jupytercon2017-widgets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ipywidgets\n",
    " * Interactive widgets for the Jupyter notebook\n",
    " * [Documentation: https://ipywidgets.readthedocs.io](https://ipywidgets.readthedocs.io)\n",
    " * [Github: https://github.com/jupyter-widgets/ipywidgets](https://github.com/jupyter-widgets/ipywidgets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "329d60384de6418abd474855f8f015a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slider = widgets.FloatSlider(min=0, max=10, step=0.5)\n",
    "slider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04f4a23edfbd43a28bc1e7c7c2bdd8a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = widgets.FloatText(value=1)\n",
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text.value = 5\n",
    "slider.value = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "widgets.jslink((text, 'value'), (slider, 'value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "slider.value = 3.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bqplot\n",
    " * Interactive plotting in the Jupyter notebook\n",
    " * [Documentation: https://bqplot.readthedocs.io](https://bqplot.readthedocs.io)\n",
    " * [Github: https://github.com/bloomberg/bqplot](https://github.com/bloomberg/bqplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5296bb4b96b4439a6aa2794c5784f11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bqplot.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.linspace(0, 2, 50)\n",
    "y = x**2\n",
    "\n",
    "fig = plt.figure()\n",
    "scatter = plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.animation_duration = 500\n",
    "scatter.y = x**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scatter.selected_style = {'stroke':'red', 'fill': 'orange'}\n",
    "plt.brush_selector();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12, 13, 14, 15]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter.selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scatter.selected = [1,2,10,40]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ipyleaflet\n",
    " * leaflet / Jupyter bridge enabling interactive maps in the notebook\n",
    " * https://github.com/ellisonbg/ipyleaflet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2627cd863d354a3fa39307901e0431b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipyleaflet\n",
    "ipyleaflet.Map(center = [40.7580264,-73.9297485], zoom = 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "_.add_layer(ipyleaflet.ImageOverlay(url='https://jupyter.org//assets/nav_logo.svg', bounds=_.bounds, opacity=0.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integration with vaex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import vaex\n",
    "dstaxi = vaex.open(\"/Users/maartenbreddels/vaex/data/nytaxi/nyc_taxi2015.hdf5\") # mmapped, doesn't cost extra memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afb2d38ebd8943648a94b837e47cf403",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dstaxi.plot_widget(\"pickup_longitude\", \"pickup_latitude\", f=\"log\", backend=\"ipyleaflet\", shape=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1126d96f6931422682b0317d4b5c273e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dstaxi.plot_widget(\"dropoff_longitude\", \"dropoff_latitude\", f=\"log\", backend=\"ipyleaflet\",\n",
    "                   z=\"dropoff_hour\", type=\"slice\", z_shape=24, shape=400, z_relative=True,\n",
    "                   limits=[None, None, (-0.5, 23.5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds = vaex.datasets.helmi_de_zeeuw.fetch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "define(\"vaex.ext.bqplot\", [\"jupyter-js-widgets\", \"bqplot\", \"underscore\"],\n",
       "       function(widgets, bqplot, _) {\n",
       "    \"use strict\";\n",
       "/*\n",
       "widgets.BoxView.prototype.remove_original = widgets.BoxView.prototype.remove\n",
       "widgets.BoxView.prototype.remove = function() {\n",
       "    console.log(\"remove monkey patch\")\n",
       "    if(this.children_views)\n",
       "        this.children_views.remove()\n",
       "    if(!this._is_removed) // protect against inf loop, if we monkey patch twice (during dev that can happen)\n",
       "        this.remove_original()\n",
       "    else\n",
       "        this._is_removed = true\n",
       "}*/\n",
       "\n",
       "    var Image = bqplot.Mark.extend({\n",
       "\n",
       "        render: function() {\n",
       "            var base_render_promise = Image.__super__.render.apply(this);\n",
       "            var el = this.d3el || this.el;\n",
       "            window.last_el = el;\n",
       "            window.last_image = this;\n",
       "            this.im = el.append(\"image\")\n",
       "                .attr(\"class\", \"dot_img\")\n",
       "                .attr(\"xlink:href\", this.model.get('src'))\n",
       "                .attr(\"x\", 0) //this.model.get('x'))\n",
