Analytical | A simple light-weight analytics wrapper for Swift | Analytics library

I have table in Oracle SQL. Table contains different values for different objects and I need to select every record except one with max value grouped by objects, but max value can have duplicate record in the table below. I need to use analytical functions.

I'm attempting to solve a set of equations related to biological processes. One equation (of about 5) is for a pharmacokinetic (PK) curve of the form C = Co(exp(k1*t)-exp(k2*t). The need is to simultaneously solve the derivative of this equation along with some enzyme binding equations and initial results where not as expected. After troubleshooting, realized that the PK derivative doesn't numerically integrate by itself, if k is negative using the desolve ode function. I've attempted every method (lsode, lsoda, etc) in the ode function, with no success. I've tried adjusting rtol, it doesn't resolve.

Is there an alternative to the deSolve ode function I should investigate? Or another way to get at this problem?

Below is the code with a simplified equation to demonstrate the problem. When k is negative, the integrated solution does not match the analytical result. When k is positive, results are as expected.

First Image, result with k=0.2: Analytical and Integrated results match when k is positive

Second Image, result with k=-0.2: Integrated result does not match analytical when k is negative

Can anyone tell me what is wrong with this code? It is from https://jakevdp.github.io/blog/2012/09/05/quantum-python/ . Everything in it worked out except the title of the plot.I can't figure it out.

It should look like this

but when the code is run, it polts this

Here is the code given:-

I have an analytical function challenge on Bigquery that is messing with my mind. Sorry if I am missing any fundamental function here, but I couldn't find it.

Anyway I think this can lead to a good discussion.

I would like to get a rank among groups (dense_rank or row_number or something like that), but on a clustered fashion, which is the tricky bit.

For example, I would like to create clusters based not only on two partition columns (see image below) but based on the order among them as well. This is why I am calling it cluster. Each cluster should have the same rank if it's adjacent, but a different number if it is not (it was split by other cluster).

So, for cluster "a, x", all rows for the first cluster have number 1, then all rows for the second cluster have number 2, and so on.

How can I achieve this? Is there an analytical function out of the box for this or does this require a bit of auxiliary columns?

Thanks in advance.

I would like to remove the DOI from the bibliographic references in my markdown script. Is there a way I can do this?

Here is my markdown file:

I'm trying to plot a mathematical expression in python. I have a sum of functions f_i of the following type

-x/r^2*exp(-rx)+2/r^3*(1-exp(-rx))-x/r^2*exp(-r x_i)

where x_i values between 1/360 and 50. r is quite small, meaning 0.0001. I'm interested in plotting the behavior of these functions (actually I'm interested in plotting sum f_i(x) n_i, for some real n_i) as x converges to zero. I know the exact analytical expression, which I can reproduce. However, the plot for very small x tends to start oscillating and doesn't seem to converge. I'm now wondering if this has to do with the floating-point precision in python. I'm considering very small x, like 0.0000001

I'm trying to get the selected value from a Form::select in a blade's javascript file.

This is my blade:

I have a spring batch job. It processes a large number of items. For each item, it calls an external service (assume a stored procedure or a REST service. This does some business calculations and updates a database. These results are used to generate some analytical reports.). Each item is independent, so I am partitioning the external calls in 10 partitions in the same JVM. For example, if there are 50 items to process, each partition will have 50/10 = 5 items to process. This external service can result a SUCCESS or FAILURE return code. All the business logic is encapsulated in this external service and therefore worker step is a tasklet which just calls the external service and receives a SUCCESS/FAILURE flag. I want to store all the SUCCESS/FAILURE flag for each item and get them when job is over. These are the approaches I can think of:

1. Each worker step can store the item and its SUCCESS/FAILURE in a collection and store that in job execution context. Spring batch persists the execution context and I can retrieve it at the end of the job. This is the most naïve way, and causes thread contention when all 10 worker steps try to access and modify the same collection.
2. The concurrent exceptions in 1st approach can be avoided by using a concurrent collection like CopyOnWriteArrayList. But this is too costly and the whole purpose of partitioning is defeated when each worker step is waiting to access the list.
3. I can write the item ID and success/failure to an external table or message queue. This will avoid the issues in above 2 approaches but we are going out of spring bath framework to achieve this. I mean we are not using spring batch job execution context and using an external database or message queue.

Are there any better ways to do this?

I am designing a new Data landscape, currently developing my Proof of concept. In here I use the following architecture: Azure functions --> Azure event hub --> Azure Blob storage --> Azure factory --> Azure databricks --> Azure SQL server.

What I am strugging with at the moment is the idea about how to optimize "data retrieval" to feed my ETL process on Azure Databricks.

I am handling transactional factory data that is sumbitted by minute to Azure blob storage via the channels in front of it. Thus I end up with 86000 files each day that need to be handled. Indeed, this is a huge number of separate files to process. Currently I use the following piece of code to build a list of filenames that are currently present on the azure blob storage. Next, I retrieve them by reading each file using a loop.

The problem I am facing is the time this process takes. Of course we are talking here about an enormous amount of small files that need to be read. So I am no expecting this process to complete in a few minutes.

I am aware that upscaling the databricks cluster may solve the problem but I am not certainly sure that only that will solve it, looking at the number of files I need to transfer in this case. I am running to following code by databricks.