nipunbatra/mpl-grid-spec.ipynb

## mpl-grid-spec.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = plt.GridSpec(4, 4, wspace=0.4, hspace=0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSpec(4, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb854dcb9b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for r in range(4):\n",
    "    for c in range(3):\n",
    "        ax = plt.subplot(grid[r, c])\n",
    "        ax.set_title((r, c))\n",
    "cbar_ax = plt.subplot(grid[:, 3])\n",
    "cbar_ax.imshow(np.random.randn(20, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {},
	"outputs": [],
	"source": [
	"grid = plt.GridSpec(4, 4, wspace=0.4, hspace=0.3)\n"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"GridSpec(4, 4)"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"grid"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.image.AxesImage at 0x7fb854dcb9b0>"
	]
	},
	"execution_count": 12,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 13 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"for r in range(4):\n",
	" for c in range(3):\n",
	" ax = plt.subplot(grid[r, c])\n",
	" ax.set_title((r, c))\n",
	"cbar_ax = plt.subplot(grid[:, 3])\n",
	"cbar_ax.imshow(np.random.randn(20, 5))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"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": "3.7.1"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}