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.

1 files changed, 26 insertions, 11 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))