cwhanse review

Co-Authored-By: Cliff Hansen <[email protected]>

Author: echedey-ls

pvlib/modelchain.py

@@ -330,10 +330,8 @@ class ModelChain:
         'interp' and 'no_loss'. The ModelChain instance will be passed as the
         first argument to a user-defined function.
 
-    spectral_model : str or function, optional
-        If not specified, the model will be inferred from the parameters that
-        are common to all of system.arrays[i].module_parameters. Valid strings
-        are:
+    spectral_model : str or function, default ``'no_loss'``
+        Valid strings are:
 
         - ``'sapm'``
         - ``'first_solar'``
@@ -342,6 +340,9 @@ class ModelChain:
         The ModelChain instance will be passed as the first argument to
         a user-defined function.
 
+        See :py:func:`~pvlib.modelchain.ModelChain.infer_spectral_model` to
+        infer the spectral model from system and weather information.
+
     temperature_model : str or function, optional
         Valid strings are: 'sapm', 'pvsyst', 'faiman', 'fuentes', 'noct_sam'.
         The ModelChain instance will be passed as the first argument to a
@@ -870,14 +871,30 @@ def spectral_model(self, model):
                 self._spectral_model = self.no_spectral_loss
             else:
                 raise ValueError(model + ' is not a valid spectral loss model')
-        elif model is None:
-            # None is a valid value, model inference will be done later
-            self._spectral_model = None
         else:
             self._spectral_model = partial(model, self)
 
-    def infer_spectral_model(self):
-        """Infer spectral model from system attributes."""
+    def infer_spectral_model(self, weather=None):
+        """
+        Infer spectral model from system attributes, and optionally from
+        the input weather dataframe, and set it to the ``ModelChain`` instance.
+
+        Parameters
+        ----------
+        weather : pd.DataFrame or collection of str, optional
+            An object with columns of available input data to help infer
+            the spectral model. If ``None``, the spectral model will be
+            inferred from the system attributes only.
+
+        Returns
+        -------
+        Inferred spectral correction model : function
+
+        Examples
+        --------
+        >>> mc = ModelChain(system, location)
+        >>> mc.spectral_model = mc.infer_spectral_model(weather=weather)
+        """
         module_parameters = tuple(
             array.module_parameters for array in self.system.arrays)
         params = _common_keys(module_parameters)
@@ -892,37 +909,13 @@ def infer_spectral_model(self):
             and (self.system._infer_cell_type() is not None)
         ):
             # This suggests models that provide default parameters per cell
-            # type can be used. However, some of them depend on other weather
+            # type can be used. However, some models depend on other weather
             # parameters, so we need to check if they are available.
-            # At this point, weather may not be available, so let's take that
-            # into account.
-            if self.results is not None and self.results.weather is not None:
-                # weather is available
-                if "precipitable_water" in self.results.weather:
+            if weather is not None:  # weather is available
+                if "precipitable_water" in weather:
                     return self.first_solar_spectral_loss
-                else:
-                    return self.no_spectral_loss
-            else:
-                # weather is not available, so it's unknown what model to use.
-                # Warn user about this and default to no_spectral_loss.
-                warnings.warn(
-                    "Weather data is not available to infer spectral model. "
-                    "Defaulting to 'no_loss'. Explicitly set the "
-                    "spectral model with the 'spectral_model' kwarg to "
-                    "suppress this warning."
-                )
-                return self.no_spectral_loss
-        else:
-            raise ValueError(
-                "could not infer spectral model from "
-                "system.arrays[i].module_parameters. Check that "
-                "the module_parameters for all Arrays in "
-                "system.arrays contain valid "
-                "valid spectral coefficients, a valid "
-                "Material or Technology value, or a valid model "
-                "string; explicitly set the model with the "
-                "'spectral_model' kwarg."
-            )
+
+        return self.no_spectral_loss
 
     def first_solar_spectral_loss(self):
         self.results.spectral_modifier = self.system.first_solar_spectral_loss(
@@ -1715,7 +1708,7 @@ def run_model(self, weather):
         # spectral model is optional, and weather may impact model inference
         # check if spectral model inference is requested by means of "None"
         if self._spectral_model is None:
-            self._spectral_model = self.infer_spectral_model()
+            self._spectral_model = self.infer_spectral_model(weather=weather)
         self.spectral_model()
 
         self.effective_irradiance_model()