User API

IO

oospectro.io.load_spectrum(spectrum_path, lambda_min=100, lambda_max=1000, delimiter=',')

Load a spectrum file.

Parameters

spectrum_pathstring

File path.

lambda_minscalar, optional

Cut the data at this minimum wavelength in nm.

lambda_maxscalar, optional

Cut the data at this maximum wavelength in nm.

delimiterstring, optional

Delimiter between columns in the datafile.

Returns

valuesarrays

(lamdbas, intensities)

Thickness

class oospectro.thickness.OptimizeResult

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(key, default=<unrepresentable>, /)

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict 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)

Determine the tickness by Fast Fourier Transform.

Parameters

lambdasarray

Wavelength values in nm.

intensitiesarray

Intensity values.

refractive_indexscalar, optional

Value of the refractive index of the medium.

num_half_spacescalar, optional

Number of points to compute FFT’s half space. If None, default corresponds to 10*len(lambdas).

debugboolean, optional

Show plot of the transformed signal and the peak detection.

Returns

resultsInstance 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)

Return the thickness from a min-max detection.

Parameters

lambdasarray

Wavelength values in nm.

intensitiesarray

Intensity values.

refractive_indexscalar, optional

Value of the refractive index of the medium.

min_peak_prominencescalar, optional

Required prominence of peaks.

min_peak_distancescalar, optional

Minimum distance between peaks.

methodstring, optional

Either ‘linreg’ for linear regression or ‘ransac’ for Randon Sampling Consensus.

debugboolean, optional

Show plots of peak detection and lin regression.

Returns

resultsInstance 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.