y-axis labels don't get displayed properly with Plotly - python

I've a problem with displaying the y-axis labels properly with plotly.
This is my index:
index = ['2015-11','2015-12','2016-01','2016-02','2016-03','2016-04','2016-05',
'2016-06','2016-07','2016-08','2016-09','2016-10','2016-11']
the data
data = [[0.115, 0.077, 0.0, 0.038, 0.0, 0.038, 0.038, 0.077, 0.0, 0.077, 0.077, 0.038],
[0.073, 0.055, 0.083, 0.055, 0.018, 0.055, 0.073, 0.037, 0.028, 0.037, 0.009, 0.0],
[0.099, 0.027, 0.036, 0.045, 0.063, 0.153, 0.027, 0.045, 0.063, 0.027, 0.0, 0.0],
[0.076, 0.038, 0.053, 0.061, 0.098, 0.068, 0.038, 0.061, 0.023, 0.0, 0.0, 0.0],
[0.142, 0.062, 0.027, 0.08, 0.097, 0.044, 0.071, 0.027, 0.0, 0.0, 0.0, 0.0],
[0.169, 0.026, 0.026, 0.026, 0.013, 0.013, 0.091, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.138, 0.121, 0.052, 0.017, 0.034, 0.017, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.297, 0.081, 0.054, 0.054, 0.054, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.095, 0.016, 0.024, 0.04, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.102, 0.023, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.054, 0.027, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.087, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
I create a heatmap with following code:
import plotly.figure_factory as ff
from plotly.offline import iplot
import re
cols = range(12)
index = index
df = pd.DataFrame(data, columns = cols)
df.index = index
x = df.columns.tolist()
y = df.index.tolist()
z = df.values
annotation_text = np.char.mod('%.0f%%', df*100).tolist()
annotation_text = [[re.sub('^0%$','', x) for x in l] for l in annotation_text]
colorscale=[[0.0, 'rgb(248, 248, 255)'],
[0.04, 'rgb(224, 228, 236)'],
[0.08, 'rgb(196, 210, 226)'],
[0.12, 'rgb(158, 178, 226)'],
[0.16, 'rgb(134, 158, 227)'],
[0.2, 'rgb(122, 146, 227)'],
[1.0, 'rgb(65, 105, 225)'],
]
fig = ff.create_annotated_heatmap(z, x=x, y=y, colorscale= colorscale,
annotation_text = annotation_text)
fig.layout.yaxis.autorange = 'reversed'
offline.iplot(fig, filename='annotated_heatmap_color.html')
Which produces the correct heatmap but with the y-axis labels missing
When I change the index to shorter values like '5-11' with
index = [x[3:] for x in index]
the labels show up.
I don't understand the logic behind that and would like to know how to fix it.


Plotly.py uses plotly.js under the hood, which is transforming your date strings to a numerical date format and misplacing them on your non numerical axis.
To explicit a categorical axis you just have to add:
fig.layout.yaxis.type = 'category'

Related

How to loop over a dataframe and create list

So, i have the following data below and i want to loop through the dataframe and perform some functions and at the end save the results from the function in a list. I am have trouble creating a list. i only get a single value in the list and not the two means which i intend to get. Anybody with a more effective way to solve this problem please share.
dict = {'PassengerId' : [0.0, 0.001, 0.002, 0.003, 0.004, 0.006, 0.007, 0.008, 0.009, 0.01],
'Survived' : [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
'Pclass' : [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.5],
'Age' : [0.271, 0.472, 0.321, 0.435, 0.435, np.nan, 0.673, 0.02, 0.334, 0.171],
'SibSp' : [0.125, 0.125, 0.0, 0.125, 0.0, 0.0, 0.0, 0.375, 0.0, 0.125],
'Parch' : [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.167, 0.333, 0.0],
'Fare' : [0.014, 0.139, 0.015, 0.104, 0.016, 0.017, 0.101, 0.041, 0.022, 0.059]}
import pandas as pd
dicts = pd.DataFrame(dicts, columns = dicts.keys())
def Mean(self):
list_mean = []
list_all = []
for i, row in dicts.iterrows():
if (row['Age'] > 0.2) & (row['Fare'] < 0.1):
list_all.append(row['PassengerId'])
elif (row['Age'] > 0.2) & (row['Fare'] > 0.1):
list_all.clear()
list_all.append(row['PassengerId'])
return list_mean.append(np.mean(list_all))
Mean()
Help Please!!

