ideolog | Interactive viewer for '.log ' files | Code Inspection library

TL;DR I want to have the functionality where a channel has two extra fields that tell the producer whether it is allowed to send to the channel and if so tell the producer what value the consumer expects. Although I know how to do it with shared memory, I believe that this approach goes against Go's ideology of "Do not communicate by sharing memory; instead, share memory by communicating."

Context:

I wish to have a server S that runs (besides others) three goroutines:

1. Listener that just receives UDP packets and sends them to the demultplexer.
2. Demultiplexer that takes network packets and based on some data sends it into one of several channels
3. Processing task which listens to one specific channel and processes data received on that channel.

To check whether some devices on the network are still alive, the processing task will periodically send out nonces over the network and then wait for k seconds. In those k seconds, other participants of my protocol that received the nonce will send a reply containing (besides other information) the nonce. The demultiplexer will receive the packets from the listener, parse them and send them to the processing_channel. After the k seconds elapsed, the processing task processes the messages pushed onto the processing_channel by the demultiplexer.

I want the demultiplexer to not just blindly send any response (of the correct type) it received onto the the processing_channel, but to instead check whether the processing task is currently even expecting any messages and if so which nonce value it expects. I made this design decision in order to drop unwanted packets a soon as possible.

My approach:

In other languages, I would have a class with the following fields (in pseudocode):

I am trying to make a chart that is very similar to the one here I can't seem to figure out how to outline the bars and make sure the fill is in the right order.

This is the current code ive compiled so far and make a few comments on what I've been trying to fix

I have DATASET1 with three main columns: country, ID (of political parties) and ideology (a measurement of political position in a 0-10 scale). Each row is a different political party amongst a given country.

I need to transfer this data to DATASET2, where each row is a country (in fact, the real data I am working with has thousands of rows for each country, representing interviewees in a public opinion survey, but I have grouped it into country-level data to make it easier to work with and solve this problem). In this dataset I have some groups of columns with data related to the most important political parties of each country - PARTY A, PARTY B, PARTY C, etc. One of these groups of columns has the same ID as in the other dataset, so we can use it to match data from this other dataset.

The output I want: columns with "ideology" of each party corresponding to PARTY A, B, etc. but also for the other parties that are not contemplated in the dataset 2 (something like "ideology_otherparty_1", "ideology_otherparty_2", etc.)

I am trying to find a way of doing this using TIDYR functions like gather and spread together with other functions like "match" or "case_when to match PARTY_A, etc. with the corresponding rows in DATASET1. The problem is I don't know how to combine these functions in order to make it work.

HERE IS A SAMPLE OF THE DATA I HAVE :

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when i load the paige while running a tail on the log

I am trying to measure political ideology on Twitter (by using Rtweet). I now have a dataframe consisting of +100 politicians user_id's along with two ideal point scores on 'factor 1' and 'factor 2' (both factors have a range of 1-4). It looks like this (called kandidat):

I would then like to detect if random Twitter users follow one or more of the politicians from my dataset. If they e.g. follow two of the politicians in my dataset - "Politician1" and "Politician2" - I will then assign a mean of the two politicians ideal point scores on the two factors to the user. An example of a Twitteruser following these two politicians could then be factor 1 = (3.5+1.0)/2 = 2.25 and factor 2 = (2.0+4.0)/2 = 3.00.

So I've tried to create a simplified loop including only two journalists from Twitter called 'testusers', who both follow a large share of the politicians in my dataset. The loop should then check whether the respective journalists follow one or more of the politicians: If they follow, then the loop should assign the mean of the values like described above. If not, they should be automatically removed from the dataset. The loop below does run, but unfortunately provides a wrong output (see table below the code):

In my project I'm using own format of log, as following

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However when I try to import my model from another python file (in pycharm) I am getting an error stating that the module for my app cannot be found.

OS:

I am trying to grep /var/log/secure to find the unique IP addresses that tried to use my instance. Every time, I try to grep I get the lines where the IP is located. How can I grep in a way where I just get the IP I want and store it in a text file. I'll post examples to clarify what I am looking for.