csv | fast C # CSV parser : implements stream reader | CSV Processing library

When recognizing hand gesture classes, I always get the same class, although I tried changing the parameters and even passed the data without normalization:

I ran the following code in RStudio:

I have an iterable of bytes, such as

On the Python using r front of the file path, can deal with escape sequence such as :

I have a very simple file:

My elements are created from data in a CSV file that updates every 1 minute.

I'm trying to update these elements as follows:

1. Remove those whose data is no longer in the CSV file
2. Create new ones that appeared in the CSV file
3. Keep without edit those that still exist in the CSV file

The CSV file looks like this:

Facebook constructed what it calls a relative wealth index for >19M micro regions (2.4km grid cells) around the world. They've shared the data (zip) in a csv file that lists the quad key ID, lat/long (which I believe is the top left corner of the tile cell), and the index value for the tile. It looks like this:

In their technical paper, they note that these 2.4km grid cells correspond to Bing tile level 14.

I've not worked with Bing tiles before. What's the best way to a) create or access a 2.4 tile grid that covers a polygon (e.g., Kenya) and b) join the wealth index values from the csv to this grid shapefile? I'd like to have a grid polygon with this wealth index attribute that I can use in a future analysis that extracts information from a raster by grid cell.

What I know/think I know so far:

• sf::st_make_grid() would create a grid, but I don't think it would be the Bing grid.
• Packages like {rosm} will plot bing tiles, but this is not quite what I'm looking for.
• Folks have created functions that take the quadkey input and return the upper left corner coordinate, e.g., https://gis.stackexchange.com/a/359636/22560. I'm not sure what, if anything, I can do with this.

[moved question from gis.stackexchange.com]

Edit 1: The RWI csv files no longer include the quadkey, but you can use the python package linked above to calculate it. There's a helpful tutorial here.

I am trying to extract a specific field in a specific line of a CSV file.

I'm able to do it according to the number row but sometimes the row number of the file will change so this is not that flexible.

I wanted to try and do it to extract a specific name before the field I'm interested in.

Is there a way to check the convergence when fitting a distribution in SciPy?

My goal is to fit a SciPy distribution (namely Johnson S_U distr.) to dozens of datasets as a part of an automated data-monitoring system. Mostly it works fine, but a few datasets are anomalous and clearly do not follow the Johnson S_U distribution. Fits on these datasets diverge silently, i.e. without any warning/error/whatever! On the contrary, if I switch to R and try to fit there I never ever get a convergence, which is correct - regardless of the fit settings, the R algorithm denies to declare a convergence.

data: Two datasets are available in Dropbox:

• data-converging-fit.csv ... a standard data where fit converges nicely (you may think this is an ugly, skewed and heavy-central-mass blob but the Johnson S_U is flexible enough to fit such a beast!):

• data-diverging-fit.csv ... an anomalous data where fit diverges:

code to fit the distribution: