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koukyo1994/MatfileSpeedTest.ipynb

Created November 25, 2020 01:07
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配列の読み書きのスピードテスト
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MatfileSpeedTest.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CPU Info"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l\u001b[?7l\u001b[0m\u001b[32m\u001b[1m 'c.\n",
" ,xNMM.\n",
" .OMMMMo\n",
" OMMM0,\n",
" .;loddo:' loolloddol;.\n",
" cKMMMMMMMMMMNWMMMMMMMMMM0:\n",
"\u001b[0m\u001b[33m\u001b[1m .KMMMMMMMMMMMMMMMMMMMMMMMWd.\n",
" XMMMMMMMMMMMMMMMMMMMMMMMX.\n",
"\u001b[0m\u001b[31m\u001b[1m;MMMMMMMMMMMMMMMMMMMMMMMM:\n",
":MMMMMMMMMMMMMMMMMMMMMMMM:\n",
"\u001b[0m\u001b[31m\u001b[1m.MMMMMMMMMMMMMMMMMMMMMMMMX.\n",
" kMMMMMMMMMMMMMMMMMMMMMMMMWd.\n",
" \u001b[0m\u001b[35m\u001b[1m.XMMMMMMMMMMMMMMMMMMMMMMMMMMk\n",
" .XMMMMMMMMMMMMMMMMMMMMMMMMK.\n",
" \u001b[0m\u001b[34m\u001b[1mkMMMMMMMMMMMMMMMMMMMMMMd\n",
" ;KMMMMMMMWXXWMMMMMMMk.\n",
" .cooc,. .,coo:.\u001b[0m\n",
"\u001b[17A\u001b[9999999D\u001b[33C\u001b[0m\u001b[1m\u001b[32m\u001b[1mhidehisa\u001b[0m@\u001b[32m\u001b[1maraihidenaonoMacBook-Pro.local\u001b[0m \n",
"\u001b[33C\u001b[0m---------------------------------------\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mOS\u001b[0m\u001b[0m:\u001b[0m macOS Catalina 10.15.7 19H2 x86_64\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mHost\u001b[0m\u001b[0m:\u001b[0m MacBookPro13,3\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mKernel\u001b[0m\u001b[0m:\u001b[0m 19.6.0\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mUptime\u001b[0m\u001b[0m:\u001b[0m 18 hours, 51 mins\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mPackages\u001b[0m\u001b[0m:\u001b[0m 184 (brew)\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mShell\u001b[0m\u001b[0m:\u001b[0m fish 2.7.1\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mResolution\u001b[0m\u001b[0m:\u001b[0m 1680x1050\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mDE\u001b[0m\u001b[0m:\u001b[0m Aqua\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mWM\u001b[0m\u001b[0m:\u001b[0m Quartz Compositor\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mWM Theme\u001b[0m\u001b[0m:\u001b[0m Blue (Dark)\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mTerminal\u001b[0m\u001b[0m:\u001b[0m vscode\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mCPU\u001b[0m\u001b[0m:\u001b[0m Intel i7-6820HQ (8) @ 2.70GHz\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mGPU\u001b[0m\u001b[0m:\u001b[0m Intel HD Graphics 530, AMD Radeon Pro 455\u001b[0m \n",
"\u001b[33C\u001b[0m\u001b[33m\u001b[1mMemory\u001b[0m\u001b[0m:\u001b[0m 10414MiB / 16384MiB\u001b[0m \n",
"\n",
"\u001b[33C\u001b[30m\u001b[40m \u001b[31m\u001b[41m \u001b[32m\u001b[42m \u001b[33m\u001b[43m \u001b[34m\u001b[44m \u001b[35m\u001b[45m \u001b[36m\u001b[46m \u001b[37m\u001b[47m \u001b[m\n",
"\u001b[33C\u001b[38;5;8m\u001b[48;5;8m \u001b[38;5;9m\u001b[48;5;9m \u001b[38;5;10m\u001b[48;5;10m \u001b[38;5;11m\u001b[48;5;11m \u001b[38;5;12m\u001b[48;5;12m \u001b[38;5;13m\u001b[48;5;13m \u001b[38;5;14m\u001b[48;5;14m \u001b[38;5;15m\u001b[48;5;15m \u001b[m\n",
"\n",
"\n",
"\u001b[?25h\u001b[?7h"
]
}
],
"source": [
"!neofetch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"import joblib\n",
"import numpy as np\n",
"\n",
"from scipy.sparse import csr_matrix\n",
"from scipy.io import savemat, loadmat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Case1: 乱数行列"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"a = np.random.randn(256, 256, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.save"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.45 ms ± 576 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.save(\"a.npy\", a)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"363 µs ± 10 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.load(\"a.npy\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 1.5M 11 25 09:56 \u001b[01;31m\u001b[Ka.npy\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep a.npy"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"!rm -f a.npy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.savez_compressed\n",
"\n",
