More like arguments

Author: dcherian

numpy_groupies/aggregate_numpy.py

@@ -44,7 +44,7 @@ def _sum(group_idx, a, size, fill_value, dtype=None):
 
 def _prod(group_idx, a, size, fill_value, dtype=None):
     dtype = minimum_dtype_scalar(fill_value, dtype, a)
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     if fill_value != 1:
         ret[group_idx] = 1  # product starts from 1
     np.multiply.at(ret, group_idx, a)
@@ -57,7 +57,7 @@ def _len(group_idx, a, size, fill_value, dtype=None):
 
 def _last(group_idx, a, size, fill_value, dtype=None):
     dtype = minimum_dtype(fill_value, dtype or a.dtype)
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     # repeated indexing gives last value, see:
     # the phrase "leaving behind the last value"  on this page:
     # http://wiki.scipy.org/Tentative_NumPy_Tutorial
@@ -67,14 +67,14 @@ def _last(group_idx, a, size, fill_value, dtype=None):
 
 def _first(group_idx, a, size, fill_value, dtype=None):
     dtype = minimum_dtype(fill_value, dtype or a.dtype)
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     ret[group_idx[::-1]] = a[::-1]  # same trick as _last, but in reverse
     return ret
 
 
 def _all(group_idx, a, size, fill_value, dtype=None):
     check_boolean(fill_value)
-    ret = np.full(size, fill_value, dtype=bool)
+    ret = np.full(size, fill_value, dtype=bool, like=a)
     if not fill_value:
         ret[group_idx] = True
     ret[group_idx.compress(np.logical_not(a))] = False
@@ -83,7 +83,7 @@ def _all(group_idx, a, size, fill_value, dtype=None):
 
 def _any(group_idx, a, size, fill_value, dtype=None):
     check_boolean(fill_value)
-    ret = np.full(size, fill_value, dtype=bool)
+    ret = np.full(size, fill_value, dtype=bool, like=a)
     if fill_value:
         ret[group_idx] = False
     ret[group_idx.compress(a)] = True
@@ -93,7 +93,7 @@ def _any(group_idx, a, size, fill_value, dtype=None):
 def _min(group_idx, a, size, fill_value, dtype=None):
     dtype = minimum_dtype(fill_value, dtype or a.dtype)
     dmax = maxval(fill_value, dtype)
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     if fill_value != dmax:
         ret[group_idx] = dmax  # min starts from maximum
     np.minimum.at(ret, group_idx, a)
@@ -103,7 +103,7 @@ def _min(group_idx, a, size, fill_value, dtype=None):
 def _max(group_idx, a, size, fill_value, dtype=None):
     dtype = minimum_dtype(fill_value, dtype or a.dtype)
     dmin = minval(fill_value, dtype)
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     if fill_value != dmin:
         ret[group_idx] = dmin  # max starts from minimum
     np.maximum.at(ret, group_idx, a)
@@ -115,7 +115,7 @@ def _argmax(group_idx, a, size, fill_value, dtype=int, _nansqueeze=False):
     group_max = _max(group_idx, a_, size, np.nan)
     # nan should never be maximum, so use a and not a_
     is_max = a == group_max[group_idx]
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     group_idx_max = group_idx[is_max]
     (argmax,) = is_max.nonzero()
     ret[group_idx_max[::-1]] = argmax[
@@ -129,7 +129,7 @@ def _argmin(group_idx, a, size, fill_value, dtype=int, _nansqueeze=False):
     group_min = _min(group_idx, a_, size, np.nan)
     # nan should never be minimum, so use a and not a_
     is_min = a == group_min[group_idx]
-    ret = np.full(size, fill_value, dtype=dtype)
+    ret = np.full(size, fill_value, dtype=dtype, like=a)
     group_idx_min = group_idx[is_min]
     (argmin,) = is_min.nonzero()
     ret[group_idx_min[::-1]] = argmin[
@@ -144,7 +144,7 @@ def _mean(group_idx, a, size, fill_value, dtype=np.dtype(np.float64)):
     counts = np.bincount(group_idx, minlength=size)
     if iscomplexobj(a):
         dtype = a.dtype  # TODO: this is a bit clumsy
-        sums = np.empty(size, dtype=dtype)
+        sums = np.empty(size, dtype=dtype, like=a)
         sums.real = np.bincount(group_idx, weights=a.real, minlength=size)
         sums.imag = np.bincount(group_idx, weights=a.imag, minlength=size)
     else: