173 lines
3.5 KiB
Plaintext
173 lines
3.5 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "array([[0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.]])"
|
|
},
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"a = np.zeros((5,5))\n",
|
|
"a"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "array([[ 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0.],\n [ 0., 0., 10., 0., 0.],\n [ 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0.]])"
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"a[2,2] = 10\n",
|
|
"a"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"outputs": [],
|
|
"source": [
|
|
"index_array = np.array([2,2], dtype=int)"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"234 ns ± 7.47 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n",
|
|
"311 ns ± 2.15 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%timeit a[tuple(index_array.tolist())]\n",
|
|
"%timeit a[index_array[0], index_array[1]]"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"outputs": [],
|
|
"source": [
|
|
"def array_change(array):\n",
|
|
" array[1] = 1"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "array([[ 0., 0., 0., 0., 0.],\n [ 1., 1., 1., 1., 1.],\n [ 0., 1., 10., 0., 0.],\n [ 1., 1., 1., 1., 1.],\n [ 0., 0., 0., 0., 0.]])"
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"array_change(a[2:])\n",
|
|
"a"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "array([[ 0., 0., 0., 0., 0.],\n [ 1., 1., 1., 1., 1.],\n [ 0., 1., 10., 0., 0.],\n [ 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0.]])"
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"a"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"outputs": [],
|
|
"source": [],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|