{
 "metadata": {
  "name": "",
  "signature": "sha256:c3013e986ac2d69c3ebd1e542f9eb4b343e966610f8571ba8b1a0880b5ae6cfb"
 },
 "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": 16
    },
    {
     "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 0xa3ebbf0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jcyYnWdbsbONzSDdKnHZsJ0m5b5NOmHPQ8BmiKnNzc4yNjbVbjY5Eto0O2bY2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\nQgghypBzEEIIUYacgxBCiDL+P9L/N2YTYg1tAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa3a75f0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "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": 18,
       "text": [
        "(x    521.769083\n",
        " y    108.000000\n",
        " dtype: float64, 1.7808903784058139)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "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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AwEAEKMqLriguKXYKHcg58aXmUjp0cDaC8jF8x0VBNqRVxXhlqYLKuedgX6Qa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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9ff38d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "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": 20,
       "text": [
        "'electrode69'"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}