Problem bytes to str - jython

I'm new in jython and I try to convert bytes to str.
do you know how I can do this?
Thanks


from array import array
a = array('b', [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47])
print ', '.join(map(str, a))


try:
from array import array
a = array('b', [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47])
print a.tostring()
print a.tounicode()


You can use String.join() with array as a sequence:
import array
ar = array.array('b', [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])
ars = ', '.join(str(x) for x in ar)
print(ars)

Related

How to merge classes in multiclass image segmentation

I am performing an image segmentation with a u-net model.
My mask has classes from 0-50.
I also have a text file dictionary with codes representing each class.
For example -
{1: '1234', 2:'5678', 3:'1245'} etc.
How do I combine when the 2 first string characters are the same so for example above key 1 and 3 are the same because they both start with "12".
How can I do this for all classes?

firstTwoCharDict = {}
for key, value in dictionary.items():
if key == 0:
value == value
firstTwoCharDict[key] = value
else:
value = value[:2]
firstTwoCharDict[key] = value
newDict = {}
for key, value in firstTwoCharDict.items():
if value not in newDict:
newDict[value] = [key]
else:
newDict[value].append(key)
This provides this
{'62': [1, 39],
'90': [2, 5, 9, 20, 32, 42, 47, 72, 88, 91, 95],
'97': [3, 49, 55],
'98': [4, 24, 34, 40, 53, 76, 81, 90, 96],
'31': [6, 17, 30, 48, 83],
'69': [7, 13, 15, 16, 27, 44, 51, 54, 56, 75],
'79': [8, 50],
'71': [10, 19, 22, 35, 61, 63, 65],
'99': [11, 12, 21, 46, 52, 69, 78, 84, 89],
'48': [14, 36, 74],
'60': [18],
'64': [23, 38, 66, 97]
```
Now i have an 2d array with integers, how do I replace them with they keys if the array values are equal to the values in the dict?

pandas compare two data frames and highlight the differences

I'm trying to compare 2 dataframes and highlight the differences in the second one like this:
I have tried using concat and drop duplicates but I am not sure how to check for the specific cells and also how to highlight them at the end

Possible solution is the following:
import pandas as pd
# set test data
data1 = {"A": [10, 11, 23, 44], "B": [22, 23, 56, 55], "C": [31, 21, 34, 66], "D": [25, 45, 21, 45]}
data2 = {"A": [10, 11, 23, 44, 56, 23], "B": [44, 223, 56, 55, 73, 56], "C": [31, 21, 45, 66, 22, 22], "D": [25, 45, 26, 45, 34, 12]}
# create dataframes
df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)
# define function to highlight differences in dataframes
def highlight_diff(data, other, color='yellow'):
attr = 'background-color: {}'.format(color)
return pd.DataFrame(np.where(data.ne(other), attr, ''),
index=data.index, columns=data.columns)
# apply style using function
df2.style.apply(highlight_diff, axis=None, other=df1)
Returns

Adding new dimentions to NP array

I was trying to understand why do we add new dimention to our array?
x = np.expand_dims(x, axis=0)
This insert a new axis to array of x. What is the purpose of it?

An example of when expand_dims can be needed:
We have 2 arrays:
a1 = np.arange(20).reshape(4,5)
a2 = np.array([10, 20, 30, 40, 50])
Even though a1 is a 2-D array and a2 is a 1-D array,
it is possible to add them:
a1 + a2
yields:
array([[11, 22, 33, 44, 55],
[16, 27, 38, 49, 60],
[21, 32, 43, 54, 65],
[26, 37, 48, 59, 70]])
i.e. consecutive elements of a2 are added to consecutive columns
in a1 (there are 5 elements in a2 and 5 columns in a1).
