def load_ool_module():
"""
Compile an out-of-line module, return the corresponding ffi and
module objects.
"""
from cffi import FFI
defs = """
double sin(double x);
double cos(double x);
void vsSin(int n, float* x, float* y);
void vdSin(int n, double* x, double* y);
int foo(int a, int b, int c);
"""
source = """
static int foo(int a, int b, int c)
{
return a + b * c;
}
void vsSin(int n, float* x, float* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
void vdSin(int n, double* x, double* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
"""
ffi = FFI()
ffi.set_source('cffi_usecases_ool', source)
ffi.cdef(defs, override=True)
tmpdir = static_temp_directory('test_cffi')
ffi.compile(tmpdir=tmpdir)
sys.path.append(tmpdir)
try:
import cffi_usecases_ool as mod
cffi_support.register_module(mod)
return mod.ffi, mod
finally:
sys.path.remove(tmpdir)
def load_ool_module():
"""
Compile an out-of-line module, return the corresponding ffi and
module objects.
"""
from cffi import FFI
defs = """
double sin(double x);
double cos(double x);
void vsSin(int n, float* x, float* y);
void vdSin(int n, double* x, double* y);
int foo(int a, int b, int c);
"""
source = """
static int foo(int a, int b, int c)
{
return a + b * c;
}
void vsSin(int n, float* x, float* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
void vdSin(int n, double* x, double* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
"""
ffi = FFI()
ffi.set_source('cffi_usecases_ool', source)
ffi.cdef(defs, override=True)
tmpdir = static_temp_directory('test_cffi')
ffi.compile(tmpdir=tmpdir)
sys.path.append(tmpdir)
try:
import cffi_usecases_ool as mod
cffi_support.register_module(mod)
return mod.ffi, mod
finally:
sys.path.remove(tmpdir)
import numpy as np
from numba import njit, cffi_support
import _ndtri
from _ndtri import lib, ffi
cffi_support.register_module(_ndtri)
_ndtri = lib.ndtri
@njit
def ndtri(q):
return _ndtri(q)
from pywigxjpf_ffi import ffi, lib
import pywigxjpf_ffi
import time
import numba
from numba import cffi_support
cffi_support.register_module(pywigxjpf_ffi)
nb_wig3jj = pywigxjpf_ffi.lib.wig3jj
lib.wig_table_init(100, 9)
lib.wig_temp_init(100)
print(lib.wig3jj(5, 6, 7, 3, 4, -7))
print(lib.wig6jj(6, 6, 6, 6, 6, 6))
print(lib.wig9jj(6, 6, 6, 7, 7, 6, 7, 7, 8))
print(lib.wig9jj(6, 6, 6, 7, 7, 6, 7, 7, 8))
print(lib.wig6jj(6, 6, 6, 6, 6, int(4 + 0.5 * 4)))
# Benchmark 1, make calls for a lot of trivially-0 symbols.
@numba.jit(nopython=True)
def benchmark(jjmax):
sum = 0.0
calls = 0
for jj1 in range(0, jjmax + 1):
for jj2 in range(0, jjmax + 1):
for jj3 in range(0, jjmax + 1):
for mm1 in range(-jjmax, jjmax + 1):
for mm2 in range(-jjmax, jjmax + 1):
for mm3 in range(-jjmax, jjmax + 1):
w = nb_wig3jj(jj1, jj2, jj3, \
# Import our Rmath module
import _rmath_ffi
dnorm = _rmath_ffi.lib.dnorm
pnorm = _rmath_ffi.lib.pnorm
dunif = _rmath_ffi.lib.dunif
punif = _rmath_ffi.lib.punif
dgamma = _rmath_ffi.lib.dgamma
pgamma = _rmath_ffi.lib.pgamma
from numba import cffi_support
cffi_support.register_module(_rmath_ffi)
@nb.jit(nopython=True)
def pnorm_nb(x):
y = np.empty_like(x)
for k in range(x.shape[0]):
y[k] = pnorm(x[k], 0.0, 1.0, 1, 0)
return y
@nb.vectorize(nopython=True)
def pnorm_nb_vec(x):
return pnorm(x, 0.0, 1.0, 1, 0)
x = np.random.normal(size=(100,))
from numba import jit, prange, types, cffi_support
import numpy as np
import time
import sys
from _writer import ffi, lib
import _writer
# from ._writer import ffi, lib
# from . import _writer
cffi_support.register_module(_writer)
write = _writer.lib.write_data
@jit((types.Array(types.float32, 2, "A"), types.int32),
nopython=True,
nogil=True,
parallel=True,
fastmath=True,
cache=False)
def activity_score(a, buffer_size):
"""Calculate activity score for column pairs. If
Args:
a (np.array[np.float32]): 2d array with data
buffer_size (int32): size of buffer per thread. If buffer_size>a.shape[1]**2, "small data" mode is used
Returns:
if "small data" mode:
np.array[np.float32]: 1D array with maximum interquantile range for each column pair
np.array[np.float32]: 1D array with (1-spearman correlation) score for each column pair
else:
from __future__ import unicode_literals
from . import _cprob_ffi
from ._cprob_ffi.lib import *
try:
from numba import cffi_support as _cffi_support
_cffi_support.register_module(_cprob_ffi)
except ImportError:
pass
from numba import njit
import chess
import itertools
import functools
from chess.polyglot import POLYGLOT_RANDOM_ARRAY, zobrist_hash
from numba import cffi_support
from cffi import FFI
ffi = FFI()
import khash_numba._khash_ffi as khash_ffi
cffi_support.register_module(khash_ffi)
khash_init = khash_ffi.lib.khash_int2int_init
khash_get = khash_ffi.lib.khash_int2int_get
khash_set = khash_ffi.lib.khash_int2int_set
khash_destroy = khash_ffi.lib.khash_int2int_destroy
@njit
def create_index_table(ids):
table = khash_init()
for j in range(len(ids)):
khash_set(table, ids[j], j)
return table
import numpy as np
from numba import njit, cffi_support
import _hyper
from _hyper import lib, ffi
cffi_support.register_module(_hyper)
_hyp2f1 = lib.hyp2f1
_vd_hyp2f1 = lib.vd_hyp2f1
@njit
def hyp2f1(a, b, c, x):
"""Gauss hypergeometric function 2F1.
Parameters
----------
a, b, c, x : float
"""
if x == 1.0:
return np.inf
return _hyp2f1(a, b, c, x)
@njit
def vd_hyp2f1(a, b, c, x):
"""
Gauss hypergeometric function 2F1 vectorized for float x.
void vdSin(int n, double* x, double* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
"""
ffi_ool = FFI()
ffi.set_source('cffi_usecases_ool', source)
ffi.cdef(defs, override=True)
tmpdir = static_temp_directory('test_cffi')
ffi.compile(tmpdir=tmpdir)
sys.path.append(tmpdir)
try:
import cffi_usecases_ool
cffi_support.register_module(cffi_usecases_ool)
cffi_sin_ool = cffi_usecases_ool.lib.sin
cffi_cos_ool = cffi_usecases_ool.lib.cos
cffi_foo = cffi_usecases_ool.lib.foo
vsSin = cffi_usecases_ool.lib.vsSin
vdSin = cffi_usecases_ool.lib.vdSin
finally:
sys.path.remove(tmpdir)
def use_cffi_sin(x):
return cffi_sin(x) * 2
def use_two_funcs(x):
return cffi_sin(x) - cffi_cos(x)
import numpy as np
from numba import njit, cffi_support
import _hyper
from _hyper import lib, ffi
cffi_support.register_module(_hyper)
_hyp2f1 = lib.hyp2f1
_vd_hyp2f1 = lib.vd_hyp2f1
@njit
def hyp2f1(a, b, c, x):
"""Gauss hypergeometric function 2F1.
