Merge branch 'grass8' into t.stac-lazy-load

.github/workflows/additional_checks.yml

@@ -45,7 +45,7 @@ jobs:
           exclude: mswindows .*\.bat .*/testsuite/data/.*
 
       - name: Set up Python
-        uses: actions/setup-python@39cd14951b08e74b54015e9e001cdefcf80e669f # v5.1.1
+        uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 

.github/workflows/apt.txt

@@ -8,13 +8,16 @@ libgdal-dev
 libgl1-mesa-dev
 libglu1-mesa-dev
 libnetcdf-dev
-libopenblas-dev
+liblapacke-dev
 libpng-dev
 libproj-dev
 libreadline-dev
+libudunits2-dev
+libudunits2-data
 libzstd-dev
 proj-bin
 pdal
 sqlite3
 subversion
+udunits-bin
 zlib1g-dev

.github/workflows/black.yml

@@ -34,7 +34,7 @@ jobs:
         with:
           persist-credentials: false
       - name: Set up Python
-        uses: actions/setup-python@39cd14951b08e74b54015e9e001cdefcf80e669f # v5.1.1
+        uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0
         with:
           python-version: ${{ env.PYTHON_VERSION }}
           cache: "pip"

.github/workflows/ci.yml

@@ -57,7 +57,7 @@ jobs:
               sudo apt-get install -y --no-install-recommends --no-install-suggests
 
       - name: Set up Python ${{ matrix.python-version }} as default Python
-        uses: actions/setup-python@39cd14951b08e74b54015e9e001cdefcf80e669f # v5.1.1
+        uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -102,6 +102,8 @@ jobs:
         run: |
           GDAL_VERSION="$(gdal-config --version)"
           export GDAL_VERSION
+          UDUNITS2_XML_PATH=/usr/share/xml/udunits/udunits2.xml
+          export UDUNITS2_XML_PATH
           pip install -r grass-addons/.github/workflows/extra_requirements.txt
 
       - name: Set up R
@@ -119,7 +121,7 @@ jobs:
           ../.github/workflows/test.sh
 
       - name: Make HTML test report available
-        uses: actions/upload-artifact@834a144ee995460fba8ed112a2fc961b36a5ec5a # v4.3.6
+        uses: actions/upload-artifact@50769540e7f4bd5e21e526ee35c689e35e0d6874 # v4.4.0
         with:
           name: testreport-grass-${{ matrix.grass-version }}-python-${{ matrix.python-version }}
           path: grass-addons/src/testreport

.github/workflows/clang-format-check.yml

@@ -18,7 +18,7 @@ jobs:
       - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
         with:
           persist-credentials: false
-      - uses: DoozyX/clang-format-lint-action@caa179272c6ee7f1d25dfb503ee0c410c26ebd98 # v0.18.1
+      - uses: DoozyX/clang-format-lint-action@c71d0bf4e21876ebec3e5647491186f8797fde31 # v0.18.2
         with:
           source: "."
           clangFormatVersion: 18.1.8

.github/workflows/extra_requirements.txt

@@ -4,5 +4,5 @@ pandas
 scikit-learn
 GDAL==${GDAL_VERSION}
 pygbif
-cf_units
+cf_units @ git+https://github.com/SciTools/cf-units@f57a7f1fdba4e9424e4dbbc01b614e521a88086c
 thredds_crawler

.github/workflows/flake8.yml

@@ -29,7 +29,7 @@ jobs:
       - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
 
       - name: Set up Python
-        uses: actions/setup-python@39cd14951b08e74b54015e9e001cdefcf80e669f # v5.1.1
+        uses: actions/setup-python@f677139bbe7f9c59b41e40162b753c062f5d49a3 # v5.2.0
         with:
           python-version: ${{ env.PYTHON_VERSION }}
 

src/imagery/i.sentinel/i.sentinel.download/i.sentinel.download.html

@@ -197,7 +197,7 @@ <h3>List filtered products</h3>
 g.region n=42 w=12 s=41 e=13 res=0:01 -p
 i.sentinel.download -l settings=credentials.txt producttype=S2MSI2A start=2018-01-01 end=2018-12-31 clouds=1 footprints=s2_scenes_footprints
 
