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Refactor Interpolation to Use make_interp_spline with Extrapolation Handling #2401

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Refactor Interpolation to Use make_interp_spline with Extrapolation Handling #2401

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13 changes: 8 additions & 5 deletions docs/examples/shading/plot_partial_module_shading_simple.py
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Expand Up @@ -38,7 +38,7 @@
from pvlib import pvsystem, singlediode
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
from scipy.interpolate import make_interp_spline
import matplotlib.pyplot as plt

from scipy.constants import e as qe, k as kB
Expand Down Expand Up @@ -178,10 +178,13 @@


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)
x = np.flipud(df['i'])
y = np.flipud(df['v'])

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# Create a spline interpolation with linear (k=1) and extrapolation (default behavior)

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spline = make_interp_spline(x, y, k=1, bc_type='clamped') # Extrapolation is handled by default

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return spline(i)


def combine_series(dfs):
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9 changes: 6 additions & 3 deletions pvlib/iam.py
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Expand Up @@ -470,7 +470,8 @@
'''
# Contributed by Anton Driesse (@adriesse), PV Performance Labs. July, 2019

from scipy.interpolate import interp1d
from scipy.interpolate import make_interp_spline
import numpy as np

# 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}
Expand All @@ -483,8 +484,10 @@
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')
theta_ref = np.asarray(theta_ref)
iam_ref = np.asarray(iam_ref)

interpolator = make_interp_spline(theta_ref, iam_ref, k=method, bc_type='clamped')

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aoi_input = aoi

aoi = np.asanyarray(aoi)
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13 changes: 6 additions & 7 deletions pvlib/spectrum/response.py
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Expand Up @@ -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 = (
Expand Down Expand Up @@ -67,14 +67,13 @@
resolution = 5.0
wavelength = np.arange(280, 1200 + resolution, resolution)

interpolator = interp1d(SR_DATA[0], SR_DATA[1],
kind='cubic',
bounds_error=False,
fill_value=0.0,
copy=False,
assume_sorted=True)
x_data = np.sort(SR_DATA[0])
y_data = SR_DATA[1]

interpolator = make_interp_spline(x_data, y_data, k=3, bc_type='clamped')


sr = pd.Series(data=interpolator(wavelength), index=wavelength)

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sr.index.name = 'wavelength'
sr.name = 'spectral_response'
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