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Hello everyone! What did I do this week? This was the last week of GSoC. I fixed some previous code and some errors with the Icons-picker API. Right now I am working to add Animated Icons support for the Icons Picker. The back-end part of it is done, so if an API request is made with Animated icons name in the JSON, the custom font will have only those icons. Now I have to implement the use of API and Animated icons selector in front end. Also, this week I submitted the final evaluation. This is the link that have all the project contributions I made: https://github.com/kbhutani0001/GSoC-2019-report/blob/master/README.md What is coming up next week? Nothing :'( GSoC ends next week. But I will still keep contributing to EOS and other open source projects Did I get stuck anywhere? Yes, in the front end of Animated icons selector, the animated icons are written in css so they are inheriting other CSS too. That caused a bit of a problem. Till next time, Cheers!

Hello everyone! This week is the second last week and next week we will have our final evaluations. This week I just fixed and improved my previous code.  The other student was working on the Icons cheatsheet and after he completed it, I used some of his components and used them in my code too so that the UI looks consistent. Also the API was having some issues with custom font generation so I am currently working on that.  Thanks for reading Cheers!

Today I tested a new feature of Flask version 1.1.1. [mythcat@desk my_flask]$ pip list | grep Flask Flask 1.1.1 Flask-Login 0.4.1 Flask-Mail 0.9.1 Flask-Migrate 2.5.2 Flask-Script 2.0.6 Flask-SQLAlchemy 2.4.0 Flask-WTF 0.14.2 This

I unsuccessfully tried to resolve the issue with dimensions in NMF. The main problem that I was facing was with specifying the correct dimensions for matrix operations within NMF algorithm and I have yet to identify the correct dimensions. Furthermore, there is no incremental algorithms for NMF as in PCA. Thus, if a large data is given, it becomes infeasible to perform NMF. There are several heuristics to go around this issue with scalability, namely parallel NMF, which divides columns (i.e. features) of the data matrix into several subsets, perform NMF independently on each of the column subsets, and then join the results at the end. Unfortunately, this algorithm was not feasible for LiberTEM because with LiberTEM, one only has access to the rows of the data matrix (i.e., images) and not to the full columns (i.e., feature vectors) at each step. I also tried to clean up jupyter notebook and reorganize.  What did I do this week? Work on NMF What will I work on next week? Write documentation

These are the ten most rated questions at Stack Overflow last week.Between brackets: [question score / answers count]Build date: 2019-08-24 20:58:57 GMTConcatenation of the result of a function with a mutable default argument - [33/2]Give function defaults arguments from a dictionary in Python - [11/6]Slicing behavior of python range()[:] - [11/2]How to orient matplotlib ax objects to plot consecutively along a timeseries line (affine_transform, mtransform)? - [8/1]How to look up identical column names in two dataframes and combine the matched columns - [6/5]No of Pairs of consecutive prime numbers having difference of 6 like (23,29) from 1 to 2 billion - [6/5]How to export a graph with alternative text in Excel to PDF using Python or R? - [6/2]how to limit the duplicate to 5 in pandas data frames? - [6/2]Reading multiple large csv files of size 10GB plus parallel in python - [6/1]Why cv2.rectangle sometimes return np.ndarray, while sometimes cv2.UMat - [6/0]

Alas, time has caught up to me. I will not be able to get the touch trackball controls working in time for the submission deadline. Instead, I need to use this remaining time to package up my work and create a more thorough write-up that details what I have done and what is left to be done. I'm still excited, though, since I got all of the minimum requirements of my proposal done and have gotten a good start on a few extras. I will be working beyond the end of GSoC to get iOS support to a ready state. See you soon!

Hi everyone!   It’s time for the last GSOC blog. I must say that it has been nice working with all of you. I have learned a lot and faced many professional challenges. In the end it was worth it. I hope GSOC experience will help me in the future and that my project will be useful to open source. You can check my final report at https://github.com/teabolt/gsoc19-eli5-gradcam.   Shout out to both of my mentors for taking me on board! Konstantin, https://github.com/lopuhin Thank you for the constant help, knowledge, and advice! A mentor that I would recommend to any student! Mikhail, https://github.com/kmike Thanks for jumping in at the right moments with important observations and help making the right decisions!   Shout out to other students Anubhav, https://github.com/anubhavp28 Vipul, https://github.com/vipulgupta2048 Leonardo, https://github.com/leovictorsr Ashwin, https://github.com/AshwinB-hat Though not here for the final evaluation, thanks for sticking around and hopefully you had other good endeavours this summer!   Finally shout out to all the other participants in Scrapy, especially Ian, https://github.com/imduffy15 For helping me get into GSOC in the first place, and for all your engineering advice! Cathal, https://github.com/cathalgarvey Thanks for your work as an org-admin!   So that’s it. Hope to see you in open source or in another GSOC.   Peace, Tomas Baltrunas