pvlib.spectrum.qe_to_sr#

pvlib.spectrum.qe_to_sr(qe, wavelength=None, normalize=False)[source]#

Convert quantum efficiencies to spectral responsivities. If wavelength is not provided, the quantum efficiency qe must be a pandas.Series or pandas.DataFrame, with the wavelengths in the index.

Provide wavelengths in nanometers, [nm].

Conversion is described in [1].

Added in version 0.11.0.

Parameters:

qe (numeric, pandas.Series or pandas.DataFrame) – Quantum efficiency. If pandas subtype, index must be the wavelength in nanometers, [nm].
wavelength (numeric, optional) – Points where quantum efficiency is measured, in nanometers, [nm].
normalize (bool, default False) – If True, the spectral response is normalized so that the maximum value is 1. For pandas.DataFrame, normalization is done for each column. For 2D arrays, normalization is done for each sub-array.

Returns:

spectral_response (numeric, same type as qe) – Spectral response, [A/W].

Notes

If qe is of type pandas.Series or pandas.DataFrame, column names will remain unchanged in the returned object.
If wavelength is provided it will be used independently of the datatype of qe.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from pvlib import spectrum
>>> wavelengths = np.array([350, 550, 750])
>>> quantum_efficiency = np.array([0.86, 0.90, 0.94])
>>> spectral_response = spectrum.qe_to_sr(quantum_efficiency, wavelengths)
>>> print(spectral_response)
array([0.24277287, 0.39924442, 0.56862085])

>>> quantum_efficiency_series = pd.Series(quantum_efficiency, index=wavelengths, name="dataset")
>>> sr = spectrum.qe_to_sr(quantum_efficiency_series)
>>> print(sr)
350    0.242773
550    0.399244
750    0.568621
Name: dataset, dtype: float64

>>> sr = spectrum.qe_to_sr(quantum_efficiency_series, normalize=True)
>>> print(sr)
350    0.426950
550    0.702128
750    1.000000
Name: dataset, dtype: float64

References