Commit 78c48535 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

revert most changes, go for minimalistic solution

parent 2ebc9aa1
......@@ -46,7 +46,7 @@ def make_random_mask():
if __name__ == '__main__':
ift.random.seed(42)
np.random.seed(42)
# Choose space on which the signal field is defined
if len(sys.argv) == 2:
......
......@@ -140,9 +140,9 @@ class Field(object):
Field
The newly created Field.
"""
from . import random
from .random 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)
......
......@@ -17,68 +17,57 @@
import numpy as np
_initialized = False
def seed(_seed):
global _initialized
if _initialized:
# print("WARNING: re-intializing random generator")
np.random.seed(_seed)
else:
_initialized = True
np.random.seed(_seed)
np.random.seed(_seed)
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)
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
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 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)
@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
@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)
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