But if you have another array:
a3 = np.array([10, 20, 30, 40])
and you want to add each element of a3 to each row in a1 and
you attempt to run:
a1 + a3
you will get ValueError exception.
To successfully perform this operation, a3 must be a columnar array,
i.e.:
it must have 2 dimensions,
it must have the same number of rows as a1,
and a single column.
So, you can run:
a1 + np.expand_dims(a3, axis=1)
getting:
array([[11, 12, 13, 14, 15],
[26, 27, 28, 29, 30],
[41, 42, 43, 44, 45],
[56, 57, 58, 59, 60]])

I need to filter the column from the beginning of a sentence

In my code, I can filter a column from exact texts, and it works without problems. However, it is necessary to filter another column with the beginning of a sentence.
The phrases in this column are:
A_2020.092222
A_2020.090787
B_2020.983898
B_2020.209308
So, I need to receive everything that starts with A_20 and B_20.
Thanks in advance
My code:
from bs4 import BeautifulSoup
import pandas as pd
import zipfile, urllib.request, shutil, time, csv, datetime, os, sys, os.path
#location
dt = datetime.datetime.now()
file_csv = "/home/Downloads/source.CSV"
file_csv_new = "/var/www/html/Data/Test.csv"
#open CSV
with open(file_csv, 'r', encoding='CP1251') as file:
reader = csv.reader(file, delimiter=';')
data = list(reader)
#list to dataframe
df = pd.DataFrame(data)
#filter UF
df = df.loc[df[9].isin(['PR','SC','RS'])]
#filter key
# A_ & B_
df = df.loc[df[35].isin(['A_20','B_20'])]
#print (df)
#Empty DataFrame
#Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]
#Index: []
#[0 rows x 119 columns]```

Give the following a try:
lst1 = ['A_2020.092222', 'A_2020.090787 ', 'B_2020.983898', 'B_2020.209308', 'C_2020.209308', 'D_2020.209308']
df = pd.DataFrame(lst1, columns =['Name'])
df.loc[df.Name.str.startswith(('A_20','B_20'))]

Pymc size / indexing issue

I am trying to model Kruschke's "filtration-condensation experiment" with pymc 2.3.5. (numpy 1.10.1)
Basicaly there are:
4 groups
each group has 40 individuals
each individual has 64 Bernoulli trials (correct/incorrect)
What I am modeling:
each individual's results are Binomial distribution (e.g. 45 correct out of 64).
my belief about each individual's performance is Beta distribution.
this Beta distribution is influenced by group to which individual belongs (through parameters A=mu*kappa and B=(1-mu)*kappa)
my belief about how strong each group's influence is Gamma distribution (kappa variable)
my belief about each group's average is Beta distribution (mu variable)
The problem:
when I do modeling with "size=" parameters, pymc get's lost
when I seperate each distribution manually (no size=) the pymc does good job
I include the code below:
Data
import numpy as np
import seaborn as sns
import pymc as pm
from pymc.Matplot import plot as mcplot
%matplotlib inline
# Data
ncond = 4
nSubj = 40
trials = 64
N = np.repeat([trials], (ncond * nSubj))
z = np.array([45, 63, 58, 64, 58, 63, 51, 60, 59, 47, 63, 61, 60, 51, 59, 45,
61, 59, 60, 58, 63, 56, 63, 64, 64, 60, 64, 62, 49, 64, 64, 58, 64, 52, 64, 64,
64, 62, 64, 61, 59, 59, 55, 62, 51, 58, 55, 54, 59, 57, 58, 60, 54, 42, 59, 57,
59, 53, 53, 42, 59, 57, 29, 36, 51, 64, 60, 54, 54, 38, 61, 60, 61, 60, 62, 55,
38, 43, 58, 60, 44, 44, 32, 56, 43, 36, 38, 48, 32, 40, 40, 34, 45, 42, 41, 32,
48, 36, 29, 37, 53, 55, 50, 47, 46, 44, 50, 56, 58, 42, 58, 54, 57, 54, 51, 49,
52, 51, 49, 51, 46, 46, 42, 49, 46, 56, 42, 53, 55, 51, 55, 49, 53, 55, 40, 46,
56, 47, 54, 54, 42, 34, 35, 41, 48, 46, 39, 55, 30, 49, 27, 51, 41, 36, 45, 41,
