From 89c9b321ed596a34a30cd861c152166f94fe6cda Mon Sep 17 00:00:00 2001 From: georgievgeorgi Date: Tue, 19 Nov 2024 19:43:16 +0000 Subject: [PATCH] deploy: bcb351ebaa571f064db7863ca82bf52f6900f607 --- .nojekyll | 0 index.html | 7 + ramanchada2.html | 1612 +++++ ramanchada2/auxiliary.html | 259 + ramanchada2/auxiliary/spectra.html | 265 + ramanchada2/auxiliary/spectra/datasets2.html | 1827 ++++++ ramanchada2/auxiliary/spectra/simulated.html | 309 + ramanchada2/io.html | 267 + ramanchada2/io/HSDS.html | 654 ++ ramanchada2/io/experimental.html | 272 + ramanchada2/io/experimental/bw_format.html | 324 + .../io/experimental/neegala_format.html | 316 + ramanchada2/io/experimental/rc1_parser.html | 267 + .../rc1_parser/binary_readers.html | 769 +++ .../io/experimental/rc1_parser/io.html | 452 ++ .../rc1_parser/third_party_readers.html | 410 ++ .../rc1_parser/txt_format_readers.html | 721 +++ ramanchada2/io/experimental/read_csv.html | 313 + ramanchada2/io/experimental/read_spe.html | 310 + 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.../misc/spectrum_deco/spectrum_method.html | 307 + ramanchada2/misc/types.html | 273 + ramanchada2/misc/types/fit_peaks_result.html | 961 +++ ramanchada2/misc/types/peak_candidates.html | 1377 ++++ .../misc/types/positive_not_multiple.html | 287 + .../misc/types/pydantic_base_model.html | 491 ++ ramanchada2/misc/types/spectrum.html | 266 + .../types/spectrum/applied_processings.html | 833 +++ ramanchada2/misc/types/spectrum/metadata.html | 678 ++ ramanchada2/misc/utils.html | 289 + ramanchada2/misc/utils/argmin2d.html | 579 ++ ramanchada2/misc/utils/matchsets.html | 1029 +++ .../misc/utils/ramanshift_to_wavelength.html | 406 ++ ramanchada2/misc/utils/svd.html | 340 + ramanchada2/protocols.html | 263 + .../calib_ne_si_argmin2d_iter_gg.html | 626 ++ ramanchada2/protocols/calibration.html | 262 + .../calibration/calibration_component.html | 989 +++ .../calibration/calibration_model.html | 1552 +++++ .../protocols/calibration/xcalibration.html | 2819 +++++++++ .../protocols/calibration/ycalibration.html | 1535 +++++ ramanchada2/protocols/metadata_helper.html | 617 ++ ramanchada2/protocols/spectraframe.html | 1584 +++++ ramanchada2/protocols/twinning.html | 1283 ++++ ramanchada2/spectral_components.html | 274 + ramanchada2/spectral_components/baseline.html | 261 + .../baseline/analytical.html | 255 + .../baseline/baseline_base.html | 310 + .../baseline/numerical.html | 366 ++ .../spectral_components/peak_profile.html | 270 + .../peak_profile/delta.html | 477 ++ .../peak_profile/gauss.html | 492 ++ .../peak_profile/voigt.html | 320 + .../spectral_component.html | 321 + .../spectral_component_collection.html | 552 ++ .../spectral_components/spectral_peak.html | 439 ++ ramanchada2/spectrum.html | 290 + ramanchada2/spectrum/arithmetics.html | 274 + ramanchada2/spectrum/arithmetics/add.html | 292 + ramanchada2/spectrum/arithmetics/mul.html | 292 + ramanchada2/spectrum/arithmetics/sub.html | 292 + ramanchada2/spectrum/arithmetics/truediv.html | 291 + ramanchada2/spectrum/baseline.html | 273 + .../spectrum/baseline/add_baseline.html | 445 ++ .../spectrum/baseline/baseline_rc1.html | 465 ++ .../spectrum/baseline/moving_minimum.html | 366 ++ ramanchada2/spectrum/calc.html | 271 + .../spectrum/calc/central_moments.html | 333 + ramanchada2/spectrum/calibration.html | 278 + .../spectrum/calibration/by_deltas.html | 1510 +++++ .../spectrum/calibration/change_x_units.html | 529 ++ .../spectrum/calibration/normalize.html | 383 ++ .../spectrum/calibration/scale_xaxis.html | 445 ++ .../spectrum/calibration/scale_yaxis.html | 345 ++ .../spectrum/calibration/set_new_xaxis.html | 353 ++ ramanchada2/spectrum/creators.html | 282 + .../spectrum/creators/from_cache_or_calc.html | 443 ++ ramanchada2/spectrum/creators/from_chada.html | 303 + .../spectrum/creators/from_delta_lines.html | 425 ++ .../spectrum/creators/from_local_file.html | 464 ++ .../spectrum/creators/from_simulation.html | 412 ++ .../from_spectral_component_collection.html | 339 + .../spectrum/creators/from_stream.html | 456 ++ .../spectrum/creators/from_test_spe.html | 344 + .../creators/from_theoretical_lines.html | 357 ++ .../creators/hdr_from_multi_exposure.html | 348 ++ ramanchada2/spectrum/filters.html | 285 + .../spectrum/filters/add_gaussian_noise.html | 381 ++ .../filters/add_gaussian_noise_drift.html | 480 ++ .../spectrum/filters/add_poisson_noise.html | 379 ++ ramanchada2/spectrum/filters/convolve.html | 403 ++ ramanchada2/spectrum/filters/drop_spikes.html | 615 ++ ramanchada2/spectrum/filters/dropna.html | 352 ++ .../spectrum/filters/moving_average.html | 414 ++ .../spectrum/filters/moving_median.html | 435 ++ ramanchada2/spectrum/filters/pad_zeros.html | 367 ++ ramanchada2/spectrum/filters/resampling.html | 857 +++ .../spectrum/filters/sharpen_lines.html | 593 ++ ramanchada2/spectrum/filters/smoothing.html | 401 ++ ramanchada2/spectrum/filters/trim_axes.html | 366 ++ ramanchada2/spectrum/multimap.html | 264 + ramanchada2/spectrum/multimap/spc.html | 390 ++ ramanchada2/spectrum/peaks.html | 276 + ramanchada2/spectrum/peaks/find_peaks.html | 768 +++ .../find_peaks_BayesianGaussianMixture.html | 401 ++ ramanchada2/spectrum/peaks/fit_peaks.html | 706 +++ .../spectrum/peaks/get_fitted_peaks.html | 431 ++ ramanchada2/spectrum/spectrum.html | 5514 +++++++++++++++++ ramanchada2/theoretical_lines.html | 264 + .../theoretical_lines/model_from_lines.html | 637 ++ search.js | 46 + 131 files changed, 68803 insertions(+) create mode 100644 .nojekyll create mode 100644 index.html create mode 100644 ramanchada2.html create mode 100644 ramanchada2/auxiliary.html create mode 100644 ramanchada2/auxiliary/spectra.html create mode 100644 ramanchada2/auxiliary/spectra/datasets2.html create mode 100644 ramanchada2/auxiliary/spectra/simulated.html create mode 100644 ramanchada2/io.html create mode 100644 ramanchada2/io/HSDS.html create mode 100644 ramanchada2/io/experimental.html create mode 100644 ramanchada2/io/experimental/bw_format.html create mode 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100644 index 00000000..e69de29b diff --git a/index.html b/index.html new file mode 100644 index 00000000..9c020d73 --- /dev/null +++ b/index.html @@ -0,0 +1,7 @@ + + + + + + + diff --git a/ramanchada2.html b/ramanchada2.html new file mode 100644 index 00000000..1840a548 --- /dev/null +++ b/ramanchada2.html @@ -0,0 +1,1612 @@ + + + + + + + ramanchada2 API documentation + + + + + + + + + + + + +
+
+

+ramanchada2

+ +

Purpose

+ +

ramanchada2 software package is meant to fill the gap between the theoretical +Raman analysis and the experimental Raman spectroscopy by providing means to +compare data of different origin. The software is in early development stage +but still able to solve practical problems.

+ +

Features

+ +

Read simulated data

+ +

Process simulated data by VASP and CRYSTAL and provide same interface. +CRYSTAL data contain intensities for multiple orientations -- laser beam +incidents perpendicularly or parallelly to the observation and information +for mono-crystals. VASP data provide data only for poly-crystals but in +different format. So the perpendicular and parallel intensities are calculated +by an implemented algorithm.

+ +

Models

+ +

LMFIT theoretical models can be build by spectral information obtained by +simulations or by provided by the user. These models can be fit to experimental +data, providing calibration information. At poor initial calibration the minimisation +procedure naturally fails. An iterative procedure aiming to solve this problem +was adopted in the code. On the first iteration the experimental spectrum lines +are artificially broadened. This makes it possible for the minimisation procedure +to find a parameters that are close enough to be used as an initial guess for +the second iteration. In few iterations the algorithm is able to fit to the original +experimental data. This idea is implemented and is at proof-of-concept level.

+ +

Generate spectra

+ +

Spectra can be generated by the theoretical models. Random Poissonian noise and +artificial random-generated baseline can be added to the generated spectra, making +them convenient tools to test new methods for analysis.

+ +

Spectrum manipulation

+ +

A number of filters can be applied to spectra (experimental and generated). +Scaling on both x and y axes is possible. Scaling could be linear or arbitrary +user defined function. A convolution is possible with set of predefined functions +as well as user defined model.

+ +

Concept

+ +

The code is object oriented, written in python. Main elements are Spectrum and +theoretical models. Theoretical models are based on LMFIT library, while +Spectrum is a custom made class. Spectrum object contains data for x and y axes +and metadata coming from experimental files or other sources. It is planned +to add information about the uncertainties in x and y. All filters and manipulation +procedures are available as class methods. Measures are taken to preserve spectrum +instances immutable, so filters are generating new spectra, preserving the original +unchanged. Additionally, Spectrum has information about its history -- the sequence +of applied filters.

+ +

File formats

+ +

.cha

+ +

ramanchada software package introduced .cha file format, which is an HDF5 +with a simple layout.

+ +

Cache in .cha files

+ +

The concept to keep previous variants of data is employed in ramanchada2. If +configured so, the software saves the data for all Spectrum instances to a +tree-organized .cha file. When a particular chain of operations is requested +by the user, the software checks if the final result is present in the cache file, +if so it is provided, otherwise the software checks for its parent. When a parent +or some of the grand parents are present, they are taken as a starting point and +the needed steps are applied to provide the final result. The current implementation +uses h5py library to access local hdf files. It is foreseen to have implementation +with h5pyd that support network operations.

+ +

Nexus format

+ +

The latest ramanchada2 package allows export of a spectrum to NeXus format.

+ +

Decorated Functions in Spectrum

+ +

Function: __add__

+ +

Docstring: No docstring available

+ +
+ +

Function: __init__

+ +

Docstring: No docstring available

+ +
+ +

Function: __mul__

+ +

Docstring: No docstring available

+ +
+ +

Function: __sub__

+ +

Docstring: No docstring available

+ +
+ +

Function: __truediv__

+ +

Docstring: No docstring available

+ +
+ +

Function: abs_nm_to_shift_cm_1

+ +

Docstring: + Convert wavelength to Ramanshift in wavenumber

+ +
Args:
+    spe: internal use only
+    laser_wave_length_nm: Laser wave length
+
+Returns: Corrected x-values
+
+ +
+ +

Function: abs_nm_to_shift_cm_1_filter

+ +

Docstring: + Convert wavelength to Ramanshift in wavenumber

+ +
Args:
+    spe: internal use only
+    laser_wave_length_nm: Laser wave length
+
+Returns: Spectrum with corrected x-values
+
+ +
+ +

Function: add_baseline

+ +

Docstring: + Add artificial baseline to the spectrum. + A random baseline is generated in frequency domain using uniform random numbers. + The baseline in frequency domain is tapered with bohman window to reduce the bandwidth + of the baseline to first n_freq frequencies and is transformed to "time" domain. + Additionaly by using func parameter the user can define arbitrary function + to be added as baseline.

+ +
Args:
+    n_freq:
+        Must be `> 2`. Number of lowest frequency bins distinct from zero.
+    amplitude:
+        Upper boundary for the uniform random generator.
+    pedestal:
+        Additive constant pedestal to the spectrum.
+    func:
+        Callable. User-defined function to be added as baseline. Example: `func = lambda x: x*.01 + x**2*.0001`.
+    rng_seed:
+        `int`, optional. Seed for the random generator.
+
+ +
+ +

Function: add_gaussian_noise

+ +

Docstring: + Add gaussian noise to the spectrum.

+ +
Random number i.i.d. $N(0, \sigma)$ is added to every sample
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    sigma:
+        Sigma of the gaussian noise.
+    rng_seed:
+        `int` or rng state, optional, seed for the random generator.
+        If a state is provided, it is updated in-place.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: add_gaussian_noise_drift

+ +

Docstring: + Add cumulative gaussian noise to the spectrum.

+ +
Exponential-moving-average-like gaussian noise is added
+to each sample. The goal is to mimic the low-frequency noise
+(or random substructures in spectra).
+The additive noise is
+$$a_i = coef*\sum_{j=0}^{i-1}g_j + g_i,$$
+
+where
+$$g_i = \mathcal{N}(0, 1+\frac{coef}{\sqrt 2}).$$
+
+This way drifting is possible while keeping the
+$$\sigma(\Delta(a)) \approx 1.$$
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    sigma:
+        Sigma of the gaussian noise.
+    coef:
+        `float` in `[0, 1]`, drifting coefficient. If `coef == 0`,
+        the result is identical to `add_gaussian_noise()`.
+    rng_seed:
+        `int` or rng state, optional. Seed for the random generator.
+        If a state is provided, it is updated in-place.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: add_poisson_noise

+ +

Docstring: + Add poisson noise to the spectrum.

+ +
For each particular sample the noise is proportional to $\sqrt{scale*a_i}$.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    scale:
+        `float`, optional, default is `1`. Scale the amplitude of the noise.
+    rng_seed:
+        `int` or rng state, optional. Seed for the random generator.
+        If a state is provided, it is updated in-place.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: apply_processing

+ +

Docstring: No docstring available

+ +
+ +

Function: bayesian_gaussian_mixture

+ +

Docstring: + Decompose the spectrum to Bayesian Gaussian Mixture

+ +
Args:
+    spe: internal use only
+    n_samples: optional. Defaults to 5000.
+        Resampled dataset size
+    n_components: optional. Defaults to 50.
+        Number of expected gaussian components
+    max_iter: optional. Defaults to 100.
+        Maximal number of iterations.
+    moving_minimum_window: optional. Defaults to None.
+        If None no moving minimum is subtracted, otherwise as specified.
+    random_state: optional. Defaults to None.
+        Random generator seed to be used.
+    trim_range: optional. Defaults to None:
+        If None ignore trimming, otherwise trim range is in x-axis values.
+
+Returns:
+    BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture
+
+ +
+ +

Function: calibrate_by_deltas_filter

+ +

Docstring: No docstring available

+ +
+ +

Function: calibrate_by_deltas_model

+ +

Docstring: + - Builds a composite model based on a set of user specified delta lines. + - Initial guess is calculated based on 10-th and 90-th percentiles of + the distributions.

+ +
The phasespace of the model is flat with big amount of narrow minima.
+In order to find the best fit, the experimental data are successively
+convolved with gaussians with different widths startign from wide to
+narrow. The model for the calibration is 3-th order polynomial, which
+potentialy can be changed for higher order polynomial. In order to avoid
+solving the inverse of the calibration function, the result is tabulated
+and interpolated linarly for each bin of the spectrum.
+This alogrithm is useful for corse calibration.
+
+ +
+ +

Function: central_moments

+ +

Docstring: No docstring available

+ +
+ +

Function: convolve

+ +

Docstring: + Convole spectrum with arbitrary lineshape.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    lineshape:callable, `str` or `np.ndarray`.
+         If callable: should have a single positional argument `x`, e.g.
+        `lambda x: np.exp((x/5)**2)`.
+        If predefined peak profile: can be `gaussian`, `lorentzian`, `voigt`,
+        `pvoigt`, `moffat` or `pearson4`.
+        If `np.ndarray`: lineshape in samples.
+    **kwargs:
+        Additional kwargs will be passed to lineshape function.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: derivative_sharpening

+ +

Docstring: + Derivative-based sharpening.

+ +
Sharpen the spectrum subtracting second derivative and add fourth derivative.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    filter_fraction `float` in (0; 1]: Default is 0.6
+        Depth of filtration
+    signal_width: The width of features to be enhanced in sample count
+    der2_factor: Second derivative scaling factor
+    der4_factor: Fourth derivative scaling factor
+
+Returns: modified Spectrum
+
+ +
+ +

Function: drop_spikes

+ +

Docstring: + Removes single-bin spikes.

+ +
Remove x, y pairs recognised as spikes using left and right
+successive differences and standard-deviation-based threshold.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    n_sigma: optional, default is `10`.
+        Threshold is `n_sigma` times the standard deviation.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: dropna

+ +

Docstring: + Remove non finite numbers on both axes

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+
+Returns: modified Spectrum
+
+ +
+ +

Function: find_peak_multipeak

+ +

Docstring: + Find groups of peaks in spectrum.

+ +
Args:
+    spe: internal use only
+    prominence: Optional. Defaults to None
+        If None the prominence value will be `spe.y_nose`. Reasonable value for
+        promience is `const * spe.y_noise_MAD`.
+    wlen: optional. Defaults to None.
+        wlen value used in `scipy.signal.find_peaks`. If wlen is None, 200 will be used.
+    width: optional. Defaults to None.
+        width value used in `scipy.signal.find_peaks`. If width is None, 2 will be used.
+    hht_chain: optional. Defaults to None.
+        List of hht_chain window sizes. If None, no hht sharpening is performed.
+    bgm_kwargs: kwargs for bayesian_gaussian_mixture
+    sharpening 'hht' or None. Defaults to None.
+        If 'hht' hht sharpening will be performed before finding peaks.
+    strategy: optional. Defauts to 'topo'.
+        Peakfinding method
+
+Returns:
+    ListPeakCandidateMultiModel: Located peak groups
+
+ +
+ +

Function: find_peak_multipeak_filter

+ +

Docstring: + Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    *args, **kwargs: same as `find_peak_multipeak`
+
+ +
+ +

Function: fit_peak_multimodel

+ +

Docstring: No docstring available

+ +
+ +

Function: fit_peak_positions

+ +

Docstring: + Calculate peak positions and amplitudes.

+ +
Sequence of multiple processings:
+- `subtract_moving_minimum`
+- `find_peak_multipeak`
+- filter peaks with x-location better than threshold
+
+Args:
+    spe: internal use only
+    mov_min: optional. Defaults to 40
+        subtract moving_minimum with the specified window.
+    center_err_threshold: optional. Defaults to 0.5.
+        threshold for centroid standard deviation. Only peaks
+        with better uncertainty will be returned.
+
+    find_peaks_kw: optional
+        keyword arguments to be used with find_peak_multipeak
+    fit_peaks_kw: optional
+        keyword arguments to be used with fit_peaks_multipeak
+
+Returns:
+    Dict[float, float]: {positions: amplitudes}
+
+ +
+ +

Function: fit_peaks_filter

+ +

Docstring: + Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    should_break: same as in fit_peaks_multipeak
+    *args, **kwargs: same as `fit_peaks_multipeak`
+
+ +
+ +

Function: from_cache_or_calc

+ +

Docstring: + Load spectrum from cache or calculate if needed.

+ +
The cache is a nested structure of spectra. All processings applied to
+a spectrum result to spectra of the initial one. If part of the requred
+processings are available, only the needed steps are calculated and added
+to the cache.
+
+Args:
+    required_steps: List of required steps in the form
+        [{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
+    cachefile: optional. Defaults to None.
+        Filename of the cache. If None no cache is used
+
+ +
+ +

Function: from_chada

+ +

Docstring: No docstring available

+ +
+ +

Function: from_delta_lines

+ +

Docstring: + Generate Spectrum with delta lines.

+ +
Args:
+    deltas:
+        Keys of the dictionary are the `x` positions of the deltas; values are the amplitudes of the corresponding
+        deltas.
+    xcal:
+        Callable, optional. `x` axis calibration function.
+    nbins:
+        `int`, optional. Number of bins in the spectrum.
+    xaxis:
+        `Array-like`, optional. The xaxis of the new spectrum. If `xaxis` is provided,
+        `xcal` should be `None` and `nbins` is ignored.
+
+Example:
+
+This will produce spectrum with 1000 bins in the range `[-1000, 2000)`:
+
+
+

+
xcal = lambda x: x*3 -1000, nbins=1000
+

+

+
+ +
+ +

Function: from_local_file

+ +

Docstring: + Read experimental spectrum from a local file.

+ +
Args:
+    in_file_name:
+        Path to a local file containing a spectrum.
+    filetype:
+        Specify the filetype. Filetype can be any of: `spc`, `sp`, `spa`, `0`, `1`, `2`, `wdf`, `ngs`, `jdx`, `dx`,
+        `txt`, `txtr`, `csv`, `prn`, `rruf`, `spe` (Princeton Instruments) or `None`.
+        `None` used to determine by extension of the file.
+    backend:
+        `native`, `rc1_parser` or `None`. `None` means both.
+
+Raises:
+    ValueError:
+        When called with unsupported file formats.
+
+ +
+ +

Function: from_simulation

+ +

Docstring: + Generate spectrum from simulation file.

+ +
The returned spectrum has only few x/y pairs -- one for each simulated line. Values along
+the x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.
+
+Args:
+    in_file:
+        Path to a local file, or file-like object.
+    sim_type:
+        If `vasp`: `.dat` file from VASP simulation. If `crystal_out`: `.out` file from CRYSTAL simulation, not
+        preferred. If `crystal_dat`: `.dat` file from CRYSTAL simulation.
+    use:
+        One of the directions `I_tot`, `I_perp`, `I_par`, `I_xx`, `I_xy`,
+        `I_xz`, `I_yy`, `I_yz`, `I_zz`, `I_tot`, `I_perp`, `I_par` are
+        available for both CRYSTAL and VASP. `I_xx`, `I_xy`, `I_xz`,
+        `I_yy`, `I_yz`, `I_zz` are available only for CRYSTAL. If a Dict is
+        passed, the key should be directions and values should be weighting factor.
+        For example, `use={'I_perp': .1, 'I_par': .9}`
+
+ +
+ +

Function: from_spectral_component_collection

+ +

Docstring: + from_spectral_component_collection

+ +
Args:
+    spe_components:
+        SpectralComponentCollection
+    x:
+        `int` or array-like, optional, default `2000`. `x` axis of the spectrum.
+
+ +
+ +

Function: from_stream

+ +

Docstring: No docstring available

+ +
+ +

Function: from_test_spe

+ +

Docstring: Create new spectrum from test data.

+ +
Args:
+    index:
+        `int` or `None`, optional, default is `None`. If `int`: will be used as an index of filtered list. If
+        `None`: a random spectrum will be taken.
+    **kwargs:
+        The rest of the parameters will be used as filter.
+
+ +
+ +

Function: from_theoretical_lines

+ +

Docstring: + Generate spectrum from lmfit shapes.

+ +
Args:
+    shapes:
+        The shapes to be used for spectrum generation.
+    params:
+        Shape parameters to be applied to be used with shapes.
+    x:
+        Array with `x` values, by default `np.array(2000)`.
+
+ +
+ +

Function: gen_samples

+ +

Docstring: No docstring available

+ +
+ +

Function: get_spikes

+ +

Docstring: + Get single-bin spikes only.

+ +
Get x, y pairs recognised as spikes using left and right
+successive differences and standard-deviation-based threshold
+and linear interpolation.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    n_sigma: optional, default is `10`.
+        Threshold is `n_sigma` times the standard deviation.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: hdr_from_multi_exposure

+ +

Docstring: Create an HDR spectrum from several spectra with different exposures.

+ +
The resulting spectrum will have the details in low-intensity peaks
+from long-exposure-time spectrum. As long-exposure-time
+spectrum might be sturated, the information for high-intensity
+peaks will be taken from short-exposure-time spectrum.
+This function will work on a very limited number of spectra,
+because we still do not have standardized metadata.
+
+ +
+ +

Function: hht_sharpening

+ +

Docstring: + Hilbert-Huang based sharpening.

+ +
In order to reduce the overshooting, moving minimum is subtracted from the result
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    movmin: optional. Default is 100
+        Window size for moving minimum
+
+Returns: modified Spectrum
+
+ +
+ +

Function: hht_sharpening_chain

+ +

Docstring: + Hilbert-Huang based chain sharpening.

+ +
Sequence of Hilbert-Huang sharpening procedures are performed.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    movmin: List[int], optional. Default is [150, 50]
+        The numer of values in the list defines how many iterations
+        of HHT_sharpening will be performed and the values define
+        the moving minimum window sizes for the corresponding operations.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: moving_average

+ +

Docstring: + Moving average filter.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    window_size:
+        `int`, optional, default is `10`.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: moving_average_convolve

+ +

Docstring: + Moving average filter.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    window_size:
+        `int`, optional, default is `10`.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: moving_median

+ +

Docstring: + Moving median filter.

+ +
The resultant spectrum is moving minimum of the input.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    window_size:
+        `int`, optional, default is `10`.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: moving_minimum

+ +

Docstring: + Moving minimum baseline estimator. + Successive values are calculated as minima of rolling rectangular window.

+ +
+ +

Function: normalize

+ +

Docstring: + Normalize the spectrum.

+ +
Args:
+    strategy:
+        If `unity`: normalize to `sum(y)`. If `min_unity`: subtract the minimum and normalize to 'unity'. If
+        `unity_density`: normalize to `Σ(y_i*Δx_i)`. If `unity_area`: same as `unity_density`. If `minmax`: scale
+        amplitudes in range `[0, 1]`. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
+
+ +
+ +

Function: pad_zeros

+ +

Docstring: + Extend x-axis by 100% in both directions.

+ +
The x-axis of resultant spectrum will be:
+$[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.
+The length of the new spectrum is 3 times the original. The added values
+are with an uniform step. In the middle is the original spectrum with
+original x and y values. The coresponding y vallues for the newly added
+x-values are always zeros.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+
+Returns: modified Spectrum
+
+ +
+ +

Function: recover_spikes

+ +

Docstring: + Recover single-bin spikes.

+ +
Recover x, y pairs recognised as spikes using left and right
+successive differences and standard-deviation-based threshold
+and linear interpolation.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    n_sigma: optional, default is `10`.
+        Threshold is `n_sigma` times the standard deviation.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: resample_NUDFT

+ +

Docstring: + Resample the spectrum using Non-uniform discrete fourier transform.

+ +
The x-axis of the result will be uniform. The corresponding y-values
+will be calculated with NUDFT and inverse FFT.
+
+Args:
+    spe: internal use only
+    x_range: optional. Defaults to (0, 4000).
+        The x_range of the new spectrum.
+    xnew_bins: optional. Defaults to 100.
+        Number of bins of the new spectrum
+    window: optional, Defaults to None.
+        The window to be used for lowpass filter. If None 'blackmanharris' is used.
+        If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+    cumulative: optional. Defaults to False.
+        If True, the resultant spectrum will be cumulative and normalized
+        (in analogy with CDF).
+
+Returns:
+    (x_values, y_values)
+
+ +
+ +

Function: resample_NUDFT_filter

+ +

Docstring: + Resample the spectrum using Non-uniform discrete fourier transform.

+ +
The x-axis of the result will be uniform. The corresponding y-values
+will be calculated with NUDFT and inverse FFT.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    x_range: optional. Defaults to (0, 4000).
+        The x_range of the new spectrum.
+    xnew_bins: optional. Defaults to 100.
+        Number of bins of the new spectrum
+    window: optional, Defaults to None.
+        The window to be used for lowpass filter. If None 'blackmanharris' is used.
+        If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+    cumulative: optional. Defaults to False.
+        If True, the resultant spectrum will be cumulative and normalized
+        (in analogy with CDF).
+
+Returns: modified Spectrum
+
+ +
+ +

Function: resample_spline

+ +

Docstring: + Resample the spectrum using spline interpolation.

+ +
The x-axis of the result will be uniform. The corresponding y-values
+will be calculated with spline interpolation.
+
+Args:
+    spe: internal use only
+    x_range: optional. Defaults to (0, 4000).
+        The x_range of the new spectrum.
+    xnew_bins: optional. Defaults to 100.
+        Number of bins of the new spectrum
+    spline: optional, Defaults to 'pchip'.
+        Name of the spline funcion to be used.
+    cumulative: optional. Defaults to False.
+        If True, the resultant spectrum will be cumulative and normalized
+        (in analogy with CDF).
+
+Returns:
+    (x_values, y_values)
+
+ +
+ +

Function: resample_spline_filter

+ +

Docstring: + Resample the spectrum using spline interpolation.

+ +
The x-axis of the result will be uniform. The corresponding y-values
+will be calculated with spline interpolation.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    x_range: optional. Defaults to (0, 4000).
+        The x_range of the new spectrum.
+    xnew_bins: optional. Defaults to 100.
+        Number of bins of the new spectrum
+    spline: optional, Defaults to 'pchip'.
+        Name of the spline funcion to be used.
+    cumulative: optional. Defaults to False.
+        If True, the resultant spectrum will be cumulative and normalized
+        (in analogy with CDF).
+
+Returns: modified Spectrum
+
+ +
+ +

Function: scale_xaxis_fun

+ +

Docstring: + Apply arbitrary calibration function to the x-axis values.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    fun: function to be applied
+    args: Additional arguments to the provided functions
+
+Returns: Corrected spectrum
+
+Raises:
+    ValueError: If the new x-values are not strictly monotonically increasing.
+
+ +
+ +

Function: scale_xaxis_linear

+ +

Docstring: + Scale x-axis using a factor.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    factor: Defaults to 1.
+        Multiply x-axis values with `factor`
+    preserve_integral: optional. Defaults to False.
+        If True, preserves the integral in sence
+        $\sum y_{orig;\,i}*{\Delta x_{orig}}_i = \sum y_{new;\,i}*{\Delta x_{new}}_i = $
+Returns: Corrected spectrum
+
+ +
+ +

Function: scale_yaxis_linear

+ +

Docstring: + Scale y-axis values

+ +
This function provides the same result as `spe*const`
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    factor optional. Defaults to 1.
+        Y-values scaling factor
+
+Returns: corrected spectrum
+
+ +
+ +

Function: set_new_xaxis

+ +

Docstring: + Substitute x-axis values with new ones

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    xaxis: new x-axis values
+
+Returns: corrected spectrum
+
+Raises:
+    ValueError: If the provided array does not match the shape of the spectrum.
+
+ +
+ +

Function: shift_cm_1_to_abs_nm

+ +

Docstring: + Convert Ramanshift in wavenumber to wavelength

+ +
Args:
+    spe: internal use only
+    laser_wave_length_nm: Laser wave length
+
+Returns: Corrected x-values
+
+ +
+ +

Function: shift_cm_1_to_abs_nm_filter

+ +

Docstring: + Convert Ramanshift in wavenumber to wavelength

+ +
Args:
+    spe: internal use only
+    laser_wave_length_nm: Laser wave length
+
+Returns: Spectrum with corrected x-values
+
+ +
+ +

Function: smoothing_RC1

+ +

Docstring: + Smooth the spectrum.

+ +
The spectrum will be smoothed using the specified filter.
+This method is inherited from ramanchada1 for compatibility reasons.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    method: method to be used
+    **kwargs: keyword arguments to be passed to the selected method
+
+Returns: modified Spectrum
+
+ +
+ +

Function: spe_distribution

+ +

Docstring: No docstring available

+ +
+ +

Function: spike_indices

+ +

Docstring: + Find spikes in spectrum

+ +
Single-bin spikes are located using left and right successive
+differences. The threshold is based on the standart deviation
+of the metric which makes this algorithm less optimal.
+
+Args:
+    spe: internal use only
+    n_sigma: Threshold value should be `n_sigma` times the standart
+      deviation of the metric.
+
+Returns: List of spike indices
+
+ +
+ +

Function: subtract_baseline_rc1_als

+ +

Docstring: No docstring available

+ +
+ +

Function: subtract_baseline_rc1_snip

+ +

Docstring: No docstring available

+ +
+ +

Function: subtract_moving_median

+ +

Docstring: + Subtract moving median filter.

+ +
The resultant spectrum is moving minimum of the input subtracted from the input.
+
+Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    window_size:
+        `int`, optional, default is `10`.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: subtract_moving_minimum

+ +

Docstring: No docstring available

+ +
+ +

Function: trim_axes

+ +

Docstring: + Trim axes of the spectrum.

+ +
Args:
+    old_spe: internal use only
+    new_spe: internal use only
+    method: 'x-axis' or 'bins'
+        If 'x-axis' boundaries will be interpreted as x-axis values.
+        If 'bins' boundaries will be interpreted as indices.
+    boundaries: lower and upper boundary for the trimming.
+
+Returns: modified Spectrum
+
+ +
+ +

Function: xcal_argmin2d_iter_lowpass

+ +

Docstring: + Calibrate spectrum

+ +
The calibration is done in multiple steps. Both the spectrum and the reference
+are passed through a low-pass filter to preserve only general structure of the
+spectrum. `low_pass_nfreqs` defines the number of frequencies to be preserved in
+each step. Once all steps with low-pass filter a final step without a low-pass
+filter is performed. Each calibration step is performed using
+`~ramanchada2.spectrum.calibration.by_deltas.xcal_fine()` algorithm.
+
+Args:
+    old_spe (Spectrum): internal use only
+    new_spe (Spectrum): internal use only
+    ref (Dict[float, float]): wavenumber - amplitude pairs
+    low_pass_nfreqs (List[int], optional): The number of elements defines the
+        number of low-pass steps and their values define the amount of frequencies
+        to keep. Defaults to [100, 500].
+
+ +
+ +

Function: xcal_fine

+ +

Docstring: + Iterative calibration with provided reference based on ~ramanchada2.misc.utils.argmin2d.align()

+ +
Iteratively apply polynomial of `poly_order` degree to match
+the found peaks to the reference locations. The pairs are created
+using `~ramanchada2.misc.utils.argmin2d.align()` algorithm.
+
+Args:
+    old_spe (Spectrum): internal use only
+    new_spe (Spectrum): internal use only
+    ref (Union[Dict[float, float], List[float]]): _description_
+    ref (Dict[float, float]):
+        If a dict is provided - wavenumber - amplitude pairs.
+        If a list is provided - wavenumbers only.
+    poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
+    should_fit (bool, optional): Whether the peaks should be fit or to
+        associate the positions with the maxima. Defaults to False.
+    find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
+
+ +
+ +

Function: xcal_fine_RBF

+ +

Docstring: Wavelength calibration using Radial basis fuction interpolation

+ +
Please be cautious! Interpolation might not be the most appropriate
+approach for this type of calibration.
+
+**kwargs are passed to RBFInterpolator
+
+ +
+ +

Function: y_noise_savgol

+ +

Docstring: No docstring available

+ +
+ +

Function: y_noise_savgol_DL

+ +

Docstring: No docstring available

+ +
+
+ + + + + + 
  1#!/usr/bin/env python3
+  2
+  3
+  4"""
+  5# Purpose
+  6`ramanchada2` software package is meant to fill the gap between the theoretical
+  7Raman analysis and the experimental Raman spectroscopy by providing means to
+  8compare data of different origin. The software is in early development stage
+  9but still able to solve practical problems.
+ 10
+ 11# Features
+ 12
+ 13## Read simulated data
+ 14Process simulated data by [VASP][] and [CRYSTAL][] and provide same interface.
+ 15CRYSTAL data contain intensities for multiple orientations -- laser beam
+ 16incidents perpendicularly or parallelly to the observation and information
+ 17for mono-crystals. VASP data provide data only for poly-crystals but in
+ 18different format. So the perpendicular and parallel intensities are calculated
+ 19by an implemented [algorithm][].
+ 20
+ 21## Models
+ 22[LMFIT][] theoretical models can be build by spectral information obtained by
+ 23simulations or by provided by the user. These models can be fit to experimental
+ 24data, providing calibration information. At poor initial calibration the minimisation
+ 25procedure naturally fails. An iterative procedure aiming to solve this problem
+ 26was adopted in the code. On the first iteration the experimental spectrum lines
+ 27are artificially broadened. This makes it possible for the minimisation procedure
+ 28to find a parameters that are close enough to be used as an initial guess for
+ 29the second iteration. In few iterations the algorithm is able to fit to the original
+ 30experimental data. This idea is implemented and is at proof-of-concept level.
+ 31
+ 32## Generate spectra
+ 33Spectra can be generated by the theoretical models. Random Poissonian noise and
+ 34artificial random-generated baseline can be added to the generated spectra, making
+ 35them convenient tools to test new methods for analysis.
+ 36
+ 37## Spectrum manipulation
+ 38A number of filters can be applied to spectra (experimental and generated).
+ 39Scaling on both x and y axes is possible. Scaling could be linear or arbitrary
+ 40user defined function. A convolution is possible with set of predefined functions
+ 41as well as user defined model.
+ 42
+ 43# Concept
+ 44The code is object oriented, written in python. Main elements are Spectrum and
+ 45theoretical models. Theoretical models are based on LMFIT library, while
+ 46Spectrum is a custom made class. Spectrum object contains data for x and y axes
+ 47and metadata coming from experimental files or other sources. It is planned
+ 48to add information about the uncertainties in x and y. All filters and manipulation
+ 49procedures are available as class methods. Measures are taken to preserve spectrum
+ 50instances immutable, so filters are generating new spectra, preserving the original
+ 51unchanged. Additionally, Spectrum has information about its history -- the sequence
+ 52of applied filters.
+ 53
+ 54# File formats
+ 55
+ 56## `.cha`
+ 57[ramanchada][] software package introduced `.cha` file format, which is an [HDF5][]
+ 58with a simple layout.
+ 59
+ 60### Cache in .cha files
+ 61The concept to keep previous variants of data is employed in `ramanchada2`. If
+ 62configured so, the software saves the data for all Spectrum instances to a
+ 63tree-organized `.cha` file. When a particular chain of operations is requested
+ 64by the user, the software checks if the final result is present in the cache file,
+ 65if so it is provided, otherwise the software checks for its parent. When a parent
+ 66or some of the grand parents are present, they are taken as a starting point and
+ 67the needed steps are applied to provide the final result. The current implementation
+ 68uses [h5py][] library to access local hdf files. It is foreseen to have implementation
+ 69with [h5pyd][] that support network operations.
+ 70
+ 71## Nexus format
+ 72
+ 73The latest ramanchada2 package allows export of a spectrum to [NeXus][] format.
+ 74
+ 75
+ 76[CRYSTAL]: https://www.crystal.unito.it/index.php
+ 77[HDF5]: https://hdfgroup.org/
+ 78[LMFIT]: https://lmfit.github.io/lmfit-py/index.html
+ 79[VASP]: https://www.vasp.at/
+ 80[algorithm]: https://doi.org/10.1103/PhysRevB.54.7830
+ 81[h5py]: https://h5py.org/
+ 82[h5pyd]: https://github.com/HDFGroup/h5pyd
+ 83[ramanchada]: https://github.com/h2020charisma/ramanchada
+ 84[NeXus]: https://www.nexusformat.org/
+ 85
+ 86.. include:: ../../../../../../spectrum_functions.md
+ 87"""
+ 88
+ 89from __future__ import annotations
+ 90
+ 91from . import spectrum
+ 92from . import theoretical_lines
+ 93__all__ = [
+ 94    'auxiliary',
+ 95    'io',
+ 96    'misc',
+ 97    'protocols',
+ 98    'spectral_components',
+ 99    'spectrum',
+100    'theoretical_lines'
+101]
+102__version__ = '1.2.0'
+103
+104
+105import logging
+106
+107
+108class CustomFormatter(logging.Formatter):
+109    green = "\x1b[32m"
+110    blue = "\x1b[34m"
+111    yellow = "\x1b[33m"
+112    red = "\x1b[31m"
+113    bold_red = "\x1b[31;1m"
+114    reset = "\x1b[0m"
+115    fmt = "%(asctime)s %(name)s %(levelname)s - %(message)s"
+116    fmt = "%(levelname)s - %(filename)s:%(lineno)d %(funcName)s() - %(message)s"
+117
+118    FORMATS = {
+119        logging.DEBUG: green + fmt + reset,
+120        logging.INFO: blue + fmt + reset,
+121        logging.WARNING: yellow + fmt + reset,
+122        logging.ERROR: red + fmt + reset,
+123        logging.CRITICAL: bold_red + fmt + reset
+124    }
+125
+126    def format(self, record):
+127        log_fmt = self.FORMATS.get(record.levelno)
+128        formatter = logging.Formatter(log_fmt)
+129        return formatter.format(record)
+130
+131
+132def basicConfig(level=logging.INFO):
+133    ch = logging.StreamHandler()
+134    ch.setLevel(level)
+135    ch.setFormatter(CustomFormatter())
+136    logging.basicConfig(handlers=[ch], force=True)
+137
+138
+139stream = logging.StreamHandler()
+140stream.setFormatter(CustomFormatter())
+141logging.basicConfig(handlers=[stream], force=True)
+142logger = logging.getLogger(__name__)
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/auxiliary.html b/ramanchada2/auxiliary.html new file mode 100644 index 00000000..616836e8 --- /dev/null +++ b/ramanchada2/auxiliary.html @@ -0,0 +1,259 @@ + + + + + + + ramanchada2.auxiliary API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.auxiliary

+ + + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/auxiliary/spectra.html b/ramanchada2/auxiliary/spectra.html new file mode 100644 index 00000000..9baa880d --- /dev/null +++ b/ramanchada2/auxiliary/spectra.html @@ -0,0 +1,265 @@ + + + + + + + ramanchada2.auxiliary.spectra API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.auxiliary.spectra

+ + + + + + + 
1from . import datasets2, simulated
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/auxiliary/spectra/datasets2.html b/ramanchada2/auxiliary/spectra/datasets2.html new file mode 100644 index 00000000..14efa8f6 --- /dev/null +++ b/ramanchada2/auxiliary/spectra/datasets2.html @@ -0,0 +1,1827 @@ + + + + + + + ramanchada2.auxiliary.spectra.datasets2 API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.auxiliary.spectra.datasets2

+ + + + + + + 
   1import importlib.resources
+   2from functools import reduce
+   3
+   4import pandas as pd
+   5
+   6data = [
+   7    {
+   8        "OP": "01",
+   9        "device": "BWtek",
+  10        "filename": "./FMNT-M_BW532/NeonSNQ043_iR532_Probe_5msx2.txt",
+  11        "laser_wl": "532",
+  12        "provider": "FNMT",
+  13        "sample": "Neon",
+  14    },
+  15    {
+  16        "OP": "01",
+  17        "device": "BWtek",
+  18        "filename": "./FMNT-M_BW532/NeonSNQ043_iR532_Probe_100msx2.txt",
+  19        "laser_wl": "532",
+  20        "provider": "FNMT",
+  21        "sample": "Neon",
+  22    },
+  23    {
+  24        "OP": "01",
+  25        "device": "BWtek",
+  26        "filename": "./FMNT-M_BW532/PST10_iR532_Probe_100_3000msx7.txt",
+  27        "laser_wl": "532",
+  28        "provider": "FNMT",
+  29        "sample": "PST",
+  30    },
+  31    {
+  32        "OP": "01",
+  33        "device": "BWtek",
+  34        "filename": "./FMNT-M_BW532/S0B10_iR532_Probe_100_60000msx2.txt",
+  35        "laser_wl": "532",
+  36        "provider": "FNMT",
+  37        "sample": "S0B",
+  38    },
+  39    {
+  40        "OP": "01",
+  41        "device": "BWtek",
+  42        "filename": "./FMNT-M_BW532/S0N10_iR532_Probe_100_30000msx3.txt",
+  43        "laser_wl": "532",
+  44        "provider": "FNMT",
+  45        "sample": "S0N",
+  46    },
+  47    {
+  48        "OP": "01",
+  49        "device": "BWtek",
+  50        "filename": "./FMNT-M_BW532/S0P10_iR532_Probe_100_60000msx2.txt",
+  51        "laser_wl": "532",
+  52        "provider": "FNMT",
+  53        "sample": "S0P",
+  54    },
+  55    {
+  56        "OP": "01",
+  57        "device": "BWtek",
+  58        "filename": "./FMNT-M_BW532/S1N10_iR532_Probe_100_22000msx2.txt",
+  59        "laser_wl": "532",
+  60        "provider": "FNMT",
+  61        "sample": "S1N",
+  62    },
+  63    {
+  64        "OP": "01",
+  65        "device": "BWtek",
+  66        "filename": "./FMNT-M_BW532/Sil10_iR532_Probe_100_60000msx2.txt",
+  67        "laser_wl": "532",
+  68        "provider": "FNMT",
+  69        "sample": "Sil_",
+  70    },
+  71    {
+  72        "OP": "01",
+  73        "device": "BWtek",
+  74        "filename": "./FMNT-M_BW532/nCAL10_iR532_Probe_100_2500msx3.txt",
+  75        "laser_wl": "532",
+  76        "provider": "FNMT",
+  77        "sample": "nCAL",
+  78    },
+  79    {
+  80        "OP": "01",
+  81        "device": "BWtek",
+  82        "filename": "./FMNT-M_BW532/sCAL10_iR532_Probe_100_3200msx4.txt",
+  83        "laser_wl": "532",
+  84        "provider": "FNMT",
+  85        "sample": "sCAL",
+  86    },
+  87    {
+  88        "OP": "01",
+  89        "device": "BWtek",
+  90        "filename": "./FMNT-M_BW532/LED532_Probe_40msx3_1.txt",
+  91        "laser_wl": "532",
+  92        "provider": "FNMT",
+  93        "sample": "LED532_EL0-9001",
+  94    },
+  95    {
+  96        "OP": "01",
+  97        "device": "BWtek",
+  98        "filename": "./FMNT-M_BW532/NIST532_Probe_3000msx8_1.txt",
+  99        "laser_wl": "532",
+ 100        "provider": "FNMT",
+ 101        "sample": "NIST532_SRM2242a",
+ 102    },
+ 103    {
+ 104        "OP": "01",
+ 105        "device": "Horiba",
+ 106        "filename": "./FMNT-M_Ho785/NeonSNQ043_iR785_OP01.txt",
+ 107        "laser_wl": "785",
+ 108        "provider": "FNMT",
+ 109        "sample": "Neon",
+ 110    },
+ 111    {
+ 112        "OP": "01",
+ 113        "device": "Horiba",
+ 114        "filename": "./FMNT-M_Ho785/PST10_iR785_OP01_40000msx4.txt",
+ 115        "laser_wl": "785",
+ 116        "provider": "FNMT",
+ 117        "sample": "PST",
+ 118    },
+ 119    {
+ 120        "OP": "01",
+ 121        "device": "Horiba",
+ 122        "filename": "./FMNT-M_Ho785/S0B10_iR785_OP01_6000msx4.txt",
+ 123        "laser_wl": "785",
+ 124        "provider": "FNMT",
+ 125        "sample": "S0B",
+ 126    },
+ 127    {
+ 128        "OP": "01",
+ 129        "device": "Horiba",
+ 130        "filename": "./FMNT-M_Ho785/S0N10_iR785_OP01_6000msx4.txt",
+ 131        "laser_wl": "785",
+ 132        "provider": "FNMT",
+ 133        "sample": "S0N",
+ 134    },
+ 135    {
+ 136        "OP": "01",
+ 137        "device": "Horiba",
+ 138        "filename": "./FMNT-M_Ho785/S0P10_iR785_OP01_6000msx4.txt",
+ 139        "laser_wl": "785",
+ 140        "provider": "FNMT",
+ 141        "sample": "S0P",
+ 142    },
+ 143    {
+ 144        "OP": "01",
+ 145        "device": "Horiba",
+ 146        "filename": "./FMNT-M_Ho785/S1N10_iR785_OP01_6000msx4.txt",
+ 147        "laser_wl": "785",
+ 148        "provider": "FNMT",
+ 149        "sample": "S1N",
+ 150    },
+ 151    {
+ 152        "OP": "01",
+ 153        "device": "Horiba",
+ 154        "filename": "./FMNT-M_Ho785/Sil10_iR785_OP01_6000msx4.txt",
+ 155        "laser_wl": "785",
+ 156        "provider": "FNMT",
+ 157        "sample": "Sil",
+ 158    },
+ 159    {
+ 160        "OP": "01",
+ 161        "device": "Horiba",
+ 162        "filename": "./FMNT-M_Ho785/nCAL10_iR785_OP01_6000msx4.txt",
+ 163        "laser_wl": "785",
+ 164        "provider": "FNMT",
+ 165        "sample": "nCAL",
+ 166    },
+ 167    {
+ 168        "OP": "01",
+ 169        "device": "Horiba",
+ 170        "filename": "./FMNT-M_Ho785/sCAL10_iR785_OP01_4000msx4.txt",
+ 171        "laser_wl": "785",
+ 172        "provider": "FNMT",
+ 173        "sample": "sCAL",
+ 174    },
+ 175    {
+ 176        "OP": "02",
+ 177        "device": "Horiba",
+ 178        "filename": "./FMNT-M_Ho785/NeonSNQ043_iR785_OP02.txt",
+ 179        "laser_wl": "785",
+ 180        "provider": "FNMT",
+ 181        "sample": "Neon",
+ 182    },
+ 183    {
+ 184        "OP": "02",
+ 185        "device": "Horiba",
+ 186        "filename": "./FMNT-M_Ho785/PST10_iR785_OP02_50000msx2.txt",
+ 187        "laser_wl": "785",
+ 188        "provider": "FNMT",
+ 189        "sample": "PST",
+ 190    },
+ 191    {
+ 192        "OP": "02",
+ 193        "device": "Horiba",
+ 194        "filename": "./FMNT-M_Ho785/S0B10_iR785_OP02_25000msx2.txt",
+ 195        "laser_wl": "785",
+ 196        "provider": "FNMT",
+ 197        "sample": "S0B",
+ 198    },
+ 199    {
+ 200        "OP": "02",
+ 201        "device": "Horiba",
+ 202        "filename": "./FMNT-M_Ho785/S0N10_iR785_OP02_25000msx2.txt",
+ 203        "laser_wl": "785",
+ 204        "provider": "FNMT",
+ 205        "sample": "S0N",
+ 206    },
+ 207    {
+ 208        "OP": "02",
+ 209        "device": "Horiba",
+ 210        "filename": "./FMNT-M_Ho785/S0P10_iR785_OP02_25000msx2.txt",
+ 211        "laser_wl": "785",
+ 212        "provider": "FNMT",
+ 213        "sample": "S0P",
+ 214    },
+ 215    {
+ 216        "OP": "02",
+ 217        "device": "Horiba",
+ 218        "filename": "./FMNT-M_Ho785/S1N10_iR785_OP02_25000msx2.txt",
+ 219        "laser_wl": "785",
+ 220        "provider": "FNMT",
+ 221        "sample": "S1N",
+ 222    },
+ 223    {
+ 224        "OP": "02",
+ 225        "device": "Horiba",
+ 226        "filename": "./FMNT-M_Ho785/Sil10_iR785_OP02_25000msx2.txt",
+ 227        "laser_wl": "785",
+ 228        "provider": "FNMT",
+ 229        "sample": "Sil",
+ 230    },
+ 231    {
+ 232        "OP": "02",
+ 233        "device": "Horiba",
+ 234        "filename": "./FMNT-M_Ho785/nCAL10_iR785_OP02_20000msx2.txt",
+ 235        "laser_wl": "785",
+ 236        "provider": "FNMT",
+ 237        "sample": "nCAL",
+ 238    },
+ 239    {
+ 240        "OP": "02",
+ 241        "device": "Horiba",
+ 242        "filename": "./FMNT-M_Ho785/sCAL10_iR785_OP02_15000msx2.txt",
+ 243        "laser_wl": "785",
+ 244        "provider": "FNMT",
+ 245        "sample": "sCAL",
+ 246    },
+ 247    {
+ 248        "OP": "03",
+ 249        "device": "Horiba",
+ 250        "filename": "./FMNT-M_Ho785/NeonSNQ043_iR785_OP03.txt",
+ 251        "laser_wl": "785",
+ 252        "provider": "FNMT",
+ 253        "sample": "Neon",
+ 254    },
+ 255    {
+ 256        "OP": "03",
+ 257        "device": "Horiba",
+ 258        "filename": "./FMNT-M_Ho785/PST10_iR785_OP03_8000msx2.txt",
+ 259        "laser_wl": "785",
+ 260        "provider": "FNMT",
+ 261        "sample": "PST",
+ 262    },
+ 263    {
+ 264        "OP": "03",
+ 265        "device": "Horiba",
+ 266        "filename": "./FMNT-M_Ho785/S0B10_iR785_OP03_8000msx2.txt",
+ 267        "laser_wl": "785",
+ 268        "provider": "FNMT",
+ 269        "sample": "S0B",
+ 270    },
+ 271    {
+ 272        "OP": "03",
+ 273        "device": "Horiba",
+ 274        "filename": "./FMNT-M_Ho785/S0N10_iR785_OP03_8000msx2.txt",
+ 275        "laser_wl": "785",
+ 276        "provider": "FNMT",
+ 277        "sample": "S0N",
+ 278    },
+ 279    {
+ 280        "OP": "03",
+ 281        "device": "Horiba",
+ 282        "filename": "./FMNT-M_Ho785/S0P10_iR785_OP03_8000msx2.txt",
+ 283        "laser_wl": "785",
+ 284        "provider": "FNMT",
+ 285        "sample": "S0P",
+ 286    },
+ 287    {
+ 288        "OP": "03",
+ 289        "device": "Horiba",
+ 290        "filename": "./FMNT-M_Ho785/S1N10_iR785_OP03_8000msx2.txt",
+ 291        "laser_wl": "785",
+ 292        "provider": "FNMT",
+ 293        "sample": "S1N",
+ 294    },
+ 295    {
+ 296        "OP": "03",
+ 297        "device": "Horiba",
+ 298        "filename": "./FMNT-M_Ho785/Sil10_iR785_OP03_8000msx2.txt",
+ 299        "laser_wl": "785",
+ 300        "provider": "FNMT",
+ 301        "sample": "Sil",
+ 302    },
+ 303    {
+ 304        "OP": "03",
+ 305        "device": "Horiba",
+ 306        "filename": "./FMNT-M_Ho785/nCAL10_iR785_OP03_8000msx2.txt",
+ 307        "laser_wl": "785",
+ 308        "provider": "FNMT",
+ 309        "sample": "nCAL",
+ 310    },
+ 311    {
+ 312        "OP": "03",
+ 313        "device": "Horiba",
+ 314        "filename": "./FMNT-M_Ho785/sCAL10_iR785_OP03_8000msx2.txt",
+ 315        "laser_wl": "785",
+ 316        "provider": "FNMT",
+ 317        "sample": "sCAL",
+ 318    },
+ 319    {
+ 320        "OP": "03",
+ 321        "device": "Horiba",
+ 322        "filename": "./FMNT-M_Ho785/LED785_Lens_1000x10_2.txt",
+ 323        "laser_wl": "785",
+ 324        "provider": "FNMT",
+ 325        "sample": "NIR785_EL0-9002",
+ 326    },
+ 327    {
+ 328        "OP": "03",
+ 329        "device": "Horiba",
+ 330        "filename": "./FMNT-M_Ho785/NIST785_Lens_80000x5_2.txt",
+ 331        "laser_wl": "785",
+ 332        "provider": "FNMT",
+ 333        "sample": "NIST785_SRM2241",
+ 334    },
+ 335    {
+ 336        "OP": "050",
+ 337        "device": "BWtek",
+ 338        "filename": "./ICV_BW532/Ne_532nm_x50_800ms.txt",
+ 339        "laser_wl": "532",
+ 340        "provider": "ICV",
+ 341        "sample": "Neon",
+ 342    },
+ 343    {
+ 344        "OP": "050",
+ 345        "device": "BWtek",
+ 346        "filename": "./ICV_BW532/Ne_532nm_x50_25ms.txt",
+ 347        "laser_wl": "532",
+ 348        "provider": "ICV",
+ 349        "sample": "Neon",
+ 350    },
+ 351    {
+ 352        "OP": "050",
+ 353        "device": "BWtek",
+ 354        "filename": "./ICV_BW532/PST02_iRPlus532_Z050_100_2500msx5.txt",
+ 355        "laser_wl": "532",
+ 356        "provider": "ICV",
+ 357        "sample": "PST",
+ 358    },
+ 359    {
+ 360        "OP": "050",
+ 361        "device": "BWtek",
+ 362        "filename": "./ICV_BW532/S0B02_iRPlus532_Z050_100_30000ms.txt",
+ 363        "laser_wl": "532",
+ 364        "provider": "ICV",
+ 365        "sample": "S0B",
+ 366    },
+ 367    {
+ 368        "OP": "050",
+ 369        "device": "BWtek",
+ 370        "filename": "./ICV_BW532/S0N02_iRPlus532_Z050_100_40000ms.txt",
+ 371        "laser_wl": "532",
+ 372        "provider": "ICV",
+ 373        "sample": "S0N",
+ 374    },
+ 375    {
+ 376        "OP": "050",
+ 377        "device": "BWtek",
+ 378        "filename": "./ICV_BW532/S0P02_iRPlus532_Z050_100_30000ms.txt",
+ 379        "laser_wl": "532",
+ 380        "provider": "ICV",
+ 381        "sample": "S0P",
+ 382    },
+ 383    {
+ 384        "OP": "050",
+ 385        "device": "BWtek",
+ 386        "filename": "./ICV_BW532/S1N02_iRPlus532_Z050_100_12000ms.txt",
+ 387        "laser_wl": "532",
+ 388        "provider": "ICV",
+ 389        "sample": "S1N",
+ 390    },
+ 391    {
+ 392        "OP": "050",
+ 393        "device": "BWtek",
+ 394        "filename": "./ICV_BW532/nCAL02_iRPlus532_Z050_100_9000ms.txt",
+ 395        "laser_wl": "532",
+ 396        "provider": "ICV",
+ 397        "sample": "nCAL",
+ 398    },
+ 399    {
+ 400        "OP": "050",
+ 401        "device": "BWtek",
+ 402        "filename": "./ICV_BW532/sCAL02_iRPlus532_Z050_100_20000ms.txt",
+ 403        "laser_wl": "532",
+ 404        "provider": "ICV",
+ 405        "sample": "sCAL",
+ 406    },
+ 407    {
+ 408        "OP": "100",
+ 409        "device": "BWtek",
+ 410        "filename": "./ICV_BW532/Ne_532nm_x100_2000ms.txt",
+ 411        "laser_wl": "532",
+ 412        "provider": "ICV",
+ 413        "sample": "Neon",
+ 414    },
+ 415    {
+ 416        "OP": "100",
+ 417        "device": "BWtek",
+ 418        "filename": "./ICV_BW532/Ne_532nm_x100_110ms.txt",
+ 419        "laser_wl": "532",
+ 420        "provider": "ICV",
+ 421        "sample": "Neon",
+ 422    },
+ 423    {
+ 424        "OP": "100",
+ 425        "device": "BWtek",
+ 426        "filename": "./ICV_BW532/PST02_iRPlus532_Z100_100_50000ms.txt",
+ 427        "laser_wl": "532",
+ 428        "provider": "ICV",
+ 429        "sample": "PST",
+ 430    },
+ 431    {
+ 432        "OP": "100",
+ 433        "device": "BWtek",
+ 434        "filename": "./ICV_BW532/S0B02_iRPlus532_Z100_100_22000ms.txt",
+ 435        "laser_wl": "532",
+ 436        "provider": "ICV",
+ 437        "sample": "S0B",
+ 438    },
+ 439    {
+ 440        "OP": "100",
+ 441        "device": "BWtek",
+ 442        "filename": "./ICV_BW532/S0N02_iRPlus532_Z100_100_28000ms.txt",
+ 443        "laser_wl": "532",
+ 444        "provider": "ICV",
+ 445        "sample": "S0N",
+ 446    },
+ 447    {
+ 448        "OP": "100",
+ 449        "device": "BWtek",
+ 450        "filename": "./ICV_BW532/S0P02_iRPlus532_Z100_100_20000ms.txt",
+ 451        "laser_wl": "532",
+ 452        "provider": "ICV",
+ 453        "sample": "S0P",
+ 454    },
+ 455    {
+ 456        "OP": "100",
+ 457        "device": "BWtek",
+ 458        "filename": "./ICV_BW532/S1N02_iRPlus532_Z100_100_6500ms.txt",
+ 459        "laser_wl": "532",
+ 460        "provider": "ICV",
+ 461        "sample": "S1N",
+ 462    },
+ 463    {
+ 464        "OP": "100",
+ 465        "device": "BWtek",
+ 466        "filename": "./ICV_BW532/nCAL02_iRPlus532_Z100_100_30000ms.txt",
+ 467        "laser_wl": "532",
+ 468        "provider": "ICV",
+ 469        "sample": "nCAL",
+ 470    },
+ 471    {
+ 472        "OP": "100",
+ 473        "device": "BWtek",
+ 474        "filename": "./ICV_BW532/sCAL02_iRPlus532_Z100_100_65000ms.txt",
+ 475        "laser_wl": "532",
+ 476        "provider": "ICV",
+ 477        "sample": "sCAL",
+ 478    },
+ 479    {
+ 480        "OP": "100",
+ 481        "device": "BWtek",
+ 482        "filename": "./ICV_BW532/Visible_532nm_130ms_100X_2.txt",
+ 483        "laser_wl": "532",
+ 484        "provider": "ICV",
+ 485        "sample": "LED532_EL0-9001",
+ 486    },
+ 487    {
+ 488        "OP": "100",
+ 489        "device": "BWtek",
+ 490        "filename": "./ICV_BW532/NISTSRM2242aC_BW532_100x_800msx10.txt",
+ 491        "laser_wl": "532",
+ 492        "provider": "ICV",
+ 493        "sample": "NIST532_SRM2242a",
+ 494    },
+ 495    {
+ 496        "OP": "020",
+ 497        "device": "BWtek",
+ 498        "filename": "./ICV_BW785/PST02_iRPlus785_Z020_100_1300ms.txt",
+ 499        "laser_wl": "785",
+ 500        "provider": "ICV",
+ 501        "sample": "PST",
+ 502    },
+ 503    {
+ 504        "OP": "020",
+ 505        "device": "BWtek",
+ 506        "filename": "./ICV_BW785/S0B02_iRPlus785_Z020_100_full.txt",
+ 507        "laser_wl": "785",
+ 508        "provider": "ICV",
+ 509        "sample": "S0B",
+ 510    },
+ 511    {
+ 512        "OP": "020",
+ 513        "device": "BWtek",
+ 514        "filename": "./ICV_BW785/S0B02_iRPlus785_Z020_100_12000ms.txt",
+ 515        "laser_wl": "785",
+ 516        "provider": "ICV",
+ 517        "sample": "S0B",
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+1054    },
+1055    {
+1056        "OP": "20x",
+1057        "device": "BWtek",
+1058        "filename": "./ICV_BW785/twinning/0_5A_P_iRaman785_20X_80(223mW)_7000msx5ac_5.txt",
+1059        "laser_wl": "785",
+1060        "provider": "ICV",
+1061        "sample": "TiO2",
+1062        "laser_power_mW": "232",
+1063        "laser_power_percent": "80",
+1064        "time_ms": "7000",
+1065        "replicate": "5",
+1066    },
+1067    {
+1068        "OP": "20x",
+1069        "device": "BWtek",
+1070        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_1.txt",
+1071        "laser_wl": "785",
+1072        "provider": "FNMT-B",
+1073        "sample": "TiO2",
+1074        "laser_power_mW": "292",
+1075        "laser_power_percent": "100",
+1076        "time_ms": "6000",
+1077        "replicate": "1",
+1078    },
+1079    {
+1080        "OP": "20x",
+1081        "device": "BWtek",
+1082        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_2.txt",
+1083        "laser_wl": "785",
+1084        "provider": "FNMT-B",
+1085        "sample": "TiO2",
+1086        "laser_power_mW": "292",
+1087        "laser_power_percent": "100",
+1088        "time_ms": "6000",
+1089        "replicate": "2",
+1090    },
+1091    {
+1092        "OP": "20x",
+1093        "device": "BWtek",
+1094        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_3.txt",
+1095        "laser_wl": "785",
+1096        "provider": "FNMT-B",
+1097        "sample": "TiO2",
+1098        "laser_power_mW": "292",
+1099        "laser_power_percent": "100",
+1100        "time_ms": "6000",
+1101        "replicate": "3",
+1102    },
+1103    {
+1104        "OP": "20x",
+1105        "device": "BWtek",
+1106        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_4.txt",
+1107        "laser_wl": "785",
+1108        "provider": "FNMT-B",
+1109        "sample": "TiO2",
+1110        "laser_power_mW": "292",
+1111        "laser_power_percent": "100",
+1112        "time_ms": "6000",
+1113        "replicate": "4",
+1114    },
+1115    {
+1116        "OP": "20x",
+1117        "device": "BWtek",
+1118        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_5.txt",
+1119        "laser_wl": "785",
+1120        "provider": "FNMT-B",
+1121        "sample": "TiO2",
+1122        "laser_power_mW": "292",
+1123        "laser_power_percent": "100",
+1124        "time_ms": "6000",
+1125        "replicate": "5",
+1126    },
+1127    {
+1128        "OP": "20x",
+1129        "device": "BWtek",
+1130        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_1.txt",
+1131        "laser_wl": "785",
+1132        "provider": "FNMT-B",
+1133        "sample": "TiO2",
+1134        "laser_power_mW": "46",
+1135        "laser_power_percent": "20",
+1136        "time_ms": "6000",
+1137        "replicate": "1",
+1138    },
+1139    {
+1140        "OP": "20x",
+1141        "device": "BWtek",
+1142        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_2.txt",
+1143        "laser_wl": "785",
+1144        "provider": "FNMT-B",
+1145        "sample": "TiO2",
+1146        "laser_power_mW": "46",
+1147        "laser_power_percent": "20",
+1148        "time_ms": "6000",
+1149        "replicate": "2",
+1150    },
+1151    {
+1152        "OP": "20x",
+1153        "device": "BWtek",
+1154        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_3.txt",
+1155        "laser_wl": "785",
+1156        "provider": "FNMT-B",
+1157        "sample": "TiO2",
+1158        "laser_power_mW": "46",
+1159        "laser_power_percent": "20",
+1160        "time_ms": "6000",
+1161        "replicate": "3",
+1162    },
+1163    {
+1164        "OP": "20x",
+1165        "device": "BWtek",
+1166        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_4.txt",
+1167        "laser_wl": "785",
+1168        "provider": "FNMT-B",
+1169        "sample": "TiO2",
+1170        "laser_power_mW": "46",
+1171        "laser_power_percent": "20",
+1172        "time_ms": "6000",
+1173        "replicate": "4",
+1174    },
+1175    {
+1176        "OP": "20x",
+1177        "device": "BWtek",
+1178        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_5.txt",
+1179        "laser_wl": "785",
+1180        "provider": "FNMT-B",
+1181        "sample": "TiO2",
+1182        "laser_power_mW": "46",
+1183        "laser_power_percent": "20",
+1184        "time_ms": "6000",
+1185        "replicate": "5",
+1186    },
+1187    {
+1188        "OP": "20x",
+1189        "device": "BWtek",
+1190        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_1.txt",
+1191        "laser_wl": "785",
+1192        "provider": "FNMT-B",
+1193        "sample": "TiO2",
+1194        "laser_power_mW": "106",
+1195        "laser_power_percent": "40",
+1196        "time_ms": "6000",
+1197        "replicate": "1",
+1198    },
+1199    {
+1200        "OP": "20x",
+1201        "device": "BWtek",
+1202        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_2.txt",
+1203        "laser_wl": "785",
+1204        "provider": "FNMT-B",
+1205        "sample": "TiO2",
+1206        "laser_power_mW": "106",
+1207        "laser_power_percent": "40",
+1208        "time_ms": "6000",
+1209        "replicate": "2",
+1210    },
+1211    {
+1212        "OP": "20x",
+1213        "device": "BWtek",
+1214        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_3.txt",
+1215        "laser_wl": "785",
+1216        "provider": "FNMT-B",
+1217        "sample": "TiO2",
+1218        "laser_power_mW": "106",
+1219        "laser_power_percent": "40",
+1220        "time_ms": "6000",
+1221        "replicate": "3",
+1222    },
+1223    {
+1224        "OP": "20x",
+1225        "device": "BWtek",
+1226        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_4.txt",
+1227        "laser_wl": "785",
+1228        "provider": "FNMT-B",
+1229        "sample": "TiO2",
+1230        "laser_power_mW": "106",
+1231        "laser_power_percent": "40",
+1232        "time_ms": "6000",
+1233        "replicate": "4",
+1234    },
+1235    {
+1236        "OP": "20x",
+1237        "device": "BWtek",
+1238        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_5.txt",
+1239        "laser_wl": "785",
+1240        "provider": "FNMT-B",
+1241        "sample": "TiO2",
+1242        "laser_power_mW": "106",
+1243        "laser_power_percent": "40",
+1244        "time_ms": "6000",
+1245        "replicate": "5",
+1246    },
+1247    {
+1248        "OP": "20x",
+1249        "device": "BWtek",
+1250        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_1.txt",
+1251        "laser_wl": "785",
+1252        "provider": "FNMT-B",
+1253        "sample": "TiO2",
+1254        "laser_power_mW": "166",
+1255        "laser_power_percent": "60",
+1256        "time_ms": "6000",
+1257        "replicate": "1",
+1258    },
+1259    {
+1260        "OP": "20x",
+1261        "device": "BWtek",
+1262        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_2.txt",
+1263        "laser_wl": "785",
+1264        "provider": "FNMT-B",
+1265        "sample": "TiO2",
+1266        "laser_power_mW": "166",
+1267        "laser_power_percent": "60",
+1268        "time_ms": "6000",
+1269        "replicate": "2",
+1270    },
+1271    {
+1272        "OP": "20x",
+1273        "device": "BWtek",
+1274        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_3.txt",
+1275        "laser_wl": "785",
+1276        "provider": "FNMT-B",
+1277        "sample": "TiO2",
+1278        "laser_power_mW": "166",
+1279        "laser_power_percent": "60",
+1280        "time_ms": "6000",
+1281        "replicate": "3",
+1282    },
+1283    {
+1284        "OP": "20x",
+1285        "device": "BWtek",
+1286        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_4.txt",
+1287        "laser_wl": "785",
+1288        "provider": "FNMT-B",
+1289        "sample": "TiO2",
+1290        "laser_power_mW": "166",
+1291        "laser_power_percent": "60",
+1292        "time_ms": "6000",
+1293        "replicate": "4",
+1294    },
+1295    {
+1296        "OP": "20x",
+1297        "device": "BWtek",
+1298        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_5.txt",
+1299        "laser_wl": "785",
+1300        "provider": "FNMT-B",
+1301        "sample": "TiO2",
+1302        "laser_power_mW": "166",
+1303        "laser_power_percent": "60",
+1304        "time_ms": "6000",
+1305        "replicate": "5",
+1306    },
+1307    {
+1308        "OP": "20x",
+1309        "device": "BWtek",
+1310        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_1.txt",
+1311        "laser_wl": "785",
+1312        "provider": "FNMT-B",
+1313        "sample": "TiO2",
+1314        "laser_power_mW": "227",
+1315        "laser_power_percent": "80",
+1316        "time_ms": "6000",
+1317        "replicate": "1",
+1318    },
+1319    {
+1320        "OP": "20x",
+1321        "device": "BWtek",
+1322        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_2.txt",
+1323        "laser_wl": "785",
+1324        "provider": "FNMT-B",
+1325        "sample": "TiO2",
+1326        "laser_power_mW": "227",
+1327        "laser_power_percent": "80",
+1328        "time_ms": "6000",
+1329        "replicate": "2",
+1330    },
+1331    {
+1332        "OP": "20x",
+1333        "device": "BWtek",
+1334        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_3.txt",
+1335        "laser_wl": "785",
+1336        "provider": "FNMT-B",
+1337        "sample": "TiO2",
+1338        "laser_power_mW": "227",
+1339        "laser_power_percent": "80",
+1340        "time_ms": "6000",
+1341        "replicate": "3",
+1342    },
+1343    {
+1344        "OP": "20x",
+1345        "device": "BWtek",
+1346        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_4.txt",
+1347        "laser_wl": "785",
+1348        "provider": "FNMT-B",
+1349        "sample": "TiO2",
+1350        "laser_power_mW": "227",
+1351        "laser_power_percent": "80",
+1352        "time_ms": "6000",
+1353        "replicate": "4",
+1354    },
+1355    {
+1356        "OP": "20x",
+1357        "device": "BWtek",
+1358        "filename": "./FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_5.txt",
+1359        "laser_wl": "785",
+1360        "provider": "FNMT-B",
+1361        "sample": "TiO2",
+1362        "laser_power_mW": "227",
+1363        "laser_power_percent": "80",
+1364        "time_ms": "6000",
+1365        "replicate": "5",
+1366    },
+1367    {
+1368        "OP": "20x",
+1369        "device": "BWtek",
+1370        "filename": "./FNMT-B_BW785/NIST_785nm_100%_2500msx3_1.txt",
+1371        "laser_wl": "785",
+1372        "provider": "FNMT-B",
+1373        "sample": "Neon",
+1374    },
+1375]
+1376
+1377
+1378df = pd.DataFrame.from_dict(data)
+1379loc = importlib.resources.files(__name__)
+1380
+1381
+1382def filtered_df(**kwargs):
+1383    filters = [
+1384        reduce(lambda a, b: a | b, [df[k] == i for i in v])
+1385        for k, v in kwargs.items()
+1386        if v
+1387    ]
+1388    if filters:
+1389        return df[reduce(lambda a, b: a & b, filters)]
+1390    else:
+1391        return df
+1392
+1393
+1394def get_filters(**kwargs):
+1395    df_tmp = filtered_df(**kwargs)
+1396    return {c: df_tmp[c].unique() for c in df_tmp.columns if c != "filename"}
+1397
+1398
+1399def get_filenames(**kwargs):
+1400    df_tmp = filtered_df(**kwargs)
+1401    return df_tmp["filename"]
+1402
+1403
+1404def prepend_prefix(filenames):
+1405    return [str(loc.joinpath(fn)) for fn in filenames]
+
+ + +
+
+
+ data = + + [{'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/NeonSNQ043_iR532_Probe_5msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/NeonSNQ043_iR532_Probe_100msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/PST10_iR532_Probe_100_3000msx7.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S0B10_iR532_Probe_100_60000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S0N10_iR532_Probe_100_30000msx3.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S0P10_iR532_Probe_100_60000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S1N10_iR532_Probe_100_22000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/Sil10_iR532_Probe_100_60000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'Sil_'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/nCAL10_iR532_Probe_100_2500msx3.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/sCAL10_iR532_Probe_100_3200msx4.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/LED532_Probe_40msx3_1.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'LED532_EL0-9001'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/NIST532_Probe_3000msx8_1.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'NIST532_SRM2242a'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NeonSNQ043_iR785_OP01.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/PST10_iR785_OP01_40000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0B10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0N10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0P10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S1N10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/Sil10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Sil'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/nCAL10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/sCAL10_iR785_OP01_4000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NeonSNQ043_iR785_OP02.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/PST10_iR785_OP02_50000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0B10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0N10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0P10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S1N10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/Sil10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Sil'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/nCAL10_iR785_OP02_20000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/sCAL10_iR785_OP02_15000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NeonSNQ043_iR785_OP03.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/PST10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '03', 'device': 'Horiba', 'filename': 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'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '154', 'laser_power_percent': '60', 'time_ms': '7000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_60(154mW)_7000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '154', 'laser_power_percent': '60', 'time_ms': '7000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_60(154mW)_7000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '154', 'laser_power_percent': '60', 'time_ms': '7000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_60(154mW)_7000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '154', 'laser_power_percent': '60', 'time_ms': '7000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_80(223mW)_7000msx5ac_1.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '232', 'laser_power_percent': '80', 'time_ms': '7000', 'replicate': '1'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_80(223mW)_7000msx5ac_2.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '232', 'laser_power_percent': '80', 'time_ms': '7000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_80(223mW)_7000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '232', 'laser_power_percent': '80', 'time_ms': '7000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_80(223mW)_7000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '232', 'laser_power_percent': '80', 'time_ms': '7000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './ICV_BW785/twinning/0_5A_P_iRaman785_20X_80(223mW)_7000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'TiO2', 'laser_power_mW': '232', 'laser_power_percent': '80', 'time_ms': '7000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_1.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '292', 'laser_power_percent': '100', 'time_ms': '6000', 'replicate': '1'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_2.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '292', 'laser_power_percent': '100', 'time_ms': '6000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '292', 'laser_power_percent': '100', 'time_ms': '6000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '292', 'laser_power_percent': '100', 'time_ms': '6000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_100(292mW)_6000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '292', 'laser_power_percent': '100', 'time_ms': '6000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_1.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '46', 'laser_power_percent': '20', 'time_ms': '6000', 'replicate': '1'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_2.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '46', 'laser_power_percent': '20', 'time_ms': '6000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '46', 'laser_power_percent': '20', 'time_ms': '6000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '46', 'laser_power_percent': '20', 'time_ms': '6000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_20(46mW)_6000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '46', 'laser_power_percent': '20', 'time_ms': '6000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_1.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '106', 'laser_power_percent': '40', 'time_ms': '6000', 'replicate': '1'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_2.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '106', 'laser_power_percent': '40', 'time_ms': '6000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '106', 'laser_power_percent': '40', 'time_ms': '6000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '106', 'laser_power_percent': '40', 'time_ms': '6000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_40(106mW)_6000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '106', 'laser_power_percent': '40', 'time_ms': '6000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_1.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '166', 'laser_power_percent': '60', 'time_ms': '6000', 'replicate': '1'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_2.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '166', 'laser_power_percent': '60', 'time_ms': '6000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '166', 'laser_power_percent': '60', 'time_ms': '6000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '166', 'laser_power_percent': '60', 'time_ms': '6000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_60(166mW)_6000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '166', 'laser_power_percent': '60', 'time_ms': '6000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_1.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '227', 'laser_power_percent': '80', 'time_ms': '6000', 'replicate': '1'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_2.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '227', 'laser_power_percent': '80', 'time_ms': '6000', 'replicate': '2'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_3.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '227', 'laser_power_percent': '80', 'time_ms': '6000', 'replicate': '3'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_4.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '227', 'laser_power_percent': '80', 'time_ms': '6000', 'replicate': '4'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/twinning/0_5A_P_iRaman785_Probe_80(227mW)_6000msx5ac_5.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'TiO2', 'laser_power_mW': '227', 'laser_power_percent': '80', 'time_ms': '6000', 'replicate': '5'}, {'OP': '20x', 'device': 'BWtek', 'filename': './FNMT-B_BW785/NIST_785nm_100%_2500msx3_1.txt', 'laser_wl': '785', 'provider': 'FNMT-B', 'sample': 'Neon'}] + + +
+ + + + +
+
+
+ df = + + OP device ... time_ms replicate +0 01 BWtek ... NaN NaN +1 01 BWtek ... NaN NaN +2 01 BWtek ... NaN NaN +3 01 BWtek ... NaN NaN +4 01 BWtek ... NaN NaN +.. ... ... ... ... ... +141 20x BWtek ... 6000 2 +142 20x BWtek ... 6000 3 +143 20x BWtek ... 6000 4 +144 20x BWtek ... 6000 5 +145 20x BWtek ... NaN NaN + +[146 rows x 10 columns] + + +
+ + + + +
+
+
+ loc = + + PosixPath('/home/runner/work/ramanchada2/ramanchada2/.tox/docs/lib/python3.11/site-packages/ramanchada2/auxiliary/spectra/datasets2') + + +
+ + + + +
+
+ +
+ + def + filtered_df(**kwargs): + + + +
+ + 
1383def filtered_df(**kwargs):
+1384    filters = [
+1385        reduce(lambda a, b: a | b, [df[k] == i for i in v])
+1386        for k, v in kwargs.items()
+1387        if v
+1388    ]
+1389    if filters:
+1390        return df[reduce(lambda a, b: a & b, filters)]
+1391    else:
+1392        return df
+
+ + + + +
+
+ +
+ + def + get_filters(**kwargs): + + + +
+ + 
1395def get_filters(**kwargs):
+1396    df_tmp = filtered_df(**kwargs)
+1397    return {c: df_tmp[c].unique() for c in df_tmp.columns if c != "filename"}
+
+ + + + +
+
+ +
+ + def + get_filenames(**kwargs): + + + +
+ + 
1400def get_filenames(**kwargs):
+1401    df_tmp = filtered_df(**kwargs)
+1402    return df_tmp["filename"]
+
+ + + + +
+
+ +
+ + def + prepend_prefix(filenames): + + + +
+ + 
1405def prepend_prefix(filenames):
+1406    return [str(loc.joinpath(fn)) for fn in filenames]
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/auxiliary/spectra/simulated.html b/ramanchada2/auxiliary/spectra/simulated.html new file mode 100644 index 00000000..b7cff702 --- /dev/null +++ b/ramanchada2/auxiliary/spectra/simulated.html @@ -0,0 +1,309 @@ + + + + + + + ramanchada2.auxiliary.spectra.simulated API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.auxiliary.spectra.simulated

+ + + + + + + 
 1import importlib.resources
+ 2
+ 3loc = importlib.resources.files(__name__)
+ 4
+ 5
+ 6FILES = {
+ 7    'calcite_crystal_raw': './crystal/calcite_CRYSTAL_PBE_raw_data.dat',
+ 8    'calcite_crystal_convolved': './crystal/calcite_CRYSTAL_PBE_convoluted.dat',
+ 9    'calcite_vasp': './vasp/snCAL_vasp_raman_ALL.dat',
+10}
+11
+12
+13for f in FILES:
+14    FILES[f] = str(loc.joinpath(FILES[f]))
+
+ + +
+
+
+ loc = + + PosixPath('/home/runner/work/ramanchada2/ramanchada2/.tox/docs/lib/python3.11/site-packages/ramanchada2/auxiliary/spectra/simulated') + + +
+ + + + +
+
+
+ FILES = + + {'calcite_crystal_raw': '/home/runner/work/ramanchada2/ramanchada2/.tox/docs/lib/python3.11/site-packages/ramanchada2/auxiliary/spectra/simulated/crystal/calcite_CRYSTAL_PBE_raw_data.dat', 'calcite_crystal_convolved': '/home/runner/work/ramanchada2/ramanchada2/.tox/docs/lib/python3.11/site-packages/ramanchada2/auxiliary/spectra/simulated/crystal/calcite_CRYSTAL_PBE_convoluted.dat', 'calcite_vasp': '/home/runner/work/ramanchada2/ramanchada2/.tox/docs/lib/python3.11/site-packages/ramanchada2/auxiliary/spectra/simulated/vasp/snCAL_vasp_raman_ALL.dat'} + + +
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io.html b/ramanchada2/io.html new file mode 100644 index 00000000..b7084bd4 --- /dev/null +++ b/ramanchada2/io.html @@ -0,0 +1,267 @@ + + + + + + + ramanchada2.io API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io

+ + + + + + + 
1#!/usr/bin/env python3
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/HSDS.html b/ramanchada2/io/HSDS.html new file mode 100644 index 00000000..9a4b61f2 --- /dev/null +++ b/ramanchada2/io/HSDS.html @@ -0,0 +1,654 @@ + + + + + + + ramanchada2.io.HSDS API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.HSDS

+ + + + + + + 
  1import logging
+  2from typing import Dict, Tuple
+  3
+  4import h5py
+  5import numpy as np
+  6import numpy.typing as npt
+  7from pydantic import validate_call
+  8
+  9from ramanchada2.misc.exceptions import ChadaReadNotFoundError
+ 10
+ 11logger = logging.getLogger()
+ 12
+ 13
+ 14# https://manual.nexusformat.org/examples/napi/python.html
+ 15# https://manual.nexusformat.org/examples/python/simple_example_basic/index.html
+ 16@validate_call(config=dict(arbitrary_types_allowed=True))
+ 17def write_nexus(filename: str,
+ 18                dataset: str,
+ 19                x: npt.NDArray, y: npt.NDArray, meta: Dict, h5module=None):
+ 20    _h5 = h5module or h5py
+ 21    try:
+ 22        with _h5.File(filename, 'a') as f:
+ 23            f.attrs['default'] = dataset
+ 24            try:
+ 25                nxentry = f.require_group('sample')
+ 26            except:  # noqa: E722
+ 27                pass
+ 28
+ 29            nxentry = f.require_group('instrument')
+ 30            for m in meta:
+ 31                print(m, meta[m])
+ 32
+ 33            try:
+ 34                nxentry = f.require_group(dataset)
+ 35                nxentry.attrs["NX_class"] = 'NXentry'
+ 36                nxentry.attrs['default'] = 'data'
+ 37            except:  # noqa: E722
+ 38                pass
+ 39
+ 40            try:
+ 41                nxdata = nxentry.require_group('data')
+ 42                nxdata.attrs["NX_class"] = 'NXdata'
+ 43                nxdata.attrs['signal'] = 'spectrum'
+ 44                nxdata.attrs['axes'] = 'raman_shift'
+ 45                nxdata.attrs['raman_shift_indices'] = [0,]
+ 46            except:  # noqa: E722
+ 47                pass
+ 48
+ 49            try:
+ 50                tth = nxdata.require_group('raman_shift', data=x)
+ 51                tth.attrs['units'] = 'cm-1'
+ 52                tth.attrs['long_name'] = 'Raman shift (cm-1)'
+ 53            except:  # noqa: E722
+ 54                pass
+ 55
+ 56            try:
+ 57                counts = nxdata.create_dataset('spectrum', data=y)
+ 58                counts.attrs['units'] = 'au'
+ 59                counts.attrs['long_name'] = 'spectrum'
+ 60            except:  # noqa: E722
+ 61                pass
+ 62
+ 63    except ValueError as e:
+ 64        logger.warning(repr(e))
+ 65
+ 66
+ 67class DatasetExistsError(Exception):
+ 68    pass
+ 69
+ 70
+ 71def sanitize_key(key: str) -> str:
+ 72    return ''.join(c if ord(c) < 128 else '_' for c in key)
+ 73
+ 74
+ 75@validate_call(config=dict(arbitrary_types_allowed=True))
+ 76def write_cha(filename: str,
+ 77              dataset: str,
+ 78              x: npt.NDArray, y: npt.NDArray, meta: Dict, h5module=None):
+ 79    data = np.stack([x, y])
+ 80    sanitized_meta = {sanitize_key(k): v for k, v in meta.items()}
+ 81    try:
+ 82        _h5 = h5module or h5py
+ 83        with _h5.File(filename, mode='a') as h5:
+ 84            if h5.get(dataset) is None:
+ 85                ds = h5.create_dataset(dataset, data=data)
+ 86                ds.attrs.update(sanitized_meta)
+ 87
+ 88            else:
+ 89                raise DatasetExistsError(f'dataset `{dataset}` already exists in file `{filename}`')
+ 90    except ValueError as e:
+ 91        raise e
+ 92
+ 93
+ 94def read_cha(filename: str,
+ 95             dataset: str, h5module=None
+ 96             ) -> Tuple[npt.NDArray, npt.NDArray, Dict]:
+ 97    _h5 = h5module or h5py
+ 98    with _h5.File(filename, mode='r') as h5:
+ 99        data = h5.get(dataset)
+100        if data is None:
+101            raise ChadaReadNotFoundError(f'dataset `{dataset}` not found in file `{filename}`')
+102        x, y = data[:]
+103        meta = dict(data.attrs)
+104    return x, y, meta
+105
+106
+107def filter_dataset(topdomain, domain, process_file, sample=None, wavelength=None, instrument=None,
+108                   provider=None, investigation=None, kwargs={}, h5module=None):
+109    _h5 = h5module or h5py
+110    with _h5.File(domain) as dataset:
+111        if (sample is not None) and (dataset["annotation_sample"].attrs["sample"] == sample):
+112            process_file(topdomain, domain, **kwargs)
+113
+114
+115def visit_domain(topdomain="/", process_dataset=None, kwargs={}, h5module=None):
+116    _h5 = h5module or h5py
+117    if topdomain.endswith("/"):
+118        with _h5.Folder(topdomain) as domain:
+119            domain._getSubdomains()
+120            for domain in domain._subdomains:
+121                if domain["class"] == "folder":
+122                    visit_domain("{}/".format(domain["name"]), process_dataset, kwargs, h5module=_h5)
+123                else:
+124                    if not (process_dataset is None):
+125                        process_dataset(topdomain, domain["name"], **kwargs, h5module=_h5)
+126    else:
+127        if not (process_dataset is None):
+128            process_dataset(None, topdomain, **kwargs, h5module=_h5)
+
+ + +
+
+
+ logger = +<RootLogger root (WARNING)> + + +
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + write_nexus( filename: str, dataset: str, x: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], y: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], meta: Dict, h5module=None): + + + +
+ + 
17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def write_nexus(filename: str,
+19                dataset: str,
+20                x: npt.NDArray, y: npt.NDArray, meta: Dict, h5module=None):
+21    _h5 = h5module or h5py
+22    try:
+23        with _h5.File(filename, 'a') as f:
+24            f.attrs['default'] = dataset
+25            try:
+26                nxentry = f.require_group('sample')
+27            except:  # noqa: E722
+28                pass
+29
+30            nxentry = f.require_group('instrument')
+31            for m in meta:
+32                print(m, meta[m])
+33
+34            try:
+35                nxentry = f.require_group(dataset)
+36                nxentry.attrs["NX_class"] = 'NXentry'
+37                nxentry.attrs['default'] = 'data'
+38            except:  # noqa: E722
+39                pass
+40
+41            try:
+42                nxdata = nxentry.require_group('data')
+43                nxdata.attrs["NX_class"] = 'NXdata'
+44                nxdata.attrs['signal'] = 'spectrum'
+45                nxdata.attrs['axes'] = 'raman_shift'
+46                nxdata.attrs['raman_shift_indices'] = [0,]
+47            except:  # noqa: E722
+48                pass
+49
+50            try:
+51                tth = nxdata.require_group('raman_shift', data=x)
+52                tth.attrs['units'] = 'cm-1'
+53                tth.attrs['long_name'] = 'Raman shift (cm-1)'
+54            except:  # noqa: E722
+55                pass
+56
+57            try:
+58                counts = nxdata.create_dataset('spectrum', data=y)
+59                counts.attrs['units'] = 'au'
+60                counts.attrs['long_name'] = 'spectrum'
+61            except:  # noqa: E722
+62                pass
+63
+64    except ValueError as e:
+65        logger.warning(repr(e))
+
+ + + + +
+
+ +
+ + class + DatasetExistsError(builtins.Exception): + + + +
+ + 
68class DatasetExistsError(Exception):
+69    pass
+
+ + +

Common base class for all non-exit exceptions.

+
+ + +
+
+ +
+ + def + sanitize_key(key: str) -> str: + + + +
+ + 
72def sanitize_key(key: str) -> str:
+73    return ''.join(c if ord(c) < 128 else '_' for c in key)
+
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + write_cha( filename: str, dataset: str, x: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], y: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], meta: Dict, h5module=None): + + + +
+ + 
76@validate_call(config=dict(arbitrary_types_allowed=True))
+77def write_cha(filename: str,
+78              dataset: str,
+79              x: npt.NDArray, y: npt.NDArray, meta: Dict, h5module=None):
+80    data = np.stack([x, y])
+81    sanitized_meta = {sanitize_key(k): v for k, v in meta.items()}
+82    try:
+83        _h5 = h5module or h5py
+84        with _h5.File(filename, mode='a') as h5:
+85            if h5.get(dataset) is None:
+86                ds = h5.create_dataset(dataset, data=data)
+87                ds.attrs.update(sanitized_meta)
+88
+89            else:
+90                raise DatasetExistsError(f'dataset `{dataset}` already exists in file `{filename}`')
+91    except ValueError as e:
+92        raise e
+
+ + + + +
+
+ +
+ + def + read_cha( filename: str, dataset: str, h5module=None) -> Tuple[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Dict]: + + + +
+ + 
 95def read_cha(filename: str,
+ 96             dataset: str, h5module=None
+ 97             ) -> Tuple[npt.NDArray, npt.NDArray, Dict]:
+ 98    _h5 = h5module or h5py
+ 99    with _h5.File(filename, mode='r') as h5:
+100        data = h5.get(dataset)
+101        if data is None:
+102            raise ChadaReadNotFoundError(f'dataset `{dataset}` not found in file `{filename}`')
+103        x, y = data[:]
+104        meta = dict(data.attrs)
+105    return x, y, meta
+
+ + + + +
+
+ +
+ + def + filter_dataset( topdomain, domain, process_file, sample=None, wavelength=None, instrument=None, provider=None, investigation=None, kwargs={}, h5module=None): + + + +
+ + 
108def filter_dataset(topdomain, domain, process_file, sample=None, wavelength=None, instrument=None,
+109                   provider=None, investigation=None, kwargs={}, h5module=None):
+110    _h5 = h5module or h5py
+111    with _h5.File(domain) as dataset:
+112        if (sample is not None) and (dataset["annotation_sample"].attrs["sample"] == sample):
+113            process_file(topdomain, domain, **kwargs)
+
+ + + + +
+
+ +
+ + def + visit_domain(topdomain='/', process_dataset=None, kwargs={}, h5module=None): + + + +
+ + 
116def visit_domain(topdomain="/", process_dataset=None, kwargs={}, h5module=None):
+117    _h5 = h5module or h5py
+118    if topdomain.endswith("/"):
+119        with _h5.Folder(topdomain) as domain:
+120            domain._getSubdomains()
+121            for domain in domain._subdomains:
+122                if domain["class"] == "folder":
+123                    visit_domain("{}/".format(domain["name"]), process_dataset, kwargs, h5module=_h5)
+124                else:
+125                    if not (process_dataset is None):
+126                        process_dataset(topdomain, domain["name"], **kwargs, h5module=_h5)
+127    else:
+128        if not (process_dataset is None):
+129            process_dataset(None, topdomain, **kwargs, h5module=_h5)
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental.html b/ramanchada2/io/experimental.html new file mode 100644 index 00000000..91bf2551 --- /dev/null +++ b/ramanchada2/io/experimental.html @@ -0,0 +1,272 @@ + + + + + + + ramanchada2.io.experimental API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental

+ + + + + + + 
1from .read_csv import read_csv
+2from .read_spe import read_spe
+3from .read_txt import read_txt
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/bw_format.html b/ramanchada2/io/experimental/bw_format.html new file mode 100644 index 00000000..3983294a --- /dev/null +++ b/ramanchada2/io/experimental/bw_format.html @@ -0,0 +1,324 @@ + + + + + + + ramanchada2.io.experimental.bw_format API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.bw_format

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from typing import List, Tuple, Dict
+ 4
+ 5import pandas
+ 6
+ 7
+ 8def bw_format(lines: List[str]) -> Tuple[pandas.DataFrame, Dict]:
+ 9    section_split = 0
+10    for i, ll in enumerate(lines):
+11        if ll.count(';') > 1:
+12            section_split = i
+13            break
+14    meta = lines[:section_split]
+15    spec = lines[section_split:]
+16
+17    meta_dict = dict([m.replace(',', '.').strip().split(';') for m in meta])
+18
+19    spec_split = [s.replace(',', '.').replace(' ', '').strip('\r\n ;').split(';') for s in spec]
+20    spe_parsed = pandas.DataFrame.from_records(data=spec_split[1:], columns=spec_split[0])
+21    spe_parsed = spe_parsed.apply(pandas.to_numeric, errors='coerce')
+22    spe_parsed = spe_parsed.dropna(axis=0)
+23
+24    return spe_parsed, meta_dict
+
+ + +
+
+ +
+ + def + bw_format(lines: List[str]) -> Tuple[pandas.core.frame.DataFrame, Dict]: + + + +
+ + 
 9def bw_format(lines: List[str]) -> Tuple[pandas.DataFrame, Dict]:
+10    section_split = 0
+11    for i, ll in enumerate(lines):
+12        if ll.count(';') > 1:
+13            section_split = i
+14            break
+15    meta = lines[:section_split]
+16    spec = lines[section_split:]
+17
+18    meta_dict = dict([m.replace(',', '.').strip().split(';') for m in meta])
+19
+20    spec_split = [s.replace(',', '.').replace(' ', '').strip('\r\n ;').split(';') for s in spec]
+21    spe_parsed = pandas.DataFrame.from_records(data=spec_split[1:], columns=spec_split[0])
+22    spe_parsed = spe_parsed.apply(pandas.to_numeric, errors='coerce')
+23    spe_parsed = spe_parsed.dropna(axis=0)
+24
+25    return spe_parsed, meta_dict
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/neegala_format.html b/ramanchada2/io/experimental/neegala_format.html new file mode 100644 index 00000000..f9dc5c51 --- /dev/null +++ b/ramanchada2/io/experimental/neegala_format.html @@ -0,0 +1,316 @@ + + + + + + + ramanchada2.io.experimental.neegala_format API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.neegala_format

+ + + + + + + 
 1from typing import Dict, List, Tuple
+ 2
+ 3import pandas
+ 4
+ 5
+ 6def neegala_format(lines: List[str]) -> Tuple[pandas.DataFrame, Dict]:
+ 7    for i, ll in enumerate(lines):
+ 8        if ll.startswith('Pixels,Wavelength,Wavenumbers,Raman_Shift,Raw_Data,Background_Data,Processed_Data'):
+ 9            start_spe = i
+10            break
+11    else:
+12        raise ValueError('The input is not neegala format')
+13    meta = dict([i.split(',', 1) for i in lines[:start_spe]])
+14    spe_lines = [ll.strip().split(',') for ll in lines[start_spe:]]
+15
+16    data = pandas.DataFrame.from_records(data=spe_lines[1:], columns=spe_lines[0]
+17                                         ).apply(pandas.to_numeric, errors='coerce'
+18                                                 ).dropna(axis=0)
+19    return data, meta
+
+ + +
+
+ +
+ + def + neegala_format(lines: List[str]) -> Tuple[pandas.core.frame.DataFrame, Dict]: + + + +
+ + 
 7def neegala_format(lines: List[str]) -> Tuple[pandas.DataFrame, Dict]:
+ 8    for i, ll in enumerate(lines):
+ 9        if ll.startswith('Pixels,Wavelength,Wavenumbers,Raman_Shift,Raw_Data,Background_Data,Processed_Data'):
+10            start_spe = i
+11            break
+12    else:
+13        raise ValueError('The input is not neegala format')
+14    meta = dict([i.split(',', 1) for i in lines[:start_spe]])
+15    spe_lines = [ll.strip().split(',') for ll in lines[start_spe:]]
+16
+17    data = pandas.DataFrame.from_records(data=spe_lines[1:], columns=spe_lines[0]
+18                                         ).apply(pandas.to_numeric, errors='coerce'
+19                                                 ).dropna(axis=0)
+20    return data, meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/rc1_parser.html b/ramanchada2/io/experimental/rc1_parser.html new file mode 100644 index 00000000..2c19c8fe --- /dev/null +++ b/ramanchada2/io/experimental/rc1_parser.html @@ -0,0 +1,267 @@ + + + + + + + ramanchada2.io.experimental.rc1_parser API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.rc1_parser

+ + + + + + + 
1from .io import parse
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/rc1_parser/binary_readers.html b/ramanchada2/io/experimental/rc1_parser/binary_readers.html new file mode 100644 index 00000000..22ce0136 --- /dev/null +++ b/ramanchada2/io/experimental/rc1_parser/binary_readers.html @@ -0,0 +1,769 @@ + + + + + + + ramanchada2.io.experimental.rc1_parser.binary_readers API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.rc1_parser.binary_readers

+ + + + + + + 
  1import numpy as np
+  2import struct
+  3
+  4
+  5def readSPA(filename: str, flip=True):
+  6    """
+  7    function to read k-vector and spectrum from a *.SPA file
+  8
+  9    Args:
+ 10        filename:
+ 11            Full path to the file to be read.
+ 12
+ 13    Returns:
+ 14        k-vector and spectrum as separate arrays: `(np.array, np.array)`.
+ 15    """
+ 16    k = None
+ 17    spec = None
+ 18    with open(filename, 'rb') as f:
+ 19        # the data offset is saved at offset 386
+ 20        f.seek(386, 0)
+ 21        offset = struct.unpack("i", f.read(4))[0]
+ 22        # the number of data points is saved at offset 564
+ 23        f.seek(564, 0)
+ 24        n = struct.unpack("i", f.read(4))[0]
+ 25        # the max and min wavenumbers are saved at 576 and 580 respectively
+ 26        f.seek(576, 0)
+ 27        w_max = struct.unpack("f", f.read(4))[0]
+ 28        w_min = struct.unpack("f", f.read(4))[0]
+ 29        k = np.linspace(w_min, w_max, n)
+ 30        # read the data points
+ 31        f.seek(offset, 0)
+ 32        spec = np.array([struct.unpack("f", f.read(4))[0] for i in range(n)])
+ 33        if flip:
+ 34            spec = np.flip(spec)
+ 35        # set corrupt counts to zero
+ 36        spec[spec > 1e5] = 0
+ 37    return k, spec, {}
+ 38
+ 39
+ 40def read_ngs(file):
+ 41    with open(file, "rb") as f:
+ 42        # read NextGen string
+ 43        s = f.read(10)
+ 44        # if the string does not match, abort
+ 45        nextgen_string = s.decode("utf-8").lower()
+ 46        if nextgen_string != 'ngsnextgen':
+ 47            print('Not a readable file !')
+ 48            return
+ 49        # read DataMatrix string form byte #18
+ 50        s = f.seek(18)
+ 51        # read length of string as single byte
+ 52        s = f.read(1)
+ 53        ll = int.from_bytes(s, "big")
+ 54        # read the actual string
+ 55        s = f.read(ll)
+ 56        datamatrix_string = s.decode("utf-8").lower()
+ 57        # if the string does not match, abort
+ 58        if datamatrix_string != 'datamatrix':
+ 59            print('Not a readable file !')
+ 60            return
+ 61        # read filename at byte #38
+ 62        s = f.seek(38)
+ 63        # read length of string as single byte
+ 64        s = f.read(1)
+ 65        ll = int.from_bytes(s, "big")
+ 66        # read the actual string
+ 67        s = f.read(ll)
+ 68        file_name = s.decode("utf-8")
+ 69        # Read no. of channels as 32 bit integer, 16 bytes from end of filename
+ 70        s = f.seek(16, 1)
+ 71        s = f.read(4)
+ 72        n = struct.unpack('i', s)[0]
+ 73        print(f'Reading Labspec .ngs file {file_name} with {n} channels.')
+ 74        # Read data block, starting 8 bytes from end of channel num. Each y count is a 32 bit integer.
+ 75        y = read_4byte_datablock(f, n, 8)
+ 76        # Read parameter block. Before, there's a rather complicated sequence of skipping obsolete parameters...
+ 77        f.seek(2, 1)
+ 78        s = f.read(1)
+ 79        ll = int.from_bytes(s, "big")
+ 80        # Skip bytes as long as they are zeros
+ 81        if ll == 0:
+ 82            while ll == 0:
+ 83                s = f.read(1)
+ 84                ll = int.from_bytes(s, "big")
+ 85            f.seek(1, 1)
+ 86            s = f.read(1)
+ 87            ll = int.from_bytes(s, "big")
+ 88
+ 89        f.seek(ll, 1)
+ 90        s = f.read(1)
+ 91        ll = int.from_bytes(s, "big")
+ 92
+ 93        f.seek(ll, 1)
+ 94        f.seek(2, 1)
+ 95        s = f.read(1)
+ 96        ll = int.from_bytes(s, "big")
+ 97
+ 98        f.seek(ll, 1)
+ 99        s = f.read(1)
+100        ll = int.from_bytes(s, "big")
+101
+102        f.seek(ll, 1)
+103        f.seek(16, 1)
+104        # Finally, read the start and end wavenumbers (start_x)
+105        s = f.read(4)
+106        start_x = struct.unpack('f', s)[0]
+107        s = f.read(4)
+108        end_x = struct.unpack('f', s)[0]
+109        # Check whether end_x is equal to no. of channels
+110        if end_x == n:
+111            x = read_4byte_datablock(f, n, 20)
+112        else:
+113            # Construct x axis
+114            x = np.linspace(start_x, end_x, n)
+115        meta = read_ngs_meta(f)
+116    return x, y, meta
+117
+118
+119def read_4byte_datablock(f, length, skip=8):
+120    s = f.seek(skip, 1)
+121    y = np.zeros(length)
+122    for ii in range(length):
+123        s = f.read(4)
+124        y[ii] = struct.unpack('f', s)[0]
+125    return y
+126
+127
+128def read_bytestring(f):
+129    # read length of string as single byte
+130    s = f.read(1)
+131    ll = int.from_bytes(s, "big")
+132    # read the actual string
+133    s = f.read(ll)
+134    return s.decode('iso-8859-1')
+135
+136
+137def read_ngs_meta(f):
+138    position = f.tell()+1
+139    f.seek(position)
+140    abl = ''
+141    while abl != 'Table':
+142        s = f.read(1)
+143        ll = int.from_bytes(s, "big")
+144        if ll == 5:
+145            f.seek(-1, 1)
+146            abl = read_bytestring(f)
+147        else:
+148            position += 1
+149            f.seek(position)
+150    abl = ''
+151    position = f.tell()
+152    f.seek(position)
+153    while abl != 'Table':
+154        s = f.read(1)
+155        ll = int.from_bytes(s, "big")
+156        if ll == 5:
+157            f.seek(-1, 1)
+158            abl = read_bytestring(f)
+159            position = f.tell()
+160        else:
+161            position += 1
+162            f.seek(position)
+163    abl = read_bytestring(f)
+164    # Make dictionary
+165    meta = {}
+166    if abl.upper() == 'ACQ':
+167        abl = read_bytestring(f)
+168        f.seek(4, 1)
+169        # read no. of params
+170        s = f.read(1)
+171        ll = int.from_bytes(s, "big")
+172        f.seek(1, 1)
+173        par_names = []
+174        # Read parameter names
+175        for ii in range(ll):
+176            abl = read_bytestring(f)
+177            par_names.append(abl)
+178        # Skip some stuff
+179        f.seek(2, 1)
+180        abl = read_bytestring(f)
+181        abl = read_bytestring(f)
+182        f.seek(10, 1)
+183        s = f.read(1)
+184        # read no. of values
+185        ll = int.from_bytes(s, "big")
+186        f.seek(1, 1)
+187        # Read parameter values
+188        par_values = []
+189        for ii in range(ll):
+190            abl = read_bytestring(f)
+191            par_values.append(abl)
+192        f.seek(2, 1)
+193        # Read parameter units
+194        par_units = []
+195        for ii in range(ll):
+196            abl = read_bytestring(f)
+197            # add unit to dict key and exchange for new key
+198            par_units.append(abl)
+199        # add units to par names
+200        par = [name + f' [{unit}]' for name, unit in zip(par_names, par_units)]
+201        meta = dict(zip(par, par_values))
+202    return meta
+
+ + +
+
+ +
+ + def + readSPA(filename: str, flip=True): + + + +
+ + 
 6def readSPA(filename: str, flip=True):
+ 7    """
+ 8    function to read k-vector and spectrum from a *.SPA file
+ 9
+10    Args:
+11        filename:
+12            Full path to the file to be read.
+13
+14    Returns:
+15        k-vector and spectrum as separate arrays: `(np.array, np.array)`.
+16    """
+17    k = None
+18    spec = None
+19    with open(filename, 'rb') as f:
+20        # the data offset is saved at offset 386
+21        f.seek(386, 0)
+22        offset = struct.unpack("i", f.read(4))[0]
+23        # the number of data points is saved at offset 564
+24        f.seek(564, 0)
+25        n = struct.unpack("i", f.read(4))[0]
+26        # the max and min wavenumbers are saved at 576 and 580 respectively
+27        f.seek(576, 0)
+28        w_max = struct.unpack("f", f.read(4))[0]
+29        w_min = struct.unpack("f", f.read(4))[0]
+30        k = np.linspace(w_min, w_max, n)
+31        # read the data points
+32        f.seek(offset, 0)
+33        spec = np.array([struct.unpack("f", f.read(4))[0] for i in range(n)])
+34        if flip:
+35            spec = np.flip(spec)
+36        # set corrupt counts to zero
+37        spec[spec > 1e5] = 0
+38    return k, spec, {}
+
+ + +

function to read k-vector and spectrum from a *.SPA file

+ +
Arguments:
+ +
    +
  • filename: Full path to the file to be read.
    • +
+ +
Returns:
+ +
+

k-vector and spectrum as separate arrays: (np.array, np.array).

+
+
+ + +
+
+ +
+ + def + read_ngs(file): + + + +
+ + 
 41def read_ngs(file):
+ 42    with open(file, "rb") as f:
+ 43        # read NextGen string
+ 44        s = f.read(10)
+ 45        # if the string does not match, abort
+ 46        nextgen_string = s.decode("utf-8").lower()
+ 47        if nextgen_string != 'ngsnextgen':
+ 48            print('Not a readable file !')
+ 49            return
+ 50        # read DataMatrix string form byte #18
+ 51        s = f.seek(18)
+ 52        # read length of string as single byte
+ 53        s = f.read(1)
+ 54        ll = int.from_bytes(s, "big")
+ 55        # read the actual string
+ 56        s = f.read(ll)
+ 57        datamatrix_string = s.decode("utf-8").lower()
+ 58        # if the string does not match, abort
+ 59        if datamatrix_string != 'datamatrix':
+ 60            print('Not a readable file !')
+ 61            return
+ 62        # read filename at byte #38
+ 63        s = f.seek(38)
+ 64        # read length of string as single byte
+ 65        s = f.read(1)
+ 66        ll = int.from_bytes(s, "big")
+ 67        # read the actual string
+ 68        s = f.read(ll)
+ 69        file_name = s.decode("utf-8")
+ 70        # Read no. of channels as 32 bit integer, 16 bytes from end of filename
+ 71        s = f.seek(16, 1)
+ 72        s = f.read(4)
+ 73        n = struct.unpack('i', s)[0]
+ 74        print(f'Reading Labspec .ngs file {file_name} with {n} channels.')
+ 75        # Read data block, starting 8 bytes from end of channel num. Each y count is a 32 bit integer.
+ 76        y = read_4byte_datablock(f, n, 8)
+ 77        # Read parameter block. Before, there's a rather complicated sequence of skipping obsolete parameters...
+ 78        f.seek(2, 1)
+ 79        s = f.read(1)
+ 80        ll = int.from_bytes(s, "big")
+ 81        # Skip bytes as long as they are zeros
+ 82        if ll == 0:
+ 83            while ll == 0:
+ 84                s = f.read(1)
+ 85                ll = int.from_bytes(s, "big")
+ 86            f.seek(1, 1)
+ 87            s = f.read(1)
+ 88            ll = int.from_bytes(s, "big")
+ 89
+ 90        f.seek(ll, 1)
+ 91        s = f.read(1)
+ 92        ll = int.from_bytes(s, "big")
+ 93
+ 94        f.seek(ll, 1)
+ 95        f.seek(2, 1)
+ 96        s = f.read(1)
+ 97        ll = int.from_bytes(s, "big")
+ 98
+ 99        f.seek(ll, 1)
+100        s = f.read(1)
+101        ll = int.from_bytes(s, "big")
+102
+103        f.seek(ll, 1)
+104        f.seek(16, 1)
+105        # Finally, read the start and end wavenumbers (start_x)
+106        s = f.read(4)
+107        start_x = struct.unpack('f', s)[0]
+108        s = f.read(4)
+109        end_x = struct.unpack('f', s)[0]
+110        # Check whether end_x is equal to no. of channels
+111        if end_x == n:
+112            x = read_4byte_datablock(f, n, 20)
+113        else:
+114            # Construct x axis
+115            x = np.linspace(start_x, end_x, n)
+116        meta = read_ngs_meta(f)
+117    return x, y, meta
+
+ + + + +
+
+ +
+ + def + read_4byte_datablock(f, length, skip=8): + + + +
+ + 
120def read_4byte_datablock(f, length, skip=8):
+121    s = f.seek(skip, 1)
+122    y = np.zeros(length)
+123    for ii in range(length):
+124        s = f.read(4)
+125        y[ii] = struct.unpack('f', s)[0]
+126    return y
+
+ + + + +
+
+ +
+ + def + read_bytestring(f): + + + +
+ + 
129def read_bytestring(f):
+130    # read length of string as single byte
+131    s = f.read(1)
+132    ll = int.from_bytes(s, "big")
+133    # read the actual string
+134    s = f.read(ll)
+135    return s.decode('iso-8859-1')
+
+ + + + +
+
+ +
+ + def + read_ngs_meta(f): + + + +
+ + 
138def read_ngs_meta(f):
+139    position = f.tell()+1
+140    f.seek(position)
+141    abl = ''
+142    while abl != 'Table':
+143        s = f.read(1)
+144        ll = int.from_bytes(s, "big")
+145        if ll == 5:
+146            f.seek(-1, 1)
+147            abl = read_bytestring(f)
+148        else:
+149            position += 1
+150            f.seek(position)
+151    abl = ''
+152    position = f.tell()
+153    f.seek(position)
+154    while abl != 'Table':
+155        s = f.read(1)
+156        ll = int.from_bytes(s, "big")
+157        if ll == 5:
+158            f.seek(-1, 1)
+159            abl = read_bytestring(f)
+160            position = f.tell()
+161        else:
+162            position += 1
+163            f.seek(position)
+164    abl = read_bytestring(f)
+165    # Make dictionary
+166    meta = {}
+167    if abl.upper() == 'ACQ':
+168        abl = read_bytestring(f)
+169        f.seek(4, 1)
+170        # read no. of params
+171        s = f.read(1)
+172        ll = int.from_bytes(s, "big")
+173        f.seek(1, 1)
+174        par_names = []
+175        # Read parameter names
+176        for ii in range(ll):
+177            abl = read_bytestring(f)
+178            par_names.append(abl)
+179        # Skip some stuff
+180        f.seek(2, 1)
+181        abl = read_bytestring(f)
+182        abl = read_bytestring(f)
+183        f.seek(10, 1)
+184        s = f.read(1)
+185        # read no. of values
+186        ll = int.from_bytes(s, "big")
+187        f.seek(1, 1)
+188        # Read parameter values
+189        par_values = []
+190        for ii in range(ll):
+191            abl = read_bytestring(f)
+192            par_values.append(abl)
+193        f.seek(2, 1)
+194        # Read parameter units
+195        par_units = []
+196        for ii in range(ll):
+197            abl = read_bytestring(f)
+198            # add unit to dict key and exchange for new key
+199            par_units.append(abl)
+200        # add units to par names
+201        par = [name + f' [{unit}]' for name, unit in zip(par_names, par_units)]
+202        meta = dict(zip(par, par_values))
+203    return meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/rc1_parser/io.html b/ramanchada2/io/experimental/rc1_parser/io.html new file mode 100644 index 00000000..f13045b1 --- /dev/null +++ b/ramanchada2/io/experimental/rc1_parser/io.html @@ -0,0 +1,452 @@ + + + + + + + ramanchada2.io.experimental.rc1_parser.io API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.rc1_parser.io

+ + + + + + + 
 1import os.path
+ 2from .third_party_readers import readSPC, readWDF, readOPUS
+ 3from .txt_format_readers import read_JCAMP, readTXT
+ 4from .binary_readers import readSPA, read_ngs
+ 5
+ 6
+ 7class UnsupportedFileTypeError(Exception):
+ 8    pass
+ 9
+10
+11def parse(source_path, file_type=None):
+12    filename, file_extension = os.path.splitext(source_path)
+13
+14    if file_type is None:
+15        file_type = file_extension[1:].lower()  # without the leading dot
+16
+17    if file_type in {'spc', 'sp'}:
+18        reader = readSPC
+19    elif file_type in {'spa'}:
+20        reader = readSPA
+21    elif file_type in {'0', '1', '2'}:
+22        reader = readOPUS
+23    elif file_type in {'wdf'}:
+24        reader = readWDF
+25    elif file_type in {'ngs'}:
+26        reader = read_ngs
+27    elif file_type in {'jdx', 'dx'}:
+28        reader = read_JCAMP
+29    elif file_type in {'txt', 'txtr', 'csv', 'prn', 'rruf'}:
+30        reader = readTXT
+31    else:
+32        raise UnsupportedFileTypeError(
+33            f'file type "{file_type}" is not supported'
+34        )
+35
+36    x_data, y_data, metadata = reader(source_path)
+37    if metadata is None:
+38        metadata = {}
+39    # Get rid of bytes that are found in some of the formats
+40    metadata = cleanMeta(metadata)
+41    # Flatten metadata
+42    metadata = dict(zip(metadata.keys(), [str(v) for v in metadata.values()]))
+43    # Extract metadata from native metadata and spectrum data,
+44    # store in metadata dictionary, and include in CHADA archive.
+45    metadata["Original file"] = os.path.basename(source_path)
+46    return x_data, y_data, metadata
+47
+48
+49def cleanMeta(meta):
+50    # This cleans complex-strcutures metadata, and returns a dict
+51    if isinstance(meta, dict):
+52        meta = {i: meta[i] for i in meta if i != ""}
+53        for key, value in meta.items():
+54            meta[key] = cleanMeta(value)
+55    if isinstance(meta, list):
+56        for ii, value in enumerate(meta):
+57            meta[ii] = cleanMeta(value)
+58    if isinstance(meta, str):
+59        meta = meta.replace('\\x00', '')
+60        meta = meta.replace('\x00', '')
+61    if isinstance(meta, bytes):
+62        try:
+63            meta = meta.decode('utf-8')
+64            meta = cleanMeta(meta)
+65        except Exception:
+66            meta = {}
+67    return meta
+
+ + +
+
+ +
+ + class + UnsupportedFileTypeError(builtins.Exception): + + + +
+ + 
8class UnsupportedFileTypeError(Exception):
+9    pass
+
+ + +

Common base class for all non-exit exceptions.

+
+ + +
+
+ +
+ + def + parse(source_path, file_type=None): + + + +
+ + 
12def parse(source_path, file_type=None):
+13    filename, file_extension = os.path.splitext(source_path)
+14
+15    if file_type is None:
+16        file_type = file_extension[1:].lower()  # without the leading dot
+17
+18    if file_type in {'spc', 'sp'}:
+19        reader = readSPC
+20    elif file_type in {'spa'}:
+21        reader = readSPA
+22    elif file_type in {'0', '1', '2'}:
+23        reader = readOPUS
+24    elif file_type in {'wdf'}:
+25        reader = readWDF
+26    elif file_type in {'ngs'}:
+27        reader = read_ngs
+28    elif file_type in {'jdx', 'dx'}:
+29        reader = read_JCAMP
+30    elif file_type in {'txt', 'txtr', 'csv', 'prn', 'rruf'}:
+31        reader = readTXT
+32    else:
+33        raise UnsupportedFileTypeError(
+34            f'file type "{file_type}" is not supported'
+35        )
+36
+37    x_data, y_data, metadata = reader(source_path)
+38    if metadata is None:
+39        metadata = {}
+40    # Get rid of bytes that are found in some of the formats
+41    metadata = cleanMeta(metadata)
+42    # Flatten metadata
+43    metadata = dict(zip(metadata.keys(), [str(v) for v in metadata.values()]))
+44    # Extract metadata from native metadata and spectrum data,
+45    # store in metadata dictionary, and include in CHADA archive.
+46    metadata["Original file"] = os.path.basename(source_path)
+47    return x_data, y_data, metadata
+
+ + + + +
+
+ +
+ + def + cleanMeta(meta): + + + +
+ + 
50def cleanMeta(meta):
+51    # This cleans complex-strcutures metadata, and returns a dict
+52    if isinstance(meta, dict):
+53        meta = {i: meta[i] for i in meta if i != ""}
+54        for key, value in meta.items():
+55            meta[key] = cleanMeta(value)
+56    if isinstance(meta, list):
+57        for ii, value in enumerate(meta):
+58            meta[ii] = cleanMeta(value)
+59    if isinstance(meta, str):
+60        meta = meta.replace('\\x00', '')
+61        meta = meta.replace('\x00', '')
+62    if isinstance(meta, bytes):
+63        try:
+64            meta = meta.decode('utf-8')
+65            meta = cleanMeta(meta)
+66        except Exception:
+67            meta = {}
+68    return meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/rc1_parser/third_party_readers.html b/ramanchada2/io/experimental/rc1_parser/third_party_readers.html new file mode 100644 index 00000000..616e180b --- /dev/null +++ b/ramanchada2/io/experimental/rc1_parser/third_party_readers.html @@ -0,0 +1,410 @@ + + + + + + + ramanchada2.io.experimental.rc1_parser.third_party_readers API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.rc1_parser.third_party_readers

+ + + + + + + 
 1import numpy as np
+ 2
+ 3from renishawWiRE import WDFReader
+ 4from spc_io import SPC
+ 5from brukeropusreader import read_file
+ 6
+ 7
+ 8def readWDF(file):
+ 9    s = WDFReader(file)
+10    y_data = s.spectra
+11    x_data = s.xdata
+12    if np.mean(np.diff(x_data)) < 0:
+13        y_data = np.flip(y_data)
+14        x_data = np.flip(x_data)
+15    static_metadata = {
+16        "laser wavelength": s.laser_length,
+17        "no. of accumulations": s.accumulation_count,
+18        "spectral unit": s.spectral_unit.name,
+19        "OEM software name": s.application_name,
+20        "OEM software version": s.application_version
+21        }
+22    return x_data, y_data, static_metadata
+23
+24
+25def readSPC(file):
+26    with open(file, 'rb') as f:
+27        spc = SPC.from_bytes_io(f)
+28    if len(spc) != 1:
+29        raise ValueError(f'Only 1 sub supported, {len(spc)} found')
+30    x_data = spc[0].xarray
+31    y_data = spc[0].yarray
+32    static_metadata = spc.log_book.text
+33    return x_data, y_data, static_metadata
+34
+35
+36def readOPUS(file, obj_no=0):
+37    opus_data = read_file(file)
+38    x = opus_data.get_range("AB")
+39    y = opus_data["AB"]
+40    meta = {}
+41    for key in opus_data:
+42        if key == "AB":
+43            continue
+44        if isinstance(opus_data[key], dict):
+45            for subkey in opus_data[key]:
+46                meta["{}.{}".format(key, subkey)] = opus_data[key][subkey]
+47        else:
+48            meta[key] = opus_data[key]
+49    return x, y, meta
+
+ + +
+
+ +
+ + def + readWDF(file): + + + +
+ + 
 9def readWDF(file):
+10    s = WDFReader(file)
+11    y_data = s.spectra
+12    x_data = s.xdata
+13    if np.mean(np.diff(x_data)) < 0:
+14        y_data = np.flip(y_data)
+15        x_data = np.flip(x_data)
+16    static_metadata = {
+17        "laser wavelength": s.laser_length,
+18        "no. of accumulations": s.accumulation_count,
+19        "spectral unit": s.spectral_unit.name,
+20        "OEM software name": s.application_name,
+21        "OEM software version": s.application_version
+22        }
+23    return x_data, y_data, static_metadata
+
+ + + + +
+
+ +
+ + def + readSPC(file): + + + +
+ + 
26def readSPC(file):
+27    with open(file, 'rb') as f:
+28        spc = SPC.from_bytes_io(f)
+29    if len(spc) != 1:
+30        raise ValueError(f'Only 1 sub supported, {len(spc)} found')
+31    x_data = spc[0].xarray
+32    y_data = spc[0].yarray
+33    static_metadata = spc.log_book.text
+34    return x_data, y_data, static_metadata
+
+ + + + +
+
+ +
+ + def + readOPUS(file, obj_no=0): + + + +
+ + 
37def readOPUS(file, obj_no=0):
+38    opus_data = read_file(file)
+39    x = opus_data.get_range("AB")
+40    y = opus_data["AB"]
+41    meta = {}
+42    for key in opus_data:
+43        if key == "AB":
+44            continue
+45        if isinstance(opus_data[key], dict):
+46            for subkey in opus_data[key]:
+47                meta["{}.{}".format(key, subkey)] = opus_data[key][subkey]
+48        else:
+49            meta[key] = opus_data[key]
+50    return x, y, meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/rc1_parser/txt_format_readers.html b/ramanchada2/io/experimental/rc1_parser/txt_format_readers.html new file mode 100644 index 00000000..d0683b78 --- /dev/null +++ b/ramanchada2/io/experimental/rc1_parser/txt_format_readers.html @@ -0,0 +1,721 @@ + + + + + + + ramanchada2.io.experimental.rc1_parser.txt_format_readers API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.rc1_parser.txt_format_readers

+ + + + + + + 
  1import numpy as np
+  2import os
+  3import re
+  4import pandas as pd
+  5import logging
+  6
+  7logger = logging.getLogger(__name__)
+  8
+  9
+ 10def read_JCAMP(file, verbose=True):
+ 11    x = []
+ 12    y = []
+ 13    # open .txt and read as lines
+ 14    with open(file) as d:
+ 15        lines = d.readlines()
+ 16    end_index = [i for i, s in enumerate(lines) if 'END' in s][-1]
+ 17    lines = lines[:end_index]
+ 18    # ## Marks meta data
+ 19    meta_lines = [ll for ll in lines if ll.startswith('##')][:-1]
+ 20    meta = dict([mm.strip('##').strip('\n').split('=') for mm in meta_lines])
+ 21    data_lines = [ll for ll in lines if not ll.startswith('##')]
+ 22    # read up to second last line
+ 23    for ll in data_lines[:-1]:
+ 24        # split line into individual numbers
+ 25        items = ll.strip('\n').strip().split()
+ 26        # 1st is x, the rest is y values
+ 27        x.append(float(items[0]))
+ 28        [y.append(float(item)) for item in items[1:]]
+ 29    # convert to np.array
+ 30    # calc num of y per x
+ 31    y_per_x = len(y)//len(x)
+ 32    x_increment = np.mean(np.diff(np.array(x))) / y_per_x
+ 33    new_x = []
+ 34    for xx in x:
+ 35        for ii in range(y_per_x):
+ 36            new_x.append(xx + x_increment*ii)
+ 37    # Read last line (may not be complete)
+ 38    items = data_lines[-1].strip('\n').strip().split()
+ 39    for ii, item in enumerate(items[1:]):
+ 40        # 1st is x, the rest is y values
+ 41        new_x.append(float(items[0]) + x_increment*ii)
+ 42        y.append(float(item))
+ 43    return np.array(new_x), np.array(y), meta
+ 44
+ 45
+ 46def readTXT(file, x_col=0, y_col=0, verbose=True):
+ 47    # open .txt and read as lines
+ 48    with open(file) as d:
+ 49        lines = d.readlines()
+ 50    # Find data lines and convert to np.array
+ 51    start, stop = startStop(lines)
+ 52    logger.debug("Importing " + str(stop-start+1) +
+ 53                 " data lines starting from line " + str(start) +
+ 54                 " in " + os.path.basename(file) + ".")
+ 55    data_lines = lines[start:stop]
+ 56    data = dataFromTxtLines(data_lines)
+ 57    # if columns not specified, assign x (Raman shift) and y (counts) axes
+ 58    if x_col == y_col == 0:
+ 59        # x axis is the one with mean closest to 1750
+ 60        score = 1./np.abs(data.mean(0)-1750)
+ 61        # x axis must be monotonous!
+ 62        s = np.sign(np.diff(data, axis=0))
+ 63        mono = np.array([np.all(c == c[0]) for c in s.T]) * 1.
+ 64        score *= mono
+ 65        x_col = np.argmax(score)
+ 66        # y axis is the one with maximal std/mean
+ 67        score = np.nan_to_num(data.std(0)/data.mean(0), nan=0)
+ 68        # Do not choose x axis again for y
+ 69        score[x_col] = -1000
+ 70        y_col = np.argmax(score)
+ 71        # if there's mroe than 2 columns and a header line
+ 72        if startStop(lines)[0] > 0 and data.shape[1] > 2:
+ 73            logger.debug("Found more than 2 data columns in " +
+ 74                         os.path.basename(file) + ".")
+ 75            header_line = lines[startStop(lines)[0]-1].strip('\n')
+ 76            header_line = [s.casefold() for s in re.split(';|,|\t', header_line)]
+ 77            # x axis is header line with "Shift"
+ 78            indices = [i for i, s in enumerate(header_line) if 'shift' in s]
+ 79            if indices != []:
+ 80                x_col = indices[0]
+ 81                logger.debug("X data: assigning column labelled '" +
+ 82                             header_line[x_col] + "'.")
+ 83            else:
+ 84                logger.debug("X data: assigning column # " + str(x_col) + ".")
+ 85            # y axis is header line with "Subtracted"
+ 86            indices = [i for i, s in enumerate(header_line) if 'subtracted' in s]
+ 87            if indices != []:
+ 88                y_col = indices[0]
+ 89                logger.debug("Y data: assigning column labelled '" +
+ 90                             header_line[y_col] + "'.")
+ 91            else:
+ 92                logger.debug("Y data: assigning column # " + str(y_col) + ".")
+ 93    x, y = data[:, x_col], data[:, y_col]
+ 94    # Only use unique x data points
+ 95    x, unique_ind = np.unique(x, return_index=True)
+ 96    y = y[unique_ind]
+ 97    # is x inverted?
+ 98    if all(np.diff(x) <= 0):
+ 99        x = np.flip(x)
+100        y = np.flip(y)
+101    meta_lines = lines[:startStop(lines)[0]]
+102    logger.debug("Importing " + str(start-1) + " metadata lines from " +
+103                 os.path.basename(file) + ".")
+104    meta_lines = [re.split(';|,|\t|=', ll.strip()) for ll in meta_lines]
+105    ml = {}
+106    for ll in meta_lines:
+107        ml.update({ll[0]: ll[1:]})
+108    # is x axis pixel numbers instead of Raman shifts?
+109    if all(np.diff(x) == 1) and (x[0] == 0 or x[0] == 1):
+110        if "Start WN" in ml:
+111            start_x = np.int(np.array(ml["Start WN"])[0])
+112        if "End WN" in ml:
+113            stop_x = np.int(np.array(ml["End WN"])[0])
+114        x = np.linspace(start_x, stop_x, len(x))
+115        logger.debug("X data: using linspace from " + str(start_x) + " to " +
+116                     str(stop_x) + " 1/cm.")
+117    return x, y, ml
+118
+119
+120def dataFromTxtLines(data_lines):
+121    data = []
+122    for ii, ll in enumerate(data_lines):
+123        ll = ll.strip('\n').replace("\t", " ")
+124        if ";" in ll:
+125            separator = ";"
+126        elif "," in ll and "." in ll:
+127            separator = ","
+128        elif "," in ll and " " in ll:
+129            separator = " "
+130        elif "," in ll:
+131            separator = ","
+132        else:
+133            separator = " "
+134        items = ll.split(separator)
+135        items = [item.replace(",", ".") for item in items]
+136        data.append(items)
+137    D = pd.DataFrame(np.array(data))
+138    D = D.replace(r'^\s*$', 0, regex=True)
+139    D = D.apply(pd.to_numeric)
+140    D = D.dropna()
+141    return D.to_numpy()
+142
+143
+144def isDataLine(line):
+145    line = line.strip("\n").replace("\t", " ")
+146    blank = all([c == " " for c in line])
+147    # has more than 75% digits
+148    digits = np.sum([d.isdigit() for d in line]) / len(line) > .25
+149    # apart from digits, has only ".", ";", ",", " "
+150    chars = all([c in '.,;+-eE ' for c in line if not c.isdigit()])
+151    return (not blank) & digits & chars
+152
+153
+154def startStop(lines):
+155    start_line, stop_line = 0, 0
+156    for ii, line in enumerate(lines):
+157        # if this is a data line and the following 5 lines are also data lines, then here is the start line
+158        if (len(lines) - ii) > 5 and start_line == 0:
+159            if all([isDataLine(ll) for ll in lines[ii:ii+5]]):
+160                start_line = ii
+161        # if this is a data line and the following 5 lines are also data lines, then here is the start line
+162        if (not isDataLine(line)) and stop_line <= start_line:
+163            stop_line = ii
+164    if stop_line <= start_line:
+165        stop_line = len(lines)-1
+166    return start_line, stop_line
+167
+168
+169def getYDataType(y_data):
+170    types = {0: "Single spectrum", 1: "Line scan", 2: "Map", 3: "Map series / volume"}
+171    return types[len(y_data.shape)-1]
+
+ + +
+
+
+ logger = +<Logger ramanchada2.io.experimental.rc1_parser.txt_format_readers (WARNING)> + + +
+ + + + +
+
+ +
+ + def + read_JCAMP(file, verbose=True): + + + +
+ + 
11def read_JCAMP(file, verbose=True):
+12    x = []
+13    y = []
+14    # open .txt and read as lines
+15    with open(file) as d:
+16        lines = d.readlines()
+17    end_index = [i for i, s in enumerate(lines) if 'END' in s][-1]
+18    lines = lines[:end_index]
+19    # ## Marks meta data
+20    meta_lines = [ll for ll in lines if ll.startswith('##')][:-1]
+21    meta = dict([mm.strip('##').strip('\n').split('=') for mm in meta_lines])
+22    data_lines = [ll for ll in lines if not ll.startswith('##')]
+23    # read up to second last line
+24    for ll in data_lines[:-1]:
+25        # split line into individual numbers
+26        items = ll.strip('\n').strip().split()
+27        # 1st is x, the rest is y values
+28        x.append(float(items[0]))
+29        [y.append(float(item)) for item in items[1:]]
+30    # convert to np.array
+31    # calc num of y per x
+32    y_per_x = len(y)//len(x)
+33    x_increment = np.mean(np.diff(np.array(x))) / y_per_x
+34    new_x = []
+35    for xx in x:
+36        for ii in range(y_per_x):
+37            new_x.append(xx + x_increment*ii)
+38    # Read last line (may not be complete)
+39    items = data_lines[-1].strip('\n').strip().split()
+40    for ii, item in enumerate(items[1:]):
+41        # 1st is x, the rest is y values
+42        new_x.append(float(items[0]) + x_increment*ii)
+43        y.append(float(item))
+44    return np.array(new_x), np.array(y), meta
+
+ + + + +
+
+ +
+ + def + readTXT(file, x_col=0, y_col=0, verbose=True): + + + +
+ + 
 47def readTXT(file, x_col=0, y_col=0, verbose=True):
+ 48    # open .txt and read as lines
+ 49    with open(file) as d:
+ 50        lines = d.readlines()
+ 51    # Find data lines and convert to np.array
+ 52    start, stop = startStop(lines)
+ 53    logger.debug("Importing " + str(stop-start+1) +
+ 54                 " data lines starting from line " + str(start) +
+ 55                 " in " + os.path.basename(file) + ".")
+ 56    data_lines = lines[start:stop]
+ 57    data = dataFromTxtLines(data_lines)
+ 58    # if columns not specified, assign x (Raman shift) and y (counts) axes
+ 59    if x_col == y_col == 0:
+ 60        # x axis is the one with mean closest to 1750
+ 61        score = 1./np.abs(data.mean(0)-1750)
+ 62        # x axis must be monotonous!
+ 63        s = np.sign(np.diff(data, axis=0))
+ 64        mono = np.array([np.all(c == c[0]) for c in s.T]) * 1.
+ 65        score *= mono
+ 66        x_col = np.argmax(score)
+ 67        # y axis is the one with maximal std/mean
+ 68        score = np.nan_to_num(data.std(0)/data.mean(0), nan=0)
+ 69        # Do not choose x axis again for y
+ 70        score[x_col] = -1000
+ 71        y_col = np.argmax(score)
+ 72        # if there's mroe than 2 columns and a header line
+ 73        if startStop(lines)[0] > 0 and data.shape[1] > 2:
+ 74            logger.debug("Found more than 2 data columns in " +
+ 75                         os.path.basename(file) + ".")
+ 76            header_line = lines[startStop(lines)[0]-1].strip('\n')
+ 77            header_line = [s.casefold() for s in re.split(';|,|\t', header_line)]
+ 78            # x axis is header line with "Shift"
+ 79            indices = [i for i, s in enumerate(header_line) if 'shift' in s]
+ 80            if indices != []:
+ 81                x_col = indices[0]
+ 82                logger.debug("X data: assigning column labelled '" +
+ 83                             header_line[x_col] + "'.")
+ 84            else:
+ 85                logger.debug("X data: assigning column # " + str(x_col) + ".")
+ 86            # y axis is header line with "Subtracted"
+ 87            indices = [i for i, s in enumerate(header_line) if 'subtracted' in s]
+ 88            if indices != []:
+ 89                y_col = indices[0]
+ 90                logger.debug("Y data: assigning column labelled '" +
+ 91                             header_line[y_col] + "'.")
+ 92            else:
+ 93                logger.debug("Y data: assigning column # " + str(y_col) + ".")
+ 94    x, y = data[:, x_col], data[:, y_col]
+ 95    # Only use unique x data points
+ 96    x, unique_ind = np.unique(x, return_index=True)
+ 97    y = y[unique_ind]
+ 98    # is x inverted?
+ 99    if all(np.diff(x) <= 0):
+100        x = np.flip(x)
+101        y = np.flip(y)
+102    meta_lines = lines[:startStop(lines)[0]]
+103    logger.debug("Importing " + str(start-1) + " metadata lines from " +
+104                 os.path.basename(file) + ".")
+105    meta_lines = [re.split(';|,|\t|=', ll.strip()) for ll in meta_lines]
+106    ml = {}
+107    for ll in meta_lines:
+108        ml.update({ll[0]: ll[1:]})
+109    # is x axis pixel numbers instead of Raman shifts?
+110    if all(np.diff(x) == 1) and (x[0] == 0 or x[0] == 1):
+111        if "Start WN" in ml:
+112            start_x = np.int(np.array(ml["Start WN"])[0])
+113        if "End WN" in ml:
+114            stop_x = np.int(np.array(ml["End WN"])[0])
+115        x = np.linspace(start_x, stop_x, len(x))
+116        logger.debug("X data: using linspace from " + str(start_x) + " to " +
+117                     str(stop_x) + " 1/cm.")
+118    return x, y, ml
+
+ + + + +
+
+ +
+ + def + dataFromTxtLines(data_lines): + + + +
+ + 
121def dataFromTxtLines(data_lines):
+122    data = []
+123    for ii, ll in enumerate(data_lines):
+124        ll = ll.strip('\n').replace("\t", " ")
+125        if ";" in ll:
+126            separator = ";"
+127        elif "," in ll and "." in ll:
+128            separator = ","
+129        elif "," in ll and " " in ll:
+130            separator = " "
+131        elif "," in ll:
+132            separator = ","
+133        else:
+134            separator = " "
+135        items = ll.split(separator)
+136        items = [item.replace(",", ".") for item in items]
+137        data.append(items)
+138    D = pd.DataFrame(np.array(data))
+139    D = D.replace(r'^\s*$', 0, regex=True)
+140    D = D.apply(pd.to_numeric)
+141    D = D.dropna()
+142    return D.to_numpy()
+
+ + + + +
+
+ +
+ + def + isDataLine(line): + + + +
+ + 
145def isDataLine(line):
+146    line = line.strip("\n").replace("\t", " ")
+147    blank = all([c == " " for c in line])
+148    # has more than 75% digits
+149    digits = np.sum([d.isdigit() for d in line]) / len(line) > .25
+150    # apart from digits, has only ".", ";", ",", " "
+151    chars = all([c in '.,;+-eE ' for c in line if not c.isdigit()])
+152    return (not blank) & digits & chars
+
+ + + + +
+
+ +
+ + def + startStop(lines): + + + +
+ + 
155def startStop(lines):
+156    start_line, stop_line = 0, 0
+157    for ii, line in enumerate(lines):
+158        # if this is a data line and the following 5 lines are also data lines, then here is the start line
+159        if (len(lines) - ii) > 5 and start_line == 0:
+160            if all([isDataLine(ll) for ll in lines[ii:ii+5]]):
+161                start_line = ii
+162        # if this is a data line and the following 5 lines are also data lines, then here is the start line
+163        if (not isDataLine(line)) and stop_line <= start_line:
+164            stop_line = ii
+165    if stop_line <= start_line:
+166        stop_line = len(lines)-1
+167    return start_line, stop_line
+
+ + + + +
+
+ +
+ + def + getYDataType(y_data): + + + +
+ + 
170def getYDataType(y_data):
+171    types = {0: "Single spectrum", 1: "Line scan", 2: "Map", 3: "Map series / volume"}
+172    return types[len(y_data.shape)-1]
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/read_csv.html b/ramanchada2/io/experimental/read_csv.html new file mode 100644 index 00000000..07172225 --- /dev/null +++ b/ramanchada2/io/experimental/read_csv.html @@ -0,0 +1,313 @@ + + + + + + + ramanchada2.io.experimental.read_csv API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.read_csv

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from typing import TextIO, Tuple, Dict
+ 4
+ 5from numpy.typing import NDArray
+ 6import numpy as np
+ 7
+ 8
+ 9def read_csv(data_in: TextIO) -> Tuple[NDArray, NDArray, Dict]:
+10    lines = data_in.readlines()
+11    positions, intensities = np.genfromtxt(lines, delimiter=',', dtype=float).T
+12    filter_nan = ~(
+13        np.isnan(positions) |
+14        np.isnan(intensities)
+15    )
+16    positions = positions[filter_nan]
+17    intensities = intensities[filter_nan]
+18    meta: Dict[str, None] = dict()
+19    return positions, intensities, meta
+
+ + +
+
+ +
+ + def + read_csv( data_in: <class 'TextIO'>) -> Tuple[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Dict]: + + + +
+ + 
10def read_csv(data_in: TextIO) -> Tuple[NDArray, NDArray, Dict]:
+11    lines = data_in.readlines()
+12    positions, intensities = np.genfromtxt(lines, delimiter=',', dtype=float).T
+13    filter_nan = ~(
+14        np.isnan(positions) |
+15        np.isnan(intensities)
+16    )
+17    positions = positions[filter_nan]
+18    intensities = intensities[filter_nan]
+19    meta: Dict[str, None] = dict()
+20    return positions, intensities, meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/read_spe.html b/ramanchada2/io/experimental/read_spe.html new file mode 100644 index 00000000..7c856e48 --- /dev/null +++ b/ramanchada2/io/experimental/read_spe.html @@ -0,0 +1,310 @@ + + + + + + + ramanchada2.io.experimental.read_spe API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.read_spe

+ + + + + + + 
 1import spe_loader
+ 2
+ 3
+ 4def read_spe(filename):
+ 5    """Princeton Instruments spe format"""
+ 6    teledyn = spe_loader.load_from_files([filename])
+ 7    if len(teledyn.data) != 1 or len(teledyn.data[0]) != 1 or len(teledyn.data[0][0]) != 1:
+ 8        raise ValueError('only single spectrum files are supported')
+ 9    positions = teledyn.wavelength
+10    intensities = teledyn.data[0][0][0]
+11    meta = {}
+12    meta['@axes'] = ['Wavelengths']
+13    meta['@signal'] = ''
+14    return positions, intensities, meta
+
+ + +
+
+ +
+ + def + read_spe(filename): + + + +
+ + 
 5def read_spe(filename):
+ 6    """Princeton Instruments spe format"""
+ 7    teledyn = spe_loader.load_from_files([filename])
+ 8    if len(teledyn.data) != 1 or len(teledyn.data[0]) != 1 or len(teledyn.data[0][0]) != 1:
+ 9        raise ValueError('only single spectrum files are supported')
+10    positions = teledyn.wavelength
+11    intensities = teledyn.data[0][0][0]
+12    meta = {}
+13    meta['@axes'] = ['Wavelengths']
+14    meta['@signal'] = ''
+15    return positions, intensities, meta
+
+ + +

Princeton Instruments spe format

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/read_txt.html b/ramanchada2/io/experimental/read_txt.html new file mode 100644 index 00000000..0b11e5bd --- /dev/null +++ b/ramanchada2/io/experimental/read_txt.html @@ -0,0 +1,367 @@ + + + + + + + ramanchada2.io.experimental.read_txt API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.read_txt

