verify its you/purchase authentification issue with google play

for 2 weeks i came across an issue. When i try to buy something with google play, it tells me that i need to enter a 5-digit code which I get by SMS on my phone. After that it sends me on a similar screen which says Verify it’s you/Purchase verification as you see in the picture i posted. I tried everything, passwords, that 5digit code that i got earlier, card info etc. I honestly dont know what to do since i couldnt find any info about this issue so here I am. Can someone tell what do i need to put in that blank space ?enter image description here

How do I trouble shoot my invoice issue

Recently Some of my invoice found in my admin panel not showing a proper page, just like showing Plain text page, how do I trouble shoot what’s wrong with the invoice? just randomly happen

enter image description here

Performance issue in python- network creation based on the Euclidean distance with nested for loops is too slow

I want to create a network in which the links are formed based on a similarity metric defined as the Euclidean distance between the nodes. The distance is calculated using socio-demographic features of customers such as gender and age. The problem is the code takes 200 seconds to just create the network and as I am tuning my model and the code executes at least 100 times, the long execution time of this piece is making the whole code run slowly.

So, the nodes are in fact customers. I defined a class for them. They have two attributes gender (numerical; specified by number 0 or 1) and age (varies from 24 to 44) which are stored in a csv file. I have generated a sample csv file here :

#number of customers
ncons = 5000
gender = (random.randint(0, 1) for i in range(ncons))
age = (random.randint(22, 45) for i in range(ncons))
customer_df = pd.DataFrame(
    {'customer_gender': gender,
     'customer_age': age
    })
customer_df.to_csv('customer_df.csv', mode = 'w', index=False)

The Euclidean distance delta_ik is of the form enter image description herefollowing. In the formula, n is the number of attributes (here n=2, age and gender). For customers i and k, S_f,i - S_f,k is the difference between attribute f = 1,2 which is divided by the maximum range of attribute f for all the customers (max d_f). So the distance is the distance in the values of socio-demographic attributes, not geographical positions.

Then I define the similarity metric H_ik which creates a number between 0 and 1 from delta_ik as follow:customer similarity. Finally, For customers i and k, I generate a random number rho between 0 and 1. If rho is smaller than H_ik, the nodes are connected.

So, the code that keeps delta_ik in a matrix and then uses that to generate the network looks as below:

import random
import pandas as pd
import time
import csv
import networkx as nx
import numpy as np
import math
#Read the csv file containing the part worth utilities of 184 consumers
def readCSVPWU():
    global headers
    global Attr
    Attr = ()
    with open('customer_df.csv') as csvfile:
        csvreader = csv.reader(csvfile,delimiter=',')
        headers = next(csvreader)  # skip the first row of the CSV file.
        #CSV header cells are string and should be turned to a float number.
        for i in range(len(headers)):   
            if headers(i).isnumeric():
                headers(i) = float(headers(i))
        for row in csvreader:
            AttrS = row
            Attr.append(AttrS)
    #convert strings to float numbers
    Attr = ((float(j) for j in i) for i in Attr)
    #Return the CSV as a matrix with 17 columns and 184 rows 
    return Attr

#customer class
class Customer:
    def __init__(self, PWU = None, Ut = None):
        self.Ut = Ut
        self.PWU = Attr(random.randint(0,len(Attr)-1))  # Pick random row from survey utility data  


#Generate a network by connecting nodes based on their similarity metric
def Network_generation(cust_agent):
    start_time = time.time() # track execution time

    #we form links/connections between consumeragentsbasedontheirdegreeofsocio-demographic similarity.
    global ncons
    Gcons = nx.Graph()
    #add nodes
    (Gcons.add_node(i, data = cust_agent(i)) for i in range(ncons))
    #**********Compute the node to node distance
    #Initialize Deltaik with zero's
    Deltaik = ((0 for xi in range(ncons)) for yi in range(ncons)) 
    #For each attribute, find the maximum range of that attribute; for instance max age diff = max age - min age = 53-32=21
    maxdiff = ()
    allval = ()
    #the last two columns of Attr keep income and age data
    #Make a 2D numpy array to slice the last 2 columns (#THE ACTUAL CSV FILE HAS MORE THAN 2 COLUMNS)
    np_Attr = np.array(Attr)
    #Take the last two columns, income and age of the participants, respectively
    socio = np_Attr(:, (len(Attr(0))-2, len(Attr(0))-1))
    #convert numpy array to a list of list
    socio = socio.tolist()
    #Max diff for each attribute

    for f in range(len(socio(0))):
        for node1 in Gcons.nodes():
        #keep all values of an attribute to find the max range
            allval.append((Gcons.nodes(node1)('data').PWU(-2:)(f)))
        maxdiff.append((max(allval)-min(allval)))
        allval = ()
# THE SECOND MOST TIME CONSUMING PART ********************

    for node1 in Gcons.nodes():
        for node2 in Gcons.nodes():
            tempdelta = 0
            #for each feature (attribute)
            for f in range(len(socio(0))):
                Deltaik(node1)(node2) = (Gcons.nodes(node1)('data').PWU(-2:)(f)-Gcons.nodes(node2)('data').PWU(-2:)(f))
                #max difference
                insidepar = (Deltaik(node1)(node2) / maxdiff(f))**2
                tempdelta += insidepar
            Deltaik(node1)(node2) = math.sqrt(tempdelta)
     # THE END OF THE SECOND MOST TIME CONSUMING PART ********************
       
    #Find maximum of a matrix
    maxdel = max(map(max, Deltaik))
    #Find the homopholic weight
    import copy
    Hik = copy.deepcopy(Deltaik)
    for i in range(len(Deltaik)):
        for j in range(len(Deltaik(0))):
            
            Hik(i)(j) =1 - (Deltaik(i)(j)/maxdel)
    #Define a dataframe to save Hik
    dfHik = pd.DataFrame(columns = list(range(ncons) ),index = list(range(ncons) ))
    temp_h = ()
    #For every consumer pair $i$ and $k$, a random number $rho$ from a uniform distribution $U(0,1)$ is drawn and compared with $H_{i,k}$ . The two consumers are connected in the social network if $rho$ is smaller than $H_{i,k}$~cite{wolf2015changing}.
# THE MOST TIME CONSUMING PART ********************
    for node1 in Gcons.nodes():
        for node2 in Gcons.nodes():
            #Add Hik to the dataframe
            temp_h.append(Hik(node1)(node2))
            rho = np.random.uniform(0,1,1)
            if node1 != node2:
                if rho < Hik(node1)(node2):
                    Gcons.add_edge(node1, node2)
        #Row idd for consumer idd keeps homophily with every other consumer
        dfHik.loc(node1) = temp_h
        temp_h = ()
    # nx.draw(Gcons, with_labels=True)            
    print("Simulation time: %.3f seconds" % (time.time() - start_time))
# THE END OF THE MOST TIME CONSUMING PART ********************

    return Gcons     
#%%
#number of customers
ncons = 5000
gender = (random.randint(0, 1) for i in range(ncons))
age = (random.randint(22, 39) for i in range(ncons))
customer_df = pd.DataFrame(
    {'customer_gender': gender,
     'customer_age': age
    })
customer_df.to_csv('customer_df.csv', mode = 'w', index=False)
readCSVPWU()
customer_agent = dict(enumerate((Customer(PWU = (), Ut = ()) for ij in range(ncons)))) # Ut=()
G = Network_generation(customer_agent)

I realized that there are two nested loops that are more time consuming than others, but I am not sure how to write them more efficiently. I would tremendously appreciate if you could please give me some advice on the ways to decrease the elapsed time.

Thank you so much

screen – Backgound/Text resizing issue

Hello,
I have a screen design where the text is above the wave.
This is the original design

Original Design

but when I try to do it in different android screen size, i get this

Overlapping

The text is not part of the background. Does anyone have any suggestion on how to work around this issue? Should I make the text part of the background?

xiaomi – If I buy a new screen, can I fix this issue in my phone?

I have a xiaomi mi 9t pro (I guess it’s also named k20pro), it’s that 2019 xiaomi phone with moving front camera, no notch. And for the last 2 months I got this pink-blurry-stain in the bottom-right side of my screen. It’s slowly growing, already making it harder to use the phone. Also it has something like a “blind spot” for the touchscreen, a little above the keyboard (fortunatelly). I was wondering if I could fix all these issues by replacing this phone’s screen.

The technicians in my city say they can do the job, but I’ll have to buy the replacement by myself, and I’m a bit worried to buy the wrong screen. It’s very easy to find, but I would like to be sure that I’m buying the right item. In example: This seller shows the correct names for the phone, he has amoled blue frame (my phone version/model). Is it all I need?

Thanks in advance =)

stain screen

How to solve the issue of my query results of in my sheet?

https://docs.google.com/spreadsheets/d/1VsEvt2KnYGnlN_qHjkjPohVHF5MUqfyHD7tSk0VpkqA/edit#gid=1418057437

I have this sheet that I enter all data of patients in (Data_Entry) tab and I want these data to be sorted by Room number and bed number in the form of 00-00. For example Room 4 bed2 (4-2) and so on. I added a helper column L that modify the room/bed number in a way I remove the “-” and I multiply by 10 if the number entered has no “-” because some rooms are only one bedded room. Then I query all the data in All_patients Tab to be sorted first by unit(Column I), then by ward(Column B), then by modified Room&Bed (helper Column L). Because sorting by Column C (actual numbers like 4-3) doesn’t result in correct ascending numbers.

But I had a problem at the results in All_Patients tab that the original helper column L(Data_Entry) which is Column K in the results tab(All_Patients) doesn’t show all the values of the numbers.So, that resulted in an incorrect ascending order by room& bed number.

=ArrayFormula(IFs(REGEXMATCH(C5:C, "-"), SUBSTITUTE(C5:C,"-",""), C5:C<>"-",C5:C*10)) this formula is in L5 of (Data_ENtry)tab that modifies the room/bed numbers in actual numbers that can be sorted if needed.

=query(Data_Entry!B5:L,"select B,C,D,E,H,F,G,K,I,L where F is not null AND B<>'Ward' order by I,B,L",0) this formula is in B5 of (All_Patients) tab that brings all entry data and sort them by unit,ward,room.

there is an issue

there an issue with the https not coming up right when coming to the website

.

magento2 – Magento 2.4 issue with pricing and custom options

I have a site with custom options with added cost on some products. If you add an item with a custom option to the cart, it shows the right markup. If you add another of the same product to the cart, but without a custom option, it will change the price of the item with the custom option back to the original price, but keep the custom option. Example URL is: https://www.kbchorsesupplies.com/neck-strap-horse-yearling.html

path aliases – Facing issue to have node preview alias

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