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[Fix] Docstrings and small issues in dirac and obs

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Fabian Joswig 2024-12-24 16:59:37 +01:00
parent 3eac9214b4
commit be22624f83
2 changed files with 27 additions and 21 deletions
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8
pyerrors/dirac.py
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@ -32,8 +32,8 @@ def epsilon_tensor(i, j, k):
elem : int elem : int
Element (i,j,k) of the epsilon tensor of rank 3 Element (i,j,k) of the epsilon tensor of rank 3
""" """
test_set = set((i, j, k)) test_set = {i, j, k}
if not (test_set <= set((1, 2, 3)) or test_set <= set((0, 1, 2))): if not (test_set <= {1, 2, 3} or test_set <= {0, 1, 2}):
raise ValueError("Unexpected input", i, j, k) raise ValueError("Unexpected input", i, j, k)
return (i - j) * (j - k) * (k - i) / 2 return (i - j) * (j - k) * (k - i) / 2
@ -50,8 +50,8 @@ def epsilon_tensor_rank4(i, j, k, o):
elem : int elem : int
Element (i,j,k,o) of the epsilon tensor of rank 4 Element (i,j,k,o) of the epsilon tensor of rank 4
""" """
test_set = set((i, j, k, o)) test_set = {i, j, k, o}
if not (test_set <= set((1, 2, 3, 4)) or test_set <= set((0, 1, 2, 3))): if not (test_set <= {1, 2, 3, 4} or test_set <= {0, 1, 2, 3}):
raise ValueError("Unexpected input", i, j, k, o) raise ValueError("Unexpected input", i, j, k, o)
return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12 return (i - j) * (j - k) * (k - i) * (i - o) * (j - o) * (o - k) / 12

