pvlib · jason-rpkt · Apr 23, 2026 · Apr 24, 2026 · Apr 27, 2026 · May 5, 2026
diff --git a/docs/examples/shading/plot_partial_module_shading_simple.py b/docs/examples/shading/plot_partial_module_shading_simple.py
@@ -38,7 +38,6 @@
 from pvlib import pvsystem, singlediode
 import pandas as pd
 import numpy as np
-from scipy.interpolate import interp1d
 import matplotlib.pyplot as plt
 
 from scipy.constants import e as qe, k as kB
@@ -178,10 +177,8 @@ def plot_curves(dfs, labels, title):
 
 
 def interpolate(df, i):
-    """convenience wrapper around scipy.interpolate.interp1d"""
-    f_interp = interp1d(np.flipud(df['i']), np.flipud(df['v']), kind='linear',
-                        fill_value='extrapolate')
-    return f_interp(i)
+    """convenience wrapper around numpy.interp"""
+    return np.interp(i, np.flipud(df['i']), np.flipud(df['v']))
 
 
 def combine_series(dfs):

diff --git a/pvlib/iam.py b/pvlib/iam.py
@@ -13,6 +13,7 @@
 import functools
 from scipy.optimize import minimize
 from pvlib.tools import cosd, sind, acosd
+from scipy.interpolate import make_interp_spline
 
 # a dict of required parameter names for each IAM model
 # keys are the function names for the IAM models
@@ -440,7 +441,7 @@ def interp(aoi, theta_ref, iam_ref, method='linear', normalize=True):
     method : str, default 'linear'
         Specifies the interpolation method.
         Useful options are: 'linear', 'quadratic', 'cubic'.
-        See scipy.interpolate.interp1d for more options.
+        See scipy.interpolate for more options.
 
     normalize : boolean, default True
         When true, the interpolated values are divided by the interpolated
@@ -469,9 +470,6 @@ def interp(aoi, theta_ref, iam_ref, method='linear', normalize=True):
     pvlib.iam.sapm
     '''
     # Contributed by Anton Driesse (@adriesse), PV Performance Labs. July, 2019
-
-    from scipy.interpolate import interp1d
-
     # Scipy doesn't give the clearest feedback, so check number of points here.
     MIN_REF_VALS = {'linear': 2, 'quadratic': 3, 'cubic': 4, 1: 2, 2: 3, 3: 4}
 
@@ -483,10 +481,21 @@ def interp(aoi, theta_ref, iam_ref, method='linear', normalize=True):
         raise ValueError("Negative value(s) found in 'iam_ref'. "
                          "This is not physically possible.")
 
-    interpolator = interp1d(theta_ref, iam_ref, kind=method,
-                            fill_value='extrapolate')
-    aoi_input = aoi
+    if method == "linear":
+        interpolator = make_interp_spline(theta_ref, iam_ref, k=1)
+
+    elif method == "quadratic":
+        interpolator = make_interp_spline(theta_ref, iam_ref, k=2)
 
+    elif method == "cubic":
+        interpolator = make_interp_spline(theta_ref, iam_ref, k=3)
+
+    else:
+        raise ValueError(
+            f"Interpolation method '{method}' is not supported"
+            " in pvlib-python.")
+
+    aoi_input = aoi
     aoi = np.asanyarray(aoi)
     aoi = np.abs(aoi)
     iam = interpolator(aoi)

diff --git a/pvlib/spectrum/response.py b/pvlib/spectrum/response.py
@@ -6,7 +6,7 @@
 import numpy as np
 import pandas as pd
 import scipy.constants
-from scipy.interpolate import interp1d
+from scipy.interpolate import make_interp_spline
 
