Make DatetimeIndex timezone naive for compatibility with xarray/numpy engine

[madtoinou]

Fixes #1052 and #648. Xarray relies on numpy engine, which is not timezone aware. Causes issues for plotting and resampling.

Summary

When constructing a TimeSeries from a pd.DataFrame or a pd.Series with a DatetimeIndex containing timezone information, this attribute is dropped without conversion using tz_localize(None) (conversion can causes shift in the timestamps, making the index invalid). The timezone information can be recovered by calling ts.time_index.tz_localize(original_tz) prior to exporting the data.

Other Information

The DatetimeIndex not used as index are silently converted to UTC by the pd.DataFrame constructor without saving the timezone information. Thus, timezone aware dataset should use the time range as index to take advantage of the bugfix and avoid the error raised by the TimeSeries constructor due to index invalidity.

In order to have the desired x-axis when plotting the TimeSeries (with a timezone unaware time_index due to the bugfix), the user can simply leverage the matplotlib.pyplot rcParams['timezone'] parameter to automatically convert the time axis back to the original timezone.

[codecov-commenter]

Codecov Report

Base: 93.89% // Head: 93.88% // Decreases project coverage by -0.00% ⚠️

Coverage data is based on head (34bdec4) compared to base (1547d5b).
Patch coverage: 100.00% of modified lines in pull request are covered.

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1343      +/-   ##
==========================================
- Coverage   93.89%   93.88%   -0.01%     
==========================================
  Files          78       78              
  Lines        8516     8510       -6     
==========================================
- Hits         7996     7990       -6     
  Misses        520      520              

Impacted Files	Coverage Δ
darts/timeseries.py	92.34% <100.00%> (-0.02%)	⬇️
...arts/models/forecasting/torch_forecasting_model.py	87.08% <0.00%> (-0.06%)	⬇️
darts/models/forecasting/block_rnn_model.py	98.24% <0.00%> (-0.04%)	⬇️
darts/models/forecasting/nhits.py	99.27% <0.00%> (-0.01%)	⬇️

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[hrzn]

Looks good! I just have suggestions for slightly better warning messages, and also to remove the print() statements.

[hrzn]

Thanks 👍