In reply to@_slack_fatiando_U01E7LSJCHH:matrix.org
So, from the JOSS article, this would be the difference, right? "Some of these interpolationand data processing methods already exist in GMT ... . However, there are no model selection tools in GMT and it can be difficult to separate parts of the processing that are done internally by its modules. Verde is designed to be modular, easily extended, ... .It can be used to implement new interpolation methods".

In reply to@_slack_fatiando_U01F18B993Q:matrix.org
I'm wondering if anyone would have some pointers for me. I'm trying to use Verde on a very small scale project compared to the data you are processing in the tutorials (surveying lakes/ponds). Scipygridder seemed to have quite nicely interpolated the points without going into the free space (https://www.fatiando.org/verde/latest/gallery/scipygridder.html). When I tried the same approach on my data I have the interpolation is quite far away from the surveyed data (see the screenshot). Does anybody know if I can easily tune this? Is this an issue of scale?

In reply to@_slack_fatiando_UMSRSPEMA:matrix.org
Scipy applies something like vd.convexhull_mask by default, it will interpolate inside of the convex hull formed by the data. You might want to try using vd.Spline and then masking with vd.distance_mask instead.

In reply to@_slack_fatiando_UMSRSPEMA:matrix.org
Or you could also apply distance_mask on top of the Scipy results.

In reply to@_slack_fatiando_UMSRSPEMA:matrix.org
Exactly. We are missing some options from GMT’s greenspline still, like the splines in tension. But those are relatively easy to add and we’ve been focusing on the bigger picture of the API for Verde v1.*. Now we’re getting performance down and making a viable alternative to surface which is still the best gridder out there in my opinion.

In reply to@_slack_fatiando_U01E7LSJCHH:matrix.org
Do you have any material where I can undestarnd the idea behind Green function without many formulas? I am not a (geo)physicists so it is hard for me to understand the idea from the fornulas.

In reply toundefined

In reply to@_slack_fatiando_U01E7LSJCHH:matrix.org
Hi. I am trying to understand the remove-compute-restore method that I see in verde Tutorial (in Transform 2020). Thanks in advance.

In reply to@_slack_fatiando_UMSRSPEMA:matrix.org
Federico Esteban anything in particular?

In reply to@_slack_fatiando_U0156QCM6AH:matrix.org
Here's a possible Verde public dataset http://auspass.edu.au/research/AusMoho.html

In reply to@_slack_fatiando_UT0BDHKPE:matrix.org
Hi all, i wonder if there is a way to BlockReduce categorical data. I.E. i would like to decimate a dataset, but maintin their categorical features….

In reply to@_slack_fatiando_UMGLPTLAW:matrix.org
Perhaps run through pandas groupby then blockreduxe?

In reply toundefined

In reply to@_slack_fatiando_UMSRSPEMA:matrix.org
I guess it would depend on the reduction function. If it can handle categorical data, then BlockReduce with center_coordinates=True should work. Please let us know if it does actually. We’ve never had a use case for this.

In reply to@_slack_fatiando_UT0BDHKPE:matrix.org
Many thanks, I will try these options and let know

In reply to@_slack_fatiando_UMSRSPEMA:matrix.org
Out of curiosity, what kind of reduction do you want to do on categorical data?

In reply toundefined

In reply to@_slack_fatiando_UT0BDHKPE:matrix.org
actually my problem is not really a reduction, I just want to decimate data. I convert a lines in vertices (it represent a seismic line), but instead of having a vertices every 10 meters I want to reduce the vertices to a 50 m spacing. And I want to keep all the attributes (columns of the dataframe) associate with the vertices (that contains some categoricals). Since I use Blockreduce with success in the past, but never with categorical variables I wonder if there is an option. Honestly I’m not sure this is the best approach to do that…