Welcome to oospectro’s documentation!¶

Author:	François Boulogne
Devel:	project
Bugs:	github
Generated:	Feb 01, 2021
License:	BSD

Contents:

Introduction¶

For data acquisition of Ocean Optics spectrometers, you can use seabreeze.

Install¶

Via pip¶

oospectro is availlable on pypi.

Run

pip install oospectro

Note about dependencies¶

numpy
scipy >= 1.1.0
scikit-image
matplotlib

Example¶

from oospectro import load_spectrum, thickness_from_minmax

spectrum = 'spectra/sample1/007084.xy'
lambdas, intensities = load_spectrum(spectrum, lambda_min=450)
result = thickness_from_minmax(lambdas, intensities,
                               refractive_index=1.33,
                               min_peak_prominence=0.02,
                               method='ransac', debug=True)
# result is a class with various attributes
# related to the algorithm outputs.
# Print the thickness
print(result.thickness)

Large thicknesses can be determined by FFT (requires a large amount of oscillations)

from oospectro import load_spectrum, thickness_from_fft

spectrum = 'spectra/sample1/003582.xy'
lambdas, intensities = load_spectrum(spectrum, lambda_min=450)
result = thickness_from_fft(lambdas, intensities,
                            refractive_index=1.33)
print(result.thickness)

User API¶

IO¶

oospectro.io.load_spectrum(spectrum_path, lambda_min=100, lambda_max=1000, delimiter=', ')[source]¶

Load a spectrum file.

Parameters:	spectrum_path : string File path. lambda_min : scalar, optional Cut the data at this minimum wavelength in nm. lambda_max : scalar, optional Cut the data at this maximum wavelength in nm. delimiter : string, optional Delimiter between columns in the datafile.
Returns:	values : arrays (lamdbas, intensities)

Thickness¶

class oospectro.thickness.OptimizeResult[source]¶

Bases: dict

Represents the optimization result.

Notes

This class has been copied from scipy.optimize

clear() → None. Remove all items from D.¶

copy() → a shallow copy of D¶

fromkeys(iterable, value=None, /)¶: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶: Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶: If key is not found, d is returned if given, otherwise KeyError is raised

popitem() → (k, v), remove and return some (key, value) pair as a¶: 2-tuple; but raise KeyError if D is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.¶: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values¶

oospectro.thickness.thickness_from_fft(lambdas, intensities, refractive_index=1.0, num_half_space=None, debug=False)[source]¶

Determine the tickness by Fast Fourier Transform.

Parameters:

lambdas : array: Wavelength values in nm.
intensities : array: Intensity values.
refractive_index : scalar, optional: Value of the refractive index of the medium.
num_half_space : scalar, optional: Number of points to compute FFT’s half space. If None, default corresponds to 10*len(lambdas).
debug : boolean, optional: Show plot of the transformed signal and the peak detection.

Returns:

results : Instance of OptimizeResult class.: The attribute thickness gives the thickness value in nm.

oospectro.thickness.thickness_from_minmax(lambdas, intensities, refractive_index=1.0, min_peak_prominence=0.01, min_peak_distance=10, method='linreg', debug=False)[source]¶

Return the thickness from a min-max detection.

Parameters:

lambdas : array: Wavelength values in nm.
intensities : array: Intensity values.
refractive_index : scalar, optional: Value of the refractive index of the medium.
min_peak_prominence : scalar, optional: Required prominence of peaks.
min_peak_distance : scalar, optional: Minimum distance between peaks.
method : string, optional: Either ‘linreg’ for linear regression or ‘ransac’ for Randon Sampling Consensus.
debug : boolean, optional: Show plots of peak detection and lin regression.

Returns:

results : Instance of OptimizeResult class.: The attribute thickness gives the thickness value in nm.

Notes

For more details about min_peak_prominence and min_peak_distance, see the documentation of scipy.signal.find_peaks. This function is used to find extrema.

Acknowledgements¶

We thank Lorène Champougny for provinding valuable data to benchmark the library, Jonas Miguet and Marina Pasquet for useful comments.