40
pyerrors/obs.py
View file

@ -112,7 +112,7 @@ class Obs:
else: else:
self.idl[name] = list(idx) self.idl[name] = list(idx)
else: else:
raise TypeError('incompatible type for idl[%s].' % (name)) raise TypeError('incompatible type for idl[%s].' % name)
else: else:
for name, sample in sorted(zip(names, samples)): for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1) self.idl[name] = range(1, len(sample) + 1)
@ -388,7 +388,7 @@ class Obs:
if self.tag is not None: if self.tag is not None:
print("Description:", self.tag) print("Description:", self.tag)
if not hasattr(self, 'e_dvalue'): if not hasattr(self, 'e_dvalue'):
print('Result\t %3.8e' % (self.value)) print('Result\t %3.8e' % self.value)
else: else:
if self.value == 0.0: if self.value == 0.0:
percentage = np.nan percentage = np.nan
@ -446,7 +446,7 @@ class Obs:
my_string_list.append(my_string) my_string_list.append(my_string)
print('\n'.join(my_string_list)) print('\n'.join(my_string_list))
def reweight(self, weight): def reweight(self, weight, all_configs=False):
"""Reweight the obs with given rewighting factors. """Reweight the obs with given rewighting factors.
Parameters Parameters
@ -459,7 +459,7 @@ class Obs:
the reweighting factor on all configurations in weight.idl and not the reweighting factor on all configurations in weight.idl and not
on the configurations in obs[i].idl. Default False. on the configurations in obs[i].idl. Default False.
""" """
return reweight(weight, [self])[0] return reweight(weight, [self], all_configs=all_configs)[0]
def is_zero_within_error(self, sigma=1): def is_zero_within_error(self, sigma=1):
"""Checks whether the observable is zero within 'sigma' standard errors. """Checks whether the observable is zero within 'sigma' standard errors.
@ -1078,7 +1078,7 @@ def _expand_deltas(deltas, idx, shape, gapsize):
are found in idx, the data is expanded. are found in idx, the data is expanded.
""" """
if isinstance(idx, range): if isinstance(idx, range):
if (idx.step == gapsize): if idx.step == gapsize:
return deltas return deltas
ret = np.zeros((idx[-1] - idx[0] + gapsize) // gapsize) ret = np.zeros((idx[-1] - idx[0] + gapsize) // gapsize)
for i in range(shape): for i in range(shape):
@ -1188,6 +1188,10 @@ def derived_observable(func, data, array_mode=False, **kwargs):
of func. Use cautiously, supplying the wrong derivative will of func. Use cautiously, supplying the wrong derivative will
not be intercepted. not be intercepted.
array_mode: bool
If True, the function is applied to the full array of data.
Default: False
Notes Notes
----- -----
For simple mathematical operations it can be practical to use anonymous For simple mathematical operations it can be practical to use anonymous
@ -1210,7 +1214,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
for name in o.cov_names: for name in o.cov_names:
if name in allcov: if name in allcov:
if not np.allclose(allcov[name], o.covobs[name].cov): if not np.allclose(allcov[name], o.covobs[name].cov):
raise Exception('Inconsistent covariance matrices for %s!' % (name)) raise Exception('Inconsistent covariance matrices for %s!' % name)
else: else:
allcov[name] = o.covobs[name].cov allcov[name] = o.covobs[name].cov
@ -1260,7 +1264,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
for mc_name in obs.mc_names: for mc_name in obs.mc_names:
mc_idl_d = [name for name in obs.idl if name.startswith(mc_name + '|')] mc_idl_d = [name for name in obs.idl if name.startswith(mc_name + '|')]
new_mc_idl_d = [name for name in new_idl_d if name.startswith(mc_name + '|')] new_mc_idl_d = [name for name in new_idl_d if name.startswith(mc_name + '|')]
if len(mc_idl_d) > 0 and len(mc_idl_d) < len(new_mc_idl_d): if 0 < len(mc_idl_d) < len(new_mc_idl_d):
scalef_d[mc_name] = sum([len(new_idl_d[name]) for name in new_mc_idl_d]) / sum([len(new_idl_d[name]) for name in mc_idl_d]) scalef_d[mc_name] = sum([len(new_idl_d[name]) for name in new_mc_idl_d]) / sum([len(new_idl_d[name]) for name in mc_idl_d])
return scalef_d return scalef_d
@ -1386,7 +1390,7 @@ def _reduce_deltas(deltas, idx_old, idx_new):
return np.array(deltas)[indices] return np.array(deltas)[indices]
def reweight(weight, obs, **kwargs): def reweight(weight, obs, all_configs=False):
"""Reweight a list of observables. """Reweight a list of observables.
Parameters Parameters
@ -1417,7 +1421,7 @@ def reweight(weight, obs, **kwargs):
new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name])) new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))
tmp_obs = Obs(new_samples, sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)]) tmp_obs = Obs(new_samples, sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
if kwargs.get('all_configs'): if all_configs:
new_weight = weight new_weight = weight
else: else:
new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(obs[i].names)], sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)]) new_weight = Obs([w_deltas[name] + weight.r_values[name] for name in sorted(obs[i].names)], sorted(obs[i].names), idl=[obs[i].idl[name] for name in sorted(obs[i].names)])
@ -1471,8 +1475,8 @@ def correlate(obs_a, obs_b):
return o return o
def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs): def covariance(obs, visualize=False, correlation=False, smooth=None):
r'''Calculates the error covariance matrix of a set of observables. r"""Calculates the error covariance matrix of a set of observables.
WARNING: This function should be used with care, especially for observables with support on multiple WARNING: This function should be used with care, especially for observables with support on multiple
ensembles with differing autocorrelations. See the notes below for details. ensembles with differing autocorrelations. See the notes below for details.
@ -1503,7 +1507,7 @@ def covariance(obs, visualize=False, correlation=False, smooth=None, **kwargs):
For observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements. For observables defined on a single ensemble our approximation is equivalent to assuming that the integrated autocorrelation time of an off-diagonal element is equal to the geometric mean of the integrated autocorrelation times of the corresponding diagonal elements.
$$\tau_{\mathrm{int}, ij}=\sqrt{\tau_{\mathrm{int}, i}\times \tau_{\mathrm{int}, j}}$$ $$\tau_{\mathrm{int}, ij}=\sqrt{\tau_{\mathrm{int}, i}\times \tau_{\mathrm{int}, j}}$$
This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors). This construction ensures that the estimated covariance matrix is positive semi-definite (up to numerical rounding errors).
''' """
length = len(obs) length = len(obs)
@ -1557,7 +1561,7 @@ def invert_corr_cov_cholesky(corr, inverrdiag):
if condn > 0.1 / np.finfo(float).eps: if condn > 0.1 / np.finfo(float).eps:
raise ValueError(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})") raise ValueError(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1e13: if condn > 1e13:
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning) warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % condn, RuntimeWarning)
chol = np.linalg.cholesky(corr) chol = np.linalg.cholesky(corr)
chol_inv = scipy.linalg.solve_triangular(chol, inverrdiag, lower=True) chol_inv = scipy.linalg.solve_triangular(chol, inverrdiag, lower=True)
@ -1714,6 +1718,8 @@ def import_jackknife(jacks, name, idl=None):
the N jackknife samples as first to Nth entry. the N jackknife samples as first to Nth entry.
name : str name : str
name of the ensemble the samples are defined on. name of the ensemble the samples are defined on.
idl : list, optional
list of ranges or lists on which the samples are defined
""" """
length = len(jacks) - 1 length = len(jacks) - 1
prj = (np.ones((length, length)) - (length - 1) * np.identity(length)) prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
@ -1789,7 +1795,7 @@ def cov_Obs(means, cov, name, grad=None):
Parameters Parameters
---------- ----------
mean : list of floats or float means : list of floats or float
N mean value(s) of the new Obs N mean value(s) of the new Obs
cov : list or array cov : list or array
2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance 2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
@ -1821,7 +1827,7 @@ def cov_Obs(means, cov, name, grad=None):
for i in range(len(means)): for i in range(len(means)):
ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad))) ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
if ol[0].covobs[name].N != len(means): if ol[0].covobs[name].N != len(means):
raise ValueError('You have to provide %d mean values!' % (ol[0].N)) raise ValueError('You have to provide %d mean values!' % ol[0].N)
if len(ol) == 1: if len(ol) == 1:
return ol[0] return ol[0]
return ol return ol
@ -1843,14 +1849,14 @@ def _determine_gap(o, e_content, e_name):
def _check_lists_equal(idl): def _check_lists_equal(idl):
''' """
Use groupby to efficiently check whether all elements of idl are identical. Use groupby to efficiently check whether all elements of idl are identical.
Returns True if all elements are equal, otherwise False. Returns True if all elements are equal, otherwise False.
Parameters Parameters
---------- ----------
idl : list of lists, ranges or np.ndarrays idl : list of lists, ranges or np.ndarrays
''' """
g = groupby([np.nditer(el) if isinstance(el, np.ndarray) else el for el in idl]) g = groupby([np.nditer(el) if isinstance(el, np.ndarray) else el for el in idl])
if next(g, True) and not next(g, False): if next(g, True) and not next(g, False):
return True return True