remove all bare excepts and trailing whitespace

chainladder/adjustments/bootstrap.py

@@ -108,7 +108,7 @@ def fit(self, X, y=None, sample_weight=None):
             if self.hat_adj:
                 try:
                     self.hat_ = self._get_hat(X, exp_incr_triangle)
-                except:
+                except Exception:
                     warn("Could not compute hat matrix.  Setting hat_adj to False")
                     self.had_adj = False
                     self.hat_ = None

chainladder/adjustments/tests/test_berqsherm.py

@@ -40,4 +40,4 @@ def test_adjusted_values():
         berq_triangle["Incurred"].values.sum(), 1126985253.661, atol=1e-2
     )
     assert np.isclose(berq_triangle["Paid"].values.sum(), 182046766.054, atol=1e-2)
-    assert np.isclose(berq_triangle["Closed"].values.sum(), 8798.982, atol=1e-2)
+    assert np.isclose(berq_triangle["Closed"].values.sum(), 8798.982, atol=1e-2)

chainladder/core/base.py

@@ -290,24 +290,24 @@ def _get_date_axes(
             end=origin_date.max(),
             freq=origin_grain
         ).to_timestamp(how="s")
-        
+
         development_range: DatetimeIndex = pd.period_range(
             start=development_date.min(),
             end=development_date.max(),
             freq=development_grain,
         ).to_timestamp(how="e")
-        
+
         # If the development is semi-annual, we need to adjust further because of "2Q-DEC".
         if development_grain[:2] == "2Q":
             from pandas.tseries.offsets import DateOffset
 
             development_range += DateOffset(months=-3)
-        
+
         c = pd.DataFrame(
             TriangleBase._cartesian_product(origin_range, development_range),
             columns=["__origin__", "__development__"],
         )
-        
+
         return c[c["__development__"] > c["__origin__"]]
 
     @property

chainladder/core/display.py

@@ -239,4 +239,4 @@ def _dimensionality(self) -> str:
             return 'single'
 
         else :
-            return 'multi'
+            return 'multi'

chainladder/core/dunders.py

@@ -63,7 +63,7 @@ def _compatibility_check(self, x, y):
         x, y = set_common_backend([x, y])
         if (
             x.origin_grain != y.origin_grain
-            or (x.development_grain != y.development_grain and 
+            or (x.development_grain != y.development_grain and
                 min(x.shape[-1], y.shape[-1]) > 1)
         ):
             raise ValueError(
@@ -100,7 +100,7 @@ def _prep_index(self, x, y):
                 x = x.sort_index()
                 try:
                     y = y.loc[x.index]
-                except:
+                except Exception:
                     x = x.groupby(list(common))
                     y = y.groupby(list(common))
             return x, y
@@ -112,7 +112,7 @@ def _prep_index(self, x, y):
 
     def _prep_columns(self, x, y):
         x_backend, y_backend = x.array_backend, y.array_backend
-        
+
         if len(x.columns) == 1 and len(y.columns) > 1:
             x.vdims = y.vdims
         elif len(y.columns) == 1 and len(x.columns) > 1:
@@ -121,30 +121,30 @@ def _prep_columns(self, x, y):
             y.vdims = x.vdims
         elif x.shape[1] == y.shape[1] and np.array_equal(x.columns, y.columns):
             return x, y
-        else:            
+        else:
             # Find columns to add to each triangle
-            cols_to_add_to_x = [col for col in y.columns if col not in x.columns] 
-            cols_to_add_to_y = [col for col in x.columns if col not in y.columns] 
-            
+            cols_to_add_to_x = [col for col in y.columns if col not in x.columns]
+            cols_to_add_to_y = [col for col in x.columns if col not in y.columns]
+
             # Create new columns only if necessary
             if cols_to_add_to_x:
                 new_x_cols = list(x.columns) + list(cols_to_add_to_x)
                 x = x.reindex(columns=new_x_cols, fill_value=0)
-            
+
             if cols_to_add_to_y:
                 new_y_cols = list(y.columns) + list(cols_to_add_to_y)
                 y = y.reindex(columns=new_y_cols, fill_value=0)
-            
+
             # Ensure both triangles have the same column order
             x = x[new_x_cols]
             y = y[new_x_cols]
-        
+
         # Reset backends only if they've changed
         if x.array_backend != x_backend:
             x = x.set_backend(x_backend, inplace=True)
         if y.array_backend != y_backend:
             y = y.set_backend(y_backend, inplace=True)
-        
+
         return x, y
 
