Logging
Say we query a NetCDF file for a few timestamps
pvgis series select \
data/sample_era5landplus_t2m_over_esti.nc \
8.628 45.812 \
--neighbor-lookup nearest \
'2014-12-31 12:00:00, 2015-12-31 12:00:00,2016-12-31 12:00:00,2017-12-31 12:00:00,2018-12-31 12:00:00'
We can use some logging flags to get details around several steps of the
requested operation. In example, let's add -l to the same command
pvgis series select \
data/sample_era5landplus_t2m_over_esti.nc \
8.628 45.812 \
--neighbor-lookup nearest \
'2014-12-31 12:00:00, 2015-12-31 12:00:00,2016-12-31 12:00:00,2017-12-31 12:00:00,2018-12-31 12:00:00' \
-l
We can also try --log
pvgis --log series \
select \
data/sample_era5landplus_t2m_over_esti.nc \
8.628 45.812 \
--neighbor-lookup nearest \
'2014-12-31 12:00:00, 2015-12-31 12:00:00,2016-12-31 12:00:00,2017-12-31 12:00:00,2018-12-31 12:00:00'
or even --log-rich
pvgis --log-rich series \
select \
data/sample_era5landplus_t2m_over_esti.nc \
8.628 45.812 \
--neighbor-lookup nearest \
'2014-12-31 12:00:00, 2015-12-31 12:00:00,2016-12-31 12:00:00,2017-12-31 12:00:00,2018-12-31 12:00:00'
[08/07/26 16:04:18]
DEBUG Object DatetimeIndex(['2014-12-31 12:00:00', '2015-12-31 12:00:00', caching.py:62
'2016-12-31 12:00:00', '2017-12-31 12:00:00',
'2018-12-31 12:00:00'],
dtype='datetime64[us]', freq=None) is unhashable.
DEBUG Object ['t2m'] is unhashable. caching.py:62
DEBUG Data file in /builds/pvgis/pvgis/data : sample_era5landplus_t2m_over_esti.nc select.py:105
DEBUG Scale factor : None, Offset : None select.py:107
DEBUG Requested location coordinates : 8.628, 45.812 select.py:117
DEBUG i Executing data selection function : select_location_time_series() open.py:504
DEBUG ✓ Location specific dimensions detected in 't2m' : longitude, latitude open.py:410
DEBUG ✓ Coordinates : 8.628, 45.812. open.py:430
DEBUG < Returning selected location from time series : <xarray.DataArray 't2m' (time: 96432)> Size: open.py:575
386kB
array([1.5442892 , 1.5442892 , 1.7939669 , ..., 0.80042255, 0.68867517,
0.7804081 ], shape=(96432,), dtype=float32)
Coordinates:
* time (time) datetime64 771kB 2014-01-01 ... 2024-12-31T23:00:00
latitude float64 8B 45.8
longitude float64 8B 8.6
Attributes:
_FillValue: nan
units: C
DEBUG Selected location from time series : <xarray.DataArray 't2m' (time: 96432)> Size: 386kB select.py:135
array([1.5442892 , 1.5442892 , 1.7939669 , ..., 0.80042255, 0.68867517,
0.7804081 ], shape=(96432,), dtype=float32)
Coordinates:
* time (time) datetime64 771kB 2014-01-01 ... 2024-12-31T23:00:00
latitude float64 8B 45.8
longitude float64 8B 8.6
Attributes:
_FillValue: nan
units: C
DEBUG Selected timestamps from location time series : <xarray.DataArray 't2m' (time: 5)> Size: 20B select.py:217
array([-0.1983587, 3.7281141, 7.7046404, 5.1725235, 12.022321 ],
dtype=float32)
Coordinates:
* time (time) datetime64 40B 2014-12-31T12:00:00 ... 2018-12-31T1...
latitude float64 8B 45.8
longitude float64 8B 8.6
Attributes:
_FillValue: nan
units: C
DEBUG Object DatetimeIndex(['2014-12-31 12:00:00', '2015-12-31 12:00:00', caching.py:62
'2016-12-31 12:00:00', '2017-12-31 12:00:00',
'2018-12-31 12:00:00'],
dtype='datetime64[us]', freq=None) is unhashable.
-0.1983587,3.7281141,7.7046404,5.1725235,12.022321
Finally, we can export to a log file via --log-file series_select.log and increase the level using -llll
pvgis --log-file ./docs/data/series_select.log \
-llll \
series select \
data/sample_era5landplus_t2m_over_esti.nc \
8.628 45.812 \
--neighbor-lookup nearest \
'2014-12-31 12:00:00, 2015-12-31 12:00:00,2016-12-31 12:00:00,2017-12-31 12:00:00,2018-12-31 12:00:00'