diff --git a/workshop/jupyter/content/data/README.md b/workshop/jupyter/content/data/README.md index d61dc6e..db558e8 100644 --- a/workshop/jupyter/content/data/README.md +++ b/workshop/jupyter/content/data/README.md @@ -16,3 +16,7 @@ and sometimes also GeoJSON. NB "Antarctica" has been removed from the "countries" datasets as it gave problems with reprojections... + +The poi-timis.geojson dataset has been downloaded from geofabrik.de, then filtered +on certain types and bounds. Data provided by Open Street Map Editors under the +Open Database License (ODbL) 1.0. diff --git a/workshop/jupyter/content/data/poi-timis.geojson b/workshop/jupyter/content/data/poi-timis.geojson new file mode 100644 index 0000000..d1f6134 --- /dev/null +++ b/workshop/jupyter/content/data/poi-timis.geojson @@ -0,0 +1,703 @@ +{ +"license": "Data provided by Open Street Map Editors under the Open Database License (ODbL) 1.0.", +"type": "FeatureCollection", +"name": "poi-timis", +"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC::CRS84" } }, +"xy_coordinate_resolution": 1e-08, +"features": [ +{ "type": "Feature", "properties": { "fid": 1094, "fclass": "fast_food", "name": "McDonald's" }, "geometry": { "type": "Point", "coordinates": [ 21.2260394, 45.7534964 ] } }, +{ "type": "Feature", "properties": { "fid": 1188, "fclass": "restaurant", "name": "Restaurant Tinecz" }, "geometry": { "type": "Point", "coordinates": [ 21.2221875, 45.7710081 ] } }, +{ "type": "Feature", 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"properties": { "fid": 93215, "fclass": "fast_food", "name": "Suta de Grame" }, "geometry": { "type": "Point", "coordinates": [ 21.2505908, 45.7574681 ] } }, +{ "type": "Feature", "properties": { "fid": 93218, "fclass": "cafe", "name": "Streinu" }, "geometry": { "type": "Point", "coordinates": [ 21.2085693, 45.7258737 ] } }, +{ "type": "Feature", "properties": { "fid": 93226, "fclass": "fast_food", "name": "Kebabistan" }, "geometry": { "type": "Point", "coordinates": [ 21.2208156, 45.763283 ] } }, +{ "type": "Feature", "properties": { "fid": 93360, "fclass": "bakery", "name": "Novapane" }, "geometry": { "type": "Point", "coordinates": [ 21.2390548, 45.7340296 ] } }, +{ "type": "Feature", "properties": { "fid": 93758, "fclass": "fast_food", "name": "Food Corner" }, "geometry": { "type": "Point", "coordinates": [ 21.2369706, 45.7471643 ] } }, +{ "type": "Feature", "properties": { "fid": 93987, "fclass": "restaurant", "name": "Enjoy Sushi" }, "geometry": { "type": "Point", "coordinates": [ 21.2293091, 45.755346 ] } }, +{ "type": "Feature", "properties": { "fid": 93996, "fclass": "nightclub", "name": "Marianes's Club" }, "geometry": { "type": "Point", "coordinates": [ 21.2061629, 45.7438802 ] } }, +{ "type": "Feature", "properties": { "fid": 93998, "fclass": "fast_food", "name": "Escobar the Burger" }, "geometry": { "type": "Point", "coordinates": [ 21.2384045, 45.7459355 ] } }, +{ "type": "Feature", "properties": { "fid": 94042, "fclass": "bakery", "name": "Mini Melanie" }, "geometry": { "type": "Point", "coordinates": [ 21.2000057, 45.7329721 ] } }, +{ "type": "Feature", "properties": { "fid": 94048, "fclass": "bar", "name": "Cafe Bar Lumi" }, "geometry": { "type": "Point", "coordinates": [ 21.2009773, 45.730446 ] } }, +{ "type": "Feature", "properties": { "fid": 94051, "fclass": "restaurant", "name": "Boom Pub" }, "geometry": { "type": "Point", "coordinates": [ 21.2282438, 45.7574378 ] } }, +{ "type": "Feature", "properties": { "fid": 94283, "fclass": "restaurant", "name": "Ciorbărie" }, "geometry": { "type": "Point", "coordinates": [ 21.2379861, 45.7340724 ] } }, +{ "type": "Feature", "properties": { "fid": 94435, "fclass": "bakery", "name": "" }, "geometry": { "type": "Point", "coordinates": [ 21.1974408, 45.7370266 ] } }, +{ "type": "Feature", "properties": { "fid": 94445, "fclass": "bakery", "name": "Sucitorul Fermecat" }, "geometry": { "type": "Point", "coordinates": [ 21.2033903, 45.7369103 ] } }, +{ "type": "Feature", "properties": { "fid": 94453, "fclass": "fast_food", "name": "Spoon Burger" }, "geometry": { "type": "Point", "coordinates": [ 21.2178207, 45.7594269 ] } }, +{ "type": "Feature", "properties": { "fid": 94836, "fclass": "restaurant", "name": "Pizza Hola" }, "geometry": { "type": "Point", "coordinates": [ 21.2587608, 45.7593038 ] } }, +{ "type": "Feature", "properties": { "fid": 95294, "fclass": "cafe", "name": "Meron" }, "geometry": { "type": "Point", "coordinates": [ 21.2242694, 45.7521864 ] } }, +{ "type": "Feature", "properties": { "fid": 95298, "fclass": "restaurant", "name": "indian bistro" }, "geometry": { "type": "Point", "coordinates": [ 21.2273437, 45.7581783 ] } }, +{ "type": "Feature", "properties": { "fid": 95333, "fclass": "restaurant", "name": "Brutarie" }, "geometry": { "type": "Point", "coordinates": [ 21.2473218, 45.7627358 ] } }, +{ "type": "Feature", "properties": { "fid": 95391, "fclass": "cafe", "name": "Stația de cafea Marschall" }, "geometry": { "type": "Point", "coordinates": [ 21.2202553, 45.7490845 ] } }, +{ "type": "Feature", "properties": { "fid": 95450, "fclass": "nightclub", "name": "Gentlemen's Poker Club" }, "geometry": { "type": "Point", "coordinates": [ 21.2329791, 45.7551223 ] } }, +{ "type": "Feature", "properties": { "fid": 96020, "fclass": "restaurant", "name": "Pulcinella Mosnița" }, "geometry": { "type": "Point", "coordinates": [ 21.3336095, 45.7335597 ] } }, +{ "type": "Feature", "properties": { "fid": 96182, "fclass": "bar", "name": "" }, "geometry": { "type": "Point", "coordinates": [ 21.1894968, 45.7501613 ] } }, +{ "type": "Feature", "properties": { "fid": 96183, "fclass": "restaurant", "name": "Ceaune și Ciolane" }, "geometry": { "type": "Point", "coordinates": [ 21.1955626, 45.767871 ] } }, +{ "type": "Feature", "properties": { "fid": 96186, "fclass": "fast_food", "name": "Kaufland Grill" }, "geometry": { "type": "Point", "coordinates": [ 21.212651, 45.7391032 ] } }, +{ "type": "Feature", "properties": { "fid": 96225, "fclass": "bakery", "name": "PEK ELA PAN" }, "geometry": { "type": "Point", "coordinates": [ 21.2249718, 45.7414861 ] } }, +{ "type": "Feature", "properties": { "fid": 96617, "fclass": "pub", "name": "Alf" }, "geometry": { "type": "Point", "coordinates": [ 21.2361151, 45.7462289 ] } }, +{ "type": "Feature", "properties": { "fid": 96724, "fclass": "restaurant", "name": "Pizza Contemporană" }, "geometry": { "type": "Point", "coordinates": [ 21.2508094, 45.7631577 ] } }, +{ "type": "Feature", "properties": { "fid": 96839, "fclass": "restaurant", "name": "Pulcinelo" }, "geometry": { "type": "Point", "coordinates": [ 21.3144993, 45.7196877 ] } }, +{ "type": "Feature", "properties": { "fid": 97009, "fclass": "fast_food", "name": "Atelierul de langoși" }, "geometry": { "type": "Point", "coordinates": [ 21.2482373, 45.7629307 ] } }, +{ "type": "Feature", "properties": { "fid": 97045, "fclass": "restaurant", "name": "E10 Hub & Pub" }, "geometry": { "type": "Point", "coordinates": [ 21.2142365, 45.7441546 ] } }, +{ "type": "Feature", "properties": { "fid": 97922, "fclass": "fast_food", "name": "King'Z Fast-food Tunisian" }, "geometry": { "type": "Point", "coordinates": [ 21.2068634, 45.7428938 ] } }, +{ "type": "Feature", "properties": { "fid": 98437, "fclass": "cafe", "name": "La povești în Traian" }, "geometry": { "type": "Point", "coordinates": [ 21.2489004, 45.756218 ] } }, +{ "type": "Feature", "properties": { "fid": 98442, "fclass": "bakery", "name": "Brutăria Nisi" }, "geometry": { "type": "Point", "coordinates": [ 21.2016124, 45.7336376 ] } }, +{ "type": "Feature", "properties": { "fid": 98612, "fclass": "hotel", "name": "HG Galaxy" }, "geometry": { "type": "Point", "coordinates": [ 21.2230403, 45.7587196 ] } } +] +} diff --git a/workshop/jupyter/content/notebooks/05-raster-data.ipynb b/workshop/jupyter/content/notebooks/05-raster-data.ipynb index 345cb70..13159a2 100644 --- a/workshop/jupyter/content/notebooks/05-raster-data.ipynb +++ b/workshop/jupyter/content/notebooks/05-raster-data.ipynb @@ -64,15 +64,17 @@ "gdal.UseExceptions()\n", "ds = gdal.Open('../data/world.rgb.tif')\n", "band = ds.GetRasterBand(1)\n", - "band.GetDescription()" + "print('band:', band.GetDescription())\n", + "min_val, max_val, mean_val, std_val = band.ComputeStatistics(False)\n", + "print('min:', min_val, 'max:', max_val, 'mean:', mean_val,'std:', std_val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "A typical use case for using gdal in python is resampling a grid and creating overviews, so the tiff can be used as a\n", - "Cloud Optimized GeoTiff (COG)" + "A typical use case for using gdal in python is translating and resampling a grid and creating overviews,\n", + "so the grid can be used as a Cloud Optimized GeoTiff (COG) in web application." ] }, { @@ -82,45 +84,12 @@ "outputs": [], "source": [ "# Resample\n", - "gdal.Warp('output_4326_cog.tif','../data/world.rgb.tif',dstSRS='EPSG:4326',format='COG')\n" - - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The COG format is optimal for storing large grid files in the cloud. Clients such as GDAL and OpenLayers are able\n", - "to fetch and render parts of the file, without downloading the full file. Below is an example using OpenLayers to\n", - "interact with the generated COG." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import HTML\n", - "\n", - "HTML('''\n", - "\n", - "\n", - "\n", - "
\n", - "\n", - "''')\n", - "\n" + "ds = gdal.Warp('../data/output/world_3857_cog.tif','../data/world.rgb.tif',\n", + " dstSRS='EPSG:3857', format='COG',\n", + " outputBounds=(-180, -85, 180, 85), outputBoundsSRS='EPSG:4326')\n", + "# plot first band using matplotlib\n", + "import matplotlib.pyplot as plt\n", + "r = plt.imshow(ds.GetRasterBand(1).ReadAsArray())\n" ] }, { diff --git a/workshop/jupyter/content/notebooks/07-visualization.ipynb b/workshop/jupyter/content/notebooks/07-visualization.ipynb index 762720e..bc0fedf 100644 --- a/workshop/jupyter/content/notebooks/07-visualization.ipynb +++ b/workshop/jupyter/content/notebooks/07-visualization.ipynb @@ -5,13 +5,12 @@ "metadata": {}, "source": [ "# Visualization\n", - "When it comes to visualizing geospatial data with/on maps with Python, a great number of tools and techniques\n", - "are available. In this lesson we will explore several of these:\n", + "In the previous chapters you have already seen some mechanisms to display geometry in a jupyter notebook, such\n", + "as Shapely and Matplotlib. In this chapter we'll look at more advanded visualization libraries:\n", "\n", "* [Folium](#Folium)\n", "* [ipyleaflet](#ipyleaflet)\n", - "* [Bokeh](#Bokeh)\n", - "* [pydeck](#pydeck)\n" + "* [Bokeh](#Bokeh)\n" ] }, { @@ -19,15 +18,14 @@ "metadata": {}, "source": [ "## Folium\n", - "Whenever you visit a website that has some kind of interactive map, it\n", - "is quite probable that you are witnessing a map that has been made with\n", - "a JavaScript library called [Leaflet](http://leafletjs.com). Other popular libraries you may encounter are\n", - "[OpenLayers](https://openlayers.org) and [maplibre](https://maplibre.org/).\n", + "Whenever you visit a website that has some kind of interactive map, the map\n", + "functionality is likely provided with a javascript library \n", + "such as [Leaflet](http://leafletjs.com), [OpenLayers](https://openlayers.org),\n", + "[maplibre](https://maplibre.org/), or [google maps](https://geemap.org/).\n", "\n", - "The Python module \n", - "[Folium](https://github.com/python-visualization/folium) makes\n", + "The Python module [Folium](https://github.com/python-visualization/folium) makes\n", "it possible to visualize data that has been manipulated in Python on an\n", - "interactive Leaflet map in a jupyter notebook or website generated from python code.\n", + "interactive Leaflet map in a jupyter notebook or python based website.\n", "\n", "### Basics\n", "We will start with the most minimal map using the default OpenStreetMap base map.\n", @@ -46,59 +44,15 @@ "source": [ "import folium\n", "\n", - "folium_map = folium.Map(location=[-1.4563, -48.5013])" + "folium.Map(location=[45.75, 21.25], zoom_start=12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `location` keyword argument (there are many more, with sensible defaults) provides the Map centre in Latitude and Longitude (Northing, Easting). To display the Map in a Jupyter notebook, simply ask for its object representation:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "pycharm": { - "name": "#%%\n" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "folium_map" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "You could even save this map to a file and serve it via a webserver: \n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "folium_map.save('test/07-folium-1.html')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now [open this map here](test/07-folium-1.html) as \"regular\" HTML! \n" + "The `location` keyword argument (there are many more, with sensible defaults) provides the Map centre\n", + "in Latitude and Longitude (Northing, Easting), `zoom_start` indicates the initial zoom level.\n" ] }, { @@ -107,13 +61,12 @@ "source": [ "### GeoJSON overlay\n", "It gets interesting when you can overlay the map with data manipulated\n", - "via Python. Here we overlay the map with the Polygons of all countries, though\n", - "that set is in a lower resolution clearly.\n" + "via Python. Here we overlay the map with the Polygons of all countries.\n" ] - }, + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" @@ -123,7 +76,7 @@ "source": [ "countries = f'../data/countries.json'\n", "\n", - "folium_map2 = folium.Map(\n", + "folium_map = folium.Map(\n", " location=[0, 0],\n", " zoom_start=2 \n", ")\n", @@ -131,33 +84,11 @@ "folium.GeoJson(\n", " countries,\n", " name='countries'\n", - ").add_to(folium_map2)\n", + ").add_to(folium_map)\n", "\n", - "folium.LayerControl().add_to(folium_map2)\n", + "folium.LayerControl().add_to(folium_map)\n", "\n", - "folium_map2" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Also save this map\n", - "folium_map2.save('test/07-folium-2.html')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And [open this map with overlay here](test/07-folium-2.html). \n", - "\n" + "display(folium_map)" ] }, { @@ -165,8 +96,9 @@ "metadata": {}, "source": [ "### Folium and Streamlit\n", - "Folium can also be [combined with Streamlit](https://folium.streamlit.app/). \n", + "Just like with Juptyer, Folium can also be combined with python web frameworks such as Streamlit\n", "[Streamlit](https://streamlit.io) is a platform to create interactive web apps for your python data scripts.\n", + "See for example this [Folium+Streamlit example](https://folium.streamlit.app/). \n", "\n" ] }, @@ -202,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" @@ -214,7 +146,7 @@ "from ipyleaflet import *\n", "\n", "m = Map(center=(-1.4563, -48.5013), zoom=10, basemap=basemaps.OpenStreetMap.Mapnik)\n", - "m" + "display(m)" ] }, { @@ -227,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" @@ -237,7 +169,7 @@ "source": [ "strava_all = basemap_to_tiles(basemaps.Strava.All)\n", "m.add_layer(strava_all)\n", - "m" + "display(m)" ] }, { @@ -250,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" @@ -271,7 +203,7 @@ " label.value = str(kwargs.get('coordinates'))\n", "\n", "m.on_interaction(handle_interaction)\n", - "m" + "display(m)" ] }, { @@ -284,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "pycharm": { "name": "#%% \n" @@ -305,8 +237,9 @@ " 'fillOpacity': 0.5\n", " }\n", "geo = GeoJSON(data=data, hover_style={'fillColor': 'red'}, name='Rivers-Lakes')\n", + "m.zoom = 8\n", "m.add_layer(geo)\n", - "m" + "display(m)" ] }, { @@ -319,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" @@ -340,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" @@ -359,83 +292,53 @@ "source": [ "### ipyleaflet - Adding Popups\n", "\n", - "#### 1. Adding a static popup" + "For this example we'll prepare a map of the following scenario\n", + "Seeing all the pubs and restaurants as a point on the map and on click show their name" ] }, - { + { "cell_type": "code", - "execution_count": 12, "metadata": {}, + "execution_count": 9, "outputs": [], "source": [ + "import json\n", + "from ipyleaflet import Map, Marker, MarkerCluster, Popup\n", "from ipywidgets import HTML\n", - "from ipyleaflet import Map, Marker, Popup\n", - "center = (-1.4217175,-48.4921731)\n", - "m = Map(center=center, zoom=17, close_popup_on_click=False)\n", - "marker = Marker(location=(-1.4217175,-48.4921731))\n", - "m.add_layer(marker)\n", - "message2 = HTML()\n", - "message2.value = \"Hey!! I'm speaking at FOSS4G 2024 🔥\"\n", - "marker.popup = message2\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 2. Using Custom data in popup\n", "\n", - "For this example we'll prepare map of following scenario\n", - "Seeing all the cities as a point on map and on click show their name" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "#Preparing data \n", - "import geopandas as gpd\n", - "all_cities = gpd.read_file('../data/shape/populated_places.shp')\n", - "all_countries = gpd.read_file('../data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp')\n", - "all_cities.dropna(subset=[\"NAME\",\"geometry\"])\n", - "Brazil = all_countries[all_countries['NAME'] == 'Brazil']\n", - "Brazilian_cities = all_cities[all_cities.within(Brazil.squeeze().geometry)]\n", - "Brazilian_cities\n", - "\n", - "# Creating Map\n", - "from ipyleaflet import Map, Marker, Popup\n", - "from ipywidgets import HTML\n", - "center = (-15.749997,-47.9499962)\n", - "m = Map(center=center, zoom=3, close_popup_on_click=False)\n", + "m = Map(center=(45.75, 21.25),zoom=12)\n", "\n", - "# Adding data as marker \n", - "for index, row in Brazilian_cities.iterrows():\n", - " message2 = HTML()\n", - " marker = Marker(location=(row['geometry'].y, row['geometry'].x))\n", - " message2.value = row['NAME']\n", - " # message2.description = row['NAME']\n", - " marker.popup = message2\n", - " m.add_layer(marker)\n", - "# print(index)\n", + "# Load GeoJSON\n", + "with open('../data/poi-timis.geojson') as f:\n", + " data = json.load(f)\n", "\n", - "#load map\n", - "m" + "# Create markers from Point features\n", + "markers = []\n", + "for feature in data['features']:\n", + " lon, lat = feature['geometry']['coordinates']\n", + " pophtml = f\"{feature['properties'].get('name')}
{feature['properties'].get('fclass')}\"\n", + " marker = Marker(\n", + " location=(lat, lon),\n", + " title=feature['properties'].get('name', '')\n", + " )\n", + " marker.popup = HTML(value=pophtml)\n", + " markers.append(marker)\n", + "\n", + "# Create cluster layer\n", + "m.add(MarkerCluster(markers=markers, disable_clustering_at_zoom=16))\n", + "display(m)" ] }, { - "cell_type": "markdown", + "cell_type": "markdown", "metadata": {}, "source": [ "### Other interesting ipyleaflet functions\n", "\n", "1. AntPath \n", - "2. Marker Cluster\n", - "3. Heatmap\n", - "4. Velocity\n", - "5. Choropleth\n", + "2. Heatmap\n", + "3. Velocity\n", + "4. Choropleth\n", "\n", "check out out at https://ipyleaflet.readthedocs.io/" ] @@ -478,111 +381,12 @@ "metadata": {}, "source": [ "### Bokeh - making a simple plot\n", - "First, we learn the basic logic of plotting in Bokeh by making a simple interactive plot with a few points.