Version 0.5.2 - Bugfix and performance improvements

• Fix bugs when the validation framework encountered empty datasets for various reasons.
• Add dataset adapters for masking and anomaly calculation.
• Improve performance of moving average calculation and ISMN readers.
• ISMN readers are approx. an order of magnitude faster now.

Small Bugfixes, and refactoring

• Fix bug in jobs argument passing to Validation class.
• Add support to use a pre initialized DataManager instance in the Validation class.
• Add support for per dataset reading method names in the DataManager. This
  relaxes the assumption that every dataset has a read_ts method.

Improved validation framework and scaling module

• Fix bug in temporal resampling if input was a pandas.Series

• Major refactoring of validation framwork. Please see updated documentation and
  example for detailed changes. The most important breaking changes are:

  • 'type' is no longer used in the dataset dictionary.
  • the temporal matcher does no longer need to be specified since a reasonable
    default was developed that should handle most cases
  • metrics calculators are now given as dictionaries of functions. Please see
    the docs for an explanation and an example.
  • cell_based_jobs keyword was removed in favor of a more general definition of jobs.

  New features are the possibility to use unrelated masking datasets and the
  possibility to temporally match any number of datasets and give them in sets
  of k datasets to multiple metric calculators.

• Changes in the scaling module, escpecially CDF matching. The new CDF scaling
  module is more modular and does not make any assumptions about how unique the
  percentiles for the CDF matching have to be. CDF matching now returns NaN
  values if non unique percentiles are in the data. There are new functions that
  rescale based on pre-calculated percentiles so these can be used if the user
  wants to make sure that the percentiles are unique before matching.

Improvements and slight interface changes

• Fix bug in validation framework due to error prone string formatting in warnings.
• Remove grid functionality. Use pygeogrids from now on.
• Fix bug in moving average calculation when input had size 1.
• Add recursive calculation of Pearson correlation coefficent.
• Change H-SAF reading interface to use pygeobase consistently. This changes the
  interface slightly as the read_img method is now called just read
• H07 reader now returns more variables.
• Resampling interface now respects dtype of input data.
• Improvements in ISMN plotting interface make it possible to use the plot not
  only show it.

Climatology calculation fixes

• make sure that climatologies are always 366 elements
• add new options to climatology calculation for filling NaN values
• add option to climatology calculation for wraparound before the smoothing

v0.3.5

• fix bug in anomaly calculation that occurred when the climatology series had
  a name already
• add option in anomaly calculation to respect leap years during matching
• improve testing of scaling functions
• add linear CDF scaling based on stored percentiles
• add utility function for MATLAB like percentile calculation pytesmo.utils.ml_percentile
• add utility function for making sure elements in an array are unique by
  using iterative interpolation pytesmo.utils.interp_uniq

v0.3.4

• fix #63 by moving data preparation before period checks
• fix bug in exponential and boxcar filter. Problem was that nan values were not ignored correctly

v0.3.3

• add option to temporal resampling to exclude window boundaries
• fix #48 by reintroducting netcdf imports
• fix #60 by importing correctly from pygeogrids
• fix #56 by allowing read_bulk keyword for ASCAT_SSM
• fix #58 by using cKDTree keyword if available
• lookup table indexing fixed, see #59

v0.3.2

• hotfix for temporal resampling problem when time series where of unequal lenghts

v0.3.1

• added validation framework and example on how to use it
• fix bug (issue #51) in temporal matching
• added test data as git submodule