Fix issues caught by pylint

Issue:
https://github.com/genenetwork/gn-gemtext-threads/blob/main/topics/gn1-migration-to-gn2/clustering.gmi

* Fix a myriad of issues caught by pylint to ensure the code passes all tests.

2 files changed, 72 insertions, 49 deletions

diff --git a/gn3/computations/correlations2.py b/gn3/computations/correlations2.py
index 6c456db..93db3fa 100644
--- a/gn3/computations/correlations2.py
+++ b/gn3/computations/correlations2.py
@@ -1,15 +1,25 @@
+"""
+DESCRIPTION:
+    TODO: Add a description for the module
+
+FUNCTIONS:
+compute_correlation:
+    TODO: Describe what the function does..."""
+
 from math import sqrt
 from functools import reduce
 ## From GN1: mostly for clustering and heatmap generation
 
-def items_with_values(dbdata, userdata):
+def __items_with_values(dbdata, userdata):
     """Retains only corresponding items in the data items that are not `None` values.
-This should probably be renamed to something sensible"""
+    This should probably be renamed to something sensible"""
     def both_not_none(item1, item2):
+        """Check that both items are not the value `None`."""
         if (item1 is not None) and (item2 is not None):
             return (item1, item2)
         return None
     def split_lists(accumulator, item):
+        """Separate the 'x' and 'y' items."""
         return [accumulator[0] + [item[0]], accumulator[1] + [item[1]]]
     return reduce(
         split_lists,
@@ -17,19 +27,24 @@ This should probably be renamed to something sensible"""
         [[], []])
 
 def compute_correlation(dbdata, userdata):
-    x, y = items_with_values(dbdata, userdata)
-    if len(x) < 6:
-        return (0.0, len(x))
-    meanx = sum(x)/len(x)
-    meany = sum(y)/len(y)
+    """Compute some form of correlation.
+
+    This is extracted from
+    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/utility/webqtlUtil.py#L622-L647
+    """
+    x_items, y_items = __items_with_values(dbdata, userdata)
+    if len(x_items) < 6:
+        return (0.0, len(x_items))
+    meanx = sum(x_items)/len(x_items)
+    meany = sum(y_items)/len(y_items)
     def cal_corr_vals(acc, item):
         xitem, yitem = item
         return [
             acc[0] + ((xitem - meanx) * (yitem - meany)),
             acc[1] + ((xitem - meanx) * (xitem - meanx)),
             acc[2] + ((yitem - meany) * (yitem - meany))]
-    xyd, sxd, syd = reduce(cal_corr_vals, zip(x, y), [0.0, 0.0, 0.0])
+    xyd, sxd, syd = reduce(cal_corr_vals, zip(x_items, y_items), [0.0, 0.0, 0.0])
     try:
-        return ((xyd/(sqrt(sxd)*sqrt(syd))), len(x))
-    except ZeroDivisionError as zde:
-        return(0, len(x))
+        return ((xyd/(sqrt(sxd)*sqrt(syd))), len(x_items))
+    except ZeroDivisionError:
+        return(0, len(x_items))
diff --git a/gn3/computations/slink.py b/gn3/computations/slink.py
index 8d51f29..4aac6b3 100644
--- a/gn3/computations/slink.py
+++ b/gn3/computations/slink.py
@@ -7,13 +7,14 @@ slink:
     TODO: Describe what the function does...
 """
 import logging
-from functools import partial
 
 class LengthError(BaseException):
-    pass
+    """Raised whenever child lists/tuples are not the same length as the parent
+    list of tuple."""
 
 class MirrorError(BaseException):
-    pass
+    """Raised if the distance from child A to child B is not the same as the
+    distance from child B to child A."""
 
 def __is_list_or_tuple(item):
     return type(item) in [list, tuple]
@@ -50,19 +51,20 @@ def __raise_valueerror_if_child_list_distance_from_itself_is_not_zero(lists):
 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]))
+    inner_coords = range(len(lists))
+    coords = ((i, j) for i in inner_coords for j in inner_coords)
+    def __is_same_reversed(coord):
+        return lists[coord[0]][coord[1]] == lists[coord[1]][coord[0]]
+    if not all(map(__is_same_reversed, coords)):
+        raise MirrorError((
+            "Distance from one child to the other should be the same in both "
+            "directions."))
 