+ + + + + + + 
 1from typing import Dict, TextIO, Tuple
+ 2
+ 3import numpy as np
+ 4from numpy.typing import NDArray
+ 5
+ 6from .bw_format import bw_format
+ 7from .neegala_format import neegala_format
+ 8from .rruf_format import rruf_format
+ 9
+10""" There are 4 types of TXT data files that can be distinguished by their first line:
+11    1. File Version;BWRam4.11_11
+12    2. File Version;BWSpec4.11_1
+13    3. <wavenumber>	<intensity>
+14    4. #Wave		#Intensity
+15      <wavenumber>	<intensity>
+16    """
+17
+18
+19def read_txt(data_in: TextIO) -> Tuple[NDArray, NDArray, Dict]:
+20    lines = data_in.readlines()
+21    if lines[0].startswith('File Version;BW'):
+22        data, meta = bw_format(lines)
+23        positions = data['RamanShift'].to_numpy()
+24        intensities = data['DarkSubtracted#1'].to_numpy()
+25        meta['@axes'] = ['RamanShift']
+26        meta['@signal'] = 'DarkSubtracted'
+27    elif lines[0].startswith('##'):
+28        # rruf format
+29        positions, intensities, meta = rruf_format(lines)
+30        meta['@axes'] = ['']
+31        meta['@signal'] = ''
+32    elif ',' in lines[0] and not lines[0].split(',')[0].isdigit():
+33        data, meta = neegala_format(lines)
+34        positions = data['Raman_Shift'].to_numpy()
+35        intensities = data['Processed_Data'].to_numpy()
+36        meta['@axes'] = ['Raman_Shift']
+37        meta['@signal'] = 'Processed_Data'
+38    else:  # assume two column spectrum
+39        meta = dict()
+40        if lines[0].startswith('#'):
+41            # assume header row
+42            data = np.genfromtxt(lines, names=True, loose=False)
+43            meta['@axes'] = [data.dtype.names[0]]
+44            meta['@signal'] = data.dtype.names[1]
+45            data = np.array(data.tolist())
+46        else:
+47            data = np.genfromtxt(lines, loose=False)
+48            meta['@axes'] = ['']
+49            meta['@signal'] = ''
+50        positions, intensities = data.T
+51    return positions, intensities, meta
+
+ + +
+
+ +
+ + def + read_txt( data_in: <class 'TextIO'>) -> Tuple[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Dict]: + + + +
+ + 
20def read_txt(data_in: TextIO) -> Tuple[NDArray, NDArray, Dict]:
+21    lines = data_in.readlines()
+22    if lines[0].startswith('File Version;BW'):
+23        data, meta = bw_format(lines)
+24        positions = data['RamanShift'].to_numpy()
+25        intensities = data['DarkSubtracted#1'].to_numpy()
+26        meta['@axes'] = ['RamanShift']
+27        meta['@signal'] = 'DarkSubtracted'
+28    elif lines[0].startswith('##'):
+29        # rruf format
+30        positions, intensities, meta = rruf_format(lines)
+31        meta['@axes'] = ['']
+32        meta['@signal'] = ''
+33    elif ',' in lines[0] and not lines[0].split(',')[0].isdigit():
+34        data, meta = neegala_format(lines)
+35        positions = data['Raman_Shift'].to_numpy()
+36        intensities = data['Processed_Data'].to_numpy()
+37        meta['@axes'] = ['Raman_Shift']
+38        meta['@signal'] = 'Processed_Data'
+39    else:  # assume two column spectrum
+40        meta = dict()
+41        if lines[0].startswith('#'):
+42            # assume header row
+43            data = np.genfromtxt(lines, names=True, loose=False)
+44            meta['@axes'] = [data.dtype.names[0]]
+45            meta['@signal'] = data.dtype.names[1]
+46            data = np.array(data.tolist())
+47        else:
+48            data = np.genfromtxt(lines, loose=False)
+49            meta['@axes'] = ['']
+50            meta['@signal'] = ''
+51        positions, intensities = data.T
+52    return positions, intensities, meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/experimental/rruf_format.html b/ramanchada2/io/experimental/rruf_format.html new file mode 100644 index 00000000..ac5ef30e --- /dev/null +++ b/ramanchada2/io/experimental/rruf_format.html @@ -0,0 +1,319 @@ + + + + + + + ramanchada2.io.experimental.rruf_format API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.experimental.rruf_format

+ + + + + + + 
 1from typing import Dict, List, Tuple
+ 2
+ 3import pandas
+ 4from numpy.typing import NDArray
+ 5
+ 6
+ 7def rruf_format(lines: List[str]) -> Tuple[NDArray, NDArray, Dict]:
+ 8    for i, ll in enumerate(lines):
+ 9        if not ll.startswith('##'):
+10            start_spe = i
+11            break
+12    meta = dict([ll.strip()[2:].split('=') for ll in lines[:start_spe]])
+13    for i, ll in enumerate(lines):
+14        if ll.startswith('##END'):
+15            stop_spe = i
+16            break
+17    data = pandas.DataFrame.from_records(
+18        data=[ll.split(',') for ll in lines[start_spe:stop_spe]]
+19        ).apply(pandas.to_numeric).dropna(axis=0)
+20    positions, intensities = data.to_numpy().T
+21    return positions, intensities, meta
+
+ + +
+
+ +
+ + def + rruf_format( lines: List[str]) -> Tuple[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Dict]: + + + +
+ + 
 8def rruf_format(lines: List[str]) -> Tuple[NDArray, NDArray, Dict]:
+ 9    for i, ll in enumerate(lines):
+10        if not ll.startswith('##'):
+11            start_spe = i
+12            break
+13    meta = dict([ll.strip()[2:].split('=') for ll in lines[:start_spe]])
+14    for i, ll in enumerate(lines):
+15        if ll.startswith('##END'):
+16            stop_spe = i
+17            break
+18    data = pandas.DataFrame.from_records(
+19        data=[ll.split(',') for ll in lines[start_spe:stop_spe]]
+20        ).apply(pandas.to_numeric).dropna(axis=0)
+21    positions, intensities = data.to_numpy().T
+22    return positions, intensities, meta
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/output.html b/ramanchada2/io/output.html new file mode 100644 index 00000000..d03da621 --- /dev/null +++ b/ramanchada2/io/output.html @@ -0,0 +1,264 @@ + + + + + + + ramanchada2.io.output API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.output

+ + + + + + + 
1#!/usr/bin/env python3
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/output/write_csv.html b/ramanchada2/io/output/write_csv.html new file mode 100644 index 00000000..98e4f3d1 --- /dev/null +++ b/ramanchada2/io/output/write_csv.html @@ -0,0 +1,290 @@ + + + + + + + ramanchada2.io.output.write_csv API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.output.write_csv

+ + + + + + + 
1from typing import List
+2
+3
+4def write_csv(x, y, delimiter=',') -> List[str]:
+5    return [f'{x[i]}{delimiter}{y[i]}\n' for i in range(len(x))]
+
+ + +
+
+ +
+ + def + write_csv(x, y, delimiter=',') -> List[str]: + + + +
+ + 
5def write_csv(x, y, delimiter=',') -> List[str]:
+6    return [f'{x[i]}{delimiter}{y[i]}\n' for i in range(len(x))]
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated.html b/ramanchada2/io/simulated.html new file mode 100644 index 00000000..1b5db04c --- /dev/null +++ b/ramanchada2/io/simulated.html @@ -0,0 +1,269 @@ + + + + + + + ramanchada2.io.simulated API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3from .read_simulated_lines import read_simulated_lines
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/crystal.html b/ramanchada2/io/simulated/crystal.html new file mode 100644 index 00000000..166c73e3 --- /dev/null +++ b/ramanchada2/io/simulated/crystal.html @@ -0,0 +1,265 @@ + + + + + + + ramanchada2.io.simulated.crystal API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.crystal

+ + + + + + + 
1#!/usr/bin/env python3
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/crystal/discrete_lines_dat.html b/ramanchada2/io/simulated/crystal/discrete_lines_dat.html new file mode 100644 index 00000000..c9446f5a --- /dev/null +++ b/ramanchada2/io/simulated/crystal/discrete_lines_dat.html @@ -0,0 +1,318 @@ + + + + + + + ramanchada2.io.simulated.crystal.discrete_lines_dat API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.crystal.discrete_lines_dat

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from io import TextIOBase
+ 4
+ 5import pandas as pd
+ 6from pydantic import validate_call
+ 7
+ 8
+ 9@validate_call(config=dict(arbitrary_types_allowed=True))
+10def lines_from_crystal_dat(data_in: TextIOBase) -> pd.DataFrame:
+11    return pd.DataFrame(
+12        data=[
+13            lin.split()
+14            for lin in data_in.readlines()
+15            if not lin.startswith('#')
+16        ],
+17        columns=[
+18            'Frequencies', 'I_tot', 'I_par', 'I_perp',
+19            'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'
+20        ],
+21        dtype=float)
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + lines_from_crystal_dat(data_in: io.TextIOBase) -> pandas.core.frame.DataFrame: + + + +
+ + 
10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def lines_from_crystal_dat(data_in: TextIOBase) -> pd.DataFrame:
+12    return pd.DataFrame(
+13        data=[
+14            lin.split()
+15            for lin in data_in.readlines()
+16            if not lin.startswith('#')
+17        ],
+18        columns=[
+19            'Frequencies', 'I_tot', 'I_par', 'I_perp',
+20            'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'
+21        ],
+22        dtype=float)
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/crystal/discrete_lines_out.html b/ramanchada2/io/simulated/crystal/discrete_lines_out.html new file mode 100644 index 00000000..92df7bc2 --- /dev/null +++ b/ramanchada2/io/simulated/crystal/discrete_lines_out.html @@ -0,0 +1,402 @@ + + + + + + + ramanchada2.io.simulated.crystal.discrete_lines_out API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.crystal.discrete_lines_out

+ + + + + + + 
 1import re
+ 2from io import TextIOBase
+ 3from typing import List
+ 4
+ 5import pandas as pd
+ 6from pydantic import validate_call
+ 7
+ 8from ramanchada2.misc.exceptions import InputParserError
+ 9
+10
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def lines_from_crystal_out(data_in: TextIOBase) -> pd.DataFrame:
+13    def advance_to(content: str) -> None:
+14        while content not in data_in.readline():
+15            continue
+16
+17    def read_paragraph() -> List[str]:
+18        ret = list()
+19        while True:
+20            line = data_in.readline().rstrip()
+21            if line == '':
+22                break
+23            ret.append(line)
+24        return ret
+25
+26    def parse(regex, lines: List[str]) -> List[List[str]]:
+27        ret = list()
+28        if len(lines) < 2:
+29            raise InputParserError()
+30        for match in [regex.match(line) for line in lines[2:]]:
+31            if match:
+32                ret.append(match.groups())
+33            else:
+34                raise InputParserError()
+35        return ret
+36
+37    def skip_line():
+38        data_in.readline()
+39
+40    advance_to('POLYCRYSTALLINE ISOTROPIC INTENSITIES (ARBITRARY UNITS)')
+41    skip_line()  # empty line
+42    polyXtal_lines = read_paragraph()
+43    advance_to('SINGLE CRYSTAL DIRECTIONAL INTENSITIES (ARBITRARY UNITS)')
+44    skip_line()  # empty line
+45    monoXtal_lines = read_paragraph()
+46    # data_in is processed
+47
+48    polyXtal_regex = re.compile(r'\s*(\d+)-\s*(\d+)\s*([\d.]+)\s*\((\w+)\s*\)' + r'\s*([\d.]+)'*3)
+49    polyXtal_parsed = parse(polyXtal_regex, polyXtal_lines)
+50    polyXtal_df = pd.DataFrame.from_records(
+51        polyXtal_parsed,
+52        columns=['ModeL', 'ModeU', 'Frequencies', 'Origin', 'I_tot', 'I_par', 'I_perp'])
+53    polyXtal_df = polyXtal_df.astype(
+54        dict(zip(polyXtal_df.keys(), [*[int]*2, float, str, *[float]*3])))
+55
+56    monoXtal_regex = re.compile(r'\s*(\d+)-\s*(\d+)\s*([\d.]+)\s*\((\w+)\s*\)' + r'\s*([\d.]+)'*6)
+57    monoXtal_parsed = parse(monoXtal_regex, monoXtal_lines)
+58    monoXtal_df = pd.DataFrame.from_records(
+59        monoXtal_parsed,
+60        columns=['ModeL', 'ModeU', 'Frequencies', 'Origin', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'])
+61    monoXtal_df = monoXtal_df.astype(
+62        dict(zip(monoXtal_df.keys(), [*[int]*2, float, str, *[float]*6])))
+63    merge = pd.merge(polyXtal_df, monoXtal_df)
+64    return merge
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + lines_from_crystal_out(data_in: io.TextIOBase) -> pandas.core.frame.DataFrame: + + + +
+ + 
12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def lines_from_crystal_out(data_in: TextIOBase) -> pd.DataFrame:
+14    def advance_to(content: str) -> None:
+15        while content not in data_in.readline():
+16            continue
+17
+18    def read_paragraph() -> List[str]:
+19        ret = list()
+20        while True:
+21            line = data_in.readline().rstrip()
+22            if line == '':
+23                break
+24            ret.append(line)
+25        return ret
+26
+27    def parse(regex, lines: List[str]) -> List[List[str]]:
+28        ret = list()
+29        if len(lines) < 2:
+30            raise InputParserError()
+31        for match in [regex.match(line) for line in lines[2:]]:
+32            if match:
+33                ret.append(match.groups())
+34            else:
+35                raise InputParserError()
+36        return ret
+37
+38    def skip_line():
+39        data_in.readline()
+40
+41    advance_to('POLYCRYSTALLINE ISOTROPIC INTENSITIES (ARBITRARY UNITS)')
+42    skip_line()  # empty line
+43    polyXtal_lines = read_paragraph()
+44    advance_to('SINGLE CRYSTAL DIRECTIONAL INTENSITIES (ARBITRARY UNITS)')
+45    skip_line()  # empty line
+46    monoXtal_lines = read_paragraph()
+47    # data_in is processed
+48
+49    polyXtal_regex = re.compile(r'\s*(\d+)-\s*(\d+)\s*([\d.]+)\s*\((\w+)\s*\)' + r'\s*([\d.]+)'*3)
+50    polyXtal_parsed = parse(polyXtal_regex, polyXtal_lines)
+51    polyXtal_df = pd.DataFrame.from_records(
+52        polyXtal_parsed,
+53        columns=['ModeL', 'ModeU', 'Frequencies', 'Origin', 'I_tot', 'I_par', 'I_perp'])
+54    polyXtal_df = polyXtal_df.astype(
+55        dict(zip(polyXtal_df.keys(), [*[int]*2, float, str, *[float]*3])))
+56
+57    monoXtal_regex = re.compile(r'\s*(\d+)-\s*(\d+)\s*([\d.]+)\s*\((\w+)\s*\)' + r'\s*([\d.]+)'*6)
+58    monoXtal_parsed = parse(monoXtal_regex, monoXtal_lines)
+59    monoXtal_df = pd.DataFrame.from_records(
+60        monoXtal_parsed,
+61        columns=['ModeL', 'ModeU', 'Frequencies', 'Origin', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'])
+62    monoXtal_df = monoXtal_df.astype(
+63        dict(zip(monoXtal_df.keys(), [*[int]*2, float, str, *[float]*6])))
+64    merge = pd.merge(polyXtal_df, monoXtal_df)
+65    return merge
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/lines_from_raw_dat.html b/ramanchada2/io/simulated/lines_from_raw_dat.html new file mode 100644 index 00000000..ed297b72 --- /dev/null +++ b/ramanchada2/io/simulated/lines_from_raw_dat.html @@ -0,0 +1,300 @@ + + + + + + + ramanchada2.io.simulated.lines_from_raw_dat API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.lines_from_raw_dat

+ + + + + + + 
 1from io import TextIOBase
+ 2
+ 3import pandas as pd
+ 4from pydantic import validate_call
+ 5
+ 6
+ 7@validate_call(config=dict(arbitrary_types_allowed=True))
+ 8def lines_from_raw_dat(data_in: TextIOBase) -> pd.DataFrame:
+ 9    df = pd.read_table(data_in, sep=r'\s+', dtype=float)
+10    df.rename(columns={'#FREQUENCIES': 'Frequencies'}, inplace=True)
+11    return df
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + lines_from_raw_dat(data_in: io.TextIOBase) -> pandas.core.frame.DataFrame: + + + +
+ + 
 8@validate_call(config=dict(arbitrary_types_allowed=True))
+ 9def lines_from_raw_dat(data_in: TextIOBase) -> pd.DataFrame:
+10    df = pd.read_table(data_in, sep=r'\s+', dtype=float)
+11    df.rename(columns={'#FREQUENCIES': 'Frequencies'}, inplace=True)
+12    return df
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/read_simulated_lines.html b/ramanchada2/io/simulated/read_simulated_lines.html new file mode 100644 index 00000000..51b85f7e --- /dev/null +++ b/ramanchada2/io/simulated/read_simulated_lines.html @@ -0,0 +1,381 @@ + + + + + + + ramanchada2.io.simulated.read_simulated_lines API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.read_simulated_lines

+ + + + + + + 
 1from io import TextIOBase
+ 2from typing import Dict, List, Literal, Set, Tuple
+ 3
+ 4from pydantic import validate_call
+ 5
+ 6from .crystal.discrete_lines_dat import lines_from_crystal_dat
+ 7from .crystal.discrete_lines_out import lines_from_crystal_out
+ 8from .lines_from_raw_dat import lines_from_raw_dat
+ 9from .vasp.vasp_simulation_dat import lines_from_vasp_dat
+10
+11
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def read_simulated_lines(data_in: TextIOBase,
+14                         sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],
+15                         use: Set[Literal[
+16                             'I_tot', 'I_perp', 'I_par',
+17                             'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'
+18                             ]] = {'I_tot'}
+19                         ) -> Tuple[List[str], List[float], Dict[str, List[float]]]:
+20    positions: List[float]
+21    intensities: Dict[str, List[float]] = dict()
+22    if sim_type.startswith('crystal'):
+23        if sim_type.endswith('out'):
+24            tbl = lines_from_crystal_out(data_in)
+25        elif sim_type.endswith('dat'):
+26            tbl = lines_from_crystal_dat(data_in)
+27        else:
+28            raise Exception('This should never happen')
+29        names = [f'_{i}' for i in range(len(tbl['I_tot']))]
+30        for key in use:
+31            intensities.update({key: tbl[key].to_list()})
+32        positions = tbl['Frequencies'].to_list()
+33
+34    elif sim_type == 'vasp':
+35        tbl = lines_from_vasp_dat(data_in)
+36        names = [f'_{mode}'.replace('.', '_') for mode in tbl['mode']]
+37        for key in use:
+38            if key in {'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'}:
+39                raise ValueError('vasp simulation does not support monocrystal intensities')
+40            if key == 'I_tot':
+41                intensities.update({key: tbl['activity'].to_list()})
+42            else:
+43                intensities.update({key: tbl[key].to_list()})
+44        positions = tbl['freq(cm-1)'].to_list()
+45
+46    elif sim_type == 'raw_dat':
+47        tbl = lines_from_raw_dat(data_in)
+48        names = [''] * len(tbl['I_tot'])
+49        for key in use:
+50            intensities.update({key: tbl[key].to_list()})
+51        positions = tbl['Frequencies'].to_list()
+52    else:
+53        raise Exception('This should never happen')
+54    return names, positions, intensities
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + read_simulated_lines( data_in: io.TextIOBase, sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'], use: Set[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz']] = {'I_tot'}) -> Tuple[List[str], List[float], Dict[str, List[float]]]: + + + +
+ + 
13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def read_simulated_lines(data_in: TextIOBase,
+15                         sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],
+16                         use: Set[Literal[
+17                             'I_tot', 'I_perp', 'I_par',
+18                             'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'
+19                             ]] = {'I_tot'}
+20                         ) -> Tuple[List[str], List[float], Dict[str, List[float]]]:
+21    positions: List[float]
+22    intensities: Dict[str, List[float]] = dict()
+23    if sim_type.startswith('crystal'):
+24        if sim_type.endswith('out'):
+25            tbl = lines_from_crystal_out(data_in)
+26        elif sim_type.endswith('dat'):
+27            tbl = lines_from_crystal_dat(data_in)
+28        else:
+29            raise Exception('This should never happen')
+30        names = [f'_{i}' for i in range(len(tbl['I_tot']))]
+31        for key in use:
+32            intensities.update({key: tbl[key].to_list()})
+33        positions = tbl['Frequencies'].to_list()
+34
+35    elif sim_type == 'vasp':
+36        tbl = lines_from_vasp_dat(data_in)
+37        names = [f'_{mode}'.replace('.', '_') for mode in tbl['mode']]
+38        for key in use:
+39            if key in {'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'}:
+40                raise ValueError('vasp simulation does not support monocrystal intensities')
+41            if key == 'I_tot':
+42                intensities.update({key: tbl['activity'].to_list()})
+43            else:
+44                intensities.update({key: tbl[key].to_list()})
+45        positions = tbl['freq(cm-1)'].to_list()
+46
+47    elif sim_type == 'raw_dat':
+48        tbl = lines_from_raw_dat(data_in)
+49        names = [''] * len(tbl['I_tot'])
+50        for key in use:
+51            intensities.update({key: tbl[key].to_list()})
+52        positions = tbl['Frequencies'].to_list()
+53    else:
+54        raise Exception('This should never happen')
+55    return names, positions, intensities
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/vasp.html b/ramanchada2/io/simulated/vasp.html new file mode 100644 index 00000000..8ae4111e --- /dev/null +++ b/ramanchada2/io/simulated/vasp.html @@ -0,0 +1,264 @@ + + + + + + + ramanchada2.io.simulated.vasp API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.vasp

+ + + + + + + 
1#!/usr/bin/env python3
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/io/simulated/vasp/vasp_simulation_dat.html b/ramanchada2/io/simulated/vasp/vasp_simulation_dat.html new file mode 100644 index 00000000..ef416e3a --- /dev/null +++ b/ramanchada2/io/simulated/vasp/vasp_simulation_dat.html @@ -0,0 +1,337 @@ + + + + + + + ramanchada2.io.simulated.vasp.vasp_simulation_dat API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.io.simulated.vasp.vasp_simulation_dat

+ + + + + + + 
 1from io import TextIOBase
+ 2
+ 3import pandas
+ 4from pydantic import validate_call
+ 5
+ 6
+ 7@validate_call(config=dict(arbitrary_types_allowed=True))
+ 8def lines_from_vasp_dat(data_in: TextIOBase) -> pandas.DataFrame:
+ 9    """
+10    calculates perpendicular and parallel intensities using
+11    https://doi.org/10.1103/PhysRevB.54.7830
+12    """
+13    lines = data_in.readlines()
+14    lines_split = [ll.strip(' \r\n#').split() for ll in lines]
+15    df = pandas.DataFrame.from_records(data=lines_split[1:], columns=lines_split[0])
+16    df = df.apply(pandas.to_numeric)
+17
+18    alpha = df['alpha']
+19    beta2 = df['beta2']
+20    perp_par_ratio = 3*beta2/(45*alpha**2 + 4*beta2)
+21    perp_par_ratio = perp_par_ratio.fillna(0)
+22
+23    i_tot = df['activity']
+24    i_perp = i_tot * perp_par_ratio
+25    i_par = i_tot * (1 - perp_par_ratio)
+26    df = df.merge(i_par.to_frame(name='I_par'), left_index=True, right_index=True)
+27    df = df.merge(i_perp.to_frame(name='I_perp'), left_index=True, right_index=True)
+28    return df
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + lines_from_vasp_dat(data_in: io.TextIOBase) -> pandas.core.frame.DataFrame: + + + +
+ + 
 8@validate_call(config=dict(arbitrary_types_allowed=True))
+ 9def lines_from_vasp_dat(data_in: TextIOBase) -> pandas.DataFrame:
+10    """
+11    calculates perpendicular and parallel intensities using
+12    https://doi.org/10.1103/PhysRevB.54.7830
+13    """
+14    lines = data_in.readlines()
+15    lines_split = [ll.strip(' \r\n#').split() for ll in lines]
+16    df = pandas.DataFrame.from_records(data=lines_split[1:], columns=lines_split[0])
+17    df = df.apply(pandas.to_numeric)
+18
+19    alpha = df['alpha']
+20    beta2 = df['beta2']
+21    perp_par_ratio = 3*beta2/(45*alpha**2 + 4*beta2)
+22    perp_par_ratio = perp_par_ratio.fillna(0)
+23
+24    i_tot = df['activity']
+25    i_perp = i_tot * perp_par_ratio
+26    i_par = i_tot * (1 - perp_par_ratio)
+27    df = df.merge(i_par.to_frame(name='I_par'), left_index=True, right_index=True)
+28    df = df.merge(i_perp.to_frame(name='I_perp'), left_index=True, right_index=True)
+29    return df
+
+ + +

calculates perpendicular and parallel intensities using +https://doi.org/10.1103/PhysRevB.54.7830

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc.html b/ramanchada2/misc.html new file mode 100644 index 00000000..ec791e83 --- /dev/null +++ b/ramanchada2/misc.html @@ -0,0 +1,270 @@ + + + + + + + ramanchada2.misc API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc

+ + + + + + + 
1#!/usr/bin/env python3
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/base_class.html b/ramanchada2/misc/base_class.html new file mode 100644 index 00000000..cd97d4a1 --- /dev/null +++ b/ramanchada2/misc/base_class.html @@ -0,0 +1,386 @@ + + + + + + + ramanchada2.misc.base_class API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.base_class

+ + + + + + + 
 1class BaseClass(object):
+ 2    def __init__(self):
+ 3        self._origin = []
+ 4
+ 5    def __repr__(self):
+ 6        return '/'.join(
+ 7            [i.replace('/', '_') for i in self.origin_list_str]
+ 8            ).replace(' ', '')
+ 9
+10    @property
+11    def origin(self):
+12        return self._origin
+13
+14    @origin.setter
+15    def origin(self, value):
+16        self._origin = list(value)
+17
+18    @property
+19    def origin_list_str(self):
+20        ret = list()
+21        for name, args, kwargs in self.origin:
+22            str_args = [str(i) for i in args]
+23            str_kwargs = [f'{k}={repr(v)}' for k, v in kwargs.items()]
+24            sss = f'{name}({", ".join(str_args + str_kwargs)})'
+25            ret.append(sss)
+26        return ret
+
+ + +
+
+ +
+ + class + BaseClass: + + + +
+ + 
 2class BaseClass(object):
+ 3    def __init__(self):
+ 4        self._origin = []
+ 5
+ 6    def __repr__(self):
+ 7        return '/'.join(
+ 8            [i.replace('/', '_') for i in self.origin_list_str]
+ 9            ).replace(' ', '')
+10
+11    @property
+12    def origin(self):
+13        return self._origin
+14
+15    @origin.setter
+16    def origin(self, value):
+17        self._origin = list(value)
+18
+19    @property
+20    def origin_list_str(self):
+21        ret = list()
+22        for name, args, kwargs in self.origin:
+23            str_args = [str(i) for i in args]
+24            str_kwargs = [f'{k}={repr(v)}' for k, v in kwargs.items()]
+25            sss = f'{name}({", ".join(str_args + str_kwargs)})'
+26            ret.append(sss)
+27        return ret
+
+ + + + +
+ +
+ origin + + + +
+ + 
11    @property
+12    def origin(self):
+13        return self._origin
+
+ + + + +
+
+ +
+ origin_list_str + + + +
+ + 
19    @property
+20    def origin_list_str(self):
+21        ret = list()
+22        for name, args, kwargs in self.origin:
+23            str_args = [str(i) for i in args]
+24            str_kwargs = [f'{k}={repr(v)}' for k, v in kwargs.items()]
+25            sss = f'{name}({", ".join(str_args + str_kwargs)})'
+26            ret.append(sss)
+27        return ret
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/constants.html b/ramanchada2/misc/constants.html new file mode 100644 index 00000000..91f4ecef --- /dev/null +++ b/ramanchada2/misc/constants.html @@ -0,0 +1,1128 @@ + + + + + + + ramanchada2.misc.constants API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.constants

+ + + + + + + 
  1from .. import spectrum as rc2spectrum
+  2
+  3from .utils.ramanshift_to_wavelength import (abs_nm_to_shift_cm_1_dict,
+  4                                             shift_cm_1_to_abs_nm_dict)
+  5
+  6from typing import Final
+  7
+  8PST_RS_dict = {620.9: 16, 795.8: 10, 1001.4: 100, 1031.8: 27, 1155.3: 13, 1450.5: 8,
+  9               1583.1: 12, 1602.3: 28, 2852.4: 9, 2904.5: 13, 3054.3: 32}
+ 10
+ 11
+ 12neon_nist_wl_nm = {200.7009: 80,
+ 13                   202.55599999999998: 80,
+ 14                   208.54659999999998: 150,
+ 15                   209.61060000000003: 200,
+ 16                   209.6248: 120,
+ 17                   256.2123: 80,
+ 18                   256.7121: 90,
+ 19                   262.3107: 80,
+ 20                   262.98850000000004: 80,
+ 21                   263.6069: 90,
+ 22                   263.82890000000003: 80,
+ 23                   264.40970000000004: 80,
+ 24                   276.2921: 80,
+ 25                   279.20189999999997: 90,
+ 26                   279.4221: 80,
+ 27                   280.9485: 100,
+ 28                   290.6592: 80,
+ 29                   290.6816: 80,
+ 30                   291.0061: 90,
+ 31                   291.0408: 90,
+ 32                   291.11379999999997: 80,
+ 33                   291.5122: 80,
+ 34                   292.5618: 80,
+ 35                   293.2103: 80,
+ 36                   294.0653: 80,
+ 37                   294.6044: 90,
+ 38                   295.5725: 150,
+ 39                   296.3236: 150,
+ 40                   296.71840000000003: 150,
+ 41                   297.2997: 100,
+ 42                   297.47189: 30,
+ 43                   297.9461: 100,
+ 44                   298.26696000000004: 30,
+ 45                   300.1668: 150,
+ 46                   301.7311: 120,
+ 47                   302.7016: 300,
+ 48                   302.8864: 300,
+ 49                   303.07869999999997: 100,
+ 50                   303.4461: 120,
+ 51                   303.59229999999997: 100,
+ 52                   303.772: 100,
+ 53                   303.9586: 100,
+ 54                   304.40880000000004: 100,
+ 55                   304.5556: 100,
+ 56                   304.7556: 120,
+ 57                   305.43449999999996: 100,
+ 58                   305.46770000000004: 100,
+ 59                   305.73906999999997: 30,
+ 60                   305.91060000000004: 100,
+ 61                   306.2491: 100,
+ 62                   306.3301: 100,
+ 63                   307.0887: 100,
+ 64                   307.1529: 100,
+ 65                   307.5731: 100,
+ 66                   308.8166: 120,
+ 67                   309.2092: 100,
+ 68                   309.2901: 120,
+ 69                   309.4006: 100,
+ 70                   309.51030000000003: 100,
+ 71                   309.7131: 100,
+ 72                   311.798: 100,
+ 73                   311.816: 120,
+ 74                   314.1332: 300,
+ 75                   314.3721: 100,
+ 76                   314.8681: 100,
+ 77                   316.4429: 100,
+ 78                   316.5648: 100,
+ 79                   318.8743: 100,
+ 80                   319.4579: 120,
+ 81                   319.85859999999997: 500,
+ 82                   320.8965: 60,
+ 83                   320.9356: 120,
+ 84                   321.37350000000004: 120,
+ 85                   321.4329: 150,
+ 86                   321.8193: 150,
+ 87                   322.4818: 120,
+ 88                   322.9573: 120,
+ 89                   323.007: 200,
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+404
+405
+406polystyrene_rs = {620.9: 16, 795.8: 10, 1001.4: 100, 1031.8: 27, 1155.3: 13, 1450.5: 8,
+407                  1583.1: 12, 1602.3: 28, 2852.4: 9, 2904.5: 13, 3054.3: 32}
+408
+409
+410neon_rs_dict = {
+411    785: {k: v for k, v in abs_nm_to_shift_cm_1_dict(neon_nist_wl_nm, 785).items() if 100 < k < 3000},
+412    633: {k: v for k, v in abs_nm_to_shift_cm_1_dict(neon_nist_wl_nm, 633).items() if 100 < k < 4500},
+413    532: {k: v for k, v in abs_nm_to_shift_cm_1_dict(neon_nist_wl_nm, 532).items() if 100 < k < 4000},
+414    514: {k: v for k, v in abs_nm_to_shift_cm_1_dict(neon_nist_wl_nm, 514).items() if 100 < k < 4000},
+415}
+416
+417neon_wl_dict = {
+418    785: {k: v for k, v in shift_cm_1_to_abs_nm_dict(neon_rs_dict[785], 785).items()},
+419    633: {k: v for k, v in shift_cm_1_to_abs_nm_dict(neon_rs_dict[633], 633).items()},
+420    532: {k: v for k, v in shift_cm_1_to_abs_nm_dict(neon_rs_dict[532], 532).items()},
+421    514: {k: v for k, v in shift_cm_1_to_abs_nm_dict(neon_rs_dict[514], 514).items()},
+422}
+423
+424
+425neon_rs_spe = {
+426    785: rc2spectrum.from_delta_lines(neon_rs_dict[785], xcal=lambda x: x, nbins=4500).convolve('gaussian', sigma=1),
+427    633: rc2spectrum.from_delta_lines(neon_rs_dict[633], xcal=lambda x: x, nbins=4500).convolve('gaussian', sigma=2),
+428    532: rc2spectrum.from_delta_lines(neon_rs_dict[532], xcal=lambda x: x, nbins=4500).convolve('gaussian', sigma=3),
+429    514: rc2spectrum.from_delta_lines(neon_rs_dict[514], xcal=lambda x: x, nbins=4500).convolve('gaussian', sigma=3),
+430}
+431
+432neon_wl_spe = {
+433    785: rc2spectrum.from_delta_lines(neon_wl_dict[785], xcal=lambda x: x/22.85 + 788, nbins=4500
+434                                      ).convolve('gaussian', sigma=1.5),
+435    633: rc2spectrum.from_delta_lines(neon_wl_dict[633], xcal=lambda x: x/17.64 + 633, nbins=4500
+436                                      ).convolve('gaussian', sigma=1.5),
+437    532: rc2spectrum.from_delta_lines(neon_wl_dict[532], xcal=lambda x: x/31.69 + 535, nbins=4500
+438                                      ).convolve('gaussian', sigma=1.5),
+439    514: rc2spectrum.from_delta_lines(neon_wl_dict[514], xcal=lambda x: x/31.69 + 514, nbins=4500
+440                                      ).convolve('gaussian', sigma=1.5),
+441}
+442
+443
+444neon_wl_785_nist_dict = {
+445    792.6841221569385: 5.37437266023976, 793.7330415754924: 0.5689277951354417,
+446    794.3457330415755: 3.501094091821991, 808.4285595693893: 7.202537154952434,
+447    811.8949671772428: 1.7505470455873593, 812.945295404814: 0.5251641209139684,
+448    813.6892778993436: 7.439824945294173, 825.9868433097755: 1.3120972814694163,
+449    826.5665828089599: 7.94695465954125, 830.0568927789934: 13.129102844632756,
+450    831.5448577680525: 4.3763676148399115, 836.6214442013129: 2.1881838071971296,
+451    837.234135667396: 4.376367614845547, 837.8030634573304: 35.010940919002735,
+452    841.8664541909187: 12.03028296382178, 846.3807439824946: 1.750547045586516,
+453    848.4814004376367: 0.568927789934395, 849.5754923413567: 30.634573304125958,
+454    854.5207877461706: 0.6564551422317848, 857.1903719912473: 1.3129102839767155,
+455    859.1597374179431: 17.50547045950853, 863.4923413566739: 15.317286652068542,
+456    865.469184130712: 29.20051773802159, 866.8621444201312: 4.376367614840063,
+457    868.0: 5.689277899344474, 868.218818380744: 6.564551422319682,
+458    870.4507658643327: 1.3129102839772355, 877.190371991244: 4.376367613743546,
+459    878.1094091903724: 26.2582056879645, 878.4157549234137: 17.505470458778227,
+460    883.1422319474835: 0.262582056892803, 885.4179431072209: 13.129102844635762,
+461    886.5947874281137: 7.437997551100201, 891.9824945295405: 2.6258205687594547,
+462    898.8971553610503: 0.8752735229757134, 908.0: 4.376367614838839,
+463    914.9146608315099: 5.251641137834712, 920.2100656455142: 3.9387308533338343,
+464    922.0688951698448: 3.3797527091026245, 927.6061269146609: 0.39387308533920273,
+465    928.7877461706784: 8.752735229757006, 930.144420131291: 3.5010940918132034,
+466    931.1072210065647: 0.35010940916879774, 931.4573304157549: 1.3129102844605525,
+467    932.6827133479212: 3.0634573302989097, 937.3654266958424: 0.6564551422320757,
+468    942.5733041575493: 2.188183807192464, 945.9431072210066: 1.3129102839781117,
+469    948.7002188183808: 2.188183807191779, 953.4704595185996: 2.6258205687594476,
+470    954.7833698030635: 1.3129102839777373, 957.7592997811817: 5.2516411378370345,
+471    966.5995623632385: 7.877461706780519, 980.910284463895: 4.376367614839546
+472}
+473
+474neon_wl_633_nist_dict = {
+475    638.3287981859411: 5.6689342403499765, 640.2562358276643: 11.337868480727586,
+476    650.6870748299319: 8.503401360545043, 653.3514739229025: 0.566893424036351,
+477    659.9274376417234: 5.668934240348053, 665.2562358276643: 0.8503401360546983,
+478    667.8639455782313: 2.8344671200402334, 671.7755102040816: 0.39682539682552465,
+479    692.9773242630386: 56.68934240353873, 702.4444444444445: 17.006802721074873,
+480    703.2947845804988: 45.351473922858105, 705.1655328798186: 1.1337868476493322,
+481    705.9591836734694: 5.668934240348062, 717.4671201814059: 45.351473922836774,
+482    721.3786848072563: 8.503401360545148, 723.5895691609977: 8.50340136054297,
+483    724.5532879818594: 45.351473922833904, 734.4739229024943: 8.503401360542986,
+484    747.2857142857143: 1.7006802717293952, 748.9297052154195: 17.00680272108454,
+485    749.2698412698412: 5.6689342403646865, 752.3310657596371: 8.503401360527796,
+486    753.6349206349206: 17.006802721048707, 754.4852607709751: 7.369614512457517,
+487    772.5124716553288: 0.05668934240365808, 774.156462585034: 6.802721088429488,
+488    783.9637188208617: 0.11337868480724644, 792.6951245635115: 6.971961625854252,
+489    793.77097505694: 0.7369614305162637, 794.3945578231699: 4.535147381418968,
+490    808.4417565814348: 9.329816961551543, 811.9115646254535: 2.2675736482945825,
+491    812.9319727898597: 0.6802720427342306, 813.6689342404153: 9.63718817885658,
+492    825.9704271162868: 1.6978631010464373, 826.5881899888147: 10.231887665802304,
+493    830.1088435374149: 17.00680272107302, 831.5827664399093: 5.668934240348589,
+494    836.6281179138335: 2.8344671202589966, 837.2517006802728: 5.6689342195710095,
+495    837.8185941043079: 45.351473923298364, 841.8913979246645: 15.8101670633123,
+496    846.3786848072561: 2.26757369591529, 848.4761904759695: 0.7369614416713715,
+497    849.6099773242671: 39.68253967290885, 854.5419501133787: 0.8503401360543106,
+498    857.2063492063492: 1.7006802717279004, 859.1904761904761: 22.67573696142483,
+499    863.4988662131518: 19.841269841214043, 864.7460316744742: 3.401354571136033,
+500    865.4928040619619: 38.396369552201875, 866.9002267573604: 5.668934237269633,
+501    868.034013605442: 7.369614508413964, 868.26077097504: 8.503401360112813,
+502    870.4716553287982: 1.7006802717287801, 877.2176870748299: 5.668934240361571,
+503    878.1247165532881: 34.01360544219461, 878.4081632653063: 22.675736961434616,
+504    883.1700680272108: 0.3401360544216929
+505}
+506
+507neon_wl_532_nist_dict = {
+508    540.0804670242978: 6.311139160610751, 556.3000946670874: 1.5777847897339283,
+509    565.7036920164089: 1.5777847897345527, 571.9517197854212: 1.5777847897346757,
+510    574.8548437993057: 1.5777847897345527, 576.4641842852635: 2.2088987059726164,
+511    580.4717576522562: 1.5777847897345527, 582.0495424424108: 1.5777847897345523,
+512    585.2682234143263: 6.311139160610822, 587.3193436415273: 1.5777847897341903,
+513    588.234458819817: 3.1555695802011297, 590.285579047018: 0.15777847901546926,
+514    590.6642473966551: 0.15777847901546949, 594.5140422846324: 1.5777847897344977,
+515    596.5651625118334: 1.5777847897347166, 597.5336867539808: 3.5612432617200778,
+516    598.837172609656: 0.4733354370464895, 603.0340801514673: 3.155569580200976,
+517    607.4518775639002: 3.155569580201294, 609.6607762701167: 0.9466708739111769,
+518    612.8794572420321: 0.31555695803092476, 614.3310192489744: 3.1555695802009724,
+519    616.3821394761754: 3.1555695802010364, 618.2439255285578: 0.4733354370463865,
+520    621.7466077627012: 3.1555695802013357, 626.6692963079836: 3.1555695802012638,
+521    630.5190911959609: 0.31555695803092476, 632.8542126853799: 0.9466708729394551,
+522    633.4853266014544: 3.1555695792292964, 638.3449037551278: 3.1555695802011074,
+523    640.2698011991164: 6.31113916061083, 650.6831808141369: 4.733354370433662,
+524    653.3338592615967: 0.3155569580309239, 659.928999684443: 3.155569580201134,
+525    665.2303565793626: 0.4733354370463867, 667.8494793310192: 1.5777847897339252,
+526    671.7308299147995: 0.22088987062164733
+527}
+528
+529
+530neon_wl_514_nist_dict = {  # generated with `neon_wl_spe[514].fit_peak_positions()`
+531    533.1227516566739: 1.893341747575223, 534.1325339224629: 3.155569580094588,
+532    534.3534237930478: 1.893341747594008, 540.0965604291574: 6.311139160528673,
+533    556.316188071947: 1.5777847894388257, 565.6882297254654: 1.5777847894388244,
+534    571.9678131902808: 1.577784789438823, 574.8709372041653: 1.5777847894384802,
+535    576.480277690123: 2.208898705700221, 580.4878510571158: 1.577784789439162,
+536    582.0340801514673: 1.577784789438819, 585.2843168191858: 6.3111391605299945,
+537    587.3038813505837: 1.5777847894388177, 588.2189965288734: 3.1555695799820027,
+538    590.2701167560745: 0.15777847901555297, 590.6803408015146: 0.1577784790155361,
+539    594.5301356894919: 1.577784789438816, 596.5812559166928: 1.577784789438815,
+540    597.5497766401437: 3.5613026786141493, 598.8217103187125: 0.4733354370463101,
+541    603.0186178605237: 3.155569579981991, 607.4679709687598: 3.1555695799819894,
+542    609.6453139791731: 0.9466708736863838, 612.8639949510887: 0.3155569580308466,
+543    614.347112653834: 3.155569579981985, 616.3982328810349: 3.155569579981983,
+544    618.2600189334174: 0.47333543704630987, 621.7627011675606: 3.1555695799819796,
+545    626.6853897128431: 3.1555695799819743, 630.5036289050173: 0.3155569580308466,
+546    632.8387503944462: 0.9466708736863778, 633.469864310508: 3.15556957998197,
+547    638.3294414641841: 3.1555695799826475, 640.2543389081728: 6.311139160528551
+548}
+549
+550
+551neon_rs_514_nist_dict = abs_nm_to_shift_cm_1_dict(neon_wl_532_nist_dict, 514)
+552neon_rs_532_nist_dict = abs_nm_to_shift_cm_1_dict(neon_wl_532_nist_dict, 532)
+553neon_rs_633_nist_dict = abs_nm_to_shift_cm_1_dict(neon_wl_633_nist_dict, 633)
+554neon_rs_785_nist_dict = abs_nm_to_shift_cm_1_dict(neon_wl_785_nist_dict, 785)
+555
+556neon_wl_D3_3 = [
+557    533.07775, 540.05616, 556.27662, 565.66588, 571.92248, 574.82985, 576.44188,
+558    580.44496, 580.44496, 582.01558, 585.24878, 587.28275, 588.1895, 590.24623,
+559    594.4834, 596.5471, 598.79074, 602.99968, 607.43376, 609.6163, 612.84498,
+560    614.30627, 616.35937, 618.2146, 621.72812, 626.64952, 630.47893, 633.44276,
+561    638.29914, 640.2248, 650.65277, 653.28824, 659.89528, 667.82766, 671.7043,
+562    692.94672, 702.405, 703.24128, 705.91079, 717.3938, 724.51665, 748.88712,
+563    753.57739, 754.40439, 794.31805, 808.24576, 811.85495, 813.64061, 830.03248,
+564    836.57464, 837.7607, 846.33569, 849.53591, 854.46952, 857.13535, 859.12583,
+565    863.46472, 870.41122, 877.16575, 878.37539, 885.38669, 891.95007, 898.85564,
+566    898.85564, 914.8672, 920.17588, 927.55191, 930.08532, 932.65072, 937.33079,
+567    942.53797, 945.9211, 948.66825, 953.4164, 954.74052, 966.542
+568]
+569
+570neon_wl_D3_3_dict = dict(zip(neon_wl_D3_3, [1]*len(neon_wl_D3_3)))
+571
+572NEON_WL: Final = {
+573    785: neon_wl_785_nist_dict,
+574    633: neon_wl_633_nist_dict,
+575    532: neon_wl_532_nist_dict,
+576    514: neon_wl_532_nist_dict,
+577}
+
+ + +
+
+
+ PST_RS_dict = + + {620.9: 16, 795.8: 10, 1001.4: 100, 1031.8: 27, 1155.3: 13, 1450.5: 8, 1583.1: 12, 1602.3: 28, 2852.4: 9, 2904.5: 13, 3054.3: 32} + + +
+ + + + +
+
+
+ neon_nist_wl_nm = + + {200.7009: 80, 202.55599999999998: 80, 208.54659999999998: 150, 209.61060000000003: 200, 209.6248: 120, 256.2123: 80, 256.7121: 90, 262.3107: 80, 262.98850000000004: 80, 263.6069: 90, 263.82890000000003: 80, 264.40970000000004: 80, 276.2921: 80, 279.20189999999997: 90, 279.4221: 80, 280.9485: 100, 290.6592: 80, 290.6816: 80, 291.0061: 90, 291.0408: 90, 291.11379999999997: 80, 291.5122: 80, 292.5618: 80, 293.2103: 80, 294.0653: 80, 294.6044: 90, 295.5725: 150, 296.3236: 150, 296.71840000000003: 150, 297.2997: 100, 297.47189: 30, 297.9461: 100, 298.26696000000004: 30, 300.1668: 150, 301.7311: 120, 302.7016: 300, 302.8864: 300, 303.07869999999997: 100, 303.4461: 120, 303.59229999999997: 100, 303.772: 100, 303.9586: 100, 304.40880000000004: 100, 304.5556: 100, 304.7556: 120, 305.43449999999996: 100, 305.46770000000004: 100, 305.73906999999997: 30, 305.91060000000004: 100, 306.2491: 100, 306.3301: 100, 307.0887: 100, 307.1529: 100, 307.5731: 100, 308.8166: 120, 309.2092: 100, 309.2901: 120, 309.4006: 100, 309.51030000000003: 100, 309.7131: 100, 311.798: 100, 311.816: 120, 314.1332: 300, 314.3721: 100, 314.8681: 100, 316.4429: 100, 316.5648: 100, 318.8743: 100, 319.4579: 120, 319.85859999999997: 500, 320.8965: 60, 320.9356: 120, 321.37350000000004: 120, 321.4329: 150, 321.8193: 150, 322.4818: 120, 322.9573: 120, 323.007: 200, 323.0419: 120, 323.2022: 120, 323.2372: 150, 324.3396: 100, 324.4095: 100, 324.8345: 100, 325.0355: 100, 329.7726: 150, 330.974: 150, 331.97220000000004: 300, 332.3745: 1000, 332.71529999999996: 150, 332.9158: 100, 333.48359999999997: 200, 334.4395: 150, 334.5453: 300, 334.5829: 150, 335.5016: 200, 335.78200000000004: 120, 336.0597: 200, 336.2161: 120, 336.2707: 100, 336.7218: 120, 336.98076000000003: 50, 336.99072: 70, 337.1799: 100, 337.8216: 500, 338.8417: 150, 338.8945: 120, 339.27979999999997: 300, 340.48220000000003: 100, 340.6947: 120, 341.3148: 100, 341.69140000000004: 120, 341.7688: 120, 341.79031: 50, 341.80055000000004: 5, 342.8687: 120, 344.77024: 20, 345.41944: 10, 345.661: 100, 345.9321: 100, 346.05237: 10, 346.43382: 10, 346.65781000000004: 20, 347.25706: 50, 347.95189999999997: 150, 348.0718: 200, 348.1933: 200, 349.80636: 10, 350.12159: 20, 351.51902: 20, 352.04711000000003: 100, 354.28470000000004: 120, 355.78049999999996: 120, 356.1198: 100, 356.8502: 250, 357.4181: 100, 357.4612: 200, 359.35257: 50, 359.36388999999997: 30, 360.01685000000003: 10, 363.3664: 10, 364.3927: 150, 366.40729999999996: 200, 368.22420999999997: 10, 368.57352000000003: 10, 369.42130000000003: 200, 370.12244: 4, 370.9622: 150, 371.3079: 250, 372.7107: 250, 376.6259: 800, 377.7133: 1000, 381.84270000000004: 100, 382.9749: 120, 421.9745: 150, 423.38500000000005: 100, 425.0649: 120, 436.9862: 120, 437.93999999999994: 70, 437.95500000000004: 150, 438.5059: 100, 439.1991: 200, 439.799: 150, 440.9299: 150, 441.3215: 100, 442.13890000000004: 100, 442.85159999999996: 100, 442.8634: 100, 443.09040000000005: 150, 443.0942: 150, 445.7049: 120, 452.27200000000005: 100, 453.77545: 100, 456.90569999999997: 100, 470.43949000000003: 150, 470.88594: 120, 471.00649999999996: 100, 471.20633: 150, 471.5344: 150, 475.2732: 50, 478.89258: 100, 479.02195: 50, 482.7338: 100, 488.49170000000004: 100, 500.51587: 50, 503.77511999999996: 50, 514.49384: 50, 533.07775: 60, 534.10938: 100, 534.32834: 60, 540.05618: 200, 556.27662: 50, 565.66588: 50, 571.92248: 50, 574.82985: 50, 576.4418800000001: 70, 580.44496: 50, 582.01558: 50, 585.24879: 200, 587.2827500000001: 50, 588.18952: 100, 590.24623: 5, 590.64294: 5, 594.48342: 50, 596.5471: 50, 597.46273: 50, 597.5534: 60, 598.79074: 15, 602.99969: 100, 607.43377: 100, 609.61631: 30, 612.8449899999999: 10, 614.3062600000001: 100, 616.35939: 100, 618.2146: 15, 621.72812: 100, 626.6495: 100, 630.47889: 10, 632.81646: 30, 633.4427800000001: 100, 638.29917: 100, 640.2248: 200, 650.65281: 150, 653.28822: 10, 659.89529: 100, 665.2092700000001: 15, 667.82762: 50, 671.7043: 7, 692.94673: 1000, 702.40504: 300, 703.24131: 800, 705.12923: 20, 705.91074: 100, 717.39381: 800, 721.3199999999999: 150, 723.5188: 150, 724.51666: 800, 734.3945: 150, 747.24386: 30, 748.88712: 300, 749.2102: 100, 752.2818: 150, 753.57741: 300, 754.4044299999999: 130, 772.4623300000001: 1, 774.0738: 120, 783.9052899999999: 2, 792.6201: 120, 792.71177: 3, 793.69961: 13, 794.3181400000001: 80, 808.2457999999999: 60, 808.4345000000001: 100, 811.85492: 40, 812.89108: 12, 813.64054: 170, 825.9379000000001: 30, 826.4807000000001: 100, 826.60772: 70, 826.71162: 10, 830.03258: 300, 831.4995000000001: 100, 836.57466: 50, 837.2106: 100, 837.7608: 800, 841.71606: 30, 841.84274: 250, 846.33575: 40, 848.44435: 13, 849.53598: 700, 854.46958: 15, 857.13524: 30, 859.12584: 400, 863.4647000000001: 350, 864.70411: 60, 865.4383099999999: 600, 865.5522000000001: 80, 866.8255999999999: 100, 867.94925: 130, 868.19211: 150, 870.41116: 30, 877.1656300000001: 100, 878.06226: 600, 878.3753300000001: 400, 883.0907199999999: 6, 885.38668: 300, 886.53063: 20, 886.57552: 150, 891.9500599999999: 60, 898.85564: 20, 907.9462: 100, 914.86716: 120, 920.1759099999999: 90, 922.0060100000001: 60, 922.1580099999999: 20, 922.66903: 20, 927.55196: 9, 928.7563: 200, 930.0852699999999: 80, 931.0583899999999: 8, 931.39726: 30, 932.65068: 70, 937.33078: 15, 942.5378800000001: 50, 945.9209500000001: 30, 948.66818: 50, 953.4162899999999: 60, 954.7404899999999: 30, 957.7013000000001: 120, 966.54197: 180, 980.8860000000001: 100, 1029.5417400000001: 4, 1056.24075: 80, 1079.80429: 60, 1084.44772: 90, 1114.3020000000001: 300, 1117.7523999999999: 500, 1139.04339: 150, 1140.91343: 90, 1152.27459: 300, 1152.5019399999999: 150, 1153.63445: 90, 1160.15366: 30, 1161.40807: 130, 1168.80017: 30, 1176.67924: 150, 1178.90435: 130, 1178.98891: 30, 1198.4912: 70, 1206.6334000000002: 200, 1245.9388999999999: 40, 1268.9200999999998: 60, 1291.2014: 80, 1321.9241: 40, 1523.0714: 50, 1716.1929: 20, 1803.5812: 20, 1808.3181: 40, 1808.3263: 9, 1822.1087: 15, 1822.7015999999999: 13, 1827.6642: 140, 1828.2614: 100, 1830.3967: 70, 1835.9094: 20, 1838.4826: 60, 1838.9937000000002: 90, 1840.2836: 40, 1842.2401999999997: 60, 1845.864: 13, 1847.58: 40, 1859.1541000000002: 70, 1859.7698: 100, 1861.8908: 16, 1862.5158999999999: 20, 2104.127: 30, 2170.811: 30, 2224.736: 13, 2242.814: 13, 2253.038: 80, 2266.179: 13, 2310.048: 25, 2326.027: 40, 2337.296: 50, 2356.5330000000004: 30, 2363.648: 170, 2370.166: 12, 2370.913: 60, 2395.1400000000003: 110, 2395.643: 50, 2397.816: 60, 2409.857: 11, 2416.143: 20, 2424.9610000000002: 30, 2436.5009999999997: 70, 2437.161: 40, 2444.786: 20, 2445.939: 30, 2477.6490000000003: 17, 2492.889: 30, 2516.17: 13, 2552.433: 50, 2838.62: 6, 3020.049: 6, 3317.3089999999997: 8, 3335.238: 17, 3389.9809999999998: 5, 3390.3019999999997: 4, 3391.31: 12, 3413.1339999999996: 4, 3447.143: 6, 3583.4809999999998: 8} + + +
+ + + + +
+
+
+ polystyrene_rs = + + {620.9: 16, 795.8: 10, 1001.4: 100, 1031.8: 27, 1155.3: 13, 1450.5: 8, 1583.1: 12, 1602.3: 28, 2852.4: 9, 2904.5: 13, 3054.3: 32} + + +
+ + + + +
+
+
+ neon_rs_dict = + + {785: {np.float64(122.46893256885171): np.float64(120.0), np.float64(123.92790418673181): np.float64(3.0), np.float64(139.62846387796614): np.float64(13.0), np.float64(149.43939261183922): np.float64(80.0), np.float64(366.3796839579363): np.float64(60.0), np.float64(369.2675936274158): np.float64(100.0), np.float64(421.38180516260735): np.float64(40.0), np.float64(437.0823975157964): np.float64(12.0), np.float64(448.4137962350071): np.float64(170.0), np.float64(631.4057156452395): np.float64(30.0), np.float64(639.3574108984682): np.float64(100.0), np.float64(641.2166700808558): np.float64(70.0), np.float64(642.737084741243): np.float64(10.0), np.float64(691.1336413932653): np.float64(300.0), np.float64(712.3880633377877): np.float64(100.0), np.float64(785.3477634818161): np.float64(50.0), np.float64(794.42757260046): np.float64(100.0), np.float64(802.2720828079191): np.float64(800.0), np.float64(858.3625927463404): np.float64(30.0), np.float64(860.1503620255945): np.float64(250.0), np.float64(923.2117794361885): np.float64(40.0), np.float64(952.5766542670426): np.float64(13.0), np.float64(967.7216907334014): np.float64(700.0), np.float64(1035.6867269022846): np.float64(15.0), np.float64(1072.08315781891): np.float64(30.0), np.float64(1099.115138430138): np.float64(400.0), np.float64(1157.604148115537): np.float64(350.0), np.float64(1174.2039492465462): np.float64(60.0), np.float64(1184.0148919843368): np.float64(600.0), np.float64(1185.5352862129364): np.float64(80.0), np.float64(1202.5075530881757): np.float64(100.0), np.float64(1217.4425451131438): np.float64(130.0), np.float64(1220.665437642399): np.float64(150.0), np.float64(1250.0302210935215): np.float64(30.0), np.float64(1338.4980195800945): np.float64(100.0), np.float64(1350.1394500377296): np.float64(600.0), np.float64(1354.1986085623885): np.float64(400.0), np.float64(1414.9886119309567): np.float64(6.0), np.float64(1444.3533419670161): np.float64(300.0), np.float64(1458.9273939198806): np.float64(20.0), np.float64(1459.498530694608): np.float64(150.0), np.float64(1527.4634843309643): np.float64(60.0), np.float64(1613.5965041854067): np.float64(20.0), np.float64(1724.9850603188252): np.float64(100.0), np.float64(1808.3048539141466): np.float64(120.0), np.float64(1871.3662202368241): np.float64(90.0), np.float64(1892.9372168038894): np.float64(60.0), np.float64(1894.7249573620707): np.float64(20.0), np.float64(1900.7309751098303): np.float64(20.0), np.float64(1957.786316393365): np.float64(9.0), np.float64(1971.7663781767853): np.float64(200.0), np.float64(1987.151135078183): np.float64(80.0), np.float64(1998.388557694021): np.float64(8.0), np.float64(2002.2962579980554): np.float64(30.0), np.float64(2016.7254712832068): np.float64(70.0), np.float64(2070.2611413719483): np.float64(15.0), np.float64(2129.200339961183): np.float64(50.0), np.float64(2167.1455819255425): np.float64(30.0), np.float64(2197.759988274157): np.float64(50.0), np.float64(2250.2557050497553): np.float64(60.0), np.float64(2264.8031148954296): np.float64(30.0), np.float64(2297.184477570986): np.float64(120.0), np.float64(2392.6912977297347): np.float64(180.0), np.float64(2543.988860402575): np.float64(100.0)}, 633: {np.float64(131.15349323856475): np.float64(100.0), np.float64(178.27466380474812): np.float64(200.0), np.float64(428.6085469303318): np.float64(150.0), np.float64(490.60888429816987): np.float64(10.0), np.float64(643.8689658571262): np.float64(100.0), np.float64(764.9250424124886): np.float64(15.0), np.float64(823.8643500376157): np.float64(50.0), np.float64(910.2849841405953): np.float64(7.0), np.float64(1366.664578090937): np.float64(1000.0), np.float64(1560.9884142372662): np.float64(300.0), np.float64(1577.918319348396): np.float64(800.0), np.float64(1615.9907404166095): np.float64(20.0), np.float64(1631.6913325599187): np.float64(100.0), np.float64(1858.4430565740402): np.float64(800.0), np.float64(1934.3157870391897): np.float64(150.0), np.float64(1976.4473852543142): np.float64(150.0), np.float64(1995.483197701751): np.float64(800.0), np.float64(2181.1285988136524): np.float64(150.0), np.float64(2415.276207094919): np.float64(30.0), np.float64(2444.6410422620947): np.float64(300.0), np.float64(2450.3992858353195): np.float64(100.0), np.float64(2504.8972680083675): np.float64(150.0), np.float64(2527.75145967322): np.float64(300.0), np.float64(2542.2988104564824): np.float64(130.0), np.float64(2852.1732140759405): np.float64(1.0), np.float64(2879.1234484825845): np.float64(120.0), np.float64(3041.1452207757484): np.float64(2.0), np.float64(3181.4037390207895): np.float64(120.0), np.float64(3182.86271063867): np.float64(3.0), np.float64(3198.563270329904): np.float64(13.0), np.float64(3208.3741990637773): np.float64(80.0), np.float64(3425.314490409874): np.float64(60.0), np.float64(3428.2024000793535): np.float64(100.0), np.float64(3480.3166116145453): np.float64(40.0), np.float64(3496.0172039677345): 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np.float64(3652.8826702068427): np.float64(30.0), np.float64(3668.507349887903): np.float64(100.0), np.float64(3788.618101889852): np.float64(100.0), np.float64(3835.7392724560355): np.float64(200.0)}} + + +
+ + + + +
+
+
+ neon_wl_dict = + + {785: {np.float64(792.6201): np.float64(120.0), np.float64(792.71177): np.float64(3.0), np.float64(793.69961): np.float64(13.0), np.float64(794.3181400000001): np.float64(80.0), np.float64(808.2457999999999): np.float64(60.0), np.float64(808.4345000000001): np.float64(100.0), np.float64(811.85492): np.float64(40.0), np.float64(812.89108): np.float64(12.0), np.float64(813.64054): np.float64(170.0), np.float64(825.9379000000001): np.float64(30.0), np.float64(826.4807): np.float64(100.0), np.float64(826.60772): np.float64(70.0), np.float64(826.7116200000002): np.float64(10.0), np.float64(830.0325800000002): np.float64(300.0), np.float64(831.4995000000001): np.float64(100.0), np.float64(836.5746600000001): np.float64(50.0), np.float64(837.2106): np.float64(100.0), np.float64(837.7608000000001): np.float64(800.0), np.float64(841.7160600000001): np.float64(30.0), np.float64(841.84274): np.float64(250.0), np.float64(846.33575): np.float64(40.0), np.float64(848.4443499999999): np.float64(13.0), np.float64(849.5359799999999): np.float64(700.0), np.float64(854.46958): np.float64(15.0), np.float64(857.13524): np.float64(30.0), np.float64(859.1258399999999): np.float64(400.0), np.float64(863.4647000000001): np.float64(350.0), np.float64(864.7041100000001): np.float64(60.0), np.float64(865.43831): np.float64(600.0), np.float64(865.5522000000001): np.float64(80.0), np.float64(866.8255999999999): np.float64(100.0), np.float64(867.94925): np.float64(130.0), np.float64(868.1921100000001): np.float64(150.0), np.float64(870.4111599999999): np.float64(30.0), np.float64(877.1656300000001): np.float64(100.0), np.float64(878.06226): np.float64(600.0), np.float64(878.3753300000001): np.float64(400.0), np.float64(883.0907199999999): np.float64(6.0), np.float64(885.3866799999998): np.float64(300.0), np.float64(886.53063): np.float64(20.0), np.float64(886.57552): np.float64(150.0), np.float64(891.9500599999998): np.float64(60.0), np.float64(898.85564): np.float64(20.0), np.float64(907.9462): np.float64(100.0), np.float64(914.8671600000001): np.float64(120.0), np.float64(920.1759099999999): np.float64(90.0), np.float64(922.00601): np.float64(60.0), np.float64(922.1580099999999): np.float64(20.0), np.float64(922.66903): np.float64(20.0), np.float64(927.5519599999999): np.float64(9.0), np.float64(928.7563): np.float64(200.0), np.float64(930.0852699999999): np.float64(80.0), np.float64(931.0583899999999): np.float64(8.0), np.float64(931.39726): np.float64(30.0), np.float64(932.6506799999999): np.float64(70.0), np.float64(937.3307799999999): np.float64(15.0), np.float64(942.5378800000001): np.float64(50.0), np.float64(945.9209500000001): np.float64(30.0), np.float64(948.66818): np.float64(50.0), np.float64(953.41629): np.float64(60.0), np.float64(954.74049): np.float64(30.0), np.float64(957.7013000000001): np.float64(120.0), np.float64(966.5419700000001): np.float64(180.0), np.float64(980.8860000000001): 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np.float64(618.2146): np.float64(15.0), np.float64(621.72812): np.float64(100.0), np.float64(626.6495): np.float64(100.0), np.float64(630.47889): np.float64(10.0), np.float64(632.81646): np.float64(30.0), np.float64(633.4427800000001): np.float64(100.0), np.float64(638.29917): np.float64(100.0), np.float64(640.2248): np.float64(200.0), np.float64(650.65281): np.float64(150.0), np.float64(653.28822): np.float64(10.0), np.float64(659.89529): np.float64(100.0), np.float64(665.2092700000001): np.float64(15.0), np.float64(667.82762): np.float64(50.0), np.float64(671.7043): np.float64(7.0)}, 514: {np.float64(533.07775): np.float64(60.0), np.float64(534.10938): np.float64(100.0), np.float64(534.32834): np.float64(60.0), np.float64(540.05618): np.float64(200.0), np.float64(556.27662): np.float64(50.0), np.float64(565.66588): np.float64(50.0), np.float64(571.92248): np.float64(50.0), np.float64(574.82985): np.float64(50.0), np.float64(576.4418800000001): np.float64(70.0), np.float64(580.44496): 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np.float64(638.29917): np.float64(100.0), np.float64(640.2248): np.float64(200.0)}} + + +
+ + + + +
+
+
+ neon_rs_spe = + + {785: from_delta_lines({np.float64(122.46893256885171): np.float64(120.0), np.float64(123.92790418673181): np.float64(3.0), np.float64(139.62846387796614): np.float64(13.0), np.float64(149.43939261183922): np.float64(80.0), np.float64(366.3796839579363): np.float64(60.0), np.float64(369.2675936274158): np.float64(100.0), np.float64(421.38180516260735): np.float64(40.0), np.float64(437.0823975157964): np.float64(12.0), np.float64(448.4137962350071): np.float64(170.0), np.float64(631.4057156452395): np.float64(30.0), np.float64(639.3574108984682): np.float64(100.0), np.float64(641.2166700808558): np.float64(70.0), np.float64(642.737084741243): np.float64(10.0), np.float64(691.1336413932653): np.float64(300.0), np.float64(712.3880633377877): np.float64(100.0), np.float64(785.3477634818161): np.float64(50.0), np.float64(794.42757260046): np.float64(100.0), np.float64(802.2720828079191): np.float64(800.0), np.float64(858.3625927463404): np.float64(30.0), np.float64(860.1503620255945): np.float64(250.0), np.float64(923.2117794361885): np.float64(40.0), np.float64(952.5766542670426): np.float64(13.0), np.float64(967.7216907334014): np.float64(700.0), np.float64(1035.6867269022846): np.float64(15.0), np.float64(1072.08315781891): np.float64(30.0), np.float64(1099.115138430138): np.float64(400.0), np.float64(1157.604148115537): np.float64(350.0), np.float64(1174.2039492465462): np.float64(60.0), np.float64(1184.0148919843368): np.float64(600.0), np.float64(1185.5352862129364): np.float64(80.0), np.float64(1202.5075530881757): np.float64(100.0), np.float64(1217.4425451131438): np.float64(130.0), np.float64(1220.665437642399): np.float64(150.0), np.float64(1250.0302210935215): np.float64(30.0), np.float64(1338.4980195800945): np.float64(100.0), np.float64(1350.1394500377296): np.float64(600.0), np.float64(1354.1986085623885): np.float64(400.0), np.float64(1414.9886119309567): np.float64(6.0), np.float64(1444.3533419670161): np.float64(300.0), np.float64(1458.9273939198806): np.float64(20.0), np.float64(1459.498530694608): np.float64(150.0), np.float64(1527.4634843309643): np.float64(60.0), np.float64(1613.5965041854067): np.float64(20.0), np.float64(1724.9850603188252): np.float64(100.0), np.float64(1808.3048539141466): np.float64(120.0), np.float64(1871.3662202368241): np.float64(90.0), np.float64(1892.9372168038894): np.float64(60.0), np.float64(1894.7249573620707): np.float64(20.0), np.float64(1900.7309751098303): np.float64(20.0), np.float64(1957.786316393365): np.float64(9.0), np.float64(1971.7663781767853): np.float64(200.0), np.float64(1987.151135078183): np.float64(80.0), np.float64(1998.388557694021): np.float64(8.0), np.float64(2002.2962579980554): np.float64(30.0), np.float64(2016.7254712832068): np.float64(70.0), np.float64(2070.2611413719483): np.float64(15.0), np.float64(2129.200339961183): np.float64(50.0), np.float64(2167.1455819255425): np.float64(30.0), np.float64(2197.759988274157): np.float64(50.0), np.float64(2250.2557050497553): np.float64(60.0), np.float64(2264.8031148954296): np.float64(30.0), np.float64(2297.184477570986): np.float64(120.0), np.float64(2392.6912977297347): np.float64(180.0), np.float64(2543.988860402575): np.float64(100.0)}, xcal=<function <lambda>>, nbins=4500).convolve('gaussian', sigma=1), 633: from_delta_lines({np.float64(131.15349323856475): np.float64(100.0), np.float64(178.27466380474812): np.float64(200.0), np.float64(428.6085469303318): np.float64(150.0), np.float64(490.60888429816987): np.float64(10.0), np.float64(643.8689658571262): np.float64(100.0), np.float64(764.9250424124886): np.float64(15.0), np.float64(823.8643500376157): np.float64(50.0), np.float64(910.2849841405953): np.float64(7.0), np.float64(1366.664578090937): np.float64(1000.0), np.float64(1560.9884142372662): np.float64(300.0), np.float64(1577.918319348396): np.float64(800.0), np.float64(1615.9907404166095): np.float64(20.0), np.float64(1631.6913325599187): np.float64(100.0), np.float64(1858.4430565740402): np.float64(800.0), np.float64(1934.3157870391897): np.float64(150.0), np.float64(1976.4473852543142): np.float64(150.0), np.float64(1995.483197701751): np.float64(800.0), np.float64(2181.1285988136524): np.float64(150.0), np.float64(2415.276207094919): np.float64(30.0), np.float64(2444.6410422620947): np.float64(300.0), np.float64(2450.3992858353195): np.float64(100.0), np.float64(2504.8972680083675): np.float64(150.0), np.float64(2527.75145967322): np.float64(300.0), np.float64(2542.2988104564824): np.float64(130.0), np.float64(2852.1732140759405): np.float64(1.0), np.float64(2879.1234484825845): np.float64(120.0), np.float64(3041.1452207757484): np.float64(2.0), np.float64(3181.4037390207895): np.float64(120.0), np.float64(3182.86271063867): np.float64(3.0), np.float64(3198.563270329904): np.float64(13.0), np.float64(3208.3741990637773): np.float64(80.0), np.float64(3425.314490409874): np.float64(60.0), np.float64(3428.2024000793535): np.float64(100.0), np.float64(3480.3166116145453): np.float64(40.0), np.float64(3496.0172039677345): np.float64(12.0), np.float64(3507.348602686945): np.float64(170.0), np.float64(3690.340522097177): np.float64(30.0), np.float64(3698.292217350406): np.float64(100.0), np.float64(3700.151476532794): np.float64(70.0), np.float64(3701.671891193181): np.float64(10.0), np.float64(3750.068447845203): np.float64(300.0), np.float64(3771.3228697897257): np.float64(100.0), np.float64(3844.282569933754): np.float64(50.0), np.float64(3853.362379052398): np.float64(100.0), np.float64(3861.206889259857): np.float64(800.0), np.float64(3917.2973991982785): np.float64(30.0), np.float64(3919.0851684775325): np.float64(250.0), np.float64(3982.1465858881265): np.float64(40.0), np.float64(4011.5114607189803): np.float64(13.0), np.float64(4026.6564971853395): np.float64(700.0), np.float64(4094.6215333542223): np.float64(15.0), np.float64(4131.017964270848): np.float64(30.0), np.float64(4158.049944882076): np.float64(400.0), np.float64(4216.538954567475): np.float64(350.0), np.float64(4233.138755698485): np.float64(60.0), np.float64(4242.949698436275): np.float64(600.0), np.float64(4244.470092664874): np.float64(80.0), np.float64(4261.442359540114): np.float64(100.0), np.float64(4276.377351565082): np.float64(130.0), np.float64(4279.600244094337): np.float64(150.0), np.float64(4308.96502754546): np.float64(30.0), np.float64(4397.432826032033): np.float64(100.0), np.float64(4409.074256489667): np.float64(600.0), np.float64(4413.133415014327): np.float64(400.0), np.float64(4473.923418382895): np.float64(6.0)}, xcal=<function <lambda>>, nbins=4500).convolve('gaussian', sigma=2), 532: from_delta_lines({np.float64(280.400374063339): np.float64(200.0), np.float64(820.3246859611342): np.float64(50.0), np.float64(1118.7121507719057): np.float64(50.0), np.float64(1312.1053685299726): np.float64(50.0), np.float64(1400.5402962651492): np.float64(50.0), np.float64(1449.1897851185397): np.float64(70.0), np.float64(1568.8301417453613): np.float64(50.0), np.float64(1615.3218462004172): np.float64(50.0), np.float64(1710.2420754482148): np.float64(200.0), np.float64(1769.419306271512): np.float64(50.0), np.float64(1795.6695062543874): np.float64(100.0), np.float64(1854.9105300823699): np.float64(5.0), np.float64(1866.2898133610774): np.float64(5.0), np.float64(1975.6654877605317): np.float64(50.0), np.float64(2033.8567623301808): np.float64(50.0), np.float64(2059.546783125738): np.float64(50.0), np.float64(2062.0864460269026): np.float64(60.0), np.float64(2096.667422736005): np.float64(15.0), np.float64(2213.236028525559): np.float64(100.0), np.float64(2334.292358356692): np.float64(100.0), np.float64(2393.2318928421078): np.float64(30.0), np.float64(2479.652593999227): np.float64(10.0), np.float64(2518.4671736471323): np.float64(100.0), np.float64(2572.691915262086): np.float64(100.0), np.float64(2621.3796761996955): np.float64(15.0), np.float64(2712.79156071062): np.float64(100.0), np.float64(2839.1085285308986): np.float64(100.0), np.float64(2936.033203089824): np.float64(10.0), np.float64(2994.6222331219114): np.float64(30.0), np.float64(3010.246912802971): np.float64(100.0), np.float64(3130.3576648049207): np.float64(100.0), np.float64(3177.4788353711037): np.float64(200.0), np.float64(3427.8127184966875): np.float64(150.0), np.float64(3489.8130558645257): np.float64(10.0), np.float64(3643.073137423482): np.float64(100.0), np.float64(3764.1292139788443): np.float64(15.0), np.float64(3823.0685216039715): np.float64(50.0), np.float64(3909.4891557069514): np.float64(7.0)}, xcal=<function <lambda>>, nbins=4500).convolve('gaussian', sigma=3), 514: from_delta_lines({np.float64(696.2632587120148): np.float64(60.0), np.float64(732.4961676014019): np.float64(100.0), np.float64(740.1684816286361): np.float64(60.0), np.float64(938.6608111482706): np.float64(200.0), np.float64(1478.5851230460657): np.float64(50.0), np.float64(1776.9725878568374): np.float64(50.0), np.float64(1970.3658056149043): np.float64(50.0), np.float64(2058.800733350081): np.float64(50.0), np.float64(2107.450222203471): np.float64(70.0), np.float64(2227.090578830293): np.float64(50.0), np.float64(2273.5822832853487): np.float64(50.0), np.float64(2368.5025125331463): np.float64(200.0), np.float64(2427.6797433564434): np.float64(50.0), np.float64(2453.929943339319): np.float64(100.0), np.float64(2513.1709671673016): np.float64(5.0), np.float64(2524.550250446009): np.float64(5.0), np.float64(2633.925924845463): np.float64(50.0), np.float64(2692.1171994151123): np.float64(50.0), np.float64(2717.8072202106696): np.float64(50.0), np.float64(2720.3468831118344): np.float64(60.0), np.float64(2754.9278598209366): np.float64(15.0), np.float64(2871.4964656104908): np.float64(100.0), np.float64(2992.5527954416234): np.float64(100.0), np.float64(3051.492329927039): np.float64(30.0), np.float64(3137.913031084159): np.float64(10.0), np.float64(3176.7276107320636): np.float64(100.0), np.float64(3230.9523523470175): np.float64(100.0), np.float64(3279.640113284627): np.float64(15.0), np.float64(3371.051997795552): np.float64(100.0), np.float64(3497.3689656158303): np.float64(100.0), np.float64(3594.293640174755): np.float64(10.0), np.float64(3652.8826702068427): np.float64(30.0), np.float64(3668.507349887903): np.float64(100.0), np.float64(3788.618101889852): np.float64(100.0), np.float64(3835.7392724560355): np.float64(200.0)}, xcal=<function <lambda>>, nbins=4500).convolve('gaussian', sigma=3)} + + +
+ + + + +
+
+
+ neon_wl_spe = + + {785: from_delta_lines({np.float64(792.6201): np.float64(120.0), np.float64(792.71177): np.float64(3.0), np.float64(793.69961): np.float64(13.0), np.float64(794.3181400000001): np.float64(80.0), np.float64(808.2457999999999): np.float64(60.0), np.float64(808.4345000000001): np.float64(100.0), np.float64(811.85492): np.float64(40.0), np.float64(812.89108): np.float64(12.0), np.float64(813.64054): np.float64(170.0), np.float64(825.9379000000001): np.float64(30.0), np.float64(826.4807): np.float64(100.0), np.float64(826.60772): np.float64(70.0), np.float64(826.7116200000002): np.float64(10.0), np.float64(830.0325800000002): np.float64(300.0), np.float64(831.4995000000001): np.float64(100.0), np.float64(836.5746600000001): np.float64(50.0), np.float64(837.2106): np.float64(100.0), np.float64(837.7608000000001): np.float64(800.0), np.float64(841.7160600000001): np.float64(30.0), np.float64(841.84274): np.float64(250.0), np.float64(846.33575): np.float64(40.0), np.float64(848.4443499999999): np.float64(13.0), np.float64(849.5359799999999): np.float64(700.0), np.float64(854.46958): np.float64(15.0), np.float64(857.13524): np.float64(30.0), np.float64(859.1258399999999): np.float64(400.0), np.float64(863.4647000000001): np.float64(350.0), np.float64(864.7041100000001): np.float64(60.0), np.float64(865.43831): np.float64(600.0), np.float64(865.5522000000001): np.float64(80.0), np.float64(866.8255999999999): np.float64(100.0), np.float64(867.94925): np.float64(130.0), np.float64(868.1921100000001): np.float64(150.0), np.float64(870.4111599999999): np.float64(30.0), np.float64(877.1656300000001): np.float64(100.0), np.float64(878.06226): np.float64(600.0), np.float64(878.3753300000001): np.float64(400.0), np.float64(883.0907199999999): np.float64(6.0), np.float64(885.3866799999998): np.float64(300.0), np.float64(886.53063): np.float64(20.0), np.float64(886.57552): np.float64(150.0), np.float64(891.9500599999998): np.float64(60.0), np.float64(898.85564): np.float64(20.0), np.float64(907.9462): np.float64(100.0), np.float64(914.8671600000001): np.float64(120.0), np.float64(920.1759099999999): np.float64(90.0), np.float64(922.00601): np.float64(60.0), np.float64(922.1580099999999): np.float64(20.0), np.float64(922.66903): np.float64(20.0), np.float64(927.5519599999999): np.float64(9.0), np.float64(928.7563): np.float64(200.0), np.float64(930.0852699999999): np.float64(80.0), np.float64(931.0583899999999): np.float64(8.0), np.float64(931.39726): np.float64(30.0), np.float64(932.6506799999999): np.float64(70.0), np.float64(937.3307799999999): np.float64(15.0), np.float64(942.5378800000001): np.float64(50.0), np.float64(945.9209500000001): np.float64(30.0), np.float64(948.66818): np.float64(50.0), np.float64(953.41629): np.float64(60.0), np.float64(954.74049): np.float64(30.0), np.float64(957.7013000000001): np.float64(120.0), np.float64(966.5419700000001): np.float64(180.0), np.float64(980.8860000000001): np.float64(100.0)}, xcal=<function <lambda>>, nbins=4500).convolve('gaussian', sigma=1.5), 633: from_delta_lines({np.float64(638.29917): np.float64(100.0), np.float64(640.2248): np.float64(200.0), np.float64(650.65281): np.float64(150.0), np.float64(653.28822): np.float64(10.0), np.float64(659.89529): np.float64(100.0), np.float64(665.2092700000001): np.float64(15.0), np.float64(667.82762): np.float64(50.0), np.float64(671.7043): np.float64(7.0), np.float64(692.94673): np.float64(1000.0), np.float64(702.40504): np.float64(300.0), np.float64(703.24131): np.float64(800.0), np.float64(705.12923): np.float64(20.0), np.float64(705.91074): np.float64(100.0), np.float64(717.39381): np.float64(800.0), np.float64(721.3199999999999): np.float64(150.0), np.float64(723.5188): np.float64(150.0), np.float64(724.51666): np.float64(800.0), np.float64(734.3945): np.float64(150.0), np.float64(747.24386): np.float64(30.0), np.float64(748.8871199999999): np.float64(300.0), np.float64(749.2102): 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np.float64(883.0907199999999): np.float64(6.0)}, xcal=<function <lambda>>, nbins=4500).convolve('gaussian', sigma=1.5), 532: from_delta_lines({np.float64(540.05618): np.float64(200.0), np.float64(556.27662): np.float64(50.0), np.float64(565.66588): np.float64(50.0), np.float64(571.92248): np.float64(50.0), np.float64(574.82985): np.float64(50.0), np.float64(576.4418800000001): np.float64(70.0), np.float64(580.44496): np.float64(50.0), np.float64(582.01558): np.float64(50.0), np.float64(585.24879): np.float64(200.0), np.float64(587.2827500000001): np.float64(50.0), np.float64(588.18952): np.float64(100.0), np.float64(590.24623): np.float64(5.0), np.float64(590.64294): np.float64(5.0), np.float64(594.48342): np.float64(50.0), np.float64(596.5471): np.float64(50.0), np.float64(597.46273): np.float64(50.0), np.float64(597.5534): np.float64(60.0), np.float64(598.79074): np.float64(15.0), np.float64(602.99969): np.float64(100.0), np.float64(607.43377): np.float64(100.0), 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+ + + + +
+
+
+ neon_wl_785_nist_dict = + + {792.6841221569385: 5.37437266023976, 793.7330415754924: 0.5689277951354417, 794.3457330415755: 3.501094091821991, 808.4285595693893: 7.202537154952434, 811.8949671772428: 1.7505470455873593, 812.945295404814: 0.5251641209139684, 813.6892778993436: 7.439824945294173, 825.9868433097755: 1.3120972814694163, 826.5665828089599: 7.94695465954125, 830.0568927789934: 13.129102844632756, 831.5448577680525: 4.3763676148399115, 836.6214442013129: 2.1881838071971296, 837.234135667396: 4.376367614845547, 837.8030634573304: 35.010940919002735, 841.8664541909187: 12.03028296382178, 846.3807439824946: 1.750547045586516, 848.4814004376367: 0.568927789934395, 849.5754923413567: 30.634573304125958, 854.5207877461706: 0.6564551422317848, 857.1903719912473: 1.3129102839767155, 859.1597374179431: 17.50547045950853, 863.4923413566739: 15.317286652068542, 865.469184130712: 29.20051773802159, 866.8621444201312: 4.376367614840063, 868.0: 5.689277899344474, 868.218818380744: 6.564551422319682, 870.4507658643327: 1.3129102839772355, 877.190371991244: 4.376367613743546, 878.1094091903724: 26.2582056879645, 878.4157549234137: 17.505470458778227, 883.1422319474835: 0.262582056892803, 885.4179431072209: 13.129102844635762, 886.5947874281137: 7.437997551100201, 891.9824945295405: 2.6258205687594547, 898.8971553610503: 0.8752735229757134, 908.0: 4.376367614838839, 914.9146608315099: 5.251641137834712, 920.2100656455142: 3.9387308533338343, 922.0688951698448: 3.3797527091026245, 927.6061269146609: 0.39387308533920273, 928.7877461706784: 8.752735229757006, 930.144420131291: 3.5010940918132034, 931.1072210065647: 0.35010940916879774, 931.4573304157549: 1.3129102844605525, 932.6827133479212: 3.0634573302989097, 937.3654266958424: 0.6564551422320757, 942.5733041575493: 2.188183807192464, 945.9431072210066: 1.3129102839781117, 948.7002188183808: 2.188183807191779, 953.4704595185996: 2.6258205687594476, 954.7833698030635: 1.3129102839777373, 957.7592997811817: 5.2516411378370345, 966.5995623632385: 7.877461706780519, 980.910284463895: 4.376367614839546} + + +
+ + + + +
+
+
+ neon_wl_633_nist_dict = + + {638.3287981859411: 5.6689342403499765, 640.2562358276643: 11.337868480727586, 650.6870748299319: 8.503401360545043, 653.3514739229025: 0.566893424036351, 659.9274376417234: 5.668934240348053, 665.2562358276643: 0.8503401360546983, 667.8639455782313: 2.8344671200402334, 671.7755102040816: 0.39682539682552465, 692.9773242630386: 56.68934240353873, 702.4444444444445: 17.006802721074873, 703.2947845804988: 45.351473922858105, 705.1655328798186: 1.1337868476493322, 705.9591836734694: 5.668934240348062, 717.4671201814059: 45.351473922836774, 721.3786848072563: 8.503401360545148, 723.5895691609977: 8.50340136054297, 724.5532879818594: 45.351473922833904, 734.4739229024943: 8.503401360542986, 747.2857142857143: 1.7006802717293952, 748.9297052154195: 17.00680272108454, 749.2698412698412: 5.6689342403646865, 752.3310657596371: 8.503401360527796, 753.6349206349206: 17.006802721048707, 754.4852607709751: 7.369614512457517, 772.5124716553288: 0.05668934240365808, 774.156462585034: 6.802721088429488, 783.9637188208617: 0.11337868480724644, 792.6951245635115: 6.971961625854252, 793.77097505694: 0.7369614305162637, 794.3945578231699: 4.535147381418968, 808.4417565814348: 9.329816961551543, 811.9115646254535: 2.2675736482945825, 812.9319727898597: 0.6802720427342306, 813.6689342404153: 9.63718817885658, 825.9704271162868: 1.6978631010464373, 826.5881899888147: 10.231887665802304, 830.1088435374149: 17.00680272107302, 831.5827664399093: 5.668934240348589, 836.6281179138335: 2.8344671202589966, 837.2517006802728: 5.6689342195710095, 837.8185941043079: 45.351473923298364, 841.8913979246645: 15.8101670633123, 846.3786848072561: 2.26757369591529, 848.4761904759695: 0.7369614416713715, 849.6099773242671: 39.68253967290885, 854.5419501133787: 0.8503401360543106, 857.2063492063492: 1.7006802717279004, 859.1904761904761: 22.67573696142483, 863.4988662131518: 19.841269841214043, 864.7460316744742: 3.401354571136033, 865.4928040619619: 38.396369552201875, 866.9002267573604: 5.668934237269633, 868.034013605442: 7.369614508413964, 868.26077097504: 8.503401360112813, 870.4716553287982: 1.7006802717287801, 877.2176870748299: 5.668934240361571, 878.1247165532881: 34.01360544219461, 878.4081632653063: 22.675736961434616, 883.1700680272108: 0.3401360544216929} + + +
+ + + + +
+
+
+ neon_wl_532_nist_dict = + + {540.0804670242978: 6.311139160610751, 556.3000946670874: 1.5777847897339283, 565.7036920164089: 1.5777847897345527, 571.9517197854212: 1.5777847897346757, 574.8548437993057: 1.5777847897345527, 576.4641842852635: 2.2088987059726164, 580.4717576522562: 1.5777847897345527, 582.0495424424108: 1.5777847897345523, 585.2682234143263: 6.311139160610822, 587.3193436415273: 1.5777847897341903, 588.234458819817: 3.1555695802011297, 590.285579047018: 0.15777847901546926, 590.6642473966551: 0.15777847901546949, 594.5140422846324: 1.5777847897344977, 596.5651625118334: 1.5777847897347166, 597.5336867539808: 3.5612432617200778, 598.837172609656: 0.4733354370464895, 603.0340801514673: 3.155569580200976, 607.4518775639002: 3.155569580201294, 609.6607762701167: 0.9466708739111769, 612.8794572420321: 0.31555695803092476, 614.3310192489744: 3.1555695802009724, 616.3821394761754: 3.1555695802010364, 618.2439255285578: 0.4733354370463865, 621.7466077627012: 3.1555695802013357, 626.6692963079836: 3.1555695802012638, 630.5190911959609: 0.31555695803092476, 632.8542126853799: 0.9466708729394551, 633.4853266014544: 3.1555695792292964, 638.3449037551278: 3.1555695802011074, 640.2698011991164: 6.31113916061083, 650.6831808141369: 4.733354370433662, 653.3338592615967: 0.3155569580309239, 659.928999684443: 3.155569580201134, 665.2303565793626: 0.4733354370463867, 667.8494793310192: 1.5777847897339252, 671.7308299147995: 0.22088987062164733} + + +
+ + + + +
+
+
+ neon_wl_514_nist_dict = + + {533.1227516566739: 1.893341747575223, 534.1325339224629: 3.155569580094588, 534.3534237930478: 1.893341747594008, 540.0965604291574: 6.311139160528673, 556.316188071947: 1.5777847894388257, 565.6882297254654: 1.5777847894388244, 571.9678131902808: 1.577784789438823, 574.8709372041653: 1.5777847894384802, 576.480277690123: 2.208898705700221, 580.4878510571158: 1.577784789439162, 582.0340801514673: 1.577784789438819, 585.2843168191858: 6.3111391605299945, 587.3038813505837: 1.5777847894388177, 588.2189965288734: 3.1555695799820027, 590.2701167560745: 0.15777847901555297, 590.6803408015146: 0.1577784790155361, 594.5301356894919: 1.577784789438816, 596.5812559166928: 1.577784789438815, 597.5497766401437: 3.5613026786141493, 598.8217103187125: 0.4733354370463101, 603.0186178605237: 3.155569579981991, 607.4679709687598: 3.1555695799819894, 609.6453139791731: 0.9466708736863838, 612.8639949510887: 0.3155569580308466, 614.347112653834: 3.155569579981985, 616.3982328810349: 3.155569579981983, 618.2600189334174: 0.47333543704630987, 621.7627011675606: 3.1555695799819796, 626.6853897128431: 3.1555695799819743, 630.5036289050173: 0.3155569580308466, 632.8387503944462: 0.9466708736863778, 633.469864310508: 3.15556957998197, 638.3294414641841: 3.1555695799826475, 640.2543389081728: 6.311139160528551} + + +
+ + + + +
+
+
+ neon_rs_514_nist_dict = + + {np.float64(939.4934887766555): np.float64(6.311139160610751), np.float64(1479.3436997493238): np.float64(1.5777847897339283), np.float64(1778.1542160402296): np.float64(1.5777847897345527), np.float64(1971.2596823698889): np.float64(1.5777847897346757), np.float64(2059.5571041787043): np.float64(1.5777847897345527), np.float64(2108.1214353517908): np.float64(2.2088987059726164), np.float64(2227.885922096006): np.float64(1.5777847897345527), np.float64(2274.584829386518): np.float64(1.5777847897345523), np.float64(2369.0698659052264): np.float64(6.311139160610822), np.float64(2428.7406668822714): np.float64(1.5777847897341903), np.float64(2455.228778146369): np.float64(3.1555695802011297), np.float64(2514.3003438672263): np.float64(0.15777847901546926), np.float64(2525.1610021530646): np.float64(0.15777847901546949), np.float64(2634.792359322113): np.float64(1.5777847897344977), np.float64(2692.6247455413572): np.float64(1.5777847897347166), np.float64(2719.79478148628): np.float64(3.5612432617200778), np.float64(2756.222768871403): np.float64(0.4733354370464895), np.float64(2872.442212982116): np.float64(3.155569580200976), np.float64(2993.043532906524): np.float64(3.155569580201294), np.float64(3052.68875600141): np.float64(0.9466708739111769), np.float64(3138.830689029075): np.float64(0.31555695803092476), np.float64(3177.383680584302): np.float64(3.1555695802009724), np.float64(3231.551159994985): np.float64(3.1555695802010364), np.float64(3280.4073806618967): np.float64(0.4733354370463865), np.float64(3371.5302648035954): np.float64(3.1555695802013357), np.float64(3497.873070711381): np.float64(3.1555695802012638), np.float64(3595.304917297004): np.float64(0.31555695803092476), np.float64(3653.825354794028): np.float64(0.9466708729394551), np.float64(3669.567630755911): np.float64(3.1555695792292964), np.float64(3789.740526516658): np.float64(3.1555695802011074), np.float64(3836.8370859752126): np.float64(6.31113916061083), np.float64(4086.7905162200564): np.float64(4.733354370433662), np.float64(4149.142790608222): np.float64(0.3155569580309239), np.float64(4302.107648445758): np.float64(3.155569580201134), np.float64(4422.86616519335): np.float64(0.4733354370463867), np.float64(4481.819069066493): np.float64(1.5777847897339252), np.float64(4568.337572645141): np.float64(0.22088987062164733)} + + +
+ + + + +
+
+
+ neon_rs_532_nist_dict = + + {np.float64(281.2330516917239): np.float64(6.311139160610751), np.float64(821.0832626643922): np.float64(1.5777847897339283), np.float64(1119.893778955298): np.float64(1.5777847897345527), np.float64(1312.9992452849574): np.float64(1.5777847897346757), np.float64(1401.2966670937728): np.float64(1.5777847897345527), np.float64(1449.8609982668593): np.float64(2.2088987059726164), np.float64(1569.6254850110745): np.float64(1.5777847897345527), np.float64(1616.3243923015864): np.float64(1.5777847897345523), np.float64(1710.8094288202951): np.float64(6.311139160610822), np.float64(1770.4802297973397): np.float64(1.5777847897341903), np.float64(1796.9683410614377): np.float64(3.1555695802011297), np.float64(1856.0399067822946): np.float64(0.15777847901546926), np.float64(1866.9005650681333): np.float64(0.15777847901546949), np.float64(1976.5319222371818): np.float64(1.5777847897344977), np.float64(2034.3643084564255): np.float64(1.5777847897347166), np.float64(2061.5343444013483): np.float64(3.5612432617200778), np.float64(2097.962331786471): np.float64(0.4733354370464895), np.float64(2214.1817758971847): np.float64(3.155569580200976), np.float64(2334.7830958215923): np.float64(3.155569580201294), np.float64(2394.428318916479): np.float64(0.9466708739111769), np.float64(2480.5702519441434): np.float64(0.31555695803092476), np.float64(2519.123243499371): np.float64(3.1555695802009724), np.float64(2573.290722910054): np.float64(3.1555695802010364), np.float64(2622.1469435769654): np.float64(0.4733354370463865), np.float64(2713.269827718664): np.float64(3.1555695802013357), np.float64(2839.6126336264497): np.float64(3.1555695802012638), np.float64(2937.0444802120724): np.float64(0.31555695803092476), np.float64(2995.564917709097): np.float64(0.9466708729394551), np.float64(3011.3071936709794): np.float64(3.1555695792292964), np.float64(3131.480089431727): np.float64(3.1555695802011074), np.float64(3178.576648890281): np.float64(6.31113916061083), np.float64(3428.5300791351247): np.float64(4.733354370433662), np.float64(3490.88235352329): np.float64(0.3155569580309239), np.float64(3643.8472113608263): np.float64(3.155569580201134), np.float64(3764.6057281084186): np.float64(0.4733354370463867), np.float64(3823.558631981562): np.float64(1.5777847897339252), np.float64(3910.0771355602096): np.float64(0.22088987062164733)} + + +
+ + + + +
+
+
+ neon_rs_633_nist_dict = + + {np.float64(131.88066389220208): np.float64(5.6689342403499765), np.float64(179.04156354222116): np.float64(11.337868480727586), np.float64(429.41787963591844): np.float64(8.503401360545043), np.float64(492.0908418445599): np.float64(0.566893424036351), np.float64(644.6071724264865): np.float64(5.668934240348053), np.float64(765.9863340283906): np.float64(0.8503401360546983), np.float64(824.6787920356078): np.float64(2.8344671200402334), np.float64(911.8631038159482): np.float64(0.39682539682552465), np.float64(1367.3016979214524): np.float64(56.68934240353873), np.float64(1561.7870442143164): np.float64(17.006802721074873), np.float64(1578.9995182981806): np.float64(45.351473922858105), np.float64(1616.7208386258383): np.float64(1.1337868476493322), np.float64(1632.6634252703236): np.float64(5.668934240348062), np.float64(1859.8673670412886): np.float64(45.351473922836774), np.float64(1935.4435930445873): np.float64(8.503401360545148), np.float64(1977.799152530638): np.float64(8.50340136054297), np.float64(1996.1809387646144): np.float64(45.351473922833904), np.float64(2182.601046783266): np.float64(8.503401360542986), np.float64(2416.025740338238): np.float64(1.7006802717293952), np.float64(2445.4003213358865): np.float64(17.00680272108454), np.float64(2451.46172714364): np.float64(5.6689342403646865), np.float64(2505.7677416665): np.float64(8.503401360527796), np.float64(2528.764109463943): np.float64(17.006802721048707), np.float64(2543.718919624388): np.float64(7.369614512457517), np.float64(2853.013478270963): np.float64(0.05668934240365808), np.float64(2880.502872777571): np.float64(6.802721088429488), np.float64(3042.0959734648623): np.float64(0.11337868480724644), np.float64(3182.5978156792344): np.float64(6.971961625854252), np.float64(3199.69602075866): np.float64(0.7369614305162637), np.float64(3209.585254214946): np.float64(4.535147381418968), np.float64(3428.313429608364): np.float64(9.329816961551543), np.float64(3481.175964530883): np.float64(2.2675736482945825), np.float64(3496.636018035287): np.float64(0.6802720427342306), np.float64(3507.7774966574075): np.float64(9.63718817885658), np.float64(3690.8173192671397): np.float64(1.6978631010464373), np.float64(3699.8656425642857): np.float64(10.231887665802304), np.float64(3751.175292738612): np.float64(17.00680272107302), np.float64(3772.5270806058697): np.float64(5.668934240348589), np.float64(3845.04636147525): np.float64(2.8344671202589966), np.float64(3853.948730839821): np.float64(5.6689342195710095), np.float64(3862.03029427443): np.float64(45.351473923298364), np.float64(3919.7717095729495): np.float64(15.8101670633123), np.float64(3982.7459657037134): np.float64(2.26757369591529), np.float64(4011.9537603014455): np.float64(0.7369614416713715), np.float64(4027.681711437721): np.float64(39.68253967290885), np.float64(4095.6126602455083): np.float64(0.8503401360543106), np.float64(4131.985776490957): np.float64(1.7006802717279004), np.float64(4158.925592793552): np.float64(22.67573696142483), np.float64(4216.997191919614): np.float64(19.841269841214043), np.float64(4233.699393684956): np.float64(3.401354571136033), np.float64(4243.677226254282): np.float64(38.396369552201875), np.float64(4262.435461241407): np.float64(5.668934237269633), np.float64(4277.502418460247): np.float64(7.369614508413964), np.float64(4280.511087990217): np.float64(8.503401360112813), np.float64(4309.7634684110535): np.float64(1.7006802717287801), np.float64(4398.109361819262): np.float64(5.668934240361571), np.float64(4409.8842779054585): np.float64(34.01360544219461), np.float64(4413.558952264782): np.float64(22.675736961434616), np.float64(4474.940806054933): np.float64(0.3401360544216929)} + + +
+ + + + +
+
+
+ neon_rs_785_nist_dict = + + {np.float64(123.48791116373066): np.float64(5.37437266023976), np.float64(140.15913593139325): np.float64(0.5689277951354417), np.float64(149.87670877344723): np.float64(3.501094091821991), np.float64(369.17670041748454): np.float64(7.202537154952434), np.float64(421.9893714023522): np.float64(1.7505470455873593), np.float64(437.9028038877788): np.float64(0.5251641209139684), np.float64(449.1499620289975): np.float64(7.439824945294173), np.float64(632.1231345393229): np.float64(1.3120972814694163), np.float64(640.6145857383626): np.float64(7.94695465954125), np.float64(691.4865250966848): np.float64(13.129102844632756), np.float64(713.0440635821694): np.float64(4.3763676148399115), np.float64(786.0162082399527): np.float64(2.1881838071971296), np.float64(794.7633449059146): np.float64(4.376367614845547), np.float64(802.8742305220228): np.float64(35.010940919002735), np.float64(860.4849683432024): np.float64(12.03028296382178), np.float64(923.8399042850345): np.float64(1.750547045586516), np.float64(953.0913228444969): np.float64(0.568927789934395), np.float64(968.2691464716178): np.float64(30.634573304125958), np.float64(1036.3880472238798): np.float64(0.6564551422317848), np.float64(1072.833530550081): np.float64(1.3129102839767155), np.float64(1099.5743744242316): np.float64(17.50547045950853), np.float64(1157.9748768745785): np.float64(15.317286652068542), np.float64(1184.4270910599143): np.float64(29.20051773802159), np.float64(1202.993892324344): np.float64(4.376367614840063), np.float64(1218.1161759957727): np.float64(5.689277899344474), np.float64(1221.0197632407637): np.float64(6.564551422319682), np.float64(1250.5529672316122): np.float64(1.3129102839772355), np.float64(1338.8195774819583): np.float64(4.376367613743546), np.float64(1350.7509554394137): np.float64(26.2582056879645), np.float64(1354.7225333663616): np.float64(17.505470458778227), np.float64(1415.6491106733881): np.float64(0.262582056892803), np.float64(1444.7521380071198): np.float64(13.129102844635762), np.float64(1459.7436530032244): np.float64(7.437997551100201), np.float64(1527.8711561888636): np.float64(2.6258205687594547), np.float64(1614.11032165422): np.float64(0.8752735229757134), np.float64(1725.6376441538746): np.float64(4.376367614838839), np.float64(1808.8723496292414): np.float64(5.251641137834712), np.float64(1871.7695912247377): np.float64(3.9387308533338343), np.float64(1893.6769091320486): np.float64(3.3797527091026245), np.float64(1958.415869313852): np.float64(0.39387308533920273), np.float64(1972.1309218099645): np.float64(8.752735229757006), np.float64(1987.834861812345): np.float64(3.5010940918132034), np.float64(1998.9518308621705): np.float64(0.35010940916879774), np.float64(2002.9886670182382): np.float64(1.3129102844605525), np.float64(2017.093726910536): np.float64(3.0634573302989097), np.float64(2070.6554715489538): np.float64(0.6564551422320757), np.float64(2129.599076084452): np.float64(2.188183807192464), np.float64(2167.393207461413): np.float64(1.3129102839781117), np.float64(2198.1159744687006): np.float64(2.188183807191779), np.float64(2250.851593771674): np.float64(2.6258205687594476), np.float64(2265.27350978513): np.float64(1.3129102839777373), np.float64(2297.8168019125974): np.float64(5.2516411378370345), np.float64(2393.3077473486487): np.float64(7.877461706780519), np.float64(2544.241255373098): np.float64(4.376367614839546)} + + +
+ + + + +
+
+
+ neon_wl_D3_3 = + + [533.07775, 540.05616, 556.27662, 565.66588, 571.92248, 574.82985, 576.44188, 580.44496, 580.44496, 582.01558, 585.24878, 587.28275, 588.1895, 590.24623, 594.4834, 596.5471, 598.79074, 602.99968, 607.43376, 609.6163, 612.84498, 614.30627, 616.35937, 618.2146, 621.72812, 626.64952, 630.47893, 633.44276, 638.29914, 640.2248, 650.65277, 653.28824, 659.89528, 667.82766, 671.7043, 692.94672, 702.405, 703.24128, 705.91079, 717.3938, 724.51665, 748.88712, 753.57739, 754.40439, 794.31805, 808.24576, 811.85495, 813.64061, 830.03248, 836.57464, 837.7607, 846.33569, 849.53591, 854.46952, 857.13535, 859.12583, 863.46472, 870.41122, 877.16575, 878.37539, 885.38669, 891.95007, 898.85564, 898.85564, 914.8672, 920.17588, 927.55191, 930.08532, 932.65072, 937.33079, 942.53797, 945.9211, 948.66825, 953.4164, 954.74052, 966.542] + + +
+ + + + +
+
+
+ neon_wl_D3_3_dict = + + {533.07775: 1, 540.05616: 1, 556.27662: 1, 565.66588: 1, 571.92248: 1, 574.82985: 1, 576.44188: 1, 580.44496: 1, 582.01558: 1, 585.24878: 1, 587.28275: 1, 588.1895: 1, 590.24623: 1, 594.4834: 1, 596.5471: 1, 598.79074: 1, 602.99968: 1, 607.43376: 1, 609.6163: 1, 612.84498: 1, 614.30627: 1, 616.35937: 1, 618.2146: 1, 621.72812: 1, 626.64952: 1, 630.47893: 1, 633.44276: 1, 638.29914: 1, 640.2248: 1, 650.65277: 1, 653.28824: 1, 659.89528: 1, 667.82766: 1, 671.7043: 1, 692.94672: 1, 702.405: 1, 703.24128: 1, 705.91079: 1, 717.3938: 1, 724.51665: 1, 748.88712: 1, 753.57739: 1, 754.40439: 1, 794.31805: 1, 808.24576: 1, 811.85495: 1, 813.64061: 1, 830.03248: 1, 836.57464: 1, 837.7607: 1, 846.33569: 1, 849.53591: 1, 854.46952: 1, 857.13535: 1, 859.12583: 1, 863.46472: 1, 870.41122: 1, 877.16575: 1, 878.37539: 1, 885.38669: 1, 891.95007: 1, 898.85564: 1, 914.8672: 1, 920.17588: 1, 927.55191: 1, 930.08532: 1, 932.65072: 1, 937.33079: 1, 942.53797: 1, 945.9211: 1, 948.66825: 1, 953.4164: 1, 954.74052: 1, 966.542: 1} + + +
+ + + + +
+
+
+ NEON_WL: Final = + + {785: {792.6841221569385: 5.37437266023976, 793.7330415754924: 0.5689277951354417, 794.3457330415755: 3.501094091821991, 808.4285595693893: 7.202537154952434, 811.8949671772428: 1.7505470455873593, 812.945295404814: 0.5251641209139684, 813.6892778993436: 7.439824945294173, 825.9868433097755: 1.3120972814694163, 826.5665828089599: 7.94695465954125, 830.0568927789934: 13.129102844632756, 831.5448577680525: 4.3763676148399115, 836.6214442013129: 2.1881838071971296, 837.234135667396: 4.376367614845547, 837.8030634573304: 35.010940919002735, 841.8664541909187: 12.03028296382178, 846.3807439824946: 1.750547045586516, 848.4814004376367: 0.568927789934395, 849.5754923413567: 30.634573304125958, 854.5207877461706: 0.6564551422317848, 857.1903719912473: 1.3129102839767155, 859.1597374179431: 17.50547045950853, 863.4923413566739: 15.317286652068542, 865.469184130712: 29.20051773802159, 866.8621444201312: 4.376367614840063, 868.0: 5.689277899344474, 868.218818380744: 6.564551422319682, 870.4507658643327: 1.3129102839772355, 877.190371991244: 4.376367613743546, 878.1094091903724: 26.2582056879645, 878.4157549234137: 17.505470458778227, 883.1422319474835: 0.262582056892803, 885.4179431072209: 13.129102844635762, 886.5947874281137: 7.437997551100201, 891.9824945295405: 2.6258205687594547, 898.8971553610503: 0.8752735229757134, 908.0: 4.376367614838839, 914.9146608315099: 5.251641137834712, 920.2100656455142: 3.9387308533338343, 922.0688951698448: 3.3797527091026245, 927.6061269146609: 0.39387308533920273, 928.7877461706784: 8.752735229757006, 930.144420131291: 3.5010940918132034, 931.1072210065647: 0.35010940916879774, 931.4573304157549: 1.3129102844605525, 932.6827133479212: 3.0634573302989097, 937.3654266958424: 0.6564551422320757, 942.5733041575493: 2.188183807192464, 945.9431072210066: 1.3129102839781117, 948.7002188183808: 2.188183807191779, 953.4704595185996: 2.6258205687594476, 954.7833698030635: 1.3129102839777373, 957.7592997811817: 5.2516411378370345, 966.5995623632385: 7.877461706780519, 980.910284463895: 4.376367614839546}, 633: {638.3287981859411: 5.6689342403499765, 640.2562358276643: 11.337868480727586, 650.6870748299319: 8.503401360545043, 653.3514739229025: 0.566893424036351, 659.9274376417234: 5.668934240348053, 665.2562358276643: 0.8503401360546983, 667.8639455782313: 2.8344671200402334, 671.7755102040816: 0.39682539682552465, 692.9773242630386: 56.68934240353873, 702.4444444444445: 17.006802721074873, 703.2947845804988: 45.351473922858105, 705.1655328798186: 1.1337868476493322, 705.9591836734694: 5.668934240348062, 717.4671201814059: 45.351473922836774, 721.3786848072563: 8.503401360545148, 723.5895691609977: 8.50340136054297, 724.5532879818594: 45.351473922833904, 734.4739229024943: 8.503401360542986, 747.2857142857143: 1.7006802717293952, 748.9297052154195: 17.00680272108454, 749.2698412698412: 5.6689342403646865, 752.3310657596371: 8.503401360527796, 753.6349206349206: 17.006802721048707, 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3.1555695802011074, 640.2698011991164: 6.31113916061083, 650.6831808141369: 4.733354370433662, 653.3338592615967: 0.3155569580309239, 659.928999684443: 3.155569580201134, 665.2303565793626: 0.4733354370463867, 667.8494793310192: 1.5777847897339252, 671.7308299147995: 0.22088987062164733}} + + +
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/exceptions.html b/ramanchada2/misc/exceptions.html new file mode 100644 index 00000000..ea21a014 --- /dev/null +++ b/ramanchada2/misc/exceptions.html @@ -0,0 +1,357 @@ + + + + + + + ramanchada2.misc.exceptions API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.exceptions

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3
+ 4class ApplicationException(Exception):
+ 5    pass
+ 6
+ 7
+ 8class InputParserError(ApplicationException):
+ 9    pass
+10
+11
+12class ChadaReadNotFoundError(ApplicationException):
+13    pass
+
+ + +
+
+ +
+ + class + ApplicationException(builtins.Exception): + + + +
+ + 
5class ApplicationException(Exception):
+6    pass
+
+ + +

Common base class for all non-exit exceptions.

+
+ + +
+
+ +
+ + class + InputParserError(ApplicationException): + + + +
+ + 
 9class InputParserError(ApplicationException):
+10    pass
+
+ + +

Common base class for all non-exit exceptions.

+
+ + +
+
+ +
+ + class + ChadaReadNotFoundError(ApplicationException): + + + +
+ + 
13class ChadaReadNotFoundError(ApplicationException):
+14    pass
+
+ + +

Common base class for all non-exit exceptions.

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/plottable.html b/ramanchada2/misc/plottable.html new file mode 100644 index 00000000..c8b270d9 --- /dev/null +++ b/ramanchada2/misc/plottable.html @@ -0,0 +1,343 @@ + + + + + + + ramanchada2.misc.plottable API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.plottable

+ + + + + + + 
 1from __future__ import annotations
+ 2from abc import ABC, abstractmethod
+ 3from matplotlib.axes import Axes
+ 4import matplotlib.pyplot as plt
+ 5
+ 6
+ 7class Plottable(ABC):
+ 8    def plot(self, ax=None, label=' ', **kwargs) -> Axes:
+ 9        if ax is None:
+10            fig, ax = plt.subplots(1)
+11        self._plot(ax, label=label, **kwargs)
+12        ax.legend()
+13        return ax
+14
+15    @abstractmethod
+16    def _plot(self, ax, **kwargs):
+17        pass
+
+ + +
+
+ +
+ + class + Plottable(abc.ABC): + + + +
+ + 
 8class Plottable(ABC):
+ 9    def plot(self, ax=None, label=' ', **kwargs) -> Axes:
+10        if ax is None:
+11            fig, ax = plt.subplots(1)
+12        self._plot(ax, label=label, **kwargs)
+13        ax.legend()
+14        return ax
+15
+16    @abstractmethod
+17    def _plot(self, ax, **kwargs):
+18        pass
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + def + plot(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes: + + + +
+ + 
 9    def plot(self, ax=None, label=' ', **kwargs) -> Axes:
+10        if ax is None:
+11            fig, ax = plt.subplots(1)
+12        self._plot(ax, label=label, **kwargs)
+13        ax.legend()
+14        return ax
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/spectrum_deco.html b/ramanchada2/misc/spectrum_deco.html new file mode 100644 index 00000000..a697a748 --- /dev/null +++ b/ramanchada2/misc/spectrum_deco.html @@ -0,0 +1,273 @@ + + + + + + + ramanchada2.misc.spectrum_deco API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.spectrum_deco

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3from .spectrum_filter import add_spectrum_filter
+4from .spectrum_constructor import add_spectrum_constructor
+5from .spectrum_method import add_spectrum_method
+6from .dynamically_added import dynamically_added_filters
+7from .dynamically_added import dynamically_added_constructors
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/spectrum_deco/dynamically_added.html b/ramanchada2/misc/spectrum_deco/dynamically_added.html new file mode 100644 index 00000000..9b48d748 --- /dev/null +++ b/ramanchada2/misc/spectrum_deco/dynamically_added.html @@ -0,0 +1,301 @@ + + + + + + + ramanchada2.misc.spectrum_deco.dynamically_added API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.spectrum_deco.dynamically_added

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3from typing import Set
+4
+5dynamically_added_filters: Set[str] = set()
+6dynamically_added_constructors: Set[str] = set()
+
+ + +
+
+
+ dynamically_added_filters: Set[str] = + + {'smoothing_RC1', 'subtract_baseline_rc1_snip', 'derivative_sharpening', 'resample_NUDFT_filter', 'subtract_moving_minimum', 'convolve', 'moving_minimum', 'add_baseline', 'add_gaussian_noise', 'subtract_baseline_rc1_als', '__sub__', 'hht_sharpening', 'drop_spikes', 'add_gaussian_noise_drift', 'calibrate_by_deltas_filter', 'resample_spline_filter', 'abs_nm_to_shift_cm_1_filter', 'xcal_argmin2d_iter_lowpass', '__add__', 'normalize', 'find_peak_multipeak_filter', 'xcal_fine_RBF', '__truediv__', 'moving_average_convolve', 'pad_zeros', 'scale_xaxis_linear', 'moving_median', 'scale_xaxis_fun', 'trim_axes', 'subtract_moving_median', 'xcal_fine', 'dropna', 'shift_cm_1_to_abs_nm_filter', 'set_new_xaxis', 'scale_yaxis_linear', 'hht_sharpening_chain', 'add_poisson_noise', 'recover_spikes', '__mul__', 'get_spikes', 'fit_peaks_filter', 'moving_average'} + + +
+ + + + +
+
+
+ dynamically_added_constructors: Set[str] = + + {'from_delta_lines', 'from_local_file', 'from_simulation', 'hdr_from_multi_exposure', 'from_stream', 'from_cache_or_calc', 'from_spectral_component_collection', 'from_theoretical_lines', 'from_test_spe', 'from_chada'} + + +
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/spectrum_deco/spectrum_constructor.html b/ramanchada2/misc/spectrum_deco/spectrum_constructor.html new file mode 100644 index 00000000..bcb287de --- /dev/null +++ b/ramanchada2/misc/spectrum_deco/spectrum_constructor.html @@ -0,0 +1,366 @@ + + + + + + + ramanchada2.misc.spectrum_deco.spectrum_constructor API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.spectrum_deco.spectrum_constructor

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from functools import wraps
+ 4from ramanchada2.spectrum.spectrum import Spectrum
+ 5from .dynamically_added import dynamically_added_constructors
+ 6
+ 7
+ 8class add_spectrum_constructor:
+ 9    def __init__(self, set_applied_processing=True):
+10        self.set_proc = set_applied_processing
+11
+12    def __call__(self, fun):
+13        @wraps(fun)
+14        def retf(*args, cachefile_=None, **kwargs) -> Spectrum:
+15            spe = fun(*args, **kwargs)
+16            if self.set_proc:
+17                spe._applied_processings.assign(proc=fun.__name__, args=args, kwargs=kwargs)
+18                if cachefile_:
+19                    spe._cachefile = cachefile_
+20                spe.write_cache()
+21            return spe
+22        if hasattr(Spectrum, fun.__name__):
+23            raise ValueError(f'redefining {fun.__name__}')
+24        setattr(Spectrum, fun.__name__, retf)
+25        dynamically_added_constructors.add(fun.__name__)
+26        return retf
+
+ + +
+
+ +
+ + class + add_spectrum_constructor: + + + +
+ + 
 9class add_spectrum_constructor:
+10    def __init__(self, set_applied_processing=True):
+11        self.set_proc = set_applied_processing
+12
+13    def __call__(self, fun):
+14        @wraps(fun)
+15        def retf(*args, cachefile_=None, **kwargs) -> Spectrum:
+16            spe = fun(*args, **kwargs)
+17            if self.set_proc:
+18                spe._applied_processings.assign(proc=fun.__name__, args=args, kwargs=kwargs)
+19                if cachefile_:
+20                    spe._cachefile = cachefile_
+21                spe.write_cache()
+22            return spe
+23        if hasattr(Spectrum, fun.__name__):
+24            raise ValueError(f'redefining {fun.__name__}')
+25        setattr(Spectrum, fun.__name__, retf)
+26        dynamically_added_constructors.add(fun.__name__)
+27        return retf
+
+ + + + +
+ +
+ + add_spectrum_constructor(set_applied_processing=True) + + + +
+ + 
10    def __init__(self, set_applied_processing=True):
+11        self.set_proc = set_applied_processing
+
+ + + + +
+
+
+ set_proc + + +
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/spectrum_deco/spectrum_filter.html b/ramanchada2/misc/spectrum_deco/spectrum_filter.html new file mode 100644 index 00000000..53d9846c --- /dev/null +++ b/ramanchada2/misc/spectrum_deco/spectrum_filter.html @@ -0,0 +1,341 @@ + + + + + + + ramanchada2.misc.spectrum_deco.spectrum_filter API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.spectrum_deco.spectrum_filter

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from functools import wraps
+ 4from copy import copy, deepcopy
+ 5from ramanchada2.spectrum.spectrum import Spectrum
+ 6from .dynamically_added import dynamically_added_filters
+ 7import logging
+ 8
+ 9logger = logging.getLogger(__name__)
+10
+11
+12def add_spectrum_filter(fun):
+13    @wraps(fun)
+14    def retf(old_spe: Spectrum, *args, **kwargs) -> Spectrum:
+15        new_spe = copy(old_spe)
+16        new_spe._applied_processings.append(proc=fun.__name__,
+17                                            args=deepcopy(args),
+18                                            kwargs=deepcopy(kwargs))
+19        fun(old_spe, new_spe, *args, **kwargs)
+20        new_spe.write_cache()
+21        return new_spe
+22    if hasattr(Spectrum, fun.__name__):
+23        raise ValueError(f'redefining {fun.__name__}')
+24    Spectrum._available_processings.add(fun.__name__)
+25    setattr(Spectrum, fun.__name__, retf)
+26    dynamically_added_filters.add(fun.__name__)
+27    return retf
+
+ + +
+
+
+ logger = +<Logger ramanchada2.misc.spectrum_deco.spectrum_filter (WARNING)> + + +
+ + + + +
+
+ +
+ + def + add_spectrum_filter(fun): + + + +
+ + 
13def add_spectrum_filter(fun):
+14    @wraps(fun)
+15    def retf(old_spe: Spectrum, *args, **kwargs) -> Spectrum:
+16        new_spe = copy(old_spe)
+17        new_spe._applied_processings.append(proc=fun.__name__,
+18                                            args=deepcopy(args),
+19                                            kwargs=deepcopy(kwargs))
+20        fun(old_spe, new_spe, *args, **kwargs)
+21        new_spe.write_cache()
+22        return new_spe
+23    if hasattr(Spectrum, fun.__name__):
+24        raise ValueError(f'redefining {fun.__name__}')
+25    Spectrum._available_processings.add(fun.__name__)
+26    setattr(Spectrum, fun.__name__, retf)
+27    dynamically_added_filters.add(fun.__name__)
+28    return retf
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/spectrum_deco/spectrum_method.html b/ramanchada2/misc/spectrum_deco/spectrum_method.html new file mode 100644 index 00000000..5bce61a4 --- /dev/null +++ b/ramanchada2/misc/spectrum_deco/spectrum_method.html @@ -0,0 +1,307 @@ + + + + + + + ramanchada2.misc.spectrum_deco.spectrum_method API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.spectrum_deco.spectrum_method

+ + + + + + + 
 1#!/usr/bin/env python
+ 2
+ 3from functools import wraps
+ 4from ramanchada2.spectrum.spectrum import Spectrum
+ 5
+ 6
+ 7def add_spectrum_method(fun):
+ 8    @wraps(fun)
+ 9    def retf(spe: Spectrum, *args, **kwargs):
+10        ret = fun(spe, *args, **kwargs)
+11        return ret
+12    if hasattr(Spectrum, fun.__name__):
+13        raise ValueError(f'redefining {fun.__name__}')
+14    setattr(Spectrum, fun.__name__, retf)
+15    return retf
+
+ + +
+
+ +
+ + def + add_spectrum_method(fun): + + + +
+ + 
 8def add_spectrum_method(fun):
+ 9    @wraps(fun)
+10    def retf(spe: Spectrum, *args, **kwargs):
+11        ret = fun(spe, *args, **kwargs)
+12        return ret
+13    if hasattr(Spectrum, fun.__name__):
+14        raise ValueError(f'redefining {fun.__name__}')
+15    setattr(Spectrum, fun.__name__, retf)
+16    return retf
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types.html b/ramanchada2/misc/types.html new file mode 100644 index 00000000..2da872b5 --- /dev/null +++ b/ramanchada2/misc/types.html @@ -0,0 +1,273 @@ + + + + + + + ramanchada2.misc.types API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3from .peak_candidates import (ListPeakCandidateMultiModel,
+4                              PeakCandidateMultiModel, PeakModel)
+5from .positive_not_multiple import PositiveOddInt
+6from .spectrum import *  # noqa
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/fit_peaks_result.html b/ramanchada2/misc/types/fit_peaks_result.html new file mode 100644 index 00000000..333966d8 --- /dev/null +++ b/ramanchada2/misc/types/fit_peaks_result.html @@ -0,0 +1,961 @@ + + + + + + + ramanchada2.misc.types.fit_peaks_result API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.fit_peaks_result

+ + + + + + + 
  1import re
+  2from collections import UserList, defaultdict
+  3from typing import List
+  4
+  5import numpy as np
+  6import pandas as pd
+  7from lmfit.model import Model, ModelResult, Parameters
+  8
+  9from ..plottable import Plottable
+ 10from .peak_candidates import ListPeakCandidateMultiModel
+ 11from ramanchada2.spectrum.spectrum import Spectrum
+ 12
+ 13
+ 14class FitPeaksResult(UserList, Plottable):
+ 15    def valuesdict(self):
+ 16        ret = dict()
+ 17        for i in self:
+ 18            ret.update(i.params.valuesdict())
+ 19        return ret
+ 20
+ 21    @property
+ 22    def locations(self):
+ 23        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('center')])
+ 24
+ 25    @property
+ 26    def centers(self):
+ 27        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('center')])
+ 28
+ 29    @property
+ 30    def fwhm(self):
+ 31        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('fwhm')])
+ 32
+ 33    def boundaries(self, n_sigma=5):
+ 34        bounds = list()
+ 35        for group in self:
+ 36            pos = np.array([v for k, v in group.values.items() if k.endswith('center')])
+ 37            sig = np.array([v for k, v in group.values.items() if k.endswith('fwhm')])
+ 38            sig /= 2.35
+ 39            sig *= n_sigma
+ 40            bounds.append((np.min(pos - sig), np.max(pos+sig)))
+ 41        return bounds
+ 42
+ 43    def center_amplitude(self, threshold):
+ 44        return np.array([
+ 45            (v.value, peak.params[k[:-6] + 'amplitude'].value)
+ 46            for peak in self
+ 47            for k, v in peak.params.items()
+ 48            if k.endswith('center')
+ 49            if hasattr(v, 'stderr') and v.stderr is not None and v.stderr < threshold
+ 50        ]).T
+ 51
+ 52    @property
+ 53    def centers_err(self):
+ 54        return np.array([
+ 55            (v.value, v.stderr)
+ 56            for peak in self
+ 57            for k, v in peak.params.items()
+ 58            if k.endswith('center')
+ 59            if hasattr(v, 'stderr') and v.stderr is not None
+ 60            ])
+ 61
+ 62    @property
+ 63    def fwhms(self):
+ 64        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('fwhm')])
+ 65
+ 66    @property
+ 67    def amplitudes(self):
+ 68        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('amplitude')])
+ 69
+ 70    def dumps(self):
+ 71        return [peak.dumps() for peak in self]
+ 72
+ 73    @classmethod
+ 74    def loads(cls, json_str: List[str]):
+ 75        self = cls()
+ 76        for p in json_str:
+ 77            params = Parameters()
+ 78            modres = ModelResult(Model(lambda x: x, None), params)
+ 79            self.append(modres.loads(p))
+ 80        return self
+ 81
+ 82    def _plot(self, ax, peak_candidate_groups=None, individual_peaks=False, xarr=None,
+ 83              label=None,  # ignore label from kwargs
+ 84              **kwargs):
+ 85        def group_plot(x, fitres, label=''):
+ 86            if individual_peaks:
+ 87                color = None
+ 88                for component in fitres.eval_components(x=x).values():
+ 89                    line, = ax.plot(x, component, color=color,
+ 90                                    label=(label if color is None else None),
+ 91                                    **kwargs)
+ 92                    color = line.get_c()
+ 93            else:
+ 94                ax.plot(x, fitres.eval(x=x), **kwargs)
+ 95
+ 96        if peak_candidate_groups is None:
+ 97            for group_i, (bound, fitres) in enumerate(zip(self.boundaries(), self)):
+ 98                x = np.linspace(*bound, 200)
+ 99                group_plot(x, fitres, label=f'group {group_i}')
+100        elif isinstance(peak_candidate_groups, ListPeakCandidateMultiModel):
+101            for group_i, (cand, fitres) in enumerate(zip(peak_candidate_groups, self)):
+102                x = np.linspace(*cand.boundaries, 2000)
+103                group_plot(x, fitres, label=f'group {group_i}')
+104        else:
+105            for group_i, fitres in enumerate(self):
+106                left, right = peak_candidate_groups[group_i].boundaries(n_sigma=3)
+107                x = np.linspace(left, right, 100)
+108                group_plot(x, fitres, label=f'group {group_i}')
+109
+110    def to_dataframe(self):
+111        return pd.DataFrame(
+112            [
+113                dict(name=f'g{group:02d}_{key}', value=val.value, stderr=val.stderr)
+114                for group, res in enumerate(self)
+115                for key, val in res.params.items()
+116            ]
+117        ).sort_values('name')
+118
+119    def to_dataframe_peaks(self):
+120        regex = re.compile(r'p([0-9]+)_(.*)')
+121        ret = defaultdict(dict)
+122        for group_i, group in enumerate(self):
+123            for par in group.params:
+124                m = regex.match(par)
+125                if m is None:
+126                    continue
+127                peak_i, par_name = m.groups()
+128                ret[f'g{group_i:02d}_p{peak_i}'][par_name] = group.params[par].value
+129                ret[f'g{group_i:02d}_p{peak_i}'][f'{par_name}_stderr'] = group.params[par].stderr
+130        return pd.DataFrame.from_dict(ret, orient='index')
+131
+132    def to_csv(self, path_or_buf=None, sep=',', **kwargs):
+133        return self.to_dataframe_peaks().to_csv(path_or_buf=path_or_buf, sep=sep, **kwargs)
+134
+135    def gen_fake_spectrum(self, xarr):
+136        summ = np.zeros_like(xarr)
+137        last_i = 0
+138        last_y = 0
+139        for m in self:
+140            mx = m.userkws['x']
+141            xi = np.searchsorted(xarr, mx)
+142            evalm = m.eval()
+143            summ[xi] = evalm
+144            summ[np.arange(last_i, xi[0])] = np.interp(np.arange(last_i, xi[0]), [last_i, xi[0]], [last_y, evalm[0]])
+145            last_i = xi[-1]
+146            last_y = evalm[-1]
+147        fake_spe = Spectrum(x=xarr, y=summ)
+148        return fake_spe
+
+ + +
+
+ +
+ + class + FitPeaksResult(collections.UserList, ramanchada2.misc.plottable.Plottable): + + + +
+ + 
 15class FitPeaksResult(UserList, Plottable):
+ 16    def valuesdict(self):
+ 17        ret = dict()
+ 18        for i in self:
+ 19            ret.update(i.params.valuesdict())
+ 20        return ret
+ 21
+ 22    @property
+ 23    def locations(self):
+ 24        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('center')])
+ 25
+ 26    @property
+ 27    def centers(self):
+ 28        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('center')])
+ 29
+ 30    @property
+ 31    def fwhm(self):
+ 32        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('fwhm')])
+ 33
+ 34    def boundaries(self, n_sigma=5):
+ 35        bounds = list()
+ 36        for group in self:
+ 37            pos = np.array([v for k, v in group.values.items() if k.endswith('center')])
+ 38            sig = np.array([v for k, v in group.values.items() if k.endswith('fwhm')])
+ 39            sig /= 2.35
+ 40            sig *= n_sigma
+ 41            bounds.append((np.min(pos - sig), np.max(pos+sig)))
+ 42        return bounds
+ 43
+ 44    def center_amplitude(self, threshold):
+ 45        return np.array([
+ 46            (v.value, peak.params[k[:-6] + 'amplitude'].value)
+ 47            for peak in self
+ 48            for k, v in peak.params.items()
+ 49            if k.endswith('center')
+ 50            if hasattr(v, 'stderr') and v.stderr is not None and v.stderr < threshold
+ 51        ]).T
+ 52
+ 53    @property
+ 54    def centers_err(self):
+ 55        return np.array([
+ 56            (v.value, v.stderr)
+ 57            for peak in self
+ 58            for k, v in peak.params.items()
+ 59            if k.endswith('center')
+ 60            if hasattr(v, 'stderr') and v.stderr is not None
+ 61            ])
+ 62
+ 63    @property
+ 64    def fwhms(self):
+ 65        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('fwhm')])
+ 66
+ 67    @property
+ 68    def amplitudes(self):
+ 69        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('amplitude')])
+ 70
+ 71    def dumps(self):
+ 72        return [peak.dumps() for peak in self]
+ 73
+ 74    @classmethod
+ 75    def loads(cls, json_str: List[str]):
+ 76        self = cls()
+ 77        for p in json_str:
+ 78            params = Parameters()
+ 79            modres = ModelResult(Model(lambda x: x, None), params)
+ 80            self.append(modres.loads(p))
+ 81        return self
+ 82
+ 83    def _plot(self, ax, peak_candidate_groups=None, individual_peaks=False, xarr=None,
+ 84              label=None,  # ignore label from kwargs
+ 85              **kwargs):
+ 86        def group_plot(x, fitres, label=''):
+ 87            if individual_peaks:
+ 88                color = None
+ 89                for component in fitres.eval_components(x=x).values():
+ 90                    line, = ax.plot(x, component, color=color,
+ 91                                    label=(label if color is None else None),
+ 92                                    **kwargs)
+ 93                    color = line.get_c()
+ 94            else:
+ 95                ax.plot(x, fitres.eval(x=x), **kwargs)
+ 96
+ 97        if peak_candidate_groups is None:
+ 98            for group_i, (bound, fitres) in enumerate(zip(self.boundaries(), self)):
+ 99                x = np.linspace(*bound, 200)
+100                group_plot(x, fitres, label=f'group {group_i}')
+101        elif isinstance(peak_candidate_groups, ListPeakCandidateMultiModel):
+102            for group_i, (cand, fitres) in enumerate(zip(peak_candidate_groups, self)):
+103                x = np.linspace(*cand.boundaries, 2000)
+104                group_plot(x, fitres, label=f'group {group_i}')
+105        else:
+106            for group_i, fitres in enumerate(self):
+107                left, right = peak_candidate_groups[group_i].boundaries(n_sigma=3)
+108                x = np.linspace(left, right, 100)
+109                group_plot(x, fitres, label=f'group {group_i}')
+110
+111    def to_dataframe(self):
+112        return pd.DataFrame(
+113            [
+114                dict(name=f'g{group:02d}_{key}', value=val.value, stderr=val.stderr)
+115                for group, res in enumerate(self)
+116                for key, val in res.params.items()
+117            ]
+118        ).sort_values('name')
+119
+120    def to_dataframe_peaks(self):
+121        regex = re.compile(r'p([0-9]+)_(.*)')
+122        ret = defaultdict(dict)
+123        for group_i, group in enumerate(self):
+124            for par in group.params:
+125                m = regex.match(par)
+126                if m is None:
+127                    continue
+128                peak_i, par_name = m.groups()
+129                ret[f'g{group_i:02d}_p{peak_i}'][par_name] = group.params[par].value
+130                ret[f'g{group_i:02d}_p{peak_i}'][f'{par_name}_stderr'] = group.params[par].stderr
+131        return pd.DataFrame.from_dict(ret, orient='index')
+132
+133    def to_csv(self, path_or_buf=None, sep=',', **kwargs):
+134        return self.to_dataframe_peaks().to_csv(path_or_buf=path_or_buf, sep=sep, **kwargs)
+135
+136    def gen_fake_spectrum(self, xarr):
+137        summ = np.zeros_like(xarr)
+138        last_i = 0
+139        last_y = 0
+140        for m in self:
+141            mx = m.userkws['x']
+142            xi = np.searchsorted(xarr, mx)
+143            evalm = m.eval()
+144            summ[xi] = evalm
+145            summ[np.arange(last_i, xi[0])] = np.interp(np.arange(last_i, xi[0]), [last_i, xi[0]], [last_y, evalm[0]])
+146            last_i = xi[-1]
+147            last_y = evalm[-1]
+148        fake_spe = Spectrum(x=xarr, y=summ)
+149        return fake_spe
+
+ + +

A more or less complete user-defined wrapper around list objects.

+
+ + +
+ +
+ + def + valuesdict(self): + + + +
+ + 
16    def valuesdict(self):
+17        ret = dict()
+18        for i in self:
+19            ret.update(i.params.valuesdict())
+20        return ret
+
+ + + + +
+
+ +
+ locations + + + +
+ + 
22    @property
+23    def locations(self):
+24        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('center')])
+
+ + + + +
+
+ +
+ centers + + + +
+ + 
26    @property
+27    def centers(self):
+28        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('center')])
+
+ + + + +
+
+ +
+ fwhm + + + +
+ + 
30    @property
+31    def fwhm(self):
+32        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('fwhm')])
+
+ + + + +
+
+ +
+ + def + boundaries(self, n_sigma=5): + + + +
+ + 
34    def boundaries(self, n_sigma=5):
+35        bounds = list()
+36        for group in self:
+37            pos = np.array([v for k, v in group.values.items() if k.endswith('center')])
+38            sig = np.array([v for k, v in group.values.items() if k.endswith('fwhm')])
+39            sig /= 2.35
+40            sig *= n_sigma
+41            bounds.append((np.min(pos - sig), np.max(pos+sig)))
+42        return bounds
+
+ + + + +
+
+ +
+ + def + center_amplitude(self, threshold): + + + +
+ + 
44    def center_amplitude(self, threshold):
+45        return np.array([
+46            (v.value, peak.params[k[:-6] + 'amplitude'].value)
+47            for peak in self
+48            for k, v in peak.params.items()
+49            if k.endswith('center')
+50            if hasattr(v, 'stderr') and v.stderr is not None and v.stderr < threshold
+51        ]).T
+
+ + + + +
+
+ +
+ centers_err + + + +
+ + 
53    @property
+54    def centers_err(self):
+55        return np.array([
+56            (v.value, v.stderr)
+57            for peak in self
+58            for k, v in peak.params.items()
+59            if k.endswith('center')
+60            if hasattr(v, 'stderr') and v.stderr is not None
+61            ])
+
+ + + + +
+
+ +
+ fwhms + + + +
+ + 
63    @property
+64    def fwhms(self):
+65        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('fwhm')])
+
+ + + + +
+
+ +
+ amplitudes + + + +
+ + 
67    @property
+68    def amplitudes(self):
+69        return np.array([v for peak in self for k, v in peak.values.items() if k.endswith('amplitude')])
+
+ + + + +
+
+ +
+ + def + dumps(self): + + + +
+ + 
71    def dumps(self):
+72        return [peak.dumps() for peak in self]
+
+ + + + +
+
+ +
+
@classmethod
+ + def + loads(cls, json_str: List[str]): + + + +
+ + 
74    @classmethod
+75    def loads(cls, json_str: List[str]):
+76        self = cls()
+77        for p in json_str:
+78            params = Parameters()
+79            modres = ModelResult(Model(lambda x: x, None), params)
+80            self.append(modres.loads(p))
+81        return self
+
+ + + + +
+
+ +
+ + def + to_dataframe(self): + + + +
+ + 
111    def to_dataframe(self):
+112        return pd.DataFrame(
+113            [
+114                dict(name=f'g{group:02d}_{key}', value=val.value, stderr=val.stderr)
+115                for group, res in enumerate(self)
+116                for key, val in res.params.items()
+117            ]
+118        ).sort_values('name')
+
+ + + + +
+
+ +
+ + def + to_dataframe_peaks(self): + + + +
+ + 
120    def to_dataframe_peaks(self):
+121        regex = re.compile(r'p([0-9]+)_(.*)')
+122        ret = defaultdict(dict)
+123        for group_i, group in enumerate(self):
+124            for par in group.params:
+125                m = regex.match(par)
+126                if m is None:
+127                    continue
+128                peak_i, par_name = m.groups()
+129                ret[f'g{group_i:02d}_p{peak_i}'][par_name] = group.params[par].value
+130                ret[f'g{group_i:02d}_p{peak_i}'][f'{par_name}_stderr'] = group.params[par].stderr
+131        return pd.DataFrame.from_dict(ret, orient='index')
+
+ + + + +
+
+ +
+ + def + to_csv(self, path_or_buf=None, sep=',', **kwargs): + + + +
+ + 
133    def to_csv(self, path_or_buf=None, sep=',', **kwargs):
+134        return self.to_dataframe_peaks().to_csv(path_or_buf=path_or_buf, sep=sep, **kwargs)
+
+ + + + +
+
+ +
+ + def + gen_fake_spectrum(self, xarr): + + + +
+ + 
136    def gen_fake_spectrum(self, xarr):
+137        summ = np.zeros_like(xarr)
+138        last_i = 0
+139        last_y = 0
+140        for m in self:
+141            mx = m.userkws['x']
+142            xi = np.searchsorted(xarr, mx)
+143            evalm = m.eval()
+144            summ[xi] = evalm
+145            summ[np.arange(last_i, xi[0])] = np.interp(np.arange(last_i, xi[0]), [last_i, xi[0]], [last_y, evalm[0]])
+146            last_i = xi[-1]
+147            last_y = evalm[-1]
+148        fake_spe = Spectrum(x=xarr, y=summ)
+149        return fake_spe
+
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/peak_candidates.html b/ramanchada2/misc/types/peak_candidates.html new file mode 100644 index 00000000..72bfcd6b --- /dev/null +++ b/ramanchada2/misc/types/peak_candidates.html @@ -0,0 +1,1377 @@ + + + + + + + ramanchada2.misc.types.peak_candidates API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.peak_candidates

+ + + + + + + 
  1from __future__ import annotations
+  2
+  3from typing import List, Tuple
+  4
+  5import numpy as np
+  6from pydantic import PositiveFloat
+  7
+  8from ..plottable import Plottable
+  9from .pydantic_base_model import PydBaseModel, PydRootModel
+ 10
+ 11
+ 12class PeakModel(PydBaseModel):
+ 13    amplitude: PositiveFloat
+ 14    position: float
+ 15    sigma: PositiveFloat
+ 16    skew: float = 0
+ 17
+ 18    @property
+ 19    def fwhm(self) -> float:
+ 20        return self.sigma * 2.355
+ 21
+ 22    @property
+ 23    def lwhm(self) -> float:
+ 24        """
+ 25        Left width at half maximum.
+ 26        """
+ 27        return self.fwhm*(1-self.skew)/2
+ 28
+ 29    @property
+ 30    def rwhm(self) -> float:
+ 31        """
+ 32        Right width at half maximum.
+ 33        """
+ 34        return self.fwhm*(1+self.skew)/2
+ 35
+ 36    def serialize(self):
+ 37        return self.model_dump_json()
+ 38
+ 39
+ 40class PeakCandidateMultiModel(PydBaseModel, Plottable):
+ 41    peaks: List[PeakModel]
+ 42    base_slope: float = 0
+ 43    base_intercept: float = 0
+ 44    boundaries: Tuple[float, float]
+ 45
+ 46    def plot_params_baseline(self):
+ 47        x = np.array(self.boundaries)
+ 48        return (x, self.base_slope*x + self.base_intercept)
+ 49
+ 50    def plot_params_errorbar(self):
+ 51        x = self.positions
+ 52        xleft = self.lwhms
+ 53        xright = self.rwhms
+ 54        y_err = (self.amplitudes/2)
+ 55        y = y_err + self.peak_bases
+ 56        return x, y, y_err, (xleft, xright)
+ 57
+ 58    @property
+ 59    def positions(self):
+ 60        return np.array([p.position for p in self.peaks])
+ 61
+ 62    @property
+ 63    def sigmas(self):
+ 64        return np.array([p.sigma for p in self.peaks])
+ 65
+ 66    @property
+ 67    def fwhms(self):
+ 68        return np.array([p.fwhm for p in self.peaks])
+ 69
+ 70    @property
+ 71    def lwhms(self):
+ 72        return np.array([p.lwhm for p in self.peaks])
+ 73
+ 74    @property
+ 75    def rwhms(self):
+ 76        return np.array([p.rwhm for p in self.peaks])
+ 77
+ 78    @property
+ 79    def skews(self):
+ 80        return np.array([p.skew for p in self.peaks])
+ 81
+ 82    @property
+ 83    def amplitudes(self):
+ 84        return np.array([p.amplitude for p in self.peaks])
+ 85
+ 86    @property
+ 87    def bases(self):
+ 88        return self.positions * self.base_slope + self.base_intercept
+ 89
+ 90    def get_pos_ampl_dict(self):
+ 91        return dict(zip(self.positions, self.amplitudes))
+ 92
+ 93    def get_ampl_pos_fwhm(self):
+ 94        return np.array([
+ 95            self.amplitudes,
+ 96            self.positions,
+ 97            self.fwhms]).T
+ 98
+ 99    @property
+100    def peak_bases(self):
+101        return self.positions * self.base_slope + self.base_intercept
+102
+103    def _plot(self, ax, *args, label=" ", **kwargs):
+104        ax.errorbar(*self.plot_params_errorbar(), label=label)
+105        ax.plot(*self.plot_params_baseline())
+106
+107    def serialize(self):
+108        return self.model_dump_json()
+109
+110
+111class ListPeakCandidateMultiModel(PydRootModel, Plottable):
+112    root: List[PeakCandidateMultiModel]
+113
+114    def get_ampl_pos_fwhm(self):
+115        return np.concatenate([cands.get_ampl_pos_fwhm() for cands in self.root])
+116
+117    def get_pos_ampl_dict(self):
+118        return {k: v for cands in self.root for k, v in cands.get_pos_ampl_dict().items()}
+119
+120    def _plot(self, ax, *args, label=" ", **kwargs):
+121        for i, gr in enumerate(self.root):
+122            gr.plot(ax=ax, *args, label=f'{label}_{i}', **kwargs)
+123
+124    def __getitem__(self, key) -> PeakCandidateMultiModel:
+125        return self.root[key]
+126
+127    def __iter__(self):
+128        return iter(self.root)
+129
+130    def __len__(self):
+131        return len(self.root)
+132
+133    def serialize(self):
+134        return self.model_dump_json()
+
+ + +
+
+ +
+ + class + PeakModel(ramanchada2.misc.types.pydantic_base_model.PydBaseModel): + + + +
+ + 
13class PeakModel(PydBaseModel):
+14    amplitude: PositiveFloat
+15    position: float
+16    sigma: PositiveFloat
+17    skew: float = 0
+18
+19    @property
+20    def fwhm(self) -> float:
+21        return self.sigma * 2.355
+22
+23    @property
+24    def lwhm(self) -> float:
+25        """
+26        Left width at half maximum.
+27        """
+28        return self.fwhm*(1-self.skew)/2
+29
+30    @property
+31    def rwhm(self) -> float:
+32        """
+33        Right width at half maximum.
+34        """
+35        return self.fwhm*(1+self.skew)/2
+36
+37    def serialize(self):
+38        return self.model_dump_json()
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ amplitude: typing.Annotated[float, Gt(gt=0)] + + +
+ + + + +
+
+
+ position: float + + +
+ + + + +
+
+
+ sigma: typing.Annotated[float, Gt(gt=0)] + + +
+ + + + +
+
+
+ skew: float + + +
+ + + + +
+
+ +
+ fwhm: float + + + +
+ + 
19    @property
+20    def fwhm(self) -> float:
+21        return self.sigma * 2.355
+
+ + + + +
+
+ +
+ lwhm: float + + + +
+ + 
23    @property
+24    def lwhm(self) -> float:
+25        """
+26        Left width at half maximum.
+27        """
+28        return self.fwhm*(1-self.skew)/2
+
+ + +

Left width at half maximum.

+
+ + +
+
+ +
+ rwhm: float + + + +
+ + 
30    @property
+31    def rwhm(self) -> float:
+32        """
+33        Right width at half maximum.
+34        """
+35        return self.fwhm*(1+self.skew)/2
+
+ + +

Right width at half maximum.

