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   "source": [
    "### Plotting from an analytic function expression\n",
    "\n",
    "#### Plot in cartesian coordinate system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "\t\t<text><tspan font-family=\"Arial\" >Math function example</tspan></text>\n",
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      "text/plain": [
       "#<Gnuplot::Plot:0x000000018118b0 @datasets=Hamster::Vector[#<Gnuplot::Dataset:0x00000001811680 @type=:math_function, @data=\"x*sin(x)\", @options=Hamster::Hash[:with => \"lines\", :lt => {:rgb=>\"blue\", :lw=>3}]>], @options=Hamster::Hash[:xrange => -10..10, :title => \"Math function example\", :xlabel => \"x*sin(x)\", :ylabel => \"x\"], @already_plotted=true, @cmd=\"plot \", @terminal=#<Gnuplot::Terminal:0x00000001811158 @cmd=\"gnuplot\", @current_datablock=0, @in=#<IO:fd 22>>>"
      ]
     },
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    }
   ],
   "source": [
    "require 'pilot-gnuplot'\n",
    "include Gnuplot\n",
    "\n",
    "# To use gnuplot this way iRuby should be modified.\n",
    "# For some reason there are also problems with\n",
    "# svg's so I'm using png in all other examples.\n",
    "cartesian = Plot.new(\n",
    "  ['x*sin(x)', with: 'lines', lt: { rgb: 'blue', lw: 3 }],\n",
    "  xrange: -10..10,\n",
    "  title: 'Math function example', \n",
    "  ylabel: 'x',\n",
    "  xlabel: 'x*sin(x)'\n",
    ")"
   ]
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  {
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     "data": {
      "image/png": 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",
      "text/plain": [
       "#<File:cartesian.png>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# github doesn't show svg so I inserted here a png too\n",
    "cartesian.to_png('cartesian.png')\n",
    "File.open('cartesian.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot in polar coordinate system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "#<File:polar.png>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polar = Plot.new(\n",
    "  ['abs(sin(3*t))', with: 'filledcurves', lt: { rgb: 'red' }],\n",
    "  title: 'Plot in polar example',\n",
    "  polar: true,\n",
    "  samples: 500\n",
    ")\n",
    "polar.to_png('polar.png', size: [500, 500])\n",
    "File.open('polar.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot from a parametric function expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "#<File:parametric.png>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parametric = Plot.new(\n",
    "  ['1.5*cos(t) - cos(30*t), 1.5*sin(t) - sin(30*t)', title: 'Parametric curve', lw: 2],\n",
    "  title: 'Parametric plot example',\n",
    "  parametric: true,\n",
    "  samples: 1000,\n",
    "  xrange: -3..3,\n",
    "  yrange: -3..3\n",
    ")\n",
    "parametric.to_png('parametric.png', size: [500, 500])\n",
    "File.open('parametric.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Ruby 2.1.2",
   "language": "ruby",
   "name": "ruby"
  },
  "language_info": {
   "file_extension": "rb",
   "mimetype": "application/x-ruby",
   "name": "ruby",
   "version": "2.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}