Merge branch 'Unidata:main' into main

.codespellexclude

@@ -0,0 +1,18 @@
+            r'^(z|lv_|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)'
+  | ``(thta, u, v, dx, dy, dim_order='yx')``                                                                         |
+       Changed signature from ``(thta, u, v, dx, dy, dim_order='yx')``
+    dpres = gempak.PRES.values
+    pres = 1
+    thta = 2
+                lambda grid: grid if grid.PARM in parameter else False,
+                            col_head.SELV,
+                'SELV': col_head.SELV,
+                                col_head.SELV,
+                                row_head.SELV,
+                'SELV': row_head.SELV,
+                'SELV': col_head.SELV,
+# GFS, NAM, RAP, or other gridded dataset (e.g., NARR).
+# This attribute can be set to False if the vector components are grid relative (e.g., for NAM
+    components that are earth-relative. The primary exception is NAM output with wind
+                                    col_head.SELV,
+                                    row_head.SELV,

.codespellignore

@@ -1,19 +1 @@
-            r'^(z|lv_|bottom_top|sigma|h(ei)?ght|altitude|depth|isobaric|pres|isotherm)'
-  | ``(thta, u, v, dx, dy, dim_order='yx')``                                                                         |
-       Changed signature from ``(thta, u, v, dx, dy, dim_order='yx')``
-    dpres = gempak.PRES.values
-    pres = 1
-    thta = 2
-                lambda grid: grid if grid.PARM in parameter else False,
-                            col_head.SELV,
-                'SELV': col_head.SELV,
-                                col_head.SELV,
-                                row_head.SELV,
-                'SELV': row_head.SELV,
-                'SELV': col_head.SELV,
-Klystron Warmup    Integer*2  N/A  0 to 1  1  0=Normal, 1=Preheat  146
-# GFS, NAM, RAP, or other gridded dataset (e.g., NARR).
-# This attribute can be set to False if the vector components are grid relative (e.g., for NAM
-    components that are earth-relative. The primary exception is NAM output with wind
-                                    col_head.SELV,
-                                    row_head.SELV,
+trough

.github/workflows/docs-conda.yml

@@ -22,8 +22,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - python-version: 3.9
-            os: Windows
           - python-version: '3.10'
             os: Windows
           - python-version: 3.11

.github/workflows/docs.yml

@@ -27,7 +27,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.9, '3.10', 3.11]
+        python-version: ['3.10', 3.11]
         check-links: [false]
         include:
           - python-version: 3.12

.github/workflows/tests-conda.yml

@@ -28,11 +28,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.9, 3.12]
+        python-version: ['3.10', 3.12]
         os: [macOS, Windows]
         include:
-          - python-version: '3.10'
-            os: macOS
           - python-version: 3.11
             os: Windows
 

.github/workflows/tests-pypi.yml

@@ -25,13 +25,13 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.9, '3.10', 3.11, 3.12]
+        python-version: ['3.10', 3.11, 3.12]
         dep-versions: [Latest]
         no-extras: ['']
         include:
-          - python-version: 3.9
+          - python-version: '3.10'
             dep-versions: Minimum
-          - python-version: 3.9
+          - python-version: '3.10'
             dep-versions: Minimum
             no-extras: 'No Extras'
           - python-version: 3.12

README.md

@@ -31,7 +31,7 @@ on a future ``1.x`` version.
 For additional MetPy examples not included in this repository, please see the [Unidata Python
 Gallery](https://unidata.github.io/python-gallery/).
 
-We support Python >= 3.9.
+We support Python >= 3.10.
 
 Need Help?
 ----------

ci/doc_requirements.txt

@@ -1,8 +1,8 @@
-sphinx==7.3.7
-pydata-sphinx-theme==0.15.2
+sphinx==8.0.2
+pydata-sphinx-theme==0.15.4
 sphinx-design==0.6.1
 sphinx-gallery==0.17.1
-myst-parser==3.0.1
+myst-parser==4.0.0
 netCDF4==1.7.1
-geopandas==0.14.4
+geopandas==1.0.1
 rtree==1.3.0

ci/extra_requirements.txt

@@ -1,3 +1,3 @@
 cartopy==0.23.0
-dask==2024.8.0
-shapely==2.0.5
+dask==2024.8.1
+shapely==2.0.6

ci/linting_requirements.txt

@@ -1,4 +1,4 @@
-ruff==0.6.1
+ruff==0.6.2
 
 flake8==7.1.1
 pycodestyle==2.12.1

ci/requirements.txt

@@ -1,9 +1,9 @@
 matplotlib==3.9.2
-numpy==2.0.1
+numpy==2.1.0
 pandas==2.2.2
 pooch==1.8.2
 pint==0.24.3
 pyproj==3.6.1
-scipy==1.13.1
+scipy==1.14.1
 traitlets==5.14.3
 xarray==2024.7.0

