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nmpowell/matplotlib_colourmap_from_css_gradient.ipynb

Last active April 11, 2023 23:59
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Generating a qualitative matplotlib colourmap from a CSS HSL gradient. This is for https://github.com/nmpowell/carbon-intensity-forecast-tracking
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matplotlib_colourmap_from_css_gradient.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "a8b8063b",
"metadata": {},
"source": [
"# Generating a matplotlib colourmap from a CSS HSL gradient\n",
"\n",
"I copied a nice gradient, green to red via yellow, from [Josh Comeau](https://www.joshwcomeau.com/gradient-generator?colors=00ea00|ffea00|ff0000&angle=0&colorMode=hcl&precision=20&easingCurve=0.25|0.75|0.75|0.25), which is designed to avoid the perceptual muddy middle of some colour gradients."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "cfb1a7f4",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as mcolors\n",
"from matplotlib.colors import BoundaryNorm, ListedColormap\n",
"import colorsys\n",
"\n",
"plt.rcParams[\"figure.figsize\"] = [12, 1]\n",
"plt.rcParams[\"figure.dpi\"] = 250\n",
"\n",
"# The middle boundaries of the qualitative ranges; not the limits.\n",
"# The actual minimum in the data could be e.g. 0 or -np.inf; the actual maximum could be 300 or np.inf.\n",
"values_from = [40, 120, 200, 291]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7ae97fe1",
"metadata": {},
"outputs": [],
"source": [
"def hsl_to_rgb(h, s, l):\n",
" return colorsys.hls_to_rgb(h / 360, l / 100, s / 100)\n",
"\n",
"# Use the CSS values from the gradient generator\n",
"css_hsl = [\n",
" (120, 100, 46),\n",
" (101, 100, 46),\n",
" (94, 100, 46),\n",
" (88, 100, 46),\n",
" (82, 100, 46),\n",
" (78, 100, 46),\n",
" (73, 100, 46),\n",
" (69, 100, 46),\n",
" (65, 100, 46),\n",
" (62, 100, 46),\n",
" (58, 100, 47),\n",
" (55, 100, 50),\n",
" (52, 100, 50),\n",
" (48, 100, 50),\n",
" (44, 100, 50),\n",
" (41, 100, 50),\n",
" (37, 100, 50),\n",
" (33, 100, 50),\n",
" (29, 100, 50),\n",
" (24, 100, 50),\n",
" (19, 100, 50),\n",
" (13, 100, 50),\n",
" (0, 100, 50),\n",
"]\n",
"\n",
"colours = [hsl_to_rgb(*hsl) for hsl in css_hsl]\n",
"\n",
"colour_positions = np.linspace(0, 1, len(colours))\n",
"\n",
"cmap = mcolors.LinearSegmentedColormap.from_list(\"green_yellow_red_cmap\", list(zip(colour_positions, colours)))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "87be1327",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 3000x250 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def preview_colourmap(cmap):\n",
" gradient = np.linspace(0, 1, 256)\n",
" gradient = np.vstack((gradient, gradient))\n",
"\n",
" fig, ax = plt.subplots()\n",
" ax.imshow(gradient, aspect=\"auto\", cmap=cmap)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plt.show()\n",
"\n",
