v0.10.4-beta

Bump version: 0.10.3-beta → 0.10.4-beta

v0.10.3-beta

Bump version: 0.10.2-beta → 0.10.3-beta

v0.10.2-beta

• Added utilities with ensemble, run length, and subset algorithms to the documentation.

v0.10.1-beta

• Migrated to a major.minor.patch-release semantic versioning system.
• Removed attributes in netCDF output from Indicators that are not in the CF-convention.
• Added fit indicator to fit the parameters of a distribution to a series.

v0.10-beta

• Remove support for Python 2 compatibility
• Added support for period of the year subsetting in checks.missing_any.
• Allow passing positive longitude values when subsetting data with negative longitudes
• Improved runlength calculations for small grid size arrays via ufunc_1dim flag
• Indicator class has var_name attribute, instead of using identifier to name output variables.

v0.9-beta

This is a significant jump in the release. Many modifications have been made and will be added to the documentation in the coming days. Among the many changes:

• New indices have been added with documentation and call examples
• Run_length based operations have been optimized
• Support for CF non-standard calendars
• Automated/improved unit conversion and management via pint library
• Added ensemble utilities for creation and analysis of muti-model climate ensembles
• Added subsetting utilities for spatio-temporal subsets of xarray data objects
• Added streamflow indicators
• Refactoring of the code : separation of indices.py into a directory with sub-files (simple, threshold and multivariate); ensembles and subset utilities separated into distinct modules (pulled from utils.py)
• Indicators are now split into packages named by realms. import xclim.atmos to load indicators related to atmospheric variables.

v0.8-beta

This is a testing release. No significant changes have been made since v0.7-beta

v0.7-beta

• Support for resampling of data structured using non-standard CF-Time calendars.
• Added several ICCLIM and other indicators.
• Dropped support for Python 3.4.
• Now under Apache v2.0 license.
• Stable PyPI-based dependencies.
• Dask optimizations for better memory management.
• Introduced class-based indicator calculations with data integrity verification and CF-Compliant-like metadata writing functionality.

Class-based indicators are new methods that allow index calculation with error-checking and provide on-the-fly metadata checks for CF-Compliant (and CF-compliant-like) data that are passed to them. When written to NetCDF, outputs of these indicators will append appropriate metadata based on the indicator, threshold values, moving window length, and time period / resampling frequency examined.