Phase 2 Goal 3 - Adding Validation for using Historical Reconstruction Data

[claalmve]

Description of PR

Summary of changes and related issue As a part of Phase 2 Goal 3, we are working to include Historical Reconstruction data as an available option for cava_data function. In order to do so, we must validate the inputs (year range, ignore SSPs) to do so.

Type of change

• [ ] Bug fix (non-breaking change which fixes an issue)
• [x] New feature (non-breaking change which adds functionality)
• [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
• [ ] This change requires a documentation update

Checklist:

• [x] My code follows the style guidelines of this project
• [x] I have performed a self-review of my code
• [x] I have commented my code, particularly in hard-to-understand areas
• [x] I have made corresponding changes to the documentation
• [x] My changes generate no new warnings
• [x] I have added tests that prove my fix is effective or that my feature works
• [ ] New and existing unit tests pass locally with my changes
• [ ] Any dependent changes have been merged and published in downstream modules

[vicford]

Getting this error on the WL example in the notebook - I don't think this is tied to this PR??

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In [7], line 1
----> 1 data = cava_data(
      2     ## Set-up
      3     example_locs.iloc[:1],
      4     downscaling_method="Dynamical",  # default for now
      5     approach="warming_level",
      6     warming_level="2.0",
      7     
      8     ## Likely seaonal event specific arguments
      9     variable="Air Temperature at 2m", 
     10     metric_calc="max", # daily high temperature
     11     percentile=60, # likeliness percentile
     12     season="winter", # season
     13     units="degC", # change units
     14     
     15     ## Export
     16     export_method="both",
     17     file_format="NetCDF",
     18 )

File ~/src/climakitae/climakitae/explore/vulnerability.py:516, in cava_data(input_locations, variable, approach, downscaling_method, time_start_year, time_end_year, historical_data, ssp_data, warming_level, metric_calc, heat_idx_threshold, one_in_x, percentile, season, units, wrf_bc, export_method, separate_files, file_format)
    499     data = data.sel(
    500         simulation=[
    501             sim
   (...)
    512         ]
    513     )
    515 # Find the closest gridcell from the data to the lat/lon point
--> 516 closest_cell = get_closest_gridcell(data, lat, lon, print_coords=False).squeeze(
    517     dim="scenario"
    518 )
    520 # Storing data differently if all data points should be in one file vs each data point in its own file
    521 if not separate_files:
    522     # Renaming gridcell so that it can be concatenated with other lat/lon gridcells

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/common.py:394, in DataWithCoords.squeeze(self, dim, drop, axis)
    364 def squeeze(
    365     self: T_DataWithCoords,
    366     dim: Hashable | Iterable[Hashable] | None = None,
    367     drop: bool = False,
    368     axis: int | Iterable[int] | None = None,
    369 ) -> T_DataWithCoords:
    370     """Return a new object with squeezed data.
    371 
    372     Parameters
   (...)
    392     numpy.squeeze
    393     """
--> 394     dims = get_squeeze_dims(self, dim, axis)
    395     return self.isel(drop=drop, **{d: 0 for d in dims})

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/common.py:348, in get_squeeze_dims(xarray_obj, dim, axis)
    345     alldims = list(xarray_obj.sizes.keys())
    346     dim = [alldims[a] for a in axis]
--> 348 if any(xarray_obj.sizes[k] > 1 for k in dim):
    349     raise ValueError(
    350         "cannot select a dimension to squeeze out "
    351         "which has length greater than one"
    352     )
    353 return dim

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/common.py:348, in <genexpr>(.0)
    345     alldims = list(xarray_obj.sizes.keys())
    346     dim = [alldims[a] for a in axis]
--> 348 if any(xarray_obj.sizes[k] > 1 for k in dim):
    349     raise ValueError(
    350         "cannot select a dimension to squeeze out "
    351         "which has length greater than one"
    352     )
    353 return dim

File /srv/conda/envs/notebook/lib/python3.9/site-packages/xarray/core/utils.py:458, in Frozen.__getitem__(self, key)
    457 def __getitem__(self, key: K) -> V:
--> 458     return self.mapping[key]

KeyError: 'scenario'