probably | Probabilistic Data Structures in Python

This is probably me being stupid but what am I doing wrong here? I'm not sure if I need the code but I'm doing this in Visual Studio 2019.

I'm writing a Firebase function (Gist) which

1. Queries a realtime database ref (events) in the following fashion:

   await admin.database().ref('/events_geo').once('value').then(snapshots => {

2. Iterates through all the events

   snapshots.forEach(snapshot => {

3. Events are filtered by a criteria for further processing

4. Several queries are fired off towards realtime DB to get details related to the event

   await database().ref("/ratings").orderByChild('fk_event').equalTo(snapshot.key).once('value').then(snapshots => {

5. Data is prepared for SendGrid and the processing is finished

All of the data processing works perfectly fine but I can't get the outer await (point 1 in my list) to wait for the inner awaits (queries towards realtime DB) and thus when SendGrid should be called the data is empty. The data arrives a little while later. Example output from Firebase function logs can be seen below:

10:54:12.642 AM Function execution started

10:54:13.945 AM There are no emails to be sent in afterEventHostMailGoodRating

10:54:14.048 AM There are no emails to be sent in afterEventHostMailBadRating

10:54:14.052 AM Function execution took 1412 ms, finished with status: 'ok'

10:54:14.148 AM

Super hyggelig aften :)

super oplevelse, ... long string generated

Gist showing the function in question

I'm probably mixing up my async/awaits because of the awaits inside the await. But I don't see how else the code could be written without splitting it out into many atomic pieces but that would still require stitching a bunch of awaits together and make it harder to read.

So, two questions in total. Can this code work and what would be the ideal way to handle this pattern of making further processing on top of data fetched from Realtime DB?

Best regards, Simon

I am trying to create scatter plots of all the combinations for the columns: insulin, sspg, glucose (mclust, diabetes dataset, in R) with class as the colo(u)r. By that I mean insulin with sspg, insulin with glucose and sspg with glucose.

And I would like to do that with tidyverse, purrr, mappings and pipe operations. I can't quite get it to work, since I'm relatively new to R and functional programming.

When I load the data I've got the columns: class, glucose, insulin and sspg. I also used pivot_longer to get the columns: attr and value but I was not able to plot it and don't know how to create the combinations.

I assume that there will be an iwalk() or map2() function at the end and that I might have to use group_by() and nest() and maybe combn(., m=2) for the combinations or something like that. But it will probably have some way simpler solution that I can not see myself.

My attempts have amounted to this:

I'm trying to understand how the "fetch" phase of the CPU pipeline interacts with memory.

Let's say I have these instructions:

i'm a beginner on MYSQL db and i'm trying to play around with the query and relations.

i have created 2 tables, one is 'users' which contain the field staff_ID and the other is 'reports' which also contain the table field staff_ID of the user submitting the reports.

on the relations (see picture) i have connect the 2 staff id field.

every user can submit more than one reports, so i'm try to query and get only the reports of one users(staff_ID). I understood i have to use the JOIN keyword in order to obtain the data..

i tried the following query but it gave me all the result for all the users.

Hi so I'm starting to learn PHP and one of the first steps was to run the Development Server to start practicing, the line I used was:

I would like to know whether there is a recommended way of measuring execution time in Tensorflow Federated. To be more specific, if one would like to extract the execution time for each client in a certain round, e.g., for each client involved in a FedAvg round, saving the time stamp before the local training starts and the time stamp just before sending back the updates, what is the best (or just correct) strategy to do this? Furthermore, since the clients' code run in parallel, are such a time stamps untruthful (especially considering the hypothesis that different clients may be using differently sized models for local training)?

To be very practical, using tf.timestamp() at the beginning and at the end of @tf.function client_update(model, dataset, server_message, client_optimizer) -- this is probably a simplified signature -- and then subtracting such time stamps is appropriate?

I have the feeling that this is not the right way to do this given that clients run in parallel on the same machine.

Thanks to anyone can help me on that.

This probably ins't typical setup, but due to higher decisions we endup having multiple kafka clusters within one app, multiple topics per each, and each might have different serializing strategy. Json/avro. And avro might be with confluent schema registry or using single object encoding.

Well I got it working somehow, by building my own abstractions and registry which analyzes the configuration and creates most of stuff manually, but I feel I needed to repeat stuff like topic names, schema registry url on several places multiple times just to create all needed beans. Ugly as hell.

I'd like to ask, if there is some better way and support for this I just might have overlooked.

I need to create N representations of kafka clusters, configuring it once. Configure topics respective to given kafka cluster, configure confluent schema registry for topics where applicable etc, so that I can create instance of Avro schema file, send it to KafkaTemplate and it will work.

What I want the Macro to accomplish:

I want the user to be able to fill in data from E2 to E9 on the spreadsheet. When the user presses the "Add Car" button the macro is supposed to be executed. The makro then should take the handwritten data, copy everything from E2:E9 and put it into a table that starts at with C13 and spans over 7 columns, always putting the new set of data in the next free row. It is also supposed to check for duplicates and give an alert while not overwriting the original set of data

So my problem is, that I want the Macro I'm writing to take the information put into certain cells and then copy them into a table underneath.

I'm starting the Macro like this