Merge pull request #185 from ndawe/stretch

[MRG] stretch() improvements

Author: ndawe

root_numpy/_utils.py

@@ -17,10 +17,6 @@
 VLEN = np.vectorize(len)
 
 
-def _is_object_field(arr, col):
-    return arr.dtype[col] == 'O'
-
-
 def rec2array(rec, fields=None):
     """Convert a record array into a ndarray with a homogeneous data type.
 
@@ -72,7 +68,7 @@ def stack(recs, fields=None):
     return np.hstack([rec[fields] for rec in recs])
 
 
-def stretch(arr, fields):
+def stretch(arr, fields=None):
     """Stretch an array.
 
     Stretch an array by ``hstack()``-ing  multiple array fields while
@@ -83,8 +79,8 @@ def stretch(arr, fields):
     ----------
     arr : NumPy structured or record array
         The array to be stretched.
-    fields : list of strings
-        A list of column names to stretch.
+    fields : list of strings, optional (default=None)
+        A list of column names to stretch. If None, then stretch all fields.
 
     Returns
     -------
@@ -103,44 +99,51 @@ def stretch(arr, fields):
         dtype=[('scalar', '<i8'), ('array', '<f8')])
 
     """
-    dt = []
-    has_array_field = False
-    has_scalar_filed = False
-    first_array = None
-
-    # Construct dtype
-    for c in fields:
-        if _is_object_field(arr, c):
-            dt.append((c, arr[c][0].dtype))
-            has_array_field = True
-            first_array = c if first_array is None else first_array
-        else:
-            # Assume scalar
-            dt.append((c, arr[c].dtype))
-            has_scalar_filed = True
-
-    if not has_array_field:
-        raise RuntimeError("No array column specified")
-
-    len_array = VLEN(arr[first_array])
-    numrec = np.sum(len_array)
-    ret = np.empty(numrec, dtype=dt)
-
-    for c in fields:
-        if _is_object_field(arr, c):
-            # FIXME: this is rather inefficient since the stack
-            # is copied over to the return value
-            stack = np.hstack(arr[c])
-            if len(stack) != numrec:
+    dtype = []
+    len_array = None
+
+    if fields is None:
+        fields = arr.dtype.names
+
+    # Construct dtype and check consistency
+    for field in fields:
+        dt = arr.dtype[field]
+        if dt == 'O' or len(dt.shape):
+            if dt == 'O':
+                # Variable-length array field
+                lengths = VLEN(arr[field])
+            else:
+                lengths = np.repeat(dt.shape[0], arr.shape[0])
+            # Fixed-length array field
+            if len_array is None:
+                len_array = lengths
+            elif not np.array_equal(lengths, len_array):
                 raise ValueError(
-                    "Array lengths do not match: "
-                    "expected %d but found %d in %s" %
-                    (numrec, len(stack), c))
-            ret[c] = stack
+                    "inconsistent lengths of array columns in input")
+            if dt == 'O':
+                dtype.append((field, arr[field][0].dtype))
+            else:
+                dtype.append((field, arr[field].dtype, dt.shape[1:]))
+        else:
+            # Scalar field
+            dtype.append((field, dt))
+
+    if len_array is None:
+        raise RuntimeError("no array column in input")
+
+    # Build stretched output
+    ret = np.empty(np.sum(len_array), dtype=dtype)
+    for field in fields:
+        dt = arr.dtype[field]
+        if dt == 'O' or len(dt.shape) == 1:
+            # Variable-length or 1D fixed-length array field
+            ret[field] = np.hstack(arr[field])
+        elif len(dt.shape):
+            # Multidimensional fixed-length array field
+            ret[field] = np.vstack(arr[field])
         else:
-            # FIXME: this is rather inefficient since the repeat result
-            # is copied over to the return value
-            ret[c] = np.repeat(arr[c], len_array)
+            # Scalar field
+            ret[field] = np.repeat(arr[field], len_array)
 
     return ret
 

root_numpy/tests.py

@@ -574,56 +574,53 @@ def test_fill_graph():
 