Parameters
----------
a, b, c, x : float
"""
if x == 1.0:
return np.inf
return _hyp2f1(a, b, c, x)
@njit
def vd_hyp2f1(a, b, c, x):
"""
Gauss hypergeometric function 2F1 vectorized for float x.
import importlib
import numba
import numpy as np
from numba import types
from numba.typed import Dict
from numba.cffi_support import register_module
loaded = importlib.import_module('_utils')
register_module(loaded)
get_seconds_since_epoch = loaded.lib.get_seconds_since_epoch
print_char_int = loaded.lib.print_char
print_float = loaded.lib.print_float
print_bar = loaded.lib.print_bar
print_info_bar = loaded.lib.print_info_bar
print_remaining = loaded.lib.print_remaining
@numba.jit(nopython=True)
def numba_ord(chr):
if chr == '=':
return 61
elif chr == '.':
return 46
elif chr == '>':
return 62
elif chr == '[':
return 91
elif chr == ']':
import numpy as np
from numba import cffi_support, float32, float64, vectorize
import _chi2
from _chi2 import lib, ffi
cffi_support.register_module(_chi2)
_chdtr = lib.chdtr
_chdtrc = lib.chdtrc
_chdtri = lib.chdtri
@vectorize([float64(float64, float64),
float32(float32, float32)])
def cdf(x, df):
return _chdtr(df, x)
@vectorize([float64(float64, float64),
float32(float32, float32)])
def ppf(p, df):
return _chdtri(df, 1.0-p)
@vectorize([float64(float64, float64),
float32(float32, float32)])
def sf(p, df):
return _chdtrc(df, p)
@vectorize([float64(float64, float64),
float32(float32, float32)])
def isf(p, df):
return _chdtri(df, p)
import numpy as np
from numba import jit, cffi_support
from . import _cnufft
cffi_support.register_module(_cnufft)
nufft3d1 = _cnufft.lib.nufft3d1
@jit(nopython=True)
def njit_nufft3d1(x,
y,
z,
c,
ms,
mt,
mu,
df=1,
eps=1e-15,
iflag=1,
direct=False):
nj = x.shape[0]
x = np.ascontiguousarray(x)
y = np.ascontiguousarray(y)
z = np.ascontiguousarray(z)
c = np.ascontiguousarray(c)
fk = np.ascontiguousarray(np.zeros((ms * mt * mu * 2)))
return_code = nufft3d1(np.int32(nj),
cffi_support.ffi.from_buffer(x.view(np.float64)),
cffi_support.ffi.from_buffer(y.view(np.float64)),
from __future__ import unicode_literals
from . import _ellf_ffi
from ._ellf_ffi.lib import *
try:
from numba import cffi_support as _cffi_support
_cffi_support.register_module(_ellf_ffi)
except ImportError:
pass
from __future__ import print_function
import numpy as np
import numba as nb
from numba import cffi_support
import _rmath_ffi
cffi_support.register_module(_rmath_ffi)
pnorm = _rmath_ffi.lib.pnorm
qnorm = _rmath_ffi.lib.qnorm
runif = _rmath_ffi.lib.runif
set_seed = _rmath_ffi.lib.set_seed
set_seed(123, 456)
@nb.njit
def f():
zlo = np.random.random()
zhi = np.random.random()
if zlo > 0.0:
plo = pnorm(-zhi, 0.0, 1.0, 1, 0)
phi = pnorm(-zlo, 0.0, 1.0, 1, 0)
else:
plo = pnorm(zlo, 0.0, 1.0, 1, 0)
phi = pnorm(zhi, 0.0, 1.0, 1, 0)
p = runif(plo, phi)
import cffi
ffi = cffi.FFI()
import numpy as np
from numba import cffi_support
import numbaflow._tensorflow_ffi as _tensorflow_ffi
cffi_support.register_module(_tensorflow_ffi)
get_info_struct = _tensorflow_ffi.lib.create_run_info
close_info_struct = _tensorflow_ffi.lib.close_run_info
sess_run = _tensorflow_ffi.lib.sess_run
def create_eval_fn():
info_struct = get_info_struct()
def evaluator(pieces, masks):
returned_pointer = sess_run(
info_struct,
ffi.cast("uint8_t *", ffi.from_buffer(pieces)),
ffi.cast("uint64_t *", ffi.from_buffer(masks)),
len(masks),
len(pieces))
result_buff = ffi.buffer(returned_pointer)
"""Wrappers to the functions in the extension module built by `build_ssht_cffi.py`
The function signatures are defined in the SSHT docs.
