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Merge branch 'develop' into feature/eigenvalue_smoothing

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Fabian Joswig 2022-03-31 11:46:23 +01:00 • committed by GitHub
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11 changed files with 157 additions and 82 deletions
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29
pyerrors/correlators.py
View file

@ -794,7 +794,34 @@ class Corr:
else:
raise Exception("'save' has to be a string.")
return
def spaghetti_plot(self, logscale=True):
"""Produces a spaghetti plot of the correlator suited to monitor exceptional configurations.
Parameters
----------
logscale : bool
Determines whether the scale of the y-axis is logarithmic or standard.
"""
if self.N != 1:
raise Exception("Correlator needs to be projected first.")
mc_names = list(set([item for sublist in [o[0].mc_names for o in self.content if o is not None] for item in sublist]))
x0_vals = [n for (n, o) in zip(np.arange(self.T), self.content) if o is not None]
for name in mc_names:
data = np.array([o[0].deltas[name] + o[0].r_values[name] for o in self.content if o is not None]).T
fig = plt.figure()
ax = fig.add_subplot(111)
for dat in data:
ax.plot(x0_vals, dat, ls='-', marker='')
if logscale is True:
ax.set_yscale('log')
ax.set_xlabel(r'$x_0 / a$')
plt.title(name)
plt.draw()
def dump(self, filename, datatype="json.gz", **kwargs):
"""Dumps the Corr into a file of chosen type

8
pyerrors/fits.py
View file

@ -464,8 +464,10 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
corr = covariance(y, correlation=True, **kwargs)
covdiag = np.diag(1 / np.asarray(dy_f))
condn = np.linalg.cond(corr)
if condn > 1e8:
warnings.warn("Correlation matrix may be ill-conditioned, condition number: %1.2e" % (condn), RuntimeWarning)
if condn > 0.1 / np.finfo(float).eps:
raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1 / np.sqrt(np.finfo(float).eps):
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = np.linalg.inv(chol)
chol_inv = np.dot(chol_inv, covdiag)
@ -623,7 +625,7 @@ def qqplot(x, o_y, func, p):
fit_stop = my_x[-1]
samples = np.arange(fit_start, fit_stop, 0.01)
plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)))
plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
plt.xlabel('Theoretical quantiles')
plt.ylabel('Ordered Values')

6
pyerrors/input/json.py
View file

@ -358,7 +358,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
ret = Obs([[ddi[0] + values[0] for ddi in di] for di in od['deltas']], od['names'], idl=od['idl'])
ret.is_merged = od['is_merged']
else:
ret = Obs([], [])
ret = Obs([], [], means=[])
ret._value = values[0]
for name in cd:
co = cd[name][0]
@ -383,7 +383,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
ret.append(Obs([list(di[:, i] + values[i]) for di in od['deltas']], od['names'], idl=od['idl']))
ret[-1].is_merged = od['is_merged']
else:
ret.append(Obs([], []))
ret.append(Obs([], [], means=[]))
ret[-1]._value = values[i]
print('Created Obs with means= ', values[i])
for name in cd:
@ -410,7 +410,7 @@ def _parse_json_dict(json_dict, verbose=True, full_output=False):
ret.append(Obs([di[:, i] + values[i] for di in od['deltas']], od['names'], idl=od['idl']))
ret[-1].is_merged = od['is_merged']
else:
ret.append(Obs([], []))
ret.append(Obs([], [], means=[]))
ret[-1]._value = values[i]
for name in cd:
co = cd[name][i]

