{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyferret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyferret.start(verify=False, journal=False, unmapped=True, quiet=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, '')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyferret.run('set redirect/clobber/file=\"out.txt\" stdout')\n",
    "pyferret.run('set window/xpixel=300/aspect=0.8 1')\n",
    "pyferret.run('list i[i=1:10]')\n",
    "pyferret.run('plot sin(i[i=1:10])')\n",
    "pyferret.run('frame/file=\"plot.png\"/format=PNG')\n",
    "pyferret.run('cancel window 1')\n",
    "pyferret.run('cancel redirect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             VARIABLE : I\r\n",
      "                        axis ABSTRACT\r\n",
      "             SUBSET   : 10 points (X)\r\n",
      " 1    /  1:   1.00\r\n",
      " 2    /  2:   2.00\r\n",
      " 3    /  3:   3.00\r\n",
      " 4    /  4:   4.00\r\n",
      " 5    /  5:   5.00\r\n",
      " 6    /  6:   6.00\r\n",
      " 7    /  7:   7.00\r\n",
      " 8    /  8:   8.00\r\n",
      " 9    /  9:   9.00\r\n",
      " 10   / 10:  10.00\r\n"
     ]
    }
   ],
   "source": [
    "! cat out.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}