"圧縮あり、書き出しは遅めだが読み込みが速い"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"68.5 ms ± 2.66 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.savez_compressed(\"a.npy\", a)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65.6 µs ± 3.02 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.load(\"a.npy.npz\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 1.4M 11 25 09:56 \u001b[01;31m\u001b[Ka.npy.npz\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep a.npy.npz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"取り出し方に癖がある"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['arr_0']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aa = np.load(\"a.npy.npz\")\n",
"aa.files"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0.20239102, 0.37383372, -0.79941785],\n",
" [-0.85540574, 0.17240746, -0.98539357],\n",
" [-0.87391825, -0.86141655, 0.34941402],\n",
" ...,\n",
" [-0.51699032, 2.93163159, 0.36189887],\n",
" [-0.51422701, -0.19698399, 1.31475487],\n",
" [ 1.70792745, -0.2228165 , -0.41474589]],\n",
"\n",
" [[ 1.18903922, -0.46300179, -0.49396092],\n",
" [ 0.40682345, -2.53154819, -1.53560004],\n",
" [-1.29830327, -1.27992439, -0.72188917],\n",
" ...,\n",
" [ 0.18440747, -0.32896796, 0.44594645],\n",
" [-1.42360919, -0.10762281, -2.2276768 ],\n",
" [ 1.70897435, -0.81933952, -0.20320236]],\n",
"\n",
" [[ 0.25962003, 1.48201361, -1.22802066],\n",
" [-1.42737811, 0.61574337, 0.1009645 ],\n",
" [-0.25704913, -0.82313048, 0.82107737],\n",
" ...,\n",
" [ 0.27376284, 0.30428687, -1.10083257],\n",
" [-0.91431796, -0.38424368, -1.4240811 ],\n",
" [ 0.64929156, -0.40993174, 0.95160479]],\n",
"\n",
" ...,\n",
"\n",
" [[ 0.21310458, 0.364045 , 0.840645 ],\n",
" [ 1.05534974, 0.07496168, 1.76842987],\n",
" [-0.02294783, 0.68710474, 0.96094913],\n",
" ...,\n",
" [-1.45985302, -0.35835899, -0.49365589],\n",
" [ 0.54136375, 0.4323057 , 1.35250782],\n",
" [ 0.7536575 , 1.32436637, -0.42235029]],\n",
"\n",
" [[-0.37174953, 0.77438195, 1.12398299],\n",
" [-0.10213659, -0.8338731 , -0.7724271 ],\n",
" [-0.35257496, 1.22800204, -0.99073415],\n",
" ...,\n",
" [-0.62863125, -0.81999371, 0.82252486],\n",
" [-0.62148623, 0.09642251, 1.51298272],\n",
" [-0.16196264, 0.9259189 , 0.78179996]],\n",
"\n",
" [[ 0.72380544, 1.18419219, -1.72712512],\n",
" [ 0.89111329, 0.0299966 , -2.50459415],\n",
" [-0.56849416, -0.99600025, -1.77963208],\n",
" ...,\n",
" [-0.42582803, -0.04800713, -0.32263953],\n",
" [ 1.0303978 , 0.98871888, 0.09722896],\n",
" [ 2.34839695, 1.02545279, 0.10599268]]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aa[\"arr_0\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"!rm -f a.npy.npz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pickle\n",
"\n",
"圧縮なし、書き出しがはやい"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.58 ms ± 47.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"a.pkl\", \"wb\") as f:\n",
" pickle.dump(a, f)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"157 µs ± 46.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"a.pkl\", \"rb\") as f:\n",
" pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 1.5M 11 25 09:56 \u001b[01;31m\u001b[Ka.pkl\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep a.pkl"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"!rm -f a.pkl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### joblib\n",
"\n",
"圧縮あり、書き出しは`savez_compressed`並だが読み込みが遅いぞ？"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"63.8 ms ± 1.73 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"a.joblib\", \"wb\") as f:\n",
" joblib.dump(a, f, compress=3)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.67 ms ± 206 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"a.joblib\", \"rb\") as f:\n",
" joblib.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 1.4M 11 25 09:57 \u001b[01;31m\u001b[Ka.joblib\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep a.joblib"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"!rm -f a.joblib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### csr_matrix + savemat\n",
"\n",
"疎行列なら圧縮率が高い、読み込みが遅いが圧縮あり系の中だと圧縮は一番速いかも"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"二次元配列しか扱えないのでややめんどい"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"11.1 ms ± 400 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"mat0 = csr_matrix(a[:, :, 0])\n",
"mat1 = csr_matrix(a[:, :, 1])\n",
"mat2 = csr_matrix(a[:, :, 2])\n",
