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19 changes: 19 additions & 0 deletions contrib_colormaps/colormaps/black.csv
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0.13725490196078433,0.16862745098039217,0.16862745098039217
0.10588235294117647,0.10588235294117647,0.10588235294117647
0.10196078431372549,0.06666666666666667,0.06274509803921569
127 changes: 127 additions & 0 deletions examples/colormaps/black.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Black"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This colormap is constructed from [shades of black](https://en.wikipedia.org/wiki/Shades_of_black). It is meant to be used for qualitative data in an application where a dark colormap is appropriate.\n",
"\n",
"This colormap __is not__:\n",
"* colorblind friendly\n",
"* perceptually uniform"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from contrib_colormaps.plotting import swatch\n",
"import holoviews as hv\n",
"hv.extension('bokeh', 'matplotlib', logo=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"hv.render(swatch('black'), backend='matplotlib')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bokeh.plotting import show\n",
"show(hv.render(swatch('black'), backend='bokeh'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import contrib_colormaps as cc\n",
"\n",
"N = len(cc.black)\n",
"data = np.vstack([np.roll(np.arange(N), -i) for i in range(N)])\n",
"dbox = np.vstack([np.hstack([data, np.flip(data, axis=1)]),\n",
" np.hstack([np.flip(data, axis=0), np.flip(data, axis=[0,1])])])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as mcolors\n",
"\n",
"fig, ax = plt.subplots(figsize=(9,9))\n",
"ax.imshow(dbox, cmap=cc.m_black)\n",
"ax.axis('off')\n",
"ax.set_aspect('equal')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bokeh.plotting import figure, show, output_notebook\n",
"\n",
"output_notebook()\n",
"p = figure()\n",
"p.image([dbox], x=0, y=0, dw=1, dh=1, palette=cc.b_black)\n",
"show(p)"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}