Add functions to fit and convert IAM models

docs/examples/reflections/plot_convert_iam_models.py

@@ -99,7 +99,8 @@
 physical_iam_default = physical(aoi, **physical_params_default)
 
 
-# ... using a custom weight function.
+# ... using a custom weight function. The weight function must take ``aoi``
+# as it's argument and return a vector of the same length as ``aoi``.
 def weight_function(aoi):
     return cosd(aoi)
 

docs/examples/reflections/plot_fit_iam_models.py

@@ -38,8 +38,8 @@
 # mimic measured data and then we'll fit the physical model to the perturbed
 # data.
 
-# Create and perturb IAM data.
-aoi = np.linspace(0, 90, 100)
+# Create some IAM data.
+aoi = np.linspace(0, 85, 10)
 params = {'a_r': 0.16}
 iam = martin_ruiz(aoi, **params)
 data = iam * np.array([uniform(0.98, 1.02) for _ in range(len(iam))])
@@ -69,7 +69,8 @@
 # function to :py:func:`pvlib.iam.fit`.
 #
 
-# Define a custom weight function.
+# Define a custom weight function.  The weight function must take ``aoi``
+# as it's argument and return a vector of the same length as ``aoi``.
 def weight_function(aoi):
     return cosd(aoi)
 

pvlib/iam.py

@@ -11,7 +11,6 @@
 import numpy as np
 import pandas as pd
 import functools
-import warnings
 from scipy.optimize import minimize
 from pvlib.tools import cosd, sind, acosd
 
@@ -1094,18 +1093,6 @@ def _process_return(target_name, optimize_result):
     return target_params
 
 
-def _min_scipy():
-    '''convert and fit require scipy>=1.5.0
-    '''
-    from scipy import __version__
-    major, minor = __version__.split('.')[:2]
-    if (int(major) >= 1) & (int(minor) >= 5):
-        return True
-    else:
-        warnings.warn('iam.convert and iam.fit require scipy>=1.5.0')
-        return False
-
-
 def convert(source_name, source_params, target_name, weight=_sin_weight,
             fix_n=True, xtol=None):
     """
@@ -1192,43 +1179,38 @@ def convert(source_name, source_params, target_name, weight=_sin_weight,
     pvlib.iam.martin_ruiz
     pvlib.iam.physical
     """
-    if _min_scipy():
-
-        source = _get_model(source_name)
-        target = _get_model(target_name)
-
-        aoi = np.linspace(0, 90, 100)
-        _check_params(source_name, source_params)
-        source_iam = source(aoi, **source_params)
-
-        if target_name == "physical":
-            # we can do some special set-up to improve the fit when the
-            # target model is physical
-            if source_name == "ashrae":
-                residual_function, guess, bounds = \
-                    _ashrae_to_physical(aoi, source_iam, weight, fix_n,
-                                        source_params['b'])
-            elif source_name == "martin_ruiz":
-                residual_function, guess, bounds = \
-                    _martin_ruiz_to_physical(aoi, source_iam, weight,
-                                             source_params['a_r'])
+    source = _get_model(source_name)
+    target = _get_model(target_name)
+
+    aoi = np.linspace(0, 90, 100)
+    _check_params(source_name, source_params)
+    source_iam = source(aoi, **source_params)
+
+    if target_name == "physical":
+        # we can do some special set-up to improve the fit when the
+        # target model is physical
+        if source_name == "ashrae":
+            residual_function, guess, bounds = \
+                _ashrae_to_physical(aoi, source_iam, weight, fix_n,
+                                    source_params['b'])
+        elif source_name == "martin_ruiz":
+            residual_function, guess, bounds = \
+                _martin_ruiz_to_physical(aoi, source_iam, weight,
+                                         source_params['a_r'])
 
