python why does max(max(float_2d_array)) give wrong answer? - python

for example:
a = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.2852976787772832, 0.7965388321000092, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 6.409872844109646, 0.17506688391255013, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
print max(max(a))
print max(a)
The result is:
1.28529767878
[0.0, 0.0, 1.2852976787772832, 0.7965388321000092, 0.0, 0.0, 0.0, 0.0, 0.0]
This is clearly wrong, the max value should be 6.409872844109646.
b = []
for i in a:
b.extend(i)
print max(b)
6.40987284411
This is python 2.7, Cpython.
Thank you very much.


Lists are sorted element-wise. Since the index of 1.2852976787772832 is one place ahead of that of 6.409872844109646 in the candidate sublists, the list containing the former gets picked as the maximum.
In the same index in the second list, we have a 0 and 1.2852976787772832 is clearly greater than 0:
[0.0, 0.0, 1.2852976787772832, 0.7965388321000092, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 6.409872844109646, 0.17506688391255013, 0.0, 0.0, 0.0, 0.0]
# ^ here's your tie-breaker
In fact, the next index containing 6.4... is never checked.
I'm not sure how you expect the maximum sublist to be selected: sublist with maximum sum, sublist containing maximum number? You'll have to code the behavior you want if the default behavior does not cut it.


Moses already explained why you got the wrong result: first element greater than the other "wins" when comparing lists.
To get the maximum value you have to flatten your list:
print(max(x for l in a for x in l))

Related

convert a string into a list of floats

The string to be converted is:
strng= "0.000000000000000E+000, 2*2.400000000000000E-002 , 97*0.000000000000000E+000 ,"
which I was able to split using strng.split(',')
[' 0.000000000000000E+000', ' 2*2.400000000000000E-002 ', ' 97*0.000000000000000E+000 ', '\n']
however ' 2*2.400000000000000E-002 ' is not a string but actually two elemts of a list
and ' 97*0.000000000000000E+000 is actually 97 elements of a list.
Here is an ugly attempt to accomplish this:
a=strng.split("=").split(',')
lst=[]
for item in a:
print("intem=", item)
i= item.split("*")
if len(i) >1:
print(int(i[0])*i[1])
lst.append(int(i[0])*i[1])
else:
lst.append(i[0])
print("================")
print(lst)
What is a more elegant way to accomplish this?

Considering
string = "0.000000000000000E+000, 2*2.400000000000000E-002 , 97*0.000000000000000E+000 ,"
You can apply the following steps:
Split and strip the original string:
# Split by commas
expressions = string.split(',')
# Remove leading and trailing white space of each element
expressions = [expression.strip() for expression in expressions]
# Remove empty elements
expressions = [expression for expression in expressions if expression]
or, alternatively,
expressions = [expression.strip() for expression in string.split(',') if expression.strip()]
This will evaluate to
['0.000000000000000E+000', '2*2.400000000000000E-002', '97*0.000000000000000E+000']
Create a new list and evaluate each expression:
result = []
for expression in expressions:
# Split by the multiplication sign
operands = expression.split('*')
# A single element, just add to `result`
if len(operands) == 1:
result.append(float(operands[0]))
# Two operands, add to `result` repeatdely
elif len(operands) == 2:
o1, o2 = operands
o1 = int(o1)
o2 = float(o2)
for _ in range(o1):
result.append(o2)
This will evaluate to
[0.0, 0.024, 0.024, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

Here is another way to do this with string manipulations but this is only so that you for your understanding on how it works.
string = "0.000000000000000E+000, 2*2.400000000000000E-002 , 97*0.000000000000000E+000 ,"
out = [eval(i.strip().replace('*','*[')+']' if '*' in i else '['+i.strip()+']') for i in strng.split(',') if len(i)>0]
print(out)
[[0.0], [0.024, 0.024], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]

Can pyspark.ml.stat.Summarizer return sparse vector results?

computing aggregations of sparse vectors using pyspark.ml.stat.Summarizer returns dense vector results - is there a way to force sparse vector operations?
(just converting the result isn't a good solution as the intermediate results will waste a lot of resources)
code to reproduce:
import pyspark
from pyspark.sql.functions import col
from pyspark.ml.stat import Summarizer
from pyspark.ml.linalg import SparseVector, DenseVector
sc = pyspark.SparkContext.getOrCreate()
sql_context = pyspark.SQLContext(sc)
df = sc.parallelize([ ( SparseVector(100, {1: 1.0}),)]).toDF(['v'])
print(df.head())
print(df.select(Summarizer.mean(col('v'))).head())
output:
Row(v=SparseVector(100, {1: 1.0}))
Row(mean(v)=DenseVector([0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]))
EDIT
an alternative implementation for aggregating sparse vectors would also be relevant

Why is my variable being overwritten by the function even though it's indirectly referenced?

