From d82f28b2425a537be50facd1279d656b53236a3b Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Thu, 19 Mar 2020 11:46:10 +0100
Subject: [PATCH] demo implementation
---
demos/getting_started_1.py | 2 +-
nifty6/__init__.py | 2 +
nifty6/field.py | 4 +-
nifty6/random.py | 111 +++++++++++++++++++++----------------
4 files changed, 68 insertions(+), 51 deletions(-)
diff --git a/demos/getting_started_1.py b/demos/getting_started_1.py
index 886a4ceaa..0054f3f40 100644
--- a/demos/getting_started_1.py
+++ b/demos/getting_started_1.py
@@ -46,7 +46,7 @@ def make_random_mask():
if __name__ == '__main__':
- np.random.seed(42)
+ ift.random.init(42)
# Choose space on which the signal field is defined
if len(sys.argv) == 2:
diff --git a/nifty6/__init__.py b/nifty6/__init__.py
index 7ba7583e9..e2e8ec76b 100644
--- a/nifty6/__init__.py
+++ b/nifty6/__init__.py
@@ -1,5 +1,7 @@
from .version import __version__
+from . import random
+
from .domains.domain import Domain
from .domains.structured_domain import StructuredDomain
from .domains.unstructured_domain import UnstructuredDomain
diff --git a/nifty6/field.py b/nifty6/field.py
index 306dad503..d325ff8ee 100644
--- a/nifty6/field.py
+++ b/nifty6/field.py
@@ -140,9 +140,9 @@ class Field(object):
Field
The newly created Field.
"""
- from .random import Random
+ from . import random
domain = DomainTuple.make(domain)
- generator_function = getattr(Random, random_type)
+ generator_function = getattr(random, random_type)
arr = generator_function(dtype=dtype, shape=domain.shape, **kwargs)
return Field(domain, arr)
diff --git a/nifty6/random.py b/nifty6/random.py
index 673bf6596..6c4a71adf 100644
--- a/nifty6/random.py
+++ b/nifty6/random.py
@@ -11,59 +11,74 @@
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
-# Copyright(C) 2013-2019 Max-Planck-Society
+# Copyright(C) 2013-2020 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import numpy as np
+_initialized = False
-class Random(object):
- @staticmethod
- def pm1(dtype, shape):
- if np.issubdtype(dtype, np.complexfloating):
- x = np.array([1+0j, 0+1j, -1+0j, 0-1j], dtype=dtype)
- x = x[np.random.randint(4, size=shape)]
- else:
- x = 2*np.random.randint(2, size=shape) - 1
- return x.astype(dtype, copy=False)
+def init(seed):
+ global _initialized
+ if _initialized:
+ print("WARNING: re-intializing random generator")
+ np.random.seed(seed)
+ else:
+ _initialized = True
+ np.random.seed(seed)
- @staticmethod
- def normal(dtype, shape, mean=0., std=1.):
- if not (np.issubdtype(dtype, np.floating) or
- np.issubdtype(dtype, np.complexfloating)):
- raise TypeError("dtype must be float or complex")
- if not np.isscalar(mean) or not np.isscalar(std):
- raise TypeError("mean and std must be scalars")
- if np.issubdtype(type(std), np.complexfloating):
- raise TypeError("std must not be complex")
- if ((not np.issubdtype(dtype, np.complexfloating)) and
- np.issubdtype(type(mean), np.complexfloating)):
- raise TypeError("mean must not be complex for a real result field")
- if np.issubdtype(dtype, np.complexfloating):
- x = np.empty(shape, dtype=dtype)
- x.real = np.random.normal(mean.real, std*np.sqrt(0.5), shape)
- x.imag = np.random.normal(mean.imag, std*np.sqrt(0.5), shape)
- else:
- x = np.random.normal(mean, std, shape).astype(dtype, copy=False)
- return x
+def pm1(dtype, shape):
+ global _initialized
+ if not _initialized:
+ raise RuntimeError("RNG not initialized")
+ if np.issubdtype(dtype, np.complexfloating):
+ x = np.array([1+0j, 0+1j, -1+0j, 0-1j], dtype=dtype)
+ x = x[np.random.randint(4, size=shape)]
+ else:
+ x = 2*np.random.randint(2, size=shape) - 1
+ return x.astype(dtype, copy=False)
- @staticmethod
- def uniform(dtype, shape, low=0., high=1.):
- if not np.isscalar(low) or not np.isscalar(high):
- raise TypeError("low and high must be scalars")
- if (np.issubdtype(type(low), np.complexfloating) or
- np.issubdtype(type(high), np.complexfloating)):
- raise TypeError("low and high must not be complex")
- if np.issubdtype(dtype, np.complexfloating):
- x = np.empty(shape, dtype=dtype)
- x.real = np.random.uniform(low, high, shape)
- x.imag = np.random.uniform(low, high, shape)
- elif np.issubdtype(dtype, np.integer):
- if not (np.issubdtype(type(low), np.integer) and
- np.issubdtype(type(high), np.integer)):
- raise TypeError("low and high must be integer")
- x = np.random.randint(low, high+1, shape)
- else:
- x = np.random.uniform(low, high, shape)
- return x.astype(dtype, copy=False)
+def normal(dtype, shape, mean=0., std=1.):
+ global _initialized
+ if not _initialized:
+ raise RuntimeError("RNG not initialized")
+ if not (np.issubdtype(dtype, np.floating) or
+ np.issubdtype(dtype, np.complexfloating)):
+ raise TypeError("dtype must be float or complex")
+ if not np.isscalar(mean) or not np.isscalar(std):
+ raise TypeError("mean and std must be scalars")
+ if np.issubdtype(type(std), np.complexfloating):
+ raise TypeError("std must not be complex")
+ if ((not np.issubdtype(dtype, np.complexfloating)) and
+ np.issubdtype(type(mean), np.complexfloating)):
+ raise TypeError("mean must not be complex for a real result field")
+ if np.issubdtype(dtype, np.complexfloating):
+ x = np.empty(shape, dtype=dtype)
+ x.real = np.random.normal(mean.real, std*np.sqrt(0.5), shape)
+ x.imag = np.random.normal(mean.imag, std*np.sqrt(0.5), shape)
+ else:
+ x = np.random.normal(mean, std, shape).astype(dtype, copy=False)
+ return x
+
+def uniform(dtype, shape, low=0., high=1.):
+ global _initialized
+ if not _initialized:
+ raise RuntimeError("RNG not initialized")
+ if not np.isscalar(low) or not np.isscalar(high):
+ raise TypeError("low and high must be scalars")
+ if (np.issubdtype(type(low), np.complexfloating) or
+ np.issubdtype(type(high), np.complexfloating)):
+ raise TypeError("low and high must not be complex")
+ if np.issubdtype(dtype, np.complexfloating):
+ x = np.empty(shape, dtype=dtype)
+ x.real = np.random.uniform(low, high, shape)
+ x.imag = np.random.uniform(low, high, shape)
+ elif np.issubdtype(dtype, np.integer):
+ if not (np.issubdtype(type(low), np.integer) and
+ np.issubdtype(type(high), np.integer)):
+ raise TypeError("low and high must be integer")
+ x = np.random.randint(low, high+1, shape)
+ else:
+ x = np.random.uniform(low, high, shape)
+ return x.astype(dtype, copy=False)
--
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