numpy_do.py 3.77 KB
Newer Older
1
2
3
4
5
6
7
8
9
10
11
12
13
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
14
# Copyright(C) 2013-2019 Max-Planck-Society
15
#
16
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
17

18
19
20
# Data object module for NIFTy that uses simple numpy ndarrays.

import numpy as np
Philipp Arras's avatar
Philipp Arras committed
21
from numpy import empty, empty_like, exp, full, log
22
from numpy import ndarray as data_object
Philipp Arras's avatar
Philipp Arras committed
23
from numpy import ones, sqrt, tanh, vdot, zeros
24
from numpy import sin, cos, tan, sinh, cosh, sinc
Martin Reinecke's avatar
Martin Reinecke committed
25
from numpy import absolute, sign, clip
Martin Reinecke's avatar
Martin Reinecke committed
26
27
from .random import Random

Martin Reinecke's avatar
Martin Reinecke committed
28
29
30
31
32
33
__all__ = ["ntask", "rank", "master", "local_shape", "data_object", "full",
           "empty", "zeros", "ones", "empty_like", "vdot", "exp",
           "log", "tanh", "sqrt", "from_object", "from_random",
           "local_data", "ibegin", "ibegin_from_shape", "np_allreduce_sum",
           "np_allreduce_min", "np_allreduce_max",
           "distaxis", "from_local_data", "from_global_data", "to_global_data",
Martin Reinecke's avatar
Martin Reinecke committed
34
           "redistribute", "default_distaxis", "is_numpy", "absmax", "norm",
Martin Reinecke's avatar
Martin Reinecke committed
35
           "lock", "locked", "uniform_full", "to_global_data_rw",
Martin Reinecke's avatar
tweaks    
Martin Reinecke committed
36
           "ensure_not_distributed", "ensure_default_distributed",
Martin Reinecke's avatar
Martin Reinecke committed
37
           "clip", "sin", "cos", "tan", "sinh",
38
           "cosh", "absolute", "sign", "sinc"]
Martin Reinecke's avatar
Martin Reinecke committed
39

Martin Reinecke's avatar
Martin Reinecke committed
40
41
42
43
ntask = 1
rank = 0
master = True

44

Martin Reinecke's avatar
Martin Reinecke committed
45
46
47
48
def is_numpy():
    return True


Martin Reinecke's avatar
Martin Reinecke committed
49
50
51
52
53
54
55
56
57
58
59
60
61
62
def from_object(object, dtype, copy, set_locked):
    if dtype is None:
        dtype = object.dtype
    dtypes_equal = dtype == object.dtype
    if set_locked and dtypes_equal and locked(object):
        return object
    if not dtypes_equal and not copy:
        raise ValueError("cannot change data type without copying")
    if set_locked and not copy:
        raise ValueError("cannot lock object without copying")
    res = np.array(object, dtype=dtype, copy=copy)
    if set_locked:
        lock(res)
    return res
Martin Reinecke's avatar
Martin Reinecke committed
63
64
65
66
67


def from_random(random_type, shape, dtype=np.float64, **kwargs):
    generator_function = getattr(Random, random_type)
    return generator_function(dtype=dtype, shape=shape, **kwargs)
Martin Reinecke's avatar
Martin Reinecke committed
68

Martin Reinecke's avatar
Martin Reinecke committed
69

Martin Reinecke's avatar
Martin Reinecke committed
70
71
72
73
def local_data(arr):
    return arr


74
75
76
77
def ibegin_from_shape(glob_shape, distaxis=-1):
    return (0,)*len(glob_shape)


Martin Reinecke's avatar
Martin Reinecke committed
78
79
80
81
82
83
84
85
def ibegin(arr):
    return (0,)*arr.ndim


def np_allreduce_sum(arr):
    return arr


86
87
88
89
def np_allreduce_min(arr):
    return arr


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
90
91
92
93
def np_allreduce_max(arr):
    return arr


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
94
def distaxis(arr):
Martin Reinecke's avatar
Martin Reinecke committed
95
    return -1
Martin Reinecke's avatar
Martin Reinecke committed
96
97


Martin Reinecke's avatar
Martin Reinecke committed
98
def from_local_data(shape, arr, distaxis=-1):
Martin Reinecke's avatar
Martin Reinecke committed
99
    if tuple(shape) != arr.shape:
Martin Reinecke's avatar
Martin Reinecke committed
100
101
102
103
        raise ValueError
    return arr


104
def from_global_data(arr, sum_up=False, distaxis=-1):
Martin Reinecke's avatar
Martin Reinecke committed
105
106
107
    return arr


Martin Reinecke's avatar
Martin Reinecke committed
108
def to_global_data(arr):
Martin Reinecke's avatar
Martin Reinecke committed
109
110
111
    return arr


112
113
114
115
def to_global_data_rw(arr):
    return arr.copy()


Martin Reinecke's avatar
Martin Reinecke committed
116
def redistribute(arr, dist=None, nodist=None):
Martin Reinecke's avatar
Martin Reinecke committed
117
118
119
    return arr


Martin Reinecke's avatar
fixes    
Martin Reinecke committed
120
def default_distaxis():
Martin Reinecke's avatar
Martin Reinecke committed
121
122
123
    return -1


124
def local_shape(glob_shape, distaxis=-1):
Martin Reinecke's avatar
Martin Reinecke committed
125
    return glob_shape
126
127
128
129
130
131
132
133


def lock(arr):
    arr.flags.writeable = False


def locked(arr):
    return not arr.flags.writeable
Martin Reinecke's avatar
Martin Reinecke committed
134
135
136
137


def uniform_full(shape, fill_value, dtype=None, distaxis=-1):
    return np.broadcast_to(fill_value, shape)
Martin Reinecke's avatar
Martin Reinecke committed
138
139
140
141
142
143
144
145


def ensure_not_distributed(arr, axes):
    return arr, arr


def ensure_default_distributed(arr):
    return arr
Martin Reinecke's avatar
Martin Reinecke committed
146
147
148


def absmax(arr):
149
    return np.linalg.norm(arr.rehape(-1), ord=np.inf)
Martin Reinecke's avatar
Martin Reinecke committed
150
151
152


def norm(arr, ord=2):
153
    return np.linalg.norm(arr.reshape(-1), ord=ord)