"preview_colourmap(cmap)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4aaa01b8",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAACUcAAADyCAYAAABk+uZcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAACZzAAAmcwHzbHUKAAAMKElEQVR4nO3cwW0bSRRF0RLbEmho5wW9maQmkMloInA2DkIwwJU3ggkJzfZCg8mg0O6LcwKo/wK4qIdt27YBAAAAAAAAAAAQc9p7AAAAAAAAAAAAwAziKAAAAAAAAAAAIEkcBQAAAAAAAAAAJImjAAAAAAAAAACAJHEUAAAAAAAAAACQJI4CAAAAAAAAAACSxFEAAAAAAAAAAECSOAoAAAAAAAAAAEgSRwEAAAAAAAAAAEniKAAAAAAAAAAAIEkcBQAAAAAAAAAAJImjAAAAAAAAAACAJHEUAAAAAAAAAACQJI4CAAAAAAAAAACSxFEAAAAAAAAAAECSOAoAAAAAAAAAAEgSRwEAAAAAAAAAAEniKAAAAAAAAAAAIEkcBQAAAAAAAAAAJImjAAAAAAAAAACAJHEUAAAAAAAAAACQJI4CAAAAAAAAAACSxFEAAAAAAAAAAEDSp9kHnp+fx+12G8uyjMvlMvscAAAAAAAAAADwh7ter2Nd13E+n8fr6+u0Ow/btm3TXh9jLMsy7vf7zBMAAAAAAAAAAMABnU6nsa7rtPen/xz1fxx1GmN8nX0NABq+7D0AAA7m894DAOCIfu09AAAO5ufeAwCg5ccY4z4+2qKZpsdRl8tlvLy8fIRR32dfA4CGf/ceAAAH8/fUP5EBIOrb3gMA4GD+2XsAALT8NcZ4GR9t0Uynqa8DAAAAAAAAAADsRBwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJAkjgIAAAAAAAAAAJLEUQAAAAAAAAAAQJI4CgAAAAAAAAAASBJHAQAAAAAAAAAASeIoAAAAAAAAAAAgSRwFAAAAAAAAAAAkiaMAAAAAAAAAAIAkcRQAAAAAAAAAAJD0sG3bNvPA09PTeH9//8iwvs68BAAdX/YeAAAH83nvAQBwRL/2HgAAB/Nz7wEA0PJjjHEfYzw+Po63t7dpd6bHUcuyjPv9PvMEAAAAAAAAAABwQKfTaazrOu39T9Ne/s/5fB63220syzIul8vscwAAAAAAAAAAwB/uer2OdV3H+Xyeemf6z1EAAAAAAAAAAAB7OO09AAAAAAAAAAAAYAZxFAAAAAAAAAAAkCSOAgAAAAAAAAAAksRRAAAAAAAAAABAkjgKAAAAAAAAAABIEkcBAAAAAAAAAABJ4igAAAAAAAAAACBJHAUAAAAAAAAAACSJowAAAAAAAAAAgCRxFAAAAAAAAAAAkCSOAgAAAAAAAAAAksRRAAAAAAAAAABAkjgKAAAAAAAAAABIEkcBAAAAAAAAAABJ4igAAAAAAAAAACBJHAUAAAAAAAAAACSJowAAAAAAAAAAgCRxFAAAAAAAAAAAkCSOAgAAAAAAAAAAksRRAAAAAAAAAABAkjgKAAAAAAAAAABIEkcBAAAAAAAAAABJvwH8ulAX/2KYOAAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 3000x250 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Select 5 colours from the original colormap\n",
"selected_colours = [cmap(i) for i in np.linspace(0, 1, 5)]\n",
"\n",
"# Create a qualitative colormap with 5 distinct colours\n",
"qualitative_cmap = mcolors.ListedColormap(selected_colours)\n",
"\n",
"# Preview the qualitative colormap\n",
"preview_colourmap(qualitative_cmap)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "06d49306",
"metadata": {},
"outputs": [],
"source": [
"# Define the new boundaries\n",
"boundaries = [0, *values_from, max(values_from)+1]\n",
"\n",
"# Create a new BoundaryNorm with the new boundaries\n",
"norm = BoundaryNorm(boundaries, qualitative_cmap.N)\n",
"\n",
"# Create a ListedColormap with the desired colors\n",
"listed_cmap = ListedColormap(qualitative_cmap.colors)"
]
},
{
"cell_type": "markdown",
"id": "f3146c32",
"metadata": {},
"source": [
"## Testing"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "74d63713",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" <th>G</th>\n",
" <th>H</th>\n",
" <th>I</th>\n",
" <th>J</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>One</th>\n",
" <td>23</td>\n",
" <td>181</td>\n",
" <td>129</td>\n",
" <td>385</td>\n",
" <td>410</td>\n",
" <td>123</td>\n",
" <td>144</td>\n",
" <td>28</td>\n",
" <td>386</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Two</th>\n",
" <td>282</td>\n",
" <td>74</td>\n",
" <td>109</td>\n",
" <td>413</td>\n",
" <td>318</td>\n",
" <td>29</td>\n",
" <td>169</td>\n",
" <td>12</td>\n",
" <td>51</td>\n",
" <td>328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Three</th>\n",
" <td>52</td>\n",
" <td>389</td>\n",
" <td>223</td>\n",
" <td>149</td>\n",
" <td>191</td>\n",
" <td>134</td>\n",
" <td>170</td>\n",
" <td>105</td>\n",
" <td>250</td>\n",
" <td>288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Four</th>\n",
" <td>296</td>\n",
" <td>399</td>\n",
" <td>147</td>\n",
" <td>237</td>\n",
" <td>78</td>\n",
" <td>358</td>\n",
" <td>43</td>\n",
" <td>272</td>\n",