 
 _PLANCK_BY_LIGHT_SPEED_OVER_ELEMENTAL_CHARGE_BY_BILLION = (
@@ -66,16 +66,14 @@ def get_example_spectral_response(wavelength=None):
     if wavelength is None:
         resolution = 5.0
         wavelength = np.arange(280, 1200 + resolution, resolution)
+    x = SR_DATA[0]
+    y = SR_DATA[1]
+    spline = make_interp_spline(x, y, k=3)
 
-    interpolator = interp1d(SR_DATA[0], SR_DATA[1],
-                            kind='cubic',
-                            bounds_error=False,
-                            fill_value=0.0,
-                            copy=False,
-                            assume_sorted=True)
-
-    sr = pd.Series(data=interpolator(wavelength), index=wavelength)
+    values = spline(wavelength)
+    values[(wavelength < x[0]) | (wavelength > x[-1])] = 0.0
 
+    sr = pd.Series(data=values, index=wavelength)
     sr.index.name = 'wavelength'
     sr.name = 'spectral_response'
 

diff --git a/tests/test_iam.py b/tests/test_iam.py
@@ -171,8 +171,8 @@ def test_martin_ruiz_diffuse():
 
 def test_iam_interp():
 
-    aoi_meas = [0.0, 45.0, 65.0, 75.0]
-    iam_meas = [1.0,  0.9,  0.8,  0.6]
+    aoi_meas = np.array([0.0, 45.0, 65.0, 75.0])
+    iam_meas = np.array([1.0,  0.9,  0.8,  0.6])
 
     # simple default linear method
     aoi = 55.0
@@ -200,18 +200,52 @@ def test_iam_interp():
     assert_series_equal(iam, expected)
 
     # check beyond reference values
-    aoi = [-45, 0, 45, 85, 90, 95, 100, 105, 110]
-    expected = [0.9, 1.0, 0.9, 0.4, 0.3, 0.2, 0.1, 0.0, 0.0]
+    aoi = np.array([-45, 0, 45, 85, 90, 95, 100, 105, 110])
+    expected = np.array([0.9, 1.0, 0.9, 0.4, 0.3, 0.2, 0.1, 0.0, 0.0])
     iam = _iam.interp(aoi, aoi_meas, iam_meas)
     assert_allclose(iam, expected)
 
     # check exception clause
     with pytest.raises(ValueError):
-        _iam.interp(0.0, [0], [1])
+        _iam.interp(0.0, np.array([0]), np.array([1]))
 
     # check exception clause
     with pytest.raises(ValueError):
-        _iam.interp(0.0, [0, 90], [1, -1])
+        _iam.interp(0.0, np.array([0, 90]), np.array([1, -1]))
+
+    # check linear after updating interp1d
+    theta_ref = np.array([0, 60, 90])
+    iam_ref = np.array([1.0, 0.8, 0.0])
+
+    aoi = np.array([0, 30, 60])
+    iam = _iam.interp(
+        aoi, theta_ref, iam_ref,
+        method="linear", normalize=False)
+    expected = np.array([1.0, 0.9, 0.8])
+    np.testing.assert_allclose(iam, expected)
+
+    # check quadratic
+    theta_ref = np.array([0, 30, 60, 90])
+    iam_ref = 1.0 - 1e-4 * theta_ref**2
+    aoi = np.array([15, 45, 75])
+    iam = _iam.interp(
+        aoi,
+        theta_ref,
+        iam_ref,
+        method="quadratic",
+        normalize=False
+    )
+
+    expected = 1.0 - 1e-4 * aoi**2
+    np.testing.assert_allclose(iam, expected, rtol=1e-12)
+
+    # check exception clause - list input for theta_ref
+    with pytest.raises(TypeError):
+        _iam.interp(0.0, [0, 60, 90], np.array([1.0, 0.8, 0.0]))
+
+    # check exception clause - list input for iam_ref
+    with pytest.raises(TypeError):
+        _iam.interp(0.0, np.array([0, 60, 90]), [1.0, 0.8, 0.0])
 
 
 @pytest.mark.parametrize('aoi,expected', [