philippemiron/seminf_haxby.ipynb

## seminf_haxby.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.colors import LinearSegmentedColormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# If the following cell is put into a file `cmcustom.py`, it can be later imported with:\n",
    "- `import cmcustom`\n",
    "- `cmap = cmcustom.seminf_haxby` or `cmcustom.seminf_haxby_r`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple conversion of J.M. Lilly's MATLAB code\n",
    "# https://www.dropbox.com/s/y9sh2enm1dfu90m/seminfhaxby.m?dl=0\n",
    "\n",
    "seminf_haxby_data = [[255, 255, 255], \n",
    "                    [208, 216, 251],\n",
    "                    [186, 197, 247],\n",
    "                    [143, 161, 241],\n",
    "                    [97, 122, 236],\n",
    "                    [0, 39, 224],\n",
    "                    [25, 101, 240],\n",
    "                    [12, 129, 248],\n",
    "                    [24, 175, 255],\n",
    "                    [49, 190, 255],\n",
    "                    [67, 202, 255],\n",
    "                    [96, 225, 240],\n",
    "                    [105, 235, 225],\n",
    "                    [123, 235, 200],\n",
    "                    [138, 236, 174],\n",
    "                    [172, 245, 168],\n",
    "                    [205, 255, 162],\n",
    "                    [223, 245, 141],\n",
    "                    [240, 236, 120],\n",
    "                    [247, 215, 103],\n",
    "                    [255, 189, 86],\n",
    "                    [255, 160, 68],\n",
    "                    [244, 116, 74],\n",
    "                    [238, 79,  77]]\n",
    "\n",
    "seminf_haxby_data = np.array(seminf_haxby_data) / 255.\n",
    "seminf_haxby_data[0] *= 0.95 #JML modifcation\n",
    "\n",
    "seminf_haxby = LinearSegmentedColormap.from_list('seminf-haxby', seminf_haxby_data)\n",
    "seminf_haxby_r = seminf_haxby.reversed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "cm = [seminf_haxby, seminf_haxby_r] \n",
    "for i in range(0, len(cm)):\n",
    "    plt.subplot(len(cm), 1, i + 1)\n",
    "    plt.imshow(np.linspace(0, 100, 256)[None, :], aspect='auto', cmap=cm[i])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"from matplotlib.colors import LinearSegmentedColormap"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# If the following cell is put into a file `cmcustom.py`, it can be later imported with:\n",
	"- `import cmcustom`\n",
	"- `cmap = cmcustom.seminf_haxby` or `cmcustom.seminf_haxby_r`"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"# Simple conversion of J.M. Lilly's MATLAB code\n",
	"# https://www.dropbox.com/s/y9sh2enm1dfu90m/seminfhaxby.m?dl=0\n",
	"\n",
	"seminf_haxby_data = [[255, 255, 255], \n",
	" [208, 216, 251],\n",
	" [186, 197, 247],\n",
	" [143, 161, 241],\n",
	" [97, 122, 236],\n",
	" [0, 39, 224],\n",
	" [25, 101, 240],\n",
	" [12, 129, 248],\n",
	" [24, 175, 255],\n",
	" [49, 190, 255],\n",
	" [67, 202, 255],\n",
	" [96, 225, 240],\n",
	" [105, 235, 225],\n",
	" [123, 235, 200],\n",
	" [138, 236, 174],\n",
	" [172, 245, 168],\n",
	" [205, 255, 162],\n",
	" [223, 245, 141],\n",
	" [240, 236, 120],\n",
	" [247, 215, 103],\n",
	" [255, 189, 86],\n",
	" [255, 160, 68],\n",
	" [244, 116, 74],\n",
	" [238, 79, 77]]\n",
	"\n",
	"seminf_haxby_data = np.array(seminf_haxby_data) / 255.\n",
	"seminf_haxby_data[0] *= 0.95 #JML modifcation\n",
	"\n",
	"seminf_haxby = LinearSegmentedColormap.from_list('seminf-haxby', seminf_haxby_data)\n",
	"seminf_haxby_r = seminf_haxby.reversed()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [
	{
	"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": [
	"cm = [seminf_haxby, seminf_haxby_r] \n",
	"for i in range(0, len(cm)):\n",
	" plt.subplot(len(cm), 1, i + 1)\n",
	" plt.imshow(np.linspace(0, 100, 256)[None, :], aspect='auto', cmap=cm[i])\n",
	"plt.show()"
	]
	}
	],
	"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",
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