MNT: Correct spelling errors identified by codespell

Also work around a couple (reasonable) false alarms.

.github/workflows/draft-release.yml

@@ -33,13 +33,13 @@ jobs:
             const issues = items.data.filter(i => !i.pull_request);
             const issue_text = '### Issues Closed\n' +
                                issues.map(i => `* [Issue ${i.number}](${i.html_url}) - ${i.title}\n`)
-                               .join('') + `\nIn this release ${issues.length} issues were closed.`;
+                               .join('') + `\n${issues.length} issues were closed in this release.`;
 
             // Now do the same for the PRs
             const prs = items.data.filter(i => !!i.pull_request);
             const pr_text = '### Pull Requests Merged\n' +
                             prs.map(i => `* [PR ${i.number}](${i.html_url}) - ${i.title}, by @${i.user.login}\n`)
-                            .join('') + `\nIn this release ${prs.length} pull requests were closed.`;
+                            .join('') + `\n${prs.length} pull requests were closed in this release.`;
 
             // Add the list of people who contributed PRs
             const contrib_text = '### Contributors\n' +

docs/userguide/gempak.rst

@@ -1502,7 +1502,7 @@ blue is uncertain of parity, and white is unevaluated.
       </tr>
       <tr>
         <td>ENS_SWSPRD(input_arg1 & input_arg2)</td>
-        <td>Compute the spred, similar to ENS_SSPRD, of a scalar diagnostic field over an ensemble. The spread is weighted and input_arg2 specifies the name of a weight grid to be used in the calculation.                 NOTE: For uniform weights ens_ssprd (input_arg1) might be expected to give the same result as ens_swsprd(input_arg1 &amp; w1) where  w1 is uniform field of 1.  This does not happen because of the division by (N-1) in ens_ssprd.  The same is also true in comparing ens_vsprd and ens_vwsprd results.</td>
+        <td>Compute the spread, similar to ENS_SSPRD, of a scalar diagnostic field over an ensemble. The spread is weighted and input_arg2 specifies the name of a weight grid to be used in the calculation.                 NOTE: For uniform weights ens_ssprd (input_arg1) might be expected to give the same result as ens_swsprd(input_arg1 &amp; w1) where  w1 is uniform field of 1.  This does not happen because of the division by (N-1) in ens_ssprd.  The same is also true in comparing ens_vsprd and ens_vwsprd results.</td>
         <td></td>
         <td></td>
         <td></td>
@@ -1526,7 +1526,7 @@ blue is uncertain of parity, and white is unevaluated.
       </tr>
       <tr>
         <td>ENS_VWSPRD(input_arg1 & input_arg2)</td>
-        <td>Compute the spred, similar to ENS_VSPRD, of a vector diagnostic field over an ensemble. The spread is weighted and input_arg2 specifies the name of a weight grid to be used in the calculation.  Also, see NOTE for function ENS_SWSPRD.</td>
+        <td>Compute the spread, similar to ENS_VSPRD, of a vector diagnostic field over an ensemble. The spread is weighted and input_arg2 specifies the name of a weight grid to be used in the calculation.  Also, see NOTE for function ENS_SWSPRD.</td>
         <td></td>
         <td></td>
         <td></td>
@@ -1955,7 +1955,7 @@ blue is uncertain of parity, and white is unevaluated.
       </tr>
       <tr>
         <td>dctrop</td>
-        <td>Hurricane/tropical storm reprots</td>
+        <td>Hurricane/tropical storm reports</td>
         <td></td>
         <td></td>
         <td></td>
@@ -4643,7 +4643,7 @@ blue is uncertain of parity, and white is unevaluated.
       </tr>
       <tr>
         <td>SK12</td>
-        <td>maximum sustain surface wind spped fcst for 12-hr period</td>
+        <td>maximum sustain surface wind speed fcst for 12-hr period</td>
         <td></td>
         <td></td>
         <td></td>
@@ -7419,7 +7419,7 @@ blue is uncertain of parity, and white is unevaluated.
       </tr>
       <tr>
         <td>RADFRQ</td>
-        <td>tThe update frequency for RADAR composites.</td>
+        <td>The update frequency for RADAR composites.</td>
         <td></td>
         <td></td>
         <td></td>

docs/userguide/media.rst

@@ -3,7 +3,7 @@ Talks and Other Media
 ---------------------
 
 * `AMS 2020 talk`_ on MetPy being ready for a 1.0 release
-* `AMS 2019 talk`_ on bringing GEMPAK-like syntax to MetPy's declaritive plotting interface
+* `AMS 2019 talk`_ on bringing GEMPAK-like syntax to MetPy's declarative plotting interface
 * `AMS 2019 poster`_ on recent development and community building with MetPy
 * `SciPy 2018 poster`_ and `abstract <http://johnrleeman.com/pubs/2018/Leeman_2018_SciPy_Abstract.pdf>`_ on building community by John Leeman
 * `SciPy 2018 talk`_ on prototyping MetPy's future declarative plotting interface

examples/plots/Station_Plot_with_Layout.py

@@ -154,7 +154,7 @@
 # or instead, a custom layout can be used:
 
