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Created May 2, 2017 03:09
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jupyter_magic.ipynb
{
"cells": [
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "%load_ext cython",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%%cython\n\ncpdef int fact1(int n):\n \"\"\"Doc\n \n Parameters\n ----------\n n : int\n \n \"\"\"\n if (n <= 1):\n return 1\n \n else:\n return n * fact1(n - 1)\n \ndef fact2(n):\n if (n <= 1):\n return 1\n else:\n return n * fact2(n - 1)\n \ndef fact3(int n):\n if (n <= 1):\n return 1\n else:\n return n * fact3(n - 1)\n \ncpdef int fact4(int n):\n if n <= 1:\n return 1\n else:\n return n * fact4(n - 1)",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 1000 fact1(5)",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": "51.1 ns ± 1.57 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 1000 fact2(5)",
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": "338 ns ± 50.8 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 1000 fact3(5)",
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": "235 ns ± 3.75 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 1000 fact4(5)",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": "57.1 ns ± 0.471 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "def fact_python(n):\n if (n <= 1):\n return 1\n else:\n return n * fact_python(n - 1)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 1000 fact_python(5)",
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": "895 ns ± 71.2 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "%quickref",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%%writefile hello.py\nprint(\"Hello World\")",
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": "Overwriting hello.py\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# %load hello.py\nprint(\"Hello World\")",
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": "Hello World\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%save save_test.py 2",
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "File `save_test.py` exists. Overwrite (y/[N])? y\nThe following commands were written to file `save_test.py`:\nget_ipython().run_cell_magic('cython', '', '\\ncpdef int fact1(int n):\\n \"\"\"Doc\\n \\n Parameters\\n ----------\\n n : int\\n \\n \"\"\"\\n if (n <= 1):\\n return 1\\n \\n else:\\n return n * fact1(n - 1)\\n \\ndef fact2(n):\\n if (n <= 1):\\n return 1\\n else:\\n return n * fact2(n - 1)\\n \\ndef fact3(int n):\\n if (n <= 1):\\n return 1\\n else:\\n return n * fact3(n - 1)\\n \\ncpdef int fact4(int n):\\n if n <= 1:\\n return 1\\n else:\\n return n * fact4(n - 1)')\n"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# %load save_test.py\nget_ipython().run_cell_magic('cython', '', '\\ncpdef int fact1(int n):\\n \"\"\"Doc\\n \\n Parameters\\n ----------\\n n : int\\n \\n \"\"\"\\n if (n <= 1):\\n return 1\\n \\n else:\\n return n * fact1(n - 1)\\n \\ndef fact2(n):\\n if (n <= 1):\\n return 1\\n else:\\n return n * fact2(n - 1)\\n \\ndef fact3(int n):\\n if (n <= 1):\\n return 1\\n else:\\n return n * fact3(n - 1)\\n \\ncpdef int fact4(int n):\\n if n <= 1:\\n return 1\\n else:\\n return n * fact4(n - 1)')\n",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%save save_test.py 7",
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "File `save_test.py` exists. Overwrite (y/[N])? y\nThe following commands were written to file `save_test.py`:\ndef fact_python(n):\n if (n <= 1):\n return 1\n else:\n return n * fact_python(n - 1)\n"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# %load save_test.py\ndef fact_python(n):\n if (n <= 1):\n return 1\n else:\n return n * fact_python(n - 1)",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "import numpy as np",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "%%cython\nimport numpy as np\ncimport numpy as np\n\nDTYPE = np.float\nctypedef np.float_t DTYPE_t\n\ndef sum_by_row(np.ndarray[DTYPE_t, ndim=2] f):\n cdef int n_row = f.shape[0]\n cdef int n_col = f.shape[1]\n \n cdef np.ndarray[DTYPE_t, ndim=1] result = np.zeros([n_col], dtype=DTYPE)\n \n cdef int i\n cdef int j\n \n for i in range(n_row):\n for c in range(n_col):\n result[c] += f[i, c]\n\n return result",
"execution_count": 17,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%%cython\nimport numpy as np\ncimport numpy as np\n\nDTYPE = np.float\nctypedef np.float_t DTYPE_t\n\ndef sum_by_row2(np.ndarray[DTYPE_t, ndim=2] f):\n cdef int n_row = f.shape[0]\n cdef int n_col = f.shape[1]\n \n cdef np.ndarray[DTYPE_t, ndim=1] result = np.zeros([n_col], dtype=DTYPE)\n \n cdef int i\n cdef int j\n \n for i in range(n_row):\n for c in range(n_col):\n result[c] += f[i, c]\n\n return result",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%%cython\nimport numpy as np\ncimport numpy as np\n\ndef sum_by_row3(np.ndarray f):\n cdef int n_row = f.shape[0]\n cdef int n_col = f.shape[1]\n \n cdef np.ndarray result = np.zeros([n_col])\n \n cdef int i\n cdef int j\n \n for i in range(n_row):\n for c in range(n_col):\n result[c] += f[i, c]\n\n return result",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "A = np.random.randn(32, 32)",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 10000 sum_by_row(A)",
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": "3.06 µs ± 180 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 10000 A.sum(0)",
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": "3.41 µs ± 190 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 10000 sum_by_row2(A)",
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": "3.53 µs ± 222 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 10000 sum_by_row3(A)",
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": "220 µs ± 8.94 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "def sum_by_row_python(f):\n n_row = f.shape[0]\n n_col = f.shape[1]\n \n result = np.zeros([n_col])\n \n \n for i in range(n_row):\n for c in range(n_col):\n result[c] += f[i, c]\n\n return result",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%timeit -n 1000 sum_by_row_python(A)",
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": "355 µs ± 14.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
"name": "stdout"
}
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.1",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"gist": {
"id": "0993e87adb7d0332a843c32f35dd8145",
"data": {
"description": "jupyter_magic.ipynb",
"public": true
}
},
"_draft": {
"nbviewer_url": "https://gist.github.com/0993e87adb7d0332a843c32f35dd8145"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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