_obs():
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue