Difference between revisions of "Staff Priorities - Masha"
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* 'pandas' data analysis Python package for reading forecast from gzip archive file: | * 'pandas' data analysis Python package for reading forecast from gzip archive file: | ||
** tested with iPython that adding data extraction from archive adds only 100ms to 500ms to read data with Pandas vs. 3.5s to load data with numpy.loadtxt | ** tested with iPython that adding data extraction from archive adds only 100ms to 500ms to read data with Pandas vs. 3.5s to load data with numpy.loadtxt | ||
| − | ** post question to user group if can load data directly with Pandas when filename is stored within archive | + | ** post question to user group if can load data directly from archive with Pandas when filename is stored within archive |
* Re-run test case using best available catalog for Canterbury experiment with zero days of delay for providing slip models to the forecasts: | * Re-run test case using best available catalog for Canterbury experiment with zero days of delay for providing slip models to the forecasts: | ||
** Generating one-month and one-day forecasts | ** Generating one-month and one-day forecasts | ||
Revision as of 21:09, 13 October 2015
Current Activities
Yesterday
- CSEP V15.10.0 release: could not install on csep-op due to the nightly processing still running
- Tested NZ image successfully
- Updated/checked David Jackson's email on CSEP lists
- Read papers for the CSEP status meeting on Wednesday
- 'pandas' data analysis Python package for optimized file IO functionality: problems reading forecast from gzip archive because of storing original filename as part of the archive
- Re-run test case using best available catalog for Canterbury experiment with zero days of delay for providing slip models to the forecasts:
- Generating one-month and one-day forecasts
Today
- Install and test CSEP V15.10.0 on csep-op
- Read papers for the CSEP status meeting on Wednesday
- 'pandas' data analysis Python package for reading forecast from gzip archive file:
- tested with iPython that adding data extraction from archive adds only 100ms to 500ms to read data with Pandas vs. 3.5s to load data with numpy.loadtxt
- post question to user group if can load data directly from archive with Pandas when filename is stored within archive
- Re-run test case using best available catalog for Canterbury experiment with zero days of delay for providing slip models to the forecasts:
- Generating one-month and one-day forecasts
Blocked: No
Followups:
- With John Y.:
- csep-op: Added my email to recipients list for Oceanic Transform Faults status email, got email sent to my account, but not to the mailing list.
TODO
- Trac ticket #353: Optimize RELM N-test by avoiding event to bin look up when masking bit is set for the whole forecast
