Merge branch 'main' into reclass

continuous_integration/environment-ci.yml

@@ -13,7 +13,7 @@ dependencies:
   - cartopy
   - cvxopt
   - xarray
-  - metpy
+  - metpy<1.6
   - pytest-cov
   - pytest-mpl
   - coveralls

doc/environment.yml

@@ -11,11 +11,11 @@ dependencies:
   - netcdf4
   - pytest
   - wradlib
-  - metpy
+  - metpy<1.6
   - cartopy
   - cvxopt
   - xarray
-  - sphinx
+  - sphinx<7.2
   - ipython
   - pandoc
   - pkg-config

doc/source/conf.py

@@ -49,18 +49,18 @@
     "sphinx.ext.napoleon",
     "sphinx_copybutton",
     "sphinx_gallery.gen_gallery",
-    "sphinx_gallery.load_style",
     "sphinx_design",
     "nbsphinx",
     "myst_nb",
     "ablog",
 ]
 
 exclude_patterns = ["_build", "**.ipynb_checkpoints"]
-
-# only include examples if the BUILD_PYART_EXAMPLES env. variable is set
-extensions.append("sphinx_gallery.gen_gallery")
-sphinx_gallery_conf = {"examples_dirs": "../../examples", "gallery_dirs": "examples"}
+sphinx_gallery_conf = {
+    "examples_dirs": "../../examples",
+    "gallery_dirs": "examples",
+    "abort_on_example_error": True,
+}
 
 
 # Configuration options for plot_directive. See:
@@ -124,7 +124,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -203,7 +203,7 @@
 post_auto_excerpt = 2
 
 # Don't execute the jupyter notebooks
-jupyter_execute_notebooks = "off"
+nb_execution_mode = "off"
 
 # Extra variables that will be available to the templates. Used to create the
 # links to the Github repository sources and issues
@@ -263,6 +263,9 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 
+# Make MyST generate anchors for headings
+myst_heading_anchors = 2
+
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3/", None),

examples/retrieve/plot_convective_stratiform.py

@@ -21,7 +21,7 @@
 
 ######################################
 # How the algorithm works
-# ----------
+# -----------------------
 # This first section describes how the convective-stratiform algorithm works (see references for full details). This
 # algorithm is a feature detection algorithm and classifies fields as "convective" or "stratiform". The algorithm is
 # designed to detect features in a reflectivity field but can also detect features in fields such as rain rate or
@@ -446,7 +446,7 @@
 
 ######################################
 # Summary of recommendations and best practices
-# ----------
+# ---------------------------------------------
 # * Tune your parameters to your specific purpose
 # * Use a rescaled field if possible (i.e. linear reflectivity, rain or snow rate)
 # * Keep ``estimate_flag=True`` to see uncertainty in classification

examples/retrieve/plot_feature_detection.py

@@ -23,7 +23,7 @@
 
 ######################################
 # How the algorithm works
-# ----------
+# -----------------------
 # The feature detection algorithm works by identifying features that exceed the background value by an amount that
 # varies with the background value. The algorithm is heavily customizable and is designed to work with a variety of
 # datasets. Here, we show several examples of how to use the algorithm with different types of radar data.
@@ -33,7 +33,7 @@
 
 ######################################
 # Part 1: Warm-season convective-stratiform classification
-# ----------
+# --------------------------------------------------------
 # **Classification of summer convective example**
 #
 # Our first example classifies echo from a summer convective event.
@@ -397,8 +397,7 @@
 
 ######################################
 # Part 2: Cool-season feature detection
-# ----------
-######################################
+# -------------------------------------
 # **Winter storm example**
 #
 # In this example, we will show how to algorithm can be used to detect features (snow bands) in winter storms. Here, we
@@ -778,7 +777,7 @@ def quick_image_mute(field, muted_ref):
 plt.show()
 ######################################
 # Summary of recommendations and best practices
-# ----------
+# ---------------------------------------------
 # * Tune your parameters to your specific purpose
 # * Use a rescaled field if possible (i.e. linear reflectivity, rain or snow rate)
 # * Keep ``estimate_flag=True`` to see uncertainty in classification

examples/retrieve/plot_hydrometeor.py

@@ -1,7 +1,7 @@
 """
-=========================================
+=============================================
 Calculate and Plot hydrometeor classification
-=========================================
+=============================================
 
 Calculates a hydrometeor classification and displays the results
 """

pyart/io/nexrad_level2.py

@@ -592,7 +592,7 @@ def _decompress_records(file_handler):
     cbuf = file_handler.read()  # read all data from the file
     decompressor = bz2.BZ2Decompressor()
     skip = _structure_size(VOLUME_HEADER) + CONTROL_WORD_SIZE
-    buf = decompressor.decompress(cbuf[skip:])
+    buf = bytearray(decompressor.decompress(cbuf[skip:]))
     while len(decompressor.unused_data):
         cbuf = decompressor.unused_data
         decompressor = bz2.BZ2Decompressor()