Add the drizzle library to requirements.txt. It's on pypi, but triggered some compilation when I installed it, so it might not work on all platforms. An alternative would be to avoid the drizzle import until runtime, but users will need it to simulate images in our default configuration.
Add minimum dependency versions to requirement.txt, and load requirements.txt in setup.py. Maybe you'll eventually want two package lists (a minimum one and one for automated test builds).
Avoid runtime tensorflow imports, by making manada.Analysis unavailable if tensorflow is missing.
If you don't have tensorflow, you'll get a notification manada.Analysis disabled since tensorflow is missing when importing manada.
If you later try manada.Analysis (which works fine if you have tensorflow) you'll get an AttributeError. (Since this is rather uninformative, I thought the notification was warranted.)
import manada.Analysis, from manada import Analysis, etc. will still trigger the tensorflow ModuleNotFound error if you don't have tensorflow.
This aims to make manada easier to install / use:
drizzle
library torequirements.txt
. It's on pypi, but triggered some compilation when I installed it, so it might not work on all platforms. An alternative would be to avoid the drizzle import until runtime, but users will need it to simulate images in our default configuration.requirement.txt
, and loadrequirements.txt
insetup.py
. Maybe you'll eventually want two package lists (a minimum one and one for automated test builds).manada.Analysis
unavailable if tensorflow is missing.manada.Analysis disabled since tensorflow is missing
when importing manada.manada.Analysis
(which works fine if you have tensorflow) you'll get anAttributeError
. (Since this is rather uninformative, I thought the notification was warranted.)import manada.Analysis
,from manada import Analysis
, etc. will still trigger the tensorflowModuleNotFound
error if you don't have tensorflow.