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Boxiang Sun
cython
Commits
1d86d5fe
Commit
1d86d5fe
authored
Mar 18, 2018
by
gabrieldemarmiesse
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Quite some progress with this tutorial. Only two parts missing.
parent
c00d9ed6
Changes
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7 changed files
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145 additions
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112 deletions
+145
-112
docs/examples/userguide/convolve_fused_types.pyx
docs/examples/userguide/convolve_fused_types.pyx
+1
-1
docs/examples/userguide/convolve_infer_types.pyx
docs/examples/userguide/convolve_infer_types.pyx
+2
-1
docs/examples/userguide/convolve_memview.pyx
docs/examples/userguide/convolve_memview.pyx
+1
-1
docs/examples/userguide/convolve_py.py
docs/examples/userguide/convolve_py.py
+1
-1
docs/examples/userguide/convolve_typed.pyx
docs/examples/userguide/convolve_typed.pyx
+4
-8
docs/src/userguide/convolve_types_html.png
docs/src/userguide/convolve_types_html.png
+0
-0
docs/src/userguide/numpy_tutorial.rst
docs/src/userguide/numpy_tutorial.rst
+136
-100
No files found.
docs/examples/userguide/convolve_fused_types.pyx
View file @
1d86d5fe
...
...
@@ -8,7 +8,7 @@ ctypedef fused my_type:
@
cython
.
boundscheck
(
False
)
@
cython
.
wraparound
(
False
)
cpdef
naive_convolve
_fused_types
(
my_type
[:,:]
f
,
my_type
[:,:]
g
):
cpdef
naive_convolve
(
my_type
[:,:]
f
,
my_type
[:,:]
g
):
if
g
.
shape
[
0
]
%
2
!=
1
or
g
.
shape
[
1
]
%
2
!=
1
:
raise
ValueError
(
"Only odd dimensions on filter supported"
)
...
...
docs/examples/userguide/convolve_infer_types.pyx
View file @
1d86d5fe
# cython: infer_types=True
import
numpy
as
np
cimport
cython
DTYPE
=
np
.
intc
@
cython
.
boundscheck
(
False
)
@
cython
.
wraparound
(
False
)
def
naive_convolve
_infer_types
(
int
[:,::
1
]
f
,
int
[:,::
1
]
g
):
def
naive_convolve
(
int
[:,::
1
]
f
,
int
[:,::
1
]
g
):
if
g
.
shape
[
0
]
%
2
!=
1
or
g
.
shape
[
1
]
%
2
!=
1
:
raise
ValueError
(
"Only odd dimensions on filter supported"
)
...
...
docs/examples/userguide/convolve_memview.pyx
View file @
1d86d5fe
...
...
@@ -2,7 +2,7 @@ import numpy as np
DTYPE
=
np
.
intc
def
naive_convolve
_memview
(
int
[:,:]
f
,
int
[:,:]
g
):
def
naive_convolve
(
int
[:,:]
f
,
int
[:,:]
g
):
if
g
.
shape
[
0
]
%
2
!=
1
or
g
.
shape
[
1
]
%
2
!=
1
:
raise
ValueError
(
"Only odd dimensions on filter supported"
)
...
...
docs/examples/userguide/convolve_py.py
View file @
1d86d5fe
from
__future__
import
division
import
numpy
as
np
def
naive_convolve
_py
(
f
,
g
):
def
naive_convolve
(
f
,
g
):
# f is an image and is indexed by (v, w)
# g is a filter kernel and is indexed by (s, t),
# it needs odd dimensions
...
...
docs/examples/userguide/convolve_typed.pyx
View file @
1d86d5fe
import
numpy
as
np
# "def" can type its arguments but not have a return type. The type of the
# arguments for a "def" function is checked at run-time when entering the
# function.
# We now need to fix a datatype for our arrays. I've used the variable
# DTYPE for this, which is assigned to the usual NumPy runtime
# type info object.
DTYPE
=
np
.
intc
# The arrays f, g and h is typed as "np.ndarray" instances. The only effect
# this has is to a) insert checks that the function arguments really are
# NumPy arrays, and b) make some attribute access like f.shape[0] much
# more efficient. (In this example this doesn't matter though.)
def
naive_convolve_types
(
f
,
g
):
def
naive_convolve
(
f
,
g
):
if
g
.
shape
[
0
]
%
2
!=
1
or
g
.
shape
[
1
]
%
2
!=
1
:
raise
ValueError
(
"Only odd dimensions on filter supported"
)
assert
f
.
dtype
==
DTYPE
and
g
.
dtype
==
DTYPE
...
...
@@ -46,6 +40,8 @@ def naive_convolve_types(f, g):
cdef
int
value
for
x
in
range
(
xmax
):
for
y
in
range
(
ymax
):
# Cython has built-in C functions for min and max
# This makes the following lines very fast.
s_from
=
max
(
smid
-
x
,
-
smid
)
s_to
=
min
((
xmax
-
x
)
-
smid
,
smid
+
1
)
t_from
=
max
(
tmid
-
y
,
-
tmid
)
...
...
docs/src/userguide/convolve_types_html.png
0 → 100644
View file @
1d86d5fe
37.1 KB
docs/src/userguide/numpy_tutorial.rst
View file @
1d86d5fe
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