+
+ + +
+
+ +
+ + def + serialize(self): + + + +
+ + 
37    def serialize(self):
+38        return self.model_dump_json()
+
+ + + + +
+
+
+ model_config = +{'arbitrary_types_allowed': True} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'amplitude': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'position': FieldInfo(annotation=float, required=True), 'sigma': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'skew': FieldInfo(annotation=float, required=False, default=0)} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+ + class + PeakCandidateMultiModel(ramanchada2.misc.types.pydantic_base_model.PydBaseModel, ramanchada2.misc.plottable.Plottable): + + + +
+ + 
 41class PeakCandidateMultiModel(PydBaseModel, Plottable):
+ 42    peaks: List[PeakModel]
+ 43    base_slope: float = 0
+ 44    base_intercept: float = 0
+ 45    boundaries: Tuple[float, float]
+ 46
+ 47    def plot_params_baseline(self):
+ 48        x = np.array(self.boundaries)
+ 49        return (x, self.base_slope*x + self.base_intercept)
+ 50
+ 51    def plot_params_errorbar(self):
+ 52        x = self.positions
+ 53        xleft = self.lwhms
+ 54        xright = self.rwhms
+ 55        y_err = (self.amplitudes/2)
+ 56        y = y_err + self.peak_bases
+ 57        return x, y, y_err, (xleft, xright)
+ 58
+ 59    @property
+ 60    def positions(self):
+ 61        return np.array([p.position for p in self.peaks])
+ 62
+ 63    @property
+ 64    def sigmas(self):
+ 65        return np.array([p.sigma for p in self.peaks])
+ 66
+ 67    @property
+ 68    def fwhms(self):
+ 69        return np.array([p.fwhm for p in self.peaks])
+ 70
+ 71    @property
+ 72    def lwhms(self):
+ 73        return np.array([p.lwhm for p in self.peaks])
+ 74
+ 75    @property
+ 76    def rwhms(self):
+ 77        return np.array([p.rwhm for p in self.peaks])
+ 78
+ 79    @property
+ 80    def skews(self):
+ 81        return np.array([p.skew for p in self.peaks])
+ 82
+ 83    @property
+ 84    def amplitudes(self):
+ 85        return np.array([p.amplitude for p in self.peaks])
+ 86
+ 87    @property
+ 88    def bases(self):
+ 89        return self.positions * self.base_slope + self.base_intercept
+ 90
+ 91    def get_pos_ampl_dict(self):
+ 92        return dict(zip(self.positions, self.amplitudes))
+ 93
+ 94    def get_ampl_pos_fwhm(self):
+ 95        return np.array([
+ 96            self.amplitudes,
+ 97            self.positions,
+ 98            self.fwhms]).T
+ 99
+100    @property
+101    def peak_bases(self):
+102        return self.positions * self.base_slope + self.base_intercept
+103
+104    def _plot(self, ax, *args, label=" ", **kwargs):
+105        ax.errorbar(*self.plot_params_errorbar(), label=label)
+106        ax.plot(*self.plot_params_baseline())
+107
+108    def serialize(self):
+109        return self.model_dump_json()
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ peaks: List[PeakModel] + + +
+ + + + +
+
+
+ base_slope: float + + +
+ + + + +
+
+
+ base_intercept: float + + +
+ + + + +
+
+
+ boundaries: Tuple[float, float] + + +
+ + + + +
+
+ +
+ + def + plot_params_baseline(self): + + + +
+ + 
47    def plot_params_baseline(self):
+48        x = np.array(self.boundaries)
+49        return (x, self.base_slope*x + self.base_intercept)
+
+ + + + +
+
+ +
+ + def + plot_params_errorbar(self): + + + +
+ + 
51    def plot_params_errorbar(self):
+52        x = self.positions
+53        xleft = self.lwhms
+54        xright = self.rwhms
+55        y_err = (self.amplitudes/2)
+56        y = y_err + self.peak_bases
+57        return x, y, y_err, (xleft, xright)
+
+ + + + +
+
+ +
+ positions + + + +
+ + 
59    @property
+60    def positions(self):
+61        return np.array([p.position for p in self.peaks])
+
+ + + + +
+
+ +
+ sigmas + + + +
+ + 
63    @property
+64    def sigmas(self):
+65        return np.array([p.sigma for p in self.peaks])
+
+ + + + +
+
+ +
+ fwhms + + + +
+ + 
67    @property
+68    def fwhms(self):
+69        return np.array([p.fwhm for p in self.peaks])
+
+ + + + +
+
+ +
+ lwhms + + + +
+ + 
71    @property
+72    def lwhms(self):
+73        return np.array([p.lwhm for p in self.peaks])
+
+ + + + +
+
+ +
+ rwhms + + + +
+ + 
75    @property
+76    def rwhms(self):
+77        return np.array([p.rwhm for p in self.peaks])
+
+ + + + +
+
+ +
+ skews + + + +
+ + 
79    @property
+80    def skews(self):
+81        return np.array([p.skew for p in self.peaks])
+
+ + + + +
+
+ +
+ amplitudes + + + +
+ + 
83    @property
+84    def amplitudes(self):
+85        return np.array([p.amplitude for p in self.peaks])
+
+ + + + +
+
+ +
+ bases + + + +
+ + 
87    @property
+88    def bases(self):
+89        return self.positions * self.base_slope + self.base_intercept
+
+ + + + +
+
+ +
+ + def + get_pos_ampl_dict(self): + + + +
+ + 
91    def get_pos_ampl_dict(self):
+92        return dict(zip(self.positions, self.amplitudes))
+
+ + + + +
+
+ +
+ + def + get_ampl_pos_fwhm(self): + + + +
+ + 
94    def get_ampl_pos_fwhm(self):
+95        return np.array([
+96            self.amplitudes,
+97            self.positions,
+98            self.fwhms]).T
+
+ + + + +
+
+ +
+ peak_bases + + + +
+ + 
100    @property
+101    def peak_bases(self):
+102        return self.positions * self.base_slope + self.base_intercept
+
+ + + + +
+
+ +
+ + def + serialize(self): + + + +
+ + 
108    def serialize(self):
+109        return self.model_dump_json()
+
+ + + + +
+
+
+ model_config = +{'arbitrary_types_allowed': True} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'peaks': FieldInfo(annotation=List[PeakModel], required=True), 'base_slope': FieldInfo(annotation=float, required=False, default=0), 'base_intercept': FieldInfo(annotation=float, required=False, default=0), 'boundaries': FieldInfo(annotation=Tuple[float, float], required=True)} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
Inherited Members
+
+ +
+
+
+
+ +
+ + class + ListPeakCandidateMultiModel(pydantic.main.BaseModel, typing.Generic[~RootModelRootType]): + + + +
+ + 
112class ListPeakCandidateMultiModel(PydRootModel, Plottable):
+113    root: List[PeakCandidateMultiModel]
+114
+115    def get_ampl_pos_fwhm(self):
+116        return np.concatenate([cands.get_ampl_pos_fwhm() for cands in self.root])
+117
+118    def get_pos_ampl_dict(self):
+119        return {k: v for cands in self.root for k, v in cands.get_pos_ampl_dict().items()}
+120
+121    def _plot(self, ax, *args, label=" ", **kwargs):
+122        for i, gr in enumerate(self.root):
+123            gr.plot(ax=ax, *args, label=f'{label}_{i}', **kwargs)
+124
+125    def __getitem__(self, key) -> PeakCandidateMultiModel:
+126        return self.root[key]
+127
+128    def __iter__(self):
+129        return iter(self.root)
+130
+131    def __len__(self):
+132        return len(self.root)
+133
+134    def serialize(self):
+135        return self.model_dump_json()
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

+ +

A Pydantic BaseModel for the root object of the model.

+ +
Attributes:
+ +
    +
  • root: The root object of the model.
    • +
  • __pydantic_root_model__: Whether the model is a RootModel.
    • +
  • __pydantic_private__: Private fields in the model.
    • +
  • __pydantic_extra__: Extra fields in the model.
    • +
+
+ + +
+
+ root: List[PeakCandidateMultiModel] + + +
+ + + + +
+
+ +
+ + def + get_ampl_pos_fwhm(self): + + + +
+ + 
115    def get_ampl_pos_fwhm(self):
+116        return np.concatenate([cands.get_ampl_pos_fwhm() for cands in self.root])
+
+ + + + +
+
+ +
+ + def + get_pos_ampl_dict(self): + + + +
+ + 
118    def get_pos_ampl_dict(self):
+119        return {k: v for cands in self.root for k, v in cands.get_pos_ampl_dict().items()}
+
+ + + + +
+
+ +
+ + def + serialize(self): + + + +
+ + 
134    def serialize(self):
+135        return self.model_dump_json()
+
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/positive_not_multiple.html b/ramanchada2/misc/types/positive_not_multiple.html new file mode 100644 index 00000000..d3c8423c --- /dev/null +++ b/ramanchada2/misc/types/positive_not_multiple.html @@ -0,0 +1,287 @@ + + + + + + + ramanchada2.misc.types.positive_not_multiple API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.positive_not_multiple

+ + + + + + + 
1from pydantic import PositiveInt
+2
+3# For more info check https://github.com/pydantic/pydantic/issues/10111
+4# PositiveOddInt = typing.Annotated[int,
+5#                                   annotated_types.Ge(0),
+6#                                   annotated_types.Not(annotated_types.MultipleOf(2))]
+7
+8# FIXME This is a quickfix
+9PositiveOddInt = PositiveInt
+
+ + +
+
+
+ PositiveOddInt = +typing.Annotated[int, Gt(gt=0)] + + +
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/pydantic_base_model.html b/ramanchada2/misc/types/pydantic_base_model.html new file mode 100644 index 00000000..e748b357 --- /dev/null +++ b/ramanchada2/misc/types/pydantic_base_model.html @@ -0,0 +1,491 @@ + + + + + + + ramanchada2.misc.types.pydantic_base_model API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.pydantic_base_model

+ + + + + + + 
 1from abc import ABC, abstractmethod
+ 2
+ 3from pydantic import BaseModel, RootModel
+ 4
+ 5
+ 6class PydBaseModel(BaseModel, ABC):
+ 7    model_config = dict(
+ 8        arbitrary_types_allowed=True
+ 9    )
+10
+11    @abstractmethod
+12    def serialize(self):
+13        pass
+14
+15
+16class PydRootModel(RootModel, ABC):
+17    model_config = dict(
+18        arbitrary_types_allowed=True
+19    )
+20
+21    @abstractmethod
+22    def serialize(self):
+23        pass
+
+ + +
+
+ +
+ + class + PydBaseModel(pydantic.main.BaseModel, abc.ABC): + + + +
+ + 
 7class PydBaseModel(BaseModel, ABC):
+ 8    model_config = dict(
+ 9        arbitrary_types_allowed=True
+10    )
+11
+12    @abstractmethod
+13    def serialize(self):
+14        pass
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ model_config = +{'arbitrary_types_allowed': True} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+ +
+
@abstractmethod
+ + def + serialize(self): + + + +
+ + 
12    @abstractmethod
+13    def serialize(self):
+14        pass
+
+ + + + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = +{} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+ + class + PydRootModel(pydantic.main.BaseModel, typing.Generic[~RootModelRootType]): + + + +
+ + 
17class PydRootModel(RootModel, ABC):
+18    model_config = dict(
+19        arbitrary_types_allowed=True
+20    )
+21
+22    @abstractmethod
+23    def serialize(self):
+24        pass
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

+ +

A Pydantic BaseModel for the root object of the model.

+ +
Attributes:
+ +
    +
  • root: The root object of the model.
    • +
  • __pydantic_root_model__: Whether the model is a RootModel.
    • +
  • __pydantic_private__: Private fields in the model.
    • +
  • __pydantic_extra__: Extra fields in the model.
    • +
+
+ + +
+ +
+
@abstractmethod
+ + def + serialize(self): + + + +
+ + 
22    @abstractmethod
+23    def serialize(self):
+24        pass
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/spectrum.html b/ramanchada2/misc/types/spectrum.html new file mode 100644 index 00000000..69f21dc4 --- /dev/null +++ b/ramanchada2/misc/types/spectrum.html @@ -0,0 +1,266 @@ + + + + + + + ramanchada2.misc.types.spectrum API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.spectrum

+ + + + + + + 
1from .applied_processings import SpeProcessingListModel, SpeProcessingModel
+2from .metadata import SpeMetadataFieldTyping, SpeMetadataModel
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/spectrum/applied_processings.html b/ramanchada2/misc/types/spectrum/applied_processings.html new file mode 100644 index 00000000..d6aa792c --- /dev/null +++ b/ramanchada2/misc/types/spectrum/applied_processings.html @@ -0,0 +1,833 @@ + + + + + + + ramanchada2.misc.types.spectrum.applied_processings API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.spectrum.applied_processings

+ + + + + + + 
 1from __future__ import annotations
+ 2
+ 3from typing import Dict, List
+ 4
+ 5from pydantic import (BaseModel, Field, RootModel, field_validator,
+ 6                      validate_call)
+ 7
+ 8
+ 9class SpeProcessingModel(BaseModel):
+10    proc: str = Field(...)
+11    args: List = list()
+12    kwargs: Dict = dict()
+13
+14    @property
+15    def is_constructor(self):
+16        from ramanchada2.misc.spectrum_deco.dynamically_added import \
+17            dynamically_added_constructors
+18        return self.proc in dynamically_added_constructors
+19
+20    @field_validator('proc', mode='before')
+21    @validate_call
+22    def check_proc(cls, val: str):
+23        from ramanchada2.misc.spectrum_deco.dynamically_added import (
+24            dynamically_added_constructors, dynamically_added_filters)
+25        if val in dynamically_added_filters:
+26            return val
+27        if val in dynamically_added_constructors:
+28            return val
+29        raise ValueError(f'processing {val} not supported')
+30
+31
+32class SpeProcessingListModel(RootModel):
+33    root: List[SpeProcessingModel]
+34
+35    def __len__(self):
+36        return len(self.root)
+37
+38    def append(self, proc, args=[], kwargs={}):
+39        self.root.append(SpeProcessingModel(proc=proc, args=args, kwargs=kwargs))
+40
+41    @validate_call
+42    def extend_left(self, proc_list: List[SpeProcessingModel]):
+43        self.root = proc_list + self.root
+44
+45    def pop(self):
+46        return self.root.pop()
+47
+48    def clear(self):
+49        return self.root.clear()
+50
+51    def assign(self, *args, **kwargs):
+52        self.clear()
+53        self.append(*args, **kwargs)
+54
+55    def _string_list(self):
+56        ret = list()
+57        for elem in self.root:
+58            args = [f'{repr(i)}' for i in elem.args]
+59            kwargs = [f'{k}={repr(v)}' for k, v in elem.kwargs.items()]
+60            comb = ', '.join(args + kwargs)
+61            ret.append(f'{elem.proc}({comb})')
+62        return ret
+63
+64    def repr(self):
+65        return '.'.join(self._string_list())
+66
+67    def cache_path(self):
+68        return '/'.join([
+69            i.replace(' ', '').replace('/', '_')
+70            for i in self._string_list()])
+71
+72    def to_list(self):
+73        return [i.model_dump() for i in self.root]
+
+ + +
+
+ +
+ + class + SpeProcessingModel(pydantic.main.BaseModel): + + + +
+ + 
10class SpeProcessingModel(BaseModel):
+11    proc: str = Field(...)
+12    args: List = list()
+13    kwargs: Dict = dict()
+14
+15    @property
+16    def is_constructor(self):
+17        from ramanchada2.misc.spectrum_deco.dynamically_added import \
+18            dynamically_added_constructors
+19        return self.proc in dynamically_added_constructors
+20
+21    @field_validator('proc', mode='before')
+22    @validate_call
+23    def check_proc(cls, val: str):
+24        from ramanchada2.misc.spectrum_deco.dynamically_added import (
+25            dynamically_added_constructors, dynamically_added_filters)
+26        if val in dynamically_added_filters:
+27            return val
+28        if val in dynamically_added_constructors:
+29            return val
+30        raise ValueError(f'processing {val} not supported')
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ proc: str + + +
+ + + + +
+
+
+ args: List + + +
+ + + + +
+
+
+ kwargs: Dict + + +
+ + + + +
+
+ +
+ is_constructor + + + +
+ + 
15    @property
+16    def is_constructor(self):
+17        from ramanchada2.misc.spectrum_deco.dynamically_added import \
+18            dynamically_added_constructors
+19        return self.proc in dynamically_added_constructors
+
+ + + + +
+
+ +
+
@field_validator('proc', mode='before')
+
@validate_call
+ + def + check_proc(cls, val: str): + + + +
+ + 
21    @field_validator('proc', mode='before')
+22    @validate_call
+23    def check_proc(cls, val: str):
+24        from ramanchada2.misc.spectrum_deco.dynamically_added import (
+25            dynamically_added_constructors, dynamically_added_filters)
+26        if val in dynamically_added_filters:
+27            return val
+28        if val in dynamically_added_constructors:
+29            return val
+30        raise ValueError(f'processing {val} not supported')
+
+ + + + +
+
+
+ model_config: ClassVar[pydantic.config.ConfigDict] = +{} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'proc': FieldInfo(annotation=str, required=True), 'args': FieldInfo(annotation=List, required=False, default=[]), 'kwargs': FieldInfo(annotation=Dict, required=False, default={})} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+ + class + SpeProcessingListModel(pydantic.main.BaseModel, typing.Generic[~RootModelRootType]): + + + +
+ + 
33class SpeProcessingListModel(RootModel):
+34    root: List[SpeProcessingModel]
+35
+36    def __len__(self):
+37        return len(self.root)
+38
+39    def append(self, proc, args=[], kwargs={}):
+40        self.root.append(SpeProcessingModel(proc=proc, args=args, kwargs=kwargs))
+41
+42    @validate_call
+43    def extend_left(self, proc_list: List[SpeProcessingModel]):
+44        self.root = proc_list + self.root
+45
+46    def pop(self):
+47        return self.root.pop()
+48
+49    def clear(self):
+50        return self.root.clear()
+51
+52    def assign(self, *args, **kwargs):
+53        self.clear()
+54        self.append(*args, **kwargs)
+55
+56    def _string_list(self):
+57        ret = list()
+58        for elem in self.root:
+59            args = [f'{repr(i)}' for i in elem.args]
+60            kwargs = [f'{k}={repr(v)}' for k, v in elem.kwargs.items()]
+61            comb = ', '.join(args + kwargs)
+62            ret.append(f'{elem.proc}({comb})')
+63        return ret
+64
+65    def repr(self):
+66        return '.'.join(self._string_list())
+67
+68    def cache_path(self):
+69        return '/'.join([
+70            i.replace(' ', '').replace('/', '_')
+71            for i in self._string_list()])
+72
+73    def to_list(self):
+74        return [i.model_dump() for i in self.root]
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

+ +

A Pydantic BaseModel for the root object of the model.

+ +
Attributes:
+ +
    +
  • root: The root object of the model.
    • +
  • __pydantic_root_model__: Whether the model is a RootModel.
    • +
  • __pydantic_private__: Private fields in the model.
    • +
  • __pydantic_extra__: Extra fields in the model.
    • +
+
+ + +
+
+ root: List[SpeProcessingModel] + + +
+ + + + +
+
+ +
+ + def + append(self, proc, args=[], kwargs={}): + + + +
+ + 
39    def append(self, proc, args=[], kwargs={}):
+40        self.root.append(SpeProcessingModel(proc=proc, args=args, kwargs=kwargs))
+
+ + + + +
+
+ +
+
@validate_call
+ + def + extend_left( self, proc_list: List[SpeProcessingModel]): + + + +
+ + 
42    @validate_call
+43    def extend_left(self, proc_list: List[SpeProcessingModel]):
+44        self.root = proc_list + self.root
+
+ + + + +
+
+ +
+ + def + pop(self): + + + +
+ + 
46    def pop(self):
+47        return self.root.pop()
+
+ + + + +
+
+ +
+ + def + clear(self): + + + +
+ + 
49    def clear(self):
+50        return self.root.clear()
+
+ + + + +
+
+ +
+ + def + assign(self, *args, **kwargs): + + + +
+ + 
52    def assign(self, *args, **kwargs):
+53        self.clear()
+54        self.append(*args, **kwargs)
+
+ + + + +
+
+ +
+ + def + repr(self): + + + +
+ + 
65    def repr(self):
+66        return '.'.join(self._string_list())
+
+ + + + +
+
+ +
+ + def + cache_path(self): + + + +
+ + 
68    def cache_path(self):
+69        return '/'.join([
+70            i.replace(' ', '').replace('/', '_')
+71            for i in self._string_list()])
+
+ + + + +
+
+ +
+ + def + to_list(self): + + + +
+ + 
73    def to_list(self):
+74        return [i.model_dump() for i in self.root]
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/types/spectrum/metadata.html b/ramanchada2/misc/types/spectrum/metadata.html new file mode 100644 index 00000000..004f536f --- /dev/null +++ b/ramanchada2/misc/types/spectrum/metadata.html @@ -0,0 +1,678 @@ + + + + + + + ramanchada2.misc.types.spectrum.metadata API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.types.spectrum.metadata

+ + + + + + + 
 1import datetime
+ 2import json
+ 3from typing import Any, Dict, List, Union
+ 4
+ 5import numpy as np
+ 6import numpy.typing as npt
+ 7from pydantic import Field, StrictBool, StrictInt, StrictStr, field_validator
+ 8
+ 9from ..pydantic_base_model import PydBaseModel, PydRootModel
+10
+11SpeMetadataFieldTyping = Union[
+12    npt.NDArray,
+13    StrictBool,
+14    StrictInt, float,
+15    datetime.datetime,
+16    List[Any], Dict[str, Any],
+17    StrictStr, None]
+18
+19
+20class SpeMetadataFieldModel(PydRootModel):
+21    root: SpeMetadataFieldTyping = Field(union_mode='left_to_right')
+22
+23    @field_validator('root', mode='before')
+24    def pre_validate(cls, val):
+25        if isinstance(val, np.ndarray):
+26            return val
+27        if isinstance(val, str):
+28            if val.startswith('ramanchada2_model@'):
+29                # The format is:
+30                # ramanchada2_model@ModelName#<DATA>
+31                pos_at = val.index('@')
+32                pos_hash = val.index('#')
+33                model_name = val[pos_at+1:pos_hash]
+34                from ramanchada2.misc import types
+35                model = getattr(types, model_name)
+36                return model.model_validate(val[pos_hash+1:])
+37            if (val.startswith('[') and val.endswith(']') or
+38               val.startswith('{') and val.endswith('}')):
+39                return json.loads(val.replace("'", '"').replace(r'b"', '"'))
+40        return val
+41
+42    def serialize(self):
+43        if isinstance(self.root, list) or isinstance(self.root, dict):
+44            return json.dumps(self.root)
+45        if isinstance(self.root, PydBaseModel):
+46            return f'ramanchada2_model@{type(self.root).__name__}#' + self.json()
+47        if isinstance(self.root, datetime.datetime):
+48            return self.root.isoformat()
+49        if isinstance(self.root, PydBaseModel):
+50            return self.root.serialize()
+51        return self.root
+52
+53
+54class SpeMetadataModel(PydRootModel):
+55    root: Dict[str, SpeMetadataFieldModel]
+56
+57    @field_validator('root', mode='before')
+58    def pre_validate(cls, val):
+59        if val is None or val == '':
+60            val = {}
+61        elif isinstance(val, list):
+62            val = {'%d' % k: v for k, v in enumerate(val)}
+63        return val
+64
+65    def __str__(self):
+66        return str(self.serialize())
+67
+68    def serialize(self):
+69        return {k: v.serialize() for k, v in sorted(self.root.items())}
+70
+71    def __getitem__(self, key: str) -> SpeMetadataFieldTyping:
+72        return self.root[key].root
+73
+74    def _update(self, val: Dict):
+75        self.root.update(self.model_validate(val).root)
+76
+77    def _del_key(self, key: str):
+78        del self.root[key]
+79
+80    def _flush(self):
+81        self.root = {}
+82
+83    def get_all_keys(self) -> list[str]:
+84        """
+85        Returns a list of all keys in the metadata model.
+86        """
+87        return list(self.root.keys())
+
+ + +
+
+
+ SpeMetadataFieldTyping = + + typing.Union[numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], typing.Annotated[bool, Strict(strict=True)], typing.Annotated[int, Strict(strict=True)], float, datetime.datetime, typing.List[typing.Any], typing.Dict[str, typing.Any], typing.Annotated[str, Strict(strict=True)], NoneType] + + +
+ + + + +
+
+ +
+ + class + SpeMetadataFieldModel(pydantic.main.BaseModel, typing.Generic[~RootModelRootType]): + + + +
+ + 
21class SpeMetadataFieldModel(PydRootModel):
+22    root: SpeMetadataFieldTyping = Field(union_mode='left_to_right')
+23
+24    @field_validator('root', mode='before')
+25    def pre_validate(cls, val):
+26        if isinstance(val, np.ndarray):
+27            return val
+28        if isinstance(val, str):
+29            if val.startswith('ramanchada2_model@'):
+30                # The format is:
+31                # ramanchada2_model@ModelName#<DATA>
+32                pos_at = val.index('@')
+33                pos_hash = val.index('#')
+34                model_name = val[pos_at+1:pos_hash]
+35                from ramanchada2.misc import types
+36                model = getattr(types, model_name)
+37                return model.model_validate(val[pos_hash+1:])
+38            if (val.startswith('[') and val.endswith(']') or
+39               val.startswith('{') and val.endswith('}')):
+40                return json.loads(val.replace("'", '"').replace(r'b"', '"'))
+41        return val
+42
+43    def serialize(self):
+44        if isinstance(self.root, list) or isinstance(self.root, dict):
+45            return json.dumps(self.root)
+46        if isinstance(self.root, PydBaseModel):
+47            return f'ramanchada2_model@{type(self.root).__name__}#' + self.json()
+48        if isinstance(self.root, datetime.datetime):
+49            return self.root.isoformat()
+50        if isinstance(self.root, PydBaseModel):
+51            return self.root.serialize()
+52        return self.root
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

+ +

A Pydantic BaseModel for the root object of the model.

+ +
Attributes:
+ +
    +
  • root: The root object of the model.
    • +
  • __pydantic_root_model__: Whether the model is a RootModel.
    • +
  • __pydantic_private__: Private fields in the model.
    • +
  • __pydantic_extra__: Extra fields in the model.
    • +
+
+ + +
+
+ root: Union[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Annotated[bool, Strict(strict=True)], Annotated[int, Strict(strict=True)], float, datetime.datetime, List[Any], Dict[str, Any], Annotated[str, Strict(strict=True)], NoneType] + + +
+ + + + +
+
+ +
+
@field_validator('root', mode='before')
+ + def + pre_validate(cls, val): + + + +
+ + 
24    @field_validator('root', mode='before')
+25    def pre_validate(cls, val):
+26        if isinstance(val, np.ndarray):
+27            return val
+28        if isinstance(val, str):
+29            if val.startswith('ramanchada2_model@'):
+30                # The format is:
+31                # ramanchada2_model@ModelName#<DATA>
+32                pos_at = val.index('@')
+33                pos_hash = val.index('#')
+34                model_name = val[pos_at+1:pos_hash]
+35                from ramanchada2.misc import types
+36                model = getattr(types, model_name)
+37                return model.model_validate(val[pos_hash+1:])
+38            if (val.startswith('[') and val.endswith(']') or
+39               val.startswith('{') and val.endswith('}')):
+40                return json.loads(val.replace("'", '"').replace(r'b"', '"'))
+41        return val
+
+ + + + +
+
+ +
+ + def + serialize(self): + + + +
+ + 
43    def serialize(self):
+44        if isinstance(self.root, list) or isinstance(self.root, dict):
+45            return json.dumps(self.root)
+46        if isinstance(self.root, PydBaseModel):
+47            return f'ramanchada2_model@{type(self.root).__name__}#' + self.json()
+48        if isinstance(self.root, datetime.datetime):
+49            return self.root.isoformat()
+50        if isinstance(self.root, PydBaseModel):
+51            return self.root.serialize()
+52        return self.root
+
+ + + + +
+
+
+ +
+ + class + SpeMetadataModel(pydantic.main.BaseModel, typing.Generic[~RootModelRootType]): + + + +
+ + 
55class SpeMetadataModel(PydRootModel):
+56    root: Dict[str, SpeMetadataFieldModel]
+57
+58    @field_validator('root', mode='before')
+59    def pre_validate(cls, val):
+60        if val is None or val == '':
+61            val = {}
+62        elif isinstance(val, list):
+63            val = {'%d' % k: v for k, v in enumerate(val)}
+64        return val
+65
+66    def __str__(self):
+67        return str(self.serialize())
+68
+69    def serialize(self):
+70        return {k: v.serialize() for k, v in sorted(self.root.items())}
+71
+72    def __getitem__(self, key: str) -> SpeMetadataFieldTyping:
+73        return self.root[key].root
+74
+75    def _update(self, val: Dict):
+76        self.root.update(self.model_validate(val).root)
+77
+78    def _del_key(self, key: str):
+79        del self.root[key]
+80
+81    def _flush(self):
+82        self.root = {}
+83
+84    def get_all_keys(self) -> list[str]:
+85        """
+86        Returns a list of all keys in the metadata model.
+87        """
+88        return list(self.root.keys())
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

+ +

A Pydantic BaseModel for the root object of the model.

+ +
Attributes:
+ +
    +
  • root: The root object of the model.
    • +
  • __pydantic_root_model__: Whether the model is a RootModel.
    • +
  • __pydantic_private__: Private fields in the model.
    • +
  • __pydantic_extra__: Extra fields in the model.
    • +
+
+ + +
+
+ root: Dict[str, SpeMetadataFieldModel] + + +
+ + + + +
+
+ +
+
@field_validator('root', mode='before')
+ + def + pre_validate(cls, val): + + + +
+ + 
58    @field_validator('root', mode='before')
+59    def pre_validate(cls, val):
+60        if val is None or val == '':
+61            val = {}
+62        elif isinstance(val, list):
+63            val = {'%d' % k: v for k, v in enumerate(val)}
+64        return val
+
+ + + + +
+
+ +
+ + def + serialize(self): + + + +
+ + 
69    def serialize(self):
+70        return {k: v.serialize() for k, v in sorted(self.root.items())}
+
+ + + + +
+
+ +
+ + def + get_all_keys(self) -> list[str]: + + + +
+ + 
84    def get_all_keys(self) -> list[str]:
+85        """
+86        Returns a list of all keys in the metadata model.
+87        """
+88        return list(self.root.keys())
+
+ + +

Returns a list of all keys in the metadata model.

+
+ + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/utils.html b/ramanchada2/misc/utils.html new file mode 100644 index 00000000..5b936d7a --- /dev/null +++ b/ramanchada2/misc/utils.html @@ -0,0 +1,289 @@ + + + + + + + ramanchada2.misc.utils API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.utils

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from .ramanshift_to_wavelength import (abs_nm_to_shift_cm_1,
+ 4                                       shift_cm_1_to_abs_nm,
+ 5                                       abs_nm_to_shift_cm_1_dict,
+ 6                                       shift_cm_1_to_abs_nm_dict,
+ 7                                       laser_wl_nm,
+ 8                                       )
+ 9
+10from .svd import (svd_inverse,
+11                  svd_solve,
+12                  )
+13
+14from .argmin2d import (argmin2d,
+15                       find_closest_pairs,
+16                       find_closest_pairs_idx,
+17                       align, align_shift
+18                       )
+19
+20from .matchsets import (
+21                       match_peaks,
+22                       match_peaks_cluster
+23                       )
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/utils/argmin2d.html b/ramanchada2/misc/utils/argmin2d.html new file mode 100644 index 00000000..a26b8db3 --- /dev/null +++ b/ramanchada2/misc/utils/argmin2d.html @@ -0,0 +1,579 @@ + + + + + + + ramanchada2.misc.utils.argmin2d API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.utils.argmin2d

+ + + + + + + 
  1from typing import List, Optional, Union
+  2
+  3import numpy as np
+  4import numpy.typing as npt
+  5from pydantic import PositiveInt, validate_call
+  6from scipy import linalg
+  7
+  8
+  9def argmin2d(A, median_limit: Optional[float] = None):
+ 10    if median_limit is None:
+ 11        median_limit = 10
+ 12    ymin_idx = np.argmin(A, axis=0)
+ 13    xmin_idx = np.argmin(A, axis=1)
+ 14    x_idx = np.unique(xmin_idx[xmin_idx[ymin_idx[xmin_idx]] == xmin_idx])
+ 15    y_idx = np.unique(ymin_idx[ymin_idx[xmin_idx[ymin_idx]] == ymin_idx])
+ 16    dist = A[y_idx, x_idx]
+ 17    filt = dist < np.median(dist) * median_limit
+ 18    x_idx = x_idx[filt]
+ 19    y_idx = y_idx[filt]
+ 20    matches = np.stack([y_idx, x_idx]).T
+ 21    return matches
+ 22
+ 23
+ 24def find_closest_pairs_idx(x, y, **kw_args):
+ 25    outer_dif = np.abs(np.subtract.outer(x, y))
+ 26    return argmin2d(outer_dif, **kw_args).T
+ 27
+ 28
+ 29def find_closest_pairs(x, y, **kw_args):
+ 30    x_idx, y_idx = find_closest_pairs_idx(x, y, **kw_args)
+ 31    return x[x_idx], y[y_idx]
+ 32
+ 33
+ 34@validate_call(config=dict(arbitrary_types_allowed=True))
+ 35def align(x, y,
+ 36          p0: Union[List[float], npt.NDArray] = [0, 1, 0, 0],
+ 37          func=lambda x, a0, a1, a2, a3: (a0*np.ones_like(x), a1*x, a2*x**2/1, a3*(x/1000)**3),
+ 38          max_iter: PositiveInt = 1000,
+ 39          **kw_args):
+ 40    """
+ 41    Iteratively finds best match between x and y and evaluates the x scaling parameters.
+ 42
+ 43    Finds best parameters *p that minimise L2 distance between scaled x and original y
+ 44    min((lambda(x, *p)-y)**2 | *p)
+ 45
+ 46    Args:
+ 47        x (ArrayLike[float]): values that need to match the reference
+ 48        y (ArrayLike[float]): reference values
+ 49        p0 (Union[List[float], npt.NDArray], optional): initial values for the parameters `p`.
+ 50            Defaults to [0, 1, 0, 0].
+ 51        func (Callable, optional): Objective function to minimize. Returns list penalties
+ 52            calculated for each `p`. The total objective function is sum of the elements.
+ 53            Defaults to polynomial of 3-th degree.
+ 54        max_iter (PositiveInt, optional): max number of iterations. Defaults to 1000.
+ 55
+ 56    Returns:
+ 57        ArrayLike[float]: array of parameters `p` that minimize the objective funciton
+ 58    """
+ 59
+ 60    if isinstance(p0, list):
+ 61        p = np.array(p0)
+ 62    else:
+ 63        p = p0
+ 64    loss = np.inf
+ 65    cur_x = x
+ 66    for it in range(max_iter):
+ 67        cur_x = np.sum(func(x, *p), axis=0)
+ 68        x_idx, y_idx = find_closest_pairs_idx(cur_x, y, **kw_args)
+ 69        x_match, y_match = x[x_idx], y[y_idx]
+ 70        p_bak = p
+ 71        obj_mat = np.stack(func(x_match, *np.ones_like(p)), axis=1)
+ 72        p, *_ = linalg.lstsq(obj_mat, y_match, cond=1e-8)
+ 73        loss_bak = loss
+ 74        loss = np.sum((x_match-y_match)**2)/len(x_match)**2
+ 75        if np.allclose(p, p_bak):
+ 76            break
+ 77        if loss > loss_bak:
+ 78            pass
+ 79            return p_bak
+ 80    return p
+ 81
+ 82
+ 83@validate_call(config=dict(arbitrary_types_allowed=True))
+ 84def align_shift(x, y,
+ 85                p0: float = 0,
+ 86                max_iter: PositiveInt = 1000,
+ 87                **kw_args):
+ 88    loss = np.inf
+ 89    cur_x = x
+ 90    p = p0
+ 91    for it in range(max_iter):
+ 92        cur_x = x + p
+ 93        x_idx, y_idx = find_closest_pairs_idx(cur_x, y, **kw_args)
+ 94        x_match, y_match = x[x_idx], y[y_idx]
+ 95        p_bak = p
+ 96        p = np.mean(y_match-x_match)
+ 97        loss_bak = loss
+ 98        loss = np.sum((y_match-x_match)**2)
+ 99        if np.allclose(p, p_bak):
+100            break
+101        if loss > loss_bak:
+102            return p_bak
+103    return p
+
+ + +
+
+ +
+ + def + argmin2d(A, median_limit: Optional[float] = None): + + + +
+ + 
10def argmin2d(A, median_limit: Optional[float] = None):
+11    if median_limit is None:
+12        median_limit = 10
+13    ymin_idx = np.argmin(A, axis=0)
+14    xmin_idx = np.argmin(A, axis=1)
+15    x_idx = np.unique(xmin_idx[xmin_idx[ymin_idx[xmin_idx]] == xmin_idx])
+16    y_idx = np.unique(ymin_idx[ymin_idx[xmin_idx[ymin_idx]] == ymin_idx])
+17    dist = A[y_idx, x_idx]
+18    filt = dist < np.median(dist) * median_limit
+19    x_idx = x_idx[filt]
+20    y_idx = y_idx[filt]
+21    matches = np.stack([y_idx, x_idx]).T
+22    return matches
+
+ + + + +
+
+ +
+ + def + find_closest_pairs_idx(x, y, **kw_args): + + + +
+ + 
25def find_closest_pairs_idx(x, y, **kw_args):
+26    outer_dif = np.abs(np.subtract.outer(x, y))
+27    return argmin2d(outer_dif, **kw_args).T
+
+ + + + +
+
+ +
+ + def + find_closest_pairs(x, y, **kw_args): + + + +
+ + 
30def find_closest_pairs(x, y, **kw_args):
+31    x_idx, y_idx = find_closest_pairs_idx(x, y, **kw_args)
+32    return x[x_idx], y[y_idx]
+
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + align( x, y, p0: Union[List[float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = [0, 1, 0, 0], func=<function <lambda>>, max_iter: typing.Annotated[int, Gt(gt=0)] = 1000, **kw_args): + + + +
+ + 
35@validate_call(config=dict(arbitrary_types_allowed=True))
+36def align(x, y,
+37          p0: Union[List[float], npt.NDArray] = [0, 1, 0, 0],
+38          func=lambda x, a0, a1, a2, a3: (a0*np.ones_like(x), a1*x, a2*x**2/1, a3*(x/1000)**3),
+39          max_iter: PositiveInt = 1000,
+40          **kw_args):
+41    """
+42    Iteratively finds best match between x and y and evaluates the x scaling parameters.
+43
+44    Finds best parameters *p that minimise L2 distance between scaled x and original y
+45    min((lambda(x, *p)-y)**2 | *p)
+46
+47    Args:
+48        x (ArrayLike[float]): values that need to match the reference
+49        y (ArrayLike[float]): reference values
+50        p0 (Union[List[float], npt.NDArray], optional): initial values for the parameters `p`.
+51            Defaults to [0, 1, 0, 0].
+52        func (Callable, optional): Objective function to minimize. Returns list penalties
+53            calculated for each `p`. The total objective function is sum of the elements.
+54            Defaults to polynomial of 3-th degree.
+55        max_iter (PositiveInt, optional): max number of iterations. Defaults to 1000.
+56
+57    Returns:
+58        ArrayLike[float]: array of parameters `p` that minimize the objective funciton
+59    """
+60
+61    if isinstance(p0, list):
+62        p = np.array(p0)
+63    else:
+64        p = p0
+65    loss = np.inf
+66    cur_x = x
+67    for it in range(max_iter):
+68        cur_x = np.sum(func(x, *p), axis=0)
+69        x_idx, y_idx = find_closest_pairs_idx(cur_x, y, **kw_args)
+70        x_match, y_match = x[x_idx], y[y_idx]
+71        p_bak = p
+72        obj_mat = np.stack(func(x_match, *np.ones_like(p)), axis=1)
+73        p, *_ = linalg.lstsq(obj_mat, y_match, cond=1e-8)
+74        loss_bak = loss
+75        loss = np.sum((x_match-y_match)**2)/len(x_match)**2
+76        if np.allclose(p, p_bak):
+77            break
+78        if loss > loss_bak:
+79            pass
+80            return p_bak
+81    return p
+
+ + +

Iteratively finds best match between x and y and evaluates the x scaling parameters.

+ +

Finds best parameters p that minimise L2 distance between scaled x and original y +min((lambda(x, *p)-y)*2 | *p)

+ +
Arguments:
+ +
    +
  • x (ArrayLike[float]): values that need to match the reference
    • +
  • y (ArrayLike[float]): reference values
    • +
  • p0 (Union[List[float], npt.NDArray], optional): initial values for the parameters p. +Defaults to [0, 1, 0, 0].
    • +
  • func (Callable, optional): Objective function to minimize. Returns list penalties +calculated for each p. The total objective function is sum of the elements. +Defaults to polynomial of 3-th degree.
    • +
  • max_iter (PositiveInt, optional): max number of iterations. Defaults to 1000.
    • +
+ +
Returns:
+ +
+

ArrayLike[float]: array of parameters p that minimize the objective funciton

+
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + align_shift( x, y, p0: float = 0, max_iter: typing.Annotated[int, Gt(gt=0)] = 1000, **kw_args): + + + +
+ + 
 84@validate_call(config=dict(arbitrary_types_allowed=True))
+ 85def align_shift(x, y,
+ 86                p0: float = 0,
+ 87                max_iter: PositiveInt = 1000,
+ 88                **kw_args):
+ 89    loss = np.inf
+ 90    cur_x = x
+ 91    p = p0
+ 92    for it in range(max_iter):
+ 93        cur_x = x + p
+ 94        x_idx, y_idx = find_closest_pairs_idx(cur_x, y, **kw_args)
+ 95        x_match, y_match = x[x_idx], y[y_idx]
+ 96        p_bak = p
+ 97        p = np.mean(y_match-x_match)
+ 98        loss_bak = loss
+ 99        loss = np.sum((y_match-x_match)**2)
+100        if np.allclose(p, p_bak):
+101            break
+102        if loss > loss_bak:
+103            return p_bak
+104    return p
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/utils/matchsets.html b/ramanchada2/misc/utils/matchsets.html new file mode 100644 index 00000000..8a9289a1 --- /dev/null +++ b/ramanchada2/misc/utils/matchsets.html @@ -0,0 +1,1029 @@ + + + + + + + ramanchada2.misc.utils.matchsets API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.utils.matchsets

+ + + + + + + 
  1import pandas as pd
+  2from sklearn.cluster import KMeans
+  3from scipy.optimize import linear_sum_assignment
+  4import numpy as np
+  5from typing import Dict, List
+  6
+  7
+  8def match_peaks_cluster(
+  9    spe_pos_dict: Dict[float, float],
+ 10    ref: Dict[float, float],
+ 11    _filter_range=True,
+ 12    cost_intensity=0.25,
+ 13):
+ 14    wl_label = "Wavelength"
+ 15    intensity_label = "Intensity"
+ 16    source_label = "Source"
+ 17
+ 18    # Min-Max normalize the reference values
+ 19    min_value = min(ref.values())
+ 20    max_value = max(ref.values())
+ 21    if len(ref.keys()) > 1:
+ 22        normalized_ref = {
+ 23            key: (value - min_value) / (max_value - min_value)
+ 24            for key, value in ref.items()
+ 25        }
+ 26    else:
+ 27        normalized_ref = ref
+ 28
+ 29    min_value_spe = min(spe_pos_dict.values())
+ 30    max_value_spe = max(spe_pos_dict.values())
+ 31    # Min-Max normalize the spe_pos_dict
+ 32    if len(spe_pos_dict.keys()) > 1:
+ 33        normalized_spe = {
+ 34            key: (value - min_value_spe) / (max_value_spe - min_value_spe)
+ 35            for key, value in spe_pos_dict.items()
+ 36        }
+ 37    else:
+ 38        normalized_spe = spe_pos_dict
+ 39    data_list = [
+ 40        {wl_label: key, intensity_label: value, source_label: "spe"}
+ 41        for key, value in normalized_spe.items()
+ 42    ] + [
+ 43        {wl_label: key, intensity_label: value, source_label: "reference"}
+ 44        for key, value in normalized_ref.items()
+ 45    ]
+ 46
+ 47    # Create a DataFrame from the list of dictionaries
+ 48    df = pd.DataFrame(data_list)
+ 49    df["intensity4cluster"] = df[intensity_label] * cost_intensity
+ 50
+ 51    if _filter_range:
+ 52        _tollerance = 50
+ 53        left_limit = max(min(ref.keys()), min(spe_pos_dict.keys())) - _tollerance
+ 54        right_limit = min(max(ref.keys()), max(spe_pos_dict.keys())) + _tollerance
+ 55        df = df.loc[(df[wl_label] <= right_limit) & (df[wl_label] >= left_limit)]
+ 56        n_spe = len(df[df[source_label] == "spe"])
+ 57        n_ref = len(df[df[source_label] == "reference"])
+ 58    else:
+ 59        n_ref = len(ref.keys())
+ 60        n_spe = len(spe_pos_dict.keys())
+ 61
+ 62    feature_matrix = df[[wl_label, "intensity4cluster"]].to_numpy()
+ 63
+ 64    n_ref = len(ref.keys())
+ 65    n_spe = len(spe_pos_dict.keys())
+ 66    kmeans = KMeans(n_clusters=n_ref if n_ref > n_spe else n_spe, random_state=68)
+ 67    kmeans.fit(feature_matrix)
+ 68    labels = kmeans.labels_
+ 69    # Extract cluster labels, x values, and y values
+ 70    df["Cluster"] = labels
+ 71    grouped = df.groupby("Cluster")
+ 72    x_spe = np.array([])
+ 73    x_reference = np.array([])
+ 74    x_distance = np.array([])
+ 75    clusters = np.array([])
+ 76
+ 77    # Iterate through each group
+ 78    for cluster, group in grouped:
+ 79        # Get the unique sources within the group
+ 80        unique_sources = group["Source"].unique()
+ 81        if "reference" in unique_sources and "spe" in unique_sources:
+ 82            x = None
+ 83            r = None
+ 84            e_min = None
+ 85            for _, row_spe in group.loc[group[source_label] == "spe"].iterrows():
+ 86                w_spe = row_spe[wl_label]
+ 87                for _, row_ref in group.loc[
+ 88                    group[source_label] == "reference"
+ 89                ].iterrows():
+ 90                    w_ref = row_ref[wl_label]
+ 91                    e = (w_spe - w_ref) ** 2 + (
+ 92                        row_spe[intensity_label] - row_ref[intensity_label]
+ 93                    ) ** 2
+ 94                    if (e_min is None) or (e < e_min):
+ 95                        x = w_spe
+ 96                        r = w_ref
+ 97                        e_min = e
+ 98            if x is not None and r is not None and e_min is not None:
+ 99                x_spe = np.append(x_spe, x)
+100                x_reference = np.append(x_reference, r)
+101                x_distance = np.append(x_distance, e_min)
+102                clusters = np.append(clusters, cluster)
+103    sort_indices = np.argsort(x_spe)
+104    return (
+105        x_spe[sort_indices],
+106        x_reference[sort_indices],
+107        x_distance[sort_indices],
+108        df,
+109    )
+110
+111
+112def cost_function_position(
+113    p1: Dict[float, float],
+114    p2: Dict[float, float],
+115    order_weight=1.0,
+116    priority_weight=1.0,
+117):
+118    order_penalty = order_weight * abs(p1[0] - p2[0])
+119    return order_penalty
+120
+121
+122def cost_function(
+123    p1: Dict[float, float],
+124    p2: Dict[float, float],
+125    order_weight=1.0,
+126    priority_weight=0.1,
+127):
+128    """
+129    Modified cost function with an order preservation penalty and priority weighting.
+130    - `order_weight` increases penalty for large differences in the x-axis values.
+131    - `priority_weight` decreases the cost for higher values in the y-axis for set_b points.
+132    """
+133    order_penalty = order_weight * abs(p1[0] - p2[0])
+134    priority_bonus = (
+135        priority_weight * p2[1]
+136    )  # Rewards points in set_b with higher second dimension values
+137    return order_penalty - priority_bonus
+138
+139
+140def normalize_tuples(tuples):
+141    second_values = np.array([x[1] for x in tuples])
+142    min_val, max_val = second_values.min(), second_values.max()
+143    normalized_values = (second_values - min_val) / (max_val - min_val)
+144    # Replace the original second dimension with the normalized values
+145    return [(tuples[i][0], normalized_values[i]) for i in range(len(tuples))]
+146
+147
+148def cost_matrix_peaks(
+149    spectrum_a_dict: Dict[float, float],
+150    spectrum_b_dict: Dict[float, float],
+151    threshold_max_distance=9,
+152    cost_func=None,
+153):
+154    if cost_func is None:
+155        cost_func = cost_function_position
+156    peaks_a = np.array(list(spectrum_a_dict.keys()))
+157    intensities_a = np.array(list(spectrum_a_dict.values()))
+158    peaks_b = np.array(list(spectrum_b_dict.keys()))
+159    intensities_b = np.array(list(spectrum_b_dict.values()))
+160
+161    num_peaks_b = len(peaks_b)  # Number of reference peaks to match
+162
+163    # Normalize intensities using min-max normalization
+164    def normalize_intensities(intensities):
+165        min_intensity = np.min(intensities)
+166        max_intensity = np.max(intensities)
+167        return (intensities - min_intensity) / (max_intensity - min_intensity)
+168
+169    intensities_a_normalized = normalize_intensities(intensities_a)
+170    intensities_b_normalized = normalize_intensities(intensities_b)
+171
+172    num_peaks_a = len(peaks_a)
+173    cost_matrix = np.full(
+174        (num_peaks_a, num_peaks_b), np.inf
+175    )  # Initialize with infinity
+176
+177    for i in range(num_peaks_a):
+178        for j in range(num_peaks_b):
+179            cost = cost_func(
+180                [peaks_a[i], intensities_a_normalized[i]],
+181                [peaks_b[j], intensities_b_normalized[j]],
+182                priority_weight=1,
+183            )
+184            cost_matrix[i, j] = cost
+185    return cost_matrix
+186
+187
+188def match_peaks(
+189    spectrum_a_dict: Dict[float, float],
+190    spectrum_b_dict: Dict[float, float],
+191    threshold_max_distance=9,
+192    df=False,
+193    cost_func=None,
+194):
+195    """
+196    Match peaks between two spectra based on their positions and intensities.
+197
+198    Uses scipy linear_sum_assignment to match peaks based on cost function
+199    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html
+200
+201    Parameters:
+202    ----------
+203    spectrum_a_dict : dict
+204        A dictionary representing the first spectrum, where keys are peak
+205        positions (float) and values are peak intensities (float).
+206
+207    spectrum_b_dict : dict
+208        A dictionary representing the second spectrum, where keys are peak
+209        positions (float) and values are peak intensities (float).
+210
+211    threshold_max_distance : float, optional
+212        The maximum allowed distance for two peaks to be considered a match.
+213        Default is 8.
+214
+215    df : bool, optional
+216        If True, return a DataFrame with matched peaks and their respective
+217        intensities; if False, return None
+218
+219    Returns:
+220    -------
+221    matched_peaks : (matched_peaks_a,matched_peaks_b,matched_distances, df)
+222
+223    Examples:
+224    ---------
+225    >>> spectrum_a = {100: 10, 105: 20, 110: 15}
+226    >>> spectrum_b = {102: 12, 106: 22, 111: 16}
+227    >>> match_peaks(spectrum_a, spectrum_b)
+228
+229    """
+230    cost_matrix = cost_matrix_peaks(
+231        spectrum_a_dict,
+232        spectrum_b_dict,
+233        threshold_max_distance=threshold_max_distance,
+234        cost_func=cost_function if cost_func is None else cost_func,
+235    )
+236
+237    # Use the Hungarian algorithm to find the optimal assignment
+238    try:
+239        row_ind, col_ind = linear_sum_assignment(cost_matrix)
+240
+241    except Exception as err:
+242        raise err
+243
+244    # Prepare matched peaks and distances
+245    # I am sure this could be done in a more efficient way
+246    matched_peaks_a: List[float] = []
+247    matched_peaks_b: List[float] = []
+248    matched_distances: List[float] = []
+249    intensity_a: List[float] = []
+250    intensity_b: List[float] = []
+251
+252    peaks_a = np.array(list(spectrum_a_dict.keys()))
+253    intensities_a = np.array(list(spectrum_a_dict.values()))
+254    peaks_b = np.array(list(spectrum_b_dict.keys()))
+255    intensities_b = np.array(list(spectrum_b_dict.values()))
+256
+257    last_matched_reference = -np.inf
+258    last_matched_cost = np.inf
+259    for i in range(len(row_ind)):
+260        cost = cost_matrix[row_ind[i], col_ind[i]]
+261        if abs(peaks_a[row_ind[i]] - peaks_b[col_ind[i]]) >= threshold_max_distance:
+262            continue
+263        if cost < np.inf:  # Only consider valid pairs
+264            current_reference = peaks_b[col_ind[i]]
+265            if current_reference >= last_matched_reference:
+266                matched_peaks_a.append(peaks_a[row_ind[i]])
+267                matched_peaks_b.append(current_reference)
+268                matched_distances.append(cost)
+269                last_matched_reference = current_reference
+270                last_matched_cost = cost
+271                intensity_a.append(intensities_a[row_ind[i]])
+272                intensity_b.append(intensities_b[col_ind[i]])
+273            elif last_matched_cost > cost:
+274                matched_peaks_a[-1] = peaks_a[row_ind[i]]
+275                matched_peaks_b[-1] = current_reference
+276                matched_distances[-1] = cost
+277                intensity_a[-1] = intensities_a[row_ind[i]]
+278                intensity_b[-1] = intensities_b[col_ind[i]]
+279                last_matched_cost = cost
+280
+281    matched_peaks_a_np = np.array(matched_peaks_a)
+282    matched_peaks_b_np = np.array(matched_peaks_b)
+283    matched_distances_np = np.array(matched_distances)
+284
+285    # Sort matched peaks by peaks_a
+286    # linear_sum_assignment shall give the row_ind sorted
+287    # sorted_indices = np.argsort(matched_peaks_a)
+288    # matched_peaks_a = matched_peaks_a[sorted_indices]
+289    # matched_peaks_b = matched_peaks_b[sorted_indices]
+290    # matched_distances = matched_distances[sorted_indices]
+291
+292    if df:
+293        df = pd.DataFrame(
+294            {
+295                "spe": matched_peaks_a,
+296                "reference": matched_peaks_b,
+297                "distances": matched_distances,
+298                "intensity_a": intensity_a,
+299                "intensity_b": intensity_b,
+300            }
+301        )
+302    else:
+303        df = None
+304    return (
+305        matched_peaks_a_np,
+306        matched_peaks_b_np,
+307        matched_distances_np,
+308        cost_matrix,
+309        df,
+310    )
+
+ + +
+
+ +
+ + def + match_peaks_cluster( spe_pos_dict: Dict[float, float], ref: Dict[float, float], _filter_range=True, cost_intensity=0.25): + + + +
+ + 
  9def match_peaks_cluster(
+ 10    spe_pos_dict: Dict[float, float],
+ 11    ref: Dict[float, float],
+ 12    _filter_range=True,
+ 13    cost_intensity=0.25,
+ 14):
+ 15    wl_label = "Wavelength"
+ 16    intensity_label = "Intensity"
+ 17    source_label = "Source"
+ 18
+ 19    # Min-Max normalize the reference values
+ 20    min_value = min(ref.values())
+ 21    max_value = max(ref.values())
+ 22    if len(ref.keys()) > 1:
+ 23        normalized_ref = {
+ 24            key: (value - min_value) / (max_value - min_value)
+ 25            for key, value in ref.items()
+ 26        }
+ 27    else:
+ 28        normalized_ref = ref
+ 29
+ 30    min_value_spe = min(spe_pos_dict.values())
+ 31    max_value_spe = max(spe_pos_dict.values())
+ 32    # Min-Max normalize the spe_pos_dict
+ 33    if len(spe_pos_dict.keys()) > 1:
+ 34        normalized_spe = {
+ 35            key: (value - min_value_spe) / (max_value_spe - min_value_spe)
+ 36            for key, value in spe_pos_dict.items()
+ 37        }
+ 38    else:
+ 39        normalized_spe = spe_pos_dict
+ 40    data_list = [
+ 41        {wl_label: key, intensity_label: value, source_label: "spe"}
+ 42        for key, value in normalized_spe.items()
+ 43    ] + [
+ 44        {wl_label: key, intensity_label: value, source_label: "reference"}
+ 45        for key, value in normalized_ref.items()
+ 46    ]
+ 47
+ 48    # Create a DataFrame from the list of dictionaries
+ 49    df = pd.DataFrame(data_list)
+ 50    df["intensity4cluster"] = df[intensity_label] * cost_intensity
+ 51
+ 52    if _filter_range:
+ 53        _tollerance = 50
+ 54        left_limit = max(min(ref.keys()), min(spe_pos_dict.keys())) - _tollerance
+ 55        right_limit = min(max(ref.keys()), max(spe_pos_dict.keys())) + _tollerance
+ 56        df = df.loc[(df[wl_label] <= right_limit) & (df[wl_label] >= left_limit)]
+ 57        n_spe = len(df[df[source_label] == "spe"])
+ 58        n_ref = len(df[df[source_label] == "reference"])
+ 59    else:
+ 60        n_ref = len(ref.keys())
+ 61        n_spe = len(spe_pos_dict.keys())
+ 62
+ 63    feature_matrix = df[[wl_label, "intensity4cluster"]].to_numpy()
+ 64
+ 65    n_ref = len(ref.keys())
+ 66    n_spe = len(spe_pos_dict.keys())
+ 67    kmeans = KMeans(n_clusters=n_ref if n_ref > n_spe else n_spe, random_state=68)
+ 68    kmeans.fit(feature_matrix)
+ 69    labels = kmeans.labels_
+ 70    # Extract cluster labels, x values, and y values
+ 71    df["Cluster"] = labels
+ 72    grouped = df.groupby("Cluster")
+ 73    x_spe = np.array([])
+ 74    x_reference = np.array([])
+ 75    x_distance = np.array([])
+ 76    clusters = np.array([])
+ 77
+ 78    # Iterate through each group
+ 79    for cluster, group in grouped:
+ 80        # Get the unique sources within the group
+ 81        unique_sources = group["Source"].unique()
+ 82        if "reference" in unique_sources and "spe" in unique_sources:
+ 83            x = None
+ 84            r = None
+ 85            e_min = None
+ 86            for _, row_spe in group.loc[group[source_label] == "spe"].iterrows():
+ 87                w_spe = row_spe[wl_label]
+ 88                for _, row_ref in group.loc[
+ 89                    group[source_label] == "reference"
+ 90                ].iterrows():
+ 91                    w_ref = row_ref[wl_label]
+ 92                    e = (w_spe - w_ref) ** 2 + (
+ 93                        row_spe[intensity_label] - row_ref[intensity_label]
+ 94                    ) ** 2
+ 95                    if (e_min is None) or (e < e_min):
+ 96                        x = w_spe
+ 97                        r = w_ref
+ 98                        e_min = e
+ 99            if x is not None and r is not None and e_min is not None:
+100                x_spe = np.append(x_spe, x)
+101                x_reference = np.append(x_reference, r)
+102                x_distance = np.append(x_distance, e_min)
+103                clusters = np.append(clusters, cluster)
+104    sort_indices = np.argsort(x_spe)
+105    return (
+106        x_spe[sort_indices],
+107        x_reference[sort_indices],
+108        x_distance[sort_indices],
+109        df,
+110    )
+
+ + + + +
+
+ +
+ + def + cost_function_position( p1: Dict[float, float], p2: Dict[float, float], order_weight=1.0, priority_weight=1.0): + + + +
+ + 
113def cost_function_position(
+114    p1: Dict[float, float],
+115    p2: Dict[float, float],
+116    order_weight=1.0,
+117    priority_weight=1.0,
+118):
+119    order_penalty = order_weight * abs(p1[0] - p2[0])
+120    return order_penalty
+
+ + + + +
+
+ +
+ + def + cost_function( p1: Dict[float, float], p2: Dict[float, float], order_weight=1.0, priority_weight=0.1): + + + +
+ + 
123def cost_function(
+124    p1: Dict[float, float],
+125    p2: Dict[float, float],
+126    order_weight=1.0,
+127    priority_weight=0.1,
+128):
+129    """
+130    Modified cost function with an order preservation penalty and priority weighting.
+131    - `order_weight` increases penalty for large differences in the x-axis values.
+132    - `priority_weight` decreases the cost for higher values in the y-axis for set_b points.
+133    """
+134    order_penalty = order_weight * abs(p1[0] - p2[0])
+135    priority_bonus = (
+136        priority_weight * p2[1]
+137    )  # Rewards points in set_b with higher second dimension values
+138    return order_penalty - priority_bonus
+
+ + +

Modified cost function with an order preservation penalty and priority weighting.

+ +
    +
  • order_weight increases penalty for large differences in the x-axis values.
    • +
  • priority_weight decreases the cost for higher values in the y-axis for set_b points.
    • +
+
+ + +
+
+ +
+ + def + normalize_tuples(tuples): + + + +
+ + 
141def normalize_tuples(tuples):
+142    second_values = np.array([x[1] for x in tuples])
+143    min_val, max_val = second_values.min(), second_values.max()
+144    normalized_values = (second_values - min_val) / (max_val - min_val)
+145    # Replace the original second dimension with the normalized values
+146    return [(tuples[i][0], normalized_values[i]) for i in range(len(tuples))]
+
+ + + + +
+
+ +
+ + def + cost_matrix_peaks( spectrum_a_dict: Dict[float, float], spectrum_b_dict: Dict[float, float], threshold_max_distance=9, cost_func=None): + + + +
+ + 
149def cost_matrix_peaks(
+150    spectrum_a_dict: Dict[float, float],
+151    spectrum_b_dict: Dict[float, float],
+152    threshold_max_distance=9,
+153    cost_func=None,
+154):
+155    if cost_func is None:
+156        cost_func = cost_function_position
+157    peaks_a = np.array(list(spectrum_a_dict.keys()))
+158    intensities_a = np.array(list(spectrum_a_dict.values()))
+159    peaks_b = np.array(list(spectrum_b_dict.keys()))
+160    intensities_b = np.array(list(spectrum_b_dict.values()))
+161
+162    num_peaks_b = len(peaks_b)  # Number of reference peaks to match
+163
+164    # Normalize intensities using min-max normalization
+165    def normalize_intensities(intensities):
+166        min_intensity = np.min(intensities)
+167        max_intensity = np.max(intensities)
+168        return (intensities - min_intensity) / (max_intensity - min_intensity)
+169
+170    intensities_a_normalized = normalize_intensities(intensities_a)
+171    intensities_b_normalized = normalize_intensities(intensities_b)
+172
+173    num_peaks_a = len(peaks_a)
+174    cost_matrix = np.full(
+175        (num_peaks_a, num_peaks_b), np.inf
+176    )  # Initialize with infinity
+177
+178    for i in range(num_peaks_a):
+179        for j in range(num_peaks_b):
+180            cost = cost_func(
+181                [peaks_a[i], intensities_a_normalized[i]],
+182                [peaks_b[j], intensities_b_normalized[j]],
+183                priority_weight=1,
+184            )
+185            cost_matrix[i, j] = cost
+186    return cost_matrix
+
+ + + + +
+
+ +
+ + def + match_peaks( spectrum_a_dict: Dict[float, float], spectrum_b_dict: Dict[float, float], threshold_max_distance=9, df=False, cost_func=None): + + + +
+ + 
189def match_peaks(
+190    spectrum_a_dict: Dict[float, float],
+191    spectrum_b_dict: Dict[float, float],
+192    threshold_max_distance=9,
+193    df=False,
+194    cost_func=None,
+195):
+196    """
+197    Match peaks between two spectra based on their positions and intensities.
+198
+199    Uses scipy linear_sum_assignment to match peaks based on cost function
+200    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html
+201
+202    Parameters:
+203    ----------
+204    spectrum_a_dict : dict
+205        A dictionary representing the first spectrum, where keys are peak
+206        positions (float) and values are peak intensities (float).
+207
+208    spectrum_b_dict : dict
+209        A dictionary representing the second spectrum, where keys are peak
+210        positions (float) and values are peak intensities (float).
+211
+212    threshold_max_distance : float, optional
+213        The maximum allowed distance for two peaks to be considered a match.
+214        Default is 8.
+215
+216    df : bool, optional
+217        If True, return a DataFrame with matched peaks and their respective
+218        intensities; if False, return None
+219
+220    Returns:
+221    -------
+222    matched_peaks : (matched_peaks_a,matched_peaks_b,matched_distances, df)
+223
+224    Examples:
+225    ---------
+226    >>> spectrum_a = {100: 10, 105: 20, 110: 15}
+227    >>> spectrum_b = {102: 12, 106: 22, 111: 16}
+228    >>> match_peaks(spectrum_a, spectrum_b)
+229
+230    """
+231    cost_matrix = cost_matrix_peaks(
+232        spectrum_a_dict,
+233        spectrum_b_dict,
+234        threshold_max_distance=threshold_max_distance,
+235        cost_func=cost_function if cost_func is None else cost_func,
+236    )
+237
+238    # Use the Hungarian algorithm to find the optimal assignment
+239    try:
+240        row_ind, col_ind = linear_sum_assignment(cost_matrix)
+241
+242    except Exception as err:
+243        raise err
+244
+245    # Prepare matched peaks and distances
+246    # I am sure this could be done in a more efficient way
+247    matched_peaks_a: List[float] = []
+248    matched_peaks_b: List[float] = []
+249    matched_distances: List[float] = []
+250    intensity_a: List[float] = []
+251    intensity_b: List[float] = []
+252
+253    peaks_a = np.array(list(spectrum_a_dict.keys()))
+254    intensities_a = np.array(list(spectrum_a_dict.values()))
+255    peaks_b = np.array(list(spectrum_b_dict.keys()))
+256    intensities_b = np.array(list(spectrum_b_dict.values()))
+257
+258    last_matched_reference = -np.inf
+259    last_matched_cost = np.inf
+260    for i in range(len(row_ind)):
+261        cost = cost_matrix[row_ind[i], col_ind[i]]
+262        if abs(peaks_a[row_ind[i]] - peaks_b[col_ind[i]]) >= threshold_max_distance:
+263            continue
+264        if cost < np.inf:  # Only consider valid pairs
+265            current_reference = peaks_b[col_ind[i]]
+266            if current_reference >= last_matched_reference:
+267                matched_peaks_a.append(peaks_a[row_ind[i]])
+268                matched_peaks_b.append(current_reference)
+269                matched_distances.append(cost)
+270                last_matched_reference = current_reference
+271                last_matched_cost = cost
+272                intensity_a.append(intensities_a[row_ind[i]])
+273                intensity_b.append(intensities_b[col_ind[i]])
+274            elif last_matched_cost > cost:
+275                matched_peaks_a[-1] = peaks_a[row_ind[i]]
+276                matched_peaks_b[-1] = current_reference
+277                matched_distances[-1] = cost
+278                intensity_a[-1] = intensities_a[row_ind[i]]
+279                intensity_b[-1] = intensities_b[col_ind[i]]
+280                last_matched_cost = cost
+281
+282    matched_peaks_a_np = np.array(matched_peaks_a)
+283    matched_peaks_b_np = np.array(matched_peaks_b)
+284    matched_distances_np = np.array(matched_distances)
+285
+286    # Sort matched peaks by peaks_a
+287    # linear_sum_assignment shall give the row_ind sorted
+288    # sorted_indices = np.argsort(matched_peaks_a)
+289    # matched_peaks_a = matched_peaks_a[sorted_indices]
+290    # matched_peaks_b = matched_peaks_b[sorted_indices]
+291    # matched_distances = matched_distances[sorted_indices]
+292
+293    if df:
+294        df = pd.DataFrame(
+295            {
+296                "spe": matched_peaks_a,
+297                "reference": matched_peaks_b,
+298                "distances": matched_distances,
+299                "intensity_a": intensity_a,
+300                "intensity_b": intensity_b,
+301            }
+302        )
+303    else:
+304        df = None
+305    return (
+306        matched_peaks_a_np,
+307        matched_peaks_b_np,
+308        matched_distances_np,
+309        cost_matrix,
+310        df,
+311    )
+
+ + +

Match peaks between two spectra based on their positions and intensities.

+ +

Uses scipy linear_sum_assignment to match peaks based on cost function +https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html

+ +

Parameters:

+ +

spectrum_a_dict : dict + A dictionary representing the first spectrum, where keys are peak + positions (float) and values are peak intensities (float).

+ +

spectrum_b_dict : dict + A dictionary representing the second spectrum, where keys are peak + positions (float) and values are peak intensities (float).

+ +

threshold_max_distance : float, optional + The maximum allowed distance for two peaks to be considered a match. + Default is 8.

+ +

df : bool, optional + If True, return a DataFrame with matched peaks and their respective + intensities; if False, return None

+ +

Returns:

+ +

matched_peaks : (matched_peaks_a,matched_peaks_b,matched_distances, df)

+ +

Examples:

+ +
+
>>> spectrum_a = {100: 10, 105: 20, 110: 15}
+>>> spectrum_b = {102: 12, 106: 22, 111: 16}
+>>> match_peaks(spectrum_a, spectrum_b)
+
+
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/utils/ramanshift_to_wavelength.html b/ramanchada2/misc/utils/ramanshift_to_wavelength.html new file mode 100644 index 00000000..4ab43d5a --- /dev/null +++ b/ramanchada2/misc/utils/ramanshift_to_wavelength.html @@ -0,0 +1,406 @@ + + + + + + + ramanchada2.misc.utils.ramanshift_to_wavelength API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.utils.ramanshift_to_wavelength

+ + + + + + + 
 1import numpy as np
+ 2
+ 3
+ 4def shift_cm_1_to_abs_nm_dict(deltas, laser_wave_length_nm):
+ 5    arr = np.array(list(deltas.items()), dtype=float)
+ 6    arr[:, 0] = 1/(1/laser_wave_length_nm - arr[:, 0] * 1e-7)
+ 7    return dict(arr)
+ 8
+ 9
+10def abs_nm_to_shift_cm_1_dict(deltas, laser_wave_length_nm):
+11    arr = np.array(list(deltas.items()), dtype=float)
+12    arr[:, 0] = 1e7*(1/laser_wave_length_nm - 1/arr[:, 0])
+13    return dict(arr)
+14
+15
+16def abs_nm_to_shift_cm_1(wl, laser_wave_length_nm):
+17    return 1e7*(1/laser_wave_length_nm - 1/wl)
+18
+19
+20def shift_cm_1_to_abs_nm(wn, laser_wave_length_nm):
+21    shift_nm = wn * 1e-7
+22    absolute_nm = 1/(1/laser_wave_length_nm - shift_nm)
+23    return absolute_nm
+24
+25
+26def laser_wl_nm(raman_shift_cm_1, wave_length_nm):
+27    return 1/(1/wave_length_nm+raman_shift_cm_1*1e-7)
+
+ + +
+
+ +
+ + def + shift_cm_1_to_abs_nm_dict(deltas, laser_wave_length_nm): + + + +
+ + 
5def shift_cm_1_to_abs_nm_dict(deltas, laser_wave_length_nm):
+6    arr = np.array(list(deltas.items()), dtype=float)
+7    arr[:, 0] = 1/(1/laser_wave_length_nm - arr[:, 0] * 1e-7)
+8    return dict(arr)
+
+ + + + +
+
+ +
+ + def + abs_nm_to_shift_cm_1_dict(deltas, laser_wave_length_nm): + + + +
+ + 
11def abs_nm_to_shift_cm_1_dict(deltas, laser_wave_length_nm):
+12    arr = np.array(list(deltas.items()), dtype=float)
+13    arr[:, 0] = 1e7*(1/laser_wave_length_nm - 1/arr[:, 0])
+14    return dict(arr)
+
+ + + + +
+
+ +
+ + def + abs_nm_to_shift_cm_1(wl, laser_wave_length_nm): + + + +
+ + 
17def abs_nm_to_shift_cm_1(wl, laser_wave_length_nm):
+18    return 1e7*(1/laser_wave_length_nm - 1/wl)
+
+ + + + +
+
+ +
+ + def + shift_cm_1_to_abs_nm(wn, laser_wave_length_nm): + + + +
+ + 
21def shift_cm_1_to_abs_nm(wn, laser_wave_length_nm):
+22    shift_nm = wn * 1e-7
+23    absolute_nm = 1/(1/laser_wave_length_nm - shift_nm)
+24    return absolute_nm
+
+ + + + +
+
+ +
+ + def + laser_wl_nm(raman_shift_cm_1, wave_length_nm): + + + +
+ + 
27def laser_wl_nm(raman_shift_cm_1, wave_length_nm):
+28    return 1/(1/wave_length_nm+raman_shift_cm_1*1e-7)
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/misc/utils/svd.html b/ramanchada2/misc/utils/svd.html new file mode 100644 index 00000000..ab28e88f --- /dev/null +++ b/ramanchada2/misc/utils/svd.html @@ -0,0 +1,340 @@ + + + + + + + ramanchada2.misc.utils.svd API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.misc.utils.svd

+ + + + + + + 
 1import numpy as np
+ 2
+ 3
+ 4def svd_solve(A, b):
+ 5    """
+ 6    Solves Ax=b
+ 7    """
+ 8    u, s, vt = np.linalg.svd(A, full_matrices=False)
+ 9    v = vt.T
+10    s_1 = 1/s
+11    s_1[np.abs(s) < 1e-8 * np.max(s)] = 0
+12    x = v @ (np.diag(s_1) @ (u.T@b))
+13    return x
+14
+15
+16def svd_inverse(mat):
+17    u, s, vt = np.linalg.svd(mat, full_matrices=False)
+18    s_1 = 1/s
+19    s_1[np.abs(s) < 1e-8 * np.max(s)] = 0
+20    return vt.T @ np.diag(s_1) @ u.T
+
+ + +
+
+ +
+ + def + svd_solve(A, b): + + + +
+ + 
 5def svd_solve(A, b):
+ 6    """
+ 7    Solves Ax=b
+ 8    """
+ 9    u, s, vt = np.linalg.svd(A, full_matrices=False)
+10    v = vt.T
+11    s_1 = 1/s
+12    s_1[np.abs(s) < 1e-8 * np.max(s)] = 0
+13    x = v @ (np.diag(s_1) @ (u.T@b))
+14    return x
+
+ + +

Solves Ax=b

+
+ + +
+
+ +
+ + def + svd_inverse(mat): + + + +
+ + 
17def svd_inverse(mat):
+18    u, s, vt = np.linalg.svd(mat, full_matrices=False)
+19    s_1 = 1/s
+20    s_1[np.abs(s) < 1e-8 * np.max(s)] = 0
+21    return vt.T @ np.diag(s_1) @ u.T
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols.html b/ramanchada2/protocols.html new file mode 100644 index 00000000..37cb3185 --- /dev/null +++ b/ramanchada2/protocols.html @@ -0,0 +1,263 @@ + + + + + + + ramanchada2.protocols API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols

+ + + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/calib_ne_si_argmin2d_iter_gg.html b/ramanchada2/protocols/calib_ne_si_argmin2d_iter_gg.html new file mode 100644 index 00000000..d23e6987 --- /dev/null +++ b/ramanchada2/protocols/calib_ne_si_argmin2d_iter_gg.html @@ -0,0 +1,626 @@ + + + + + + + ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg

+ + + + + + + 
  1from typing import Literal
+  2
+  3import numpy as np
+  4from scipy import interpolate
+  5
+  6from ..misc import constants as rc2const
+  7from ..spectrum.spectrum import Spectrum
+  8
+  9
+ 10def neon_calibration(ne_cm_1: Spectrum,
+ 11                     wl: Literal[514, 532, 633, 785]):
+ 12    """
+ 13    Neon calibration
+ 14
+ 15    The approximate laser wavelengt `wl` is used to translate the neon spectrum to [nm].
+ 16    Then using :func:`~ramanchada2.spectrum.calibration.by_deltas.xcal_argmin2d_iter_lowpass`
+ 17    the approximate neon spectrum in [nm] is scaled to match the reference lines.
+ 18    This way it is calibrated to absolute wavelengths. A Makima spline is calculated so that
+ 19    it takes wavenumbers [1/cm] and return wavelength-calibrated x-axis in wavelengths [nm].
+ 20
+ 21    Args:
+ 22        ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
+ 23        wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
+ 24
+ 25    Returns:
+ 26        Callable(ArrayLike[float]): callable (spline) that applies the calibration
+ 27    """
+ 28    ref = rc2const.neon_wl_dict[wl]
+ 29    ne_nm = ne_cm_1.subtract_moving_minimum(200).shift_cm_1_to_abs_nm_filter(wl).normalize()  # type: ignore
+ 30
+ 31    ne_cal = ne_nm.xcal_argmin2d_iter_lowpass(ref=ref)
+ 32    spline = interpolate.Akima1DInterpolator(ne_cm_1.x, ne_cal.x, method='makima')
+ 33    return spline
+ 34
+ 35
+ 36def silicon_calibration(si_nm: Spectrum,
+ 37                        wl: Literal[514, 532, 633, 785],
+ 38                        find_peaks_kw={},
+ 39                        fit_peaks_kw={}):
+ 40    """
+ 41    Calculate calibration function for lazer zeroing
+ 42
+ 43    Takes wavelength-calibrated Silicon spectrum in wavelengths [nm] and using
+ 44    the Silicon peak position it calculates the real laser wavelength and a Makima
+ 45    spline that translates the wavelengt-calibrated x-axis wavelength [nm] values to
+ 46    lazer-zeroed Raman shift in wavenumbers [1/cm].
+ 47
+ 48    Args:
+ 49        si_nm: Spectrum
+ 50            Wavelength-calibrated Silicon spectrum in wavelengths [nm]
+ 51        wl: Literal[514, 532, 633, 785]
+ 52            Approximate Laser wavelength
+ 53        find_peaks_kw: dict, optional
+ 54            keywords for find_peak. Default values are
+ 55            `{'prominence': min(.8, si_nm.y_noise_MAD()*50), 'width': 2, 'wlen': 100}`
+ 56        fit_peaks_kw: dict, optional
+ 57            keywords for fit_peaks. Default values are
+ 58            `{'profile': 'Pearson4', 'vary_baseline': False}`
+ 59
+ 60    Returns:
+ 61        spline, esitmated_wavelength: int
+ 62    """
+ 63    si_nm_orig = si_nm
+ 64    fnd_kw = {'prominence': min(.8, si_nm.y_noise_MAD()*50),
+ 65              'width': 2,
+ 66              'wlen': 100,
+ 67              }
+ 68    fnd_kw.update(find_peaks_kw)
+ 69    ll = wl/(1-wl*(520-50)*1e-7)
+ 70    rr = wl/(1-wl*(520+50)*1e-7)
+ 71    si_nm = si_nm.dropna().trim_axes(method='x-axis', boundaries=(ll, rr)).normalize()  # type: ignore
+ 72    peaks = si_nm.find_peak_multipeak(**fnd_kw)  # type: ignore
+ 73    fp_kw = {'profile': 'Pearson4',
+ 74             'vary_baseline': False
+ 75             }
+ 76    fp_kw.update(fit_peaks_kw)
+ 77    fitres = si_nm.fit_peak_multimodel(candidates=peaks, **fp_kw)  # type: ignore
+ 78    si_wl = fitres.centers
+ 79    if len(si_wl) < 1:
+ 80        raise ValueError('No peaks were found. Please refind find_peaks parameters.')
+ 81    laser_wl = 1/(520.45/1e7 + 1/si_wl)
+ 82
+ 83    laser_wl = laser_wl[np.argmin(np.abs(laser_wl-wl))]
+ 84    x_cm_1 = 1e7*(1/laser_wl-1/si_nm_orig.x)
+ 85
+ 86    spline = interpolate.Akima1DInterpolator(si_nm_orig.x, x_cm_1, method='makima')
+ 87    return spline, laser_wl
+ 88
+ 89
+ 90def neon_silicon_calibration(ne_cm_1: Spectrum,
+ 91                             si_cm_1: Spectrum,
+ 92                             wl: Literal[514, 532, 633, 785],
+ 93                             sil_fit_kw={},
+ 94                             sil_find_kw={}
+ 95                             ):
+ 96    """
+ 97    Perform neon and silicon calibration together
+ 98
+ 99    Combines :func:`~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_calibration`
+100    and :func:`~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.silicon_calibration`.
+101    Returned spline is calculated using the wavlength-calibrated x-axis values translated
+102    to Raman shift wavenumbers using the calculated laser wavelength in `silicon_calibration`
+103
+104    Args:
+105        ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
+106        si_cm_1 (Spectrum): silicon spectrum to estimate laser wavelength. Should be in [1/cm].
+107        wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
+108        sil_fit_kw (dict, optional): kwargs sent as `find_peaks_kw` in `silicon_calibration`. Defaults to {}.
+109        sil_find_kw (dict, optional): kwargs sent as `fit_peaks_kw` in `silicon_calibration`. Defaults to {}.
+110
+111    Returns:
+112        Callable(ArrayLike[float]): callable (spline) that applies the calibration
+113    """
+114    ne_spline = neon_calibration(ne_cm_1, wl)
+115    si_nm = si_cm_1.scale_xaxis_fun(ne_spline)  # type: ignore
+116    si_spline, wl = silicon_calibration(si_nm, wl,
+117                                        find_peaks_kw=sil_find_kw,
+118                                        fit_peaks_kw=sil_fit_kw)
+119    ne_nm = ne_cm_1.scale_xaxis_fun(ne_spline)  # type: ignore
+120    ne_cal_cm_1 = ne_nm.abs_nm_to_shift_cm_1_filter(wl)
+121    spline = interpolate.Akima1DInterpolator(ne_cm_1.x, ne_cal_cm_1.x, method='makima')
+122    return spline
+
+ + +
+
+ +
+ + def + neon_calibration( ne_cm_1: ramanchada2.spectrum.spectrum.Spectrum, wl: Literal[514, 532, 633, 785]): + + + +
+ + 
11def neon_calibration(ne_cm_1: Spectrum,
+12                     wl: Literal[514, 532, 633, 785]):
+13    """
+14    Neon calibration
+15
+16    The approximate laser wavelengt `wl` is used to translate the neon spectrum to [nm].
+17    Then using :func:`~ramanchada2.spectrum.calibration.by_deltas.xcal_argmin2d_iter_lowpass`
+18    the approximate neon spectrum in [nm] is scaled to match the reference lines.
+19    This way it is calibrated to absolute wavelengths. A Makima spline is calculated so that
+20    it takes wavenumbers [1/cm] and return wavelength-calibrated x-axis in wavelengths [nm].
+21
+22    Args:
+23        ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
+24        wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
+25
+26    Returns:
+27        Callable(ArrayLike[float]): callable (spline) that applies the calibration
+28    """
+29    ref = rc2const.neon_wl_dict[wl]
+30    ne_nm = ne_cm_1.subtract_moving_minimum(200).shift_cm_1_to_abs_nm_filter(wl).normalize()  # type: ignore
+31
+32    ne_cal = ne_nm.xcal_argmin2d_iter_lowpass(ref=ref)
+33    spline = interpolate.Akima1DInterpolator(ne_cm_1.x, ne_cal.x, method='makima')
+34    return spline
+
+ + +

Neon calibration

+ +

The approximate laser wavelengt wl is used to translate the neon spectrum to [nm]. +Then using ~ramanchada2.spectrum.calibration.by_deltas.xcal_argmin2d_iter_lowpass() +the approximate neon spectrum in [nm] is scaled to match the reference lines. +This way it is calibrated to absolute wavelengths. A Makima spline is calculated so that +it takes wavenumbers [1/cm] and return wavelength-calibrated x-axis in wavelengths [nm].

+ +
Arguments:
+ +
    +
  • ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
    • +
  • wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
    • +
+ +
Returns:
+ +
+

Callable(ArrayLike[float]): callable (spline) that applies the calibration

+
+
+ + +
+
+ +
+ + def + silicon_calibration( si_nm: ramanchada2.spectrum.spectrum.Spectrum, wl: Literal[514, 532, 633, 785], find_peaks_kw={}, fit_peaks_kw={}): + + + +
+ + 
37def silicon_calibration(si_nm: Spectrum,
+38                        wl: Literal[514, 532, 633, 785],
+39                        find_peaks_kw={},
+40                        fit_peaks_kw={}):
+41    """
+42    Calculate calibration function for lazer zeroing
+43
+44    Takes wavelength-calibrated Silicon spectrum in wavelengths [nm] and using
+45    the Silicon peak position it calculates the real laser wavelength and a Makima
+46    spline that translates the wavelengt-calibrated x-axis wavelength [nm] values to
+47    lazer-zeroed Raman shift in wavenumbers [1/cm].
+48
+49    Args:
+50        si_nm: Spectrum
+51            Wavelength-calibrated Silicon spectrum in wavelengths [nm]
+52        wl: Literal[514, 532, 633, 785]
+53            Approximate Laser wavelength
+54        find_peaks_kw: dict, optional
+55            keywords for find_peak. Default values are
+56            `{'prominence': min(.8, si_nm.y_noise_MAD()*50), 'width': 2, 'wlen': 100}`
+57        fit_peaks_kw: dict, optional
+58            keywords for fit_peaks. Default values are
+59            `{'profile': 'Pearson4', 'vary_baseline': False}`
+60
+61    Returns:
+62        spline, esitmated_wavelength: int
+63    """
+64    si_nm_orig = si_nm
+65    fnd_kw = {'prominence': min(.8, si_nm.y_noise_MAD()*50),
+66              'width': 2,
+67              'wlen': 100,
+68              }
+69    fnd_kw.update(find_peaks_kw)
+70    ll = wl/(1-wl*(520-50)*1e-7)
+71    rr = wl/(1-wl*(520+50)*1e-7)
+72    si_nm = si_nm.dropna().trim_axes(method='x-axis', boundaries=(ll, rr)).normalize()  # type: ignore
+73    peaks = si_nm.find_peak_multipeak(**fnd_kw)  # type: ignore
+74    fp_kw = {'profile': 'Pearson4',
+75             'vary_baseline': False
+76             }
+77    fp_kw.update(fit_peaks_kw)
+78    fitres = si_nm.fit_peak_multimodel(candidates=peaks, **fp_kw)  # type: ignore
+79    si_wl = fitres.centers
+80    if len(si_wl) < 1:
+81        raise ValueError('No peaks were found. Please refind find_peaks parameters.')
+82    laser_wl = 1/(520.45/1e7 + 1/si_wl)
+83
+84    laser_wl = laser_wl[np.argmin(np.abs(laser_wl-wl))]
+85    x_cm_1 = 1e7*(1/laser_wl-1/si_nm_orig.x)
+86
+87    spline = interpolate.Akima1DInterpolator(si_nm_orig.x, x_cm_1, method='makima')
+88    return spline, laser_wl
+
+ + +

Calculate calibration function for lazer zeroing

+ +

Takes wavelength-calibrated Silicon spectrum in wavelengths [nm] and using +the Silicon peak position it calculates the real laser wavelength and a Makima +spline that translates the wavelengt-calibrated x-axis wavelength [nm] values to +lazer-zeroed Raman shift in wavenumbers [1/cm].

+ +
Arguments:
+ +
    +
  • si_nm: Spectrum +Wavelength-calibrated Silicon spectrum in wavelengths [nm]
    • +
  • wl: Literal[514, 532, 633, 785] +Approximate Laser wavelength
    • +
  • find_peaks_kw: dict, optional +keywords for find_peak. Default values are +{'prominence': min(.8, si_nm.y_noise_MAD()*50), 'width': 2, 'wlen': 100}
    • +
  • fit_peaks_kw: dict, optional +keywords for fit_peaks. Default values are +{'profile': 'Pearson4', 'vary_baseline': False}
    • +
+ +
Returns:
+ +
+

spline, esitmated_wavelength: int

+
+
+ + +
+
+ +
+ + def + neon_silicon_calibration( ne_cm_1: ramanchada2.spectrum.spectrum.Spectrum, si_cm_1: ramanchada2.spectrum.spectrum.Spectrum, wl: Literal[514, 532, 633, 785], sil_fit_kw={}, sil_find_kw={}): + + + +
+ + 
 91def neon_silicon_calibration(ne_cm_1: Spectrum,
+ 92                             si_cm_1: Spectrum,
+ 93                             wl: Literal[514, 532, 633, 785],
+ 94                             sil_fit_kw={},
+ 95                             sil_find_kw={}
+ 96                             ):
+ 97    """
+ 98    Perform neon and silicon calibration together
+ 99
+100    Combines :func:`~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_calibration`
+101    and :func:`~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.silicon_calibration`.
+102    Returned spline is calculated using the wavlength-calibrated x-axis values translated
+103    to Raman shift wavenumbers using the calculated laser wavelength in `silicon_calibration`
+104
+105    Args:
+106        ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
+107        si_cm_1 (Spectrum): silicon spectrum to estimate laser wavelength. Should be in [1/cm].
+108        wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
+109        sil_fit_kw (dict, optional): kwargs sent as `find_peaks_kw` in `silicon_calibration`. Defaults to {}.
+110        sil_find_kw (dict, optional): kwargs sent as `fit_peaks_kw` in `silicon_calibration`. Defaults to {}.
+111
+112    Returns:
+113        Callable(ArrayLike[float]): callable (spline) that applies the calibration
+114    """
+115    ne_spline = neon_calibration(ne_cm_1, wl)
+116    si_nm = si_cm_1.scale_xaxis_fun(ne_spline)  # type: ignore
+117    si_spline, wl = silicon_calibration(si_nm, wl,
+118                                        find_peaks_kw=sil_find_kw,
+119                                        fit_peaks_kw=sil_fit_kw)
+120    ne_nm = ne_cm_1.scale_xaxis_fun(ne_spline)  # type: ignore
+121    ne_cal_cm_1 = ne_nm.abs_nm_to_shift_cm_1_filter(wl)
+122    spline = interpolate.Akima1DInterpolator(ne_cm_1.x, ne_cal_cm_1.x, method='makima')
+123    return spline
+
+ + +

Perform neon and silicon calibration together

+ +

Combines ~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_calibration() +and ~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.silicon_calibration(). +Returned spline is calculated using the wavlength-calibrated x-axis values translated +to Raman shift wavenumbers using the calculated laser wavelength in silicon_calibration

+ +
Arguments:
+ +
    +
  • ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
    • +
  • si_cm_1 (Spectrum): silicon spectrum to estimate laser wavelength. Should be in [1/cm].
    • +
  • wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
    • +
  • sil_fit_kw (dict, optional): kwargs sent as find_peaks_kw in silicon_calibration. Defaults to {}.
    • +
  • sil_find_kw (dict, optional): kwargs sent as fit_peaks_kw in silicon_calibration. Defaults to {}.
    • +
+ +
Returns:
+ +
+

Callable(ArrayLike[float]): callable (spline) that applies the calibration

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/calibration.html b/ramanchada2/protocols/calibration.html new file mode 100644 index 00000000..c8ef4c2e --- /dev/null +++ b/ramanchada2/protocols/calibration.html @@ -0,0 +1,262 @@ + + + + + + + ramanchada2.protocols.calibration API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.calibration

+ + + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/calibration/calibration_component.html b/ramanchada2/protocols/calibration/calibration_component.html new file mode 100644 index 00000000..42caf85b --- /dev/null +++ b/ramanchada2/protocols/calibration/calibration_component.html @@ -0,0 +1,989 @@ + + + + + + + ramanchada2.protocols.calibration.calibration_component API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.calibration.calibration_component

+ + + + + + + 
  1import logging
+  2
+  3import matplotlib.pyplot as plt
+  4import numpy as np
+  5import pandas as pd
+  6from matplotlib.axes import Axes
+  7from typing import Literal
+  8
+  9from ramanchada2.misc.plottable import Plottable
+ 10from ramanchada2.spectrum import Spectrum
+ 11
+ 12
+ 13logger = logging.getLogger(__name__)
+ 14
+ 15
+ 16class ProcessingModel:
+ 17    def __init__(self):
+ 18        pass
+ 19
+ 20
+ 21class CalibrationComponent(Plottable):
+ 22    nonmonotonic: Literal["ignore", "nan", "error"] = "nan"
+ 23
+ 24    def __init__(self, laser_wl, spe, spe_units, ref, ref_units, sample=None):
+ 25        super(CalibrationComponent, self).__init__()
+ 26        self.laser_wl = laser_wl
+ 27        self.spe = spe
+ 28        self.spe_units = spe_units
+ 29        self.ref = ref
+ 30        self.ref_units = ref_units
+ 31        self.name = "not estimated"
+ 32        self.model = None
+ 33        self.model_units = None
+ 34        self.peaks = None
+ 35        self.sample = sample
+ 36        self.enabled = True
+ 37        self.fit_res = None
+ 38
+ 39    def set_model(self, model, model_units, peaks, name=None):
+ 40        self.model = model
+ 41        self.model_units = model_units
+ 42        self.peaks = peaks
+ 43        self.name = "calibration component" if name is None else name
+ 44
+ 45    def __str__(self):
+ 46        return (
+ 47            f"{self.name} spe ({self.spe_units}) reference ({self.ref_units}) "
+ 48            f"model ({self.model_units}) {self.model}"
+ 49        )
+ 50
+ 51    def convert_units(self, old_spe, spe_unit="cm-1", newspe_unit="nm", laser_wl=None):
+ 52        if laser_wl is None:
+ 53            laser_wl = self.laser_wl
+ 54        logger.debug(
+ 55            "convert laser_wl {} {} --> {}".format(laser_wl, spe_unit, newspe_unit)
+ 56        )
+ 57        if spe_unit != newspe_unit:
+ 58            new_spe = old_spe.__copy__()
+ 59            if spe_unit == "nm":
+ 60                new_spe = old_spe.abs_nm_to_shift_cm_1_filter(
+ 61                    laser_wave_length_nm=laser_wl
+ 62                )
+ 63            elif spe_unit == "cm-1":
+ 64                new_spe = old_spe.shift_cm_1_to_abs_nm_filter(
+ 65                    laser_wave_length_nm=laser_wl
+ 66                )
+ 67            else:
+ 68                raise Exception(
+ 69                    "Unsupported conversion {} to {}", spe_unit, newspe_unit
+ 70                )
+ 71        else:
+ 72            new_spe = old_spe.__copy__()
+ 73        #    new_spe = old_spe.__copy__()
+ 74        return new_spe
+ 75
+ 76    def process(self, old_spe: Spectrum, spe_units="cm-1", convert_back=False):
+ 77        raise NotImplementedError(self)
+ 78
+ 79    def derive_model(
+ 80        self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None
+ 81    ):
+ 82        raise NotImplementedError(self)
+ 83
+ 84    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+ 85        if ax is None:
+ 86            fig, ax = plt.subplots(3, 1, figsize=(12, 4))
+ 87        elif not isinstance(ax, (list, np.ndarray)):
+ 88            raise ValueError(
+ 89                "ax should be a list or array of Axes when creating multiple subplots."
+ 90            )
+ 91
+ 92        self._plot(ax[0], label=label, **kwargs)
+ 93        ax[0].legend()
+ 94        return ax
+ 95
+ 96    def _plot(self, ax, **kwargs):
+ 97        pass
+ 98
+ 99    def __getstate__(self):
+100        # Return the state to be serialized, excluding transient_data
+101        state = self.__dict__.copy()
+102        del state["fit_res"]
+103        return state
+104
+105    def __setstate__(self, state):
+106        self.__dict__.update(state)
+107        self.fit_res = None
+108
+109    def fitres2df(self, spe):
+110        df = pd.DataFrame(
+111            list(
+112                zip(
+113                    self.fit_res.centers,
+114                    self.fit_res.fwhm,
+115                    np.array(
+116                        [
+117                            v
+118                            for peak in self.fit_res
+119                            for k, v in peak.values.items()
+120                            if k.endswith("height")
+121                        ]
+122                    ),
+123                    np.array(
+124                        [
+125                            v
+126                            for peak in self.fit_res
+127                            for k, v in peak.values.items()
+128                            if k.endswith("amplitude")
+129                        ]
+130                    ),
+131                )
+132            ),
+133            columns=["center", "fwhm", "height", "amplitude"],
+134        )
+135        return df[(df["center"] >= min(spe.x)) & (df["center"] <= max(spe.x))]
+
+ + +
+
+
+ logger = +<Logger ramanchada2.protocols.calibration.calibration_component (WARNING)> + + +
+ + + + +
+
+ +
+ + class + ProcessingModel: + + + +
+ + 
17class ProcessingModel:
+18    def __init__(self):
+19        pass
+
+ + + + +
+
+ +
+ + class + CalibrationComponent(ramanchada2.misc.plottable.Plottable): + + + +
+ + 
 22class CalibrationComponent(Plottable):
+ 23    nonmonotonic: Literal["ignore", "nan", "error"] = "nan"
+ 24
+ 25    def __init__(self, laser_wl, spe, spe_units, ref, ref_units, sample=None):
+ 26        super(CalibrationComponent, self).__init__()
+ 27        self.laser_wl = laser_wl
+ 28        self.spe = spe
+ 29        self.spe_units = spe_units
+ 30        self.ref = ref
+ 31        self.ref_units = ref_units
+ 32        self.name = "not estimated"
+ 33        self.model = None
+ 34        self.model_units = None
+ 35        self.peaks = None
+ 36        self.sample = sample
+ 37        self.enabled = True
+ 38        self.fit_res = None
+ 39
+ 40    def set_model(self, model, model_units, peaks, name=None):
+ 41        self.model = model
+ 42        self.model_units = model_units
+ 43        self.peaks = peaks
+ 44        self.name = "calibration component" if name is None else name
+ 45
+ 46    def __str__(self):
+ 47        return (
+ 48            f"{self.name} spe ({self.spe_units}) reference ({self.ref_units}) "
+ 49            f"model ({self.model_units}) {self.model}"
+ 50        )
+ 51
+ 52    def convert_units(self, old_spe, spe_unit="cm-1", newspe_unit="nm", laser_wl=None):
+ 53        if laser_wl is None:
+ 54            laser_wl = self.laser_wl
+ 55        logger.debug(
+ 56            "convert laser_wl {} {} --> {}".format(laser_wl, spe_unit, newspe_unit)
+ 57        )
+ 58        if spe_unit != newspe_unit:
+ 59            new_spe = old_spe.__copy__()
+ 60            if spe_unit == "nm":
+ 61                new_spe = old_spe.abs_nm_to_shift_cm_1_filter(
+ 62                    laser_wave_length_nm=laser_wl
+ 63                )
+ 64            elif spe_unit == "cm-1":
+ 65                new_spe = old_spe.shift_cm_1_to_abs_nm_filter(
+ 66                    laser_wave_length_nm=laser_wl
+ 67                )
+ 68            else:
+ 69                raise Exception(
+ 70                    "Unsupported conversion {} to {}", spe_unit, newspe_unit
+ 71                )
+ 72        else:
+ 73            new_spe = old_spe.__copy__()
+ 74        #    new_spe = old_spe.__copy__()
+ 75        return new_spe
+ 76
+ 77    def process(self, old_spe: Spectrum, spe_units="cm-1", convert_back=False):
+ 78        raise NotImplementedError(self)
+ 79
+ 80    def derive_model(
+ 81        self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None
+ 82    ):
+ 83        raise NotImplementedError(self)
+ 84
+ 85    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+ 86        if ax is None:
+ 87            fig, ax = plt.subplots(3, 1, figsize=(12, 4))
+ 88        elif not isinstance(ax, (list, np.ndarray)):
+ 89            raise ValueError(
+ 90                "ax should be a list or array of Axes when creating multiple subplots."
+ 91            )
+ 92
+ 93        self._plot(ax[0], label=label, **kwargs)
+ 94        ax[0].legend()
+ 95        return ax
+ 96
+ 97    def _plot(self, ax, **kwargs):
+ 98        pass
+ 99
+100    def __getstate__(self):
+101        # Return the state to be serialized, excluding transient_data
+102        state = self.__dict__.copy()
+103        del state["fit_res"]
+104        return state
+105
+106    def __setstate__(self, state):
+107        self.__dict__.update(state)
+108        self.fit_res = None
+109
+110    def fitres2df(self, spe):
+111        df = pd.DataFrame(
+112            list(
+113                zip(
+114                    self.fit_res.centers,
+115                    self.fit_res.fwhm,
+116                    np.array(
+117                        [
+118                            v
+119                            for peak in self.fit_res
+120                            for k, v in peak.values.items()
+121                            if k.endswith("height")
+122                        ]
+123                    ),
+124                    np.array(
+125                        [
+126                            v
+127                            for peak in self.fit_res
+128                            for k, v in peak.values.items()
+129                            if k.endswith("amplitude")
+130                        ]
+131                    ),
+132                )
+133            ),
+134            columns=["center", "fwhm", "height", "amplitude"],
+135        )
+136        return df[(df["center"] >= min(spe.x)) & (df["center"] <= max(spe.x))]
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + CalibrationComponent(laser_wl, spe, spe_units, ref, ref_units, sample=None) + + + +
+ + 
25    def __init__(self, laser_wl, spe, spe_units, ref, ref_units, sample=None):
+26        super(CalibrationComponent, self).__init__()
+27        self.laser_wl = laser_wl
+28        self.spe = spe
+29        self.spe_units = spe_units
+30        self.ref = ref
+31        self.ref_units = ref_units
+32        self.name = "not estimated"
+33        self.model = None
+34        self.model_units = None
+35        self.peaks = None
+36        self.sample = sample
+37        self.enabled = True
+38        self.fit_res = None
+
+ + + + +
+
+
+ nonmonotonic: Literal['ignore', 'nan', 'error'] = +'nan' + + +
+ + + + +
+
+
+ laser_wl + + +
+ + + + +
+
+
+ spe + + +
+ + + + +
+
+
+ spe_units + + +
+ + + + +
+
+
+ ref + + +
+ + + + +
+
+
+ ref_units + + +
+ + + + +
+
+
+ name + + +
+ + + + +
+
+
+ model + + +
+ + + + +
+
+
+ model_units + + +
+ + + + +
+
+
+ peaks + + +
+ + + + +
+
+
+ sample + + +
+ + + + +
+
+
+ enabled + + +
+ + + + +
+
+
+ fit_res + + +
+ + + + +
+
+ +
+ + def + set_model(self, model, model_units, peaks, name=None): + + + +
+ + 
40    def set_model(self, model, model_units, peaks, name=None):
+41        self.model = model
+42        self.model_units = model_units
+43        self.peaks = peaks
+44        self.name = "calibration component" if name is None else name
+
+ + + + +
+
+ +
+ + def + convert_units(self, old_spe, spe_unit='cm-1', newspe_unit='nm', laser_wl=None): + + + +
+ + 
52    def convert_units(self, old_spe, spe_unit="cm-1", newspe_unit="nm", laser_wl=None):
+53        if laser_wl is None:
+54            laser_wl = self.laser_wl
+55        logger.debug(
+56            "convert laser_wl {} {} --> {}".format(laser_wl, spe_unit, newspe_unit)
+57        )
+58        if spe_unit != newspe_unit:
+59            new_spe = old_spe.__copy__()
+60            if spe_unit == "nm":
+61                new_spe = old_spe.abs_nm_to_shift_cm_1_filter(
+62                    laser_wave_length_nm=laser_wl
+63                )
+64            elif spe_unit == "cm-1":
+65                new_spe = old_spe.shift_cm_1_to_abs_nm_filter(
+66                    laser_wave_length_nm=laser_wl
+67                )
+68            else:
+69                raise Exception(
+70                    "Unsupported conversion {} to {}", spe_unit, newspe_unit
+71                )
+72        else:
+73            new_spe = old_spe.__copy__()
+74        #    new_spe = old_spe.__copy__()
+75        return new_spe
+
+ + + + +
+
+ +
+ + def + process( self, old_spe: ramanchada2.spectrum.spectrum.Spectrum, spe_units='cm-1', convert_back=False): + + + +
+ + 
77    def process(self, old_spe: Spectrum, spe_units="cm-1", convert_back=False):
+78        raise NotImplementedError(self)
+
+ + + + +
+
+ +
+ + def + derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None): + + + +
+ + 
80    def derive_model(
+81        self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None
+82    ):
+83        raise NotImplementedError(self)
+
+ + + + +
+
+ +
+ + def + plot(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes: + + + +
+ + 
85    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+86        if ax is None:
+87            fig, ax = plt.subplots(3, 1, figsize=(12, 4))
+88        elif not isinstance(ax, (list, np.ndarray)):
+89            raise ValueError(
+90                "ax should be a list or array of Axes when creating multiple subplots."
+91            )
+92
+93        self._plot(ax[0], label=label, **kwargs)
+94        ax[0].legend()
+95        return ax
+
+ + + + +
+
+ +
+ + def + fitres2df(self, spe): + + + +
+ + 
110    def fitres2df(self, spe):
+111        df = pd.DataFrame(
+112            list(
+113                zip(
+114                    self.fit_res.centers,
+115                    self.fit_res.fwhm,
+116                    np.array(
+117                        [
+118                            v
+119                            for peak in self.fit_res
+120                            for k, v in peak.values.items()
+121                            if k.endswith("height")
+122                        ]
+123                    ),
+124                    np.array(
+125                        [
+126                            v
+127                            for peak in self.fit_res
+128                            for k, v in peak.values.items()
+129                            if k.endswith("amplitude")
+130                        ]
+131                    ),
+132                )
+133            ),
+134            columns=["center", "fwhm", "height", "amplitude"],
+135        )
+136        return df[(df["center"] >= min(spe.x)) & (df["center"] <= max(spe.x))]
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/calibration/calibration_model.html b/ramanchada2/protocols/calibration/calibration_model.html new file mode 100644 index 00000000..6087686b --- /dev/null +++ b/ramanchada2/protocols/calibration/calibration_model.html @@ -0,0 +1,1552 @@ + + + + + + + ramanchada2.protocols.calibration.calibration_model API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.calibration.calibration_model

+ + + + + + + 
  1import pickle
+  2import warnings
+  3
+  4from typing import Dict, Literal
+  5import matplotlib.pyplot as plt
+  6from matplotlib.axes import Axes
+  7
+  8import ramanchada2.misc.constants as rc2const
+  9from ramanchada2.misc.plottable import Plottable
+ 10from ramanchada2.spectrum import Spectrum
+ 11from .calibration_component import ProcessingModel
+ 12from .xcalibration import LazerZeroingComponent, XCalibrationComponent
+ 13
+ 14
+ 15class CalibrationModel(ProcessingModel, Plottable):
+ 16    nonmonotonic: Literal["ignore", "nan", "error"] = "nan"
+ 17
+ 18    """
+ 19    A class representing a calibration model for Raman spectrum.
+ 20    """
+ 21
+ 22    def __init__(self, laser_wl: int):
+ 23        """
+ 24        Initializes a CalibrationModel instance.
+ 25
+ 26        Args:
+ 27            laser_wl:
+ 28                The wavelength of the laser used for calibration.
+ 29
+ 30        Example:
+ 31        ```python
+ 32        # Create an instance of CalibrationModel
+ 33        import ramanchada2 as rc2
+ 34        import ramanchada2.misc.constants as rc2const
+ 35        from ramanchada2.protocols.calibration import CalibrationModel
+ 36        laser_wl=785
+ 37        calmodel = CalibrationModel.calibration_model_factory(
+ 38            laser_wl,
+ 39            spe_neon,
+ 40            spe_sil,
+ 41            neon_wl=rc2const.NEON_WL[laser_wl],
+ 42            find_kw={"wlen": 200, "width": 1},
+ 43            fit_peaks_kw={},
+ 44            should_fit=False,
+ 45        )
+ 46        # Store (optional)
+ 47        calmodel.save(modelfile)
+ 48        # Load (optional)
+ 49        calmodel = CalibrationModel.from_file(modelfile)
+ 50        # Apply to new spectrum
+ 51        calmodel.apply_calibration_x(
+ 52            spe_to_calibrate,
+ 53            spe_units="cm-1"
+ 54            )
+ 55        ```
+ 56        """
+ 57        super(ProcessingModel, self).__init__()
+ 58        super(Plottable, self).__init__()
+ 59        self.set_laser_wavelength(laser_wl)
+ 60        self.prominence_coeff = 3
+ 61
+ 62    def set_laser_wavelength(self, laser_wl):
+ 63        """
+ 64        Sets the wavelength of the laser used for calibration.
+ 65        """
+ 66        self.clear()
+ 67        self.laser_wl = laser_wl
+ 68
+ 69    def clear(self):
+ 70        """
+ 71        Clears the calibration model.
+ 72        """
+ 73        self.laser_wl = None
+ 74        self.components = []
+ 75
+ 76    def save(self, filename):
+ 77        """
+ 78        Saves the calibration model to a file.
+ 79        """
+ 80        with open(filename, "wb") as file:
+ 81            pickle.dump(self, file)
+ 82
+ 83    @staticmethod
+ 84    def from_file(filename):
+ 85        """
+ 86        Loads a calibration model from a file.
+ 87        """
+ 88        with open(filename, "rb") as file:
+ 89            return pickle.load(file)
+ 90
+ 91    def derive_model_x(
+ 92        self,
+ 93        spe_neon: Spectrum,
+ 94        spe_neon_units: str,
+ 95        ref_neon: Dict,
+ 96        ref_neon_units: str,
+ 97        spe_sil: Spectrum,
+ 98        spe_sil_units="cm-1",
+ 99        ref_sil={520.45: 1},
+100        ref_sil_units="cm-1",
+101        find_kw={"wlen": 200, "width": 1},
+102        fit_kw={},
+103        should_fit=False,
+104        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+105        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+106        extrapolate=True,
+107    ):
+108        """
+109        Derives x-calibration models using Neon and Silicon spectra.
+110        """
+111        self.components.clear()
+112        if ref_neon_units is None:
+113            ref_neon_units = "nm"
+114        if spe_neon_units is None:
+115            spe_neon_units = "cm-1"
+116        find_kw["prominence"] = spe_neon.y_noise_MAD() * self.prominence_coeff
+117        model_neon = self._derive_model_curve(
+118            spe_neon,
+119            rc2const.NEON_WL[self.laser_wl] if ref_neon is None else ref_neon,
+120            spe_units=spe_neon_units,
+121            ref_units=ref_neon_units,
+122            find_kw=find_kw,
+123            fit_peaks_kw=fit_kw,
+124            should_fit=should_fit,
+125            name="Neon calibration",
+126            match_method=match_method,
+127            interpolator_method=interpolator_method,
+128            extrapolate=extrapolate,
+129        )
+130        model_neon.nonmonotonic = self.nonmonotonic
+131        spe_sil_ne_calib = model_neon.process(
+132            spe_sil, spe_units=spe_sil_units, convert_back=False
+133        )
+134
+135        find_kw["prominence"] = spe_sil_ne_calib.y_noise_MAD() * self.prominence_coeff
+136        model_si = self._derive_model_zero(
+137            spe_sil_ne_calib,
+138            ref=ref_sil,
+139            spe_units=model_neon.model_units,
+140            ref_units=ref_sil_units,
+141            find_kw=find_kw,
+142            fit_peaks_kw=fit_kw,
+143            should_fit=True,
+144            name="Si laser zeroing",
+145        )
+146        return (model_neon, model_si)
+147
+148    def _derive_model_curve(
+149        self,
+150        spe: Spectrum,
+151        ref=Dict[float, float],
+152        spe_units="cm-1",
+153        ref_units="nm",
+154        find_kw=None,
+155        fit_peaks_kw=None,
+156        should_fit=False,
+157        name="X calibration",
+158        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+159        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+160        extrapolate=True,
+161    ):
+162        if find_kw is None:
+163            find_kw = {}
+164        if fit_peaks_kw is None:
+165            fit_peaks_kw = {}
+166        reference_peaks = rc2const.NEON_WL[self.laser_wl] if ref is None else ref
+167        calibration_x = XCalibrationComponent(
+168            self.laser_wl,
+169            spe=spe,
+170            spe_units=spe_units,
+171            ref=reference_peaks,
+172            ref_units=ref_units,
+173            match_method=match_method,
+174            interpolator_method=interpolator_method,
+175            extrapolate=extrapolate,
+176        )
+177        calibration_x.nonmonotonic = self.nonmonotonic
+178        calibration_x.derive_model(
+179            find_kw=find_kw, fit_peaks_kw=fit_peaks_kw, should_fit=should_fit, name=name
+180        )
+181        self.components.append(calibration_x)
+182        return calibration_x
+183
+184    def derive_model_curve(
+185        self,
+186        spe: Spectrum,
+187        ref=None,
+188        spe_units="cm-1",
+189        ref_units="nm",
+190        find_kw={},
+191        fit_peaks_kw={},
+192        should_fit=False,
+193        name="X calibration",
+194        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+195        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+196        extrapolate=True,
+197    ):
+198        warnings.warn(
+199            message="Do not use directly. Use derive_model_x instead.",
+200            category=DeprecationWarning,
+201        )
+202        return self._derive_model_curve(
+203            spe=spe,
+204            ref=ref,
+205            spe_units=spe_units,
+206            ref_units=ref_units,
+207            find_kw=find_kw,
+208            fit_peaks_kw=fit_peaks_kw,
+209            should_fit=should_fit,
+210            name=name,
+211            match_method=match_method,
+212            interpolator_method=interpolator_method,
+213            extrapolate=True,
+214        )
+215
+216    def _derive_model_zero(
+217        self,
+218        spe: Spectrum,
+219        ref=None,
+220        spe_units="nm",
+221        ref_units="cm-1",
+222        find_kw=None,
+223        fit_peaks_kw=None,
+224        should_fit=False,
+225        name="Laser zeroing",
+226        profile="Pearson4",
+227    ):
+228        if ref is None:
+229            ref = {520.45: 1}
+230        if find_kw is None:
+231            find_kw = {}
+232        if fit_peaks_kw is None:
+233            fit_peaks_kw = {}
+234        calibration_shift = LazerZeroingComponent(
+235            self.laser_wl, spe, spe_units, ref, ref_units
+236        )
+237        calibration_shift.profile = profile
+238        calibration_shift.derive_model(
+239            find_kw=find_kw, fit_peaks_kw=fit_peaks_kw, should_fit=should_fit, name=name
+240        )
+241        _laser_zeroing_component = None
+242        for i, item in enumerate(self.components):
+243            if isinstance(item, LazerZeroingComponent):
+244                self.components[i] = calibration_shift
+245                _laser_zeroing_component = self.components[i]
+246        if (
+247            _laser_zeroing_component is None
+248        ):  # LaserZeroing component should present only once
+249            self.components.append(calibration_shift)
+250        return calibration_shift
+251
+252    def derive_model_zero(
+253        self,
+254        spe: Spectrum,
+255        ref={520.45: 1},
+256        spe_units="nm",
+257        ref_units="cm-1",
+258        find_kw=None,
+259        fit_peaks_kw=None,
+260        should_fit=False,
+261        name="X Shift",
+262        profile="Pearson4",
+263    ):
+264        if find_kw is None:
+265            find_kw = {}
+266        if fit_peaks_kw is None:
+267            fit_peaks_kw = {}
+268        warnings.warn(
+269            message="Do not use directly. Use derive_model_x instead.",
+270            category=DeprecationWarning,
+271        )
+272        return self._derive_model_zero(
+273            spe=spe,
+274            ref=ref,
+275            spe_units=spe_units,
+276            ref_units=ref_units,
+277            find_kw=find_kw,
+278            fit_peaks_kw=fit_peaks_kw,
+279            should_fit=should_fit,
+280            name=name,
+281            profile=profile,
+282        )
+283
+284    def apply_calibration_x(self, old_spe: Spectrum, spe_units="cm-1"):
+285        # neon calibration converts to nm
+286        # silicon calibration takes nm and converts back to cm-1 using laser zeroing
+287        new_spe = old_spe
+288        model_units = spe_units
+289        for model in self.components:
+290            # TODO: tbd find out if to convert units
+291            if model.enabled:
+292                new_spe = model.process(new_spe, model_units, convert_back=False)
+293                model_units = model.model_units
+294        return new_spe
+295
+296    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+297        if ax is None:
+298            fig, ax = plt.subplots(1, 1, figsize=(12, 4))
+299        self._plot(ax, **kwargs)
+300        return ax
+301
+302    def _plot(self, ax, **kwargs):
+303        for index, model in enumerate(self.components):
+304            model._plot(ax, **kwargs)
+305            break
+306
+307    @staticmethod
+308    def calibration_model_factory(
+309        laser_wl,
+310        spe_neon: Spectrum,
+311        spe_sil: Spectrum,
+312        neon_wl=None,
+313        find_kw=None,
+314        fit_peaks_kw=None,
+315        should_fit=False,
+316        prominence_coeff=3,
+317        si_profile="Pearson4",
+318        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+319        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+320        extrapolate=True,
+321    ):
+322        if neon_wl is None:
+323            neon_wl = rc2const.NEON_WL[laser_wl]
+324        if find_kw is None:
+325            find_kw = {"wlen": 100, "width": 1}
+326        if fit_peaks_kw is None:
+327            fit_peaks_kw = {}
+328        calmodel = CalibrationModel(laser_wl)
+329        calmodel.prominence_coeff = prominence_coeff
+330        find_kw["prominence"] = spe_neon.y_noise_MAD() * calmodel.prominence_coeff
+331
+332        model_neon = calmodel._derive_model_curve(
+333            spe=spe_neon,
+334            ref=neon_wl,
+335            spe_units="cm-1",
+336            ref_units="nm",
+337            find_kw=find_kw,
+338            fit_peaks_kw=fit_peaks_kw,
+339            should_fit=should_fit,
+340            name="Neon calibration",
+341            match_method=match_method,
+342            interpolator_method=interpolator_method,
+343            extrapolate=extrapolate,
+344        )
+345        spe_sil_ne_calib = model_neon.process(
+346            spe_sil, spe_units="cm-1", convert_back=False
+347        )
+348        find_kw["prominence"] = (
+349            spe_sil_ne_calib.y_noise_MAD() * calmodel.prominence_coeff
+350        )
+351        calmodel.derive_model_zero(
+352            spe=spe_sil_ne_calib,
+353            ref={520.45: 1},
+354            spe_units=model_neon.model_units,
+355            ref_units="cm-1",
+356            find_kw=find_kw,
+357            fit_peaks_kw=fit_peaks_kw,
+358            should_fit=True,
+359            name="Si calibration",
+360            profile=si_profile,
+361        )
+362        return calmodel
+
+ + +
+
+ +
+ + class + CalibrationModel(ramanchada2.protocols.calibration.calibration_component.ProcessingModel, ramanchada2.misc.plottable.Plottable): + + + +
+ + 
 16class CalibrationModel(ProcessingModel, Plottable):
+ 17    nonmonotonic: Literal["ignore", "nan", "error"] = "nan"
+ 18
+ 19    """
+ 20    A class representing a calibration model for Raman spectrum.
+ 21    """
+ 22
+ 23    def __init__(self, laser_wl: int):
+ 24        """
+ 25        Initializes a CalibrationModel instance.
+ 26
+ 27        Args:
+ 28            laser_wl:
+ 29                The wavelength of the laser used for calibration.
+ 30
+ 31        Example:
+ 32        ```python
+ 33        # Create an instance of CalibrationModel
+ 34        import ramanchada2 as rc2
+ 35        import ramanchada2.misc.constants as rc2const
+ 36        from ramanchada2.protocols.calibration import CalibrationModel
+ 37        laser_wl=785
+ 38        calmodel = CalibrationModel.calibration_model_factory(
+ 39            laser_wl,
+ 40            spe_neon,
+ 41            spe_sil,
+ 42            neon_wl=rc2const.NEON_WL[laser_wl],
+ 43            find_kw={"wlen": 200, "width": 1},
+ 44            fit_peaks_kw={},
+ 45            should_fit=False,
+ 46        )
+ 47        # Store (optional)
+ 48        calmodel.save(modelfile)
+ 49        # Load (optional)
+ 50        calmodel = CalibrationModel.from_file(modelfile)
+ 51        # Apply to new spectrum
+ 52        calmodel.apply_calibration_x(
+ 53            spe_to_calibrate,
+ 54            spe_units="cm-1"
+ 55            )
+ 56        ```
+ 57        """
+ 58        super(ProcessingModel, self).__init__()
+ 59        super(Plottable, self).__init__()
+ 60        self.set_laser_wavelength(laser_wl)
+ 61        self.prominence_coeff = 3
+ 62
+ 63    def set_laser_wavelength(self, laser_wl):
+ 64        """
+ 65        Sets the wavelength of the laser used for calibration.
+ 66        """
+ 67        self.clear()
+ 68        self.laser_wl = laser_wl
+ 69
+ 70    def clear(self):
+ 71        """
+ 72        Clears the calibration model.
+ 73        """
+ 74        self.laser_wl = None
+ 75        self.components = []
+ 76
+ 77    def save(self, filename):
+ 78        """
+ 79        Saves the calibration model to a file.
+ 80        """
+ 81        with open(filename, "wb") as file:
+ 82            pickle.dump(self, file)
+ 83
+ 84    @staticmethod
+ 85    def from_file(filename):
+ 86        """
+ 87        Loads a calibration model from a file.
+ 88        """
+ 89        with open(filename, "rb") as file:
+ 90            return pickle.load(file)
+ 91
+ 92    def derive_model_x(
+ 93        self,
+ 94        spe_neon: Spectrum,
+ 95        spe_neon_units: str,
+ 96        ref_neon: Dict,
+ 97        ref_neon_units: str,
+ 98        spe_sil: Spectrum,
+ 99        spe_sil_units="cm-1",
+100        ref_sil={520.45: 1},
+101        ref_sil_units="cm-1",
+102        find_kw={"wlen": 200, "width": 1},
+103        fit_kw={},
+104        should_fit=False,
+105        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+106        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+107        extrapolate=True,
+108    ):
+109        """
+110        Derives x-calibration models using Neon and Silicon spectra.
+111        """
+112        self.components.clear()
+113        if ref_neon_units is None:
+114            ref_neon_units = "nm"
+115        if spe_neon_units is None:
+116            spe_neon_units = "cm-1"
+117        find_kw["prominence"] = spe_neon.y_noise_MAD() * self.prominence_coeff
+118        model_neon = self._derive_model_curve(
+119            spe_neon,
+120            rc2const.NEON_WL[self.laser_wl] if ref_neon is None else ref_neon,
+121            spe_units=spe_neon_units,
+122            ref_units=ref_neon_units,
+123            find_kw=find_kw,
+124            fit_peaks_kw=fit_kw,
+125            should_fit=should_fit,
+126            name="Neon calibration",
+127            match_method=match_method,
+128            interpolator_method=interpolator_method,
+129            extrapolate=extrapolate,
+130        )
+131        model_neon.nonmonotonic = self.nonmonotonic
+132        spe_sil_ne_calib = model_neon.process(
+133            spe_sil, spe_units=spe_sil_units, convert_back=False
+134        )
+135
+136        find_kw["prominence"] = spe_sil_ne_calib.y_noise_MAD() * self.prominence_coeff
+137        model_si = self._derive_model_zero(
+138            spe_sil_ne_calib,
+139            ref=ref_sil,
+140            spe_units=model_neon.model_units,
+141            ref_units=ref_sil_units,
+142            find_kw=find_kw,
+143            fit_peaks_kw=fit_kw,
+144            should_fit=True,
+145            name="Si laser zeroing",
+146        )
+147        return (model_neon, model_si)
+148
+149    def _derive_model_curve(
+150        self,
+151        spe: Spectrum,
+152        ref=Dict[float, float],
+153        spe_units="cm-1",
+154        ref_units="nm",
+155        find_kw=None,
+156        fit_peaks_kw=None,
+157        should_fit=False,
+158        name="X calibration",
+159        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+160        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+161        extrapolate=True,
+162    ):
+163        if find_kw is None:
+164            find_kw = {}
+165        if fit_peaks_kw is None:
+166            fit_peaks_kw = {}
+167        reference_peaks = rc2const.NEON_WL[self.laser_wl] if ref is None else ref
+168        calibration_x = XCalibrationComponent(
+169            self.laser_wl,
+170            spe=spe,
+171            spe_units=spe_units,
+172            ref=reference_peaks,
+173            ref_units=ref_units,
+174            match_method=match_method,
+175            interpolator_method=interpolator_method,
+176            extrapolate=extrapolate,
+177        )
+178        calibration_x.nonmonotonic = self.nonmonotonic
+179        calibration_x.derive_model(
+180            find_kw=find_kw, fit_peaks_kw=fit_peaks_kw, should_fit=should_fit, name=name
+181        )
+182        self.components.append(calibration_x)
+183        return calibration_x
+184
+185    def derive_model_curve(
+186        self,
+187        spe: Spectrum,
+188        ref=None,
+189        spe_units="cm-1",
+190        ref_units="nm",
+191        find_kw={},
+192        fit_peaks_kw={},
+193        should_fit=False,
+194        name="X calibration",
+195        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+196        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+197        extrapolate=True,
+198    ):
+199        warnings.warn(
+200            message="Do not use directly. Use derive_model_x instead.",
+201            category=DeprecationWarning,
+202        )
+203        return self._derive_model_curve(
+204            spe=spe,
+205            ref=ref,
+206            spe_units=spe_units,
+207            ref_units=ref_units,
+208            find_kw=find_kw,
+209            fit_peaks_kw=fit_peaks_kw,
+210            should_fit=should_fit,
+211            name=name,
+212            match_method=match_method,
+213            interpolator_method=interpolator_method,
+214            extrapolate=True,
+215        )
+216
+217    def _derive_model_zero(
+218        self,
+219        spe: Spectrum,
+220        ref=None,
+221        spe_units="nm",
+222        ref_units="cm-1",
+223        find_kw=None,
+224        fit_peaks_kw=None,
+225        should_fit=False,
+226        name="Laser zeroing",
+227        profile="Pearson4",
+228    ):
+229        if ref is None:
+230            ref = {520.45: 1}
+231        if find_kw is None:
+232            find_kw = {}
+233        if fit_peaks_kw is None:
+234            fit_peaks_kw = {}
+235        calibration_shift = LazerZeroingComponent(
+236            self.laser_wl, spe, spe_units, ref, ref_units
+237        )
+238        calibration_shift.profile = profile
+239        calibration_shift.derive_model(
+240            find_kw=find_kw, fit_peaks_kw=fit_peaks_kw, should_fit=should_fit, name=name
+241        )
+242        _laser_zeroing_component = None
+243        for i, item in enumerate(self.components):
+244            if isinstance(item, LazerZeroingComponent):
+245                self.components[i] = calibration_shift
+246                _laser_zeroing_component = self.components[i]
+247        if (
+248            _laser_zeroing_component is None
+249        ):  # LaserZeroing component should present only once
+250            self.components.append(calibration_shift)
+251        return calibration_shift
+252
+253    def derive_model_zero(
+254        self,
+255        spe: Spectrum,
+256        ref={520.45: 1},
+257        spe_units="nm",
+258        ref_units="cm-1",
+259        find_kw=None,
+260        fit_peaks_kw=None,
+261        should_fit=False,
+262        name="X Shift",
+263        profile="Pearson4",
+264    ):
+265        if find_kw is None:
+266            find_kw = {}
+267        if fit_peaks_kw is None:
+268            fit_peaks_kw = {}
+269        warnings.warn(
+270            message="Do not use directly. Use derive_model_x instead.",
+271            category=DeprecationWarning,
+272        )
+273        return self._derive_model_zero(
+274            spe=spe,
+275            ref=ref,
+276            spe_units=spe_units,
+277            ref_units=ref_units,
+278            find_kw=find_kw,
+279            fit_peaks_kw=fit_peaks_kw,
+280            should_fit=should_fit,
+281            name=name,
+282            profile=profile,
+283        )
+284
+285    def apply_calibration_x(self, old_spe: Spectrum, spe_units="cm-1"):
+286        # neon calibration converts to nm
+287        # silicon calibration takes nm and converts back to cm-1 using laser zeroing
+288        new_spe = old_spe
+289        model_units = spe_units
+290        for model in self.components:
+291            # TODO: tbd find out if to convert units
+292            if model.enabled:
+293                new_spe = model.process(new_spe, model_units, convert_back=False)
+294                model_units = model.model_units
+295        return new_spe
+296
+297    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+298        if ax is None:
+299            fig, ax = plt.subplots(1, 1, figsize=(12, 4))
+300        self._plot(ax, **kwargs)
+301        return ax
+302
+303    def _plot(self, ax, **kwargs):
+304        for index, model in enumerate(self.components):
+305            model._plot(ax, **kwargs)
+306            break
+307
+308    @staticmethod
+309    def calibration_model_factory(
+310        laser_wl,
+311        spe_neon: Spectrum,
+312        spe_sil: Spectrum,
+313        neon_wl=None,
+314        find_kw=None,
+315        fit_peaks_kw=None,
+316        should_fit=False,
+317        prominence_coeff=3,
+318        si_profile="Pearson4",
+319        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+320        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+321        extrapolate=True,
+322    ):
+323        if neon_wl is None:
+324            neon_wl = rc2const.NEON_WL[laser_wl]
+325        if find_kw is None:
+326            find_kw = {"wlen": 100, "width": 1}
+327        if fit_peaks_kw is None:
+328            fit_peaks_kw = {}
+329        calmodel = CalibrationModel(laser_wl)
+330        calmodel.prominence_coeff = prominence_coeff
+331        find_kw["prominence"] = spe_neon.y_noise_MAD() * calmodel.prominence_coeff
+332
+333        model_neon = calmodel._derive_model_curve(
+334            spe=spe_neon,
+335            ref=neon_wl,
+336            spe_units="cm-1",
+337            ref_units="nm",
+338            find_kw=find_kw,
+339            fit_peaks_kw=fit_peaks_kw,
+340            should_fit=should_fit,
+341            name="Neon calibration",
+342            match_method=match_method,
+343            interpolator_method=interpolator_method,
+344            extrapolate=extrapolate,
+345        )
+346        spe_sil_ne_calib = model_neon.process(
+347            spe_sil, spe_units="cm-1", convert_back=False
+348        )
+349        find_kw["prominence"] = (
+350            spe_sil_ne_calib.y_noise_MAD() * calmodel.prominence_coeff
+351        )
+352        calmodel.derive_model_zero(
+353            spe=spe_sil_ne_calib,
+354            ref={520.45: 1},
+355            spe_units=model_neon.model_units,
+356            ref_units="cm-1",
+357            find_kw=find_kw,
+358            fit_peaks_kw=fit_peaks_kw,
+359            should_fit=True,
+360            name="Si calibration",
+361            profile=si_profile,
+362        )
+363        return calmodel
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + CalibrationModel(laser_wl: int) + + + +
+ + 
23    def __init__(self, laser_wl: int):
+24        """
+25        Initializes a CalibrationModel instance.
+26
+27        Args:
+28            laser_wl:
+29                The wavelength of the laser used for calibration.
+30
+31        Example:
+32        ```python
+33        # Create an instance of CalibrationModel
+34        import ramanchada2 as rc2
+35        import ramanchada2.misc.constants as rc2const
+36        from ramanchada2.protocols.calibration import CalibrationModel
+37        laser_wl=785
+38        calmodel = CalibrationModel.calibration_model_factory(
+39            laser_wl,
+40            spe_neon,
+41            spe_sil,
+42            neon_wl=rc2const.NEON_WL[laser_wl],
+43            find_kw={"wlen": 200, "width": 1},
+44            fit_peaks_kw={},
+45            should_fit=False,
+46        )
+47        # Store (optional)
+48        calmodel.save(modelfile)
+49        # Load (optional)
+50        calmodel = CalibrationModel.from_file(modelfile)
+51        # Apply to new spectrum
+52        calmodel.apply_calibration_x(
+53            spe_to_calibrate,
+54            spe_units="cm-1"
+55            )
+56        ```
+57        """
+58        super(ProcessingModel, self).__init__()
+59        super(Plottable, self).__init__()
+60        self.set_laser_wavelength(laser_wl)
+61        self.prominence_coeff = 3
+
+ + +

Initializes a CalibrationModel instance.

+ +
Arguments:
+ +
    +
  • laser_wl: The wavelength of the laser used for calibration.
    • +
+ +

Example:

+ +
+
# Create an instance of CalibrationModel
+import ramanchada2 as rc2
+import ramanchada2.misc.constants as rc2const
+from ramanchada2.protocols.calibration import CalibrationModel
+laser_wl=785
+calmodel = CalibrationModel.calibration_model_factory(
+    laser_wl,
+    spe_neon,
+    spe_sil,
+    neon_wl=rc2const.NEON_WL[laser_wl],
+    find_kw={"wlen": 200, "width": 1},
+    fit_peaks_kw={},
+    should_fit=False,
+)
+# Store (optional)
+calmodel.save(modelfile)
+# Load (optional)
+calmodel = CalibrationModel.from_file(modelfile)
+# Apply to new spectrum
+calmodel.apply_calibration_x(
+    spe_to_calibrate,
+    spe_units="cm-1"
+    )
+
+
+
+ + +
+
+
+ nonmonotonic: Literal['ignore', 'nan', 'error'] = +'nan' + + +
+ + +

A class representing a calibration model for Raman spectrum.

+
+ + +
+
+
+ prominence_coeff + + +
+ + + + +
+
+ +
+ + def + set_laser_wavelength(self, laser_wl): + + + +
+ + 
63    def set_laser_wavelength(self, laser_wl):
+64        """
+65        Sets the wavelength of the laser used for calibration.
+66        """
+67        self.clear()
+68        self.laser_wl = laser_wl
+
+ + +

Sets the wavelength of the laser used for calibration.

+
+ + +
+
+ +
+ + def + clear(self): + + + +
+ + 
70    def clear(self):
+71        """
+72        Clears the calibration model.
+73        """
+74        self.laser_wl = None
+75        self.components = []
+
+ + +

Clears the calibration model.

+
+ + +
+
+ +
+ + def + save(self, filename): + + + +
+ + 
77    def save(self, filename):
+78        """
+79        Saves the calibration model to a file.
+80        """
+81        with open(filename, "wb") as file:
+82            pickle.dump(self, file)
+
+ + +

Saves the calibration model to a file.

+
+ + +
+
+ +
+
@staticmethod
+ + def + from_file(filename): + + + +
+ + 
84    @staticmethod
+85    def from_file(filename):
+86        """
+87        Loads a calibration model from a file.
+88        """
+89        with open(filename, "rb") as file:
+90            return pickle.load(file)
+
+ + +

Loads a calibration model from a file.

+
+ + +
+
+ +
+ + def + derive_model_x( self, spe_neon: ramanchada2.spectrum.spectrum.Spectrum, spe_neon_units: str, ref_neon: Dict, ref_neon_units: str, spe_sil: ramanchada2.spectrum.spectrum.Spectrum, spe_sil_units='cm-1', ref_sil={520.45: 1}, ref_sil_units='cm-1', find_kw={'wlen': 200, 'width': 1}, fit_kw={}, should_fit=False, match_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster', interpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf', extrapolate=True): + + + +
+ + 
 92    def derive_model_x(
+ 93        self,
+ 94        spe_neon: Spectrum,
+ 95        spe_neon_units: str,
+ 96        ref_neon: Dict,
+ 97        ref_neon_units: str,
+ 98        spe_sil: Spectrum,
+ 99        spe_sil_units="cm-1",
+100        ref_sil={520.45: 1},
+101        ref_sil_units="cm-1",
+102        find_kw={"wlen": 200, "width": 1},
+103        fit_kw={},
+104        should_fit=False,
+105        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+106        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+107        extrapolate=True,
+108    ):
+109        """
+110        Derives x-calibration models using Neon and Silicon spectra.
+111        """
+112        self.components.clear()
+113        if ref_neon_units is None:
+114            ref_neon_units = "nm"
+115        if spe_neon_units is None:
+116            spe_neon_units = "cm-1"
+117        find_kw["prominence"] = spe_neon.y_noise_MAD() * self.prominence_coeff
+118        model_neon = self._derive_model_curve(
+119            spe_neon,
+120            rc2const.NEON_WL[self.laser_wl] if ref_neon is None else ref_neon,
+121            spe_units=spe_neon_units,
+122            ref_units=ref_neon_units,
+123            find_kw=find_kw,
+124            fit_peaks_kw=fit_kw,
+125            should_fit=should_fit,
+126            name="Neon calibration",
+127            match_method=match_method,
+128            interpolator_method=interpolator_method,
+129            extrapolate=extrapolate,
+130        )
+131        model_neon.nonmonotonic = self.nonmonotonic
+132        spe_sil_ne_calib = model_neon.process(
+133            spe_sil, spe_units=spe_sil_units, convert_back=False
+134        )
+135
+136        find_kw["prominence"] = spe_sil_ne_calib.y_noise_MAD() * self.prominence_coeff
+137        model_si = self._derive_model_zero(
+138            spe_sil_ne_calib,
+139            ref=ref_sil,
+140            spe_units=model_neon.model_units,
+141            ref_units=ref_sil_units,
+142            find_kw=find_kw,
+143            fit_peaks_kw=fit_kw,
+144            should_fit=True,
+145            name="Si laser zeroing",
+146        )
+147        return (model_neon, model_si)
+
+ + +

Derives x-calibration models using Neon and Silicon spectra.

