fixed tests

sup3r/models/linear.py

@@ -47,7 +47,8 @@ def load(cls, model_dir, verbose=False):
  ----------
  model_dir : str
  Directory to load LinearInterp model files from. Must
- have a model_params.json file containing all of the init args.
+ have a model_params.json file containing "meta" key with all of the
+ class init args.
  verbose : bool
  Flag to log information about the loaded model.
 
@@ -61,7 +62,7 @@ def load(cls, model_dir, verbose=False):
  with open(fp_params, 'r') as f:
  params = json.load(f)
 
- meta = params.get('meta', {'class': 'LinearInterp'})
+ meta = params['meta']
  args = signature(cls.__init__).parameters
  kwargs = {k: v for k, v in meta.items() if k in args}
  model = cls(**kwargs)

sup3r/models/surface.py

@@ -125,7 +125,8 @@ def load(cls, model_dir, verbose=False):
  ----------
  model_dir : str
  Directory to load SurfaceSpatialMetModel model files from. Must
- have a model_params.json file containing all of the init args.
+ have a model_params.json file containing "meta" key with all of the
+ class init args.
  verbose : bool
  Flag to log information about the loaded model.
 
@@ -140,7 +141,7 @@ def load(cls, model_dir, verbose=False):
  with open(fp_params, 'r') as f:
  params = json.load(f)
 
- meta = params.get('meta', {'class': 'SurfaceSpatialMetModel'})
+ meta = params['meta']
  args = signature(cls.__init__).parameters
  kwargs = {k: v for k, v in meta.items() if k in args}
  model = cls(**kwargs)
@@ -444,17 +445,18 @@ def downscale_pres(self, single_lr_pres, topo_lr, topo_hr):
  warn(msg)
 
  const = 101325 * (1 - (1 - topo_lr / self._pres_div)**self._pres_exp)
- single_lr_pres = single_lr_pres.copy() + const
+ lr_pres_adj = single_lr_pres.copy() + const
 
- if np.min(single_lr_pres) < 0.0:
+ if np.min(lr_pres_adj) < 0.0:
  msg = ('Spatial interpolation of surface pressure '
  'resulted in negative values. Incorrectly '
  'scaled/unscaled values or incorrect units are '
- 'the most likely causes.')
+ 'the most likely causes. All pressure data should be '
+ 'in Pascals.')
  logger.error(msg)
  raise ValueError(msg)
 
- hi_res_pres = self.downscale_arr(single_lr_pres, self._s_enhance,
+ hi_res_pres = self.downscale_arr(lr_pres_adj, self._s_enhance,
  method=self._interp_method)
 
  const = 101325 * (1 - (1 - topo_hr / self._pres_div)**self._pres_exp)

tests/test_surface_model.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 """Test the temperature and relative humidity scaling relationships of the
 SurfaceSpatialMetModel"""
+import json
 import os
 import tempfile
 import pytest
@@ -53,7 +54,14 @@ def test_surface_model(s_enhance=5):
 
  low_res, true_hi_res, topo_lr, topo_hr = get_inputs(s_enhance)
 
- model = SurfaceSpatialMetModel.load(features=FEATURES, s_enhance=s_enhance)
+ kwargs = {'meta': {'features': FEATURES, 's_enhance': s_enhance}}
+ with tempfile.TemporaryDirectory() as td:
+ fp_params = os.path.join(td, 'model_params.json')
+ with open(fp_params, 'w') as f:
+ json.dump(kwargs, f)
+
+ model = SurfaceSpatialMetModel.load(model_dir=td)
+
  hi_res = model.generate(low_res, exogenous_data=[topo_lr, topo_hr])
 
  diff = true_hi_res - hi_res
@@ -116,14 +124,21 @@ def test_multi_step_surface(s_enhance=2, t_enhance=2):
  t_enhance=t_enhance)
 
  with tempfile.TemporaryDirectory() as td:
- fp = os.path.join(td, 'model')
- model.save(fp)
+ temporal_dir = os.path.join(td, 'model')
+ model.save(temporal_dir)
+
+ surface_model_kwargs = {'meta': {'features': FEATURES,
+ 's_enhance': s_enhance}}
+
+ surface_dir = os.path.join(td, 'surface/')
+ os.makedirs(surface_dir)
+ fp_params = os.path.join(surface_dir, 'model_params.json')
+ with open(fp_params, 'w') as f:
+ json.dump(surface_model_kwargs, f)
 
- surface_model_kwargs = {'features': FEATURES, 's_enhance': s_enhance}
- temporal_model_kwargs = {'model_dirs': fp}
  ms_model = MultiStepSurfaceMetGan.load(
- surface_model_kwargs=surface_model_kwargs,
- temporal_model_kwargs=temporal_model_kwargs)
+ surface_model_kwargs={'model_dir': surface_dir},
+ temporal_model_kwargs={'model_dirs': temporal_dir})
 
  for model in ms_model.models:
  assert isinstance(model.s_enhance, int)