-Writing footprints into <s2_scenes_footprints>...
+Writing footprints into &lt;s2_scenes_footprints&lt;...
 197 scene(s) found.
 [...]
 S2B_MSIL2A_20180210T100139_N9999_R122_T32TQM_20221022T182154 2018-02-10T10:01:39   0% S2MSI2A 0.98 GB
@@ -207,6 +207,7 @@ <h3>List filtered products</h3>
 S2A_MSIL2A_20181023T100051_N9999_R122_T33TTG_20221023T032324 2018-10-23T10:00:51   0% S2MSI2A 0.99 GB
 S2A_MSIL2A_20181205T101401_N9999_R022_T32TQM_20221022T180259 2018-12-05T10:14:01   0% S2MSI2A 625.52 MB
 [...]
+</pre></div>
 <p>
 Find Sentinel-2 L1C products (S2MSI1C) of <b>last 60 days</b> (default) covering current computation region extent:
 
@@ -282,7 +283,7 @@ <h3>Download Sentinel products by ID</h3>
 
 <div class="code"><pre>
 i.sentinel.download settings=credentials.txt id=S2B_MSIL2A_20180210T100139_N9999_R122_T32TQM_20221022T182154 output=s2_data/
-<pre></div>
+</pre></div>
 
 <h2>REQUIREMENTS</h2>
 

src/imagery/i.sentinel/i.sentinel.download/i.sentinel.download.py

@@ -39,7 +39,6 @@
 # %option G_OPT_V_OUTPUT
 # % key: footprints
 # % description: Name for output vector map with footprints
-# % label: Only supported for download from ESA_Copernicus Open Access Hub
 # % required: no
 # % guisection: Output
 # %end
@@ -53,7 +52,6 @@
 # % key: area_relation
 # % type: string
 # % description: Spatial relation of footprint to AOI
-# % label: ESA Copernicus Open Access Hub allows all three, USGS Earth Explorer only 'Intersects' option
 # % options: Intersects,Contains,IsWithin
 # % answer: Intersects
 # % required: no
@@ -70,7 +68,6 @@
 # % key: producttype
 # % type: string
 # % description: Sentinel product type to filter
-# % label: USGS Earth Explorer only supports S2MSI1C
 # % required: no
 # % options: SLC,GRD,OCN,S2MSI1C,S2MSI2A,S3OL1EFR,S3OL1ERR,S3OL1SPC,S3SL1RBT,S3OL2WFR,S3OL2WRR,S3OL2LFR,S3OL2LRR,S3SL2LST,S3SL2FRP,S3SY2SYN,S3SY2VGP,S3SY2VG1,S3SY2V10,S3SY2AOD,S3SR2LAN
 # % answer: S2MSI2A
@@ -98,7 +95,6 @@
 # % key: query
 # % type: string
 # % description: Extra search keywords to use in the query
-# % label: USGS Earth Explorer only supports query options "identifier", "filename" (in ESA name format) or "usgs_identifier" (in USGS name format)
 # % guisection: Filter
 # %end
 # %option
@@ -113,7 +109,6 @@
 # % type: integer
 # % multiple: yes
 # % description: Relative orbit number to download (Sentinel-1: from 1 to 175; Sentinel-2: from 1 to 143; Sentinel-3: from 1 to 385)
-# % label:_Only supported by ESA Copernicus Open Access Hub.
 # % guisection: Filter
 # %end
 # %option
@@ -174,7 +169,7 @@
 # %rules
 # % requires: -b,map
 # % required: output,-l,-p
-# % excludes: id,map,area_relation,clouds,producttype,start,end,limit,query,sort,order
+# % excludes: id,map,area_relation,clouds,start,end,limit,query,sort,order
 # % excludes: -p,-l
 # %end
 