Why is it that this code:
import random, decimal
data = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3333333333333333, 0.05555555555555555, 0.05555555555555555, 0.05555555555555555, 0.007936507936507936, 0.007352941176470588, 0.005714285714285714, 0.038461538461538464, 0.006024096385542169, 0.00392156862745098, 0.004048582995951417, 0.007874015748031496, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03333333333333333, 0.027777777777777776, 0.010638297872340425, 0.006493506493506494, 0.0058823529411764705, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.0044444444444444444, 0.005813953488372093, 0.003952569169960474, 0.004132231404958678, 0.005128205128205128, 0.015625, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02040816326530612, 0.004201680672268907, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.00398406374501992, 0.010752688172043012, 0.012195121951219513, 0.012195121951219513, 0.017857142857142856, 0.02564102564102564, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.05555555555555555, 0.0045662100456621, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.005050505050505051, 0.005494505494505495, 0.004048582995951417, 0.004149377593360996, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0125, 0.00641025641025641, 0.009345794392523364, 0.003952569169960474, 0.003952569169960474, 0.004878048780487805, 0.09090909090909091,
0.0, 0.023255813953488372, 0.006493506493506494, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.07142857142857142, 1.0, 0.006493506493506494, 0.003952569169960474, 0.011111111111111112, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.007194244604316547, 0.003952569169960474, 0.005263157894736842, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09090909090909091, 0.005263157894736842, 0.003952569169960474, 0.014285714285714285, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02857142857142857, 0.004149377593360996, 0.0044444444444444444, 0.00625, 0.009259259259259259, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.012345679012345678, 0.004166666666666667, 0.003952569169960474, 0.003952569169960474, 0.008403361344537815, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.022222222222222223, 0.005376344086021506, 0.003952569169960474, 0.003952569169960474, 0.006666666666666667, 0.037037037037037035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0625, 0.010752688172043012, 0.003968253968253968, 0.003952569169960474, 0.0053475935828877, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004016064257028112, 0.003952569169960474, 0.004016064257028112, 0.015625, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.021739130434782608, 0.007692307692307693, 0.00546448087431694, 0.003952569169960474, 0.003952569169960474, 0.004830917874396135, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02564102564102564, 0.006756756756756757, 0.004366812227074236, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.004, 0.005494505494505495, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.041666666666666664, 0.008771929824561403, 0.004524886877828055, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.004975124378109453, 0.01282051282051282, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.043478260869565216, 0.015151515151515152, 0.004694835680751174, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.005050505050505051, 0.012345679012345678,
0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05555555555555555, 0.005847953216374269, 0.0045662100456621, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.005128205128205128, 0.0125, 0.1111111111111111, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.01818181818181818, 0.005813953488372093, 0.004424778761061947, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.004098360655737705, 0.007518796992481203, 0.09090909090909091, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.007352941176470588, 0.003952569169960474, 0.003952569169960474, 0.003952569169960474, 0.0047169811320754715, 0.007407407407407408, 0.007575757575757576, 0.0625, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
[[0.2958373547424454, 0.7017788649452896, 0.062273689823917136, 0.5972807574704273, 0.9954328393432262, 0.06846660132933566, 0.951370577952487, 0.13857649386421894, 0.9155594510837761, 0.25332617322833023], [0.9435904403108412, 0.8308103147735971, 0.5642138942684856, 0.569439896252594, 0.008026089778913156, 0.14708273242122766, 0.025129344862662672, 0.10605633957699719, 0.017149039312628126, 0.9685317671516491, 0.9604996293414068, 0.13009421347484584, 0.011168020783984038, 0.9660212281911376, 0.009785512162735856, 0.774118311034036], [0.39828514946703536, 0.9719812893589492, 0.29744104658316833, 0.2407147714636478, 0.17834097142046978, 0.30339140369565476, 0.23414714143729679, 0.010316054064037888, 0.9665891555382209, 0.2709023215219119, 0.18678849879884543, 0.6853950395070337, 0.49793613573495626, 0.7369771877120291, 0.5822901326349779, 0.35804830353223377]]
def rnd():
return float(decimal.Decimal(random.randrange(-200, 200))/100)
def neuronPositivityTotal(aIndex, imageData, weightsBalances):
""" Calculates the total positivity of all nodes.."""
# Step 1: Level 1 Neurons
for z in range(len(aIndex[2])):
value = 0.0
# weightsBalances[2] is the level 1 values
balance = weightsBalances[2][z][1]
weights = weightsBalances[2][z][0]
for y in range(len(imageData)):
for x in range(len(imageData[y])):
value += imageData[x][y]*weights[x][y]
value += balance
aIndex[2][z] = value
activationIndex = [[0.0 for x in range(10)], [0.0 for x in range(16)], [0.0 for x in range(16)]]
answerWB = {}
n1WB = {}
n2WB = {}
for x in range(10):
answerWB[x] = [[rnd() for y in range(16)], rnd()]
for x in range(16):
n2WB[x] = [[rnd() for y in range(16)], rnd()]
n1WB[x] = [[[rnd() for y in range(28)] for x in range(28)], rnd()]
weightsBalances = [answerWB, n2WB, n1WB]
print(activationIndex)
neuronPositivityTotal(activationIndex, data, weightsBalances)
print(activationIndex)
Outputs:
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.7268240240758053, 2.1047003177465773, -0.14702590533853588, 1.6819534808112164, 2.1421710797784743, -2.0840036534506106, -0.6067792109947316, 0.40979446487814936, 1.9165752482288656, 0.5045996467720566, -0.08187273316902777, -0.9549459648875294, -0.19719768439019592, -3.4785770307824064, -4.423173617897677, -1.8256438294590946]]
Even though there are no global tags in the program? It's part of a larger deep learning project that I'm working on and I can' figure it out. The value of activationIndex is only ever set once and the values are used with the aIndex variable. (Using python 3.8)

