`forecast.get_data` doesn't allow extra kwargs

[odow]

Describe the bug

forecast.get_processed_data accepts **kwargs (good) and passes these to get_data (good) and process_data (good):

pvlib-python/pvlib/forecast.py

Line 306 in 91c6a55

return self.process_data(self.get_data(*args, **kwargs), **kwargs)

but get_data doesn't allow superfluous keyword arguments (bad):

pvlib-python/pvlib/forecast.py

Lines 202 to 204 in 91c6a55

	def get_data(self, latitude, longitude, start, end,
	vert_level=None, query_variables=None,
	close_netcdf_data=True):

Is the fix just to add **kwargs as the last argument to get_data? If so, you can assign this issue to me and I'll make a PR.

To Reproduce

import datetime
import pandas
from pvlib import forecast

forecast.NDFD().get_processed_data(
    38.238875, -117.363598, 
    pandas.Timestamp(datetime.datetime.now(), tz='US/Central'),
    pandas.Timestamp(datetime.datetime.now(), tz='US/Central') + pandas.Timedelta(hours = 12),
    how = 'liujordan'
)

Yields

Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
  File "/anaconda3/envs/tonopah/lib/python3.7/site-packages/pvlib/forecast.py", line 306, in get_processed_data
    return self.process_data(self.get_data(*args, **kwargs), **kwargs)
TypeError: get_data() got an unexpected keyword argument 'how'

Expected behavior

I expect that get_processed_data is equivalent to:

model = forecast.NDFD()
data = model.get_data(
    38.238875, -117.363598, 
    pandas.Timestamp(datetime.datetime.now(), tz='US/Central'),
    pandas.Timestamp(datetime.datetime.now(), tz='US/Central') + pandas.Timedelta(hours = 12)
)
model.process_data(data, how = 'liujordan', extra_kwarg=False)

Note how I can pass extra_kwarg without any problem.

Versions:

• pvlib.__version__: 0.6.3
• pandas.__version__: 0.24.2
• python: 3.7.3

[wholmgren]

Good catch. I'm surprised that we've not seen this until now.

I agree that adding **kwargs to get_data is the cleanest way forward. I try to avoid using kwargs in the pvlib API, but we're already using it extensively within forecast.py and I don't see another good option.