Split conda environment into two files, because one appeared to be too large for the virtual machines.

Author: Verstegen

index.Rmd

@@ -45,25 +45,30 @@ mkdir data
 mkdir output
 ```
 
-Next, create a text file, (re)name it (to) `raster.yaml`, and copy the following content into the file:
+Like in the previous lesson, we will create a conda environment with one `.yaml` file, and then update it with a second `.yaml` file. Use the following content for the two files:
 ```
 name: raster
-channels:
-  - conda-forge
 dependencies:
   - python
   - numpy
   - matplotlib
   - spyder
   - owslib
   - gdal
-  - rasterio
   - geopandas
+```
+
+```
+name: raster
+channels:
+  - conda-forge
+dependencies:
+  - rasterio
   - rasterstats
   - affine
 ```
 
-Use this file to create an environment for this tutorial, as you have learned in the refresher lesson. NOTE: This can take a while (5-10 minutes); just go and get a coffee/tea/water/juice. Activate the environment, open Spyder, create a script in the root folder and start coding.
+Use these two files to create an environment for this tutorial. NOTE: The second one can take a while (5-10 minutes); just go and get a coffee/tea/water/juice. Finally, activate the environment, open Spyder, create a script in the root folder and start coding.
 
 
 ## Reading raster data and accessing metadata

index.html

@@ -873,7 +873,7 @@
 <div class="contents col-xs-12 col-md-10">
 <h1 id="wur-geoscripting"><a href="https://geoscripting-wur.github.io/">WUR Geoscripting</a> <img src="https://www.wur.nl/upload/854757ab-168f-46d7-b415-f8b501eebaa5_WUR_RGB_standard_2021-site.svg" alt="WUR logo" style="height: 35px;"/></h1>
 <h1 id="week-3-lesson-10-handling-raster-data-with-python">Week 3, Lesson 10: Handling Raster data with Python</h1>
-<p><em>Jan Verbesselt, Jorge Mendes de Jesus, Aldo Bergsma, Dainius Masiliūnas, David Swinkels, Judith Verstegen, Corné Vreugdenhil</em> - 2022-01-09</p>
+<p><em>Jan Verbesselt, Jorge Mendes de Jesus, Aldo Bergsma, Dainius Masiliūnas, David Swinkels, Judith Verstegen, Corné Vreugdenhil</em> - 2022-01-17</p>
 <h2 id="introduction">Introduction</h2>
 <p>Today we will work with Python packages for spatial raster analysis. Python has some dedicated packages to handle rasters:</p>
 <ul>
@@ -902,22 +902,24 @@ <h2 id="setting-up-the-python-environment">Setting up the Python Environment</h2
 mkdir data
 mkdir output</code></pre>
 </div>
-<p>Next, create a text file, (re)name it (to) <code>raster.yaml</code>, and copy the following content into the file:</p>
+<p>Like in the previous lesson, we will create a conda environment with one <code>.yaml</code> file, and then update it with a second <code>.yaml</code> file. Use the following content for the two files:</p>
 <pre><code>name: raster
-channels:
-  - conda-forge
 dependencies:
   - python
   - numpy
   - matplotlib
   - spyder
   - owslib
   - gdal
+  - geopandas</code></pre>
+<pre><code>name: raster
+channels:
+  - conda-forge
+dependencies:
   - rasterio
-  - geopandas
   - rasterstats
   - affine</code></pre>
-<p>Use this file to create an environment for this tutorial, as you have learned in the refresher lesson. NOTE: This can take a while (5-10 minutes); just go and get a coffee/tea/water/juice. Activate the environment, open Spyder, create a script in the root folder and start coding.</p>
+<p>Use these two files to create an environment for this tutorial. NOTE: The second one can take a while (5-10 minutes); just go and get a coffee/tea/water/juice. Finally, activate the environment, open Spyder, create a script in the root folder and start coding.</p>
 <h2 id="reading-raster-data-and-accessing-metadata">Reading raster data and accessing metadata</h2>
 <h3 id="via-a-web-coverage-service">Via a Web Coverage Service</h3>
 <p>A Web Coverage Service (WCS) loads raster data in a similar way as Web Feature Services (WFS) load vector data. <a href="https://www.opengeospatial.org/standards/wcs">Web Coverage Services</a> are a standard by the Open Geospatial Consortium and allow the downloading of geospatial raster data with multiple types of format encoding: GeoTIFF, netCDF, JPEG2000 etc. A <a href="https://www.opengeospatial.org/standards/wms">Web Map Service</a> [WMS] also exists for rasters; it allows downloading of images but without the data values.</p>