Passing an argument to a function makes an alias pointing to that same object in the local parameter name within the function. When you do:
aIndex[2][z] = value
it's equivalent to doing:
activationIndex[2][z] = value
because aIndex is an alias to the same list bound to activationIndex; the "pointer" to that list is copied (so saying aIndex = 'foo' won't change activationIndex, as it just assigns a new "pointer"), but mutations to the pointed-to object will be seen through both aliases until one of them is rebound to a new object.
If you want to sever that aliasing linkage to the caller, deepcopy aIndex when you receive it, adding:
from copy import deepcopy # At top of file
and changing the beginning of neuronPositivityTotal to:
def neuronPositivityTotal(aIndex, imageData, weightsBalances):
""" Calculates the total positivity of all nodes.."""
aIndex = deepcopy(aIndex)
# ... rest of function ...

Python statistics.median_grouped returns value outside of input range

I'm analyzing some data and got this list, median_grouped of which seems to be outside of range of values in the list.
screen:
statistics.median_grouped(rcdistance)
-0.1250640605965987
min(rcdistance)
0.0
Here's the list:
[0.0, 0.21098454268727887, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.800735254367722, 6.800735254367722, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.3691164850450193, 0.8341463454328069, 6.954672651889972, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.894523526205462, 0.5202041513165154, 0.3195953715186193, 1.19650727442225, 1.9829608504017993, 0.320822054594543, 0.8021892543724501, 0.06639592707563217, 0.33791893636471976, 1.3469748280785028, 0.46225975601521924, 0.29146628081088805, 0.31432316149828754, 0.24174865833529519, 0.10137129170776715, 2.07576600608641, 0.2615844609784234, 0.6481662780506829, 0.13578192040175857, 0.13470214233110803, 0.34278013217398423, 0.09326076007769707, 0.4336006232739294, 0.6007209936117036, 2.8535831869013726, 0.7286885646881327, 0.6175406688934962, 0.6432741795490186, 0.25089604017432937, 0.2247559442381167, 0.10569371849097034, 0.13555324220681084, 11.575372015987313, 0.13669258957306155, 1.634337739537961, 0.7023579970496473, 0.9488940841470294, 1.9556804571337925, 0.8370864316757329, 1.2024907508700595, 2.2424077713328843, 0.49703062007687854, 0.7817924904836159, 2.3319974222716975, 1.8335319342837306, 0.0, 3.28868490484547, 1.4314796804850678, 1.3638514553202294, 0.3513379545492983, 0.16043624593306288, 12.960363339703447, 1.4495399509870706, 0.2861815531492466, 0.23131921608674785, 0.07470703125, 0.3156641038900055, 0.12637431907281702, 0.6356548946706172, 2.1073361751465227, 0.1782201479382565, 1.3450383951599099, 0.8871288611532409, 1.4173304274383594, 0.14168172737548348, 0.3703729958352881, 1.658888478625415, 1.2477698697766813, 1.7575586601929727, 2.167596692301178, 2.051780602511496, 0.37814640268331945, 2.1041473318539876, 1.1896854668558041, 0.9250246006039865, 1.3271447098141402, 1.6116094170310153, 1.0963143124740196, 1.4711895830945183, 0.1623601238081846, 1.577846710293568, 0.07569225630868787, 1.200957612567313, 0.18243840079429852, 0.7718713876122335, 0.6783927775524854, 1.7536286642036438, 1.7548529400031645, 0.16162109375, 0.6676365879769467, 0.2727575294371441, 13.361847527117655, 2.2281143134718224, 0.5390126302014291, 0.9547223635736837, 0.17379417516519388, 0.15405041289356222, 0.918123792176202, 0.2754296890581234, 0.5438064589731, 1.6679939428923882, 0.33831407770944383, 0.13401630343709894, 0.4036704845489422, 2.4687540199150484, 1.364673723134251, 1.462182233800534, 0.2524997193740947, 0.6879091779122948, 0.07512909587034285, 1.168234753379213, 0.460947068971782, 0.9278222935828773, 0.5815480934256779, 0.15330106772382537, 0.16744291813233583, 0.11594392249731117, 2.424850113159381, 0.8373170681343546, 4.714464894570124, 0.8284206942102329, 6.691576922679101, 0.9362133734023534, 0.3749359394034013, 0.7812593459523988, 5.344434281645955, 0.34577846245001914, 2.6886532105742007, 0.261063966541952, 0.14849250175991532, 0.3498228476287005]

