trtoolbox package

Submodules

trtoolbox.expfit module

class trtoolbox.expfit.Results(pre, tcs, time)[source]

Bases: object

Object containing fit results

data

Time trace subjected to fitting.

Type:np.array
time

Time array

Type:np.array
tcs

Time constants

Type:np.array
pre

Prefactors

Type:np.array
var

Variance of tcs

Type:np.array
traces

Individual expontential fit traces

Type:np.array
fit

Fitted time trace

Type:np.array
create_traces()[source]

Creates individual exponential traces

plot_results()[source]

Plots result.

plot_results_traces()[source]

Plots individual exponential traces.

print_results()[source]

Prints time constants.

trtoolbox.expfit.calculate_error(res, data)[source]

Returns the standard error of the optimized parameters.

Parameters:
  • res (scipy.optimize.OptimizeResult) – Results object obtained with least squares.
  • data (np.array) – Data matrix.
Returns:

perr – Standard error of the parameters.
Return type:

np.array
trtoolbox.expfit.create_tr(pre, tcs, time)[source]

Creates fitted time trace

Parameters:
  • pre (np.array) – Prefactors
  • tcs (np.array) – Time constants
  • time (np.array) – Time array
Returns:

tr – Fitted time trace
Return type:

np.array
trtoolbox.expfit.dofit(data, time, init)[source]

Do exponential fitting

Parameters:
  • data (np.array) – Time trace subjected to fitting
  • time (np.array) – Time array
  • init (np.array) – Initial guesses. Prefactors first column, time constants second
Returns:

res – Results object
Return type:

self.Results()
trtoolbox.expfit.opt_func(pre_plus_tcs, data, time)[source]

Optimization function

Parameters:
  • pre_plus_tcs (np.array) – Prefactors first column, time constants second
  • data (np.array) – Time trace subjected to fitting
  • time (np.array) – Time array
Returns:

r – Residuals
Return type:

np.array

trtoolbox.globalanalysis module

class trtoolbox.globalanalysis.RateConstants(ks)[source]

Bases: object

Container for rate contants related stuff

ks

Rate constants

Type:np.array
tcs

Time constants

Type:np.array
nb_exps

shape of the ks-matrix

Type:tuple
kmatrix

K-matrix used for generating differential equations.

Type:np.array
style

Which style of k-matrix was used. ‘dec’: parallel decaying processes ‘seq’: sequential model ‘back’: sequential model with back reactions ‘custom’: custom k-matrix

Type:str
alphas

Defines starting population ratio of ‘custom’ is choosen

Type:np.array
ks_err

Standard error of the fit (rate constants).

Type:np.array
tcs_err

Standard error of the fit (time constants).

Type:np.array
create_kmatrix(style=None)[source]

Creates K-matrix. Rate constants should be over rows.

Parameters:style (str) – Determines how the K-matrix is generated
set_ks(ks)[source]

Sets ks and also tcs accordingly.

Parameters:ks (np.array) – Rate constants
class trtoolbox.globalanalysis.Results[source]

Bases: trtoolbox.pclasses.Data

Object containing fit results.

offset

True if an offset was used.

Type:bool
spectral_offset

optional if offset was chosen

Type:np.array
method

Chosen method (default is ‘svd’).

Type:str
rate_constants

Object containing everything on the rate constants

Type:RateConstants
xas

Decay/Evolution/Species associated spectra.

Type:np.array
profile

Concentration profile determined by ks

Type:np.array
artefact

If True, the first two species are merged

Type:bool
estimates

Contribution of das to the dataset for each datapoint.

Type:np.array
fitdata

Fitted dataset.

Type:np.array
fittraces

optional if method=’svd’ was chosen. Fitted abstract time traces.

Type:np.array
svdtraces

SVD abstract time traces.

Type:np.array
r2

R^2 of fit.

Type:float
plot_fitdata(interpolate=False, step=0.5)[source]

Plots fitted data.

Parameters:
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_fitdata_3d(interpolate=False, step=0.5)[source]

3D plot fitted data.

