linear and spatial model features->lr_features

sup3r/models/linear.py

@@ -17,11 +17,11 @@ class LinearInterp(AbstractInterface):
     """Simple model to do linear interpolation on the spatial and temporal axes
     """
 
-    def __init__(self, features, s_enhance, t_enhance, t_centered=False):
+    def __init__(self, lr_features, s_enhance, t_enhance, t_centered=False):
         """
         Parameters
         ----------
-        features : list
+        lr_features : list
             List of feature names that this model will operate on for both
             input and output. This must match the feature axis ordering in the
             array input to generate().
@@ -35,7 +35,7 @@ def __init__(self, features, s_enhance, t_enhance, t_centered=False):
             time-centered (e.g. interp 01:00 02:00 to 00:45 01:15 01:45 02:15)
         """
 
-        self._features = features
+        self._lr_features = lr_features
         self._s_enhance = s_enhance
         self._t_enhance = t_enhance
         self._t_centered = t_centered
@@ -78,11 +78,10 @@ class init args.
     @property
     def meta(self):
         """Get meta data dictionary that defines the model params"""
-        return {'features': self._features,
+        return {'lr_features': self._lr_features,
                 's_enhance': self._s_enhance,
                 't_enhance': self._t_enhance,
                 't_centered': self._t_centered,
-                'lr_features': self.lr_features,
                 'hr_out_features': self.hr_out_features,
                 'class': self.__class__.__name__,
                 }
@@ -92,13 +91,13 @@ def lr_features(self):
         """Get the list of input feature names that the generative model was
         trained on.
         """
-        return self._features
+        return self._lr_features
 
     @property
     def hr_out_features(self):
         """Get the list of output feature names that the generative model
         outputs"""
-        return self._features
+        return self._lr_features
 
     @property
     def hr_exo_features(self):

sup3r/models/surface.py

@@ -42,14 +42,14 @@ class SurfaceSpatialMetModel(LinearInterp):
     """Weight for the delta-topography feature for the relative humidity linear
     regression model."""
 
-    def __init__(self, features, s_enhance, noise_adders=None,
+    def __init__(self, lr_features, s_enhance, noise_adders=None,
                  temp_lapse=None, w_delta_temp=None, w_delta_topo=None,
                  pres_div=None, pres_exp=None, interp_method='LANCZOS',
                  fix_bias=True):
         """
         Parameters
         ----------
-        features : list
+        lr_features : list
             List of feature names that this model will operate on for both
             input and output. This must match the feature axis ordering in the
             array input to generate(). Typically this is a list containing:
@@ -63,11 +63,11 @@ def __init__(self, features, s_enhance, noise_adders=None,
             Option to add gaussian noise to spatial model output. Noise will be
             normally distributed with mean of 0 and standard deviation =
             noise_adders. noise_adders can be a single value or a list
-            corresponding to the features list. None is no noise. The addition
-            of noise has been shown to help downstream temporal-only models
-            produce diurnal cycles in regions where there is minimal change in
-            topography. A noise_adders around 0.07C (temperature) and 0.1%
-            (relative humidity) have been shown to be effective. This is
+            corresponding to the lr_features list. None is no noise. The
+            addition of noise has been shown to help downstream temporal-only
+            models produce diurnal cycles in regions where there is minimal
+            change in topography. A noise_adders around 0.07C (temperature) and
+            0.1% (relative humidity) have been shown to be effective. This is
             unnecessary if daily min/max temperatures are provided as low res
             training features.
         temp_lapse : None | float
@@ -98,7 +98,7 @@ def __init__(self, features, s_enhance, noise_adders=None,
             low-resolution deviation from the input data
         """
 
-        self._features = features
+        self._lr_features = lr_features
         self._s_enhance = s_enhance
         self._noise_adders = noise_adders
         self._temp_lapse = temp_lapse or self.TEMP_LAPSE
@@ -111,7 +111,7 @@ def __init__(self, features, s_enhance, noise_adders=None,
         self._interp_method = getattr(Image.Resampling, interp_method)
 
         if isinstance(self._noise_adders, (int, float)):
-            self._noise_adders = [self._noise_adders] * len(self._features)
+            self._noise_adders = [self._noise_adders] * len(self._lr_features)
 
     def __len__(self):
         """Get number of model steps (match interface of MultiStepGan)"""
@@ -162,21 +162,21 @@ def input_dims(self):
     @property
     def feature_inds_temp(self):
         """Get the feature index values for the temperature features."""
-        inds = [i for i, name in enumerate(self._features)
+        inds = [i for i, name in enumerate(self._lr_features)
                 if fnmatch(name, 'temperature_*')]
         return inds
 
