Black reformatting

src/scores/continuous.py

@@ -56,42 +56,42 @@ def mse(fcst, obs, reduce_dims=None, preserve_dims=None, weights=None):
     return _mse
 
 
-def rmse(fcst, obs, reduce_dims = None, preserve_dims = None, weights=None):    
+def rmse(fcst, obs, reduce_dims=None, preserve_dims=None, weights=None):
     """Calculate the Root Mean Squared Error from xarray or pandas objects.
 
-      A detailed explanation is on [Wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation)
-
-      
-      Dimensional reduction is not supported for pandas and the user should
-      convert their data to xarray to formulate the call to the metric.
-      At most one of `reduce_dims` and `preserve_dims` may be specified.
-      Specifying both will result in an exception.
-
-
-      Args:
-          fcst Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]: Forecast
-              or predicted variables in xarray or pandas.
-          obs (Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]): Observed
-              variables in xarray or pandas.
-          reduce_dims (Union[str, Iterable[str]): Optionally specify which dimensions to reduce when
-              calculating RMSE. All other dimensions will be preserved.
-          preserve_dims (Union[str, Iterable[str]): Optionally specify which dimensions to preserve
-              when calculating RMSE. All other dimensions will be reduced.
-              As a special case, 'all' will allow all dimensions to be
-              preserved. In this case, the result will be in the same
-              shape/dimensionality as the forecast, and the errors will be
-              the absolute error at each point (i.e. single-value comparison
-              against observed), and the forecast and observed dimensions
-              must match precisely.
-        weights: Not yet implemented. Allow weighted averaging (e.g. by
-            area, by latitude, by population, custom)
-            
-      Returns:
-          Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]:  An object containing
-              a single floating point number representing the root mean squared
-              error for the supplied data. All dimensions will be reduced.
-              Otherwise: Returns an object representing the root mean squared error,
-              reduced along the relevant dimensions and weighted appropriately.
+    A detailed explanation is on [Wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation)
+
+
+    Dimensional reduction is not supported for pandas and the user should
+    convert their data to xarray to formulate the call to the metric.
+    At most one of `reduce_dims` and `preserve_dims` may be specified.
+    Specifying both will result in an exception.
+
+
+    Args:
+        fcst Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]: Forecast
+            or predicted variables in xarray or pandas.
+        obs (Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]): Observed
+            variables in xarray or pandas.
+        reduce_dims (Union[str, Iterable[str]): Optionally specify which dimensions to reduce when
+            calculating RMSE. All other dimensions will be preserved.
+        preserve_dims (Union[str, Iterable[str]): Optionally specify which dimensions to preserve
+            when calculating RMSE. All other dimensions will be reduced.
+            As a special case, 'all' will allow all dimensions to be
+            preserved. In this case, the result will be in the same
+            shape/dimensionality as the forecast, and the errors will be
+            the absolute error at each point (i.e. single-value comparison
+            against observed), and the forecast and observed dimensions
+            must match precisely.
+      weights: Not yet implemented. Allow weighted averaging (e.g. by
+          area, by latitude, by population, custom)
+
+    Returns:
+        Union[xr.Dataset, xr.DataArray, pd.Dataframe, pd.Series]:  An object containing
+            a single floating point number representing the root mean squared
+            error for the supplied data. All dimensions will be reduced.
+            Otherwise: Returns an object representing the root mean squared error,
+            reduced along the relevant dimensions and weighted appropriately.
 
     """
     _mse = mse(fcst=fcst, obs=obs, reduce_dims=reduce_dims, preserve_dims=preserve_dims, weights=weights)
@@ -100,6 +100,7 @@ def rmse(fcst, obs, reduce_dims = None, preserve_dims = None, weights=None):
 
     return _rmse
 
+
 def mae(fcst, obs, reduce_dims=None, preserve_dims=None, weights=None):
     """Calculates the mean absolute error from forecast and observed data.
 

