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-rw-r--r--gn3/computations/slink.py75
-rw-r--r--tests/unit/computations/test_slink.py174
2 files changed, 249 insertions, 0 deletions
diff --git a/gn3/computations/slink.py b/gn3/computations/slink.py
new file mode 100644
index 0000000..7b4016a
--- /dev/null
+++ b/gn3/computations/slink.py
@@ -0,0 +1,75 @@
+class LengthError(BaseException):
+    pass
+
+class MirrorError(BaseException):
+    pass
+
+def raise_valueerror_if_data_is_not_lists_or_tuples(lists):
+    """Check that `lists` is a list of lists: If not, raise an exception."""
+    def is_list_or_tuple(item):
+        return type(item) == type([]) or type(item) == type(tuple)
+
+    if (not is_list_or_tuple(lists)) or (not all(map(is_list_or_tuple, lists))):
+        raise ValueError("Expected list or tuple")
+
+def raise_valueerror_if_lists_empty(lists):
+    """Check that the list and its direct children are not empty."""
+    def empty(lst):
+        return len(lst) == 0
+    if (empty(lists)) or not all(map(lambda x: not empty(x), lists)):
+        raise ValueError("List/Tuple should NOT be empty!")
+
+def raise_lengtherror_if_child_lists_are_not_same_as_parent(lists):
+    def len_is_same_as_parent(lst):
+        return len(lst) == len(lists)
+    if not all(map(len_is_same_as_parent, lists)):
+        raise LengthError("All children lists should be same length as the parent.")
+
+def raise_valueerror_if_child_list_distance_from_itself_is_not_zero(lists):
+    def get_child_distance(child):
+        idx = lists.index(child)
+        return lists[idx][idx]
+    def distance_is_zero(dist):
+        return dist == 0
+    children_distances = map(get_child_distance, lists)
+    if not all(map(distance_is_zero, children_distances)):
+        raise ValueError("Distance of each child list/tuple from itself should be zero!")
+
+def raise_mirrorerror_of_distances_one_way_are_not_same_other_way(lists):
+    """Check that the distance from A to B, is the same as the distance from B to A.
+If the two distances are different, throw an exception."""
+    for i in range(len(lists)):
+        for j in range(len(lists)):
+            if lists[i][j] != lists[j][i]:
+                raise MirrorError(
+                    ("Distance from one child({}) to the other ({}) "
+                     "should be the same in both directions.").format(
+                         lists[i][j], lists[j][i]))
+
+def raise_valueerror_on_negative_distances(lists):
+    """Check that distances between 'somethings' are all positive, otherwise,
+raise an exception."""
+    def zero_or_positive(val):
+        return val >= 0;
+    # flatten lists
+    flattened = [distance for child in lists for distance in child]
+    if not all(map(zero_or_positive, flattened)):
+        raise ValueError("Distances should be positive.")
+
+def nearest(lists, i, j):
+    """Computes some form of distance.
+This is 'copied' over from genenetwork1, from https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/slink.py#L42-L64.
+
+This description should be updated once the form/type of 'distance' identified."""
+
+    #### Guard Functions: Should we do this a different way? ####
+    raise_valueerror_if_data_is_not_lists_or_tuples(lists)
+    raise_valueerror_if_lists_empty(lists)
+    raise_lengtherror_if_child_lists_are_not_same_as_parent(lists)
+    raise_valueerror_if_child_list_distance_from_itself_is_not_zero(lists)
+    raise_mirrorerror_of_distances_one_way_are_not_same_other_way(lists)
+    raise_valueerror_on_negative_distances(lists)
+    #### END: Guard Functions ####
+    return None
+    if type(i) == int and type(j) == int: # From member i to member j
+        return lists[i][j]
diff --git a/tests/unit/computations/test_slink.py b/tests/unit/computations/test_slink.py
new file mode 100644
index 0000000..166eff9
--- /dev/null
+++ b/tests/unit/computations/test_slink.py
@@ -0,0 +1,174 @@
+"""Module contains tests for slink"""
+import unittest
+from unittest import TestCase
+
+from gn3.computations.slink import nearest
+from gn3.computations.slink import LengthError
+from gn3.computations.slink import MirrorError
+
+class TestSlink(TestCase):
+    """Class for testing slink functions"""
+
+    def test_nearest_expects_list_of_lists(self):
+        # This might be better handled with type-hints and mypy
+        for item in [9, "some string", 5.432,
+                     [1,2,3], ["test", 7.4]]:
+            with self.subTest(item=item):
+                with self.assertRaises(ValueError, msg="Expected list or tuple"):
+                    nearest(item, 1, 1)
+
+    def test_nearest_does_not_allow_empty_lists(self):
+        for lst in [[],
+                    [[],[]],
+                    [[],[],[]],
+                    [[0, 1, 2],[],[1, 2, 0]]]:
+            with self.subTest(lst=lst):
+                with self.assertRaises(ValueError):
+                    nearest(lst, 1, 1)
+
+    def test_nearest_expects_exception_if_all_child_lists_are_not_of_equal_length_to_length_of_parent_list(self):
+        for lst in [[[0,1]],
+                    [[0,1,2],[3,4,5]],
+                    [[0,1,2,3],[4,5,6],[7,8,9,0]],
+                    [[0,1,2,3,4],[5,6,7,8,9],[1,2,3,4,5],[2,3],[3,4,5,6,7]]]:
+            with self.subTest(lst=lst):
+                with self.assertRaises(LengthError):
+                    nearest(lst, 1, 1)
+
+    def test_nearest_expects_exception_if_distance_of_child_from_itself_is_not_zero(self):
