Pytesmo

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- Fix metadata for new version of pypi.

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- Add return\_clim keyword to anomaly calculation. Useful for getting
both anomaly and climatology in one pandas.DataFrame. Also used in
time series anomaly plot.
- Fix bug in julian2date which led to negative microseconds in some
edge cases.

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- Moved the ASCAT readers to the ascat package. The functionality is
the same, just replace ``import pytesmo.io.sat.ascat`` by
``import ascat`` and everything should work the same as before.
- The H07 reader now returns also ssm mean as a value between 0 and
100. Before it was between 0 and 1 and inconsistent with the other
ssm values.
- Fix small bug in julian date calculation and add tests for it.
- Add hamming window to resample module

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- 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.

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- 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.

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- Fix bug in temporal resampling if input was a pandas.Series
- Major refactoring of validation framework. 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.