Some of changes you have to made in you solution to resolve this issue. And for vectorized answer checkout my Code section.
1.
Return statement return list_mean should placed in function block not in if-block
Change:
. . .
if (row['Age'] > self.age) & (row['Fare'] < self.fare):
list_mean.append(row['PassengerId'])
return list_mean
. . .
To:
. . .
list_mean = []
for i, row in dicts.iterrows():
if (row['Age'] > self.age) & (row['Fare'] < self.fare):
list_mean.append(row['PassengerId'])
return list_mean
. . .
CODE :(Vectorized-Version-Solution) No need of defining explicit class to perform this action
import numpy as np
dict_ = {
'PassengerId':
[0.0, 0.001, 0.002, 0.003, 0.004, 0.006, 0.007, 0.008, 0.009, 0.01],
'Survived': [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
'Pclass': [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.5],
'Age':
[0.271, 0.472, 0.321, 0.435, 0.435, np.nan, 0.673, 0.02, 0.334, 0.171],
'SibSp': [0.125, 0.125, 0.0, 0.125, 0.0, 0.0, 0.0, 0.375, 0.0, 0.125],
'Parch': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.167, 0.333, 0.0],
'Fare':
[0.014, 0.139, 0.015, 0.104, 0.016, 0.017, 0.101, 0.041, 0.022, 0.059]
}
import pandas as pd
dicts = pd.DataFrame(dict_, columns=dict_.keys())
l1 = dicts['PassengerId'][np.logical_and(dicts['Age'] > 0.2, dicts['Fare'] < 0.1)]
l2 = dicts['PassengerId'][np.logical_and(dicts['Age'] > 0.2, dicts['Fare'] > 0.1)]
print( (sum(list(l1))/len(l1), sum(list(l2))/len(l2)) )
OUTPUT :
(0.00375, 0.0036666666666666666)

import pandas as pd
import numpy as np
dict = {'PassengerId' : [0.0, 0.001, 0.002, 0.003, 0.004, 0.006, 0.007, 0.008, 0.009, 0.01],
'Survived' : [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
'Pclass' : [1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.5],
'Age' : [0.271, 0.472, 0.321, 0.435, 0.435, np.nan, 0.673, 0.02, 0.334, 0.171],
'SibSp' : [0.125, 0.125, 0.0, 0.125, 0.0, 0.0, 0.0, 0.375, 0.0, 0.125],
'Parch' : [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.167, 0.333, 0.0],
'Fare' : [0.014, 0.139, 0.015, 0.104, 0.016, 0.017, 0.101, 0.041, 0.022, 0.059]}
df = pd.DataFrame(dict, columns = dict.keys())
def calculate_mean():
l1, l2 = [], []
for i, row in df.iterrows():
if row['Age'] > 0.2 and row['Fare'] < 0.1:
l1.append(row['PassengerId'])
elif row['Age'] > 0.2 and row['Fare'] > 0.1:
l2.append(row['PassengerId'])
return np.mean(l1), np.mean(l2)
print(calculate_mean()) # (0.00375, 0.0036666666666666666)