     def _prep_origin_development(self, obj, other):

chainladder/core/pandas.py

@@ -91,7 +91,7 @@ def to_frame(self, origin_as_datetime=True, keepdims=False,
 
             valuation = pd.DataFrame(
                 obj.valuation.values.reshape(obj.shape[-2:], order='F'),
-                index=obj.odims if origin_as_datetime else obj.origin, 
+                index=obj.odims if origin_as_datetime else obj.origin,
                 columns=obj.ddims
             ).unstack().rename('valuation').reset_index().rename(
                 columns={'level_0': 'development', 'level_1': 'origin'})

chainladder/core/slice.py

@@ -248,7 +248,7 @@ def __setitem__(self, key, value):
             self.vdims = self.vdims if key in self.vdims else np.append(self.vdims, key)
             try:
                 self.values = xp.concatenate((self.values, value.values), axis=1)
-            except:
+            except Exception:
                 # For misaligned triangle support
                 conc = (self.values, (self.iloc[:, 0] * 0 + value).values)
                 self.values = xp.concatenate(conc, axis=1)

chainladder/core/tests/rtest_correlation.py

@@ -1,5 +1,5 @@
 ### Building out a dev environment with a working copy
-### of R ChainLadder is difficult.  These tests are 
+### of R ChainLadder is difficult.  These tests are
 ### Currently inactive, but available should the compatibility
 ### of the installs improve at a later date.
 
@@ -11,7 +11,7 @@
     from rpy2.robjects.packages import importr
     from rpy2.robjects import r
     CL = importr("ChainLadder")
-except:
+except Exception:
     pass
 
 def dev_corr_r(data, ci):

chainladder/core/tests/test_correlation.py

@@ -17,4 +17,4 @@ def test_dev_corr_sparse():
 
 def test_validate_critical():
     with pytest.raises(ValueError):
-        raa.valuation_correlation(p_critical=1.5, total=True)
+        raa.valuation_correlation(p_critical=1.5, total=True)

chainladder/core/tests/test_display.py

@@ -7,7 +7,7 @@ def test_heatmap_render(raa):
     try:
         raa.heatmap()
 
-    except:
+    except Exception:
         assert False
 
 
@@ -24,7 +24,7 @@ def test_to_frame(raa):
         cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=False)
         cl.Chainladder().fit(raa).ultimate_.to_frame(origin_as_datetime=True)
 
-    except:
+    except Exception:
         assert False
 
 

chainladder/core/tests/test_triangle.py

@@ -7,7 +7,7 @@
 
 try:
     from IPython.core.display import HTML
-except:
+except Exception:
     HTML = None
 
 
@@ -787,17 +787,17 @@ def test_semi_annual_grain():
                         "2009-01-01", "2009-07-01", "2010-01-01",
                         "2009-07-01", "2010-01-01",
                         "2010-01-01"],
-        'loss': [100, 200, 300, 400, 500, 600, 700, 
-                150, 300, 450, 500, 550, 600, 
-                200, 250, 350, 400, 450, 
+        'loss': [100, 200, 300, 400, 500, 600, 700,
+                150, 300, 450, 500, 550, 600,
+                200, 250, 350, 400, 450,
                 50, 100, 150, 200,
                 100, 200, 300,
-                50, 150, 
+                50, 150,
                 100]
     }
 
