{
 "metadata": {
  "name": "",
  "signature": "sha256:d4aec791bcba3763fc3861c89a7b3ca000dce7d25b40f46a1ab64f2bcd63c475"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from path_helpers import path\n",
      "from droplet_planning import svg_polygons_to_df\n",
      "from dmf_device_ui.tesselate import tesselate_shapes_frame\n",
      "from dmf_device_ui.plot import plot_shapes\n",
      "from dmf_device_ui.point_query import get_shapes_pymunk_space\n",
      "from dmf_device_ui.device import ShapesCanvas\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Loading chipmunk for Windows (32bit) [C:\\Users\\Christian\\Documents\\microdrop-1.0\\python-2.7.9\\lib\\site-packages\\pymunk\\chipmunk.dll]\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Load device SVG"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "drawing_shape = pd.Series([1920, 1080], index=['width', 'height'])\n",
      "device_svg = 'static/device.svg'\n",
      "\n",
      "device_canvas = ShapesCanvas.from_svg(device_svg, padding_factor=0.1, canvas_shape=drawing_shape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "--------------------------------------------------"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scale and center SVG points to drawing shape"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svg_shape = device_canvas.source_shape\n",
      "\n",
      "svg_axis = device_canvas.df_shapes[['x', 'y']].plot(kind='scatter', x='x', y='y',\n",
      "                                                    xlim=(0, svg_shape.width),\n",
      "                                                    ylim=(0, svg_shape.height))                                          \n",
      "drawing_axis = (device_canvas.df_canvas_shapes[['x', 'y']]\n",
      "                .plot(kind='scatter', x='x', y='y',\n",
      "                      xlim=(0, device_canvas.canvas_shape.width),\n",
      "                      ylim=(0, device_canvas.canvas_shape.height)))\n",
      "svg_axis.set_aspect(True)\n",
      "drawing_axis.set_aspect(True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x9f79b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jcyYnWdbsbONzSDdKnHZsJ0m5b5NOmHPQ8BmiKnNzc4yNjbVbjY5Eto0O2bY2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\nQgghypBzEEIIUYacgxBCiDL+P9L/N2YTYg1tAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa014b70>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get x/y-offset and scale for later use"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "device_canvas.canvas_offset, device_canvas.canvas_scale"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "(x    521.769083\n",
        " y    108.000000\n",
        " dtype: float64, 1.7808903784058139)"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "--------------------------------------------------"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Tesselate *scaled* shapes into convex shapes and construct pymunk `Space`"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Draw tesselations in drawing layout"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "axis = plot_shapes(device_canvas.df_canvas_tesselations, ['path_id', 'triangle_i'],\n",
      "                   fc='none')\n",
      "plot_shapes(device_canvas.df_canvas_shapes, ['path_id'], alpha=.3, axis=axis)\n",
      "axis.set_aspect(True)\n",
      "axis.set_xlim(0, device_canvas.canvas_shape.width)\n",
      "axis.set_ylim(0, device_canvas.canvas_shape.height)\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bSUVFhZzIy4Ejja/FbKHrXV1d2lktVhcDHxgQWP+ibo2vNiwsTN7Feu7cOTp1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\nCElEQVSOUv4/vad1dshb4dcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9fa0310>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Demonstrate electrode lookup based on drawing coordinates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "device_canvas.find_shape(1000, 480)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "'electrode69'"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}