Plotting a histogram using a range of values and their frequency as a dictionary

Assume that I have the following dictionary:
scenario_summary = {'Day1': {'22459-22585': 0.0, '22585-22711': 0.0, '22711-22837': 0.0, '22837-22963': 0.0, '22963-23089': 0.0, '23089-23215': 0.0, '23215-23341': 0.0, '23341-23467': 0.0, '23467-23593': 0.0, '23593-23719': 0.0, '23719-23845': 0.0, '23845-23971': 0.0, '23971-24097': 0.0, '24097-24223': 0.0, '24223-24349': 0.0, '24349-24475': 0.0, '24475-24601': 0.0, '24601-24727': 0.0, '24727-24853': 0.0, '24853-24979': 0.0, '24979-25105': 0.0, '25105-25231': 0.0, '25231-25357': 0.0, '25357-25483': 0.0, '25483-25609': 0.0, '25609-25735': 0.0, '25735-25861': 0.0, '25861-25987': 0.0, '25987-26113': 1.0, '26113-26239': 1.0, '26239-26365': 0.0, '26365-26491': 2.0, '26491-26617': 5.0, '26617-26743': 5.0, '26743-26869': 5.0, '26869-26995': 12.0, '26995-27121': 19.0, '27121-27247': 7.000000000000001, '27247-27373': 11.0, '27373-27499': 15.0, '27499-27625': 7.000000000000001, '27625-27751': 4.0, '27751-27877': 4.0, '27877-28003': 2.0, '28003-28129': 0.0, '28129-28255': 0.0, '28255-28381': 0.0, '28381-28507': 0.0, '28507-28633': 0.0, '28633-28759': 0.0, '28759-28885': 0.0, '28885-29011': 0.0, '29011-29137': 0.0, '29137-29263': 0.0, '29263-29389': 0.0, '29389-29515': 0.0, '29515-29641': 0.0, '29641-29767': 0.0, '29767-29893': 0.0, '29893-30019': 0.0, '30019-30145': 0.0, '30145-30271': 0.0, '30271-30397': 0.0, '30397-30523': 0.0, '30523-30649': 0.0, '30649-30775': 0.0, '30775-30901': 0.0, '30901-31027': 0.0, '31027-31153': 0.0, '31153-31279': 0.0, '31279-31405': 0.0, '31405-31531': 0.0, '31531-31657': 0.0, '31657-31783': 0.0, '31783-31909': 0.0, '31909-32035': 0.0, '32035-32161': 0.0, '32161-32287': 0.0, '32287-32413': 0.0, '32413-32539': 0.0, '32539-32665': 0.0, '32665-32791': 0.0, '32791-32917': 0.0, '32917-33043': 0.0, '33043-33169': 0.0, '33169-33295': 0.0, '33295-33421': 0.0, '33421-33547': 0.0, '33547-33673': 0.0, '33673-33799': 0.0, '33799-33925': 0.0, '33925-34051': 0.0, '34051-34177': 0.0, '34177-34303': 0.0, '34303-34429': 0.0, '34429-34555': 0.0, '34555-34681': 0.0, '34681-34807': 0.0}}
As you can see, the dictionary consists of a range of values in string and their frequency. I would like to plot this as a histogram, but I don't know how I would be able to transform the string into a form that pandas or plotly would understand. What would your approach be? Or is there an easier way to do it, instead of hardcoding things? Or, would another module be easier option in doing so?
Thanks!

