Pablo Winant albop

## 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))

## test_mul_22.py
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]

## compare_products.py
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]

## 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

## generated_interp.py
from math import floor
from numba import njit

####
####  Working
####

@njit
def native_index_1d(mat, vec):
    return mat[vec[0]]

## eval_cubic_cuda.py

from __future__ import division

from numba import double, int64

from numba import jit, njit

import numpy as np
from numpy import zeros, array

## 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)


## index.html
<html>

<head>

<style>

.annotation_table {
color: #000000;
font-family: monospace;
margin: 5px;

## watcher.py
"""
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):

## 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']