Fix cropping cross 0 deg

cfr/climate.py

@@ -550,13 +550,21 @@ def plot(self, **kwargs):
 
             kwargs['title'] = f'{self.da.name}, {date_str}' 
 
+        if len(set(np.diff(self.da.lon))) > 1:
+            # the case when longitudes cross 0 degree
+            fd_plot = self.wrap_lon(mode='180')
+            kwargs['central_longitude'] = 0
+        else:
+            fd_plot = self
+
         _kwargs.update(kwargs)
-        if len(self.da.dims) == 3:
-            vals = self.da.values[0]
-        elif len(self.da.dims) == 2:
-            vals = self.da.values
+
+        if len(fd_plot.da.dims) == 3:
+            vals = fd_plot.da.values[0]
+        elif len(fd_plot.da.dims) == 2:
+            vals = fd_plot.da.values
 
-        fig, ax = visual.plot_field_map(vals, self.da.lat, self.da.lon, **_kwargs)
+        fig, ax = visual.plot_field_map(vals, fd_plot.da.lat, fd_plot.da.lon, **_kwargs)
 
         return fig, ax
 
@@ -655,6 +663,8 @@ def regrid(self, lats, lons, periodic_lon=False):
     def crop(self, lat_min=-90, lat_max=90, lon_min=0, lon_max=360):
         ''' Crop the climate field based on the range of latitude and longitude.
 
+        Note that in cases when the crop range is crossing the 0 degree of longitude, `lon_min` should be less than 0.
+
         Args:
             lat_min (float): the lower bound of latitude to crop.
             lat_max (float): the upper bound of latitude to crop.
@@ -663,7 +673,13 @@ def crop(self, lat_min=-90, lat_max=90, lon_min=0, lon_max=360):
         '''
 
         mask_lat = (self.da.lat >= lat_min) & (self.da.lat <= lat_max)
-        mask_lon = (self.da.lon >= lon_min) & (self.da.lon <= lon_max)
+        if lon_min >= 0:
+            mask_lon = (self.da.lon >= lon_min) & (self.da.lon <= lon_max)
+        else:
+            # the case when longitudes in mode [-180, 180]
+            lon_min = np.mod(lon_min, 360)
+            lon_max = np.mod(lon_max, 360)
+            mask_lon = (self.da.lon >= lon_min) | (self.da.lon <= lon_max)
 
         da = self.da.sel({
                 'lat': self.da.lat[mask_lat],
@@ -682,7 +698,9 @@ def geo_mean(self, lat_min=-90, lat_max=90, lon_min=0, lon_max=360):
             lon_min (float): the lower bound of longitude for the calculation.
             lon_max (float): the upper bound of longitude for the calculation.
         '''
-        m = utils.geo_mean(self.da, lat_min=lat_min, lat_max=lat_max, lon_min=lon_min, lon_max=lon_max)
+        fdc = self.crop(lat_min=lat_min, lat_max=lat_max, lon_min=lon_min, lon_max=lon_max)
+        wgts = np.cos(np.deg2rad(fdc.da['lat']))
+        m = fdc.da.weighted(wgts).mean(('lon', 'lat'))
         ts = EnsTS(time=m['time'].values, value=m.values)
         return ts
 

cfr/utils.py

@@ -488,6 +488,14 @@ def coefficient_efficiency(ref, test, valid=None):
     return CE
 
 def geo_mean(da, lat_min=-90, lat_max=90, lon_min=0, lon_max=360, lat_name='lat', lon_name='lon'):
+    ''' Calculate the geographical mean value of the climate field.
+
+    Args:
+        lat_min (float): the lower bound of latitude for the calculation.
+        lat_max (float): the upper bound of latitude for the calculation.
+        lon_min (float): the lower bound of longitude for the calculation.
+        lon_max (float): the upper bound of longitude for the calculation.
+    '''
     # calculation
     mask_lat = (da[lat_name] >= lat_min) & (da[lat_name] <= lat_max)
     mask_lon = (da[lon_name] >= lon_min) & (da[lon_name] <= lon_max)

cfr/visual.py

@@ -264,7 +264,7 @@ def plot_field_map(field_var, lat, lon, levels=50, add_cyclic_point=True,
                    site_markersize=50, site_color=sns.xkcd_rgb['amber'],
                    projection='Robinson', transform=ccrs.PlateCarree(),
                    proj_args=None, latlon_range=None, central_longitude=180,
-                   lon_ticks=[60, 120, 180, 240, 300], lat_ticks=[-90, -45, 0, 45, 90],
+                   lon_ticks=[0, 60, 120, 180, 240, 300], lat_ticks=[-90, 45, 0, 45, 90],
                    land_color=sns.xkcd_rgb['light grey'], ocean_color=sns.xkcd_rgb['light grey'],
                    land_zorder=None, ocean_zorder=None, signif_values=None, signif_range=[0.05, 9999], hatch='..',
                    clim=None, cmap=None, cmap_under=None, cmap_over=None, cmap_bad=None, extend='both', mode=None, add_gridlines=False,
@@ -324,7 +324,6 @@ def plot_field_map(field_var, lat, lon, levels=50, add_cyclic_point=True,
         ax (object, optional): `matplotlib.axes`. Defaults to None.
 
     '''
-
     if mode is None:
         ndim = len(np.shape(lat))
         if ndim == 1:
@@ -383,19 +382,23 @@ def plot_field_map(field_var, lat, lon, levels=50, add_cyclic_point=True,
     if title:
         plt.title(title, fontsize=title_size, fontweight=title_weight)
 
-    if latlon_range:
+    if latlon_range is not None:
         lat_min, lat_max, lon_min, lon_max = latlon_range
         ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=transform)
-        lon_formatter = LongitudeFormatter(zero_direction_label=False)
-        lat_formatter = LatitudeFormatter()
-        ax.xaxis.set_major_formatter(lon_formatter)
-        ax.yaxis.set_major_formatter(lat_formatter)
-        lon_ticks = np.array(lon_ticks)
         lat_ticks = np.array(lat_ticks)
+        lon_ticks = np.array(lon_ticks)
+        if lon_min < 0:
+            lon_ticks = np.sort(np.mod(lon_ticks+180, 360) - 180)
+
         mask_lon = (lon_ticks >= lon_min) & (lon_ticks <= lon_max)
         mask_lat = (lat_ticks >= lat_min) & (lat_ticks <= lat_max)
-        ax.set_xticks(lon_ticks[mask_lon], crs=ccrs.PlateCarree())
-        ax.set_yticks(lat_ticks[mask_lat], crs=ccrs.PlateCarree())
+        if lon_min >= 0:
+            lon_formatter = LongitudeFormatter(zero_direction_label=False)
+            lat_formatter = LatitudeFormatter()
+            ax.xaxis.set_major_formatter(lon_formatter)
+            ax.yaxis.set_major_formatter(lat_formatter)
+            ax.set_xticks(lon_ticks[mask_lon], crs=ccrs.PlateCarree())
+            ax.set_yticks(lat_ticks[mask_lat], crs=ccrs.PlateCarree())
     else:
         ax.set_global()
 

docsrc/cg-updating-docs.rst

@@ -3,7 +3,7 @@ Updating the Documentation
 
 About the `cfr` documentation
 """""""""""""""""""""""""""""""""
-`cfr`'s documentation is built automatically from the function and class docstrings, via `Sphinx The Docs <https://www.sphinx-doc.org>`_.
+`cfr`'s documentation is built automatically from the function and class docstrings, via `Sphinx <https://www.sphinx-doc.org>`_.
 It is therefore especially important for your code to include a docstring, and to modify the docstrings of the functions/classes you modified to make sure the documentation is current.
 
 Updating docstrings

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='cfr',  # required
-    version='2023.9.8',
+    version='2023.9.9',
     description='cfr: a Python package for Climate Field Reconstruction',
     long_description=long_description,
     long_description_content_type='text/x-rst',