HajimeKawahara/waxis.ipynb

## waxis.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=100\n",
    "x = np.linspace(0.0,1.0,N)\n",
    "f = x**2\n",
    "p = -x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def x2p(x):\n",
    "    return 1/x\n",
    "\n",
    "def p2x(p):\n",
    "    return 1/p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_35727/4108865532.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return 1/p\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x,f)\n",
    "ax.secondary_xaxis('top',functions=(x2p, p2x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "72bc7f8b1808a6f5ada3c6a20601509b8b1843160436d276d47f2ba819b3753b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"N=100\n",
	"x = np.linspace(0.0,1.0,N)\n",
	"f = x**2\n",
	"p = -x"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [],
	"source": [
	"def x2p(x):\n",
	" return 1/x\n",
	"\n",
	"def p2x(p):\n",
	" return 1/p"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 13,
	"metadata": {},
	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"/tmp/ipykernel_35727/4108865532.py:5: RuntimeWarning: divide by zero encountered in true_divide\n",
	" return 1/p\n"
	]
	},
	{
	"data": {
	"image/png": 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",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"fig = plt.figure()\n",
	"ax = fig.add_subplot(111)\n",
	"ax.plot(x,f)\n",
	"ax.secondary_xaxis('top',functions=(x2p, p2x))\n",
	"plt.show()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3.8.8 ('base')",
	"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": "3.8.8"
	},
	"orig_nbformat": 4,
	"vscode": {
	"interpreter": {
	"hash": "72bc7f8b1808a6f5ada3c6a20601509b8b1843160436d276d47f2ba819b3753b"
	}
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}