pvlib.spectrum.sr_to_qe#

pvlib.spectrum.sr_to_qe(sr, wavelength=None, normalize=False)[source]#

Convert spectral responsivities to quantum efficiencies. If wavelength is not provided, the spectral responsivity sr 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:

sr (numeric, pandas.Series or pandas.DataFrame) – Spectral response, [A/W]. Index must be the wavelength in nanometers, [nm].
wavelength (numeric, optional) – Points where spectral response is measured, in nanometers, [nm].
normalize (bool, default False) – If True, the quantum efficiency 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:

quantum_efficiency (numeric, same type as sr) – Quantum efficiency, in the interval [0, 1].

Notes

If sr 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 sr.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from pvlib import spectrum
>>> wavelengths = np.array([350, 550, 750])
>>> spectral_response = np.array([0.25, 0.40, 0.57])
>>> quantum_efficiency = spectrum.sr_to_qe(spectral_response, wavelengths)
>>> print(quantum_efficiency)
array([0.88560142, 0.90170326, 0.94227991])

>>> spectral_response_series = pd.Series(spectral_response, index=wavelengths, name="dataset")
>>> qe = spectrum.sr_to_qe(spectral_response_series)
>>> print(qe)
350    0.885601
550    0.901703
750    0.942280
Name: dataset, dtype: float64

>>> qe = spectrum.sr_to_qe(spectral_response_series, normalize=True)
>>> print(qe)
350    0.939850
550    0.956938
750    1.000000
Name: dataset, dtype: float64

References