 
 def test_stretch():
-    nrec = 5
-    arr = np.empty(nrec,
+    arr = np.empty(5,
         dtype=[
             ('scalar', np.int),
-            ('df1', 'O'),
-            ('df2', 'O'),
-            ('df3', 'O')])
-
-    for i in range(nrec):
-        df1 = np.array(range(i + 1), dtype=np.float)
-        df2 = np.array(range(i + 1), dtype=np.int) * 2
-        df3 = np.array(range(i + 1), dtype=np.double) * 3
-        arr[i] = (i, df1, df2, df3)
+            ('vl1', 'O'),
+            ('vl2', 'O'),
+            ('vl3', 'O'),
+            ('fl1', np.int, (2, 2)),
+            ('fl2', np.float, (2, 3)),
+            ('fl3', np.double, (3, 2))])
+
+    for i in range(arr.shape[0]):
+        vl1 = np.array(range(i + 1), dtype=np.int)
+        vl2 = np.array(range(i + 2), dtype=np.float) * 2
+        vl3 = np.array(range(2), dtype=np.double) * 3
+        fl1 = np.array(range(4), dtype=np.int).reshape((2, 2))
+        fl2 = np.array(range(6), dtype=np.float).reshape((2, 3))
+        fl3 = np.array(range(6), dtype=np.double).reshape((3, 2))
+        arr[i] = (i, vl1, vl2, vl3, fl1, fl2, fl3)
+
+    # no array columns included
+    assert_raises(RuntimeError, rnp.stretch, arr, ['scalar',])
 
-    stretched = rnp.stretch(
-        arr, ['scalar', 'df1', 'df2', 'df3'])
+    # lengths don't match
+    assert_raises(ValueError, rnp.stretch, arr, ['scalar', 'vl1', 'vl2',])
+    assert_raises(ValueError, rnp.stretch, arr, ['scalar', 'fl1', 'fl3',])
+    assert_raises(ValueError, rnp.stretch, arr)
 
+    # variable-length stretch
+    stretched = rnp.stretch(arr, ['scalar', 'vl1',])
     assert_equal(stretched.dtype,
-        [('scalar', np.int),
-         ('df1', np.float),
-         ('df2', np.int),
-         ('df3', np.double)])
-    assert_equal(stretched.size, 15)
-
-    assert_almost_equal(stretched['df1'][14], 4.0)
-    assert_almost_equal(stretched['df2'][14], 8)
-    assert_almost_equal(stretched['df3'][14], 12.0)
-    assert_almost_equal(stretched['scalar'][14], 4)
-    assert_almost_equal(stretched['scalar'][13], 4)
-    assert_almost_equal(stretched['scalar'][12], 4)
-    assert_almost_equal(stretched['scalar'][11], 4)
-    assert_almost_equal(stretched['scalar'][10], 4)
-    assert_almost_equal(stretched['scalar'][9], 3)
-
-    arr = np.empty(1, dtype=[('scalar', np.int),])
-    arr[0] = (1,)
-    assert_raises(RuntimeError, rnp.stretch, arr, ['scalar',])
+                 [('scalar', np.int),
+                  ('vl1', np.int)])
+    assert_equal(stretched.shape[0], 15)
+    assert_array_equal(
+        stretched['scalar'],
+        np.repeat(arr['scalar'], np.vectorize(len)(arr['vl1'])))
 
-    nrec = 5
-    arr = np.empty(nrec,
-        dtype=[
-            ('scalar', np.int),
-            ('df1', 'O'),
-            ('df2', 'O')])
-
-    for i in range(nrec):
-        df1 = np.array(range(i + 1), dtype=np.float)
-        df2 = np.array(range(i + 2), dtype=np.int) * 2
-        arr[i] = (i, df1, df2)
-    assert_raises(ValueError, rnp.stretch, arr, ['scalar', 'df1', 'df2'])
+    # fixed-length stretch
+    stretched = rnp.stretch(arr, ['scalar', 'vl3', 'fl1', 'fl2',])
+    assert_equal(stretched.dtype,
+                 [('scalar', np.int),
+                  ('vl3', np.double),
+                  ('fl1', np.int, (2,)),
+                  ('fl2', np.float, (3,))])
+    assert_equal(stretched.shape[0], 10)
+    assert_array_equal(
+        stretched['scalar'], np.repeat(arr['scalar'], 2))
 
 
 def test_blockwise_inner_join():