Use Julia environment

.github/workflows/pr.yml

@@ -32,6 +32,9 @@ jobs:
         with:
           cache-compiled: "true"
           cache-registries: "true"
+      - name: Preload some libraries
+        run: |
+          julia --project -e "using GeoDataFrames"
 
       - name: Set up Quarto
         uses: quarto-dev/quarto-actions/setup@v2
@@ -43,9 +46,13 @@ jobs:
           # Extra repositories:
           extra-repositories: |
             https://josiahparry.r-universe.dev
+
       - name: R deps
         uses: r-lib/actions/setup-r-dependencies@v2
         with:
           cache-version: 2
           extra-packages: |
             any::sf
+
+      - name: Render Quarto Project
+        uses: quarto-dev/quarto-actions/render@v2

Project.toml

@@ -1,2 +1,3 @@
 [deps]
 GeoDataFrames = "62cb38b5-d8d2-4862-a48e-6a340996859f"
+Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"

blog.qmd

@@ -32,6 +32,21 @@ knitr::opts_chunk$set(message = FALSE, warning = FALSE)
 # install python packages needed for this document:
 pkgs = c("geopandas", "shapely", "pandas", "rasterio", "matplotlib", "folium", "mapclassify")
 reticulate::py_install(pkgs)
+x = .libPaths()
+write(x, stderr())
+Sys.setenv(LD_LIBRARY_PATH = "")
+```
+
+```{julia}
+using Pkg
+println(stderr, ENV)
+using Libdl
+println.(stderr, Libdl.DL_LOAD_PATH);
+println(stderr, "===")
+println.(stderr, Libdl.dllist());
+Pkg.status()
+Pkg.activate(".")
+Pkg.status()
 ```
 
 # Introduction
@@ -71,7 +86,7 @@ See detailed description of [R](https://ogh23.robinlovelace.net/tidy#vector-data
 
 ## Python
 
-```{python}
+```python
 import pandas as pd
 from shapely import Point
 import geopandas as gpd
@@ -95,7 +110,7 @@ library(tmap)
 
 ## Julia
 
-```julia
+```{julia}
 using GeoDataFrames
 ```
 
@@ -110,7 +125,7 @@ Most projects start with pre-generated data, but it's useful to create datasets
 
 ## Python
 
-```{python}
+```python
 poi = gpd.GeoDataFrame([
     {"name": "Faculty",        "geometry": Point(16.9418, 52.4643)},
     {"name": "Hotel ForZa",    "geometry": Point(16.9474, 52.4436)},
@@ -142,7 +157,7 @@ The following commands download data from the internet.
 
 ## Python
 
-```{python}
+```python
 import urllib.request
 import zipfile
 import os
@@ -164,12 +179,12 @@ if (!dir.exists("data")) {
 
 ## Julia
 
-```julia
+```{julia}
 using Downloads
 u = "https://github.com/Robinlovelace/opengeohub2023/releases/download/data/data.zip"
 f = basename(u)
 if !isfile(f)
-    download(u, f)
+    Downloads.download(u, f)
 end
 ```
 
@@ -185,7 +200,7 @@ We can unzip the data in R and Python: it contains spatial information about bus
 
 ## Python
 
-```{python}
+```python
 with zipfile.ZipFile(f, 'r') as zip_ref:
     zip_ref.extractall()
 ```
@@ -207,7 +222,7 @@ Note: we recommend using open file formats such as GeoPackage (`.gpkg`), as outl
 
 ## Python
 
-```{python}
+```python
 pol_all = gpd.read_file("zip://data.zip!data/osm/gis_osm_transport_a_free_1.shp")
 pol_all
 ```
@@ -221,24 +236,29 @@ pol_all
 
 ## Julia
 
-```julia
-df = GeoDataFrames.read("/vsizip/data.zip/data/osm/gis_osm_transport_a_free_1.shp")
-df
+```{julia}
+pol_all = GeoDataFrames.read("/vsizip/data.zip/data/osm/gis_osm_transport_a_free_1.shp")
+pol_all
 ```
 
 :::
 
 <!---
   This should also work in Python and Julia no? It's a GDAL feature.
 --->
-Note: in R, you can read-in the dataset from the URL in a single line of code without first downloading the zip file:
+Note: in R and Julia, you can read-in the dataset from the URL in a single line of code without first downloading the zip file:
 
 ```{r}
 #| eval: false
 uz = paste0("/vsizip//vsicurl/", u, "/data/osm/gis_osm_transport_a_free_1.shp")
 pol_all = sf::read_sf(uz)
 ```
 
+```{julia}
+zipurl = "/vsizip/vsicurl/" * u * "/data/osm/gis_osm_transport_a_free_1.shp"
+pol_all = GeoDataFrames.read(zipurl)
+```
+
 ## Subsetting by attributes
 
 The following commands select a subset of the data based on attribute values (looking for a specific string in the `name` column).
@@ -247,7 +267,7 @@ The following commands select a subset of the data based on attribute values (lo
 
 ## Python
 
-```{python}
+```python
 pol = pol_all[pol_all['name'].str.contains('Port*.+Poz', na=False)]
 pol
 ```
@@ -256,10 +276,17 @@ pol
 
 ```{r}
 pol = pol_all |>
-  filter(str_detect(name, "Port*.+Poz"))
+    filter(str_detect(name, "Port*.+Poz"))
 pol
 ```
 
+## Julia
+```{julia}
+using DataFrames
+pol = dropmissing(pol_all, :name)
+pol = subset!(pol, :name => n -> occursin.(r"Port*.+Poz", n))
+```
+
 :::
 
 ## Basic plotting
@@ -271,7 +298,7 @@ Note that by default, R's `plot()` method for `{sf}` objects creates a plot for
 
 ## Python
 
-```{python}
+```python
 pol.plot();
 ```
 