src/scores/functions.py

@@ -1,21 +1,22 @@
 import numpy as np
 
+
 def apply_weights(values, weights=None):
-    '''
+    """
     Returns:
         A new array with the elements of values multiplied by the specified weights.
-        
+
     Args:
-        - weights: The weightings to be used at every location in the values array. If weights contains additional 
-        dimensions, these will be taken to mean that multiple weightings are wanted simultaneoulsy, and these 
+        - weights: The weightings to be used at every location in the values array. If weights contains additional
+        dimensions, these will be taken to mean that multiple weightings are wanted simultaneoulsy, and these
         dimensions will be added to the new array.
         - values: The unweighted values to be used as the basis for weighting calculation
 
 
-    Note - this weighting function is different to the .weighted method contained in xarray in that xarray's 
-    method does not allow NaNs to be present in the weights or data.    
-    '''
-    
+    Note - this weighting function is different to the .weighted method contained in xarray in that xarray's
+    method does not allow NaNs to be present in the weights or data.
+    """
+
     if weights is not None:
         result = values * weights
         return result
@@ -24,11 +25,11 @@ def apply_weights(values, weights=None):
 
 
 def create_latitude_weights(latitudes):
-    '''
+    """
     A common way of weighting errors is to make them proportional to the amount of area
     which is contained in a particular region. This is approximated by the cosine
     of the latitude on an LLXY grid. Nuances not accounted for include the variation in
     latitude across the region, or the irregularity of the surface of the earth.
-    '''
+    """
     weights = np.cos(np.deg2rad(latitudes))
-    return weights
+    return weights

src/scores/probability/crps_impl.py

@@ -341,7 +341,7 @@ def crps_cdf(
             include_components=include_components,
         )
 
-    weighted = scores.functions.apply_weights(result, weights)        
+    weighted = scores.functions.apply_weights(result, weights)
 
     dims_to_collapse = scores.utils.dims_complement(result, dims=dims)
     result = weighted.mean(dim=dims_to_collapse)

src/scores/utils.py

@@ -5,28 +5,28 @@
 import xarray as xr
 
 
-WARN_ALL_DATA_CONFLICT_MSG = '''
+WARN_ALL_DATA_CONFLICT_MSG = """
 You are requesting to reduce or preserve every dimension by specifying the string 'all'.
 In this case, 'all' is also a named dimension in your data, leading to an ambiguity.
 In order to reduce or preserve the named data dimension, specify ['all'] as a list item
 rather than relying on string interpretation. The program will continue to interpret the
 string as an instruction to reduce or preserve every dimension.
-'''
+"""
 
-ERROR_SPECIFIED_NONPRESENT_PRESERVE_DIMENSION = '''
+ERROR_SPECIFIED_NONPRESENT_PRESERVE_DIMENSION = """
 You are requesting to preserve a dimension which does not appear in your data (fcst or obs).
 It is ambiguous how to proceed therefore an exception has been raised instead.
-'''
+"""
 
-ERROR_SPECIFIED_NONPRESENT_REDUCE_DIMENSION = '''
+ERROR_SPECIFIED_NONPRESENT_REDUCE_DIMENSION = """
 You are requesting to reduce a dimension which does not appear in your data (fcst or obs).
 It is ambiguous how to proceed therefore an exception has been raised instead.
-'''
+"""
 
-ERROR_OVERSPECIFIED_PRESERVE_REDUCE = '''
+ERROR_OVERSPECIFIED_PRESERVE_REDUCE = """
 You have specified both preserve_dims and reduce_dims. This method doesn't know how
 to properly interpret that, therefore an exception has been raised.
-'''
+"""
 
 
 class DimensionError(Exception):
@@ -76,7 +76,6 @@ def gather_dimensions(fcst_dims, obs_dims, weights_dims=None, reduce_dims=None,
 
     # Handle preserve_dims case
     if preserve_dims is not None:
-
         if preserve_dims == "all":
             return set([])
 
@@ -206,5 +205,3 @@ def check_dims(xr_data, expected_dims, mode=None):
                     f"Dimensions {list(xr_data[data_var].dims)} of data variable "
                     f"'{data_var}' are not {mode} to the dimensions {sorted(dims_set)}"
                 )
-
-