+
+ + +
+
+ +
+ + def + derive_model_curve( self, spe: ramanchada2.spectrum.spectrum.Spectrum, ref=None, spe_units='cm-1', ref_units='nm', find_kw={}, fit_peaks_kw={}, should_fit=False, name='X calibration', match_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster', interpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf', extrapolate=True): + + + +
+ + 
185    def derive_model_curve(
+186        self,
+187        spe: Spectrum,
+188        ref=None,
+189        spe_units="cm-1",
+190        ref_units="nm",
+191        find_kw={},
+192        fit_peaks_kw={},
+193        should_fit=False,
+194        name="X calibration",
+195        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+196        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+197        extrapolate=True,
+198    ):
+199        warnings.warn(
+200            message="Do not use directly. Use derive_model_x instead.",
+201            category=DeprecationWarning,
+202        )
+203        return self._derive_model_curve(
+204            spe=spe,
+205            ref=ref,
+206            spe_units=spe_units,
+207            ref_units=ref_units,
+208            find_kw=find_kw,
+209            fit_peaks_kw=fit_peaks_kw,
+210            should_fit=should_fit,
+211            name=name,
+212            match_method=match_method,
+213            interpolator_method=interpolator_method,
+214            extrapolate=True,
+215        )
+
+ + + + +
+
+ +
+ + def + derive_model_zero( self, spe: ramanchada2.spectrum.spectrum.Spectrum, ref={520.45: 1}, spe_units='nm', ref_units='cm-1', find_kw=None, fit_peaks_kw=None, should_fit=False, name='X Shift', profile='Pearson4'): + + + +
+ + 
253    def derive_model_zero(
+254        self,
+255        spe: Spectrum,
+256        ref={520.45: 1},
+257        spe_units="nm",
+258        ref_units="cm-1",
+259        find_kw=None,
+260        fit_peaks_kw=None,
+261        should_fit=False,
+262        name="X Shift",
+263        profile="Pearson4",
+264    ):
+265        if find_kw is None:
+266            find_kw = {}
+267        if fit_peaks_kw is None:
+268            fit_peaks_kw = {}
+269        warnings.warn(
+270            message="Do not use directly. Use derive_model_x instead.",
+271            category=DeprecationWarning,
+272        )
+273        return self._derive_model_zero(
+274            spe=spe,
+275            ref=ref,
+276            spe_units=spe_units,
+277            ref_units=ref_units,
+278            find_kw=find_kw,
+279            fit_peaks_kw=fit_peaks_kw,
+280            should_fit=should_fit,
+281            name=name,
+282            profile=profile,
+283        )
+
+ + + + +
+
+ +
+ + def + apply_calibration_x( self, old_spe: ramanchada2.spectrum.spectrum.Spectrum, spe_units='cm-1'): + + + +
+ + 
285    def apply_calibration_x(self, old_spe: Spectrum, spe_units="cm-1"):
+286        # neon calibration converts to nm
+287        # silicon calibration takes nm and converts back to cm-1 using laser zeroing
+288        new_spe = old_spe
+289        model_units = spe_units
+290        for model in self.components:
+291            # TODO: tbd find out if to convert units
+292            if model.enabled:
+293                new_spe = model.process(new_spe, model_units, convert_back=False)
+294                model_units = model.model_units
+295        return new_spe
+
+ + + + +
+
+ +
+ + def + plot(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes: + + + +
+ + 
297    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+298        if ax is None:
+299            fig, ax = plt.subplots(1, 1, figsize=(12, 4))
+300        self._plot(ax, **kwargs)
+301        return ax
+
+ + + + +
+
+ +
+
@staticmethod
+ + def + calibration_model_factory( laser_wl, spe_neon: ramanchada2.spectrum.spectrum.Spectrum, spe_sil: ramanchada2.spectrum.spectrum.Spectrum, neon_wl=None, find_kw=None, fit_peaks_kw=None, should_fit=False, prominence_coeff=3, si_profile='Pearson4', match_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster', interpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf', extrapolate=True): + + + +
+ + 
308    @staticmethod
+309    def calibration_model_factory(
+310        laser_wl,
+311        spe_neon: Spectrum,
+312        spe_sil: Spectrum,
+313        neon_wl=None,
+314        find_kw=None,
+315        fit_peaks_kw=None,
+316        should_fit=False,
+317        prominence_coeff=3,
+318        si_profile="Pearson4",
+319        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+320        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+321        extrapolate=True,
+322    ):
+323        if neon_wl is None:
+324            neon_wl = rc2const.NEON_WL[laser_wl]
+325        if find_kw is None:
+326            find_kw = {"wlen": 100, "width": 1}
+327        if fit_peaks_kw is None:
+328            fit_peaks_kw = {}
+329        calmodel = CalibrationModel(laser_wl)
+330        calmodel.prominence_coeff = prominence_coeff
+331        find_kw["prominence"] = spe_neon.y_noise_MAD() * calmodel.prominence_coeff
+332
+333        model_neon = calmodel._derive_model_curve(
+334            spe=spe_neon,
+335            ref=neon_wl,
+336            spe_units="cm-1",
+337            ref_units="nm",
+338            find_kw=find_kw,
+339            fit_peaks_kw=fit_peaks_kw,
+340            should_fit=should_fit,
+341            name="Neon calibration",
+342            match_method=match_method,
+343            interpolator_method=interpolator_method,
+344            extrapolate=extrapolate,
+345        )
+346        spe_sil_ne_calib = model_neon.process(
+347            spe_sil, spe_units="cm-1", convert_back=False
+348        )
+349        find_kw["prominence"] = (
+350            spe_sil_ne_calib.y_noise_MAD() * calmodel.prominence_coeff
+351        )
+352        calmodel.derive_model_zero(
+353            spe=spe_sil_ne_calib,
+354            ref={520.45: 1},
+355            spe_units=model_neon.model_units,
+356            ref_units="cm-1",
+357            find_kw=find_kw,
+358            fit_peaks_kw=fit_peaks_kw,
+359            should_fit=True,
+360            name="Si calibration",
+361            profile=si_profile,
+362        )
+363        return calmodel
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/calibration/xcalibration.html b/ramanchada2/protocols/calibration/xcalibration.html new file mode 100644 index 00000000..2588dc9a --- /dev/null +++ b/ramanchada2/protocols/calibration/xcalibration.html @@ -0,0 +1,2819 @@ + + + + + + + ramanchada2.protocols.calibration.xcalibration API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.calibration.xcalibration

+ + + + + + + 
  1import logging
+  2from typing import Literal, Dict
+  3import numpy as np
+  4from scipy.interpolate import RBFInterpolator, PchipInterpolator, CubicSpline
+  5import pandas as pd
+  6
+  7from ramanchada2.misc.utils.matchsets import (
+  8    cost_function_position,
+  9    match_peaks,
+ 10    match_peaks_cluster,
+ 11)
+ 12from ramanchada2.misc.utils import find_closest_pairs_idx
+ 13from ramanchada2.spectrum import Spectrum
+ 14from .calibration_component import CalibrationComponent
+ 15import json
+ 16
+ 17logger = logging.getLogger(__name__)
+ 18
+ 19
+ 20class XCalibrationComponent(CalibrationComponent):
+ 21    def __init__(
+ 22        self,
+ 23        laser_wl,
+ 24        spe: Spectrum,
+ 25        ref: Dict[float, float],
+ 26        spe_units: Literal["cm-1", "nm"] = "cm-1",
+ 27        ref_units: Literal["cm-1", "nm"] = "nm",
+ 28        sample="Neon",
+ 29        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+ 30        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+ 31        extrapolate=True,
+ 32    ):
+ 33        super(XCalibrationComponent, self).__init__(
+ 34            laser_wl, spe, spe_units, ref, ref_units, sample
+ 35        )
+ 36        self.spe_pos_dict = None
+ 37        self.match_method = match_method
+ 38        self.cost_function = cost_function_position
+ 39        self.interpolator_method = interpolator_method
+ 40        self.extrapolate = extrapolate
+ 41
+ 42    # @staticmethod
+ 43    # def from_json(filepath: str):
+ 44    #    rbf_intrpolator, other_data = load_xcalibration_model(filepath)
+ 45    #    calibration_x = XCalibrationComponent(laser_wl, spe, spe_units, ref, ref_units)
+ 46    #    calibration_x.model = rbf_intrpolator
+ 47    #    return calibration_x
+ 48
+ 49    def process(
+ 50        self,
+ 51        old_spe: Spectrum,
+ 52        spe_units: Literal["cm-1", "nm"] = "cm-1",
+ 53        convert_back=False,
+ 54    ):
+ 55        logger.debug(
+ 56            "convert spe_units {} --> model units {}".format(
+ 57                spe_units, self.model_units
+ 58            )
+ 59        )
+ 60        new_spe = self.convert_units(old_spe, spe_units, self.model_units)
+ 61        logger.debug("process", self)
+ 62        if self.model is None:
+ 63            return new_spe
+ 64        elif self.enabled:
+ 65            if isinstance(self.model, float):
+ 66                new_spe.x = new_spe.x + self.model
+ 67            else:
+ 68                if isinstance(self.model, CustomPChipInterpolator):
+ 69                    new_spe.x = self.model(new_spe.x)
+ 70                elif isinstance(self.model, CustomRBFInterpolator):
+ 71                    new_spe.x = self.model(new_spe.x.reshape(-1, 1))
+ 72                    if not self.extrapolate:
+ 73                        min_train, max_train = self.model.y.min(), self.model.y.max()
+ 74                        out_of_bounds = (new_spe.x < min_train) | (
+ 75                            new_spe.x > max_train
+ 76                        )
+ 77                        new_spe.x[out_of_bounds] = np.nan
+ 78
+ 79                elif isinstance(self.model, CustomCubicSplineInterpolator):
+ 80                    new_spe.x = self.model(new_spe.x)
+ 81                if not np.all(np.diff(new_spe.x) > 0):
+ 82                    if self.nonmonotonic == "nan":
+ 83                        new_spe.x = np.where(
+ 84                            np.diff(new_spe.x, prepend=new_spe.x[0]) < 0,
+ 85                            np.nan,
+ 86                            new_spe.x,
+ 87                        )
+ 88                    elif self.nonmonotonic == "error":
+ 89                        assert self.nonmonotonic
+ 90                    else:
+ 91                        pass
+ 92        if convert_back:
+ 93            return self.convert_units(new_spe, self.model_units, spe_units)
+ 94        else:
+ 95            return new_spe
+ 96
+ 97    def _plot(self, ax, **kwargs):
+ 98        ax.stem(
+ 99            self.spe_pos_dict.keys(),
+100            self.spe_pos_dict.values(),
+101            linefmt="b-",
+102            basefmt=" ",
+103            label="{} peaks".format(self.sample),
+104        )
+105        ax.twinx().stem(
+106            self.ref.keys(),
+107            self.ref.values(),
+108            linefmt="r-",
+109            basefmt=" ",
+110            label="Reference {}".format(self.sample),
+111        )
+112
+113        if self.ref_units == "cm-1":
+114            _units = r"$\mathrm{{[{self.ref_units}]}}$"
+115        else:
+116            _units = self.ref_units
+117        ax.set_xlabel(_units)
+118        ax.legend()
+119
+120    def _plot_peaks(self, ax, **kwargs):
+121        # self.model.peaks
+122        pass
+123        # fig, ax = plt.subplots(3,1,figsize=(12,4))
+124        # spe.plot(ax=ax[0].twinx(),label=spe_units)
+125        # spe_to_process.plot(ax=ax[1],label=ref_units)
+126
+127    def derive_model(
+128        self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None
+129    ):
+130        if find_kw is None:
+131            find_kw = {"sharpening": None}
+132        if fit_peaks_kw is None:
+133            fit_peaks_kw = {}
+134        # convert to ref_units
+135        logger.debug(
+136            "[{}]: convert spe_units {} to ref_units {}".format(
+137                self.name, self.spe_units, self.ref_units
+138            )
+139        )
+140        peaks_df = self.fit_peaks(find_kw, fit_peaks_kw, should_fit)
+141        x_spe, x_reference, x_distance, cost_matrix, df = self.match_peaks(
+142            threshold_max_distance=8, return_df=True
+143        )
+144
+145        self.cost_matrix = cost_matrix
+146        self.matched_peaks = df
+147        # if df is None:
+148        #    self.matched_peaks = pd.DataFrame({
+149        #        'spe': x_spe,
+150        #        'reference': x_reference,
+151        #        'distances': x_distance
+152        #    })
+153
+154        sum_of_differences = np.sum(np.abs(x_spe - x_reference)) / len(x_spe)
+155        logger.debug(
+156            "sum_of_differences original {} {}".format(
+157                sum_of_differences, self.ref_units
+158            )
+159        )
+160        if len(x_reference) == 1:
+161            _offset = x_reference[0] - x_spe[0]
+162            logger.debug(
+163                "ref",
+164                x_reference[0],
+165                "sample",
+166                x_spe[0],
+167                "offset",
+168                _offset,
+169                self.ref_units,
+170            )
+171            self.set_model(_offset, self.ref_units, peaks_df, name)
+172        else:
+173            try:
+174                if self.interpolator_method == "pchip":
+175                    # kwargs = {"kernel": "thin_plate_spline"}
+176                    interp = CustomPChipInterpolator(x_spe, x_reference)
+177                elif self.interpolator_method == "cubic_spline":
+178                    kwargs = {"bc_type": "clamped"}
+179                    interp = CustomPChipInterpolator(x_spe, x_reference)
+180                elif self.interpolator_method == "rbf":
+181                    kwargs = {
+182                        "kernel": "thin_plate_spline",
+183                        "neighbors": len(x_spe) / 3,
+184                        "smoothing": 0,
+185                    }
+186                    interp = CustomRBFInterpolator(
+187                        x_spe.reshape(-1, 1), x_reference, **kwargs
+188                    )
+189                self.set_model(interp, self.ref_units, peaks_df, name)
+190            except Exception as err:
+191                print(err)
+192                raise err
+193
+194    def match_peaks(self, threshold_max_distance=9, return_df=False):
+195        if self.match_method == "cluster":
+196            x_spe, x_reference, x_distance, _ = match_peaks_cluster(
+197                self.spe_pos_dict, self.ref
+198            )
+199            cost_matrix = None
+200            df = pd.DataFrame(
+201                {"spe": x_spe, "reference": x_reference, "distances": x_distance}
+202            )
+203            return x_spe, x_reference, x_distance, cost_matrix, df
+204        elif self.match_method == "argmin2d":
+205            x = np.array(list(self.spe_pos_dict.keys()))
+206            y = np.array(list(self.ref.keys()))
+207            x_idx, y_idx = find_closest_pairs_idx(x, y)
+208            x_spe = x[x_idx]
+209            x_reference = y[y_idx]
+210            df = pd.DataFrame(
+211                {
+212                    "spe": x_spe,
+213                    "reference": x_reference,
+214                    "distances": x_spe - x_reference,
+215                }
+216            )
+217            return x_spe, x_reference, x_spe - x_reference, None, df
+218        else:
+219            try:
+220                x_spe, x_reference, x_distance, cost_matrix, df = match_peaks(
+221                    self.spe_pos_dict,
+222                    self.ref,
+223                    threshold_max_distance=threshold_max_distance,
+224                    df=return_df,
+225                    cost_func=self.cost_function,
+226                )
+227                return x_spe, x_reference, x_distance, cost_matrix, df
+228            except Exception as err:
+229                raise err
+230
+231    def fit_peaks(self, find_kw, fit_peaks_kw, should_fit):
+232        spe_to_process = self.convert_units(self.spe, self.spe_units, self.ref_units)
+233        logger.debug("max x", max(spe_to_process.x), self.ref_units)
+234
+235        peaks_df = None
+236        self.fit_res = None
+237
+238        # instead of fit_peak_positions - we don't want movmin here
+239        # baseline removal might be done during preprocessing
+240        center_err_threshold = 0.5
+241        find_kw.update(dict(sharpening=None))
+242        cand = spe_to_process.find_peak_multipeak(**find_kw)
+243        # print(cand.get_ampl_pos_fwhm())
+244
+245        self.fit_res = spe_to_process.fit_peak_multimodel(
+246            profile="Gaussian", candidates=cand, **fit_peaks_kw, no_fit=not should_fit
+247        )
+248        peaks_df = self.fitres2df(spe_to_process)
+249        # self.fit_res.to_dataframe_peaks()
+250        if should_fit:
+251            pos, amp = self.fit_res.center_amplitude(threshold=center_err_threshold)
+252            self.spe_pos_dict = dict(zip(pos, amp))
+253        else:
+254            self.spe_pos_dict = cand.get_pos_ampl_dict()
+255        return peaks_df
+256
+257
+258class LazerZeroingComponent(CalibrationComponent):
+259    def __init__(
+260        self,
+261        laser_wl,
+262        spe: Spectrum,
+263        spe_units: Literal["cm-1", "nm"] = "nm",
+264        ref=None,
+265        ref_units: Literal["cm-1", "nm"] = "cm-1",
+266        sample="Silicon",
+267        profile="Pearson4",
+268    ):
+269        if ref is None:
+270            ref = {520.45: 1}
+271        super(LazerZeroingComponent, self).__init__(
+272            laser_wl, spe, spe_units, ref, ref_units, sample
+273        )
+274        self.profile = profile
+275
+276    def derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):
+277        if find_kw is None:
+278            find_kw = {}
+279        if fit_peaks_kw is None:
+280            fit_peaks_kw = {}
+281
+282        cand = self.spe.find_peak_multipeak(**find_kw)
+283        logger.debug(self.name, cand)
+284        self.fit_res = self.spe.fit_peak_multimodel(
+285            profile=self.profile, candidates=cand, **fit_peaks_kw
+286        )
+287        # df = self.fit_res.to_dataframe_peaks()
+288        df = self.fitres2df(self.spe)
+289        # highest peak first
+290        df = df.sort_values(by="height", ascending=False)
+291        # df = df.sort_values(by='amplitude', ascending=False)
+292        if df.empty:
+293            raise Exception("No peaks found")
+294        else:
+295            if "position" in df.columns:
+296                zero_peak_nm = df.iloc[0]["position"]
+297            elif "center" in df.columns:
+298                zero_peak_nm = df.iloc[0]["center"]
+299            # https://www.elodiz.com/calibration-and-validation-of-raman-instruments/
+300            zero_peak_cm1 = self.zero_nm_to_shift_cm_1(
+301                zero_peak_nm, zero_peak_nm, list(self.ref.keys())[0]
+302            )
+303            self.set_model(
+304                zero_peak_nm,
+305                "nm",
+306                df,
+307                "Laser zeroing using {} nm {} cm-1 ({}) ".format(
+308                    zero_peak_nm, zero_peak_cm1, self.profile
+309                ),
+310            )
+311            logger.info(self.name, f"peak {self.profile} at {zero_peak_nm} nm")
+312        # laser_wl should be calculated  based on the peak position and set instead of the nominal
+313
+314    def zero_nm_to_shift_cm_1(self, wl, zero_pos_nm, zero_ref_cm_1=520.45):
+315        return 1e7 * (1 / zero_pos_nm - 1 / wl) + zero_ref_cm_1
+316
+317    # we do not do shift (as initially implemented)
+318    # just convert the spectrum nm->cm-1 using the Si measured peak in nm and reference in cm-1
+319    # https://www.elodiz.com/calibration-and-validation-of-raman-instruments/
+320    def process(
+321        self,
+322        old_spe: Spectrum,
+323        spe_units: Literal["cm-1", "nm"] = "nm",
+324        convert_back=False,
+325    ):
+326        wl_si_ref = list(self.ref.keys())[0]
+327        logger.debug(self.name, "process", self.model, wl_si_ref)
+328        new_x = self.zero_nm_to_shift_cm_1(old_spe.x, self.model, wl_si_ref)
+329        new_spe = Spectrum(x=new_x, y=old_spe.y, metadata=old_spe.meta)
+330        # new_spe = old_spe.lazer_zero_nm_to_shift_cm_1(self.model, wl_si_ref)
+331        # print("old si", old_spe.x)
+332        # print("new si", new_spe.x)
+333        return new_spe
+334
+335    def _plot(self, ax, **kwargs):
+336        # spe_sil.plot(label="{} original".format(si_tag),ax=ax)
+337        # spe_sil_calib.plot(ax = ax,label="{} laser zeroed".format(si_tag),fmt=":")
+338        # ax.set_xlim(520.45-50,520.45+50)
+339        # ax.set_xlabel("cm-1")
+340        pass
+341
+342
+343class CustomRBFInterpolator(RBFInterpolator):
+344    def __init__(self, *args, **kwargs):
+345        super().__init__(*args, **kwargs)
+346
+347    @staticmethod
+348    def from_dict(rbf_dict=None):
+349        if rbf_dict is None:
+350            rbf_dict = {}
+351        interpolator_loaded = CustomRBFInterpolator(
+352            rbf_dict["y"],
+353            rbf_dict["d"],
+354            epsilon=rbf_dict["epsilon"],
+355            smoothing=rbf_dict["smoothing"],
+356            kernel=rbf_dict["kernel"],
+357            neighbors=rbf_dict["neighbors"],
+358        )
+359        interpolator_loaded._coeffs = rbf_dict["coeffs"]
+360        interpolator_loaded._scale = rbf_dict["scale"]
+361        interpolator_loaded._shift = rbf_dict["shift"]
+362        return interpolator_loaded
+363
+364    def to_dict(self):
+365        return {
+366            "y": self.y,
+367            "d": self.d,
+368            "d_dtype": self.d_dtype,
+369            "d_shape": self.d_shape,
+370            "epsilon": self.epsilon,
+371            "kernel": self.kernel,
+372            "neighbors": self.neighbors,
+373            "powers": self.powers,
+374            "smoothing": self.smoothing,
+375            "coeffs": self._coeffs,
+376            "scale": self._scale,
+377            "shift": self._shift,
+378        }
+379
+380    def plot(self, ax):
+381        ax.scatter(
+382            self.y.reshape(-1),
+383            self.d.reshape(-1),
+384            marker="+",
+385            color="blue",
+386            label="Matched peaks",
+387        )
+388
+389        x_range = np.linspace(self.y.min(), self.y.max(), 100)
+390        predicted_x = self(x_range.reshape(-1, 1))
+391
+392        ax.plot(
+393            x_range, predicted_x, color="red", linestyle="-", label="Calibration curve"
+394        )
+395        ax.set_xlabel("Ne peaks, nm")
+396        ax.set_ylabel("Reference peaks, nm")
+397        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+398        ax.legend()
+399
+400    def __str__(self):
+401        return f"Calibration curve {len(self.y)} points) {self.kernel}"
+402
+403
+404class CustomPChipInterpolator(PchipInterpolator):
+405    def __init__(self, x, y):
+406        super().__init__(x, y)
+407        self.x = x  # Store x values
+408        self.y = y  # Store y values
+409
+410    @staticmethod
+411    def from_dict(pchip_dict=None):
+412        if pchip_dict is None:
+413            pchip_dict = {}
+414        # Load the PCHIP interpolator from a dictionary
+415        interpolator_loaded = CustomPChipInterpolator(
+416            np.array(pchip_dict["x"]),  # Convert back to numpy arrays
+417            np.array(pchip_dict["y"]),
+418        )
+419        return interpolator_loaded
+420
+421    def to_dict(self):
+422        # Save the current x and y data to a dictionary
+423        return {
+424            "x": self.x.tolist(),  # Convert numpy arrays to lists for JSON serialization
+425            "y": self.y.tolist(),
+426        }
+427
+428    def save_coefficients(self, filename):
+429        """Save the x and y coefficients to a JSON file."""
+430        coeffs = self.to_dict()
+431        with open(filename, "w") as f:
+432            json.dump(coeffs, f)
+433
+434    @classmethod
+435    def load_coefficients(cls, filename):
+436        """Load the coefficients from a JSON file."""
+437        with open(filename, "r") as f:
+438            coeffs = json.load(f)
+439        return cls.from_dict(coeffs)
+440
+441    def plot(self, ax):
+442        """Plot the interpolation curve and the original points."""
+443        ax.scatter(self.x, self.y, marker="+", color="blue", label="Data Points")
+444
+445        x_range = np.linspace(self.x.min(), self.x.max(), 100)
+446        predicted_y = self(x_range)
+447
+448        ax.plot(
+449            x_range, predicted_y, color="red", linestyle="-", label="Calibration curve"
+450        )
+451        ax.set_xlabel("Peaks, nm")
+452        ax.set_ylabel("Reference peaks, nm")
+453        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+454        ax.legend()
+455
+456    def __str__(self):
+457        return f"Calibration curve {len(self.y)} points) (PchipInterpolator)"
+458
+459
+460class CustomCubicSplineInterpolator(CubicSpline):
+461    def __init__(self, *args, **kwargs):
+462        super().__init__(*args, **kwargs)
+463
+464    @staticmethod
+465    def from_dict(spline_dict=None):
+466        if spline_dict is None:
+467            spline_dict = {}
+468        interpolator_loaded = CustomCubicSplineInterpolator(
+469            spline_dict["x"],
+470            spline_dict["y"],
+471            bc_type=spline_dict.get("bc_type", "clamped"),
+472            extrapolate=spline_dict.get("extrapolate", True),
+473        )
+474        return interpolator_loaded
+475
+476    def to_dict(self):
+477        return {
+478            "x": self.x,
+479            "y": self.y,
+480            "bc_type": self.bc_type,
+481            "extrapolate": self.extrapolate,
+482        }
+483
+484    def plot(self, ax):
+485        ax.scatter(self.x, self.y, marker="+", color="blue", label="Data points")
+486        x_range = np.linspace(self.x.min(), self.x.max(), 100)
+487        predicted_y = self(x_range)
+488
+489        ax.plot(
+490            x_range, predicted_y, color="red", linestyle="-", label="Cubic spline curve"
+491        )
+492        ax.set_xlabel("X values")
+493        ax.set_ylabel("Y values")
+494        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+495        ax.legend()
+496
+497    def __str__(self):
+498        return f"Cubic Spline Interpolator with {len(self.x)} points."
+
+ + +
+
+
+ logger = +<Logger ramanchada2.protocols.calibration.xcalibration (WARNING)> + + +
+ + + + +
+
+ +
+ + class + XCalibrationComponent(ramanchada2.protocols.calibration.calibration_component.CalibrationComponent): + + + +
+ + 
 21class XCalibrationComponent(CalibrationComponent):
+ 22    def __init__(
+ 23        self,
+ 24        laser_wl,
+ 25        spe: Spectrum,
+ 26        ref: Dict[float, float],
+ 27        spe_units: Literal["cm-1", "nm"] = "cm-1",
+ 28        ref_units: Literal["cm-1", "nm"] = "nm",
+ 29        sample="Neon",
+ 30        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+ 31        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+ 32        extrapolate=True,
+ 33    ):
+ 34        super(XCalibrationComponent, self).__init__(
+ 35            laser_wl, spe, spe_units, ref, ref_units, sample
+ 36        )
+ 37        self.spe_pos_dict = None
+ 38        self.match_method = match_method
+ 39        self.cost_function = cost_function_position
+ 40        self.interpolator_method = interpolator_method
+ 41        self.extrapolate = extrapolate
+ 42
+ 43    # @staticmethod
+ 44    # def from_json(filepath: str):
+ 45    #    rbf_intrpolator, other_data = load_xcalibration_model(filepath)
+ 46    #    calibration_x = XCalibrationComponent(laser_wl, spe, spe_units, ref, ref_units)
+ 47    #    calibration_x.model = rbf_intrpolator
+ 48    #    return calibration_x
+ 49
+ 50    def process(
+ 51        self,
+ 52        old_spe: Spectrum,
+ 53        spe_units: Literal["cm-1", "nm"] = "cm-1",
+ 54        convert_back=False,
+ 55    ):
+ 56        logger.debug(
+ 57            "convert spe_units {} --> model units {}".format(
+ 58                spe_units, self.model_units
+ 59            )
+ 60        )
+ 61        new_spe = self.convert_units(old_spe, spe_units, self.model_units)
+ 62        logger.debug("process", self)
+ 63        if self.model is None:
+ 64            return new_spe
+ 65        elif self.enabled:
+ 66            if isinstance(self.model, float):
+ 67                new_spe.x = new_spe.x + self.model
+ 68            else:
+ 69                if isinstance(self.model, CustomPChipInterpolator):
+ 70                    new_spe.x = self.model(new_spe.x)
+ 71                elif isinstance(self.model, CustomRBFInterpolator):
+ 72                    new_spe.x = self.model(new_spe.x.reshape(-1, 1))
+ 73                    if not self.extrapolate:
+ 74                        min_train, max_train = self.model.y.min(), self.model.y.max()
+ 75                        out_of_bounds = (new_spe.x < min_train) | (
+ 76                            new_spe.x > max_train
+ 77                        )
+ 78                        new_spe.x[out_of_bounds] = np.nan
+ 79
+ 80                elif isinstance(self.model, CustomCubicSplineInterpolator):
+ 81                    new_spe.x = self.model(new_spe.x)
+ 82                if not np.all(np.diff(new_spe.x) > 0):
+ 83                    if self.nonmonotonic == "nan":
+ 84                        new_spe.x = np.where(
+ 85                            np.diff(new_spe.x, prepend=new_spe.x[0]) < 0,
+ 86                            np.nan,
+ 87                            new_spe.x,
+ 88                        )
+ 89                    elif self.nonmonotonic == "error":
+ 90                        assert self.nonmonotonic
+ 91                    else:
+ 92                        pass
+ 93        if convert_back:
+ 94            return self.convert_units(new_spe, self.model_units, spe_units)
+ 95        else:
+ 96            return new_spe
+ 97
+ 98    def _plot(self, ax, **kwargs):
+ 99        ax.stem(
+100            self.spe_pos_dict.keys(),
+101            self.spe_pos_dict.values(),
+102            linefmt="b-",
+103            basefmt=" ",
+104            label="{} peaks".format(self.sample),
+105        )
+106        ax.twinx().stem(
+107            self.ref.keys(),
+108            self.ref.values(),
+109            linefmt="r-",
+110            basefmt=" ",
+111            label="Reference {}".format(self.sample),
+112        )
+113
+114        if self.ref_units == "cm-1":
+115            _units = r"$\mathrm{{[{self.ref_units}]}}$"
+116        else:
+117            _units = self.ref_units
+118        ax.set_xlabel(_units)
+119        ax.legend()
+120
+121    def _plot_peaks(self, ax, **kwargs):
+122        # self.model.peaks
+123        pass
+124        # fig, ax = plt.subplots(3,1,figsize=(12,4))
+125        # spe.plot(ax=ax[0].twinx(),label=spe_units)
+126        # spe_to_process.plot(ax=ax[1],label=ref_units)
+127
+128    def derive_model(
+129        self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None
+130    ):
+131        if find_kw is None:
+132            find_kw = {"sharpening": None}
+133        if fit_peaks_kw is None:
+134            fit_peaks_kw = {}
+135        # convert to ref_units
+136        logger.debug(
+137            "[{}]: convert spe_units {} to ref_units {}".format(
+138                self.name, self.spe_units, self.ref_units
+139            )
+140        )
+141        peaks_df = self.fit_peaks(find_kw, fit_peaks_kw, should_fit)
+142        x_spe, x_reference, x_distance, cost_matrix, df = self.match_peaks(
+143            threshold_max_distance=8, return_df=True
+144        )
+145
+146        self.cost_matrix = cost_matrix
+147        self.matched_peaks = df
+148        # if df is None:
+149        #    self.matched_peaks = pd.DataFrame({
+150        #        'spe': x_spe,
+151        #        'reference': x_reference,
+152        #        'distances': x_distance
+153        #    })
+154
+155        sum_of_differences = np.sum(np.abs(x_spe - x_reference)) / len(x_spe)
+156        logger.debug(
+157            "sum_of_differences original {} {}".format(
+158                sum_of_differences, self.ref_units
+159            )
+160        )
+161        if len(x_reference) == 1:
+162            _offset = x_reference[0] - x_spe[0]
+163            logger.debug(
+164                "ref",
+165                x_reference[0],
+166                "sample",
+167                x_spe[0],
+168                "offset",
+169                _offset,
+170                self.ref_units,
+171            )
+172            self.set_model(_offset, self.ref_units, peaks_df, name)
+173        else:
+174            try:
+175                if self.interpolator_method == "pchip":
+176                    # kwargs = {"kernel": "thin_plate_spline"}
+177                    interp = CustomPChipInterpolator(x_spe, x_reference)
+178                elif self.interpolator_method == "cubic_spline":
+179                    kwargs = {"bc_type": "clamped"}
+180                    interp = CustomPChipInterpolator(x_spe, x_reference)
+181                elif self.interpolator_method == "rbf":
+182                    kwargs = {
+183                        "kernel": "thin_plate_spline",
+184                        "neighbors": len(x_spe) / 3,
+185                        "smoothing": 0,
+186                    }
+187                    interp = CustomRBFInterpolator(
+188                        x_spe.reshape(-1, 1), x_reference, **kwargs
+189                    )
+190                self.set_model(interp, self.ref_units, peaks_df, name)
+191            except Exception as err:
+192                print(err)
+193                raise err
+194
+195    def match_peaks(self, threshold_max_distance=9, return_df=False):
+196        if self.match_method == "cluster":
+197            x_spe, x_reference, x_distance, _ = match_peaks_cluster(
+198                self.spe_pos_dict, self.ref
+199            )
+200            cost_matrix = None
+201            df = pd.DataFrame(
+202                {"spe": x_spe, "reference": x_reference, "distances": x_distance}
+203            )
+204            return x_spe, x_reference, x_distance, cost_matrix, df
+205        elif self.match_method == "argmin2d":
+206            x = np.array(list(self.spe_pos_dict.keys()))
+207            y = np.array(list(self.ref.keys()))
+208            x_idx, y_idx = find_closest_pairs_idx(x, y)
+209            x_spe = x[x_idx]
+210            x_reference = y[y_idx]
+211            df = pd.DataFrame(
+212                {
+213                    "spe": x_spe,
+214                    "reference": x_reference,
+215                    "distances": x_spe - x_reference,
+216                }
+217            )
+218            return x_spe, x_reference, x_spe - x_reference, None, df
+219        else:
+220            try:
+221                x_spe, x_reference, x_distance, cost_matrix, df = match_peaks(
+222                    self.spe_pos_dict,
+223                    self.ref,
+224                    threshold_max_distance=threshold_max_distance,
+225                    df=return_df,
+226                    cost_func=self.cost_function,
+227                )
+228                return x_spe, x_reference, x_distance, cost_matrix, df
+229            except Exception as err:
+230                raise err
+231
+232    def fit_peaks(self, find_kw, fit_peaks_kw, should_fit):
+233        spe_to_process = self.convert_units(self.spe, self.spe_units, self.ref_units)
+234        logger.debug("max x", max(spe_to_process.x), self.ref_units)
+235
+236        peaks_df = None
+237        self.fit_res = None
+238
+239        # instead of fit_peak_positions - we don't want movmin here
+240        # baseline removal might be done during preprocessing
+241        center_err_threshold = 0.5
+242        find_kw.update(dict(sharpening=None))
+243        cand = spe_to_process.find_peak_multipeak(**find_kw)
+244        # print(cand.get_ampl_pos_fwhm())
+245
+246        self.fit_res = spe_to_process.fit_peak_multimodel(
+247            profile="Gaussian", candidates=cand, **fit_peaks_kw, no_fit=not should_fit
+248        )
+249        peaks_df = self.fitres2df(spe_to_process)
+250        # self.fit_res.to_dataframe_peaks()
+251        if should_fit:
+252            pos, amp = self.fit_res.center_amplitude(threshold=center_err_threshold)
+253            self.spe_pos_dict = dict(zip(pos, amp))
+254        else:
+255            self.spe_pos_dict = cand.get_pos_ampl_dict()
+256        return peaks_df
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + XCalibrationComponent( laser_wl, spe: ramanchada2.spectrum.spectrum.Spectrum, ref: Dict[float, float], spe_units: Literal['cm-1', 'nm'] = 'cm-1', ref_units: Literal['cm-1', 'nm'] = 'nm', sample='Neon', match_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster', interpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf', extrapolate=True) + + + +
+ + 
22    def __init__(
+23        self,
+24        laser_wl,
+25        spe: Spectrum,
+26        ref: Dict[float, float],
+27        spe_units: Literal["cm-1", "nm"] = "cm-1",
+28        ref_units: Literal["cm-1", "nm"] = "nm",
+29        sample="Neon",
+30        match_method: Literal["cluster", "argmin2d", "assignment"] = "cluster",
+31        interpolator_method: Literal["rbf", "pchip", "cubic_spline"] = "rbf",
+32        extrapolate=True,
+33    ):
+34        super(XCalibrationComponent, self).__init__(
+35            laser_wl, spe, spe_units, ref, ref_units, sample
+36        )
+37        self.spe_pos_dict = None
+38        self.match_method = match_method
+39        self.cost_function = cost_function_position
+40        self.interpolator_method = interpolator_method
+41        self.extrapolate = extrapolate
+
+ + + + +
+
+
+ spe_pos_dict + + +
+ + + + +
+
+
+ match_method + + +
+ + + + +
+
+
+ cost_function + + +
+ + + + +
+
+
+ interpolator_method + + +
+ + + + +
+
+
+ extrapolate + + +
+ + + + +
+
+ +
+ + def + process( self, old_spe: ramanchada2.spectrum.spectrum.Spectrum, spe_units: Literal['cm-1', 'nm'] = 'cm-1', convert_back=False): + + + +
+ + 
50    def process(
+51        self,
+52        old_spe: Spectrum,
+53        spe_units: Literal["cm-1", "nm"] = "cm-1",
+54        convert_back=False,
+55    ):
+56        logger.debug(
+57            "convert spe_units {} --> model units {}".format(
+58                spe_units, self.model_units
+59            )
+60        )
+61        new_spe = self.convert_units(old_spe, spe_units, self.model_units)
+62        logger.debug("process", self)
+63        if self.model is None:
+64            return new_spe
+65        elif self.enabled:
+66            if isinstance(self.model, float):
+67                new_spe.x = new_spe.x + self.model
+68            else:
+69                if isinstance(self.model, CustomPChipInterpolator):
+70                    new_spe.x = self.model(new_spe.x)
+71                elif isinstance(self.model, CustomRBFInterpolator):
+72                    new_spe.x = self.model(new_spe.x.reshape(-1, 1))
+73                    if not self.extrapolate:
+74                        min_train, max_train = self.model.y.min(), self.model.y.max()
+75                        out_of_bounds = (new_spe.x < min_train) | (
+76                            new_spe.x > max_train
+77                        )
+78                        new_spe.x[out_of_bounds] = np.nan
+79
+80                elif isinstance(self.model, CustomCubicSplineInterpolator):
+81                    new_spe.x = self.model(new_spe.x)
+82                if not np.all(np.diff(new_spe.x) > 0):
+83                    if self.nonmonotonic == "nan":
+84                        new_spe.x = np.where(
+85                            np.diff(new_spe.x, prepend=new_spe.x[0]) < 0,
+86                            np.nan,
+87                            new_spe.x,
+88                        )
+89                    elif self.nonmonotonic == "error":
+90                        assert self.nonmonotonic
+91                    else:
+92                        pass
+93        if convert_back:
+94            return self.convert_units(new_spe, self.model_units, spe_units)
+95        else:
+96            return new_spe
+
+ + + + +
+
+ +
+ + def + derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None): + + + +
+ + 
128    def derive_model(
+129        self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None
+130    ):
+131        if find_kw is None:
+132            find_kw = {"sharpening": None}
+133        if fit_peaks_kw is None:
+134            fit_peaks_kw = {}
+135        # convert to ref_units
+136        logger.debug(
+137            "[{}]: convert spe_units {} to ref_units {}".format(
+138                self.name, self.spe_units, self.ref_units
+139            )
+140        )
+141        peaks_df = self.fit_peaks(find_kw, fit_peaks_kw, should_fit)
+142        x_spe, x_reference, x_distance, cost_matrix, df = self.match_peaks(
+143            threshold_max_distance=8, return_df=True
+144        )
+145
+146        self.cost_matrix = cost_matrix
+147        self.matched_peaks = df
+148        # if df is None:
+149        #    self.matched_peaks = pd.DataFrame({
+150        #        'spe': x_spe,
+151        #        'reference': x_reference,
+152        #        'distances': x_distance
+153        #    })
+154
+155        sum_of_differences = np.sum(np.abs(x_spe - x_reference)) / len(x_spe)
+156        logger.debug(
+157            "sum_of_differences original {} {}".format(
+158                sum_of_differences, self.ref_units
+159            )
+160        )
+161        if len(x_reference) == 1:
+162            _offset = x_reference[0] - x_spe[0]
+163            logger.debug(
+164                "ref",
+165                x_reference[0],
+166                "sample",
+167                x_spe[0],
+168                "offset",
+169                _offset,
+170                self.ref_units,
+171            )
+172            self.set_model(_offset, self.ref_units, peaks_df, name)
+173        else:
+174            try:
+175                if self.interpolator_method == "pchip":
+176                    # kwargs = {"kernel": "thin_plate_spline"}
+177                    interp = CustomPChipInterpolator(x_spe, x_reference)
+178                elif self.interpolator_method == "cubic_spline":
+179                    kwargs = {"bc_type": "clamped"}
+180                    interp = CustomPChipInterpolator(x_spe, x_reference)
+181                elif self.interpolator_method == "rbf":
+182                    kwargs = {
+183                        "kernel": "thin_plate_spline",
+184                        "neighbors": len(x_spe) / 3,
+185                        "smoothing": 0,
+186                    }
+187                    interp = CustomRBFInterpolator(
+188                        x_spe.reshape(-1, 1), x_reference, **kwargs
+189                    )
+190                self.set_model(interp, self.ref_units, peaks_df, name)
+191            except Exception as err:
+192                print(err)
+193                raise err
+
+ + + + +
+
+ +
+ + def + match_peaks(self, threshold_max_distance=9, return_df=False): + + + +
+ + 
195    def match_peaks(self, threshold_max_distance=9, return_df=False):
+196        if self.match_method == "cluster":
+197            x_spe, x_reference, x_distance, _ = match_peaks_cluster(
+198                self.spe_pos_dict, self.ref
+199            )
+200            cost_matrix = None
+201            df = pd.DataFrame(
+202                {"spe": x_spe, "reference": x_reference, "distances": x_distance}
+203            )
+204            return x_spe, x_reference, x_distance, cost_matrix, df
+205        elif self.match_method == "argmin2d":
+206            x = np.array(list(self.spe_pos_dict.keys()))
+207            y = np.array(list(self.ref.keys()))
+208            x_idx, y_idx = find_closest_pairs_idx(x, y)
+209            x_spe = x[x_idx]
+210            x_reference = y[y_idx]
+211            df = pd.DataFrame(
+212                {
+213                    "spe": x_spe,
+214                    "reference": x_reference,
+215                    "distances": x_spe - x_reference,
+216                }
+217            )
+218            return x_spe, x_reference, x_spe - x_reference, None, df
+219        else:
+220            try:
+221                x_spe, x_reference, x_distance, cost_matrix, df = match_peaks(
+222                    self.spe_pos_dict,
+223                    self.ref,
+224                    threshold_max_distance=threshold_max_distance,
+225                    df=return_df,
+226                    cost_func=self.cost_function,
+227                )
+228                return x_spe, x_reference, x_distance, cost_matrix, df
+229            except Exception as err:
+230                raise err
+
+ + + + +
+
+ +
+ + def + fit_peaks(self, find_kw, fit_peaks_kw, should_fit): + + + +
+ + 
232    def fit_peaks(self, find_kw, fit_peaks_kw, should_fit):
+233        spe_to_process = self.convert_units(self.spe, self.spe_units, self.ref_units)
+234        logger.debug("max x", max(spe_to_process.x), self.ref_units)
+235
+236        peaks_df = None
+237        self.fit_res = None
+238
+239        # instead of fit_peak_positions - we don't want movmin here
+240        # baseline removal might be done during preprocessing
+241        center_err_threshold = 0.5
+242        find_kw.update(dict(sharpening=None))
+243        cand = spe_to_process.find_peak_multipeak(**find_kw)
+244        # print(cand.get_ampl_pos_fwhm())
+245
+246        self.fit_res = spe_to_process.fit_peak_multimodel(
+247            profile="Gaussian", candidates=cand, **fit_peaks_kw, no_fit=not should_fit
+248        )
+249        peaks_df = self.fitres2df(spe_to_process)
+250        # self.fit_res.to_dataframe_peaks()
+251        if should_fit:
+252            pos, amp = self.fit_res.center_amplitude(threshold=center_err_threshold)
+253            self.spe_pos_dict = dict(zip(pos, amp))
+254        else:
+255            self.spe_pos_dict = cand.get_pos_ampl_dict()
+256        return peaks_df
+
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ +
+ + class + LazerZeroingComponent(ramanchada2.protocols.calibration.calibration_component.CalibrationComponent): + + + +
+ + 
259class LazerZeroingComponent(CalibrationComponent):
+260    def __init__(
+261        self,
+262        laser_wl,
+263        spe: Spectrum,
+264        spe_units: Literal["cm-1", "nm"] = "nm",
+265        ref=None,
+266        ref_units: Literal["cm-1", "nm"] = "cm-1",
+267        sample="Silicon",
+268        profile="Pearson4",
+269    ):
+270        if ref is None:
+271            ref = {520.45: 1}
+272        super(LazerZeroingComponent, self).__init__(
+273            laser_wl, spe, spe_units, ref, ref_units, sample
+274        )
+275        self.profile = profile
+276
+277    def derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):
+278        if find_kw is None:
+279            find_kw = {}
+280        if fit_peaks_kw is None:
+281            fit_peaks_kw = {}
+282
+283        cand = self.spe.find_peak_multipeak(**find_kw)
+284        logger.debug(self.name, cand)
+285        self.fit_res = self.spe.fit_peak_multimodel(
+286            profile=self.profile, candidates=cand, **fit_peaks_kw
+287        )
+288        # df = self.fit_res.to_dataframe_peaks()
+289        df = self.fitres2df(self.spe)
+290        # highest peak first
+291        df = df.sort_values(by="height", ascending=False)
+292        # df = df.sort_values(by='amplitude', ascending=False)
+293        if df.empty:
+294            raise Exception("No peaks found")
+295        else:
+296            if "position" in df.columns:
+297                zero_peak_nm = df.iloc[0]["position"]
+298            elif "center" in df.columns:
+299                zero_peak_nm = df.iloc[0]["center"]
+300            # https://www.elodiz.com/calibration-and-validation-of-raman-instruments/
+301            zero_peak_cm1 = self.zero_nm_to_shift_cm_1(
+302                zero_peak_nm, zero_peak_nm, list(self.ref.keys())[0]
+303            )
+304            self.set_model(
+305                zero_peak_nm,
+306                "nm",
+307                df,
+308                "Laser zeroing using {} nm {} cm-1 ({}) ".format(
+309                    zero_peak_nm, zero_peak_cm1, self.profile
+310                ),
+311            )
+312            logger.info(self.name, f"peak {self.profile} at {zero_peak_nm} nm")
+313        # laser_wl should be calculated  based on the peak position and set instead of the nominal
+314
+315    def zero_nm_to_shift_cm_1(self, wl, zero_pos_nm, zero_ref_cm_1=520.45):
+316        return 1e7 * (1 / zero_pos_nm - 1 / wl) + zero_ref_cm_1
+317
+318    # we do not do shift (as initially implemented)
+319    # just convert the spectrum nm->cm-1 using the Si measured peak in nm and reference in cm-1
+320    # https://www.elodiz.com/calibration-and-validation-of-raman-instruments/
+321    def process(
+322        self,
+323        old_spe: Spectrum,
+324        spe_units: Literal["cm-1", "nm"] = "nm",
+325        convert_back=False,
+326    ):
+327        wl_si_ref = list(self.ref.keys())[0]
+328        logger.debug(self.name, "process", self.model, wl_si_ref)
+329        new_x = self.zero_nm_to_shift_cm_1(old_spe.x, self.model, wl_si_ref)
+330        new_spe = Spectrum(x=new_x, y=old_spe.y, metadata=old_spe.meta)
+331        # new_spe = old_spe.lazer_zero_nm_to_shift_cm_1(self.model, wl_si_ref)
+332        # print("old si", old_spe.x)
+333        # print("new si", new_spe.x)
+334        return new_spe
+335
+336    def _plot(self, ax, **kwargs):
+337        # spe_sil.plot(label="{} original".format(si_tag),ax=ax)
+338        # spe_sil_calib.plot(ax = ax,label="{} laser zeroed".format(si_tag),fmt=":")
+339        # ax.set_xlim(520.45-50,520.45+50)
+340        # ax.set_xlabel("cm-1")
+341        pass
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + LazerZeroingComponent( laser_wl, spe: ramanchada2.spectrum.spectrum.Spectrum, spe_units: Literal['cm-1', 'nm'] = 'nm', ref=None, ref_units: Literal['cm-1', 'nm'] = 'cm-1', sample='Silicon', profile='Pearson4') + + + +
+ + 
260    def __init__(
+261        self,
+262        laser_wl,
+263        spe: Spectrum,
+264        spe_units: Literal["cm-1", "nm"] = "nm",
+265        ref=None,
+266        ref_units: Literal["cm-1", "nm"] = "cm-1",
+267        sample="Silicon",
+268        profile="Pearson4",
+269    ):
+270        if ref is None:
+271            ref = {520.45: 1}
+272        super(LazerZeroingComponent, self).__init__(
+273            laser_wl, spe, spe_units, ref, ref_units, sample
+274        )
+275        self.profile = profile
+
+ + + + +
+
+
+ profile + + +
+ + + + +
+
+ +
+ + def + derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None): + + + +
+ + 
277    def derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):
+278        if find_kw is None:
+279            find_kw = {}
+280        if fit_peaks_kw is None:
+281            fit_peaks_kw = {}
+282
+283        cand = self.spe.find_peak_multipeak(**find_kw)
+284        logger.debug(self.name, cand)
+285        self.fit_res = self.spe.fit_peak_multimodel(
+286            profile=self.profile, candidates=cand, **fit_peaks_kw
+287        )
+288        # df = self.fit_res.to_dataframe_peaks()
+289        df = self.fitres2df(self.spe)
+290        # highest peak first
+291        df = df.sort_values(by="height", ascending=False)
+292        # df = df.sort_values(by='amplitude', ascending=False)
+293        if df.empty:
+294            raise Exception("No peaks found")
+295        else:
+296            if "position" in df.columns:
+297                zero_peak_nm = df.iloc[0]["position"]
+298            elif "center" in df.columns:
+299                zero_peak_nm = df.iloc[0]["center"]
+300            # https://www.elodiz.com/calibration-and-validation-of-raman-instruments/
+301            zero_peak_cm1 = self.zero_nm_to_shift_cm_1(
+302                zero_peak_nm, zero_peak_nm, list(self.ref.keys())[0]
+303            )
+304            self.set_model(
+305                zero_peak_nm,
+306                "nm",
+307                df,
+308                "Laser zeroing using {} nm {} cm-1 ({}) ".format(
+309                    zero_peak_nm, zero_peak_cm1, self.profile
+310                ),
+311            )
+312            logger.info(self.name, f"peak {self.profile} at {zero_peak_nm} nm")
+313        # laser_wl should be calculated  based on the peak position and set instead of the nominal
+
+ + + + +
+
+ +
+ + def + zero_nm_to_shift_cm_1(self, wl, zero_pos_nm, zero_ref_cm_1=520.45): + + + +
+ + 
315    def zero_nm_to_shift_cm_1(self, wl, zero_pos_nm, zero_ref_cm_1=520.45):
+316        return 1e7 * (1 / zero_pos_nm - 1 / wl) + zero_ref_cm_1
+
+ + + + +
+
+ +
+ + def + process( self, old_spe: ramanchada2.spectrum.spectrum.Spectrum, spe_units: Literal['cm-1', 'nm'] = 'nm', convert_back=False): + + + +
+ + 
321    def process(
+322        self,
+323        old_spe: Spectrum,
+324        spe_units: Literal["cm-1", "nm"] = "nm",
+325        convert_back=False,
+326    ):
+327        wl_si_ref = list(self.ref.keys())[0]
+328        logger.debug(self.name, "process", self.model, wl_si_ref)
+329        new_x = self.zero_nm_to_shift_cm_1(old_spe.x, self.model, wl_si_ref)
+330        new_spe = Spectrum(x=new_x, y=old_spe.y, metadata=old_spe.meta)
+331        # new_spe = old_spe.lazer_zero_nm_to_shift_cm_1(self.model, wl_si_ref)
+332        # print("old si", old_spe.x)
+333        # print("new si", new_spe.x)
+334        return new_spe
+
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ +
+ + class + CustomRBFInterpolator(scipy.interpolate._rbfinterp.RBFInterpolator): + + + +
+ + 
344class CustomRBFInterpolator(RBFInterpolator):
+345    def __init__(self, *args, **kwargs):
+346        super().__init__(*args, **kwargs)
+347
+348    @staticmethod
+349    def from_dict(rbf_dict=None):
+350        if rbf_dict is None:
+351            rbf_dict = {}
+352        interpolator_loaded = CustomRBFInterpolator(
+353            rbf_dict["y"],
+354            rbf_dict["d"],
+355            epsilon=rbf_dict["epsilon"],
+356            smoothing=rbf_dict["smoothing"],
+357            kernel=rbf_dict["kernel"],
+358            neighbors=rbf_dict["neighbors"],
+359        )
+360        interpolator_loaded._coeffs = rbf_dict["coeffs"]
+361        interpolator_loaded._scale = rbf_dict["scale"]
+362        interpolator_loaded._shift = rbf_dict["shift"]
+363        return interpolator_loaded
+364
+365    def to_dict(self):
+366        return {
+367            "y": self.y,
+368            "d": self.d,
+369            "d_dtype": self.d_dtype,
+370            "d_shape": self.d_shape,
+371            "epsilon": self.epsilon,
+372            "kernel": self.kernel,
+373            "neighbors": self.neighbors,
+374            "powers": self.powers,
+375            "smoothing": self.smoothing,
+376            "coeffs": self._coeffs,
+377            "scale": self._scale,
+378            "shift": self._shift,
+379        }
+380
+381    def plot(self, ax):
+382        ax.scatter(
+383            self.y.reshape(-1),
+384            self.d.reshape(-1),
+385            marker="+",
+386            color="blue",
+387            label="Matched peaks",
+388        )
+389
+390        x_range = np.linspace(self.y.min(), self.y.max(), 100)
+391        predicted_x = self(x_range.reshape(-1, 1))
+392
+393        ax.plot(
+394            x_range, predicted_x, color="red", linestyle="-", label="Calibration curve"
+395        )
+396        ax.set_xlabel("Ne peaks, nm")
+397        ax.set_ylabel("Reference peaks, nm")
+398        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+399        ax.legend()
+400
+401    def __str__(self):
+402        return f"Calibration curve {len(self.y)} points) {self.kernel}"
+
+ + +

Radial basis function (RBF) interpolation in N dimensions.

+ +

Parameters

+ +

y : (npoints, ndims) array_like + 2-D array of data point coordinates. +d : (npoints, ...) array_like + N-D array of data values at y. The length of d along the first + axis must be equal to the length of y. Unlike some interpolators, the + interpolation axis cannot be changed. +neighbors : int, optional + If specified, the value of the interpolant at each evaluation point + will be computed using only this many nearest data points. All the data + points are used by default. +smoothing : float or (npoints, ) array_like, optional + Smoothing parameter. The interpolant perfectly fits the data when this + is set to 0. For large values, the interpolant approaches a least + squares fit of a polynomial with the specified degree. Default is 0. +kernel : str, optional + Type of RBF. This should be one of

+ +
    - 'linear'               : ``-r``
+    - 'thin_plate_spline'    : ``r**2 * log(r)``
+    - 'cubic'                : ``r**3``
+    - 'quintic'              : ``-r**5``
+    - 'multiquadric'         : ``-sqrt(1 + r**2)``
+    - 'inverse_multiquadric' : ``1/sqrt(1 + r**2)``
+    - 'inverse_quadratic'    : ``1/(1 + r**2)``
+    - 'gaussian'             : ``exp(-r**2)``
+
+Default is 'thin_plate_spline'.
+
+ +

epsilon : float, optional + Shape parameter that scales the input to the RBF. If kernel is + 'linear', 'thin_plate_spline', 'cubic', or 'quintic', this defaults to + 1 and can be ignored because it has the same effect as scaling the + smoothing parameter. Otherwise, this must be specified. +degree : int, optional + Degree of the added polynomial. For some RBFs the interpolant may not + be well-posed if the polynomial degree is too small. Those RBFs and + their corresponding minimum degrees are

+ +
    - 'multiquadric'      : 0
+    - 'linear'            : 0
+    - 'thin_plate_spline' : 1
+    - 'cubic'             : 1
+    - 'quintic'           : 2
+
+The default value is the minimum degree for `kernel` or 0 if there is
+no minimum degree. Set this to -1 for no added polynomial.
+
+ +

Notes

+ +

An RBF is a scalar valued function in N-dimensional space whose value at +\( x \) can be expressed in terms of \( r=||x - c|| \), where \( c \) +is the center of the RBF.

+ +

An RBF interpolant for the vector of data values \( d \), which are from +locations \( y \), is a linear combination of RBFs centered at \( y \) +plus a polynomial with a specified degree. The RBF interpolant is written +as

+ +

$$f(x) = K(x, y) a + P(x) b,$$

+ +

where \( K(x, y) \) is a matrix of RBFs with centers at \( y \) +evaluated at the points \( x \), and \( P(x) \) is a matrix of +monomials, which span polynomials with the specified degree, evaluated at +\( x \). The coefficients \( a \) and \( b \) are the solution to the +linear equations

+ +

$$(K(y, y) + \lambda I) a + P(y) b = d$$

+ +

and

+ +

$$P(y)^T a = 0,$$

+ +

where \( \lambda \) is a non-negative smoothing parameter that controls +how well we want to fit the data. The data are fit exactly when the +smoothing parameter is 0.

+ +

The above system is uniquely solvable if the following requirements are +met:

+ +
- \\( P(y) \\) must have full column rank. \\( P(y) \\) always has full
+  column rank when `degree` is -1 or 0. When `degree` is 1,
+  \\( P(y) \\) has full column rank if the data point locations are not
+  all collinear (N=2), coplanar (N=3), etc.
+- If `kernel` is 'multiquadric', 'linear', 'thin_plate_spline',
+  'cubic', or 'quintic', then `degree` must not be lower than the
+  minimum value listed above.
+- If `smoothing` is 0, then each data point location must be distinct.
+
+ +

When using an RBF that is not scale invariant ('multiquadric', +'inverse_multiquadric', 'inverse_quadratic', or 'gaussian'), an appropriate +shape parameter must be chosen (e.g., through cross validation). Smaller +values for the shape parameter correspond to wider RBFs. The problem can +become ill-conditioned or singular when the shape parameter is too small.

+ +

The memory required to solve for the RBF interpolation coefficients +increases quadratically with the number of data points, which can become +impractical when interpolating more than about a thousand data points. +To overcome memory limitations for large interpolation problems, the +neighbors argument can be specified to compute an RBF interpolant for +each evaluation point using only the nearest data points.

+ +

New in version 1.7.0.

+ +

See Also

+ +

NearestNDInterpolator +LinearNDInterpolator +CloughTocher2DInterpolator

+ +

References

+ +

Examples

+ +

Demonstrate interpolating scattered data to a grid in 2-D.

+ +
+
>>> import numpy as np
+>>> import matplotlib.pyplot as plt
+>>> from scipy.interpolate import RBFInterpolator
+>>> from scipy.stats.qmc import Halton
+
+
+ +
+
>>> rng = np.random.default_rng()
+>>> xobs = 2*Halton(2, seed=rng).random(100) - 1
+>>> yobs = np.sum(xobs, axis=1)*np.exp(-6*np.sum(xobs**2, axis=1))
+
+
+ +
+
>>> xgrid = np.mgrid[-1:1:50j, -1:1:50j]
+>>> xflat = xgrid.reshape(2, -1).T
+>>> yflat = RBFInterpolator(xobs, yobs)(xflat)
+>>> ygrid = yflat.reshape(50, 50)
+
+
+ +
+
>>> fig, ax = plt.subplots()
+>>> ax.pcolormesh(*xgrid, ygrid, vmin=-0.25, vmax=0.25, shading='gouraud')
+>>> p = ax.scatter(*xobs.T, c=yobs, s=50, ec='k', vmin=-0.25, vmax=0.25)
+>>> fig.colorbar(p)
+>>> plt.show()
+
+
+ +
+
+
    +
+
+
+ + +
+ +
+ + CustomRBFInterpolator(*args, **kwargs) + + + +
+ + 
345    def __init__(self, *args, **kwargs):
+346        super().__init__(*args, **kwargs)
+
+ + + + +
+
+ +
+
@staticmethod
+ + def + from_dict(rbf_dict=None): + + + +
+ + 
348    @staticmethod
+349    def from_dict(rbf_dict=None):
+350        if rbf_dict is None:
+351            rbf_dict = {}
+352        interpolator_loaded = CustomRBFInterpolator(
+353            rbf_dict["y"],
+354            rbf_dict["d"],
+355            epsilon=rbf_dict["epsilon"],
+356            smoothing=rbf_dict["smoothing"],
+357            kernel=rbf_dict["kernel"],
+358            neighbors=rbf_dict["neighbors"],
+359        )
+360        interpolator_loaded._coeffs = rbf_dict["coeffs"]
+361        interpolator_loaded._scale = rbf_dict["scale"]
+362        interpolator_loaded._shift = rbf_dict["shift"]
+363        return interpolator_loaded
+
+ + + + +
+
+ +
+ + def + to_dict(self): + + + +
+ + 
365    def to_dict(self):
+366        return {
+367            "y": self.y,
+368            "d": self.d,
+369            "d_dtype": self.d_dtype,
+370            "d_shape": self.d_shape,
+371            "epsilon": self.epsilon,
+372            "kernel": self.kernel,
+373            "neighbors": self.neighbors,
+374            "powers": self.powers,
+375            "smoothing": self.smoothing,
+376            "coeffs": self._coeffs,
+377            "scale": self._scale,
+378            "shift": self._shift,
+379        }
+
+ + + + +
+
+ +
+ + def + plot(self, ax): + + + +
+ + 
381    def plot(self, ax):
+382        ax.scatter(
+383            self.y.reshape(-1),
+384            self.d.reshape(-1),
+385            marker="+",
+386            color="blue",
+387            label="Matched peaks",
+388        )
+389
+390        x_range = np.linspace(self.y.min(), self.y.max(), 100)
+391        predicted_x = self(x_range.reshape(-1, 1))
+392
+393        ax.plot(
+394            x_range, predicted_x, color="red", linestyle="-", label="Calibration curve"
+395        )
+396        ax.set_xlabel("Ne peaks, nm")
+397        ax.set_ylabel("Reference peaks, nm")
+398        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+399        ax.legend()
+
+ + + + +
+
+
+ +
+ + class + CustomPChipInterpolator(scipy.interpolate._cubic.PchipInterpolator): + + + +
+ + 
405class CustomPChipInterpolator(PchipInterpolator):
+406    def __init__(self, x, y):
+407        super().__init__(x, y)
+408        self.x = x  # Store x values
+409        self.y = y  # Store y values
+410
+411    @staticmethod
+412    def from_dict(pchip_dict=None):
+413        if pchip_dict is None:
+414            pchip_dict = {}
+415        # Load the PCHIP interpolator from a dictionary
+416        interpolator_loaded = CustomPChipInterpolator(
+417            np.array(pchip_dict["x"]),  # Convert back to numpy arrays
+418            np.array(pchip_dict["y"]),
+419        )
+420        return interpolator_loaded
+421
+422    def to_dict(self):
+423        # Save the current x and y data to a dictionary
+424        return {
+425            "x": self.x.tolist(),  # Convert numpy arrays to lists for JSON serialization
+426            "y": self.y.tolist(),
+427        }
+428
+429    def save_coefficients(self, filename):
+430        """Save the x and y coefficients to a JSON file."""
+431        coeffs = self.to_dict()
+432        with open(filename, "w") as f:
+433            json.dump(coeffs, f)
+434
+435    @classmethod
+436    def load_coefficients(cls, filename):
+437        """Load the coefficients from a JSON file."""
+438        with open(filename, "r") as f:
+439            coeffs = json.load(f)
+440        return cls.from_dict(coeffs)
+441
+442    def plot(self, ax):
+443        """Plot the interpolation curve and the original points."""
+444        ax.scatter(self.x, self.y, marker="+", color="blue", label="Data Points")
+445
+446        x_range = np.linspace(self.x.min(), self.x.max(), 100)
+447        predicted_y = self(x_range)
+448
+449        ax.plot(
+450            x_range, predicted_y, color="red", linestyle="-", label="Calibration curve"
+451        )
+452        ax.set_xlabel("Peaks, nm")
+453        ax.set_ylabel("Reference peaks, nm")
+454        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+455        ax.legend()
+456
+457    def __str__(self):
+458        return f"Calibration curve {len(self.y)} points) (PchipInterpolator)"
+
+ + +

PCHIP 1-D monotonic cubic interpolation.

+ +

x and y are arrays of values used to approximate some function f, +with y = f(x). The interpolant uses monotonic cubic splines +to find the value of new points. (PCHIP stands for Piecewise Cubic +Hermite Interpolating Polynomial).

+ +

Parameters

+ +

x : ndarray, shape (npoints, ) + A 1-D array of monotonically increasing real values. x cannot + include duplicate values (otherwise f is overspecified) +y : ndarray, shape (..., npoints, ...) + A N-D array of real values. y's length along the interpolation + axis must be equal to the length of x. Use the axis + parameter to select the interpolation axis.

+ +
*Deprecated since version 1.13.0:*
+Complex data is deprecated and will raise an error in SciPy 1.15.0.
+If you are trying to use the real components of the passed array,
+use ``np.real`` on ``y``.
+
+ +

axis : int, optional + Axis in the y array corresponding to the x-coordinate values. Defaults + to axis=0. +extrapolate : bool, optional + Whether to extrapolate to out-of-bounds points based on first + and last intervals, or to return NaNs.

+ +

Methods

+ +

__call__ +derivative +antiderivative +roots

+ +

See Also

+ +

CubicHermiteSpline : Piecewise-cubic interpolator. +Akima1DInterpolator : Akima 1D interpolator. +CubicSpline : Cubic spline data interpolator. +PPoly : Piecewise polynomial in terms of coefficients and breakpoints.

+ +

Notes

+ +

The interpolator preserves monotonicity in the interpolation data and does +not overshoot if the data is not smooth.

+ +

The first derivatives are guaranteed to be continuous, but the second +derivatives may jump at \( x_k \).

+ +

Determines the derivatives at the points \( x_k \), \( f'_k \), +by using PCHIP algorithm 1.

+ +

Let \( h_k = x_{k+1} - x_k \), and \( d_k = (y_{k+1} - y_k) / h_k \) +are the slopes at internal points \( x_k \). +If the signs of \( d_k \) and \( d_{k-1} \) are different or either of +them equals zero, then \( f'_k = 0 \). Otherwise, it is given by the +weighted harmonic mean

+ +

$$\frac{w_1 + w_2}{f'_k} = \frac{w_1}{d_{k-1}} + \frac{w_2}{d_k}$$

+ +

where \( w_1 = 2 h_k + h_{k-1} \) and \( w_2 = h_k + 2 h_{k-1} \).

+ +

The end slopes are set using a one-sided scheme 2.

+ +

References

+ +
+
+
    +
  1. +

    F. N. Fritsch and J. Butland, +A method for constructing local +monotone piecewise cubic interpolants, +SIAM J. Sci. Comput., 5(2), 300-304 (1984). +:doi:10.1137/0905021. ↩

    +
    2. + +
  2. +

    see, e.g., C. Moler, Numerical Computing with Matlab, 2004. +:doi:10.1137/1.9780898717952 ↩

    +
    3. +
+
+
+ + +
+ +
+ + CustomPChipInterpolator(x, y) + + + +
+ + 
406    def __init__(self, x, y):
+407        super().__init__(x, y)
+408        self.x = x  # Store x values
+409        self.y = y  # Store y values
+
+ + + + +
+
+
+ x + + +
+ + + + +
+
+
+ y + + +
+ + + + +
+
+ +
+
@staticmethod
+ + def + from_dict(pchip_dict=None): + + + +
+ + 
411    @staticmethod
+412    def from_dict(pchip_dict=None):
+413        if pchip_dict is None:
+414            pchip_dict = {}
+415        # Load the PCHIP interpolator from a dictionary
+416        interpolator_loaded = CustomPChipInterpolator(
+417            np.array(pchip_dict["x"]),  # Convert back to numpy arrays
+418            np.array(pchip_dict["y"]),
+419        )
+420        return interpolator_loaded
+
+ + + + +
+
+ +
+ + def + to_dict(self): + + + +
+ + 
422    def to_dict(self):
+423        # Save the current x and y data to a dictionary
+424        return {
+425            "x": self.x.tolist(),  # Convert numpy arrays to lists for JSON serialization
+426            "y": self.y.tolist(),
+427        }
+
+ + + + +
+
+ +
+ + def + save_coefficients(self, filename): + + + +
+ + 
429    def save_coefficients(self, filename):
+430        """Save the x and y coefficients to a JSON file."""
+431        coeffs = self.to_dict()
+432        with open(filename, "w") as f:
+433            json.dump(coeffs, f)
+
+ + +

Save the x and y coefficients to a JSON file.

+
+ + +
+
+ +
+
@classmethod
+ + def + load_coefficients(cls, filename): + + + +
+ + 
435    @classmethod
+436    def load_coefficients(cls, filename):
+437        """Load the coefficients from a JSON file."""
+438        with open(filename, "r") as f:
+439            coeffs = json.load(f)
+440        return cls.from_dict(coeffs)
+
+ + +

Load the coefficients from a JSON file.

+
+ + +
+
+ +
+ + def + plot(self, ax): + + + +
+ + 
442    def plot(self, ax):
+443        """Plot the interpolation curve and the original points."""
+444        ax.scatter(self.x, self.y, marker="+", color="blue", label="Data Points")
+445
+446        x_range = np.linspace(self.x.min(), self.x.max(), 100)
+447        predicted_y = self(x_range)
+448
+449        ax.plot(
+450            x_range, predicted_y, color="red", linestyle="-", label="Calibration curve"
+451        )
+452        ax.set_xlabel("Peaks, nm")
+453        ax.set_ylabel("Reference peaks, nm")
+454        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+455        ax.legend()
+
+ + +

Plot the interpolation curve and the original points.

+
+ + +
+
+
+ +
+ + class + CustomCubicSplineInterpolator(scipy.interpolate._cubic.CubicSpline): + + + +
+ + 
461class CustomCubicSplineInterpolator(CubicSpline):
+462    def __init__(self, *args, **kwargs):
+463        super().__init__(*args, **kwargs)
+464
+465    @staticmethod
+466    def from_dict(spline_dict=None):
+467        if spline_dict is None:
+468            spline_dict = {}
+469        interpolator_loaded = CustomCubicSplineInterpolator(
+470            spline_dict["x"],
+471            spline_dict["y"],
+472            bc_type=spline_dict.get("bc_type", "clamped"),
+473            extrapolate=spline_dict.get("extrapolate", True),
+474        )
+475        return interpolator_loaded
+476
+477    def to_dict(self):
+478        return {
+479            "x": self.x,
+480            "y": self.y,
+481            "bc_type": self.bc_type,
+482            "extrapolate": self.extrapolate,
+483        }
+484
+485    def plot(self, ax):
+486        ax.scatter(self.x, self.y, marker="+", color="blue", label="Data points")
+487        x_range = np.linspace(self.x.min(), self.x.max(), 100)
+488        predicted_y = self(x_range)
+489
+490        ax.plot(
+491            x_range, predicted_y, color="red", linestyle="-", label="Cubic spline curve"
+492        )
+493        ax.set_xlabel("X values")
+494        ax.set_ylabel("Y values")
+495        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+496        ax.legend()
+497
+498    def __str__(self):
+499        return f"Cubic Spline Interpolator with {len(self.x)} points."
+
+ + +

Cubic spline data interpolator.

+ +

Interpolate data with a piecewise cubic polynomial which is twice +continuously differentiable 1. The result is represented as a PPoly +instance with breakpoints matching the given data.

+ +

Parameters

+ +

x : array_like, shape (n,) + 1-D array containing values of the independent variable. + Values must be real, finite and in strictly increasing order. +y : array_like + Array containing values of the dependent variable. It can have + arbitrary number of dimensions, but the length along axis + (see below) must match the length of x. Values must be finite. +axis : int, optional + Axis along which y is assumed to be varying. Meaning that for + x[i] the corresponding values are np.take(y, i, axis=axis). + Default is 0. +bc_type : string or 2-tuple, optional + Boundary condition type. Two additional equations, given by the + boundary conditions, are required to determine all coefficients of + polynomials on each segment 2.

+ +
If `bc_type` is a string, then the specified condition will be applied
+at both ends of a spline. Available conditions are:
+
+* 'not-a-knot' (default): The first and second segment at a curve end
+  are the same polynomial. It is a good default when there is no
+  information on boundary conditions.
+* 'periodic': The interpolated functions is assumed to be periodic
+  of period ``x[-1] - x[0]``. The first and last value of `y` must be
+  identical: ``y[0] == y[-1]``. This boundary condition will result in
+  ``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``.
+* 'clamped': The first derivative at curves ends are zero. Assuming
+  a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition.
+* 'natural': The second derivative at curve ends are zero. Assuming
+  a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition.
+
+If `bc_type` is a 2-tuple, the first and the second value will be
+applied at the curve start and end respectively. The tuple values can
+be one of the previously mentioned strings (except 'periodic') or a
+tuple `(order, deriv_values)` allowing to specify arbitrary
+derivatives at curve ends:
+
+* `order`: the derivative order, 1 or 2.
+* `deriv_value`: array_like containing derivative values, shape must
+  be the same as `y`, excluding ``axis`` dimension. For example, if
+  `y` is 1-D, then `deriv_value` must be a scalar. If `y` is 3-D with
+  the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2-D
+  and have the shape (n0, n1).
+
+ +

extrapolate : {bool, 'periodic', None}, optional + If bool, determines whether to extrapolate to out-of-bounds points + based on first and last intervals, or to return NaNs. If 'periodic', + periodic extrapolation is used. If None (default), extrapolate is + set to 'periodic' for bc_type='periodic' and to True otherwise.

+ +

Attributes

+ +

x : ndarray, shape (n,) + Breakpoints. The same x which was passed to the constructor. +c : ndarray, shape (4, n-1, ...) + Coefficients of the polynomials on each segment. The trailing + dimensions match the dimensions of y, excluding axis. + For example, if y is 1-d, then c[k, i] is a coefficient for + (x-x[i])**(3-k) on the segment between x[i] and x[i+1]. +axis : int + Interpolation axis. The same axis which was passed to the + constructor.

+ +

Methods

+ +

__call__ +derivative +antiderivative +integrate +roots

+ +

See Also

+ +

Akima1DInterpolator : Akima 1D interpolator. +PchipInterpolator : PCHIP 1-D monotonic cubic interpolator. +PPoly : Piecewise polynomial in terms of coefficients and breakpoints.

+ +

Notes

+ +

Parameters bc_type and extrapolate work independently, i.e. the +former controls only construction of a spline, and the latter only +evaluation.

+ +

When a boundary condition is 'not-a-knot' and n = 2, it is replaced by +a condition that the first derivative is equal to the linear interpolant +slope. When both boundary conditions are 'not-a-knot' and n = 3, the +solution is sought as a parabola passing through given points.

+ +

When 'not-a-knot' boundary conditions is applied to both ends, the +resulting spline will be the same as returned by splrep (with s=0) +and InterpolatedUnivariateSpline, but these two methods use a +representation in B-spline basis.

+ +

New in version 0.18.0.

+ +

Examples

+ +

In this example the cubic spline is used to interpolate a sampled sinusoid. +You can see that the spline continuity property holds for the first and +second derivatives and violates only for the third derivative.

+ +
+
>>> import numpy as np
+>>> from scipy.interpolate import CubicSpline
+>>> import matplotlib.pyplot as plt
+>>> x = np.arange(10)
+>>> y = np.sin(x)
+>>> cs = CubicSpline(x, y)
+>>> xs = np.arange(-0.5, 9.6, 0.1)
+>>> fig, ax = plt.subplots(figsize=(6.5, 4))
+>>> ax.plot(x, y, 'o', label='data')
+>>> ax.plot(xs, np.sin(xs), label='true')
+>>> ax.plot(xs, cs(xs), label="S")
+>>> ax.plot(xs, cs(xs, 1), label="S'")
+>>> ax.plot(xs, cs(xs, 2), label="S''")
+>>> ax.plot(xs, cs(xs, 3), label="S'''")
+>>> ax.set_xlim(-0.5, 9.5)
+>>> ax.legend(loc='lower left', ncol=2)
+>>> plt.show()
+
+
+ +

In the second example, the unit circle is interpolated with a spline. A +periodic boundary condition is used. You can see that the first derivative +values, ds/dx=0, ds/dy=1 at the periodic point (1, 0) are correctly +computed. Note that a circle cannot be exactly represented by a cubic +spline. To increase precision, more breakpoints would be required.

+ +
+
>>> theta = 2 * np.pi * np.linspace(0, 1, 5)
+>>> y = np.c_[np.cos(theta), np.sin(theta)]
+>>> cs = CubicSpline(theta, y, bc_type='periodic')
+>>> print("ds/dx={:.1f} ds/dy={:.1f}".format(cs(0, 1)[0], cs(0, 1)[1]))
+ds/dx=0.0 ds/dy=1.0
+>>> xs = 2 * np.pi * np.linspace(0, 1, 100)
+>>> fig, ax = plt.subplots(figsize=(6.5, 4))
+>>> ax.plot(y[:, 0], y[:, 1], 'o', label='data')
+>>> ax.plot(np.cos(xs), np.sin(xs), label='true')
+>>> ax.plot(cs(xs)[:, 0], cs(xs)[:, 1], label='spline')
+>>> ax.axes.set_aspect('equal')
+>>> ax.legend(loc='center')
+>>> plt.show()
+
+
+ +

The third example is the interpolation of a polynomial y = x3 on the +interval 0 <= x<= 1. A cubic spline can represent this function exactly. +To achieve that we need to specify values and first derivatives at +endpoints of the interval. Note that y' = 3 * x2 and thus y'(0) = 0 and +y'(1) = 3.

+ +
+
>>> cs = CubicSpline([0, 1], [0, 1], bc_type=((1, 0), (1, 3)))
+>>> x = np.linspace(0, 1)
+>>> np.allclose(x**3, cs(x))
+True
+
+
+ +

References

+ +
+
+
    +
  1. +

    Cubic Spline Interpolation + +on Wikiversity. ↩

    +
    2. + +
  2. +

    Carl de Boor, "A Practical Guide to Splines", Springer-Verlag, 1978. ↩

    +
    3. +
+
+
+ + +
+ +
+ + CustomCubicSplineInterpolator(*args, **kwargs) + + + +
+ + 
462    def __init__(self, *args, **kwargs):
+463        super().__init__(*args, **kwargs)
+
+ + + + +
+
+ +
+
@staticmethod
+ + def + from_dict(spline_dict=None): + + + +
+ + 
465    @staticmethod
+466    def from_dict(spline_dict=None):
+467        if spline_dict is None:
+468            spline_dict = {}
+469        interpolator_loaded = CustomCubicSplineInterpolator(
+470            spline_dict["x"],
+471            spline_dict["y"],
+472            bc_type=spline_dict.get("bc_type", "clamped"),
+473            extrapolate=spline_dict.get("extrapolate", True),
+474        )
+475        return interpolator_loaded
+
+ + + + +
+
+ +
+ + def + to_dict(self): + + + +
+ + 
477    def to_dict(self):
+478        return {
+479            "x": self.x,
+480            "y": self.y,
+481            "bc_type": self.bc_type,
+482            "extrapolate": self.extrapolate,
+483        }
+
+ + + + +
+
+ +
+ + def + plot(self, ax): + + + +
+ + 
485    def plot(self, ax):
+486        ax.scatter(self.x, self.y, marker="+", color="blue", label="Data points")
+487        x_range = np.linspace(self.x.min(), self.x.max(), 100)
+488        predicted_y = self(x_range)
+489
+490        ax.plot(
+491            x_range, predicted_y, color="red", linestyle="-", label="Cubic spline curve"
+492        )
+493        ax.set_xlabel("X values")
+494        ax.set_ylabel("Y values")
+495        ax.grid(which="both", linestyle="--", linewidth=0.5, color="gray")
+496        ax.legend()
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/calibration/ycalibration.html b/ramanchada2/protocols/calibration/ycalibration.html new file mode 100644 index 00000000..38176003 --- /dev/null +++ b/ramanchada2/protocols/calibration/ycalibration.html @@ -0,0 +1,1535 @@ + + + + + + + ramanchada2.protocols.calibration.ycalibration API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.calibration.ycalibration

+ + + + + + + 
  1import json
+  2import os.path
+  3from typing import Optional, Tuple
+  4
+  5import numpy as np
+  6from pydantic import BaseModel, ValidationError
+  7
+  8from ramanchada2.misc.plottable import Plottable
+  9from ramanchada2.spectrum import Spectrum
+ 10from .calibration_component import CalibrationComponent
+ 11
+ 12
+ 13class YCalibrationCertificate(BaseModel, Plottable):
+ 14    """
+ 15    Class for intensity calibration certificates
+ 16
+ 17    Usage:
+ 18
+ 19        1. Use for specific SRM
+ 20        >>> cert = YCalibrationCertificate(
+ 21        ...             id="NIST785_SRM2241",
+ 22        ...             description="optical glass",
+ 23        ...             url="https://tsapps.nist.gov/srmext/certificates/2241.pdf",
+ 24        ...             wavelength=785,
+ 25        ...             params="A0 = 9.71937e-02, A1 = 2.28325e-04, A2 = -5.86762e-08, A3 = 2.16023e-10, A4 = -9.77171e-14, A5 = 1.15596e-17",
+ 26        ...             equation="A0 + A1 * x + A2 * x**2 + A3 * x**3 + A4 * x**4 + A5 * x**5",
+ 27        ...             temperature_c=(20, 25),
+ 28        ...             raman_shift=(200, 3500)
+ 29        ...         )
+ 30        ...
+ 31        >>> cert.plot()
+ 32
+ 33    """  # noqa: E501
+ 34
+ 35    id: str
+ 36    description: Optional[str]
+ 37    url: Optional[str]
+ 38    wavelength: int
+ 39    params: str
+ 40    equation: str
+ 41    temperature_c: Optional[Tuple[int, int]]
+ 42    raman_shift: Optional[Tuple[int, int]]
+ 43
+ 44    @property
+ 45    def response_function(self):
+ 46        local_vars = {}
+ 47        for param in self.params.split(","):
+ 48            key, value = param.split("=")
+ 49            key = key.strip()
+ 50            value = value.strip()
+ 51            local_vars[key] = eval(value)
+ 52
+ 53        def evaluate_expression(x_value):
+ 54            local_vars["x"] = x_value
+ 55            return eval(self.equation, {"np": np}, local_vars)
+ 56
+ 57        return evaluate_expression
+ 58
+ 59    def Y(self, x_value):
+ 60        return self.response_function(x_value)
+ 61
+ 62    def _plot(self, ax, **kwargs):
+ 63        if self.raman_shift is None:
+ 64            x = np.linspace(100, 4000)
+ 65        else:
+ 66            x = np.linspace(self.raman_shift[0], self.raman_shift[1])
+ 67        kwargs.pop("label", None)
+ 68        ax.plot(
+ 69            x, self.Y(x), label="{} ({}nm)".format(self.id, self.wavelength), **kwargs
+ 70        )
+ 71        _units = "cm^{-1}"
+ 72        ax.set_xlabel(rf"Raman shift $\mathrm{{[{_units}]}}$")
+ 73        ax.set_ylabel("Intensity")
+ 74
+ 75    @staticmethod
+ 76    def load(wavelength=785, key="NIST785_SRM2241"):
+ 77        return CertificatesDict().get(wavelength, key)
+ 78
+ 79
+ 80class CertificatesDict:
+ 81    """
+ 82    Class for loading y calibration certificates
+ 83
+ 84    Usage:
+ 85       Load single certificate
+ 86       >>> cert = CertificatesDict.load(wavelength="785", key="NIST785_SRM2241")
+ 87       >>> cert.plot()
+ 88
+ 89       Load all certificates for wavelength. Iterate :
+ 90
+ 91        >>> certificates = CertificatesDict()
+ 92        ... plt.figure()
+ 93        ... ax=None
+ 94        ... certs = certificates.get_certificates(wavelength=532)
+ 95        ... ax = certs[cert].plot(ax=ax)
+ 96        >>> plt.show()
+ 97    """
+ 98
+ 99    def __init__(self):
+100        self.load_certificates(
+101            os.path.join(os.path.dirname(__file__), "config_certs.json")
+102        )
+103
+104    def load_certificates(self, file_path):
+105
+106        with open(file_path, "r") as f:
+107            certificates_data = json.load(f)
+108            certificates = {}
+109            self.laser_wl = []
+110            for wavelength, certificates_dict in certificates_data.items():
+111                certificates[wavelength] = {}
+112                self.laser_wl.append(wavelength)
+113                for certificate_id, certificate_data in certificates_dict.items():
+114                    certificate_data["wavelength"] = int(wavelength)
+115                    certificate_data["id"] = certificate_id
+116                    try:
+117                        certificate = YCalibrationCertificate.model_construct(
+118                            **certificate_data
+119                        )
+120                        certificates[wavelength][certificate_id] = certificate
+121                    except ValidationError as e:
+122                        print(f"Validation error for certificate {certificate_id}: {e}")
+123            self.config_certs = certificates
+124
+125    def get_laser_wl(self):
+126        return self.laser_wl
+127
+128    def get_certificates(self, wavelength=785):
+129        return self.config_certs[str(wavelength)]
+130
+131    def get(self, wavelength=532, key="NIST532_SRM2242a"):
+132        return self.config_certs[str(wavelength)][key]
+133
+134    @staticmethod
+135    def load(wavelength=785, key="NIST785_SRM2241"):
+136        return CertificatesDict().get(wavelength, key)
+137
+138
+139class YCalibrationComponent(CalibrationComponent):
+140    """
+141    Class for relative intensity calibration. Uses response functions loaded in
+142    ResponseFunctionEvaluator. Functions are defined in json file.
+143
+144    Usage:
+145
+146        >>> laser_wl = 785
+147        >>> ycert = YCalibrationCertificate.load(wavelength=785, key="SRM2241")
+148        >>> ycal = YCalibrationComponent(laser_wl, reference_spe_xcalibrated=spe_srm,certificate=ycert)
+149        >>> fig, ax = plt.subplots(1, 1, figsize=(15,4))
+150        >>> spe_srm.plot(ax=ax)
+151        >>> spe_to_correct.plot(ax=ax)
+152        >>> spe_ycalibrated = ycal.process(spe_to_correct)
+153        >>> spe_ycalibrated.plot(label="y-calibrated",color="green",ax=ax.twinx())
+154    """
+155
+156    def __init__(
+157        self, laser_wl, reference_spe_xcalibrated, certificate: YCalibrationCertificate
+158    ):
+159        super(YCalibrationComponent, self).__init__(
+160            laser_wl,
+161            spe=reference_spe_xcalibrated,
+162            spe_units=None,
+163            ref=certificate,
+164            ref_units=None,
+165        )
+166        self.laser_wl = laser_wl
+167        self.spe = reference_spe_xcalibrated
+168        self.ref = certificate
+169        self.name = "Y calibration"
+170        self.model = self.spe.spe_distribution(trim_range=certificate.raman_shift)
+171        self.model_units = "cm-1"
+172
+173    def derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):
+174        # measured reference spectrum as distribution, so we can resample
+175        self.model = self.spe.spe_distribution(trim_range=self.ref.raman_shift)
+176
+177    def safe_divide(self, spe_to_correct, spe_reference_resampled):
+178        numerator = spe_to_correct.y
+179        # numerator_noise = spe_to_correct.y_noise
+180
+181        scaling_denominator = spe_reference_resampled.y / self.ref.Y(
+182            spe_reference_resampled.x
+183        )
+184        # print(np.median(scaling_denominator), np.mean(scaling_denominator), np.std(scaling_denominator))
+185
+186        # denominator_noise = spe_reference_resampled.y_noise
+187        denominator = spe_reference_resampled.y
+188        # Create a mask for dividing only where value is above noise !
+189        # mask = (abs(scaling_denominator) > 0) & (kind_of_snr > 0.9)
+190        # mask =  (abs(denominator) > abs(denominator_noise)) &
+191        mask = (abs(scaling_denominator) > 0) & (numerator > 0) & (denominator > 0)
+192        # & (abs(numerator) > numerator_noise) & (abs(scaling_denominator) > 0)
+193        # & (abs(denominator-numerator) > min(denominator_noise,numerator_noise))
+194        result = np.zeros_like(numerator)
+195        # Perform division where mask is true
+196        result[mask] = numerator[mask] / scaling_denominator[mask]
+197        return result
+198
+199    def safe_mask(self, spe_to_correct, spe_reference_resampled):
+200        ref_noise = spe_reference_resampled.y_noise_MAD()
+201        return (spe_reference_resampled.y >= 0) & (
+202            abs(spe_reference_resampled.y) > ref_noise
+203        )
+204
+205    def safe_factor(self, spe_to_correct, spe_reference_resampled):
+206        numerator = spe_to_correct.y
+207        # numerator_noise = spe_to_correct.y_noise
+208
+209        Y = self.ref.Y(spe_reference_resampled.x)
+210        mask = self.safe_mask(spe_to_correct, spe_reference_resampled)
+211        if mask is None:
+212            scaling_factor = Y / spe_reference_resampled.y
+213        else:
+214            scaling_factor = np.zeros_like(spe_reference_resampled.y)
+215            scaling_factor[mask] = Y[mask] / spe_reference_resampled.y[mask]
+216
+217        result = numerator * scaling_factor
+218        return result
+219
+220    def process(self, old_spe: Spectrum, spe_units="nm", convert_back=False):
+221        # resample using probability density function
+222        _tmp = self.model.pdf(old_spe.x)
+223        _tmp = (
+224            _tmp * max(self.spe.y) / max(_tmp)
+225        )  # pdf sampling is normalized to area unity, scaling back
+226        spe_reference_resampled = Spectrum(old_spe.x, _tmp)
+227        # new_spe = Spectrum(old_spe.x,self.safe_divide(old_spe,spe_reference_resampled))
+228        new_spe = Spectrum(
+229            old_spe.x, self.safe_factor(old_spe, spe_reference_resampled)
+230        )
+231        return new_spe
+232
+233    def _plot(self, ax, **kwargs):
+234        if self.ref is not None:
+235            self.ref.plot(ax, **kwargs)
+
+ + +
+
+ +
+ + class + YCalibrationCertificate(pydantic.main.BaseModel, ramanchada2.misc.plottable.Plottable): + + + +
+ + 
14class YCalibrationCertificate(BaseModel, Plottable):
+15    """
+16    Class for intensity calibration certificates
+17
+18    Usage:
+19
+20        1. Use for specific SRM
+21        >>> cert = YCalibrationCertificate(
+22        ...             id="NIST785_SRM2241",
+23        ...             description="optical glass",
+24        ...             url="https://tsapps.nist.gov/srmext/certificates/2241.pdf",
+25        ...             wavelength=785,
+26        ...             params="A0 = 9.71937e-02, A1 = 2.28325e-04, A2 = -5.86762e-08, A3 = 2.16023e-10, A4 = -9.77171e-14, A5 = 1.15596e-17",
+27        ...             equation="A0 + A1 * x + A2 * x**2 + A3 * x**3 + A4 * x**4 + A5 * x**5",
+28        ...             temperature_c=(20, 25),
+29        ...             raman_shift=(200, 3500)
+30        ...         )
+31        ...
+32        >>> cert.plot()
+33
+34    """  # noqa: E501
+35
+36    id: str
+37    description: Optional[str]
+38    url: Optional[str]
+39    wavelength: int
+40    params: str
+41    equation: str
+42    temperature_c: Optional[Tuple[int, int]]
+43    raman_shift: Optional[Tuple[int, int]]
+44
+45    @property
+46    def response_function(self):
+47        local_vars = {}
+48        for param in self.params.split(","):
+49            key, value = param.split("=")
+50            key = key.strip()
+51            value = value.strip()
+52            local_vars[key] = eval(value)
+53
+54        def evaluate_expression(x_value):
+55            local_vars["x"] = x_value
+56            return eval(self.equation, {"np": np}, local_vars)
+57
+58        return evaluate_expression
+59
+60    def Y(self, x_value):
+61        return self.response_function(x_value)
+62
+63    def _plot(self, ax, **kwargs):
+64        if self.raman_shift is None:
+65            x = np.linspace(100, 4000)
+66        else:
+67            x = np.linspace(self.raman_shift[0], self.raman_shift[1])
+68        kwargs.pop("label", None)
+69        ax.plot(
+70            x, self.Y(x), label="{} ({}nm)".format(self.id, self.wavelength), **kwargs
+71        )
+72        _units = "cm^{-1}"
+73        ax.set_xlabel(rf"Raman shift $\mathrm{{[{_units}]}}$")
+74        ax.set_ylabel("Intensity")
+75
+76    @staticmethod
+77    def load(wavelength=785, key="NIST785_SRM2241"):
+78        return CertificatesDict().get(wavelength, key)
+
+ + +

Class for intensity calibration certificates

+ +
Usage:
+ +
+
    +
  1. Use for specific SRM + +
    +
    >>> cert = YCalibrationCertificate(
+...             id="NIST785_SRM2241",
+...             description="optical glass",
+...             url="https://tsapps.nist.gov/srmext/certificates/2241.pdf&quot;,
+...             wavelength=785,
+...             params="A0 = 9.71937e-02, A1 = 2.28325e-04, A2 = -5.86762e-08, A3 = 2.16023e-10, A4 = -9.77171e-14, A5 = 1.15596e-17",
+...             equation="A0 + A1 * x + A2 * x**2 + A3 * x**3 + A4 * x**4 + A5 * x**5",
+...             temperature_c=(20, 25),
+...             raman_shift=(200, 3500)
+...         )
+...
+>>> cert.plot()
+
    +
    +
    2. +
+
+
+ + +
+
+ id: str + + +
+ + + + +
+
+
+ description: Optional[str] + + +
+ + + + +
+
+
+ url: Optional[str] + + +
+ + + + +
+
+
+ wavelength: int + + +
+ + + + +
+
+
+ params: str + + +
+ + + + +
+
+
+ equation: str + + +
+ + + + +
+
+
+ temperature_c: Optional[Tuple[int, int]] + + +
+ + + + +
+
+
+ raman_shift: Optional[Tuple[int, int]] + + +
+ + + + +
+
+ +
+ response_function + + + +
+ + 
45    @property
+46    def response_function(self):
+47        local_vars = {}
+48        for param in self.params.split(","):
+49            key, value = param.split("=")
+50            key = key.strip()
+51            value = value.strip()
+52            local_vars[key] = eval(value)
+53
+54        def evaluate_expression(x_value):
+55            local_vars["x"] = x_value
+56            return eval(self.equation, {"np": np}, local_vars)
+57
+58        return evaluate_expression
+
+ + + + +
+
+ +
+ + def + Y(self, x_value): + + + +
+ + 
60    def Y(self, x_value):
+61        return self.response_function(x_value)
+
+ + + + +
+
+ +
+
@staticmethod
+ + def + load(wavelength=785, key='NIST785_SRM2241'): + + + +
+ + 
76    @staticmethod
+77    def load(wavelength=785, key="NIST785_SRM2241"):
+78        return CertificatesDict().get(wavelength, key)
+
+ + + + +
+
+
+ model_config: ClassVar[pydantic.config.ConfigDict] = +{} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'id': FieldInfo(annotation=str, required=True), 'description': FieldInfo(annotation=Union[str, NoneType], required=True), 'url': FieldInfo(annotation=Union[str, NoneType], required=True), 'wavelength': FieldInfo(annotation=int, required=True), 'params': FieldInfo(annotation=str, required=True), 'equation': FieldInfo(annotation=str, required=True), 'temperature_c': FieldInfo(annotation=Union[Tuple[int, int], NoneType], required=True), 'raman_shift': FieldInfo(annotation=Union[Tuple[int, int], NoneType], required=True)} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
Inherited Members
+
+ +
+
+
+
+ +
+ + class + CertificatesDict: + + + +
+ + 
 81class CertificatesDict:
+ 82    """
+ 83    Class for loading y calibration certificates
+ 84
+ 85    Usage:
+ 86       Load single certificate
+ 87       >>> cert = CertificatesDict.load(wavelength="785", key="NIST785_SRM2241")
+ 88       >>> cert.plot()
+ 89
+ 90       Load all certificates for wavelength. Iterate :
+ 91
+ 92        >>> certificates = CertificatesDict()
+ 93        ... plt.figure()
+ 94        ... ax=None
+ 95        ... certs = certificates.get_certificates(wavelength=532)
+ 96        ... ax = certs[cert].plot(ax=ax)
+ 97        >>> plt.show()
+ 98    """
+ 99
+100    def __init__(self):
+101        self.load_certificates(
+102            os.path.join(os.path.dirname(__file__), "config_certs.json")
+103        )
+104
+105    def load_certificates(self, file_path):
+106
+107        with open(file_path, "r") as f:
+108            certificates_data = json.load(f)
+109            certificates = {}
+110            self.laser_wl = []
+111            for wavelength, certificates_dict in certificates_data.items():
+112                certificates[wavelength] = {}
+113                self.laser_wl.append(wavelength)
+114                for certificate_id, certificate_data in certificates_dict.items():
+115                    certificate_data["wavelength"] = int(wavelength)
+116                    certificate_data["id"] = certificate_id
+117                    try:
+118                        certificate = YCalibrationCertificate.model_construct(
+119                            **certificate_data
+120                        )
+121                        certificates[wavelength][certificate_id] = certificate
+122                    except ValidationError as e:
+123                        print(f"Validation error for certificate {certificate_id}: {e}")
+124            self.config_certs = certificates
+125
+126    def get_laser_wl(self):
+127        return self.laser_wl
+128
+129    def get_certificates(self, wavelength=785):
+130        return self.config_certs[str(wavelength)]
+131
+132    def get(self, wavelength=532, key="NIST532_SRM2242a"):
+133        return self.config_certs[str(wavelength)][key]
+134
+135    @staticmethod
+136    def load(wavelength=785, key="NIST785_SRM2241"):
+137        return CertificatesDict().get(wavelength, key)
+
+ + +

Class for loading y calibration certificates

+ +
Usage:
+ +
+

Load single certificate

+ +
+
>>> cert = CertificatesDict.load(wavelength="785", key="NIST785_SRM2241")
+>>> cert.plot()
+
+
+ +

Load all certificates for wavelength. Iterate :

+ +
+
>>> certificates = CertificatesDict()
+... plt.figure()
+... ax=None
+... certs = certificates.get_certificates(wavelength=532)
+... ax = certs[cert].plot(ax=ax)
+>>> plt.show()
+
+
+
+
+ + +
+ +
+ + def + load_certificates(self, file_path): + + + +
+ + 
105    def load_certificates(self, file_path):
+106
+107        with open(file_path, "r") as f:
+108            certificates_data = json.load(f)
+109            certificates = {}
+110            self.laser_wl = []
+111            for wavelength, certificates_dict in certificates_data.items():
+112                certificates[wavelength] = {}
+113                self.laser_wl.append(wavelength)
+114                for certificate_id, certificate_data in certificates_dict.items():
+115                    certificate_data["wavelength"] = int(wavelength)
+116                    certificate_data["id"] = certificate_id
+117                    try:
+118                        certificate = YCalibrationCertificate.model_construct(
+119                            **certificate_data
+120                        )
+121                        certificates[wavelength][certificate_id] = certificate
+122                    except ValidationError as e:
+123                        print(f"Validation error for certificate {certificate_id}: {e}")
+124            self.config_certs = certificates
+
+ + + + +
+
+ +
+ + def + get_laser_wl(self): + + + +
+ + 
126    def get_laser_wl(self):
+127        return self.laser_wl
+
+ + + + +
+
+ +
+ + def + get_certificates(self, wavelength=785): + + + +
+ + 
129    def get_certificates(self, wavelength=785):
+130        return self.config_certs[str(wavelength)]
+
+ + + + +
+
+ +
+ + def + get(self, wavelength=532, key='NIST532_SRM2242a'): + + + +
+ + 
132    def get(self, wavelength=532, key="NIST532_SRM2242a"):
+133        return self.config_certs[str(wavelength)][key]
+
+ + + + +
+
+ +
+
@staticmethod
+ + def + load(wavelength=785, key='NIST785_SRM2241'): + + + +
+ + 
135    @staticmethod
+136    def load(wavelength=785, key="NIST785_SRM2241"):
+137        return CertificatesDict().get(wavelength, key)
+
+ + + + +
+
+
+ +
+ + class + YCalibrationComponent(ramanchada2.protocols.calibration.calibration_component.CalibrationComponent): + + + +
+ + 
140class YCalibrationComponent(CalibrationComponent):
+141    """
+142    Class for relative intensity calibration. Uses response functions loaded in
+143    ResponseFunctionEvaluator. Functions are defined in json file.
+144
+145    Usage:
+146
+147        >>> laser_wl = 785
+148        >>> ycert = YCalibrationCertificate.load(wavelength=785, key="SRM2241")
+149        >>> ycal = YCalibrationComponent(laser_wl, reference_spe_xcalibrated=spe_srm,certificate=ycert)
+150        >>> fig, ax = plt.subplots(1, 1, figsize=(15,4))
+151        >>> spe_srm.plot(ax=ax)
+152        >>> spe_to_correct.plot(ax=ax)
+153        >>> spe_ycalibrated = ycal.process(spe_to_correct)
+154        >>> spe_ycalibrated.plot(label="y-calibrated",color="green",ax=ax.twinx())
+155    """
+156
+157    def __init__(
+158        self, laser_wl, reference_spe_xcalibrated, certificate: YCalibrationCertificate
+159    ):
+160        super(YCalibrationComponent, self).__init__(
+161            laser_wl,
+162            spe=reference_spe_xcalibrated,
+163            spe_units=None,
+164            ref=certificate,
+165            ref_units=None,
+166        )
+167        self.laser_wl = laser_wl
+168        self.spe = reference_spe_xcalibrated
+169        self.ref = certificate
+170        self.name = "Y calibration"
+171        self.model = self.spe.spe_distribution(trim_range=certificate.raman_shift)
+172        self.model_units = "cm-1"
+173
+174    def derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):
+175        # measured reference spectrum as distribution, so we can resample
+176        self.model = self.spe.spe_distribution(trim_range=self.ref.raman_shift)
+177
+178    def safe_divide(self, spe_to_correct, spe_reference_resampled):
+179        numerator = spe_to_correct.y
+180        # numerator_noise = spe_to_correct.y_noise
+181
+182        scaling_denominator = spe_reference_resampled.y / self.ref.Y(
+183            spe_reference_resampled.x
+184        )
+185        # print(np.median(scaling_denominator), np.mean(scaling_denominator), np.std(scaling_denominator))
+186
+187        # denominator_noise = spe_reference_resampled.y_noise
+188        denominator = spe_reference_resampled.y
+189        # Create a mask for dividing only where value is above noise !
+190        # mask = (abs(scaling_denominator) > 0) & (kind_of_snr > 0.9)
+191        # mask =  (abs(denominator) > abs(denominator_noise)) &
+192        mask = (abs(scaling_denominator) > 0) & (numerator > 0) & (denominator > 0)
+193        # & (abs(numerator) > numerator_noise) & (abs(scaling_denominator) > 0)
+194        # & (abs(denominator-numerator) > min(denominator_noise,numerator_noise))
+195        result = np.zeros_like(numerator)
+196        # Perform division where mask is true
+197        result[mask] = numerator[mask] / scaling_denominator[mask]
+198        return result
+199
+200    def safe_mask(self, spe_to_correct, spe_reference_resampled):
+201        ref_noise = spe_reference_resampled.y_noise_MAD()
+202        return (spe_reference_resampled.y >= 0) & (
+203            abs(spe_reference_resampled.y) > ref_noise
+204        )
+205
+206    def safe_factor(self, spe_to_correct, spe_reference_resampled):
+207        numerator = spe_to_correct.y
+208        # numerator_noise = spe_to_correct.y_noise
+209
+210        Y = self.ref.Y(spe_reference_resampled.x)
+211        mask = self.safe_mask(spe_to_correct, spe_reference_resampled)
+212        if mask is None:
+213            scaling_factor = Y / spe_reference_resampled.y
+214        else:
+215            scaling_factor = np.zeros_like(spe_reference_resampled.y)
+216            scaling_factor[mask] = Y[mask] / spe_reference_resampled.y[mask]
+217
+218        result = numerator * scaling_factor
+219        return result
+220
+221    def process(self, old_spe: Spectrum, spe_units="nm", convert_back=False):
+222        # resample using probability density function
+223        _tmp = self.model.pdf(old_spe.x)
+224        _tmp = (
+225            _tmp * max(self.spe.y) / max(_tmp)
+226        )  # pdf sampling is normalized to area unity, scaling back
+227        spe_reference_resampled = Spectrum(old_spe.x, _tmp)
+228        # new_spe = Spectrum(old_spe.x,self.safe_divide(old_spe,spe_reference_resampled))
+229        new_spe = Spectrum(
+230            old_spe.x, self.safe_factor(old_spe, spe_reference_resampled)
+231        )
+232        return new_spe
+233
+234    def _plot(self, ax, **kwargs):
+235        if self.ref is not None:
+236            self.ref.plot(ax, **kwargs)
+
+ + +

Class for relative intensity calibration. Uses response functions loaded in +ResponseFunctionEvaluator. Functions are defined in json file.

+ +
Usage:
+ +
+
+
>>> laser_wl = 785
+>>> ycert = YCalibrationCertificate.load(wavelength=785, key="SRM2241")
+>>> ycal = YCalibrationComponent(laser_wl, reference_spe_xcalibrated=spe_srm,certificate=ycert)
+>>> fig, ax = plt.subplots(1, 1, figsize=(15,4))
+>>> spe_srm.plot(ax=ax)
+>>> spe_to_correct.plot(ax=ax)
+>>> spe_ycalibrated = ycal.process(spe_to_correct)
+>>> spe_ycalibrated.plot(label="y-calibrated",color="green",ax=ax.twinx())
+
+
+
+
+ + +
+ +
+ + YCalibrationComponent( laser_wl, reference_spe_xcalibrated, certificate: YCalibrationCertificate) + + + +
+ + 
157    def __init__(
+158        self, laser_wl, reference_spe_xcalibrated, certificate: YCalibrationCertificate
+159    ):
+160        super(YCalibrationComponent, self).__init__(
+161            laser_wl,
+162            spe=reference_spe_xcalibrated,
+163            spe_units=None,
+164            ref=certificate,
+165            ref_units=None,
+166        )
+167        self.laser_wl = laser_wl
+168        self.spe = reference_spe_xcalibrated
+169        self.ref = certificate
+170        self.name = "Y calibration"
+171        self.model = self.spe.spe_distribution(trim_range=certificate.raman_shift)
+172        self.model_units = "cm-1"
+
+ + + + +
+
+
+ laser_wl + + +
+ + + + +
+
+
+ spe + + +
+ + + + +
+
+
+ ref + + +
+ + + + +
+
+
+ name + + +
+ + + + +
+
+
+ model + + +
+ + + + +
+
+
+ model_units + + +
+ + + + +
+
+ +
+ + def + derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None): + + + +
+ + 
174    def derive_model(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):
+175        # measured reference spectrum as distribution, so we can resample
+176        self.model = self.spe.spe_distribution(trim_range=self.ref.raman_shift)
+
+ + + + +
+
+ +
+ + def + safe_divide(self, spe_to_correct, spe_reference_resampled): + + + +
+ + 
178    def safe_divide(self, spe_to_correct, spe_reference_resampled):
+179        numerator = spe_to_correct.y
+180        # numerator_noise = spe_to_correct.y_noise
+181
+182        scaling_denominator = spe_reference_resampled.y / self.ref.Y(
+183            spe_reference_resampled.x
+184        )
+185        # print(np.median(scaling_denominator), np.mean(scaling_denominator), np.std(scaling_denominator))
+186
+187        # denominator_noise = spe_reference_resampled.y_noise
+188        denominator = spe_reference_resampled.y
+189        # Create a mask for dividing only where value is above noise !
+190        # mask = (abs(scaling_denominator) > 0) & (kind_of_snr > 0.9)
+191        # mask =  (abs(denominator) > abs(denominator_noise)) &
+192        mask = (abs(scaling_denominator) > 0) & (numerator > 0) & (denominator > 0)
+193        # & (abs(numerator) > numerator_noise) & (abs(scaling_denominator) > 0)
+194        # & (abs(denominator-numerator) > min(denominator_noise,numerator_noise))
+195        result = np.zeros_like(numerator)
+196        # Perform division where mask is true
+197        result[mask] = numerator[mask] / scaling_denominator[mask]
+198        return result
+
+ + + + +
+
+ +
+ + def + safe_mask(self, spe_to_correct, spe_reference_resampled): + + + +
+ + 
200    def safe_mask(self, spe_to_correct, spe_reference_resampled):
+201        ref_noise = spe_reference_resampled.y_noise_MAD()
+202        return (spe_reference_resampled.y >= 0) & (
+203            abs(spe_reference_resampled.y) > ref_noise
+204        )
+
+ + + + +
+
+ +
+ + def + safe_factor(self, spe_to_correct, spe_reference_resampled): + + + +
+ + 
206    def safe_factor(self, spe_to_correct, spe_reference_resampled):
+207        numerator = spe_to_correct.y
+208        # numerator_noise = spe_to_correct.y_noise
+209
+210        Y = self.ref.Y(spe_reference_resampled.x)
+211        mask = self.safe_mask(spe_to_correct, spe_reference_resampled)
+212        if mask is None:
+213            scaling_factor = Y / spe_reference_resampled.y
+214        else:
+215            scaling_factor = np.zeros_like(spe_reference_resampled.y)
+216            scaling_factor[mask] = Y[mask] / spe_reference_resampled.y[mask]
+217
+218        result = numerator * scaling_factor
+219        return result
+
+ + + + +
+
+ +
+ + def + process( self, old_spe: ramanchada2.spectrum.spectrum.Spectrum, spe_units='nm', convert_back=False): + + + +
+ + 
221    def process(self, old_spe: Spectrum, spe_units="nm", convert_back=False):
+222        # resample using probability density function
+223        _tmp = self.model.pdf(old_spe.x)
+224        _tmp = (
+225            _tmp * max(self.spe.y) / max(_tmp)
+226        )  # pdf sampling is normalized to area unity, scaling back
+227        spe_reference_resampled = Spectrum(old_spe.x, _tmp)
+228        # new_spe = Spectrum(old_spe.x,self.safe_divide(old_spe,spe_reference_resampled))
+229        new_spe = Spectrum(
+230            old_spe.x, self.safe_factor(old_spe, spe_reference_resampled)
+231        )
+232        return new_spe
+
+ + + + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/metadata_helper.html b/ramanchada2/protocols/metadata_helper.html new file mode 100644 index 00000000..dcc06593 --- /dev/null +++ b/ramanchada2/protocols/metadata_helper.html @@ -0,0 +1,617 @@ + + + + + + + ramanchada2.protocols.metadata_helper API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.metadata_helper

+ + + + + + + 
 1class MetadataExtractor:
+ 2    def extract(self, spectrum, filename=None):
+ 3        raise NotImplementedError("Subclasses should implement this method.")
+ 4
+ 5
+ 6class TemplateMetadataExtractor(MetadataExtractor):
+ 7    def __init__(self, template):
+ 8        self.template = template
+ 9
+10    def extract(self, spectrum, filename=None):
+11        return {key: spectrum.get(key) for key in self.template}
+12
+13
+14class FilenameMetadataExtractor(MetadataExtractor):
+15    def extract(self, spectrum, filename):
+16        return {"filename": filename}
+17
+18
+19class SpectrumMetadataExtractor(MetadataExtractor):
+20    def extract(self, spectrum, filename=None):
+21        return spectrum.get_metadata()
+22
+23
+24class ChainedMetadataExtractor(MetadataExtractor):
+25    def __init__(self, *extractors):
+26        self.extractors = extractors
+27
+28    def extract(self, spectrum, filename=None):
+29        metadata = {}
+30        for extractor in self.extractors:
+31            metadata.update(extractor.extract(spectrum, filename))
+32        return metadata
+
+ + +
+
+ +
+ + class + MetadataExtractor: + + + +
+ + 
2class MetadataExtractor:
+3    def extract(self, spectrum, filename=None):
+4        raise NotImplementedError("Subclasses should implement this method.")
+
+ + + + +
+ +
+ + def + extract(self, spectrum, filename=None): + + + +
+ + 
3    def extract(self, spectrum, filename=None):
+4        raise NotImplementedError("Subclasses should implement this method.")
+
+ + + + +
+
+
+ +
+ + class + TemplateMetadataExtractor(MetadataExtractor): + + + +
+ + 
 7class TemplateMetadataExtractor(MetadataExtractor):
+ 8    def __init__(self, template):
+ 9        self.template = template
+10
+11    def extract(self, spectrum, filename=None):
+12        return {key: spectrum.get(key) for key in self.template}
+
+ + + + +
+ +
+ + TemplateMetadataExtractor(template) + + + +
+ + 
8    def __init__(self, template):
+9        self.template = template
+
+ + + + +
+
+
+ template + + +
+ + + + +
+
+ +
+ + def + extract(self, spectrum, filename=None): + + + +
+ + 
11    def extract(self, spectrum, filename=None):
+12        return {key: spectrum.get(key) for key in self.template}
+
+ + + + +
+
+
+ +
+ + class + FilenameMetadataExtractor(MetadataExtractor): + + + +
+ + 
15class FilenameMetadataExtractor(MetadataExtractor):
+16    def extract(self, spectrum, filename):
+17        return {"filename": filename}
+
+ + + + +
+ +
+ + def + extract(self, spectrum, filename): + + + +
+ + 
16    def extract(self, spectrum, filename):
+17        return {"filename": filename}
+
+ + + + +
+
+
+ +
+ + class + SpectrumMetadataExtractor(MetadataExtractor): + + + +
+ + 
20class SpectrumMetadataExtractor(MetadataExtractor):
+21    def extract(self, spectrum, filename=None):
+22        return spectrum.get_metadata()
+
+ + + + +
+ +
+ + def + extract(self, spectrum, filename=None): + + + +
+ + 
21    def extract(self, spectrum, filename=None):
+22        return spectrum.get_metadata()
+
+ + + + +
+
+
+ +
+ + class + ChainedMetadataExtractor(MetadataExtractor): + + + +
+ + 
25class ChainedMetadataExtractor(MetadataExtractor):
+26    def __init__(self, *extractors):
+27        self.extractors = extractors
+28
+29    def extract(self, spectrum, filename=None):
+30        metadata = {}
+31        for extractor in self.extractors:
+32            metadata.update(extractor.extract(spectrum, filename))
+33        return metadata
+
+ + + + +
+ +
+ + ChainedMetadataExtractor(*extractors) + + + +
+ + 
26    def __init__(self, *extractors):
+27        self.extractors = extractors
+
+ + + + +
+
+
+ extractors + + +
+ + + + +
+
+ +
+ + def + extract(self, spectrum, filename=None): + + + +
+ + 
29    def extract(self, spectrum, filename=None):
+30        metadata = {}
+31        for extractor in self.extractors:
+32            metadata.update(extractor.extract(spectrum, filename))
+33        return metadata
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/spectraframe.html b/ramanchada2/protocols/spectraframe.html new file mode 100644 index 00000000..099a84d3 --- /dev/null +++ b/ramanchada2/protocols/spectraframe.html @@ -0,0 +1,1584 @@ + + + + + + + ramanchada2.protocols.spectraframe API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.spectraframe

+ + + + + + + 
  1from typing import Dict, List, Optional
+  2
+  3import numpy as np
+  4import pandas as pd
+  5from pydantic import BaseModel, ValidationError
+  6
+  7from ramanchada2.protocols.metadata_helper import SpectrumMetadataExtractor
+  8from ramanchada2.spectrum import Spectrum
+  9
+ 10
+ 11class SpectraFrameSchema(BaseModel):
+ 12    file_name: Optional[str] = None
+ 13    sample: Optional[str] = None
+ 14    provider: Optional[str] = None
+ 15    device: Optional[str] = None
+ 16    device_id: Optional[str] = None
+ 17    laser_wl: int
+ 18    laser_power_mW: Optional[float] = None
+ 19    laser_power_percent: Optional[float] = None
+ 20    time_ms: Optional[float] = None
+ 21    replicate: Optional[int] = None
+ 22    optical_path: Optional[str] = None
+ 23    spectrum: Optional[Spectrum] = None
+ 24
+ 25    class Config:
+ 26        arbitrary_types_allowed = True  # Allow arbitrary types
+ 27
+ 28
+ 29# Define a custom DataFrame class with dynamic column mapping and validation
+ 30class SpectraFrame(pd.DataFrame):
+ 31
+ 32    @classmethod
+ 33    def validate_columns(cls, df: pd.DataFrame, column_mapping: Dict[str, str]):
+ 34        """
+ 35        Validate a DataFrame against the schema with dynamic column mapping.
+ 36
+ 37        Args:
+ 38            df (pd.DataFrame): The DataFrame to validate.
+ 39            column_mapping (Dict[str, str]): A mapping from expected column names (in the schema)
+ 40                                             to actual column names in the DataFrame.
+ 41        """
+ 42        # Rename DataFrame columns according to the provided mapping
+ 43        df_mapped = df.rename(columns=column_mapping)
+ 44        # Validate each row against the schema
+ 45        errors = []
+ 46        for index, row in df_mapped.iterrows():
+ 47            try:
+ 48                # Convert row to dictionary and validate using Pydantic schema
+ 49                SpectraFrameSchema(**row.to_dict())
+ 50            except ValidationError as e:
+ 51                errors.append((index, e.errors()))
+ 52
+ 53        if errors:
+ 54            for idx, err in errors:
+ 55                print(f"Row {idx} has errors: {err}")
+ 56
+ 57            raise ValueError("DataFrame validation failed")
+ 58        else:
+ 59            print("DataFrame validation passed!")
+ 60        return df_mapped
+ 61
+ 62    @classmethod
+ 63    def from_dataframe(cls, df: pd.DataFrame, column_mapping: Dict[str, str]):
+ 64        """
+ 65        Create an instance of SpectraFrame with dynamic column validation.
+ 66
+ 67        Args:
+ 68            df (pd.DataFrame): The input DataFrame.
+ 69            column_mapping (Dict[str, str]): The dynamic mapping for column names.
+ 70
+ 71        Returns:
+ 72            SpectraFrame: A validated SpectraFrame object.
+ 73        """
+ 74        if column_mapping is None:
+ 75            column_mapping = {}
+ 76        # Validate columns before creating the MyFrame
+ 77        df_mapped = cls.validate_columns(df, column_mapping)
+ 78        return cls(df_mapped)
+ 79
+ 80    @classmethod
+ 81    def from_metadata(
+ 82        cls, spectra: List[Spectrum], metadata_extractor: SpectrumMetadataExtractor
+ 83    ):
+ 84        data = []
+ 85        for spectrum in spectra:
+ 86            metadata = metadata_extractor.extract(spectrum, None)
+ 87            data.append({"spectrum": spectrum, **metadata})
+ 88        return cls(pd.DataFrame(data))
+ 89
+ 90    @classmethod
+ 91    def from_template(
+ 92        cls, template_file: str, metadata_extractor: SpectrumMetadataExtractor
+ 93    ):
+ 94        return
+ 95
+ 96    def average(
+ 97        self,
+ 98        grouping_cols=[
+ 99            "sample",
+100            "provider",
+101            "device",
+102            "laser_wl",
+103            "laser_power_percent",
+104            "laser_power_mW",
+105            "time_ms",
+106        ],
+107        source="spectrum",
+108        target="spectrum",
+109    ):
+110        processed_rows = []
+111
+112        for group_keys, group in self.groupby(grouping_cols):
+113            # Iterate over each row in the group
+114            spe_average = None
+115            for index, row in group.iterrows():
+116                if spe_average is None:
+117                    spe_average = row[source]
+118                else:
+119                    spe_average = spe_average + row[source]
+120            spe_average = spe_average / group.shape[0]
+121
+122            processed_row = row.copy()[grouping_cols]  # Make a copy of the row
+123            processed_row[target] = spe_average
+124            processed_rows.append(processed_row)
+125
+126        df = pd.DataFrame(processed_rows)
+127        df.sort_values(by="laser_power_percent")
+128        return SpectraFrame.from_dataframe(df, column_mapping={})
+129
+130    # tbd make it more generic
+131    def trim(self, source="spectrum", target="spectrum", **kwargs):
+132        kwargs.setdefault("method", "x-axis")
+133        kwargs.setdefault("boundaries", (50, 4000))
+134        for index, row in self.iterrows():
+135            self.at[index, target] = row[source].trim_axes(**kwargs)
+136        return self
+137
+138    def baseline_snip(self, source="spectrum", target="spectrum", **kwargs):
+139        kwargs.setdefault("niter", 40)
+140        for index, row in self.iterrows():
+141            self.at[index, target] = row[source].subtract_baseline_rc1_snip(**kwargs)
+142        return self
+143
+144    def spe_area(self, boundaries=(50, 3000), source="spectrum", target="area"):
+145        for index, row in self.iterrows():
+146            spe = row[source]
+147            sc = spe.trim_axes(method="x-axis", boundaries=boundaries)
+148            self.at[index, target] = np.sum(sc.y * np.diff(sc.x_bin_boundaries))
+149
+150    def multiply(self, multiplier: float, source="spectrum", target="spectrum"):
+151        for index, row in self.iterrows():
+152            self.at[index, target] = row[source] * multiplier
+
+ + +
+
+ +
+ + class + SpectraFrameSchema(pydantic.main.BaseModel): + + + +
+ + 
12class SpectraFrameSchema(BaseModel):
+13    file_name: Optional[str] = None
+14    sample: Optional[str] = None
+15    provider: Optional[str] = None
+16    device: Optional[str] = None
+17    device_id: Optional[str] = None
+18    laser_wl: int
+19    laser_power_mW: Optional[float] = None
+20    laser_power_percent: Optional[float] = None
+21    time_ms: Optional[float] = None
+22    replicate: Optional[int] = None
+23    optical_path: Optional[str] = None
+24    spectrum: Optional[Spectrum] = None
+25
+26    class Config:
+27        arbitrary_types_allowed = True  # Allow arbitrary types
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ file_name: Optional[str] + + +
+ + + + +
+
+
+ sample: Optional[str] + + +
+ + + + +
+
+
+ provider: Optional[str] + + +
+ + + + +
+
+
+ device: Optional[str] + + +
+ + + + +
+
+
+ device_id: Optional[str] + + +
+ + + + +
+
+
+ laser_wl: int + + +
+ + + + +
+
+
+ laser_power_mW: Optional[float] + + +
+ + + + +
+
+
+ laser_power_percent: Optional[float] + + +
+ + + + +
+
+
+ time_ms: Optional[float] + + +
+ + + + +
+
+
+ replicate: Optional[int] + + +
+ + + + +
+
+
+ optical_path: Optional[str] + + +
+ + + + +
+
+
+ spectrum: Optional[ramanchada2.spectrum.spectrum.Spectrum] + + +
+ + + + +
+
+
+ model_config: ClassVar[pydantic.config.ConfigDict] = +{'arbitrary_types_allowed': True} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'file_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'sample': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'provider': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'device': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'device_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'laser_wl': FieldInfo(annotation=int, required=True), 'laser_power_mW': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'laser_power_percent': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'time_ms': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'replicate': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'optical_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'spectrum': FieldInfo(annotation=Union[Spectrum, NoneType], required=False, default=None)} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+ + class + SpectraFrameSchema.Config: + + + +
+ + 
26    class Config:
+27        arbitrary_types_allowed = True  # Allow arbitrary types
+
+ + + + +
+
+ arbitrary_types_allowed = +True + + +
+ + + + +
+
+
+ +
+ + class + SpectraFrame(pandas.core.frame.DataFrame): + + + +
+ + 
 31class SpectraFrame(pd.DataFrame):
+ 32
+ 33    @classmethod
+ 34    def validate_columns(cls, df: pd.DataFrame, column_mapping: Dict[str, str]):
+ 35        """
+ 36        Validate a DataFrame against the schema with dynamic column mapping.
+ 37
+ 38        Args:
+ 39            df (pd.DataFrame): The DataFrame to validate.
+ 40            column_mapping (Dict[str, str]): A mapping from expected column names (in the schema)
+ 41                                             to actual column names in the DataFrame.
+ 42        """
+ 43        # Rename DataFrame columns according to the provided mapping
+ 44        df_mapped = df.rename(columns=column_mapping)
+ 45        # Validate each row against the schema
+ 46        errors = []
+ 47        for index, row in df_mapped.iterrows():
+ 48            try:
+ 49                # Convert row to dictionary and validate using Pydantic schema
+ 50                SpectraFrameSchema(**row.to_dict())
+ 51            except ValidationError as e:
+ 52                errors.append((index, e.errors()))
+ 53
+ 54        if errors:
+ 55            for idx, err in errors:
+ 56                print(f"Row {idx} has errors: {err}")
+ 57
+ 58            raise ValueError("DataFrame validation failed")
+ 59        else:
+ 60            print("DataFrame validation passed!")
+ 61        return df_mapped
+ 62
+ 63    @classmethod
+ 64    def from_dataframe(cls, df: pd.DataFrame, column_mapping: Dict[str, str]):
+ 65        """
+ 66        Create an instance of SpectraFrame with dynamic column validation.
+ 67
+ 68        Args:
+ 69            df (pd.DataFrame): The input DataFrame.
+ 70            column_mapping (Dict[str, str]): The dynamic mapping for column names.
+ 71
+ 72        Returns:
+ 73            SpectraFrame: A validated SpectraFrame object.
+ 74        """
+ 75        if column_mapping is None:
+ 76            column_mapping = {}
+ 77        # Validate columns before creating the MyFrame
+ 78        df_mapped = cls.validate_columns(df, column_mapping)
+ 79        return cls(df_mapped)
+ 80
+ 81    @classmethod
+ 82    def from_metadata(
+ 83        cls, spectra: List[Spectrum], metadata_extractor: SpectrumMetadataExtractor
+ 84    ):
+ 85        data = []
+ 86        for spectrum in spectra:
+ 87            metadata = metadata_extractor.extract(spectrum, None)
+ 88            data.append({"spectrum": spectrum, **metadata})
+ 89        return cls(pd.DataFrame(data))
+ 90
+ 91    @classmethod
+ 92    def from_template(
+ 93        cls, template_file: str, metadata_extractor: SpectrumMetadataExtractor
+ 94    ):
+ 95        return
+ 96
+ 97    def average(
+ 98        self,
+ 99        grouping_cols=[
+100            "sample",
+101            "provider",
+102            "device",
+103            "laser_wl",
+104            "laser_power_percent",
+105            "laser_power_mW",
+106            "time_ms",
+107        ],
+108        source="spectrum",
+109        target="spectrum",
+110    ):
+111        processed_rows = []
+112
+113        for group_keys, group in self.groupby(grouping_cols):
+114            # Iterate over each row in the group
+115            spe_average = None
+116            for index, row in group.iterrows():
+117                if spe_average is None:
+118                    spe_average = row[source]
+119                else:
+120                    spe_average = spe_average + row[source]
+121            spe_average = spe_average / group.shape[0]
+122
+123            processed_row = row.copy()[grouping_cols]  # Make a copy of the row
+124            processed_row[target] = spe_average
+125            processed_rows.append(processed_row)
+126
+127        df = pd.DataFrame(processed_rows)
+128        df.sort_values(by="laser_power_percent")
+129        return SpectraFrame.from_dataframe(df, column_mapping={})
+130
+131    # tbd make it more generic
+132    def trim(self, source="spectrum", target="spectrum", **kwargs):
+133        kwargs.setdefault("method", "x-axis")
+134        kwargs.setdefault("boundaries", (50, 4000))
+135        for index, row in self.iterrows():
+136            self.at[index, target] = row[source].trim_axes(**kwargs)
+137        return self
+138
+139    def baseline_snip(self, source="spectrum", target="spectrum", **kwargs):
+140        kwargs.setdefault("niter", 40)
+141        for index, row in self.iterrows():
+142            self.at[index, target] = row[source].subtract_baseline_rc1_snip(**kwargs)
+143        return self
+144
+145    def spe_area(self, boundaries=(50, 3000), source="spectrum", target="area"):
+146        for index, row in self.iterrows():
+147            spe = row[source]
+148            sc = spe.trim_axes(method="x-axis", boundaries=boundaries)
+149            self.at[index, target] = np.sum(sc.y * np.diff(sc.x_bin_boundaries))
+150
+151    def multiply(self, multiplier: float, source="spectrum", target="spectrum"):
+152        for index, row in self.iterrows():
+153            self.at[index, target] = row[source] * multiplier
+
+ + +

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

+ +

Data structure also contains labeled axes (rows and columns). +Arithmetic operations align on both row and column labels. Can be +thought of as a dict-like container for Series objects. The primary +pandas data structure.

+ +

Parameters

+ +

data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame + Dict can contain Series, arrays, constants, dataclass or list-like objects. If + data is a dict, column order follows insertion-order. If a dict contains Series + which have an index defined, it is aligned by its index. This alignment also + occurs if data is a Series or a DataFrame itself. Alignment is done on + Series/DataFrame inputs.

+ +
If data is a list of dicts, column order follows insertion-order.
+
+ +

index : Index or array-like + Index to use for resulting frame. Will default to RangeIndex if + no indexing information part of input data and no index provided. +columns : Index or array-like + Column labels to use for resulting frame when data does not have them, + defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels, + will perform column selection instead. +dtype : dtype, default None + Data type to force. Only a single dtype is allowed. If None, infer. +copy : bool or None, default None + Copy data from inputs. + For dict data, the default of None behaves like copy=True. For DataFrame + or 2d ndarray input, the default of None behaves like copy=False. + If data is a dict containing one or more Series (possibly of different dtypes), + copy=False will ensure that these inputs are not copied.

+ +
*Changed in version 1.3.0.*
+
+ +

See Also

+ +

DataFrame.from_records : Constructor from tuples, also record arrays. +DataFrame.from_dict : From dicts of Series, arrays, or dicts. +read_csv : Read a comma-separated values (csv) file into DataFrame. +read_table : Read general delimited file into DataFrame. +read_clipboard : Read text from clipboard into DataFrame.

+ +

Notes

+ +

Please reference the :ref:User Guide <basics.dataframe> for more information.

+ +

Examples

+ +

Constructing DataFrame from a dictionary.

+ +
+
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
+>>> df = pd.DataFrame(data=d)
+>>> df
+   col1  col2
+0     1     3
+1     2     4
+
+
+ +

Notice that the inferred dtype is int64.

+ +
+
>>> df.dtypes
+col1    int64
+col2    int64
+dtype: object
+
+
+ +

To enforce a single dtype:

+ +
+
>>> df = pd.DataFrame(data=d, dtype=np.int8)
+>>> df.dtypes
+col1    int8
+col2    int8
+dtype: object
+
+
+ +

Constructing DataFrame from a dictionary including Series:

+ +
+
>>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
+>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
+   col1  col2
+0     0   NaN
+1     1   NaN
+2     2   2.0
+3     3   3.0
+
+
+ +

Constructing DataFrame from numpy ndarray:

+ +
+
>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
+...                    columns=['a', 'b', 'c'])
+>>> df2
+   a  b  c
+0  1  2  3
+1  4  5  6
+2  7  8  9
+
+
+ +

Constructing DataFrame from a numpy ndarray that has labeled columns:

+ +
+
>>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
+...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
+>>> df3 = pd.DataFrame(data, columns=['c', 'a'])
+...
+>>> df3
+   c  a
+0  3  1
+1  6  4
+2  9  7
+
+
+ +

Constructing DataFrame from dataclass:

+ +
+
>>> from dataclasses import make_dataclass
+>>> Point = make_dataclass("Point", [("x", int), ("y", int)])
+>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
+   x  y
+0  0  0
+1  0  3
+2  2  3
+
+
+ +

Constructing DataFrame from Series/DataFrame:

+ +
+
>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
+>>> df = pd.DataFrame(data=ser, index=["a", "c"])
+>>> df
+   0
+a  1
+c  3
+
+
+ +
+
>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
+>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
+>>> df2
+   x
+a  1
+c  3
+
+
+
+ + +
+ +
+
@classmethod
+ + def + validate_columns(cls, df: pandas.core.frame.DataFrame, column_mapping: Dict[str, str]): + + + +
+ + 
33    @classmethod
+34    def validate_columns(cls, df: pd.DataFrame, column_mapping: Dict[str, str]):
+35        """
+36        Validate a DataFrame against the schema with dynamic column mapping.
+37
+38        Args:
+39            df (pd.DataFrame): The DataFrame to validate.
+40            column_mapping (Dict[str, str]): A mapping from expected column names (in the schema)
+41                                             to actual column names in the DataFrame.
+42        """
+43        # Rename DataFrame columns according to the provided mapping
+44        df_mapped = df.rename(columns=column_mapping)
+45        # Validate each row against the schema
+46        errors = []
+47        for index, row in df_mapped.iterrows():
+48            try:
+49                # Convert row to dictionary and validate using Pydantic schema
+50                SpectraFrameSchema(**row.to_dict())
+51            except ValidationError as e:
+52                errors.append((index, e.errors()))
+53
+54        if errors:
+55            for idx, err in errors:
+56                print(f"Row {idx} has errors: {err}")
+57
+58            raise ValueError("DataFrame validation failed")
+59        else:
+60            print("DataFrame validation passed!")
+61        return df_mapped
+
+ + +

Validate a DataFrame against the schema with dynamic column mapping.

+ +
Arguments:
+ +
    +
  • df (pd.DataFrame): The DataFrame to validate.
    • +
  • column_mapping (Dict[str, str]): A mapping from expected column names (in the schema) +to actual column names in the DataFrame.
    • +
+
+ + +
+
+ +
+
@classmethod
+ + def + from_dataframe(cls, df: pandas.core.frame.DataFrame, column_mapping: Dict[str, str]): + + + +
+ + 
63    @classmethod
+64    def from_dataframe(cls, df: pd.DataFrame, column_mapping: Dict[str, str]):
+65        """
+66        Create an instance of SpectraFrame with dynamic column validation.
+67
+68        Args:
+69            df (pd.DataFrame): The input DataFrame.
+70            column_mapping (Dict[str, str]): The dynamic mapping for column names.
+71
+72        Returns:
+73            SpectraFrame: A validated SpectraFrame object.
+74        """
+75        if column_mapping is None:
+76            column_mapping = {}
+77        # Validate columns before creating the MyFrame
+78        df_mapped = cls.validate_columns(df, column_mapping)
+79        return cls(df_mapped)
+
+ + +

Create an instance of SpectraFrame with dynamic column validation.

+ +
Arguments:
+ +
    +
  • df (pd.DataFrame): The input DataFrame.
    • +
  • column_mapping (Dict[str, str]): The dynamic mapping for column names.
    • +
+ +
Returns:
+ +
+

SpectraFrame: A validated SpectraFrame object.

+
+
+ + +
+
+ +
+
@classmethod
+ + def + from_metadata( cls, spectra: List[ramanchada2.spectrum.spectrum.Spectrum], metadata_extractor: ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor): + + + +
+ + 
81    @classmethod
+82    def from_metadata(
+83        cls, spectra: List[Spectrum], metadata_extractor: SpectrumMetadataExtractor
+84    ):
+85        data = []
+86        for spectrum in spectra:
+87            metadata = metadata_extractor.extract(spectrum, None)
+88            data.append({"spectrum": spectrum, **metadata})
+89        return cls(pd.DataFrame(data))
+
+ + + + +
+
+ +
+
@classmethod
+ + def + from_template( cls, template_file: str, metadata_extractor: ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor): + + + +
+ + 
91    @classmethod
+92    def from_template(
+93        cls, template_file: str, metadata_extractor: SpectrumMetadataExtractor
+94    ):
+95        return
+
+ + + + +
+
+ +
+ + def + average( self, grouping_cols=['sample', 'provider', 'device', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'], source='spectrum', target='spectrum'): + + + +
+ + 
 97    def average(
+ 98        self,
+ 99        grouping_cols=[
+100            "sample",
+101            "provider",
+102            "device",
+103            "laser_wl",
+104            "laser_power_percent",
+105            "laser_power_mW",
+106            "time_ms",
+107        ],
+108        source="spectrum",
+109        target="spectrum",
+110    ):
+111        processed_rows = []
+112
+113        for group_keys, group in self.groupby(grouping_cols):
+114            # Iterate over each row in the group
+115            spe_average = None
+116            for index, row in group.iterrows():
+117                if spe_average is None:
+118                    spe_average = row[source]
+119                else:
+120                    spe_average = spe_average + row[source]
+121            spe_average = spe_average / group.shape[0]
+122
+123            processed_row = row.copy()[grouping_cols]  # Make a copy of the row
+124            processed_row[target] = spe_average
+125            processed_rows.append(processed_row)
+126
+127        df = pd.DataFrame(processed_rows)
+128        df.sort_values(by="laser_power_percent")
+129        return SpectraFrame.from_dataframe(df, column_mapping={})
+
+ + + + +
+
+ +
+ + def + trim(self, source='spectrum', target='spectrum', **kwargs): + + + +
+ + 
132    def trim(self, source="spectrum", target="spectrum", **kwargs):
+133        kwargs.setdefault("method", "x-axis")
+134        kwargs.setdefault("boundaries", (50, 4000))
+135        for index, row in self.iterrows():
+136            self.at[index, target] = row[source].trim_axes(**kwargs)
+137        return self
+
+ + + + +
+
+ +
+ + def + baseline_snip(self, source='spectrum', target='spectrum', **kwargs): + + + +
+ + 
139    def baseline_snip(self, source="spectrum", target="spectrum", **kwargs):
+140        kwargs.setdefault("niter", 40)
+141        for index, row in self.iterrows():
+142            self.at[index, target] = row[source].subtract_baseline_rc1_snip(**kwargs)
+143        return self
+
+ + + + +
+
+ +
+ + def + spe_area(self, boundaries=(50, 3000), source='spectrum', target='area'): + + + +
+ + 
145    def spe_area(self, boundaries=(50, 3000), source="spectrum", target="area"):
+146        for index, row in self.iterrows():
+147            spe = row[source]
+148            sc = spe.trim_axes(method="x-axis", boundaries=boundaries)
+149            self.at[index, target] = np.sum(sc.y * np.diff(sc.x_bin_boundaries))
+
+ + + + +
+
+ +
+ + def + multiply(self, multiplier: float, source='spectrum', target='spectrum'): + + + +
+ + 
151    def multiply(self, multiplier: float, source="spectrum", target="spectrum"):
+152        for index, row in self.iterrows():
+153            self.at[index, target] = row[source] * multiplier
+
+ + +

Get Multiplication of dataframe and other, element-wise (binary operator mul).

+ +

Equivalent to dataframe * other, but with support to substitute a fill_value +for missing data in one of the inputs. With reverse version, rmul.

+ +

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to +arithmetic operators: +, -, *, /, //, %, **.

+ +

Parameters

+ +

other : scalar, sequence, Series, dict or DataFrame + Any single or multiple element data structure, or list-like object. +axis : {0 or 'index', 1 or 'columns'} + Whether to compare by the index (0 or 'index') or columns. + (1 or 'columns'). For Series input, axis to match Series index on. +level : int or label + Broadcast across a level, matching Index values on the + passed MultiIndex level. +fill_value : float or None, default None + Fill existing missing (NaN) values, and any new element needed for + successful DataFrame alignment, with this value before computation. + If data in both corresponding DataFrame locations is missing + the result will be missing.

+ +

Returns

+ +

DataFrame + Result of the arithmetic operation.

+ +

See Also

+ +

DataFrame.add : Add DataFrames. +DataFrame.sub : Subtract DataFrames. +DataFrame.mul : Multiply DataFrames. +DataFrame.div : Divide DataFrames (float division). +DataFrame.truediv : Divide DataFrames (float division). +DataFrame.floordiv : Divide DataFrames (integer division). +DataFrame.mod : Calculate modulo (remainder after division). +DataFrame.pow : Calculate exponential power.

+ +

Notes

+ +

Mismatched indices will be unioned together.

+ +

Examples

+ +
+
>>> df = pd.DataFrame({'angles': [0, 3, 4],
+...                    'degrees': [360, 180, 360]},
+...                   index=['circle', 'triangle', 'rectangle'])
+>>> df
+           angles  degrees
+circle          0      360
+triangle        3      180
+rectangle       4      360
+
+
+ +

Add a scalar with operator version which return the same +results.

+ +
+
>>> df + 1
+           angles  degrees
+circle          1      361
+triangle        4      181
+rectangle       5      361
+
+
+ +
+
>>> df.add(1)
+           angles  degrees
+circle          1      361
+triangle        4      181
+rectangle       5      361
+
+
+ +

Divide by constant with reverse version.

+ +
+
>>> df.div(10)
+           angles  degrees
+circle        0.0     36.0
+triangle      0.3     18.0
+rectangle     0.4     36.0
+
+
+ +
+
>>> df.rdiv(10)
+             angles   degrees
+circle          inf  0.027778
+triangle   3.333333  0.055556
+rectangle  2.500000  0.027778
+
+
+ +

Subtract a list and Series by axis with operator version.

+ +
+
>>> df - [1, 2]
+           angles  degrees
+circle         -1      358
+triangle        2      178
+rectangle       3      358
+
+
+ +
+
>>> df.sub([1, 2], axis='columns')
+           angles  degrees
+circle         -1      358
+triangle        2      178
+rectangle       3      358
+
+
+ +
+
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),
+...        axis='index')
+           angles  degrees
+circle         -1      359
+triangle        2      179
+rectangle       3      359
+
+
+ +

Multiply a dictionary by axis.

+ +
+
>>> df.mul({'angles': 0, 'degrees': 2})
+            angles  degrees
+circle           0      720
+triangle         0      360
+rectangle        0      720
+
+
+ +
+
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')
+            angles  degrees
+circle           0        0
+triangle         6      360
+rectangle       12     1080
+
+
+ +

Multiply a DataFrame of different shape with operator version.

+ +
+
>>> other = pd.DataFrame({'angles': [0, 3, 4]},
+...                      index=['circle', 'triangle', 'rectangle'])
+>>> other
+           angles
+circle          0
+triangle        3
+rectangle       4
+
+
+ +
+
>>> df * other
+           angles  degrees
+circle          0      NaN
+triangle        9      NaN
+rectangle      16      NaN
+
+
+ +
+
>>> df.mul(other, fill_value=0)
+           angles  degrees
+circle          0      0.0
+triangle        9      0.0
+rectangle      16      0.0
+
+
+ +

Divide by a MultiIndex by level.

