libpyhat.transform.baseline_code package

Submodules

libpyhat.transform.baseline_code.airpls module

class libpyhat.transform.baseline_code.airpls.AirPLS(smoothness_param=100, max_iters=10, conv_thresh=0.001, verbose=False)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.airpls.airpls_baseline(intensities, smoothness_param=100, max_iters=10, conv_thresh=0.001, verbose=False)

Baseline corr. using adaptive iteratively reweighted penalized least squares. Also known as airPLS, 2010. http://pubs.rsc.org/EN/content/articlehtml/2010/an/b922045c https://code.google.com/p/airpls/ https://airpls.googlecode.com/svn/trunk/airPLS.py

libpyhat.transform.baseline_code.als module

class libpyhat.transform.baseline_code.als.ALS(asymmetry_param=0.05, smoothness_param=1000000.0, max_iters=10, conv_thresh=1e-05, verbose=False)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.als.als_baseline(intensities, asymmetry_param=0.05, smoothness_param=1000000.0, max_iters=10, conv_thresh=1e-05, verbose=False)

Perform asymmetric least squares baseline removal. * http://www.science.uva.nl/~hboelens/publications/draftpub/Eilers_2005.pdf

smoothness_param: Relative importance of smoothness of the predicted response. asymmetry_param (p): if y > z, w = p, otherwise w = 1-p.

Setting p=1 is effectively a hinge loss.

libpyhat.transform.baseline_code.common module

class libpyhat.transform.baseline_code.common.Baseline

Bases: object

fit(bands, intensities, segment=False)

Fits one baseline per spectrum and stores them as self.baseline. When segment=True, automatically detects discontinuities in the bands and fits a separate baseline per segment.

fit_transform(bands, intensities, segment=False)

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

class libpyhat.transform.baseline_code.common.WhittakerSmoother(signal, smoothness_param, deriv_order=1)

Bases: object

smooth(w)

libpyhat.transform.baseline_code.common.iterative_threshold(signal, num_stds=3)

libpyhat.transform.baseline_code.dietrich module

class libpyhat.transform.baseline_code.dietrich.Dietrich(half_window=16, num_erosions=10)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.dietrich.dietrich_baseline(bands, intensities, half_window=16, num_erosions=10)

Fast and precise automatic baseline correction of … NMR spectra, 1991. http://www.sciencedirect.com/science/article/pii/002223649190402F http://www.inmr.net/articles/AutomaticBaseline.html

libpyhat.transform.baseline_code.fabc module

class libpyhat.transform.baseline_code.fabc.FABC(dilation_param=50, smoothness_param=1000.0)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.fabc.fabc_baseline(intensities, dilation_param=50, smoothness_param=1000.0)

Fully Automatic Baseline Correction, by Carlos Cobas (2006). http://www.sciencedirect.com/science/article/pii/S1090780706002266

libpyhat.transform.baseline_code.kajfosz_kwiatek module

class libpyhat.transform.baseline_code.kajfosz_kwiatek.KajfoszKwiatek(top_width=0, bottom_width=50, exponent=2, tangent=False)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.kajfosz_kwiatek.kajfosz_kwiatek_baseline(bands, intensities, top_width=0, bottom_width=50, exponent=2, tangent=False)

This function uses an enhanced version of the algorithm published by Kajfosz, J. and Kwiatek, W.M. (1987) “Non-polynomial approximation of background in x-ray spectra.” Nucl. Instrum. Methods B22, 78-81.

top_width:

Specifies the width of the polynomials which are concave upward. The top_width is the full width in energy units at which the magnitude of the polynomial is 0.1 of max. The default is 0, which means that concave upward polynomials are not used.

bottom_width:

Specifies the width of the polynomials which are concave downward. The bottom_width is the full width in energy units at which the magnitude of the polynomial is 0.1 of max. The default is 50.

exponent:

Specifies the power of polynomial which is used. The power must be an integer. The default is 2, i.e. parabolas. Higher exponents, for example EXPONENT=4, results in polynomials with flatter tops and steeper sides, which can better fit spectra with steeply sloping backgrounds.

tangent:

Specifies that the polynomials are to be tangent to the slope of the spectrum. The default is vertical polynomials. This option works best on steeply sloping spectra. It has trouble in spectra with big peaks because the polynomials are very tilted up inside the peaks.