       "                .attr(\"y\", 0) //this.model.get('y'))\n",
       "                .attr(\"width\", 1)//this.model.get('width'))\n",
       "                .attr(\"height\", 1)// this.model.get('height'))\n",
       "                .attr(\"preserveAspectRatio\", this.model.get('preserve_aspect_ratio'));\n",
       "\n",
       "            this.width = this.parent.plotarea_width;\n",
       "            this.height = this.parent.plotarea_height;\n",
       "            this.map_id = widgets.uuid();\n",
       "            this.display_el_classes = [\"event_layer\"];\n",
       "            var that = this;\n",
       "            this.event_metadata = {\n",
       "            \"mouse_over\": {\n",
       "                \"msg_name\": \"hover\",\n",
       "                \"lookup_data\": false,\n",
       "                \"hit_test\": true\n",
       "            },\n",
       "            \"legend_clicked\":  {\n",
       "                \"msg_name\": \"legend_click\",\n",
       "                \"hit_test\": true\n",
       "            },\n",
       "            \"element_clicked\": {\n",
       "                \"msg_name\": \"element_click\",\n",
       "                \"lookup_data\": false,\n",
       "                \"hit_test\": true\n",
       "            },\n",
       "            \"parent_clicked\": {\n",
       "                \"msg_name\": \"background_click\",\n",
       "                \"hit_test\": false\n",
       "            }}\n",
       "            console.log('listen to...')\n",
       "            this.is_removed = false;\n",
       "            this.model.set(\"view_count\", this.model.get(\"view_count\")+1)\n",
       "            this.model.save_changes()\n",
       "            return base_render_promise.then(function() {\n",
       "                console.log(\"promise...\")\n",
       "                that.event_listeners = {};\n",
       "                that.create_listeners();\n",
       "                console.log(\"drawing...\")\n",
       "                that.draw();\n",
       "            });\n",
       "        },\n",
       "        remove: function() {\n",
       "            var result = Image.__super__.remove.apply(this, arguments);\n",
       "            console.log(\"remove\")\n",
       "            if(!this.is_removed) {\n",
       "                console.log(\"removed\")\n",
       "                this.model.set(\"view_count\", this.model.get(\"view_count\")-1)\n",
       "                this.model.save_changes()\n",
       "                this.is_removed = true;\n",
       "            }\n",
       "            return result;\n",
       "        },\n",
       "        set_positional_scales: function() {\n",
       "            var x_scale = this.scales.x,\n",
       "                y_scale = this.scales.y;\n",
       "            this.listenTo(x_scale, \"domain_changed\", function() {\n",
       "                if (!this.model.dirty) {\n",
       "                    var animate = true;\n",
       "                    //this.update_xy_position(animate); }\n",
       "                    this.set_ranges()\n",
       "                 }\n",
       "            });\n",
       "            this.listenTo(y_scale, \"domain_changed\", function() {\n",
       "                if (!this.model.dirty) {\n",
       "                    var animate = true;\n",
       "                    //this.update_xy_position(animate);\n",
       "                    this.set_ranges()\n",
       "                }\n",
       "            });\n",
       "        },\n",
       "        set_ranges: function() {\n",
       "                console.log(\"set ranges\")\n",
       "\t            var x_scale = this.scales.x,\n",
       "\t                y_scale = this.scales.y\n",
       "\t                ;\n",
       "\t            if(x_scale) {\n",
       "\t                x_scale.set_range(this.parent.padded_range(\"x\", x_scale.model));\n",
       "\t            }\n",
       "\t            if(y_scale) {\n",
       "\t                y_scale.set_range(this.parent.padded_range(\"y\", y_scale.model));\n",
       "\t            }\n",
       "\t            var x_scale = this.scales.x, y_scale = this.scales.y;\n",
       "\t            var that = this;\n",
       "\t            var animation_duration = this.parent.model.get(\"animation_duration\");// : 0;\n",
       "                var el = this.d3el || this.el;\n",
       "\t            el.selectAll(\".dot_img\").transition()\n",
       "\t                .duration(animation_duration)\n",
       "\t                .attr(\"transform\", function(d) {\n",
       "                        var sx = x_scale.scale(1) - x_scale.scale(0);\n",
       "                        var sy = y_scale.scale(1) - y_scale.scale(0);\n",
       "                        var tx = x_scale.scale(that.model.get('x')) + x_scale.offset\n",