 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;
+        return val >= 0
     # flatten lists
     flattened = __flatten_list_of_lists(lists)
     if not all(map(zero_or_positive, flattened)):
@@ -76,7 +78,8 @@ def nearest(lists, i, j):
     Computes shortest distance between member(s) in `i` and member(s) in `j`.
 
     Description:
-    This is 'copied' over from genenetwork1, from https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/slink.py#L42-L64.
+    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 to better describe what 'member' means in
     the context where the function is used.
@@ -108,18 +111,20 @@ def nearest(lists, i, j):
     __raise_mirrorerror_of_distances_one_way_are_not_same_other_way(lists)
     __raise_valueerror_on_negative_distances(lists)
     #### END: Guard Functions ####
-    if type(i) == int and type(j) == int: # From member i to member j
+    if isinstance(i, int) and isinstance(j, int): # From member i to member j
         return lists[i][j]
-    elif type(i) == int and __is_list_or_tuple(j):
+
+    if isinstance(i, int) and __is_list_or_tuple(j):
         return min(map(lambda j_new: nearest(lists, i, j_new), j[:-1]))
-    elif type(j) == int and __is_list_or_tuple(i):
+    if isinstance(j, int) and __is_list_or_tuple(i):
         return min(map(lambda i_new: nearest(lists, i_new, j), i[:-1]))
-    elif __is_list_or_tuple(i) and __is_list_or_tuple(j):
+
+    if __is_list_or_tuple(i) and __is_list_or_tuple(j):
         coordinate_pairs = __flatten_list_of_lists(
             [[(itemi, itemj) for itemj in j[:-1]] for itemi in i[:-1]])
         return min(map(lambda x: nearest(lists, x[0], x[1]), coordinate_pairs))
-    else:
-        raise ValueError("member values (i or j) should be lists/tuples of integers or integers")
+
+    raise ValueError("member values (i or j) should be lists/tuples of integers or integers")
 
 def slink(lists):
     """
@@ -144,36 +149,39 @@ def slink(lists):
     """
     try:
         size = len(lists)
-        listindex = range(size)
         listindexcopy = list(range(size))
-        listscopy = [[item for item in child] for child in lists]
-        initSize = size
+        listscopy = [child[:] for child in lists]
+        init_size = size
         candidate = []
-        while initSize >2:
+        while init_size > 2:
             mindist = 1e10
-            for i in range(initSize):
-                for j in range(i+1,initSize):
+            for i in range(init_size):
+                for j in range(i+1, init_size):
                     if listscopy[i][j] < mindist:
-                        mindist =  listscopy[i][j]
-                        candidate=[[i,j]]
+                        mindist = listscopy[i][j]
+                        candidate = [[i, j]]
                     elif listscopy[i][j] == mindist:
-                        mindist =  listscopy[i][j]
-                        candidate.append([i,j])
+                        mindist = listscopy[i][j]
+                        candidate.append([i, j])
                     else:
                         pass
-            newmem = (listindexcopy[candidate[0][0]],listindexcopy[candidate[0][1]],mindist)
+            newmem = (
+                listindexcopy[candidate[0][0]], listindexcopy[candidate[0][1]],
+                mindist)
             listindexcopy.pop(candidate[0][1])
             listindexcopy[candidate[0][0]] = newmem
 
-            initSize -= 1
-            for i in range(initSize):
-                for j in range(i+1,initSize):
-                    listscopy[i][j] = nearest(lists,listindexcopy[i],listindexcopy[j])
+            init_size -= 1
+            for i in range(init_size):
+                for j in range(i+1, init_size):
+                    listscopy[i][j] = nearest(
+                        lists, listindexcopy[i], listindexcopy[j])
                     listscopy[j][i] = listscopy[i][j]
-        listindexcopy.append(nearest(lists,listindexcopy[0],listindexcopy[1]))
+        listindexcopy.append(
+            nearest(lists, listindexcopy[0], listindexcopy[1]))
         return listindexcopy
-    except Exception as e:
-        # TODO: Look into making the logging log output to the system's
-        #    configured logger(s)
-        logging.warning("Exception: {}, {}".format(type(e), e))
+    except (LengthError, MirrorError, TypeError, IndexError) as exc:
+        # Look into making the logging log output to the system's
+        #   configured logger(s)
+        logging.warning("Exception: %s, %s", type(exc), exc)
         return []