 # Just winds, temps, and dewpoint, with colors. Dewpoint and temp will be plotted
-# out to Farenheit tenths. Extra data will be ignored
+# out to Fahrenheit tenths. Extra data will be ignored
 custom_layout = StationPlotLayout()
 custom_layout.add_barb('eastward_wind', 'northward_wind', units='knots')
 custom_layout.add_value('NW', 'air_temperature', fmt='.1f', units='degF', color='darkred')

src/metpy/calc/basic.py

@@ -794,9 +794,9 @@ def smooth_gaussian(scalar_grid, n):
     sgma = n / (2 * np.pi)
 
     # Construct sigma sequence so smoothing occurs only in horizontal direction
-    nax = len(scalar_grid.shape)
+    num_ax = len(scalar_grid.shape)
     # Assume the last two axes represent the horizontal directions
-    sgma_seq = [sgma if i > nax - 3 else 0 for i in range(nax)]
+    sgma_seq = [sgma if i > num_ax - 3 else 0 for i in range(num_ax)]
 
     # Compute smoothed field
     return gaussian_filter(scalar_grid, sgma_seq, truncate=2 * np.sqrt(2))
@@ -837,7 +837,7 @@ def smooth_window(scalar_grid, window, passes=1, normalize_weights=True):
     function will leave an unsmoothed edge of size `(n - 1) / 2` for each `n` in the shape of
     `window` around the data). If a masked value or NaN values exists in the array, it will
     propagate to any point that uses that particular grid point in the smoothing calculation.
-    Applying the smoothing function multiple times will propogate NaNs further throughout the
+    Applying the smoothing function multiple times will propagate NaNs further throughout the
     domain.
 
     See Also
@@ -861,7 +861,7 @@ def _trailing_dims(indexer):
         # Add ... to the front of an indexer, since we are working with trailing dimensions.
         return (Ellipsis,) + tuple(indexer)
 
-    # Verify that shape in all dimensions is odd (need to have a neighboorhood around a
+    # Verify that shape in all dimensions is odd (need to have a neighborhood around a
     # central point)
     if any((size % 2 == 0) for size in window.shape):
         raise ValueError('The shape of the smoothing window must be odd in all dimensions.')
@@ -921,7 +921,7 @@ def smooth_rectangular(scalar_grid, size, passes=1):
     function will leave an unsmoothed edge of size `(n - 1) / 2` for each `n` in `size` around
     the data). If a masked value or NaN values exists in the array, it will propagate to any
     point that uses that particular grid point in the smoothing calculation. Applying the
-    smoothing function multiple times will propogate NaNs further throughout the domain.
+    smoothing function multiple times will propagate NaNs further throughout the domain.
 
     See Also
     --------
@@ -960,7 +960,7 @@ def smooth_circular(scalar_grid, radius, passes=1):
     function will leave an unsmoothed edge of size `radius` around the data). If a masked
     value or NaN values exists in the array, it will propagate to any point that uses that
     particular grid point in the smoothing calculation. Applying the smoothing function
-    multiple times will propogate NaNs further throughout the domain.
+    multiple times will propagate NaNs further throughout the domain.
 
     See Also
     --------
@@ -1007,7 +1007,7 @@ def smooth_n_point(scalar_grid, n=5, passes=1):
     the end points with their original values (this function will leave an unsmoothed edge of
     size 1 around the data). If a masked value or NaN values exists in the array, it will
     propagate to any point that uses that particular grid point in the smoothing calculation.
-    Applying the smoothing function multiple times will propogate NaNs further throughout the
+    Applying the smoothing function multiple times will propagate NaNs further throughout the
     domain.
 
     See Also

src/metpy/calc/cross_sections.py

@@ -94,7 +94,7 @@ def latitude_from_cross_section(cross):
 
 @exporter.export
 def unit_vectors_from_cross_section(cross, index='index'):
-    r"""Calculate the unit tanget and unit normal vectors from a cross-section.
+    r"""Calculate the unit tangent and unit normal vectors from a cross-section.
 
