{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import julia\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "julia.install()\n",
    "j=julia.Julia()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\amira\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\julia\\core.py:691: FutureWarning: Accessing `Julia().<name>` to obtain Julia objects is deprecated.  Use `from julia import Main; Main.<name>` or `jl = Julia(); jl.eval('<name>')`.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "fn=j.include('test.jl')\n",
    "x= np.array([[1,2,3],[4,5,6]])\n",
    "fn(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from julia import Base\n",
    "Base.sind(90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from julia import Main\n",
    "# Main.xs = [1, 2, 3]\n",
    "# Main.eval(\"sin.(xs)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Julia Magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initializing Julia interpreter. This may take some time...\n"
     ]
    }
   ],
   "source": [
    "%load_ext julia.magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [4, 5]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%julia [1 2; 3 4] .+ 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = [1, 2, 3]\n",
    "%julia $arr .+ 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PyCall.jlwrap dataPlotTest>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Main.include(\"script.jl\")\n",
    "# Main.testFunc01(x, y)\n",
    "# Main.testFunc02(a, b, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(Main.testFunc01(1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1   8  27]\n",
      " [ 64 125 216]]\n"
     ]
    }
   ],
   "source": [
    "x= np.array([[1,2,3],[4,5,6]])\n",
    "print(Main.testFunc01(x, x,x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y= np.array([1,2,5,3,4,5,6,8,7,6,8])\n",
    "Main.dataPlotTest(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "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",
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