docs/_static/theme-unidata.css

@@ -19,7 +19,7 @@ html {
 
 .bd-header .navbar-nav>.nav-item>.nav-link, 
 .bd-header .dropdown-toggle,
-.bd-header .fa-solid,
+.bd-header .pst-navbar-icon,
 .bd-header .btn {
     color: #fff !important;
  }

docs/api/index.rst

@@ -9,14 +9,14 @@ Reference Guide
 .. autosummary::
    :toctree: generated/
 
-   metpy.constants
-   metpy.units
-   metpy.io
-   metpy.calc
-   metpy.plots
-   metpy.plots.ctables
-   metpy.interpolate
-   metpy.xarray
+   constants
+   units
+   io
+   calc
+   plots
+   plots.ctables
+   interpolate
+   xarray
 
 .. toctree::
    :maxdepth: 1

docs/devel/infrastructureguide.rst

@@ -10,7 +10,7 @@ Versioning
 ----------
 
 To manage identifying the version of the code, MetPy relies upon `setuptools_scm
-<https://github.com/pypa/setuptools_scm>`_. ``setuptools_scm`` takes the current version of the
+<https://github.com/pypa/setuptools-scm>`_. ``setuptools_scm`` takes the current version of the
 source from git tags and any additional commits. For development, the version will have a
 string like ``0.10.0.post209+gff2e549f.d20190918``, which comes from ``git describe``. This
 version means that the current code is 209 commits past the 0.10.0 tag, on git hash

docs/index.rst

@@ -14,7 +14,7 @@ MetPy
 =====
 
 MetPy is a collection of tools in Python for reading, visualizing, and performing calculations
-with weather data. MetPy supports Python >= 3.9 and is freely available under a permissive
+with weather data. MetPy supports Python >= 3.10 and is freely available under a permissive
 `open source license <https://github.com/Unidata/MetPy/blob/main/LICENSE>`_.
 
 If you're new to MetPy, check out our :doc:`Getting Started <userguide/startingguide>` guide.

docs/userguide/installguide.rst

@@ -7,7 +7,7 @@ Requirements
 ------------
 In general, MetPy tries to support minor versions of dependencies released within the last two
 years. For Python itself, that generally means supporting the last two minor releases; MetPy
-currently supports Python >= 3.9.
+currently supports Python >= 3.10.
 
 .. literalinclude:: ../../pyproject.toml
    :start-at: matplotlib

docs/userguide/media.rst

@@ -25,7 +25,7 @@ Talks and Other Media
 .. _`2016 AMS Annual Meeting`: https://ams.confex.com/ams/96Annual/webprogram/Paper286983.html
 .. _`SSEC/Wisconsin AOS Python Workshop`: https://www.youtube.com/watch?v=RRvJI_vouQc
 .. _`SciPy 2016`: https://www.youtube.com/watch?v=moLKGjbXvgE
-.. _`Episode 100 of Podcast.__init__`: https://www.pythonpodcast.com/episode-100-metpy-with-ryan-may-sean-arms-and-john-leeman/
+.. _`Episode 100 of Podcast.__init__`: https://www.pythonpodcast.com/episodepage/episode-100-metpy-with-ryan-may-sean-arms-and-john-leeman/
 .. _`SciPy 2017 talk`: https://www.youtube.com/watch?v=qCo9bkT9sow
 .. _`SciPy 2017 poster`: https://github.com/jrleeman/CAPE-SciPy-2017/blob/master/Poster/SciPy_Poster_2017.pdf
 .. _`2018 AMS Annual Meeting`: https://ams.confex.com/ams/98Annual/webprogram/Paper333578.html

examples/Advanced_Sounding_With_Complex_Layout.py

@@ -214,7 +214,7 @@
 # mixed layer parcel properties!
 ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
 ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
-mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, prof, depth=50 * units.hPa)
+mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, Td, depth=50 * units.hPa)
 
 # most unstable parcel properties!
 mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)

examples/cross_section.py

@@ -96,7 +96,6 @@
 
 # Adjust the y-axis to be logarithmic
 ax.set_yscale('symlog')
-ax.set_yticklabels(np.arange(1000, 50, -100))
 ax.set_ylim(cross['isobaric'].max(), cross['isobaric'].min())
 ax.set_yticks(np.arange(1000, 50, -100))
 

examples/formats/NEXRAD_Level_2_File.py

@@ -73,7 +73,8 @@
 fig = plt.figure(figsize=(15, 8))
 add_metpy_logo(fig, 190, 85, size='large')
 
