Global s/np.issubdtype/safe_issubdtype/. Probably most cases were OK, but this is easier than carefully checking each!

njsmith · njsmith · commit cd0b84247ec3 · 2015-03-03T23:44:05.000-08:00
diff --git a/patsy/build.py b/patsy/build.py
@@ -17,7 +17,8 @@
                                CategoricalSniffer,
                                categorical_to_int)
 from patsy.util import (atleast_2d_column_default,
-                        have_pandas, asarray_or_pandas)
+                        have_pandas, asarray_or_pandas,
+                        safe_issubdtype)
 from patsy.design_info import DesignMatrix, DesignInfo
 from patsy.redundancy import pick_contrasts_for_term
 from patsy.desc import ModelDesc
@@ -76,7 +77,7 @@ def eval(self, data, NA_action):
                                 % (self.factor.name(), self._expected_columns,
                                    result.shape[1]),
                                 self.factor)
-        if not np.issubdtype(np.asarray(result).dtype, np.number):
+        if not safe_issubdtype(np.asarray(result).dtype, np.number):
             raise PatsyError("when evaluating numeric factor %s, "
                                 "I got non-numeric data of type '%s'"
                                 % (self.factor.name(), result.dtype),
diff --git a/patsy/categorical.py b/patsy/categorical.py
@@ -45,7 +45,8 @@
                         safe_is_pandas_categorical,
                         pandas_Categorical_from_codes,
                         pandas_Categorical_categories,
-                        pandas_Categorical_codes)
+                        pandas_Categorical_codes,
+                        safe_issubdtype)
 
 if have_pandas:
     import pandas
@@ -123,7 +124,7 @@ def guess_categorical(data):
     if isinstance(data, _CategoricalBox):
         return True
     data = np.asarray(data)
-    if np.issubdtype(data.dtype, np.number):
+    if safe_issubdtype(data.dtype, np.number):
         return False
     return True
 
@@ -190,7 +191,7 @@ def sniff(self, data):
                 data = data.data
         # fastpath to avoid doing an item-by-item iteration over boolean
         # arrays, as requested by #44
-        if hasattr(data, "dtype") and np.issubdtype(data.dtype, np.bool_):
+        if hasattr(data, "dtype") and safe_issubdtype(data.dtype, np.bool_):
             self._level_set = set([True, False])
             return True
 
@@ -333,7 +334,7 @@ def categorical_to_int(data, levels, NA_action, origin=None):
 
     # fastpath to avoid doing an item-by-item iteration over boolean arrays,
     # as requested by #44
-    if hasattr(data, "dtype") and np.issubdtype(data.dtype, np.bool_):
+    if hasattr(data, "dtype") and safe_issubdtype(data.dtype, np.bool_):
         if level_to_int[False] == 0 and level_to_int[True] == 1:
             return data.astype(np.int_)
     out = np.empty(len(data), dtype=int)
diff --git a/patsy/contrasts.py b/patsy/contrasts.py
@@ -15,7 +15,7 @@
 import numpy as np
 from patsy import PatsyError
 from patsy.compat import triu_indices, tril_indices, diag_indices
-from patsy.util import repr_pretty_delegate, repr_pretty_impl
+from patsy.util import repr_pretty_delegate, repr_pretty_impl, safe_issubdtype
 
 class ContrastMatrix(object):
     """A simple container for a matrix used for coding categorical factors.
@@ -567,7 +567,7 @@ def code_contrast_matrix(intercept, levels, contrast, default=None):
     if isinstance(contrast, ContrastMatrix):
         return contrast
     as_array = np.asarray(contrast)
-    if np.issubdtype(as_array.dtype, np.number):
+    if safe_issubdtype(as_array.dtype, np.number):
         return ContrastMatrix(as_array,
                               _name_levels("custom", range(as_array.shape[1])))
     if intercept:
diff --git a/patsy/design_info.py b/patsy/design_info.py
@@ -19,7 +19,7 @@
 from patsy import PatsyError
 from patsy.util import atleast_2d_column_default
 from patsy.compat import OrderedDict
-from patsy.util import repr_pretty_delegate, repr_pretty_impl
+from patsy.util import repr_pretty_delegate, repr_pretty_impl, safe_issubdtype
 from patsy.constraint import linear_constraint
 
 class DesignInfo(object):
@@ -278,7 +278,7 @@ def from_array(cls, array_like, default_column_prefix="column"):
             raise ValueError("design matrix can't have >2 dimensions")
         columns = getattr(arr, "columns", range(arr.shape[1]))
         if (hasattr(columns, "dtype")
-            and not np.issubdtype(columns.dtype, np.integer)):
+            and not safe_issubdtype(columns.dtype, np.integer)):
             column_names = [str(obj) for obj in columns]
         else:
             column_names = ["%s%s" % (default_column_prefix, i)
@@ -527,7 +527,7 @@ def __new__(cls, input_array, design_info=None,
             return input_array
         self = atleast_2d_column_default(input_array).view(cls)
         # Upcast integer to floating point
-        if np.issubdtype(self.dtype, np.integer):
+        if safe_issubdtype(self.dtype, np.integer):
             self = np.asarray(self, dtype=float).view(cls)
         if self.ndim > 2:
             raise ValueError("DesignMatrix must be 2d")
@@ -539,7 +539,7 @@ def __new__(cls, input_array, design_info=None,
                              "(got %s, wanted %s)"
                              % (len(design_info.column_names), self.shape[1]))
         self.design_info = design_info
-        if not np.issubdtype(self.dtype, np.floating):
+        if not safe_issubdtype(self.dtype, np.floating):
             raise ValueError("design matrix must be real-valued floating point")
         return self
 
diff --git a/patsy/state.py b/patsy/state.py
@@ -27,7 +27,7 @@
 import numpy as np
 from patsy.util import (atleast_2d_column_default,
                         asarray_or_pandas, pandas_friendly_reshape,
-                        wide_dtype_for)
+                        wide_dtype_for, safe_issubdtype)
 from patsy.compat import wraps
 
 # These are made available in the patsy.* namespace
@@ -107,7 +107,7 @@ def transform(self, x):
         # heterogenous types. And in that case we're going to be munging the
         # types anyway, so copying isn't a big deal.
         x_arr = np.asarray(x)
-        if np.issubdtype(x_arr.dtype, np.integer):
+        if safe_issubdtype(x_arr.dtype, np.integer):
             dt = float
         else:
             dt = x_arr.dtype
diff --git a/patsy/util.py b/patsy/util.py
@@ -270,10 +270,10 @@ def test_to_uniqueify_list():
 
 def wide_dtype_for(arr):
     arr = np.asarray(arr)
-    if (np.issubdtype(arr.dtype, np.integer)
-        or np.issubdtype(arr.dtype, np.floating)):
+    if (safe_issubdtype(arr.dtype, np.integer)
+        or safe_issubdtype(arr.dtype, np.floating)):
         return widest_float
-    elif np.issubdtype(arr.dtype, np.complexfloating):
+    elif safe_issubdtype(arr.dtype, np.complexfloating):
         return widest_complex
     raise ValueError("cannot widen a non-numeric type %r" % (arr.dtype,))
 