Since the bins (ranges) are already defined and their counts are already aggregated at an initial level, maybe it can help if you build something that overlays a histogram (distribution) on the top of the existing bin ranges:
import matplotlib
%matplotlib inline
def plot_hist(bins,input_dict):
df1 = pd.DataFrame(input_dict).reset_index()
df1['min'] = df1['index'].apply(lambda x:x.split('-')[0]).astype(int)
df1['max'] = df1['index'].apply(lambda x:x.split('-')[1]).astype(int)
df1['group'] = pd.cut(df1['max'],bins,labels=False)
df2 = df1.groupby('group' [['Day1','min','max']].agg({'min':'min','max':'max','Day1':'sum'}).reset_index()
df2['range_new'] = df2['min'].astype(str) + str('-') + df2['max'].astype(str)
df2.plot(x='range_new',y='Day1',kind='bar')
...and call the function by choosing bins lesser than the length of the dictionary - or the first level of 98 bins that are already there, like, say if you want a distribution of 20 groups aggregate:
plot_hist(20,scenario_summary)
Result Image :
hope it helps...

A histogram is basically a simple bar chart, where each bar represents a bin (usually in the form of a range) and a frequency of the elements that fall into that bin.
This is exactly the data that you already have. So instead of computing values for a histogram (as it would be done with plt.hist), you can simply pass your data to plt.bar, as it is. The result would then be this:
The code with your data, as a MCVE :
import matplotlib.pyplot as plt
scenario_summary = { 'Day1': {
'22459-22585': 0.0, '22585-22711': 0.0, '22711-22837': 0.0,
'22837-22963': 0.0, '22963-23089': 0.0, '23089-23215': 0.0,
'23215-23341': 0.0, '23341-23467': 0.0, '23467-23593': 0.0,
'23593-23719': 0.0, '23719-23845': 0.0, '23845-23971': 0.0,
'23971-24097': 0.0, '24097-24223': 0.0, '24223-24349': 0.0,
'24349-24475': 0.0, '24475-24601': 0.0, '24601-24727': 0.0,
'24727-24853': 0.0, '24853-24979': 0.0, '24979-25105': 0.0,
'25105-25231': 0.0, '25231-25357': 0.0, '25357-25483': 0.0,
'25483-25609': 0.0, '25609-25735': 0.0, '25735-25861': 0.0,
'25861-25987': 0.0, '25987-26113': 1.0, '26113-26239': 1.0,
'26239-26365': 0.0, '26365-26491': 2.0, '26491-26617': 5.0,
'26617-26743': 5.0, '26743-26869': 5.0, '26869-26995': 12.0,
'26995-27121': 19.0, '27121-27247': 7.0, '27247-27373': 11.0,
'27373-27499': 15.0, '27499-27625': 7.0, '27625-27751': 4.0,
'27751-27877': 4.0, '27877-28003': 2.0, '28003-28129': 0.0,
'28129-28255': 0.0, '28255-28381': 0.0, '28381-28507': 0.0,
'28507-28633': 0.0, '28633-28759': 0.0, '28759-28885': 0.0,
'28885-29011': 0.0, '29011-29137': 0.0, '29137-29263': 0.0,
'29263-29389': 0.0, '29389-29515': 0.0, '29515-29641': 0.0,
'29641-29767': 0.0, '29767-29893': 0.0, '29893-30019': 0.0,
'30019-30145': 0.0, '30145-30271': 0.0, '30271-30397': 0.0,
'30397-30523': 0.0, '30523-30649': 0.0, '30649-30775': 0.0,
'30775-30901': 0.0, '30901-31027': 0.0, '31027-31153': 0.0,
'31153-31279': 0.0, '31279-31405': 0.0, '31405-31531': 0.0,
'31531-31657': 0.0, '31657-31783': 0.0, '31783-31909': 0.0,
'31909-32035': 0.0, '32035-32161': 0.0, '32161-32287': 0.0,
'32287-32413': 0.0, '32413-32539': 0.0, '32539-32665': 0.0,
'32665-32791': 0.0, '32791-32917': 0.0, '32917-33043': 0.0,
'33043-33169': 0.0, '33169-33295': 0.0, '33295-33421': 0.0,
'33421-33547': 0.0, '33547-33673': 0.0, '33673-33799': 0.0,
'33799-33925': 0.0, '33925-34051': 0.0, '34051-34177': 0.0,
'34177-34303': 0.0, '34303-34429': 0.0, '34429-34555': 0.0,
'34555-34681': 0.0, '34681-34807': 0.0}}
data = scenario_summary['Day1']
x = range(len(data))
y = list(data.values())
plt.figure(figsize=(16, 9))
plt.bar(x, y)
plt.subplots_adjust(bottom=0.2)
plt.xticks(x, data.keys(), rotation='vertical')
plt.show()