+ +
+
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],
+...                              'degrees': [360, 180, 360, 360, 540, 720]},
+...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],
+...                                    ['circle', 'triangle', 'rectangle',
+...                                     'square', 'pentagon', 'hexagon']])
+>>> df_multindex
+             angles  degrees
+A circle          0      360
+  triangle        3      180
+  rectangle       4      360
+B square          4      360
+  pentagon        5      540
+  hexagon         6      720
+
+
+ +
+
>>> df.div(df_multindex, level=1, fill_value=0)
+             angles  degrees
+A circle        NaN      1.0
+  triangle      1.0      1.0
+  rectangle     1.0      1.0
+B square        0.0      0.0
+  pentagon      0.0      0.0
+  hexagon       0.0      0.0
+
+
+
+ + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/protocols/twinning.html b/ramanchada2/protocols/twinning.html new file mode 100644 index 00000000..6101e742 --- /dev/null +++ b/ramanchada2/protocols/twinning.html @@ -0,0 +1,1283 @@ + + + + + + + ramanchada2.protocols.twinning API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.protocols.twinning

+ + + + + + + 
  1import matplotlib.pyplot as plt
+  2import numpy as np
+  3from matplotlib.axes import Axes
+  4from sklearn.linear_model import LinearRegression
+  5
+  6from ramanchada2.misc.plottable import Plottable
+  7from ramanchada2.protocols.spectraframe import SpectraFrame
+  8from ramanchada2.spectrum import Spectrum
+  9
+ 10
+ 11class TwinningComponent(Plottable):
+ 12    """
+ 13    TwinningComponent is an implementation of CHARISMA Twinning protocol CWA18134 Sep 2024
+ 14    https://www.cencenelec.eu/media/CEN-CENELEC/CWAs/RI/2024/cwa18134-1.pdf
+ 15    It expects reference spectra and test spectra (to be twinned) as SpectraFrame objects.
+ 16
+ 17    Attributes:
+ 18        grouping_cols (list): The SpectraFrame may contain replicates, which will be averaged by grouping by
+ 19            columns except replicates, e.g.
+ 20            ['sample', 'provider', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'].
+ 21
+ 22        reference (SpectraFrame): The averaged reference spectra.
+ 23
+ 24        twinned (SpectraFrame): The averaged spectra to be twinned.
+ 25
+ 26        boundaries (tuple): A tuple representing the boundaries for analysis (default: (50, 2000)).
+ 27
+ 28        linreg_reference (tuple): Placeholder for storing the result of a linear regression on the reference spectra.
+ 29            Defaults to a tuple (None, None) which can later hold the regression slope and intercept.
+ 30
+ 31        linreg_twinned (tuple): Placeholder for storing the result of a linear regression on the twinned spectra.
+ 32            Defaults to a tuple (None, None) which can later hold the regression slope and intercept.
+ 33
+ 34        correction_factor (float): A scaling factor derived as ratio of slopes as defined in CWA18134.
+ 35
+ 36        peak (float): The position of the peak (in nm) of interest for analysis, with a default value of 144 (TiO2).
+ 37
+ 38    Methods:
+ 39        __init__(self, twinned: SpectraFrame, reference: SpectraFrame, boundaries=None, peak_at=144):
+ 40            Initializes a new TwinningComponent object by averaging the provided twinned and reference spectra
+ 41            based on predefined grouping columns. Optionally, boundaries for analysis and a peak position can be
+ 42            specified.
+ 43
+ 44    Parameters:
+ 45        twinned (SpectraFrame): The SpectraFrame representing the twinned data.
+ 46        reference (SpectraFrame): The SpectraFrame representing the reference data.
+ 47        boundaries (tuple, optional): Optional boundary values to restrict the analysis region (default: (50, 2000)).
+ 48        peak_at (int, optional): The peak position to focus the analysis on (default: 144 for TiO2).
+ 49    """
+ 50
+ 51    def __init__(
+ 52        self,
+ 53        twinned: SpectraFrame,
+ 54        reference: SpectraFrame,
+ 55        boundaries=None,
+ 56        reference_band_nm=144.0,
+ 57        grouping_cols=[
+ 58            "sample",
+ 59            "provider",
+ 60            "device",
+ 61            "laser_wl",
+ 62            "laser_power_percent",
+ 63            "laser_power_mW",
+ 64            "time_ms",
+ 65        ],
+ 66    ):
+ 67        self.grouping_cols = grouping_cols
+ 68        self.twinned = twinned.average(grouping_cols=self.grouping_cols)
+ 69        self.reference = reference.average(grouping_cols=self.grouping_cols)
+ 70        self.boundaries = (50, 2000) if boundaries is None else boundaries
+ 71        self.linreg_reference = (None, None)
+ 72        self.linreg_twinned = (None, None)
+ 73        self.correction_factor: float = 1.0
+ 74        self.reference_band_nm = reference_band_nm
+ 75
+ 76    def normalize_by_laserpower_time(self, source="spectrum", target="spectrum"):
+ 77        for (index_refere4nce, row_reference), (index_twinned, row_twinned) in zip(
+ 78            self.reference.iterrows(), self.twinned.iterrows()
+ 79        ):
+ 80            laser_power_ratio = (
+ 81                row_reference["laser_power_mW"] / row_twinned["laser_power_mW"]
+ 82            )
+ 83            time_ratio = row_reference["time_ms"] / row_twinned["time_ms"]
+ 84            spe = row_twinned[source]
+ 85            self.twinned.at[index_twinned, target] = (
+ 86                spe * laser_power_ratio * time_ratio
+ 87            )
+ 88            self.twinned.at[index_twinned, "laser_power_ratio"] = laser_power_ratio
+ 89            self.twinned.at[index_twinned, "time_ratio"] = time_ratio
+ 90
+ 91    def calc_peak_intensity(
+ 92        self,
+ 93        spe: Spectrum,
+ 94        boundaries=None,
+ 95        prominence_coeff=0.01,
+ 96        no_fit=False,
+ 97        peak_intensity="height",
+ 98    ):
+ 99        try:
+100            if boundaries is None:
+101                boundaries = (self.reference_band_nm - 50, self.reference_band_nm + 50)
+102            # TODO: Check if the MyPy type ignores below can be handled better.
+103            spe = spe.trim_axes(method="x-axis", boundaries=boundaries)  # type: ignore
+104            prominence = spe.y_noise_MAD() * prominence_coeff
+105            candidates = spe.find_peak_multipeak(prominence=prominence)  # type: ignore
+106            fit_res = spe.fit_peak_multimodel(  # type: ignore
+107                profile="Voigt", candidates=candidates, no_fit=no_fit
+108            )
+109            df = fit_res.to_dataframe_peaks()
+110            df["sorted"] = abs(
+111                df["center"] - self.reference_band_nm
+112            )  # closest peak to 144
+113            df_sorted = df.sort_values(by="sorted")
+114
+115            # we get actual y value, not height or amplitude
+116            index_left = np.searchsorted(
+117                spe.x, df_sorted["center"].iloc[0], side="left", sorter=None
+118            )
+119            index_right = np.searchsorted(
+120                spe.x, df_sorted["center"].iloc[0], side="right", sorter=None
+121            )
+122            if index_right == index_left:
+123                peak_intensity = spe.y[index_left]
+124                peak_position = spe.x[index_left]
+125            else:
+126                peak_intensity = (spe.y[index_right] + spe.y[index_left]) / 2.0
+127                peak_position = (spe.x[index_right] + spe.x[index_left]) / 2
+128
+129            # _label = "intensity = {:.3f} {} ={:.3f} amplitude={:.3f} center={:.1f}".format(
+130            #    intensity_val,peak_intensity,df_sorted.iloc[0][peak_intensity],
+131            #    df_sorted.iloc[0]["amplitude"],df_sorted.iloc[0]["center"])
+132
+133            return peak_intensity, peak_position, fit_res
+134        except Exception as err:
+135            print(err)
+136            return None, None, None
+137
+138    def laser_power_regression(
+139        self, df: SpectraFrame, boundaries=None, no_fit=False, source="spectrum"
+140    ):
+141        # fig, ax = plt.subplots(df.shape[0],1,figsize=(12,12))
+142        r = 0
+143        for index, row in df.iterrows():
+144            spe = row[source]
+145            if boundaries is None:
+146                boundaries = (self.reference_band_nm - 50, self.reference_band_nm + 50)
+147            peak_intensity, peak_position, fit_res = self.calc_peak_intensity(
+148                spe, boundaries=boundaries, no_fit=no_fit
+149            )
+150            df.at[index, "peak_intensity"] = peak_intensity
+151            df.at[index, "peak_position"] = peak_position
+152            # spe.trim_axes(method='x-axis',boundaries=boundaries).plot(ax=ax[r], fmt=':',label=row["laser_power_mW"])
+153            # if fit_res is not None:
+154            #    fit_res.plot(ax=ax[r])
+155            r = r + 1
+156        # plt.savefig("test_twinning_peaks_{}.png".format(title))
+157        # print(df[['laser_power_mW','peak_intensity']])
+158        return LinearRegression().fit(
+159            df[["laser_power_mW"]].values, df["peak_intensity"].values
+160        )
+161
+162    def process(
+163        self, spe: SpectraFrame, source="spectrum", target="spectrum_harmonized"
+164    ):
+165        spe.multiply(self.correction_factor, source=source, target=target)
+166        spe.spe_area(
+167            source=target, target="area_harmonized", boundaries=self.boundaries
+168        )
+169
+170    def derive_model(self):
+171        self.reference.trim(
+172            boundaries=self.boundaries, source="spectrum", target="spe_processed"
+173        )
+174        self.twinned.trim(
+175            boundaries=self.boundaries, source="spectrum", target="spe_processed"
+176        )
+177
+178        self.normalize_by_laserpower_time(
+179            source="spe_processed", target="spe_processed"
+180        )
+181
+182        self.reference.baseline_snip(source="spe_processed", target="spe_processed")
+183        self.twinned.baseline_snip(source="spe_processed", target="spe_processed")
+184
+185        boundaries4area = self.boundaries
+186        self.reference.spe_area(
+187            target="area", boundaries=boundaries4area, source="spe_processed"
+188        )
+189        self.twinned.spe_area(
+190            target="area", boundaries=boundaries4area, source="spe_processed"
+191        )
+192
+193        model_reference = self.laser_power_regression(
+194            self.reference, no_fit=False, source="spe_processed"
+195        )
+196        self.linreg_reference = (model_reference.intercept_, model_reference.coef_[0])
+197
+198        model_twinned = self.laser_power_regression(
+199            self.twinned, no_fit=False, source="spe_processed"
+200        )
+201        self.linreg_twinned = (model_twinned.intercept_, model_twinned.coef_[0])
+202
+203        self.correction_factor = model_reference.coef_[0] / model_twinned.coef_[0]
+204        self.twinned["correction_factor"] = self.correction_factor
+205
+206    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+207        if ax is None:
+208            fig, ax = plt.subplots(1, 2, figsize=(10, 4))
+209        self._plot(ax, label=label, **kwargs)
+210
+211        return ax
+212
+213    def _plot(self, ax, **kwargs):
+214        A = self.reference
+215        B = self.twinned
+216        regression_A = self.linreg_reference
+217        regression_B = self.linreg_twinned
+218        # fig, axes = plt.subplots(1,2, figsize=(10,4))
+219        ax[0].plot(
+220            A["laser_power_mW"], A["peak_intensity"], "o", label=A["device_id"].unique()
+221        )
+222
+223        A_pred = A["laser_power_mW"] * regression_A[1] + regression_A[0]
+224        ax[0].plot(
+225            A["laser_power_mW"],
+226            A_pred,
+227            "-",
+228            label="{:.2e} * LP + {:.2e}".format(regression_A[1], regression_A[0]),
+229        )
+230
+231        ax[0].plot(
+232            B["laser_power_mW"], B["peak_intensity"], "+", label=B["device_id"].unique()
+233        )
+234
+235        # axes[0].plot(
+236        #     B["laser_power_mW"],
+237        #     B["peak_intensity"] * correction_factor,
+238        #     "+",
+239        #     label="{} corrected".format(B["device_id"].unique()),
+240        # )
+241
+242        B_pred = B["laser_power_mW"] * regression_B[1] + regression_B[0]
+243        ax[0].plot(
+244            B["laser_power_mW"],
+245            B_pred,
+246            "-",
+247            label="{:.2e} * LP + {:.2e}".format(regression_B[1], regression_B[0]),
+248        )
+249
+250        ax[0].set_ylabel("Peak intensity of the (fitted) peak @ 144cm-1")
+251        ax[0].set_xlabel("laser power, %")
+252        ax[0].legend()
+253        bar_width = 0.2  # Adjust this value to control the width of the groups
+254        bar_positions = np.arange(len(A["laser_power_percent"].values))
+255        ax[1].bar(
+256            bar_positions - bar_width,
+257            A["area"],
+258            width=bar_width,
+259            label=str(A["device_id"].unique()),
+260        )
+261        bar_positions = np.arange(len(B["laser_power_percent"].values))
+262        ax[1].bar(
+263            bar_positions,
+264            B["area"],
+265            width=bar_width,
+266            label=str(B["device_id"].unique()),
+267        )
+268        ax[1].bar(
+269            bar_positions + bar_width,
+270            B["area_harmonized"],
+271            width=bar_width,
+272            label="{} harmonized CF={:.2e}".format(
+273                B["device_id"].unique(), self.correction_factor
+274            ),
+275        )
+276        ax[1].set_ylabel("spectrum area")
+277        ax[1].set_xlabel("laser power, %")
+278        # Set the x-axis positions and labels
+279        plt.xticks(bar_positions, B["laser_power_percent"])
+280        ax[1].legend()
+281        plt.tight_layout()
+
+ + +
+
+ +
+ + class + TwinningComponent(ramanchada2.misc.plottable.Plottable): + + + +
+ + 
 12class TwinningComponent(Plottable):
+ 13    """
+ 14    TwinningComponent is an implementation of CHARISMA Twinning protocol CWA18134 Sep 2024
+ 15    https://www.cencenelec.eu/media/CEN-CENELEC/CWAs/RI/2024/cwa18134-1.pdf
+ 16    It expects reference spectra and test spectra (to be twinned) as SpectraFrame objects.
+ 17
+ 18    Attributes:
+ 19        grouping_cols (list): The SpectraFrame may contain replicates, which will be averaged by grouping by
+ 20            columns except replicates, e.g.
+ 21            ['sample', 'provider', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'].
+ 22
+ 23        reference (SpectraFrame): The averaged reference spectra.
+ 24
+ 25        twinned (SpectraFrame): The averaged spectra to be twinned.
+ 26
+ 27        boundaries (tuple): A tuple representing the boundaries for analysis (default: (50, 2000)).
+ 28
+ 29        linreg_reference (tuple): Placeholder for storing the result of a linear regression on the reference spectra.
+ 30            Defaults to a tuple (None, None) which can later hold the regression slope and intercept.
+ 31
+ 32        linreg_twinned (tuple): Placeholder for storing the result of a linear regression on the twinned spectra.
+ 33            Defaults to a tuple (None, None) which can later hold the regression slope and intercept.
+ 34
+ 35        correction_factor (float): A scaling factor derived as ratio of slopes as defined in CWA18134.
+ 36
+ 37        peak (float): The position of the peak (in nm) of interest for analysis, with a default value of 144 (TiO2).
+ 38
+ 39    Methods:
+ 40        __init__(self, twinned: SpectraFrame, reference: SpectraFrame, boundaries=None, peak_at=144):
+ 41            Initializes a new TwinningComponent object by averaging the provided twinned and reference spectra
+ 42            based on predefined grouping columns. Optionally, boundaries for analysis and a peak position can be
+ 43            specified.
+ 44
+ 45    Parameters:
+ 46        twinned (SpectraFrame): The SpectraFrame representing the twinned data.
+ 47        reference (SpectraFrame): The SpectraFrame representing the reference data.
+ 48        boundaries (tuple, optional): Optional boundary values to restrict the analysis region (default: (50, 2000)).
+ 49        peak_at (int, optional): The peak position to focus the analysis on (default: 144 for TiO2).
+ 50    """
+ 51
+ 52    def __init__(
+ 53        self,
+ 54        twinned: SpectraFrame,
+ 55        reference: SpectraFrame,
+ 56        boundaries=None,
+ 57        reference_band_nm=144.0,
+ 58        grouping_cols=[
+ 59            "sample",
+ 60            "provider",
+ 61            "device",
+ 62            "laser_wl",
+ 63            "laser_power_percent",
+ 64            "laser_power_mW",
+ 65            "time_ms",
+ 66        ],
+ 67    ):
+ 68        self.grouping_cols = grouping_cols
+ 69        self.twinned = twinned.average(grouping_cols=self.grouping_cols)
+ 70        self.reference = reference.average(grouping_cols=self.grouping_cols)
+ 71        self.boundaries = (50, 2000) if boundaries is None else boundaries
+ 72        self.linreg_reference = (None, None)
+ 73        self.linreg_twinned = (None, None)
+ 74        self.correction_factor: float = 1.0
+ 75        self.reference_band_nm = reference_band_nm
+ 76
+ 77    def normalize_by_laserpower_time(self, source="spectrum", target="spectrum"):
+ 78        for (index_refere4nce, row_reference), (index_twinned, row_twinned) in zip(
+ 79            self.reference.iterrows(), self.twinned.iterrows()
+ 80        ):
+ 81            laser_power_ratio = (
+ 82                row_reference["laser_power_mW"] / row_twinned["laser_power_mW"]
+ 83            )
+ 84            time_ratio = row_reference["time_ms"] / row_twinned["time_ms"]
+ 85            spe = row_twinned[source]
+ 86            self.twinned.at[index_twinned, target] = (
+ 87                spe * laser_power_ratio * time_ratio
+ 88            )
+ 89            self.twinned.at[index_twinned, "laser_power_ratio"] = laser_power_ratio
+ 90            self.twinned.at[index_twinned, "time_ratio"] = time_ratio
+ 91
+ 92    def calc_peak_intensity(
+ 93        self,
+ 94        spe: Spectrum,
+ 95        boundaries=None,
+ 96        prominence_coeff=0.01,
+ 97        no_fit=False,
+ 98        peak_intensity="height",
+ 99    ):
+100        try:
+101            if boundaries is None:
+102                boundaries = (self.reference_band_nm - 50, self.reference_band_nm + 50)
+103            # TODO: Check if the MyPy type ignores below can be handled better.
+104            spe = spe.trim_axes(method="x-axis", boundaries=boundaries)  # type: ignore
+105            prominence = spe.y_noise_MAD() * prominence_coeff
+106            candidates = spe.find_peak_multipeak(prominence=prominence)  # type: ignore
+107            fit_res = spe.fit_peak_multimodel(  # type: ignore
+108                profile="Voigt", candidates=candidates, no_fit=no_fit
+109            )
+110            df = fit_res.to_dataframe_peaks()
+111            df["sorted"] = abs(
+112                df["center"] - self.reference_band_nm
+113            )  # closest peak to 144
+114            df_sorted = df.sort_values(by="sorted")
+115
+116            # we get actual y value, not height or amplitude
+117            index_left = np.searchsorted(
+118                spe.x, df_sorted["center"].iloc[0], side="left", sorter=None
+119            )
+120            index_right = np.searchsorted(
+121                spe.x, df_sorted["center"].iloc[0], side="right", sorter=None
+122            )
+123            if index_right == index_left:
+124                peak_intensity = spe.y[index_left]
+125                peak_position = spe.x[index_left]
+126            else:
+127                peak_intensity = (spe.y[index_right] + spe.y[index_left]) / 2.0
+128                peak_position = (spe.x[index_right] + spe.x[index_left]) / 2
+129
+130            # _label = "intensity = {:.3f} {} ={:.3f} amplitude={:.3f} center={:.1f}".format(
+131            #    intensity_val,peak_intensity,df_sorted.iloc[0][peak_intensity],
+132            #    df_sorted.iloc[0]["amplitude"],df_sorted.iloc[0]["center"])
+133
+134            return peak_intensity, peak_position, fit_res
+135        except Exception as err:
+136            print(err)
+137            return None, None, None
+138
+139    def laser_power_regression(
+140        self, df: SpectraFrame, boundaries=None, no_fit=False, source="spectrum"
+141    ):
+142        # fig, ax = plt.subplots(df.shape[0],1,figsize=(12,12))
+143        r = 0
+144        for index, row in df.iterrows():
+145            spe = row[source]
+146            if boundaries is None:
+147                boundaries = (self.reference_band_nm - 50, self.reference_band_nm + 50)
+148            peak_intensity, peak_position, fit_res = self.calc_peak_intensity(
+149                spe, boundaries=boundaries, no_fit=no_fit
+150            )
+151            df.at[index, "peak_intensity"] = peak_intensity
+152            df.at[index, "peak_position"] = peak_position
+153            # spe.trim_axes(method='x-axis',boundaries=boundaries).plot(ax=ax[r], fmt=':',label=row["laser_power_mW"])
+154            # if fit_res is not None:
+155            #    fit_res.plot(ax=ax[r])
+156            r = r + 1
+157        # plt.savefig("test_twinning_peaks_{}.png".format(title))
+158        # print(df[['laser_power_mW','peak_intensity']])
+159        return LinearRegression().fit(
+160            df[["laser_power_mW"]].values, df["peak_intensity"].values
+161        )
+162
+163    def process(
+164        self, spe: SpectraFrame, source="spectrum", target="spectrum_harmonized"
+165    ):
+166        spe.multiply(self.correction_factor, source=source, target=target)
+167        spe.spe_area(
+168            source=target, target="area_harmonized", boundaries=self.boundaries
+169        )
+170
+171    def derive_model(self):
+172        self.reference.trim(
+173            boundaries=self.boundaries, source="spectrum", target="spe_processed"
+174        )
+175        self.twinned.trim(
+176            boundaries=self.boundaries, source="spectrum", target="spe_processed"
+177        )
+178
+179        self.normalize_by_laserpower_time(
+180            source="spe_processed", target="spe_processed"
+181        )
+182
+183        self.reference.baseline_snip(source="spe_processed", target="spe_processed")
+184        self.twinned.baseline_snip(source="spe_processed", target="spe_processed")
+185
+186        boundaries4area = self.boundaries
+187        self.reference.spe_area(
+188            target="area", boundaries=boundaries4area, source="spe_processed"
+189        )
+190        self.twinned.spe_area(
+191            target="area", boundaries=boundaries4area, source="spe_processed"
+192        )
+193
+194        model_reference = self.laser_power_regression(
+195            self.reference, no_fit=False, source="spe_processed"
+196        )
+197        self.linreg_reference = (model_reference.intercept_, model_reference.coef_[0])
+198
+199        model_twinned = self.laser_power_regression(
+200            self.twinned, no_fit=False, source="spe_processed"
+201        )
+202        self.linreg_twinned = (model_twinned.intercept_, model_twinned.coef_[0])
+203
+204        self.correction_factor = model_reference.coef_[0] / model_twinned.coef_[0]
+205        self.twinned["correction_factor"] = self.correction_factor
+206
+207    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+208        if ax is None:
+209            fig, ax = plt.subplots(1, 2, figsize=(10, 4))
+210        self._plot(ax, label=label, **kwargs)
+211
+212        return ax
+213
+214    def _plot(self, ax, **kwargs):
+215        A = self.reference
+216        B = self.twinned
+217        regression_A = self.linreg_reference
+218        regression_B = self.linreg_twinned
+219        # fig, axes = plt.subplots(1,2, figsize=(10,4))
+220        ax[0].plot(
+221            A["laser_power_mW"], A["peak_intensity"], "o", label=A["device_id"].unique()
+222        )
+223
+224        A_pred = A["laser_power_mW"] * regression_A[1] + regression_A[0]
+225        ax[0].plot(
+226            A["laser_power_mW"],
+227            A_pred,
+228            "-",
+229            label="{:.2e} * LP + {:.2e}".format(regression_A[1], regression_A[0]),
+230        )
+231
+232        ax[0].plot(
+233            B["laser_power_mW"], B["peak_intensity"], "+", label=B["device_id"].unique()
+234        )
+235
+236        # axes[0].plot(
+237        #     B["laser_power_mW"],
+238        #     B["peak_intensity"] * correction_factor,
+239        #     "+",
+240        #     label="{} corrected".format(B["device_id"].unique()),
+241        # )
+242
+243        B_pred = B["laser_power_mW"] * regression_B[1] + regression_B[0]
+244        ax[0].plot(
+245            B["laser_power_mW"],
+246            B_pred,
+247            "-",
+248            label="{:.2e} * LP + {:.2e}".format(regression_B[1], regression_B[0]),
+249        )
+250
+251        ax[0].set_ylabel("Peak intensity of the (fitted) peak @ 144cm-1")
+252        ax[0].set_xlabel("laser power, %")
+253        ax[0].legend()
+254        bar_width = 0.2  # Adjust this value to control the width of the groups
+255        bar_positions = np.arange(len(A["laser_power_percent"].values))
+256        ax[1].bar(
+257            bar_positions - bar_width,
+258            A["area"],
+259            width=bar_width,
+260            label=str(A["device_id"].unique()),
+261        )
+262        bar_positions = np.arange(len(B["laser_power_percent"].values))
+263        ax[1].bar(
+264            bar_positions,
+265            B["area"],
+266            width=bar_width,
+267            label=str(B["device_id"].unique()),
+268        )
+269        ax[1].bar(
+270            bar_positions + bar_width,
+271            B["area_harmonized"],
+272            width=bar_width,
+273            label="{} harmonized CF={:.2e}".format(
+274                B["device_id"].unique(), self.correction_factor
+275            ),
+276        )
+277        ax[1].set_ylabel("spectrum area")
+278        ax[1].set_xlabel("laser power, %")
+279        # Set the x-axis positions and labels
+280        plt.xticks(bar_positions, B["laser_power_percent"])
+281        ax[1].legend()
+282        plt.tight_layout()
+
+ + +

TwinningComponent is an implementation of CHARISMA Twinning protocol CWA18134 Sep 2024 +https://www.cencenelec.eu/media/CEN-CENELEC/CWAs/RI/2024/cwa18134-1.pdf +It expects reference spectra and test spectra (to be twinned) as SpectraFrame objects.

+ +
Attributes:
+ +
    +
  • grouping_cols (list): The SpectraFrame may contain replicates, which will be averaged by grouping by +columns except replicates, e.g. +['sample', 'provider', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'].
    • +
  • reference (SpectraFrame): The averaged reference spectra.
    • +
  • twinned (SpectraFrame): The averaged spectra to be twinned.
    • +
  • boundaries (tuple): A tuple representing the boundaries for analysis (default: (50, 2000)).
    • +
  • linreg_reference (tuple): Placeholder for storing the result of a linear regression on the reference spectra. +Defaults to a tuple (None, None) which can later hold the regression slope and intercept.
    • +
  • linreg_twinned (tuple): Placeholder for storing the result of a linear regression on the twinned spectra. +Defaults to a tuple (None, None) which can later hold the regression slope and intercept.
    • +
  • correction_factor (float): A scaling factor derived as ratio of slopes as defined in CWA18134.
    • +
  • peak (float): The position of the peak (in nm) of interest for analysis, with a default value of 144 (TiO2).
    • +
+ +
Methods:
+ +
+

__init__(self, twinned: SpectraFrame, reference: SpectraFrame, boundaries=None, peak_at=144): + Initializes a new TwinningComponent object by averaging the provided twinned and reference spectra + based on predefined grouping columns. Optionally, boundaries for analysis and a peak position can be + specified.

+
+ +
Arguments:
+ +
    +
  • twinned (SpectraFrame): The SpectraFrame representing the twinned data.
    • +
  • reference (SpectraFrame): The SpectraFrame representing the reference data.
    • +
  • boundaries (tuple, optional): Optional boundary values to restrict the analysis region (default: (50, 2000)).
    • +
  • peak_at (int, optional): The peak position to focus the analysis on (default: 144 for TiO2).
    • +
+
+ + +
+ +
+ + TwinningComponent( twinned: ramanchada2.protocols.spectraframe.SpectraFrame, reference: ramanchada2.protocols.spectraframe.SpectraFrame, boundaries=None, reference_band_nm=144.0, grouping_cols=['sample', 'provider', 'device', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms']) + + + +
+ + 
52    def __init__(
+53        self,
+54        twinned: SpectraFrame,
+55        reference: SpectraFrame,
+56        boundaries=None,
+57        reference_band_nm=144.0,
+58        grouping_cols=[
+59            "sample",
+60            "provider",
+61            "device",
+62            "laser_wl",
+63            "laser_power_percent",
+64            "laser_power_mW",
+65            "time_ms",
+66        ],
+67    ):
+68        self.grouping_cols = grouping_cols
+69        self.twinned = twinned.average(grouping_cols=self.grouping_cols)
+70        self.reference = reference.average(grouping_cols=self.grouping_cols)
+71        self.boundaries = (50, 2000) if boundaries is None else boundaries
+72        self.linreg_reference = (None, None)
+73        self.linreg_twinned = (None, None)
+74        self.correction_factor: float = 1.0
+75        self.reference_band_nm = reference_band_nm
+
+ + + + +
+
+
+ grouping_cols + + +
+ + + + +
+
+
+ twinned + + +
+ + + + +
+
+
+ reference + + +
+ + + + +
+
+
+ boundaries + + +
+ + + + +
+
+
+ linreg_reference + + +
+ + + + +
+
+
+ linreg_twinned + + +
+ + + + +
+
+
+ correction_factor: float + + +
+ + + + +
+
+
+ reference_band_nm + + +
+ + + + +
+
+ +
+ + def + normalize_by_laserpower_time(self, source='spectrum', target='spectrum'): + + + +
+ + 
77    def normalize_by_laserpower_time(self, source="spectrum", target="spectrum"):
+78        for (index_refere4nce, row_reference), (index_twinned, row_twinned) in zip(
+79            self.reference.iterrows(), self.twinned.iterrows()
+80        ):
+81            laser_power_ratio = (
+82                row_reference["laser_power_mW"] / row_twinned["laser_power_mW"]
+83            )
+84            time_ratio = row_reference["time_ms"] / row_twinned["time_ms"]
+85            spe = row_twinned[source]
+86            self.twinned.at[index_twinned, target] = (
+87                spe * laser_power_ratio * time_ratio
+88            )
+89            self.twinned.at[index_twinned, "laser_power_ratio"] = laser_power_ratio
+90            self.twinned.at[index_twinned, "time_ratio"] = time_ratio
+
+ + + + +
+
+ +
+ + def + calc_peak_intensity( self, spe: ramanchada2.spectrum.spectrum.Spectrum, boundaries=None, prominence_coeff=0.01, no_fit=False, peak_intensity='height'): + + + +
+ + 
 92    def calc_peak_intensity(
+ 93        self,
+ 94        spe: Spectrum,
+ 95        boundaries=None,
+ 96        prominence_coeff=0.01,
+ 97        no_fit=False,
+ 98        peak_intensity="height",
+ 99    ):
+100        try:
+101            if boundaries is None:
+102                boundaries = (self.reference_band_nm - 50, self.reference_band_nm + 50)
+103            # TODO: Check if the MyPy type ignores below can be handled better.
+104            spe = spe.trim_axes(method="x-axis", boundaries=boundaries)  # type: ignore
+105            prominence = spe.y_noise_MAD() * prominence_coeff
+106            candidates = spe.find_peak_multipeak(prominence=prominence)  # type: ignore
+107            fit_res = spe.fit_peak_multimodel(  # type: ignore
+108                profile="Voigt", candidates=candidates, no_fit=no_fit
+109            )
+110            df = fit_res.to_dataframe_peaks()
+111            df["sorted"] = abs(
+112                df["center"] - self.reference_band_nm
+113            )  # closest peak to 144
+114            df_sorted = df.sort_values(by="sorted")
+115
+116            # we get actual y value, not height or amplitude
+117            index_left = np.searchsorted(
+118                spe.x, df_sorted["center"].iloc[0], side="left", sorter=None
+119            )
+120            index_right = np.searchsorted(
+121                spe.x, df_sorted["center"].iloc[0], side="right", sorter=None
+122            )
+123            if index_right == index_left:
+124                peak_intensity = spe.y[index_left]
+125                peak_position = spe.x[index_left]
+126            else:
+127                peak_intensity = (spe.y[index_right] + spe.y[index_left]) / 2.0
+128                peak_position = (spe.x[index_right] + spe.x[index_left]) / 2
+129
+130            # _label = "intensity = {:.3f} {} ={:.3f} amplitude={:.3f} center={:.1f}".format(
+131            #    intensity_val,peak_intensity,df_sorted.iloc[0][peak_intensity],
+132            #    df_sorted.iloc[0]["amplitude"],df_sorted.iloc[0]["center"])
+133
+134            return peak_intensity, peak_position, fit_res
+135        except Exception as err:
+136            print(err)
+137            return None, None, None
+
+ + + + +
+
+ +
+ + def + laser_power_regression( self, df: ramanchada2.protocols.spectraframe.SpectraFrame, boundaries=None, no_fit=False, source='spectrum'): + + + +
+ + 
139    def laser_power_regression(
+140        self, df: SpectraFrame, boundaries=None, no_fit=False, source="spectrum"
+141    ):
+142        # fig, ax = plt.subplots(df.shape[0],1,figsize=(12,12))
+143        r = 0
+144        for index, row in df.iterrows():
+145            spe = row[source]
+146            if boundaries is None:
+147                boundaries = (self.reference_band_nm - 50, self.reference_band_nm + 50)
+148            peak_intensity, peak_position, fit_res = self.calc_peak_intensity(
+149                spe, boundaries=boundaries, no_fit=no_fit
+150            )
+151            df.at[index, "peak_intensity"] = peak_intensity
+152            df.at[index, "peak_position"] = peak_position
+153            # spe.trim_axes(method='x-axis',boundaries=boundaries).plot(ax=ax[r], fmt=':',label=row["laser_power_mW"])
+154            # if fit_res is not None:
+155            #    fit_res.plot(ax=ax[r])
+156            r = r + 1
+157        # plt.savefig("test_twinning_peaks_{}.png".format(title))
+158        # print(df[['laser_power_mW','peak_intensity']])
+159        return LinearRegression().fit(
+160            df[["laser_power_mW"]].values, df["peak_intensity"].values
+161        )
+
+ + + + +
+
+ +
+ + def + process( self, spe: ramanchada2.protocols.spectraframe.SpectraFrame, source='spectrum', target='spectrum_harmonized'): + + + +
+ + 
163    def process(
+164        self, spe: SpectraFrame, source="spectrum", target="spectrum_harmonized"
+165    ):
+166        spe.multiply(self.correction_factor, source=source, target=target)
+167        spe.spe_area(
+168            source=target, target="area_harmonized", boundaries=self.boundaries
+169        )
+
+ + + + +
+
+ +
+ + def + derive_model(self): + + + +
+ + 
171    def derive_model(self):
+172        self.reference.trim(
+173            boundaries=self.boundaries, source="spectrum", target="spe_processed"
+174        )
+175        self.twinned.trim(
+176            boundaries=self.boundaries, source="spectrum", target="spe_processed"
+177        )
+178
+179        self.normalize_by_laserpower_time(
+180            source="spe_processed", target="spe_processed"
+181        )
+182
+183        self.reference.baseline_snip(source="spe_processed", target="spe_processed")
+184        self.twinned.baseline_snip(source="spe_processed", target="spe_processed")
+185
+186        boundaries4area = self.boundaries
+187        self.reference.spe_area(
+188            target="area", boundaries=boundaries4area, source="spe_processed"
+189        )
+190        self.twinned.spe_area(
+191            target="area", boundaries=boundaries4area, source="spe_processed"
+192        )
+193
+194        model_reference = self.laser_power_regression(
+195            self.reference, no_fit=False, source="spe_processed"
+196        )
+197        self.linreg_reference = (model_reference.intercept_, model_reference.coef_[0])
+198
+199        model_twinned = self.laser_power_regression(
+200            self.twinned, no_fit=False, source="spe_processed"
+201        )
+202        self.linreg_twinned = (model_twinned.intercept_, model_twinned.coef_[0])
+203
+204        self.correction_factor = model_reference.coef_[0] / model_twinned.coef_[0]
+205        self.twinned["correction_factor"] = self.correction_factor
+
+ + + + +
+
+ +
+ + def + plot(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes: + + + +
+ + 
207    def plot(self, ax=None, label=" ", **kwargs) -> Axes:
+208        if ax is None:
+209            fig, ax = plt.subplots(1, 2, figsize=(10, 4))
+210        self._plot(ax, label=label, **kwargs)
+211
+212        return ax
+
+ + + + +
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components.html b/ramanchada2/spectral_components.html new file mode 100644 index 00000000..9bd7f012 --- /dev/null +++ b/ramanchada2/spectral_components.html @@ -0,0 +1,274 @@ + + + + + + + ramanchada2.spectral_components API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3from .baseline import *  # noqa
+4from .peak_profile import *  # noqa
+5from .spectral_component_collection import SpectralComponentCollection  # noqa
+6
+7#  raise DeprecationWarning('This module is deprecated')
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/baseline.html b/ramanchada2/spectral_components/baseline.html new file mode 100644 index 00000000..a85d5f58 --- /dev/null +++ b/ramanchada2/spectral_components/baseline.html @@ -0,0 +1,261 @@ + + + + + + + ramanchada2.spectral_components.baseline API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.baseline

+ + + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/baseline/analytical.html b/ramanchada2/spectral_components/baseline/analytical.html new file mode 100644 index 00000000..771f18db --- /dev/null +++ b/ramanchada2/spectral_components/baseline/analytical.html @@ -0,0 +1,255 @@ + + + + + + + ramanchada2.spectral_components.baseline.analytical API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.baseline.analytical

+ + + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/baseline/baseline_base.html b/ramanchada2/spectral_components/baseline/baseline_base.html new file mode 100644 index 00000000..f78e953d --- /dev/null +++ b/ramanchada2/spectral_components/baseline/baseline_base.html @@ -0,0 +1,310 @@ + + + + + + + ramanchada2.spectral_components.baseline.baseline_base API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.baseline.baseline_base

+ + + + + + + 
1from ..spectral_component import SpectralComponent
+2
+3
+4class BaseLineBase(SpectralComponent):
+5    ...
+
+ + +
+
+ +
+ + class + BaseLineBase(ramanchada2.spectral_components.spectral_component.SpectralComponent): + + + +
+ + 
5class BaseLineBase(SpectralComponent):
+6    ...
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/baseline/numerical.html b/ramanchada2/spectral_components/baseline/numerical.html new file mode 100644 index 00000000..58212478 --- /dev/null +++ b/ramanchada2/spectral_components/baseline/numerical.html @@ -0,0 +1,366 @@ + + + + + + + ramanchada2.spectral_components.baseline.numerical API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.baseline.numerical

+ + + + + + + 
 1from .baseline_base import BaseLineBase
+ 2
+ 3
+ 4class BaselineNumerical(BaseLineBase):
+ 5    def __init__(self, x, y):
+ 6        """
+ 7        Numerical baseline
+ 8
+ 9        Args:
+10            x:
+11                array-like
+12            y:
+13                array-like
+14        """
+
+ + +
+
+ +
+ + class + BaselineNumerical(ramanchada2.spectral_components.baseline.baseline_base.BaseLineBase): + + + +
+ + 
 5class BaselineNumerical(BaseLineBase):
+ 6    def __init__(self, x, y):
+ 7        """
+ 8        Numerical baseline
+ 9
+10        Args:
+11            x:
+12                array-like
+13            y:
+14                array-like
+15        """
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + BaselineNumerical(x, y) + + + +
+ + 
 6    def __init__(self, x, y):
+ 7        """
+ 8        Numerical baseline
+ 9
+10        Args:
+11            x:
+12                array-like
+13            y:
+14                array-like
+15        """
+
+ + +

Numerical baseline

+ +
Arguments:
+ +
    +
  • x: array-like
    • +
  • y: array-like
    • +
+
+ + +
+
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/peak_profile.html b/ramanchada2/spectral_components/peak_profile.html new file mode 100644 index 00000000..d7fad704 --- /dev/null +++ b/ramanchada2/spectral_components/peak_profile.html @@ -0,0 +1,270 @@ + + + + + + + ramanchada2.spectral_components.peak_profile API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.peak_profile

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3
+4from .gauss import GaussPeak  # noqa
+5from .voigt import VoigtPeak  # noqa
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/peak_profile/delta.html b/ramanchada2/spectral_components/peak_profile/delta.html new file mode 100644 index 00000000..502b19e1 --- /dev/null +++ b/ramanchada2/spectral_components/peak_profile/delta.html @@ -0,0 +1,477 @@ + + + + + + + ramanchada2.spectral_components.peak_profile.delta API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.peak_profile.delta

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from __future__ import annotations
+ 4import numpy as np
+ 5
+ 6from uncertainties import unumpy
+ 7
+ 8from ..spectral_peak import SpectralPeak
+ 9
+10
+11class DeltasPeak(SpectralPeak):
+12    def __init__(self, **kwargs):
+13        super().__init__(**kwargs)
+14        if {'a', 'x0'} - kwargs.keys():
+15            raise ValueError("'a', 'x0' arguments required")
+16        self.a = kwargs['a']
+17        self.x0 = kwargs['x0']
+18
+19    def __call__(self, x: unumpy.uarray):
+20        ret = np.zeros_like(x)
+21        ret[x == self.x0] = self.a
+22        return ret
+23
+24    @property
+25    def delta(self):
+26        return self.x0, self.a
+27
+28    @property
+29    def pos_amp_fwhm(self):
+30        return self.x0, self.a, 0
+31
+32    @property
+33    def limit_3sigma(self):
+34        return self.x0-1, self.x0+1
+
+ + +
+
+ +
+ + class + DeltasPeak(ramanchada2.spectral_components.spectral_peak.SpectralPeak): + + + +
+ + 
12class DeltasPeak(SpectralPeak):
+13    def __init__(self, **kwargs):
+14        super().__init__(**kwargs)
+15        if {'a', 'x0'} - kwargs.keys():
+16            raise ValueError("'a', 'x0' arguments required")
+17        self.a = kwargs['a']
+18        self.x0 = kwargs['x0']
+19
+20    def __call__(self, x: unumpy.uarray):
+21        ret = np.zeros_like(x)
+22        ret[x == self.x0] = self.a
+23        return ret
+24
+25    @property
+26    def delta(self):
+27        return self.x0, self.a
+28
+29    @property
+30    def pos_amp_fwhm(self):
+31        return self.x0, self.a, 0
+32
+33    @property
+34    def limit_3sigma(self):
+35        return self.x0-1, self.x0+1
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + DeltasPeak(**kwargs) + + + +
+ + 
13    def __init__(self, **kwargs):
+14        super().__init__(**kwargs)
+15        if {'a', 'x0'} - kwargs.keys():
+16            raise ValueError("'a', 'x0' arguments required")
+17        self.a = kwargs['a']
+18        self.x0 = kwargs['x0']
+
+ + + + +
+
+
+ a + + +
+ + + + +
+
+
+ x0 + + +
+ + + + +
+
+ +
+ delta + + + +
+ + 
25    @property
+26    def delta(self):
+27        return self.x0, self.a
+
+ + + + +
+
+ +
+ pos_amp_fwhm + + + +
+ + 
29    @property
+30    def pos_amp_fwhm(self):
+31        return self.x0, self.a, 0
+
+ + + + +
+
+ +
+ limit_3sigma + + + +
+ + 
33    @property
+34    def limit_3sigma(self):
+35        return self.x0-1, self.x0+1
+
+ + + + +
+
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/peak_profile/gauss.html b/ramanchada2/spectral_components/peak_profile/gauss.html new file mode 100644 index 00000000..b126113d --- /dev/null +++ b/ramanchada2/spectral_components/peak_profile/gauss.html @@ -0,0 +1,492 @@ + + + + + + + ramanchada2.spectral_components.peak_profile.gauss API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.peak_profile.gauss

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from __future__ import annotations
+ 4import numpy as np
+ 5
+ 6from uncertainties import unumpy
+ 7
+ 8from ..spectral_peak import SpectralPeak
+ 9
+10
+11class GaussPeak(SpectralPeak):
+12    def __init__(self, **kwargs):
+13        super().__init__(**kwargs)
+14        if {'a', 'x0', 'w'} - kwargs.keys():
+15            raise ValueError("'a', 'x0', 'w' arguments required")
+16        self.a = kwargs['a']
+17        self.x0 = kwargs['x0']
+18        self.w = kwargs['w']
+19
+20    def __call__(self, x: unumpy.uarray):
+21        ret = 1/np.sqrt(2*np.pi)/self.w * self.a * unumpy.exp(-(x-self.x0)**2/2/self.w**2)
+22        return ret
+23
+24    @property
+25    def delta(self):
+26        return self.x0, self.a
+27
+28    @property
+29    def pos_amp_fwhm(self):
+30        return self.x0, self.a/np.sqrt(2*np.pi)/self.w, self.w*2*np.sqrt(2*np.log(2))
+31
+32    @property
+33    def limit_3sigma(self):
+34        return self.x0-3*self.w, self.x0+3*self.w
+
+ + +
+
+ +
+ + class + GaussPeak(ramanchada2.spectral_components.spectral_peak.SpectralPeak): + + + +
+ + 
12class GaussPeak(SpectralPeak):
+13    def __init__(self, **kwargs):
+14        super().__init__(**kwargs)
+15        if {'a', 'x0', 'w'} - kwargs.keys():
+16            raise ValueError("'a', 'x0', 'w' arguments required")
+17        self.a = kwargs['a']
+18        self.x0 = kwargs['x0']
+19        self.w = kwargs['w']
+20
+21    def __call__(self, x: unumpy.uarray):
+22        ret = 1/np.sqrt(2*np.pi)/self.w * self.a * unumpy.exp(-(x-self.x0)**2/2/self.w**2)
+23        return ret
+24
+25    @property
+26    def delta(self):
+27        return self.x0, self.a
+28
+29    @property
+30    def pos_amp_fwhm(self):
+31        return self.x0, self.a/np.sqrt(2*np.pi)/self.w, self.w*2*np.sqrt(2*np.log(2))
+32
+33    @property
+34    def limit_3sigma(self):
+35        return self.x0-3*self.w, self.x0+3*self.w
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + GaussPeak(**kwargs) + + + +
+ + 
13    def __init__(self, **kwargs):
+14        super().__init__(**kwargs)
+15        if {'a', 'x0', 'w'} - kwargs.keys():
+16            raise ValueError("'a', 'x0', 'w' arguments required")
+17        self.a = kwargs['a']
+18        self.x0 = kwargs['x0']
+19        self.w = kwargs['w']
+
+ + + + +
+
+
+ a + + +
+ + + + +
+
+
+ x0 + + +
+ + + + +
+
+
+ w + + +
+ + + + +
+
+ +
+ delta + + + +
+ + 
25    @property
+26    def delta(self):
+27        return self.x0, self.a
+
+ + + + +
+
+ +
+ pos_amp_fwhm + + + +
+ + 
29    @property
+30    def pos_amp_fwhm(self):
+31        return self.x0, self.a/np.sqrt(2*np.pi)/self.w, self.w*2*np.sqrt(2*np.log(2))
+
+ + + + +
+
+ +
+ limit_3sigma + + + +
+ + 
33    @property
+34    def limit_3sigma(self):
+35        return self.x0-3*self.w, self.x0+3*self.w
+
+ + + + +
+
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/peak_profile/voigt.html b/ramanchada2/spectral_components/peak_profile/voigt.html new file mode 100644 index 00000000..13ac6331 --- /dev/null +++ b/ramanchada2/spectral_components/peak_profile/voigt.html @@ -0,0 +1,320 @@ + + + + + + + ramanchada2.spectral_components.peak_profile.voigt API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.peak_profile.voigt

+ + + + + + + 
1#!/usr/bin/env python3
+2
+3from ..spectral_peak import SpectralPeak
+4
+5
+6class VoigtPeak(SpectralPeak):
+7    def __init__(self, **kwargs):
+8        super().__init__(**kwargs)
+
+ + +
+
+ +
+ + class + VoigtPeak(ramanchada2.spectral_components.spectral_peak.SpectralPeak): + + + +
+ + 
7class VoigtPeak(SpectralPeak):
+8    def __init__(self, **kwargs):
+9        super().__init__(**kwargs)
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
Inherited Members
+
+ + + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/spectral_component.html b/ramanchada2/spectral_components/spectral_component.html new file mode 100644 index 00000000..65bd5145 --- /dev/null +++ b/ramanchada2/spectral_components/spectral_component.html @@ -0,0 +1,321 @@ + + + + + + + ramanchada2.spectral_components.spectral_component API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.spectral_component

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from __future__ import annotations
+ 4
+ 5from ramanchada2.misc.plottable import Plottable
+ 6from ramanchada2.misc.base_class import BaseClass
+ 7
+ 8
+ 9class SpectralComponent(Plottable, BaseClass):
+10    def __init__(self, **kwargs):
+11        super(Plottable, self).__init__()
+12        super(BaseClass, self).__init__()
+13        self._origin = [(type(self).__name__, (), kwargs)]
+
+ + +
+
+ +
+ + class + SpectralComponent(ramanchada2.misc.plottable.Plottable, ramanchada2.misc.base_class.BaseClass): + + + +
+ + 
10class SpectralComponent(Plottable, BaseClass):
+11    def __init__(self, **kwargs):
+12        super(Plottable, self).__init__()
+13        super(BaseClass, self).__init__()
+14        self._origin = [(type(self).__name__, (), kwargs)]
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/spectral_component_collection.html b/ramanchada2/spectral_components/spectral_component_collection.html new file mode 100644 index 00000000..0d917327 --- /dev/null +++ b/ramanchada2/spectral_components/spectral_component_collection.html @@ -0,0 +1,552 @@ + + + + + + + ramanchada2.spectral_components.spectral_component_collection API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.spectral_component_collection

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from __future__ import annotations
+ 4
+ 5import numpy as np
+ 6
+ 7from ramanchada2.misc.base_class import BaseClass
+ 8from ramanchada2.misc.plottable import Plottable
+ 9from .spectral_peak import SpectralPeak
+10
+11
+12class SpectralComponentCollection(Plottable, BaseClass):
+13    def __init__(self, peaks, **kwargs):
+14        super(Plottable, self).__init__()
+15        super(BaseClass, self).__init__()
+16        self._peaks = set(peaks)
+17        self.kwargs = kwargs
+18        self.reset_origin()
+19
+20    def reset_origin(self):
+21        self._origin = [(type(self).__name__,
+22                         [tuple(sorted(self._peaks, key=lambda x: repr(x)))],
+23                         self.kwargs)]
+24
+25    def __call__(self, x):
+26        ret = np.array([p(x) for p in self._peaks]).sum(axis=0)
+27        return ret
+28
+29    def get_deltas(self):
+30        pos, ampl = zip(*[p.delta for p in self._peaks])
+31        return pos, ampl
+32
+33    def get_curve(self):
+34        ...
+35
+36    @property
+37    def limit_3sigma(self):
+38        lims = [p.limit_3sigma for p in self._peaks]
+39        return np.min(lims), np.max(lims)
+40
+41    def __iadd__(self, peak: SpectralPeak):
+42        self._peaks.add(peak)
+43        self.reset_origin()
+44
+45    def _plot(self, ax, draw='combined line', **kwargs):
+46        if draw == 'deltas':
+47            stem_kwargs = dict(basefmt='', markerfmt='rD')
+48            stem_kwargs.update(kwargs)
+49            ax.stem(*self.get_deltas(), **stem_kwargs)
+50        elif draw == 'crosses':
+51            x0, a, fwhm = zip(*[i.pos_amp_fwhm for i in self._peaks])
+52            x0 = np.array(x0)
+53            a = np.array(a)
+54            fwhm = np.array(fwhm)
+55            err_kwargs = dict(linewidth=0, elinewidth=1)
+56            err_kwargs.update(kwargs)
+57            ax.errorbar(x0, a/2, xerr=fwhm/2, yerr=a/2, **err_kwargs)
+58        elif draw == 'combined line':
+59            x = np.arange(*self.limit_3sigma)
+60            ax.plot(x, self(x), **kwargs)
+61        elif draw == 'individual lines':
+62            for p in self._peaks:
+63                p.plot(ax, draw='line', **kwargs)
+64        else:
+65            raise TypeError("draw can be 'combined line', 'individual lines', 'crosses' or 'deltas'")
+
+ + +
+
+ +
+ + class + SpectralComponentCollection(ramanchada2.misc.plottable.Plottable, ramanchada2.misc.base_class.BaseClass): + + + +
+ + 
13class SpectralComponentCollection(Plottable, BaseClass):
+14    def __init__(self, peaks, **kwargs):
+15        super(Plottable, self).__init__()
+16        super(BaseClass, self).__init__()
+17        self._peaks = set(peaks)
+18        self.kwargs = kwargs
+19        self.reset_origin()
+20
+21    def reset_origin(self):
+22        self._origin = [(type(self).__name__,
+23                         [tuple(sorted(self._peaks, key=lambda x: repr(x)))],
+24                         self.kwargs)]
+25
+26    def __call__(self, x):
+27        ret = np.array([p(x) for p in self._peaks]).sum(axis=0)
+28        return ret
+29
+30    def get_deltas(self):
+31        pos, ampl = zip(*[p.delta for p in self._peaks])
+32        return pos, ampl
+33
+34    def get_curve(self):
+35        ...
+36
+37    @property
+38    def limit_3sigma(self):
+39        lims = [p.limit_3sigma for p in self._peaks]
+40        return np.min(lims), np.max(lims)
+41
+42    def __iadd__(self, peak: SpectralPeak):
+43        self._peaks.add(peak)
+44        self.reset_origin()
+45
+46    def _plot(self, ax, draw='combined line', **kwargs):
+47        if draw == 'deltas':
+48            stem_kwargs = dict(basefmt='', markerfmt='rD')
+49            stem_kwargs.update(kwargs)
+50            ax.stem(*self.get_deltas(), **stem_kwargs)
+51        elif draw == 'crosses':
+52            x0, a, fwhm = zip(*[i.pos_amp_fwhm for i in self._peaks])
+53            x0 = np.array(x0)
+54            a = np.array(a)
+55            fwhm = np.array(fwhm)
+56            err_kwargs = dict(linewidth=0, elinewidth=1)
+57            err_kwargs.update(kwargs)
+58            ax.errorbar(x0, a/2, xerr=fwhm/2, yerr=a/2, **err_kwargs)
+59        elif draw == 'combined line':
+60            x = np.arange(*self.limit_3sigma)
+61            ax.plot(x, self(x), **kwargs)
+62        elif draw == 'individual lines':
+63            for p in self._peaks:
+64                p.plot(ax, draw='line', **kwargs)
+65        else:
+66            raise TypeError("draw can be 'combined line', 'individual lines', 'crosses' or 'deltas'")
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+ + SpectralComponentCollection(peaks, **kwargs) + + + +
+ + 
14    def __init__(self, peaks, **kwargs):
+15        super(Plottable, self).__init__()
+16        super(BaseClass, self).__init__()
+17        self._peaks = set(peaks)
+18        self.kwargs = kwargs
+19        self.reset_origin()
+
+ + + + +
+
+
+ kwargs + + +
+ + + + +
+
+ +
+ + def + reset_origin(self): + + + +
+ + 
21    def reset_origin(self):
+22        self._origin = [(type(self).__name__,
+23                         [tuple(sorted(self._peaks, key=lambda x: repr(x)))],
+24                         self.kwargs)]
+
+ + + + +
+
+ +
+ + def + get_deltas(self): + + + +
+ + 
30    def get_deltas(self):
+31        pos, ampl = zip(*[p.delta for p in self._peaks])
+32        return pos, ampl
+
+ + + + +
+
+ +
+ + def + get_curve(self): + + + +
+ + 
34    def get_curve(self):
+35        ...
+
+ + + + +
+
+ +
+ limit_3sigma + + + +
+ + 
37    @property
+38    def limit_3sigma(self):
+39        lims = [p.limit_3sigma for p in self._peaks]
+40        return np.min(lims), np.max(lims)
+
+ + + + +
+
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectral_components/spectral_peak.html b/ramanchada2/spectral_components/spectral_peak.html new file mode 100644 index 00000000..eb666fdb --- /dev/null +++ b/ramanchada2/spectral_components/spectral_peak.html @@ -0,0 +1,439 @@ + + + + + + + ramanchada2.spectral_components.spectral_peak API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectral_components.spectral_peak

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from __future__ import annotations
+ 4from abc import ABC, abstractmethod
+ 5
+ 6import numpy as np
+ 7from uncertainties import unumpy
+ 8
+ 9from ramanchada2.spectral_components.spectral_component import SpectralComponent
+10
+11
+12class SpectralPeak(SpectralComponent, ABC):
+13    def __init__(self, **kwargs):
+14        super().__init__(**kwargs)
+15
+16    def _plot(self, ax, draw='line', **kwargs):
+17        if draw == 'line':
+18            x = np.arange(*self.limit_3sigma)
+19            y = self(x)
+20            ax.errorbar(unumpy.nominal_values(x), unumpy.nominal_values(y),
+21                        yerr=unumpy.std_devs(y), **kwargs)
+22        elif draw == 'delta':
+23            args = dict(basefmt='', markerfmt='rD')
+24            args.update(kwargs)
+25            ax.stem(*self.delta, **args)
+26        elif draw == 'cross':
+27            x0, a, fwhm = self.pos_amp_fwhm
+28            ax.errorbar(x0, a/2, xerr=fwhm/2, yerr=a/2, **kwargs)
+29        else:
+30            raise TypeError("draw can be 'line', 'cross' or 'delta'")
+31
+32    @abstractmethod
+33    def delta(self): pass
+34
+35    @abstractmethod
+36    def limit_3sigma(self): pass
+37
+38    @abstractmethod
+39    def pos_amp_fwhm(self): pass
+
+ + +
+
+ +
+ + class + SpectralPeak(ramanchada2.spectral_components.spectral_component.SpectralComponent, abc.ABC): + + + +
+ + 
13class SpectralPeak(SpectralComponent, ABC):
+14    def __init__(self, **kwargs):
+15        super().__init__(**kwargs)
+16
+17    def _plot(self, ax, draw='line', **kwargs):
+18        if draw == 'line':
+19            x = np.arange(*self.limit_3sigma)
+20            y = self(x)
+21            ax.errorbar(unumpy.nominal_values(x), unumpy.nominal_values(y),
+22                        yerr=unumpy.std_devs(y), **kwargs)
+23        elif draw == 'delta':
+24            args = dict(basefmt='', markerfmt='rD')
+25            args.update(kwargs)
+26            ax.stem(*self.delta, **args)
+27        elif draw == 'cross':
+28            x0, a, fwhm = self.pos_amp_fwhm
+29            ax.errorbar(x0, a/2, xerr=fwhm/2, yerr=a/2, **kwargs)
+30        else:
+31            raise TypeError("draw can be 'line', 'cross' or 'delta'")
+32
+33    @abstractmethod
+34    def delta(self): pass
+35
+36    @abstractmethod
+37    def limit_3sigma(self): pass
+38
+39    @abstractmethod
+40    def pos_amp_fwhm(self): pass
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+
@abstractmethod
+ + def + delta(self): + + + +
+ + 
33    @abstractmethod
+34    def delta(self): pass
+
+ + + + +
+
+ +
+
@abstractmethod
+ + def + limit_3sigma(self): + + + +
+ + 
36    @abstractmethod
+37    def limit_3sigma(self): pass
+
+ + + + +
+
+ +
+
@abstractmethod
+ + def + pos_amp_fwhm(self): + + + +
+ + 
39    @abstractmethod
+40    def pos_amp_fwhm(self): pass
+
+ + + + +
+
+
Inherited Members
+
+ + +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum.html b/ramanchada2/spectrum.html new file mode 100644 index 00000000..fba9e186 --- /dev/null +++ b/ramanchada2/spectrum.html @@ -0,0 +1,290 @@ + + + + + + + ramanchada2.spectrum API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum

+ + + + + + + 
 1from .arithmetics import *  # noqa
+ 2from .baseline import *  # noqa
+ 3from .calc import *  # noqa
+ 4from .calibration import *  # noqa
+ 5from .creators.from_cache_or_calc import from_cache_or_calc
+ 6from .creators.from_chada import from_chada
+ 7from .creators.from_delta_lines import from_delta_lines
+ 8from .creators.from_local_file import from_local_file
+ 9from .creators.from_simulation import from_simulation
+10from .creators.from_spectral_component_collection import \
+11    from_spectral_component_collection
+12from .creators.from_stream import from_stream
+13from .creators.from_test_spe import from_test_spe
+14from .creators.from_theoretical_lines import from_theoretical_lines
+15from .creators.hdr_from_multi_exposure import hdr_from_multi_exposure
+16from .filters import *  # noqa
+17from .multimap import *  # noqa
+18from .peaks import *  # noqa
+19from .spectrum import Spectrum  # noqa
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/arithmetics.html b/ramanchada2/spectrum/arithmetics.html new file mode 100644 index 00000000..f2862bce --- /dev/null +++ b/ramanchada2/spectrum/arithmetics.html @@ -0,0 +1,274 @@ + + + + + + + ramanchada2.spectrum.arithmetics API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.arithmetics

+ + + + + + + 
1import os
+2import glob
+3
+4__all__ = [
+5    os.path.basename(f)[:-3]
+6    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+7    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+8]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/arithmetics/add.html b/ramanchada2/spectrum/arithmetics/add.html new file mode 100644 index 00000000..592feaf6 --- /dev/null +++ b/ramanchada2/spectrum/arithmetics/add.html @@ -0,0 +1,292 @@ + + + + + + + ramanchada2.spectrum.arithmetics.add API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.arithmetics.add

+ + + + + + + 
 1from typing import Union
+ 2
+ 3import numpy as np
+ 4from numpy.typing import NDArray
+ 5from pydantic import validate_call
+ 6
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 8
+ 9from ..spectrum import Spectrum
+10
+11
+12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def __add__(
+15        old_spe: Spectrum,
+16        new_spe: Spectrum,
+17        arg: Union[Spectrum, NDArray, float]):
+18    if isinstance(arg, Spectrum):
+19        if not (old_spe.x == arg.x).all():
+20            ValueError('x axes should be equal')
+21        new_spe.y = old_spe.y + arg.y
+22    elif isinstance(arg, np.ndarray):
+23        if old_spe.y.shape != arg.shape:
+24            ValueError(f'shapes does not match {old_spe.y.shape} != {arg.shape}')
+25        new_spe.y = old_spe.y + arg
+26    elif isinstance(arg, float):
+27        new_spe.y = old_spe.y + arg
+28    else:
+29        ValueError('This should never happen')
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/arithmetics/mul.html b/ramanchada2/spectrum/arithmetics/mul.html new file mode 100644 index 00000000..a6d2654e --- /dev/null +++ b/ramanchada2/spectrum/arithmetics/mul.html @@ -0,0 +1,292 @@ + + + + + + + ramanchada2.spectrum.arithmetics.mul API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.arithmetics.mul

+ + + + + + + 
 1from typing import Union
+ 2
+ 3import numpy as np
+ 4from numpy.typing import NDArray
+ 5from pydantic import validate_call
+ 6
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 8
+ 9from ..spectrum import Spectrum
+10
+11
+12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def __mul__(
+15        old_spe: Spectrum,
+16        new_spe: Spectrum,
+17        arg: Union[Spectrum, NDArray, float]):
+18    if isinstance(arg, Spectrum):
+19        if not (old_spe.x == arg.x).all():
+20            ValueError('x axes should be equal')
+21        new_spe.y = old_spe.y * arg.y
+22    elif isinstance(arg, np.ndarray):
+23        if old_spe.y.shape != arg.shape:
+24            ValueError(f'shapes does not match {old_spe.y.shape} != {arg.shape}')
+25        new_spe.y = old_spe.y * arg
+26    elif isinstance(arg, float):
+27        new_spe.y = old_spe.y * arg
+28    else:
+29        ValueError('This should never happen')
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/arithmetics/sub.html b/ramanchada2/spectrum/arithmetics/sub.html new file mode 100644 index 00000000..32e493e6 --- /dev/null +++ b/ramanchada2/spectrum/arithmetics/sub.html @@ -0,0 +1,292 @@ + + + + + + + ramanchada2.spectrum.arithmetics.sub API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.arithmetics.sub

+ + + + + + + 
 1from typing import Union
+ 2
+ 3import numpy as np
+ 4from numpy.typing import NDArray
+ 5from pydantic import validate_call
+ 6
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 8
+ 9from ..spectrum import Spectrum
+10
+11
+12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def __sub__(
+15        old_spe: Spectrum,
+16        new_spe: Spectrum,
+17        arg: Union[Spectrum, NDArray, float]):
+18    if isinstance(arg, Spectrum):
+19        if not (old_spe.x == arg.x).all():
+20            ValueError('x axes should be equal')
+21        new_spe.y = old_spe.y - arg.y
+22    elif isinstance(arg, np.ndarray):
+23        if old_spe.y.shape != arg.shape:
+24            ValueError(f'shapes does not match {old_spe.y.shape} != {arg.shape}')
+25        new_spe.y = old_spe.y - arg
+26    elif isinstance(arg, float):
+27        new_spe.y = old_spe.y - arg
+28    else:
+29        ValueError('This should never happen')
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/arithmetics/truediv.html b/ramanchada2/spectrum/arithmetics/truediv.html new file mode 100644 index 00000000..158b54fa --- /dev/null +++ b/ramanchada2/spectrum/arithmetics/truediv.html @@ -0,0 +1,291 @@ + + + + + + + ramanchada2.spectrum.arithmetics.truediv API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.arithmetics.truediv

+ + + + + + + 
 1from typing import Union
+ 2
+ 3import numpy as np
+ 4from numpy.typing import NDArray
+ 5from pydantic import validate_call
+ 6
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 8from ..spectrum import Spectrum
+ 9
+10
+11@add_spectrum_filter
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def __truediv__(
+14        old_spe: Spectrum,
+15        new_spe: Spectrum,
+16        arg: Union[Spectrum, NDArray, float]):
+17    if isinstance(arg, Spectrum):
+18        if not (old_spe.x == arg.x).all():
+19            ValueError('x axes should be equal')
+20        new_spe.y = old_spe.y / arg.y
+21    elif isinstance(arg, np.ndarray):
+22        if old_spe.y.shape != arg.shape:
+23            ValueError(f'shapes does not match {old_spe.y.shape} != {arg.shape}')
+24        new_spe.y = old_spe.y / arg
+25    elif isinstance(arg, float):
+26        new_spe.y = old_spe.y / arg
+27    else:
+28        ValueError('This should never happen')
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/baseline.html b/ramanchada2/spectrum/baseline.html new file mode 100644 index 00000000..b0c3a9c7 --- /dev/null +++ b/ramanchada2/spectrum/baseline.html @@ -0,0 +1,273 @@ + + + + + + + ramanchada2.spectrum.baseline API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.baseline

+ + + + + + + 
1import os
+2import glob
+3
+4__all__ = [
+5    os.path.basename(f)[:-3]
+6    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+7    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+8]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/baseline/add_baseline.html b/ramanchada2/spectrum/baseline/add_baseline.html new file mode 100644 index 00000000..cc027f33 --- /dev/null +++ b/ramanchada2/spectrum/baseline/add_baseline.html @@ -0,0 +1,445 @@ + + + + + + + ramanchada2.spectrum.baseline.add_baseline API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.baseline.add_baseline

+ + + + + + + 
 1from typing import Union, Callable
+ 2
+ 3from pydantic import validate_call, Field
+ 4import numpy as np
+ 5from scipy import signal, fft
+ 6
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 8from ..spectrum import Spectrum
+ 9
+10
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def generate_baseline(
+13        n_freq: int = Field(..., gt=2),
+14        size: int = Field(..., gt=2),
+15        # validation for rng_seed is removed because
+16        # it makes in-place modification impossible
+17        rng_seed=None):
+18    if isinstance(rng_seed, dict):
+19        rng = np.random.default_rng()
+20        rng.bit_generator.state = rng_seed
+21    else:
+22        rng = np.random.default_rng(rng_seed)
+23    k = rng.normal(0, size, size=(2, n_freq))
+24    k[1][0] = 0
+25    z = k[0] + k[1]*1j
+26    w = signal.windows.bohman(2*len(z))[-len(z):]
+27    z *= w
+28    z = np.concatenate([z, np.zeros(size-n_freq)])
+29    base = fft.irfft(z)
+30    base = base[:size]
+31    base -= base.min()
+32    base /= base.max()
+33    if isinstance(rng_seed, dict):
+34        rng_seed.update(rng.bit_generator.state)
+35    return base
+36
+37
+38@add_spectrum_filter
+39@validate_call(config=dict(arbitrary_types_allowed=True))
+40def add_baseline(old_spe: Spectrum, new_spe: Spectrum, /, n_freq: int, amplitude: float, pedestal: float = 0,
+41                 func: Union[Callable, None] = None, rng_seed=None):
+42    """
+43    Add artificial baseline to the spectrum.
+44    A random baseline is generated in frequency domain using uniform random numbers.
+45    The baseline in frequency domain is tapered with bohman window to reduce the bandwidth
+46    of the baseline to first `n_freq` frequencies and is transformed to "time" domain.
+47    Additionaly by using `func` parameter the user can define arbitrary function
+48    to be added as baseline.
+49
+50    Args:
+51        n_freq:
+52            Must be `> 2`. Number of lowest frequency bins distinct from zero.
+53        amplitude:
+54            Upper boundary for the uniform random generator.
+55        pedestal:
+56            Additive constant pedestal to the spectrum.
+57        func:
+58            Callable. User-defined function to be added as baseline. Example: `func = lambda x: x*.01 + x**2*.0001`.
+59        rng_seed:
+60            `int`, optional. Seed for the random generator.
+61    """
+62    size = len(old_spe.y)
+63    base = generate_baseline(n_freq=n_freq, size=size, rng_seed=rng_seed)
+64    y = old_spe.y + amplitude*base + pedestal
+65    if func is not None:
+66        y += func(old_spe.x) + old_spe.y
+67    new_spe.y = y
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + generate_baseline( n_freq: int = FieldInfo(annotation=int, required=True, metadata=[Gt(gt=2)]), size: int = FieldInfo(annotation=int, required=True, metadata=[Gt(gt=2)]), rng_seed=None): + + + +
+ + 
12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def generate_baseline(
+14        n_freq: int = Field(..., gt=2),
+15        size: int = Field(..., gt=2),
+16        # validation for rng_seed is removed because
+17        # it makes in-place modification impossible
+18        rng_seed=None):
+19    if isinstance(rng_seed, dict):
+20        rng = np.random.default_rng()
+21        rng.bit_generator.state = rng_seed
+22    else:
+23        rng = np.random.default_rng(rng_seed)
+24    k = rng.normal(0, size, size=(2, n_freq))
+25    k[1][0] = 0
+26    z = k[0] + k[1]*1j
+27    w = signal.windows.bohman(2*len(z))[-len(z):]
+28    z *= w
+29    z = np.concatenate([z, np.zeros(size-n_freq)])
+30    base = fft.irfft(z)
+31    base = base[:size]
+32    base -= base.min()
+33    base /= base.max()
+34    if isinstance(rng_seed, dict):
+35        rng_seed.update(rng.bit_generator.state)
+36    return base
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_baseline( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, n_freq: int, amplitude: float, pedestal: float = 0, func: Optional[Callable] = None, rng_seed=None): + + + +
+ + 
39@add_spectrum_filter
+40@validate_call(config=dict(arbitrary_types_allowed=True))
+41def add_baseline(old_spe: Spectrum, new_spe: Spectrum, /, n_freq: int, amplitude: float, pedestal: float = 0,
+42                 func: Union[Callable, None] = None, rng_seed=None):
+43    """
+44    Add artificial baseline to the spectrum.
+45    A random baseline is generated in frequency domain using uniform random numbers.
+46    The baseline in frequency domain is tapered with bohman window to reduce the bandwidth
+47    of the baseline to first `n_freq` frequencies and is transformed to "time" domain.
+48    Additionaly by using `func` parameter the user can define arbitrary function
+49    to be added as baseline.
+50
+51    Args:
+52        n_freq:
+53            Must be `> 2`. Number of lowest frequency bins distinct from zero.
+54        amplitude:
+55            Upper boundary for the uniform random generator.
+56        pedestal:
+57            Additive constant pedestal to the spectrum.
+58        func:
+59            Callable. User-defined function to be added as baseline. Example: `func = lambda x: x*.01 + x**2*.0001`.
+60        rng_seed:
+61            `int`, optional. Seed for the random generator.
+62    """
+63    size = len(old_spe.y)
+64    base = generate_baseline(n_freq=n_freq, size=size, rng_seed=rng_seed)
+65    y = old_spe.y + amplitude*base + pedestal
+66    if func is not None:
+67        y += func(old_spe.x) + old_spe.y
+68    new_spe.y = y
+
+ + +

Add artificial baseline to the spectrum. +A random baseline is generated in frequency domain using uniform random numbers. +The baseline in frequency domain is tapered with bohman window to reduce the bandwidth +of the baseline to first n_freq frequencies and is transformed to "time" domain. +Additionaly by using func parameter the user can define arbitrary function +to be added as baseline.

+ +
Arguments:
+ +
    +
  • n_freq: Must be > 2. Number of lowest frequency bins distinct from zero.
    • +
  • amplitude: Upper boundary for the uniform random generator.
    • +
  • pedestal: Additive constant pedestal to the spectrum.
    • +
  • func: Callable. User-defined function to be added as baseline. Example: func = lambda x: x*.01 + x**2*.0001.
    • +
  • rng_seed: int, optional. Seed for the random generator.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/baseline/baseline_rc1.html b/ramanchada2/spectrum/baseline/baseline_rc1.html new file mode 100644 index 00000000..5c3770d2 --- /dev/null +++ b/ramanchada2/spectrum/baseline/baseline_rc1.html @@ -0,0 +1,465 @@ + + + + + + + ramanchada2.spectrum.baseline.baseline_rc1 API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.baseline.baseline_rc1

+ + + + + + + 
 1from typing import Literal, Union
+ 2
+ 3import numpy as np
+ 4import pandas as pd
+ 5from pydantic import PositiveInt, validate_call
+ 6from scipy import sparse
+ 7from scipy.signal import wiener
+ 8from scipy.sparse.linalg import spsolve
+ 9
+10from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+11from ramanchada2.misc.types import PositiveOddInt
+12
+13from ..spectrum import Spectrum
+14
+15
+16@validate_call
+17def baseline_als(y, lam: float = 1e5, p: float = 0.001, niter: PositiveInt = 100,
+18                 smooth: Union[PositiveOddInt, Literal[0]] = PositiveOddInt(7)):
+19    if smooth > 0:
+20        y = wiener(y, smooth)
+21    L = len(y)
+22    D = sparse.csc_matrix(np.diff(np.eye(L), 2))
+23    w = np.ones(L)
+24    for i in range(niter):
+25        W = sparse.spdiags(w, 0, L, L)
+26        Z = W + lam * D.dot(D.transpose())
+27        z = spsolve(Z, w*y)
+28        w = p * (y > z) + (1-p) * (y < z)
+29    return z
+30
+31
+32def baseline_snip(y0, niter: PositiveInt = 30):
+33    # y can't have negatives. fix by offset:
+34    y_offset = y0.min()
+35    y = y0 - y_offset
+36    # Spectrum must be row of a DataFrame
+37    raman_spectra = pd.DataFrame(y).T
+38    spectrum_points = len(raman_spectra.columns)
+39    raman_spectra_transformed = np.log(np.log(np.sqrt(raman_spectra + 1) + 1) + 1)
+40    working_spectra = np.zeros(raman_spectra.shape)
+41    for pp in np.arange(1, niter+1):
+42        r1 = raman_spectra_transformed.iloc[:, pp:spectrum_points - pp]
+43        r2 = (np.roll(raman_spectra_transformed, - pp, axis=1)[:, pp:spectrum_points - pp] +
+44              np.roll(raman_spectra_transformed, pp, axis=1)[:, pp:spectrum_points - pp])/2
+45        working_spectra = np.minimum(r1, r2)
+46        raman_spectra_transformed.iloc[:, pp:spectrum_points-pp] = working_spectra
+47    baseline = (np.exp(np.exp(raman_spectra_transformed)-1)-1)**2 - 1
+48    # Re-convert to np.array and apply inverse y offset to baseline
+49    return baseline.to_numpy()[0].T + y_offset
+50
+51
+52@add_spectrum_filter
+53@validate_call(config=dict(arbitrary_types_allowed=True))
+54def subtract_baseline_rc1_als(
+55        old_spe: Spectrum,
+56        new_spe: Spectrum,
+57        lam=1e5, p=0.001, niter=100, smooth=7
+58        ):
+59    new_spe.y = old_spe.y - baseline_als(old_spe.y, lam=lam, p=p, niter=niter, smooth=smooth)
+60
+61
+62@add_spectrum_filter
+63@validate_call(config=dict(arbitrary_types_allowed=True))
+64def subtract_baseline_rc1_snip(
+65        old_spe: Spectrum,
+66        new_spe: Spectrum,
+67        niter=30
+68        ):
+69    new_spe.y = old_spe.y - baseline_snip(old_spe.y, niter=niter)
+
+ + +
+
+ +
+
@validate_call
+ + def + baseline_als( y, lam: float = 100000.0, p: float = 0.001, niter: typing.Annotated[int, Gt(gt=0)] = 100, smooth: Union[Annotated[int, Gt(gt=0)], Literal[0]] = 7): + + + +
+ + 
17@validate_call
+18def baseline_als(y, lam: float = 1e5, p: float = 0.001, niter: PositiveInt = 100,
+19                 smooth: Union[PositiveOddInt, Literal[0]] = PositiveOddInt(7)):
+20    if smooth > 0:
+21        y = wiener(y, smooth)
+22    L = len(y)
+23    D = sparse.csc_matrix(np.diff(np.eye(L), 2))
+24    w = np.ones(L)
+25    for i in range(niter):
+26        W = sparse.spdiags(w, 0, L, L)
+27        Z = W + lam * D.dot(D.transpose())
+28        z = spsolve(Z, w*y)
+29        w = p * (y > z) + (1-p) * (y < z)
+30    return z
+
+ + + + +
+
+ +
+ + def + baseline_snip(y0, niter: typing.Annotated[int, Gt(gt=0)] = 30): + + + +
+ + 
33def baseline_snip(y0, niter: PositiveInt = 30):
+34    # y can't have negatives. fix by offset:
+35    y_offset = y0.min()
+36    y = y0 - y_offset
+37    # Spectrum must be row of a DataFrame
+38    raman_spectra = pd.DataFrame(y).T
+39    spectrum_points = len(raman_spectra.columns)
+40    raman_spectra_transformed = np.log(np.log(np.sqrt(raman_spectra + 1) + 1) + 1)
+41    working_spectra = np.zeros(raman_spectra.shape)
+42    for pp in np.arange(1, niter+1):
+43        r1 = raman_spectra_transformed.iloc[:, pp:spectrum_points - pp]
+44        r2 = (np.roll(raman_spectra_transformed, - pp, axis=1)[:, pp:spectrum_points - pp] +
+45              np.roll(raman_spectra_transformed, pp, axis=1)[:, pp:spectrum_points - pp])/2
+46        working_spectra = np.minimum(r1, r2)
+47        raman_spectra_transformed.iloc[:, pp:spectrum_points-pp] = working_spectra
+48    baseline = (np.exp(np.exp(raman_spectra_transformed)-1)-1)**2 - 1
+49    # Re-convert to np.array and apply inverse y offset to baseline
+50    return baseline.to_numpy()[0].T + y_offset
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_baseline_rc1_als( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, lam=100000.0, p=0.001, niter=100, smooth=7): + + + +
+ + 
53@add_spectrum_filter
+54@validate_call(config=dict(arbitrary_types_allowed=True))
+55def subtract_baseline_rc1_als(
+56        old_spe: Spectrum,
+57        new_spe: Spectrum,
+58        lam=1e5, p=0.001, niter=100, smooth=7
+59        ):
+60    new_spe.y = old_spe.y - baseline_als(old_spe.y, lam=lam, p=p, niter=niter, smooth=smooth)
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_baseline_rc1_snip( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, niter=30): + + + +
+ + 
63@add_spectrum_filter
+64@validate_call(config=dict(arbitrary_types_allowed=True))
+65def subtract_baseline_rc1_snip(
+66        old_spe: Spectrum,
+67        new_spe: Spectrum,
+68        niter=30
+69        ):
+70    new_spe.y = old_spe.y - baseline_snip(old_spe.y, niter=niter)
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/baseline/moving_minimum.html b/ramanchada2/spectrum/baseline/moving_minimum.html new file mode 100644 index 00000000..fca8e849 --- /dev/null +++ b/ramanchada2/spectrum/baseline/moving_minimum.html @@ -0,0 +1,366 @@ + + + + + + + ramanchada2.spectrum.baseline.moving_minimum API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.baseline.moving_minimum

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import validate_call, PositiveInt
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5from ..spectrum import Spectrum
+ 6
+ 7
+ 8@validate_call(config=dict(arbitrary_types_allowed=True))
+ 9def _moving_minimum(arr, window_size: PositiveInt):
+10    mov_min_left = [min(arr[max(0, i):min(i+window_size, len(arr))])
+11                    for i in range(len(arr))
+12                    ]
+13    mov_min_right = [min(arr[max(0, i-window_size):min(i, len(arr))])
+14                     for i in range(1, len(arr)+1)
+15                     ]
+16    return np.maximum.reduce([mov_min_left, mov_min_right])
+17
+18
+19@add_spectrum_filter
+20@validate_call(config=dict(arbitrary_types_allowed=True))
+21def moving_minimum(
+22        old_spe: Spectrum,
+23        new_spe: Spectrum,
+24        window_size: int):
+25    """
+26    Moving minimum baseline estimator.
+27    Successive values are calculated as minima of rolling rectangular window.
+28    """
+29    new_spe.y = _moving_minimum(old_spe.y, window_size)
+30
+31
+32@add_spectrum_filter
+33@validate_call(config=dict(arbitrary_types_allowed=True))
+34def subtract_moving_minimum(
+35        old_spe: Spectrum,
+36        new_spe: Spectrum,
+37        window_size: int):
+38    new_spe.y = old_spe.y - _moving_minimum(old_spe.y, window_size)
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_minimum( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, window_size: int): + + + +
+ + 
20@add_spectrum_filter
+21@validate_call(config=dict(arbitrary_types_allowed=True))
+22def moving_minimum(
+23        old_spe: Spectrum,
+24        new_spe: Spectrum,
+25        window_size: int):
+26    """
+27    Moving minimum baseline estimator.
+28    Successive values are calculated as minima of rolling rectangular window.
+29    """
+30    new_spe.y = _moving_minimum(old_spe.y, window_size)
+
+ + +

Moving minimum baseline estimator. +Successive values are calculated as minima of rolling rectangular window.

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_moving_minimum( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, window_size: int): + + + +
+ + 
33@add_spectrum_filter
+34@validate_call(config=dict(arbitrary_types_allowed=True))
+35def subtract_moving_minimum(
+36        old_spe: Spectrum,
+37        new_spe: Spectrum,
+38        window_size: int):
+39    new_spe.y = old_spe.y - _moving_minimum(old_spe.y, window_size)
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calc.html b/ramanchada2/spectrum/calc.html new file mode 100644 index 00000000..e65d85c7 --- /dev/null +++ b/ramanchada2/spectrum/calc.html @@ -0,0 +1,271 @@ + + + + + + + ramanchada2.spectrum.calc API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calc

+ + + + + + + 
1import os
+2import glob
+3
+4__all__ = [
+5    os.path.basename(f)[:-3]
+6    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+7    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+8]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calc/central_moments.html b/ramanchada2/spectrum/calc/central_moments.html new file mode 100644 index 00000000..924b992a --- /dev/null +++ b/ramanchada2/spectrum/calc/central_moments.html @@ -0,0 +1,333 @@ + + + + + + + ramanchada2.spectrum.calc.central_moments API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calc.central_moments

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_method
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_method
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def central_moments(spe: Spectrum, /,
+12                    boundaries=(-np.inf, np.inf), moments=[1, 2, 3, 4], normalize=False
+13                    ):
+14    mom = dict()
+15    filter_idx = (spe.x >= boundaries[0]) & (spe.x < boundaries[1])
+16    x = spe.x[filter_idx]
+17    p = spe.y[filter_idx]
+18    p -= p.min()
+19    p /= p.sum()
+20    mom[1] = np.sum(x*p)
+21    mom[2] = np.sum((x - mom[1])**2 * p)
+22    for i in moments:
+23        if i <= 2:
+24            continue
+25        mom[i] = np.sum((x - mom[1])**i * p)
+26        if normalize and i > 2:
+27            mom[i] /= mom[2] ** i/2
+28    return [mom[i] for i in moments]
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + central_moments( spe: ramanchada2.spectrum.spectrum.Spectrum, /, boundaries=(-inf, inf), moments=[1, 2, 3, 4], normalize=False): + + + +
+ + 
10@add_spectrum_method
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def central_moments(spe: Spectrum, /,
+13                    boundaries=(-np.inf, np.inf), moments=[1, 2, 3, 4], normalize=False
+14                    ):
+15    mom = dict()
+16    filter_idx = (spe.x >= boundaries[0]) & (spe.x < boundaries[1])
+17    x = spe.x[filter_idx]
+18    p = spe.y[filter_idx]
+19    p -= p.min()
+20    p /= p.sum()
+21    mom[1] = np.sum(x*p)
+22    mom[2] = np.sum((x - mom[1])**2 * p)
+23    for i in moments:
+24        if i <= 2:
+25            continue
+26        mom[i] = np.sum((x - mom[1])**i * p)
+27        if normalize and i > 2:
+28            mom[i] /= mom[2] ** i/2
+29    return [mom[i] for i in moments]
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration.html b/ramanchada2/spectrum/calibration.html new file mode 100644 index 00000000..3241962f --- /dev/null +++ b/ramanchada2/spectrum/calibration.html @@ -0,0 +1,278 @@ + + + + + + + ramanchada2.spectrum.calibration API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3import os
+ 4import glob
+ 5
+ 6__all__ = [
+ 7    os.path.basename(f)[:-3]
+ 8    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+ 9    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+10]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration/by_deltas.html b/ramanchada2/spectrum/calibration/by_deltas.html new file mode 100644 index 00000000..e650f112 --- /dev/null +++ b/ramanchada2/spectrum/calibration/by_deltas.html @@ -0,0 +1,1510 @@ + + + + + + + ramanchada2.spectrum.calibration.by_deltas API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration.by_deltas

+ + + + + + + 
  1from typing import Dict, List, Literal, Optional, Union
+  2
+  3import lmfit
+  4import numpy as np
+  5import numpy.typing as npt
+  6from pydantic import BaseModel, NonNegativeInt, validate_call
+  7from scipy import interpolate
+  8from scipy import fft, signal
+  9
+ 10from ramanchada2.misc.spectrum_deco import (add_spectrum_filter,
+ 11                                            add_spectrum_method)
+ 12
+ 13from ...misc import utils as rc2utils
+ 14from ..spectrum import Spectrum
+ 15
+ 16
+ 17class DeltaSpeModel:
+ 18    def __init__(self, deltas: Dict[float, float], shift=0, scale=1):
+ 19        components = list()
+ 20        self.params = lmfit.Parameters()
+ 21        self.minx = np.min(list(deltas.keys()))
+ 22        self.maxx = np.max(list(deltas.keys()))
+ 23        self.params.add('shift', value=shift, vary=True)
+ 24        self.params.add('scale', value=scale, vary=True, min=.1, max=10)
+ 25        self.params.add('scale2', value=0, vary=False, min=-.1, max=.1)
+ 26        self.params.add('scale3', value=0, vary=False, min=-1e-3, max=1e-3)
+ 27        self.params.add('sigma', value=1, vary=True)
+ 28        self.params.add('gain', value=1, vary=True)
+ 29        for comp_i, (k, v) in enumerate(deltas.items()):
+ 30            prefix = f'comp_{comp_i}'
+ 31            components.append(lmfit.models.GaussianModel(prefix=f'comp_{comp_i}_'))
+ 32            self.params.add(prefix + '_center', expr=f'shift + {k}*scale + {k}**2*scale2 + {k}**3*scale3', vary=False)
+ 33            self.params.add(prefix + '_amplitude', expr=f'{v}*gain', vary=False)
+ 34            self.params.add(prefix + '_sigma', expr='sigma', vary=False)
+ 35        self.model = np.sum(components)
+ 36
+ 37    def fit(self, spe, sigma, ax=None, no_fit=False):
+ 38        self.params['sigma'].set(value=sigma if sigma > 1 else 1)
+ 39        spe_conv = spe.convolve('gaussian', sigma=sigma/np.mean(np.diff(spe.x)))
+ 40        if no_fit:
+ 41            fit_res = self.model.fit(spe_conv.y, x=spe_conv.x, params=self.params, max_nfev=-1)
+ 42        else:
+ 43            fit_res = self.model.fit(spe_conv.y, x=spe_conv.x, params=self.params)
+ 44        self.params = fit_res.params
+ 45        if ax is not None:
+ 46            spe_conv.plot(ax=ax)
+ 47            ax.plot(spe_conv.x, fit_res.eval(x=spe_conv.x), 'r')
+ 48
+ 49
+ 50class ParamBounds(BaseModel):
+ 51    min: float = -np.inf
+ 52    max: float = np.inf
+ 53
+ 54
+ 55class FitBounds(BaseModel):
+ 56    shift: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+ 57    scale: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+ 58    scale2: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+ 59    scale3: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+ 60
+ 61
+ 62@add_spectrum_method
+ 63@validate_call(config=dict(arbitrary_types_allowed=True))
+ 64def calibrate_by_deltas_model(spe: Spectrum, /,
+ 65                              deltas: Dict[float, float],
+ 66                              bounds: Optional[FitBounds] = None,
+ 67                              convolution_steps: Union[None, List[float]] = [15, 1],
+ 68                              scale2=True, scale3=False,
+ 69                              init_guess: Literal[None, 'cumulative'] = None,
+ 70                              ax=None, **kwargs
+ 71                              ):
+ 72    """
+ 73    - Builds a composite model based on a set of user specified delta lines.
+ 74    - Initial guess is calculated based on 10-th and 90-th percentiles of
+ 75      the distributions.
+ 76
+ 77    The phasespace of the model is flat with big amount of narrow minima.
+ 78    In order to find the best fit, the experimental data are successively
+ 79    convolved with gaussians with different widths startign from wide to
+ 80    narrow. The model for the calibration is 3-th order polynomial, which
+ 81    potentialy can be changed for higher order polynomial. In order to avoid
+ 82    solving the inverse of the calibration function, the result is tabulated
+ 83    and interpolated linarly for each bin of the spectrum.
+ 84    This alogrithm is useful for corse calibration.
+ 85    """
+ 86    mod = DeltaSpeModel(deltas)
+ 87    spe_padded = spe
+ 88
+ 89    if init_guess == 'cumulative':
+ 90        deltasx = np.array(list(deltas.keys()))
+ 91
+ 92        deltas_cs = np.cumsum(list(deltas.values()))
+ 93        deltas_cs /= deltas_cs[-1]
+ 94
+ 95        deltas_idx10 = np.argmin(np.abs(deltas_cs-.1))
+ 96        deltas_idx90 = np.argmin(np.abs(deltas_cs-.9))
+ 97        x1, x2 = deltasx[[deltas_idx10, deltas_idx90]]
+ 98
+ 99        spe_cs = np.cumsum(
+100            spe_padded.moving_average(50).subtract_moving_minimum(10).moving_average(5).y)  # type: ignore
+101
+102        spe_cs /= spe_cs[-1]
+103        spe_idx10 = np.argmin(np.abs(spe_cs-.1))
+104        spe_idx90 = np.argmin(np.abs(spe_cs-.9))
+105        y1, y2 = spe_padded.x[[spe_idx10, spe_idx90]]
+106
+107        scale = (y1-y2)/(x1-x2)
+108        shift = -scale * x1 + y1
+109    else:
+110        scale = 1
+111        shift = 0
+112    gain = np.sum(spe.y)/np.sum(list(deltas.values()))
+113    if bounds is not None:
+114        mod.params['scale'].set(value=scale, min=bounds.scale.min, max=bounds.scale.max)
+115        mod.params['shift'].set(value=shift, min=bounds.shift.min, max=bounds.shift.max)
+116    else:
+117        mod.params['scale'].set(value=scale)
+118        mod.params['shift'].set(value=shift)
+119    mod.params['gain'].set(value=gain)
+120    mod.params['sigma'].set(value=2.5)
+121
+122    if ax is not None:
+123        spe_padded.plot(ax=ax)
+124
+125    if convolution_steps is not None:
+126        for sig in convolution_steps:
+127            mod.fit(spe=spe_padded, sigma=sig, ax=ax, **kwargs)
+128
+129    if scale2:
+130        if bounds is not None:
+131            mod.params['scale2'].set(vary=True, value=0, min=bounds.scale2.min, max=bounds.scale2.max)
+132        else:
+133            mod.params['scale2'].set(vary=True, value=0)
+134        mod.fit(spe_padded, sigma=0.05, ax=ax, **kwargs)
+135    if scale3:
+136        if bounds is not None:
+137            mod.params['scale2'].set(vary=True, value=0, min=bounds.scale2.min, max=bounds.scale2.max)
+138            mod.params['scale3'].set(vary=True, value=0, min=bounds.scale3.min, max=bounds.scale3.max)
+139        else:
+140            mod.params['scale2'].set(vary=True, value=0)
+141            mod.params['scale3'].set(vary=True, value=0)
+142        mod.fit(spe_padded, sigma=0.05, ax=ax, **kwargs)
+143    return mod.model, mod.params
+144
+145
+146@add_spectrum_filter
+147@validate_call(config=dict(arbitrary_types_allowed=True))
+148def calibrate_by_deltas_filter(old_spe: Spectrum,
+149                               new_spe: Spectrum, /,
+150                               deltas: Dict[float, float],
+151                               convolution_steps,
+152                               init_guess=None,
+153                               **kwargs
+154                               ):
+155    mod, par = old_spe.calibrate_by_deltas_model(  # type: ignore
+156        deltas=deltas,
+157        convolution_steps=convolution_steps,
+158        init_guess=init_guess,
+159        **kwargs)
+160
+161    deltasx = np.array(list(deltas.keys()))
+162    dxl, dxr = deltasx[[0, -1]]
+163    xl = dxl - (dxr - dxl)
+164    xr = dxl + (dxr - dxl)
+165    true_x = np.linspace(xl, xr, len(old_spe.x)*6)
+166    meas_x = (par['shift'].value + true_x * par['scale'] +
+167              true_x**2 * par['scale2'] + true_x**3 * par['scale3'])
+168    x_cal = np.zeros_like(old_spe.x)
+169    for i in range(len(old_spe.x)):
+170        idx = np.argmax(meas_x > old_spe.x[i])
+171        pt_rto = (old_spe.x[i] - meas_x[idx-1])/(meas_x[idx] - meas_x[idx-1])
+172        x_cal[i] = (true_x[idx] - true_x[idx-1])*pt_rto + true_x[idx-1]
+173    new_spe.x = x_cal
+174
+175
+176@add_spectrum_filter
+177@validate_call(config=dict(arbitrary_types_allowed=True))
+178def xcal_fine(old_spe: Spectrum,
+179              new_spe: Spectrum, /, *,
+180              ref: Union[Dict[float, float], List[float]],
+181              should_fit=False,
+182              poly_order: NonNegativeInt,
+183              find_peaks_kw={},
+184              ):
+185    """
+186    Iterative calibration with provided reference based on :func:`~ramanchada2.misc.utils.argmin2d.align`
+187
+188    Iteratively apply polynomial of `poly_order` degree to match
+189    the found peaks to the reference locations. The pairs are created
+190    using :func:`~ramanchada2.misc.utils.argmin2d.align` algorithm.
+191
+192    Args:
+193        old_spe (Spectrum): internal use only
+194        new_spe (Spectrum): internal use only
+195        ref (Union[Dict[float, float], List[float]]): _description_
+196        ref (Dict[float, float]):
+197            If a dict is provided - wavenumber - amplitude pairs.
+198            If a list is provided - wavenumbers only.
+199        poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
+200        should_fit (bool, optional): Whether the peaks should be fit or to
+201            associate the positions with the maxima. Defaults to False.
+202        find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
+203    """
+204
+205    if isinstance(ref, dict):
+206        ref_pos = np.array(list(ref.keys()))
+207    else:
+208        ref_pos = np.array(ref)
+209
+210    if should_fit:
+211        spe_pos_dict = old_spe.fit_peak_positions(center_err_threshold=1, find_peaks_kw=find_peaks_kw)  # type: ignore
+212    else:
+213        find_kw = dict(sharpening=None)
+214        find_kw.update(find_peaks_kw)
+215        spe_pos_dict = old_spe.find_peak_multipeak(**find_kw).get_pos_ampl_dict()  # type: ignore
+216    spe_cent = np.array(list(spe_pos_dict.keys()))
+217
+218    if poly_order == 0:
+219        p = rc2utils.align_shift(spe_cent, ref_pos)
+220        spe_cal = old_spe.scale_xaxis_fun(lambda x: x + p)  # type: ignore
+221    else:
+222        def cal_func(x, *a):
+223            return [par*(x/1000)**power for power, par in enumerate(a)]
+224
+225        p0 = np.resize([0, 1000, 0], poly_order + 1)
+226        p = rc2utils.align(spe_cent, ref_pos, p0=p0, func=cal_func)
+227        spe_cal = old_spe.scale_xaxis_fun(  # type: ignore
+228            (lambda x, *args: np.sum(cal_func(x, *args), axis=0)), args=p)
+229    new_spe.x = spe_cal.x
+230
+231
+232@add_spectrum_filter
+233@validate_call(config=dict(arbitrary_types_allowed=True))
+234def xcal_fine_RBF(old_spe: Spectrum,
+235                  new_spe: Spectrum, /, *,
+236                  ref: Union[Dict[float, float], List[float], npt.NDArray],
+237                  should_fit=False,
+238                  kernel: Literal['thin_plate_spline', 'cubic', 'quintic',
+239                                  'multiquadric', 'inverse_multiquadric',
+240                                  'inverse_quadratic', 'gaussian',
+241                                  ] = 'thin_plate_spline',
+242                  find_peaks_kw={},
+243                  **kwargs,
+244                  ):
+245    """Wavelength calibration using Radial basis fuction interpolation
+246
+247    Please be cautious! Interpolation might not be the most appropriate
+248    approach for this type of calibration.
+249
+250    **kwargs are passed to RBFInterpolator
+251    """
+252
+253    if isinstance(ref, dict):
+254        ref_pos = np.array(list(ref.keys()))
+255    else:
+256        ref_pos = np.array(ref)
+257
+258    if should_fit:
+259        spe_pos_dict = old_spe.fit_peak_positions(center_err_threshold=1, find_peaks_kw=find_peaks_kw)  # type: ignore
+260    else:
+261        find_kw = dict(sharpening=None)
+262        find_kw.update(find_peaks_kw)
+263        spe_pos_dict = old_spe.find_peak_multipeak(**find_kw).get_pos_ampl_dict()  # type: ignore
+264    spe_cent = np.array(list(spe_pos_dict.keys()))
+265
+266    spe_idx, ref_idx = rc2utils.find_closest_pairs_idx(spe_cent, ref_pos)
+267    if len(ref_idx) == 1:
+268        _offset = (ref_pos[ref_idx][0] - spe_cent[spe_idx][0])
+269        new_spe.x = old_spe.x + _offset
+270    else:
+271        kwargs["kernel"] = kernel
+272        interp = interpolate.RBFInterpolator(spe_cent[spe_idx].reshape(-1, 1), ref_pos[ref_idx], **kwargs)
+273        new_spe.x = interp(old_spe.x.reshape(-1, 1))
+274
+275
+276@add_spectrum_filter
+277@validate_call(config=dict(arbitrary_types_allowed=True))
+278def xcal_argmin2d_iter_lowpass(old_spe: Spectrum,
+279                               new_spe: Spectrum, /, *,
+280                               ref: Dict[float, float],
+281                               low_pass_nfreqs: List[int] = [100, 500]):
+282    """
+283    Calibrate spectrum
+284
+285    The calibration is done in multiple steps. Both the spectrum and the reference
+286    are passed through a low-pass filter to preserve only general structure of the
+287    spectrum. `low_pass_nfreqs` defines the number of frequencies to be preserved in
+288    each step. Once all steps with low-pass filter a final step without a low-pass
+289    filter is performed. Each calibration step is performed using
+290    :func:`~ramanchada2.spectrum.calibration.by_deltas.xcal_fine` algorithm.
+291
+292    Args:
+293        old_spe (Spectrum): internal use only
+294        new_spe (Spectrum): internal use only
+295        ref (Dict[float, float]): wavenumber - amplitude pairs
+296        low_pass_nfreqs (List[int], optional): The number of elements defines the
+297            number of low-pass steps and their values define the amount of frequencies
+298            to keep. Defaults to [100, 500].
+299    """
+300    def semi_spe_from_dict(deltas: dict, xaxis):
+301        y = np.zeros_like(xaxis)
+302        for pos, ampl in deltas.items():
+303            idx = np.argmin(np.abs(xaxis - pos))
+304            y[idx] += ampl
+305        # remove overflows and underflows
+306        y[0] = 0
+307        y[-1] = 0
+308        return y
+309
+310    def low_pass(x, nbin, window=signal.windows.blackmanharris):
+311        h = window(nbin*2-1)[nbin-1:]
+312        X = fft.rfft(x)
+313        X[:nbin] *= h  # apply the window
+314        X[nbin:] = 0  # clear upper frequencies
+315        return fft.irfft(X, n=len(x))
+316
+317    spe = old_spe.__copy__()
+318    for low_pass_i in low_pass_nfreqs:
+319        xaxis = spe.x
+320        y_ref_semi_spe = semi_spe_from_dict(ref, spe.x)
+321        y_ref_semi_spe = low_pass(y_ref_semi_spe, low_pass_i)
+322
+323        r = xaxis[signal.find_peaks(y_ref_semi_spe)[0]]
+324
+325        spe_low = spe.__copy__()
+326        spe_low.y = low_pass(spe.y, low_pass_i)
+327
+328        spe_cal = spe_low.xcal_fine(ref=r, should_fit=False, poly_order=2)
+329        spe.x = spe_cal.x
+330    spe_cal_fin = spe.xcal_fine(ref=ref, should_fit=False, poly_order=2)
+331    new_spe.x = spe_cal_fin.x
+
+ + +
+
+ +
+ + class + DeltaSpeModel: + + + +
+ + 
18class DeltaSpeModel:
+19    def __init__(self, deltas: Dict[float, float], shift=0, scale=1):
+20        components = list()
+21        self.params = lmfit.Parameters()
+22        self.minx = np.min(list(deltas.keys()))
+23        self.maxx = np.max(list(deltas.keys()))
+24        self.params.add('shift', value=shift, vary=True)
+25        self.params.add('scale', value=scale, vary=True, min=.1, max=10)
+26        self.params.add('scale2', value=0, vary=False, min=-.1, max=.1)
+27        self.params.add('scale3', value=0, vary=False, min=-1e-3, max=1e-3)
+28        self.params.add('sigma', value=1, vary=True)
+29        self.params.add('gain', value=1, vary=True)
+30        for comp_i, (k, v) in enumerate(deltas.items()):
+31            prefix = f'comp_{comp_i}'
+32            components.append(lmfit.models.GaussianModel(prefix=f'comp_{comp_i}_'))
+33            self.params.add(prefix + '_center', expr=f'shift + {k}*scale + {k}**2*scale2 + {k}**3*scale3', vary=False)
+34            self.params.add(prefix + '_amplitude', expr=f'{v}*gain', vary=False)
+35            self.params.add(prefix + '_sigma', expr='sigma', vary=False)
+36        self.model = np.sum(components)
+37
+38    def fit(self, spe, sigma, ax=None, no_fit=False):
+39        self.params['sigma'].set(value=sigma if sigma > 1 else 1)
+40        spe_conv = spe.convolve('gaussian', sigma=sigma/np.mean(np.diff(spe.x)))
+41        if no_fit:
+42            fit_res = self.model.fit(spe_conv.y, x=spe_conv.x, params=self.params, max_nfev=-1)
+43        else:
+44            fit_res = self.model.fit(spe_conv.y, x=spe_conv.x, params=self.params)
+45        self.params = fit_res.params
+46        if ax is not None:
+47            spe_conv.plot(ax=ax)
+48            ax.plot(spe_conv.x, fit_res.eval(x=spe_conv.x), 'r')
+
+ + + + +
+ +
+ + DeltaSpeModel(deltas: Dict[float, float], shift=0, scale=1) + + + +
+ + 
19    def __init__(self, deltas: Dict[float, float], shift=0, scale=1):
+20        components = list()
+21        self.params = lmfit.Parameters()
+22        self.minx = np.min(list(deltas.keys()))
+23        self.maxx = np.max(list(deltas.keys()))
+24        self.params.add('shift', value=shift, vary=True)
+25        self.params.add('scale', value=scale, vary=True, min=.1, max=10)
+26        self.params.add('scale2', value=0, vary=False, min=-.1, max=.1)
+27        self.params.add('scale3', value=0, vary=False, min=-1e-3, max=1e-3)
+28        self.params.add('sigma', value=1, vary=True)
+29        self.params.add('gain', value=1, vary=True)
+30        for comp_i, (k, v) in enumerate(deltas.items()):
+31            prefix = f'comp_{comp_i}'
+32            components.append(lmfit.models.GaussianModel(prefix=f'comp_{comp_i}_'))
+33            self.params.add(prefix + '_center', expr=f'shift + {k}*scale + {k}**2*scale2 + {k}**3*scale3', vary=False)
+34            self.params.add(prefix + '_amplitude', expr=f'{v}*gain', vary=False)
+35            self.params.add(prefix + '_sigma', expr='sigma', vary=False)
+36        self.model = np.sum(components)
+
+ + + + +
+
+
+ params + + +
+ + + + +
+
+
+ minx + + +
+ + + + +
+
+
+ maxx + + +
+ + + + +
+
+
+ model + + +
+ + + + +
+
+ +
+ + def + fit(self, spe, sigma, ax=None, no_fit=False): + + + +
+ + 
38    def fit(self, spe, sigma, ax=None, no_fit=False):
+39        self.params['sigma'].set(value=sigma if sigma > 1 else 1)
+40        spe_conv = spe.convolve('gaussian', sigma=sigma/np.mean(np.diff(spe.x)))
+41        if no_fit:
+42            fit_res = self.model.fit(spe_conv.y, x=spe_conv.x, params=self.params, max_nfev=-1)
+43        else:
+44            fit_res = self.model.fit(spe_conv.y, x=spe_conv.x, params=self.params)
+45        self.params = fit_res.params
+46        if ax is not None:
+47            spe_conv.plot(ax=ax)
+48            ax.plot(spe_conv.x, fit_res.eval(x=spe_conv.x), 'r')
+
+ + + + +
+
+
+ +
+ + class + ParamBounds(pydantic.main.BaseModel): + + + +
+ + 
51class ParamBounds(BaseModel):
+52    min: float = -np.inf
+53    max: float = np.inf
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ min: float + + +
+ + + + +
+
+
+ max: float + + +
+ + + + +
+
+
+ model_config: ClassVar[pydantic.config.ConfigDict] = +{} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'min': FieldInfo(annotation=float, required=False, default=-inf), 'max': FieldInfo(annotation=float, required=False, default=inf)} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+ + class + FitBounds(pydantic.main.BaseModel): + + + +
+ + 
56class FitBounds(BaseModel):
+57    shift: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+58    scale: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+59    scale2: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+60    scale3: ParamBounds = ParamBounds(min=-np.inf, max=np.inf)
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ shift: ParamBounds + + +
+ + + + +
+
+
+ scale: ParamBounds + + +
+ + + + +
+
+
+ scale2: ParamBounds + + +
+ + + + +
+
+
+ scale3: ParamBounds + + +
+ + + + +
+
+
+ model_config: ClassVar[pydantic.config.ConfigDict] = +{} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'shift': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf)), 'scale': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf)), 'scale2': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf)), 'scale3': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf))} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + calibrate_by_deltas_model( spe: ramanchada2.spectrum.spectrum.Spectrum, /, deltas: Dict[float, float], bounds: Optional[FitBounds] = None, convolution_steps: Optional[List[float]] = [15, 1], scale2=True, scale3=False, init_guess: Literal[None, 'cumulative'] = None, ax=None, **kwargs): + + + +
+ + 
 63@add_spectrum_method
+ 64@validate_call(config=dict(arbitrary_types_allowed=True))
+ 65def calibrate_by_deltas_model(spe: Spectrum, /,
+ 66                              deltas: Dict[float, float],
+ 67                              bounds: Optional[FitBounds] = None,
+ 68                              convolution_steps: Union[None, List[float]] = [15, 1],
+ 69                              scale2=True, scale3=False,
+ 70                              init_guess: Literal[None, 'cumulative'] = None,
+ 71                              ax=None, **kwargs
+ 72                              ):
+ 73    """
+ 74    - Builds a composite model based on a set of user specified delta lines.
+ 75    - Initial guess is calculated based on 10-th and 90-th percentiles of
+ 76      the distributions.
+ 77
+ 78    The phasespace of the model is flat with big amount of narrow minima.
+ 79    In order to find the best fit, the experimental data are successively
+ 80    convolved with gaussians with different widths startign from wide to
+ 81    narrow. The model for the calibration is 3-th order polynomial, which
+ 82    potentialy can be changed for higher order polynomial. In order to avoid
+ 83    solving the inverse of the calibration function, the result is tabulated
+ 84    and interpolated linarly for each bin of the spectrum.
+ 85    This alogrithm is useful for corse calibration.
+ 86    """
+ 87    mod = DeltaSpeModel(deltas)
+ 88    spe_padded = spe
+ 89
+ 90    if init_guess == 'cumulative':
+ 91        deltasx = np.array(list(deltas.keys()))
+ 92
+ 93        deltas_cs = np.cumsum(list(deltas.values()))
+ 94        deltas_cs /= deltas_cs[-1]
+ 95
+ 96        deltas_idx10 = np.argmin(np.abs(deltas_cs-.1))
+ 97        deltas_idx90 = np.argmin(np.abs(deltas_cs-.9))
+ 98        x1, x2 = deltasx[[deltas_idx10, deltas_idx90]]
+ 99
+100        spe_cs = np.cumsum(
+101            spe_padded.moving_average(50).subtract_moving_minimum(10).moving_average(5).y)  # type: ignore
+102
+103        spe_cs /= spe_cs[-1]
+104        spe_idx10 = np.argmin(np.abs(spe_cs-.1))
+105        spe_idx90 = np.argmin(np.abs(spe_cs-.9))
+106        y1, y2 = spe_padded.x[[spe_idx10, spe_idx90]]
+107
+108        scale = (y1-y2)/(x1-x2)
+109        shift = -scale * x1 + y1
+110    else:
+111        scale = 1
+112        shift = 0
+113    gain = np.sum(spe.y)/np.sum(list(deltas.values()))
+114    if bounds is not None:
+115        mod.params['scale'].set(value=scale, min=bounds.scale.min, max=bounds.scale.max)
+116        mod.params['shift'].set(value=shift, min=bounds.shift.min, max=bounds.shift.max)
+117    else:
+118        mod.params['scale'].set(value=scale)
+119        mod.params['shift'].set(value=shift)
+120    mod.params['gain'].set(value=gain)
+121    mod.params['sigma'].set(value=2.5)
+122
+123    if ax is not None:
+124        spe_padded.plot(ax=ax)
+125
+126    if convolution_steps is not None:
+127        for sig in convolution_steps:
+128            mod.fit(spe=spe_padded, sigma=sig, ax=ax, **kwargs)
+129
+130    if scale2:
+131        if bounds is not None:
+132            mod.params['scale2'].set(vary=True, value=0, min=bounds.scale2.min, max=bounds.scale2.max)
+133        else:
+134            mod.params['scale2'].set(vary=True, value=0)
+135        mod.fit(spe_padded, sigma=0.05, ax=ax, **kwargs)
+136    if scale3:
+137        if bounds is not None:
+138            mod.params['scale2'].set(vary=True, value=0, min=bounds.scale2.min, max=bounds.scale2.max)
+139            mod.params['scale3'].set(vary=True, value=0, min=bounds.scale3.min, max=bounds.scale3.max)
+140        else:
+141            mod.params['scale2'].set(vary=True, value=0)
+142            mod.params['scale3'].set(vary=True, value=0)
+143        mod.fit(spe_padded, sigma=0.05, ax=ax, **kwargs)
+144    return mod.model, mod.params
+
+ + +
    +
  • Builds a composite model based on a set of user specified delta lines.
    • +
  • Initial guess is calculated based on 10-th and 90-th percentiles of +the distributions.
    • +
+ +

The phasespace of the model is flat with big amount of narrow minima. +In order to find the best fit, the experimental data are successively +convolved with gaussians with different widths startign from wide to +narrow. The model for the calibration is 3-th order polynomial, which +potentialy can be changed for higher order polynomial. In order to avoid +solving the inverse of the calibration function, the result is tabulated +and interpolated linarly for each bin of the spectrum. +This alogrithm is useful for corse calibration.

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + calibrate_by_deltas_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, deltas: Dict[float, float], convolution_steps, init_guess=None, **kwargs): + + + +
+ + 
147@add_spectrum_filter
+148@validate_call(config=dict(arbitrary_types_allowed=True))
+149def calibrate_by_deltas_filter(old_spe: Spectrum,
+150                               new_spe: Spectrum, /,
+151                               deltas: Dict[float, float],
+152                               convolution_steps,
+153                               init_guess=None,
+154                               **kwargs
+155                               ):
+156    mod, par = old_spe.calibrate_by_deltas_model(  # type: ignore
+157        deltas=deltas,
+158        convolution_steps=convolution_steps,
+159        init_guess=init_guess,
+160        **kwargs)
+161
+162    deltasx = np.array(list(deltas.keys()))
+163    dxl, dxr = deltasx[[0, -1]]
+164    xl = dxl - (dxr - dxl)
+165    xr = dxl + (dxr - dxl)
+166    true_x = np.linspace(xl, xr, len(old_spe.x)*6)
+167    meas_x = (par['shift'].value + true_x * par['scale'] +
+168              true_x**2 * par['scale2'] + true_x**3 * par['scale3'])
+169    x_cal = np.zeros_like(old_spe.x)
+170    for i in range(len(old_spe.x)):
+171        idx = np.argmax(meas_x > old_spe.x[i])
+172        pt_rto = (old_spe.x[i] - meas_x[idx-1])/(meas_x[idx] - meas_x[idx-1])
+173        x_cal[i] = (true_x[idx] - true_x[idx-1])*pt_rto + true_x[idx-1]
+174    new_spe.x = x_cal
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + xcal_fine( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, *, ref: Union[Dict[float, float], List[float]], should_fit=False, poly_order: typing.Annotated[int, Ge(ge=0)], find_peaks_kw={}): + + + +
+ + 
177@add_spectrum_filter
+178@validate_call(config=dict(arbitrary_types_allowed=True))
+179def xcal_fine(old_spe: Spectrum,
+180              new_spe: Spectrum, /, *,
+181              ref: Union[Dict[float, float], List[float]],
+182              should_fit=False,
+183              poly_order: NonNegativeInt,
+184              find_peaks_kw={},
+185              ):
+186    """
+187    Iterative calibration with provided reference based on :func:`~ramanchada2.misc.utils.argmin2d.align`
+188
+189    Iteratively apply polynomial of `poly_order` degree to match
+190    the found peaks to the reference locations. The pairs are created
+191    using :func:`~ramanchada2.misc.utils.argmin2d.align` algorithm.
+192
+193    Args:
+194        old_spe (Spectrum): internal use only
+195        new_spe (Spectrum): internal use only
+196        ref (Union[Dict[float, float], List[float]]): _description_
+197        ref (Dict[float, float]):
+198            If a dict is provided - wavenumber - amplitude pairs.
+199            If a list is provided - wavenumbers only.
+200        poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
+201        should_fit (bool, optional): Whether the peaks should be fit or to
+202            associate the positions with the maxima. Defaults to False.
+203        find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
+204    """
+205
+206    if isinstance(ref, dict):
+207        ref_pos = np.array(list(ref.keys()))
+208    else:
+209        ref_pos = np.array(ref)
+210
+211    if should_fit:
+212        spe_pos_dict = old_spe.fit_peak_positions(center_err_threshold=1, find_peaks_kw=find_peaks_kw)  # type: ignore
+213    else:
+214        find_kw = dict(sharpening=None)
+215        find_kw.update(find_peaks_kw)
+216        spe_pos_dict = old_spe.find_peak_multipeak(**find_kw).get_pos_ampl_dict()  # type: ignore
+217    spe_cent = np.array(list(spe_pos_dict.keys()))
+218
+219    if poly_order == 0:
+220        p = rc2utils.align_shift(spe_cent, ref_pos)
+221        spe_cal = old_spe.scale_xaxis_fun(lambda x: x + p)  # type: ignore
+222    else:
+223        def cal_func(x, *a):
+224            return [par*(x/1000)**power for power, par in enumerate(a)]
+225
+226        p0 = np.resize([0, 1000, 0], poly_order + 1)
+227        p = rc2utils.align(spe_cent, ref_pos, p0=p0, func=cal_func)
+228        spe_cal = old_spe.scale_xaxis_fun(  # type: ignore
+229            (lambda x, *args: np.sum(cal_func(x, *args), axis=0)), args=p)
+230    new_spe.x = spe_cal.x
+
+ + +

Iterative calibration with provided reference based on ~ramanchada2.misc.utils.argmin2d.align()

+ +

Iteratively apply polynomial of poly_order degree to match +the found peaks to the reference locations. The pairs are created +using ~ramanchada2.misc.utils.argmin2d.align() algorithm.

+ +
Arguments:
+ +
    +
  • old_spe (Spectrum): internal use only
    • +
  • new_spe (Spectrum): internal use only
    • +
  • ref (Union[Dict[float, float], List[float]]): _description_
    • +
  • ref (Dict[float, float]): If a dict is provided - wavenumber - amplitude pairs. +If a list is provided - wavenumbers only.
    • +
  • poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
    • +
  • should_fit (bool, optional): Whether the peaks should be fit or to +associate the positions with the maxima. Defaults to False.
    • +
  • find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
    • +
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + xcal_fine_RBF( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, *, ref: Union[Dict[float, float], List[float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]], should_fit=False, kernel: Literal['thin_plate_spline', 'cubic', 'quintic', 'multiquadric', 'inverse_multiquadric', 'inverse_quadratic', 'gaussian'] = 'thin_plate_spline', find_peaks_kw={}, **kwargs): + + + +
+ + 
233@add_spectrum_filter
+234@validate_call(config=dict(arbitrary_types_allowed=True))
+235def xcal_fine_RBF(old_spe: Spectrum,
+236                  new_spe: Spectrum, /, *,
+237                  ref: Union[Dict[float, float], List[float], npt.NDArray],
+238                  should_fit=False,
+239                  kernel: Literal['thin_plate_spline', 'cubic', 'quintic',
+240                                  'multiquadric', 'inverse_multiquadric',
+241                                  'inverse_quadratic', 'gaussian',
+242                                  ] = 'thin_plate_spline',
+243                  find_peaks_kw={},
+244                  **kwargs,
+245                  ):
+246    """Wavelength calibration using Radial basis fuction interpolation
+247
+248    Please be cautious! Interpolation might not be the most appropriate
+249    approach for this type of calibration.
+250
+251    **kwargs are passed to RBFInterpolator
+252    """
+253
+254    if isinstance(ref, dict):
+255        ref_pos = np.array(list(ref.keys()))
+256    else:
+257        ref_pos = np.array(ref)
+258
+259    if should_fit:
+260        spe_pos_dict = old_spe.fit_peak_positions(center_err_threshold=1, find_peaks_kw=find_peaks_kw)  # type: ignore
+261    else:
+262        find_kw = dict(sharpening=None)
+263        find_kw.update(find_peaks_kw)
+264        spe_pos_dict = old_spe.find_peak_multipeak(**find_kw).get_pos_ampl_dict()  # type: ignore
+265    spe_cent = np.array(list(spe_pos_dict.keys()))
+266
+267    spe_idx, ref_idx = rc2utils.find_closest_pairs_idx(spe_cent, ref_pos)
+268    if len(ref_idx) == 1:
+269        _offset = (ref_pos[ref_idx][0] - spe_cent[spe_idx][0])
+270        new_spe.x = old_spe.x + _offset
+271    else:
+272        kwargs["kernel"] = kernel
+273        interp = interpolate.RBFInterpolator(spe_cent[spe_idx].reshape(-1, 1), ref_pos[ref_idx], **kwargs)
+274        new_spe.x = interp(old_spe.x.reshape(-1, 1))
+
+ + +

Wavelength calibration using Radial basis fuction interpolation

+ +

Please be cautious! Interpolation might not be the most appropriate +approach for this type of calibration.

+ +

**kwargs are passed to RBFInterpolator

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + xcal_argmin2d_iter_lowpass( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, *, ref: Dict[float, float], low_pass_nfreqs: List[int] = [100, 500]): + + + +
+ + 
277@add_spectrum_filter
+278@validate_call(config=dict(arbitrary_types_allowed=True))
+279def xcal_argmin2d_iter_lowpass(old_spe: Spectrum,
+280                               new_spe: Spectrum, /, *,
+281                               ref: Dict[float, float],
+282                               low_pass_nfreqs: List[int] = [100, 500]):
+283    """
+284    Calibrate spectrum
+285
+286    The calibration is done in multiple steps. Both the spectrum and the reference
+287    are passed through a low-pass filter to preserve only general structure of the
+288    spectrum. `low_pass_nfreqs` defines the number of frequencies to be preserved in
+289    each step. Once all steps with low-pass filter a final step without a low-pass
+290    filter is performed. Each calibration step is performed using
+291    :func:`~ramanchada2.spectrum.calibration.by_deltas.xcal_fine` algorithm.
+292
+293    Args:
+294        old_spe (Spectrum): internal use only
+295        new_spe (Spectrum): internal use only
+296        ref (Dict[float, float]): wavenumber - amplitude pairs
+297        low_pass_nfreqs (List[int], optional): The number of elements defines the
+298            number of low-pass steps and their values define the amount of frequencies
+299            to keep. Defaults to [100, 500].
+300    """
+301    def semi_spe_from_dict(deltas: dict, xaxis):
+302        y = np.zeros_like(xaxis)
+303        for pos, ampl in deltas.items():
+304            idx = np.argmin(np.abs(xaxis - pos))
+305            y[idx] += ampl
+306        # remove overflows and underflows
+307        y[0] = 0
+308        y[-1] = 0
+309        return y
+310
+311    def low_pass(x, nbin, window=signal.windows.blackmanharris):
+312        h = window(nbin*2-1)[nbin-1:]
+313        X = fft.rfft(x)
+314        X[:nbin] *= h  # apply the window
+315        X[nbin:] = 0  # clear upper frequencies
+316        return fft.irfft(X, n=len(x))
+317
+318    spe = old_spe.__copy__()
+319    for low_pass_i in low_pass_nfreqs:
+320        xaxis = spe.x
+321        y_ref_semi_spe = semi_spe_from_dict(ref, spe.x)
+322        y_ref_semi_spe = low_pass(y_ref_semi_spe, low_pass_i)
+323
+324        r = xaxis[signal.find_peaks(y_ref_semi_spe)[0]]
+325
+326        spe_low = spe.__copy__()
+327        spe_low.y = low_pass(spe.y, low_pass_i)
+328
+329        spe_cal = spe_low.xcal_fine(ref=r, should_fit=False, poly_order=2)
+330        spe.x = spe_cal.x
+331    spe_cal_fin = spe.xcal_fine(ref=ref, should_fit=False, poly_order=2)
+332    new_spe.x = spe_cal_fin.x
+
+ + +

Calibrate spectrum

+ +

The calibration is done in multiple steps. Both the spectrum and the reference +are passed through a low-pass filter to preserve only general structure of the +spectrum. low_pass_nfreqs defines the number of frequencies to be preserved in +each step. Once all steps with low-pass filter a final step without a low-pass +filter is performed. Each calibration step is performed using +~ramanchada2.spectrum.calibration.by_deltas.xcal_fine() algorithm.

+ +
Arguments:
+ +
    +
  • old_spe (Spectrum): internal use only
    • +
  • new_spe (Spectrum): internal use only
    • +
  • ref (Dict[float, float]): wavenumber - amplitude pairs
    • +
  • low_pass_nfreqs (List[int], optional): The number of elements defines the +number of low-pass steps and their values define the amount of frequencies +to keep. Defaults to [100, 500].
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration/change_x_units.html b/ramanchada2/spectrum/calibration/change_x_units.html new file mode 100644 index 00000000..f1c6514f --- /dev/null +++ b/ramanchada2/spectrum/calibration/change_x_units.html @@ -0,0 +1,529 @@ + + + + + + + ramanchada2.spectrum.calibration.change_x_units API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration.change_x_units

+ + + + + + + 
 1from pydantic import validate_call
+ 2
+ 3from ramanchada2.misc.spectrum_deco import (add_spectrum_filter,
+ 4                                            add_spectrum_method)
+ 5from ramanchada2.misc.utils.ramanshift_to_wavelength import \
+ 6    abs_nm_to_shift_cm_1 as util_abs_nm_to_shift_cm_1
+ 7from ramanchada2.misc.utils.ramanshift_to_wavelength import \
+ 8    shift_cm_1_to_abs_nm as util_shift_cm_1_to_abs_nm
+ 9
+10from ..spectrum import Spectrum
+11
+12
+13@add_spectrum_method
+14@validate_call(config=dict(arbitrary_types_allowed=True))
+15def abs_nm_to_shift_cm_1(spe: Spectrum, /,
+16                         laser_wave_length_nm: float):
+17    """
+18    Convert wavelength to Ramanshift in wavenumber
+19
+20    Args:
+21        spe: internal use only
+22        laser_wave_length_nm: Laser wave length
+23
+24    Returns: Corrected x-values
+25    """
+26    return util_abs_nm_to_shift_cm_1(spe.x, laser_wave_length_nm=laser_wave_length_nm)
+27
+28
+29@add_spectrum_method
+30@validate_call(config=dict(arbitrary_types_allowed=True))
+31def shift_cm_1_to_abs_nm(spe: Spectrum, /,
+32                         laser_wave_length_nm: float):
+33    """
+34    Convert Ramanshift in wavenumber to wavelength
+35
+36    Args:
+37        spe: internal use only
+38        laser_wave_length_nm: Laser wave length
+39
+40    Returns: Corrected x-values
+41    """
+42    return util_shift_cm_1_to_abs_nm(spe.x, laser_wave_length_nm=laser_wave_length_nm)
+43
+44
+45@add_spectrum_filter
+46@validate_call(config=dict(arbitrary_types_allowed=True))
+47def abs_nm_to_shift_cm_1_filter(old_spe: Spectrum,
+48                                new_spe: Spectrum, /,
+49                                laser_wave_length_nm: float):
+50    """
+51    Convert wavelength to Ramanshift in wavenumber
+52
+53    Args:
+54        spe: internal use only
+55        laser_wave_length_nm: Laser wave length
+56
+57    Returns: Spectrum with corrected x-values
+58    """
+59    new_spe.x = util_abs_nm_to_shift_cm_1(old_spe.x, laser_wave_length_nm=laser_wave_length_nm)
+60
+61
+62@add_spectrum_filter
+63@validate_call(config=dict(arbitrary_types_allowed=True))
+64def shift_cm_1_to_abs_nm_filter(old_spe: Spectrum,
+65                                new_spe: Spectrum, /,
+66                                laser_wave_length_nm: float):
+67    """
+68    Convert Ramanshift in wavenumber to wavelength
+69
+70    Args:
+71        spe: internal use only
+72        laser_wave_length_nm: Laser wave length
+73
+74    Returns: Spectrum with corrected x-values
+75    """
+76    new_spe.x = util_shift_cm_1_to_abs_nm(old_spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + abs_nm_to_shift_cm_1( spe: ramanchada2.spectrum.spectrum.Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
14@add_spectrum_method
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def abs_nm_to_shift_cm_1(spe: Spectrum, /,
+17                         laser_wave_length_nm: float):
+18    """
+19    Convert wavelength to Ramanshift in wavenumber
+20
+21    Args:
+22        spe: internal use only
+23        laser_wave_length_nm: Laser wave length
+24
+25    Returns: Corrected x-values
+26    """
+27    return util_abs_nm_to_shift_cm_1(spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert wavelength to Ramanshift in wavenumber

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + shift_cm_1_to_abs_nm( spe: ramanchada2.spectrum.spectrum.Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
30@add_spectrum_method
+31@validate_call(config=dict(arbitrary_types_allowed=True))
+32def shift_cm_1_to_abs_nm(spe: Spectrum, /,
+33                         laser_wave_length_nm: float):
+34    """
+35    Convert Ramanshift in wavenumber to wavelength
+36
+37    Args:
+38        spe: internal use only
+39        laser_wave_length_nm: Laser wave length
+40
+41    Returns: Corrected x-values
+42    """
+43    return util_shift_cm_1_to_abs_nm(spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert Ramanshift in wavenumber to wavelength

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + abs_nm_to_shift_cm_1_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
46@add_spectrum_filter
+47@validate_call(config=dict(arbitrary_types_allowed=True))
+48def abs_nm_to_shift_cm_1_filter(old_spe: Spectrum,
+49                                new_spe: Spectrum, /,
+50                                laser_wave_length_nm: float):
+51    """
+52    Convert wavelength to Ramanshift in wavenumber
+53
+54    Args:
+55        spe: internal use only
+56        laser_wave_length_nm: Laser wave length
+57
+58    Returns: Spectrum with corrected x-values
+59    """
+60    new_spe.x = util_abs_nm_to_shift_cm_1(old_spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert wavelength to Ramanshift in wavenumber

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Spectrum with corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + shift_cm_1_to_abs_nm_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
63@add_spectrum_filter
+64@validate_call(config=dict(arbitrary_types_allowed=True))
+65def shift_cm_1_to_abs_nm_filter(old_spe: Spectrum,
+66                                new_spe: Spectrum, /,
+67                                laser_wave_length_nm: float):
+68    """
+69    Convert Ramanshift in wavenumber to wavelength
+70
+71    Args:
+72        spe: internal use only
+73        laser_wave_length_nm: Laser wave length
+74
+75    Returns: Spectrum with corrected x-values
+76    """
+77    new_spe.x = util_shift_cm_1_to_abs_nm(old_spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert Ramanshift in wavenumber to wavelength

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Spectrum with corrected x-values

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration/normalize.html b/ramanchada2/spectrum/calibration/normalize.html new file mode 100644 index 00000000..0179778b --- /dev/null +++ b/ramanchada2/spectrum/calibration/normalize.html @@ -0,0 +1,383 @@ + + + + + + + ramanchada2.spectrum.calibration.normalize API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration.normalize

+ + + + + + + 
 1from typing import Literal
+ 2
+ 3import numpy as np
+ 4from pydantic import validate_call
+ 5
+ 6from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 7
+ 8from ..spectrum import Spectrum
+ 9
+10
+11@add_spectrum_filter
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def normalize(old_spe: Spectrum,
+14              new_spe: Spectrum, /,
+15              strategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax',
+16                                'L1', 'L2'] = 'minmax'):
+17    """
+18    Normalize the spectrum.
+19
+20    Args:
+21        strategy:
+22            If `unity`: normalize to `sum(y)`. If `min_unity`: subtract the minimum and normalize to 'unity'. If
+23            `unity_density`: normalize to `Σ(y_i*Δx_i)`. If `unity_area`: same as `unity_density`. If `minmax`: scale
+24            amplitudes in range `[0, 1]`. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
+25    """
+26    if strategy == 'unity':
+27        res = old_spe.y
+28        res /= np.sum(res)
+29        new_spe.y = res
+30    elif strategy == 'min_unity':
+31        res = old_spe.y - np.min(old_spe.y)
+32        res /= np.sum(res)
+33        new_spe.y = res
+34    if strategy == 'unity_density' or strategy == 'unity_area':
+35        res = old_spe.y
+36        res /= np.sum(res * np.diff(old_spe.x_bin_boundaries))
+37        new_spe.y = res
+38    elif strategy == 'minmax':
+39        res = old_spe.y - np.min(old_spe.y)
+40        res /= np.max(res)
+41        new_spe.y = res
+42    elif strategy == 'L1':
+43        res = old_spe.y
+44        res /= np.linalg.norm(res, 1)
+45        new_spe.y = res
+46    elif strategy == 'L2':
+47        res = old_spe.y
+48        res /= np.linalg.norm(res)
+49        new_spe.y = res
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + normalize( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, strategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax', 'L1', 'L2'] = 'minmax'): + + + +
+ + 
12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def normalize(old_spe: Spectrum,
+15              new_spe: Spectrum, /,
+16              strategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax',
+17                                'L1', 'L2'] = 'minmax'):
+18    """
+19    Normalize the spectrum.
+20
+21    Args:
+22        strategy:
+23            If `unity`: normalize to `sum(y)`. If `min_unity`: subtract the minimum and normalize to 'unity'. If
+24            `unity_density`: normalize to `Σ(y_i*Δx_i)`. If `unity_area`: same as `unity_density`. If `minmax`: scale
+25            amplitudes in range `[0, 1]`. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
+26    """
+27    if strategy == 'unity':
+28        res = old_spe.y
+29        res /= np.sum(res)
+30        new_spe.y = res
+31    elif strategy == 'min_unity':
+32        res = old_spe.y - np.min(old_spe.y)
+33        res /= np.sum(res)
+34        new_spe.y = res
+35    if strategy == 'unity_density' or strategy == 'unity_area':
+36        res = old_spe.y
+37        res /= np.sum(res * np.diff(old_spe.x_bin_boundaries))
+38        new_spe.y = res
+39    elif strategy == 'minmax':
+40        res = old_spe.y - np.min(old_spe.y)
+41        res /= np.max(res)
+42        new_spe.y = res
+43    elif strategy == 'L1':
+44        res = old_spe.y
+45        res /= np.linalg.norm(res, 1)
+46        new_spe.y = res
+47    elif strategy == 'L2':
+48        res = old_spe.y
+49        res /= np.linalg.norm(res)
+50        new_spe.y = res
+
+ + +

Normalize the spectrum.

+ +
Arguments:
+ +
    +
  • strategy: If unity: normalize to sum(y). If min_unity: subtract the minimum and normalize to 'unity'. If +unity_density: normalize to Σ(y_i*Δx_i). If unity_area: same as unity_density. If minmax: scale +amplitudes in range [0, 1]. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration/scale_xaxis.html b/ramanchada2/spectrum/calibration/scale_xaxis.html new file mode 100644 index 00000000..b3afd4d4 --- /dev/null +++ b/ramanchada2/spectrum/calibration/scale_xaxis.html @@ -0,0 +1,445 @@ + + + + + + + ramanchada2.spectrum.calibration.scale_xaxis API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration.scale_xaxis

+ + + + + + + 
 1from typing import Callable, Union
+ 2
+ 3import numpy as np
+ 4import numpy.typing as npt
+ 5from pydantic import validate_call
+ 6
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 8
+ 9from ..spectrum import Spectrum
+10
+11
+12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def scale_xaxis_linear(old_spe: Spectrum,
+15                       new_spe: Spectrum, /,
+16                       factor: float = 1,
+17                       preserve_integral: bool = False):
+18    r"""
+19    Scale x-axis using a factor.
+20
+21    Args:
+22        old_spe: internal use only
+23        new_spe: internal use only
+24        factor: Defaults to 1.
+25            Multiply x-axis values with `factor`
+26        preserve_integral: optional. Defaults to False.
+27            If True, preserves the integral in sence
+28            $\sum y_{orig;\,i}*{\Delta x_{orig}}_i = \sum y_{new;\,i}*{\Delta x_{new}}_i = $
+29    Returns: Corrected spectrum
+30    """
+31    new_spe.x = old_spe.x * factor
+32    if preserve_integral:
+33        new_spe.y = old_spe.y / factor
+34
+35
+36@add_spectrum_filter
+37@validate_call(config=dict(arbitrary_types_allowed=True))
+38def scale_xaxis_fun(old_spe: Spectrum,
+39                    new_spe: Spectrum, /,
+40                    fun: Callable[[Union[int, npt.NDArray]], float],
+41                    args=[]):
+42    """
+43    Apply arbitrary calibration function to the x-axis values.
+44
+45    Args:
+46        old_spe: internal use only
+47        new_spe: internal use only
+48        fun: function to be applied
+49        args: Additional arguments to the provided functions
+50
+51    Returns: Corrected spectrum
+52
+53    Raises:
+54        ValueError: If the new x-values are not strictly monotonically increasing.
+55    """
+56    new_spe.x = fun(old_spe.x, *args)
+57    if (np.diff(new_spe.x) < 0).any():
+58        raise ValueError('The provided function is not a monoton increasing funciton.')
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + scale_xaxis_linear( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, factor: float = 1, preserve_integral: bool = False): + + + +
+ + 
13@add_spectrum_filter
+14@validate_call(config=dict(arbitrary_types_allowed=True))
+15def scale_xaxis_linear(old_spe: Spectrum,
+16                       new_spe: Spectrum, /,
+17                       factor: float = 1,
+18                       preserve_integral: bool = False):
+19    r"""
+20    Scale x-axis using a factor.
+21
+22    Args:
+23        old_spe: internal use only
+24        new_spe: internal use only
+25        factor: Defaults to 1.
+26            Multiply x-axis values with `factor`
+27        preserve_integral: optional. Defaults to False.
+28            If True, preserves the integral in sence
+29            $\sum y_{orig;\,i}*{\Delta x_{orig}}_i = \sum y_{new;\,i}*{\Delta x_{new}}_i = $
+30    Returns: Corrected spectrum
+31    """
+32    new_spe.x = old_spe.x * factor
+33    if preserve_integral:
+34        new_spe.y = old_spe.y / factor
+
+ + +

Scale x-axis using a factor.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • factor: Defaults to 1. +Multiply x-axis values with factor
    • +
  • preserve_integral: optional. Defaults to False. +If True, preserves the integral in sence +$\sum y_{orig;\,i}*{\Delta x_{orig}}_i = \sum y_{new;\,i}*{\Delta x_{new}}_i = $
    • +
+ +

Returns: Corrected spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + scale_xaxis_fun( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, fun: Callable[[Union[int, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float], args=[]): + + + +
+ + 
37@add_spectrum_filter
+38@validate_call(config=dict(arbitrary_types_allowed=True))
+39def scale_xaxis_fun(old_spe: Spectrum,
+40                    new_spe: Spectrum, /,
+41                    fun: Callable[[Union[int, npt.NDArray]], float],
+42                    args=[]):
+43    """
+44    Apply arbitrary calibration function to the x-axis values.
+45
+46    Args:
+47        old_spe: internal use only
+48        new_spe: internal use only
+49        fun: function to be applied
+50        args: Additional arguments to the provided functions
+51
+52    Returns: Corrected spectrum
+53
+54    Raises:
+55        ValueError: If the new x-values are not strictly monotonically increasing.
+56    """
+57    new_spe.x = fun(old_spe.x, *args)
+58    if (np.diff(new_spe.x) < 0).any():
+59        raise ValueError('The provided function is not a monoton increasing funciton.')
+
+ + +

Apply arbitrary calibration function to the x-axis values.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • fun: function to be applied
    • +
  • args: Additional arguments to the provided functions
    • +
+ +

Returns: Corrected spectrum

+ +
Raises:
+ +
    +
  • ValueError: If the new x-values are not strictly monotonically increasing.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration/scale_yaxis.html b/ramanchada2/spectrum/calibration/scale_yaxis.html new file mode 100644 index 00000000..af01ae5c --- /dev/null +++ b/ramanchada2/spectrum/calibration/scale_yaxis.html @@ -0,0 +1,345 @@ + + + + + + + ramanchada2.spectrum.calibration.scale_yaxis API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration.scale_yaxis

+ + + + + + + 
 1from pydantic import validate_call
+ 2
+ 3from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 4
+ 5from ..spectrum import Spectrum
+ 6
+ 7
+ 8@add_spectrum_filter
+ 9@validate_call(config=dict(arbitrary_types_allowed=True))
+10def scale_yaxis_linear(old_spe: Spectrum,
+11                       new_spe: Spectrum,
+12                       factor: float = 1):
+13    """
+14    Scale y-axis values
+15
+16    This function provides the same result as `spe*const`
+17
+18    Args:
+19        old_spe: internal use only
+20        new_spe: internal use only
+21        factor optional. Defaults to 1.
+22            Y-values scaling factor
+23
+24    Returns: corrected spectrum
+25    """
+26    new_spe.y = old_spe.y * factor
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + scale_yaxis_linear( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, factor: float = 1): + + + +
+ + 
 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def scale_yaxis_linear(old_spe: Spectrum,
+12                       new_spe: Spectrum,
+13                       factor: float = 1):
+14    """
+15    Scale y-axis values
+16
+17    This function provides the same result as `spe*const`
+18
+19    Args:
+20        old_spe: internal use only
+21        new_spe: internal use only
+22        factor optional. Defaults to 1.
+23            Y-values scaling factor
+24
+25    Returns: corrected spectrum
+26    """
+27    new_spe.y = old_spe.y * factor
+
+ + +

Scale y-axis values

+ +

This function provides the same result as spe*const

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • factor optional. Defaults to 1. +Y-values scaling factor
    • +
+ +

Returns: corrected spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/calibration/set_new_xaxis.html b/ramanchada2/spectrum/calibration/set_new_xaxis.html new file mode 100644 index 00000000..32fe21a0 --- /dev/null +++ b/ramanchada2/spectrum/calibration/set_new_xaxis.html @@ -0,0 +1,353 @@ + + + + + + + ramanchada2.spectrum.calibration.set_new_xaxis API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.calibration.set_new_xaxis

+ + + + + + + 
 1import numpy.typing as npt
+ 2from pydantic import validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def set_new_xaxis(old_spe: Spectrum,
+12                  new_spe: Spectrum, /,
+13                  xaxis: npt.NDArray):
+14    """
+15    Substitute x-axis values with new ones
+16
+17    Args:
+18        old_spe: internal use only
+19        new_spe: internal use only
+20        xaxis: new x-axis values
+21
+22    Returns: corrected spectrum
+23
+24    Raises:
+25        ValueError: If the provided array does not match the shape of the spectrum.
+26    """
+27    if old_spe.x.shape != xaxis.shape:
+28        raise ValueError('Shape of xaxis should match the shape of xaxis of the spectrum')
+29    new_spe.x = xaxis
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + set_new_xaxis( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, xaxis: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def set_new_xaxis(old_spe: Spectrum,
+13                  new_spe: Spectrum, /,
+14                  xaxis: npt.NDArray):
+15    """
+16    Substitute x-axis values with new ones
+17
+18    Args:
+19        old_spe: internal use only
+20        new_spe: internal use only
+21        xaxis: new x-axis values
+22
+23    Returns: corrected spectrum
+24
+25    Raises:
+26        ValueError: If the provided array does not match the shape of the spectrum.
+27    """
+28    if old_spe.x.shape != xaxis.shape:
+29        raise ValueError('Shape of xaxis should match the shape of xaxis of the spectrum')
+30    new_spe.x = xaxis
+
+ + +

Substitute x-axis values with new ones

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • xaxis: new x-axis values
    • +
+ +

Returns: corrected spectrum

+ +
Raises:
+ +
    +
  • ValueError: If the provided array does not match the shape of the spectrum.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators.html b/ramanchada2/spectrum/creators.html new file mode 100644 index 00000000..4bdcc99c --- /dev/null +++ b/ramanchada2/spectrum/creators.html @@ -0,0 +1,282 @@ + + + + + + + ramanchada2.spectrum.creators API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3import os
+ 4import glob
+ 5
+ 6__all__ = [
+ 7    os.path.basename(f)[:-3]
+ 8    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+ 9    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+10]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_cache_or_calc.html b/ramanchada2/spectrum/creators/from_cache_or_calc.html new file mode 100644 index 00000000..1b180404 --- /dev/null +++ b/ramanchada2/spectrum/creators/from_cache_or_calc.html @@ -0,0 +1,443 @@ + + + + + + + ramanchada2.spectrum.creators.from_cache_or_calc API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_cache_or_calc

+ + + + + + + 
 1import logging
+ 2from typing import Optional
+ 3
+ 4from pydantic import validate_call
+ 5
+ 6import ramanchada2.misc.types.spectrum as spe_t
+ 7from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+ 8
+ 9from ..spectrum import Spectrum
+10
+11logger = logging.getLogger(__name__)
+12
+13
+14@add_spectrum_constructor(set_applied_processing=False)
+15@validate_call
+16def from_cache_or_calc(required_steps: spe_t.SpeProcessingListModel,
+17                       cachefile: Optional[str] = None):
+18    """
+19    Load spectrum from cache or calculate if needed.
+20
+21    The cache is a nested structure of spectra. All processings applied to
+22    a spectrum result to spectra of the initial one. If part of the requred
+23    processings are available, only the needed steps are calculated and added
+24    to the cache.
+25
+26    Args:
+27        required_steps: List of required steps in the form
+28            [{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
+29        cachefile: optional. Defaults to None.
+30            Filename of the cache. If None no cache is used
+31    """
+32    def recall():
+33        if len(required_steps):
+34            last_proc = required_steps.pop()
+35            if last_proc.is_constructor:
+36                spe = Spectrum.apply_creator(last_proc, cachefile_=cachefile)
+37            else:
+38                spe = recur(required_steps=required_steps)
+39                spe._cachefile = cachefile
+40                spe = spe.apply_processing(last_proc)
+41            return spe
+42        else:
+43            raise Exception('no starting point')
+44
+45    def recur(required_steps: spe_t.SpeProcessingListModel):
+46        try:
+47            if cachefile:
+48                spe = get_cache()
+49            else:
+50                spe = recall()
+51        except Exception:
+52            spe = recall()
+53        spe._cachefile = cachefile
+54        return spe
+55
+56    def get_cache():
+57        try:
+58            cache_path = required_steps.cache_path()
+59            if cache_path:
+60                cache_path = '/cache/'+cache_path+'/_data'
+61            else:
+62                cache_path = 'raw'
+63            spe = Spectrum.from_chada(cachefile, cache_path)
+64            spe._applied_processings.extend_left(required_steps.root)
+65            return spe
+66        except Exception as e:
+67            logger.info(repr(e))
+68            raise e
+69
+70    return recur(required_steps)
+
+ + +
+
+
+ logger = +<Logger ramanchada2.spectrum.creators.from_cache_or_calc (WARNING)> + + +
+ + + + +
+
+ +
+
@add_spectrum_constructor(set_applied_processing=False)
+
@validate_call
+ + def + from_cache_or_calc( required_steps: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel, cachefile: Optional[str] = None): + + + +
+ + 
15@add_spectrum_constructor(set_applied_processing=False)
+16@validate_call
+17def from_cache_or_calc(required_steps: spe_t.SpeProcessingListModel,
+18                       cachefile: Optional[str] = None):
+19    """
+20    Load spectrum from cache or calculate if needed.
+21
+22    The cache is a nested structure of spectra. All processings applied to
+23    a spectrum result to spectra of the initial one. If part of the requred
+24    processings are available, only the needed steps are calculated and added
+25    to the cache.
+26
+27    Args:
+28        required_steps: List of required steps in the form
+29            [{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
+30        cachefile: optional. Defaults to None.
+31            Filename of the cache. If None no cache is used
+32    """
+33    def recall():
+34        if len(required_steps):
+35            last_proc = required_steps.pop()
+36            if last_proc.is_constructor:
+37                spe = Spectrum.apply_creator(last_proc, cachefile_=cachefile)
+38            else:
+39                spe = recur(required_steps=required_steps)
+40                spe._cachefile = cachefile
+41                spe = spe.apply_processing(last_proc)
+42            return spe
+43        else:
+44            raise Exception('no starting point')
+45
+46    def recur(required_steps: spe_t.SpeProcessingListModel):
+47        try:
+48            if cachefile:
+49                spe = get_cache()
+50            else:
+51                spe = recall()
+52        except Exception:
+53            spe = recall()
+54        spe._cachefile = cachefile
+55        return spe
+56
+57    def get_cache():
+58        try:
+59            cache_path = required_steps.cache_path()
+60            if cache_path:
+61                cache_path = '/cache/'+cache_path+'/_data'
+62            else:
+63                cache_path = 'raw'
+64            spe = Spectrum.from_chada(cachefile, cache_path)
+65            spe._applied_processings.extend_left(required_steps.root)
+66            return spe
+67        except Exception as e:
+68            logger.info(repr(e))
+69            raise e
+70
+71    return recur(required_steps)
+
+ + +

Load spectrum from cache or calculate if needed.

+ +

The cache is a nested structure of spectra. All processings applied to +a spectrum result to spectra of the initial one. If part of the requred +processings are available, only the needed steps are calculated and added +to the cache.

+ +
Arguments:
+ +
    +
  • required_steps: List of required steps in the form +[{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
    • +
  • cachefile: optional. Defaults to None. +Filename of the cache. If None no cache is used
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_chada.html b/ramanchada2/spectrum/creators/from_chada.html new file mode 100644 index 00000000..fc370722 --- /dev/null +++ b/ramanchada2/spectrum/creators/from_chada.html @@ -0,0 +1,303 @@ + + + + + + + ramanchada2.spectrum.creators.from_chada API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_chada

+ + + + + + + 
 1from pydantic import validate_call
+ 2
+ 3from ramanchada2.io.HSDS import read_cha
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_constructor(set_applied_processing=False)
+10@validate_call
+11def from_chada(filename: str, dataset: str = '/raw', h5module=None):
+12    x, y, meta = read_cha(filename, dataset, h5module=h5module)
+13    return Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+
+ + +
+
+ +
+
@add_spectrum_constructor(set_applied_processing=False)
+
@validate_call
+ + def + from_chada(filename: str, dataset: str = '/raw', h5module=None): + + + +
+ + 
10@add_spectrum_constructor(set_applied_processing=False)
+11@validate_call
+12def from_chada(filename: str, dataset: str = '/raw', h5module=None):
+13    x, y, meta = read_cha(filename, dataset, h5module=h5module)
+14    return Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_delta_lines.html b/ramanchada2/spectrum/creators/from_delta_lines.html new file mode 100644 index 00000000..eb3eb8b0 --- /dev/null +++ b/ramanchada2/spectrum/creators/from_delta_lines.html @@ -0,0 +1,425 @@ + + + + + + + ramanchada2.spectrum.creators.from_delta_lines API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_delta_lines

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3from typing import Dict, Callable, Optional
+ 4
+ 5import numpy as np
+ 6import numpy.typing as npt
+ 7from pydantic import validate_call, PositiveInt
+ 8
+ 9from ..spectrum import Spectrum
+10from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+11
+12
+13@add_spectrum_constructor()
+14@validate_call(config=dict(arbitrary_types_allowed=True))
+15def from_delta_lines(
+16        deltas: Dict[float, float],
+17        xcal: Optional[Callable[[float], float]] = None,
+18        nbins: PositiveInt = 2000,
+19        xaxis: Optional[npt.NDArray] = None,
+20        **kwargs
+21        ):
+22    """
+23    Generate `Spectrum` with delta lines.
+24
+25    Args:
+26        deltas:
+27            Keys of the dictionary are the `x` positions of the deltas; values are the amplitudes of the corresponding
+28            deltas.
+29        xcal:
+30            Callable, optional. `x` axis calibration function.
+31        nbins:
+32            `int`, optional. Number of bins in the spectrum.
+33        xaxis:
+34            `Array-like`, optional. The xaxis of the new spectrum. If `xaxis` is provided,
+35            `xcal` should be `None` and `nbins` is ignored.
+36
+37    Example:
+38
+39    This will produce spectrum with 1000 bins in the range `[-1000, 2000)`:
+40    ```py
+41    xcal = lambda x: x*3 -1000, nbins=1000
+42    ```
+43    """
+44    if xaxis is not None and (xcal is not None):
+45        raise ValueError('xaxis and xcal/nbins are mutually exclusive')
+46    if xaxis is not None:
+47        x = xaxis
+48    else:
+49        if xcal is None:
+50            dk = list(deltas.keys())
+51            dkmin, dkmax = np.min(dk), np.max(dk)
+52            if dkmin == dkmax:
+53                dkmin, dkmax = dkmin*.8, dkmax*1.2
+54            else:
+55                dkmin -= (dkmax-dkmin) * .1
+56                dkmax += (dkmax-dkmin) * .1
+57            x = np.linspace(dkmin, dkmax, nbins, endpoint=False, dtype=float)
+58        else:
+59            x = np.linspace(xcal(0), xcal(nbins), nbins, endpoint=False)
+60    y = np.zeros_like(x)
+61    for pos, ampl in deltas.items():
+62        idx = np.argmin(np.abs(x - pos))
+63        y[idx] += ampl
+64    spe = Spectrum(x=x, y=y, **kwargs)
+65    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_delta_lines( deltas: Dict[float, float], xcal: Optional[Callable[[float], float]] = None, nbins: typing.Annotated[int, Gt(gt=0)] = 2000, xaxis: Optional[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = None, **kwargs): + + + +
+ + 
14@add_spectrum_constructor()
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def from_delta_lines(
+17        deltas: Dict[float, float],
+18        xcal: Optional[Callable[[float], float]] = None,
+19        nbins: PositiveInt = 2000,
+20        xaxis: Optional[npt.NDArray] = None,
+21        **kwargs
+22        ):
+23    """
+24    Generate `Spectrum` with delta lines.
+25
+26    Args:
+27        deltas:
+28            Keys of the dictionary are the `x` positions of the deltas; values are the amplitudes of the corresponding
+29            deltas.
+30        xcal:
+31            Callable, optional. `x` axis calibration function.
+32        nbins:
+33            `int`, optional. Number of bins in the spectrum.
+34        xaxis:
+35            `Array-like`, optional. The xaxis of the new spectrum. If `xaxis` is provided,
+36            `xcal` should be `None` and `nbins` is ignored.
+37
+38    Example:
+39
+40    This will produce spectrum with 1000 bins in the range `[-1000, 2000)`:
+41    ```py
+42    xcal = lambda x: x*3 -1000, nbins=1000
+43    ```
+44    """
+45    if xaxis is not None and (xcal is not None):
+46        raise ValueError('xaxis and xcal/nbins are mutually exclusive')
+47    if xaxis is not None:
+48        x = xaxis
+49    else:
+50        if xcal is None:
+51            dk = list(deltas.keys())
+52            dkmin, dkmax = np.min(dk), np.max(dk)
+53            if dkmin == dkmax:
+54                dkmin, dkmax = dkmin*.8, dkmax*1.2
+55            else:
+56                dkmin -= (dkmax-dkmin) * .1
+57                dkmax += (dkmax-dkmin) * .1
+58            x = np.linspace(dkmin, dkmax, nbins, endpoint=False, dtype=float)
+59        else:
+60            x = np.linspace(xcal(0), xcal(nbins), nbins, endpoint=False)
+61    y = np.zeros_like(x)
+62    for pos, ampl in deltas.items():
+63        idx = np.argmin(np.abs(x - pos))
+64        y[idx] += ampl
+65    spe = Spectrum(x=x, y=y, **kwargs)
+66    return spe
+
+ + +

Generate Spectrum with delta lines.

+ +
Arguments:
+ +
    +
  • deltas: Keys of the dictionary are the x positions of the deltas; values are the amplitudes of the corresponding +deltas.
    • +
  • xcal: Callable, optional. x axis calibration function.
    • +
  • nbins: int, optional. Number of bins in the spectrum.
    • +
  • xaxis: Array-like, optional. The xaxis of the new spectrum. If xaxis is provided, +xcal should be None and nbins is ignored.
    • +
+ +

Example:

+ +

This will produce spectrum with 1000 bins in the range [-1000, 2000):

+ +
+
xcal = lambda x: x*3 -1000, nbins=1000
+
+
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_local_file.html b/ramanchada2/spectrum/creators/from_local_file.html new file mode 100644 index 00000000..1eb8ef2b --- /dev/null +++ b/ramanchada2/spectrum/creators/from_local_file.html @@ -0,0 +1,464 @@ + + + + + + + ramanchada2.spectrum.creators.from_local_file API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_local_file

+ +

Create spectrum from local files.

