UGT | Universal Game Translator - Uses Google | Game Engine library

I have around 3000 files (phylogenetic tree files) in which there are some specific genes that I want to insert {Foreground} after : .

For instance;

data frame with following strucutre : 'data.frame': 4371 obs. of 6 variables:

Following asking a previous question, I've tried to use batch transactions with Py2Neo to speed things up. I've adapted my code quite a bit, but seem unable to build and execute a batch of transactions. The matching works fine, it's only the transaction piece at the bottom which I'm having issues with - I thought I would include my entire code, just in case though. The current error I'm getting is as follows:

I am trying to run a Classifier model on top of an OD Model (used for localizing objects). To decrease the latency, I used multiprocessing for both OD as well as Classifier model. The output is correct but I am getting repetitive results.

I have a machine with 8 cores and so I am multiprocessing with pool=mp.Pool(8) I am using map_async and have an iterable as the list of image paths. To get the results as list I am using .get().

In the starting I didn't implement pool.join() after pool.close(), which I identified after going through a few sites. The error in output I am getting is because of the chunksize I am passing to the pool.map_async(). The number of repetitions of the same output is same as the chunksize. But according to my understanding of chunksize, it should just create batches of size same as chunksize and pass each batch to one process.

I'm trying to access genome data within SciKit Allele, a tool used for genome data, based on Numpy.

I'm not great with python, but am trying to iterate through each variant and extract the relevant column in the array to then create nodes in a Neo4j database using the Neo4j Rest Client.

The code below generates an array of all variants and all data types:

I have been working on a PHP page that calculates probabilities of different outcomes while randomly selecting a sample group from a larger group consisted of two types of people (+ and -).

For example, it can calculate the probability of having 0 (or n) smokers in a group of 1000 people randomly chosen from across the United States, considering that 0.15 of Americans are smokers (+).

It works very well while working with populations below 10000 people, but when it comes to bigger populations of like 1000000, it echoes 0 for all probabilities unless the precision (number of digits after .) is increased to like 3000. Even in that case it takes forever.

The code works by calculating the probability of 0 positives, and doing some calculations on it to get the probability of 1 positive, and so on. This is while most of these probabilities are useless.

I have been thinking that if I can figure out a fast way of calculating almost the exact (99.999% or higher) value of very big factorials (like 1000000!), there wouldn't be the need for starting from 0, and the calculation could have been started from the place where it is needed, and with very low precision to reduce the time it takes.

Here is the code:

Thank you for looking at this....

Need to reduce the precision of IoT Sensor data timestamps and merge.

I have two csv files with the following data

CSV-1