     @property
     def feature_inds_pres(self):
         """Get the feature index values for the pressure features."""
-        inds = [i for i, name in enumerate(self._features)
+        inds = [i for i, name in enumerate(self._lr_features)
                 if fnmatch(name, 'pressure_*')]
         return inds
 
     @property
     def feature_inds_rh(self):
         """Get the feature index values for the relative humidity features."""
-        inds = [i for i, name in enumerate(self._features)
+        inds = [i for i, name in enumerate(self._lr_features)
                 if fnmatch(name, 'relativehumidity_*')]
         return inds
 
@@ -195,22 +195,23 @@ def _get_temp_rh_ind(self, idf_rh):
             Index in the feature list for a temperature_*m feature with the
             same hub height as the idf_rh input.
         """
-        name_rh = self._features[idf_rh]
+        name_rh = self._lr_features[idf_rh]
         hh_suffix = name_rh.split('_')[-1]
         idf_temp = None
         for i in self.feature_inds_temp:
-            same_hh = self._features[i].endswith(hh_suffix)
+            same_hh = self._lr_features[i].endswith(hh_suffix)
             not_minmax = not any(mm in name_rh for mm in ('_min_', '_max_'))
-            both_mins = '_min_' in name_rh and '_min_' in self._features[i]
-            both_maxs = '_max_' in name_rh and '_max_' in self._features[i]
+            both_mins = '_min_' in name_rh and '_min_' in self._lr_features[i]
+            both_maxs = '_max_' in name_rh and '_max_' in self._lr_features[i]
 
             if same_hh and (not_minmax or both_mins or both_maxs):
                 idf_temp = i
                 break
 
         if idf_temp is None:
             msg = ('Could not find temperature feature corresponding to '
-                   '"{}" in feature list: {}'.format(name_rh, self._features))
+                   '"{}" in feature list: {}'
+                   .format(name_rh, self._lr_features))
             logger.error(msg)
             raise KeyError(msg)
 
@@ -567,7 +568,6 @@ def meta(self):
                 'weight_for_delta_topo': self._w_delta_topo,
                 'pressure_divisor': self._pres_div,
                 'pressure_exponent': self._pres_exp,
-                'features': self._features,
                 'lr_features': self.lr_features,
                 'hr_out_features': self.hr_out_features,
                 'interp_method': str(self._interp_method),

tests/forward_pass/test_forward_pass_exo.py

@@ -776,7 +776,7 @@ def test_fwp_wind_hi_res_topo_plus_linear():
     _ = s_model.generate(np.ones((4, 10, 10, 3)),
                          exogenous_data=exo_tmp)
 
-    t_model = LinearInterp(features=['U_100m', 'V_100m'],
+    t_model = LinearInterp(lr_features=['U_100m', 'V_100m'],
                            s_enhance=1,
                            t_enhance=4)
     t_model.meta['input_resolution'] = {'spatial': '4km',

tests/forward_pass/test_multi_step.py

@@ -195,7 +195,7 @@ def test_spatial_gan_then_linear_interp():
     model1 = Sup3rGan(fp_gen, fp_disc)
     _ = model1.generate(np.ones((4, 10, 10, len(FEATURES))))
 
-    model2 = LinearInterp(features=FEATURES, s_enhance=3, t_enhance=4)
+    model2 = LinearInterp(lr_features=FEATURES, s_enhance=3, t_enhance=4)
 
     model1.set_norm_stats({'U_100m': 0.1, 'V_100m': 0.2},
                           {'U_100m': 0.04, 'V_100m': 0.02})

tests/forward_pass/test_surface_model.py

@@ -54,8 +54,8 @@ def test_surface_model(s_enhance=5):
 
     low_res, true_hi_res, topo_lr, topo_hr = get_inputs(s_enhance)
 
-    kwargs = {'meta': {'features': FEATURES, 'lr_features': FEATURES,
-                       'hr_out_features': FEATURES, 's_enhance': s_enhance}}
+    kwargs = {'meta': {'lr_features': FEATURES, 'hr_out_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:
@@ -136,8 +136,7 @@ def test_multi_step_surface(s_enhance=2, t_enhance=2):
         temporal_dir = os.path.join(td, 'model')
         model.save(temporal_dir)
 
-        surface_model_kwargs = {'meta': {'features': FEATURES,
-                                         'lr_features': FEATURES,
+        surface_model_kwargs = {'meta': {'lr_features': FEATURES,
                                          'hr_out_features': FEATURES,
                                          's_enhance': s_enhance}}