-for var_data, var_range, ax_rect in zip((ref, rho), (ref_range, rho_range), spec):
+for var_data, var_range, ax_rect in zip((ref, rho), (ref_range, rho_range), spec,
+                                        strict=False):
     # Turn into an array, then mask
     data = np.ma.array(var_data)
     data[np.isnan(data)] = np.ma.masked

examples/formats/NEXRAD_Level_3_File.py

@@ -26,7 +26,7 @@
 ctables = (('NWSStormClearReflectivity', -20, 0.5),  # dBZ
            ('NWS8bitVel', -100, 1.0))  # m/s
 
-for v, ctable, ax_rect in zip(('N0Q', 'N0U'), ctables, spec):
+for v, ctable, ax_rect in zip(('N0Q', 'N0U'), ctables, spec, strict=False):
     # Open the file
     name = get_test_data(f'nids/KOUN_SDUS54_{v}TLX_201305202016', as_file_obj=False)
     f = Level3File(name)

examples/gridding/Inverse_Distance_Verification.py

@@ -61,10 +61,10 @@ def draw_circle(ax, x, y, r, m, label):
 #
 # The variable ``indices`` represents the index of each matched coordinate within the
 # cKDTree's ``data`` list.
-grid_points = np.array(list(zip(sim_gridx, sim_gridy)))
+grid_points = np.array(list(zip(sim_gridx, sim_gridy, strict=False)))
 
 radius = 40
-obs_tree = cKDTree(list(zip(xp, yp)))
+obs_tree = cKDTree(list(zip(xp, yp, strict=False)))
 indices = obs_tree.query_ball_point(grid_points, r=radius)
 
 ###########################################
@@ -83,7 +83,7 @@ def draw_circle(ax, x, y, r, m, label):
 barnes_dist = dist_2(sim_gridx[1], sim_gridy[1], x2, y2)
 barnes_obs = zp[indices[1]]
 
-kappa = calc_kappa(average_spacing(list(zip(xp, yp))))
+kappa = calc_kappa(average_spacing(list(zip(xp, yp, strict=False))))
 
 barnes_val = barnes_point(barnes_dist, barnes_obs, kappa)
 
@@ -121,7 +121,7 @@ def draw_circle(ax, x, y, r, m, label):
 mx, my = obs_tree.data[indices[0]].T
 mz = zp[indices[0]]
 
-for x, y, z in zip(mx, my, mz):
+for x, y, z in zip(mx, my, mz, strict=False):
     d = np.sqrt((sim_gridx[0] - x)**2 + (y - sim_gridy[0])**2)
     ax.plot([sim_gridx[0], x], [sim_gridy[0], y], '--')
 
@@ -160,7 +160,7 @@ def draw_circle(ax, x, y, r, m, label):
 mx, my = obs_tree.data[indices[1]].T
 mz = zp[indices[1]]
 
-for x, y, z in zip(mx, my, mz):
+for x, y, z in zip(mx, my, mz, strict=False):
     d = np.sqrt((sim_gridx[1] - x)**2 + (y - sim_gridy[1])**2)
     ax.plot([sim_gridx[1], x], [sim_gridy[1], y], '--')
 

examples/gridding/Natural_Neighbor_Verification.py

@@ -93,7 +93,8 @@
 ax.set_title('Triangulation of observations and test grid cell '
              'natural neighbor interpolation values')
 
-members, circumcenters = geometry.find_natural_neighbors(tri, list(zip(sim_gridx, sim_gridy)))
+members, circumcenters = geometry.find_natural_neighbors(tri, list(zip(sim_gridx, sim_gridy,
+                                                                       strict=False)))
 
 val = natural_neighbor_point(xp, yp, zp, (sim_gridx[0], sim_gridy[0]), tri, members[0],
                              circumcenters)
@@ -164,7 +165,7 @@ def draw_circle(ax, x, y, r, m, label):
 # spatial data structure that we use here simply to show areal ratios.
 # Notice that the two natural neighbor triangle circumcenters are also vertices
 # in the Voronoi plot (green dots), and the observations are in the polygons (blue dots).
-vort = Voronoi(list(zip(xp, yp)))
+vort = Voronoi(list(zip(xp, yp, strict=False)))
 
 fig, ax = plt.subplots(1, 1, figsize=(15, 10))
 ax.ishold = lambda: True  # Work-around for Matplotlib 3.0.0 incompatibility
@@ -175,7 +176,7 @@ def draw_circle(ax, x, y, r, m, label):
 x_0 = xp[nn_ind]
 y_0 = yp[nn_ind]
 