You can use pandas module to convert dictionary data into data frame:
import pandas as pd
import matplotlib.pyplot as plt
scenario_summary = {'Day1': {'22459-22585': 0.0, '22585-22711': 0.0, '22711-22837': 0.0,
'22837-22963': 0.0, '22963-23089': 0.0, '23089-23215': 0.0,
'23215-23341': 0.0, '23341-23467': 0.0, '23467-23593': 0.0,
'23593-23719': 0.0, '23719-23845': 0.0, '23845-23971': 0.0,
'23971-24097': 0.0, '24097-24223': 0.0, '24223-24349': 0.0,
'24349-24475': 0.0, '24475-24601': 0.0, '24601-24727': 0.0,
'24727-24853': 0.0, '24853-24979': 0.0, '24979-25105': 0.0,
'25105-25231': 0.0, '25231-25357': 0.0, '25357-25483': 0.0,
'25483-25609': 0.0, '25609-25735': 0.0, '25735-25861': 0.0,
'25861-25987': 0.0, '25987-26113': 1.0, '26113-26239': 1.0,
'26239-26365': 0.0, '26365-26491': 2.0, '26491-26617': 5.0,
'26617-26743': 5.0, '26743-26869': 5.0, '26869-26995': 12.0,
'26995-27121': 19.0, '27121-27247': 7.000000000000001, '27247-27373': 11.0,
'27373-27499': 15.0, '27499-27625': 7.000000000000001, '27625-27751': 4.0,
'27751-27877': 4.0, '27877-28003': 2.0, '28003-28129': 0.0,
'28129-28255': 0.0, '28255-28381': 0.0, '28381-28507': 0.0,
'28507-28633': 0.0, '28633-28759': 0.0, '28759-28885': 0.0,
'28885-29011': 0.0, '29011-29137': 0.0, '29137-29263': 0.0,
'29263-29389': 0.0, '29389-29515': 0.0, '29515-29641': 0.0,
'29641-29767': 0.0, '29767-29893': 0.0, '29893-30019': 0.0,
'30019-30145': 0.0, '30145-30271': 0.0, '30271-30397': 0.0,
'30397-30523': 0.0, '30523-30649': 0.0, '30649-30775': 0.0,
'30775-30901': 0.0, '30901-31027': 0.0, '31027-31153': 0.0,
'31153-31279': 0.0, '31279-31405': 0.0, '31405-31531': 0.0,
'31531-31657': 0.0, '31657-31783': 0.0, '31783-31909': 0.0,
'31909-32035': 0.0, '32035-32161': 0.0, '32161-32287': 0.0,
'32287-32413': 0.0, '32413-32539': 0.0, '32539-32665': 0.0,
'32665-32791': 0.0, '32791-32917': 0.0, '32917-33043': 0.0,
'33043-33169': 0.0, '33169-33295': 0.0, '33295-33421': 0.0,
'33421-33547': 0.0, '33547-33673': 0.0, '33673-33799': 0.0,
'33799-33925': 0.0, '33925-34051': 0.0, '34051-34177': 0.0,
'34177-34303': 0.0, '34303-34429': 0.0, '34429-34555': 0.0,
'34555-34681': 0.0, '34681-34807': 0.0}}
# convert to data frame
data_frame = pd.DataFrame.from_dict(scenario_summary)
# plot data
plt.hist(data_frame['Day1'], density=1, bins=20)
plt.show()

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