Hello! My name is Ishaan Jain, a Information Technology undergrad at Manipal University Jaipur. I will be working on developing an information theoretic approach to filter out artificial information and real information in geospatial datasets for Xbitinfo during Google Summer of Code 2023

What did I do this week?

Created pull request PR#234 and completed a basic implementation of the aritificial information fillter.

What is coming up next?
Will optimize and try to make the function more generalised.

Hello! My name is Ishaan Jain, a Information Technology undergrad at Manipal University Jaipur. I will be working on developing an information theoretic approach to filter out artificial information and real information in geospatial datasets for Xbitinfo during Google Summer of Code 2023
What did I do this week?

1. Integration of Artificial Information Filter: One of the key tasks I tackled was integrating the artificial information filter as a parameter within the get_keepbits function. This addition represents a significant step forward in making the function more versatile and adaptable to different datasets and use cases. The filter allows us to selectively identify and manage artificial information within the data, contributing to improved data quality.

2. Challenges with Different Dimensions: During the implementation of the artificial information filter, I encountered some issues related to handling datasets with varying dimensions. Ensuring that the filter works seamlessly across datasets of different sizes and structures is a complex task, and I dedicated time to address these challenges effectively.

What is coming up next?
Will complete the addition of filter to get_keepbits function.

Hello! My name is Ishaan Jain, a Information Technology undergrad at Manipal University Jaipur. I will be working on developing an information theoretic approach to filter out artificial information and real information in geospatial datasets for Xbitinfo during Google Summer of Code 2023

What did I do this week?

Introduced a new parameter to my function, namely the "threshold." By default, this threshold is set at 70%. This parameter is crucial as it helps determine where the keepbits should be positioned within the dataset. Specifically, it ensures that keepbits are strategically placed when the information content of a variable reaches 70% or more. This addition adds flexibility and adaptability to the function, allowing it to better manage keepbits in a dynamic manner.

What is coming up next?

In the coming weeks, I plan to further refine and test these updates to ensure the function's robustness across various datasets and scenarios.

Hello! My name is Ishaan Jain, a Information Technology undergrad at Manipal University Jaipur. I will be working on developing an information theoretic approach to filter out artificial information and real information in geospatial datasets for Xbitinfo during Google Summer of Code 2023

What did I do this week?

Worked on variables like like E2,Q2,QVAPOR,RH2,SH2,MLCAPE,PSFC,T2,TRAD,TSK,TLP,U10,V10. The function which returns true keepbits is working for the decoded DataArray for most variables but in some cases the true keepbits are not coming right for original DataArray .

Hello! My name is Ishaan Jain, a Information Technology undergrad at Manipal University Jaipur. I will be working on developing an information theoretic approach to filter out artificial information and real information in geospatial datasets for Xbitinfo during Google Summer of Code 2023.

What did I do this week?

This week was dedicated to fine-tuning the function responsible for calculating true keepbits, a critical aspect of our data analysis. I focused on experimenting with different values of tolerance to achieve accurate results. Despite my efforts, I encountered some challenges. Although the function produced correct keepbits for the decoded dataset, it still struggled to yield appropriate keepbits for the original dataset in some cases.

What is coming up next?

In the upcoming week, I plan to take a deeper dive into the function's inner workings and experiment with different strategies to improve its accuracy for the original dataset.