-
Notifications
You must be signed in to change notification settings - Fork 35
Generic functions for cohort means and sums #730
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Comments
Sounds great @timothymillar! |
timothymillar
added a commit
to timothymillar/sgkit
that referenced
this issue
Dec 7, 2021
timothymillar
added a commit
to timothymillar/sgkit
that referenced
this issue
Dec 7, 2021
timothymillar
added a commit
to timothymillar/sgkit
that referenced
this issue
Dec 7, 2021
timothymillar
referenced
this issue
in timothymillar/sgkit
Dec 8, 2021
timothymillar
added a commit
to timothymillar/sgkit
that referenced
this issue
Jun 18, 2022
timothymillar
added a commit
to timothymillar/sgkit
that referenced
this issue
Jun 20, 2022
timothymillar
added a commit
to timothymillar/sgkit
that referenced
this issue
Jun 20, 2022
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
I've come across a couple of situations where I want to take mean of sample values for each cohort in a dataset. From what I can tell, there is not a simple way to achieve this using dask or xarray.
We already have a numba jitted function to do this for a specific case (
_cohort_observed_heterozygosity
) so it would make sense to generalize that to standard function inutils.py
or similar. A higher level function could be made available which automatically calculates the cohort dimension size and runsda.mapblocks
etc. We could also to the same for cohort sums.The text was updated successfully, but these errors were encountered: