Fix issue with numpy interop (#51)

Reading from an Array:
  - When an Array of real or complex numbers with more than two dimensions was
  benig converted to numpy this conversion didn't return an np.array
  with a proper shape.

Creating an Array:
  - When an Array of real numbers with more than two dimensions was
  built. The shape in the device (arrayfire) was not correct.
  - For complex numbers it occurrs with more than one dimension.

Author: jrecuerda

khiva/array.py

@@ -14,7 +14,6 @@
 import ctypes
 import logging
 import sys
-from collections import deque
 from enum import Enum
 
 import numpy as np
@@ -177,21 +176,24 @@ def _create_array(self, data):
         if isinstance(data, pd.DataFrame):
             data = data.values
         shape = np.array(data.shape)
-        shape = shape[shape > 1]
-        shape = deque(shape)
-        shape.rotate(1)
+
+        if data.size > 1:
+            trimmed_dims = shape
+            for _ in range(0, 3):
+                if trimmed_dims[-1] == 1:
+                    trimmed_dims = trimmed_dims[:-1]
+            shape = trimmed_dims[::-1]
+        else:
+            shape = np.array([1])
+
         c_array_n = (ctypes.c_longlong * len(shape))(*(np.array(shape)).astype(np.longlong))
         c_ndims = ctypes.c_uint(len(shape))
         c_complex = np.iscomplexobj(data)
 
         if c_complex:
-            data = np.array([data.real, data.imag])
-            c = deque(range(1, len(data.shape)))
-            c.rotate(1)
-            c.append(0)
-            array_joint = np.transpose(data, c).flatten()
-        else:
-            array_joint = data.flatten()
+            data = np.dstack((data.real.flatten(), data.imag.flatten()))
+
+        array_joint = data.flatten()
 
         c_array_joint = (_get_array_type(self.khiva_type.value) * len(array_joint))(
             *array_joint)
@@ -212,25 +214,22 @@ def _get_data(self):
         c_result_array = (_get_array_type(self.khiva_type.value) * self.result_l)(*initialized_result_array)
         KhivaLibrary().c_khiva_library.get_data(ctypes.pointer(self.arr_reference), ctypes.pointer(c_result_array))
 
-        dims = self.get_dims()
-        if dims[dims > 1].size > 0:
-            dims = dims[dims > 1]
-        else:
-            dims = np.array([1])
-
         a = np.array(c_result_array)
 
         if self._is_complex():
             a = np.array(np.split(a, self.result_l / 2))
             a = np.apply_along_axis(lambda args: [complex(*args)], 1, a)
-            a = a.reshape(dims)
-            c = deque(range(len(a.shape)))
-            c.rotate(-1)
-            a = np.transpose(a, c)
+
+        # Clean up the last n dimensions if these are equal to 1
+        if a.size > 1:
+            trimmed_dims = self.get_dims()
+            for _ in range(0, 3):
+                if trimmed_dims[-1] == 1:
+                    trimmed_dims = trimmed_dims[:-1]
         else:
-            dims = deque(dims)
-            dims.rotate(1)
-            a = a.reshape(dims)
+            trimmed_dims = np.array([1])
+
+        a = a.reshape(trimmed_dims[::-1])
 
         a = a.astype(_get_numpy_type(self.khiva_type.value))
         return a

tests/unit_tests/array_unit_tests.py

@@ -30,21 +30,43 @@ class ArrayTest(unittest.TestCase):
     def setUp(self):
         set_backend(KHIVABackend.KHIVA_BACKEND_CPU)
 
+    def test_real_1d_creation(self):
+        a = Array([1, 5, 3, 1])
+        np.testing.assert_array_equal(a.dims, np.array([4, 1, 1, 1]))
+
+    def test_single_value_creation(self):
+        a = Array([1])
+        np.testing.assert_array_equal(a.dims, np.array([1, 1, 1, 1]))
+
     def test_real_1d(self):
         a = Array([1, 2, 3, 4, 5, 6, 7, 8])
         expected = np.array([1, 2, 3, 4, 5, 6, 7, 8])
         np.testing.assert_array_equal(a.to_numpy(), expected)
 
+    def test_real_2d_creation(self):
+        a = Array([[1, 5, 3, 1], [2, 6, 9, 8], [3, 4, 1, 3]])
+        np.testing.assert_array_equal(a.dims, np.array([4, 3, 1, 1]))
+
     def test_real_2d(self):
         a = Array([[1, 2, 3, 4], [5, 6, 7, 8]])
         expected = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
         np.testing.assert_array_equal(a.to_numpy(), expected)
 
+    def test_real_3d_creation(self):
+        a = Array([[[1, 5, 3, 1], [2, 6, 9, 8], [3, 4, 1, 3]],
+                   [[3, 7, 4, 2], [4, 8, 1, 9], [1, 5, 9, 2]]])
+        np.testing.assert_array_equal(a.dims, np.array([4, 3, 2, 1]))
+
     def test_real_3d(self):
         a = Array([[[1, 5], [2, 6]], [[3, 7], [4, 8]]])
         expected = np.array([[[1, 5], [2, 6]], [[3, 7], [4, 8]]])
         np.testing.assert_array_equal(a.to_numpy(), expected)
 
+    def test_real_3d_large_column(self):
+        a = Array([[[1, 5, 3], [2, 6, 9]], [[3, 7, 4], [4, 8, 1]]])
+        expected = np.array([[[1, 5, 3], [2, 6, 9]], [[3, 7, 4], [4, 8, 1]]])
+        np.testing.assert_array_equal(a.to_numpy(), expected)
+
     def test_real_4d(self):
         a = Array([[[[1, 9], [2, 10]], [[3, 11], [4, 12]]], [[[5, 13], [6, 14]], [[7, 15], [8, 16]]]])
         expected = np.array([[[[1, 9], [2, 10]], [[3, 11], [4, 12]]], [[[5, 13], [6, 14]], [[7, 15], [8, 16]]]])
@@ -254,7 +276,7 @@ def testCols(self):
     def testRow(self):
         a = Array(np.transpose([[1, 2], [3, 4]]), dtype.s32)
         c = a.get_row(0)
-        np.testing.assert_array_equal(c.to_numpy(), [1, 2])
+        np.testing.assert_array_equal(c.to_numpy(), np.transpose(np.array([[1, 2]])))
 
     def testRows(self):
         a = Array(np.transpose([[1, 2], [3, 4], [5, 6]]), dtype.s32)

tests/unit_tests/matrix_unit_tests.py

@@ -68,8 +68,8 @@ def test_find_best_n_motifs_multiple_profiles(self):
         find_best_n_motifs_result = find_best_n_motifs(stomp_result[0], stomp_result[1], 3, 1)
         a = find_best_n_motifs_result[1].to_numpy()
         b = find_best_n_motifs_result[2].to_numpy()
-        np.testing.assert_array_almost_equal(a, np.array([[12, 12], [12, 12]]), decimal=self.DECIMAL)
-        np.testing.assert_array_almost_equal(b, np.array([[1, 1], [1, 1]]), decimal=self.DECIMAL)
+        np.testing.assert_array_almost_equal(a, np.array([[[12], [12]], [[12], [12]]]), decimal=self.DECIMAL)
+        np.testing.assert_array_almost_equal(b, np.array([[[1], [1]], [[1], [1]]]), decimal=self.DECIMAL)
 
     def test_find_best_n_motifs_mirror(self):
         stomp_result = stomp_self_join(Array([10.1, 11, 10.2, 10.15, 10.775, 10.1, 11, 10.2], dtype.f32), 3)