For more info, see: cars9.uchicago.edu/software/idl/mca_utility_routines.html#FIT_BACKGROUND

libpyhat.transform.baseline_code.median module

class libpyhat.transform.baseline_code.median.MedianFilter(window_size=501)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.median.median_baseline(intensities, window_size=501)

Perform median filtering baseline removal. Window should be wider than FWHM of the peaks. “A Model-free Algorithm for the Removal of Baseline Artifacts” Friedrichs 1995

libpyhat.transform.baseline_code.min_spline module

This baseline removal method is a simple alternative inspired by the wavelet+spline method described for ChemCam. It divides the spectrum up into windows of a user-specified size, finds the minimum value within each window that is also a local minimum, and then fits a cubic spline.

libpyhat.transform.baseline_code.min_spline.min_interp(wvl, spectrum, window=50, kind='cubic')

class libpyhat.transform.baseline_code.min_spline.minimum_interp(window=50, kind='cubic')

Bases: Baseline

libpyhat.transform.baseline_code.polyfit module

class libpyhat.transform.baseline_code.polyfit.PolyFit(poly_order=5, num_stdv=3.0)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.polyfit.polyfit_baseline(bands, intensities, poly_order=5, num_stdv=3.0, max_iter=200)

Iteratively fits a polynomial, discarding far away points as peaks. Similar in spirit to ALS and related methods. Automated method for subtraction of fluorescence from biological Raman spectra Lieber & Mahadevan-Jansen 2003

libpyhat.transform.baseline_code.rubberband module

class libpyhat.transform.baseline_code.rubberband.Rubberband(num_iters=8, num_ranges=64)

Bases: Baseline

param_ranges()

Returns a dict of parameter -> (min,max,scale) mappings. Min and max are scalars, scale is one of {‘linear’,’log’,’integer’}.

libpyhat.transform.baseline_code.rubberband.rubberband_baseline(bands, intensities, num_iters=8, num_ranges=64)

Bruker OPUS method. If num_iters=0, uses basic method from OPUS. Method detailed in Pirzer et al., 2008 US Patent No. US7359815B2

libpyhat.transform.baseline_code.wavelet_a_trous module

Algorithm based on Appendix A of Starck, J.L., Murtagh, F., 2006. Handbook of Astronomical Data Analysis, 2nd ed. Springer-Verlag.

This is the algorithm used as the basis for ChemCam’s denoise and baseline removal as described in Wiens, R.C., et al., 2013. Pre-flight calibration and initial data processing for the ChemCam laser-induced breakdown spectroscopy instrument on the Mars Science Laboratory rover. Spectrochimica Acta Part B: Atomic Spectroscopy 82, 1–27. doi:10.1016/j.sab.2013.02.003

libpyhat.transform.baseline_code.wavelet_a_trous.wavelet_a_trous(spectrum, n_resolutions)

libpyhat.transform.baseline_code.wavelet_spline module

This baseline removal method is based on the description of continuum removal in Wiens, R.C., et al., 2013. Pre-flight calibration and initial data processing for the ChemCam laser-induced breakdown spectroscopy instrument on the Mars Science Laboratory rover. Spectrochimica Acta Part B: Atomic Spectroscopy 82, 1–27. doi:10.1016/j.sab.2013.02.003

It uses the wavelet a trous algorithm described in Starck, J.L., Murtagh, F., 2006. Handbook of Astronomical Data Analysis, 2nd ed. Springer-Verlag. to decompose the spectrum, then reconstructs the spectrum up to a specified level, finds the local minima in that smoothed spectrum, then fits a cubic spline to the nearest local minima in the actual spectrum.

Note: this doesn’t give identical results to the continuum removal used by ChemCam, but it is pretty close.

libpyhat.transform.baseline_code.wavelet_spline.wave_spline(wvl, spectrum, level=6, levelmin=2)

class libpyhat.transform.baseline_code.wavelet_spline.wavelet_spline(level=6, levelmin=2)

Bases: Baseline

Module contents