"savemat(\"a.mat\", {\"mat0\": mat0, \"mat1\": mat1, \"mat2\": mat2})"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.58 ms ± 130 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"aa = loadmat(\"a.mat\")\n",
"np.stack([\n",
" aa[\"mat0\"].toarray(), \n",
" aa[\"mat1\"].toarray(), \n",
" aa[\"mat2\"].toarray()\n",
"], axis=2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 2.3M 11 25 09:58 \u001b[01;31m\u001b[Ka.mat\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep a.mat"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"!rm -f a.mat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Case2: 疎な行列\n",
"\n",
"セグメンテーション用のラベルなどを配列で保存する場合"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"b = np.zeros([256, 256, 3])\n",
"for _ in range(10):\n",
" ystart = np.random.randint(0, 256 - 20)\n",
" xstart = np.random.randint(0, 256 - 20)\n",
" color = np.random.randint(0, 255, [10, 10, 3])\n",
" b[ystart:ystart + 10, xstart:xstart + 10, :] = color"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.save"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.43 ms ± 451 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.save(\"b.npy\", b)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"420 µs ± 25.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.load(\"b.npy\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 1.5M 11 25 10:00 \u001b[01;31m\u001b[Kb.npy\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep b.npy"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"!rm -f b.npy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.savez_compressed\n",
"\n",
"めっちゃ圧縮されてる、書き出しも速くなってる"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.16 ms ± 52.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.savez_compressed(\"b.npy\", b)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"70.3 µs ± 3.24 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"np.load(\"b.npy.npz\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 9.5K 11 25 10:00 \u001b[01;31m\u001b[Kb.npy.npz\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep b.npy.npz"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"!rm -f b.npy.npz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pickle"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.69 ms ± 45.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"b.pkl\", \"wb\") as f:\n",
" pickle.dump(b, f)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"132 µs ± 10.2 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"b.pkl\", \"rb\") as f:\n",
" pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 1.5M 11 25 10:01 \u001b[01;31m\u001b[Kb.pkl\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep b.pkl"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"!rm -f b.pkl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### joblib\n",
"\n",
"割と圧縮される。相変わらず読み込みが遅い"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.49 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"b.joblib\", \"wb\") as f:\n",
" joblib.dump(b, f, compress=3)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.23 ms ± 158 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"with open(\"b.joblib\", \"rb\") as f:\n",
" joblib.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 15K 11 25 10:02 \u001b[01;31m\u001b[Kb.joblib\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep b.joblib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### csr_matrix + savemat\n",
"\n",
"圧縮率は前の二つよりは低い。書き出しが速い。"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.34 ms ± 256 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"mat0 = csr_matrix(b[:, :, 0])\n",
"mat1 = csr_matrix(b[:, :, 1])\n",
"mat2 = csr_matrix(b[:, :, 2])\n",
"savemat(\"b.mat\", {\"mat0\": mat0, \"mat1\": mat1, \"mat2\": mat2})"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"583 µs ± 10.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"bb = loadmat(\"b.mat\")\n",
"np.stack([\n",
" bb[\"mat0\"].toarray(), \n",
" bb[\"mat1\"].toarray(), \n",
" bb[\"mat2\"].toarray()\n",
"], axis=2)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-rw-r--r-- 1 hidehisa staff 38K 11 25 10:04 \u001b[01;31m\u001b[Kb.mat\u001b[m\u001b[K\n"
]
}
],
"source": [
"!ls -lh | grep b.mat"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"!rm -f b.mat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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