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"""
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):
"""Retains only corresponding items in the data items that are not `None` values.
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,
filter(lambda x: x is not None, map(both_not_none, dbdata, userdata)),
[[], []])
def compute_correlation(dbdata, userdata):
"""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_items, y_items), [0.0, 0.0, 0.0])
try:
return ((xyd/(sqrt(sxd)*sqrt(syd))), len(x_items))
except ZeroDivisionError:
return(0, len(x_items))
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