Parameters:
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_profile()[source]

Plots concentration profile.

plot_results()[source]

Plots the concentration profile, DAS, fitted data and fitted abstract time traces if method=’svd’ was chosen.

plot_spectra()[source]

Plots interactive spectra.

plot_traces()[source]

Plots interactive time traces.

plot_xas()[source]

Plots decay associated spectra.

print_results()[source]

Prints time constants.

save_to_files(path, comment='')[source]

Saving results to .dat files.

Parameters:
  • path (str) – Path for saving.
  • comment (str) – Personal comment.
tcs
trtoolbox.globalanalysis.calc_r2(data, res)[source]

Returns R^2 in percent.

Parameters:
  • data (np.array) – Data matrix.
  • res (scipy.optimize.OptimizeResult) – Results object obtained with least squares.
Returns:

r2 – R^2.
Return type:

float
trtoolbox.globalanalysis.calculate_error(res, data)[source]

Returns the standard error of the optimized parameters.

Parameters:
  • res (scipy.optimize.OptimizeResult) – Results object obtained with least squares.
  • data (np.array) – Data matrix.
Returns:

perr – Standard error of the parameters.
Return type:

np.array
trtoolbox.globalanalysis.calculate_estimate(das, data)[source]

Computes contributions of DAS in the raw data.

Parameters:
  • das (np.array) – DAS
  • data (np.array) – Data matrix.
Returns:

est – Contributions of the individual DAS.
Return type:

np.array
trtoolbox.globalanalysis.calculate_fitdata(rate_constants, time, data)[source]

Computes the final fitted dataset.

Parameters:
  • rate_constants (RateConstants) – RateConstants object.
  • time (np.array) – Time array.
  • data (np.array) – Data matrix.
Returns:

fitdata – Fitted dataset.
Return type:

np.array
trtoolbox.globalanalysis.convert_tcs(arr)[source]

Converts time to rate constants and vice versa. Necessary due to zeros.

Parameters:arr (np.array) –
Returns:
Return type:np.array
trtoolbox.globalanalysis.create_profile(time, rate_constants)[source]

Computes a concentration profile according to the model() function.

Parameters:
  • time (np.array) – Time array.
  • rate_constants (RateConstants) – RateConstants object.
Returns:

profile – Concentration profile matrix.
Return type:

np.array
trtoolbox.globalanalysis.create_tr_expfit(rate_constants, pre, time)[source]

Function returning exponential time traces for a given set of parameters. Uses expfit module.

Parameters:
  • rate_constants (RateConstants) – RateConstants object.
  • pre (np.array) – Exponential pre-factors.
  • time (np.array) – Time array.
Returns:

profile – Concentration profile matrix.
Return type:

np.array
trtoolbox.globalanalysis.create_tr_odeint(rate_constants, pre, time)[source]
Function returning exponential time traces for a given set of parameters.
Uses odeint function.
Parameters:
  • rate_constants (RateConstants) – RateConstants object.
  • pre (np.array) – Exponential pre-factors.
  • time (np.array) – Time array.
Returns:

profile – Concentration profile matrix.
Return type:

np.array
trtoolbox.globalanalysis.create_xas(profile, data)[source]

Obtains decay associated spectra.

Parameters:
  • profile (np.array) – Concentration profile matrix
  • data (np.array) – Data matrix.
Returns:

xas – Decay/Evolution/Species associated spectra
Return type:

np.array
trtoolbox.globalanalysis.doglobalanalysis(data, time, wn, tcs, method='svd_expfit', svds=5, offset=False, offindex=-1, style='seq', kmatrix=None, alphas=None, artefact=False, silent=False)[source]

Wrapper for global fit routine.

