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‎guide/03-the-gis/managing-your-gis-servers.ipynb‎

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{
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"cell_type": "code",
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"execution_count": 76,
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"metadata": {
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"collapsed": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"df.index = df['date']\n",
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"df['count'] = df['count'] \n",
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"\n",
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"ax = df.plot(kind='bar', x=df['date'])\n",
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"ax = df['count'].plot(kind='bar', x=df['date'])\n",
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"ticklabels = ['']*len(df.index)\n",
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"ticklabels[::4] = [item.strftime('%b %d') for item in df.index[::4]]\n",
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"ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.3"
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"version": "3.6.6"
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}
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},
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"nbformat": 4,

‎samples/02_power_users_developers/population_exploration_dashboard.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Explore the Population!\n",
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"This Dashboard uses the Geoenrichment API to alow users to pan around the world and explore the population of a selected AOI. Let's start by logging into ArcGIS Online and loading the *"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"extensions": {
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"jupyter_dashboards": {
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"version": 1,
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"views": {
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"grid_default": {
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"hidden": true
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},
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"report_default": {
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"hidden": true
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}
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}
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}
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}
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},
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"outputs": [],
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"source": [
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"from IPython.display import display, clear_output\n",
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"import ipywidgets as widgets\n",
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"\n",
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"from arcgis.features import SpatialDataFrame\n",
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"from arcgis.geoenrichment import enrich\n",
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"from arcgis.raster import ImageryLayer\n",
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"from arcgis.geometry import Geometry\n",
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"from arcgis.gis import GIS\n",
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"\n",
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"gis = GIS(\"http://www.arcgis.com/home\", \"name\", \"pword\")\n",
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"img_svc ='https://landscape7.arcgis.com/arcgis/rest/services/World_Population_Density_Estimate_2016/ImageServer/'\n",
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"img_lyr = ImageryLayer(img_svc, gis=gis)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"extensions": {
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"jupyter_dashboards": {
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"version": 1,
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"views": {
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"grid_default": {
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"col": 8,
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"height": 4,
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"hidden": false,
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"row": 24,
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"width": 4
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},
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"report_default": {}
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}
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}
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>\n",
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".intro {\n",
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" padding: 10px; \n",
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" color: #202020;\n",
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" font-family: 'Helvetica'\n",
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"}\n",
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".map {\n",
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" border: solid;\n",
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" height: 450;\n",
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"}\n",
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"</style>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"%%html\n",
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"<style>\n",
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".intro {\n",
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" padding: 10px; \n",
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" color: #202020;\n",
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" font-family: 'Helvetica'\n",
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"}\n",
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".map {\n",
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" border: solid;\n",
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" height: 450;\n",
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"}\n",
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"</style>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"extensions": {
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"jupyter_dashboards": {
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"version": 1,
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"views": {
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"grid_default": {
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"hidden": true
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},
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"report_default": {
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"hidden": true
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}
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}
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}
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}
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},
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"outputs": [],
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"source": [
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"# run_btn on_click\n",
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"def running(button_object):\n",
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" \n",
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" viz_map.clear_graphics()\n",
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"\n",
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" #print('GeoEnriched Cell Data')\n",
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" #print('-' * 75)\n",
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"\n",
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" the_geom = Geometry(viz_map.extent)\n",
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" enriched = SpatialDataFrame(enrich([the_geom], gis=gis))\n",
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" \n",
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" if 'TOTPOP' not in list(enriched):\n",
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" enriched_pop = -1\n",
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" else:\n",
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" enriched_pop = enriched.TOTPOP[0]\n",
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" \n",
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" samples = img_lyr.get_samples(the_geom.project_as(4326))# sample_count=img_lyr.properties.maxImageHeight)\n",
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" sample_sum = sum([float(sample['value']) for sample in samples])\n",
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" \n",
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" print('GeoEnrichment Population Results: {}'.format(enriched_pop))\n",
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" #print('the Geom: {}'.format(the_geom))\n",
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" #print('Sample Count: {}'.format(img_lyr.properties.maxImageHeight))\n",
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" #print('Image Server Results: {}'.format(sample_sum))\n",
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" \n",
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" title = 'GeoEnriched Cell'\n",
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"\n",
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" for feature in enriched.to_featureset():\n",
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" \n",
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" # Expand Attributes With New Lines\n",
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" content = \"{}\".format(\n",
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" '<br/>'.join(['%s: %s' % (key.upper(), value) for (key, value) in feature.attributes.items()])\n",
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" )\n",
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" \n",
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" # Add Cell to Map\n",
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" viz_map.draw(feature.geometry, popup={'title': title, 'content': content})\n",
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"\n",
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" #print('-' * 75)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"extensions": {
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"jupyter_dashboards": {
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"version": 1,
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"views": {
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"grid_default": {
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"col": 0,
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"height": 24,
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"hidden": false,
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"row": 0,
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"width": null
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},
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"report_default": {
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"hidden": false
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}
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}
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}
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},
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "066baf6fe6154fa4a77ca91cb6ee1ab4",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"MapView(basemaps=['dark-gray', 'dark-gray-vector', 'gray', 'gray-vector', 'hybrid', 'national-geographic', 'oc…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1e9068b1e8bd4d8d975bc837a6453846",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(Button(button_style='success', description='Fetch Extent', layout=Layout(justify_content='cente…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"GeoEnrichment Population Results: 1146296\n",
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"GeoEnrichment Population Results: 321066\n"
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]
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}
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],
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"source": [
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"# Create & Display Map\n",
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"viz_map = gis.map('St Louis')\n",
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"viz_map.add_layer(img_lyr)\n",
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"display(viz_map)\n",
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"\n",
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"# Create Submit Button & Set on_click\n",
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"run_btn = widgets.Button(\n",
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" description='Fetch Extent',\n",
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" button_style='success',\n",
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" tooltip='Collect GeoEnrichment for Extent',\n",
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" layout=widgets.Layout(justify_content='center', margin='0px 0px 0px 10px')\n",
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")\n",
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"run_btn.on_click(running)\n",
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"\n",
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"# Handle Widget Layout\n",
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"params = widgets.HBox(\n",
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" [run_btn], \n",
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" layout=widgets.Layout(justify_content='center', margin='10px')\n",
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")\n",
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"display(params)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"extensions": {
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"jupyter_dashboards": {
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"version": 1,
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"views": {
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"grid_default": {},
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"report_default": {}
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}
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}
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}
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"extensions": {
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"jupyter_dashboards": {
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"activeView": "grid_default",
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"version": 1,
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"views": {
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"grid_default": {
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"cellMargin": 10,
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"defaultCellHeight": 20,
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"maxColumns": 12,
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"name": "grid",
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"type": "grid"
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},
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"report_default": {
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"name": "report",
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"type": "report"
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}
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}
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}
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎talks/uc2018/IntegratingMLandDeepLearning/1_TensorFlow/3 - City Detection.ipynb‎

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"\n",
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" for iteration in range(1000):\n",
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" # TrafficLand API source JSON\n",
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" source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key=1594c8892d7fbd18181a8a6a44958b28\"\n",
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" trafficland_API_key = os.environ['TRAFFICLAND_API_KEY'] # \n",
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" source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key={0}\".format(trafficland_API_key)\n",
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" df = pd.read_json(source_json)\n",
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" ddot_df = df.loc[df.provider == 'DDOT']\n",
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" \n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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"version": "3.6.6"
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}
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},
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"nbformat": 4,

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