" <td>235</td>\n",
" <td>409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Five</th>\n",
" <td>80</td>\n",
" <td>214</td>\n",
" <td>321</td>\n",
" <td>388</td>\n",
" <td>146</td>\n",
" <td>224</td>\n",
" <td>324</td>\n",
" <td>53</td>\n",
" <td>276</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Six</th>\n",
" <td>119</td>\n",
" <td>180</td>\n",
" <td>330</td>\n",
" <td>418</td>\n",
" <td>341</td>\n",
" <td>147</td>\n",
" <td>332</td>\n",
" <td>48</td>\n",
" <td>87</td>\n",
" <td>108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Seven</th>\n",
" <td>280</td>\n",
" <td>75</td>\n",
" <td>387</td>\n",
" <td>251</td>\n",
" <td>203</td>\n",
" <td>324</td>\n",
" <td>273</td>\n",
" <td>246</td>\n",
" <td>159</td>\n",
" <td>240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eight</th>\n",
" <td>341</td>\n",
" <td>109</td>\n",
" <td>204</td>\n",
" <td>14</td>\n",
" <td>344</td>\n",
" <td>139</td>\n",
" <td>18</td>\n",
" <td>292</td>\n",
" <td>321</td>\n",
" <td>327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nine</th>\n",
" <td>167</td>\n",
" <td>280</td>\n",
" <td>351</td>\n",
" <td>410</td>\n",
" <td>392</td>\n",
" <td>18</td>\n",
" <td>374</td>\n",
" <td>58</td>\n",
" <td>45</td>\n",
" <td>320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ten</th>\n",
" <td>120</td>\n",
" <td>107</td>\n",
" <td>16</td>\n",
" <td>113</td>\n",
" <td>433</td>\n",
" <td>35</td>\n",
" <td>128</td>\n",
" <td>50</td>\n",
" <td>39</td>\n",
" <td>160</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E F G H I J\n",
"One 23 181 129 385 410 123 144 28 386 14\n",
"Two 282 74 109 413 318 29 169 12 51 328\n",
"Three 52 389 223 149 191 134 170 105 250 288\n",
"Four 296 399 147 237 78 358 43 272 235 409\n",
"Five 80 214 321 388 146 224 324 53 276 144\n",
"Six 119 180 330 418 341 147 332 48 87 108\n",
"Seven 280 75 387 251 203 324 273 246 159 240\n",
"Eight 341 109 204 14 344 139 18 292 321 327\n",
"Nine 167 280 351 410 392 18 374 58 45 320\n",
"Ten 120 107 16 113 433 35 128 50 39 160"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create some example data with a sensible range based upon the expected values\n",
"data = np.random.randint(0, values_from[-1] * 1.5, (10, 10))\n",
"columns = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']\n",
"index = ['One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine', 'Ten']\n",
"\n",
"# Create a DataFrame\n",
"df_ex = pd.DataFrame(data, columns=columns, index=index)\n",
"df_ex"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0fc536ca",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 3000x1500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams[\"figure.figsize\"] = [12, 6]\n",
"\n",
"# Plot the DataFrame with the new colormap and boundaries\n",
"fig, ax = plt.subplots()\n",
"im = ax.imshow(df_ex, cmap=listed_cmap, norm=norm)\n",
"fig.colorbar(im, ax=ax, ticks=boundaries, boundaries=boundaries)\n",
"\n",
"# Set the x and y axis labels with DataFrame column names and index\n",
"ax.set_xticks(np.arange(len(df_ex.columns)))\n",
"ax.set_yticks(np.arange(len(df_ex.index)))\n",
"ax.set_xticklabels(df_ex.columns)\n",
"ax.set_yticklabels(df_ex.index)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c22e181",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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