Dashboard widgets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 11,
        "hidden": false,
        "row": 0,
        "width": 4
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,3]\n",
    "y = numpy.array([2,5,4])\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "line1, = ax.plot(x, y, 'r-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9da3a60cca994a7790a0e17acdc9c90c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=5.0, description='Value:', max=10.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slider = widgets.FloatSlider(min=0.0,max=10.0,value=5.0,description=\"Value:\")\n",
    "display(slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def on_value_change(change):\n",
    "    line1.set_ydata(y*(10/change['new']))\n",
    "    fig.canvas.draw()\n",
    "\n",
    "slider.observe(on_value_change, names='value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "line1.set_ydata(numpy.array([2,1,3]))\n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.7027027 , 6.75675676, 5.40540541])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line1.get_ydata()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {}
      }
     }
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "grid_default",
    "version": 1,
    "views": {
     "grid_default": {
      "cellMargin": 10,
      "defaultCellHeight": 20,
      "maxColumns": 12,
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}