mxl00474 · April 18, 2020 14:55
diff --git a/Untitled.ipynb b/Untitled.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# add current to path\n",
    "sys.path.insert(0, os.path.abspath('.'))\n",
    "\n",
    "from sample import sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Call the simple function in \"sample.py\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb6e0f7c550>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.arange(-1,1.1, 0.1)\n",
    "y=sample(x)\n",
    "plt.plot(x, y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3 Debug",
   "language": "python",
   "name": "ptvsd"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
 }
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Import"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"%load_ext autoreload\n",
	"%autoreload 2"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"import sys\n",
	"import os\n",
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"\n",
	"# add current to path\n",
	"sys.path.insert(0, os.path.abspath('.'))\n",
	"\n",
	"from sample import sample"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"## Call the simple function in \"sample.py\""
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x7fb6e0f7c550>]"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"x=np.arange(-1,1.1, 0.1)\n",
	"y=sample(x)\n",
	"plt.plot(x, y)"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python3 Debug",
	"language": "python",
	"name": "ptvsd"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.6.8"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 4
	}
No results found