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statistics on 2d grids: control the `bin_centers`
Hi,
I have a question regarding a usecase of mine (maybe it is a common one, I don't know).
Let's say df
(a pandas.DataFrame
) holds scalar values for a variable
observed at different time
, latitude
and longitude
. I can compute the variable
mean over time
, binned by longitude
and latitude
on a N
xM
grid as:
df_vx = vaex.from_pandas(df)
gridded_mean = df_vx.mean("variable", binby=["longitude", "latitude"], shape=(N, M))
The shape
allows me to control the underlying grid resolution, knowing the spatial extent of the observations.
However, I would like to directlly control the resulting bin_centers
of the underlying grid, in the case of a regular grid, such that they match another grid. So far, I am achieving this by adding fake rows to df
with the appropriate latitude
and longitude
coordinates. Is there a better / more direct way to do it?
Thanks. Vadim