\n", - "\n", - "Import the necessary functionalities from Bokeh." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from bokeh.plotting import figure, save" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Initialize our plot by calling the `figure` object.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "p = figure(title='My first interactive plot!')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we create lists of x and y coordinates that we want to plot.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "x_coords = [0,1,2,3,4]\n", - "y_coords = [5,4,1,2,0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\tIn Bokeh drawing points, lines or polygons are always done using \n", - "\tlist(s) of x and y coordinates.\n", - "\n", - "Now we can plot those as points using a `.scatter()` -object. Give it a red color and size of 10.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "p.scatter(x=x_coords, y=y_coords, size=10, color='red')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can save our interactive plot into the disk with save -function \n", - "that we imported in the beginning. All interactive plots are typically \n", - "saved as html files which you can open in a web-browser.\n", - "\t\n", - "\t# Save the plot by passing the plot -object and output path\n", - "\tsave(obj=p, filename=\"../data/output/07-points.html\")\n", - "\n", - "Now you could open your interactive `points.html` plot by double-clicking it which should open it in a web browser.\n", - "\n", - "But we will plot directly in the Notebook here using `output_notebook()` and `show()`.\n" + "First, we learn the basic logic of plotting in Bokeh by making a simple interactive plot with a few points." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" @@ -590,27 +394,19 @@ }, "outputs": [], "source": [ + "# Import the necessary functionalities from Bokeh\n", + "from bokeh.plotting import figure, save\n", "from bokeh.io import output_notebook, show\n", - "output_notebook()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And then the moment of magic:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ + "# Initialize our plot by calling the `figure` object.\n", + "p = figure(title='My first interactive plot!')\n", + "# Next we create lists of x and y coordinates that we want to plot.\n", + "x_coords = [0,1,2,3,4]\n", + "y_coords = [5,4,1,2,0]\n", + "# Now we can plot those as points using a `.scatter()` -object. Give it a red color and size of 10.\n", + "p.scatter(x=x_coords, y=y_coords, size=10, color='red')\n", + "# direct the bokeh output to the notebook\n", + "output_notebook()\n", + "# show the output\n", "show(p)" ] }, @@ -623,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" @@ -633,31 +429,9 @@ "source": [ "from bokeh.plotting import figure\n", "from bokeh.io import output_notebook, show\n", - "output_notebook()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you show the figure, you can then use the wheel zoom and pan tools to navigate over any zoom level, \n", - "and Bokeh will request the appropriate tiles from the server and insert them at the correct locations in the plot:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# When using in standard Python env\n", - "# output_file(\"tile.html\")\n", + "output_notebook()", "\n", - "# range bounds supplied in web mercator coordinates\n", + "# add tile layer, bounds supplied in web mercator\n", "p = figure(tools='pan, wheel_zoom', x_range=(-10000000, -3000000), y_range=(-6000000, 0),\n", " x_axis_type='mercator', y_axis_type='mercator')\n", "p.add_tile(\"CartoDB Positron\", retina=True)\n", @@ -669,7 +443,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Creating an Interactive Maps using Bokeh and Geopandas - OPTIONAL\n", + "### Creating an Interactive Maps using Bokeh and Geopandas\n", "\n", "Creating an interactive Bokeh map from a Shapefile or