     Given a path described parametrically by :math:`\vec{l}(i) = (x(i), y(i))`, we can find
     the unit tangent vector by the formula:

src/metpy/calc/tools.py

@@ -121,7 +121,7 @@ def find_intersections(x, a, b, direction='all', log_x=False):
 
     Notes
     -----
-    This function implicity converts `xarray.DataArray` to `pint.Quantity`, with the results
+    This function implicitly converts `xarray.DataArray` to `pint.Quantity`, with the results
     given as `pint.Quantity`.
 
     """
@@ -1308,7 +1308,7 @@ def parse_angle(input_dir):
     """Calculate the meteorological angle from directional text.
 
     Works for abbrieviations or whole words (E -> 90 | South -> 180)
-    and also is able to parse 22.5 degreee angles such as ESE/East South East.
+    and also is able to parse 22.5 degree angles such as ESE/East South East.
 
     Parameters
     ----------

src/metpy/interpolate/grid.py

@@ -259,7 +259,7 @@ def interpolate_to_grid(x, y, z, interp_type='linear', hres=50000,
     rbf_func: str
         Specifies which function to use for Rbf interpolation.
         Options include: 'multiquadric', 'inverse', 'gaussian', 'linear', 'cubic',
-        'quintic', and 'thin_plate'. Defualt 'linear'. See `scipy.interpolate.Rbf` for more
+        'quintic', and 'thin_plate'. Default 'linear'. See `scipy.interpolate.Rbf` for more
         information.
     rbf_smooth: float
         Smoothing value applied to rbf interpolation.  Higher values result in more smoothing.

src/metpy/interpolate/one_dimension.py

@@ -178,7 +178,7 @@ def interpolate_1d(x, xp, *args, axis=0, fill_value=np.nan, return_list_always=F
 def log_interpolate_1d(x, xp, *args, axis=0, fill_value=np.nan):
     r"""Interpolates data with logarithmic x-scale over a specified axis.
 
-    Interpolation on a logarithmic x-scale for interpolation values in pressure coordintates.
+    Interpolation on a logarithmic x-scale for interpolation values in pressure coordinates.
 
     Parameters
     ----------

src/metpy/interpolate/points.py

@@ -321,7 +321,7 @@ def interpolate_to_points(points, values, xi, interp_type='linear', minimum_neig
     rbf_func: str
         Specifies which function to use for Rbf interpolation.
         Options include: 'multiquadric', 'inverse', 'gaussian', 'linear', 'cubic',
-        'quintic', and 'thin_plate'. Defualt 'linear'. See `scipy.interpolate.Rbf` for more
+        'quintic', and 'thin_plate'. Default 'linear'. See `scipy.interpolate.Rbf` for more
         information.
     rbf_smooth: float
         Smoothing value applied to rbf interpolation.  Higher values result in more smoothing.

src/metpy/pandas.py

@@ -14,7 +14,7 @@ def preprocess_pandas(func):
     @functools.wraps(func)
     def wrapper(*args, **kwargs):
         # not using hasattr(a, values) because it picks up dict.values()
-        # and this is more explictly handling pandas
+        # and this is more explicitly handling pandas
         args = tuple(a.values if isinstance(a, pd.Series) else a for a in args)
         kwargs = {name: (v.values if isinstance(v, pd.Series) else v)
                   for name, v in kwargs.items()}

src/metpy/plots/declarative.py

@@ -612,7 +612,7 @@ class MapPanel(Panel):
                  default_value=None)
     area.__doc__ = """A tuple or string value that indicates the graphical area of the plot.
 
-    The tuple value coresponds to longitude/latitude box based on the projection of the map
+    The tuple value corresponds to longitude/latitude box based on the projection of the map
     with the format (west-most longitude, east-most longitude, south-most latitude,
     north-most latitude). This tuple defines a box from the lower-left to the upper-right
     corner.
@@ -622,7 +622,7 @@ class MapPanel(Panel):
     For a CONUS region, the following strings can be used: 'us', 'spcus', 'ncus', and 'afus'.
     For regional plots, US postal state abbreviations can be used, such as 'co', 'ny', 'ca',
     et cetera. Providing a '+' or '-' suffix to the string value will zoom in or out,
-    respectivley. Providing multiple '+' or '-' characters will zoom in or out further.
+    respectively. Providing multiple '+' or '-' characters will zoom in or out further.
 