+
+ + + + + + 
 1"""Create spectrum from local files."""
+ 2
+ 3import os
+ 4from typing import Literal, Union
+ 5
+ 6import spc_io
+ 7from pydantic import validate_call
+ 8
+ 9from ramanchada2.io.experimental import (rc1_parser, read_csv, read_spe,
+10                                         read_txt)
+11from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+12from ramanchada2.misc.types import SpeMetadataModel
+13
+14from ..spectrum import Spectrum
+15from .from_chada import from_chada
+16
+17
+18@add_spectrum_constructor()
+19@validate_call(config=dict(arbitrary_types_allowed=True))
+20def from_local_file(
+21        in_file_name: str,
+22        filetype: Union[None, Literal['spc', 'sp', 'spa', '0', '1', '2',
+23                                      'wdf', 'ngs', 'jdx', 'dx',
+24                                      'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None,
+25        backend: Union[None, Literal['native', 'rc1_parser']] = None):
+26    """
+27    Read experimental spectrum from a local file.
+28
+29    Args:
+30        in_file_name:
+31            Path to a local file containing a spectrum.
+32        filetype:
+33            Specify the filetype. Filetype can be any of: `spc`, `sp`, `spa`, `0`, `1`, `2`, `wdf`, `ngs`, `jdx`, `dx`,
+34            `txt`, `txtr`, `csv`, `prn`, `rruf`, `spe` (Princeton Instruments) or `None`.
+35            `None` used to determine by extension of the file.
+36        backend:
+37            `native`, `rc1_parser` or `None`. `None` means both.
+38
+39    Raises:
+40        ValueError:
+41            When called with unsupported file formats.
+42    """
+43    def load_native():
+44        if filetype is None:
+45            ft = os.path.splitext(in_file_name)[1][1:]
+46        else:
+47            ft = filetype
+48        if ft in {'cha'}:
+49            return from_chada(filename=in_file_name)
+50        elif ft in {'txt', 'txtr', 'prn', 'rruf'}:
+51            with open(in_file_name) as fp:
+52                x, y, meta = read_txt(fp)
+53        elif ft in {'csv'}:
+54            with open(in_file_name) as fp:
+55                x, y, meta = read_csv(fp)
+56        elif ft in {'spc'}:
+57            with open(in_file_name, 'rb') as fp:
+58                spc = spc_io.SPC.from_bytes_io(fp)
+59                if len(spc) != 1:
+60                    raise ValueError(f'Single subfile SPCs are supported. {len(spc)} subfiles found')
+61                x = spc[0].xarray
+62                y = spc[0].yarray
+63                meta = spc.log_book.text
+64        elif ft in {'spe'}:
+65            x, y, meta = read_spe(in_file_name)
+66            spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+67        else:
+68            raise ValueError(f'filetype {ft} not supported')
+69        meta["Original file"] = os.path.basename(in_file_name)
+70        spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+71        return spe
+72
+73    def load_rc1():
+74        x, y, meta = rc1_parser.parse(in_file_name, filetype)
+75        spe = Spectrum(x=x, y=y, metadata=SpeMetadataModel.model_validate(meta))
+76        return spe
+77
+78    if backend == 'native':
+79        spe = load_native()
+80    elif backend == 'rc1_parser':
+81        spe = load_rc1()
+82    elif backend is None:
+83        try:
+84            spe = load_native()
+85        except Exception:
+86            spe = load_rc1()
+87    spe._sort_x()
+88    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_local_file( in_file_name: str, filetype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None, backend: Optional[Literal['native', 'rc1_parser']] = None): + + + +
+ + 
19@add_spectrum_constructor()
+20@validate_call(config=dict(arbitrary_types_allowed=True))
+21def from_local_file(
+22        in_file_name: str,
+23        filetype: Union[None, Literal['spc', 'sp', 'spa', '0', '1', '2',
+24                                      'wdf', 'ngs', 'jdx', 'dx',
+25                                      'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None,
+26        backend: Union[None, Literal['native', 'rc1_parser']] = None):
+27    """
+28    Read experimental spectrum from a local file.
+29
+30    Args:
+31        in_file_name:
+32            Path to a local file containing a spectrum.
+33        filetype:
+34            Specify the filetype. Filetype can be any of: `spc`, `sp`, `spa`, `0`, `1`, `2`, `wdf`, `ngs`, `jdx`, `dx`,
+35            `txt`, `txtr`, `csv`, `prn`, `rruf`, `spe` (Princeton Instruments) or `None`.
+36            `None` used to determine by extension of the file.
+37        backend:
+38            `native`, `rc1_parser` or `None`. `None` means both.
+39
+40    Raises:
+41        ValueError:
+42            When called with unsupported file formats.
+43    """
+44    def load_native():
+45        if filetype is None:
+46            ft = os.path.splitext(in_file_name)[1][1:]
+47        else:
+48            ft = filetype
+49        if ft in {'cha'}:
+50            return from_chada(filename=in_file_name)
+51        elif ft in {'txt', 'txtr', 'prn', 'rruf'}:
+52            with open(in_file_name) as fp:
+53                x, y, meta = read_txt(fp)
+54        elif ft in {'csv'}:
+55            with open(in_file_name) as fp:
+56                x, y, meta = read_csv(fp)
+57        elif ft in {'spc'}:
+58            with open(in_file_name, 'rb') as fp:
+59                spc = spc_io.SPC.from_bytes_io(fp)
+60                if len(spc) != 1:
+61                    raise ValueError(f'Single subfile SPCs are supported. {len(spc)} subfiles found')
+62                x = spc[0].xarray
+63                y = spc[0].yarray
+64                meta = spc.log_book.text
+65        elif ft in {'spe'}:
+66            x, y, meta = read_spe(in_file_name)
+67            spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+68        else:
+69            raise ValueError(f'filetype {ft} not supported')
+70        meta["Original file"] = os.path.basename(in_file_name)
+71        spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+72        return spe
+73
+74    def load_rc1():
+75        x, y, meta = rc1_parser.parse(in_file_name, filetype)
+76        spe = Spectrum(x=x, y=y, metadata=SpeMetadataModel.model_validate(meta))
+77        return spe
+78
+79    if backend == 'native':
+80        spe = load_native()
+81    elif backend == 'rc1_parser':
+82        spe = load_rc1()
+83    elif backend is None:
+84        try:
+85            spe = load_native()
+86        except Exception:
+87            spe = load_rc1()
+88    spe._sort_x()
+89    return spe
+
+ + +

Read experimental spectrum from a local file.

+ +
Arguments:
+ +
    +
  • in_file_name: Path to a local file containing a spectrum.
    • +
  • filetype: Specify the filetype. Filetype can be any of: spc, sp, spa, 0, 1, 2, wdf, ngs, jdx, dx, +txt, txtr, csv, prn, rruf, spe (Princeton Instruments) or None. +None used to determine by extension of the file.
    • +
  • backend: native, rc1_parser or None. None means both.
    • +
+ +
Raises:
+ +
    +
  • ValueError: When called with unsupported file formats.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_simulation.html b/ramanchada2/spectrum/creators/from_simulation.html new file mode 100644 index 00000000..6e1e115c --- /dev/null +++ b/ramanchada2/spectrum/creators/from_simulation.html @@ -0,0 +1,412 @@ + + + + + + + ramanchada2.spectrum.creators.from_simulation API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_simulation

+ +

Create spectrum from simulation output files.

+
+ + + + + + 
 1"""Create spectrum from simulation output files."""
+ 2
+ 3from io import TextIOBase
+ 4import numpy as np
+ 5from typing import Dict, Literal, Union
+ 6
+ 7from pydantic import PositiveFloat, PositiveInt, validate_call
+ 8
+ 9from ramanchada2.io.simulated import read_simulated_lines
+10from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+11
+12from .from_delta_lines import from_delta_lines
+13
+14_DIRECTION_LITERALS = Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz']
+15
+16
+17@add_spectrum_constructor()
+18@validate_call(config=dict(arbitrary_types_allowed=True))
+19def from_simulation(in_file: Union[str, TextIOBase],
+20                    sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],
+21                    use: Union[_DIRECTION_LITERALS, Dict[_DIRECTION_LITERALS, PositiveFloat]] = 'I_tot',
+22                    nbins: PositiveInt = 2000,
+23                    ):
+24    """
+25    Generate spectrum from simulation file.
+26
+27    The returned spectrum has only few x/y pairs -- one for each simulated line. Values along
+28    the x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.
+29
+30    Args:
+31        in_file:
+32            Path to a local file, or file-like object.
+33        sim_type:
+34            If `vasp`: `.dat` file from VASP simulation. If `crystal_out`: `.out` file from CRYSTAL simulation, not
+35            preferred. If `crystal_dat`: `.dat` file from CRYSTAL simulation.
+36        use:
+37            One of the directions `I_tot`, `I_perp`, `I_par`, `I_xx`, `I_xy`,
+38            `I_xz`, `I_yy`, `I_yz`, `I_zz`, `I_tot`, `I_perp`, `I_par` are
+39            available for both CRYSTAL and VASP. `I_xx`, `I_xy`, `I_xz`,
+40            `I_yy`, `I_yz`, `I_zz` are available only for CRYSTAL. If a Dict is
+41            passed, the key should be directions and values should be weighting factor.
+42            For example, `use={'I_perp': .1, 'I_par': .9}`
+43
+44    """
+45    if isinstance(use, str):
+46        use_directions = {use}
+47    else:
+48        use_directions = set(use.keys())
+49    if isinstance(in_file, TextIOBase):
+50        labels, x, ydict = read_simulated_lines(in_file, sim_type=sim_type, use=use_directions)
+51    else:
+52        with open(in_file) as f:
+53            labels, x, ydict = read_simulated_lines(f, sim_type=sim_type, use=use_directions)
+54    if isinstance(use, str):
+55        y = ydict[use]
+56    else:
+57        dirs = list(use.keys())
+58        fact = np.array(list(use.values()))
+59        y = np.transpose([ydict[i] for i in dirs]) @ fact
+60    spe = from_delta_lines(deltas=dict(zip(x, y)), nbins=nbins)
+61    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_simulation( in_file: Union[str, io.TextIOBase], sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'], use: Union[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Dict[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Annotated[float, Gt(gt=0)]]] = 'I_tot', nbins: typing.Annotated[int, Gt(gt=0)] = 2000): + + + +
+ + 
18@add_spectrum_constructor()
+19@validate_call(config=dict(arbitrary_types_allowed=True))
+20def from_simulation(in_file: Union[str, TextIOBase],
+21                    sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],
+22                    use: Union[_DIRECTION_LITERALS, Dict[_DIRECTION_LITERALS, PositiveFloat]] = 'I_tot',
+23                    nbins: PositiveInt = 2000,
+24                    ):
+25    """
+26    Generate spectrum from simulation file.
+27
+28    The returned spectrum has only few x/y pairs -- one for each simulated line. Values along
+29    the x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.
+30
+31    Args:
+32        in_file:
+33            Path to a local file, or file-like object.
+34        sim_type:
+35            If `vasp`: `.dat` file from VASP simulation. If `crystal_out`: `.out` file from CRYSTAL simulation, not
+36            preferred. If `crystal_dat`: `.dat` file from CRYSTAL simulation.
+37        use:
+38            One of the directions `I_tot`, `I_perp`, `I_par`, `I_xx`, `I_xy`,
+39            `I_xz`, `I_yy`, `I_yz`, `I_zz`, `I_tot`, `I_perp`, `I_par` are
+40            available for both CRYSTAL and VASP. `I_xx`, `I_xy`, `I_xz`,
+41            `I_yy`, `I_yz`, `I_zz` are available only for CRYSTAL. If a Dict is
+42            passed, the key should be directions and values should be weighting factor.
+43            For example, `use={'I_perp': .1, 'I_par': .9}`
+44
+45    """
+46    if isinstance(use, str):
+47        use_directions = {use}
+48    else:
+49        use_directions = set(use.keys())
+50    if isinstance(in_file, TextIOBase):
+51        labels, x, ydict = read_simulated_lines(in_file, sim_type=sim_type, use=use_directions)
+52    else:
+53        with open(in_file) as f:
+54            labels, x, ydict = read_simulated_lines(f, sim_type=sim_type, use=use_directions)
+55    if isinstance(use, str):
+56        y = ydict[use]
+57    else:
+58        dirs = list(use.keys())
+59        fact = np.array(list(use.values()))
+60        y = np.transpose([ydict[i] for i in dirs]) @ fact
+61    spe = from_delta_lines(deltas=dict(zip(x, y)), nbins=nbins)
+62    return spe
+
+ + +

Generate spectrum from simulation file.

+ +

The returned spectrum has only few x/y pairs -- one for each simulated line. Values along +the x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.

+ +
Arguments:
+ +
    +
  • in_file: Path to a local file, or file-like object.
    • +
  • sim_type: If vasp: .dat file from VASP simulation. If crystal_out: .out file from CRYSTAL simulation, not +preferred. If crystal_dat: .dat file from CRYSTAL simulation.
    • +
  • use: One of the directions I_tot, I_perp, I_par, I_xx, I_xy, +I_xz, I_yy, I_yz, I_zz, I_tot, I_perp, I_par are +available for both CRYSTAL and VASP. I_xx, I_xy, I_xz, +I_yy, I_yz, I_zz are available only for CRYSTAL. If a Dict is +passed, the key should be directions and values should be weighting factor. +For example, use={'I_perp': .1, 'I_par': .9}
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_spectral_component_collection.html b/ramanchada2/spectrum/creators/from_spectral_component_collection.html new file mode 100644 index 00000000..72953e70 --- /dev/null +++ b/ramanchada2/spectrum/creators/from_spectral_component_collection.html @@ -0,0 +1,339 @@ + + + + + + + ramanchada2.spectrum.creators.from_spectral_component_collection API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_spectral_component_collection

+ + + + + + + 
 1from pydantic import validate_call
+ 2
+ 3from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+ 4from ramanchada2.spectral_components.spectral_component_collection import \
+ 5    SpectralComponentCollection
+ 6
+ 7from ..spectrum import Spectrum
+ 8
+ 9
+10@add_spectrum_constructor()
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def from_spectral_component_collection(
+13        spe_components: SpectralComponentCollection,
+14        x=2000):
+15    """
+16    from_spectral_component_collection
+17
+18    Args:
+19        spe_components:
+20            SpectralComponentCollection
+21        x:
+22            `int` or array-like, optional, default `2000`. `x` axis of the spectrum.
+23    """
+24
+25    spe = Spectrum(x=x, metadata={'origin': 'generated'})  # type: ignore
+26    spe.y = spe_components(spe.x)
+27    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_spectral_component_collection( spe_components: ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection, x=2000): + + + +
+ + 
11@add_spectrum_constructor()
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def from_spectral_component_collection(
+14        spe_components: SpectralComponentCollection,
+15        x=2000):
+16    """
+17    from_spectral_component_collection
+18
+19    Args:
+20        spe_components:
+21            SpectralComponentCollection
+22        x:
+23            `int` or array-like, optional, default `2000`. `x` axis of the spectrum.
+24    """
+25
+26    spe = Spectrum(x=x, metadata={'origin': 'generated'})  # type: ignore
+27    spe.y = spe_components(spe.x)
+28    return spe
+
+ + +

from_spectral_component_collection

+ +
Arguments:
+ +
    +
  • spe_components: SpectralComponentCollection
    • +
  • x: int or array-like, optional, default 2000. x axis of the spectrum.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_stream.html b/ramanchada2/spectrum/creators/from_stream.html new file mode 100644 index 00000000..e0ad331c --- /dev/null +++ b/ramanchada2/spectrum/creators/from_stream.html @@ -0,0 +1,456 @@ + + + + + + + ramanchada2.spectrum.creators.from_stream API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_stream

+ + + + + + + 
 1import io
+ 2import os
+ 3import shutil
+ 4import tempfile
+ 5from typing import Literal, Optional, Union
+ 6
+ 7import spc_io
+ 8from pydantic import validate_call
+ 9
+10from ramanchada2.io.experimental import (rc1_parser, read_csv, read_spe,
+11                                         read_txt)
+12from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+13from ramanchada2.misc.types import SpeMetadataModel
+14
+15from ..spectrum import Spectrum
+16
+17
+18@add_spectrum_constructor()
+19@validate_call(config=dict(arbitrary_types_allowed=True))
+20def from_stream(in_stream: Union[io.TextIOBase, io.BytesIO, io.BufferedReader],
+21                filetype: Union[None, Literal['spc', 'sp', 'spa', '0', '1', '2',
+22                                              'wdf', 'ngs', 'jdx', 'dx',
+23                                              'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']],
+24                filename: Optional[str] = None,
+25                backend: Union[None, Literal['native', 'rc1_parser']] = None,
+26                ):
+27    def load_native():
+28        if filetype in {'txt', 'txtr', 'prn', 'rruf'}:
+29            if isinstance(in_stream, io.TextIOBase):
+30                fp = in_stream
+31            else:
+32                fp = io.TextIOWrapper(in_stream)
+33            x, y, meta = read_txt(fp)
+34        elif filetype in {'csv'}:
+35            if isinstance(in_stream, io.TextIOBase):
+36                fp = in_stream
+37            else:
+38                fp = io.TextIOWrapper(in_stream)
+39            x, y, meta = read_csv(fp)
+40        elif filetype in {'spc'}:
+41            if isinstance(in_stream, io.TextIOBase):
+42                raise ValueError('For spc filetype does not support io.TextIOBase')
+43            fp = in_stream
+44            spc = spc_io.SPC.from_bytes_io(fp)
+45            if len(spc) != 1:
+46                raise ValueError(f'Single subfile SPCs are supported. {len(spc)} subfiles found')
+47            x = spc[0].xarray
+48            y = spc[0].yarray
+49            meta = spc.log_book.text
+50        elif filetype in {'spe'}:
+51            if isinstance(in_stream, io.TextIOBase):
+52                raise ValueError('For spc filetype does not support io.TextIOBase')
+53            with tempfile.TemporaryDirectory(suffix='ramanchada2') as dn:
+54                fn = os.path.basename(filename or in_stream.name or f'noname.{filetype}')
+55                path = os.path.join(dn, fn)
+56                with open(path, 'wb') as fp:
+57                    shutil.copyfileobj(in_stream, fp)
+58                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+59                x, y, meta = read_spe(path)
+60            spe = Spectrum(x=x, y=y, metadata=meta)
+61        else:
+62            raise ValueError(f'filetype {filetype} not supported')
+63        meta["Original file"] = os.path.basename(filename) if filename else 'N/A loaded from stream'
+64        spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+65        return spe
+66
+67    def load_rc1():
+68        with tempfile.TemporaryDirectory(suffix='ramanchada2') as dn:
+69            fn = os.path.basename(filename or in_stream.name or f'noname.{filetype}')
+70            path = os.path.join(dn, fn)
+71            if isinstance(in_stream, io.TextIOBase):
+72                with open(path, 'w') as fp:
+73                    shutil.copyfileobj(in_stream, fp)
+74                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+75            else:
+76                with open(path, 'wb') as fp:
+77                    shutil.copyfileobj(in_stream, fp)
+78                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+79            x, y, meta = rc1_parser.parse(path, filetype)
+80        spe = Spectrum(x=x, y=y, metadata=SpeMetadataModel.model_validate(meta))
+81        return spe
+82
+83    if backend == 'native':
+84        spe = load_native()
+85    elif backend == 'rc1_parser':
+86        spe = load_rc1()
+87    elif backend is None:
+88        try:
+89            spe = load_native()
+90        except Exception:
+91            spe = load_rc1()
+92
+93    spe._sort_x()
+94    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_stream( in_stream: Union[io.TextIOBase, _io.BytesIO, _io.BufferedReader], filetype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']], filename: Optional[str] = None, backend: Optional[Literal['native', 'rc1_parser']] = None): + + + +
+ + 
19@add_spectrum_constructor()
+20@validate_call(config=dict(arbitrary_types_allowed=True))
+21def from_stream(in_stream: Union[io.TextIOBase, io.BytesIO, io.BufferedReader],
+22                filetype: Union[None, Literal['spc', 'sp', 'spa', '0', '1', '2',
+23                                              'wdf', 'ngs', 'jdx', 'dx',
+24                                              'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']],
+25                filename: Optional[str] = None,
+26                backend: Union[None, Literal['native', 'rc1_parser']] = None,
+27                ):
+28    def load_native():
+29        if filetype in {'txt', 'txtr', 'prn', 'rruf'}:
+30            if isinstance(in_stream, io.TextIOBase):
+31                fp = in_stream
+32            else:
+33                fp = io.TextIOWrapper(in_stream)
+34            x, y, meta = read_txt(fp)
+35        elif filetype in {'csv'}:
+36            if isinstance(in_stream, io.TextIOBase):
+37                fp = in_stream
+38            else:
+39                fp = io.TextIOWrapper(in_stream)
+40            x, y, meta = read_csv(fp)
+41        elif filetype in {'spc'}:
+42            if isinstance(in_stream, io.TextIOBase):
+43                raise ValueError('For spc filetype does not support io.TextIOBase')
+44            fp = in_stream
+45            spc = spc_io.SPC.from_bytes_io(fp)
+46            if len(spc) != 1:
+47                raise ValueError(f'Single subfile SPCs are supported. {len(spc)} subfiles found')
+48            x = spc[0].xarray
+49            y = spc[0].yarray
+50            meta = spc.log_book.text
+51        elif filetype in {'spe'}:
+52            if isinstance(in_stream, io.TextIOBase):
+53                raise ValueError('For spc filetype does not support io.TextIOBase')
+54            with tempfile.TemporaryDirectory(suffix='ramanchada2') as dn:
+55                fn = os.path.basename(filename or in_stream.name or f'noname.{filetype}')
+56                path = os.path.join(dn, fn)
+57                with open(path, 'wb') as fp:
+58                    shutil.copyfileobj(in_stream, fp)
+59                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+60                x, y, meta = read_spe(path)
+61            spe = Spectrum(x=x, y=y, metadata=meta)
+62        else:
+63            raise ValueError(f'filetype {filetype} not supported')
+64        meta["Original file"] = os.path.basename(filename) if filename else 'N/A loaded from stream'
+65        spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+66        return spe
+67
+68    def load_rc1():
+69        with tempfile.TemporaryDirectory(suffix='ramanchada2') as dn:
+70            fn = os.path.basename(filename or in_stream.name or f'noname.{filetype}')
+71            path = os.path.join(dn, fn)
+72            if isinstance(in_stream, io.TextIOBase):
+73                with open(path, 'w') as fp:
+74                    shutil.copyfileobj(in_stream, fp)
+75                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+76            else:
+77                with open(path, 'wb') as fp:
+78                    shutil.copyfileobj(in_stream, fp)
+79                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+80            x, y, meta = rc1_parser.parse(path, filetype)
+81        spe = Spectrum(x=x, y=y, metadata=SpeMetadataModel.model_validate(meta))
+82        return spe
+83
+84    if backend == 'native':
+85        spe = load_native()
+86    elif backend == 'rc1_parser':
+87        spe = load_rc1()
+88    elif backend is None:
+89        try:
+90            spe = load_native()
+91        except Exception:
+92            spe = load_rc1()
+93
+94    spe._sort_x()
+95    return spe
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_test_spe.html b/ramanchada2/spectrum/creators/from_test_spe.html new file mode 100644 index 00000000..50ac5d99 --- /dev/null +++ b/ramanchada2/spectrum/creators/from_test_spe.html @@ -0,0 +1,344 @@ + + + + + + + ramanchada2.spectrum.creators.from_test_spe API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_test_spe

+ +

Create spectrum from local files.

+
+ + + + + + 
 1#!/usr/bin/env python3
+ 2"""Create spectrum from local files."""
+ 3
+ 4import random
+ 5
+ 6from ramanchada2.auxiliary.spectra.datasets2 import (get_filenames,
+ 7                                                     prepend_prefix)
+ 8from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+ 9
+10from ..spectrum import Spectrum
+11
+12
+13@add_spectrum_constructor()
+14def from_test_spe(index=None, **kwargs):
+15    """Create new spectrum from test data.
+16
+17    Args:
+18        index:
+19            `int` or `None`, optional, default is `None`. If `int`: will be used as an index of filtered list. If
+20            `None`: a random spectrum will be taken.
+21        **kwargs:
+22            The rest of the parameters will be used as filter.
+23    """
+24    filtered = prepend_prefix(get_filenames(**kwargs))
+25    if index is None:
+26        fn = random.sample(filtered, 1)[0]
+27    else:
+28        fn = filtered[index]
+29    spe = Spectrum.from_local_file(fn)
+30    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+ + def + from_test_spe(index=None, **kwargs): + + + +
+ + 
14@add_spectrum_constructor()
+15def from_test_spe(index=None, **kwargs):
+16    """Create new spectrum from test data.
+17
+18    Args:
+19        index:
+20            `int` or `None`, optional, default is `None`. If `int`: will be used as an index of filtered list. If
+21            `None`: a random spectrum will be taken.
+22        **kwargs:
+23            The rest of the parameters will be used as filter.
+24    """
+25    filtered = prepend_prefix(get_filenames(**kwargs))
+26    if index is None:
+27        fn = random.sample(filtered, 1)[0]
+28    else:
+29        fn = filtered[index]
+30    spe = Spectrum.from_local_file(fn)
+31    return spe
+
+ + +

Create new spectrum from test data.

+ +
Arguments:
+ +
    +
  • index: int or None, optional, default is None. If int: will be used as an index of filtered list. If +None: a random spectrum will be taken.
    • +
  • **kwargs: The rest of the parameters will be used as filter.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/from_theoretical_lines.html b/ramanchada2/spectrum/creators/from_theoretical_lines.html new file mode 100644 index 00000000..41a6b056 --- /dev/null +++ b/ramanchada2/spectrum/creators/from_theoretical_lines.html @@ -0,0 +1,357 @@ + + + + + + + ramanchada2.spectrum.creators.from_theoretical_lines API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.from_theoretical_lines

+ + + + + + + 
 1from typing import Dict, List, Literal, Union
+ 2
+ 3import numpy as np
+ 4import numpy.typing as npt
+ 5from lmfit import lineshapes
+ 6from pydantic import validate_call
+ 7
+ 8from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+ 9
+10from ..spectrum import Spectrum
+11
+12
+13@add_spectrum_constructor()
+14@validate_call(config=dict(arbitrary_types_allowed=True))
+15def from_theoretical_lines(
+16        shapes: List[Literal[lineshapes.functions]],  # type: ignore
+17        params: List[Dict],
+18        x: Union[int, npt.NDArray[np.float64]] = 2000):
+19    """
+20    Generate spectrum from `lmfit` shapes.
+21
+22    Args:
+23        shapes:
+24            The shapes to be used for spectrum generation.
+25        params:
+26            Shape parameters to be applied to be used with shapes.
+27        x:
+28            Array with `x` values, by default `np.array(2000)`.
+29    """
+30    spe = Spectrum(x=x)
+31    x = spe.x
+32    y = np.zeros_like(x, dtype=float)
+33    for shape_name, pars in zip(shapes, params):
+34        shape = getattr(lineshapes, shape_name)
+35        y += shape(x=x, **pars)
+36    spe.y = y
+37    return spe
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_theoretical_lines( shapes: List[Literal['gaussian', 'gaussian2d', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7', 'breit_wigner', 'damped_oscillator', 'dho', 'logistic', 'lognormal', 'students_t', 'expgaussian', 'doniach', 'skewed_gaussian', 'skewed_voigt', 'thermal_distribution', 'step', 'rectangle', 'exponential', 'powerlaw', 'linear', 'parabolic', 'sine', 'expsine', 'split_lorentzian']], params: List[Dict], x: Union[int, numpy.ndarray[Any, numpy.dtype[numpy.float64]]] = 2000): + + + +
+ + 
14@add_spectrum_constructor()
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def from_theoretical_lines(
+17        shapes: List[Literal[lineshapes.functions]],  # type: ignore
+18        params: List[Dict],
+19        x: Union[int, npt.NDArray[np.float64]] = 2000):
+20    """
+21    Generate spectrum from `lmfit` shapes.
+22
+23    Args:
+24        shapes:
+25            The shapes to be used for spectrum generation.
+26        params:
+27            Shape parameters to be applied to be used with shapes.
+28        x:
+29            Array with `x` values, by default `np.array(2000)`.
+30    """
+31    spe = Spectrum(x=x)
+32    x = spe.x
+33    y = np.zeros_like(x, dtype=float)
+34    for shape_name, pars in zip(shapes, params):
+35        shape = getattr(lineshapes, shape_name)
+36        y += shape(x=x, **pars)
+37    spe.y = y
+38    return spe
+
+ + +

Generate spectrum from lmfit shapes.

+ +
Arguments:
+ +
    +
  • shapes: The shapes to be used for spectrum generation.
    • +
  • params: Shape parameters to be applied to be used with shapes.
    • +
  • x: Array with x values, by default np.array(2000).
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/creators/hdr_from_multi_exposure.html b/ramanchada2/spectrum/creators/hdr_from_multi_exposure.html new file mode 100644 index 00000000..7ad27fd1 --- /dev/null +++ b/ramanchada2/spectrum/creators/hdr_from_multi_exposure.html @@ -0,0 +1,348 @@ + + + + + + + ramanchada2.spectrum.creators.hdr_from_multi_exposure API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.creators.hdr_from_multi_exposure

+ + + + + + + 
 1from typing import List
+ 2
+ 3import numpy as np
+ 4from pydantic import validate_call
+ 5
+ 6from ramanchada2.misc.spectrum_deco import add_spectrum_constructor
+ 7
+ 8from ..spectrum import Spectrum
+ 9
+10
+11@add_spectrum_constructor(set_applied_processing=True)
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def hdr_from_multi_exposure(spes_in: List[Spectrum]):
+14    """Create an HDR spectrum from several spectra with different exposures.
+15
+16    The resulting spectrum will have the details in low-intensity peaks
+17    from long-exposure-time spectrum. As long-exposure-time
+18    spectrum might be sturated, the information for high-intensity
+19    peaks will be taken from short-exposure-time spectrum.
+20    This function will work on a very limited number of spectra,
+21    because we still do not have standardized metadata.
+22    """
+23
+24    spes = list(sorted(spes_in, key=lambda s: float(s.meta['intigration times(ms)'])))  # type: ignore
+25    if not np.all([spes[0].x == s.x for s in spes]):
+26        raise ValueError('x-axes of the spectra should be equal')
+27    spes_cpms = np.array([s.y / float(s.meta['intigration times(ms)']) for s in spes])  # type: ignore
+28    masks = np.array(list(map(lambda s: s.y > s.meta['yaxis_max'], spes)))  # type: ignore
+29    y = spes_cpms[0]
+30    for si in range(1, len(spes_cpms)):
+31        y[~masks[si]] = spes_cpms[si][~masks[si]]
+32    return Spectrum(x=spes[0].x, y=y)
+
+ + +
+
+ +
+
@add_spectrum_constructor(set_applied_processing=True)
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + hdr_from_multi_exposure(spes_in: List[ramanchada2.spectrum.spectrum.Spectrum]): + + + +
+ + 
12@add_spectrum_constructor(set_applied_processing=True)
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def hdr_from_multi_exposure(spes_in: List[Spectrum]):
+15    """Create an HDR spectrum from several spectra with different exposures.
+16
+17    The resulting spectrum will have the details in low-intensity peaks
+18    from long-exposure-time spectrum. As long-exposure-time
+19    spectrum might be sturated, the information for high-intensity
+20    peaks will be taken from short-exposure-time spectrum.
+21    This function will work on a very limited number of spectra,
+22    because we still do not have standardized metadata.
+23    """
+24
+25    spes = list(sorted(spes_in, key=lambda s: float(s.meta['intigration times(ms)'])))  # type: ignore
+26    if not np.all([spes[0].x == s.x for s in spes]):
+27        raise ValueError('x-axes of the spectra should be equal')
+28    spes_cpms = np.array([s.y / float(s.meta['intigration times(ms)']) for s in spes])  # type: ignore
+29    masks = np.array(list(map(lambda s: s.y > s.meta['yaxis_max'], spes)))  # type: ignore
+30    y = spes_cpms[0]
+31    for si in range(1, len(spes_cpms)):
+32        y[~masks[si]] = spes_cpms[si][~masks[si]]
+33    return Spectrum(x=spes[0].x, y=y)
+
+ + +

Create an HDR spectrum from several spectra with different exposures.

+ +

The resulting spectrum will have the details in low-intensity peaks +from long-exposure-time spectrum. As long-exposure-time +spectrum might be sturated, the information for high-intensity +peaks will be taken from short-exposure-time spectrum. +This function will work on a very limited number of spectra, +because we still do not have standardized metadata.

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters.html b/ramanchada2/spectrum/filters.html new file mode 100644 index 00000000..370065ae --- /dev/null +++ b/ramanchada2/spectrum/filters.html @@ -0,0 +1,285 @@ + + + + + + + ramanchada2.spectrum.filters API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3import os
+ 4import glob
+ 5
+ 6__all__ = [
+ 7    os.path.basename(f)[:-3]
+ 8    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+ 9    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+10]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/add_gaussian_noise.html b/ramanchada2/spectrum/filters/add_gaussian_noise.html new file mode 100644 index 00000000..0e00f307 --- /dev/null +++ b/ramanchada2/spectrum/filters/add_gaussian_noise.html @@ -0,0 +1,381 @@ + + + + + + + ramanchada2.spectrum.filters.add_gaussian_noise API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.add_gaussian_noise

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import PositiveFloat, validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def add_gaussian_noise(
+12        old_spe: Spectrum,
+13        new_spe: Spectrum, /,
+14        sigma: PositiveFloat,
+15        # validation for rng_seed is removed because
+16        # it makes in-place modification impossible
+17        rng_seed=None):
+18    r"""
+19    Add gaussian noise to the spectrum.
+20
+21    Random number i.i.d. $N(0, \sigma)$ is added to every sample
+22
+23    Args:
+24        old_spe: internal use only
+25        new_spe: internal use only
+26        sigma:
+27            Sigma of the gaussian noise.
+28        rng_seed:
+29            `int` or rng state, optional, seed for the random generator.
+30            If a state is provided, it is updated in-place.
+31
+32    Returns: modified Spectrum
+33    """
+34    if isinstance(rng_seed, dict):
+35        rng = np.random.default_rng()
+36        rng.bit_generator.state = rng_seed
+37    else:
+38        rng = np.random.default_rng(rng_seed)
+39    dat = old_spe.y + rng.normal(0., sigma, size=len(old_spe.y))
+40    if any(dat < 0):
+41        dat += abs(dat.min())
+42    if isinstance(rng_seed, dict):
+43        rng_seed.update(rng.bit_generator.state)
+44    new_spe.y = np.array(dat)
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_gaussian_noise( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, sigma: typing.Annotated[float, Gt(gt=0)], rng_seed=None): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def add_gaussian_noise(
+13        old_spe: Spectrum,
+14        new_spe: Spectrum, /,
+15        sigma: PositiveFloat,
+16        # validation for rng_seed is removed because
+17        # it makes in-place modification impossible
+18        rng_seed=None):
+19    r"""
+20    Add gaussian noise to the spectrum.
+21
+22    Random number i.i.d. $N(0, \sigma)$ is added to every sample
+23
+24    Args:
+25        old_spe: internal use only
+26        new_spe: internal use only
+27        sigma:
+28            Sigma of the gaussian noise.
+29        rng_seed:
+30            `int` or rng state, optional, seed for the random generator.
+31            If a state is provided, it is updated in-place.
+32
+33    Returns: modified Spectrum
+34    """
+35    if isinstance(rng_seed, dict):
+36        rng = np.random.default_rng()
+37        rng.bit_generator.state = rng_seed
+38    else:
+39        rng = np.random.default_rng(rng_seed)
+40    dat = old_spe.y + rng.normal(0., sigma, size=len(old_spe.y))
+41    if any(dat < 0):
+42        dat += abs(dat.min())
+43    if isinstance(rng_seed, dict):
+44        rng_seed.update(rng.bit_generator.state)
+45    new_spe.y = np.array(dat)
+
+ + +

Add gaussian noise to the spectrum.

+ +

Random number i.i.d. $N(0, \sigma)$ is added to every sample

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • sigma: Sigma of the gaussian noise.
    • +
  • rng_seed: int or rng state, optional, seed for the random generator. +If a state is provided, it is updated in-place.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/add_gaussian_noise_drift.html b/ramanchada2/spectrum/filters/add_gaussian_noise_drift.html new file mode 100644 index 00000000..02c5506c --- /dev/null +++ b/ramanchada2/spectrum/filters/add_gaussian_noise_drift.html @@ -0,0 +1,480 @@ + + + + + + + ramanchada2.spectrum.filters.add_gaussian_noise_drift API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.add_gaussian_noise_drift

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import PositiveFloat, confloat, validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@validate_call(config=dict(arbitrary_types_allowed=True))
+10def generate_add_gaussian_noise_drift(y, /,
+11                                      sigma: PositiveFloat,
+12                                      coef: confloat(ge=0, le=1),  # type: ignore [valid-type]
+13                                      # validation for rng_seed is removed because
+14                                      # it makes in-place modification impossible
+15                                      rng_seed=None):
+16    if isinstance(rng_seed, dict):
+17        rng = np.random.default_rng()
+18        rng.bit_generator.state = rng_seed
+19    else:
+20        rng = np.random.default_rng(rng_seed)
+21    gaus = rng.normal(0., sigma+coef/np.sqrt(2), size=len(y))
+22    cs = np.cumsum(gaus)
+23    # coef*sum(cs[:i]) + (1-coef)*gaus is identical to
+24    # coef*sum(cs[:i-1]) + gaus
+25    noise = coef*cs + gaus*(1-coef)
+26    noise -= np.std(noise)
+27    dat = y + noise
+28    if any(dat < 0):
+29        dat += abs(dat.min())
+30    if isinstance(rng_seed, dict):
+31        rng_seed.update(rng.bit_generator.state)
+32    return np.array(dat)
+33
+34
+35@add_spectrum_filter
+36@validate_call(config=dict(arbitrary_types_allowed=True))
+37def add_gaussian_noise_drift(
+38        old_spe: Spectrum,
+39        new_spe: Spectrum, /,
+40        sigma: PositiveFloat,
+41        coef: confloat(ge=0, le=1),  # type: ignore [valid-type]
+42        # validation for rng_seed is removed because
+43        # it makes in-place modification impossible
+44        rng_seed=None):
+45    r"""
+46    Add cumulative gaussian noise to the spectrum.
+47
+48    Exponential-moving-average-like gaussian noise is added
+49    to each sample. The goal is to mimic the low-frequency noise
+50    (or random substructures in spectra).
+51    The additive noise is
+52    .. math::
+53        a_i = coef*\sum_{j=0}^{i-1}g_j + g_i,
+54    where
+55    .. math::
+56        g_i = \mathcal{N}(0, 1+\frac{coef}{\sqrt 2}).
+57    This way drifting is possible while keeping the
+58    .. math::
+59        \sigma(\Delta(a)) \approx 1.
+60
+61    Args:
+62        old_spe: internal use only
+63        new_spe: internal use only
+64        sigma:
+65            Sigma of the gaussian noise.
+66        coef:
+67            `float` in `[0, 1]`, drifting coefficient. If `coef == 0`,
+68            the result is identical to `add_gaussian_noise()`.
+69        rng_seed:
+70            `int` or rng state, optional. Seed for the random generator.
+71            If a state is provided, it is updated in-place.
+72
+73    Returns: modified Spectrum
+74    """
+75    new_spe.y = generate_add_gaussian_noise_drift(old_spe.y,
+76                                                  sigma=sigma,
+77                                                  coef=coef,
+78                                                  rng_seed=rng_seed)
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + generate_add_gaussian_noise_drift( y, /, sigma: typing.Annotated[float, Gt(gt=0)], coef: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=1), None, None], rng_seed=None): + + + +
+ + 
10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def generate_add_gaussian_noise_drift(y, /,
+12                                      sigma: PositiveFloat,
+13                                      coef: confloat(ge=0, le=1),  # type: ignore [valid-type]
+14                                      # validation for rng_seed is removed because
+15                                      # it makes in-place modification impossible
+16                                      rng_seed=None):
+17    if isinstance(rng_seed, dict):
+18        rng = np.random.default_rng()
+19        rng.bit_generator.state = rng_seed
+20    else:
+21        rng = np.random.default_rng(rng_seed)
+22    gaus = rng.normal(0., sigma+coef/np.sqrt(2), size=len(y))
+23    cs = np.cumsum(gaus)
+24    # coef*sum(cs[:i]) + (1-coef)*gaus is identical to
+25    # coef*sum(cs[:i-1]) + gaus
+26    noise = coef*cs + gaus*(1-coef)
+27    noise -= np.std(noise)
+28    dat = y + noise
+29    if any(dat < 0):
+30        dat += abs(dat.min())
+31    if isinstance(rng_seed, dict):
+32        rng_seed.update(rng.bit_generator.state)
+33    return np.array(dat)
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_gaussian_noise_drift( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, sigma: typing.Annotated[float, Gt(gt=0)], coef: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=1), None, None], rng_seed=None): + + + +
+ + 
36@add_spectrum_filter
+37@validate_call(config=dict(arbitrary_types_allowed=True))
+38def add_gaussian_noise_drift(
+39        old_spe: Spectrum,
+40        new_spe: Spectrum, /,
+41        sigma: PositiveFloat,
+42        coef: confloat(ge=0, le=1),  # type: ignore [valid-type]
+43        # validation for rng_seed is removed because
+44        # it makes in-place modification impossible
+45        rng_seed=None):
+46    r"""
+47    Add cumulative gaussian noise to the spectrum.
+48
+49    Exponential-moving-average-like gaussian noise is added
+50    to each sample. The goal is to mimic the low-frequency noise
+51    (or random substructures in spectra).
+52    The additive noise is
+53    .. math::
+54        a_i = coef*\sum_{j=0}^{i-1}g_j + g_i,
+55    where
+56    .. math::
+57        g_i = \mathcal{N}(0, 1+\frac{coef}{\sqrt 2}).
+58    This way drifting is possible while keeping the
+59    .. math::
+60        \sigma(\Delta(a)) \approx 1.
+61
+62    Args:
+63        old_spe: internal use only
+64        new_spe: internal use only
+65        sigma:
+66            Sigma of the gaussian noise.
+67        coef:
+68            `float` in `[0, 1]`, drifting coefficient. If `coef == 0`,
+69            the result is identical to `add_gaussian_noise()`.
+70        rng_seed:
+71            `int` or rng state, optional. Seed for the random generator.
+72            If a state is provided, it is updated in-place.
+73
+74    Returns: modified Spectrum
+75    """
+76    new_spe.y = generate_add_gaussian_noise_drift(old_spe.y,
+77                                                  sigma=sigma,
+78                                                  coef=coef,
+79                                                  rng_seed=rng_seed)
+
+ + +

Add cumulative gaussian noise to the spectrum.

+ +

Exponential-moving-average-like gaussian noise is added +to each sample. The goal is to mimic the low-frequency noise +(or random substructures in spectra). +The additive noise is +$$a_i = coef*\sum_{j=0}^{i-1}g_j + g_i,$$

+ +

where +$$g_i = \mathcal{N}(0, 1+\frac{coef}{\sqrt 2}).$$

+ +

This way drifting is possible while keeping the +$$\sigma(\Delta(a)) \approx 1.$$

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • sigma: Sigma of the gaussian noise.
    • +
  • coef: float in [0, 1], drifting coefficient. If coef == 0, +the result is identical to add_gaussian_noise().
    • +
  • rng_seed: int or rng state, optional. Seed for the random generator. +If a state is provided, it is updated in-place.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/add_poisson_noise.html b/ramanchada2/spectrum/filters/add_poisson_noise.html new file mode 100644 index 00000000..13533425 --- /dev/null +++ b/ramanchada2/spectrum/filters/add_poisson_noise.html @@ -0,0 +1,379 @@ + + + + + + + ramanchada2.spectrum.filters.add_poisson_noise API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.add_poisson_noise

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def add_poisson_noise(
+12        old_spe: Spectrum,
+13        new_spe: Spectrum, /,
+14        scale: float = 1,
+15        # validation for rng_seed is removed because
+16        # it makes in-place modification impossible
+17        rng_seed=None):
+18    r"""
+19    Add poisson noise to the spectrum.
+20
+21    For each particular sample the noise is proportional to $\sqrt{scale*a_i}$.
+22
+23    Args:
+24        old_spe: internal use only
+25        new_spe: internal use only
+26        scale:
+27            `float`, optional, default is `1`. Scale the amplitude of the noise.
+28        rng_seed:
+29            `int` or rng state, optional. Seed for the random generator.
+30            If a state is provided, it is updated in-place.
+31
+32    Returns: modified Spectrum
+33    """
+34    if isinstance(rng_seed, dict):
+35        rng = np.random.default_rng()
+36        rng.bit_generator.state = rng_seed
+37    else:
+38        rng = np.random.default_rng(rng_seed)
+39    dat = old_spe.y + [rng.normal(0., np.sqrt(i*scale)) for i in old_spe.y]
+40    dat[dat < 0] = 0
+41    if isinstance(rng_seed, dict):
+42        rng_seed.update(rng.bit_generator.state)
+43    new_spe.y = np.array(dat)
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_poisson_noise( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, scale: float = 1, rng_seed=None): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def add_poisson_noise(
+13        old_spe: Spectrum,
+14        new_spe: Spectrum, /,
+15        scale: float = 1,
+16        # validation for rng_seed is removed because
+17        # it makes in-place modification impossible
+18        rng_seed=None):
+19    r"""
+20    Add poisson noise to the spectrum.
+21
+22    For each particular sample the noise is proportional to $\sqrt{scale*a_i}$.
+23
+24    Args:
+25        old_spe: internal use only
+26        new_spe: internal use only
+27        scale:
+28            `float`, optional, default is `1`. Scale the amplitude of the noise.
+29        rng_seed:
+30            `int` or rng state, optional. Seed for the random generator.
+31            If a state is provided, it is updated in-place.
+32
+33    Returns: modified Spectrum
+34    """
+35    if isinstance(rng_seed, dict):
+36        rng = np.random.default_rng()
+37        rng.bit_generator.state = rng_seed
+38    else:
+39        rng = np.random.default_rng(rng_seed)
+40    dat = old_spe.y + [rng.normal(0., np.sqrt(i*scale)) for i in old_spe.y]
+41    dat[dat < 0] = 0
+42    if isinstance(rng_seed, dict):
+43        rng_seed.update(rng.bit_generator.state)
+44    new_spe.y = np.array(dat)
+
+ + +

Add poisson noise to the spectrum.

+ +

For each particular sample the noise is proportional to $\sqrt{scale*a_i}$.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • scale: float, optional, default is 1. Scale the amplitude of the noise.
    • +
  • rng_seed: int or rng state, optional. Seed for the random generator. +If a state is provided, it is updated in-place.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/convolve.html b/ramanchada2/spectrum/filters/convolve.html new file mode 100644 index 00000000..00f8a8c4 --- /dev/null +++ b/ramanchada2/spectrum/filters/convolve.html @@ -0,0 +1,403 @@ + + + + + + + ramanchada2.spectrum.filters.convolve API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.convolve

+ + + + + + + 
 1from typing import Callable, Literal, Union
+ 2
+ 3import lmfit
+ 4import numpy as np
+ 5import numpy.typing as npt
+ 6from numpy.typing import NDArray
+ 7from pydantic import validate_call
+ 8from scipy import signal
+ 9
+10from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+11
+12from ..spectrum import Spectrum
+13
+14
+15@add_spectrum_filter
+16@validate_call(config=dict(arbitrary_types_allowed=True))
+17def convolve(
+18        old_spe: Spectrum,
+19        new_spe: Spectrum, /,
+20        lineshape: Union[Callable[[Union[float, NDArray]], float],
+21                         npt.NDArray,
+22                         Literal[
+23                              'gaussian', 'lorentzian',
+24                              'voigt', 'pvoigt', 'moffat',
+25                              'pearson4', 'pearson7'
+26                              ]],
+27        **kwargs):
+28    """
+29    Convole spectrum with arbitrary lineshape.
+30
+31    Args:
+32        old_spe: internal use only
+33        new_spe: internal use only
+34        lineshape:callable, `str` or `np.ndarray`.
+35             If callable: should have a single positional argument `x`, e.g.
+36            `lambda x: np.exp((x/5)**2)`.
+37            If predefined peak profile: can be `gaussian`, `lorentzian`, `voigt`,
+38            `pvoigt`, `moffat` or `pearson4`.
+39            If `np.ndarray`: lineshape in samples.
+40        **kwargs:
+41            Additional kwargs will be passed to lineshape function.
+42
+43    Returns: modified Spectrum
+44    """
+45
+46    if isinstance(lineshape, np.ndarray):
+47        new_spe.y = signal.convolve(old_spe.y, lineshape, mode='same')
+48    else:
+49        if callable(lineshape):
+50            shape_fun = lineshape
+51        else:
+52            shape_fun = getattr(lmfit.lineshapes, lineshape)
+53
+54        leny = len(old_spe.y)
+55        x = np.arange(-(leny-1)//2, (leny+1)//2, dtype=float)
+56        shape_val = shape_fun(x, **kwargs)
+57        new_spe.y = signal.convolve(old_spe.y, shape_val, mode='same')
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + convolve( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, lineshape: Union[Callable[[Union[float, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Literal['gaussian', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7']], **kwargs): + + + +
+ + 
16@add_spectrum_filter
+17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def convolve(
+19        old_spe: Spectrum,
+20        new_spe: Spectrum, /,
+21        lineshape: Union[Callable[[Union[float, NDArray]], float],
+22                         npt.NDArray,
+23                         Literal[
+24                              'gaussian', 'lorentzian',
+25                              'voigt', 'pvoigt', 'moffat',
+26                              'pearson4', 'pearson7'
+27                              ]],
+28        **kwargs):
+29    """
+30    Convole spectrum with arbitrary lineshape.
+31
+32    Args:
+33        old_spe: internal use only
+34        new_spe: internal use only
+35        lineshape:callable, `str` or `np.ndarray`.
+36             If callable: should have a single positional argument `x`, e.g.
+37            `lambda x: np.exp((x/5)**2)`.
+38            If predefined peak profile: can be `gaussian`, `lorentzian`, `voigt`,
+39            `pvoigt`, `moffat` or `pearson4`.
+40            If `np.ndarray`: lineshape in samples.
+41        **kwargs:
+42            Additional kwargs will be passed to lineshape function.
+43
+44    Returns: modified Spectrum
+45    """
+46
+47    if isinstance(lineshape, np.ndarray):
+48        new_spe.y = signal.convolve(old_spe.y, lineshape, mode='same')
+49    else:
+50        if callable(lineshape):
+51            shape_fun = lineshape
+52        else:
+53            shape_fun = getattr(lmfit.lineshapes, lineshape)
+54
+55        leny = len(old_spe.y)
+56        x = np.arange(-(leny-1)//2, (leny+1)//2, dtype=float)
+57        shape_val = shape_fun(x, **kwargs)
+58        new_spe.y = signal.convolve(old_spe.y, shape_val, mode='same')
+
+ + +

Convole spectrum with arbitrary lineshape.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • lineshape: callable, str or np.ndarray. + If callable: should have a single positional argument x, e.g. +lambda x: np.exp((x/5)**2). +If predefined peak profile: can be gaussian, lorentzian, voigt, +pvoigt, moffat or pearson4. +If np.ndarray: lineshape in samples.
    • +
  • **kwargs: Additional kwargs will be passed to lineshape function.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/drop_spikes.html b/ramanchada2/spectrum/filters/drop_spikes.html new file mode 100644 index 00000000..dabebd3c --- /dev/null +++ b/ramanchada2/spectrum/filters/drop_spikes.html @@ -0,0 +1,615 @@ + + + + + + + ramanchada2.spectrum.filters.drop_spikes API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.drop_spikes

+ + + + + + + 
  1import numpy as np
+  2from numpy.typing import NDArray
+  3from pydantic import PositiveFloat, validate_call
+  4
+  5from ramanchada2.misc.spectrum_deco import (add_spectrum_filter,
+  6                                            add_spectrum_method)
+  7
+  8from ..spectrum import Spectrum
+  9
+ 10
+ 11@add_spectrum_method
+ 12@validate_call(config=dict(arbitrary_types_allowed=True))
+ 13def spike_indices(spe: Spectrum, /, n_sigma: PositiveFloat) -> NDArray:
+ 14    """
+ 15    Find spikes in spectrum
+ 16
+ 17    Single-bin spikes are located using left and right successive
+ 18    differences. The threshold is based on the standart deviation
+ 19    of the metric which makes this algorithm less optimal.
+ 20
+ 21    Args:
+ 22        spe: internal use only
+ 23        n_sigma: Threshold value should be `n_sigma` times the standart
+ 24          deviation of the metric.
+ 25
+ 26    Returns: List of spike indices
+ 27    """
+ 28    yi = spe.y[1:-1]
+ 29    yi_1 = spe.y[:-2]
+ 30    yi1 = spe.y[2:]
+ 31    y_merit = np.abs(2*yi-yi_1-yi1) - np.abs(yi1-yi_1)
+ 32    spike_idx = y_merit > n_sigma * y_merit.std()
+ 33    spike_idx = np.concatenate(([False], spike_idx, [False]))
+ 34    return spike_idx
+ 35
+ 36
+ 37@add_spectrum_filter
+ 38@validate_call(config=dict(arbitrary_types_allowed=True))
+ 39def drop_spikes(old_spe: Spectrum,
+ 40                new_spe: Spectrum, /,
+ 41                n_sigma: PositiveFloat = 10):
+ 42    """
+ 43    Removes single-bin spikes.
+ 44
+ 45    Remove x, y pairs recognised as spikes using left and right
+ 46    successive differences and standard-deviation-based threshold.
+ 47
+ 48    Args:
+ 49        old_spe: internal use only
+ 50        new_spe: internal use only
+ 51        n_sigma: optional, default is `10`.
+ 52            Threshold is `n_sigma` times the standard deviation.
+ 53
+ 54    Returns: modified Spectrum
+ 55    """
+ 56    use_idx = ~spike_indices(old_spe, n_sigma=n_sigma)
+ 57    new_spe.x = old_spe.x[use_idx]
+ 58    new_spe.y = old_spe.y[use_idx]
+ 59
+ 60
+ 61@add_spectrum_filter
+ 62@validate_call(config=dict(arbitrary_types_allowed=True))
+ 63def recover_spikes(old_spe: Spectrum,
+ 64                   new_spe: Spectrum, /,
+ 65                   n_sigma: PositiveFloat = 10):
+ 66    """
+ 67    Recover single-bin spikes.
+ 68
+ 69    Recover x, y pairs recognised as spikes using left and right
+ 70    successive differences and standard-deviation-based threshold
+ 71    and linear interpolation.
+ 72
+ 73    Args:
+ 74        old_spe: internal use only
+ 75        new_spe: internal use only
+ 76        n_sigma: optional, default is `10`.
+ 77            Threshold is `n_sigma` times the standard deviation.
+ 78
+ 79    Returns: modified Spectrum
+ 80    """
+ 81    use_idx = ~spike_indices(old_spe, n_sigma=n_sigma)
+ 82    new_spe.y = np.interp(old_spe.x, old_spe.x[use_idx], old_spe.y[use_idx])
+ 83
+ 84
+ 85@add_spectrum_filter
+ 86@validate_call(config=dict(arbitrary_types_allowed=True))
+ 87def get_spikes(old_spe: Spectrum,
+ 88               new_spe: Spectrum, /,
+ 89               n_sigma: PositiveFloat = 10):
+ 90    """
+ 91    Get single-bin spikes only.
+ 92
+ 93    Get x, y pairs recognised as spikes using left and right
+ 94    successive differences and standard-deviation-based threshold
+ 95    and linear interpolation.
+ 96
+ 97    Args:
+ 98        old_spe: internal use only
+ 99        new_spe: internal use only
+100        n_sigma: optional, default is `10`.
+101            Threshold is `n_sigma` times the standard deviation.
+102
+103    Returns: modified Spectrum
+104    """
+105    spike_idx = spike_indices(old_spe, n_sigma=n_sigma)
+106    new_spe.x = old_spe.x[spike_idx]
+107    new_spe.y = old_spe.y[spike_idx]
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + spike_indices( spe: ramanchada2.spectrum.spectrum.Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)]) -> numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]: + + + +
+ + 
12@add_spectrum_method
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def spike_indices(spe: Spectrum, /, n_sigma: PositiveFloat) -> NDArray:
+15    """
+16    Find spikes in spectrum
+17
+18    Single-bin spikes are located using left and right successive
+19    differences. The threshold is based on the standart deviation
+20    of the metric which makes this algorithm less optimal.
+21
+22    Args:
+23        spe: internal use only
+24        n_sigma: Threshold value should be `n_sigma` times the standart
+25          deviation of the metric.
+26
+27    Returns: List of spike indices
+28    """
+29    yi = spe.y[1:-1]
+30    yi_1 = spe.y[:-2]
+31    yi1 = spe.y[2:]
+32    y_merit = np.abs(2*yi-yi_1-yi1) - np.abs(yi1-yi_1)
+33    spike_idx = y_merit > n_sigma * y_merit.std()
+34    spike_idx = np.concatenate(([False], spike_idx, [False]))
+35    return spike_idx
+
+ + +

Find spikes in spectrum

+ +

Single-bin spikes are located using left and right successive +differences. The threshold is based on the standart deviation +of the metric which makes this algorithm less optimal.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • n_sigma: Threshold value should be n_sigma times the standart +deviation of the metric.
    • +
+ +

Returns: List of spike indices

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + drop_spikes( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)] = 10): + + + +
+ + 
38@add_spectrum_filter
+39@validate_call(config=dict(arbitrary_types_allowed=True))
+40def drop_spikes(old_spe: Spectrum,
+41                new_spe: Spectrum, /,
+42                n_sigma: PositiveFloat = 10):
+43    """
+44    Removes single-bin spikes.
+45
+46    Remove x, y pairs recognised as spikes using left and right
+47    successive differences and standard-deviation-based threshold.
+48
+49    Args:
+50        old_spe: internal use only
+51        new_spe: internal use only
+52        n_sigma: optional, default is `10`.
+53            Threshold is `n_sigma` times the standard deviation.
+54
+55    Returns: modified Spectrum
+56    """
+57    use_idx = ~spike_indices(old_spe, n_sigma=n_sigma)
+58    new_spe.x = old_spe.x[use_idx]
+59    new_spe.y = old_spe.y[use_idx]
+
+ + +

Removes single-bin spikes.

+ +

Remove x, y pairs recognised as spikes using left and right +successive differences and standard-deviation-based threshold.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • n_sigma: optional, default is 10. +Threshold is n_sigma times the standard deviation.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + recover_spikes( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)] = 10): + + + +
+ + 
62@add_spectrum_filter
+63@validate_call(config=dict(arbitrary_types_allowed=True))
+64def recover_spikes(old_spe: Spectrum,
+65                   new_spe: Spectrum, /,
+66                   n_sigma: PositiveFloat = 10):
+67    """
+68    Recover single-bin spikes.
+69
+70    Recover x, y pairs recognised as spikes using left and right
+71    successive differences and standard-deviation-based threshold
+72    and linear interpolation.
+73
+74    Args:
+75        old_spe: internal use only
+76        new_spe: internal use only
+77        n_sigma: optional, default is `10`.
+78            Threshold is `n_sigma` times the standard deviation.
+79
+80    Returns: modified Spectrum
+81    """
+82    use_idx = ~spike_indices(old_spe, n_sigma=n_sigma)
+83    new_spe.y = np.interp(old_spe.x, old_spe.x[use_idx], old_spe.y[use_idx])
+
+ + +

Recover single-bin spikes.

+ +

Recover x, y pairs recognised as spikes using left and right +successive differences and standard-deviation-based threshold +and linear interpolation.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • n_sigma: optional, default is 10. +Threshold is n_sigma times the standard deviation.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + get_spikes( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)] = 10): + + + +
+ + 
 86@add_spectrum_filter
+ 87@validate_call(config=dict(arbitrary_types_allowed=True))
+ 88def get_spikes(old_spe: Spectrum,
+ 89               new_spe: Spectrum, /,
+ 90               n_sigma: PositiveFloat = 10):
+ 91    """
+ 92    Get single-bin spikes only.
+ 93
+ 94    Get x, y pairs recognised as spikes using left and right
+ 95    successive differences and standard-deviation-based threshold
+ 96    and linear interpolation.
+ 97
+ 98    Args:
+ 99        old_spe: internal use only
+100        new_spe: internal use only
+101        n_sigma: optional, default is `10`.
+102            Threshold is `n_sigma` times the standard deviation.
+103
+104    Returns: modified Spectrum
+105    """
+106    spike_idx = spike_indices(old_spe, n_sigma=n_sigma)
+107    new_spe.x = old_spe.x[spike_idx]
+108    new_spe.y = old_spe.y[spike_idx]
+
+ + +

Get single-bin spikes only.

+ +

Get x, y pairs recognised as spikes using left and right +successive differences and standard-deviation-based threshold +and linear interpolation.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • n_sigma: optional, default is 10. +Threshold is n_sigma times the standard deviation.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/dropna.html b/ramanchada2/spectrum/filters/dropna.html new file mode 100644 index 00000000..65caf24e --- /dev/null +++ b/ramanchada2/spectrum/filters/dropna.html @@ -0,0 +1,352 @@ + + + + + + + ramanchada2.spectrum.filters.dropna API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.dropna

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def dropna(old_spe: Spectrum,
+12           new_spe: Spectrum):
+13    """
+14    Remove non finite numbers on both axes
+15
+16    Args:
+17        old_spe: internal use only
+18        new_spe: internal use only
+19
+20    Returns: modified Spectrum
+21    """
+22
+23    x = old_spe.x
+24    y = old_spe.y
+25    idx = np.isfinite(x)
+26    x = x[idx]
+27    y = y[idx]
+28    idx = np.isfinite(y)
+29    x = x[idx]
+30    y = y[idx]
+31    new_spe.x = x
+32    new_spe.y = y
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + dropna( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def dropna(old_spe: Spectrum,
+13           new_spe: Spectrum):
+14    """
+15    Remove non finite numbers on both axes
+16
+17    Args:
+18        old_spe: internal use only
+19        new_spe: internal use only
+20
+21    Returns: modified Spectrum
+22    """
+23
+24    x = old_spe.x
+25    y = old_spe.y
+26    idx = np.isfinite(x)
+27    x = x[idx]
+28    y = y[idx]
+29    idx = np.isfinite(y)
+30    x = x[idx]
+31    y = y[idx]
+32    new_spe.x = x
+33    new_spe.y = y
+
+ + +

Remove non finite numbers on both axes

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/moving_average.html b/ramanchada2/spectrum/filters/moving_average.html new file mode 100644 index 00000000..1e8b2ffa --- /dev/null +++ b/ramanchada2/spectrum/filters/moving_average.html @@ -0,0 +1,414 @@ + + + + + + + ramanchada2.spectrum.filters.moving_average API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.moving_average

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import PositiveInt, validate_call
+ 3from scipy import signal
+ 4
+ 5from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 6
+ 7from ..spectrum import Spectrum
+ 8
+ 9
+10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def moving_average(old_spe: Spectrum,
+13                   new_spe: Spectrum, /,
+14                   window_size: PositiveInt = 10):
+15    """
+16    Moving average filter.
+17
+18    Args:
+19        old_spe: internal use only
+20        new_spe: internal use only
+21        window_size:
+22            `int`, optional, default is `10`.
+23
+24    Returns: modified Spectrum
+25    """
+26    y = [np.average(old_spe.y[i:min(i + window_size, len(old_spe.y))])
+27         for i in range(len(old_spe.y))]
+28    new_spe.y = np.array(y)
+29
+30
+31@add_spectrum_filter
+32@validate_call(config=dict(arbitrary_types_allowed=True))
+33def moving_average_convolve(old_spe: Spectrum,
+34                            new_spe: Spectrum, /,
+35                            window_size: PositiveInt = 10):
+36    """
+37    Moving average filter.
+38
+39    Args:
+40        old_spe: internal use only
+41        new_spe: internal use only
+42        window_size:
+43            `int`, optional, default is `10`.
+44
+45    Returns: modified Spectrum
+46    """
+47    new_spe.y = signal.convolve(old_spe.y, np.ones(window_size)/window_size, mode='same')
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_average( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, window_size: typing.Annotated[int, Gt(gt=0)] = 10): + + + +
+ + 
11@add_spectrum_filter
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def moving_average(old_spe: Spectrum,
+14                   new_spe: Spectrum, /,
+15                   window_size: PositiveInt = 10):
+16    """
+17    Moving average filter.
+18
+19    Args:
+20        old_spe: internal use only
+21        new_spe: internal use only
+22        window_size:
+23            `int`, optional, default is `10`.
+24
+25    Returns: modified Spectrum
+26    """
+27    y = [np.average(old_spe.y[i:min(i + window_size, len(old_spe.y))])
+28         for i in range(len(old_spe.y))]
+29    new_spe.y = np.array(y)
+
+ + +

Moving average filter.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_average_convolve( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, window_size: typing.Annotated[int, Gt(gt=0)] = 10): + + + +
+ + 
32@add_spectrum_filter
+33@validate_call(config=dict(arbitrary_types_allowed=True))
+34def moving_average_convolve(old_spe: Spectrum,
+35                            new_spe: Spectrum, /,
+36                            window_size: PositiveInt = 10):
+37    """
+38    Moving average filter.
+39
+40    Args:
+41        old_spe: internal use only
+42        new_spe: internal use only
+43        window_size:
+44            `int`, optional, default is `10`.
+45
+46    Returns: modified Spectrum
+47    """
+48    new_spe.y = signal.convolve(old_spe.y, np.ones(window_size)/window_size, mode='same')
+
+ + +

Moving average filter.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/moving_median.html b/ramanchada2/spectrum/filters/moving_median.html new file mode 100644 index 00000000..0da41554 --- /dev/null +++ b/ramanchada2/spectrum/filters/moving_median.html @@ -0,0 +1,435 @@ + + + + + + + ramanchada2.spectrum.filters.moving_median API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.moving_median

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import PositiveInt, validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@validate_call(config=dict(arbitrary_types_allowed=True))
+10def _moving_median(s,
+11                   window_size: PositiveInt = 10):
+12    y = ([np.median(s[:window_size]) for i in range(window_size)] +
+13         [np.median(s[i-window_size: i+window_size]) for i in range(window_size, len(s)-window_size)] +
+14         [np.median(s[-window_size:]) for i in range(window_size)]
+15         )
+16    return np.array(y)
+17
+18
+19@add_spectrum_filter
+20@validate_call(config=dict(arbitrary_types_allowed=True))
+21def moving_median(old_spe: Spectrum,
+22                  new_spe: Spectrum, /,
+23                  window_size: PositiveInt = 10):
+24    """
+25    Moving median filter.
+26
+27    The resultant spectrum is moving minimum of the input.
+28
+29    Args:
+30        old_spe: internal use only
+31        new_spe: internal use only
+32        window_size:
+33            `int`, optional, default is `10`.
+34
+35    Returns: modified Spectrum
+36    """
+37
+38    new_spe.y = _moving_median(old_spe.y, window_size)
+39
+40
+41@add_spectrum_filter
+42@validate_call(config=dict(arbitrary_types_allowed=True))
+43def subtract_moving_median(
+44        old_spe: Spectrum,
+45        new_spe: Spectrum,
+46        window_size: int):
+47    """
+48    Subtract moving median filter.
+49
+50    The resultant spectrum is moving minimum of the input subtracted from the input.
+51
+52    Args:
+53        old_spe: internal use only
+54        new_spe: internal use only
+55        window_size:
+56            `int`, optional, default is `10`.
+57
+58    Returns: modified Spectrum
+59    """
+60    new_spe.y = old_spe.y - _moving_median(old_spe.y, window_size)
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_median( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, window_size: typing.Annotated[int, Gt(gt=0)] = 10): + + + +
+ + 
20@add_spectrum_filter
+21@validate_call(config=dict(arbitrary_types_allowed=True))
+22def moving_median(old_spe: Spectrum,
+23                  new_spe: Spectrum, /,
+24                  window_size: PositiveInt = 10):
+25    """
+26    Moving median filter.
+27
+28    The resultant spectrum is moving minimum of the input.
+29
+30    Args:
+31        old_spe: internal use only
+32        new_spe: internal use only
+33        window_size:
+34            `int`, optional, default is `10`.
+35
+36    Returns: modified Spectrum
+37    """
+38
+39    new_spe.y = _moving_median(old_spe.y, window_size)
+
+ + +

Moving median filter.

+ +

The resultant spectrum is moving minimum of the input.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_moving_median( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, window_size: int): + + + +
+ + 
42@add_spectrum_filter
+43@validate_call(config=dict(arbitrary_types_allowed=True))
+44def subtract_moving_median(
+45        old_spe: Spectrum,
+46        new_spe: Spectrum,
+47        window_size: int):
+48    """
+49    Subtract moving median filter.
+50
+51    The resultant spectrum is moving minimum of the input subtracted from the input.
+52
+53    Args:
+54        old_spe: internal use only
+55        new_spe: internal use only
+56        window_size:
+57            `int`, optional, default is `10`.
+58
+59    Returns: modified Spectrum
+60    """
+61    new_spe.y = old_spe.y - _moving_median(old_spe.y, window_size)
+
+ + +

Subtract moving median filter.

+ +

The resultant spectrum is moving minimum of the input subtracted from the input.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/pad_zeros.html b/ramanchada2/spectrum/filters/pad_zeros.html new file mode 100644 index 00000000..6c008204 --- /dev/null +++ b/ramanchada2/spectrum/filters/pad_zeros.html @@ -0,0 +1,367 @@ + + + + + + + ramanchada2.spectrum.filters.pad_zeros API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.pad_zeros

+ + + + + + + 
 1import numpy as np
+ 2from pydantic import validate_call
+ 3
+ 4from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 5
+ 6from ..spectrum import Spectrum
+ 7
+ 8
+ 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def pad_zeros(old_spe: Spectrum,
+12              new_spe: Spectrum, /):
+13    """
+14    Extend x-axis by 100% in both directions.
+15
+16    The x-axis of resultant spectrum will be:
+17    $[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.
+18    The length of the new spectrum is 3 times the original. The added values
+19    are with an uniform step. In the middle is the original spectrum with
+20    original x and y values. The coresponding y vallues for the newly added
+21    x-values are always zeros.
+22
+23    Args:
+24        old_spe: internal use only
+25        new_spe: internal use only
+26
+27    Returns: modified Spectrum
+28    """
+29    lenx = len(old_spe.x)
+30    minx = np.min(old_spe.x)
+31    maxx = np.max(old_spe.x)
+32    xl = np.linspace(minx-(maxx-minx), minx, lenx, endpoint=True)[:-1]
+33    xr = np.linspace(maxx, maxx+(maxx-minx), lenx, endpoint=True)[1:]
+34
+35    new_spe.y = np.concatenate((np.zeros(lenx-1), old_spe.y, np.zeros(lenx-1)))
+36    new_spe.x = np.concatenate((xl, old_spe.x, xr))
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + pad_zeros( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def pad_zeros(old_spe: Spectrum,
+13              new_spe: Spectrum, /):
+14    """
+15    Extend x-axis by 100% in both directions.
+16
+17    The x-axis of resultant spectrum will be:
+18    $[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.
+19    The length of the new spectrum is 3 times the original. The added values
+20    are with an uniform step. In the middle is the original spectrum with
+21    original x and y values. The coresponding y vallues for the newly added
+22    x-values are always zeros.
+23
+24    Args:
+25        old_spe: internal use only
+26        new_spe: internal use only
+27
+28    Returns: modified Spectrum
+29    """
+30    lenx = len(old_spe.x)
+31    minx = np.min(old_spe.x)
+32    maxx = np.max(old_spe.x)
+33    xl = np.linspace(minx-(maxx-minx), minx, lenx, endpoint=True)[:-1]
+34    xr = np.linspace(maxx, maxx+(maxx-minx), lenx, endpoint=True)[1:]
+35
+36    new_spe.y = np.concatenate((np.zeros(lenx-1), old_spe.y, np.zeros(lenx-1)))
+37    new_spe.x = np.concatenate((xl, old_spe.x, xr))
+
+ + +

Extend x-axis by 100% in both directions.

+ +

The x-axis of resultant spectrum will be: +$[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$. +The length of the new spectrum is 3 times the original. The added values +are with an uniform step. In the middle is the original spectrum with +original x and y values. The coresponding y vallues for the newly added +x-values are always zeros.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/resampling.html b/ramanchada2/spectrum/filters/resampling.html new file mode 100644 index 00000000..5eb5f13e --- /dev/null +++ b/ramanchada2/spectrum/filters/resampling.html @@ -0,0 +1,857 @@ + + + + + + + ramanchada2.spectrum.filters.resampling API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.resampling

+ + + + + + + 
  1from typing import Any, Callable, Dict, Literal, Optional, Tuple, Union
+  2
+  3import numpy as np
+  4from pydantic import PositiveInt, validate_call
+  5from scipy import fft, signal
+  6from scipy.interpolate import (Akima1DInterpolator, CubicSpline,
+  7                               PchipInterpolator)
+  8
+  9from ramanchada2.misc.spectrum_deco import (add_spectrum_filter,
+ 10                                            add_spectrum_method)
+ 11
+ 12from ..spectrum import Spectrum
+ 13
+ 14
+ 15@add_spectrum_method
+ 16@validate_call(config=dict(arbitrary_types_allowed=True))
+ 17def resample_NUDFT(spe: Spectrum, /,
+ 18                   x_range: Tuple[float, float] = (0, 4000),
+ 19                   xnew_bins: PositiveInt = 100,
+ 20                   window: Optional[Union[Callable,
+ 21                                          Tuple[Any, ...],  # e.g. ('gaussian', sigma)
+ 22                                          Literal['barthann', 'bartlett', 'blackman', 'blackmanharris',
+ 23                                                  'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss',
+ 24                                                  'exponential', 'flattop', 'gaussian', 'general_cosine',
+ 25                                                  'general_gaussian', 'general_hamming', 'hamming', 'hann',
+ 26                                                  'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey']
+ 27                                          ]] = None,
+ 28                   cumulative: bool = False):
+ 29    """
+ 30    Resample the spectrum using Non-uniform discrete fourier transform.
+ 31
+ 32    The x-axis of the result will be uniform. The corresponding y-values
+ 33    will be calculated with NUDFT and inverse FFT.
+ 34
+ 35    Args:
+ 36        spe: internal use only
+ 37        x_range: optional. Defaults to (0, 4000).
+ 38            The x_range of the new spectrum.
+ 39        xnew_bins: optional. Defaults to 100.
+ 40            Number of bins of the new spectrum
+ 41        window: optional, Defaults to None.
+ 42            The window to be used for lowpass filter. If None 'blackmanharris' is used.
+ 43            If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+ 44        cumulative: optional. Defaults to False.
+ 45            If True, the resultant spectrum will be cumulative and normalized
+ 46            (in analogy with CDF).
+ 47
+ 48    Returns:
+ 49        (x_values, y_values)
+ 50    """
+ 51
+ 52    x_new = np.linspace(x_range[0], x_range[1], xnew_bins, endpoint=False)
+ 53    x = spe.x
+ 54    y = spe.y
+ 55    x = np.array(x)
+ 56    x_range = (np.min(x_range), np.max(x_range))
+ 57    y = y[(x >= x_range[0]) & (x < x_range[1])]
+ 58    x = x[(x >= x_range[0]) & (x < x_range[1])]
+ 59
+ 60    w = (x-x_range[0])/(x_range[1]-x_range[0])*np.pi*2
+ 61    x -= x_range[0]
+ 62
+ 63    k = np.arange(xnew_bins)
+ 64
+ 65    Y_new = np.sum([yi*np.exp(-1j*wi*k) for yi, wi in zip(y, w)], axis=0)
+ 66
+ 67    if window is None:
+ 68        window = 'blackmanharris'
+ 69
+ 70    if hasattr(window, '__call__'):
+ 71        h = (window(len(Y_new)*2))[len(Y_new):]  # type: ignore
+ 72    else:
+ 73        h = signal.windows.get_window(window, len(Y_new)*2)[len(Y_new):]
+ 74    Y_new *= h
+ 75
+ 76    y_new = fft.irfft(Y_new, n=xnew_bins)
+ 77    if cumulative:
+ 78        y_new = np.cumsum(y_new)
+ 79        y_new /= y_new[-1]
+ 80    return x_new, y_new
+ 81
+ 82
+ 83@add_spectrum_filter
+ 84@validate_call(config=dict(arbitrary_types_allowed=True))
+ 85def resample_NUDFT_filter(old_spe: Spectrum,
+ 86                          new_spe: Spectrum, /,
+ 87                          x_range: Tuple[float, float] = (0, 4000),
+ 88                          xnew_bins: PositiveInt = 100,
+ 89                          window=None,
+ 90                          cumulative: bool = False):
+ 91    """
+ 92    Resample the spectrum using Non-uniform discrete fourier transform.
+ 93
+ 94    The x-axis of the result will be uniform. The corresponding y-values
+ 95    will be calculated with NUDFT and inverse FFT.
+ 96
+ 97    Args:
+ 98        old_spe: internal use only
+ 99        new_spe: internal use only
+100        x_range: optional. Defaults to (0, 4000).
+101            The x_range of the new spectrum.
+102        xnew_bins: optional. Defaults to 100.
+103            Number of bins of the new spectrum
+104        window: optional, Defaults to None.
+105            The window to be used for lowpass filter. If None 'blackmanharris' is used.
+106            If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+107        cumulative: optional. Defaults to False.
+108            If True, the resultant spectrum will be cumulative and normalized
+109            (in analogy with CDF).
+110
+111    Returns: modified Spectrum
+112    """
+113    new_spe.x, new_spe.y = resample_NUDFT(old_spe,
+114                                          x_range=x_range,
+115                                          xnew_bins=xnew_bins,
+116                                          window=window,
+117                                          cumulative=cumulative)
+118
+119
+120@add_spectrum_method
+121@validate_call(config=dict(arbitrary_types_allowed=True))
+122def resample_spline(spe: Spectrum, /,
+123                    x_range: Tuple[float, float] = (0, 4000),
+124                    xnew_bins: PositiveInt = 100,
+125                    spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',
+126                    interp_kw_args: Optional[Dict] = None,
+127                    cumulative: bool = False):
+128    """
+129    Resample the spectrum using spline interpolation.
+130
+131    The x-axis of the result will be uniform. The corresponding y-values
+132    will be calculated with spline interpolation.
+133
+134    Args:
+135        spe: internal use only
+136        x_range: optional. Defaults to (0, 4000).
+137            The x_range of the new spectrum.
+138        xnew_bins: optional. Defaults to 100.
+139            Number of bins of the new spectrum
+140        spline: optional, Defaults to 'pchip'.
+141            Name of the spline funcion to be used.
+142        cumulative: optional. Defaults to False.
+143            If True, the resultant spectrum will be cumulative and normalized
+144            (in analogy with CDF).
+145
+146    Returns:
+147        (x_values, y_values)
+148    """
+149
+150    kw_args: Dict[str, Any] = {}
+151    if spline == 'pchip':
+152        spline_fn = PchipInterpolator
+153        kw_args['extrapolate'] = False
+154    elif spline == 'akima':
+155        spline_fn = Akima1DInterpolator
+156    elif spline == 'makima':
+157        spline_fn = Akima1DInterpolator
+158        kw_args['method'] = 'makima'
+159    elif spline == 'cubic_spline':
+160        spline_fn = CubicSpline
+161        kw_args['bc_type'] = 'natural'
+162        kw_args['extrapolate'] = False
+163
+164    if interp_kw_args is not None:
+165        kw_args.update(interp_kw_args)
+166
+167    x_new = np.linspace(x_range[0], x_range[1], xnew_bins, endpoint=False)
+168    y_new = spline_fn(spe.x, spe.y, **kw_args)(x_new)
+169
+170    y_new[np.isnan(y_new)] = 0
+171    if cumulative:
+172        y_new = np.cumsum(y_new)
+173        y_new /= y_new[-1]
+174
+175    return x_new, y_new
+176
+177
+178@add_spectrum_filter
+179@validate_call(config=dict(arbitrary_types_allowed=True))
+180def resample_spline_filter(old_spe: Spectrum,
+181                           new_spe: Spectrum, /,
+182                           x_range: Tuple[float, float] = (0, 4000),
+183                           xnew_bins: PositiveInt = 100,
+184                           spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',
+185                           interp_kw_args: Optional[Dict] = None,
+186                           cumulative: bool = False):
+187    """
+188    Resample the spectrum using spline interpolation.
+189
+190    The x-axis of the result will be uniform. The corresponding y-values
+191    will be calculated with spline interpolation.
+192
+193    Args:
+194        old_spe: internal use only
+195        new_spe: internal use only
+196        x_range: optional. Defaults to (0, 4000).
+197            The x_range of the new spectrum.
+198        xnew_bins: optional. Defaults to 100.
+199            Number of bins of the new spectrum
+200        spline: optional, Defaults to 'pchip'.
+201            Name of the spline funcion to be used.
+202        cumulative: optional. Defaults to False.
+203            If True, the resultant spectrum will be cumulative and normalized
+204            (in analogy with CDF).
+205
+206    Returns: modified Spectrum
+207    """
+208    new_spe.x, new_spe.y = resample_spline(old_spe,
+209                                           x_range=x_range,
+210                                           xnew_bins=xnew_bins,
+211                                           spline=spline,
+212                                           interp_kw_args=interp_kw_args,
+213                                           cumulative=cumulative)
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_NUDFT( spe: ramanchada2.spectrum.spectrum.Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, window: Union[Callable, Tuple[Any, ...], Literal['barthann', 'bartlett', 'blackman', 'blackmanharris', 'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss', 'exponential', 'flattop', 'gaussian', 'general_cosine', 'general_gaussian', 'general_hamming', 'hamming', 'hann', 'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey'], NoneType] = None, cumulative: bool = False): + + + +
+ + 
16@add_spectrum_method
+17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def resample_NUDFT(spe: Spectrum, /,
+19                   x_range: Tuple[float, float] = (0, 4000),
+20                   xnew_bins: PositiveInt = 100,
+21                   window: Optional[Union[Callable,
+22                                          Tuple[Any, ...],  # e.g. ('gaussian', sigma)
+23                                          Literal['barthann', 'bartlett', 'blackman', 'blackmanharris',
+24                                                  'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss',
+25                                                  'exponential', 'flattop', 'gaussian', 'general_cosine',
+26                                                  'general_gaussian', 'general_hamming', 'hamming', 'hann',
+27                                                  'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey']
+28                                          ]] = None,
+29                   cumulative: bool = False):
+30    """
+31    Resample the spectrum using Non-uniform discrete fourier transform.
+32
+33    The x-axis of the result will be uniform. The corresponding y-values
+34    will be calculated with NUDFT and inverse FFT.
+35
+36    Args:
+37        spe: internal use only
+38        x_range: optional. Defaults to (0, 4000).
+39            The x_range of the new spectrum.
+40        xnew_bins: optional. Defaults to 100.
+41            Number of bins of the new spectrum
+42        window: optional, Defaults to None.
+43            The window to be used for lowpass filter. If None 'blackmanharris' is used.
+44            If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+45        cumulative: optional. Defaults to False.
+46            If True, the resultant spectrum will be cumulative and normalized
+47            (in analogy with CDF).
+48
+49    Returns:
+50        (x_values, y_values)
+51    """
+52
+53    x_new = np.linspace(x_range[0], x_range[1], xnew_bins, endpoint=False)
+54    x = spe.x
+55    y = spe.y
+56    x = np.array(x)
+57    x_range = (np.min(x_range), np.max(x_range))
+58    y = y[(x >= x_range[0]) & (x < x_range[1])]
+59    x = x[(x >= x_range[0]) & (x < x_range[1])]
+60
+61    w = (x-x_range[0])/(x_range[1]-x_range[0])*np.pi*2
+62    x -= x_range[0]
+63
+64    k = np.arange(xnew_bins)
+65
+66    Y_new = np.sum([yi*np.exp(-1j*wi*k) for yi, wi in zip(y, w)], axis=0)
+67
+68    if window is None:
+69        window = 'blackmanharris'
+70
+71    if hasattr(window, '__call__'):
+72        h = (window(len(Y_new)*2))[len(Y_new):]  # type: ignore
+73    else:
+74        h = signal.windows.get_window(window, len(Y_new)*2)[len(Y_new):]
+75    Y_new *= h
+76
+77    y_new = fft.irfft(Y_new, n=xnew_bins)
+78    if cumulative:
+79        y_new = np.cumsum(y_new)
+80        y_new /= y_new[-1]
+81    return x_new, y_new
+
+ + +

Resample the spectrum using Non-uniform discrete fourier transform.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with NUDFT and inverse FFT.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • window: optional, Defaults to None. +The window to be used for lowpass filter. If None 'blackmanharris' is used. +If no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +
Returns:
+ +
+

(x_values, y_values)

+
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_NUDFT_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, window=None, cumulative: bool = False): + + + +
+ + 
 84@add_spectrum_filter
+ 85@validate_call(config=dict(arbitrary_types_allowed=True))
+ 86def resample_NUDFT_filter(old_spe: Spectrum,
+ 87                          new_spe: Spectrum, /,
+ 88                          x_range: Tuple[float, float] = (0, 4000),
+ 89                          xnew_bins: PositiveInt = 100,
+ 90                          window=None,
+ 91                          cumulative: bool = False):
+ 92    """
+ 93    Resample the spectrum using Non-uniform discrete fourier transform.
+ 94
+ 95    The x-axis of the result will be uniform. The corresponding y-values
+ 96    will be calculated with NUDFT and inverse FFT.
+ 97
+ 98    Args:
+ 99        old_spe: internal use only
+100        new_spe: internal use only
+101        x_range: optional. Defaults to (0, 4000).
+102            The x_range of the new spectrum.
+103        xnew_bins: optional. Defaults to 100.
+104            Number of bins of the new spectrum
+105        window: optional, Defaults to None.
+106            The window to be used for lowpass filter. If None 'blackmanharris' is used.
+107            If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+108        cumulative: optional. Defaults to False.
+109            If True, the resultant spectrum will be cumulative and normalized
+110            (in analogy with CDF).
+111
+112    Returns: modified Spectrum
+113    """
+114    new_spe.x, new_spe.y = resample_NUDFT(old_spe,
+115                                          x_range=x_range,
+116                                          xnew_bins=xnew_bins,
+117                                          window=window,
+118                                          cumulative=cumulative)
+
+ + +

Resample the spectrum using Non-uniform discrete fourier transform.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with NUDFT and inverse FFT.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • window: optional, Defaults to None. +The window to be used for lowpass filter. If None 'blackmanharris' is used. +If no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_spline( spe: ramanchada2.spectrum.spectrum.Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip', interp_kw_args: Optional[Dict] = None, cumulative: bool = False): + + + +
+ + 
121@add_spectrum_method
+122@validate_call(config=dict(arbitrary_types_allowed=True))
+123def resample_spline(spe: Spectrum, /,
+124                    x_range: Tuple[float, float] = (0, 4000),
+125                    xnew_bins: PositiveInt = 100,
+126                    spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',
+127                    interp_kw_args: Optional[Dict] = None,
+128                    cumulative: bool = False):
+129    """
+130    Resample the spectrum using spline interpolation.
+131
+132    The x-axis of the result will be uniform. The corresponding y-values
+133    will be calculated with spline interpolation.
+134
+135    Args:
+136        spe: internal use only
+137        x_range: optional. Defaults to (0, 4000).
+138            The x_range of the new spectrum.
+139        xnew_bins: optional. Defaults to 100.
+140            Number of bins of the new spectrum
+141        spline: optional, Defaults to 'pchip'.
+142            Name of the spline funcion to be used.
+143        cumulative: optional. Defaults to False.
+144            If True, the resultant spectrum will be cumulative and normalized
+145            (in analogy with CDF).
+146
+147    Returns:
+148        (x_values, y_values)
+149    """
+150
+151    kw_args: Dict[str, Any] = {}
+152    if spline == 'pchip':
+153        spline_fn = PchipInterpolator
+154        kw_args['extrapolate'] = False
+155    elif spline == 'akima':
+156        spline_fn = Akima1DInterpolator
+157    elif spline == 'makima':
+158        spline_fn = Akima1DInterpolator
+159        kw_args['method'] = 'makima'
+160    elif spline == 'cubic_spline':
+161        spline_fn = CubicSpline
+162        kw_args['bc_type'] = 'natural'
+163        kw_args['extrapolate'] = False
+164
+165    if interp_kw_args is not None:
+166        kw_args.update(interp_kw_args)
+167
+168    x_new = np.linspace(x_range[0], x_range[1], xnew_bins, endpoint=False)
+169    y_new = spline_fn(spe.x, spe.y, **kw_args)(x_new)
+170
+171    y_new[np.isnan(y_new)] = 0
+172    if cumulative:
+173        y_new = np.cumsum(y_new)
+174        y_new /= y_new[-1]
+175
+176    return x_new, y_new
+
+ + +

Resample the spectrum using spline interpolation.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with spline interpolation.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • spline: optional, Defaults to 'pchip'. +Name of the spline funcion to be used.
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +
Returns:
+ +
+

(x_values, y_values)

+
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_spline_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip', interp_kw_args: Optional[Dict] = None, cumulative: bool = False): + + + +
+ + 
179@add_spectrum_filter
+180@validate_call(config=dict(arbitrary_types_allowed=True))
+181def resample_spline_filter(old_spe: Spectrum,
+182                           new_spe: Spectrum, /,
+183                           x_range: Tuple[float, float] = (0, 4000),
+184                           xnew_bins: PositiveInt = 100,
+185                           spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',
+186                           interp_kw_args: Optional[Dict] = None,
+187                           cumulative: bool = False):
+188    """
+189    Resample the spectrum using spline interpolation.
+190
+191    The x-axis of the result will be uniform. The corresponding y-values
+192    will be calculated with spline interpolation.
+193
+194    Args:
+195        old_spe: internal use only
+196        new_spe: internal use only
+197        x_range: optional. Defaults to (0, 4000).
+198            The x_range of the new spectrum.
+199        xnew_bins: optional. Defaults to 100.
+200            Number of bins of the new spectrum
+201        spline: optional, Defaults to 'pchip'.
+202            Name of the spline funcion to be used.
+203        cumulative: optional. Defaults to False.
+204            If True, the resultant spectrum will be cumulative and normalized
+205            (in analogy with CDF).
+206
+207    Returns: modified Spectrum
+208    """
+209    new_spe.x, new_spe.y = resample_spline(old_spe,
+210                                           x_range=x_range,
+211                                           xnew_bins=xnew_bins,
+212                                           spline=spline,
+213                                           interp_kw_args=interp_kw_args,
+214                                           cumulative=cumulative)
+
+ + +

Resample the spectrum using spline interpolation.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with spline interpolation.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • spline: optional, Defaults to 'pchip'. +Name of the spline funcion to be used.
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/sharpen_lines.html b/ramanchada2/spectrum/filters/sharpen_lines.html new file mode 100644 index 00000000..6a6eccb6 --- /dev/null +++ b/ramanchada2/spectrum/filters/sharpen_lines.html @@ -0,0 +1,593 @@ + + + + + + + ramanchada2.spectrum.filters.sharpen_lines API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.sharpen_lines

+ + + + + + + 
  1from typing import List
+  2
+  3import emd
+  4import numpy as np
+  5from pydantic import PositiveInt, confloat, validate_call
+  6from scipy import fft, signal
+  7
+  8from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+  9
+ 10from ..spectrum import Spectrum
+ 11
+ 12
+ 13@add_spectrum_filter
+ 14@validate_call(config=dict(arbitrary_types_allowed=True))
+ 15def derivative_sharpening(old_spe: Spectrum,
+ 16                          new_spe: Spectrum, /,
+ 17                          filter_fraction: confloat(gt=0, le=1) = .6,  # type: ignore
+ 18                          sig_width: confloat(ge=0) = .25,  # type: ignore
+ 19                          der2_factor: float = 1,
+ 20                          der4_factor: float = .1
+ 21                          ):
+ 22    """
+ 23    Derivative-based sharpening.
+ 24
+ 25    Sharpen the spectrum subtracting second derivative and add fourth derivative.
+ 26
+ 27    Args:
+ 28        old_spe: internal use only
+ 29        new_spe: internal use only
+ 30        filter_fraction `float` in (0; 1]: Default is 0.6
+ 31            Depth of filtration
+ 32        signal_width: The width of features to be enhanced in sample count
+ 33        der2_factor: Second derivative scaling factor
+ 34        der4_factor: Fourth derivative scaling factor
+ 35
+ 36    Returns: modified Spectrum
+ 37    """
+ 38    leny = len(old_spe.y)
+ 39    Y = fft.rfft(old_spe.y, n=leny)
+ 40    h = signal.windows.hann(int(len(Y)*filter_fraction))
+ 41    h = h[(len(h))//2-1:]
+ 42    h = np.concatenate((h, np.zeros(len(Y)-len(h))))
+ 43    der = np.arange(len(Y))
+ 44    der = 1j*np.pi*der/der[-1]
+ 45    Y *= h
+ 46    Y2 = Y*der**2
+ 47    Y4 = Y2*der**2
+ 48    y0 = fft.irfft(Y, n=leny)
+ 49    y2 = fft.irfft(Y2, n=leny)
+ 50    y4 = fft.irfft(Y4, n=leny)
+ 51    new_spe.y = y0 - y2/sig_width**2*der2_factor + y4/sig_width**4*der4_factor
+ 52
+ 53
+ 54@add_spectrum_filter
+ 55@validate_call(config=dict(arbitrary_types_allowed=True))
+ 56def hht_sharpening(old_spe: Spectrum,
+ 57                   new_spe: Spectrum, /,
+ 58                   movmin=100
+ 59                   ):
+ 60    """
+ 61    Hilbert-Huang based sharpening.
+ 62
+ 63    In order to reduce the overshooting, moving minimum is subtracted from the result
+ 64
+ 65    Args:
+ 66        old_spe: internal use only
+ 67        new_spe: internal use only
+ 68        movmin: optional. Default is 100
+ 69            Window size for moving minimum
+ 70
+ 71    Returns: modified Spectrum
+ 72    """
+ 73    imfs = emd.sift.sift(old_spe.y).T
+ 74    freq_list = list()
+ 75    for ansig in signal.hilbert(imfs):
+ 76        freq_list.append(emd.spectra.freq_from_phase(
+ 77            emd.spectra.phase_from_complex_signal(ansig, ret_phase='unwrapped'), 1))
+ 78    freq = np.array(freq_list)
+ 79    freq[freq < 0] = 0
+ 80    freq[np.isnan(freq)] = 0
+ 81    imfsall = imfs.copy()
+ 82    imfsall[np.isnan(imfsall)] = 0
+ 83    imfsall[freq > .3] = 0
+ 84    imfsall *= freq**.5
+ 85    ynew = np.sum(imfsall, axis=0)
+ 86    new_spe.y = ynew
+ 87    new_spe.y = new_spe.subtract_moving_minimum(movmin).normalize().y  # type: ignore
+ 88    new_spe.y = new_spe.y * old_spe.y.max() + old_spe.y.min()
+ 89
+ 90
+ 91@add_spectrum_filter
+ 92@validate_call(config=dict(arbitrary_types_allowed=True))
+ 93def hht_sharpening_chain(old_spe: Spectrum,
+ 94                         new_spe: Spectrum, /,
+ 95                         movmin: List[PositiveInt] = [150, 50]
+ 96                         ):
+ 97    """
+ 98    Hilbert-Huang based chain sharpening.
+ 99
+100    Sequence of Hilbert-Huang sharpening procedures are performed.
+101
+102    Args:
+103        old_spe: internal use only
+104        new_spe: internal use only
+105        movmin: List[int], optional. Default is [150, 50]
+106            The numer of values in the list defines how many iterations
+107            of HHT_sharpening will be performed and the values define
+108            the moving minimum window sizes for the corresponding operations.
+109
+110    Returns: modified Spectrum
+111    """
+112    spe = old_spe
+113    for mm in movmin:
+114        spe = spe.hht_sharpening(movmin=mm)  # type: ignore
+115    new_spe.y = spe.y
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + derivative_sharpening( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, filter_fraction: typing.Annotated[float, None, Interval(gt=0, ge=None, lt=None, le=1), None, None] = 0.6, sig_width: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=None), None, None] = 0.25, der2_factor: float = 1, der4_factor: float = 0.1): + + + +
+ + 
14@add_spectrum_filter
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def derivative_sharpening(old_spe: Spectrum,
+17                          new_spe: Spectrum, /,
+18                          filter_fraction: confloat(gt=0, le=1) = .6,  # type: ignore
+19                          sig_width: confloat(ge=0) = .25,  # type: ignore
+20                          der2_factor: float = 1,
+21                          der4_factor: float = .1
+22                          ):
+23    """
+24    Derivative-based sharpening.
+25
+26    Sharpen the spectrum subtracting second derivative and add fourth derivative.
+27
+28    Args:
+29        old_spe: internal use only
+30        new_spe: internal use only
+31        filter_fraction `float` in (0; 1]: Default is 0.6
+32            Depth of filtration
+33        signal_width: The width of features to be enhanced in sample count
+34        der2_factor: Second derivative scaling factor
+35        der4_factor: Fourth derivative scaling factor
+36
+37    Returns: modified Spectrum
+38    """
+39    leny = len(old_spe.y)
+40    Y = fft.rfft(old_spe.y, n=leny)
+41    h = signal.windows.hann(int(len(Y)*filter_fraction))
+42    h = h[(len(h))//2-1:]
+43    h = np.concatenate((h, np.zeros(len(Y)-len(h))))
+44    der = np.arange(len(Y))
+45    der = 1j*np.pi*der/der[-1]
+46    Y *= h
+47    Y2 = Y*der**2
+48    Y4 = Y2*der**2
+49    y0 = fft.irfft(Y, n=leny)
+50    y2 = fft.irfft(Y2, n=leny)
+51    y4 = fft.irfft(Y4, n=leny)
+52    new_spe.y = y0 - y2/sig_width**2*der2_factor + y4/sig_width**4*der4_factor
+
+ + +

Derivative-based sharpening.

+ +

Sharpen the spectrum subtracting second derivative and add fourth derivative.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • filter_fraction float in (0; 1]: Default is 0.6 +Depth of filtration
    • +
  • signal_width: The width of features to be enhanced in sample count
    • +
  • der2_factor: Second derivative scaling factor
    • +
  • der4_factor: Fourth derivative scaling factor
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + hht_sharpening( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, movmin=100): + + + +
+ + 
55@add_spectrum_filter
+56@validate_call(config=dict(arbitrary_types_allowed=True))
+57def hht_sharpening(old_spe: Spectrum,
+58                   new_spe: Spectrum, /,
+59                   movmin=100
+60                   ):
+61    """
+62    Hilbert-Huang based sharpening.
+63
+64    In order to reduce the overshooting, moving minimum is subtracted from the result
+65
+66    Args:
+67        old_spe: internal use only
+68        new_spe: internal use only
+69        movmin: optional. Default is 100
+70            Window size for moving minimum
+71
+72    Returns: modified Spectrum
+73    """
+74    imfs = emd.sift.sift(old_spe.y).T
+75    freq_list = list()
+76    for ansig in signal.hilbert(imfs):
+77        freq_list.append(emd.spectra.freq_from_phase(
+78            emd.spectra.phase_from_complex_signal(ansig, ret_phase='unwrapped'), 1))
+79    freq = np.array(freq_list)
+80    freq[freq < 0] = 0
+81    freq[np.isnan(freq)] = 0
+82    imfsall = imfs.copy()
+83    imfsall[np.isnan(imfsall)] = 0
+84    imfsall[freq > .3] = 0
+85    imfsall *= freq**.5
+86    ynew = np.sum(imfsall, axis=0)
+87    new_spe.y = ynew
+88    new_spe.y = new_spe.subtract_moving_minimum(movmin).normalize().y  # type: ignore
+89    new_spe.y = new_spe.y * old_spe.y.max() + old_spe.y.min()
+
+ + +

Hilbert-Huang based sharpening.

+ +

In order to reduce the overshooting, moving minimum is subtracted from the result

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • movmin: optional. Default is 100 +Window size for moving minimum
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + hht_sharpening_chain( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, movmin: List[Annotated[int, Gt(gt=0)]] = [150, 50]): + + + +
+ + 
 92@add_spectrum_filter
+ 93@validate_call(config=dict(arbitrary_types_allowed=True))
+ 94def hht_sharpening_chain(old_spe: Spectrum,
+ 95                         new_spe: Spectrum, /,
+ 96                         movmin: List[PositiveInt] = [150, 50]
+ 97                         ):
+ 98    """
+ 99    Hilbert-Huang based chain sharpening.
+100
+101    Sequence of Hilbert-Huang sharpening procedures are performed.
+102
+103    Args:
+104        old_spe: internal use only
+105        new_spe: internal use only
+106        movmin: List[int], optional. Default is [150, 50]
+107            The numer of values in the list defines how many iterations
+108            of HHT_sharpening will be performed and the values define
+109            the moving minimum window sizes for the corresponding operations.
+110
+111    Returns: modified Spectrum
+112    """
+113    spe = old_spe
+114    for mm in movmin:
+115        spe = spe.hht_sharpening(movmin=mm)  # type: ignore
+116    new_spe.y = spe.y
+
+ + +

Hilbert-Huang based chain sharpening.

+ +

Sequence of Hilbert-Huang sharpening procedures are performed.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • movmin: List[int], optional. Default is [150, 50] +The numer of values in the list defines how many iterations +of HHT_sharpening will be performed and the values define +the moving minimum window sizes for the corresponding operations.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/smoothing.html b/ramanchada2/spectrum/filters/smoothing.html new file mode 100644 index 00000000..f76576c4 --- /dev/null +++ b/ramanchada2/spectrum/filters/smoothing.html @@ -0,0 +1,401 @@ + + + + + + + ramanchada2.spectrum.filters.smoothing API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.smoothing

+ + + + + + + 
 1from typing import Literal
+ 2
+ 3import numpy as np
+ 4import statsmodels.api as sm
+ 5from pydantic import validate_call
+ 6from scipy.ndimage import gaussian_filter1d
+ 7from scipy.signal import medfilt, savgol_filter, wiener
+ 8from scipy.signal.windows import boxcar
+ 9
+10from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+11
+12from ..spectrum import Spectrum
+13
+14
+15@add_spectrum_filter
+16@validate_call(config=dict(arbitrary_types_allowed=True))
+17def smoothing_RC1(old_spe: Spectrum,
+18                  new_spe: Spectrum, /, *args,
+19                  method: Literal['savgol', 'sg',
+20                                  'wiener',
+21                                  'median',
+22                                  'gauss', 'gaussian',
+23                                  'lowess',
+24                                  'boxcar'],
+25                  **kwargs):
+26    """
+27    Smooth the spectrum.
+28
+29    The spectrum will be smoothed using the specified filter.
+30    This method is inherited from ramanchada1 for compatibility reasons.
+31
+32    Args:
+33        old_spe: internal use only
+34        new_spe: internal use only
+35        method: method to be used
+36        **kwargs: keyword arguments to be passed to the selected method
+37
+38    Returns: modified Spectrum
+39    """
+40    if method == 'savgol' or method == 'sg':
+41        new_spe.y = savgol_filter(old_spe.y, **kwargs)  # window_length, polyorder
+42    elif method == 'wiener':
+43        new_spe.y = wiener(old_spe.y, **kwargs)
+44    elif method == 'gaussian' or method == 'gauss':
+45        new_spe.y = gaussian_filter1d(old_spe.y, **kwargs)  # sigma
+46    elif method == 'median':
+47        new_spe.y = medfilt(old_spe.y, **kwargs)
+48    elif method == 'lowess':
+49        kw = dict(span=11)
+50        kw.update(kwargs)
+51        x = np.linspace(0, 1, len(old_spe.y))
+52        new_spe.y = sm.nonparametric.lowess(old_spe.y, x, frac=(5*kw['span'] / len(old_spe.y)), return_sorted=False)
+53    elif method == 'boxcar':
+54        kw = dict(box_pts=11)
+55        kw.update(kwargs)
+56        box = boxcar(**kwargs, sym=True)
+57        new_spe.y = np.convolve(old_spe.y, box, mode='same')
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + smoothing_RC1( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, *args, method: Literal['savgol', 'sg', 'wiener', 'median', 'gauss', 'gaussian', 'lowess', 'boxcar'], **kwargs): + + + +
+ + 
16@add_spectrum_filter
+17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def smoothing_RC1(old_spe: Spectrum,
+19                  new_spe: Spectrum, /, *args,
+20                  method: Literal['savgol', 'sg',
+21                                  'wiener',
+22                                  'median',
+23                                  'gauss', 'gaussian',
+24                                  'lowess',
+25                                  'boxcar'],
+26                  **kwargs):
+27    """
+28    Smooth the spectrum.
+29
+30    The spectrum will be smoothed using the specified filter.
+31    This method is inherited from ramanchada1 for compatibility reasons.
+32
+33    Args:
+34        old_spe: internal use only
+35        new_spe: internal use only
+36        method: method to be used
+37        **kwargs: keyword arguments to be passed to the selected method
+38
+39    Returns: modified Spectrum
+40    """
+41    if method == 'savgol' or method == 'sg':
+42        new_spe.y = savgol_filter(old_spe.y, **kwargs)  # window_length, polyorder
+43    elif method == 'wiener':
+44        new_spe.y = wiener(old_spe.y, **kwargs)
+45    elif method == 'gaussian' or method == 'gauss':
+46        new_spe.y = gaussian_filter1d(old_spe.y, **kwargs)  # sigma
+47    elif method == 'median':
+48        new_spe.y = medfilt(old_spe.y, **kwargs)
+49    elif method == 'lowess':
+50        kw = dict(span=11)
+51        kw.update(kwargs)
+52        x = np.linspace(0, 1, len(old_spe.y))
+53        new_spe.y = sm.nonparametric.lowess(old_spe.y, x, frac=(5*kw['span'] / len(old_spe.y)), return_sorted=False)
+54    elif method == 'boxcar':
+55        kw = dict(box_pts=11)
+56        kw.update(kwargs)
+57        box = boxcar(**kwargs, sym=True)
+58        new_spe.y = np.convolve(old_spe.y, box, mode='same')
+
+ + +

Smooth the spectrum.

+ +

The spectrum will be smoothed using the specified filter. +This method is inherited from ramanchada1 for compatibility reasons.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • method: method to be used
    • +
  • **kwargs: keyword arguments to be passed to the selected method
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/filters/trim_axes.html b/ramanchada2/spectrum/filters/trim_axes.html new file mode 100644 index 00000000..045f9f7a --- /dev/null +++ b/ramanchada2/spectrum/filters/trim_axes.html @@ -0,0 +1,366 @@ + + + + + + + ramanchada2.spectrum.filters.trim_axes API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.filters.trim_axes

+ + + + + + + 
 1from typing import Literal, Tuple
+ 2
+ 3import numpy as np
+ 4from pydantic import validate_call
+ 5
+ 6from ramanchada2.misc.spectrum_deco import add_spectrum_filter
+ 7
+ 8from ..spectrum import Spectrum
+ 9
+10
+11@add_spectrum_filter
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def trim_axes(old_spe: Spectrum,
+14              new_spe: Spectrum, /,
+15              method: Literal['x-axis', 'bins'],
+16              boundaries: Tuple[float, float],
+17              ):
+18    """
+19    Trim axes of the spectrum.
+20
+21    Args:
+22        old_spe: internal use only
+23        new_spe: internal use only
+24        method: 'x-axis' or 'bins'
+25            If 'x-axis' boundaries will be interpreted as x-axis values.
+26            If 'bins' boundaries will be interpreted as indices.
+27        boundaries: lower and upper boundary for the trimming.
+28
+29    Returns: modified Spectrum
+30    """
+31    if method == 'bins':
+32        lb = int(boundaries[0])
+33        rb = int(boundaries[1])
+34    elif method == 'x-axis':
+35        lb = int(np.argmin(np.abs(old_spe.x - boundaries[0])))
+36        rb = int(np.argmin(np.abs(old_spe.x - boundaries[1])))
+37    new_spe.x = old_spe.x[lb:rb]
+38    new_spe.y = old_spe.y[lb:rb]
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + trim_axes( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, method: Literal['x-axis', 'bins'], boundaries: Tuple[float, float]): + + + +
+ + 
12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def trim_axes(old_spe: Spectrum,
+15              new_spe: Spectrum, /,
+16              method: Literal['x-axis', 'bins'],
+17              boundaries: Tuple[float, float],
+18              ):
+19    """
+20    Trim axes of the spectrum.
+21
+22    Args:
+23        old_spe: internal use only
+24        new_spe: internal use only
+25        method: 'x-axis' or 'bins'
+26            If 'x-axis' boundaries will be interpreted as x-axis values.
+27            If 'bins' boundaries will be interpreted as indices.
+28        boundaries: lower and upper boundary for the trimming.
+29
+30    Returns: modified Spectrum
+31    """
+32    if method == 'bins':
+33        lb = int(boundaries[0])
+34        rb = int(boundaries[1])
+35    elif method == 'x-axis':
+36        lb = int(np.argmin(np.abs(old_spe.x - boundaries[0])))
+37        rb = int(np.argmin(np.abs(old_spe.x - boundaries[1])))
+38    new_spe.x = old_spe.x[lb:rb]
+39    new_spe.y = old_spe.y[lb:rb]
+
+ + +

Trim axes of the spectrum.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • method: 'x-axis' or 'bins' +If 'x-axis' boundaries will be interpreted as x-axis values. +If 'bins' boundaries will be interpreted as indices.
    • +
  • boundaries: lower and upper boundary for the trimming.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/multimap.html b/ramanchada2/spectrum/multimap.html new file mode 100644 index 00000000..b7d6b90d --- /dev/null +++ b/ramanchada2/spectrum/multimap.html @@ -0,0 +1,264 @@ + + + + + + + ramanchada2.spectrum.multimap API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.multimap

+ + + + + + + 
1from .spc import read_map_spc
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/multimap/spc.html b/ramanchada2/spectrum/multimap/spc.html new file mode 100644 index 00000000..583e5f50 --- /dev/null +++ b/ramanchada2/spectrum/multimap/spc.html @@ -0,0 +1,390 @@ + + + + + + + ramanchada2.spectrum.multimap.spc API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.multimap.spc

+ + + + + + + 
 1from collections import namedtuple
+ 2from typing import Dict
+ 3
+ 4import spc_io
+ 5
+ 6from ...misc.types import SpeMetadataModel
+ 7from ..spectrum import Spectrum
+ 8
+ 9SPCMapCoordinates = namedtuple('SPCMapCoordinates', ['z', 'w'])
+10
+11
+12def read_map_spc(filename: str) -> Dict[SPCMapCoordinates, Spectrum]:
+13    spc = spc_io.SPC.from_bytes_io(open(filename, 'rb'))
+14
+15    ret = dict()
+16
+17    spc_meta = {k.strip(): v.strip() for k, v in spc.log_book.text.items()}
+18    for meas in spc:
+19        spe_meta = {}
+20        spe_meta.update(spc_meta)
+21        spe_meta.update(dict(w=meas.w, z=meas.z))
+22        ret[SPCMapCoordinates(w=meas.w, z=meas.z)] = Spectrum(x=meas.xarray, y=meas.yarray,
+23                                                              metadata=SpeMetadataModel.model_validate(spe_meta))
+24    return ret
+
+ + +
+
+
+ + class + SPCMapCoordinates(builtins.tuple): + + +
+ + +

SPCMapCoordinates(z, w)

+
+ + +
+
+ + SPCMapCoordinates(z, w) + + +
+ + +

Create new instance of SPCMapCoordinates(z, w)

+
+ + +
+
+
+ z + + +
+ + +

Alias for field number 0

+
+ + +
+
+
+ w + + +
+ + +

Alias for field number 1

+
+ + +
+
+
+ +
+ + def + read_map_spc( filename: str) -> Dict[SPCMapCoordinates, ramanchada2.spectrum.spectrum.Spectrum]: + + + +
+ + 
13def read_map_spc(filename: str) -> Dict[SPCMapCoordinates, Spectrum]:
+14    spc = spc_io.SPC.from_bytes_io(open(filename, 'rb'))
+15
+16    ret = dict()
+17
+18    spc_meta = {k.strip(): v.strip() for k, v in spc.log_book.text.items()}
+19    for meas in spc:
+20        spe_meta = {}
+21        spe_meta.update(spc_meta)
+22        spe_meta.update(dict(w=meas.w, z=meas.z))
+23        ret[SPCMapCoordinates(w=meas.w, z=meas.z)] = Spectrum(x=meas.xarray, y=meas.yarray,
+24                                                              metadata=SpeMetadataModel.model_validate(spe_meta))
+25    return ret
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/peaks.html b/ramanchada2/spectrum/peaks.html new file mode 100644 index 00000000..686da324 --- /dev/null +++ b/ramanchada2/spectrum/peaks.html @@ -0,0 +1,276 @@ + + + + + + + ramanchada2.spectrum.peaks API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.peaks

+ + + + + + + 
 1#!/usr/bin/env python3
+ 2
+ 3import os
+ 4import glob
+ 5
+ 6__all__ = [
+ 7    os.path.basename(f)[:-3]
+ 8    for f in glob.glob(os.path.dirname(__file__)+"/*.py")
+ 9    if os.path.isfile(f) and not os.path.basename(f).startswith('_')
+10]
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/peaks/find_peaks.html b/ramanchada2/spectrum/peaks/find_peaks.html new file mode 100644 index 00000000..d2782d51 --- /dev/null +++ b/ramanchada2/spectrum/peaks/find_peaks.html @@ -0,0 +1,768 @@ + + + + + + + ramanchada2.spectrum.peaks.find_peaks API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.peaks.find_peaks

+ + + + + + + 
  1from typing import List, Literal, Tuple, Union
+  2
+  3import numpy as np
+  4from pydantic import (NonNegativeFloat, NonNegativeInt, PositiveInt,
+  5                      validate_call)
+  6from scipy import signal
+  7from scipy.signal import find_peaks_cwt
+  8
+  9from ramanchada2.misc.spectrum_deco import (add_spectrum_filter,
+ 10                                            add_spectrum_method)
+ 11from ramanchada2.misc.types.peak_candidates import ListPeakCandidateMultiModel
+ 12
+ 13from ..spectrum import Spectrum
+ 14
+ 15
+ 16def peak_boundaries(spe, wlen, width, prominence):
+ 17    peaks = signal.find_peaks(spe.y, prominence=prominence, width=width, wlen=wlen)
+ 18    larr = peaks[1]['left_bases'][:]
+ 19    rarr = peaks[1]['right_bases'][:]
+ 20    lb = 0
+ 21    lbounds = list()
+ 22    rbounds = list()
+ 23    while len(larr):
+ 24        lbargmin = np.argmin(larr)
+ 25        lb = larr[lbargmin]
+ 26        rb = rarr[lbargmin]
+ 27        while True:
+ 28            group_bool = larr < rb
+ 29            if group_bool.any():
+ 30                rb = np.max(rarr[group_bool])
+ 31                rarr = rarr[~group_bool]
+ 32                larr = larr[~group_bool]
+ 33                continue
+ 34            break
+ 35        lbounds.append(lb)
+ 36        rbounds.append(rb)
+ 37    return np.array(list(zip(lbounds, rbounds)))
+ 38
+ 39
+ 40@add_spectrum_method
+ 41@validate_call(config=dict(arbitrary_types_allowed=True))
+ 42def find_peak_multipeak(
+ 43        spe: Spectrum, /,
+ 44        prominence: Union[NonNegativeFloat, None] = None,
+ 45        wlen: Union[NonNegativeInt, None] = None,
+ 46        width: Union[int, Tuple[int, int], None] = None,
+ 47        hht_chain: Union[List[PositiveInt], None] = None,
+ 48        bgm_kwargs={},
+ 49        sharpening: Union[Literal['hht'], None] = None,
+ 50        strategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo'
+ 51        ) -> ListPeakCandidateMultiModel:
+ 52    """
+ 53    Find groups of peaks in spectrum.
+ 54
+ 55    Args:
+ 56        spe: internal use only
+ 57        prominence: Optional. Defaults to None
+ 58            If None the prominence value will be `spe.y_nose`. Reasonable value for
+ 59            promience is `const * spe.y_noise_MAD`.
+ 60        wlen: optional. Defaults to None.
+ 61            wlen value used in `scipy.signal.find_peaks`. If wlen is None, 200 will be used.
+ 62        width: optional. Defaults to None.
+ 63            width value used in `scipy.signal.find_peaks`. If width is None, 2 will be used.
+ 64        hht_chain: optional. Defaults to None.
+ 65            List of hht_chain window sizes. If None, no hht sharpening is performed.
+ 66        bgm_kwargs: kwargs for bayesian_gaussian_mixture
+ 67        sharpening 'hht' or None. Defaults to None.
+ 68            If 'hht' hht sharpening will be performed before finding peaks.
+ 69        strategy: optional. Defauts to 'topo'.
+ 70            Peakfinding method
+ 71
+ 72    Returns:
+ 73        ListPeakCandidateMultiModel: Located peak groups
+ 74    """
+ 75
+ 76    if prominence is None:
+ 77        prominence = spe.y_noise
+ 78    if not wlen:
+ 79        wlen = 200
+ 80    if width is None:
+ 81        width = 2
+ 82
+ 83    if sharpening == 'hht':
+ 84        if hht_chain is None:
+ 85            hht_chain = [20]
+ 86        sharp_spe = spe.hht_sharpening_chain(movmin=hht_chain)  # type: ignore
+ 87    else:
+ 88        sharp_spe = spe
+ 89
+ 90    x_arr = sharp_spe.x
+ 91    y_arr = sharp_spe.y
+ 92
+ 93    def interpolate(x):
+ 94        x1 = int(x)
+ 95        x2 = x1 + 1
+ 96        y1 = x_arr[x1]
+ 97        y2 = x_arr[x2]
+ 98        return (y2-y1)/(x2-x1)*(x-x1)+y1
+ 99
+100    boundaries = peak_boundaries(spe, prominence=prominence, width=width, wlen=wlen)
+101    boundaries = [(li, ri) for li, ri in boundaries if (ri-li) > 4]
+102
+103    peaks, props = signal.find_peaks(y_arr,
+104                                     prominence=prominence,
+105                                     width=width,
+106                                     wlen=wlen)
+107    peak_groups = list()
+108
+109    if strategy in {'bgm', 'bayesian_gaussian_mixture'}:
+110        bgm = sharp_spe.bayesian_gaussian_mixture(**bgm_kwargs)
+111
+112        bgm_peaks = [[mean[0], np.sqrt(cov[0][0]), weight]
+113                     for mean, cov, weight in
+114                     zip(bgm.means_, bgm.covariances_, bgm.weights_)]
+115        bgm_peaks = sorted(bgm_peaks, key=lambda x: x[2], reverse=True)
+116        integral = np.sum(y_arr)
+117        n_peaks = (np.round(bgm.weights_, 2) > 0).sum()
+118        bgm_peaks = bgm_peaks[:n_peaks]
+119
+120        peak_list = list()
+121        for mean, sigma, weight in bgm_peaks:
+122            peak_list.append(dict(amplitude=weight*integral*2/sigma,
+123                                  position=mean,
+124                                  sigma=sigma,
+125                                  ))
+126        for li, ri in boundaries:
+127            peak_group = list()
+128            for peak in peak_list:
+129                if li < peak['position'] < ri:
+130                    peak_group.append(dict(position=peak['position'],
+131                                           amplitude=peak['amplitude'],
+132                                           sigma=peak['sigma'])
+133                                      )
+134            if peak_group:
+135                peak_groups.append(dict(boundaries=(x_arr[li], x_arr[ri]),
+136                                        peaks=peak_group))
+137    elif strategy == 'cwt':
+138        # TODO: cwt_args tbd
+139        peaks = find_peaks_cwt(spe.y, **bgm_kwargs)
+140        peak_list = list()
+141        for peak_index in peaks:
+142            half_max = spe.y[peak_index] / 2.0
+143            left_index = np.where(spe.y[:peak_index] <= half_max)[0][-1]
+144            right_index = np.where(spe.y[peak_index:] <= half_max)[0][0] + peak_index
+145            fwhm = spe.x[right_index] - spe.x[left_index]
+146            # rough sigma estimation based on fwhm
+147            sqrt2ln2 = 2 * np.sqrt(2 * np.log(2))
+148            # print(spe.x[peak_index], spe.y[peak_index], fwhm / sqrt2ln2 )
+149            peak_list.append(dict(amplitude=spe.y[peak_index],
+150                                  position=spe.x[peak_index],
+151                                  sigma=fwhm / sqrt2ln2,
+152                                  fwhm=fwhm))
+153        for li, ri in boundaries:
+154            peak_group = list()
+155            for peak in peak_list:
+156                if li < peak['position'] < ri:
+157                    peak_group.append(dict(position=peak['position'],
+158                                           amplitude=peak['amplitude'],
+159                                           sigma=peak['sigma']))
+160            if peak_group:
+161                peak_groups.append(dict(boundaries=(x_arr[li], x_arr[ri]),
+162                                        peaks=peak_group))
+163    elif strategy == 'topo':
+164        for li, ri in boundaries:
+165            peak_group = list()
+166            x1 = spe.x[li]
+167            x2 = spe.x[ri]
+168            y1 = spe.y[li]
+169            y2 = spe.y[ri]
+170            slope = (y2-y1)/(x2-x1)
+171            intercept = -slope*x1+y1
+172            for peak_i, peak_pos in enumerate(peaks):
+173                if li < peak_pos < ri:
+174                    pos_maximum = x_arr[peak_pos]
+175                    amplitude = props['prominences'][peak_i]
+176                    lwhm = pos_maximum - interpolate(props['left_ips'][peak_i])
+177                    rwhm = interpolate(props['right_ips'][peak_i]) - pos_maximum
+178                    fwhm = lwhm + rwhm
+179                    sigma = fwhm/2.355
+180                    skew = (rwhm-lwhm)/(rwhm+lwhm)
+181                    peak_group.append(dict(position=pos_maximum,
+182                                           amplitude=amplitude,
+183                                           sigma=sigma,
+184                                           skew=skew)
+185                                      )
+186            if peak_group:
+187                peak_groups.append(dict(base_intercept=intercept,
+188                                        base_slope=slope,
+189                                        boundaries=(x_arr[li], x_arr[ri]),
+190                                        peaks=peak_group))
+191
+192    candidates = ListPeakCandidateMultiModel.model_validate(peak_groups)
+193    return candidates
+194
+195
+196@add_spectrum_filter
+197@validate_call(config=dict(arbitrary_types_allowed=True))
+198def find_peak_multipeak_filter(
+199        old_spe: Spectrum,
+200        new_spe: Spectrum, /,
+201        *args, **kwargs):
+202    """
+203    Same as `find_peak_multipeak` but the result is stored as metadata in the returned spectrum.
+204
+205    Args:
+206        old_spe: internal use only
+207        new_spe: internal use only
+208        *args, **kwargs: same as `find_peak_multipeak`
+209    """
+210    res = old_spe.find_peak_multipeak(*args, **kwargs)  # type: ignore
+211    new_spe.result = res.model_dump()
+
+ + +
+
+ +
+ + def + peak_boundaries(spe, wlen, width, prominence): + + + +
+ + 
17def peak_boundaries(spe, wlen, width, prominence):
+18    peaks = signal.find_peaks(spe.y, prominence=prominence, width=width, wlen=wlen)
+19    larr = peaks[1]['left_bases'][:]
+20    rarr = peaks[1]['right_bases'][:]
+21    lb = 0
+22    lbounds = list()
+23    rbounds = list()
+24    while len(larr):
+25        lbargmin = np.argmin(larr)
+26        lb = larr[lbargmin]
+27        rb = rarr[lbargmin]
+28        while True:
+29            group_bool = larr < rb
+30            if group_bool.any():
+31                rb = np.max(rarr[group_bool])
+32                rarr = rarr[~group_bool]
+33                larr = larr[~group_bool]
+34                continue
+35            break
+36        lbounds.append(lb)
+37        rbounds.append(rb)
+38    return np.array(list(zip(lbounds, rbounds)))
+
+ + + + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + find_peak_multipeak( spe: ramanchada2.spectrum.spectrum.Spectrum, /, prominence: Optional[Annotated[float, Ge(ge=0)]] = None, wlen: Optional[Annotated[int, Ge(ge=0)]] = None, width: Union[int, Tuple[int, int], NoneType] = None, hht_chain: Optional[List[Annotated[int, Gt(gt=0)]]] = None, bgm_kwargs={}, sharpening: Optional[Literal['hht']] = None, strategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo') -> ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel: + + + +
+ + 
 41@add_spectrum_method
+ 42@validate_call(config=dict(arbitrary_types_allowed=True))
+ 43def find_peak_multipeak(
+ 44        spe: Spectrum, /,
+ 45        prominence: Union[NonNegativeFloat, None] = None,
+ 46        wlen: Union[NonNegativeInt, None] = None,
+ 47        width: Union[int, Tuple[int, int], None] = None,
+ 48        hht_chain: Union[List[PositiveInt], None] = None,
+ 49        bgm_kwargs={},
+ 50        sharpening: Union[Literal['hht'], None] = None,
+ 51        strategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo'
+ 52        ) -> ListPeakCandidateMultiModel:
+ 53    """
+ 54    Find groups of peaks in spectrum.
+ 55
+ 56    Args:
+ 57        spe: internal use only
+ 58        prominence: Optional. Defaults to None
+ 59            If None the prominence value will be `spe.y_nose`. Reasonable value for
+ 60            promience is `const * spe.y_noise_MAD`.
+ 61        wlen: optional. Defaults to None.
+ 62            wlen value used in `scipy.signal.find_peaks`. If wlen is None, 200 will be used.
+ 63        width: optional. Defaults to None.
+ 64            width value used in `scipy.signal.find_peaks`. If width is None, 2 will be used.
+ 65        hht_chain: optional. Defaults to None.
+ 66            List of hht_chain window sizes. If None, no hht sharpening is performed.
+ 67        bgm_kwargs: kwargs for bayesian_gaussian_mixture
+ 68        sharpening 'hht' or None. Defaults to None.
+ 69            If 'hht' hht sharpening will be performed before finding peaks.
+ 70        strategy: optional. Defauts to 'topo'.
+ 71            Peakfinding method
+ 72
+ 73    Returns:
+ 74        ListPeakCandidateMultiModel: Located peak groups
+ 75    """
+ 76
+ 77    if prominence is None:
+ 78        prominence = spe.y_noise
+ 79    if not wlen:
+ 80        wlen = 200
+ 81    if width is None:
+ 82        width = 2
+ 83
+ 84    if sharpening == 'hht':
+ 85        if hht_chain is None:
+ 86            hht_chain = [20]
+ 87        sharp_spe = spe.hht_sharpening_chain(movmin=hht_chain)  # type: ignore
+ 88    else:
+ 89        sharp_spe = spe
+ 90
+ 91    x_arr = sharp_spe.x
+ 92    y_arr = sharp_spe.y
+ 93
+ 94    def interpolate(x):
+ 95        x1 = int(x)
+ 96        x2 = x1 + 1
+ 97        y1 = x_arr[x1]
+ 98        y2 = x_arr[x2]
+ 99        return (y2-y1)/(x2-x1)*(x-x1)+y1
+100
+101    boundaries = peak_boundaries(spe, prominence=prominence, width=width, wlen=wlen)
+102    boundaries = [(li, ri) for li, ri in boundaries if (ri-li) > 4]
+103
+104    peaks, props = signal.find_peaks(y_arr,
+105                                     prominence=prominence,
+106                                     width=width,
+107                                     wlen=wlen)
+108    peak_groups = list()
+109
+110    if strategy in {'bgm', 'bayesian_gaussian_mixture'}:
+111        bgm = sharp_spe.bayesian_gaussian_mixture(**bgm_kwargs)
+112
+113        bgm_peaks = [[mean[0], np.sqrt(cov[0][0]), weight]
+114                     for mean, cov, weight in
+115                     zip(bgm.means_, bgm.covariances_, bgm.weights_)]
+116        bgm_peaks = sorted(bgm_peaks, key=lambda x: x[2], reverse=True)
+117        integral = np.sum(y_arr)
+118        n_peaks = (np.round(bgm.weights_, 2) > 0).sum()
+119        bgm_peaks = bgm_peaks[:n_peaks]
+120
+121        peak_list = list()
+122        for mean, sigma, weight in bgm_peaks:
+123            peak_list.append(dict(amplitude=weight*integral*2/sigma,
+124                                  position=mean,
+125                                  sigma=sigma,
+126                                  ))
+127        for li, ri in boundaries:
+128            peak_group = list()
+129            for peak in peak_list:
+130                if li < peak['position'] < ri:
+131                    peak_group.append(dict(position=peak['position'],
+132                                           amplitude=peak['amplitude'],
+133                                           sigma=peak['sigma'])
+134                                      )
+135            if peak_group:
+136                peak_groups.append(dict(boundaries=(x_arr[li], x_arr[ri]),
+137                                        peaks=peak_group))
+138    elif strategy == 'cwt':
+139        # TODO: cwt_args tbd
+140        peaks = find_peaks_cwt(spe.y, **bgm_kwargs)
+141        peak_list = list()
+142        for peak_index in peaks:
+143            half_max = spe.y[peak_index] / 2.0
+144            left_index = np.where(spe.y[:peak_index] <= half_max)[0][-1]
+145            right_index = np.where(spe.y[peak_index:] <= half_max)[0][0] + peak_index
+146            fwhm = spe.x[right_index] - spe.x[left_index]
+147            # rough sigma estimation based on fwhm
+148            sqrt2ln2 = 2 * np.sqrt(2 * np.log(2))
+149            # print(spe.x[peak_index], spe.y[peak_index], fwhm / sqrt2ln2 )
+150            peak_list.append(dict(amplitude=spe.y[peak_index],
+151                                  position=spe.x[peak_index],
+152                                  sigma=fwhm / sqrt2ln2,
+153                                  fwhm=fwhm))
+154        for li, ri in boundaries:
+155            peak_group = list()
+156            for peak in peak_list:
+157                if li < peak['position'] < ri:
+158                    peak_group.append(dict(position=peak['position'],
+159                                           amplitude=peak['amplitude'],
+160                                           sigma=peak['sigma']))
+161            if peak_group:
+162                peak_groups.append(dict(boundaries=(x_arr[li], x_arr[ri]),
+163                                        peaks=peak_group))
+164    elif strategy == 'topo':
+165        for li, ri in boundaries:
+166            peak_group = list()
+167            x1 = spe.x[li]
+168            x2 = spe.x[ri]
+169            y1 = spe.y[li]
+170            y2 = spe.y[ri]
+171            slope = (y2-y1)/(x2-x1)
+172            intercept = -slope*x1+y1
+173            for peak_i, peak_pos in enumerate(peaks):
+174                if li < peak_pos < ri:
+175                    pos_maximum = x_arr[peak_pos]
+176                    amplitude = props['prominences'][peak_i]
+177                    lwhm = pos_maximum - interpolate(props['left_ips'][peak_i])
+178                    rwhm = interpolate(props['right_ips'][peak_i]) - pos_maximum
+179                    fwhm = lwhm + rwhm
+180                    sigma = fwhm/2.355
+181                    skew = (rwhm-lwhm)/(rwhm+lwhm)
+182                    peak_group.append(dict(position=pos_maximum,
+183                                           amplitude=amplitude,
+184                                           sigma=sigma,
+185                                           skew=skew)
+186                                      )
+187            if peak_group:
+188                peak_groups.append(dict(base_intercept=intercept,
+189                                        base_slope=slope,
+190                                        boundaries=(x_arr[li], x_arr[ri]),
+191                                        peaks=peak_group))
+192
+193    candidates = ListPeakCandidateMultiModel.model_validate(peak_groups)
+194    return candidates
+
+ + +

Find groups of peaks in spectrum.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • prominence: Optional. Defaults to None +If None the prominence value will be spe.y_nose. Reasonable value for +promience is const * spe.y_noise_MAD.
    • +
  • wlen: optional. Defaults to None. +wlen value used in scipy.signal.find_peaks. If wlen is None, 200 will be used.
    • +
  • width: optional. Defaults to None. +width value used in scipy.signal.find_peaks. If width is None, 2 will be used.
    • +
  • hht_chain: optional. Defaults to None. +List of hht_chain window sizes. If None, no hht sharpening is performed.
    • +
  • bgm_kwargs: kwargs for bayesian_gaussian_mixture
    • +
  • sharpening 'hht' or None. Defaults to None. +If 'hht' hht sharpening will be performed before finding peaks.
    • +
  • strategy: optional. Defauts to 'topo'. +Peakfinding method
    • +
+ +
Returns:
+ +
+

ListPeakCandidateMultiModel: Located peak groups

+
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + find_peak_multipeak_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, *args, **kwargs): + + + +
+ + 
197@add_spectrum_filter
+198@validate_call(config=dict(arbitrary_types_allowed=True))
+199def find_peak_multipeak_filter(
+200        old_spe: Spectrum,
+201        new_spe: Spectrum, /,
+202        *args, **kwargs):
+203    """
+204    Same as `find_peak_multipeak` but the result is stored as metadata in the returned spectrum.
+205
+206    Args:
+207        old_spe: internal use only
+208        new_spe: internal use only
+209        *args, **kwargs: same as `find_peak_multipeak`
+210    """
+211    res = old_spe.find_peak_multipeak(*args, **kwargs)  # type: ignore
+212    new_spe.result = res.model_dump()
+
+ + +

Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • args, *kwargs: same as find_peak_multipeak
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/peaks/find_peaks_BayesianGaussianMixture.html b/ramanchada2/spectrum/peaks/find_peaks_BayesianGaussianMixture.html new file mode 100644 index 00000000..54b96cc2 --- /dev/null +++ b/ramanchada2/spectrum/peaks/find_peaks_BayesianGaussianMixture.html @@ -0,0 +1,401 @@ + + + + + + + ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture

+ + + + + + + 
 1from typing import Optional, Tuple, Union
+ 2
+ 3from pydantic import PositiveInt, validate_call
+ 4from sklearn.mixture import BayesianGaussianMixture
+ 5
+ 6from ramanchada2.misc.spectrum_deco import add_spectrum_method
+ 7
+ 8from ..spectrum import Spectrum
+ 9
+10
+11@add_spectrum_method
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def bayesian_gaussian_mixture(spe: Spectrum, /,
+14                              n_samples: PositiveInt = 5000,
+15                              n_components: PositiveInt = 50,
+16                              max_iter: PositiveInt = 100,
+17                              moving_minimum_window: Union[PositiveInt, None] = None,
+18                              random_state=None,
+19                              trim_range: Optional[Tuple[float, float]] = None,
+20                              ) -> BayesianGaussianMixture:
+21    """
+22    Decompose the spectrum to Bayesian Gaussian Mixture
+23
+24    Args:
+25        spe: internal use only
+26        n_samples: optional. Defaults to 5000.
+27            Resampled dataset size
+28        n_components: optional. Defaults to 50.
+29            Number of expected gaussian components
+30        max_iter: optional. Defaults to 100.
+31            Maximal number of iterations.
+32        moving_minimum_window: optional. Defaults to None.
+33            If None no moving minimum is subtracted, otherwise as specified.
+34        random_state: optional. Defaults to None.
+35            Random generator seed to be used.
+36        trim_range: optional. Defaults to None:
+37            If None ignore trimming, otherwise trim range is in x-axis values.
+38
+39    Returns:
+40        BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture
+41    """
+42    if moving_minimum_window is not None:
+43        spe = spe.subtract_moving_minimum(moving_minimum_window)  # type: ignore
+44    samp = spe.gen_samples(size=n_samples, trim_range=trim_range)
+45    X = [[i] for i in samp]
+46    bgm = BayesianGaussianMixture(n_components=n_components,
+47                                  random_state=random_state,
+48                                  max_iter=max_iter
+49                                  ).fit(X)
+50    return bgm
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + bayesian_gaussian_mixture( spe: ramanchada2.spectrum.spectrum.Spectrum, /, n_samples: typing.Annotated[int, Gt(gt=0)] = 5000, n_components: typing.Annotated[int, Gt(gt=0)] = 50, max_iter: typing.Annotated[int, Gt(gt=0)] = 100, moving_minimum_window: Optional[Annotated[int, Gt(gt=0)]] = None, random_state=None, trim_range: Optional[Tuple[float, float]] = None) -> sklearn.mixture._bayesian_mixture.BayesianGaussianMixture: + + + +
+ + 
12@add_spectrum_method
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def bayesian_gaussian_mixture(spe: Spectrum, /,
+15                              n_samples: PositiveInt = 5000,
+16                              n_components: PositiveInt = 50,
+17                              max_iter: PositiveInt = 100,
+18                              moving_minimum_window: Union[PositiveInt, None] = None,
+19                              random_state=None,
+20                              trim_range: Optional[Tuple[float, float]] = None,
+21                              ) -> BayesianGaussianMixture:
+22    """
+23    Decompose the spectrum to Bayesian Gaussian Mixture
+24
+25    Args:
+26        spe: internal use only
+27        n_samples: optional. Defaults to 5000.
+28            Resampled dataset size
+29        n_components: optional. Defaults to 50.
+30            Number of expected gaussian components
+31        max_iter: optional. Defaults to 100.
+32            Maximal number of iterations.
+33        moving_minimum_window: optional. Defaults to None.
+34            If None no moving minimum is subtracted, otherwise as specified.
+35        random_state: optional. Defaults to None.
+36            Random generator seed to be used.
+37        trim_range: optional. Defaults to None:
+38            If None ignore trimming, otherwise trim range is in x-axis values.
+39
+40    Returns:
+41        BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture
+42    """
+43    if moving_minimum_window is not None:
+44        spe = spe.subtract_moving_minimum(moving_minimum_window)  # type: ignore
+45    samp = spe.gen_samples(size=n_samples, trim_range=trim_range)
+46    X = [[i] for i in samp]
+47    bgm = BayesianGaussianMixture(n_components=n_components,
+48                                  random_state=random_state,
+49                                  max_iter=max_iter
+50                                  ).fit(X)
+51    return bgm
+
+ + +

Decompose the spectrum to Bayesian Gaussian Mixture

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • n_samples: optional. Defaults to 5000. +Resampled dataset size
    • +
  • n_components: optional. Defaults to 50. +Number of expected gaussian components
    • +
  • max_iter: optional. Defaults to 100. +Maximal number of iterations.
    • +
  • moving_minimum_window: optional. Defaults to None. +If None no moving minimum is subtracted, otherwise as specified.
    • +
  • random_state: optional. Defaults to None. +Random generator seed to be used.
    • +
  • trim_range: optional. Defaults to None: +If None ignore trimming, otherwise trim range is in x-axis values.
    • +
+ +
Returns:
+ +
+

BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/peaks/fit_peaks.html b/ramanchada2/spectrum/peaks/fit_peaks.html new file mode 100644 index 00000000..40608472 --- /dev/null +++ b/ramanchada2/spectrum/peaks/fit_peaks.html @@ -0,0 +1,706 @@ + + + + + + + ramanchada2.spectrum.peaks.fit_peaks API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.peaks.fit_peaks

+ + + + + + + 
  1import logging
+  2from typing import List, Literal, Union
+  3
+  4import numpy as np
+  5from lmfit.models import LinearModel, lmfit_models
+  6from pydantic import validate_call
+  7
+  8from ramanchada2.misc.spectrum_deco import (add_spectrum_filter,
+  9                                            add_spectrum_method)
+ 10from ramanchada2.misc.types.fit_peaks_result import FitPeaksResult
+ 11from ramanchada2.misc.types.peak_candidates import (
+ 12    ListPeakCandidateMultiModel, PeakCandidateMultiModel)
+ 13
+ 14from ..spectrum import Spectrum
+ 15
+ 16logger = logging.getLogger(__name__)
+ 17available_models = ['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']
+ 18available_models_type = Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']
+ 19
+ 20
+ 21@validate_call(config=dict(arbitrary_types_allowed=True))
+ 22def build_multipeak_model_params(profile: Union[available_models_type, List[available_models_type]],
+ 23                                 candidates: PeakCandidateMultiModel,
+ 24                                 baseline_model: Literal['linear', None] = 'linear',
+ 25                                 ):
+ 26    mod_list = list()
+ 27    if baseline_model == 'linear':
+ 28        mod_list.append(LinearModel(name='baseline', prefix='bl_'))
+ 29    for peak_i, peak in enumerate(candidates.peaks):
+ 30        mod_list.append(lmfit_models[profile](name=f'p{peak_i}', prefix=f'p{peak_i}_'))
+ 31    fit_model = np.sum(mod_list)
+ 32    fit_params = fit_model.make_params()
+ 33    if baseline_model == 'linear':
+ 34        fit_params['bl_slope'].set(value=candidates.base_slope, vary=False)
+ 35        fit_params['bl_intercept'].set(value=candidates.base_intercept, vary=False)
+ 36
+ 37    for peak_i, peak in enumerate(candidates.peaks):
+ 38        if profile == 'Moffat':
+ 39            fwhm_factor = 2.
+ 40            height_factor = 2./peak.sigma**.5
+ 41            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+ 42            fit_params[f'p{peak_i}_beta'].set(value=1, min=1e-4, max=10)
+ 43            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma)
+ 44
+ 45        elif profile == 'Voigt':
+ 46            fwhm_factor = 3.6013
+ 47            height_factor = 1/peak.sigma/2
+ 48            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+ 49            fit_params[f'p{peak_i}_gamma'].set(value=peak.sigma/fwhm_factor, vary=True)
+ 50            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+ 51
+ 52        elif profile == 'PseudoVoigt':
+ 53            fwhm_factor = lmfit_models[profile].fwhm_factor
+ 54            height_factor = 1/np.pi/np.sqrt(peak.sigma)/2
+ 55            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+ 56            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+ 57
+ 58        elif profile == 'Pearson4':
+ 59            fwhm_factor = 1
+ 60            # p{peak_i}_amplitude or p{peak_i}_height
+ 61            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude)
+ 62            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+ 63
+ 64        elif profile == 'Pearson7':
+ 65            fwhm_factor = 1
+ 66            height_factor = 1/2/peak.sigma
+ 67            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+ 68            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+ 69
+ 70        else:
+ 71            fwhm_factor = lmfit_models[profile].fwhm_factor
+ 72            height_factor = lmfit_models[profile].height_factor/peak.sigma/2
+ 73            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+ 74            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma)
+ 75
+ 76        fit_params[f'p{peak_i}_amplitude'].set(min=0)
+ 77        fit_params[f'p{peak_i}_fwhm'].set(min=peak.fwhm*.4, max=peak.fwhm*2)
+ 78        fit_params[f'p{peak_i}_height'].set(min=peak.amplitude*.1, max=peak.amplitude*20)
+ 79        fit_params[f'p{peak_i}_center'].set(value=peak.position)
+ 80
+ 81    return fit_model, fit_params
+ 82
+ 83
+ 84@add_spectrum_method
+ 85@validate_call(config=dict(arbitrary_types_allowed=True))
+ 86def fit_peak_multimodel(spe, /, *,
+ 87                        profile: Union[available_models_type, List[available_models_type]],
+ 88                        candidates: ListPeakCandidateMultiModel,
+ 89                        no_fit=False,
+ 90                        should_break=[False],
+ 91                        kwargs_fit={},
+ 92                        vary_baseline: bool = False,
+ 93                        bound_centers_to_group: bool = False
+ 94                        ) -> FitPeaksResult:
+ 95    """
+ 96    Fit a model based on candidates to the spectrum.
+ 97
+ 98    Args:
+ 99        spe: internal use only
+100        profile: str or List[str]
+101            possible values are: ["""+str(available_models)+"""]
+102        candidates: as provided from find_peak_multipeak
+103        no_fit: optional. Defaults to False.
+104            If true, do not perform a fit. Result will be the inital guess,
+105            based on the data from peak candidates.
+106        should_break: optional. Defaults to [False].
+107            Use mutability of the python list type to be able to externaly
+108            break the minimization procedure.
+109        kwargs_fit: optional
+110            kwargs for fit function
+111        vary_baseline: optional. Defaults to False.
+112            If False baseline will not be a free parameter and its amplitude
+113            will be taken from the peak candidates.
+114        bound_centers_to_group: optional. Defaults to False.
+115            Perform a bounded fit. Request all peak centers to be within the group
+116            interval.
+117
+118    Returns:
+119        FitPeaksResult: groups of fitted peaks
+120    """
+121
+122    def iter_cb(params, iter, resid, *args, **kws):
+123        return should_break[0]
+124    if no_fit:
+125        kwargs_fit = dict(kwargs_fit)
+126        kwargs_fit['max_nfev'] = 1
+127    fit_res = FitPeaksResult()
+128    for group in candidates.root:
+129        mod, par = build_multipeak_model_params(profile=profile, candidates=group)
+130        if bound_centers_to_group:
+131            for p in par:
+132                if p.endswith('_center'):
+133                    par[p].set(min=group.boundaries[0], max=group.boundaries[1])
+134        idx = (group.boundaries[0] < spe.x) & (spe.x < group.boundaries[1])
+135        x = spe.x[idx]
+136        y = spe.y[idx]
+137        for i in range(len(group.peaks)):
+138            par[f'p{i}_center'].set(vary=False)
+139        fr = mod.fit(y, x=x, params=par, iter_cb=iter_cb,  **kwargs_fit)
+140        for i in range(len(group.peaks)):
+141            par[f'p{i}_center'].set(vary=True)
+142        if vary_baseline:
+143            par['bl_slope'].set(vary=True)
+144            par['bl_intercept'].set(vary=True)
+145        fr = mod.fit(y, x=x, params=par, iter_cb=iter_cb, **kwargs_fit)
+146        fit_res.append(fr)
+147    return fit_res
+148
+149
+150@add_spectrum_filter
+151@validate_call(config=dict(arbitrary_types_allowed=True))
+152def fit_peaks_filter(
+153        old_spe: Spectrum,
+154        new_spe: Spectrum, /, *args,
+155        should_break=[False],
+156        kwargs_fit={},
+157        **kwargs,
+158        ):
+159    """
+160    Same as `fit_peak_multipeak` but the result is stored as metadata in the returned spectrum.
+161
+162    Args:
+163        old_spe: internal use only
+164        new_spe: internal use only
+165        should_break: same as in fit_peaks_multipeak
+166        *args, **kwargs: same as `fit_peaks_multipeak`
+167    """
+168    cand_groups = ListPeakCandidateMultiModel.model_validate(old_spe.result)
+169    new_spe.result = old_spe.fit_peak_multimodel(*args,  # type: ignore
+170                                                 candidates=cand_groups,
+171                                                 should_break=should_break,
+172                                                 kwargs_fit=kwargs_fit,
+173                                                 **kwargs).dumps()
+
+ + +
+
+
+ logger = +<Logger ramanchada2.spectrum.peaks.fit_peaks (WARNING)> + + +
+ + + + +
+
+
+ available_models = +['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'] + + +
+ + + + +
+
+
+ available_models_type = +typing.Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'] + + +
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + build_multipeak_model_params( profile: Union[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'], List[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']]], candidates: ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel, baseline_model: Literal['linear', None] = 'linear'): + + + +
+ + 
22@validate_call(config=dict(arbitrary_types_allowed=True))
+23def build_multipeak_model_params(profile: Union[available_models_type, List[available_models_type]],
+24                                 candidates: PeakCandidateMultiModel,
+25                                 baseline_model: Literal['linear', None] = 'linear',
+26                                 ):
+27    mod_list = list()
+28    if baseline_model == 'linear':
+29        mod_list.append(LinearModel(name='baseline', prefix='bl_'))
+30    for peak_i, peak in enumerate(candidates.peaks):
+31        mod_list.append(lmfit_models[profile](name=f'p{peak_i}', prefix=f'p{peak_i}_'))
+32    fit_model = np.sum(mod_list)
+33    fit_params = fit_model.make_params()
+34    if baseline_model == 'linear':
+35        fit_params['bl_slope'].set(value=candidates.base_slope, vary=False)
+36        fit_params['bl_intercept'].set(value=candidates.base_intercept, vary=False)
+37
+38    for peak_i, peak in enumerate(candidates.peaks):
+39        if profile == 'Moffat':
+40            fwhm_factor = 2.
+41            height_factor = 2./peak.sigma**.5
+42            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+43            fit_params[f'p{peak_i}_beta'].set(value=1, min=1e-4, max=10)
+44            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma)
+45
+46        elif profile == 'Voigt':
+47            fwhm_factor = 3.6013
+48            height_factor = 1/peak.sigma/2
+49            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+50            fit_params[f'p{peak_i}_gamma'].set(value=peak.sigma/fwhm_factor, vary=True)
+51            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+52
+53        elif profile == 'PseudoVoigt':
+54            fwhm_factor = lmfit_models[profile].fwhm_factor
+55            height_factor = 1/np.pi/np.sqrt(peak.sigma)/2
+56            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+57            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+58
+59        elif profile == 'Pearson4':
+60            fwhm_factor = 1
+61            # p{peak_i}_amplitude or p{peak_i}_height
+62            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude)
+63            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+64
+65        elif profile == 'Pearson7':
+66            fwhm_factor = 1
+67            height_factor = 1/2/peak.sigma
+68            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+69            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma/fwhm_factor)
+70
+71        else:
+72            fwhm_factor = lmfit_models[profile].fwhm_factor
+73            height_factor = lmfit_models[profile].height_factor/peak.sigma/2
+74            fit_params[f'p{peak_i}_amplitude'].set(value=peak.amplitude/height_factor)
+75            fit_params[f'p{peak_i}_sigma'].set(value=peak.sigma)
+76
+77        fit_params[f'p{peak_i}_amplitude'].set(min=0)
+78        fit_params[f'p{peak_i}_fwhm'].set(min=peak.fwhm*.4, max=peak.fwhm*2)
+79        fit_params[f'p{peak_i}_height'].set(min=peak.amplitude*.1, max=peak.amplitude*20)
+80        fit_params[f'p{peak_i}_center'].set(value=peak.position)
+81
+82    return fit_model, fit_params
+
+ + + + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + fit_peak_multimodel( spe, /, *, profile: Union[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'], List[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']]], candidates: ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel, no_fit=False, should_break=[False], kwargs_fit={}, vary_baseline: bool = False, bound_centers_to_group: bool = False) -> ramanchada2.misc.types.fit_peaks_result.FitPeaksResult: + + + +
+ + 
 85@add_spectrum_method
+ 86@validate_call(config=dict(arbitrary_types_allowed=True))
+ 87def fit_peak_multimodel(spe, /, *,
+ 88                        profile: Union[available_models_type, List[available_models_type]],
+ 89                        candidates: ListPeakCandidateMultiModel,
+ 90                        no_fit=False,
+ 91                        should_break=[False],
+ 92                        kwargs_fit={},
+ 93                        vary_baseline: bool = False,
+ 94                        bound_centers_to_group: bool = False
+ 95                        ) -> FitPeaksResult:
+ 96    """
+ 97    Fit a model based on candidates to the spectrum.
+ 98
+ 99    Args:
+100        spe: internal use only
+101        profile: str or List[str]
+102            possible values are: ["""+str(available_models)+"""]
+103        candidates: as provided from find_peak_multipeak
+104        no_fit: optional. Defaults to False.
+105            If true, do not perform a fit. Result will be the inital guess,
+106            based on the data from peak candidates.
+107        should_break: optional. Defaults to [False].
+108            Use mutability of the python list type to be able to externaly
+109            break the minimization procedure.
+110        kwargs_fit: optional
+111            kwargs for fit function
+112        vary_baseline: optional. Defaults to False.
+113            If False baseline will not be a free parameter and its amplitude
+114            will be taken from the peak candidates.
+115        bound_centers_to_group: optional. Defaults to False.
+116            Perform a bounded fit. Request all peak centers to be within the group
+117            interval.
+118
+119    Returns:
+120        FitPeaksResult: groups of fitted peaks
+121    """
+122
+123    def iter_cb(params, iter, resid, *args, **kws):
+124        return should_break[0]
+125    if no_fit:
+126        kwargs_fit = dict(kwargs_fit)
+127        kwargs_fit['max_nfev'] = 1
+128    fit_res = FitPeaksResult()
+129    for group in candidates.root:
+130        mod, par = build_multipeak_model_params(profile=profile, candidates=group)
+131        if bound_centers_to_group:
+132            for p in par:
+133                if p.endswith('_center'):
+134                    par[p].set(min=group.boundaries[0], max=group.boundaries[1])
+135        idx = (group.boundaries[0] < spe.x) & (spe.x < group.boundaries[1])
+136        x = spe.x[idx]
+137        y = spe.y[idx]
+138        for i in range(len(group.peaks)):
+139            par[f'p{i}_center'].set(vary=False)
+140        fr = mod.fit(y, x=x, params=par, iter_cb=iter_cb,  **kwargs_fit)
+141        for i in range(len(group.peaks)):
+142            par[f'p{i}_center'].set(vary=True)
+143        if vary_baseline:
+144            par['bl_slope'].set(vary=True)
+145            par['bl_intercept'].set(vary=True)
+146        fr = mod.fit(y, x=x, params=par, iter_cb=iter_cb, **kwargs_fit)
+147        fit_res.append(fr)
+148    return fit_res
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + fit_peaks_filter( old_spe: ramanchada2.spectrum.spectrum.Spectrum, new_spe: ramanchada2.spectrum.spectrum.Spectrum, /, *args, should_break=[False], kwargs_fit={}, **kwargs): + + + +
+ + 
151@add_spectrum_filter
+152@validate_call(config=dict(arbitrary_types_allowed=True))
+153def fit_peaks_filter(
+154        old_spe: Spectrum,
+155        new_spe: Spectrum, /, *args,
+156        should_break=[False],
+157        kwargs_fit={},
+158        **kwargs,
+159        ):
+160    """
+161    Same as `fit_peak_multipeak` but the result is stored as metadata in the returned spectrum.
+162
+163    Args:
+164        old_spe: internal use only
+165        new_spe: internal use only
+166        should_break: same as in fit_peaks_multipeak
+167        *args, **kwargs: same as `fit_peaks_multipeak`
+168    """
+169    cand_groups = ListPeakCandidateMultiModel.model_validate(old_spe.result)
+170    new_spe.result = old_spe.fit_peak_multimodel(*args,  # type: ignore
+171                                                 candidates=cand_groups,
+172                                                 should_break=should_break,
+173                                                 kwargs_fit=kwargs_fit,
+174                                                 **kwargs).dumps()
+
+ + +

Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • should_break: same as in fit_peaks_multipeak
    • +
  • args, *kwargs: same as fit_peaks_multipeak
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/peaks/get_fitted_peaks.html b/ramanchada2/spectrum/peaks/get_fitted_peaks.html new file mode 100644 index 00000000..f875ff06 --- /dev/null +++ b/ramanchada2/spectrum/peaks/get_fitted_peaks.html @@ -0,0 +1,431 @@ + + + + + + + ramanchada2.spectrum.peaks.get_fitted_peaks API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.peaks.get_fitted_peaks

+ + + + + + + 
 1import logging
+ 2from typing import Dict
+ 3
+ 4from pydantic import validate_call
+ 5
+ 6from ramanchada2.misc.spectrum_deco import add_spectrum_method
+ 7
+ 8from ..spectrum import Spectrum
+ 9
+10logger = logging.getLogger(__name__)
+11
+12
+13@add_spectrum_method
+14@validate_call(config=dict(arbitrary_types_allowed=True))
+15def fit_peak_positions(spe: Spectrum, /, *,
+16                       mov_min=40,
+17                       center_err_threshold=.5,
+18                       find_peaks_kw={},
+19                       fit_peaks_kw={},
+20                       ) -> Dict[float, float]:
+21    """
+22    Calculate peak positions and amplitudes.
+23
+24    Sequence of multiple processings:
+25    - `subtract_moving_minimum`
+26    - `find_peak_multipeak`
+27    - filter peaks with x-location better than threshold
+28
+29    Args:
+30        spe: internal use only
+31        mov_min: optional. Defaults to 40
+32            subtract moving_minimum with the specified window.
+33        center_err_threshold: optional. Defaults to 0.5.
+34            threshold for centroid standard deviation. Only peaks
+35            with better uncertainty will be returned.
+36
+37        find_peaks_kw: optional
+38            keyword arguments to be used with find_peak_multipeak
+39        fit_peaks_kw: optional
+40            keyword arguments to be used with fit_peaks_multipeak
+41
+42    Returns:
+43        Dict[float, float]: {positions: amplitudes}
+44    """
+45    ss = spe.subtract_moving_minimum(mov_min)  # type: ignore
+46    find_kw = dict(sharpening=None)
+47    find_kw.update(find_peaks_kw)
+48    cand = ss.find_peak_multipeak(**find_kw)
+49
+50    fit_kw = dict(profile='Gaussian')
+51    fit_kw.update(fit_peaks_kw)
+52    fit_res = spe.fit_peak_multimodel(candidates=cand, **fit_kw)  # type: ignore
+53
+54    pos, amp = fit_res.center_amplitude(threshold=center_err_threshold)
+55
+56    return dict(zip(pos, amp))
+
+ + +
+
+
+ logger = +<Logger ramanchada2.spectrum.peaks.get_fitted_peaks (WARNING)> + + +
+ + + + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + fit_peak_positions( spe: ramanchada2.spectrum.spectrum.Spectrum, /, *, mov_min=40, center_err_threshold=0.5, find_peaks_kw={}, fit_peaks_kw={}) -> Dict[float, float]: + + + +
+ + 
14@add_spectrum_method
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def fit_peak_positions(spe: Spectrum, /, *,
+17                       mov_min=40,
+18                       center_err_threshold=.5,
+19                       find_peaks_kw={},
+20                       fit_peaks_kw={},
+21                       ) -> Dict[float, float]:
+22    """
+23    Calculate peak positions and amplitudes.
+24
+25    Sequence of multiple processings:
+26    - `subtract_moving_minimum`
+27    - `find_peak_multipeak`
+28    - filter peaks with x-location better than threshold
+29
+30    Args:
+31        spe: internal use only
+32        mov_min: optional. Defaults to 40
+33            subtract moving_minimum with the specified window.
+34        center_err_threshold: optional. Defaults to 0.5.
+35            threshold for centroid standard deviation. Only peaks
+36            with better uncertainty will be returned.
+37
+38        find_peaks_kw: optional
+39            keyword arguments to be used with find_peak_multipeak
+40        fit_peaks_kw: optional
+41            keyword arguments to be used with fit_peaks_multipeak
+42
+43    Returns:
+44        Dict[float, float]: {positions: amplitudes}
+45    """
+46    ss = spe.subtract_moving_minimum(mov_min)  # type: ignore
+47    find_kw = dict(sharpening=None)
+48    find_kw.update(find_peaks_kw)
+49    cand = ss.find_peak_multipeak(**find_kw)
+50
+51    fit_kw = dict(profile='Gaussian')
+52    fit_kw.update(fit_peaks_kw)
+53    fit_res = spe.fit_peak_multimodel(candidates=cand, **fit_kw)  # type: ignore
+54
+55    pos, amp = fit_res.center_amplitude(threshold=center_err_threshold)
+56
+57    return dict(zip(pos, amp))
+
+ + +

Calculate peak positions and amplitudes.