       "                        var ty = y_scale.scale(that.model.get('y')) + y_scale.offset\n",
       "                        sx *= that.model.get('width')\n",
       "                        sy *= that.model.get('height')\n",
       "                        //console.log(that.model)\n",
       "                        //console.log(that.model.get('width'))\n",
       "                        //console.log(sx)\n",
       "                        //console.log(sy)\n",
       "\t                    return \"translate(\" + tx +\n",
       "\t                                    \",\" + ty + \") scale(\" + sx + \", \" + sy + \")\"});\n",
       "\t            //this.x_pixels = this.model.mark_data.map(function(el) { return x_scale.scale(el.x) + x_scale.offset; });\n",
       "\t            //this.y_pixels = this.model.mark_data.map(function(el) { return y_scale.scale(el.y) + y_scale.offset; });\n",
       "        },\n",
       "\n",
       "        create_listeners: function() {\n",
       "            Image.__super__.create_listeners.apply(this);\n",
       "\n",
       "            this.listenTo(this.model, \"change:src\", this.update_src, this);\n",
       "            this.listenTo(this.model, \"change:x\", this.update_x, this);\n",
       "            this.listenTo(this.model, \"change:y\", this.update_y, this);\n",
       "            this.listenTo(this.model, \"change:width\", this.update_width, this);\n",
       "            this.listenTo(this.model, \"change:height\", this.update_height, this);\n",
       "            this.listenTo(this.model, \"change:preserve_aspect_ratio\", this.update_preserve_aspect_ratio, this);\n",
       "            this.listenTo(this.parent, \"bg_clicked\", function() {\n",
       "                        this.event_dispatcher(\"parent_clicked\");\n",
       "                    });\n",
       "        },\n",
       "\n",
       "        update_xy_position: function(animate) {\n",
       "            //console.log(\"update_xy_position\");\n",
       "        },\n",
       "\n",
       "        update_src: function(model, new_x) {\n",
       "          //this.im.attr('x', new_x);\n",
       "          //console.log(\"update src\")\n",
       "            this.im.attr(\"xlink:href\", this.model.get('src'))\n",
       "            //this.set_ranges();\n",
       "        },\n",
       "        update_x: function(model, new_x) {\n",
       "          //this.im.attr('x', new_x);\n",
       "            this.set_ranges();\n",
       "        },\n",
       "\n",
       "        update_y: function(model, new_y) {\n",
       "          //this.im.attr('y', new_y);\n",
       "            this.set_ranges();\n",
       "        },\n",
       "\n",
       "        update_width: function(model, new_width) {\n",
       "          //this.im.attr('width', new_width);\n",
       "            this.set_ranges();\n",
       "        },\n",
       "\n",
       "        update_height: function(model, new_height) {\n",
       "          //this.im.attr('height', new_height);\n",
       "            this.set_ranges();\n",
       "        },\n",
       "\n",
       "        update_preserve_aspect_ratio: function(model, new_preserve_aspect_ratio) {\n",
       "          this.im.attr('preserveAspectRatio', new_preserve_aspect_ratio);\n",
       "        },\n",
       "\n",
       "        draw: function() {\n",
       "            console.log(\"in draw\")\n",
       "            this.set_ranges();\n",
       "            this.im.on(\"click\", _.bind(function(d, i) {\n",
       "            this.event_dispatcher(\"element_clicked\",\n",
       "\t\t\t      {\"data\": d, \"index\": 0});\n",
       "            }, this));\n",
       "\n",
       "        },\n",
       "\n",
       "    });\n",
       "\n",
       "\n",
       "   var ImageModel = bqplot.MarkModel.extend({\n",
       "        defaults: function() {\n",
       "            var more_defaults =  {\n",
       "                    _model_name: \"ImageModel\",\n",
       "                    _view_name: \"Image\",\n",
       "                    _view_module: \"vaex.ext.bqplot\",\n",
       "                    _model_module: \"vaex.ext.bqplot\",\n",
       "                    x: 0,\n",
       "                    y: 0,\n",
       "                    width: 1,\n",
       "                    height: 1,\n",
       "                    view_count: 0,\n",
       "                }\n",
       "            return _.extend(bqplot.MarkModel.prototype.defaults(), more_defaults);\n",
       "        },\n",
       "        initialize: function() {\n",
       "            ImageModel.__super__.initialize.apply(this, arguments);\n",
       "        },\n",
       "\n",
       "    });\n",
       "\n",
       "\n",
       "\n",
       "    console.log(\"blaat\")\n",
       "\t    return {\n",
       "\t        ImageModel: ImageModel,\n",
       "\t        Image:Image,\n",
       "\t    };\n",
       "})\n",
