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@flying-sheep
Created November 22, 2019 12:05
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from itertools import product\n",
"import numpy as np\n",
"import scipy.sparse as sp\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import DivergingNorm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1, 2, 5, 10, 20, 50, 100, 200, 500]),\n",
" array([0. , 0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 0.875, 1. ]))"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sizes = np.ravel(np.column_stack((\n",
" 10**np.arange(3),\n",
" 10**np.arange(1,4)//5,\n",
" 10**np.arange(1,4)//2,\n",
")))\n",
"denss = np.linspace(0, 1, len(sizes))\n",
"sizes, denss"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def gen():\n",
" for size, dens in product(sizes, denss):\n",
" smat = sp.random(size, size, dens).tocsr()\n",
" sbytes = smat.data.nbytes + smat.indices.nbytes + smat.indptr.nbytes\n",
" dbytes = smat.toarray().nbytes\n",
" yield size, dens, sbytes, dbytes, sbytes/dbytes\n",
"data = pd.DataFrame.from_records(gen(), columns='size dens sbytes dbytes ratio'.split())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data.plot.scatter('size', 'dens', c='ratio', cmap='Spectral', sharex=False, norm=DivergingNorm(1, 0))\n",
"plt.xscale('log') # logx=True messes with xlims"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f2b1657d070>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.contourf(\n",
" *np.meshgrid(sizes, denss),\n",
" data.ratio.values.reshape((len(sizes), len(sizes))).T,\n",
" 20,\n",
" cmap='Spectral',\n",
" norm=DivergingNorm(1, 0),\n",
")\n",
"plt.xscale('log')\n",
"plt.xlabel('size')\n",
"plt.ylabel('dens')\n",
"plt.colorbar()"
]
}
],
"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": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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