append list with for loop and get multiple values in each cell in python

I'm trying to run 3 optimization with for loop and store the results in one dataframe.
After each optimization (element of the for loop), I append lists of results and being able to get all the reults in one list. However, when I try to convert the list to dataframe, I get one row for each of the optimization and multiple values in each cell corresponding to the variable name and the optimization number like this:
Date = []
results = []
for idx, df in enumerate([df0,df1,df2]):
model = ConcreteModel()
model.T = Set(initialize=df.hour.tolist(), ordered=True)
...
# Solve model
solver = SolverFactory('glpk')
solver.solve(model)
Date = list(df['Date'])
results.append([Date, model.Ein.get_values().values(), model.Eout.get_values().values(),
model.Z.get_values().values(), model.NES.get_values().values(),
model.L.get_values().values()])
df_results = pd.DataFrame(results)
df_results.rename(columns = {0: 'Date', 1: 'Ein', 2:'Eout', 3:'Z', 4:'NES', 5:'L'}, inplace = True)
df_results
## The output of the df is:
Date Ein
0 [2019-01-01, 2019-01-01, 2019-01-01, 2019-01-0... (0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 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, 1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 5.0, ... (0.0, 0.0, -100.0, -100.0, -100.0, 0.0, -100.0... (16231.0, 16051.0, 15806.0, 15581.0, 15610.0, ...
1 [2019-01-16, 2019-01-16, 2019-01-16, 2019-01-1... (0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, ... (0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, ... (0.0, 1.0, 1.0, 1.0, 1.0, 0.5, 1.5, 2.5, 3.5, ... (0.0, -100.0, 0.0, 0.0, 0.0, 50.0, -100.0, -10... (17643.0, 18654.0, 20462.0, 20448.0, 20305.0, ...
2 [2019-01-31, 2019-01-31, 2019-01-31, 2019-01-3... (0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ... (0.0, 0.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 3.0, ... (0.0, 0.0, -100.0, 0.0, -100.0, -100.0, -100.0... (22155.0, 22184.0, 21510.0, 21193.0, 20884.0, ...
#The output of the list named results is:
[[['2019-01-01',
'2019-01-01',
'2019-01-01',
...
'2019-01-15',
'2019-01-15',
'2019-01-15',
'2019-01-15',
'2019-01-15',
'2019-01-15',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16'],
dict_values([0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 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, 1.0, 1.0, 1.0, 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, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
...
-1.11022302462516e-16, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.11022302462516e-16, 0.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.166666666666667, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.333333333333333, 0.0, 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, 0.666666666666667, 0.0, 0.0, 1.0, 1.0, 1.0, 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]),
dict_values([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.5, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25,
...
0.333333333333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 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, 0.166666666666667, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.833333333333333, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.166666666666667, 0.0, 0.0, 0.0, 0.666666666666667, 0.333333333333333, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625, 0.0078125, 0.00390625, 0.001953125, 0.0009765625, 0.00048828125, 0.0]),
dict_values([0.0, 0.0, 1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 4.5, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0, 4.0, 3.0, 3.0, 3.0,
...
0.142857142857143, 0.142857142857143, 1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0, 4.0, 3.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.33333333333333, 1.33333333333333, 0.666666666666667, 0.666666666666667, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625, 0.0078125, 0.00390625, 0.001953125, 0.0009765625, 0.00048828125, 0.00048828125]),
[['2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
'2019-01-16',
...
Is it because each result in the for loop has de different dictionary? How could my results in this form:
Date Ein Eout Z NES L
0 2019-01-01 1.0 0.0 1.0 -100.0 16231.0
1 2019-01-01 1.0 1.0 0.0 100.0 16051.0,
...

You're constantly appending to results creating a list of lists of the wrong dimension. I hope this solution works for you -
df_results = pd.DataFrame(zip(Date, model.Ein.get_values().values(), model.Eout.get_values().values(),
model.Z.get_values().values(), model.NES.get_values().values(),
model.L.get_values().values()))
Let me know if it doesn't.

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,
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[[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 ...

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()

Scipy Linprog - not giving expected solution

I have a set of simple linear equations and am using sciprog to solve it. Objective function is to minimize x(i)*c.
where :
c is defined below(constants)
i ranges from 1 to 28
I also have equality constraints(14 equations) based on which I could I need a solution. Infact the solution is very straightforward. Expected solution is mentioned in excel. But when I use linprog to solve this, it fails. Any reason to check for ?
from scipy.optimize import linprog
A_eq = [[1211.881188118812, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 3599.9999999999995, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 2006.5573770491803, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2185.714285714286, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2147.3684210526317, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 290.04739336492895, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 382.5, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 181.87221396731056, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 382.5, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 291.4285714285714, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 382.5, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1224.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3599.9999999999995, 0.0]
]
b_eq = [0, 0, 1397.8722944969109, 1215.3540665438804, 0, 1217.8280665438817,
177.21022795303054, 1217.8280665438822, 177.21022795303054, 177.21022795303048,
177.21022795303054, 0, 0, 0]
c = [50,27]*14
sol = linprog(c = c, A_eq = A_eq, b_eq = b_eq, bounds = (0, None))
#sol
#Out[138]:
# fun: 1210.4060665438813
# message: 'Optimization failed. Unable to find a feasible starting point.'
# nit: 13
# status: 2
# success: False
# x: nan
Initially I thought, it could be A14:AB14 causing this and thus replaced all coeff from 0 to 1. But still same result. Any clue?

Use the interior point method instead (the simplex solver is known to be rather unreliable -- not to blame on the simplex method but rather on a poor implementation). Of course this model can be solved completely in the presolve: there is nothing to optimize.
sol = linprog(c = c, A_eq = A_eq, b_eq = b_eq, bounds = (0, None), method='interior-point')
This gives:
con: array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, -2.27373675e-13,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00])
fun: 381.92688642606663
message: 'The solution was determined in presolve as there areno non-trivial constraints.'
nit: 0
slack: array([], dtype=float64)
status: 0
success: True
x: array([ 0. , 0. , 0. , 0. , 0. ,
0.69665204, 0. , 0.55604434, 0. , 0. ,
0. , 4.19872095, 0. , 0.46329471, 0. ,
6.69606445, 0. , 0.46329471, 0. , 0.60807431,
0. , 0.46329471, 0. , 0. , 0. ,
0. , 0. , 0. ])

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