Pycomlink

**Important note:** This is the last release using the v0.2.x API which is based on `Comlink` objects. In the future `xarray.Datasets` will be the basis for representing one CML with its data and metadata.

Enhancements

* Grid intersection now can be calculated using the coordinates at the lower-left corner for definition (59)

Bug fixes

* Fixed bug with calculation of MCC (58 and 60)

* `PointsToGridInterpolator` now checks if only NaNs are passed as `z` values and returne a NaN grid in this case (55)

Enhancements
--------------

* Added WAA calculation and test for method proposed by Leijnse et al 2008

* Added function to calculate WAA directly from A_obs for Leijnse et al 2008
method.

* Added WAA example notebook

* Added function to derive attenuation value `A_min_max` from min/max CML
measurements (these measurements periodically provide the min and max
value over a defined time period, typically 15 minutes).
(by DanSereb in PR 37 and 45)

* Added function to derive rain rate `R` from `A_min_max`
(by DanSereb in PR 37 and 45)

* Added example notebook with simple comparison of processing of
"instantaneous" and "min-max" CML data (by DanSereb in PR 37 and 45)

Bug fixes
---------

* Added missing kwarg for polarization in `calc_A` in `Processor`. Before,
`calc_A` always used the default polarization for the A-R relation which
leads to rain rate overestimation!

* Changed reference values in test for Ordinary Kriging interpolator, because
`pykrige v1.4.0` seems to produce slightly different results than `v1.3.1`

Enhancements
---------------

* Codebase is Python 3 now, keeping backwards compatibility to Python 2.7
via using the `future` module.

* min-max CML data can now be written to and read from cmlh5. Standard column
names are `tx_min`, `tx_max`, `rx_min` and `rx_max`. When reading from cmlh5
without specifying dedicated column names, the function tries out the
standard column names for min-max and instantaneous. If it does not find any
match it will print an error message.

* Added example file with min-max data for 75 CMLs. This dataset is derived
from the existing example dataset of 75 CMLs with instantaneous measurements.

* Added example notebook comparing min-max and instantaneous CML data

* Added TravisCI and Codecov and increased the test coverage a little

* Extended functionality for `append_data`. A maximum length or maximum
allowed age for the data can be specified

* More options for interpolation. Added option to pass `max_distance`
for IDW and Added option for resampling in `Interpolator`
(instead of just doing hourly means of variable `R`)

* Interpolated fields are now always transformed into an `xarray.Dataset`.
The `Dataset` is also stored as attribute if the `Interpolator` object

* Improved grid intersection calculation in validator

Bug fixes
---------

* `t_start` and `t_stop` have not been taken into account
in the main interpolation loop

* Fix: Catching `LinAlgError` in Kriging interpolation

Minor update

* removing `geopandas` dependecy

* update `MANIFEST.in` to include notebooks and example data in pypi releases

Backward Incompatible Changes
---------------------------------

* Complete rewrite of interpolator classes. The old interpolator class
`spatial.interpol.Interpolator()` is depreciated. New interpolator base classes
for IDW and Kriging have been added together with a convenience inteprolator
for CML data. Usage is showcased in a new example notebook.

* Some old functionality has moved to separate files.
* resampling to a given `DatetimeIndex` is now availabel in `util.temporal`
and will be removed from `validatoin.validator.Validation()` class soon.
* calculation of wet-dry error is now in module `validation.stats`
* calculation of spatial coverage with CMLs was moved to function
`spatial.coverage.calc_coverage_mask()`.
* error metric for performance evaluation of wet-dry classification is now
in `validation.stats`. Errors are now returned with meaningful names as
namedtuples. `validation.validator.calc_wet_dry_error()` is depreciated now.

Enhancements
---------------

* Read and write to and from multiple cmlh5 files (12)

* Improved `NaN` handling in `wet` indicator for baseline determination

* Speed up of KDtreeIDW using numba and by reusing
previously calculated variables

* Added example notebook for baseline determination

* Added data set of 75 CMLs (with fake locations)

* Added example notebook to show usage of new interpolator classes

* Added decorator to mark depreciated code

Bug fixes
---------

* `setup.py` now reads all packages subdirectories correctly

* Force integers for shape in `nans` helper function in `stft` module

* Always use first value of `dry_stop` timestamp list in `stft` module.
The old code did not work anyway for a list with length = 1 and would
have failed if `dry_stop` would have been a scalar value. Now we
assume that we always get a list of values (which should be true for
`mlab.find`.