Parameters:
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
  • data (np.array) – Raw data matrix.
  • tcs (np.array) – Initial time constants
  • method (np.array) – Method for global fitting. raw for fitting the residuals of fitted data and input data, est for fitting the residuals between concentration profile and contributions of DAS, svd_odeint for fitting the SVD time traces with odeint function. *svd_expfit for fitting the SVD time traces with expfit module (default).
  • svds (int) – Number of SVD components to be fitted. Default: 5.
  • offset (boolean) – Considering the last spectrum to be an offset. Default: False.
  • offindex (int) – Index of spectral offset.
  • style (str) – Which style of K-matrix to use. ‘dec’: parallel decaying processes ‘seq’: sequential model ‘back’: sequential model with back reactions. tcs-matrix should have forward time constants in the first column and backward in the second. ‘custom’: custom k-matrix
  • kmatrix (np.array) – K-matrix. Providing an 3D K-matrix is interpreted as parallel reaction pathways. This also useful if branching occurs due to heterogeneity. Starting populations can be set with the alpha attribute. For more info please see the documentation.
  • alphas (np.array) – Sets starting population of the first species.
  • artefact (bool) – If True, the first two species are merged
  • silent (bool) – Supresses print output
Returns:

gf_res – Results objects.
Return type:

globalanalysis.results
trtoolbox.globalanalysis.is_square(mat)[source]

Checks if a matrix is square.

Parameters:mat (np.array) – Matrix
Returns:
Return type:bool
trtoolbox.globalanalysis.model(s, time, rate_constants)[source]
Creates an array of differential equations according to
(kmatrix * ks).dot(s) with ks as rate constants and s as species concentration.
Parameters:
  • s (np.array) – Starting concentrations for each species.
  • time (np.array) – Time array.
  • rate_constants (RateConstants) – RateConstants object.
Returns:

arr – Array containing the differential equations.
Return type:

np.array
trtoolbox.globalanalysis.opt_func_est(ks, rate_constants, time, data)[source]
Optimization function for residuals of concentration profile
and estimated contributions of DAS
Parameters:
  • ks (np.array) – Rate constants
  • rate_constants (RateConstants) – RateConstants object.
  • time (np.array) – Time array.
  • data (np.array) – Data matrix.
Returns:

R – Flattened array of residuals.
Return type:

np.array
trtoolbox.globalanalysis.opt_func_raw(ks, rate_constants, time, data)[source]

Optimization function for residuals of fitted data - input data.

Parameters:
  • ks (np.array) – Rate constants
  • rate_constants (RateConstants) – RateConstants object.
  • time (np.array) – Time array.
  • data (np.array) – Data matrix.
Returns:

R – Flattened array of residuals.
Return type:

np.array
trtoolbox.globalanalysis.opt_func_svd(pars, rate_constants, time, svdtraces, method)[source]
Optimization function for residuals of SVD
abstract time traces - fitted traces.
Parameters:
  • pars (np.array) – Flattened parameter array
  • rate_constants (RateConstants) – RateConstants object.
  • time (np.array) – Time array.
  • svdtraces (np.array) – SVD traces
  • method (basestring) – Chosen method
Returns:

R – Flattened array of residuals.
Return type:

np.array

trtoolbox.lda module

class trtoolbox.lda.Results[source]

Bases: trtoolbox.pclasses.Data

Object containing fit results.

type

Results object type.

Type:str
taus

Time constants used for constructing D-matrix.

Type:np.array
dmatrix

D-matrix.

Type:np.array
alphas

Used alpha values for computation (tik method).

Type:np.array
lmatrix

L-matrix (tik method).

Type:np.array
lcurve

L-curve.

Type:np.array
k

Used SVD Components (tsvd method).

Type:int
method

Used method (tik or tsvd).

Type:string
x_k

Resulting exponential pre-factors.

Type:np.array
fitdata

Constructed data (dmatrix.dot(x_k))

Type:np.array
get_alpha(alpha=-1, index_alpha=-1)[source]

Gets alpha value and index.

Parameters:
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
Returns:

  • alpha (float) – Alpha value.
  • index_alpha (int) – Index of alpha value.
get_xk(alpha=-1, index_alpha=-1)[source]

Gets selected LDA map.

Parameters:
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
Returns:

  • x_k (np.array) – LDA map.
  • title (str) – Title for figure.
plot_fitdata(alpha=-1, index_alpha=-1, interpolate=False, step=0.5)[source]

Plots a nice looking heatmap of fitted data.

Parameters:
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_fitdata_3d(alpha=-1, index_alpha=-1, interpolate=False, step=0.5)[source]

Plots a 3D surface of fitted data.