     Stri = cl.Triangle(
-        pd.DataFrame(Sdata), 
+        pd.DataFrame(Sdata),
         origin='origin',
         development='development',
         columns='loss',
@@ -813,14 +813,14 @@ def test_semi_annual_grain():
                         "2008-01-01", "2009-01-01", "2010-01-01",
                         "2009-01-01", "2010-01-01",
                         "2010-01-01"],
-        'loss': [100, 600, 1000, 1300, 
-                200, 450, 650, 
+        'loss': [100, 600, 1000, 1300,
+                200, 450, 650,
                 100, 450,
                 100]
     }
 
     Atri = cl.Triangle(
-        pd.DataFrame(Adata), 
+        pd.DataFrame(Adata),
         origin='origin',
         development='development',
         columns='loss',
@@ -833,7 +833,7 @@ def test_odd_quarter_end():
         ["5/1/2023", 12, '4/30/2024', 100],
         ["8/1/2023", 9, "4/30/2024", 130],
         ["11/1/2023", 6, "4/30/2024", 160],
-        ["2/1/2024", 3, "4/30/2024", 140]], 
+        ["2/1/2024", 3, "4/30/2024", 140]],
         columns = ['origin', 'development', 'valuation', 'EarnedPremium'])
     triangle = cl.Triangle(
         data, origin='origin', origin_format='%Y-%m-%d', development='valuation', columns='EarnedPremium', trailing=True, cumulative=True
@@ -867,7 +867,7 @@ def test_single_valuation_date_preserves_exact_date():
     assert triangle.development_grain == 'M'
     assert int(triangle.valuation_date.strftime('%Y%m')) == 202510
 def test_OXDX_triangle():
-    
+
     for x in [12,6,3,1]:
         for y in [i for i in [12,6,3,1] if i <= x]:
             first_orig = '2020-01-01'
@@ -881,13 +881,13 @@ def test_OXDX_triangle():
             for i in range(12):
                 for j in range(y):
                     test_data = tri_df.copy()
-                    test_data['origin_date'] += pd.DateOffset(months=i)        
+                    test_data['origin_date'] += pd.DateOffset(months=i)
                     test_data['development_date'] += pd.DateOffset(months=i-j)
                     tri = cl.Triangle(
-                        test_data, 
-                        origin='origin_date', 
-                        development='development_date', 
-                        columns='value', 
+                        test_data,
+                        origin='origin_date',
+                        development='development_date',
+                        columns='value',
                         cumulative=True
                     )
                     assert tri.shape == (1,1,2,width)

chainladder/core/triangle.py

@@ -151,7 +151,7 @@ def __init__(
         # Store dimension metadata.
         self.columns_label: list = columns
         self.origin_label: list = origin
-        
+
         # Handle any ultimate vectors in triangles separately.
         data, ult = self._split_ult(
             data=data,
@@ -195,11 +195,11 @@ def __init__(
         # Ensure that origin_date values represent the beginning of the period.
         # i.e., 1990 means the start of 1990.
         origin_date: Series = to_period(origin_date,self.origin_grain).dt.to_timestamp(how="s")
-        
+
         # Ensure that development_date values represent the end of the period.
         # i.e., 1990 means the end of 1990 assuming annual development periods.
         development_date: Series = to_period(development_date,self.development_grain).dt.to_timestamp(how="e")
-        
+
         # Aggregate dates to the origin/development grains.
         data_agg: DataFrame = self._aggregate_data(
             data=data,
@@ -208,7 +208,7 @@ def __init__(
             index=index,
             columns=columns,
         )
-        
+
         # Fill in missing periods with zeros.
         date_axes: DataFrame = self._get_date_axes(
             data_agg["__origin__"],
@@ -253,7 +253,7 @@ def __init__(
         self.is_cumulative: bool = cumulative
         self.virtual_columns = VirtualColumns(self)
         self.is_pattern: bool = pattern
-        
+
         split: list[str] = self.origin_grain.split("-")
         self.origin_grain: str = {"A": "Y", "2Q": "S"}.get(split[0], split[0])
 
