Katdal

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* Optionally make L1 product by applying calibration corrections (194 - 198)
* Let default reference antenna in v4 datasets be "array" antenna (202, 220)
* Use katsdptelstate v0.7: generic encodings, memory backend (196, 201, 212)
* Prepare for multi-dump chunks (213, 214, 216, 217, 219)
* Allow L1 flags to be ignored (209, 210)
* Deal with deprecated dask features (204, 215)
* Remove RADOS chunk store (it's all via S3 from here on)

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* Python 3 support via python-future (finally!)
* Load L1 flags if available (164)
* Reduced memory usage (165) and speedups (155, 169, 170, 171, 182)
* S3 chunk store now uses requests directly instead of via botocore (166)
* Let lazy indexer use oindex semantics like in the past (180)
* Fix concatenated data sets (161)
* Fix IPython / Jupyter tab completion for sensor cache (176)

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* Restore NumPy 1.14 support (all data flagged otherwise)

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* Rally around the MeerKAT Visibility Format (MVF)
* First optimised converter from MVF v4 to MS: mvftoms
* Latest v4 fixes (synthetic timestamps, autodetection, NPY files in Ceph)
* Flag and zero missing chunks
* Now requires katsdptelstate (released), dask, h5py 2.3 and Python 2.7
* Restore S3 unit tests and NumPy 1.11 (on Ubuntu 16.04) support

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* New HDF5 v3.9 file format in anticipation of v4 (affects obs_params)
* Fix receiver serial numbers in recent MeerKAT data sets
* Add dask support to ChunkStore
* katdal.open() works on v4 RDB files

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* New ChunkStore and telstate-based parser for future v4 format
* Use python-casacore (>=2.2.1) to create Measurement Sets instead of blank.ms
* Read new-style noise diode sensor names, serial numbers and L0 stream metadata
* Select multiple polarisations (useful for cross-pol)
* Relax the "expected number of dumps" check to avoid spurious warnings
* Fix NumPy 1.14 warnings