Parameters:
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_lcurve()[source]

Plots L-curve.

plot_ldamap(alpha=-1, index_alpha=-1)[source]

Plots a nice looking contourmap.

Parameters:
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
plot_results()[source]

Plots interactive contourmaps of original and LDA data,

plot_solutionvector(alpha=-1, index_alpha=-1)[source]

Plots the sum of amplitudes over time constants.

Parameters:
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
plot_spectra(alpha=-1, index_alpha=-1)[source]

Plots interactive spectra.

plot_traces(alpha=-1, index_alpha=-1)[source]

Plots interactive time traces.

save_to_files(path, alpha=-1, index_alpha=-1, comment='')[source]

Saving results to .dat files.

Parameters:
  • path (str) – Path for saving.
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
  • comment (str) – Personal comment.
trtoolbox.lda.calc_lcurve(data, dmatrix, lmatrix, x_ks)[source]

Calculates L-curve.

Parameters:
  • data (np.array) – Data matrix.
  • dmatrix (np.array) – D-matrix.
  • lmatrix (np.array) – L-matrix.
  • x_ks (np.array) – LDA maps.
Returns:

lcurve – First column is resdiual norm. Second column is smoothed norm.
Return type:

np.array
trtoolbox.lda.dolda(data, time, wn, tlimits=[], tnum=100, alimits=[0.1, 5], anum=100, method='tik', seqmodel=False, k=5, prompt=False)[source]

Wrapper for doing a LDA.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
  • tlimits (list) – Limits for time constants.
  • tnum (int) – Number of time constants.
  • alimits (list) – Limits for alpha values.
  • anum (int) – Number if alpha values.
  • method (str) – Chosen method for LDA. Either ‘tik’ or ‘tsvd’.
  • seqmodel (boolean) – True for constructing the D-matrix assuming a sequential model.
  • k (int) – Just used for ‘tsvd’. Specifies the position of truncation.
  • prompt (boolean) – True for user prompts.
Returns:

res – Results object.
Return type:

lda.results
trtoolbox.lda.gen_alphas(a1, a2, n, space='log')[source]

Generates logarihmic spaced alpha values. Adds [1e-5, 1e-4, 1e-3, 1e-2] and [10, 40, 70, 100] for a better visualization of the L-curve.

Parameters:
  • a1 (float) – Bottom limit.
  • a2 (float) – Upper limit.
  • n (int) – Number of alpha values.
  • space (str) – log for logarithmic and lin for linear spaced
Returns:

alphas – Generated alpha values.
Return type:

np.array
trtoolbox.lda.gen_dmatrix(time, taus, seqmodel=False)[source]

Generates D-matrix.

Parameters:
  • time (np.array) – Time array.
  • taus (np.array) – Time constants array
  • seqmodel (boolean) – Sets a sequential model.
Returns:

dmatrix – D-matrix
Return type:

np.array
trtoolbox.lda.gen_lmatrix(dmatrix)[source]

Generates L-matrix

Parameters:dmatrix (np.array) – D-matrix
Returns:lamtrix – L-matrix
Return type:np.array
trtoolbox.lda.gen_taus(t1, t2, n)[source]

Generates logarithmic spaced time constants.

Parameters:
  • t1 (float) – Bottom limit.
  • t2 (float) – Upper limit.
  • n (int) – Number of time constants.
Returns:

taus – Generated time constants.
Return type:

np.array
trtoolbox.lda.inversesvd(dmatrix, k=-1)[source]

Returns the inverse of matrix computed via SVD.

Parameters:
  • dmatrix (np.array) – Matrix to be inversed
  • k (int) – Point of truncation. If -1 then all singular values are used.
Returns:

v – Inverse of input matrix.
Return type:

np.array
trtoolbox.lda.tik(data, dmatrix, alpha)[source]
Function for Tikhonov regularization:

min_x ||Dx - A|| + alpha*||Lx|| D-matrix contains exponential profiles, x are prefactors/amplitudes, A is the dataset, alpha is the regularization factor and L is the identity matrix.