+ +

Sequence of multiple processings:

+ +
    +
  • subtract_moving_minimum
    • +
  • find_peak_multipeak
    • +
  • filter peaks with x-location better than threshold
    • +
+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • mov_min: optional. Defaults to 40 +subtract moving_minimum with the specified window.
    • +
  • center_err_threshold: optional. Defaults to 0.5. +threshold for centroid standard deviation. Only peaks +with better uncertainty will be returned.
    • +
  • find_peaks_kw: optional +keyword arguments to be used with find_peak_multipeak
    • +
  • fit_peaks_kw: optional +keyword arguments to be used with fit_peaks_multipeak
    • +
+ +
Returns:
+ +
+

Dict[float, float]: {positions: amplitudes}

+
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/spectrum/spectrum.html b/ramanchada2/spectrum/spectrum.html new file mode 100644 index 00000000..0c27aac2 --- /dev/null +++ b/ramanchada2/spectrum/spectrum.html @@ -0,0 +1,5514 @@ + + + + + + + ramanchada2.spectrum.spectrum API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.spectrum.spectrum

+ + + + + + + 
  1from __future__ import annotations
+  2
+  3import logging
+  4from copy import deepcopy
+  5from typing import Dict, List, Optional, Set, Tuple, Union
+  6
+  7import numpy as np
+  8import numpy.typing as npt
+  9from pydantic import PositiveInt, validate_call
+ 10from scipy.signal import convolve, savgol_coeffs, savgol_filter
+ 11from scipy.stats import median_abs_deviation, rv_histogram
+ 12
+ 13from ramanchada2.io.HSDS import write_cha, write_nexus
+ 14from ramanchada2.io.output.write_csv import write_csv as io_write_csv
+ 15from ramanchada2.misc.plottable import Plottable
+ 16from ramanchada2.misc.types import PositiveOddInt, SpeMetadataModel
+ 17from ramanchada2.misc.types.spectrum import (SpeProcessingListModel,
+ 18                                             SpeProcessingModel)
+ 19
+ 20logger = logging.getLogger(__name__)
+ 21
+ 22
+ 23class Spectrum(Plottable):
+ 24    _available_processings: Set[str] = set()
+ 25
+ 26    @validate_call(config=dict(arbitrary_types_allowed=True))
+ 27    def __init__(self,
+ 28                 x: Union[npt.NDArray, int, None] = None,
+ 29                 y: Union[npt.NDArray, None] = None,
+ 30                 cachefile: Optional[str] = None,
+ 31                 metadata: Union[SpeMetadataModel, None] = None,
+ 32                 applied_processings: Union[SpeProcessingListModel, None] = None):
+ 33        super(Plottable, self).__init__()
+ 34        self._xdata = None
+ 35        self._ydata = None
+ 36        if x is not None:
+ 37            if isinstance(x, int):
+ 38                self.x = np.arange(x) * 1.
+ 39            else:
+ 40                if x.dtype != float:
+ 41                    self.x = x.astype(float)
+ 42                else:
+ 43                    self.x = x
+ 44        if y is not None:
+ 45            if y.dtype != float:
+ 46                self.y = y.astype(float)
+ 47            else:
+ 48                self.y = y
+ 49
+ 50        self._x_err: Union[npt.NDArray, None] = None
+ 51        self._y_err: Union[npt.NDArray, None] = None
+ 52
+ 53        self._cachefile = cachefile
+ 54        self._metadata = deepcopy(metadata or SpeMetadataModel(root={}))
+ 55        self._applied_processings = deepcopy(applied_processings or SpeProcessingListModel(root=[]))
+ 56        if self._xdata is not None and self._ydata is not None:
+ 57            if len(self._xdata) != len(self._ydata):
+ 58                raise ValueError(
+ 59                    f'x and y shold have same dimentions len(x)={len(self._xdata)} len(y)={len(self._ydata)}')
+ 60
+ 61    def __copy__(self):
+ 62        return Spectrum(
+ 63            x=self._xdata,
+ 64            y=self._ydata,
+ 65            cachefile=self._cachefile,
+ 66            metadata=self._metadata,
+ 67            applied_processings=self._applied_processings,
+ 68        )
+ 69
+ 70    def __repr__(self):
+ 71        return self._applied_processings.repr()
+ 72
+ 73    def applied_processings_dict(self):
+ 74        return self._applied_processings.to_list()
+ 75
+ 76    def __str__(self):
+ 77        return str(self._applied_processings.to_list())
+ 78
+ 79    def write_csv(self, filename, delimiter=',', newline='\n'):
+ 80        csv = io_write_csv(self.x, self.y, delimiter=delimiter)
+ 81        with open(filename, 'w', newline=newline) as f:
+ 82            for c in csv:
+ 83                f.write(c)
+ 84
+ 85    def write_cha(self, chafile, dataset):
+ 86        write_cha(chafile, dataset, self.x, self.y, self.meta.serialize())
+ 87
+ 88    def write_nexus(self, chafile, dataset):
+ 89        write_nexus(chafile, dataset, self.x, self.y, self.meta.serialize())
+ 90
+ 91    def write_cache(self):
+ 92        if self._cachefile:
+ 93            self.write_cha(
+ 94                self._cachefile,
+ 95                '/cache/'+self._applied_processings.cache_path()+'/_data')
+ 96
+ 97    def process(self, algorithm: str, **kwargs):
+ 98        if algorithm not in self._available_processings:
+ 99            raise ValueError('Unknown algorithm {algorithm}')
+100        return getattr(self, algorithm)(**kwargs)
+101
+102    @classmethod
+103    @validate_call(config=dict(arbitrary_types_allowed=True))
+104    def apply_creator(cls, step: SpeProcessingModel, cachefile_=None):
+105        proc = getattr(cls, step.proc)
+106        spe = proc(*step.args, **step.kwargs, cachefile_=cachefile_)
+107        return spe
+108
+109    @validate_call(config=dict(arbitrary_types_allowed=True))
+110    def apply_processing(self, step: SpeProcessingModel):
+111        proc = getattr(self, step.proc)
+112        spe = proc(*step.args, **step.kwargs)
+113        return spe
+114
+115    def _plot(self, ax, *args, **kwargs):
+116        ax.errorbar(
+117            self.x,
+118            self.y,
+119            xerr=self.x_err,
+120            yerr=self.y_err,
+121            **kwargs
+122        )
+123
+124    def _sort_x(self):
+125        idx = np.argsort(self.x)
+126        if (np.diff(idx) != 1).any():
+127            self.x = self.x[idx]
+128            self.y = self.y[idx]
+129
+130    @property
+131    def x(self):
+132        if self._xdata is None:
+133            raise ValueError('x of the spectrum is not set. self._xdata is None')
+134        return np.array(self._xdata)
+135
+136    @x.setter
+137    def x(self, val: npt.NDArray[np.float64]):
+138        self._xdata = val
+139        self._xdata.flags.writeable = False
+140
+141    @property
+142    def x_bin_boundaries(self):
+143        return np.concatenate((
+144            [(3*self.x[0] - self.x[1])/2],
+145            (self.x[1:] + self.x[:-1])/2,
+146            [(3*self.x[-1] - self.x[-2])/2]
+147        ))
+148
+149    @property
+150    def y(self) -> npt.NDArray[np.float64]:
+151        if self._ydata is None:
+152            raise ValueError('y of the spectrum is not set. self._ydata is None')
+153        return np.array(self._ydata)
+154
+155    @y.setter
+156    def y(self, val: npt.NDArray[np.float64]):
+157        self._ydata = val
+158        self._ydata.flags.writeable = False
+159
+160    @property
+161    def y_noise(self):
+162        return self.y_noise_savgol()
+163
+164    def y_noise_MAD(self):
+165        return median_abs_deviation(np.diff(self.y))
+166
+167    @validate_call(config=dict(validate_default=True))
+168    def y_noise_savgol_DL(self, order: PositiveOddInt = 1):
+169        npts = order + 2
+170        ydata = self.y - np.min(self.y)
+171        summ = np.sum((ydata - savgol_filter(ydata, npts, order))**2)
+172        coeff = savgol_coeffs(npts, order)
+173        coeff[(len(coeff) - 1) // 2] -= 1
+174        scale = np.sqrt(np.sum(coeff**2))
+175        return np.sqrt(summ/len(ydata))/scale
+176
+177    @validate_call(config=dict(validate_default=True))
+178    def y_noise_savgol(self, order: PositiveOddInt = 1):
+179        npts = order + 2
+180
+181        # subtract smoothed signal from original
+182        coeff = - savgol_coeffs(npts, order)
+183        coeff[(len(coeff)-1)//2] += 1
+184
+185        # normalize coefficients so that `sum(coeff**2) == 1`
+186        coeff /= np.sqrt(np.sum(coeff**2))
+187
+188        # remove the common floor
+189        ydata = self.y - np.min(self.y)
+190        return np.std(convolve(ydata, coeff, mode='same'))
+191
+192    @property
+193    def x_err(self):
+194        if self._x_err is None:
+195            return np.zeros_like(self._xdata)
+196        else:
+197            return self._x_err
+198
+199    @x_err.setter
+200    def x_err(self, val: Union[npt.NDArray, None]):
+201        if self._xdata is None:
+202            raise ValueError('x of the spectrum is not set. self._xdata is None')
+203        if val is not None:
+204            if val.shape != self._xdata.shape:
+205                raise ValueError(
+206                    'x_err should have same shape as xdata, expected {self._xdata.shape}, got {val.shape}')
+207        self._x_err = val
+208
+209    @property
+210    def y_err(self):
+211        if self._y_err is None:
+212            return np.zeros_like(self._ydata)
+213        else:
+214            return self._y_err
+215
+216    @y_err.setter
+217    def y_err(self, val: Union[npt.NDArray, None]):
+218        if self._ydata is None:
+219            raise ValueError('y of the spectrum is not set. self._ydata is None')
+220        if val is not None:
+221            if val.shape != self._ydata.shape:
+222                raise ValueError(
+223                    'y_err should have same shape as ydata, expected {self._ydata.shape}, got {val.shape}')
+224        self._y_err = val
+225
+226    @property
+227    def meta(self) -> SpeMetadataModel:
+228        return self._metadata
+229
+230    @meta.setter
+231    @validate_call(config=dict(arbitrary_types_allowed=True))
+232    def meta(self, val: Union[Dict, SpeMetadataModel]):
+233        if isinstance(val, dict):
+234            self._metadata = SpeMetadataModel.model_validate(val)
+235        else:
+236            self._metadata = val
+237
+238    @property
+239    def result(self):
+240        return self.meta['ramanchada2_filter_result']
+241
+242    @result.setter
+243    def result(self, res: Union[Dict, List]):
+244        return self.meta._update(dict(ramanchada2_filter_result=res))
+245
+246    @validate_call(config=dict(arbitrary_types_allowed=True))
+247    def spe_distribution(self, trim_range: Union[Tuple[float, float], None] = None):
+248        x_all = self.x_bin_boundaries
+249        if trim_range is not None:
+250            l_idx = int(np.argmin(np.abs(x_all - trim_range[0])))
+251            r_idx = int(np.argmin(np.abs(x_all - trim_range[1])))
+252            spe_dist = rv_histogram((self.y[l_idx:r_idx], x_all[l_idx:r_idx+1]))
+253        else:
+254            spe_dist = rv_histogram((self.y, x_all))
+255        return spe_dist
+256
+257    @validate_call(config=dict(arbitrary_types_allowed=True))
+258    def gen_samples(self, size: PositiveInt, trim_range=None):
+259        spe_dist = self.spe_distribution(trim_range=trim_range)
+260        samps = spe_dist.rvs(size=size)
+261        return samps
+
+ + +
+
+
+ logger = +<Logger ramanchada2.spectrum.spectrum (WARNING)> + + +
+ + + + +
+
+ +
+ + class + Spectrum(ramanchada2.misc.plottable.Plottable): + + + +
+ + 
 24class Spectrum(Plottable):
+ 25    _available_processings: Set[str] = set()
+ 26
+ 27    @validate_call(config=dict(arbitrary_types_allowed=True))
+ 28    def __init__(self,
+ 29                 x: Union[npt.NDArray, int, None] = None,
+ 30                 y: Union[npt.NDArray, None] = None,
+ 31                 cachefile: Optional[str] = None,
+ 32                 metadata: Union[SpeMetadataModel, None] = None,
+ 33                 applied_processings: Union[SpeProcessingListModel, None] = None):
+ 34        super(Plottable, self).__init__()
+ 35        self._xdata = None
+ 36        self._ydata = None
+ 37        if x is not None:
+ 38            if isinstance(x, int):
+ 39                self.x = np.arange(x) * 1.
+ 40            else:
+ 41                if x.dtype != float:
+ 42                    self.x = x.astype(float)
+ 43                else:
+ 44                    self.x = x
+ 45        if y is not None:
+ 46            if y.dtype != float:
+ 47                self.y = y.astype(float)
+ 48            else:
+ 49                self.y = y
+ 50
+ 51        self._x_err: Union[npt.NDArray, None] = None
+ 52        self._y_err: Union[npt.NDArray, None] = None
+ 53
+ 54        self._cachefile = cachefile
+ 55        self._metadata = deepcopy(metadata or SpeMetadataModel(root={}))
+ 56        self._applied_processings = deepcopy(applied_processings or SpeProcessingListModel(root=[]))
+ 57        if self._xdata is not None and self._ydata is not None:
+ 58            if len(self._xdata) != len(self._ydata):
+ 59                raise ValueError(
+ 60                    f'x and y shold have same dimentions len(x)={len(self._xdata)} len(y)={len(self._ydata)}')
+ 61
+ 62    def __copy__(self):
+ 63        return Spectrum(
+ 64            x=self._xdata,
+ 65            y=self._ydata,
+ 66            cachefile=self._cachefile,
+ 67            metadata=self._metadata,
+ 68            applied_processings=self._applied_processings,
+ 69        )
+ 70
+ 71    def __repr__(self):
+ 72        return self._applied_processings.repr()
+ 73
+ 74    def applied_processings_dict(self):
+ 75        return self._applied_processings.to_list()
+ 76
+ 77    def __str__(self):
+ 78        return str(self._applied_processings.to_list())
+ 79
+ 80    def write_csv(self, filename, delimiter=',', newline='\n'):
+ 81        csv = io_write_csv(self.x, self.y, delimiter=delimiter)
+ 82        with open(filename, 'w', newline=newline) as f:
+ 83            for c in csv:
+ 84                f.write(c)
+ 85
+ 86    def write_cha(self, chafile, dataset):
+ 87        write_cha(chafile, dataset, self.x, self.y, self.meta.serialize())
+ 88
+ 89    def write_nexus(self, chafile, dataset):
+ 90        write_nexus(chafile, dataset, self.x, self.y, self.meta.serialize())
+ 91
+ 92    def write_cache(self):
+ 93        if self._cachefile:
+ 94            self.write_cha(
+ 95                self._cachefile,
+ 96                '/cache/'+self._applied_processings.cache_path()+'/_data')
+ 97
+ 98    def process(self, algorithm: str, **kwargs):
+ 99        if algorithm not in self._available_processings:
+100            raise ValueError('Unknown algorithm {algorithm}')
+101        return getattr(self, algorithm)(**kwargs)
+102
+103    @classmethod
+104    @validate_call(config=dict(arbitrary_types_allowed=True))
+105    def apply_creator(cls, step: SpeProcessingModel, cachefile_=None):
+106        proc = getattr(cls, step.proc)
+107        spe = proc(*step.args, **step.kwargs, cachefile_=cachefile_)
+108        return spe
+109
+110    @validate_call(config=dict(arbitrary_types_allowed=True))
+111    def apply_processing(self, step: SpeProcessingModel):
+112        proc = getattr(self, step.proc)
+113        spe = proc(*step.args, **step.kwargs)
+114        return spe
+115
+116    def _plot(self, ax, *args, **kwargs):
+117        ax.errorbar(
+118            self.x,
+119            self.y,
+120            xerr=self.x_err,
+121            yerr=self.y_err,
+122            **kwargs
+123        )
+124
+125    def _sort_x(self):
+126        idx = np.argsort(self.x)
+127        if (np.diff(idx) != 1).any():
+128            self.x = self.x[idx]
+129            self.y = self.y[idx]
+130
+131    @property
+132    def x(self):
+133        if self._xdata is None:
+134            raise ValueError('x of the spectrum is not set. self._xdata is None')
+135        return np.array(self._xdata)
+136
+137    @x.setter
+138    def x(self, val: npt.NDArray[np.float64]):
+139        self._xdata = val
+140        self._xdata.flags.writeable = False
+141
+142    @property
+143    def x_bin_boundaries(self):
+144        return np.concatenate((
+145            [(3*self.x[0] - self.x[1])/2],
+146            (self.x[1:] + self.x[:-1])/2,
+147            [(3*self.x[-1] - self.x[-2])/2]
+148        ))
+149
+150    @property
+151    def y(self) -> npt.NDArray[np.float64]:
+152        if self._ydata is None:
+153            raise ValueError('y of the spectrum is not set. self._ydata is None')
+154        return np.array(self._ydata)
+155
+156    @y.setter
+157    def y(self, val: npt.NDArray[np.float64]):
+158        self._ydata = val
+159        self._ydata.flags.writeable = False
+160
+161    @property
+162    def y_noise(self):
+163        return self.y_noise_savgol()
+164
+165    def y_noise_MAD(self):
+166        return median_abs_deviation(np.diff(self.y))
+167
+168    @validate_call(config=dict(validate_default=True))
+169    def y_noise_savgol_DL(self, order: PositiveOddInt = 1):
+170        npts = order + 2
+171        ydata = self.y - np.min(self.y)
+172        summ = np.sum((ydata - savgol_filter(ydata, npts, order))**2)
+173        coeff = savgol_coeffs(npts, order)
+174        coeff[(len(coeff) - 1) // 2] -= 1
+175        scale = np.sqrt(np.sum(coeff**2))
+176        return np.sqrt(summ/len(ydata))/scale
+177
+178    @validate_call(config=dict(validate_default=True))
+179    def y_noise_savgol(self, order: PositiveOddInt = 1):
+180        npts = order + 2
+181
+182        # subtract smoothed signal from original
+183        coeff = - savgol_coeffs(npts, order)
+184        coeff[(len(coeff)-1)//2] += 1
+185
+186        # normalize coefficients so that `sum(coeff**2) == 1`
+187        coeff /= np.sqrt(np.sum(coeff**2))
+188
+189        # remove the common floor
+190        ydata = self.y - np.min(self.y)
+191        return np.std(convolve(ydata, coeff, mode='same'))
+192
+193    @property
+194    def x_err(self):
+195        if self._x_err is None:
+196            return np.zeros_like(self._xdata)
+197        else:
+198            return self._x_err
+199
+200    @x_err.setter
+201    def x_err(self, val: Union[npt.NDArray, None]):
+202        if self._xdata is None:
+203            raise ValueError('x of the spectrum is not set. self._xdata is None')
+204        if val is not None:
+205            if val.shape != self._xdata.shape:
+206                raise ValueError(
+207                    'x_err should have same shape as xdata, expected {self._xdata.shape}, got {val.shape}')
+208        self._x_err = val
+209
+210    @property
+211    def y_err(self):
+212        if self._y_err is None:
+213            return np.zeros_like(self._ydata)
+214        else:
+215            return self._y_err
+216
+217    @y_err.setter
+218    def y_err(self, val: Union[npt.NDArray, None]):
+219        if self._ydata is None:
+220            raise ValueError('y of the spectrum is not set. self._ydata is None')
+221        if val is not None:
+222            if val.shape != self._ydata.shape:
+223                raise ValueError(
+224                    'y_err should have same shape as ydata, expected {self._ydata.shape}, got {val.shape}')
+225        self._y_err = val
+226
+227    @property
+228    def meta(self) -> SpeMetadataModel:
+229        return self._metadata
+230
+231    @meta.setter
+232    @validate_call(config=dict(arbitrary_types_allowed=True))
+233    def meta(self, val: Union[Dict, SpeMetadataModel]):
+234        if isinstance(val, dict):
+235            self._metadata = SpeMetadataModel.model_validate(val)
+236        else:
+237            self._metadata = val
+238
+239    @property
+240    def result(self):
+241        return self.meta['ramanchada2_filter_result']
+242
+243    @result.setter
+244    def result(self, res: Union[Dict, List]):
+245        return self.meta._update(dict(ramanchada2_filter_result=res))
+246
+247    @validate_call(config=dict(arbitrary_types_allowed=True))
+248    def spe_distribution(self, trim_range: Union[Tuple[float, float], None] = None):
+249        x_all = self.x_bin_boundaries
+250        if trim_range is not None:
+251            l_idx = int(np.argmin(np.abs(x_all - trim_range[0])))
+252            r_idx = int(np.argmin(np.abs(x_all - trim_range[1])))
+253            spe_dist = rv_histogram((self.y[l_idx:r_idx], x_all[l_idx:r_idx+1]))
+254        else:
+255            spe_dist = rv_histogram((self.y, x_all))
+256        return spe_dist
+257
+258    @validate_call(config=dict(arbitrary_types_allowed=True))
+259    def gen_samples(self, size: PositiveInt, trim_range=None):
+260        spe_dist = self.spe_distribution(trim_range=trim_range)
+261        samps = spe_dist.rvs(size=size)
+262        return samps
+
+ + +

Helper class that provides a standard way to create an ABC using +inheritance.

+
+ + +
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + Spectrum( x: Union[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], int, NoneType] = None, y: Optional[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = None, cachefile: Optional[str] = None, metadata: Optional[ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel] = None, applied_processings: Optional[ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel] = None) + + + +
+ + 
27    @validate_call(config=dict(arbitrary_types_allowed=True))
+28    def __init__(self,
+29                 x: Union[npt.NDArray, int, None] = None,
+30                 y: Union[npt.NDArray, None] = None,
+31                 cachefile: Optional[str] = None,
+32                 metadata: Union[SpeMetadataModel, None] = None,
+33                 applied_processings: Union[SpeProcessingListModel, None] = None):
+34        super(Plottable, self).__init__()
+35        self._xdata = None
+36        self._ydata = None
+37        if x is not None:
+38            if isinstance(x, int):
+39                self.x = np.arange(x) * 1.
+40            else:
+41                if x.dtype != float:
+42                    self.x = x.astype(float)
+43                else:
+44                    self.x = x
+45        if y is not None:
+46            if y.dtype != float:
+47                self.y = y.astype(float)
+48            else:
+49                self.y = y
+50
+51        self._x_err: Union[npt.NDArray, None] = None
+52        self._y_err: Union[npt.NDArray, None] = None
+53
+54        self._cachefile = cachefile
+55        self._metadata = deepcopy(metadata or SpeMetadataModel(root={}))
+56        self._applied_processings = deepcopy(applied_processings or SpeProcessingListModel(root=[]))
+57        if self._xdata is not None and self._ydata is not None:
+58            if len(self._xdata) != len(self._ydata):
+59                raise ValueError(
+60                    f'x and y shold have same dimentions len(x)={len(self._xdata)} len(y)={len(self._ydata)}')
+
+ + + + +
+
+ +
+ + def + applied_processings_dict(self): + + + +
+ + 
74    def applied_processings_dict(self):
+75        return self._applied_processings.to_list()
+
+ + + + +
+
+ +
+ + def + write_csv(self, filename, delimiter=',', newline='\n'): + + + +
+ + 
80    def write_csv(self, filename, delimiter=',', newline='\n'):
+81        csv = io_write_csv(self.x, self.y, delimiter=delimiter)
+82        with open(filename, 'w', newline=newline) as f:
+83            for c in csv:
+84                f.write(c)
+
+ + + + +
+
+ +
+ + def + write_cha(self, chafile, dataset): + + + +
+ + 
86    def write_cha(self, chafile, dataset):
+87        write_cha(chafile, dataset, self.x, self.y, self.meta.serialize())
+
+ + + + +
+
+ +
+ + def + write_nexus(self, chafile, dataset): + + + +
+ + 
89    def write_nexus(self, chafile, dataset):
+90        write_nexus(chafile, dataset, self.x, self.y, self.meta.serialize())
+
+ + + + +
+
+ +
+ + def + write_cache(self): + + + +
+ + 
92    def write_cache(self):
+93        if self._cachefile:
+94            self.write_cha(
+95                self._cachefile,
+96                '/cache/'+self._applied_processings.cache_path()+'/_data')
+
+ + + + +
+
+ +
+ + def + process(self, algorithm: str, **kwargs): + + + +
+ + 
 98    def process(self, algorithm: str, **kwargs):
+ 99        if algorithm not in self._available_processings:
+100            raise ValueError('Unknown algorithm {algorithm}')
+101        return getattr(self, algorithm)(**kwargs)
+
+ + + + +
+
+ +
+
@classmethod
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + apply_creator( cls, step: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel, cachefile_=None): + + + +
+ + 
103    @classmethod
+104    @validate_call(config=dict(arbitrary_types_allowed=True))
+105    def apply_creator(cls, step: SpeProcessingModel, cachefile_=None):
+106        proc = getattr(cls, step.proc)
+107        spe = proc(*step.args, **step.kwargs, cachefile_=cachefile_)
+108        return spe
+
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + apply_processing( self, step: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel): + + + +
+ + 
110    @validate_call(config=dict(arbitrary_types_allowed=True))
+111    def apply_processing(self, step: SpeProcessingModel):
+112        proc = getattr(self, step.proc)
+113        spe = proc(*step.args, **step.kwargs)
+114        return spe
+
+ + + + +
+
+ +
+ x + + + +
+ + 
131    @property
+132    def x(self):
+133        if self._xdata is None:
+134            raise ValueError('x of the spectrum is not set. self._xdata is None')
+135        return np.array(self._xdata)
+
+ + + + +
+
+ +
+ x_bin_boundaries + + + +
+ + 
142    @property
+143    def x_bin_boundaries(self):
+144        return np.concatenate((
+145            [(3*self.x[0] - self.x[1])/2],
+146            (self.x[1:] + self.x[:-1])/2,
+147            [(3*self.x[-1] - self.x[-2])/2]
+148        ))
+
+ + + + +
+
+ +
+ y: numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] + + + +
+ + 
150    @property
+151    def y(self) -> npt.NDArray[np.float64]:
+152        if self._ydata is None:
+153            raise ValueError('y of the spectrum is not set. self._ydata is None')
+154        return np.array(self._ydata)
+
+ + + + +
+
+ +
+ y_noise + + + +
+ + 
161    @property
+162    def y_noise(self):
+163        return self.y_noise_savgol()
+
+ + + + +
+
+ +
+ + def + y_noise_MAD(self): + + + +
+ + 
165    def y_noise_MAD(self):
+166        return median_abs_deviation(np.diff(self.y))
+
+ + + + +
+
+ +
+
@validate_call(config=dict(validate_default=True))
+ + def + y_noise_savgol_DL(self, order: typing.Annotated[int, Gt(gt=0)] = 1): + + + +
+ + 
168    @validate_call(config=dict(validate_default=True))
+169    def y_noise_savgol_DL(self, order: PositiveOddInt = 1):
+170        npts = order + 2
+171        ydata = self.y - np.min(self.y)
+172        summ = np.sum((ydata - savgol_filter(ydata, npts, order))**2)
+173        coeff = savgol_coeffs(npts, order)
+174        coeff[(len(coeff) - 1) // 2] -= 1
+175        scale = np.sqrt(np.sum(coeff**2))
+176        return np.sqrt(summ/len(ydata))/scale
+
+ + + + +
+
+ +
+
@validate_call(config=dict(validate_default=True))
+ + def + y_noise_savgol(self, order: typing.Annotated[int, Gt(gt=0)] = 1): + + + +
+ + 
178    @validate_call(config=dict(validate_default=True))
+179    def y_noise_savgol(self, order: PositiveOddInt = 1):
+180        npts = order + 2
+181
+182        # subtract smoothed signal from original
+183        coeff = - savgol_coeffs(npts, order)
+184        coeff[(len(coeff)-1)//2] += 1
+185
+186        # normalize coefficients so that `sum(coeff**2) == 1`
+187        coeff /= np.sqrt(np.sum(coeff**2))
+188
+189        # remove the common floor
+190        ydata = self.y - np.min(self.y)
+191        return np.std(convolve(ydata, coeff, mode='same'))
+
+ + + + +
+
+ +
+ x_err + + + +
+ + 
193    @property
+194    def x_err(self):
+195        if self._x_err is None:
+196            return np.zeros_like(self._xdata)
+197        else:
+198            return self._x_err
+
+ + + + +
+
+ +
+ y_err + + + +
+ + 
210    @property
+211    def y_err(self):
+212        if self._y_err is None:
+213            return np.zeros_like(self._ydata)
+214        else:
+215            return self._y_err
+
+ + + + +
+
+ +
+ meta: ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel + + + +
+ + 
227    @property
+228    def meta(self) -> SpeMetadataModel:
+229        return self._metadata
+
+ + + + +
+
+ +
+ result + + + +
+ + 
239    @property
+240    def result(self):
+241        return self.meta['ramanchada2_filter_result']
+
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + spe_distribution(self, trim_range: Optional[Tuple[float, float]] = None): + + + +
+ + 
247    @validate_call(config=dict(arbitrary_types_allowed=True))
+248    def spe_distribution(self, trim_range: Union[Tuple[float, float], None] = None):
+249        x_all = self.x_bin_boundaries
+250        if trim_range is not None:
+251            l_idx = int(np.argmin(np.abs(x_all - trim_range[0])))
+252            r_idx = int(np.argmin(np.abs(x_all - trim_range[1])))
+253            spe_dist = rv_histogram((self.y[l_idx:r_idx], x_all[l_idx:r_idx+1]))
+254        else:
+255            spe_dist = rv_histogram((self.y, x_all))
+256        return spe_dist
+
+ + + + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + gen_samples(self, size: typing.Annotated[int, Gt(gt=0)], trim_range=None): + + + +
+ + 
258    @validate_call(config=dict(arbitrary_types_allowed=True))
+259    def gen_samples(self, size: PositiveInt, trim_range=None):
+260        spe_dist = self.spe_distribution(trim_range=trim_range)
+261        samps = spe_dist.rvs(size=size)
+262        return samps
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_minimum( old_spe: Spectrum, new_spe: Spectrum, window_size: int): + + + +
+ + 
20@add_spectrum_filter
+21@validate_call(config=dict(arbitrary_types_allowed=True))
+22def moving_minimum(
+23        old_spe: Spectrum,
+24        new_spe: Spectrum,
+25        window_size: int):
+26    """
+27    Moving minimum baseline estimator.
+28    Successive values are calculated as minima of rolling rectangular window.
+29    """
+30    new_spe.y = _moving_minimum(old_spe.y, window_size)
+
+ + +

Moving minimum baseline estimator. +Successive values are calculated as minima of rolling rectangular window.

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_moving_minimum( old_spe: Spectrum, new_spe: Spectrum, window_size: int): + + + +
+ + 
33@add_spectrum_filter
+34@validate_call(config=dict(arbitrary_types_allowed=True))
+35def subtract_moving_minimum(
+36        old_spe: Spectrum,
+37        new_spe: Spectrum,
+38        window_size: int):
+39    new_spe.y = old_spe.y - _moving_minimum(old_spe.y, window_size)
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_baseline_rc1_als( old_spe: Spectrum, new_spe: Spectrum, lam=100000.0, p=0.001, niter=100, smooth=7): + + + +
+ + 
53@add_spectrum_filter
+54@validate_call(config=dict(arbitrary_types_allowed=True))
+55def subtract_baseline_rc1_als(
+56        old_spe: Spectrum,
+57        new_spe: Spectrum,
+58        lam=1e5, p=0.001, niter=100, smooth=7
+59        ):
+60    new_spe.y = old_spe.y - baseline_als(old_spe.y, lam=lam, p=p, niter=niter, smooth=smooth)
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_baseline_rc1_snip( old_spe: Spectrum, new_spe: Spectrum, niter=30): + + + +
+ + 
63@add_spectrum_filter
+64@validate_call(config=dict(arbitrary_types_allowed=True))
+65def subtract_baseline_rc1_snip(
+66        old_spe: Spectrum,
+67        new_spe: Spectrum,
+68        niter=30
+69        ):
+70    new_spe.y = old_spe.y - baseline_snip(old_spe.y, niter=niter)
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_baseline( old_spe: Spectrum, new_spe: Spectrum, /, n_freq: int, amplitude: float, pedestal: float = 0, func: Optional[Callable] = None, rng_seed=None): + + + +
+ + 
39@add_spectrum_filter
+40@validate_call(config=dict(arbitrary_types_allowed=True))
+41def add_baseline(old_spe: Spectrum, new_spe: Spectrum, /, n_freq: int, amplitude: float, pedestal: float = 0,
+42                 func: Union[Callable, None] = None, rng_seed=None):
+43    """
+44    Add artificial baseline to the spectrum.
+45    A random baseline is generated in frequency domain using uniform random numbers.
+46    The baseline in frequency domain is tapered with bohman window to reduce the bandwidth
+47    of the baseline to first `n_freq` frequencies and is transformed to "time" domain.
+48    Additionaly by using `func` parameter the user can define arbitrary function
+49    to be added as baseline.
+50
+51    Args:
+52        n_freq:
+53            Must be `> 2`. Number of lowest frequency bins distinct from zero.
+54        amplitude:
+55            Upper boundary for the uniform random generator.
+56        pedestal:
+57            Additive constant pedestal to the spectrum.
+58        func:
+59            Callable. User-defined function to be added as baseline. Example: `func = lambda x: x*.01 + x**2*.0001`.
+60        rng_seed:
+61            `int`, optional. Seed for the random generator.
+62    """
+63    size = len(old_spe.y)
+64    base = generate_baseline(n_freq=n_freq, size=size, rng_seed=rng_seed)
+65    y = old_spe.y + amplitude*base + pedestal
+66    if func is not None:
+67        y += func(old_spe.x) + old_spe.y
+68    new_spe.y = y
+
+ + +

Add artificial baseline to the spectrum. +A random baseline is generated in frequency domain using uniform random numbers. +The baseline in frequency domain is tapered with bohman window to reduce the bandwidth +of the baseline to first n_freq frequencies and is transformed to "time" domain. +Additionaly by using func parameter the user can define arbitrary function +to be added as baseline.

+ +
Arguments:
+ +
    +
  • n_freq: Must be > 2. Number of lowest frequency bins distinct from zero.
    • +
  • amplitude: Upper boundary for the uniform random generator.
    • +
  • pedestal: Additive constant pedestal to the spectrum.
    • +
  • func: Callable. User-defined function to be added as baseline. Example: func = lambda x: x*.01 + x**2*.0001.
    • +
  • rng_seed: int, optional. Seed for the random generator.
    • +
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + central_moments( spe: Spectrum, /, boundaries=(-inf, inf), moments=[1, 2, 3, 4], normalize=False): + + + +
+ + 
10@add_spectrum_method
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def central_moments(spe: Spectrum, /,
+13                    boundaries=(-np.inf, np.inf), moments=[1, 2, 3, 4], normalize=False
+14                    ):
+15    mom = dict()
+16    filter_idx = (spe.x >= boundaries[0]) & (spe.x < boundaries[1])
+17    x = spe.x[filter_idx]
+18    p = spe.y[filter_idx]
+19    p -= p.min()
+20    p /= p.sum()
+21    mom[1] = np.sum(x*p)
+22    mom[2] = np.sum((x - mom[1])**2 * p)
+23    for i in moments:
+24        if i <= 2:
+25            continue
+26        mom[i] = np.sum((x - mom[1])**i * p)
+27        if normalize and i > 2:
+28            mom[i] /= mom[2] ** i/2
+29    return [mom[i] for i in moments]
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + scale_yaxis_linear( old_spe: Spectrum, new_spe: Spectrum, factor: float = 1): + + + +
+ + 
 9@add_spectrum_filter
+10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def scale_yaxis_linear(old_spe: Spectrum,
+12                       new_spe: Spectrum,
+13                       factor: float = 1):
+14    """
+15    Scale y-axis values
+16
+17    This function provides the same result as `spe*const`
+18
+19    Args:
+20        old_spe: internal use only
+21        new_spe: internal use only
+22        factor optional. Defaults to 1.
+23            Y-values scaling factor
+24
+25    Returns: corrected spectrum
+26    """
+27    new_spe.y = old_spe.y * factor
+
+ + +

Scale y-axis values

+ +

This function provides the same result as spe*const

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • factor optional. Defaults to 1. +Y-values scaling factor
    • +
+ +

Returns: corrected spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + normalize( old_spe: Spectrum, new_spe: Spectrum, /, strategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax', 'L1', 'L2'] = 'minmax'): + + + +
+ + 
12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def normalize(old_spe: Spectrum,
+15              new_spe: Spectrum, /,
+16              strategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax',
+17                                'L1', 'L2'] = 'minmax'):
+18    """
+19    Normalize the spectrum.
+20
+21    Args:
+22        strategy:
+23            If `unity`: normalize to `sum(y)`. If `min_unity`: subtract the minimum and normalize to 'unity'. If
+24            `unity_density`: normalize to `Σ(y_i*Δx_i)`. If `unity_area`: same as `unity_density`. If `minmax`: scale
+25            amplitudes in range `[0, 1]`. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
+26    """
+27    if strategy == 'unity':
+28        res = old_spe.y
+29        res /= np.sum(res)
+30        new_spe.y = res
+31    elif strategy == 'min_unity':
+32        res = old_spe.y - np.min(old_spe.y)
+33        res /= np.sum(res)
+34        new_spe.y = res
+35    if strategy == 'unity_density' or strategy == 'unity_area':
+36        res = old_spe.y
+37        res /= np.sum(res * np.diff(old_spe.x_bin_boundaries))
+38        new_spe.y = res
+39    elif strategy == 'minmax':
+40        res = old_spe.y - np.min(old_spe.y)
+41        res /= np.max(res)
+42        new_spe.y = res
+43    elif strategy == 'L1':
+44        res = old_spe.y
+45        res /= np.linalg.norm(res, 1)
+46        new_spe.y = res
+47    elif strategy == 'L2':
+48        res = old_spe.y
+49        res /= np.linalg.norm(res)
+50        new_spe.y = res
+
+ + +

Normalize the spectrum.

+ +
Arguments:
+ +
    +
  • strategy: If unity: normalize to sum(y). If min_unity: subtract the minimum and normalize to 'unity'. If +unity_density: normalize to Σ(y_i*Δx_i). If unity_area: same as unity_density. If minmax: scale +amplitudes in range [0, 1]. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
    • +
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + abs_nm_to_shift_cm_1( spe: Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
14@add_spectrum_method
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def abs_nm_to_shift_cm_1(spe: Spectrum, /,
+17                         laser_wave_length_nm: float):
+18    """
+19    Convert wavelength to Ramanshift in wavenumber
+20
+21    Args:
+22        spe: internal use only
+23        laser_wave_length_nm: Laser wave length
+24
+25    Returns: Corrected x-values
+26    """
+27    return util_abs_nm_to_shift_cm_1(spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert wavelength to Ramanshift in wavenumber

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + shift_cm_1_to_abs_nm( spe: Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
30@add_spectrum_method
+31@validate_call(config=dict(arbitrary_types_allowed=True))
+32def shift_cm_1_to_abs_nm(spe: Spectrum, /,
+33                         laser_wave_length_nm: float):
+34    """
+35    Convert Ramanshift in wavenumber to wavelength
+36
+37    Args:
+38        spe: internal use only
+39        laser_wave_length_nm: Laser wave length
+40
+41    Returns: Corrected x-values
+42    """
+43    return util_shift_cm_1_to_abs_nm(spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert Ramanshift in wavenumber to wavelength

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + abs_nm_to_shift_cm_1_filter( old_spe: Spectrum, new_spe: Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
46@add_spectrum_filter
+47@validate_call(config=dict(arbitrary_types_allowed=True))
+48def abs_nm_to_shift_cm_1_filter(old_spe: Spectrum,
+49                                new_spe: Spectrum, /,
+50                                laser_wave_length_nm: float):
+51    """
+52    Convert wavelength to Ramanshift in wavenumber
+53
+54    Args:
+55        spe: internal use only
+56        laser_wave_length_nm: Laser wave length
+57
+58    Returns: Spectrum with corrected x-values
+59    """
+60    new_spe.x = util_abs_nm_to_shift_cm_1(old_spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert wavelength to Ramanshift in wavenumber

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Spectrum with corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + shift_cm_1_to_abs_nm_filter( old_spe: Spectrum, new_spe: Spectrum, /, laser_wave_length_nm: float): + + + +
+ + 
63@add_spectrum_filter
+64@validate_call(config=dict(arbitrary_types_allowed=True))
+65def shift_cm_1_to_abs_nm_filter(old_spe: Spectrum,
+66                                new_spe: Spectrum, /,
+67                                laser_wave_length_nm: float):
+68    """
+69    Convert Ramanshift in wavenumber to wavelength
+70
+71    Args:
+72        spe: internal use only
+73        laser_wave_length_nm: Laser wave length
+74
+75    Returns: Spectrum with corrected x-values
+76    """
+77    new_spe.x = util_shift_cm_1_to_abs_nm(old_spe.x, laser_wave_length_nm=laser_wave_length_nm)
+
+ + +

Convert Ramanshift in wavenumber to wavelength

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • laser_wave_length_nm: Laser wave length
    • +
+ +

Returns: Spectrum with corrected x-values

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + calibrate_by_deltas_model( spe: Spectrum, /, deltas: Dict[float, float], bounds: Optional[ramanchada2.spectrum.calibration.by_deltas.FitBounds] = None, convolution_steps: Optional[List[float]] = [15, 1], scale2=True, scale3=False, init_guess: Literal[None, 'cumulative'] = None, ax=None, **kwargs): + + + +
+ + 
 63@add_spectrum_method
+ 64@validate_call(config=dict(arbitrary_types_allowed=True))
+ 65def calibrate_by_deltas_model(spe: Spectrum, /,
+ 66                              deltas: Dict[float, float],
+ 67                              bounds: Optional[FitBounds] = None,
+ 68                              convolution_steps: Union[None, List[float]] = [15, 1],
+ 69                              scale2=True, scale3=False,
+ 70                              init_guess: Literal[None, 'cumulative'] = None,
+ 71                              ax=None, **kwargs
+ 72                              ):
+ 73    """
+ 74    - Builds a composite model based on a set of user specified delta lines.
+ 75    - Initial guess is calculated based on 10-th and 90-th percentiles of
+ 76      the distributions.
+ 77
+ 78    The phasespace of the model is flat with big amount of narrow minima.
+ 79    In order to find the best fit, the experimental data are successively
+ 80    convolved with gaussians with different widths startign from wide to
+ 81    narrow. The model for the calibration is 3-th order polynomial, which
+ 82    potentialy can be changed for higher order polynomial. In order to avoid
+ 83    solving the inverse of the calibration function, the result is tabulated
+ 84    and interpolated linarly for each bin of the spectrum.
+ 85    This alogrithm is useful for corse calibration.
+ 86    """
+ 87    mod = DeltaSpeModel(deltas)
+ 88    spe_padded = spe
+ 89
+ 90    if init_guess == 'cumulative':
+ 91        deltasx = np.array(list(deltas.keys()))
+ 92
+ 93        deltas_cs = np.cumsum(list(deltas.values()))
+ 94        deltas_cs /= deltas_cs[-1]
+ 95
+ 96        deltas_idx10 = np.argmin(np.abs(deltas_cs-.1))
+ 97        deltas_idx90 = np.argmin(np.abs(deltas_cs-.9))
+ 98        x1, x2 = deltasx[[deltas_idx10, deltas_idx90]]
+ 99
+100        spe_cs = np.cumsum(
+101            spe_padded.moving_average(50).subtract_moving_minimum(10).moving_average(5).y)  # type: ignore
+102
+103        spe_cs /= spe_cs[-1]
+104        spe_idx10 = np.argmin(np.abs(spe_cs-.1))
+105        spe_idx90 = np.argmin(np.abs(spe_cs-.9))
+106        y1, y2 = spe_padded.x[[spe_idx10, spe_idx90]]
+107
+108        scale = (y1-y2)/(x1-x2)
+109        shift = -scale * x1 + y1
+110    else:
+111        scale = 1
+112        shift = 0
+113    gain = np.sum(spe.y)/np.sum(list(deltas.values()))
+114    if bounds is not None:
+115        mod.params['scale'].set(value=scale, min=bounds.scale.min, max=bounds.scale.max)
+116        mod.params['shift'].set(value=shift, min=bounds.shift.min, max=bounds.shift.max)
+117    else:
+118        mod.params['scale'].set(value=scale)
+119        mod.params['shift'].set(value=shift)
+120    mod.params['gain'].set(value=gain)
+121    mod.params['sigma'].set(value=2.5)
+122
+123    if ax is not None:
+124        spe_padded.plot(ax=ax)
+125
+126    if convolution_steps is not None:
+127        for sig in convolution_steps:
+128            mod.fit(spe=spe_padded, sigma=sig, ax=ax, **kwargs)
+129
+130    if scale2:
+131        if bounds is not None:
+132            mod.params['scale2'].set(vary=True, value=0, min=bounds.scale2.min, max=bounds.scale2.max)
+133        else:
+134            mod.params['scale2'].set(vary=True, value=0)
+135        mod.fit(spe_padded, sigma=0.05, ax=ax, **kwargs)
+136    if scale3:
+137        if bounds is not None:
+138            mod.params['scale2'].set(vary=True, value=0, min=bounds.scale2.min, max=bounds.scale2.max)
+139            mod.params['scale3'].set(vary=True, value=0, min=bounds.scale3.min, max=bounds.scale3.max)
+140        else:
+141            mod.params['scale2'].set(vary=True, value=0)
+142            mod.params['scale3'].set(vary=True, value=0)
+143        mod.fit(spe_padded, sigma=0.05, ax=ax, **kwargs)
+144    return mod.model, mod.params
+
+ + +
    +
  • Builds a composite model based on a set of user specified delta lines.
    • +
  • Initial guess is calculated based on 10-th and 90-th percentiles of +the distributions.
    • +
+ +

The phasespace of the model is flat with big amount of narrow minima. +In order to find the best fit, the experimental data are successively +convolved with gaussians with different widths startign from wide to +narrow. The model for the calibration is 3-th order polynomial, which +potentialy can be changed for higher order polynomial. In order to avoid +solving the inverse of the calibration function, the result is tabulated +and interpolated linarly for each bin of the spectrum. +This alogrithm is useful for corse calibration.

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + calibrate_by_deltas_filter( old_spe: Spectrum, new_spe: Spectrum, /, deltas: Dict[float, float], convolution_steps, init_guess=None, **kwargs): + + + +
+ + 
147@add_spectrum_filter
+148@validate_call(config=dict(arbitrary_types_allowed=True))
+149def calibrate_by_deltas_filter(old_spe: Spectrum,
+150                               new_spe: Spectrum, /,
+151                               deltas: Dict[float, float],
+152                               convolution_steps,
+153                               init_guess=None,
+154                               **kwargs
+155                               ):
+156    mod, par = old_spe.calibrate_by_deltas_model(  # type: ignore
+157        deltas=deltas,
+158        convolution_steps=convolution_steps,
+159        init_guess=init_guess,
+160        **kwargs)
+161
+162    deltasx = np.array(list(deltas.keys()))
+163    dxl, dxr = deltasx[[0, -1]]
+164    xl = dxl - (dxr - dxl)
+165    xr = dxl + (dxr - dxl)
+166    true_x = np.linspace(xl, xr, len(old_spe.x)*6)
+167    meas_x = (par['shift'].value + true_x * par['scale'] +
+168              true_x**2 * par['scale2'] + true_x**3 * par['scale3'])
+169    x_cal = np.zeros_like(old_spe.x)
+170    for i in range(len(old_spe.x)):
+171        idx = np.argmax(meas_x > old_spe.x[i])
+172        pt_rto = (old_spe.x[i] - meas_x[idx-1])/(meas_x[idx] - meas_x[idx-1])
+173        x_cal[i] = (true_x[idx] - true_x[idx-1])*pt_rto + true_x[idx-1]
+174    new_spe.x = x_cal
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + xcal_fine( old_spe: Spectrum, new_spe: Spectrum, /, *, ref: Union[Dict[float, float], List[float]], should_fit=False, poly_order: typing.Annotated[int, Ge(ge=0)], find_peaks_kw={}): + + + +
+ + 
177@add_spectrum_filter
+178@validate_call(config=dict(arbitrary_types_allowed=True))
+179def xcal_fine(old_spe: Spectrum,
+180              new_spe: Spectrum, /, *,
+181              ref: Union[Dict[float, float], List[float]],
+182              should_fit=False,
+183              poly_order: NonNegativeInt,
+184              find_peaks_kw={},
+185              ):
+186    """
+187    Iterative calibration with provided reference based on :func:`~ramanchada2.misc.utils.argmin2d.align`
+188
+189    Iteratively apply polynomial of `poly_order` degree to match
+190    the found peaks to the reference locations. The pairs are created
+191    using :func:`~ramanchada2.misc.utils.argmin2d.align` algorithm.
+192
+193    Args:
+194        old_spe (Spectrum): internal use only
+195        new_spe (Spectrum): internal use only
+196        ref (Union[Dict[float, float], List[float]]): _description_
+197        ref (Dict[float, float]):
+198            If a dict is provided - wavenumber - amplitude pairs.
+199            If a list is provided - wavenumbers only.
+200        poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
+201        should_fit (bool, optional): Whether the peaks should be fit or to
+202            associate the positions with the maxima. Defaults to False.
+203        find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
+204    """
+205
+206    if isinstance(ref, dict):
+207        ref_pos = np.array(list(ref.keys()))
+208    else:
+209        ref_pos = np.array(ref)
+210
+211    if should_fit:
+212        spe_pos_dict = old_spe.fit_peak_positions(center_err_threshold=1, find_peaks_kw=find_peaks_kw)  # type: ignore
+213    else:
+214        find_kw = dict(sharpening=None)
+215        find_kw.update(find_peaks_kw)
+216        spe_pos_dict = old_spe.find_peak_multipeak(**find_kw).get_pos_ampl_dict()  # type: ignore
+217    spe_cent = np.array(list(spe_pos_dict.keys()))
+218
+219    if poly_order == 0:
+220        p = rc2utils.align_shift(spe_cent, ref_pos)
+221        spe_cal = old_spe.scale_xaxis_fun(lambda x: x + p)  # type: ignore
+222    else:
+223        def cal_func(x, *a):
+224            return [par*(x/1000)**power for power, par in enumerate(a)]
+225
+226        p0 = np.resize([0, 1000, 0], poly_order + 1)
+227        p = rc2utils.align(spe_cent, ref_pos, p0=p0, func=cal_func)
+228        spe_cal = old_spe.scale_xaxis_fun(  # type: ignore
+229            (lambda x, *args: np.sum(cal_func(x, *args), axis=0)), args=p)
+230    new_spe.x = spe_cal.x
+
+ + +

Iterative calibration with provided reference based on ~ramanchada2.misc.utils.argmin2d.align()

+ +

Iteratively apply polynomial of poly_order degree to match +the found peaks to the reference locations. The pairs are created +using ~ramanchada2.misc.utils.argmin2d.align() algorithm.

+ +
Arguments:
+ +
    +
  • old_spe (Spectrum): internal use only
    • +
  • new_spe (Spectrum): internal use only
    • +
  • ref (Union[Dict[float, float], List[float]]): _description_
    • +
  • ref (Dict[float, float]): If a dict is provided - wavenumber - amplitude pairs. +If a list is provided - wavenumbers only.
    • +
  • poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
    • +
  • should_fit (bool, optional): Whether the peaks should be fit or to +associate the positions with the maxima. Defaults to False.
    • +
  • find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
    • +
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + xcal_fine_RBF( old_spe: Spectrum, new_spe: Spectrum, /, *, ref: Union[Dict[float, float], List[float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]], should_fit=False, kernel: Literal['thin_plate_spline', 'cubic', 'quintic', 'multiquadric', 'inverse_multiquadric', 'inverse_quadratic', 'gaussian'] = 'thin_plate_spline', find_peaks_kw={}, **kwargs): + + + +
+ + 
233@add_spectrum_filter
+234@validate_call(config=dict(arbitrary_types_allowed=True))
+235def xcal_fine_RBF(old_spe: Spectrum,
+236                  new_spe: Spectrum, /, *,
+237                  ref: Union[Dict[float, float], List[float], npt.NDArray],
+238                  should_fit=False,
+239                  kernel: Literal['thin_plate_spline', 'cubic', 'quintic',
+240                                  'multiquadric', 'inverse_multiquadric',
+241                                  'inverse_quadratic', 'gaussian',
+242                                  ] = 'thin_plate_spline',
+243                  find_peaks_kw={},
+244                  **kwargs,
+245                  ):
+246    """Wavelength calibration using Radial basis fuction interpolation
+247
+248    Please be cautious! Interpolation might not be the most appropriate
+249    approach for this type of calibration.
+250
+251    **kwargs are passed to RBFInterpolator
+252    """
+253
+254    if isinstance(ref, dict):
+255        ref_pos = np.array(list(ref.keys()))
+256    else:
+257        ref_pos = np.array(ref)
+258
+259    if should_fit:
+260        spe_pos_dict = old_spe.fit_peak_positions(center_err_threshold=1, find_peaks_kw=find_peaks_kw)  # type: ignore
+261    else:
+262        find_kw = dict(sharpening=None)
+263        find_kw.update(find_peaks_kw)
+264        spe_pos_dict = old_spe.find_peak_multipeak(**find_kw).get_pos_ampl_dict()  # type: ignore
+265    spe_cent = np.array(list(spe_pos_dict.keys()))
+266
+267    spe_idx, ref_idx = rc2utils.find_closest_pairs_idx(spe_cent, ref_pos)
+268    if len(ref_idx) == 1:
+269        _offset = (ref_pos[ref_idx][0] - spe_cent[spe_idx][0])
+270        new_spe.x = old_spe.x + _offset
+271    else:
+272        kwargs["kernel"] = kernel
+273        interp = interpolate.RBFInterpolator(spe_cent[spe_idx].reshape(-1, 1), ref_pos[ref_idx], **kwargs)
+274        new_spe.x = interp(old_spe.x.reshape(-1, 1))
+
+ + +

Wavelength calibration using Radial basis fuction interpolation

+ +

Please be cautious! Interpolation might not be the most appropriate +approach for this type of calibration.

+ +

**kwargs are passed to RBFInterpolator

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + xcal_argmin2d_iter_lowpass( old_spe: Spectrum, new_spe: Spectrum, /, *, ref: Dict[float, float], low_pass_nfreqs: List[int] = [100, 500]): + + + +
+ + 
277@add_spectrum_filter
+278@validate_call(config=dict(arbitrary_types_allowed=True))
+279def xcal_argmin2d_iter_lowpass(old_spe: Spectrum,
+280                               new_spe: Spectrum, /, *,
+281                               ref: Dict[float, float],
+282                               low_pass_nfreqs: List[int] = [100, 500]):
+283    """
+284    Calibrate spectrum
+285
+286    The calibration is done in multiple steps. Both the spectrum and the reference
+287    are passed through a low-pass filter to preserve only general structure of the
+288    spectrum. `low_pass_nfreqs` defines the number of frequencies to be preserved in
+289    each step. Once all steps with low-pass filter a final step without a low-pass
+290    filter is performed. Each calibration step is performed using
+291    :func:`~ramanchada2.spectrum.calibration.by_deltas.xcal_fine` algorithm.
+292
+293    Args:
+294        old_spe (Spectrum): internal use only
+295        new_spe (Spectrum): internal use only
+296        ref (Dict[float, float]): wavenumber - amplitude pairs
+297        low_pass_nfreqs (List[int], optional): The number of elements defines the
+298            number of low-pass steps and their values define the amount of frequencies
+299            to keep. Defaults to [100, 500].
+300    """
+301    def semi_spe_from_dict(deltas: dict, xaxis):
+302        y = np.zeros_like(xaxis)
+303        for pos, ampl in deltas.items():
+304            idx = np.argmin(np.abs(xaxis - pos))
+305            y[idx] += ampl
+306        # remove overflows and underflows
+307        y[0] = 0
+308        y[-1] = 0
+309        return y
+310
+311    def low_pass(x, nbin, window=signal.windows.blackmanharris):
+312        h = window(nbin*2-1)[nbin-1:]
+313        X = fft.rfft(x)
+314        X[:nbin] *= h  # apply the window
+315        X[nbin:] = 0  # clear upper frequencies
+316        return fft.irfft(X, n=len(x))
+317
+318    spe = old_spe.__copy__()
+319    for low_pass_i in low_pass_nfreqs:
+320        xaxis = spe.x
+321        y_ref_semi_spe = semi_spe_from_dict(ref, spe.x)
+322        y_ref_semi_spe = low_pass(y_ref_semi_spe, low_pass_i)
+323
+324        r = xaxis[signal.find_peaks(y_ref_semi_spe)[0]]
+325
+326        spe_low = spe.__copy__()
+327        spe_low.y = low_pass(spe.y, low_pass_i)
+328
+329        spe_cal = spe_low.xcal_fine(ref=r, should_fit=False, poly_order=2)
+330        spe.x = spe_cal.x
+331    spe_cal_fin = spe.xcal_fine(ref=ref, should_fit=False, poly_order=2)
+332    new_spe.x = spe_cal_fin.x
+
+ + +

Calibrate spectrum

+ +

The calibration is done in multiple steps. Both the spectrum and the reference +are passed through a low-pass filter to preserve only general structure of the +spectrum. low_pass_nfreqs defines the number of frequencies to be preserved in +each step. Once all steps with low-pass filter a final step without a low-pass +filter is performed. Each calibration step is performed using +~ramanchada2.spectrum.calibration.by_deltas.xcal_fine() algorithm.

+ +
Arguments:
+ +
    +
  • old_spe (Spectrum): internal use only
    • +
  • new_spe (Spectrum): internal use only
    • +
  • ref (Dict[float, float]): wavenumber - amplitude pairs
    • +
  • low_pass_nfreqs (List[int], optional): The number of elements defines the +number of low-pass steps and their values define the amount of frequencies +to keep. Defaults to [100, 500].
    • +
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + set_new_xaxis( old_spe: Spectrum, new_spe: Spectrum, /, xaxis: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def set_new_xaxis(old_spe: Spectrum,
+13                  new_spe: Spectrum, /,
+14                  xaxis: npt.NDArray):
+15    """
+16    Substitute x-axis values with new ones
+17
+18    Args:
+19        old_spe: internal use only
+20        new_spe: internal use only
+21        xaxis: new x-axis values
+22
+23    Returns: corrected spectrum
+24
+25    Raises:
+26        ValueError: If the provided array does not match the shape of the spectrum.
+27    """
+28    if old_spe.x.shape != xaxis.shape:
+29        raise ValueError('Shape of xaxis should match the shape of xaxis of the spectrum')
+30    new_spe.x = xaxis
+
+ + +

Substitute x-axis values with new ones

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • xaxis: new x-axis values
    • +
+ +

Returns: corrected spectrum

+ +
Raises:
+ +
    +
  • ValueError: If the provided array does not match the shape of the spectrum.
    • +
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + scale_xaxis_linear( old_spe: Spectrum, new_spe: Spectrum, /, factor: float = 1, preserve_integral: bool = False): + + + +
+ + 
13@add_spectrum_filter
+14@validate_call(config=dict(arbitrary_types_allowed=True))
+15def scale_xaxis_linear(old_spe: Spectrum,
+16                       new_spe: Spectrum, /,
+17                       factor: float = 1,
+18                       preserve_integral: bool = False):
+19    r"""
+20    Scale x-axis using a factor.
+21
+22    Args:
+23        old_spe: internal use only
+24        new_spe: internal use only
+25        factor: Defaults to 1.
+26            Multiply x-axis values with `factor`
+27        preserve_integral: optional. Defaults to False.
+28            If True, preserves the integral in sence
+29            $\sum y_{orig;\,i}*{\Delta x_{orig}}_i = \sum y_{new;\,i}*{\Delta x_{new}}_i = $
+30    Returns: Corrected spectrum
+31    """
+32    new_spe.x = old_spe.x * factor
+33    if preserve_integral:
+34        new_spe.y = old_spe.y / factor
+
+ + +

Scale x-axis using a factor.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • factor: Defaults to 1. +Multiply x-axis values with factor
    • +
  • preserve_integral: optional. Defaults to False. +If True, preserves the integral in sence +$\sum y_{orig;\,i}*{\Delta x_{orig}}_i = \sum y_{new;\,i}*{\Delta x_{new}}_i = $
    • +
+ +

Returns: Corrected spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + scale_xaxis_fun( old_spe: Spectrum, new_spe: Spectrum, /, fun: Callable[[Union[int, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float], args=[]): + + + +
+ + 
37@add_spectrum_filter
+38@validate_call(config=dict(arbitrary_types_allowed=True))
+39def scale_xaxis_fun(old_spe: Spectrum,
+40                    new_spe: Spectrum, /,
+41                    fun: Callable[[Union[int, npt.NDArray]], float],
+42                    args=[]):
+43    """
+44    Apply arbitrary calibration function to the x-axis values.
+45
+46    Args:
+47        old_spe: internal use only
+48        new_spe: internal use only
+49        fun: function to be applied
+50        args: Additional arguments to the provided functions
+51
+52    Returns: Corrected spectrum
+53
+54    Raises:
+55        ValueError: If the new x-values are not strictly monotonically increasing.
+56    """
+57    new_spe.x = fun(old_spe.x, *args)
+58    if (np.diff(new_spe.x) < 0).any():
+59        raise ValueError('The provided function is not a monoton increasing funciton.')
+
+ + +

Apply arbitrary calibration function to the x-axis values.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • fun: function to be applied
    • +
  • args: Additional arguments to the provided functions
    • +
+ +

Returns: Corrected spectrum

+ +
Raises:
+ +
    +
  • ValueError: If the new x-values are not strictly monotonically increasing.
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor(set_applied_processing=False)
+
@validate_call
+ + def + from_cache_or_calc( required_steps: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel, cachefile: Optional[str] = None): + + + +
+ + 
15@add_spectrum_constructor(set_applied_processing=False)
+16@validate_call
+17def from_cache_or_calc(required_steps: spe_t.SpeProcessingListModel,
+18                       cachefile: Optional[str] = None):
+19    """
+20    Load spectrum from cache or calculate if needed.
+21
+22    The cache is a nested structure of spectra. All processings applied to
+23    a spectrum result to spectra of the initial one. If part of the requred
+24    processings are available, only the needed steps are calculated and added
+25    to the cache.
+26
+27    Args:
+28        required_steps: List of required steps in the form
+29            [{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
+30        cachefile: optional. Defaults to None.
+31            Filename of the cache. If None no cache is used
+32    """
+33    def recall():
+34        if len(required_steps):
+35            last_proc = required_steps.pop()
+36            if last_proc.is_constructor:
+37                spe = Spectrum.apply_creator(last_proc, cachefile_=cachefile)
+38            else:
+39                spe = recur(required_steps=required_steps)
+40                spe._cachefile = cachefile
+41                spe = spe.apply_processing(last_proc)
+42            return spe
+43        else:
+44            raise Exception('no starting point')
+45
+46    def recur(required_steps: spe_t.SpeProcessingListModel):
+47        try:
+48            if cachefile:
+49                spe = get_cache()
+50            else:
+51                spe = recall()
+52        except Exception:
+53            spe = recall()
+54        spe._cachefile = cachefile
+55        return spe
+56
+57    def get_cache():
+58        try:
+59            cache_path = required_steps.cache_path()
+60            if cache_path:
+61                cache_path = '/cache/'+cache_path+'/_data'
+62            else:
+63                cache_path = 'raw'
+64            spe = Spectrum.from_chada(cachefile, cache_path)
+65            spe._applied_processings.extend_left(required_steps.root)
+66            return spe
+67        except Exception as e:
+68            logger.info(repr(e))
+69            raise e
+70
+71    return recur(required_steps)
+
+ + +

Load spectrum from cache or calculate if needed.

+ +

The cache is a nested structure of spectra. All processings applied to +a spectrum result to spectra of the initial one. If part of the requred +processings are available, only the needed steps are calculated and added +to the cache.

+ +
Arguments:
+ +
    +
  • required_steps: List of required steps in the form +[{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
    • +
  • cachefile: optional. Defaults to None. +Filename of the cache. If None no cache is used
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor(set_applied_processing=False)
+
@validate_call
+ + def + from_chada(filename: str, dataset: str = '/raw', h5module=None): + + + +
+ + 
10@add_spectrum_constructor(set_applied_processing=False)
+11@validate_call
+12def from_chada(filename: str, dataset: str = '/raw', h5module=None):
+13    x, y, meta = read_cha(filename, dataset, h5module=h5module)
+14    return Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+
+ + + + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_delta_lines( deltas: Dict[float, float], xcal: Optional[Callable[[float], float]] = None, nbins: typing.Annotated[int, Gt(gt=0)] = 2000, xaxis: Optional[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = None, **kwargs): + + + +
+ + 
14@add_spectrum_constructor()
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def from_delta_lines(
+17        deltas: Dict[float, float],
+18        xcal: Optional[Callable[[float], float]] = None,
+19        nbins: PositiveInt = 2000,
+20        xaxis: Optional[npt.NDArray] = None,
+21        **kwargs
+22        ):
+23    """
+24    Generate `Spectrum` with delta lines.
+25
+26    Args:
+27        deltas:
+28            Keys of the dictionary are the `x` positions of the deltas; values are the amplitudes of the corresponding
+29            deltas.
+30        xcal:
+31            Callable, optional. `x` axis calibration function.
+32        nbins:
+33            `int`, optional. Number of bins in the spectrum.
+34        xaxis:
+35            `Array-like`, optional. The xaxis of the new spectrum. If `xaxis` is provided,
+36            `xcal` should be `None` and `nbins` is ignored.
+37
+38    Example:
+39
+40    This will produce spectrum with 1000 bins in the range `[-1000, 2000)`:
+41    ```py
+42    xcal = lambda x: x*3 -1000, nbins=1000
+43    ```
+44    """
+45    if xaxis is not None and (xcal is not None):
+46        raise ValueError('xaxis and xcal/nbins are mutually exclusive')
+47    if xaxis is not None:
+48        x = xaxis
+49    else:
+50        if xcal is None:
+51            dk = list(deltas.keys())
+52            dkmin, dkmax = np.min(dk), np.max(dk)
+53            if dkmin == dkmax:
+54                dkmin, dkmax = dkmin*.8, dkmax*1.2
+55            else:
+56                dkmin -= (dkmax-dkmin) * .1
+57                dkmax += (dkmax-dkmin) * .1
+58            x = np.linspace(dkmin, dkmax, nbins, endpoint=False, dtype=float)
+59        else:
+60            x = np.linspace(xcal(0), xcal(nbins), nbins, endpoint=False)
+61    y = np.zeros_like(x)
+62    for pos, ampl in deltas.items():
+63        idx = np.argmin(np.abs(x - pos))
+64        y[idx] += ampl
+65    spe = Spectrum(x=x, y=y, **kwargs)
+66    return spe
+
+ + +

Generate Spectrum with delta lines.

+ +
Arguments:
+ +
    +
  • deltas: Keys of the dictionary are the x positions of the deltas; values are the amplitudes of the corresponding +deltas.
    • +
  • xcal: Callable, optional. x axis calibration function.
    • +
  • nbins: int, optional. Number of bins in the spectrum.
    • +
  • xaxis: Array-like, optional. The xaxis of the new spectrum. If xaxis is provided, +xcal should be None and nbins is ignored.
    • +
+ +

Example:

+ +

This will produce spectrum with 1000 bins in the range [-1000, 2000):

+ +
+
xcal = lambda x: x*3 -1000, nbins=1000
+
+
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_local_file( in_file_name: str, filetype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None, backend: Optional[Literal['native', 'rc1_parser']] = None): + + + +
+ + 
19@add_spectrum_constructor()
+20@validate_call(config=dict(arbitrary_types_allowed=True))
+21def from_local_file(
+22        in_file_name: str,
+23        filetype: Union[None, Literal['spc', 'sp', 'spa', '0', '1', '2',
+24                                      'wdf', 'ngs', 'jdx', 'dx',
+25                                      'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None,
+26        backend: Union[None, Literal['native', 'rc1_parser']] = None):
+27    """
+28    Read experimental spectrum from a local file.
+29
+30    Args:
+31        in_file_name:
+32            Path to a local file containing a spectrum.
+33        filetype:
+34            Specify the filetype. Filetype can be any of: `spc`, `sp`, `spa`, `0`, `1`, `2`, `wdf`, `ngs`, `jdx`, `dx`,
+35            `txt`, `txtr`, `csv`, `prn`, `rruf`, `spe` (Princeton Instruments) or `None`.
+36            `None` used to determine by extension of the file.
+37        backend:
+38            `native`, `rc1_parser` or `None`. `None` means both.
+39
+40    Raises:
+41        ValueError:
+42            When called with unsupported file formats.
+43    """
+44    def load_native():
+45        if filetype is None:
+46            ft = os.path.splitext(in_file_name)[1][1:]
+47        else:
+48            ft = filetype
+49        if ft in {'cha'}:
+50            return from_chada(filename=in_file_name)
+51        elif ft in {'txt', 'txtr', 'prn', 'rruf'}:
+52            with open(in_file_name) as fp:
+53                x, y, meta = read_txt(fp)
+54        elif ft in {'csv'}:
+55            with open(in_file_name) as fp:
+56                x, y, meta = read_csv(fp)
+57        elif ft in {'spc'}:
+58            with open(in_file_name, 'rb') as fp:
+59                spc = spc_io.SPC.from_bytes_io(fp)
+60                if len(spc) != 1:
+61                    raise ValueError(f'Single subfile SPCs are supported. {len(spc)} subfiles found')
+62                x = spc[0].xarray
+63                y = spc[0].yarray
+64                meta = spc.log_book.text
+65        elif ft in {'spe'}:
+66            x, y, meta = read_spe(in_file_name)
+67            spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+68        else:
+69            raise ValueError(f'filetype {ft} not supported')
+70        meta["Original file"] = os.path.basename(in_file_name)
+71        spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+72        return spe
+73
+74    def load_rc1():
+75        x, y, meta = rc1_parser.parse(in_file_name, filetype)
+76        spe = Spectrum(x=x, y=y, metadata=SpeMetadataModel.model_validate(meta))
+77        return spe
+78
+79    if backend == 'native':
+80        spe = load_native()
+81    elif backend == 'rc1_parser':
+82        spe = load_rc1()
+83    elif backend is None:
+84        try:
+85            spe = load_native()
+86        except Exception:
+87            spe = load_rc1()
+88    spe._sort_x()
+89    return spe
+
+ + +

Read experimental spectrum from a local file.

+ +
Arguments:
+ +
    +
  • in_file_name: Path to a local file containing a spectrum.
    • +
  • filetype: Specify the filetype. Filetype can be any of: spc, sp, spa, 0, 1, 2, wdf, ngs, jdx, dx, +txt, txtr, csv, prn, rruf, spe (Princeton Instruments) or None. +None used to determine by extension of the file.
    • +
  • backend: native, rc1_parser or None. None means both.
    • +
+ +
Raises:
+ +
    +
  • ValueError: When called with unsupported file formats.
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_simulation( in_file: Union[str, io.TextIOBase], sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'], use: Union[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Dict[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Annotated[float, Gt(gt=0)]]] = 'I_tot', nbins: typing.Annotated[int, Gt(gt=0)] = 2000): + + + +
+ + 
18@add_spectrum_constructor()
+19@validate_call(config=dict(arbitrary_types_allowed=True))
+20def from_simulation(in_file: Union[str, TextIOBase],
+21                    sim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],
+22                    use: Union[_DIRECTION_LITERALS, Dict[_DIRECTION_LITERALS, PositiveFloat]] = 'I_tot',
+23                    nbins: PositiveInt = 2000,
+24                    ):
+25    """
+26    Generate spectrum from simulation file.
+27
+28    The returned spectrum has only few x/y pairs -- one for each simulated line. Values along
+29    the x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.
+30
+31    Args:
+32        in_file:
+33            Path to a local file, or file-like object.
+34        sim_type:
+35            If `vasp`: `.dat` file from VASP simulation. If `crystal_out`: `.out` file from CRYSTAL simulation, not
+36            preferred. If `crystal_dat`: `.dat` file from CRYSTAL simulation.
+37        use:
+38            One of the directions `I_tot`, `I_perp`, `I_par`, `I_xx`, `I_xy`,
+39            `I_xz`, `I_yy`, `I_yz`, `I_zz`, `I_tot`, `I_perp`, `I_par` are
+40            available for both CRYSTAL and VASP. `I_xx`, `I_xy`, `I_xz`,
+41            `I_yy`, `I_yz`, `I_zz` are available only for CRYSTAL. If a Dict is
+42            passed, the key should be directions and values should be weighting factor.
+43            For example, `use={'I_perp': .1, 'I_par': .9}`
+44
+45    """
+46    if isinstance(use, str):
+47        use_directions = {use}
+48    else:
+49        use_directions = set(use.keys())
+50    if isinstance(in_file, TextIOBase):
+51        labels, x, ydict = read_simulated_lines(in_file, sim_type=sim_type, use=use_directions)
+52    else:
+53        with open(in_file) as f:
+54            labels, x, ydict = read_simulated_lines(f, sim_type=sim_type, use=use_directions)
+55    if isinstance(use, str):
+56        y = ydict[use]
+57    else:
+58        dirs = list(use.keys())
+59        fact = np.array(list(use.values()))
+60        y = np.transpose([ydict[i] for i in dirs]) @ fact
+61    spe = from_delta_lines(deltas=dict(zip(x, y)), nbins=nbins)
+62    return spe
+
+ + +

Generate spectrum from simulation file.

+ +

The returned spectrum has only few x/y pairs -- one for each simulated line. Values along +the x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.

+ +
Arguments:
+ +
    +
  • in_file: Path to a local file, or file-like object.
    • +
  • sim_type: If vasp: .dat file from VASP simulation. If crystal_out: .out file from CRYSTAL simulation, not +preferred. If crystal_dat: .dat file from CRYSTAL simulation.
    • +
  • use: One of the directions I_tot, I_perp, I_par, I_xx, I_xy, +I_xz, I_yy, I_yz, I_zz, I_tot, I_perp, I_par are +available for both CRYSTAL and VASP. I_xx, I_xy, I_xz, +I_yy, I_yz, I_zz are available only for CRYSTAL. If a Dict is +passed, the key should be directions and values should be weighting factor. +For example, use={'I_perp': .1, 'I_par': .9}
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_spectral_component_collection( spe_components: ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection, x=2000): + + + +
+ + 
11@add_spectrum_constructor()
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def from_spectral_component_collection(
+14        spe_components: SpectralComponentCollection,
+15        x=2000):
+16    """
+17    from_spectral_component_collection
+18
+19    Args:
+20        spe_components:
+21            SpectralComponentCollection
+22        x:
+23            `int` or array-like, optional, default `2000`. `x` axis of the spectrum.
+24    """
+25
+26    spe = Spectrum(x=x, metadata={'origin': 'generated'})  # type: ignore
+27    spe.y = spe_components(spe.x)
+28    return spe
+
+ + +

from_spectral_component_collection

+ +
Arguments:
+ +
    +
  • spe_components: SpectralComponentCollection
    • +
  • x: int or array-like, optional, default 2000. x axis of the spectrum.
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_stream( in_stream: Union[io.TextIOBase, _io.BytesIO, _io.BufferedReader], filetype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']], filename: Optional[str] = None, backend: Optional[Literal['native', 'rc1_parser']] = None): + + + +
+ + 
19@add_spectrum_constructor()
+20@validate_call(config=dict(arbitrary_types_allowed=True))
+21def from_stream(in_stream: Union[io.TextIOBase, io.BytesIO, io.BufferedReader],
+22                filetype: Union[None, Literal['spc', 'sp', 'spa', '0', '1', '2',
+23                                              'wdf', 'ngs', 'jdx', 'dx',
+24                                              'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']],
+25                filename: Optional[str] = None,
+26                backend: Union[None, Literal['native', 'rc1_parser']] = None,
+27                ):
+28    def load_native():
+29        if filetype in {'txt', 'txtr', 'prn', 'rruf'}:
+30            if isinstance(in_stream, io.TextIOBase):
+31                fp = in_stream
+32            else:
+33                fp = io.TextIOWrapper(in_stream)
+34            x, y, meta = read_txt(fp)
+35        elif filetype in {'csv'}:
+36            if isinstance(in_stream, io.TextIOBase):
+37                fp = in_stream
+38            else:
+39                fp = io.TextIOWrapper(in_stream)
+40            x, y, meta = read_csv(fp)
+41        elif filetype in {'spc'}:
+42            if isinstance(in_stream, io.TextIOBase):
+43                raise ValueError('For spc filetype does not support io.TextIOBase')
+44            fp = in_stream
+45            spc = spc_io.SPC.from_bytes_io(fp)
+46            if len(spc) != 1:
+47                raise ValueError(f'Single subfile SPCs are supported. {len(spc)} subfiles found')
+48            x = spc[0].xarray
+49            y = spc[0].yarray
+50            meta = spc.log_book.text
+51        elif filetype in {'spe'}:
+52            if isinstance(in_stream, io.TextIOBase):
+53                raise ValueError('For spc filetype does not support io.TextIOBase')
+54            with tempfile.TemporaryDirectory(suffix='ramanchada2') as dn:
+55                fn = os.path.basename(filename or in_stream.name or f'noname.{filetype}')
+56                path = os.path.join(dn, fn)
+57                with open(path, 'wb') as fp:
+58                    shutil.copyfileobj(in_stream, fp)
+59                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+60                x, y, meta = read_spe(path)
+61            spe = Spectrum(x=x, y=y, metadata=meta)
+62        else:
+63            raise ValueError(f'filetype {filetype} not supported')
+64        meta["Original file"] = os.path.basename(filename) if filename else 'N/A loaded from stream'
+65        spe = Spectrum(x=x, y=y, metadata=meta)  # type: ignore
+66        return spe
+67
+68    def load_rc1():
+69        with tempfile.TemporaryDirectory(suffix='ramanchada2') as dn:
+70            fn = os.path.basename(filename or in_stream.name or f'noname.{filetype}')
+71            path = os.path.join(dn, fn)
+72            if isinstance(in_stream, io.TextIOBase):
+73                with open(path, 'w') as fp:
+74                    shutil.copyfileobj(in_stream, fp)
+75                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+76            else:
+77                with open(path, 'wb') as fp:
+78                    shutil.copyfileobj(in_stream, fp)
+79                    print(f'shutil.copyfileobj({in_stream}, {fp}')
+80            x, y, meta = rc1_parser.parse(path, filetype)
+81        spe = Spectrum(x=x, y=y, metadata=SpeMetadataModel.model_validate(meta))
+82        return spe
+83
+84    if backend == 'native':
+85        spe = load_native()
+86    elif backend == 'rc1_parser':
+87        spe = load_rc1()
+88    elif backend is None:
+89        try:
+90            spe = load_native()
+91        except Exception:
+92            spe = load_rc1()
+93
+94    spe._sort_x()
+95    return spe
+
+ + + + +
+
+ +
+
@add_spectrum_constructor()
+ + def + from_test_spe(index=None, **kwargs): + + + +
+ + 
14@add_spectrum_constructor()
+15def from_test_spe(index=None, **kwargs):
+16    """Create new spectrum from test data.
+17
+18    Args:
+19        index:
+20            `int` or `None`, optional, default is `None`. If `int`: will be used as an index of filtered list. If
+21            `None`: a random spectrum will be taken.
+22        **kwargs:
+23            The rest of the parameters will be used as filter.
+24    """
+25    filtered = prepend_prefix(get_filenames(**kwargs))
+26    if index is None:
+27        fn = random.sample(filtered, 1)[0]
+28    else:
+29        fn = filtered[index]
+30    spe = Spectrum.from_local_file(fn)
+31    return spe
+
+ + +

Create new spectrum from test data.

+ +
Arguments:
+ +
    +
  • index: int or None, optional, default is None. If int: will be used as an index of filtered list. If +None: a random spectrum will be taken.
    • +
  • **kwargs: The rest of the parameters will be used as filter.
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor()
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + from_theoretical_lines( shapes: List[Literal['gaussian', 'gaussian2d', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7', 'breit_wigner', 'damped_oscillator', 'dho', 'logistic', 'lognormal', 'students_t', 'expgaussian', 'doniach', 'skewed_gaussian', 'skewed_voigt', 'thermal_distribution', 'step', 'rectangle', 'exponential', 'powerlaw', 'linear', 'parabolic', 'sine', 'expsine', 'split_lorentzian']], params: List[Dict], x: Union[int, numpy.ndarray[Any, numpy.dtype[numpy.float64]]] = 2000): + + + +
+ + 
14@add_spectrum_constructor()
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def from_theoretical_lines(
+17        shapes: List[Literal[lineshapes.functions]],  # type: ignore
+18        params: List[Dict],
+19        x: Union[int, npt.NDArray[np.float64]] = 2000):
+20    """
+21    Generate spectrum from `lmfit` shapes.
+22
+23    Args:
+24        shapes:
+25            The shapes to be used for spectrum generation.
+26        params:
+27            Shape parameters to be applied to be used with shapes.
+28        x:
+29            Array with `x` values, by default `np.array(2000)`.
+30    """
+31    spe = Spectrum(x=x)
+32    x = spe.x
+33    y = np.zeros_like(x, dtype=float)
+34    for shape_name, pars in zip(shapes, params):
+35        shape = getattr(lineshapes, shape_name)
+36        y += shape(x=x, **pars)
+37    spe.y = y
+38    return spe
+
+ + +

Generate spectrum from lmfit shapes.

+ +
Arguments:
+ +
    +
  • shapes: The shapes to be used for spectrum generation.
    • +
  • params: Shape parameters to be applied to be used with shapes.
    • +
  • x: Array with x values, by default np.array(2000).
    • +
+
+ + +
+
+ +
+
@add_spectrum_constructor(set_applied_processing=True)
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + hdr_from_multi_exposure(spes_in: List[Spectrum]): + + + +
+ + 
12@add_spectrum_constructor(set_applied_processing=True)
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def hdr_from_multi_exposure(spes_in: List[Spectrum]):
+15    """Create an HDR spectrum from several spectra with different exposures.
+16
+17    The resulting spectrum will have the details in low-intensity peaks
+18    from long-exposure-time spectrum. As long-exposure-time
+19    spectrum might be sturated, the information for high-intensity
+20    peaks will be taken from short-exposure-time spectrum.
+21    This function will work on a very limited number of spectra,
+22    because we still do not have standardized metadata.
+23    """
+24
+25    spes = list(sorted(spes_in, key=lambda s: float(s.meta['intigration times(ms)'])))  # type: ignore
+26    if not np.all([spes[0].x == s.x for s in spes]):
+27        raise ValueError('x-axes of the spectra should be equal')
+28    spes_cpms = np.array([s.y / float(s.meta['intigration times(ms)']) for s in spes])  # type: ignore
+29    masks = np.array(list(map(lambda s: s.y > s.meta['yaxis_max'], spes)))  # type: ignore
+30    y = spes_cpms[0]
+31    for si in range(1, len(spes_cpms)):
+32        y[~masks[si]] = spes_cpms[si][~masks[si]]
+33    return Spectrum(x=spes[0].x, y=y)
+
+ + +

Create an HDR spectrum from several spectra with different exposures.

+ +

The resulting spectrum will have the details in low-intensity peaks +from long-exposure-time spectrum. As long-exposure-time +spectrum might be sturated, the information for high-intensity +peaks will be taken from short-exposure-time spectrum. +This function will work on a very limited number of spectra, +because we still do not have standardized metadata.

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_average( old_spe: Spectrum, new_spe: Spectrum, /, window_size: typing.Annotated[int, Gt(gt=0)] = 10): + + + +
+ + 
11@add_spectrum_filter
+12@validate_call(config=dict(arbitrary_types_allowed=True))
+13def moving_average(old_spe: Spectrum,
+14                   new_spe: Spectrum, /,
+15                   window_size: PositiveInt = 10):
+16    """
+17    Moving average filter.
+18
+19    Args:
+20        old_spe: internal use only
+21        new_spe: internal use only
+22        window_size:
+23            `int`, optional, default is `10`.
+24
+25    Returns: modified Spectrum
+26    """
+27    y = [np.average(old_spe.y[i:min(i + window_size, len(old_spe.y))])
+28         for i in range(len(old_spe.y))]
+29    new_spe.y = np.array(y)
+
+ + +

Moving average filter.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_average_convolve( old_spe: Spectrum, new_spe: Spectrum, /, window_size: typing.Annotated[int, Gt(gt=0)] = 10): + + + +
+ + 
32@add_spectrum_filter
+33@validate_call(config=dict(arbitrary_types_allowed=True))
+34def moving_average_convolve(old_spe: Spectrum,
+35                            new_spe: Spectrum, /,
+36                            window_size: PositiveInt = 10):
+37    """
+38    Moving average filter.
+39
+40    Args:
+41        old_spe: internal use only
+42        new_spe: internal use only
+43        window_size:
+44            `int`, optional, default is `10`.
+45
+46    Returns: modified Spectrum
+47    """
+48    new_spe.y = signal.convolve(old_spe.y, np.ones(window_size)/window_size, mode='same')
+
+ + +

Moving average filter.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_gaussian_noise( old_spe: Spectrum, new_spe: Spectrum, /, sigma: typing.Annotated[float, Gt(gt=0)], rng_seed=None): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def add_gaussian_noise(
+13        old_spe: Spectrum,
+14        new_spe: Spectrum, /,
+15        sigma: PositiveFloat,
+16        # validation for rng_seed is removed because
+17        # it makes in-place modification impossible
+18        rng_seed=None):
+19    r"""
+20    Add gaussian noise to the spectrum.
+21
+22    Random number i.i.d. $N(0, \sigma)$ is added to every sample
+23
+24    Args:
+25        old_spe: internal use only
+26        new_spe: internal use only
+27        sigma:
+28            Sigma of the gaussian noise.
+29        rng_seed:
+30            `int` or rng state, optional, seed for the random generator.
+31            If a state is provided, it is updated in-place.
+32
+33    Returns: modified Spectrum
+34    """
+35    if isinstance(rng_seed, dict):
+36        rng = np.random.default_rng()
+37        rng.bit_generator.state = rng_seed
+38    else:
+39        rng = np.random.default_rng(rng_seed)
+40    dat = old_spe.y + rng.normal(0., sigma, size=len(old_spe.y))
+41    if any(dat < 0):
+42        dat += abs(dat.min())
+43    if isinstance(rng_seed, dict):
+44        rng_seed.update(rng.bit_generator.state)
+45    new_spe.y = np.array(dat)
+
+ + +

Add gaussian noise to the spectrum.

+ +

Random number i.i.d. $N(0, \sigma)$ is added to every sample

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • sigma: Sigma of the gaussian noise.
    • +
  • rng_seed: int or rng state, optional, seed for the random generator. +If a state is provided, it is updated in-place.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_poisson_noise( old_spe: Spectrum, new_spe: Spectrum, /, scale: float = 1, rng_seed=None): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def add_poisson_noise(
+13        old_spe: Spectrum,
+14        new_spe: Spectrum, /,
+15        scale: float = 1,
+16        # validation for rng_seed is removed because
+17        # it makes in-place modification impossible
+18        rng_seed=None):
+19    r"""
+20    Add poisson noise to the spectrum.
+21
+22    For each particular sample the noise is proportional to $\sqrt{scale*a_i}$.
+23
+24    Args:
+25        old_spe: internal use only
+26        new_spe: internal use only
+27        scale:
+28            `float`, optional, default is `1`. Scale the amplitude of the noise.
+29        rng_seed:
+30            `int` or rng state, optional. Seed for the random generator.
+31            If a state is provided, it is updated in-place.
+32
+33    Returns: modified Spectrum
+34    """
+35    if isinstance(rng_seed, dict):
+36        rng = np.random.default_rng()
+37        rng.bit_generator.state = rng_seed
+38    else:
+39        rng = np.random.default_rng(rng_seed)
+40    dat = old_spe.y + [rng.normal(0., np.sqrt(i*scale)) for i in old_spe.y]
+41    dat[dat < 0] = 0
+42    if isinstance(rng_seed, dict):
+43        rng_seed.update(rng.bit_generator.state)
+44    new_spe.y = np.array(dat)
+
+ + +

Add poisson noise to the spectrum.

+ +

For each particular sample the noise is proportional to $\sqrt{scale*a_i}$.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • scale: float, optional, default is 1. Scale the amplitude of the noise.
    • +
  • rng_seed: int or rng state, optional. Seed for the random generator. +If a state is provided, it is updated in-place.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + convolve( old_spe: Spectrum, new_spe: Spectrum, /, lineshape: Union[Callable[[Union[float, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Literal['gaussian', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7']], **kwargs): + + + +
+ + 
16@add_spectrum_filter
+17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def convolve(
+19        old_spe: Spectrum,
+20        new_spe: Spectrum, /,
+21        lineshape: Union[Callable[[Union[float, NDArray]], float],
+22                         npt.NDArray,
+23                         Literal[
+24                              'gaussian', 'lorentzian',
+25                              'voigt', 'pvoigt', 'moffat',
+26                              'pearson4', 'pearson7'
+27                              ]],
+28        **kwargs):
+29    """
+30    Convole spectrum with arbitrary lineshape.
+31
+32    Args:
+33        old_spe: internal use only
+34        new_spe: internal use only
+35        lineshape:callable, `str` or `np.ndarray`.
+36             If callable: should have a single positional argument `x`, e.g.
+37            `lambda x: np.exp((x/5)**2)`.
+38            If predefined peak profile: can be `gaussian`, `lorentzian`, `voigt`,
+39            `pvoigt`, `moffat` or `pearson4`.
+40            If `np.ndarray`: lineshape in samples.
+41        **kwargs:
+42            Additional kwargs will be passed to lineshape function.
+43
+44    Returns: modified Spectrum
+45    """
+46
+47    if isinstance(lineshape, np.ndarray):
+48        new_spe.y = signal.convolve(old_spe.y, lineshape, mode='same')
+49    else:
+50        if callable(lineshape):
+51            shape_fun = lineshape
+52        else:
+53            shape_fun = getattr(lmfit.lineshapes, lineshape)
+54
+55        leny = len(old_spe.y)
+56        x = np.arange(-(leny-1)//2, (leny+1)//2, dtype=float)
+57        shape_val = shape_fun(x, **kwargs)
+58        new_spe.y = signal.convolve(old_spe.y, shape_val, mode='same')
+
+ + +

Convole spectrum with arbitrary lineshape.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • lineshape: callable, str or np.ndarray. + If callable: should have a single positional argument x, e.g. +lambda x: np.exp((x/5)**2). +If predefined peak profile: can be gaussian, lorentzian, voigt, +pvoigt, moffat or pearson4. +If np.ndarray: lineshape in samples.
    • +
  • **kwargs: Additional kwargs will be passed to lineshape function.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + derivative_sharpening( old_spe: Spectrum, new_spe: Spectrum, /, filter_fraction: typing.Annotated[float, None, Interval(gt=0, ge=None, lt=None, le=1), None, None] = 0.6, sig_width: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=None), None, None] = 0.25, der2_factor: float = 1, der4_factor: float = 0.1): + + + +
+ + 
14@add_spectrum_filter
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def derivative_sharpening(old_spe: Spectrum,
+17                          new_spe: Spectrum, /,
+18                          filter_fraction: confloat(gt=0, le=1) = .6,  # type: ignore
+19                          sig_width: confloat(ge=0) = .25,  # type: ignore
+20                          der2_factor: float = 1,
+21                          der4_factor: float = .1
+22                          ):
+23    """
+24    Derivative-based sharpening.
+25
+26    Sharpen the spectrum subtracting second derivative and add fourth derivative.
+27
+28    Args:
+29        old_spe: internal use only
+30        new_spe: internal use only
+31        filter_fraction `float` in (0; 1]: Default is 0.6
+32            Depth of filtration
+33        signal_width: The width of features to be enhanced in sample count
+34        der2_factor: Second derivative scaling factor
+35        der4_factor: Fourth derivative scaling factor
+36
+37    Returns: modified Spectrum
+38    """
+39    leny = len(old_spe.y)
+40    Y = fft.rfft(old_spe.y, n=leny)
+41    h = signal.windows.hann(int(len(Y)*filter_fraction))
+42    h = h[(len(h))//2-1:]
+43    h = np.concatenate((h, np.zeros(len(Y)-len(h))))
+44    der = np.arange(len(Y))
+45    der = 1j*np.pi*der/der[-1]
+46    Y *= h
+47    Y2 = Y*der**2
+48    Y4 = Y2*der**2
+49    y0 = fft.irfft(Y, n=leny)
+50    y2 = fft.irfft(Y2, n=leny)
+51    y4 = fft.irfft(Y4, n=leny)
+52    new_spe.y = y0 - y2/sig_width**2*der2_factor + y4/sig_width**4*der4_factor
+
+ + +

Derivative-based sharpening.

+ +

Sharpen the spectrum subtracting second derivative and add fourth derivative.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • filter_fraction float in (0; 1]: Default is 0.6 +Depth of filtration
    • +
  • signal_width: The width of features to be enhanced in sample count
    • +
  • der2_factor: Second derivative scaling factor
    • +
  • der4_factor: Fourth derivative scaling factor
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + hht_sharpening( old_spe: Spectrum, new_spe: Spectrum, /, movmin=100): + + + +
+ + 
55@add_spectrum_filter
+56@validate_call(config=dict(arbitrary_types_allowed=True))
+57def hht_sharpening(old_spe: Spectrum,
+58                   new_spe: Spectrum, /,
+59                   movmin=100
+60                   ):
+61    """
+62    Hilbert-Huang based sharpening.
+63
+64    In order to reduce the overshooting, moving minimum is subtracted from the result
+65
+66    Args:
+67        old_spe: internal use only
+68        new_spe: internal use only
+69        movmin: optional. Default is 100
+70            Window size for moving minimum
+71
+72    Returns: modified Spectrum
+73    """
+74    imfs = emd.sift.sift(old_spe.y).T
+75    freq_list = list()
+76    for ansig in signal.hilbert(imfs):
+77        freq_list.append(emd.spectra.freq_from_phase(
+78            emd.spectra.phase_from_complex_signal(ansig, ret_phase='unwrapped'), 1))
+79    freq = np.array(freq_list)
+80    freq[freq < 0] = 0
+81    freq[np.isnan(freq)] = 0
+82    imfsall = imfs.copy()
+83    imfsall[np.isnan(imfsall)] = 0
+84    imfsall[freq > .3] = 0
+85    imfsall *= freq**.5
+86    ynew = np.sum(imfsall, axis=0)
+87    new_spe.y = ynew
+88    new_spe.y = new_spe.subtract_moving_minimum(movmin).normalize().y  # type: ignore
+89    new_spe.y = new_spe.y * old_spe.y.max() + old_spe.y.min()
+
+ + +

Hilbert-Huang based sharpening.

+ +

In order to reduce the overshooting, moving minimum is subtracted from the result

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • movmin: optional. Default is 100 +Window size for moving minimum
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + hht_sharpening_chain( old_spe: Spectrum, new_spe: Spectrum, /, movmin: List[Annotated[int, Gt(gt=0)]] = [150, 50]): + + + +
+ + 
 92@add_spectrum_filter
+ 93@validate_call(config=dict(arbitrary_types_allowed=True))
+ 94def hht_sharpening_chain(old_spe: Spectrum,
+ 95                         new_spe: Spectrum, /,
+ 96                         movmin: List[PositiveInt] = [150, 50]
+ 97                         ):
+ 98    """
+ 99    Hilbert-Huang based chain sharpening.
+100
+101    Sequence of Hilbert-Huang sharpening procedures are performed.
+102
+103    Args:
+104        old_spe: internal use only
+105        new_spe: internal use only
+106        movmin: List[int], optional. Default is [150, 50]
+107            The numer of values in the list defines how many iterations
+108            of HHT_sharpening will be performed and the values define
+109            the moving minimum window sizes for the corresponding operations.
+110
+111    Returns: modified Spectrum
+112    """
+113    spe = old_spe
+114    for mm in movmin:
+115        spe = spe.hht_sharpening(movmin=mm)  # type: ignore
+116    new_spe.y = spe.y
+
+ + +

Hilbert-Huang based chain sharpening.

+ +

Sequence of Hilbert-Huang sharpening procedures are performed.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • movmin: List[int], optional. Default is [150, 50] +The numer of values in the list defines how many iterations +of HHT_sharpening will be performed and the values define +the moving minimum window sizes for the corresponding operations.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + spike_indices( spe: Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)]) -> numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]: + + + +
+ + 
12@add_spectrum_method
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def spike_indices(spe: Spectrum, /, n_sigma: PositiveFloat) -> NDArray:
+15    """
+16    Find spikes in spectrum
+17
+18    Single-bin spikes are located using left and right successive
+19    differences. The threshold is based on the standart deviation
+20    of the metric which makes this algorithm less optimal.
+21
+22    Args:
+23        spe: internal use only
+24        n_sigma: Threshold value should be `n_sigma` times the standart
+25          deviation of the metric.
+26
+27    Returns: List of spike indices
+28    """
+29    yi = spe.y[1:-1]
+30    yi_1 = spe.y[:-2]
+31    yi1 = spe.y[2:]
+32    y_merit = np.abs(2*yi-yi_1-yi1) - np.abs(yi1-yi_1)
+33    spike_idx = y_merit > n_sigma * y_merit.std()
+34    spike_idx = np.concatenate(([False], spike_idx, [False]))
+35    return spike_idx
+
+ + +

Find spikes in spectrum

+ +

Single-bin spikes are located using left and right successive +differences. The threshold is based on the standart deviation +of the metric which makes this algorithm less optimal.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • n_sigma: Threshold value should be n_sigma times the standart +deviation of the metric.
    • +
+ +

Returns: List of spike indices

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + drop_spikes( old_spe: Spectrum, new_spe: Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)] = 10): + + + +
+ + 
38@add_spectrum_filter
+39@validate_call(config=dict(arbitrary_types_allowed=True))
+40def drop_spikes(old_spe: Spectrum,
+41                new_spe: Spectrum, /,
+42                n_sigma: PositiveFloat = 10):
+43    """
+44    Removes single-bin spikes.
+45
+46    Remove x, y pairs recognised as spikes using left and right
+47    successive differences and standard-deviation-based threshold.
+48
+49    Args:
+50        old_spe: internal use only
+51        new_spe: internal use only
+52        n_sigma: optional, default is `10`.
+53            Threshold is `n_sigma` times the standard deviation.
+54
+55    Returns: modified Spectrum
+56    """
+57    use_idx = ~spike_indices(old_spe, n_sigma=n_sigma)
+58    new_spe.x = old_spe.x[use_idx]
+59    new_spe.y = old_spe.y[use_idx]
+
+ + +

Removes single-bin spikes.

+ +

Remove x, y pairs recognised as spikes using left and right +successive differences and standard-deviation-based threshold.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • n_sigma: optional, default is 10. +Threshold is n_sigma times the standard deviation.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + recover_spikes( old_spe: Spectrum, new_spe: Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)] = 10): + + + +
+ + 
62@add_spectrum_filter
+63@validate_call(config=dict(arbitrary_types_allowed=True))
+64def recover_spikes(old_spe: Spectrum,
+65                   new_spe: Spectrum, /,
+66                   n_sigma: PositiveFloat = 10):
+67    """
+68    Recover single-bin spikes.
+69
+70    Recover x, y pairs recognised as spikes using left and right
+71    successive differences and standard-deviation-based threshold
+72    and linear interpolation.
+73
+74    Args:
+75        old_spe: internal use only
+76        new_spe: internal use only
+77        n_sigma: optional, default is `10`.
+78            Threshold is `n_sigma` times the standard deviation.
+79
+80    Returns: modified Spectrum
+81    """
+82    use_idx = ~spike_indices(old_spe, n_sigma=n_sigma)
+83    new_spe.y = np.interp(old_spe.x, old_spe.x[use_idx], old_spe.y[use_idx])
+
+ + +

Recover single-bin spikes.

+ +

Recover x, y pairs recognised as spikes using left and right +successive differences and standard-deviation-based threshold +and linear interpolation.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • n_sigma: optional, default is 10. +Threshold is n_sigma times the standard deviation.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + get_spikes( old_spe: Spectrum, new_spe: Spectrum, /, n_sigma: typing.Annotated[float, Gt(gt=0)] = 10): + + + +
+ + 
 86@add_spectrum_filter
+ 87@validate_call(config=dict(arbitrary_types_allowed=True))
+ 88def get_spikes(old_spe: Spectrum,
+ 89               new_spe: Spectrum, /,
+ 90               n_sigma: PositiveFloat = 10):
+ 91    """
+ 92    Get single-bin spikes only.
+ 93
+ 94    Get x, y pairs recognised as spikes using left and right
+ 95    successive differences and standard-deviation-based threshold
+ 96    and linear interpolation.
+ 97
+ 98    Args:
+ 99        old_spe: internal use only
+100        new_spe: internal use only
+101        n_sigma: optional, default is `10`.
+102            Threshold is `n_sigma` times the standard deviation.
+103
+104    Returns: modified Spectrum
+105    """
+106    spike_idx = spike_indices(old_spe, n_sigma=n_sigma)
+107    new_spe.x = old_spe.x[spike_idx]
+108    new_spe.y = old_spe.y[spike_idx]
+
+ + +

Get single-bin spikes only.

+ +

Get x, y pairs recognised as spikes using left and right +successive differences and standard-deviation-based threshold +and linear interpolation.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • n_sigma: optional, default is 10. +Threshold is n_sigma times the standard deviation.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + moving_median( old_spe: Spectrum, new_spe: Spectrum, /, window_size: typing.Annotated[int, Gt(gt=0)] = 10): + + + +
+ + 
20@add_spectrum_filter
+21@validate_call(config=dict(arbitrary_types_allowed=True))
+22def moving_median(old_spe: Spectrum,
+23                  new_spe: Spectrum, /,
+24                  window_size: PositiveInt = 10):
+25    """
+26    Moving median filter.
+27
+28    The resultant spectrum is moving minimum of the input.
+29
+30    Args:
+31        old_spe: internal use only
+32        new_spe: internal use only
+33        window_size:
+34            `int`, optional, default is `10`.
+35
+36    Returns: modified Spectrum
+37    """
+38
+39    new_spe.y = _moving_median(old_spe.y, window_size)
+
+ + +

Moving median filter.

+ +

The resultant spectrum is moving minimum of the input.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + subtract_moving_median( old_spe: Spectrum, new_spe: Spectrum, window_size: int): + + + +
+ + 
42@add_spectrum_filter
+43@validate_call(config=dict(arbitrary_types_allowed=True))
+44def subtract_moving_median(
+45        old_spe: Spectrum,
+46        new_spe: Spectrum,
+47        window_size: int):
+48    """
+49    Subtract moving median filter.
+50
+51    The resultant spectrum is moving minimum of the input subtracted from the input.
+52
+53    Args:
+54        old_spe: internal use only
+55        new_spe: internal use only
+56        window_size:
+57            `int`, optional, default is `10`.
+58
+59    Returns: modified Spectrum
+60    """
+61    new_spe.y = old_spe.y - _moving_median(old_spe.y, window_size)
+
+ + +

Subtract moving median filter.

+ +

The resultant spectrum is moving minimum of the input subtracted from the input.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • window_size: int, optional, default is 10.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + trim_axes( old_spe: Spectrum, new_spe: Spectrum, /, method: Literal['x-axis', 'bins'], boundaries: Tuple[float, float]): + + + +
+ + 
12@add_spectrum_filter
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def trim_axes(old_spe: Spectrum,
+15              new_spe: Spectrum, /,
+16              method: Literal['x-axis', 'bins'],
+17              boundaries: Tuple[float, float],
+18              ):
+19    """
+20    Trim axes of the spectrum.
+21
+22    Args:
+23        old_spe: internal use only
+24        new_spe: internal use only
+25        method: 'x-axis' or 'bins'
+26            If 'x-axis' boundaries will be interpreted as x-axis values.
+27            If 'bins' boundaries will be interpreted as indices.
+28        boundaries: lower and upper boundary for the trimming.
+29
+30    Returns: modified Spectrum
+31    """
+32    if method == 'bins':
+33        lb = int(boundaries[0])
+34        rb = int(boundaries[1])
+35    elif method == 'x-axis':
+36        lb = int(np.argmin(np.abs(old_spe.x - boundaries[0])))
+37        rb = int(np.argmin(np.abs(old_spe.x - boundaries[1])))
+38    new_spe.x = old_spe.x[lb:rb]
+39    new_spe.y = old_spe.y[lb:rb]
+
+ + +

Trim axes of the spectrum.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • method: 'x-axis' or 'bins' +If 'x-axis' boundaries will be interpreted as x-axis values. +If 'bins' boundaries will be interpreted as indices.
    • +
  • boundaries: lower and upper boundary for the trimming.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + add_gaussian_noise_drift( old_spe: Spectrum, new_spe: Spectrum, /, sigma: typing.Annotated[float, Gt(gt=0)], coef: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=1), None, None], rng_seed=None): + + + +
+ + 
36@add_spectrum_filter
+37@validate_call(config=dict(arbitrary_types_allowed=True))
+38def add_gaussian_noise_drift(
+39        old_spe: Spectrum,
+40        new_spe: Spectrum, /,
+41        sigma: PositiveFloat,
+42        coef: confloat(ge=0, le=1),  # type: ignore [valid-type]
+43        # validation for rng_seed is removed because
+44        # it makes in-place modification impossible
+45        rng_seed=None):
+46    r"""
+47    Add cumulative gaussian noise to the spectrum.
+48
+49    Exponential-moving-average-like gaussian noise is added
+50    to each sample. The goal is to mimic the low-frequency noise
+51    (or random substructures in spectra).
+52    The additive noise is
+53    .. math::
+54        a_i = coef*\sum_{j=0}^{i-1}g_j + g_i,
+55    where
+56    .. math::
+57        g_i = \mathcal{N}(0, 1+\frac{coef}{\sqrt 2}).
+58    This way drifting is possible while keeping the
+59    .. math::
+60        \sigma(\Delta(a)) \approx 1.
+61
+62    Args:
+63        old_spe: internal use only
+64        new_spe: internal use only
+65        sigma:
+66            Sigma of the gaussian noise.
+67        coef:
+68            `float` in `[0, 1]`, drifting coefficient. If `coef == 0`,
+69            the result is identical to `add_gaussian_noise()`.
+70        rng_seed:
+71            `int` or rng state, optional. Seed for the random generator.
+72            If a state is provided, it is updated in-place.
+73
+74    Returns: modified Spectrum
+75    """
+76    new_spe.y = generate_add_gaussian_noise_drift(old_spe.y,
+77                                                  sigma=sigma,
+78                                                  coef=coef,
+79                                                  rng_seed=rng_seed)
+
+ + +

Add cumulative gaussian noise to the spectrum.

+ +

Exponential-moving-average-like gaussian noise is added +to each sample. The goal is to mimic the low-frequency noise +(or random substructures in spectra). +The additive noise is +$$a_i = coef*\sum_{j=0}^{i-1}g_j + g_i,$$

+ +

where +$$g_i = \mathcal{N}(0, 1+\frac{coef}{\sqrt 2}).$$

+ +

This way drifting is possible while keeping the +$$\sigma(\Delta(a)) \approx 1.$$

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • sigma: Sigma of the gaussian noise.
    • +
  • coef: float in [0, 1], drifting coefficient. If coef == 0, +the result is identical to add_gaussian_noise().
    • +
  • rng_seed: int or rng state, optional. Seed for the random generator. +If a state is provided, it is updated in-place.
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_NUDFT( spe: Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, window: Union[Callable, Tuple[Any, ...], Literal['barthann', 'bartlett', 'blackman', 'blackmanharris', 'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss', 'exponential', 'flattop', 'gaussian', 'general_cosine', 'general_gaussian', 'general_hamming', 'hamming', 'hann', 'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey'], NoneType] = None, cumulative: bool = False): + + + +
+ + 
16@add_spectrum_method
+17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def resample_NUDFT(spe: Spectrum, /,
+19                   x_range: Tuple[float, float] = (0, 4000),
+20                   xnew_bins: PositiveInt = 100,
+21                   window: Optional[Union[Callable,
+22                                          Tuple[Any, ...],  # e.g. ('gaussian', sigma)
+23                                          Literal['barthann', 'bartlett', 'blackman', 'blackmanharris',
+24                                                  'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss',
+25                                                  'exponential', 'flattop', 'gaussian', 'general_cosine',
+26                                                  'general_gaussian', 'general_hamming', 'hamming', 'hann',
+27                                                  'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey']
+28                                          ]] = None,
+29                   cumulative: bool = False):
+30    """
+31    Resample the spectrum using Non-uniform discrete fourier transform.
+32
+33    The x-axis of the result will be uniform. The corresponding y-values
+34    will be calculated with NUDFT and inverse FFT.
+35
+36    Args:
+37        spe: internal use only
+38        x_range: optional. Defaults to (0, 4000).
+39            The x_range of the new spectrum.
+40        xnew_bins: optional. Defaults to 100.
+41            Number of bins of the new spectrum
+42        window: optional, Defaults to None.
+43            The window to be used for lowpass filter. If None 'blackmanharris' is used.
+44            If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+45        cumulative: optional. Defaults to False.
+46            If True, the resultant spectrum will be cumulative and normalized
+47            (in analogy with CDF).
+48
+49    Returns:
+50        (x_values, y_values)
+51    """
+52
+53    x_new = np.linspace(x_range[0], x_range[1], xnew_bins, endpoint=False)
+54    x = spe.x
+55    y = spe.y
+56    x = np.array(x)
+57    x_range = (np.min(x_range), np.max(x_range))
+58    y = y[(x >= x_range[0]) & (x < x_range[1])]
+59    x = x[(x >= x_range[0]) & (x < x_range[1])]
+60
+61    w = (x-x_range[0])/(x_range[1]-x_range[0])*np.pi*2
+62    x -= x_range[0]
+63
+64    k = np.arange(xnew_bins)
+65
+66    Y_new = np.sum([yi*np.exp(-1j*wi*k) for yi, wi in zip(y, w)], axis=0)
+67
+68    if window is None:
+69        window = 'blackmanharris'
+70
+71    if hasattr(window, '__call__'):
+72        h = (window(len(Y_new)*2))[len(Y_new):]  # type: ignore
+73    else:
+74        h = signal.windows.get_window(window, len(Y_new)*2)[len(Y_new):]
+75    Y_new *= h
+76
+77    y_new = fft.irfft(Y_new, n=xnew_bins)
+78    if cumulative:
+79        y_new = np.cumsum(y_new)
+80        y_new /= y_new[-1]
+81    return x_new, y_new
+
+ + +

Resample the spectrum using Non-uniform discrete fourier transform.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with NUDFT and inverse FFT.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • window: optional, Defaults to None. +The window to be used for lowpass filter. If None 'blackmanharris' is used. +If no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +
Returns:
+ +
+

(x_values, y_values)

+
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_NUDFT_filter( old_spe: Spectrum, new_spe: Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, window=None, cumulative: bool = False): + + + +
+ + 
 84@add_spectrum_filter
+ 85@validate_call(config=dict(arbitrary_types_allowed=True))
+ 86def resample_NUDFT_filter(old_spe: Spectrum,
+ 87                          new_spe: Spectrum, /,
+ 88                          x_range: Tuple[float, float] = (0, 4000),
+ 89                          xnew_bins: PositiveInt = 100,
+ 90                          window=None,
+ 91                          cumulative: bool = False):
+ 92    """
+ 93    Resample the spectrum using Non-uniform discrete fourier transform.
+ 94
+ 95    The x-axis of the result will be uniform. The corresponding y-values
+ 96    will be calculated with NUDFT and inverse FFT.
+ 97
+ 98    Args:
+ 99        old_spe: internal use only
+100        new_spe: internal use only
+101        x_range: optional. Defaults to (0, 4000).
+102            The x_range of the new spectrum.
+103        xnew_bins: optional. Defaults to 100.
+104            Number of bins of the new spectrum
+105        window: optional, Defaults to None.
+106            The window to be used for lowpass filter. If None 'blackmanharris' is used.
+107            If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.
+108        cumulative: optional. Defaults to False.
+109            If True, the resultant spectrum will be cumulative and normalized
+110            (in analogy with CDF).
+111
+112    Returns: modified Spectrum
+113    """
+114    new_spe.x, new_spe.y = resample_NUDFT(old_spe,
+115                                          x_range=x_range,
+116                                          xnew_bins=xnew_bins,
+117                                          window=window,
+118                                          cumulative=cumulative)
+
+ + +

Resample the spectrum using Non-uniform discrete fourier transform.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with NUDFT and inverse FFT.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • window: optional, Defaults to None. +The window to be used for lowpass filter. If None 'blackmanharris' is used. +If no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_spline( spe: Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip', interp_kw_args: Optional[Dict] = None, cumulative: bool = False): + + + +
+ + 
121@add_spectrum_method
+122@validate_call(config=dict(arbitrary_types_allowed=True))
+123def resample_spline(spe: Spectrum, /,
+124                    x_range: Tuple[float, float] = (0, 4000),
+125                    xnew_bins: PositiveInt = 100,
+126                    spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',
+127                    interp_kw_args: Optional[Dict] = None,
+128                    cumulative: bool = False):
+129    """
+130    Resample the spectrum using spline interpolation.
+131
+132    The x-axis of the result will be uniform. The corresponding y-values
+133    will be calculated with spline interpolation.
+134
+135    Args:
+136        spe: internal use only
+137        x_range: optional. Defaults to (0, 4000).
+138            The x_range of the new spectrum.
+139        xnew_bins: optional. Defaults to 100.
+140            Number of bins of the new spectrum
+141        spline: optional, Defaults to 'pchip'.
+142            Name of the spline funcion to be used.
+143        cumulative: optional. Defaults to False.
+144            If True, the resultant spectrum will be cumulative and normalized
+145            (in analogy with CDF).
+146
+147    Returns:
+148        (x_values, y_values)
+149    """
+150
+151    kw_args: Dict[str, Any] = {}
+152    if spline == 'pchip':
+153        spline_fn = PchipInterpolator
+154        kw_args['extrapolate'] = False
+155    elif spline == 'akima':
+156        spline_fn = Akima1DInterpolator
+157    elif spline == 'makima':
+158        spline_fn = Akima1DInterpolator
+159        kw_args['method'] = 'makima'
+160    elif spline == 'cubic_spline':
+161        spline_fn = CubicSpline
+162        kw_args['bc_type'] = 'natural'
+163        kw_args['extrapolate'] = False
+164
+165    if interp_kw_args is not None:
+166        kw_args.update(interp_kw_args)
+167
+168    x_new = np.linspace(x_range[0], x_range[1], xnew_bins, endpoint=False)
+169    y_new = spline_fn(spe.x, spe.y, **kw_args)(x_new)
+170
+171    y_new[np.isnan(y_new)] = 0
+172    if cumulative:
+173        y_new = np.cumsum(y_new)
+174        y_new /= y_new[-1]
+175
+176    return x_new, y_new
+
+ + +

Resample the spectrum using spline interpolation.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with spline interpolation.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • spline: optional, Defaults to 'pchip'. +Name of the spline funcion to be used.
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +
Returns:
+ +
+

(x_values, y_values)

+
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + resample_spline_filter( old_spe: Spectrum, new_spe: Spectrum, /, x_range: Tuple[float, float] = (0, 4000), xnew_bins: typing.Annotated[int, Gt(gt=0)] = 100, spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip', interp_kw_args: Optional[Dict] = None, cumulative: bool = False): + + + +
+ + 
179@add_spectrum_filter
+180@validate_call(config=dict(arbitrary_types_allowed=True))
+181def resample_spline_filter(old_spe: Spectrum,
+182                           new_spe: Spectrum, /,
+183                           x_range: Tuple[float, float] = (0, 4000),
+184                           xnew_bins: PositiveInt = 100,
+185                           spline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',
+186                           interp_kw_args: Optional[Dict] = None,
+187                           cumulative: bool = False):
+188    """
+189    Resample the spectrum using spline interpolation.
+190
+191    The x-axis of the result will be uniform. The corresponding y-values
+192    will be calculated with spline interpolation.
+193
+194    Args:
+195        old_spe: internal use only
+196        new_spe: internal use only
+197        x_range: optional. Defaults to (0, 4000).
+198            The x_range of the new spectrum.
+199        xnew_bins: optional. Defaults to 100.
+200            Number of bins of the new spectrum
+201        spline: optional, Defaults to 'pchip'.
+202            Name of the spline funcion to be used.
+203        cumulative: optional. Defaults to False.
+204            If True, the resultant spectrum will be cumulative and normalized
+205            (in analogy with CDF).
+206
+207    Returns: modified Spectrum
+208    """
+209    new_spe.x, new_spe.y = resample_spline(old_spe,
+210                                           x_range=x_range,
+211                                           xnew_bins=xnew_bins,
+212                                           spline=spline,
+213                                           interp_kw_args=interp_kw_args,
+214                                           cumulative=cumulative)
+
+ + +

Resample the spectrum using spline interpolation.

+ +

The x-axis of the result will be uniform. The corresponding y-values +will be calculated with spline interpolation.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • x_range: optional. Defaults to (0, 4000). +The x_range of the new spectrum.
    • +
  • xnew_bins: optional. Defaults to 100. +Number of bins of the new spectrum
    • +
  • spline: optional, Defaults to 'pchip'. +Name of the spline funcion to be used.
    • +
  • cumulative: optional. Defaults to False. +If True, the resultant spectrum will be cumulative and normalized +(in analogy with CDF).
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + pad_zeros( old_spe: Spectrum, new_spe: Spectrum, /): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def pad_zeros(old_spe: Spectrum,
+13              new_spe: Spectrum, /):
+14    """
+15    Extend x-axis by 100% in both directions.
+16
+17    The x-axis of resultant spectrum will be:
+18    $[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.
+19    The length of the new spectrum is 3 times the original. The added values
+20    are with an uniform step. In the middle is the original spectrum with
+21    original x and y values. The coresponding y vallues for the newly added
+22    x-values are always zeros.
+23
+24    Args:
+25        old_spe: internal use only
+26        new_spe: internal use only
+27
+28    Returns: modified Spectrum
+29    """
+30    lenx = len(old_spe.x)
+31    minx = np.min(old_spe.x)
+32    maxx = np.max(old_spe.x)
+33    xl = np.linspace(minx-(maxx-minx), minx, lenx, endpoint=True)[:-1]
+34    xr = np.linspace(maxx, maxx+(maxx-minx), lenx, endpoint=True)[1:]
+35
+36    new_spe.y = np.concatenate((np.zeros(lenx-1), old_spe.y, np.zeros(lenx-1)))
+37    new_spe.x = np.concatenate((xl, old_spe.x, xr))
+
+ + +

Extend x-axis by 100% in both directions.

+ +

The x-axis of resultant spectrum will be: +$[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$. +The length of the new spectrum is 3 times the original. The added values +are with an uniform step. In the middle is the original spectrum with +original x and y values. The coresponding y vallues for the newly added +x-values are always zeros.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + dropna( old_spe: Spectrum, new_spe: Spectrum): + + + +
+ + 
10@add_spectrum_filter
+11@validate_call(config=dict(arbitrary_types_allowed=True))
+12def dropna(old_spe: Spectrum,
+13           new_spe: Spectrum):
+14    """
+15    Remove non finite numbers on both axes
+16
+17    Args:
+18        old_spe: internal use only
+19        new_spe: internal use only
+20
+21    Returns: modified Spectrum
+22    """
+23
+24    x = old_spe.x
+25    y = old_spe.y
+26    idx = np.isfinite(x)
+27    x = x[idx]
+28    y = y[idx]
+29    idx = np.isfinite(y)
+30    x = x[idx]
+31    y = y[idx]
+32    new_spe.x = x
+33    new_spe.y = y
+
+ + +

Remove non finite numbers on both axes

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + smoothing_RC1( old_spe: Spectrum, new_spe: Spectrum, /, *args, method: Literal['savgol', 'sg', 'wiener', 'median', 'gauss', 'gaussian', 'lowess', 'boxcar'], **kwargs): + + + +
+ + 
16@add_spectrum_filter
+17@validate_call(config=dict(arbitrary_types_allowed=True))
+18def smoothing_RC1(old_spe: Spectrum,
+19                  new_spe: Spectrum, /, *args,
+20                  method: Literal['savgol', 'sg',
+21                                  'wiener',
+22                                  'median',
+23                                  'gauss', 'gaussian',
+24                                  'lowess',
+25                                  'boxcar'],
+26                  **kwargs):
+27    """
+28    Smooth the spectrum.
+29
+30    The spectrum will be smoothed using the specified filter.
+31    This method is inherited from ramanchada1 for compatibility reasons.
+32
+33    Args:
+34        old_spe: internal use only
+35        new_spe: internal use only
+36        method: method to be used
+37        **kwargs: keyword arguments to be passed to the selected method
+38
+39    Returns: modified Spectrum
+40    """
+41    if method == 'savgol' or method == 'sg':
+42        new_spe.y = savgol_filter(old_spe.y, **kwargs)  # window_length, polyorder
+43    elif method == 'wiener':
+44        new_spe.y = wiener(old_spe.y, **kwargs)
+45    elif method == 'gaussian' or method == 'gauss':
+46        new_spe.y = gaussian_filter1d(old_spe.y, **kwargs)  # sigma
+47    elif method == 'median':
+48        new_spe.y = medfilt(old_spe.y, **kwargs)
+49    elif method == 'lowess':
+50        kw = dict(span=11)
+51        kw.update(kwargs)
+52        x = np.linspace(0, 1, len(old_spe.y))
+53        new_spe.y = sm.nonparametric.lowess(old_spe.y, x, frac=(5*kw['span'] / len(old_spe.y)), return_sorted=False)
+54    elif method == 'boxcar':
+55        kw = dict(box_pts=11)
+56        kw.update(kwargs)
+57        box = boxcar(**kwargs, sym=True)
+58        new_spe.y = np.convolve(old_spe.y, box, mode='same')
+
+ + +

Smooth the spectrum.

+ +

The spectrum will be smoothed using the specified filter. +This method is inherited from ramanchada1 for compatibility reasons.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • method: method to be used
    • +
  • **kwargs: keyword arguments to be passed to the selected method
    • +
+ +

Returns: modified Spectrum

+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + bayesian_gaussian_mixture( spe: Spectrum, /, n_samples: typing.Annotated[int, Gt(gt=0)] = 5000, n_components: typing.Annotated[int, Gt(gt=0)] = 50, max_iter: typing.Annotated[int, Gt(gt=0)] = 100, moving_minimum_window: Optional[Annotated[int, Gt(gt=0)]] = None, random_state=None, trim_range: Optional[Tuple[float, float]] = None) -> sklearn.mixture._bayesian_mixture.BayesianGaussianMixture: + + + +
+ + 
12@add_spectrum_method
+13@validate_call(config=dict(arbitrary_types_allowed=True))
+14def bayesian_gaussian_mixture(spe: Spectrum, /,
+15                              n_samples: PositiveInt = 5000,
+16                              n_components: PositiveInt = 50,
+17                              max_iter: PositiveInt = 100,
+18                              moving_minimum_window: Union[PositiveInt, None] = None,
+19                              random_state=None,
+20                              trim_range: Optional[Tuple[float, float]] = None,
+21                              ) -> BayesianGaussianMixture:
+22    """
+23    Decompose the spectrum to Bayesian Gaussian Mixture
+24
+25    Args:
+26        spe: internal use only
+27        n_samples: optional. Defaults to 5000.
+28            Resampled dataset size
+29        n_components: optional. Defaults to 50.
+30            Number of expected gaussian components
+31        max_iter: optional. Defaults to 100.
+32            Maximal number of iterations.
+33        moving_minimum_window: optional. Defaults to None.
+34            If None no moving minimum is subtracted, otherwise as specified.
+35        random_state: optional. Defaults to None.
+36            Random generator seed to be used.
+37        trim_range: optional. Defaults to None:
+38            If None ignore trimming, otherwise trim range is in x-axis values.
+39
+40    Returns:
+41        BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture
+42    """
+43    if moving_minimum_window is not None:
+44        spe = spe.subtract_moving_minimum(moving_minimum_window)  # type: ignore
+45    samp = spe.gen_samples(size=n_samples, trim_range=trim_range)
+46    X = [[i] for i in samp]
+47    bgm = BayesianGaussianMixture(n_components=n_components,
+48                                  random_state=random_state,
+49                                  max_iter=max_iter
+50                                  ).fit(X)
+51    return bgm
+
+ + +

Decompose the spectrum to Bayesian Gaussian Mixture

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • n_samples: optional. Defaults to 5000. +Resampled dataset size
    • +
  • n_components: optional. Defaults to 50. +Number of expected gaussian components
    • +
  • max_iter: optional. Defaults to 100. +Maximal number of iterations.
    • +
  • moving_minimum_window: optional. Defaults to None. +If None no moving minimum is subtracted, otherwise as specified.
    • +
  • random_state: optional. Defaults to None. +Random generator seed to be used.
    • +
  • trim_range: optional. Defaults to None: +If None ignore trimming, otherwise trim range is in x-axis values.
    • +
+ +
Returns:
+ +
+

BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture

+
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + fit_peak_positions( spe: Spectrum, /, *, mov_min=40, center_err_threshold=0.5, find_peaks_kw={}, fit_peaks_kw={}) -> Dict[float, float]: + + + +
+ + 
14@add_spectrum_method
+15@validate_call(config=dict(arbitrary_types_allowed=True))
+16def fit_peak_positions(spe: Spectrum, /, *,
+17                       mov_min=40,
+18                       center_err_threshold=.5,
+19                       find_peaks_kw={},
+20                       fit_peaks_kw={},
+21                       ) -> Dict[float, float]:
+22    """
+23    Calculate peak positions and amplitudes.
+24
+25    Sequence of multiple processings:
+26    - `subtract_moving_minimum`
+27    - `find_peak_multipeak`
+28    - filter peaks with x-location better than threshold
+29
+30    Args:
+31        spe: internal use only
+32        mov_min: optional. Defaults to 40
+33            subtract moving_minimum with the specified window.
+34        center_err_threshold: optional. Defaults to 0.5.
+35            threshold for centroid standard deviation. Only peaks
+36            with better uncertainty will be returned.
+37
+38        find_peaks_kw: optional
+39            keyword arguments to be used with find_peak_multipeak
+40        fit_peaks_kw: optional
+41            keyword arguments to be used with fit_peaks_multipeak
+42
+43    Returns:
+44        Dict[float, float]: {positions: amplitudes}
+45    """
+46    ss = spe.subtract_moving_minimum(mov_min)  # type: ignore
+47    find_kw = dict(sharpening=None)
+48    find_kw.update(find_peaks_kw)
+49    cand = ss.find_peak_multipeak(**find_kw)
+50
+51    fit_kw = dict(profile='Gaussian')
+52    fit_kw.update(fit_peaks_kw)
+53    fit_res = spe.fit_peak_multimodel(candidates=cand, **fit_kw)  # type: ignore
+54
+55    pos, amp = fit_res.center_amplitude(threshold=center_err_threshold)
+56
+57    return dict(zip(pos, amp))
+
+ + +

Calculate peak positions and amplitudes.

+ +

Sequence of multiple processings:

+ + + +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • mov_min: optional. Defaults to 40 +subtract moving_minimum with the specified window.
    • +
  • center_err_threshold: optional. Defaults to 0.5. +threshold for centroid standard deviation. Only peaks +with better uncertainty will be returned.
    • +
  • find_peaks_kw: optional +keyword arguments to be used with find_peak_multipeak
    • +
  • fit_peaks_kw: optional +keyword arguments to be used with fit_peaks_multipeak
    • +
+ +
Returns:
+ +
+

Dict[float, float]: {positions: amplitudes}

+
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + find_peak_multipeak( spe: Spectrum, /, prominence: Optional[Annotated[float, Ge(ge=0)]] = None, wlen: Optional[Annotated[int, Ge(ge=0)]] = None, width: Union[int, Tuple[int, int], NoneType] = None, hht_chain: Optional[List[Annotated[int, Gt(gt=0)]]] = None, bgm_kwargs={}, sharpening: Optional[Literal['hht']] = None, strategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo') -> ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel: + + + +
+ + 
 41@add_spectrum_method
+ 42@validate_call(config=dict(arbitrary_types_allowed=True))
+ 43def find_peak_multipeak(
+ 44        spe: Spectrum, /,
+ 45        prominence: Union[NonNegativeFloat, None] = None,
+ 46        wlen: Union[NonNegativeInt, None] = None,
+ 47        width: Union[int, Tuple[int, int], None] = None,
+ 48        hht_chain: Union[List[PositiveInt], None] = None,
+ 49        bgm_kwargs={},
+ 50        sharpening: Union[Literal['hht'], None] = None,
+ 51        strategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo'
+ 52        ) -> ListPeakCandidateMultiModel:
+ 53    """
+ 54    Find groups of peaks in spectrum.
+ 55
+ 56    Args:
+ 57        spe: internal use only
+ 58        prominence: Optional. Defaults to None
+ 59            If None the prominence value will be `spe.y_nose`. Reasonable value for
+ 60            promience is `const * spe.y_noise_MAD`.
+ 61        wlen: optional. Defaults to None.
+ 62            wlen value used in `scipy.signal.find_peaks`. If wlen is None, 200 will be used.
+ 63        width: optional. Defaults to None.
+ 64            width value used in `scipy.signal.find_peaks`. If width is None, 2 will be used.
+ 65        hht_chain: optional. Defaults to None.
+ 66            List of hht_chain window sizes. If None, no hht sharpening is performed.
+ 67        bgm_kwargs: kwargs for bayesian_gaussian_mixture
+ 68        sharpening 'hht' or None. Defaults to None.
+ 69            If 'hht' hht sharpening will be performed before finding peaks.
+ 70        strategy: optional. Defauts to 'topo'.
+ 71            Peakfinding method
+ 72
+ 73    Returns:
+ 74        ListPeakCandidateMultiModel: Located peak groups
+ 75    """
+ 76
+ 77    if prominence is None:
+ 78        prominence = spe.y_noise
+ 79    if not wlen:
+ 80        wlen = 200
+ 81    if width is None:
+ 82        width = 2
+ 83
+ 84    if sharpening == 'hht':
+ 85        if hht_chain is None:
+ 86            hht_chain = [20]
+ 87        sharp_spe = spe.hht_sharpening_chain(movmin=hht_chain)  # type: ignore
+ 88    else:
+ 89        sharp_spe = spe
+ 90
+ 91    x_arr = sharp_spe.x
+ 92    y_arr = sharp_spe.y
+ 93
+ 94    def interpolate(x):
+ 95        x1 = int(x)
+ 96        x2 = x1 + 1
+ 97        y1 = x_arr[x1]
+ 98        y2 = x_arr[x2]
+ 99        return (y2-y1)/(x2-x1)*(x-x1)+y1
+100
+101    boundaries = peak_boundaries(spe, prominence=prominence, width=width, wlen=wlen)
+102    boundaries = [(li, ri) for li, ri in boundaries if (ri-li) > 4]
+103
+104    peaks, props = signal.find_peaks(y_arr,
+105                                     prominence=prominence,
+106                                     width=width,
+107                                     wlen=wlen)
+108    peak_groups = list()
+109
+110    if strategy in {'bgm', 'bayesian_gaussian_mixture'}:
+111        bgm = sharp_spe.bayesian_gaussian_mixture(**bgm_kwargs)
+112
+113        bgm_peaks = [[mean[0], np.sqrt(cov[0][0]), weight]
+114                     for mean, cov, weight in
+115                     zip(bgm.means_, bgm.covariances_, bgm.weights_)]
+116        bgm_peaks = sorted(bgm_peaks, key=lambda x: x[2], reverse=True)
+117        integral = np.sum(y_arr)
+118        n_peaks = (np.round(bgm.weights_, 2) > 0).sum()
+119        bgm_peaks = bgm_peaks[:n_peaks]
+120
+121        peak_list = list()
+122        for mean, sigma, weight in bgm_peaks:
+123            peak_list.append(dict(amplitude=weight*integral*2/sigma,
+124                                  position=mean,
+125                                  sigma=sigma,
+126                                  ))
+127        for li, ri in boundaries:
+128            peak_group = list()
+129            for peak in peak_list:
+130                if li < peak['position'] < ri:
+131                    peak_group.append(dict(position=peak['position'],
+132                                           amplitude=peak['amplitude'],
+133                                           sigma=peak['sigma'])
+134                                      )
+135            if peak_group:
+136                peak_groups.append(dict(boundaries=(x_arr[li], x_arr[ri]),
+137                                        peaks=peak_group))
+138    elif strategy == 'cwt':
+139        # TODO: cwt_args tbd
+140        peaks = find_peaks_cwt(spe.y, **bgm_kwargs)
+141        peak_list = list()
+142        for peak_index in peaks:
+143            half_max = spe.y[peak_index] / 2.0
+144            left_index = np.where(spe.y[:peak_index] <= half_max)[0][-1]
+145            right_index = np.where(spe.y[peak_index:] <= half_max)[0][0] + peak_index
+146            fwhm = spe.x[right_index] - spe.x[left_index]
+147            # rough sigma estimation based on fwhm
+148            sqrt2ln2 = 2 * np.sqrt(2 * np.log(2))
+149            # print(spe.x[peak_index], spe.y[peak_index], fwhm / sqrt2ln2 )
+150            peak_list.append(dict(amplitude=spe.y[peak_index],
+151                                  position=spe.x[peak_index],
+152                                  sigma=fwhm / sqrt2ln2,
+153                                  fwhm=fwhm))
+154        for li, ri in boundaries:
+155            peak_group = list()
+156            for peak in peak_list:
+157                if li < peak['position'] < ri:
+158                    peak_group.append(dict(position=peak['position'],
+159                                           amplitude=peak['amplitude'],
+160                                           sigma=peak['sigma']))
+161            if peak_group:
+162                peak_groups.append(dict(boundaries=(x_arr[li], x_arr[ri]),
+163                                        peaks=peak_group))
+164    elif strategy == 'topo':
+165        for li, ri in boundaries:
+166            peak_group = list()
+167            x1 = spe.x[li]
+168            x2 = spe.x[ri]
+169            y1 = spe.y[li]
+170            y2 = spe.y[ri]
+171            slope = (y2-y1)/(x2-x1)
+172            intercept = -slope*x1+y1
+173            for peak_i, peak_pos in enumerate(peaks):
+174                if li < peak_pos < ri:
+175                    pos_maximum = x_arr[peak_pos]
+176                    amplitude = props['prominences'][peak_i]
+177                    lwhm = pos_maximum - interpolate(props['left_ips'][peak_i])
+178                    rwhm = interpolate(props['right_ips'][peak_i]) - pos_maximum
+179                    fwhm = lwhm + rwhm
+180                    sigma = fwhm/2.355
+181                    skew = (rwhm-lwhm)/(rwhm+lwhm)
+182                    peak_group.append(dict(position=pos_maximum,
+183                                           amplitude=amplitude,
+184                                           sigma=sigma,
+185                                           skew=skew)
+186                                      )
+187            if peak_group:
+188                peak_groups.append(dict(base_intercept=intercept,
+189                                        base_slope=slope,
+190                                        boundaries=(x_arr[li], x_arr[ri]),
+191                                        peaks=peak_group))
+192
+193    candidates = ListPeakCandidateMultiModel.model_validate(peak_groups)
+194    return candidates
+
+ + +

Find groups of peaks in spectrum.

