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# 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)) |
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from numpy import * | |
from numba import jit | |
N = 1000000 | |
A = random.random((N,2,2)) | |
B = random.random((N,2,2)) | |
@jit(nopython=True) | |
def mulvec(A,B): | |
N = A.shape[0] |
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import numpy as np | |
import quantecon | |
from numba import jit | |
from numba import njit, prange | |
@njit | |
def cartesian_2d(x,y,out=None): | |
p = x.shape[0] | |
q = y.shape[0] |
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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 |
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from math import floor | |
from numba import njit | |
#### | |
#### Working | |
#### | |
@njit | |
def native_index_1d(mat, vec): | |
return mat[vec[0]] |
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from __future__ import division | |
from numba import double, int64 | |
from numba import jit, njit | |
import numpy as np | |
from numpy import zeros, array |
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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) | |
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<html> | |
<head> | |
<style> | |
.annotation_table { | |
color: #000000; | |
font-family: monospace; | |
margin: 5px; |
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""" | |
Filename: watcher.py | |
Authors: Pablo Winant | |
Time long computation and send a message when finished. | |
""" | |
import contextlib | |
@contextlib.contextmanager | |
def watcher(task_name=None, email=None): |
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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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