Merge pull request #185 from geocompx/184-output-datasets-frequently-…

…updated

Do not evaluate chunks that update data #184

02-spatial-data.qmd

@@ -766,6 +766,7 @@ Finally, to export the raster for permanent storage, along with the CRS definiti
 In the case of `elev`, we do it as follows:
 
 ```{python}
+#| eval: false
 new_dataset = rasterio.open(
     'output/elev.tif', 'w', 
     driver='GTiff',
@@ -785,6 +786,7 @@ Note that the CRS we (arbitrarily) set for the `elev` raster is WGS84, defined u
 Here is how we export the `grain` raster as well, using almost the exact same code just with `elev` replaced with `grain`:
 
 ```{python}
+#| eval: false
 new_dataset = rasterio.open(
     'output/grain.tif', 'w', 
     driver='GTiff',

05-geometry-operations.qmd

@@ -835,6 +835,7 @@ dst.close()
 Here is another code section to demontrate another resampling method, the maximum resampling, i.e., every new pixel gets the maximum value of all the original pixels it coincides with. Note that the transform is identical (@fig-raster-resample), so we do not need to calculate it again:
 
 ```{python}
+#| eval: false
 dst = rasterio.open('output/dem_resample_maximum.tif', 'w', **dst_kwargs)
 rasterio.warp.reproject(
     source=rasterio.band(src, 1),

06-raster-vector.qmd

@@ -711,6 +711,7 @@ Unfortunately, `rasterio` does not provide any way of extracting the contour lin
 We hereby demonstrate the first and easiest approach, using `gdal_contour`. Although we deviate from exclusively using the Python language, the benefit of `gdal_contour` is the proven algorithm, customized to spatial data, and with many relevant options. `gdal_contour` (along with other GDAL programs) should already be installed on your system since this is a dependency of `rasterio`. For example, generating 50 $m$ contours of the `dem.tif` file can be done as follows: 
 
 ```{python}
+#| eval: false
 os.system('gdal_contour -a elev data/dem.tif output/dem_contour.gpkg -i 50.0')
 ```
 

07-reproj.qmd

@@ -504,6 +504,7 @@ It is also possible to reproject into an in-memory `ndarray` object, see the [do
 To write the reprojected raster, we first create a destination file connection `dst_nlcd`, pointing at the output file path of our choice (`output/nlcd_4326.tif`), using the updated metadata object created earlier (`dst_kwargs`):
 
 ```{python}
+#| echo: false
 dst_nlcd = rasterio.open('output/nlcd_4326.tif', 'w', **dst_kwargs)
 ```
 
@@ -579,6 +580,7 @@ template.meta
 Then, we create a write-mode connection to our destination raster, using this metadata, meaning that as the resampling result is going to have identical metadata as the "template":
 
 ```{python}
+#| echo: false
 dst_nlcd_2 = rasterio.open('output/nlcd_4326_2.tif', 'w', **template.meta)
 ```
 

08-read-write-plot.qmd

@@ -402,18 +402,21 @@ The next two sections will demonstrate how to do this.
 The counterpart of `gpd.read_file` is the `.to_file` method that a `GeoDataFrame` has. It allows you to write `GeoDataFrame` objects to a wide range of geographic vector file formats, including the most common, such as `.geojson`, `.shp` and `.gpkg`. Based on the file name, `.to_file` decides automatically which driver to use. The speed of the writing process depends also on the driver.
 
 ```{python}
+#| eval: false
 world.to_file('output/world.gpkg')
 ```
 
 Note: if you try to write to the same data source again, the function will overwrite the file:
 
 ```{python}
+#| eval: false
 world.to_file('output/world.gpkg')
 ```
 
 Instead of overwriting the file, we could add a new layer to the file with `mode='a'` ("append" mode, as opposed to the default `mode='w'` for "write" mode). Appending is supported by several spatial formats, including GeoPackage. For example:
 
 ```{python}
+#| eval: false
 world.to_file('output/world_many_features.gpkg')
 world.to_file('output/world_many_features.gpkg', mode='a')
 ```
@@ -423,6 +426,7 @@ Here, `world_many_features.gpkg` will contain a polygonal layer named `world` wi
 Alternatively, you can create another, separate, layer, within the same file. The GeoPackage format also supports multiple layers within one file. For example:
 
 ```{python}
+#| eval: false
 world.to_file('output/world_many_layers.gpkg')
 world.to_file('output/world_many_layers.gpkg', layer='world2')
 ```
@@ -547,12 +551,7 @@ The raster data type can be specified when writing a raster (see above). For an
 rasterio.open('output/r.tif').meta['dtype']
 ```
 
-The file `r.tif` has the data type `np.int8`, which we specified when creating it according to the data type of the original array:
-
-```{python}
-r.dtype
-```
-
+The file `r.tif` has the data type `np.int8`, which we specified when creating it according to the data type of the original array.
 When reading the data back into the Python session, the array with the same data type is recreated:
 
 ```{python}
@@ -614,6 +613,7 @@ r.dtype
 When writing the array to file, we do not need to specify any particular `nodata` value:
 
 ```{python}
+#| eval: false
 dst = rasterio.open(
     'output/r_nodata_float.tif', 'w', 
     driver = 'GTiff',
@@ -654,6 +654,7 @@ r.dtype
 When writing the array to file, we must specify `nodata=-9999` to keep track of our "No Data" flag:
 
 ```{python}
+#| eval: false
 dst = rasterio.open(
     'output/r_nodata_int.tif', 'w', 
     driver = 'GTiff',
@@ -765,7 +766,7 @@ For example, the following code section exports the same raster plot to a file n
 
 ```{python}
 #| output: false
-
+#| eval: false
 fig, ax = plt.subplots(figsize=(5, 7))
 rasterio.plot.show(nz_elev, ax=ax)
 nz.to_crs(nz_elev.crs).plot(ax=ax, facecolor='none', edgecolor='r');