     """
 
@@ -1277,7 +1277,7 @@ class PlotVector(Plots2D):
     earth-relative.
 
     Common gridded meteorological datasets including GFS and NARR output contain wind
-    components that are earth-relative. The primary expection is NAM output with wind
+    components that are earth-relative. The primary exception is NAM output with wind
     components that are grid-relative. For any grid-relative vectors set this trait to `False`.
     """
 

src/metpy/xarray.py

@@ -105,7 +105,7 @@ class MetPyDataArrayAccessor:
 
     This accessor provides several convenient attributes and methods through the `.metpy`
     attribute on a DataArray. For example, MetPy can identify the coordinate corresponding
-    to a particular axis (given sufficent metadata):
+    to a particular axis (given sufficient metadata):
 
         >>> import xarray as xr
         >>> from metpy.units import units
@@ -588,7 +588,7 @@ def assign_latitude_longitude(self, force=False):
         Returns
         -------
         `xarray.DataArray`
-            New xarray DataArray with latitude and longtiude auxilary coordinates assigned.
+            New xarray DataArray with latitude and longtiude auxiliary coordinates assigned.
 
         Notes
         -----
@@ -679,7 +679,7 @@ def parse_cf(self, varname=None, coordinates=None):
         mapping metadata with the ``.assign_crs`` method.
 
         This method operates on individual data variables within the dataset, so do not be
-        suprised if information not associated with individual data variables is not
+        surprised if information not associated with individual data variables is not
         preserved.
 
         Parameters
@@ -820,7 +820,7 @@ def sel(self, indexers=None, method=None, tolerance=None, drop=False, **indexers
         return self._dataset.sel(indexers, method=method, tolerance=tolerance, drop=drop)
 
     def assign_crs(self, cf_attributes=None, **kwargs):
-        """Assign a CRS to this Datatset based on CF projection attributes.
+        """Assign a CRS to this Dataset based on CF projection attributes.
 
         Specify a coordinate reference system/grid mapping following the Climate and
         Forecasting (CF) conventions (see `Appendix F: Grid Mappings

tests/calc/test_cross_sections.py

@@ -148,7 +148,7 @@ def test_distances_from_cross_section_given_xy(test_cross_xy):
 
 
 def test_distances_from_cross_section_given_bad_coords(test_cross_xy):
-    """Ensure an AttributeError is raised when the cross section lacks neeed coordinates."""
+    """Ensure an AttributeError is raised when the cross section lacks need coordinates."""
     with pytest.raises(AttributeError):
         distances_from_cross_section(test_cross_xy['u_wind'].drop_vars('x'))
 

tutorials/declarative_tutorial.py

@@ -104,8 +104,8 @@
 #
 # - ``PlotBarbs()``
 #
-# More complete descrptions of these and other plotting types, as well as the map panel and
-# panel containter classes are at the end of this tutorial.
+# More complete descriptions of these and other plotting types, as well as the map panel and
+# panel container classes are at the end of this tutorial.
 #
 # Let's plot a 300-hPa map with color-filled wind speed, which we calculated and added to
 # our Dataset above, and geopotential heights over the CONUS.
@@ -187,7 +187,7 @@
 # set of attributes to control plotting a vector quantity.
 
 #########################################################################
-# We start with setting the attributes that we had before for our 300 hPa plot inlcuding,
+# We start with setting the attributes that we had before for our 300 hPa plot including,
 # Geopotential Height contours, and color-filled wind speed.
 
 # Set attributes for contours of Geopotential Heights at 300 hPa

tutorials/xarray_tutorial.py

@@ -61,7 +61,7 @@
 #
 # If you are more interested in learning about xarray's terminology and data structures, see
 # the `terminology section <http://xarray.pydata.org/en/stable/terminology.html>`_ of xarray's
-# documenation.
+# documentation.
 #
 # Coordinates and Coordinate Reference Systems
 # --------------------------------------------
@@ -434,7 +434,7 @@
 # **Undefined Unit Error**
 #
 # If the units attribute on your xarray data is not recognizable by Pint, you will likely
-# recieve an ``UndefinedUnitError``. In this case, you will likely have to update the units
+# receive an ``UndefinedUnitError``. In this case, you will likely have to update the units
 # attribute to one that can be parsed properly by Pint. It is our aim to have all valid
 # CF/UDUNITS unit strings be parseable, but this work is ongoing. If many variables in your
 # dataset are not parseable, the ``.update_attribute`` method on the MetPy accessor may come