Remove deprecated functions

docs/sphinx/source/api.rst

@@ -132,7 +132,6 @@ Normalization
    irradiance_rescale
    normalize_with_expected_power
    normalize_with_pvwatts
-   normalize_with_sapm
    pvwatts_dc_power
    sapm_dc_power
    delta_index

docs/sphinx/source/changelog/pending.rst

@@ -22,6 +22,11 @@ Requirements
 ------------
 * Specified versions in ``requirements.txt`` and ``docs/notebook_requirements.txt`` have been updated (:pull:`412`)
 
+Deprecations
+------------
+* Removed  :py:func:`~rdtools.normalization.sapm_dc_power` (:pull:`419`)
+* Removed  :py:func:`~rdtools.normalization.normalize_with_sapm` (:pull:`419`)
+
 Contributors
 ------------
 * Martin Springer (:ghuser:`martin-springer`)

rdtools/__init__.py

@@ -1,4 +1,3 @@
-from rdtools.normalization import normalize_with_sapm
 from rdtools.normalization import normalize_with_pvwatts
 from rdtools.normalization import irradiance_rescale
 from rdtools.normalization import energy_from_power

rdtools/normalization.py

@@ -1,7 +1,6 @@
 '''Functions for normalizing, rescaling, and regularizing PV system data.'''
 
 import pandas as pd
-import pvlib
 import numpy as np
 from scipy.optimize import minimize
 import warnings
@@ -176,138 +175,6 @@ def normalize_with_pvwatts(energy, pvwatts_kws):
     return energy_normalized, insolation
 
 
-@deprecated(since='2.0.0', removal='3.0.0',
-            alternative='normalize_with_expected_power')
-def sapm_dc_power(pvlib_pvsystem, met_data):
-    '''
-    Use Sandia Array Performance Model (SAPM) and PVWatts to compute the
-    effective DC power using measured irradiance, ambient temperature, and wind
-    speed. Effective irradiance and cell temperature are calculated with SAPM,
-    and DC power with PVWatts.
-
-    .. warning::
-        The ``pvlib_pvsystem`` argument must be a ``pvlib.pvsystem.LocalizedPVSystem``
-        object, which is no longer available as of pvlib 0.9.0.  To use this function
-        you'll need to use an older version of pvlib.
-
-    Parameters
-    ----------
-    pvlib_pvsystem : pvlib.pvsystem.LocalizedPVSystem
-        Object contains orientation, geographic coordinates, equipment
-        constants (including DC rated power in watts).  The object must also
-        specify either the ``temperature_model_parameters`` attribute or both
-        ``racking_model`` and ``module_type`` attributes to infer the temperature model parameters.
-    met_data : pandas.DataFrame
-        Measured irradiance components, ambient temperature, and wind speed.
-        Expected met_data DataFrame column names:
-        ['DNI', 'GHI', 'DHI', 'Temperature', 'Wind Speed']
-
-    Note
-    ----
-    All series are assumed to be right-labeled, meaning that the recorded
-    value at a given timestamp refers to the previous time interval
-
-    Returns
-    -------
-    power_dc : pandas.Series
-        DC power in watts derived using Sandia Array Performance Model and
-        PVWatts.
-    effective_poa : pandas.Series
-        Effective irradiance calculated with SAPM
-    '''
-
-    solar_position = pvlib_pvsystem.get_solarposition(met_data.index)
-
-    total_irradiance = pvlib_pvsystem\
-        .get_irradiance(solar_position['zenith'],
-                        solar_position['azimuth'],
-                        met_data['DNI'],
-                        met_data['GHI'],
-                        met_data['DHI'])
-
-    aoi = pvlib_pvsystem.get_aoi(solar_position['zenith'],
-                                 solar_position['azimuth'])
-
-    airmass = pvlib_pvsystem\
-        .get_airmass(solar_position=solar_position, model='kastenyoung1989')
-    airmass_absolute = airmass['airmass_absolute']
-
-    effective_irradiance = pvlib.pvsystem\
-        .sapm_effective_irradiance(poa_direct=total_irradiance['poa_direct'],
-                                   poa_diffuse=total_irradiance['poa_diffuse'],
-                                   airmass_absolute=airmass_absolute,
-                                   aoi=aoi,
-                                   module=pvlib_pvsystem.module)
-
-    temp_cell = pvlib_pvsystem\
-        .sapm_celltemp(total_irradiance['poa_global'],
-                       met_data['Temperature'],
-                       met_data['Wind Speed'])
-
-    power_dc = pvlib_pvsystem\
-        .pvwatts_dc(g_poa_effective=effective_irradiance,
-                    temp_cell=temp_cell)
-
-    return power_dc, effective_irradiance
-
-
-@deprecated(since='2.0.0', removal='3.0.0',
-            alternative='normalize_with_expected_power')
-def normalize_with_sapm(energy, sapm_kws):
-    '''
-    Normalize system AC energy output given measured met_data and
-    meteorological data. This method relies on the Sandia Array Performance
-    Model (SAPM) to compute the effective DC energy using measured irradiance,
-    ambient temperature, and wind speed.
-
-    Energy timeseries and met_data timeseries can be different granularities.
-
-    .. warning::
-        The ``pvlib_pvsystem`` argument must be a ``pvlib.pvsystem.LocalizedPVSystem``
-        object, which is no longer available as of pvlib 0.9.0.  To use this function
-        you'll need to use an older version of pvlib.
-
-    Parameters
-    ----------
-    energy : pandas.Series
-        Energy time series to be normalized  in watt hours.
-        Must be a right-labeled regular time series.
-    sapm_kws : dict
-        Dictionary of parameters required for sapm_dc_power function. See
-        Other Parameters.
-
-    Other Parameters
-    ---------------
-    pvlib_pvsystem : pvlib.pvsystem.LocalizedPVSystem object
-        Object contains orientation, geographic coordinates, equipment
-        constants (including DC rated power in watts).  The object must also
-        specify either the ``temperature_model_parameters`` attribute or both
-        ``racking_model`` and ``module_type`` to infer the model parameters.
-    met_data : pandas.DataFrame
-        Measured met_data, ambient temperature, and wind speed.  Expected
-        column names are ['DNI', 'GHI', 'DHI', 'Temperature', 'Wind Speed']
-
-    Note
-    ----
-    All series are assumed to be right-labeled, meaning that the recorded
-    value at a given timestamp refers to the previous time interval
-
-    Returns
-    -------
-    energy_normalized : pandas.Series
-        Energy divided by Sandia Model DC energy.
-    insolation : pandas.Series
-        Insolation associated with each normalized point
-    '''
-
-    power_dc, irrad = sapm_dc_power(**sapm_kws)
-
-    energy_normalized, insolation = normalize_with_expected_power(energy, power_dc, irrad,
-                                                                  pv_input='energy')
-
-    return energy_normalized, insolation
-
-
 def _delta_index(series):
     '''
     Takes a pandas series with a DatetimeIndex as input and

rdtools/test/bootstrap_test.py

@@ -20,6 +20,7 @@ def test_bootstrap_module(
                 cods_normalized_daily,
                 cods_normalized_daily_wo_noise,
                 decomposition_type=decomposition_type)
+
     else:
         # Rest make time serie bootstrap samples and construct of confidence intervals.
         # Test make bootstrap samples

rdtools/test/irradiance_rescale_test.py

@@ -18,6 +18,7 @@ def test_rescale(method, simple_irradiance):
     if method == "error":
         with pytest.raises(ValueError):
             irradiance_rescale(simple_irradiance, simple_irradiance * 1.05, method=method)
+
     else:
         modeled = simple_irradiance
         measured = 1.05 * simple_irradiance