@@ -831,7 +831,7 @@ def grain(self, grain="", trailing=False, inplace=False):
                 addl.ddims = addl_ts
                 obj = concat((addl, obj), axis=-1)
                 obj.values = num_to_nan(obj.values)
-        
+
         if dgrain_old != dgrain_new and obj.shape[-1] > 1:
             step = self._dstep()[dgrain_old][dgrain_new]
             d = np.sort(
@@ -847,7 +847,7 @@ def grain(self, grain="", trailing=False, inplace=False):
                 obj.ddims = ddims
 
             obj.development_grain = dgrain_new
-        
+
         obj = obj.dev_to_val() if self.is_val_tri else obj.val_to_dev()
 
         if inplace:

chainladder/development/incremental.py

@@ -146,7 +146,7 @@ def fit(self, X, y=None, sample_weight=None):
         self.sample_weight = sample_weight
         self.fit_zeta_ = self.tri_zeta * self.w_
         self.zeta_ = self._param_property(x,self.params_.slope_[...,0][..., None, :])
-        
+
         #to consolidate under full_triangle_
         y_ = xp.repeat(self.zeta_.values, len(x.odims), -2)
         obj = x.copy()
@@ -170,7 +170,7 @@ def fit(self, X, y=None, sample_weight=None):
         self.incremental_ = self.incremental_.trend(
             1/(1+future_trend)-1, axis='valuation', start=X.valuation_date,
             end=self.incremental_.valuation_date)
-        
+
         #to migrate under _zeta_to_ldf method under common, so ldf_ can be correct after tail
         self.ldf_ = obj.incr_to_cum().link_ratio
         return self
@@ -195,7 +195,7 @@ def transform(self, X):
 
     def _param_property(self, factor, params):
         from chainladder import options
-        
+
         obj = factor[factor.origin == factor.origin.min()]
         xp = factor.get_array_module()
         obj.values = params

chainladder/development/learning.py

@@ -156,13 +156,13 @@ def _prep_X_ml(self, X):
 
     def _prep_w_ml(self,X,sample_weight=None):
         weight_base = (~np.isnan(X.values)).astype(float)
-        weight = weight_base.copy()               
+        weight = weight_base.copy()
         if self.drop is not None:
             weight = weight * self._drop_func(X)
         if self.drop_valuation is not None:
             weight = weight * self._drop_valuation_func(X)
         if sample_weight is not None:
-            weight = weight * sample_weight.values 
+            weight = weight * sample_weight.values
         return weight.flatten()[weight_base.flatten()>0]
 
     def fit(self, X, y=None, sample_weight=None):
@@ -206,7 +206,7 @@ def fit(self, X, y=None, sample_weight=None):
             sample_weights = {self.weighted_step + '__sample_weight':weight}
         # Fit model
         self.estimator_ml.fit(df, self.y_ml_.fit_transform(df).squeeze(),**sample_weights)
-        #return selffit_incrementals 
+        #return selffit_incrementals
         self.triangle_ml_, self.predicted_data_ = self._get_triangle_ml(df)
         return self
 
@@ -238,4 +238,4 @@ def transform(self, X):
         X_new.ldf_.valuation_date = pd.to_datetime(options.ULT_VAL)
         X_new._set_slicers()
         X_new.predicted_data_ = predicted_data
-        return X_new
+        return X_new

chainladder/development/tests/rtest_clark.py

@@ -1,5 +1,5 @@
 ### Building out a dev environment with a working copy
-### of R ChainLadder is difficult.  These tests are 
+### of R ChainLadder is difficult.  These tests are
 ### Currently inactive, but available should the compatibility
 ### of the installs improve at a later date.
 
@@ -11,7 +11,7 @@
     from rpy2.robjects.packages import importr
     from rpy2.robjects import r
     CL = importr("ChainLadder")
-except:
+except Exception:
     pass
 
 @pytest.mark.r