{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b51b2c04fbb54c97906c10d8544e10f6",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from beakerx import *\n",
    "Plot(title=\"test title\",\n",
    "     xLabel=\"x label\",\n",
    "     yLabel=\"y label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "47239535a9ee40d3a537c27fcab850f2",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1 = Plot()\n",
    "plot1.add(Bars(displayName=\"Bar\", \n",
    "       x=[20,40,60], \n",
    "       y=[100, 120, 90], \n",
    "       width=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a7eb5d7e2956408a81ae939f70bf7f1f",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = Plot()\n",
    "plot2.add(Line(x=[1, 5, 3], y=[1, 2, 6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27930abbe0344c39ae4358143fc9f620",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot3 = Plot()\n",
    "plot3.add(Points(y=[1, 3, 6, 3, 1], \n",
    "              x=[1, 2, 3, 4, 5], \n",
    "              size=10, \n",
    "              shape=ShapeType.DIAMOND))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "661082561b1a4d9683fc71475f8d3a15",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot4 = Plot();\n",
    "plot4.add(Stems(y= [1.5, 1, 6, 5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b4cc85fa5ac4c3ea22a93de4d4b208d",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot5 = Plot(crosshair = Crosshair())\n",
    "plot5.add(Area(x = [0, 1, 2, 3], y = [3, 5, 2, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9c65082cfd54dfbac6b707a170ff66b",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot().add(ConstantLine(y=0.1)).add(ConstantLine(x=0.3, y=0.4, color=Color.gray, showLabel=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bde695e4e0a342c8a7850181eeb74af6",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot().add(Line(y=[-3, 1, 3, 4, 5])).add(ConstantBand(x=[1, 2], y=[1, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "731f6ce3f75b4bab831c9ffe14060aba",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from beakerx.plot import Text as BeakerxText\n",
    "plot = Plot()\n",
    "xs = [1, 2, 3, 4]\n",
    "ys = [8.6, 6.1, 7.4, 2.5]\n",
    "\n",
    "for i in range(0, 4):\n",
    "    plot.add(BeakerxText(x= xs[i], y= ys[i], text= 'test'))\n",
    "\n",
    "plot.add(Line(x= xs, y= ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28001f218e5e42f3b14d8408e12815a0",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tableRows = pd.read_csv('../../../doc/resources/data/interest-rates.csv')\n",
    "pp1 = Plot()\n",
    "pp1.add(Bars(y=tableRows.y1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3db7b3831f349b4818709191e5f918f",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp2 = Plot()\n",
    "pp2.add(Line(\n",
    "        x=pd.Series([10, 20, 30, 40, 50, 60, 70]), \n",
    "        y=pd.Series([0, 60, 10, 50, 20, 40, 30]), \n",
    "        width=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3d0e4f7e9cf467aa2243f127d866ee5",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = [1,5,3,2,3]\n",
    "y2 = [1,2,4,1,3]\n",
    "p = Plot()\n",
    "a1 = Area(y=y1, displayName='y1')\n",
    "a2 = Area(y=y2, displayName='y2')\n",
    "stacker = XYStacker()\n",
    "p.add(stacker.stack([a1, a2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7570a316b1cb4f829e62aac1332368b7",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SimpleTimePlot(tableRows, [\"y1\", \"y10\"], # column names\n",
    "               timeColumn=\"time\", # time is default value for a timeColumn\n",
    "               displayNames=[\"1 Year\", \"10 Year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de31adce0a1a4f15ad55d3743545acd7",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "millis = 1507541201624;\n",
    "hour = round(1000 * 60 * 60);\n",
    "xs = [];\n",
    "ys = [];\n",
    "for i in range(11):\n",
    "    xs.append(millis + hour * i);\n",
    "    ys.append(i);\n",
    "\n",
    "plot = TimePlot(timeZone=\"America/New_York\")\n",
    "# list of milliseconds\n",
    "plot.add(Points(x=xs, y=ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "539b6e98ed774879a89d89d16c48859b",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "millis = millis = 1507541201624;\n",
    "nanos  = millis * 1000 * 1000\n",
    "xs = []\n",
    "ys = []\n",
    "for i in range(11):\n",
    "    xs.append(nanos + 7 * i)\n",
    "    ys.append(i);\n",
    "\n",
    "np = NanoPlot()\n",
    "np.add(Points(x=xs, y=ys))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e8b4cd3daaa4edb8edaca163009d94f",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp = Plot()\n",
    "sp.add(YAxis(label= \"Test y axis\"))\n",
    "sp.add(Line(\n",
    "        x=pd.Series([10, 20, 30, 40, 50, 60, 70]), \n",
    "        y=pd.Series([0, 60, 10, 50, 20, 40, 30])))\n",
    "sp.add(Line(\n",
    "        x=pd.Series([5, 15, 25, 35, 45, 55, 65]), \n",
    "        y=pd.Series([5, 65, 15, 55, 25, 45, 35]), yAxis= \"Test y axis\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab97f6f6b64842779f1da74def249aa7",
       "version_major": 2,
       "version_minor": 0
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "points = 100;\n",
    "xs = [];\n",
    "for i in range(0, points):\n",
    "  xs.append(i)\n",
    "\n",
    "cplot = CombinedPlot(xLabel= \"CombinedPlot\");\n",
    "\n",
    "linearPlot = Plot(title= \"Linear x, Linear y\");\n",
    "linearPlot.add(Line(x= xs, y= xs));\n",
    "cplot.add(linearPlot, 3);\n",
    "\n",
    "logYPlot = Plot(logY= True, logX=False, title= \"Linear x, Log y\");\n",
    "logYPlot.add(Line(x= xs, y= xs));\n",
    "cplot.add(logYPlot, 3);\n",
    "\n",
    "logYPlot = Plot(logY= False, logX=True, title= \"Log x, Linear y\");\n",
    "logYPlot.add(Line(x= xs, y= xs));\n",
    "cplot.add(logYPlot, 3);\n",
    "\n",
    "cplot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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