"""
import cffi
import numba as nb
import numpy as np
from numba import cffi_support
from . import _ssht_cffi
ffi = cffi.FFI()
# allows numba to use functions from this module in nopython mode
cffi_support.register_module(_ssht_cffi)
# tells numba how to map numba types to the types defined in the cffi function signatures
cffi_support.register_type(ffi.typeof("double _Complex"), nb.types.complex128)
cffi_support.register_type(ffi.typeof("double _Complex *"),
nb.types.CPointer(nb.types.complex128))
__all__ = [
"mw_forward_sov_conv_sym",
"mw_inverse_sov_sym",
"mw_forward_sov_conv_sym_real",
"mw_inverse_sov_sym_real",
"mw_forward_sov_conv_sym_ss",
"mw_inverse_sov_sym_ss",
"mw_forward_sov_conv_sym_ss_real",
"mw_inverse_sov_sym_ss_real",
"ind2elm",
def load_ool_module():
"""
Compile an out-of-line module, return the corresponding ffi and
module objects.
"""
from cffi import FFI
numba_complex = """
typedef struct _numba_complex {
double real;
double imag;
} numba_complex;
"""
defs = numba_complex + """
double sin(double x);
double cos(double x);
int foo(int a, int b, int c);
void vsSin(int n, float* x, float* y);
void vdSin(int n, double* x, double* y);
void vector_real(numba_complex *c, double *real, int n);
void vector_imag(numba_complex *c, double *imag, int n);
"""
source = numba_complex + """
static int foo(int a, int b, int c)
{
return a + b * c;
}
void vsSin(int n, float* x, float* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
void vdSin(int n, double* x, double* y) {
int i;
for (i=0; i<n; i++)
y[i] = sin(x[i]);
}
static void vector_real(numba_complex *c, double *real, int n) {
int i;
for (i = 0; i < n; i++)
real[i] = c[i].real;
}
static void vector_imag(numba_complex *c, double *imag, int n) {
int i;
for (i = 0; i < n; i++)
imag[i] = c[i].imag;
}
"""
ffi = FFI()
ffi.set_source('cffi_usecases_ool', source)
ffi.cdef(defs, override=True)
tmpdir = temp_directory('test_cffi')
ffi.compile(tmpdir=tmpdir)
sys.path.append(tmpdir)
try:
mod = import_dynamic('cffi_usecases_ool')
cffi_support.register_module(mod)
cffi_support.register_type(mod.ffi.typeof('struct _numba_complex'),
complex128)
return mod.ffi, mod
finally:
sys.path.remove(tmpdir)
import numpy as np
from numba import cffi_support, float32, float64, vectorize
import _igam
from _igam import lib, ffi
cffi_support.register_module(_igam)
_igam = lib.igam
_igamc = lib.igamc
@vectorize([float64(float64, float64), float32(float32, float32)])
def lower(s, x):
return _igam(s, x)
@vectorize([float64(float64, float64), float32(float32, float32)])
def upper(s, x):
return _igamc(s, x)
from __future__ import unicode_literals
from . import _misc_ffi
from ._misc_ffi.lib import *
try:
from numba import cffi_support as _cffi_support
_cffi_support.register_module(_misc_ffi)
except ImportError:
pass
from __future__ import absolute_import, print_function, division
import numpy as np
import numba as nb
from numba.targets.registry import CPUDispatcher
from numba.cffi_support import register_module
from . import _wrapper
from ._wrapper import lib, ffi
register_module(_wrapper)
start_instrumentation = lib.start_instrumentation
stop_instrumentation = lib.stop_instrumentation
zero_stats = lib.zero_stats
dump_stats = lib.dump_stats
dump_stats_at = lib.dump_stats_at
def _wrap_numba(func, tag=None):
if not isinstance(func, CPUDispatcher):
raise TypeError("Not a numba dispatch function")
if tag is None:
@nb.jit(**func.targetoptions)
def inner(*args):
zero_stats()
start_instrumentation()
res = func(*args)
stop_instrumentation()
