arcgis_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import widgetsnbextension\n",
    "import ipywidgets\n",
    "import arcgis\n",
    "import notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5.1\n",
      "7.6.3\n",
      "2.1.0.2\n",
      "6.4.0\n"
     ]
    }
   ],
   "source": [
    "print(widgetsnbextension.__version__)\n",
    "print(ipywidgets.__version__)\n",
    "print(arcgis.__version__)\n",
    "print(notebook.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### arcgis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.gis import GIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MapView.set_js_cdn(\"https://ucsdonline.maps.arcgis.com/jsapi/jsapi4/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d57e4ba229d740569b4516f034c7c01a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MapView(layout=Layout(height='400px', width='100%'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"map-static-img-preview-8c66a434-2c69-4e13-acbd-d595d731432d\"><img src=\"\"></img></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"map-html-embed-preview-8c66a434-2c69-4e13-acbd-d595d731432d\"></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#my_gis = GIS(\"https://ucsdonline.maps.arcgis.com/jsapi/jsapi4\")\n",
    "my_gis = GIS()\n",
    "my_gis.map()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ipyleaflet\n",
    "Sample from <https://github.com/jupyter-widgets/ipyleaflet/blob/master/examples/BaseMap.ipynb>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e631bd7de7a4b069f5dc7da042765c8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[52, 10], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_tex…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipyleaflet import *\n",
    "\n",
    "m = Map(center=(52, 10), zoom=8, basemap=basemaps.NASAGIBS.ModisTerraTrueColorCR)\n",
    "strava_all = basemap_to_tiles(basemaps.Strava.All)\n",
    "m.add_layer(strava_all)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db26c852b6764b199b4a0a86981ef398",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Map(bottom=21848.0, center=[52, 10], controls=(ZoomControl(options=['position', 'zoom_in_text',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets\n",
    "\n",
    "ipywidgets.HBox([m, Map(center=[43.6, 1.44], zoom=10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mesa_geo\n",
    "Sample from <https://github.com/corvince/mesa-geo/README.md>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arizona\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<POLYGON ((-12527738.867 4439200.735, -12527288.246 4439202.128, -12508853.8...>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mesa\n",
    "import mesa_geo as mg\n",
    "import requests\n",
    "\n",
    "url = \"http://eric.clst.org/assets/wiki/uploads/Stuff/gz_2010_us_040_00_20m.json\"\n",
    "r = requests.get(url)\n",
    "geojson_states = r.json()\n",
    "\n",
    "# Newer example code from https://mesa-geo.readthedocs.io/en/main/tutorials/intro_tutorial.html\n",
    "\n",
    "class State(mg.GeoAgent):\n",
    "    def __init__(self, unique_id, model, geometry, crs):\n",
    "        super().__init__(unique_id, model, geometry, crs)\n",
    "\n",
    "\n",
    "class GeoModel(mesa.Model):\n",
    "    def __init__(self):\n",
    "        self.space = mg.GeoSpace()\n",
    "        # Suppress warnings\n",
    "        self.space.warn_crs_conversion = False\n",
    "\n",
    "        ac = mg.AgentCreator(agent_class=State, model=self)\n",
    "        agents = ac.from_GeoJSON(GeoJSON=geojson_states, unique_id=\"NAME\")\n",
    "        self.space.add_agents(agents)\n",
    "        \n",
    "m = GeoModel()\n",
    "\n",
    "agent = m.space.agents[0]\n",
    "print(agent.unique_id)\n",
    "agent.geometry\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kepler.gl\n",
    "Sample from <https://github.com/keplergl/kepler.gl/blob/master/bindings/kepler.gl-jupyter/notebooks/\n",
    "Load%20kepler.gl.ipynb>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Guide: https://docs.kepler.gl/docs/keplergl-jupyter\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6148b80d72e8417d8bfa8ca543346d42",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "KeplerGl(data={'earthquakes': {'index': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keplergl\n",
    "import pandas as pd\n",
    "import requests\n",
    "\n",
    "# Load data\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/uber-common/deck.gl-data/master/examples/3d-heatmap/heatmap-data.csv')\n",
    "\n",
    "# Create a kepler.gl map\n",
    "map_1 = keplergl.KeplerGl(height=400)\n",
    "\n",
    "# Add data to the map\n",
    "map_1.add_data(data=df, name='earthquakes')\n",
    "\n",
    "# Render the map\n",
    "map_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// The Round Table\n",
      "digraph {\n",
      "\tA [label=\"King Arthur\"]\n",
      "\tB [label=\"Sir Bedevere the Wise\"]\n",
      "\tL [label=\"Sir Lancelot the Brave\"]\n",
      "\tA -> B\n",
      "\tA -> L\n",
      "\tB -> L [constraint=false]\n",
      "}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from graphviz import Digraph\n",
    "from IPython.display import Image\n",
    "\n",
    "dot = Digraph(comment='The Round Table')\n",
    "dot.node('A', 'King Arthur')\n",
    "dot.node('B', 'Sir Bedevere the Wise')\n",
    "dot.node('L', 'Sir Lancelot the Brave')\n",
    "dot.edges(['AB', 'AL'])\n",
    "dot.edge('B', 'L', constraint='false')\n",
    "print(dot.source)\n",
    "\n",
    "dot.render('test-output/round-table.gv', format='png')\n",
    "# Renders Digraph output in Jupyter Notebook\n",
    "Image(filename='test-output/round-table.gv.png')"
   ]
  }
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