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i'm working on a distribution issue with gnu prolog. Im trying to distribute teachers to several school subject based on time conditions. The Code for the ideal case looks like this.

I've a file with 7946479 records, i want to read the file line by line and insert into the database(sqlite). My first approach was open the file read the records line by line and insert into the database at the same time, since it dealing with huge amount of data it taking very long time.I want to change this naive approach so when i searched on internet i saw this [python-csv-to-sqlite][1] in this they have the data in a csv file but the file i have is dat format but i like the answer to that problem so now i am trying to do it like in the solution. https://stackoverflow.com/questions/5942402/python-csv-to-sqlite

The approach they using is like first they splitting the whole file into chunks then doing the database transaction instead of writing each record one at a time.

So i started writing a code for splitting my file into chunks Here is my code,

I have aplication which runs in parallel mode. Jobs are runing using threads with implmented subroutine. Subroutine "worker3" have three argumens, one parameter and two path for files. This subroutine execute R script using system commands.

I have three variables (T2M, U50M, V50M) from which I would like to find the January average, February average, etc over Multiple Years. I have a xarry.Dataset - name Multidata:

I'm trying to access the data in a GRIB2 file at a specific longitude and latitude. I have been following along with this tutorial (https://www.youtube.com/watch?v=yLoudFv3hAY) approximately 2:52 but my GRIB file is formatted differently to the example and uses different variables

I have a 17GB GRIB file containing temperature (t2m) data for every hour of year 2020. The dimensions of Dataset are longitude, latitude, and time.

My goal is to compute the highest temperature for every coordinate (lon,lat) in data for the whole year. I can load the file fine using Xarray, though it takes 4-5 minutes:

I am working with compiling weather data from different datafiles in a folder. Stations.csv - These datafiles are defined as location points in this csv. temperature.csv - The weather data needs to be compiled in this dataframe. Issue with the iteration loop:

stations = pd.read_csv('SMHIstationset.csv', index_col='Unnamed: 0') stations.head()

stations dataframe

Dears, I need to create a xarray.datarray with the names of the dimensions equal to the names of the coordinates, however, I am not succeeding. Here is the code for reproduction:

I am getting the Error: Unprocessable Entity (HTTP 422) with the get_power function (global meteorology and surface solar energy climatology data) of the NASAPOWER library in R