other vector data file like GeoJSON\n", "consists typically of the following steps:\n", @@ -684,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" @@ -700,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" @@ -718,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" @@ -726,26 +500,15 @@ }, "outputs": [], "source": [ + "# Set coordintates for points\n", "points['x'] = points.apply(getPointCoords, geom='geometry', coord_type='x', axis=1)\n", - "points['y'] = points.apply(getPointCoords, geom='geometry', coord_type='y', axis=1)" - ] + "points['y'] = points.apply(getPointCoords, geom='geometry', coord_type='y', axis=1)\n", + "# Select top 5 points\n", + "points.head(5)\n" ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "points.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "metadata": { "pycharm": { "name": "#%% \n" @@ -753,27 +516,15 @@ }, "outputs": [], "source": [ + "# Remove geometry from columns \n", "p_df = points.drop('geometry', axis=1).copy()\n", - "p_df.head(2)" - ] + "# Select top 2 points\n", + "p_df.head(2)\n" + ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "from bokeh.models import ColumnDataSource\n", - "psource = ColumnDataSource(p_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 23, "metadata": { "pycharm": { "name": "#%% \n" @@ -781,8 +532,12 @@ }, "outputs": [], "source": [ - "# p = figure(title=\"A map of populated places from a GeoPackage\")\n", + "# As Columns\n", + "from bokeh.models import ColumnDataSource\n", + "psource = ColumnDataSource(p_df)\n", "p.scatter('x', 'y', source=psource, color='lightblue',size=15, alpha=0.7)\n", + "labels = LabelSet(x='x', y='y', text='NAME', source=psource, x_offset=5, y_offset=5)\n", + "p.add_layout(labels)\n", "show(p)" ] }, @@ -818,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" @@ -844,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" @@ -867,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" @@ -886,70 +641,6 @@ "show(p)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## pydeck\n", - "\n", - "Up till this point we've looked at 2D data visualisations. With [pydeck](https://pydeck.gl/)\n", - "we switch to WebGL-powered data visualization, including 3D and vector tiles.\n", - "pydeck is a python wrapper for the [deck.gl](https://deck.gl/) javascript library.\n", - "deck.gl visualisations typically use a vector tile background (from [mapbox](https://mapbox.com), [maptiler](https://www.maptiler.com/), or similar)\n", - "\n", - "![Example by deck.gl](images/deck.gl.jpg)\n", - "\n", - "The code snippet below creates a deck.gl view based on some sample data with tooltips.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import pydeck as pdk\n", - "import pandas as pd\n", - "\n", - "# Sample data: 6 locations with population\n", - "data = pd.DataFrame({\n", - " 'lat': [43.5081, 43.2967, 43.0500, 43.3438, 42.6507, 42.9228],\n", - " 'lon': [16.4402, 17.0177, 17.4333, 17.8078, 18.0944, 17.6158],\n", - " 'pop': [160000 , 13000, 6500, 105000, 41000, 4000],\n", - " 'city': ['Split', 'Makarska', 'Ploče', 'Mostar', 'Dubrovnik', 'Neum']\n", - "})\n", - "\n", - "# Define a scatterplot layer\n", - "layer = pdk.Layer(\n", - " 'ScatterplotLayer',\n", - " data=data,\n", - " get_position='[lon, lat]',\n", - " get_radius='pop / 10',\n", - " get_fill_color='[180, 0, 200, 140]',\n", - " pickable=True\n", - ")\n", - "\n", - "# Define map view\n", - "view_state = pdk.ViewState(\n", - " latitude=42.7,\n", - " longitude=16.8,\n", - " zoom=7,\n", - " pitch=30,\n", - " bearing=10\n", - ")\n", - "\n", - "# Render view\n", - "deck = pdk.Deck(\n", - " layers=[layer],\n", - " initial_view_state=view_state,\n", - " tooltip={\"text\": \"{city}\"}\n", - ")\n", - "\n", - "deck.show()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -975,7 +666,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.13" } }, "nbformat": 4,