       "//require([\"vaex.ext.bqplot\"])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a44f5f64a0249588a4cc3d12bdc8b57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.plot_widget(\"x\", \"y\", f=\"log\", limits=[-20, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00edb54a4b4a4ce58d632b009bf9e9b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.plot_widget(\"Lz\", \"E\", f=\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ipyvolume\n",
    " * 3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL\n",
    " * <1 year old\n",
    " * Glyphs, volume rendering, surfaces/meshes/lines\n",
    " * Live documentation http://ipyvolume.readthedocs.io/en/stable/ (since and thanks to ipywidgets 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import ipyvolume as ipv\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60abf8b2c3dd401988b4b6f4397fb155",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipv.example_ylm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 1000\n",
    "x, y, z = np.random.random((3, N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b84c1354b0434ff7866db1d652f22ffe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = ipv.figure()\n",
    "scatter = ipv.scatter(x, y, z, marker='box')\n",
    "ipv.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scatter.x = scatter.x + 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scatter.color = \"green\"\n",
    "scatter.size = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scatter.color = np.random.random((N,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scatter.size = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f20641f2eeb2496990bb9791df191078",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipv.figure()\n",
    "ipv.style.use('dark')\n",
    "quiver = ipv.quiver(*ipv.datasets.animated_stream.fetch().data[:,::,::4], size=5)\n",
    "ipv.animation_control(quiver, interval=200)\n",
    "ipv.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ipv.style.use('light')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quiver.geo = \"cat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quiver.geo = \"arrow\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 1000*1000\n",
    "x, y, z = np.random.random((3, N)).astype('f4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f03a5adae2004a84928a34f65fc4b3dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ipv.figure()\n",
    "s = ipv.scatter(x, y, z, size=0.2)\n",
    "ipv.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1161c63f111342bb9bf70e0077e8ea3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot3d = ds.plot_widget(\"x\", \"y\", \"z\", vx=\"vx\", vy=\"vy\", vz=\"vz\",\n",
    "                        backend=\"ipyvolume\", f=\"log1p\", shape=100, smooth_pre=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot3d.vcount_limits = [50, 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot3d.backend.quiver.color = \"red\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d91cf449f8194517bfb828c50485536c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.ColorPicker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "widgets.jslink((plot3d.backend.quiver, 'color'), (_, 'value'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ipv.save(\"jupytercon.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!open jupytercon.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!open ct_3d/CT_3D.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!open brain_flat/brain_flat.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# webrtc demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Billion stars in the Jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import vaex\n",
    "gaia = vaex.open(\"ws://gaia:9000/gaia-dr1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.142679769"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gaia)/1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ra_dec_limits = [[0, 360], [-90, 90]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfbcfccd1482463abaeca14da7bc9e36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gaia.plot_widget(\"ra\", \"dec\", limits=ra_dec_limits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(19.244657521033048)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia.mean(\"phot_g_mean_mag\", selection=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LlsPqa5quQjUwoCT1hA0RmikP8UnqCRsiNFOOoCT1zB43RFQ1Q4ANEQPOgJLUfmPNEBPD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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6d1978470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x6d91eaa20>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaia.plot1d(\"phot_g_mean_mag\", selection=False, n=True)\n",
    "gaia.plot1d(\"phot_g_mean_mag\", selection=True, show=True, n=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!open billion-stars-remote.mp4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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