@@ -429,6 +424,8 @@ def main():
                     id=options["id"],
                     output=outdir,
                     provider=eodag_provider,
+                    producttype=options["producttype"],
+                    footprints=options["footprints"],
                 )
             )
         except CalledModuleError:

src/raster/r.learn.ml2/r.learn.train/r.learn.train.py

@@ -445,11 +445,11 @@ def main():
     try:
         import sklearn
 
-        if sklearn.__version__ < "0.20":
-            gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
+        if sklearn.__version__ < "1.2.2":
+            gs.fatal("Package python3-scikit-learn 1.2.2 or newer is not installed")
 
     except ImportError:
-        gs.fatal("Package python3-scikit-learn 0.20 or newer is not installed")
+        gs.fatal("Package python3-scikit-learn 1.2.2 or newer is not installed")
 
     try:
         import pandas as pd
@@ -683,15 +683,15 @@ def main():
 
     # one-hot encoding
     elif norm_data is False and category_maps is not None:
-        enc = OneHotEncoder(handle_unknown="ignore", sparse=False)
+        enc = OneHotEncoder(handle_unknown="ignore", sparse_output=False)
         trans = ColumnTransformer(
             remainder="passthrough", transformers=[("onehot", enc, stack.categorical)]
         )
 
     # standardization and one-hot encoding
     elif norm_data is True and category_maps is not None:
         scaler = StandardScaler()
-        enc = OneHotEncoder(handle_unknown="ignore", sparse=False)
+        enc = OneHotEncoder(handle_unknown="ignore", sparse_output=False)
         trans = ColumnTransformer(
             remainder="passthrough",
             transformers=[

src/raster/r.sun.daily/r.sun.daily.py

@@ -58,7 +58,7 @@
 # % key_desc: float
 # % type: double
 # % description: A single value of the Linke atmospheric turbidity coefficient [-]
-# % options: 0.0-7.0
+# % options: 0.0-
 # % answer: 3.0
 # % required : no
 # %end

src/raster/r.sun.hourly/r.sun.hourly.py

@@ -49,7 +49,7 @@
 # % key: linke_value
 # % type: double
 # % description: A single value of the Linke atmospheric turbidity coefficient [-]
-# % options: 0.0-7.0
+# % options: 0.0-
 # % answer: 3.0
 # % required: no
 # %end

src/raster/r.vif/r.vif.py

@@ -216,7 +216,7 @@ def compute_vif(mapx, mapy):
     unused, resid = np.linalg.lstsq(x_i, mapy, rcond=None)[:2]
     if resid.size == 0:
         resid = 0
-    r2 = float(1 - resid / (mapy.size * mapy.var()))
+    r2 = float(1 - resid[0] / (mapy.size * mapy.var()))
     if float(r2) > 0.9999999999:
         vif = float("inf")
         sqrtvif = float("inf")

src/temporal/t.rast.import.netcdf/t.rast.import.netcdf.html

@@ -15,10 +15,18 @@ <h2>DESCRIPTION</h2>
 can be imported via <em>r.in.gdal</em> or linked with <em>r.external</em>.
 
 <p>
-<em>t.rast.import.netcdf</em> can use GDALs Virtual Raster format (VRT) if data's
+<em>t.rast.import.netcdf</em> uses GDALs Virtual Raster format (VRT) if data's
 Coordinate Reference system differs from the one of the current location where they
 are supposed to be imported.
 
+<p>
+Reprojection on import is done using GDAL warp if necessary. In that case,
+users should be aware of the extent and resolution of the data to import
+and the current computational region. Import is limited to and aligned with
+the current computational region, if the <b>r-flag</b> is set. Otherwise,
+extent and resolution in the target CRS is guessed by GDAL. Import of global
+data to coordinate systems that do not support that extent will thus fail.
+
 <p>
 Starting with GRASS GIS version 8.0, different variables or subdatasets in a
 NetCDF file can be imported as "semantic_label" into one STRDS. To achieve this,