-        else:
-            # otherwise, target model is ashrae or martin_ruiz, and scipy
-            # does fine without any special set-up
-            bounds = [(1e-04, 1)]
-            guess = [1e-03]
+    else:
+        # otherwise, target model is ashrae or martin_ruiz, and scipy
+        # does fine without any special set-up
+        bounds = [(1e-04, 1)]
+        guess = [1e-03]
 
-            def residual_function(target_param):
-                return _residual(aoi, source_iam, target, target_param, weight)
+        def residual_function(target_param):
+            return _residual(aoi, source_iam, target, target_param, weight)
 
-        optimize_result = _minimize(residual_function, guess, bounds,
-                                    xtol=xtol)
+    optimize_result = _minimize(residual_function, guess, bounds,
+                                xtol=xtol)
 
-        return _process_return(target_name, optimize_result)
-
-    else:
-        return {}
+    return _process_return(target_name, optimize_result)
 
 
 def fit(measured_aoi, measured_iam, model_name, weight=_sin_weight, xtol=None):
@@ -1295,30 +1277,26 @@ def fit(measured_aoi, measured_iam, model_name, weight=_sin_weight, xtol=None):
     pvlib.iam.martin_ruiz
     pvlib.iam.physical
     """
-    if _min_scipy():
-
-        target = _get_model(model_name)
+    target = _get_model(model_name)
 
-        if model_name == "physical":
-            bounds = [(0, 0.08), (1, 2)]
-            guess = [0.002, 1+1e-08]
+    if model_name == "physical":
+        bounds = [(0, 0.08), (1, 2)]
+        guess = [0.002, 1+1e-08]
 
-            def residual_function(target_params):
-                L, n = target_params
-                return _residual(measured_aoi, measured_iam, target, [n, 4, L],
-                                 weight)
+        def residual_function(target_params):
+            L, n = target_params
+            return _residual(measured_aoi, measured_iam, target, [n, 4, L],
+                             weight)
 
-        # otherwise, target_name is martin_ruiz or ashrae
-        else:
-            bounds = [(1e-08, 1)]
-            guess = [0.05]
+    # otherwise, target_name is martin_ruiz or ashrae
+    else:
+        bounds = [(1e-08, 1)]
+        guess = [0.05]
 
-            def residual_function(target_param):
-                return _residual(measured_aoi, measured_iam, target,
-                                 target_param, weight)
+        def residual_function(target_param):
+            return _residual(measured_aoi, measured_iam, target,
+                             target_param, weight)
 
-        optimize_result = _minimize(residual_function, guess, bounds, xtol)
+    optimize_result = _minimize(residual_function, guess, bounds, xtol)
 
-        return _process_return(model_name, optimize_result)
-    else:
-        return {}
+    return _process_return(model_name, optimize_result)

pvlib/tests/conftest.py

@@ -10,7 +10,7 @@
 
 import pvlib
 from pvlib.location import Location
-from pvlib.iam import _min_scipy
+
 
 pvlib_base_version = Version(Version(pvlib.__version__).base_version)
 
@@ -161,12 +161,6 @@ def has_numba():
 requires_pysam = pytest.mark.skipif(not has_pysam, reason="requires PySAM")
 
 
-iam_scipy_ok = _min_scipy()
-
-requires_scipy_150 = pytest.mark.skipif(not iam_scipy_ok,
-                                        reason="requires scipy>=1.5.0")
-
-
 @pytest.fixture()
 def golden():
     return Location(39.742476, -105.1786, 'America/Denver', 1830.14)

pvlib/tests/test_iam.py

@@ -8,7 +8,7 @@
 import pandas as pd
 
 import pytest
-from .conftest import assert_series_equal, requires_scipy_150
+from .conftest import assert_series_equal
 from numpy.testing import assert_allclose
 
 from pvlib import iam as _iam
@@ -397,7 +397,6 @@ def test_schlick_diffuse():
                         rtol=1e-6)
 