-for x, y, z in zip(x_0, y_0, z_0):
+for x, y, z in zip(x_0, y_0, z_0, strict=False):
     ax.annotate(f'{x}, {y}: {z:.3f} F', xy=(x, y))
 
 ax.plot(sim_gridx[0], sim_gridy[0], 'k+', markersize=10)

examples/plots/Plotting_Surface_Analysis.py

@@ -44,7 +44,7 @@ def plot_bulletin(ax, data):
     # Handle H/L points using MetPy's StationPlot class
     for field in ('HIGH', 'LOW'):
         rows = data[data.feature == field]
-        x, y = zip(*((pt.x, pt.y) for pt in rows.geometry))
+        x, y = zip(*((pt.x, pt.y) for pt in rows.geometry), strict=False)
         sp = StationPlot(ax, x, y, transform=ccrs.PlateCarree(), clip_on=True)
         sp.plot_text('C', [field[0]] * len(x), **complete_style[field])
         sp.plot_parameter('S', rows.strength, **complete_style[field])

examples/plots/US_Counties.py

@@ -22,6 +22,6 @@
 ax2 = fig.add_subplot(1, 3, 2, projection=proj)
 ax3 = fig.add_subplot(1, 3, 3, projection=proj)
 
-for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3]):
+for scale, axis in zip(['20m', '5m', '500k'], [ax1, ax2, ax3], strict=False):
     axis.set_extent([270.25, 270.9, 38.15, 38.75], ccrs.Geodetic())
     axis.add_feature(USCOUNTIES.with_scale(scale))

examples/plots/nhc_wind_probabilities.py

@@ -57,7 +57,7 @@
 # cities we selected above. Geopandas provides a spatial join method, which merges the two
 # GeoDataFrames and can tell us which wind speed probability polygon each of our city points
 # lies within. That information is stored in the 'PERCENTAGE' column below.
-cities = geopandas.sjoin(cities, wind_data, how='left', op='within')
+cities = geopandas.sjoin(cities, wind_data, how='left', predicate='within')
 cities
 
 ###########################

pyproject.toml

@@ -14,7 +14,6 @@ classifiers = [
     "Development Status :: 5 - Production/Stable",
     "Framework :: Matplotlib",
     "Programming Language :: Python",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
@@ -24,17 +23,17 @@ classifiers = [
     "Operating System :: OS Independent",
     "License :: OSI Approved :: BSD License"
 ]
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 dependencies = [
     "matplotlib>=3.5.0",
-    "numpy>=1.20.0",
+    "numpy>=1.22.0",
     "pandas>=1.4.0",
     "pint>=0.17",
     "pooch>=1.2.0",
-    "pyproj>=3.0.0",
+    "pyproj>=3.3.0",
     "scipy>=1.8.0",
-    "traitlets>=5.0.5",
-    "xarray>=0.21.0"
+    "traitlets>=5.1.0",
+    "xarray>=2022.6.0"
 ]
 
 [project.entry-points."xarray.backends"]
@@ -57,7 +56,7 @@ examples = [
 test = [
     "netCDF4",
     "packaging>=21.0",
-    "pytest>=6.2",
+    "pytest>=7.0",
     "pytest-mpl"
 ]
 extras = [
@@ -77,7 +76,8 @@ extras = [
 
 [tool.codespell]
 skip = "*.tbl,*.ipynb,AUTHORS.txt,gempak.rst,.git,./staticdata,./docs/build,*.pdf,./talks"
-exclude-file = ".codespellignore"
+exclude-file = ".codespellexclude"
+ignore-words = ".codespellignore"
 
 [tool.doc8]
 ignore-path = ["docs/build", "docs/api/generated", "docs/_templates", "docs/tutorials",
@@ -116,6 +116,8 @@ filterwarnings = [
     "ignore:Conversion of an array with ndim > 0 to a scalar is deprecated:DeprecationWarning:pyproj.geod:404",
     # Pandas >=2.2 warns about PyArrow being a future dependency
     'ignore:\nPyarrow will become a required dependency of pandas:DeprecationWarning',
+    # Not yet fixed in xarray
+    "ignore:__array__ implementation doesn't accept a copy keyword:DeprecationWarning"
 ]
 
 [tool.ruff]

src/metpy/calc/basic.py

@@ -1130,7 +1130,7 @@ def zoom_xarray(input_field, zoom, output=None, order=3, mode='constant', cval=0
     if not np.iterable(zoom):
         zoom = tuple(zoom for _ in input_field.dims)
     zoomed_dim_coords = {}
-    for dim_name, dim_zoom in zip(input_field.dims, zoom):
+    for dim_name, dim_zoom in zip(input_field.dims, zoom, strict=False):
         if dim_name in input_field.coords:
             zoomed_dim_coords[dim_name] = scipy_zoom(
                 input_field[dim_name].data, dim_zoom, order=order, mode=mode, cval=cval,