@@ -289,7 +316,7 @@ The arguments needed to change the colour of the fill and border are different i
 
 ## Python
 
-```{python}
+```python
 pol.plot(color='none', edgecolor='black');
 ```
 
@@ -310,7 +337,7 @@ Note that two steps---creating the geometry column and combining it with the ori
 
 ## Python
 
-```{python}
+```python
 # Unzip the data.zip file:
 with zipfile.ZipFile(f, 'r') as zip_ref:
     zip_ref.extractall("data")
@@ -326,8 +353,8 @@ stops
 
 ```{r}
 stops = read_csv("data/gtfs/stops.txt") |>
-  select(-stop_code) |>
-  st_as_sf(coords = c("stop_lon", "stop_lat"), crs = "EPSG:4326")
+    select(-stop_code) |>
+    st_as_sf(coords = c("stop_lon", "stop_lat"), crs = "EPSG:4326")
 stops
 ```
 
@@ -342,15 +369,15 @@ Note that the **tmap** package is hereby used in R to create these more advanced
 
 ## Python
 
-```{python}
+```python
 stops.plot(markersize=1, column='zone_id', legend=True);
 ```
 
 ## R
 
 ```{r}
 tm_shape(stops) +
-  tm_symbols(size = 0.1, col = "zone_id")
+    tm_symbols(size = 0.1, col = "zone_id")
 ```
 
 :::
@@ -361,7 +388,7 @@ We can add basic overlays in both languages as follows.
 
 ## Python
 
-```{python}
+```python
 base = stops.plot(markersize=0.1)
 poi.plot(ax=base, color='red');
 ```
@@ -389,7 +416,7 @@ Note that with **tmap**, you can use the same code to create static and interact
 
 ## Python
 
-```{python}
+```python
 stops.explore(column='zone_id', legend=True, cmap='Dark2')
 ```
 
@@ -398,7 +425,7 @@ stops.explore(column='zone_id', legend=True, cmap='Dark2')
 ```{r}
 tmap_mode("view")
 tm_shape(stops) +
-  tm_symbols(size = 0.1, col = "zone_id")
+    tm_symbols(size = 0.1, col = "zone_id")
 ```
 
 :::
@@ -411,7 +438,7 @@ The following commands reproject the data to a local projected Coordinate Refere
 
 ## Python
 
-```{python}
+```python
 poi.crs
 poi_projected = poi.to_crs(2180)
 stops_projected = stops.to_crs(2180)
@@ -443,7 +470,7 @@ For buffer operations to work in Python you must reproject the data first (which
 
 To create a new vector layers named `poi_buffer`, in both languages, we can do the following.
 
-```{python}
+```python
 poi_buffer = poi.copy()
 poi_buffer.geometry = poi_projected.buffer(150).to_crs(4326)
 ```
@@ -464,7 +491,7 @@ An interesting difference between R and Python is that the former uses the `unit
 
 ## Python
 
-```{python}
+```python
 poi_buffer.to_crs(2180).area
 ```
 
@@ -488,7 +515,7 @@ The R code is more concise because there is a special `[` notation for the speci
 
 ## Python
 
-```{python}
+```python
 poi_union = poi_buffer.unary_union
 sel = stops.intersects(poi_union)
 stops_in_b = stops[sel]
@@ -513,7 +540,7 @@ See the [Python](https://geobgu.xyz/presentations/p_2023_ogh/01-vector.html#sec-
 
 ## Python
 
-```{python}
+```python
 poi_buffer.sjoin(stops, how='left')
 ```
 
@@ -525,7 +552,7 @@ st_join(poi_buffer, stops)
 
 :::
 
-```{python}
+```python
 #| eval: false
 #| echo: false
 # Aim: check to see if there's a bug in sjoin

osm.qmd

@@ -344,7 +344,7 @@ if (!"pyrosm" %in% py_pkg_names) {
 Search for Poznan in extracts available from `pyrosm` as follows (note: this fails for me currently as documented in [github.com/HTenkanen/pyrosm/issues/217](https://github.com/HTenkanen/pyrosm/issues/217)):
 
 
-```{python}
+```python
 #| eval: false
 import pyrosm
 from pyrosm import OSM
@@ -376,7 +376,7 @@ if (!"osmnx" %in% py_pkg_names) {
 ```
 
 
-```{python}
+```python
 import osmnx as ox
 import pandas as pd
 import geopandas as gpd
@@ -387,35 +387,35 @@ poznan_polygon.plot();
 
 That is quite a big network, so let's get the area of the polygon and use that to get a smaller network from [GitHub](https://github.com/Robinlovelace/opengeohub2023/raw/main/pois_buffer_simple.geojson):
 
-```{python}
+```python
 # Get data from https://github.com/Robinlovelace/opengeohub2023/raw/main/pois_buffer_simple.geojson:
 poznan_small = gpd.read_file("https://github.com/Robinlovelace/opengeohub2023/raw/main/pois_buffer_simple.geojson")
 poznan_small.plot();
 ```
 
 Download the cycling network as follows:
 
-```{python}
+```python
 G_cycle = ox.graph_from_polygon(poznan_small.geometry[0], network_type="bike")
 ```
 
 Plot the results:
 
-```{python}
+```python
 ox.plot_graph(G_cycle)
 ```
 
 Get basic stats as follows:
 
-```{python}
+```python
 area = ox.project_gdf(poznan_small).unary_union.area
 stats = ox.basic_stats(G_cycle, area=area)
 pd.Series(stats)
 ```
 
 We can convert the object into a 'GeoDataFrame' as follows:
 
-```{python}
+```python
 cycle_gdf = ox.graph_to_gdfs(G_cycle, edges=True)
 cycle_gdf[1].plot();
 ```