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View interpolation_performance_comparison
# 2d comparison
import numpy as np
x = np.linspace(-1,1,100)
y = np.linspace(-1,1,100)
f = lambda x,y: np.sinc(x**2+y**2)
vals = np.array( [[f(xx,yy) for yy in y] for xx in x] )
N = 1000000 # number of points to evaluate
eval_points = -1+2*np.random.rand(N*2).reshape((N,2))
from numpy import *
from numba import jit
N = 1000000
A = random.random((N,2,2))
B = random.random((N,2,2))
def mulvec(A,B):
N = A.shape[0]
import numpy as np
import quantecon
from numba import jit
from numba import njit, prange
def cartesian_2d(x,y,out=None):
p = x.shape[0]
q = y.shape[0]
View erfc_libm vs erfc_python
from cffi import FFI
ffi = FFI()
ffi.cdef('double erfc(double x);')
libm = ffi.dlopen("m")
erfc = libm.erfc
from math import erfc as p_erfc
from numba import njit
from numpy import linspace
albop /
Last active Jan 21, 2016
Half working generated interpolation example
from math import floor
from numba import njit
#### Working
def native_index_1d(mat, vec):
return mat[vec[0]]
albop /
Last active Jan 19, 2016
interpolation with numba.cuda
from __future__ import division
from numba import double, int64
from numba import jit, njit
import numpy as np
from numpy import zeros, array
albop / interpolation_speed.jl
Created Jan 7, 2016
splines.jl vs interpolations.jl
View interpolation_speed.jl
d = 3 # number of dimensions
K = 50 # number of points along each dimension
N = 100000 # number of points at which to interpolate
A = rand([K for i = 1:d]...) # filtered coefficients
B = rand(N,d) # points at which to evaluate the splines
max(B, minimum(A)+0.01)
min(B, maximum(A)-0.01)
albop / index.html
Last active Aug 29, 2015
mc_sample_path: numba annotate
View index.html
.annotation_table {
color: #000000;
font-family: monospace;
margin: 5px;
Authors: Pablo Winant
Time long computation and send a message when finished.
import contextlib
def watcher(task_name=None, email=None):
albop / gist:ab49de476b6bcbdd689c
Last active Aug 29, 2015
Functions needed for vfi with dolo
View gist:ab49de476b6bcbdd689c
from dolo import yaml_import
model = yaml_import('examples/models/rbc.yaml')
s = model.calibration['states']
x = model.calibration['controls']
y = model.calibration['auxiliaries']
e = model.calibration['shocks']
v = model.calibration['values']
p = model.calibration['parameters']
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