remove infer_spectral_model

Author: cwhanse

docs/sphinx/source/whatsnew/v0.11.3.rst

@@ -4,6 +4,15 @@
 v0.11.3 (Anticipated March, 2025)
 ---------------------------------
 
+Breaking Changes
+~~~~~~~~~~~~~~~~
+* The pvlib.location.Location.pytz attribute is now read only. The
+  pytz attribute is now set internally to be consistent with the
+  pvlib.location.Location.tz attribute. (:issue:`2340`, :pull:`2341`)
+* Users must now provide ModelChain.spectral_model, or the 'no_loss' spectral
+  model is assumed. pvlib.modelchain.ModelChain no longer attempts to infer
+  the spectral from PVSystem attributes. (:issue:`2017`, :pull:`2253`)
+
 Bug fixes
 ~~~~~~~~~
 * Fix a bug in :py:func:`pvlib.bifacial.get_irradiance_poa` which may have yielded non-zero
@@ -62,11 +71,6 @@ Maintenance
 * asv 0.4.2 upgraded to asv 0.6.4 to fix CI failure due to pinned older conda.
   (:pull:`2352`)
 
-Breaking Changes
-~~~~~~~~~~~~~~~~
-* The pvlib.location.Location.pytz attribute is now read only. The
-  pytz attribute is now set internally to be consistent with the
-  pvlib.location.Location.tz attribute. (:issue:`2340`, :pull:`2341`)
 
 Contributors
 ~~~~~~~~~~~~

pvlib/modelchain.py

@@ -340,9 +340,6 @@ 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
-        get a list of available spectral models for the current system.
-
     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
@@ -876,72 +873,6 @@ def spectral_model(self, model):
         else:  # assume model is callable with 1st argument = the MC instance
             self._spectral_model = partial(model, self)
 
-    def infer_spectral_model(self, weather=None):
-        """
-        Return a spectral model key or a list of possible spectral models based
-        on the system attributes and weather data. If the weather data is not
-        provided, the spectral model will be inferred from the system
-        attributes only.
-
-        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 : string key or list of string keys
-            If no spectral model is inferred, 'no_loss' is returned.
-            If only one spectral model is inferred, a string key is returned.
-            If multiple spectral models are inferred, a list of string keys is
-            returned.
-
-            The following spectral models keys may be returned:
-
-            - ``'sapm'`` for
-              :py:func:`~pvlib.spectrum.spectral_factor_sapm`
-            - ``'first_solar'`` for
-              :py:func:`~pvlib.spectrum.spectral_factor_first_solar`.
-              Requires ``'precipitable_water'`` in the weather dataframe.
-
-            If no spectral model is inferred, 'no_loss' is returned.
-
-        Examples
-        --------
-        >>> mc = ModelChain(system, location)
-        >>> mc.spectral_model = mc.infer_spectral_model(weather=weather)
-        """
-        possible_models_result = []  # list of possible spectral models
-        module_parameters = tuple(
-            array.module_parameters for array in self.system.arrays
-        )
-        params = _common_keys(module_parameters)
-
-        # infer all the possible spectral models and append them to the list
-        if {"A4", "A3", "A2", "A1", "A0"} <= params:
-            possible_models_result.append("sapm")
-        if weather is not None:  # models that depend on weather data
-            if "precipitable_water" in weather:
-                if "first_solar_spectral_coefficients" in params:
-                    # user explicitly sets first solar spectral coefficients
-                    possible_models_result.append("first_solar")
-                # cell type is known or can be inferred
-                if ("Technology" in params or "Material" in params) and (
-                    self.system._infer_cell_type() is not None
-                ):
-                    possible_models_result.append("first_solar")
-
-        # result resolution based on the number of inferred spectral models
-        if (result_len := len(possible_models_result)) == 0:
-            # if no spectral model is inferred, return no_loss
-            return "no_loss"
-        elif result_len == 1:
-            # when only one spectral model is inferred, return it
-            return possible_models_result[0]
-        else:  # multiple spectral models are inferred (avoiding duplicates)
-            return list(set(possible_models_result))
 
     def first_solar_spectral_loss(self):
         self.results.spectral_modifier = self.system.first_solar_spectral_loss(

tests/test_modelchain.py

@@ -1280,48 +1280,6 @@ def test_singlediode_dc_arrays(location, dc_model,
         assert isinstance(dc, (pd.Series, pd.DataFrame))
 
 
-@pytest.mark.parametrize('dc_model', ['sapm', 'cec', 'cec_native'])
-def test_infer_spectral_model(location, sapm_dc_snl_ac_system,
-                              cec_dc_snl_ac_system,
-                              cec_dc_native_snl_ac_system, dc_model):
-    dc_systems = {'sapm': sapm_dc_snl_ac_system,
-                  'cec': cec_dc_snl_ac_system,
-                  'cec_native': cec_dc_native_snl_ac_system}
-    system = dc_systems[dc_model]
-    mc = ModelChain(system, location, aoi_model='physical')
-    assert isinstance(mc, ModelChain)
-
-
-def test_infer_spectral_model_with_weather(location, sapm_dc_snl_ac_system,
-                                           cec_dc_snl_ac_system, weather):
-    # instantiate example ModelChain to get the default spectral model
-    # default should resolve to no loss
-    mc = ModelChain(sapm_dc_snl_ac_system, location, aoi_model='physical')
-    assert mc.spectral_model == mc.no_spectral_loss
-    # - inference should resolve to sapm
-    mc.spectral_model = mc.infer_spectral_model(weather=weather)
-    assert mc.spectral_model == mc.sapm_spectral_loss
-    # - next inference should resolve to no loss
-    mc = ModelChain(
-        cec_dc_snl_ac_system,
-        location,
-        aoi_model="physical",
-        spectral_model=None,
-    )
-    mc.spectral_model = mc.infer_spectral_model(weather=weather)
-    assert mc.spectral_model == mc.no_spectral_loss
-
-    # infer spectral model from weather
-    # - without precipitable water in it, should resolve to no loss
-    mc.spectral_model = mc.infer_spectral_model(weather=weather)
-    assert mc.spectral_model == mc.no_spectral_loss
-    # - with precipitable water in it, should resolve to first solar
-    weather['precipitable_water'] = 1.42
-    mc.spectral_model = mc.infer_spectral_model(weather=weather)
-    assert mc.spectral_model == mc.first_solar_spectral_loss
-    assert isinstance(mc, ModelChain)
-
-
 @pytest.mark.parametrize('temp_model', [
     'sapm_temp', 'faiman_temp', 'pvsyst_temp', 'fuentes_temp',
     'noct_sam_temp'])