Details can be found in Dorlhiac, Gabriel F. et al. “PyLDM-An open source package for lifetime density analysis of time-resolved spectroscopic data.” PLoS computational biology 13.5 (2017)

Parameters:
  • data (np.array) – Data matrix to be analyzed
  • dmatrix (np.array) – D-matrix
  • alpha (float) – Regularization factor
Returns:

x_k – Expontential prefactors/amplitudes.
Return type:

np.array
trtoolbox.lda.tik_lstsq(data, dmatrix, alpha)[source]
Different implementation of the tik function.
Uses the ordinary lstsq solver of numpy.
Parameters:
  • data (np.array) – Data matrix to be analyzed.
  • dmatrix (np.array) – D-matrix.
  • alpha (float) – Regularization factor.
Returns:

res – Expontential prefactors/amplitudes.
Return type:

np.array
trtoolbox.lda.tiks(data, dmatrix, alphas)[source]
Wrapper for computing LDA for various alpha values.
Parallelization makes execution actually slower. I suspect that the svd numpy method already optimizes CPU usage.
Parameters:
  • data (np.array) – Data matrix to be analyzed.
  • dmatrix (np.array) – D-matrix.
  • alphas (np.array) – Array of regularization factors.
Returns:

x_k – 3D matrix of expontential prefactors/amplitudes.
Return type:

np.array
trtoolbox.lda.tsvd(data, dmatrix, k)[source]
Truncated SVD for LDA. Similar to Tikhonov regularization

but here we have a clear cut-off after a specified singular value.

Details can be found in Hansen PC. The truncated SVD as a method for regularization. Bit. 1987

Parameters:
  • data (np.array) – Data matrix to be analyzed.
  • dmatrix (np.array) – D-matrix.
  • k (int) – Cut-off for singular values.
Returns:

x_k – Expontential prefactors/amplitudes.
Return type:

np.array

trtoolbox.plothelper module

class trtoolbox.plothelper.MidpointNormalize(vmin=None, vmax=None, midpoint=None, clip=False)[source]

Bases: matplotlib.colors.Normalize

Class for setting 0 as midpoint in the colormap.

midpoint

Midpoint of colormap

Type:float
class trtoolbox.plothelper.PlotHelper[source]

Bases: object

Object for interactive plotting. This class ensures that all the matplotlib objects are kept referenced which ensures proper function of the sliders.

fig_traces
Type:matplotlib.figure.Figure
ax_traces
Type:matplotlib.axes.Axes
l1_traces
Type:matplotlib.lines.Line2D
l2_traces
Type:matplotlib.lines.Line2D
axfreq
Type:matplotlib.axes.Axes
sfreq
Type:matplotlib.widgets.Slider
fig_spectra
Type:matplotlib.figure.Figure
ax_spectra
Type:matplotlib.axes.Axes
l1_spectra
Type:matplotlib.lines.Line2D
l2_spectra
Type:matplotlib.lines.Line2D
axtime
Type:matplotlib.axes.Axes
stime
Type:matplotlib.widgets.Slider
fig_lda
Type:matplotlib.figure.Figure()
axs_lda
Type:np.array
map_lda
Type:matplotlib.contour.QuadContourSet
ldadata
Type:matplotlib.collections.QuadMesh
l1_lda
Type:matplotlib.lines.Line2D
axalpha
Type:matplotlib.axes.Axes
salpha
Type:matplotlib.widgets.Slider
axcolor
Type:str
static append_ldamap(res, index_alpha=-1)[source]
Appends NaN values in order to expand the taus array
to match the time array span.
Parameters:
  • res (mylda.Results) – Contains the results to be plotted.
  • index_alpha (int) – Index of selected alpha value.
Returns:

  • x_k (np.array) – LDA map of selected alpha value.
  • taus (np.array) – Extended taus array.
static do_interpolate(data, time, wn, step=0.5)[source]
static plot_contourmap(data, time, wn, title='data', newfig=True)[source]

Plots a nice looking contourmap.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
  • title (np.array) – Title of plot. Default data.
  • newfig (boolean) – Setting to False prevents the creation of a new figure.
plot_heatmap(data, time, wn, title='data', newfig=True, interpolate=False, step=0.5)[source]

Plots a nice looking heatmap.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
  • title (np.array) – Title of plot. Default data.
  • newfig (boolean) – Setting to False prevents the creation of a new figure.
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_ldaresults(res)[source]

Plots interactive resaults of LDA.

