helloworld-deanaz

A package for training and predicting temporal cell response predictions (tcrp) for florescent microscopy time-lapse data.

These details have not been verified by PyPI

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Project description

This is a test for making a shareable environment.

These details have not been verified by PyPI

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

0.0.3

Apr 30, 2020

This version

0.0.2

Apr 30, 2020

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Uploaded Apr 30, 2020 Source

Uploaded Apr 30, 2020 Python 3

Hashes for helloworld_deanaz-0.0.2.tar.gz
Algorithm	Hash digest
SHA256	`7a47a03b0632492074b9f5703badf5fdfc919b2c53ad94af6c8273dbaf79e165`
MD5	`feeb1e0223cfd18aed31d10002bb39ab`
BLAKE2b-256	`742cd6c5da0b03279c1a4d6141c4eb105037c0c34dcb1bdc33e15fd4721a7fcd`

Hashes for helloworld_deanaz-0.0.2-py3-none-any.whl
Algorithm	Hash digest
SHA256	`a4e38a5039aa077982c9fe3f976e379faa0d756716230d33cfb0a8ddf3d2e1e8`
MD5	`0054c5d52cfa13ec9710b95099339709`
BLAKE2b-256	`adc016285329f656c2a82be27f84f404615188560f1e5f416a8ee93070ba3ee9`