 
-@requires_scipy_150
 @pytest.mark.parametrize('source,source_params,target,expected', [
     ('physical', {'n': 1.5, 'K': 4.5, 'L': 0.004}, 'martin_ruiz',
      {'a_r': 0.173972}),
@@ -416,7 +415,6 @@ def test_convert(source, source_params, target, expected):
     assert_allclose(exp, tar, rtol=1e-05)
 
 
-@requires_scipy_150
 @pytest.mark.parametrize('source,source_params', [
     ('ashrae', {'b': 0.15}),
     ('ashrae', {'b': 0.05}),
@@ -429,7 +427,6 @@ def test_convert_recover(source, source_params):
     assert_allclose(exp, tar, rtol=1e-05)
 
 
-@requires_scipy_150
 def test_convert_ashrae_physical_no_fix_n():
     # convert ashrae to physical, without fixing n
     source_params = {'b': 0.15}
@@ -441,7 +438,6 @@ def test_convert_ashrae_physical_no_fix_n():
     assert_allclose(exp, tar, rtol=1e-05)
 
 
-@requires_scipy_150
 def test_convert_reverse_order_in_physical():
     source_params = {'a_r': 0.25}
     target_params = _iam.convert('martin_ruiz', source_params, 'physical')
@@ -451,7 +447,6 @@ def test_convert_reverse_order_in_physical():
     assert_allclose(exp, tar, rtol=1e-5)
 
 
-@requires_scipy_150
 def test_convert_xtol():
     source_params = {'b': 0.15}
     target_params = _iam.convert('ashrae', source_params, 'physical',
@@ -462,7 +457,6 @@ def test_convert_xtol():
     assert_allclose(exp, tar, rtol=1e-10)
 
 
-@requires_scipy_150
 def test_convert_custom_weight_func():
     aoi = np.linspace(0, 90, 100)
 
@@ -486,19 +480,16 @@ def scaled_weight(aoi):
     assert np.isclose(expected_min_res, actual_min_res, atol=1e-08)
 
 
-@requires_scipy_150
 def test_convert_model_not_implemented():
     with pytest.raises(NotImplementedError, match='model has not been'):
         _iam.convert('ashrae', {'b': 0.1}, 'foo')
 
 
-@requires_scipy_150
 def test_convert_wrong_model_parameters():
     with pytest.raises(ValueError, match='model was expecting'):
         _iam.convert('ashrae', {'B': 0.1}, 'physical')
 
 
-@requires_scipy_150
 def test_convert__minimize_fails():
     # to make scipy.optimize.minimize fail, we'll pass in a nonsense
     # weight function that only outputs nans
@@ -509,7 +500,6 @@ def nan_weight(aoi):
         _iam.convert('ashrae', {'b': 0.1}, 'physical', weight=nan_weight)
 
 
-@requires_scipy_150
 def test_fit():
     aoi = np.linspace(0, 90, 5)
     perturb = np.array([1.2, 1.01, 0.95, 1, 0.98])
@@ -523,7 +513,6 @@ def test_fit():
     assert np.isclose(expected_a_r, actual_a_r, atol=1e-04)
 
 
-@requires_scipy_150
 def test_fit_custom_weight_func():
     # define custom weight function that takes in other arguments
     def scaled_weight(aoi):
@@ -542,13 +531,11 @@ def scaled_weight(aoi):
     assert np.isclose(expected_a_r, actual_a_r, atol=1e-04)
 
 
-@requires_scipy_150
 def test_fit_model_not_implemented():
     with pytest.raises(NotImplementedError, match='model has not been'):
         _iam.fit(np.array([0, 10]), np.array([1, 0.99]), 'foo')
 
 
-@requires_scipy_150
 def test_fit__minimize_fails():
     # to make scipy.optimize.minimize fail, we'll pass in a nonsense
     # weight function that only outputs nans
@@ -560,7 +547,6 @@ def nan_weight(aoi):
                  weight=nan_weight)
 
 
-@requires_scipy_150
 def test__residual_zero_outside_range():
     # check that _residual annihilates any weights that come from aoi
     # outside of interval [0, 90] (this is important for `iam.fit`, when