+        for lst in [[[1]],
+                    [[1,2],[3,4]],
+                    [1,0,0],[0,0,5],[0,3,4],
+                    [0,0,0,0],[0,0,3,3],[0,1,2,3],[0,3,2,0]]:
+            with self.subTest(lst=lst):
+                with self.assertRaises(ValueError):
+                    nearest(lst, 1, 1)
+
+    def test_nearest_expects_exception_if_distance_from_child_a_to_child_b_is_not_distance_from_child_b_to_child_a(self):
+        for lst in [[[0,1],[2,0]],
+                    [[0,1,2],[1,0,3],[9,7,0]],
+                    [[0,1,2,3],[7,0,2,3],[2,3,0,1],[8,9,5,0]]]:
+            with self.subTest(lst=lst):
+                with self.assertRaises(MirrorError):
+                    nearest(lst, 1, 1)
+
+    def test_nearest_expects_zero_or_positive_distances(self):
+        # Based on:
+        # https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/slink.py#L87-L89
+        for lst in [[[0,-1,2,3],[-1,0,3,4],[2,3,0,5],[3,4,5,0]],
+                    [[0,1,-2,3],[1,0,3,4],[-2,3,0,5],[3,4,5,0]],
+                    [[0,1,2,3],[1,0,-3,4],[2,-3,0,5],[3,4,5,0]],
+                    [[0,1,2,-3],[1,0,3,4],[2,3,0,5],[-3,4,5,0]],
+                    [[0,1,2,3],[1,0,3,-4],[2,3,0,5],[3,-4,5,0]],
+                    [[0,1,2,3],[1,0,3,4],[2,3,0,-5],[3,4,-5,0]]]:
+            with self.subTest(lst=lst):
+                with self.assertRaises(ValueError, msg="Distances should be positive."):
+                    nearest(lst, 1, 1)
+
+    def test_nearest_returns_shortest_distance_given_coordinates_to_both_group_members(self):
+        # This test is named wrong - at least I think it is, from the expected results
+        # This tests distance when both `i`, and `j` are integers
+        # We still need to add tests for when (either one/both) (is/are) not (an) integer(s)
+        # https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/slink.py#L39-L40
+        for lst, i, j, expected in [[[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     0,0,0],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     0,1,9],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     0,2,3],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     0,3,6],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     0,4,11],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     1,0,9],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     1,1,0],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     1,2,7],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     1,3,5],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     1,4,10],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     2,0,3],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     2,1,7],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     2,2,0],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     2,3,9],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     2,4,2],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     3,0,6],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     3,1,5],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     3,2,9],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     3,3,0],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     3,4,8],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     4,0,11],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     4,1,10],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     4,2,2],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     4,3,8],
+                                    [[[0,9,3,6,11],[9,0,7,5,10],[3,7,0,9,2],[6,5,9,0,8],[11,10,2,8,0]],
+                                     4,4,0],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     0,0,0],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     0,1,9],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     0,2,5.5],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     0,3,6],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     0,4,11],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     1,0,9],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     1,1,0],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     1,2,7],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     1,3,5],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     1,4,10],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     2,0,5.5],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     2,1,7],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     2,2,0],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     2,3,9],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     2,4,2],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     3,0,6],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     3,1,5],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     3,2,9],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     3,3,0],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     3,4,3],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     4,0,11],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     4,1,10],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     4,2,2],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     4,3,3],
+                                    [[[0,9,5.5,6,11],[9,0,7,5,10],[5.5,7,0,9,2],[6,5,9,0,3],[11,10,2,3,0]],
+                                     4,4,0]]:
+            with self.subTest(lst=lst):
+                self.assertEqual(nearest(lst, i, j), expected)