Releases: cal-adapt/climakitae
1.0.1
1.0.0
Release Notes – climakitae 1.0.0
climakitae at 1.0.0 now splits the codebase into 2 repos: climakitae and climakitaegui. All GUI based code using the holoviz universe (panel, hvplot, etc.) has been moved to climakitaegui (ckg). This allows climakitae (ck) to have a more minimal install requirement allowing users to access the core data retrieval functionality of ck without the difficult build requirements of ckg.
What’s new?
- Code split into 2 repos. For GUI based functions in ckg, base classes are set in ck and then imported and extended in ckg. Some work had been previously done on this internally to ck but now the gui code is in a completely different repo.
- Functions needed only for other functions (helper functions) were moved from module level into the sub-functions.
- Documentation improvements to core functions.
Core
- All panel based user interface code moved to ckg
- New data query and retrieval tools added so that possible data outputs can be searched for and I single get_data call can be done.
Explore
- GUIs for explore tools moved to ckg. Base functions remain.
0.1.0
Release Notes – climakitae 0.1.0
climakitae is a python library being developed in tandem with a Jupyter notebook repository, cae-notebooks, for the Cal-Adapt Analytics Engine, an open-source project to enable cloud computing with official State of California downscaled climate projections.
This release represents a new stable beta version of core features, which will be built upon as existing toolkits are updated and new ones are added.
What’s new?
-
Core functions and toolsets are updated to select, subset, retrieve, preview, and export data from an updated catalog of climate projections regionally downscaled for California.
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Application class was removed, solving issues with unintended shared state
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Syntax has changed as a result, and code examples in Jupyter notebooks on the Analytics Engine Hub now reflect version 0.1.0:
| Activity | Before | Version 0.1.0 |
|---|---|---|
| Import the library | import climakitae as ck | import climakitae as ck |
| Pull up a GUI to select and subset data from the catalog | app = ck.Application app.select() |
selections = ck.Select() selections.show() |
| Print the current state of selections | app.selections | selections |
| Retrieve selected data from the catalog(in an xarray format, lazily loaded) | my_data = app.retrieve() | my_data = selections.retrieve() |
| Preview the data | app.view(my_data) | ck.view(my_data) |
| Force compute, to load data into memory | my_data = app.load(my_data) | my_data = ck.load(my_data) |
| Export data to file | app.export_as() app.export(my_data,’filename’) |
ck.export(my_data, ‘filename’,format) |
Export
- geoTIFF is deprecated (for now). netCDF and csv are available formats
- csv export better handles formatting for multiple types of data
- export is now a single line, replacing a two-step process of selecting filetype
Toolkits
- Previous ‘explore’ panels and timeseries tools are unified under the ‘explore’ module
- Functionality remains the same as immediately prior to this release
- Future updates introduce more toolkit improvements
- Required imports and calling syntax have changed:
| Activity | Before | Version 0.1.0 |
|---|---|---|
| Pull up one of the ‘explore’ toolkits (interactive panel with GUI) | app = ck.Application() app.explore.warming_levels() |
from climakitae.explore import warming_levels wl = warming_levels() wl.show() |
| timeseries toolkit | from climakitae import timeseriestools as tst timeseries = tst.Timeseries(my_data) timeseries.explore() |
from climakitae.explore import timeseries as tst timeseries = tst.TimeSeries(my_data) timeseries.explore() |