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@kmundnic
Created August 10, 2018 00:59
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Color palettes using Seaborn\n",
"\n",
"This notebook uses several palettes defined in https://seaborn.pydata.org/tutorial/color_palettes.html.\n",
"\n",
"We include a function print the palettes to be used by TikZ/PGFPlots."
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABECAYAAAAiJuZQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a17b10400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num_colors = 6\n",
"# palette = \"BuGn_r\"\n",
"# palette = \"cubehelix\" # Good for printing\n",
"# palette = \"Set2\"\n",
"# palette = \"Paired\"\n",
"palette = sns.hls_palette(6, l=.47, s=.8)\n",
"# palette = sns.cubehelix_palette(num_colors, start=.5, rot=-.75)\n",
"# palette = sns.cubehelix_palette(num_colors, start=2, rot=0, dark=0, light=.85, reverse=True)\n",
"# palette = sns.cubehelix_palette(num_colors)\n",
"# palette = sns.diverging_palette(220, 20, n=7)\n",
"# palette = sns.diverging_palette(10, 220, sep=10, n=num_colors)\n",
"sns.palplot(sns.color_palette(palette, num_colors))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plots\n",
"You can plot the colors of the palette here"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vkxDZyz6DDYO40JadMe9v3g+tJj/fJ7nAUSpqGX86W4j0YhCNUYNrcn/Kr6rs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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a793ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_palette(palette)\n",
"for i in range(num_colors):\n",
" plt.plot([0, 1], [0, i+1])\n",
"\n",
"sns.set_style(\"whitegrid\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TikZ/PGFPlots\n",
"You can also print the color to define them for use with TikZ and PGF plots."
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\\definecolor{mycolor1}{rgb}{0.103614795156,0.0949749426101,0.206221107115}\n",
"\\definecolor{mycolor2}{rgb}{0.0825938595619,0.272848105068,0.307720122318}\n",
"\\definecolor{mycolor3}{rgb}{0.170042321211,0.436797596475,0.223725555556}\n"
]
}
],
"source": [
"def print_colors(colors, indices):\n",
" counter = 0\n",
" for i in indices:\n",
" counter += 1\n",
" print('\\definecolor{mycolor' + str(counter) + '}{rgb}{' + str(colors[i][0]) + ',' + str(colors[i][1]) + ',' + str(colors[i][2]) + '}')\n",
"\n",
"colors = sns.color_palette(palette, num_colors)\n",
"indices = [0,1,2] # Set the indices of the colors you want to use from your palette. Index starts counting at 0\n",
"\n",
"print_colors(colors, indices)"
]
},
{
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