53, 32, 43, 33])
condition = np.repeat([0,1,2,3], nSubj)
Does not work
# modeling
mu = pm.Beta('mu', 1, 1, size=ncond)
kappa = pm.Gamma('gamma', 1, 0.1, size=ncond)
# Prior
theta = pm.Beta('theta', mu[condition] * kappa[condition], (1 - mu[condition]) * kappa[condition], size=len(z))
# likelihood
y = pm.Binomial('y', p=theta, n=N, value=z, observed=True)
# model
model = pm.Model([mu, kappa, theta, y])
mcmc = pm.MCMC(model)
#mcmc.use_step_method(pm.Metropolis, mu)
#mcmc.use_step_method(pm.Metropolis, theta)
#mcmc.assign_step_methods()
mcmc.sample(100000, burn=20000, thin=3)
# outputs never converge and does vary in new simulations
mcplot(mcmc.trace('mu'), common_scale=False)
Works
z1 = z[:40]
z2 = z[40:80]
z3 = z[80:120]
z4 = z[120:]
Nv = N[:40]
mu1 = pm.Beta('mu1', 1, 1)
mu2 = pm.Beta('mu2', 1, 1)
mu3 = pm.Beta('mu3', 1, 1)
mu4 = pm.Beta('mu4', 1, 1)
kappa1 = pm.Gamma('gamma1', 1, 0.1)
kappa2 = pm.Gamma('gamma2', 1, 0.1)
kappa3 = pm.Gamma('gamma3', 1, 0.1)
kappa4 = pm.Gamma('gamma4', 1, 0.1)
# Prior
theta1 = pm.Beta('theta1', mu1 * kappa1, (1 - mu1) * kappa1, size=len(Nv))
theta2 = pm.Beta('theta2', mu2 * kappa2, (1 - mu2) * kappa2, size=len(Nv))
theta3 = pm.Beta('theta3', mu3 * kappa3, (1 - mu3) * kappa3, size=len(Nv))
theta4 = pm.Beta('theta4', mu4 * kappa4, (1 - mu4) * kappa4, size=len(Nv))
# likelihood
y1 = pm.Binomial('y1', p=theta1, n=Nv, value=z1, observed=True)
y2 = pm.Binomial('y2', p=theta2, n=Nv, value=z2, observed=True)
y3 = pm.Binomial('y3', p=theta3, n=Nv, value=z3, observed=True)
y4 = pm.Binomial('y4', p=theta4, n=Nv, value=z4, observed=True)
# model
model = pm.Model([mu1, kappa1, theta1, y1, mu2, kappa2, theta2, y2,
mu3, kappa3, theta3, y3, mu4, kappa4, theta4, y4])
mcmc = pm.MCMC(model)
#mcmc.use_step_method(pm.Metropolis, mu)
#mcmc.use_step_method(pm.Metropolis, theta)
#mcmc.assign_step_methods()
mcmc.sample(100000, burn=20000, thin=3)
# outputs converge and are not too much different in every simulation
mcplot(mcmc.trace('mu1'), common_scale=False)
mcplot(mcmc.trace('mu2'), common_scale=False)
mcplot(mcmc.trace('mu3'), common_scale=False)
mcplot(mcmc.trace('mu4'), common_scale=False)
mcmc.summary()
Can someone please explain it to me why mu[condition] and gamma[condition] does not work? :)
I guess that not splitting thetas into different variables is the problem but cannot understand why and maybe there is a way to pass a shape parameter to size= on theta?

First of all, I can confirm that the first version doesn't lead to stable results. What I can't confirm is that the second one is much better; I have seen very different results also with the second code, with values for the first mu parameter varying between 0.17 and 0.9 for different runs.
The convergence problems can be cured by using good starting values for the Markov chain. This can be done by first doing a maximum a posteriori (MAP) estimate, and then starting the Markov chain from there. The MAP step is computationally inexpensive and leads to a converging Markov chain with reproducible results for both variants of your code. For reference and comparison: The values I see for the four mu parameters are around 0.94 / 0.86 / 0.72 and 0.71.
You can do the MAP estimation by inserting the following two lines of code right after the line in which you define your model with "model=pm.Model(...":
map_ = pm.MAP(model)
map_.fit()
This technique is covered in more detail in Cameron Davidson-Pilon's Bayesian Methods for Hackers, together with other helpful topics around PyMC.