Parameters:res (mylda.Results) – Contains the results to be plotted.
plot_solutionvector(res, alpha=-1, index_alpha=-1)[source]

Plots interactive solution vector.

Parameters:
  • res (mylda.Results) – Contains the data to be plotted.
  • alpha (float) – Plot for the closest alpha as specified.
  • index_alpha (int) – Plot for specified alpha at index.
plot_spectra(res, alpha=-1, index_alpha=-1, rev=True)[source]

Plots interactive spectra.

Parameters:
  • res (mysvd.Results) – Contains the data to be plotted.
  • alpha (float) – Alpha value.
  • index_alpha (int) – Index of alpha value.
  • rev (boolean) – Reverses the x-axis.
plot_surface(data, time, wn, title='data', interpolate=False, step=0.5)[source]

Plots a nice looking heatmap.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
  • title (np.array) – Title of plot. Default data.
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_traces(res, alpha=-1, index_alpha=-1)[source]

Plots interactive time traces.

Parameters:
  • res (mysvd.Results) – Contains the data to be plotted.
  • alpha (float) – Alpha value.
  • index_alpha (int) – Index of alpha value.

trtoolbox.svd module

class trtoolbox.svd.Results[source]

Bases: trtoolbox.pclasses.Data

Object containing fit results.

type

Results object type.

Type:str
u

U matrix. Represents abstract spectra

Type:np.array
s

Singular values.

Type:np.array
vt

Transposed V matrix. Represents abstract time traces.

Type:np.array
n

Number of singular components used for data reconstruction.

Type:int
svddata

Reconstructed data.

Type:np.array
plot_abstract_spectra()[source]
plot_abstract_traces()[source]
plot_data(newfig=True, interpolate=False, step=0.5)[source]

Plots a nice looking heatmap of the raw data.

Parameters:
  • newfig (boolean) – True for own figure.
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_results()[source]

Plots heatmaps of original and SVD data, interactive time traces and spectra.

plot_spectra()[source]

Plots interactive spectra.

plot_svddata(newfig=False, interpolate=False, step=0.5)[source]

Plots a nice looking heatmap of the reconstructed data.

plot_svddata_3d(interpolate=False, step=0.5)[source]

Plots 3D surface of the reconstructed data.

Parameters:
  • interpolate (boolean) – True for interpolation
  • step (float) – Step size for frequency interpolation.
plot_traces()[source]

Plots interactive time traces.

save_to_files(path)[source]

Saving results to .dat files.

Parameters:path (str) – Path for saving.
trtoolbox.svd.dosvd(data, time, wn, n=-1)[source]

Wrapper for inspecting SVD components with reconstruction.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
  • n (int, list or np.array) – Number of used SVD components. If a list or array is provided, non pythonic way of numbering is used. Meaning first component equals 1.
Returns:

res – Results object.
Return type:

svd.results
trtoolbox.svd.reconstruct(data, n)[source]

Reconstructs data with n singular components.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • n (int, list or np.array) – Number of used SVD components. If a list or array is provided, non pythonic way of numbering is used. Meaning first component equals 1.
Returns:

res – Results object.
Return type:

mysvd.results
trtoolbox.svd.show_svs(data, time, wn)[source]

Plots singular values and variance explained.

Parameters:
  • data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
  • time (np.array) – Time array.
  • wn (np.array) – Frequency array.
trtoolbox.svd.wrapper_svd(data)[source]

Simple wrapper for the scipy.linalg.svd() function.

Parameters:data (np.array) – Data matrix subjected to SVD. Assuming m x n with m as frequency and n as time. But it is actually not important.
Returns:
  • u (np.array) – U matrix. Represents abstract spectra
  • s (np.array) – Singular values.
  • vt (np.array) – Transposed V matrix. Represents abstract time traces.

Module contents