From 27f4f66b94ae5b68a8044c69cf43fe9eee7e18f8 Mon Sep 17 00:00:00 2001
From: Philipp Arras <parras@mpa-garching.mpg.de>
Date: Wed, 13 May 2020 16:55:24 +0200
Subject: [PATCH] Proper dtype handling
---
nifty6/multi_field.py | 5 +++-
nifty6/operators/energy_operators.py | 39 +++++++++++++---------------
2 files changed, 22 insertions(+), 22 deletions(-)
diff --git a/nifty6/multi_field.py b/nifty6/multi_field.py
index d6a5aadbf..b3374f3d2 100644
--- a/nifty6/multi_field.py
+++ b/nifty6/multi_field.py
@@ -103,7 +103,10 @@ class MultiField(Operator):
@staticmethod
def from_random(random_type, domain, dtype=np.float64, **kwargs):
domain = MultiDomain.make(domain)
- if dtype in [np.float64, np.complex128]:
+ if isinstance(dtype, dict):
+ dtype = {kk: np.dtype(dt) for kk, dt in dtype.items()}
+ else:
+ dtype = np.dtype(dtype)
dtype = {kk: dtype for kk in domain.keys()}
dct = {kk: Field.from_random(random_type, domain[kk], dtype[kk], **kwargs)
for kk in domain.keys()}
diff --git a/nifty6/operators/energy_operators.py b/nifty6/operators/energy_operators.py
index 7496ab07f..aab83ca78 100644
--- a/nifty6/operators/energy_operators.py
+++ b/nifty6/operators/energy_operators.py
@@ -34,18 +34,18 @@ def _check_sampling_dtype(domain, dtypes):
if dtypes is None:
return
if isinstance(domain, DomainTuple):
- dtypes = {'': dtypes}
+ np.dtype(dtypes)
+ return
elif isinstance(domain, MultiDomain):
- if dtypes in [np.float64, np.complex128]:
+ if isinstance(dtypes, dict):
+ for dt in dtypes.values():
+ np.dtype(dt)
+ if set(domain.keys()) == set(dtypes.keys()):
+ return
+ else:
+ np.dtype(dtypes)
return
- dtypes = dtypes.values()
- if set(domain.keys()) != set(dtypes.keys()):
- raise ValueError
- else:
- raise TypeError
- for dt in dtypes.values():
- if dt not in [np.float64, np.complex128]:
- raise ValueError
+ raise TypeError
def _field_to_dtype(field):
@@ -332,7 +332,8 @@ class StudentTEnergy(EnergyOperator):
E_\\theta(f) = -\\log \\text{StudentT}_\\theta(f)
= \\frac{\\theta + 1}{2} \\log(1 + \\frac{f^2}{\\theta}),
- where f is a field defined on `domain`.
+ where f is a field defined on `domain`. Assumes that the data is `float64`
+ for sampling.
Parameters
----------
@@ -342,12 +343,9 @@ class StudentTEnergy(EnergyOperator):
Degree of freedom parameter for the student t distribution
"""
- def __init__(self, domain, theta, sampling_dtype=np.float64):
+ def __init__(self, domain, theta):
self._domain = DomainTuple.make(domain)
self._theta = theta
- self._sampling_dtype = sampling_dtype
- if sampling_dtype == np.complex128:
- raise NotImplementedError('Complex data not supported yet')
def apply(self, x):
self._check_input(x)
@@ -355,9 +353,7 @@ class StudentTEnergy(EnergyOperator):
if not x.want_metric:
return res
met = makeOp((self._theta+1) / (self._theta+3), self.domain)
- if self._sampling_dtype is not None:
- met = SamplingDtypeSetter(met, self._sampling_dtype)
- return res.add_metric(met)
+ return res.add_metric(SamplingDtypeSetter(met, np.float64))
class BernoulliEnergy(EnergyOperator):
@@ -420,7 +416,8 @@ class StandardHamiltonian(EnergyOperator):
ic_samp : IterationController
Tells an internal :class:`SamplingEnabler` which convergence criterion
to use to draw Gaussian samples.
- sampling_dtype : FIXME
+ prior_dtype : numpy.dtype or dict of numpy.dtype, optional
+ Data type of prior used for sampling.
See also
--------
@@ -429,9 +426,9 @@ class StandardHamiltonian(EnergyOperator):
`<https://arxiv.org/abs/1812.04403>`_
"""
- def __init__(self, lh, ic_samp=None, _c_inp=None, sampling_dtype=np.float64):
+ def __init__(self, lh, ic_samp=None, _c_inp=None, prior_dtype=np.float64):
self._lh = lh
- self._prior = GaussianEnergy(domain=lh.domain, sampling_dtype=sampling_dtype)
+ self._prior = GaussianEnergy(domain=lh.domain, sampling_dtype=prior_dtype)
if _c_inp is not None:
_, self._prior = self._prior.simplify_for_constant_input(_c_inp)
self._ic_samp = ic_samp
--
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