100
pyerrors/obs.py
View file

@ -90,51 +90,44 @@ class Obs:
self.deltas = {}
self._covobs = {}
self._value = 0
self.N = 0
self.is_merged = {}
self.idl = {}
if len(samples):
if idl is not None:
for name, idx in sorted(zip(names, idl)):
if isinstance(idx, range):
self.idl[name] = idx
elif isinstance(idx, (list, np.ndarray)):
dc = np.unique(np.diff(idx))
if np.any(dc < 0):
raise Exception("Unsorted idx for idl[%s]" % (name))
if len(dc) == 1:
self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
else:
self.idl[name] = list(idx)
if idl is not None:
for name, idx in sorted(zip(names, idl)):
if isinstance(idx, range):
self.idl[name] = idx
elif isinstance(idx, (list, np.ndarray)):
dc = np.unique(np.diff(idx))
if np.any(dc < 0):
raise Exception("Unsorted idx for idl[%s]" % (name))
if len(dc) == 1:
self.idl[name] = range(idx[0], idx[-1] + dc[0], dc[0])
else:
raise Exception('incompatible type for idl[%s].' % (name))
else:
for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1)
self._value = 0
self.N = 0
if kwargs.get("means") is not None:
for name, sample, mean in sorted(zip(names, samples, kwargs.get("means"))):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
self.r_values[name] = mean
self.deltas[name] = sample
else:
for name, sample in sorted(zip(names, samples)):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = np.mean(sample)
self.deltas[name] = sample - self.r_values[name]
self._value += self.shape[name] * self.r_values[name]
self._value /= self.N
self.is_merged = {}
self.idl[name] = list(idx)
else:
raise Exception('incompatible type for idl[%s].' % (name))
else:
self._value = 0
self.is_merged = {}
self.N = 0
for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1)
if kwargs.get("means") is not None:
for name, sample, mean in sorted(zip(names, samples, kwargs.get("means"))):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
self.r_values[name] = mean
self.deltas[name] = sample
else:
for name, sample in sorted(zip(names, samples)):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = np.mean(sample)
self.deltas[name] = sample - self.r_values[name]
self._value += self.shape[name] * self.r_values[name]
self._value /= self.N
self._dvalue = 0.0
self.ddvalue = 0.0
@ -443,17 +436,15 @@ class Obs:
"""
return self.is_zero() or np.abs(self.value) <= sigma * self._dvalue
def is_zero(self, rtol=1.e-5, atol=1.e-8):
def is_zero(self, atol=1e-10):
"""Checks whether the observable is zero within a given tolerance.
Parameters
----------
rtol : float
Relative tolerance (for details see numpy documentation).
atol : float
Absolute tolerance (for details see numpy documentation).
"""
return np.isclose(0.0, self.value, rtol, atol) and all(np.allclose(0.0, delta, rtol, atol) for delta in self.deltas.values()) and all(np.allclose(0.0, delta.errsq(), rtol, atol) for delta in self.covobs.values())
return np.isclose(0.0, self.value, 1e-14, atol) and all(np.allclose(0.0, delta, 1e-14, atol) for delta in self.deltas.values()) and all(np.allclose(0.0, delta.errsq(), 1e-14, atol) for delta in self.covobs.values())
def plot_tauint(self, save=None):
"""Plot integrated autocorrelation time for each ensemble.
@ -584,7 +575,7 @@ class Obs:
ensemble to the error and returns a dictionary containing the fractions."""
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
if self._dvalue == 0.0:
if np.isclose(0.0, self._dvalue, atol=1e-15):
raise Exception('Error is 0.0')
labels = self.e_names
sizes = [self.e_dvalue[name] ** 2 for name in labels] / self._dvalue ** 2
@ -731,6 +722,9 @@ class Obs:
def __rsub__(self, y):
return -1 * (self - y)
def __pos__(self):
return self
def __neg__(self):
return -1 * self
@ -913,8 +907,11 @@ class CObs:
def __abs__(self):
return np.sqrt(self.real**2 + self.imag**2)
def __neg__(other):
return -1 * other
def __pos__(self):
return self
def __neg__(self):
return -1 * self
def __eq__(self, other):
return self.real == other.real and self.imag == other.imag
@ -1511,7 +1508,8 @@ def import_jackknife(jacks, name, idl=None):
length = len(jacks) - 1
prj = (np.ones((length, length)) - (length - 1) * np.identity(length))
samples = jacks[1:] @ prj
new_obs = Obs([samples], [name], idl=idl)
mean = np.mean(samples)
new_obs = Obs([samples - mean], [name], idl=idl, means=[mean])
new_obs._value = jacks[0]
return new_obs
@ -1570,7 +1568,7 @@ def cov_Obs(means, cov, name, grad=None):
co : Covobs
Covobs to be embedded into the Obs
"""
o = Obs([], [])
o = Obs([], [], means=[])
o._value = co.value
o.names.append(co.name)
o._covobs[co.name] = co

2
pyerrors/version.py
View file

@ -1 +1 @@
__version__ = "2.0.0-rc.3+dev"
__version__ = "2.1.0+dev"