+ +
Arguments:
+ +
    +
  • spe: internal use only
    • +
  • prominence: Optional. Defaults to None +If None the prominence value will be spe.y_nose. Reasonable value for +promience is const * spe.y_noise_MAD.
    • +
  • wlen: optional. Defaults to None. +wlen value used in scipy.signal.find_peaks. If wlen is None, 200 will be used.
    • +
  • width: optional. Defaults to None. +width value used in scipy.signal.find_peaks. If width is None, 2 will be used.
    • +
  • hht_chain: optional. Defaults to None. +List of hht_chain window sizes. If None, no hht sharpening is performed.
    • +
  • bgm_kwargs: kwargs for bayesian_gaussian_mixture
    • +
  • sharpening 'hht' or None. Defaults to None. +If 'hht' hht sharpening will be performed before finding peaks.
    • +
  • strategy: optional. Defauts to 'topo'. +Peakfinding method
    • +
+ +
Returns:
+ +
+

ListPeakCandidateMultiModel: Located peak groups

+
+
+ + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + find_peak_multipeak_filter( old_spe: Spectrum, new_spe: Spectrum, /, *args, **kwargs): + + + +
+ + 
197@add_spectrum_filter
+198@validate_call(config=dict(arbitrary_types_allowed=True))
+199def find_peak_multipeak_filter(
+200        old_spe: Spectrum,
+201        new_spe: Spectrum, /,
+202        *args, **kwargs):
+203    """
+204    Same as `find_peak_multipeak` but the result is stored as metadata in the returned spectrum.
+205
+206    Args:
+207        old_spe: internal use only
+208        new_spe: internal use only
+209        *args, **kwargs: same as `find_peak_multipeak`
+210    """
+211    res = old_spe.find_peak_multipeak(*args, **kwargs)  # type: ignore
+212    new_spe.result = res.model_dump()
+
+ + +

Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • args, *kwargs: same as find_peak_multipeak
    • +
+
+ + +
+
+ +
+
@add_spectrum_method
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + fit_peak_multimodel( spe, /, *, profile: Union[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'], List[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']]], candidates: ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel, no_fit=False, should_break=[False], kwargs_fit={}, vary_baseline: bool = False, bound_centers_to_group: bool = False) -> ramanchada2.misc.types.fit_peaks_result.FitPeaksResult: + + + +
+ + 
 85@add_spectrum_method
+ 86@validate_call(config=dict(arbitrary_types_allowed=True))
+ 87def fit_peak_multimodel(spe, /, *,
+ 88                        profile: Union[available_models_type, List[available_models_type]],
+ 89                        candidates: ListPeakCandidateMultiModel,
+ 90                        no_fit=False,
+ 91                        should_break=[False],
+ 92                        kwargs_fit={},
+ 93                        vary_baseline: bool = False,
+ 94                        bound_centers_to_group: bool = False
+ 95                        ) -> FitPeaksResult:
+ 96    """
+ 97    Fit a model based on candidates to the spectrum.
+ 98
+ 99    Args:
+100        spe: internal use only
+101        profile: str or List[str]
+102            possible values are: ["""+str(available_models)+"""]
+103        candidates: as provided from find_peak_multipeak
+104        no_fit: optional. Defaults to False.
+105            If true, do not perform a fit. Result will be the inital guess,
+106            based on the data from peak candidates.
+107        should_break: optional. Defaults to [False].
+108            Use mutability of the python list type to be able to externaly
+109            break the minimization procedure.
+110        kwargs_fit: optional
+111            kwargs for fit function
+112        vary_baseline: optional. Defaults to False.
+113            If False baseline will not be a free parameter and its amplitude
+114            will be taken from the peak candidates.
+115        bound_centers_to_group: optional. Defaults to False.
+116            Perform a bounded fit. Request all peak centers to be within the group
+117            interval.
+118
+119    Returns:
+120        FitPeaksResult: groups of fitted peaks
+121    """
+122
+123    def iter_cb(params, iter, resid, *args, **kws):
+124        return should_break[0]
+125    if no_fit:
+126        kwargs_fit = dict(kwargs_fit)
+127        kwargs_fit['max_nfev'] = 1
+128    fit_res = FitPeaksResult()
+129    for group in candidates.root:
+130        mod, par = build_multipeak_model_params(profile=profile, candidates=group)
+131        if bound_centers_to_group:
+132            for p in par:
+133                if p.endswith('_center'):
+134                    par[p].set(min=group.boundaries[0], max=group.boundaries[1])
+135        idx = (group.boundaries[0] < spe.x) & (spe.x < group.boundaries[1])
+136        x = spe.x[idx]
+137        y = spe.y[idx]
+138        for i in range(len(group.peaks)):
+139            par[f'p{i}_center'].set(vary=False)
+140        fr = mod.fit(y, x=x, params=par, iter_cb=iter_cb,  **kwargs_fit)
+141        for i in range(len(group.peaks)):
+142            par[f'p{i}_center'].set(vary=True)
+143        if vary_baseline:
+144            par['bl_slope'].set(vary=True)
+145            par['bl_intercept'].set(vary=True)
+146        fr = mod.fit(y, x=x, params=par, iter_cb=iter_cb, **kwargs_fit)
+147        fit_res.append(fr)
+148    return fit_res
+
+ + + + +
+
+ +
+
@add_spectrum_filter
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + fit_peaks_filter( old_spe: Spectrum, new_spe: Spectrum, /, *args, should_break=[False], kwargs_fit={}, **kwargs): + + + +
+ + 
151@add_spectrum_filter
+152@validate_call(config=dict(arbitrary_types_allowed=True))
+153def fit_peaks_filter(
+154        old_spe: Spectrum,
+155        new_spe: Spectrum, /, *args,
+156        should_break=[False],
+157        kwargs_fit={},
+158        **kwargs,
+159        ):
+160    """
+161    Same as `fit_peak_multipeak` but the result is stored as metadata in the returned spectrum.
+162
+163    Args:
+164        old_spe: internal use only
+165        new_spe: internal use only
+166        should_break: same as in fit_peaks_multipeak
+167        *args, **kwargs: same as `fit_peaks_multipeak`
+168    """
+169    cand_groups = ListPeakCandidateMultiModel.model_validate(old_spe.result)
+170    new_spe.result = old_spe.fit_peak_multimodel(*args,  # type: ignore
+171                                                 candidates=cand_groups,
+172                                                 should_break=should_break,
+173                                                 kwargs_fit=kwargs_fit,
+174                                                 **kwargs).dumps()
+
+ + +

Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.

+ +
Arguments:
+ +
    +
  • old_spe: internal use only
    • +
  • new_spe: internal use only
    • +
  • should_break: same as in fit_peaks_multipeak
    • +
  • args, *kwargs: same as fit_peaks_multipeak
    • +
+
+ + +
+
+
Inherited Members
+
+ +
+
+
+
+ + \ No newline at end of file diff --git a/ramanchada2/theoretical_lines.html b/ramanchada2/theoretical_lines.html new file mode 100644 index 00000000..278ed7ba --- /dev/null +++ b/ramanchada2/theoretical_lines.html @@ -0,0 +1,264 @@ + + + + + + + ramanchada2.theoretical_lines API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.theoretical_lines

+ + + + + + + 
1#!/usr/bin/env python3
+
+ + +
+
+ + \ No newline at end of file diff --git a/ramanchada2/theoretical_lines/model_from_lines.html b/ramanchada2/theoretical_lines/model_from_lines.html new file mode 100644 index 00000000..dae9a42b --- /dev/null +++ b/ramanchada2/theoretical_lines/model_from_lines.html @@ -0,0 +1,637 @@ + + + + + + + ramanchada2.theoretical_lines.model_from_lines API documentation + + + + + + + + + + + + +
+
+

+ramanchada2.theoretical_lines.model_from_lines

+ + + + + + + 
 1from typing import Dict, List, Literal, Tuple
+ 2
+ 3import numpy as np
+ 4from lmfit import Model, Parameters
+ 5from lmfit.models import GaussianModel, VoigtModel
+ 6from pydantic import BaseModel, Field, validate_call
+ 7
+ 8
+ 9@validate_call(config=dict(arbitrary_types_allowed=True))
+10def model_from_lines(names: List[str],
+11                     positions: List[float],
+12                     intensities: Dict[str, List[float]],
+13                     model: Literal['gaussian', 'voigt'] = 'gaussian'
+14                     ) -> Tuple[Model, Parameters]:
+15
+16    if model == 'gaussian':
+17        lm_model = GaussianModel
+18    elif model == 'voigt':
+19        lm_model = VoigtModel
+20    else:
+21        raise ValueError(f'model {model} not known')
+22    mod = np.sum([
+23        lm_model(prefix=f'{spe_type}_{name}_', name=f'{spe_type}_{name}')
+24        for spe_type in intensities
+25        for name in names
+26        ])
+27
+28    params = Parameters()
+29    params.add('pedestal', 0, min=0)
+30    params.add('sigma', 2, min=0)
+31    params.add('x0', 0)
+32    params.add('x1', 1, min=0)
+33
+34    for spe_type, spe_int in intensities.items():
+35        spe_prefix = f'{spe_type}_'
+36        params.add(spe_prefix+'amplitude', 1, min=0)
+37        for name, pos, line_int in zip(names, positions, spe_int):
+38            line_prefix = f'{spe_prefix}{name}_'
+39            params.add(line_prefix+'amplitude',
+40                       expr=f'({line_int}*{spe_prefix}amplitude)+pedestal')
+41            params.add(line_prefix+'center', expr=f'(({pos}+x0)*x1)')
+42            params.add(line_prefix+'sigma', expr='sigma')
+43
+44    return mod, params
+45
+46
+47class PydPeakModel(BaseModel):
+48    model: Literal['gaussian', 'voigt'] = Field('voigt')
+49    position: float
+50    inensity: float = Field(1, gt=0)
+51    sigma: float = Field(1, gt=0)
+52    name: str = Field('')
+53
+54
+55@validate_call(config=dict(arbitrary_types_allowed=True))
+56def model_from_list(peaks_list: List[PydPeakModel]
+57                    ) -> Tuple[Model, Parameters]:
+58    params = Parameters()
+59    params.add('amplitude', 1, min=0)
+60    params.add('sigma', 1, min=0)
+61    params.add('x0', 0)
+62    params.add('x1', 1, min=0)
+63    params.add('x2', 0, min=-1e-3, max=1e-3)
+64    params.add('x3', 0, min=-1e-5, max=1e-5)
+65
+66    peaks = list()
+67    for ii, peak in enumerate(peaks_list):
+68        if peak.model == 'gaussian':
+69            lm_model = GaussianModel
+70        elif peak.model == 'voigt':
+71            lm_model = VoigtModel
+72        else:
+73            raise ValueError(f'model {peak.model} not known')
+74        prefix = f'{peak.name}_' if peak.name else f'_{ii}_'
+75        name = f'{peak.name}' if peak.name else f'_{ii}'
+76        peaks.append(lm_model(prefix=prefix, name=name))
+77
+78        params.add(prefix+'amplitude', expr=f'({peak.inensity}*amplitude)')
+79        params.add(prefix+'center',
+80                   expr=f'{peak.position}**3*x3 + {peak.position}**2*x2 + {peak.position}*x1 + x0')
+81        params.add(prefix+'sigma', expr=f'{peak.sigma}*sigma')
+82    mod = np.sum(peaks)
+83
+84    return mod, params
+
+ + +
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + model_from_lines( names: List[str], positions: List[float], intensities: Dict[str, List[float]], model: Literal['gaussian', 'voigt'] = 'gaussian') -> Tuple[lmfit.model.Model, lmfit.parameter.Parameters]: + + + +
+ + 
10@validate_call(config=dict(arbitrary_types_allowed=True))
+11def model_from_lines(names: List[str],
+12                     positions: List[float],
+13                     intensities: Dict[str, List[float]],
+14                     model: Literal['gaussian', 'voigt'] = 'gaussian'
+15                     ) -> Tuple[Model, Parameters]:
+16
+17    if model == 'gaussian':
+18        lm_model = GaussianModel
+19    elif model == 'voigt':
+20        lm_model = VoigtModel
+21    else:
+22        raise ValueError(f'model {model} not known')
+23    mod = np.sum([
+24        lm_model(prefix=f'{spe_type}_{name}_', name=f'{spe_type}_{name}')
+25        for spe_type in intensities
+26        for name in names
+27        ])
+28
+29    params = Parameters()
+30    params.add('pedestal', 0, min=0)
+31    params.add('sigma', 2, min=0)
+32    params.add('x0', 0)
+33    params.add('x1', 1, min=0)
+34
+35    for spe_type, spe_int in intensities.items():
+36        spe_prefix = f'{spe_type}_'
+37        params.add(spe_prefix+'amplitude', 1, min=0)
+38        for name, pos, line_int in zip(names, positions, spe_int):
+39            line_prefix = f'{spe_prefix}{name}_'
+40            params.add(line_prefix+'amplitude',
+41                       expr=f'({line_int}*{spe_prefix}amplitude)+pedestal')
+42            params.add(line_prefix+'center', expr=f'(({pos}+x0)*x1)')
+43            params.add(line_prefix+'sigma', expr='sigma')
+44
+45    return mod, params
+
+ + + + +
+
+ +
+ + class + PydPeakModel(pydantic.main.BaseModel): + + + +
+ + 
48class PydPeakModel(BaseModel):
+49    model: Literal['gaussian', 'voigt'] = Field('voigt')
+50    position: float
+51    inensity: float = Field(1, gt=0)
+52    sigma: float = Field(1, gt=0)
+53    name: str = Field('')
+
+ + +

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

+ +

A base class for creating Pydantic models.

+ +
Attributes:
+ +
    +
  • __class_vars__: The names of the class variables defined on the model.
    • +
  • __private_attributes__: Metadata about the private attributes of the model.
    • +
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • +
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • +
  • __pydantic_core_schema__: The core schema of the model.
    • +
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • +
  • __pydantic_decorators__: Metadata containing the decorators defined on the model. +This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • +
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to +__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • +
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • +
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • +
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • +
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • +
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • +
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] +is set to 'allow'.
    • +
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • +
  • __pydantic_private__: Values of private attributes set on the model instance.
    • +
+
+ + +
+
+ model: Literal['gaussian', 'voigt'] + + +
+ + + + +
+
+
+ position: float + + +
+ + + + +
+
+
+ inensity: float + + +
+ + + + +
+
+
+ sigma: float + + +
+ + + + +
+
+
+ name: str + + +
+ + + + +
+
+
+ model_config: ClassVar[pydantic.config.ConfigDict] = +{} + + +
+ + +

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

+
+ + +
+
+
+ model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]] = + + {'model': FieldInfo(annotation=Literal['gaussian', 'voigt'], required=False, default='voigt'), 'position': FieldInfo(annotation=float, required=True), 'inensity': FieldInfo(annotation=float, required=False, default=1, metadata=[Gt(gt=0)]), 'sigma': FieldInfo(annotation=float, required=False, default=1, metadata=[Gt(gt=0)]), 'name': FieldInfo(annotation=str, required=False, default='')} + + +
+ + +

Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

+ +

This replaces Model.__fields__ from Pydantic V1.

+
+ + +
+
+
+ model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]] = +{} + + +
+ + +

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

+
+ + +
+
+
+ +
+
@validate_call(config=dict(arbitrary_types_allowed=True))
+ + def + model_from_list( peaks_list: List[PydPeakModel]) -> Tuple[lmfit.model.Model, lmfit.parameter.Parameters]: + + + +
+ + 
56@validate_call(config=dict(arbitrary_types_allowed=True))
+57def model_from_list(peaks_list: List[PydPeakModel]
+58                    ) -> Tuple[Model, Parameters]:
+59    params = Parameters()
+60    params.add('amplitude', 1, min=0)
+61    params.add('sigma', 1, min=0)
+62    params.add('x0', 0)
+63    params.add('x1', 1, min=0)
+64    params.add('x2', 0, min=-1e-3, max=1e-3)
+65    params.add('x3', 0, min=-1e-5, max=1e-5)
+66
+67    peaks = list()
+68    for ii, peak in enumerate(peaks_list):
+69        if peak.model == 'gaussian':
+70            lm_model = GaussianModel
+71        elif peak.model == 'voigt':
+72            lm_model = VoigtModel
+73        else:
+74            raise ValueError(f'model {peak.model} not known')
+75        prefix = f'{peak.name}_' if peak.name else f'_{ii}_'
+76        name = f'{peak.name}' if peak.name else f'_{ii}'
+77        peaks.append(lm_model(prefix=prefix, name=name))
+78
+79        params.add(prefix+'amplitude', expr=f'({peak.inensity}*amplitude)')
+80        params.add(prefix+'center',
+81                   expr=f'{peak.position}**3*x3 + {peak.position}**2*x2 + {peak.position}*x1 + x0')
+82        params.add(prefix+'sigma', expr=f'{peak.sigma}*sigma')
+83    mod = np.sum(peaks)
+84
+85    return mod, params
+
+ + + + +
+
+ + \ No newline at end of file diff --git a/search.js b/search.js new file mode 100644 index 00000000..d2454bb0 --- /dev/null +++ b/search.js @@ -0,0 +1,46 @@ +window.pdocSearch = (function(){ +/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oPurpose\n\n

ramanchada2 software package is meant to fill the gap between the theoretical\nRaman analysis and the experimental Raman spectroscopy by providing means to\ncompare data of different origin. The software is in early development stage\nbut still able to solve practical problems.

\n\n

Features

\n\n

Read simulated data

\n\n

Process simulated data by VASP and CRYSTAL and provide same interface.\nCRYSTAL data contain intensities for multiple orientations -- laser beam\nincidents perpendicularly or parallelly to the observation and information\nfor mono-crystals. VASP data provide data only for poly-crystals but in\ndifferent format. So the perpendicular and parallel intensities are calculated\nby an implemented algorithm.

\n\n

Models

\n\n

LMFIT theoretical models can be build by spectral information obtained by\nsimulations or by provided by the user. These models can be fit to experimental\ndata, providing calibration information. At poor initial calibration the minimisation\nprocedure naturally fails. An iterative procedure aiming to solve this problem\nwas adopted in the code. On the first iteration the experimental spectrum lines\nare artificially broadened. This makes it possible for the minimisation procedure\nto find a parameters that are close enough to be used as an initial guess for\nthe second iteration. In few iterations the algorithm is able to fit to the original\nexperimental data. This idea is implemented and is at proof-of-concept level.

\n\n

Generate spectra

\n\n

Spectra can be generated by the theoretical models. Random Poissonian noise and\nartificial random-generated baseline can be added to the generated spectra, making\nthem convenient tools to test new methods for analysis.

\n\n

Spectrum manipulation

\n\n

A number of filters can be applied to spectra (experimental and generated).\nScaling on both x and y axes is possible. Scaling could be linear or arbitrary\nuser defined function. A convolution is possible with set of predefined functions\nas well as user defined model.

\n\n

Concept

\n\n

The code is object oriented, written in python. Main elements are Spectrum and\ntheoretical models. Theoretical models are based on LMFIT library, while\nSpectrum is a custom made class. Spectrum object contains data for x and y axes\nand metadata coming from experimental files or other sources. It is planned\nto add information about the uncertainties in x and y. All filters and manipulation\nprocedures are available as class methods. Measures are taken to preserve spectrum\ninstances immutable, so filters are generating new spectra, preserving the original\nunchanged. Additionally, Spectrum has information about its history -- the sequence\nof applied filters.

\n\n

File formats

\n\n

.cha

\n\n

ramanchada software package introduced .cha file format, which is an HDF5\nwith a simple layout.

\n\n

Cache in .cha files

\n\n

The concept to keep previous variants of data is employed in ramanchada2. If\nconfigured so, the software saves the data for all Spectrum instances to a\ntree-organized .cha file. When a particular chain of operations is requested\nby the user, the software checks if the final result is present in the cache file,\nif so it is provided, otherwise the software checks for its parent. When a parent\nor some of the grand parents are present, they are taken as a starting point and\nthe needed steps are applied to provide the final result. The current implementation\nuses h5py library to access local hdf files. It is foreseen to have implementation\nwith h5pyd that support network operations.

\n\n

Nexus format

\n\n

The latest ramanchada2 package allows export of a spectrum to NeXus format.

\n\n

Decorated Functions in Spectrum

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Function: __add__

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Docstring: No docstring available

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Function: __init__

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Docstring: No docstring available

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Function: __mul__

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Docstring: No docstring available

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Function: __sub__

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Docstring: No docstring available

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Function: __truediv__

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Docstring: No docstring available

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Function: abs_nm_to_shift_cm_1

\n\n

Docstring: \n Convert wavelength to Ramanshift in wavenumber

\n\n
Args:\n    spe: internal use only\n    laser_wave_length_nm: Laser wave length\n\nReturns: Corrected x-values\n
\n\n
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Function: abs_nm_to_shift_cm_1_filter

\n\n

Docstring: \n Convert wavelength to Ramanshift in wavenumber

\n\n
Args:\n    spe: internal use only\n    laser_wave_length_nm: Laser wave length\n\nReturns: Spectrum with corrected x-values\n
\n\n
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Function: add_baseline

\n\n

Docstring: \n Add artificial baseline to the spectrum.\n A random baseline is generated in frequency domain using uniform random numbers.\n The baseline in frequency domain is tapered with bohman window to reduce the bandwidth\n of the baseline to first n_freq frequencies and is transformed to \"time\" domain.\n Additionaly by using func parameter the user can define arbitrary function\n to be added as baseline.

\n\n
Args:\n    n_freq:\n        Must be `> 2`. Number of lowest frequency bins distinct from zero.\n    amplitude:\n        Upper boundary for the uniform random generator.\n    pedestal:\n        Additive constant pedestal to the spectrum.\n    func:\n        Callable. User-defined function to be added as baseline. Example: `func = lambda x: x*.01 + x**2*.0001`.\n    rng_seed:\n        `int`, optional. Seed for the random generator.\n
\n\n
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Function: add_gaussian_noise

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Docstring: \n Add gaussian noise to the spectrum.

\n\n
Random number i.i.d. $N(0, \\sigma)$ is added to every sample\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    sigma:\n        Sigma of the gaussian noise.\n    rng_seed:\n        `int` or rng state, optional, seed for the random generator.\n        If a state is provided, it is updated in-place.\n\nReturns: modified Spectrum\n
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Function: add_gaussian_noise_drift

\n\n

Docstring: \n Add cumulative gaussian noise to the spectrum.

\n\n
Exponential-moving-average-like gaussian noise is added\nto each sample. The goal is to mimic the low-frequency noise\n(or random substructures in spectra).\nThe additive noise is\n$$a_i = coef*\\sum_{j=0}^{i-1}g_j + g_i,$$\n\nwhere\n$$g_i = \\mathcal{N}(0, 1+\\frac{coef}{\\sqrt 2}).$$\n\nThis way drifting is possible while keeping the\n$$\\sigma(\\Delta(a)) \\approx 1.$$\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    sigma:\n        Sigma of the gaussian noise.\n    coef:\n        `float` in `[0, 1]`, drifting coefficient. If `coef == 0`,\n        the result is identical to `add_gaussian_noise()`.\n    rng_seed:\n        `int` or rng state, optional. Seed for the random generator.\n        If a state is provided, it is updated in-place.\n\nReturns: modified Spectrum\n
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Function: add_poisson_noise

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Docstring: \n Add poisson noise to the spectrum.

\n\n
For each particular sample the noise is proportional to $\\sqrt{scale*a_i}$.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    scale:\n        `float`, optional, default is `1`. Scale the amplitude of the noise.\n    rng_seed:\n        `int` or rng state, optional. Seed for the random generator.\n        If a state is provided, it is updated in-place.\n\nReturns: modified Spectrum\n
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Function: apply_processing

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Docstring: No docstring available

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Function: bayesian_gaussian_mixture

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Docstring: \n Decompose the spectrum to Bayesian Gaussian Mixture

\n\n
Args:\n    spe: internal use only\n    n_samples: optional. Defaults to 5000.\n        Resampled dataset size\n    n_components: optional. Defaults to 50.\n        Number of expected gaussian components\n    max_iter: optional. Defaults to 100.\n        Maximal number of iterations.\n    moving_minimum_window: optional. Defaults to None.\n        If None no moving minimum is subtracted, otherwise as specified.\n    random_state: optional. Defaults to None.\n        Random generator seed to be used.\n    trim_range: optional. Defaults to None:\n        If None ignore trimming, otherwise trim range is in x-axis values.\n\nReturns:\n    BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture\n
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Function: calibrate_by_deltas_filter

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Docstring: No docstring available

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Function: calibrate_by_deltas_model

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Docstring: \n - Builds a composite model based on a set of user specified delta lines.\n - Initial guess is calculated based on 10-th and 90-th percentiles of\n the distributions.

\n\n
The phasespace of the model is flat with big amount of narrow minima.\nIn order to find the best fit, the experimental data are successively\nconvolved with gaussians with different widths startign from wide to\nnarrow. The model for the calibration is 3-th order polynomial, which\npotentialy can be changed for higher order polynomial. In order to avoid\nsolving the inverse of the calibration function, the result is tabulated\nand interpolated linarly for each bin of the spectrum.\nThis alogrithm is useful for corse calibration.\n
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Function: central_moments

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Docstring: No docstring available

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Function: convolve

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Docstring: \n Convole spectrum with arbitrary lineshape.

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    lineshape:callable, `str` or `np.ndarray`.\n         If callable: should have a single positional argument `x`, e.g.\n        `lambda x: np.exp((x/5)**2)`.\n        If predefined peak profile: can be `gaussian`, `lorentzian`, `voigt`,\n        `pvoigt`, `moffat` or `pearson4`.\n        If `np.ndarray`: lineshape in samples.\n    **kwargs:\n        Additional kwargs will be passed to lineshape function.\n\nReturns: modified Spectrum\n
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Function: derivative_sharpening

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Docstring: \n Derivative-based sharpening.

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Sharpen the spectrum subtracting second derivative and add fourth derivative.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    filter_fraction `float` in (0; 1]: Default is 0.6\n        Depth of filtration\n    signal_width: The width of features to be enhanced in sample count\n    der2_factor: Second derivative scaling factor\n    der4_factor: Fourth derivative scaling factor\n\nReturns: modified Spectrum\n
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Function: drop_spikes

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Docstring: \n Removes single-bin spikes.

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Remove x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    n_sigma: optional, default is `10`.\n        Threshold is `n_sigma` times the standard deviation.\n\nReturns: modified Spectrum\n
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Function: dropna

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Docstring: \n Remove non finite numbers on both axes

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n\nReturns: modified Spectrum\n
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Function: find_peak_multipeak

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Docstring: \n Find groups of peaks in spectrum.

\n\n
Args:\n    spe: internal use only\n    prominence: Optional. Defaults to None\n        If None the prominence value will be `spe.y_nose`. Reasonable value for\n        promience is `const * spe.y_noise_MAD`.\n    wlen: optional. Defaults to None.\n        wlen value used in `scipy.signal.find_peaks`. If wlen is None, 200 will be used.\n    width: optional. Defaults to None.\n        width value used in `scipy.signal.find_peaks`. If width is None, 2 will be used.\n    hht_chain: optional. Defaults to None.\n        List of hht_chain window sizes. If None, no hht sharpening is performed.\n    bgm_kwargs: kwargs for bayesian_gaussian_mixture\n    sharpening 'hht' or None. Defaults to None.\n        If 'hht' hht sharpening will be performed before finding peaks.\n    strategy: optional. Defauts to 'topo'.\n        Peakfinding method\n\nReturns:\n    ListPeakCandidateMultiModel: Located peak groups\n
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Function: find_peak_multipeak_filter

\n\n

Docstring: \n Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.

\n\n
Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    *args, **kwargs: same as `find_peak_multipeak`\n
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Function: fit_peak_multimodel

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Docstring: No docstring available

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Function: fit_peak_positions

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Docstring: \n Calculate peak positions and amplitudes.

\n\n
Sequence of multiple processings:\n- `subtract_moving_minimum`\n- `find_peak_multipeak`\n- filter peaks with x-location better than threshold\n\nArgs:\n    spe: internal use only\n    mov_min: optional. Defaults to 40\n        subtract moving_minimum with the specified window.\n    center_err_threshold: optional. Defaults to 0.5.\n        threshold for centroid standard deviation. Only peaks\n        with better uncertainty will be returned.\n\n    find_peaks_kw: optional\n        keyword arguments to be used with find_peak_multipeak\n    fit_peaks_kw: optional\n        keyword arguments to be used with fit_peaks_multipeak\n\nReturns:\n    Dict[float, float]: {positions: amplitudes}\n
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Function: fit_peaks_filter

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Docstring: \n Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.

\n\n
Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    should_break: same as in fit_peaks_multipeak\n    *args, **kwargs: same as `fit_peaks_multipeak`\n
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Function: from_cache_or_calc

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Docstring: \n Load spectrum from cache or calculate if needed.

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The cache is a nested structure of spectra. All processings applied to\na spectrum result to spectra of the initial one. If part of the requred\nprocessings are available, only the needed steps are calculated and added\nto the cache.\n\nArgs:\n    required_steps: List of required steps in the form\n        [{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]\n    cachefile: optional. Defaults to None.\n        Filename of the cache. If None no cache is used\n
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Function: from_chada

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Docstring: No docstring available

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Function: from_delta_lines

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Docstring: \n Generate Spectrum with delta lines.

\n\n
Args:\n    deltas:\n        Keys of the dictionary are the `x` positions of the deltas; values are the amplitudes of the corresponding\n        deltas.\n    xcal:\n        Callable, optional. `x` axis calibration function.\n    nbins:\n        `int`, optional. Number of bins in the spectrum.\n    xaxis:\n        `Array-like`, optional. The xaxis of the new spectrum. If `xaxis` is provided,\n        `xcal` should be `None` and `nbins` is ignored.\n\nExample:\n\nThis will produce spectrum with 1000 bins in the range `[-1000, 2000)`:\n\n\n
\n
xcal = lambda x: x*3 -1000, nbins=1000\n
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Function: from_local_file

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Docstring: \n Read experimental spectrum from a local file.

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Args:\n    in_file_name:\n        Path to a local file containing a spectrum.\n    filetype:\n        Specify the filetype. Filetype can be any of: `spc`, `sp`, `spa`, `0`, `1`, `2`, `wdf`, `ngs`, `jdx`, `dx`,\n        `txt`, `txtr`, `csv`, `prn`, `rruf`, `spe` (Princeton Instruments) or `None`.\n        `None` used to determine by extension of the file.\n    backend:\n        `native`, `rc1_parser` or `None`. `None` means both.\n\nRaises:\n    ValueError:\n        When called with unsupported file formats.\n
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Function: from_simulation

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Docstring: \n Generate spectrum from simulation file.

\n\n
The returned spectrum has only few x/y pairs -- one for each simulated line. Values along\nthe x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.\n\nArgs:\n    in_file:\n        Path to a local file, or file-like object.\n    sim_type:\n        If `vasp`: `.dat` file from VASP simulation. If `crystal_out`: `.out` file from CRYSTAL simulation, not\n        preferred. If `crystal_dat`: `.dat` file from CRYSTAL simulation.\n    use:\n        One of the directions `I_tot`, `I_perp`, `I_par`, `I_xx`, `I_xy`,\n        `I_xz`, `I_yy`, `I_yz`, `I_zz`, `I_tot`, `I_perp`, `I_par` are\n        available for both CRYSTAL and VASP. `I_xx`, `I_xy`, `I_xz`,\n        `I_yy`, `I_yz`, `I_zz` are available only for CRYSTAL. If a Dict is\n        passed, the key should be directions and values should be weighting factor.\n        For example, `use={'I_perp': .1, 'I_par': .9}`\n
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Function: from_spectral_component_collection

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Docstring: \n from_spectral_component_collection

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Args:\n    spe_components:\n        SpectralComponentCollection\n    x:\n        `int` or array-like, optional, default `2000`. `x` axis of the spectrum.\n
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Function: from_stream

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Docstring: No docstring available

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Function: from_test_spe

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Docstring: Create new spectrum from test data.

\n\n
Args:\n    index:\n        `int` or `None`, optional, default is `None`. If `int`: will be used as an index of filtered list. If\n        `None`: a random spectrum will be taken.\n    **kwargs:\n        The rest of the parameters will be used as filter.\n
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Function: from_theoretical_lines

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Docstring: \n Generate spectrum from lmfit shapes.

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Args:\n    shapes:\n        The shapes to be used for spectrum generation.\n    params:\n        Shape parameters to be applied to be used with shapes.\n    x:\n        Array with `x` values, by default `np.array(2000)`.\n
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Function: gen_samples

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Docstring: No docstring available

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Function: get_spikes

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Docstring: \n Get single-bin spikes only.

\n\n
Get x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    n_sigma: optional, default is `10`.\n        Threshold is `n_sigma` times the standard deviation.\n\nReturns: modified Spectrum\n
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Function: hdr_from_multi_exposure

\n\n

Docstring: Create an HDR spectrum from several spectra with different exposures.

\n\n
The resulting spectrum will have the details in low-intensity peaks\nfrom long-exposure-time spectrum. As long-exposure-time\nspectrum might be sturated, the information for high-intensity\npeaks will be taken from short-exposure-time spectrum.\nThis function will work on a very limited number of spectra,\nbecause we still do not have standardized metadata.\n
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Function: hht_sharpening

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Docstring: \n Hilbert-Huang based sharpening.

\n\n
In order to reduce the overshooting, moving minimum is subtracted from the result\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    movmin: optional. Default is 100\n        Window size for moving minimum\n\nReturns: modified Spectrum\n
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Function: hht_sharpening_chain

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Docstring: \n Hilbert-Huang based chain sharpening.

\n\n
Sequence of Hilbert-Huang sharpening procedures are performed.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    movmin: List[int], optional. Default is [150, 50]\n        The numer of values in the list defines how many iterations\n        of HHT_sharpening will be performed and the values define\n        the moving minimum window sizes for the corresponding operations.\n\nReturns: modified Spectrum\n
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Function: moving_average

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Docstring: \n Moving average filter.

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    window_size:\n        `int`, optional, default is `10`.\n\nReturns: modified Spectrum\n
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Function: moving_average_convolve

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Docstring: \n Moving average filter.

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    window_size:\n        `int`, optional, default is `10`.\n\nReturns: modified Spectrum\n
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Function: moving_median

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Docstring: \n Moving median filter.

\n\n
The resultant spectrum is moving minimum of the input.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    window_size:\n        `int`, optional, default is `10`.\n\nReturns: modified Spectrum\n
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Function: moving_minimum

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Docstring: \n Moving minimum baseline estimator.\n Successive values are calculated as minima of rolling rectangular window.

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Function: normalize

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Docstring: \n Normalize the spectrum.

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Args:\n    strategy:\n        If `unity`: normalize to `sum(y)`. If `min_unity`: subtract the minimum and normalize to 'unity'. If\n        `unity_density`: normalize to `\u03a3(y_i*\u0394x_i)`. If `unity_area`: same as `unity_density`. If `minmax`: scale\n        amplitudes in range `[0, 1]`. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.\n
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Function: pad_zeros

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Docstring: \n Extend x-axis by 100% in both directions.

\n\n
The x-axis of resultant spectrum will be:\n$[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.\nThe length of the new spectrum is 3 times the original. The added values\nare with an uniform step. In the middle is the original spectrum with\noriginal x and y values. The coresponding y vallues for the newly added\nx-values are always zeros.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n\nReturns: modified Spectrum\n
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Function: recover_spikes

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Docstring: \n Recover single-bin spikes.

\n\n
Recover x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    n_sigma: optional, default is `10`.\n        Threshold is `n_sigma` times the standard deviation.\n\nReturns: modified Spectrum\n
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Function: resample_NUDFT

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Docstring: \n Resample the spectrum using Non-uniform discrete fourier transform.

\n\n
The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.\n\nArgs:\n    spe: internal use only\n    x_range: optional. Defaults to (0, 4000).\n        The x_range of the new spectrum.\n    xnew_bins: optional. Defaults to 100.\n        Number of bins of the new spectrum\n    window: optional, Defaults to None.\n        The window to be used for lowpass filter. If None 'blackmanharris' is used.\n        If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.\n    cumulative: optional. Defaults to False.\n        If True, the resultant spectrum will be cumulative and normalized\n        (in analogy with CDF).\n\nReturns:\n    (x_values, y_values)\n
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Function: resample_NUDFT_filter

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Docstring: \n Resample the spectrum using Non-uniform discrete fourier transform.

\n\n
The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    x_range: optional. Defaults to (0, 4000).\n        The x_range of the new spectrum.\n    xnew_bins: optional. Defaults to 100.\n        Number of bins of the new spectrum\n    window: optional, Defaults to None.\n        The window to be used for lowpass filter. If None 'blackmanharris' is used.\n        If no low-pass filter is required, one can use `window=lambda x: [1]*len(x)`.\n    cumulative: optional. Defaults to False.\n        If True, the resultant spectrum will be cumulative and normalized\n        (in analogy with CDF).\n\nReturns: modified Spectrum\n
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Function: resample_spline

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Docstring: \n Resample the spectrum using spline interpolation.

\n\n
The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.\n\nArgs:\n    spe: internal use only\n    x_range: optional. Defaults to (0, 4000).\n        The x_range of the new spectrum.\n    xnew_bins: optional. Defaults to 100.\n        Number of bins of the new spectrum\n    spline: optional, Defaults to 'pchip'.\n        Name of the spline funcion to be used.\n    cumulative: optional. Defaults to False.\n        If True, the resultant spectrum will be cumulative and normalized\n        (in analogy with CDF).\n\nReturns:\n    (x_values, y_values)\n
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Function: resample_spline_filter

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Docstring: \n Resample the spectrum using spline interpolation.

\n\n
The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    x_range: optional. Defaults to (0, 4000).\n        The x_range of the new spectrum.\n    xnew_bins: optional. Defaults to 100.\n        Number of bins of the new spectrum\n    spline: optional, Defaults to 'pchip'.\n        Name of the spline funcion to be used.\n    cumulative: optional. Defaults to False.\n        If True, the resultant spectrum will be cumulative and normalized\n        (in analogy with CDF).\n\nReturns: modified Spectrum\n
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Function: scale_xaxis_fun

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Docstring: \n Apply arbitrary calibration function to the x-axis values.

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    fun: function to be applied\n    args: Additional arguments to the provided functions\n\nReturns: Corrected spectrum\n\nRaises:\n    ValueError: If the new x-values are not strictly monotonically increasing.\n
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Function: scale_xaxis_linear

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Docstring: \n Scale x-axis using a factor.

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    factor: Defaults to 1.\n        Multiply x-axis values with `factor`\n    preserve_integral: optional. Defaults to False.\n        If True, preserves the integral in sence\n        $\\sum y_{orig;\\,i}*{\\Delta x_{orig}}_i = \\sum y_{new;\\,i}*{\\Delta x_{new}}_i = $\nReturns: Corrected spectrum\n
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Function: scale_yaxis_linear

\n\n

Docstring: \n Scale y-axis values

\n\n
This function provides the same result as `spe*const`\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    factor optional. Defaults to 1.\n        Y-values scaling factor\n\nReturns: corrected spectrum\n
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Function: set_new_xaxis

\n\n

Docstring: \n Substitute x-axis values with new ones

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Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    xaxis: new x-axis values\n\nReturns: corrected spectrum\n\nRaises:\n    ValueError: If the provided array does not match the shape of the spectrum.\n
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Function: shift_cm_1_to_abs_nm

\n\n

Docstring: \n Convert Ramanshift in wavenumber to wavelength

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Args:\n    spe: internal use only\n    laser_wave_length_nm: Laser wave length\n\nReturns: Corrected x-values\n
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Function: shift_cm_1_to_abs_nm_filter

\n\n

Docstring: \n Convert Ramanshift in wavenumber to wavelength

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Args:\n    spe: internal use only\n    laser_wave_length_nm: Laser wave length\n\nReturns: Spectrum with corrected x-values\n
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Function: smoothing_RC1

\n\n

Docstring: \n Smooth the spectrum.

\n\n
The spectrum will be smoothed using the specified filter.\nThis method is inherited from ramanchada1 for compatibility reasons.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    method: method to be used\n    **kwargs: keyword arguments to be passed to the selected method\n\nReturns: modified Spectrum\n
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Function: spe_distribution

\n\n

Docstring: No docstring available

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Function: spike_indices

\n\n

Docstring: \n Find spikes in spectrum

\n\n
Single-bin spikes are located using left and right successive\ndifferences. The threshold is based on the standart deviation\nof the metric which makes this algorithm less optimal.\n\nArgs:\n    spe: internal use only\n    n_sigma: Threshold value should be `n_sigma` times the standart\n      deviation of the metric.\n\nReturns: List of spike indices\n
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Function: subtract_baseline_rc1_als

\n\n

Docstring: No docstring available

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Function: subtract_baseline_rc1_snip

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Docstring: No docstring available

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Function: subtract_moving_median

\n\n

Docstring: \n Subtract moving median filter.

\n\n
The resultant spectrum is moving minimum of the input subtracted from the input.\n\nArgs:\n    old_spe: internal use only\n    new_spe: internal use only\n    window_size:\n        `int`, optional, default is `10`.\n\nReturns: modified Spectrum\n
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Function: subtract_moving_minimum

\n\n

Docstring: No docstring available

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Function: trim_axes

\n\n

Docstring: \n Trim axes of the spectrum.

\n\n
Args:\n    old_spe: internal use only\n    new_spe: internal use only\n    method: 'x-axis' or 'bins'\n        If 'x-axis' boundaries will be interpreted as x-axis values.\n        If 'bins' boundaries will be interpreted as indices.\n    boundaries: lower and upper boundary for the trimming.\n\nReturns: modified Spectrum\n
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Function: xcal_argmin2d_iter_lowpass

\n\n

Docstring: \n Calibrate spectrum

\n\n
The calibration is done in multiple steps. Both the spectrum and the reference\nare passed through a low-pass filter to preserve only general structure of the\nspectrum. `low_pass_nfreqs` defines the number of frequencies to be preserved in\neach step. Once all steps with low-pass filter a final step without a low-pass\nfilter is performed. Each calibration step is performed using\n`~ramanchada2.spectrum.calibration.by_deltas.xcal_fine()` algorithm.\n\nArgs:\n    old_spe (Spectrum): internal use only\n    new_spe (Spectrum): internal use only\n    ref (Dict[float, float]): wavenumber - amplitude pairs\n    low_pass_nfreqs (List[int], optional): The number of elements defines the\n        number of low-pass steps and their values define the amount of frequencies\n        to keep. Defaults to [100, 500].\n
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Function: xcal_fine

\n\n

Docstring: \n Iterative calibration with provided reference based on ~ramanchada2.misc.utils.argmin2d.align()

\n\n
Iteratively apply polynomial of `poly_order` degree to match\nthe found peaks to the reference locations. The pairs are created\nusing `~ramanchada2.misc.utils.argmin2d.align()` algorithm.\n\nArgs:\n    old_spe (Spectrum): internal use only\n    new_spe (Spectrum): internal use only\n    ref (Union[Dict[float, float], List[float]]): _description_\n    ref (Dict[float, float]):\n        If a dict is provided - wavenumber - amplitude pairs.\n        If a list is provided - wavenumbers only.\n    poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3\n    should_fit (bool, optional): Whether the peaks should be fit or to\n        associate the positions with the maxima. Defaults to False.\n    find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.\n
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Function: xcal_fine_RBF

\n\n

Docstring: Wavelength calibration using Radial basis fuction interpolation

\n\n
Please be cautious! Interpolation might not be the most appropriate\napproach for this type of calibration.\n\n**kwargs are passed to RBFInterpolator\n
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Function: y_noise_savgol

\n\n

Docstring: No docstring available

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Function: y_noise_savgol_DL

\n\n

Docstring: No docstring available

\n\n
\n"}, "ramanchada2.auxiliary": {"fullname": "ramanchada2.auxiliary", "modulename": "ramanchada2.auxiliary", "kind": "module", "doc": "

\n"}, "ramanchada2.auxiliary.spectra": {"fullname": "ramanchada2.auxiliary.spectra", "modulename": "ramanchada2.auxiliary.spectra", "kind": "module", "doc": "

\n"}, "ramanchada2.auxiliary.spectra.datasets2": {"fullname": "ramanchada2.auxiliary.spectra.datasets2", "modulename": "ramanchada2.auxiliary.spectra.datasets2", "kind": "module", "doc": "

\n"}, "ramanchada2.auxiliary.spectra.datasets2.data": {"fullname": "ramanchada2.auxiliary.spectra.datasets2.data", "modulename": "ramanchada2.auxiliary.spectra.datasets2", "qualname": "data", "kind": "variable", "doc": "

\n", "default_value": "[{'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/NeonSNQ043_iR532_Probe_5msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/NeonSNQ043_iR532_Probe_100msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/PST10_iR532_Probe_100_3000msx7.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S0B10_iR532_Probe_100_60000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S0N10_iR532_Probe_100_30000msx3.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S0P10_iR532_Probe_100_60000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/S1N10_iR532_Probe_100_22000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/Sil10_iR532_Probe_100_60000msx2.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'Sil_'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/nCAL10_iR532_Probe_100_2500msx3.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/sCAL10_iR532_Probe_100_3200msx4.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/LED532_Probe_40msx3_1.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'LED532_EL0-9001'}, {'OP': '01', 'device': 'BWtek', 'filename': './FMNT-M_BW532/NIST532_Probe_3000msx8_1.txt', 'laser_wl': '532', 'provider': 'FNMT', 'sample': 'NIST532_SRM2242a'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NeonSNQ043_iR785_OP01.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/PST10_iR785_OP01_40000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0B10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0N10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0P10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S1N10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/Sil10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Sil'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/nCAL10_iR785_OP01_6000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '01', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/sCAL10_iR785_OP01_4000msx4.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NeonSNQ043_iR785_OP02.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/PST10_iR785_OP02_50000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0B10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0N10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0P10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S1N10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/Sil10_iR785_OP02_25000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Sil'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/nCAL10_iR785_OP02_20000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '02', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/sCAL10_iR785_OP02_15000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NeonSNQ043_iR785_OP03.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Neon'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/PST10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'PST'}, {'OP': '03', 'device': 'Horiba', 'filename': 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Princeton Instruments spe format

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\n"}, "ramanchada2.misc.exceptions.ApplicationException": {"fullname": "ramanchada2.misc.exceptions.ApplicationException", "modulename": "ramanchada2.misc.exceptions", "qualname": "ApplicationException", "kind": "class", "doc": "

Common base class for all non-exit exceptions.

\n", "bases": "builtins.Exception"}, "ramanchada2.misc.exceptions.InputParserError": {"fullname": "ramanchada2.misc.exceptions.InputParserError", "modulename": "ramanchada2.misc.exceptions", "qualname": "InputParserError", "kind": "class", "doc": "

Common base class for all non-exit exceptions.

\n", "bases": "ApplicationException"}, "ramanchada2.misc.exceptions.ChadaReadNotFoundError": {"fullname": "ramanchada2.misc.exceptions.ChadaReadNotFoundError", "modulename": "ramanchada2.misc.exceptions", "qualname": "ChadaReadNotFoundError", "kind": "class", "doc": "

Common base class for all non-exit exceptions.

\n", "bases": "ApplicationException"}, "ramanchada2.misc.plottable": {"fullname": "ramanchada2.misc.plottable", "modulename": "ramanchada2.misc.plottable", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.plottable.Plottable": {"fullname": "ramanchada2.misc.plottable.Plottable", "modulename": "ramanchada2.misc.plottable", "qualname": "Plottable", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

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\n", "signature": "(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes:", "funcdef": "def"}, "ramanchada2.misc.spectrum_deco": {"fullname": "ramanchada2.misc.spectrum_deco", "modulename": "ramanchada2.misc.spectrum_deco", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.spectrum_deco.dynamically_added": {"fullname": "ramanchada2.misc.spectrum_deco.dynamically_added", "modulename": "ramanchada2.misc.spectrum_deco.dynamically_added", "kind": "module", "doc": "

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\n", "annotation": ": Set[str]", "default_value": "{'smoothing_RC1', 'subtract_baseline_rc1_snip', 'derivative_sharpening', 'resample_NUDFT_filter', 'subtract_moving_minimum', 'convolve', 'moving_minimum', 'add_baseline', 'add_gaussian_noise', 'subtract_baseline_rc1_als', '__sub__', 'hht_sharpening', 'drop_spikes', 'add_gaussian_noise_drift', 'calibrate_by_deltas_filter', 'resample_spline_filter', 'abs_nm_to_shift_cm_1_filter', 'xcal_argmin2d_iter_lowpass', '__add__', 'normalize', 'find_peak_multipeak_filter', 'xcal_fine_RBF', '__truediv__', 'moving_average_convolve', 'pad_zeros', 'scale_xaxis_linear', 'moving_median', 'scale_xaxis_fun', 'trim_axes', 'subtract_moving_median', 'xcal_fine', 'dropna', 'shift_cm_1_to_abs_nm_filter', 'set_new_xaxis', 'scale_yaxis_linear', 'hht_sharpening_chain', 'add_poisson_noise', 'recover_spikes', '__mul__', 'get_spikes', 'fit_peaks_filter', 'moving_average'}"}, "ramanchada2.misc.spectrum_deco.dynamically_added.dynamically_added_constructors": {"fullname": "ramanchada2.misc.spectrum_deco.dynamically_added.dynamically_added_constructors", "modulename": "ramanchada2.misc.spectrum_deco.dynamically_added", "qualname": "dynamically_added_constructors", "kind": "variable", "doc": "

\n", "annotation": ": Set[str]", "default_value": "{'from_delta_lines', 'from_local_file', 'from_simulation', 'hdr_from_multi_exposure', 'from_stream', 'from_cache_or_calc', 'from_spectral_component_collection', 'from_theoretical_lines', 'from_test_spe', 'from_chada'}"}, "ramanchada2.misc.spectrum_deco.spectrum_constructor": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_constructor", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_constructor", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.spectrum_deco.spectrum_constructor.add_spectrum_constructor": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_constructor.add_spectrum_constructor", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_constructor", "qualname": "add_spectrum_constructor", "kind": "class", "doc": "

\n"}, "ramanchada2.misc.spectrum_deco.spectrum_constructor.add_spectrum_constructor.__init__": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_constructor.add_spectrum_constructor.__init__", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_constructor", "qualname": "add_spectrum_constructor.__init__", "kind": "function", "doc": "

\n", "signature": "(set_applied_processing=True)"}, "ramanchada2.misc.spectrum_deco.spectrum_constructor.add_spectrum_constructor.set_proc": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_constructor.add_spectrum_constructor.set_proc", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_constructor", "qualname": "add_spectrum_constructor.set_proc", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.spectrum_deco.spectrum_filter": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_filter", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_filter", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.spectrum_deco.spectrum_filter.logger": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_filter.logger", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_filter", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.misc.spectrum_deco.spectrum_filter (WARNING)>"}, "ramanchada2.misc.spectrum_deco.spectrum_filter.add_spectrum_filter": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_filter.add_spectrum_filter", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_filter", "qualname": "add_spectrum_filter", "kind": "function", "doc": "

\n", "signature": "(fun):", "funcdef": "def"}, "ramanchada2.misc.spectrum_deco.spectrum_method": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_method", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_method", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.spectrum_deco.spectrum_method.add_spectrum_method": {"fullname": "ramanchada2.misc.spectrum_deco.spectrum_method.add_spectrum_method", "modulename": "ramanchada2.misc.spectrum_deco.spectrum_method", "qualname": "add_spectrum_method", "kind": "function", "doc": "

\n", "signature": "(fun):", "funcdef": "def"}, "ramanchada2.misc.types": {"fullname": "ramanchada2.misc.types", "modulename": "ramanchada2.misc.types", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result": {"fullname": "ramanchada2.misc.types.fit_peaks_result", "modulename": "ramanchada2.misc.types.fit_peaks_result", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult", "kind": "class", "doc": "

A more or less complete user-defined wrapper around list objects.

\n", "bases": "collections.UserList, ramanchada2.misc.plottable.Plottable"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.valuesdict": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.valuesdict", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.valuesdict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.locations": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.locations", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.locations", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.centers": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.centers", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.centers", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.fwhm": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.fwhm", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.fwhm", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.boundaries": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.boundaries", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.boundaries", "kind": "function", "doc": "

\n", "signature": "(self, n_sigma=5):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.center_amplitude": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.center_amplitude", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.center_amplitude", "kind": "function", "doc": "

\n", "signature": "(self, threshold):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.centers_err": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.centers_err", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.centers_err", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.fwhms": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.fwhms", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.fwhms", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.amplitudes": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.amplitudes", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.amplitudes", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.dumps": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.dumps", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.dumps", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.loads": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.loads", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.loads", "kind": "function", "doc": "

\n", "signature": "(cls, json_str: List[str]):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.to_dataframe": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.to_dataframe", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.to_dataframe", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.to_dataframe_peaks": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.to_dataframe_peaks", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.to_dataframe_peaks", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.to_csv": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.to_csv", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.to_csv", "kind": "function", "doc": "

\n", "signature": "(self, path_or_buf=None, sep=',', **kwargs):", "funcdef": "def"}, "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.gen_fake_spectrum": {"fullname": "ramanchada2.misc.types.fit_peaks_result.FitPeaksResult.gen_fake_spectrum", "modulename": "ramanchada2.misc.types.fit_peaks_result", "qualname": "FitPeaksResult.gen_fake_spectrum", "kind": "function", "doc": "

\n", "signature": "(self, xarr):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates": {"fullname": "ramanchada2.misc.types.peak_candidates", "modulename": "ramanchada2.misc.types.peak_candidates", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakModel": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel"}, "ramanchada2.misc.types.peak_candidates.PeakModel.amplitude": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.amplitude", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.amplitude", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[float, Gt(gt=0)]"}, "ramanchada2.misc.types.peak_candidates.PeakModel.position": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.position", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.position", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakModel.sigma": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.sigma", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.sigma", "kind": "variable", "doc": "

\n", "annotation": ": typing.Annotated[float, Gt(gt=0)]"}, "ramanchada2.misc.types.peak_candidates.PeakModel.skew": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.skew", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.skew", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakModel.fwhm": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.fwhm", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.fwhm", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakModel.lwhm": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.lwhm", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.lwhm", "kind": "variable", "doc": "

Left width at half maximum.

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakModel.rwhm": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.rwhm", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.rwhm", "kind": "variable", "doc": "

Right width at half maximum.

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakModel.serialize": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.serialize", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.PeakModel.model_config": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.model_config", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "default_value": "{'arbitrary_types_allowed': True}"}, "ramanchada2.misc.types.peak_candidates.PeakModel.model_fields": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.model_fields", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'amplitude': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'position': FieldInfo(annotation=float, required=True), 'sigma': FieldInfo(annotation=float, required=True, metadata=[Gt(gt=0)]), 'skew': FieldInfo(annotation=float, required=False, default=0)}"}, "ramanchada2.misc.types.peak_candidates.PeakModel.model_computed_fields": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakModel.model_computed_fields", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel, ramanchada2.misc.plottable.Plottable"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.peaks": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.peaks", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.peaks", "kind": "variable", "doc": "

\n", "annotation": ": List[ramanchada2.misc.types.peak_candidates.PeakModel]"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.base_slope": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.base_slope", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.base_slope", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.base_intercept": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.base_intercept", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.base_intercept", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.boundaries": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.boundaries", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.boundaries", "kind": "variable", "doc": "

\n", "annotation": ": Tuple[float, float]"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.plot_params_baseline": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.plot_params_baseline", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.plot_params_baseline", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.plot_params_errorbar": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.plot_params_errorbar", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.plot_params_errorbar", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.positions": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.positions", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.positions", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.sigmas": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.sigmas", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.sigmas", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.fwhms": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.fwhms", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.fwhms", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.lwhms": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.lwhms", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.lwhms", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.rwhms": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.rwhms", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.rwhms", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.skews": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.skews", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.skews", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.amplitudes": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.amplitudes", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.amplitudes", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.bases": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.bases", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.bases", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.get_pos_ampl_dict": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.get_pos_ampl_dict", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.get_pos_ampl_dict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.get_ampl_pos_fwhm": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.get_ampl_pos_fwhm", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.get_ampl_pos_fwhm", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.peak_bases": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.peak_bases", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.peak_bases", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.serialize": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.serialize", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.model_config": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.model_config", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "default_value": "{'arbitrary_types_allowed': True}"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.model_fields": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.model_fields", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'peaks': FieldInfo(annotation=List[PeakModel], required=True), 'base_slope': FieldInfo(annotation=float, required=False, default=0), 'base_intercept': FieldInfo(annotation=float, required=False, default=0), 'boundaries': FieldInfo(annotation=Tuple[float, float], required=True)}"}, "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.model_computed_fields": {"fullname": "ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel.model_computed_fields", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "PeakCandidateMultiModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel": {"fullname": "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "ListPeakCandidateMultiModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

\n\n

A Pydantic BaseModel for the root object of the model.

\n\n
Attributes:
\n\n
    \n
  • root: The root object of the model.
    • \n
  • __pydantic_root_model__: Whether the model is a RootModel.
    • \n
  • __pydantic_private__: Private fields in the model.
    • \n
  • __pydantic_extra__: Extra fields in the model.
    • \n
\n", "bases": "pydantic.main.BaseModel, typing.Generic[~RootModelRootType]"}, "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.root": {"fullname": "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.root", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "ListPeakCandidateMultiModel.root", "kind": "variable", "doc": "

\n", "annotation": ": List[ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel]"}, "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.get_ampl_pos_fwhm": {"fullname": "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.get_ampl_pos_fwhm", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "ListPeakCandidateMultiModel.get_ampl_pos_fwhm", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.get_pos_ampl_dict": {"fullname": "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.get_pos_ampl_dict", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "ListPeakCandidateMultiModel.get_pos_ampl_dict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.serialize": {"fullname": "ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel.serialize", "modulename": "ramanchada2.misc.types.peak_candidates", "qualname": "ListPeakCandidateMultiModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.positive_not_multiple": {"fullname": "ramanchada2.misc.types.positive_not_multiple", "modulename": "ramanchada2.misc.types.positive_not_multiple", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.positive_not_multiple.PositiveOddInt": {"fullname": "ramanchada2.misc.types.positive_not_multiple.PositiveOddInt", "modulename": "ramanchada2.misc.types.positive_not_multiple", "qualname": "PositiveOddInt", "kind": "variable", "doc": "

\n", "default_value": "typing.Annotated[int, Gt(gt=0)]"}, "ramanchada2.misc.types.pydantic_base_model": {"fullname": "ramanchada2.misc.types.pydantic_base_model", "modulename": "ramanchada2.misc.types.pydantic_base_model", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.pydantic_base_model.PydBaseModel": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydBaseModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "pydantic.main.BaseModel, abc.ABC"}, "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.model_config": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.model_config", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydBaseModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "default_value": "{'arbitrary_types_allowed': True}"}, "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.serialize": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.serialize", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydBaseModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.model_fields": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.model_fields", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydBaseModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{}"}, "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.model_computed_fields": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydBaseModel.model_computed_fields", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydBaseModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.misc.types.pydantic_base_model.PydRootModel": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydRootModel", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydRootModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

\n\n

A Pydantic BaseModel for the root object of the model.

\n\n
Attributes:
\n\n
    \n
  • root: The root object of the model.
    • \n
  • __pydantic_root_model__: Whether the model is a RootModel.
    • \n
  • __pydantic_private__: Private fields in the model.
    • \n
  • __pydantic_extra__: Extra fields in the model.
    • \n
\n", "bases": "pydantic.main.BaseModel, typing.Generic[~RootModelRootType]"}, "ramanchada2.misc.types.pydantic_base_model.PydRootModel.serialize": {"fullname": "ramanchada2.misc.types.pydantic_base_model.PydRootModel.serialize", "modulename": "ramanchada2.misc.types.pydantic_base_model", "qualname": "PydRootModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum": {"fullname": "ramanchada2.misc.types.spectrum", "modulename": "ramanchada2.misc.types.spectrum", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.spectrum.applied_processings": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.proc": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.proc", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.proc", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.args": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.args", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.args", "kind": "variable", "doc": "

\n", "annotation": ": List"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.kwargs": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.kwargs", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.kwargs", "kind": "variable", "doc": "

\n", "annotation": ": Dict"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.is_constructor": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.is_constructor", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.is_constructor", "kind": "variable", "doc": "

\n"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.check_proc": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.check_proc", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.check_proc", "kind": "function", "doc": "

\n", "signature": "(cls, val: str):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.model_config": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.model_config", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.model_fields": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.model_fields", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'proc': FieldInfo(annotation=str, required=True), 'args': FieldInfo(annotation=List, required=False, default=[]), 'kwargs': FieldInfo(annotation=Dict, required=False, default={})}"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.model_computed_fields": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel.model_computed_fields", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

\n\n

A Pydantic BaseModel for the root object of the model.

\n\n
Attributes:
\n\n
    \n
  • root: The root object of the model.
    • \n
  • __pydantic_root_model__: Whether the model is a RootModel.
    • \n
  • __pydantic_private__: Private fields in the model.
    • \n
  • __pydantic_extra__: Extra fields in the model.
    • \n
\n", "bases": "pydantic.main.BaseModel, typing.Generic[~RootModelRootType]"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.root": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.root", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.root", "kind": "variable", "doc": "

\n", "annotation": ": List[ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel]"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.append": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.append", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.append", "kind": "function", "doc": "

\n", "signature": "(self, proc, args=[], kwargs={}):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.extend_left": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.extend_left", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.extend_left", "kind": "function", "doc": "

\n", "signature": "(\tself,\tproc_list: List[ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel]):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.pop": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.pop", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.pop", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.clear": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.clear", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.clear", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.assign": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.assign", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.assign", "kind": "function", "doc": "

\n", "signature": "(self, *args, **kwargs):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.repr": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.repr", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.repr", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.cache_path": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.cache_path", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.cache_path", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.to_list": {"fullname": "ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel.to_list", "modulename": "ramanchada2.misc.types.spectrum.applied_processings", "qualname": "SpeProcessingListModel.to_list", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.metadata": {"fullname": "ramanchada2.misc.types.spectrum.metadata", "modulename": "ramanchada2.misc.types.spectrum.metadata", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldTyping": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldTyping", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataFieldTyping", "kind": "variable", "doc": "

\n", "default_value": "typing.Union[numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]], typing.Annotated[bool, Strict(strict=True)], typing.Annotated[int, Strict(strict=True)], float, datetime.datetime, typing.List[typing.Any], typing.Dict[str, typing.Any], typing.Annotated[str, Strict(strict=True)], NoneType]"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataFieldModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

\n\n

A Pydantic BaseModel for the root object of the model.

\n\n
Attributes:
\n\n
    \n
  • root: The root object of the model.
    • \n
  • __pydantic_root_model__: Whether the model is a RootModel.
    • \n
  • __pydantic_private__: Private fields in the model.
    • \n
  • __pydantic_extra__: Extra fields in the model.
    • \n
\n", "bases": "pydantic.main.BaseModel, typing.Generic[~RootModelRootType]"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel.root": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel.root", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataFieldModel.root", "kind": "variable", "doc": "

\n", "annotation": ": Union[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Annotated[bool, Strict(strict=True)], Annotated[int, Strict(strict=True)], float, datetime.datetime, List[Any], Dict[str, Any], Annotated[str, Strict(strict=True)], NoneType]"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel.pre_validate": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel.pre_validate", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataFieldModel.pre_validate", "kind": "function", "doc": "

\n", "signature": "(cls, val):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel.serialize": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel.serialize", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataFieldModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#rootmodel-and-custom-root-types

\n\n

A Pydantic BaseModel for the root object of the model.

\n\n
Attributes:
\n\n
    \n
  • root: The root object of the model.
    • \n
  • __pydantic_root_model__: Whether the model is a RootModel.
    • \n
  • __pydantic_private__: Private fields in the model.
    • \n
  • __pydantic_extra__: Extra fields in the model.
    • \n
\n", "bases": "pydantic.main.BaseModel, typing.Generic[~RootModelRootType]"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.root": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.root", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataModel.root", "kind": "variable", "doc": "

\n", "annotation": ": Dict[str, ramanchada2.misc.types.spectrum.metadata.SpeMetadataFieldModel]"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.pre_validate": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.pre_validate", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataModel.pre_validate", "kind": "function", "doc": "

\n", "signature": "(cls, val):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.serialize": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.serialize", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataModel.serialize", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.get_all_keys": {"fullname": "ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel.get_all_keys", "modulename": "ramanchada2.misc.types.spectrum.metadata", "qualname": "SpeMetadataModel.get_all_keys", "kind": "function", "doc": "

Returns a list of all keys in the metadata model.

\n", "signature": "(self) -> list[str]:", "funcdef": "def"}, "ramanchada2.misc.utils": {"fullname": "ramanchada2.misc.utils", "modulename": "ramanchada2.misc.utils", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.utils.argmin2d": {"fullname": "ramanchada2.misc.utils.argmin2d", "modulename": "ramanchada2.misc.utils.argmin2d", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.utils.argmin2d.argmin2d": {"fullname": "ramanchada2.misc.utils.argmin2d.argmin2d", "modulename": "ramanchada2.misc.utils.argmin2d", "qualname": "argmin2d", "kind": "function", "doc": "

\n", "signature": "(A, median_limit: Optional[float] = None):", "funcdef": "def"}, "ramanchada2.misc.utils.argmin2d.find_closest_pairs_idx": {"fullname": "ramanchada2.misc.utils.argmin2d.find_closest_pairs_idx", "modulename": "ramanchada2.misc.utils.argmin2d", "qualname": "find_closest_pairs_idx", "kind": "function", "doc": "

\n", "signature": "(x, y, **kw_args):", "funcdef": "def"}, "ramanchada2.misc.utils.argmin2d.find_closest_pairs": {"fullname": "ramanchada2.misc.utils.argmin2d.find_closest_pairs", "modulename": "ramanchada2.misc.utils.argmin2d", "qualname": "find_closest_pairs", "kind": "function", "doc": "

\n", "signature": "(x, y, **kw_args):", "funcdef": "def"}, "ramanchada2.misc.utils.argmin2d.align": {"fullname": "ramanchada2.misc.utils.argmin2d.align", "modulename": "ramanchada2.misc.utils.argmin2d", "qualname": "align", "kind": "function", "doc": "

Iteratively finds best match between x and y and evaluates the x scaling parameters.

\n\n

Finds best parameters p that minimise L2 distance between scaled x and original y\nmin((lambda(x, *p)-y)*2 | *p)

\n\n
Arguments:
\n\n
    \n
  • x (ArrayLike[float]): values that need to match the reference
    • \n
  • y (ArrayLike[float]): reference values
    • \n
  • p0 (Union[List[float], npt.NDArray], optional): initial values for the parameters p.\nDefaults to [0, 1, 0, 0].
    • \n
  • func (Callable, optional): Objective function to minimize. Returns list penalties\ncalculated for each p. The total objective function is sum of the elements.\nDefaults to polynomial of 3-th degree.
    • \n
  • max_iter (PositiveInt, optional): max number of iterations. Defaults to 1000.
    • \n
\n\n
Returns:
\n\n
\n

ArrayLike[float]: array of parameters p that minimize the objective funciton

\n
\n", "signature": "(\tx,\ty,\tp0: Union[List[float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = [0, 1, 0, 0],\tfunc=<function <lambda>>,\tmax_iter: typing.Annotated[int, Gt(gt=0)] = 1000,\t**kw_args):", "funcdef": "def"}, "ramanchada2.misc.utils.argmin2d.align_shift": {"fullname": "ramanchada2.misc.utils.argmin2d.align_shift", "modulename": "ramanchada2.misc.utils.argmin2d", "qualname": "align_shift", "kind": "function", "doc": "

\n", "signature": "(\tx,\ty,\tp0: float = 0,\tmax_iter: typing.Annotated[int, Gt(gt=0)] = 1000,\t**kw_args):", "funcdef": "def"}, "ramanchada2.misc.utils.matchsets": {"fullname": "ramanchada2.misc.utils.matchsets", "modulename": "ramanchada2.misc.utils.matchsets", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.utils.matchsets.match_peaks_cluster": {"fullname": "ramanchada2.misc.utils.matchsets.match_peaks_cluster", "modulename": "ramanchada2.misc.utils.matchsets", "qualname": "match_peaks_cluster", "kind": "function", "doc": "

\n", "signature": "(\tspe_pos_dict: Dict[float, float],\tref: Dict[float, float],\t_filter_range=True,\tcost_intensity=0.25):", "funcdef": "def"}, "ramanchada2.misc.utils.matchsets.cost_function_position": {"fullname": "ramanchada2.misc.utils.matchsets.cost_function_position", "modulename": "ramanchada2.misc.utils.matchsets", "qualname": "cost_function_position", "kind": "function", "doc": "

\n", "signature": "(\tp1: Dict[float, float],\tp2: Dict[float, float],\torder_weight=1.0,\tpriority_weight=1.0):", "funcdef": "def"}, "ramanchada2.misc.utils.matchsets.cost_function": {"fullname": "ramanchada2.misc.utils.matchsets.cost_function", "modulename": "ramanchada2.misc.utils.matchsets", "qualname": "cost_function", "kind": "function", "doc": "

Modified cost function with an order preservation penalty and priority weighting.

\n\n
    \n
  • order_weight increases penalty for large differences in the x-axis values.
    • \n
  • priority_weight decreases the cost for higher values in the y-axis for set_b points.
    • \n
\n", "signature": "(\tp1: Dict[float, float],\tp2: Dict[float, float],\torder_weight=1.0,\tpriority_weight=0.1):", "funcdef": "def"}, "ramanchada2.misc.utils.matchsets.normalize_tuples": {"fullname": "ramanchada2.misc.utils.matchsets.normalize_tuples", "modulename": "ramanchada2.misc.utils.matchsets", "qualname": "normalize_tuples", "kind": "function", "doc": "

\n", "signature": "(tuples):", "funcdef": "def"}, "ramanchada2.misc.utils.matchsets.cost_matrix_peaks": {"fullname": "ramanchada2.misc.utils.matchsets.cost_matrix_peaks", "modulename": "ramanchada2.misc.utils.matchsets", "qualname": "cost_matrix_peaks", "kind": "function", "doc": "

\n", "signature": "(\tspectrum_a_dict: Dict[float, float],\tspectrum_b_dict: Dict[float, float],\tthreshold_max_distance=9,\tcost_func=None):", "funcdef": "def"}, "ramanchada2.misc.utils.matchsets.match_peaks": {"fullname": "ramanchada2.misc.utils.matchsets.match_peaks", "modulename": "ramanchada2.misc.utils.matchsets", "qualname": "match_peaks", "kind": "function", "doc": "

Match peaks between two spectra based on their positions and intensities.

\n\n

Uses scipy linear_sum_assignment to match peaks based on cost function\nhttps://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html

\n\n

Parameters:

\n\n

spectrum_a_dict : dict\n A dictionary representing the first spectrum, where keys are peak\n positions (float) and values are peak intensities (float).

\n\n

spectrum_b_dict : dict\n A dictionary representing the second spectrum, where keys are peak\n positions (float) and values are peak intensities (float).

\n\n

threshold_max_distance : float, optional\n The maximum allowed distance for two peaks to be considered a match.\n Default is 8.

\n\n

df : bool, optional\n If True, return a DataFrame with matched peaks and their respective\n intensities; if False, return None

\n\n

Returns:

\n\n

matched_peaks : (matched_peaks_a,matched_peaks_b,matched_distances, df)

\n\n

Examples:

\n\n
\n
>>> spectrum_a = {100: 10, 105: 20, 110: 15}\n>>> spectrum_b = {102: 12, 106: 22, 111: 16}\n>>> match_peaks(spectrum_a, spectrum_b)\n
\n
\n", "signature": "(\tspectrum_a_dict: Dict[float, float],\tspectrum_b_dict: Dict[float, float],\tthreshold_max_distance=9,\tdf=False,\tcost_func=None):", "funcdef": "def"}, "ramanchada2.misc.utils.ramanshift_to_wavelength": {"fullname": "ramanchada2.misc.utils.ramanshift_to_wavelength", "modulename": "ramanchada2.misc.utils.ramanshift_to_wavelength", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.utils.ramanshift_to_wavelength.shift_cm_1_to_abs_nm_dict": {"fullname": "ramanchada2.misc.utils.ramanshift_to_wavelength.shift_cm_1_to_abs_nm_dict", "modulename": "ramanchada2.misc.utils.ramanshift_to_wavelength", "qualname": "shift_cm_1_to_abs_nm_dict", "kind": "function", "doc": "

\n", "signature": "(deltas, laser_wave_length_nm):", "funcdef": "def"}, "ramanchada2.misc.utils.ramanshift_to_wavelength.abs_nm_to_shift_cm_1_dict": {"fullname": "ramanchada2.misc.utils.ramanshift_to_wavelength.abs_nm_to_shift_cm_1_dict", "modulename": "ramanchada2.misc.utils.ramanshift_to_wavelength", "qualname": "abs_nm_to_shift_cm_1_dict", "kind": "function", "doc": "

\n", "signature": "(deltas, laser_wave_length_nm):", "funcdef": "def"}, "ramanchada2.misc.utils.ramanshift_to_wavelength.abs_nm_to_shift_cm_1": {"fullname": "ramanchada2.misc.utils.ramanshift_to_wavelength.abs_nm_to_shift_cm_1", "modulename": "ramanchada2.misc.utils.ramanshift_to_wavelength", "qualname": "abs_nm_to_shift_cm_1", "kind": "function", "doc": "

\n", "signature": "(wl, laser_wave_length_nm):", "funcdef": "def"}, "ramanchada2.misc.utils.ramanshift_to_wavelength.shift_cm_1_to_abs_nm": {"fullname": "ramanchada2.misc.utils.ramanshift_to_wavelength.shift_cm_1_to_abs_nm", "modulename": "ramanchada2.misc.utils.ramanshift_to_wavelength", "qualname": "shift_cm_1_to_abs_nm", "kind": "function", "doc": "

\n", "signature": "(wn, laser_wave_length_nm):", "funcdef": "def"}, "ramanchada2.misc.utils.ramanshift_to_wavelength.laser_wl_nm": {"fullname": "ramanchada2.misc.utils.ramanshift_to_wavelength.laser_wl_nm", "modulename": "ramanchada2.misc.utils.ramanshift_to_wavelength", "qualname": "laser_wl_nm", "kind": "function", "doc": "

\n", "signature": "(raman_shift_cm_1, wave_length_nm):", "funcdef": "def"}, "ramanchada2.misc.utils.svd": {"fullname": "ramanchada2.misc.utils.svd", "modulename": "ramanchada2.misc.utils.svd", "kind": "module", "doc": "

\n"}, "ramanchada2.misc.utils.svd.svd_solve": {"fullname": "ramanchada2.misc.utils.svd.svd_solve", "modulename": "ramanchada2.misc.utils.svd", "qualname": "svd_solve", "kind": "function", "doc": "

Solves Ax=b

\n", "signature": "(A, b):", "funcdef": "def"}, "ramanchada2.misc.utils.svd.svd_inverse": {"fullname": "ramanchada2.misc.utils.svd.svd_inverse", "modulename": "ramanchada2.misc.utils.svd", "qualname": "svd_inverse", "kind": "function", "doc": "

\n", "signature": "(mat):", "funcdef": "def"}, "ramanchada2.protocols": {"fullname": "ramanchada2.protocols", "modulename": "ramanchada2.protocols", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg": {"fullname": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg", "modulename": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_calibration": {"fullname": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_calibration", "modulename": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg", "qualname": "neon_calibration", "kind": "function", "doc": "

Neon calibration

\n\n

The approximate laser wavelengt wl is used to translate the neon spectrum to [nm].\nThen using ~ramanchada2.spectrum.calibration.by_deltas.xcal_argmin2d_iter_lowpass()\nthe approximate neon spectrum in [nm] is scaled to match the reference lines.\nThis way it is calibrated to absolute wavelengths. A Makima spline is calculated so that\nit takes wavenumbers [1/cm] and return wavelength-calibrated x-axis in wavelengths [nm].

\n\n
Arguments:
\n\n
    \n
  • ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
    • \n
  • wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
    • \n
\n\n
Returns:
\n\n
\n

Callable(ArrayLike[float]): callable (spline) that applies the calibration

\n
\n", "signature": "(\tne_cm_1: ramanchada2.spectrum.spectrum.Spectrum,\twl: Literal[514, 532, 633, 785]):", "funcdef": "def"}, "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.silicon_calibration": {"fullname": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.silicon_calibration", "modulename": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg", "qualname": "silicon_calibration", "kind": "function", "doc": "

Calculate calibration function for lazer zeroing

\n\n

Takes wavelength-calibrated Silicon spectrum in wavelengths [nm] and using\nthe Silicon peak position it calculates the real laser wavelength and a Makima\nspline that translates the wavelengt-calibrated x-axis wavelength [nm] values to\nlazer-zeroed Raman shift in wavenumbers [1/cm].

\n\n
Arguments:
\n\n
    \n
  • si_nm: Spectrum\nWavelength-calibrated Silicon spectrum in wavelengths [nm]
    • \n
  • wl: Literal[514, 532, 633, 785]\nApproximate Laser wavelength
    • \n
  • find_peaks_kw: dict, optional\nkeywords for find_peak. Default values are\n{'prominence': min(.8, si_nm.y_noise_MAD()*50), 'width': 2, 'wlen': 100}
    • \n
  • fit_peaks_kw: dict, optional\nkeywords for fit_peaks. Default values are\n{'profile': 'Pearson4', 'vary_baseline': False}
    • \n
\n\n
Returns:
\n\n
\n

spline, esitmated_wavelength: int

\n
\n", "signature": "(\tsi_nm: ramanchada2.spectrum.spectrum.Spectrum,\twl: Literal[514, 532, 633, 785],\tfind_peaks_kw={},\tfit_peaks_kw={}):", "funcdef": "def"}, "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_silicon_calibration": {"fullname": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_silicon_calibration", "modulename": "ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg", "qualname": "neon_silicon_calibration", "kind": "function", "doc": "

Perform neon and silicon calibration together

\n\n

Combines ~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.neon_calibration()\nand ~ramanchada2.protocols.calib_ne_si_argmin2d_iter_gg.silicon_calibration().\nReturned spline is calculated using the wavlength-calibrated x-axis values translated\nto Raman shift wavenumbers using the calculated laser wavelength in silicon_calibration

\n\n
Arguments:
\n\n
    \n
  • ne_cm_1 (Spectrum): neon spectrum used for the calibration. Should be in [1/cm]
    • \n
  • si_cm_1 (Spectrum): silicon spectrum to estimate laser wavelength. Should be in [1/cm].
    • \n
  • wl (Literal[514, 532, 633, 785]): Approximate laser wavelength in [nm]
    • \n
  • sil_fit_kw (dict, optional): kwargs sent as find_peaks_kw in silicon_calibration. Defaults to {}.
    • \n
  • sil_find_kw (dict, optional): kwargs sent as fit_peaks_kw in silicon_calibration. Defaults to {}.
    • \n
\n\n
Returns:
\n\n
\n

Callable(ArrayLike[float]): callable (spline) that applies the calibration

\n
\n", "signature": "(\tne_cm_1: ramanchada2.spectrum.spectrum.Spectrum,\tsi_cm_1: ramanchada2.spectrum.spectrum.Spectrum,\twl: Literal[514, 532, 633, 785],\tsil_fit_kw={},\tsil_find_kw={}):", "funcdef": "def"}, "ramanchada2.protocols.calibration": {"fullname": "ramanchada2.protocols.calibration", "modulename": "ramanchada2.protocols.calibration", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component": {"fullname": "ramanchada2.protocols.calibration.calibration_component", "modulename": "ramanchada2.protocols.calibration.calibration_component", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.logger": {"fullname": "ramanchada2.protocols.calibration.calibration_component.logger", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.protocols.calibration.calibration_component (WARNING)>"}, "ramanchada2.protocols.calibration.calibration_component.ProcessingModel": {"fullname": "ramanchada2.protocols.calibration.calibration_component.ProcessingModel", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "ProcessingModel", "kind": "class", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.misc.plottable.Plottable"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.__init__": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.__init__", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.__init__", "kind": "function", "doc": "

\n", "signature": "(laser_wl, spe, spe_units, ref, ref_units, sample=None)"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.nonmonotonic": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.nonmonotonic", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.nonmonotonic", "kind": "variable", "doc": "

\n", "annotation": ": Literal['ignore', 'nan', 'error']", "default_value": "'nan'"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.laser_wl": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.laser_wl", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.laser_wl", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.spe": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.spe", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.spe", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.spe_units": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.spe_units", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.spe_units", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.ref": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.ref", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.ref", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.ref_units": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.ref_units", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.ref_units", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.name": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.name", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.name", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.model": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.model", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.model", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.model_units": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.model_units", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.model_units", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.peaks": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.peaks", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.peaks", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.sample": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.sample", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.sample", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.enabled": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.enabled", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.enabled", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.fit_res": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.fit_res", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.fit_res", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.set_model": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.set_model", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.set_model", "kind": "function", "doc": "

\n", "signature": "(self, model, model_units, peaks, name=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.convert_units": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.convert_units", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.convert_units", "kind": "function", "doc": "

\n", "signature": "(self, old_spe, spe_unit='cm-1', newspe_unit='nm', laser_wl=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.process": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.process", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.process", "kind": "function", "doc": "

\n", "signature": "(\tself,\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tspe_units='cm-1',\tconvert_back=False):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.derive_model": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.derive_model", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.derive_model", "kind": "function", "doc": "

\n", "signature": "(self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.plot": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.plot", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.plot", "kind": "function", "doc": "

\n", "signature": "(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes:", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.fitres2df": {"fullname": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent.fitres2df", "modulename": "ramanchada2.protocols.calibration.calibration_component", "qualname": "CalibrationComponent.fitres2df", "kind": "function", "doc": "

\n", "signature": "(self, spe):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model": {"fullname": "ramanchada2.protocols.calibration.calibration_model", "modulename": "ramanchada2.protocols.calibration.calibration_model", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.protocols.calibration.calibration_component.ProcessingModel, ramanchada2.misc.plottable.Plottable"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.__init__": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.__init__", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.__init__", "kind": "function", "doc": "

Initializes a CalibrationModel instance.

\n\n
Arguments:
\n\n
    \n
  • laser_wl: The wavelength of the laser used for calibration.
    • \n
\n\n

Example:

\n\n
\n
# Create an instance of CalibrationModel\nimport ramanchada2 as rc2\nimport ramanchada2.misc.constants as rc2const\nfrom ramanchada2.protocols.calibration import CalibrationModel\nlaser_wl=785\ncalmodel = CalibrationModel.calibration_model_factory(\n    laser_wl,\n    spe_neon,\n    spe_sil,\n    neon_wl=rc2const.NEON_WL[laser_wl],\n    find_kw={"wlen": 200, "width": 1},\n    fit_peaks_kw={},\n    should_fit=False,\n)\n# Store (optional)\ncalmodel.save(modelfile)\n# Load (optional)\ncalmodel = CalibrationModel.from_file(modelfile)\n# Apply to new spectrum\ncalmodel.apply_calibration_x(\n    spe_to_calibrate,\n    spe_units="cm-1"\n    )\n
\n
\n", "signature": "(laser_wl: int)"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.nonmonotonic": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.nonmonotonic", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.nonmonotonic", "kind": "variable", "doc": "

A class representing a calibration model for Raman spectrum.

\n", "annotation": ": Literal['ignore', 'nan', 'error']", "default_value": "'nan'"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.prominence_coeff": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.prominence_coeff", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.prominence_coeff", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.set_laser_wavelength": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.set_laser_wavelength", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.set_laser_wavelength", "kind": "function", "doc": "

Sets the wavelength of the laser used for calibration.

\n", "signature": "(self, laser_wl):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.clear": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.clear", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.clear", "kind": "function", "doc": "

Clears the calibration model.

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.save": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.save", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.save", "kind": "function", "doc": "

Saves the calibration model to a file.

\n", "signature": "(self, filename):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.from_file": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.from_file", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.from_file", "kind": "function", "doc": "

Loads a calibration model from a file.

\n", "signature": "(filename):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.derive_model_x": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.derive_model_x", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.derive_model_x", "kind": "function", "doc": "

Derives x-calibration models using Neon and Silicon spectra.

\n", "signature": "(\tself,\tspe_neon: ramanchada2.spectrum.spectrum.Spectrum,\tspe_neon_units: str,\tref_neon: Dict,\tref_neon_units: str,\tspe_sil: ramanchada2.spectrum.spectrum.Spectrum,\tspe_sil_units='cm-1',\tref_sil={520.45: 1},\tref_sil_units='cm-1',\tfind_kw={'wlen': 200, 'width': 1},\tfit_kw={},\tshould_fit=False,\tmatch_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster',\tinterpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf',\textrapolate=True):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.derive_model_curve": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.derive_model_curve", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.derive_model_curve", "kind": "function", "doc": "

\n", "signature": "(\tself,\tspe: ramanchada2.spectrum.spectrum.Spectrum,\tref=None,\tspe_units='cm-1',\tref_units='nm',\tfind_kw={},\tfit_peaks_kw={},\tshould_fit=False,\tname='X calibration',\tmatch_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster',\tinterpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf',\textrapolate=True):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.derive_model_zero": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.derive_model_zero", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.derive_model_zero", "kind": "function", "doc": "

\n", "signature": "(\tself,\tspe: ramanchada2.spectrum.spectrum.Spectrum,\tref={520.45: 1},\tspe_units='nm',\tref_units='cm-1',\tfind_kw=None,\tfit_peaks_kw=None,\tshould_fit=False,\tname='X Shift',\tprofile='Pearson4'):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.apply_calibration_x": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.apply_calibration_x", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.apply_calibration_x", "kind": "function", "doc": "

\n", "signature": "(\tself,\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tspe_units='cm-1'):", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.plot": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.plot", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.plot", "kind": "function", "doc": "

\n", "signature": "(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes:", "funcdef": "def"}, "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.calibration_model_factory": {"fullname": "ramanchada2.protocols.calibration.calibration_model.CalibrationModel.calibration_model_factory", "modulename": "ramanchada2.protocols.calibration.calibration_model", "qualname": "CalibrationModel.calibration_model_factory", "kind": "function", "doc": "

\n", "signature": "(\tlaser_wl,\tspe_neon: ramanchada2.spectrum.spectrum.Spectrum,\tspe_sil: ramanchada2.spectrum.spectrum.Spectrum,\tneon_wl=None,\tfind_kw=None,\tfit_peaks_kw=None,\tshould_fit=False,\tprominence_coeff=3,\tsi_profile='Pearson4',\tmatch_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster',\tinterpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf',\textrapolate=True):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration": {"fullname": "ramanchada2.protocols.calibration.xcalibration", "modulename": "ramanchada2.protocols.calibration.xcalibration", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.logger": {"fullname": "ramanchada2.protocols.calibration.xcalibration.logger", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.protocols.calibration.xcalibration (WARNING)>"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.__init__": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.__init__", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.__init__", "kind": "function", "doc": "

\n", "signature": "(\tlaser_wl,\tspe: ramanchada2.spectrum.spectrum.Spectrum,\tref: Dict[float, float],\tspe_units: Literal['cm-1', 'nm'] = 'cm-1',\tref_units: Literal['cm-1', 'nm'] = 'nm',\tsample='Neon',\tmatch_method: Literal['cluster', 'argmin2d', 'assignment'] = 'cluster',\tinterpolator_method: Literal['rbf', 'pchip', 'cubic_spline'] = 'rbf',\textrapolate=True)"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.spe_pos_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.spe_pos_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.spe_pos_dict", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.match_method": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.match_method", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.match_method", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.cost_function": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.cost_function", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.cost_function", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.interpolator_method": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.interpolator_method", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.interpolator_method", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.extrapolate": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.extrapolate", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.extrapolate", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.process": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.process", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.process", "kind": "function", "doc": "

\n", "signature": "(\tself,\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tspe_units: Literal['cm-1', 'nm'] = 'cm-1',\tconvert_back=False):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.derive_model": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.derive_model", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.derive_model", "kind": "function", "doc": "

\n", "signature": "(self, find_kw=None, fit_peaks_kw=None, should_fit=False, name=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.match_peaks": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.match_peaks", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.match_peaks", "kind": "function", "doc": "

\n", "signature": "(self, threshold_max_distance=9, return_df=False):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.fit_peaks": {"fullname": "ramanchada2.protocols.calibration.xcalibration.XCalibrationComponent.fit_peaks", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "XCalibrationComponent.fit_peaks", "kind": "function", "doc": "

\n", "signature": "(self, find_kw, fit_peaks_kw, should_fit):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent": {"fullname": "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "LazerZeroingComponent", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent"}, "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.__init__": {"fullname": "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.__init__", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "LazerZeroingComponent.__init__", "kind": "function", "doc": "

\n", "signature": "(\tlaser_wl,\tspe: ramanchada2.spectrum.spectrum.Spectrum,\tspe_units: Literal['cm-1', 'nm'] = 'nm',\tref=None,\tref_units: Literal['cm-1', 'nm'] = 'cm-1',\tsample='Silicon',\tprofile='Pearson4')"}, "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.profile": {"fullname": "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.profile", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "LazerZeroingComponent.profile", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.derive_model": {"fullname": "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.derive_model", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "LazerZeroingComponent.derive_model", "kind": "function", "doc": "

\n", "signature": "(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.zero_nm_to_shift_cm_1": {"fullname": "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.zero_nm_to_shift_cm_1", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "LazerZeroingComponent.zero_nm_to_shift_cm_1", "kind": "function", "doc": "

\n", "signature": "(self, wl, zero_pos_nm, zero_ref_cm_1=520.45):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.process": {"fullname": "ramanchada2.protocols.calibration.xcalibration.LazerZeroingComponent.process", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "LazerZeroingComponent.process", "kind": "function", "doc": "

\n", "signature": "(\tself,\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tspe_units: Literal['cm-1', 'nm'] = 'nm',\tconvert_back=False):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomRBFInterpolator", "kind": "class", "doc": "

Radial basis function (RBF) interpolation in N dimensions.

\n\n

Parameters

\n\n

y : (npoints, ndims) array_like\n 2-D array of data point coordinates.\nd : (npoints, ...) array_like\n N-D array of data values at y. The length of d along the first\n axis must be equal to the length of y. Unlike some interpolators, the\n interpolation axis cannot be changed.\nneighbors : int, optional\n If specified, the value of the interpolant at each evaluation point\n will be computed using only this many nearest data points. All the data\n points are used by default.\nsmoothing : float or (npoints, ) array_like, optional\n Smoothing parameter. The interpolant perfectly fits the data when this\n is set to 0. For large values, the interpolant approaches a least\n squares fit of a polynomial with the specified degree. Default is 0.\nkernel : str, optional\n Type of RBF. This should be one of

\n\n
    - 'linear'               : ``-r``\n    - 'thin_plate_spline'    : ``r**2 * log(r)``\n    - 'cubic'                : ``r**3``\n    - 'quintic'              : ``-r**5``\n    - 'multiquadric'         : ``-sqrt(1 + r**2)``\n    - 'inverse_multiquadric' : ``1/sqrt(1 + r**2)``\n    - 'inverse_quadratic'    : ``1/(1 + r**2)``\n    - 'gaussian'             : ``exp(-r**2)``\n\nDefault is 'thin_plate_spline'.\n
\n\n

epsilon : float, optional\n Shape parameter that scales the input to the RBF. If kernel is\n 'linear', 'thin_plate_spline', 'cubic', or 'quintic', this defaults to\n 1 and can be ignored because it has the same effect as scaling the\n smoothing parameter. Otherwise, this must be specified.\ndegree : int, optional\n Degree of the added polynomial. For some RBFs the interpolant may not\n be well-posed if the polynomial degree is too small. Those RBFs and\n their corresponding minimum degrees are

\n\n
    - 'multiquadric'      : 0\n    - 'linear'            : 0\n    - 'thin_plate_spline' : 1\n    - 'cubic'             : 1\n    - 'quintic'           : 2\n\nThe default value is the minimum degree for `kernel` or 0 if there is\nno minimum degree. Set this to -1 for no added polynomial.\n
\n\n

Notes

\n\n

An RBF is a scalar valued function in N-dimensional space whose value at\n\\( x \\) can be expressed in terms of \\( r=||x - c|| \\), where \\( c \\)\nis the center of the RBF.

\n\n

An RBF interpolant for the vector of data values \\( d \\), which are from\nlocations \\( y \\), is a linear combination of RBFs centered at \\( y \\)\nplus a polynomial with a specified degree. The RBF interpolant is written\nas

\n\n

$$f(x) = K(x, y) a + P(x) b,$$

\n\n

where \\( K(x, y) \\) is a matrix of RBFs with centers at \\( y \\)\nevaluated at the points \\( x \\), and \\( P(x) \\) is a matrix of\nmonomials, which span polynomials with the specified degree, evaluated at\n\\( x \\). The coefficients \\( a \\) and \\( b \\) are the solution to the\nlinear equations

\n\n

$$(K(y, y) + \\lambda I) a + P(y) b = d$$

\n\n

and

\n\n

$$P(y)^T a = 0,$$

\n\n

where \\( \\lambda \\) is a non-negative smoothing parameter that controls\nhow well we want to fit the data. The data are fit exactly when the\nsmoothing parameter is 0.

\n\n

The above system is uniquely solvable if the following requirements are\nmet:

\n\n
- \\\\( P(y) \\\\) must have full column rank. \\\\( P(y) \\\\) always has full\n  column rank when `degree` is -1 or 0. When `degree` is 1,\n  \\\\( P(y) \\\\) has full column rank if the data point locations are not\n  all collinear (N=2), coplanar (N=3), etc.\n- If `kernel` is 'multiquadric', 'linear', 'thin_plate_spline',\n  'cubic', or 'quintic', then `degree` must not be lower than the\n  minimum value listed above.\n- If `smoothing` is 0, then each data point location must be distinct.\n
\n\n

When using an RBF that is not scale invariant ('multiquadric',\n'inverse_multiquadric', 'inverse_quadratic', or 'gaussian'), an appropriate\nshape parameter must be chosen (e.g., through cross validation). Smaller\nvalues for the shape parameter correspond to wider RBFs. The problem can\nbecome ill-conditioned or singular when the shape parameter is too small.

\n\n

The memory required to solve for the RBF interpolation coefficients\nincreases quadratically with the number of data points, which can become\nimpractical when interpolating more than about a thousand data points.\nTo overcome memory limitations for large interpolation problems, the\nneighbors argument can be specified to compute an RBF interpolant for\neach evaluation point using only the nearest data points.

\n\n

New in version 1.7.0.

\n\n

See Also

\n\n

NearestNDInterpolator\nLinearNDInterpolator\nCloughTocher2DInterpolator

\n\n

References

\n\n

Examples

\n\n

Demonstrate interpolating scattered data to a grid in 2-D.

\n\n
\n
>>> import numpy as np\n>>> import matplotlib.pyplot as plt\n>>> from scipy.interpolate import RBFInterpolator\n>>> from scipy.stats.qmc import Halton\n
\n
\n\n
\n
>>> rng = np.random.default_rng()\n>>> xobs = 2*Halton(2, seed=rng).random(100) - 1\n>>> yobs = np.sum(xobs, axis=1)*np.exp(-6*np.sum(xobs**2, axis=1))\n
\n
\n\n
\n
>>> xgrid = np.mgrid[-1:1:50j, -1:1:50j]\n>>> xflat = xgrid.reshape(2, -1).T\n>>> yflat = RBFInterpolator(xobs, yobs)(xflat)\n>>> ygrid = yflat.reshape(50, 50)\n
\n
\n\n
\n
>>> fig, ax = plt.subplots()\n>>> ax.pcolormesh(*xgrid, ygrid, vmin=-0.25, vmax=0.25, shading='gouraud')\n>>> p = ax.scatter(*xobs.T, c=yobs, s=50, ec='k', vmin=-0.25, vmax=0.25)\n>>> fig.colorbar(p)\n>>> plt.show()\n
\n
\n\n
\n
\n
    \n
\n
\n", "bases": "scipy.interpolate._rbfinterp.RBFInterpolator"}, "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.__init__": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.__init__", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomRBFInterpolator.__init__", "kind": "function", "doc": "

\n", "signature": "(*args, **kwargs)"}, "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.from_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.from_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomRBFInterpolator.from_dict", "kind": "function", "doc": "

\n", "signature": "(rbf_dict=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.to_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.to_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomRBFInterpolator.to_dict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.plot": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator.plot", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomRBFInterpolator.plot", "kind": "function", "doc": "

\n", "signature": "(self, ax):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator", "kind": "class", "doc": "

PCHIP 1-D monotonic cubic interpolation.

\n\n

x and y are arrays of values used to approximate some function f,\nwith y = f(x). The interpolant uses monotonic cubic splines\nto find the value of new points. (PCHIP stands for Piecewise Cubic\nHermite Interpolating Polynomial).

\n\n

Parameters

\n\n

x : ndarray, shape (npoints, )\n A 1-D array of monotonically increasing real values. x cannot\n include duplicate values (otherwise f is overspecified)\ny : ndarray, shape (..., npoints, ...)\n A N-D array of real values. y's length along the interpolation\n axis must be equal to the length of x. Use the axis\n parameter to select the interpolation axis.

\n\n
*Deprecated since version 1.13.0:*\nComplex data is deprecated and will raise an error in SciPy 1.15.0.\nIf you are trying to use the real components of the passed array,\nuse ``np.real`` on ``y``.\n
\n\n

axis : int, optional\n Axis in the y array corresponding to the x-coordinate values. Defaults\n to axis=0.\nextrapolate : bool, optional\n Whether to extrapolate to out-of-bounds points based on first\n and last intervals, or to return NaNs.

\n\n

Methods

\n\n

__call__\nderivative\nantiderivative\nroots

\n\n

See Also

\n\n

CubicHermiteSpline : Piecewise-cubic interpolator.\nAkima1DInterpolator : Akima 1D interpolator.\nCubicSpline : Cubic spline data interpolator.\nPPoly : Piecewise polynomial in terms of coefficients and breakpoints.

\n\n

Notes

\n\n

The interpolator preserves monotonicity in the interpolation data and does\nnot overshoot if the data is not smooth.

\n\n

The first derivatives are guaranteed to be continuous, but the second\nderivatives may jump at \\( x_k \\).

\n\n

Determines the derivatives at the points \\( x_k \\), \\( f'_k \\),\nby using PCHIP algorithm 1.

\n\n

Let \\( h_k = x_{k+1} - x_k \\), and \\( d_k = (y_{k+1} - y_k) / h_k \\)\nare the slopes at internal points \\( x_k \\).\nIf the signs of \\( d_k \\) and \\( d_{k-1} \\) are different or either of\nthem equals zero, then \\( f'_k = 0 \\). Otherwise, it is given by the\nweighted harmonic mean

\n\n

$$\\frac{w_1 + w_2}{f'_k} = \\frac{w_1}{d_{k-1}} + \\frac{w_2}{d_k}$$

\n\n

where \\( w_1 = 2 h_k + h_{k-1} \\) and \\( w_2 = h_k + 2 h_{k-1} \\).

\n\n

The end slopes are set using a one-sided scheme 2.

\n\n

References

\n\n
\n
\n
    \n
  1. \n

    F. N. Fritsch and J. Butland,\nA method for constructing local\nmonotone piecewise cubic interpolants,\nSIAM J. Sci. Comput., 5(2), 300-304 (1984).\n:doi:10.1137/0905021. ↩

    \n
    2. \n\n
  2. \n

    see, e.g., C. Moler, Numerical Computing with Matlab, 2004.\n:doi:10.1137/1.9780898717952 ↩

    \n
    3. \n
\n
\n", "bases": "scipy.interpolate._cubic.PchipInterpolator"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.__init__": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.__init__", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.__init__", "kind": "function", "doc": "

\n", "signature": "(x, y)"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.x": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.x", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.x", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.y": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.y", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.y", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.from_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.from_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.from_dict", "kind": "function", "doc": "

\n", "signature": "(pchip_dict=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.to_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.to_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.to_dict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.save_coefficients": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.save_coefficients", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.save_coefficients", "kind": "function", "doc": "

Save the x and y coefficients to a JSON file.

\n", "signature": "(self, filename):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.load_coefficients": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.load_coefficients", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.load_coefficients", "kind": "function", "doc": "

Load the coefficients from a JSON file.

\n", "signature": "(cls, filename):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.plot": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.plot", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomPChipInterpolator.plot", "kind": "function", "doc": "

Plot the interpolation curve and the original points.

\n", "signature": "(self, ax):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomCubicSplineInterpolator", "kind": "class", "doc": "

Cubic spline data interpolator.

\n\n

Interpolate data with a piecewise cubic polynomial which is twice\ncontinuously differentiable 1. The result is represented as a PPoly\ninstance with breakpoints matching the given data.

\n\n

Parameters

\n\n

x : array_like, shape (n,)\n 1-D array containing values of the independent variable.\n Values must be real, finite and in strictly increasing order.\ny : array_like\n Array containing values of the dependent variable. It can have\n arbitrary number of dimensions, but the length along axis\n (see below) must match the length of x. Values must be finite.\naxis : int, optional\n Axis along which y is assumed to be varying. Meaning that for\n x[i] the corresponding values are np.take(y, i, axis=axis).\n Default is 0.\nbc_type : string or 2-tuple, optional\n Boundary condition type. Two additional equations, given by the\n boundary conditions, are required to determine all coefficients of\n polynomials on each segment 2.

\n\n
If `bc_type` is a string, then the specified condition will be applied\nat both ends of a spline. Available conditions are:\n\n* 'not-a-knot' (default): The first and second segment at a curve end\n  are the same polynomial. It is a good default when there is no\n  information on boundary conditions.\n* 'periodic': The interpolated functions is assumed to be periodic\n  of period ``x[-1] - x[0]``. The first and last value of `y` must be\n  identical: ``y[0] == y[-1]``. This boundary condition will result in\n  ``y'[0] == y'[-1]`` and ``y''[0] == y''[-1]``.\n* 'clamped': The first derivative at curves ends are zero. Assuming\n  a 1D `y`, ``bc_type=((1, 0.0), (1, 0.0))`` is the same condition.\n* 'natural': The second derivative at curve ends are zero. Assuming\n  a 1D `y`, ``bc_type=((2, 0.0), (2, 0.0))`` is the same condition.\n\nIf `bc_type` is a 2-tuple, the first and the second value will be\napplied at the curve start and end respectively. The tuple values can\nbe one of the previously mentioned strings (except 'periodic') or a\ntuple `(order, deriv_values)` allowing to specify arbitrary\nderivatives at curve ends:\n\n* `order`: the derivative order, 1 or 2.\n* `deriv_value`: array_like containing derivative values, shape must\n  be the same as `y`, excluding ``axis`` dimension. For example, if\n  `y` is 1-D, then `deriv_value` must be a scalar. If `y` is 3-D with\n  the shape (n0, n1, n2) and axis=2, then `deriv_value` must be 2-D\n  and have the shape (n0, n1).\n
\n\n

extrapolate : {bool, 'periodic', None}, optional\n If bool, determines whether to extrapolate to out-of-bounds points\n based on first and last intervals, or to return NaNs. If 'periodic',\n periodic extrapolation is used. If None (default), extrapolate is\n set to 'periodic' for bc_type='periodic' and to True otherwise.

\n\n

Attributes

\n\n

x : ndarray, shape (n,)\n Breakpoints. The same x which was passed to the constructor.\nc : ndarray, shape (4, n-1, ...)\n Coefficients of the polynomials on each segment. The trailing\n dimensions match the dimensions of y, excluding axis.\n For example, if y is 1-d, then c[k, i] is a coefficient for\n (x-x[i])**(3-k) on the segment between x[i] and x[i+1].\naxis : int\n Interpolation axis. The same axis which was passed to the\n constructor.

\n\n

Methods

\n\n

__call__\nderivative\nantiderivative\nintegrate\nroots

\n\n

See Also

\n\n

Akima1DInterpolator : Akima 1D interpolator.\nPchipInterpolator : PCHIP 1-D monotonic cubic interpolator.\nPPoly : Piecewise polynomial in terms of coefficients and breakpoints.

\n\n

Notes

\n\n

Parameters bc_type and extrapolate work independently, i.e. the\nformer controls only construction of a spline, and the latter only\nevaluation.

\n\n

When a boundary condition is 'not-a-knot' and n = 2, it is replaced by\na condition that the first derivative is equal to the linear interpolant\nslope. When both boundary conditions are 'not-a-knot' and n = 3, the\nsolution is sought as a parabola passing through given points.

\n\n

When 'not-a-knot' boundary conditions is applied to both ends, the\nresulting spline will be the same as returned by splrep (with s=0)\nand InterpolatedUnivariateSpline, but these two methods use a\nrepresentation in B-spline basis.

\n\n

New in version 0.18.0.

\n\n

Examples

\n\n

In this example the cubic spline is used to interpolate a sampled sinusoid.\nYou can see that the spline continuity property holds for the first and\nsecond derivatives and violates only for the third derivative.

\n\n
\n
>>> import numpy as np\n>>> from scipy.interpolate import CubicSpline\n>>> import matplotlib.pyplot as plt\n>>> x = np.arange(10)\n>>> y = np.sin(x)\n>>> cs = CubicSpline(x, y)\n>>> xs = np.arange(-0.5, 9.6, 0.1)\n>>> fig, ax = plt.subplots(figsize=(6.5, 4))\n>>> ax.plot(x, y, 'o', label='data')\n>>> ax.plot(xs, np.sin(xs), label='true')\n>>> ax.plot(xs, cs(xs), label="S")\n>>> ax.plot(xs, cs(xs, 1), label="S'")\n>>> ax.plot(xs, cs(xs, 2), label="S''")\n>>> ax.plot(xs, cs(xs, 3), label="S'''")\n>>> ax.set_xlim(-0.5, 9.5)\n>>> ax.legend(loc='lower left', ncol=2)\n>>> plt.show()\n
\n
\n\n

In the second example, the unit circle is interpolated with a spline. A\nperiodic boundary condition is used. You can see that the first derivative\nvalues, ds/dx=0, ds/dy=1 at the periodic point (1, 0) are correctly\ncomputed. Note that a circle cannot be exactly represented by a cubic\nspline. To increase precision, more breakpoints would be required.

\n\n
\n
>>> theta = 2 * np.pi * np.linspace(0, 1, 5)\n>>> y = np.c_[np.cos(theta), np.sin(theta)]\n>>> cs = CubicSpline(theta, y, bc_type='periodic')\n>>> print("ds/dx={:.1f} ds/dy={:.1f}".format(cs(0, 1)[0], cs(0, 1)[1]))\nds/dx=0.0 ds/dy=1.0\n>>> xs = 2 * np.pi * np.linspace(0, 1, 100)\n>>> fig, ax = plt.subplots(figsize=(6.5, 4))\n>>> ax.plot(y[:, 0], y[:, 1], 'o', label='data')\n>>> ax.plot(np.cos(xs), np.sin(xs), label='true')\n>>> ax.plot(cs(xs)[:, 0], cs(xs)[:, 1], label='spline')\n>>> ax.axes.set_aspect('equal')\n>>> ax.legend(loc='center')\n>>> plt.show()\n
\n
\n\n

The third example is the interpolation of a polynomial y = x3 on the\ninterval 0 <= x<= 1. A cubic spline can represent this function exactly.\nTo achieve that we need to specify values and first derivatives at\nendpoints of the interval. Note that y' = 3 * x2 and thus y'(0) = 0 and\ny'(1) = 3.

\n\n
\n
>>> cs = CubicSpline([0, 1], [0, 1], bc_type=((1, 0), (1, 3)))\n>>> x = np.linspace(0, 1)\n>>> np.allclose(x**3, cs(x))\nTrue\n
\n
\n\n

References

\n\n
\n
\n
    \n
  1. \n

    Cubic Spline Interpolation\n\non Wikiversity. ↩

    \n
    2. \n\n
  2. \n

    Carl de Boor, \"A Practical Guide to Splines\", Springer-Verlag, 1978. ↩

    \n
    3. \n
\n
\n", "bases": "scipy.interpolate._cubic.CubicSpline"}, "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.__init__": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.__init__", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomCubicSplineInterpolator.__init__", "kind": "function", "doc": "

\n", "signature": "(*args, **kwargs)"}, "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.from_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.from_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomCubicSplineInterpolator.from_dict", "kind": "function", "doc": "

\n", "signature": "(spline_dict=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.to_dict": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.to_dict", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomCubicSplineInterpolator.to_dict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.plot": {"fullname": "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator.plot", "modulename": "ramanchada2.protocols.calibration.xcalibration", "qualname": "CustomCubicSplineInterpolator.plot", "kind": "function", "doc": "

\n", "signature": "(self, ax):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration": {"fullname": "ramanchada2.protocols.calibration.ycalibration", "modulename": "ramanchada2.protocols.calibration.ycalibration", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate", "kind": "class", "doc": "

Class for intensity calibration certificates

\n\n
Usage:
\n\n
\n
    \n
  1. Use for specific SRM\n \n
    \n
    >>> cert = YCalibrationCertificate(\n...             id="NIST785_SRM2241",\n...             description="optical glass",\n...             url="https://tsapps.nist.gov/srmext/certificates/2241.pdf&quot;,\n...             wavelength=785,\n...             params="A0 = 9.71937e-02, A1 = 2.28325e-04, A2 = -5.86762e-08, A3 = 2.16023e-10, A4 = -9.77171e-14, A5 = 1.15596e-17",\n...             equation="A0 + A1 * x + A2 * x**2 + A3 * x**3 + A4 * x**4 + A5 * x**5",\n...             temperature_c=(20, 25),\n...             raman_shift=(200, 3500)\n...         )\n...\n>>> cert.plot()\n
    \n
    \n
    2. \n
\n
\n", "bases": "pydantic.main.BaseModel, ramanchada2.misc.plottable.Plottable"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.id": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.id", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.id", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.description": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.description", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.description", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.url": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.url", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.url", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.wavelength": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.wavelength", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.wavelength", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.params": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.params", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.params", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.equation": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.equation", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.equation", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.temperature_c": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.temperature_c", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.temperature_c", "kind": "variable", "doc": "

\n", "annotation": ": Optional[Tuple[int, int]]"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.raman_shift": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.raman_shift", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.raman_shift", "kind": "variable", "doc": "

\n", "annotation": ": Optional[Tuple[int, int]]"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.response_function": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.response_function", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.response_function", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.Y": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.Y", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.Y", "kind": "function", "doc": "

\n", "signature": "(self, x_value):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.load": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.load", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.load", "kind": "function", "doc": "

\n", "signature": "(wavelength=785, key='NIST785_SRM2241'):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.model_config": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.model_config", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.model_fields": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.model_fields", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'id': FieldInfo(annotation=str, required=True), 'description': FieldInfo(annotation=Union[str, NoneType], required=True), 'url': FieldInfo(annotation=Union[str, NoneType], required=True), 'wavelength': FieldInfo(annotation=int, required=True), 'params': FieldInfo(annotation=str, required=True), 'equation': FieldInfo(annotation=str, required=True), 'temperature_c': FieldInfo(annotation=Union[Tuple[int, int], NoneType], required=True), 'raman_shift': FieldInfo(annotation=Union[Tuple[int, int], NoneType], required=True)}"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.model_computed_fields": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate.model_computed_fields", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationCertificate.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict": {"fullname": "ramanchada2.protocols.calibration.ycalibration.CertificatesDict", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "CertificatesDict", "kind": "class", "doc": "

Class for loading y calibration certificates

\n\n
Usage:
\n\n
\n

Load single certificate

\n \n
\n
>>> cert = CertificatesDict.load(wavelength="785", key="NIST785_SRM2241")\n>>> cert.plot()\n
\n
\n \n

Load all certificates for wavelength. Iterate :

\n \n
\n
>>> certificates = CertificatesDict()\n... plt.figure()\n... ax=None\n... certs = certificates.get_certificates(wavelength=532)\n... ax = certs[cert].plot(ax=ax)\n>>> plt.show()\n
\n
\n
\n"}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.load_certificates": {"fullname": "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.load_certificates", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "CertificatesDict.load_certificates", "kind": "function", "doc": "

\n", "signature": "(self, file_path):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.get_laser_wl": {"fullname": "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.get_laser_wl", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "CertificatesDict.get_laser_wl", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.get_certificates": {"fullname": "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.get_certificates", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "CertificatesDict.get_certificates", "kind": "function", "doc": "

\n", "signature": "(self, wavelength=785):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.get": {"fullname": "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.get", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "CertificatesDict.get", "kind": "function", "doc": "

\n", "signature": "(self, wavelength=532, key='NIST532_SRM2242a'):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.load": {"fullname": "ramanchada2.protocols.calibration.ycalibration.CertificatesDict.load", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "CertificatesDict.load", "kind": "function", "doc": "

\n", "signature": "(wavelength=785, key='NIST785_SRM2241'):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent", "kind": "class", "doc": "

Class for relative intensity calibration. Uses response functions loaded in\nResponseFunctionEvaluator. Functions are defined in json file.

\n\n
Usage:
\n\n
\n
\n
>>> laser_wl = 785\n>>> ycert = YCalibrationCertificate.load(wavelength=785, key="SRM2241")\n>>> ycal = YCalibrationComponent(laser_wl, reference_spe_xcalibrated=spe_srm,certificate=ycert)\n>>> fig, ax = plt.subplots(1, 1, figsize=(15,4))\n>>> spe_srm.plot(ax=ax)\n>>> spe_to_correct.plot(ax=ax)\n>>> spe_ycalibrated = ycal.process(spe_to_correct)\n>>> spe_ycalibrated.plot(label="y-calibrated",color="green",ax=ax.twinx())\n
\n
\n
\n", "bases": "ramanchada2.protocols.calibration.calibration_component.CalibrationComponent"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.__init__": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.__init__", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.__init__", "kind": "function", "doc": "

\n", "signature": "(\tlaser_wl,\treference_spe_xcalibrated,\tcertificate: ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate)"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.laser_wl": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.laser_wl", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.laser_wl", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.spe": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.spe", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.spe", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.ref": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.ref", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.ref", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.name": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.name", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.name", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.model": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.model", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.model", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.model_units": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.model_units", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.model_units", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.derive_model": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.derive_model", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.derive_model", "kind": "function", "doc": "

\n", "signature": "(self, find_kw=None, fit_peaks_kw=None, should_fit=True, name=None):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.safe_divide": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.safe_divide", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.safe_divide", "kind": "function", "doc": "

\n", "signature": "(self, spe_to_correct, spe_reference_resampled):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.safe_mask": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.safe_mask", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.safe_mask", "kind": "function", "doc": "

\n", "signature": "(self, spe_to_correct, spe_reference_resampled):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.safe_factor": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.safe_factor", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.safe_factor", "kind": "function", "doc": "

\n", "signature": "(self, spe_to_correct, spe_reference_resampled):", "funcdef": "def"}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.process": {"fullname": "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent.process", "modulename": "ramanchada2.protocols.calibration.ycalibration", "qualname": "YCalibrationComponent.process", "kind": "function", "doc": "

\n", "signature": "(\tself,\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tspe_units='nm',\tconvert_back=False):", "funcdef": "def"}, "ramanchada2.protocols.metadata_helper": {"fullname": "ramanchada2.protocols.metadata_helper", "modulename": "ramanchada2.protocols.metadata_helper", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.metadata_helper.MetadataExtractor": {"fullname": "ramanchada2.protocols.metadata_helper.MetadataExtractor", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "MetadataExtractor", "kind": "class", "doc": "

\n"}, "ramanchada2.protocols.metadata_helper.MetadataExtractor.extract": {"fullname": "ramanchada2.protocols.metadata_helper.MetadataExtractor.extract", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "MetadataExtractor.extract", "kind": "function", "doc": "

\n", "signature": "(self, spectrum, filename=None):", "funcdef": "def"}, "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor": {"fullname": "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "TemplateMetadataExtractor", "kind": "class", "doc": "

\n", "bases": "MetadataExtractor"}, "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor.__init__": {"fullname": "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor.__init__", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "TemplateMetadataExtractor.__init__", "kind": "function", "doc": "

\n", "signature": "(template)"}, "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor.template": {"fullname": "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor.template", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "TemplateMetadataExtractor.template", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor.extract": {"fullname": "ramanchada2.protocols.metadata_helper.TemplateMetadataExtractor.extract", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "TemplateMetadataExtractor.extract", "kind": "function", "doc": "

\n", "signature": "(self, spectrum, filename=None):", "funcdef": "def"}, "ramanchada2.protocols.metadata_helper.FilenameMetadataExtractor": {"fullname": "ramanchada2.protocols.metadata_helper.FilenameMetadataExtractor", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "FilenameMetadataExtractor", "kind": "class", "doc": "

\n", "bases": "MetadataExtractor"}, "ramanchada2.protocols.metadata_helper.FilenameMetadataExtractor.extract": {"fullname": "ramanchada2.protocols.metadata_helper.FilenameMetadataExtractor.extract", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "FilenameMetadataExtractor.extract", "kind": "function", "doc": "

\n", "signature": "(self, spectrum, filename):", "funcdef": "def"}, "ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor": {"fullname": "ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "SpectrumMetadataExtractor", "kind": "class", "doc": "

\n", "bases": "MetadataExtractor"}, "ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor.extract": {"fullname": "ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor.extract", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "SpectrumMetadataExtractor.extract", "kind": "function", "doc": "

\n", "signature": "(self, spectrum, filename=None):", "funcdef": "def"}, "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor": {"fullname": "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "ChainedMetadataExtractor", "kind": "class", "doc": "

\n", "bases": "MetadataExtractor"}, "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor.__init__": {"fullname": "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor.__init__", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "ChainedMetadataExtractor.__init__", "kind": "function", "doc": "

\n", "signature": "(*extractors)"}, "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor.extractors": {"fullname": "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor.extractors", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "ChainedMetadataExtractor.extractors", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor.extract": {"fullname": "ramanchada2.protocols.metadata_helper.ChainedMetadataExtractor.extract", "modulename": "ramanchada2.protocols.metadata_helper", "qualname": "ChainedMetadataExtractor.extract", "kind": "function", "doc": "

\n", "signature": "(self, spectrum, filename=None):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe": {"fullname": "ramanchada2.protocols.spectraframe", "modulename": "ramanchada2.protocols.spectraframe", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.file_name": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.file_name", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.file_name", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.sample": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.sample", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.sample", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.provider": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.provider", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.provider", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.device": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.device", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.device", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.device_id": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.device_id", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.device_id", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.laser_wl": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.laser_wl", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.laser_wl", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.laser_power_mW": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.laser_power_mW", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.laser_power_mW", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.laser_power_percent": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.laser_power_percent", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.laser_power_percent", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.time_ms": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.time_ms", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.time_ms", "kind": "variable", "doc": "

\n", "annotation": ": Optional[float]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.replicate": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.replicate", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.replicate", "kind": "variable", "doc": "

\n", "annotation": ": Optional[int]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.optical_path": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.optical_path", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.optical_path", "kind": "variable", "doc": "

\n", "annotation": ": Optional[str]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.spectrum": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.spectrum", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.spectrum", "kind": "variable", "doc": "

\n", "annotation": ": Optional[ramanchada2.spectrum.spectrum.Spectrum]"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.Config": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.Config", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.Config", "kind": "class", "doc": "

\n"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.Config.arbitrary_types_allowed": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.Config.arbitrary_types_allowed", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.Config.arbitrary_types_allowed", "kind": "variable", "doc": "

\n", "default_value": "True"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.model_config": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.model_config", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{'arbitrary_types_allowed': True}"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.model_fields": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.model_fields", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'file_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'sample': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'provider': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'device': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'device_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'laser_wl': FieldInfo(annotation=int, required=True), 'laser_power_mW': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'laser_power_percent': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'time_ms': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'replicate': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'optical_path': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'spectrum': FieldInfo(annotation=Union[Spectrum, NoneType], required=False, default=None)}"}, "ramanchada2.protocols.spectraframe.SpectraFrameSchema.model_computed_fields": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrameSchema.model_computed_fields", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrameSchema.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.protocols.spectraframe.SpectraFrame": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame", "kind": "class", "doc": "

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

\n\n

Data structure also contains labeled axes (rows and columns).\nArithmetic operations align on both row and column labels. Can be\nthought of as a dict-like container for Series objects. The primary\npandas data structure.

\n\n

Parameters

\n\n

data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n data is a dict, column order follows insertion-order. If a dict contains Series\n which have an index defined, it is aligned by its index. This alignment also\n occurs if data is a Series or a DataFrame itself. Alignment is done on\n Series/DataFrame inputs.

\n\n
If data is a list of dicts, column order follows insertion-order.\n
\n\n

index : Index or array-like\n Index to use for resulting frame. Will default to RangeIndex if\n no indexing information part of input data and no index provided.\ncolumns : Index or array-like\n Column labels to use for resulting frame when data does not have them,\n defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n will perform column selection instead.\ndtype : dtype, default None\n Data type to force. Only a single dtype is allowed. If None, infer.\ncopy : bool or None, default None\n Copy data from inputs.\n For dict data, the default of None behaves like copy=True. For DataFrame\n or 2d ndarray input, the default of None behaves like copy=False.\n If data is a dict containing one or more Series (possibly of different dtypes),\n copy=False will ensure that these inputs are not copied.

\n\n
*Changed in version 1.3.0.*\n
\n\n

See Also

\n\n

DataFrame.from_records : Constructor from tuples, also record arrays.\nDataFrame.from_dict : From dicts of Series, arrays, or dicts.\nread_csv : Read a comma-separated values (csv) file into DataFrame.\nread_table : Read general delimited file into DataFrame.\nread_clipboard : Read text from clipboard into DataFrame.

\n\n

Notes

\n\n

Please reference the :ref:User Guide <basics.dataframe> for more information.

\n\n

Examples

\n\n

Constructing DataFrame from a dictionary.

\n\n
\n
>>> d = {'col1': [1, 2], 'col2': [3, 4]}\n>>> df = pd.DataFrame(data=d)\n>>> df\n   col1  col2\n0     1     3\n1     2     4\n
\n
\n\n

Notice that the inferred dtype is int64.

\n\n
\n
>>> df.dtypes\ncol1    int64\ncol2    int64\ndtype: object\n
\n
\n\n

To enforce a single dtype:

\n\n
\n
>>> df = pd.DataFrame(data=d, dtype=np.int8)\n>>> df.dtypes\ncol1    int8\ncol2    int8\ndtype: object\n
\n
\n\n

Constructing DataFrame from a dictionary including Series:

\n\n
\n
>>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}\n>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])\n   col1  col2\n0     0   NaN\n1     1   NaN\n2     2   2.0\n3     3   3.0\n
\n
\n\n

Constructing DataFrame from numpy ndarray:

\n\n
\n
>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n...                    columns=['a', 'b', 'c'])\n>>> df2\n   a  b  c\n0  1  2  3\n1  4  5  6\n2  7  8  9\n
\n
\n\n

Constructing DataFrame from a numpy ndarray that has labeled columns:

\n\n
\n
>>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])\n>>> df3 = pd.DataFrame(data, columns=['c', 'a'])\n...\n>>> df3\n   c  a\n0  3  1\n1  6  4\n2  9  7\n
\n
\n\n

Constructing DataFrame from dataclass:

\n\n
\n
>>> from dataclasses import make_dataclass\n>>> Point = make_dataclass("Point", [("x", int), ("y", int)])\n>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n   x  y\n0  0  0\n1  0  3\n2  2  3\n
\n
\n\n

Constructing DataFrame from Series/DataFrame:

\n\n
\n
>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])\n>>> df = pd.DataFrame(data=ser, index=["a", "c"])\n>>> df\n   0\na  1\nc  3\n
\n
\n\n
\n
>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])\n>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])\n>>> df2\n   x\na  1\nc  3\n
\n
\n", "bases": "pandas.core.frame.DataFrame"}, "ramanchada2.protocols.spectraframe.SpectraFrame.validate_columns": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.validate_columns", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.validate_columns", "kind": "function", "doc": "

Validate a DataFrame against the schema with dynamic column mapping.

\n\n
Arguments:
\n\n
    \n
  • df (pd.DataFrame): The DataFrame to validate.
    • \n
  • column_mapping (Dict[str, str]): A mapping from expected column names (in the schema)\nto actual column names in the DataFrame.
    • \n
\n", "signature": "(cls, df: pandas.core.frame.DataFrame, column_mapping: Dict[str, str]):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.from_dataframe": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.from_dataframe", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.from_dataframe", "kind": "function", "doc": "

Create an instance of SpectraFrame with dynamic column validation.

\n\n
Arguments:
\n\n
    \n
  • df (pd.DataFrame): The input DataFrame.
    • \n
  • column_mapping (Dict[str, str]): The dynamic mapping for column names.
    • \n
\n\n
Returns:
\n\n
\n

SpectraFrame: A validated SpectraFrame object.

\n
\n", "signature": "(cls, df: pandas.core.frame.DataFrame, column_mapping: Dict[str, str]):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.from_metadata": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.from_metadata", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.from_metadata", "kind": "function", "doc": "

\n", "signature": "(\tcls,\tspectra: List[ramanchada2.spectrum.spectrum.Spectrum],\tmetadata_extractor: ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.from_template": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.from_template", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.from_template", "kind": "function", "doc": "

\n", "signature": "(\tcls,\ttemplate_file: str,\tmetadata_extractor: ramanchada2.protocols.metadata_helper.SpectrumMetadataExtractor):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.average": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.average", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.average", "kind": "function", "doc": "

\n", "signature": "(\tself,\tgrouping_cols=['sample', 'provider', 'device', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'],\tsource='spectrum',\ttarget='spectrum'):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.trim": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.trim", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.trim", "kind": "function", "doc": "

\n", "signature": "(self, source='spectrum', target='spectrum', **kwargs):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.baseline_snip": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.baseline_snip", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.baseline_snip", "kind": "function", "doc": "

\n", "signature": "(self, source='spectrum', target='spectrum', **kwargs):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.spe_area": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.spe_area", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.spe_area", "kind": "function", "doc": "

\n", "signature": "(self, boundaries=(50, 3000), source='spectrum', target='area'):", "funcdef": "def"}, "ramanchada2.protocols.spectraframe.SpectraFrame.multiply": {"fullname": "ramanchada2.protocols.spectraframe.SpectraFrame.multiply", "modulename": "ramanchada2.protocols.spectraframe", "qualname": "SpectraFrame.multiply", "kind": "function", "doc": "

Get Multiplication of dataframe and other, element-wise (binary operator mul).

\n\n

Equivalent to dataframe * other, but with support to substitute a fill_value\nfor missing data in one of the inputs. With reverse version, rmul.

\n\n

Among flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to\narithmetic operators: +, -, *, /, //, %, **.

\n\n

Parameters

\n\n

other : scalar, sequence, Series, dict or DataFrame\n Any single or multiple element data structure, or list-like object.\naxis : {0 or 'index', 1 or 'columns'}\n Whether to compare by the index (0 or 'index') or columns.\n (1 or 'columns'). For Series input, axis to match Series index on.\nlevel : int or label\n Broadcast across a level, matching Index values on the\n passed MultiIndex level.\nfill_value : float or None, default None\n Fill existing missing (NaN) values, and any new element needed for\n successful DataFrame alignment, with this value before computation.\n If data in both corresponding DataFrame locations is missing\n the result will be missing.

\n\n

Returns

\n\n

DataFrame\n Result of the arithmetic operation.

\n\n

See Also

\n\n

DataFrame.add : Add DataFrames.\nDataFrame.sub : Subtract DataFrames.\nDataFrame.mul : Multiply DataFrames.\nDataFrame.div : Divide DataFrames (float division).\nDataFrame.truediv : Divide DataFrames (float division).\nDataFrame.floordiv : Divide DataFrames (integer division).\nDataFrame.mod : Calculate modulo (remainder after division).\nDataFrame.pow : Calculate exponential power.

\n\n

Notes

\n\n

Mismatched indices will be unioned together.

\n\n

Examples

\n\n
\n
>>> df = pd.DataFrame({'angles': [0, 3, 4],\n...                    'degrees': [360, 180, 360]},\n...                   index=['circle', 'triangle', 'rectangle'])\n>>> df\n           angles  degrees\ncircle          0      360\ntriangle        3      180\nrectangle       4      360\n
\n
\n\n

Add a scalar with operator version which return the same\nresults.

\n\n
\n
>>> df + 1\n           angles  degrees\ncircle          1      361\ntriangle        4      181\nrectangle       5      361\n
\n
\n\n
\n
>>> df.add(1)\n           angles  degrees\ncircle          1      361\ntriangle        4      181\nrectangle       5      361\n
\n
\n\n

Divide by constant with reverse version.

\n\n
\n
>>> df.div(10)\n           angles  degrees\ncircle        0.0     36.0\ntriangle      0.3     18.0\nrectangle     0.4     36.0\n
\n
\n\n
\n
>>> df.rdiv(10)\n             angles   degrees\ncircle          inf  0.027778\ntriangle   3.333333  0.055556\nrectangle  2.500000  0.027778\n
\n
\n\n

Subtract a list and Series by axis with operator version.

\n\n
\n
>>> df - [1, 2]\n           angles  degrees\ncircle         -1      358\ntriangle        2      178\nrectangle       3      358\n
\n
\n\n
\n
>>> df.sub([1, 2], axis='columns')\n           angles  degrees\ncircle         -1      358\ntriangle        2      178\nrectangle       3      358\n
\n
\n\n
\n
>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']),\n...        axis='index')\n           angles  degrees\ncircle         -1      359\ntriangle        2      179\nrectangle       3      359\n
\n
\n\n

Multiply a dictionary by axis.

\n\n
\n
>>> df.mul({'angles': 0, 'degrees': 2})\n            angles  degrees\ncircle           0      720\ntriangle         0      360\nrectangle        0      720\n
\n
\n\n
\n
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n            angles  degrees\ncircle           0        0\ntriangle         6      360\nrectangle       12     1080\n
\n
\n\n

Multiply a DataFrame of different shape with operator version.

\n\n
\n
>>> other = pd.DataFrame({'angles': [0, 3, 4]},\n...                      index=['circle', 'triangle', 'rectangle'])\n>>> other\n           angles\ncircle          0\ntriangle        3\nrectangle       4\n
\n
\n\n
\n
>>> df * other\n           angles  degrees\ncircle          0      NaN\ntriangle        9      NaN\nrectangle      16      NaN\n
\n
\n\n
\n
>>> df.mul(other, fill_value=0)\n           angles  degrees\ncircle          0      0.0\ntriangle        9      0.0\nrectangle      16      0.0\n
\n
\n\n

Divide by a MultiIndex by level.

\n\n
\n
>>> df_multindex = pd.DataFrame({'angles': [0, 3, 4, 4, 5, 6],\n...                              'degrees': [360, 180, 360, 360, 540, 720]},\n...                             index=[['A', 'A', 'A', 'B', 'B', 'B'],\n...                                    ['circle', 'triangle', 'rectangle',\n...                                     'square', 'pentagon', 'hexagon']])\n>>> df_multindex\n             angles  degrees\nA circle          0      360\n  triangle        3      180\n  rectangle       4      360\nB square          4      360\n  pentagon        5      540\n  hexagon         6      720\n
\n
\n\n
\n
>>> df.div(df_multindex, level=1, fill_value=0)\n             angles  degrees\nA circle        NaN      1.0\n  triangle      1.0      1.0\n  rectangle     1.0      1.0\nB square        0.0      0.0\n  pentagon      0.0      0.0\n  hexagon       0.0      0.0\n
\n
\n", "signature": "(self, multiplier: float, source='spectrum', target='spectrum'):", "funcdef": "def"}, "ramanchada2.protocols.twinning": {"fullname": "ramanchada2.protocols.twinning", "modulename": "ramanchada2.protocols.twinning", "kind": "module", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent", "kind": "class", "doc": "

TwinningComponent is an implementation of CHARISMA Twinning protocol CWA18134 Sep 2024\nhttps://www.cencenelec.eu/media/CEN-CENELEC/CWAs/RI/2024/cwa18134-1.pdf\nIt expects reference spectra and test spectra (to be twinned) as SpectraFrame objects.

\n\n
Attributes:
\n\n
    \n
  • grouping_cols (list): The SpectraFrame may contain replicates, which will be averaged by grouping by\ncolumns except replicates, e.g.\n['sample', 'provider', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'].
    • \n
  • reference (SpectraFrame): The averaged reference spectra.
    • \n
  • twinned (SpectraFrame): The averaged spectra to be twinned.
    • \n
  • boundaries (tuple): A tuple representing the boundaries for analysis (default: (50, 2000)).
    • \n
  • linreg_reference (tuple): Placeholder for storing the result of a linear regression on the reference spectra.\nDefaults to a tuple (None, None) which can later hold the regression slope and intercept.
    • \n
  • linreg_twinned (tuple): Placeholder for storing the result of a linear regression on the twinned spectra.\nDefaults to a tuple (None, None) which can later hold the regression slope and intercept.
    • \n
  • correction_factor (float): A scaling factor derived as ratio of slopes as defined in CWA18134.
    • \n
  • peak (float): The position of the peak (in nm) of interest for analysis, with a default value of 144 (TiO2).
    • \n
\n\n
Methods:
\n\n
\n

__init__(self, twinned: SpectraFrame, reference: SpectraFrame, boundaries=None, peak_at=144):\n Initializes a new TwinningComponent object by averaging the provided twinned and reference spectra\n based on predefined grouping columns. Optionally, boundaries for analysis and a peak position can be\n specified.

\n
\n\n
Arguments:
\n\n
    \n
  • twinned (SpectraFrame): The SpectraFrame representing the twinned data.
    • \n
  • reference (SpectraFrame): The SpectraFrame representing the reference data.
    • \n
  • boundaries (tuple, optional): Optional boundary values to restrict the analysis region (default: (50, 2000)).
    • \n
  • peak_at (int, optional): The peak position to focus the analysis on (default: 144 for TiO2).
    • \n
\n", "bases": "ramanchada2.misc.plottable.Plottable"}, "ramanchada2.protocols.twinning.TwinningComponent.__init__": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.__init__", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttwinned: ramanchada2.protocols.spectraframe.SpectraFrame,\treference: ramanchada2.protocols.spectraframe.SpectraFrame,\tboundaries=None,\treference_band_nm=144.0,\tgrouping_cols=['sample', 'provider', 'device', 'laser_wl', 'laser_power_percent', 'laser_power_mW', 'time_ms'])"}, "ramanchada2.protocols.twinning.TwinningComponent.grouping_cols": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.grouping_cols", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.grouping_cols", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.twinned": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.twinned", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.twinned", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.reference": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.reference", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.reference", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.boundaries": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.boundaries", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.boundaries", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.linreg_reference": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.linreg_reference", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.linreg_reference", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.linreg_twinned": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.linreg_twinned", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.linreg_twinned", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.correction_factor": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.correction_factor", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.correction_factor", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.protocols.twinning.TwinningComponent.reference_band_nm": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.reference_band_nm", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.reference_band_nm", "kind": "variable", "doc": "

\n"}, "ramanchada2.protocols.twinning.TwinningComponent.normalize_by_laserpower_time": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.normalize_by_laserpower_time", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.normalize_by_laserpower_time", "kind": "function", "doc": "

\n", "signature": "(self, source='spectrum', target='spectrum'):", "funcdef": "def"}, "ramanchada2.protocols.twinning.TwinningComponent.calc_peak_intensity": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.calc_peak_intensity", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.calc_peak_intensity", "kind": "function", "doc": "

\n", "signature": "(\tself,\tspe: ramanchada2.spectrum.spectrum.Spectrum,\tboundaries=None,\tprominence_coeff=0.01,\tno_fit=False,\tpeak_intensity='height'):", "funcdef": "def"}, "ramanchada2.protocols.twinning.TwinningComponent.laser_power_regression": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.laser_power_regression", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.laser_power_regression", "kind": "function", "doc": "

\n", "signature": "(\tself,\tdf: ramanchada2.protocols.spectraframe.SpectraFrame,\tboundaries=None,\tno_fit=False,\tsource='spectrum'):", "funcdef": "def"}, "ramanchada2.protocols.twinning.TwinningComponent.process": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.process", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.process", "kind": "function", "doc": "

\n", "signature": "(\tself,\tspe: ramanchada2.protocols.spectraframe.SpectraFrame,\tsource='spectrum',\ttarget='spectrum_harmonized'):", "funcdef": "def"}, "ramanchada2.protocols.twinning.TwinningComponent.derive_model": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.derive_model", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.derive_model", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.protocols.twinning.TwinningComponent.plot": {"fullname": "ramanchada2.protocols.twinning.TwinningComponent.plot", "modulename": "ramanchada2.protocols.twinning", "qualname": "TwinningComponent.plot", "kind": "function", "doc": "

\n", "signature": "(self, ax=None, label=' ', **kwargs) -> matplotlib.axes._axes.Axes:", "funcdef": "def"}, "ramanchada2.spectral_components": {"fullname": "ramanchada2.spectral_components", "modulename": "ramanchada2.spectral_components", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.baseline": {"fullname": "ramanchada2.spectral_components.baseline", "modulename": "ramanchada2.spectral_components.baseline", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.baseline.analytical": {"fullname": "ramanchada2.spectral_components.baseline.analytical", "modulename": "ramanchada2.spectral_components.baseline.analytical", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.baseline.baseline_base": {"fullname": "ramanchada2.spectral_components.baseline.baseline_base", "modulename": "ramanchada2.spectral_components.baseline.baseline_base", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.baseline.baseline_base.BaseLineBase": {"fullname": "ramanchada2.spectral_components.baseline.baseline_base.BaseLineBase", "modulename": "ramanchada2.spectral_components.baseline.baseline_base", "qualname": "BaseLineBase", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.spectral_components.spectral_component.SpectralComponent"}, "ramanchada2.spectral_components.baseline.numerical": {"fullname": "ramanchada2.spectral_components.baseline.numerical", "modulename": "ramanchada2.spectral_components.baseline.numerical", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.baseline.numerical.BaselineNumerical": {"fullname": "ramanchada2.spectral_components.baseline.numerical.BaselineNumerical", "modulename": "ramanchada2.spectral_components.baseline.numerical", "qualname": "BaselineNumerical", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.spectral_components.baseline.baseline_base.BaseLineBase"}, "ramanchada2.spectral_components.baseline.numerical.BaselineNumerical.__init__": {"fullname": "ramanchada2.spectral_components.baseline.numerical.BaselineNumerical.__init__", "modulename": "ramanchada2.spectral_components.baseline.numerical", "qualname": "BaselineNumerical.__init__", "kind": "function", "doc": "

Numerical baseline

\n\n
Arguments:
\n\n
    \n
  • x: array-like
    • \n
  • y: array-like
    • \n
\n", "signature": "(x, y)"}, "ramanchada2.spectral_components.peak_profile": {"fullname": "ramanchada2.spectral_components.peak_profile", "modulename": "ramanchada2.spectral_components.peak_profile", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.delta": {"fullname": "ramanchada2.spectral_components.peak_profile.delta", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.spectral_components.spectral_peak.SpectralPeak"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.__init__": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.__init__", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.a": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.a", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak.a", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.x0": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.x0", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak.x0", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.delta": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.delta", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak.delta", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.pos_amp_fwhm": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.pos_amp_fwhm", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak.pos_amp_fwhm", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.limit_3sigma": {"fullname": "ramanchada2.spectral_components.peak_profile.delta.DeltasPeak.limit_3sigma", "modulename": "ramanchada2.spectral_components.peak_profile.delta", "qualname": "DeltasPeak.limit_3sigma", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.spectral_components.spectral_peak.SpectralPeak"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.__init__": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.__init__", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.__init__", "kind": "function", "doc": "

\n", "signature": "(**kwargs)"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.a": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.a", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.a", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.x0": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.x0", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.x0", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.w": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.w", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.w", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.delta": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.delta", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.delta", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.pos_amp_fwhm": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.pos_amp_fwhm", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.pos_amp_fwhm", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.limit_3sigma": {"fullname": "ramanchada2.spectral_components.peak_profile.gauss.GaussPeak.limit_3sigma", "modulename": "ramanchada2.spectral_components.peak_profile.gauss", "qualname": "GaussPeak.limit_3sigma", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.voigt": {"fullname": "ramanchada2.spectral_components.peak_profile.voigt", "modulename": "ramanchada2.spectral_components.peak_profile.voigt", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.peak_profile.voigt.VoigtPeak": {"fullname": "ramanchada2.spectral_components.peak_profile.voigt.VoigtPeak", "modulename": "ramanchada2.spectral_components.peak_profile.voigt", "qualname": "VoigtPeak", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.spectral_components.spectral_peak.SpectralPeak"}, "ramanchada2.spectral_components.spectral_component": {"fullname": "ramanchada2.spectral_components.spectral_component", "modulename": "ramanchada2.spectral_components.spectral_component", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.spectral_component.SpectralComponent": {"fullname": "ramanchada2.spectral_components.spectral_component.SpectralComponent", "modulename": "ramanchada2.spectral_components.spectral_component", "qualname": "SpectralComponent", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.misc.plottable.Plottable, ramanchada2.misc.base_class.BaseClass"}, "ramanchada2.spectral_components.spectral_component_collection": {"fullname": "ramanchada2.spectral_components.spectral_component_collection", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.misc.plottable.Plottable, ramanchada2.misc.base_class.BaseClass"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.__init__": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.__init__", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection.__init__", "kind": "function", "doc": "

\n", "signature": "(peaks, **kwargs)"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.kwargs": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.kwargs", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection.kwargs", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.reset_origin": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.reset_origin", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection.reset_origin", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.get_deltas": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.get_deltas", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection.get_deltas", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.get_curve": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.get_curve", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection.get_curve", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.limit_3sigma": {"fullname": "ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection.limit_3sigma", "modulename": "ramanchada2.spectral_components.spectral_component_collection", "qualname": "SpectralComponentCollection.limit_3sigma", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectral_components.spectral_peak": {"fullname": "ramanchada2.spectral_components.spectral_peak", "modulename": "ramanchada2.spectral_components.spectral_peak", "kind": "module", "doc": "

\n"}, "ramanchada2.spectral_components.spectral_peak.SpectralPeak": {"fullname": "ramanchada2.spectral_components.spectral_peak.SpectralPeak", "modulename": "ramanchada2.spectral_components.spectral_peak", "qualname": "SpectralPeak", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.spectral_components.spectral_component.SpectralComponent, abc.ABC"}, "ramanchada2.spectral_components.spectral_peak.SpectralPeak.delta": {"fullname": "ramanchada2.spectral_components.spectral_peak.SpectralPeak.delta", "modulename": "ramanchada2.spectral_components.spectral_peak", "qualname": "SpectralPeak.delta", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectral_components.spectral_peak.SpectralPeak.limit_3sigma": {"fullname": "ramanchada2.spectral_components.spectral_peak.SpectralPeak.limit_3sigma", "modulename": "ramanchada2.spectral_components.spectral_peak", "qualname": "SpectralPeak.limit_3sigma", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectral_components.spectral_peak.SpectralPeak.pos_amp_fwhm": {"fullname": "ramanchada2.spectral_components.spectral_peak.SpectralPeak.pos_amp_fwhm", "modulename": "ramanchada2.spectral_components.spectral_peak", "qualname": "SpectralPeak.pos_amp_fwhm", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectrum": {"fullname": "ramanchada2.spectrum", "modulename": "ramanchada2.spectrum", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.arithmetics": {"fullname": "ramanchada2.spectrum.arithmetics", "modulename": "ramanchada2.spectrum.arithmetics", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.arithmetics.add": {"fullname": "ramanchada2.spectrum.arithmetics.add", "modulename": "ramanchada2.spectrum.arithmetics.add", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.arithmetics.mul": {"fullname": "ramanchada2.spectrum.arithmetics.mul", "modulename": "ramanchada2.spectrum.arithmetics.mul", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.arithmetics.sub": {"fullname": "ramanchada2.spectrum.arithmetics.sub", "modulename": "ramanchada2.spectrum.arithmetics.sub", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.arithmetics.truediv": {"fullname": "ramanchada2.spectrum.arithmetics.truediv", "modulename": "ramanchada2.spectrum.arithmetics.truediv", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.baseline": {"fullname": "ramanchada2.spectrum.baseline", "modulename": "ramanchada2.spectrum.baseline", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.baseline.add_baseline": {"fullname": "ramanchada2.spectrum.baseline.add_baseline", "modulename": "ramanchada2.spectrum.baseline.add_baseline", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.baseline.add_baseline.generate_baseline": {"fullname": "ramanchada2.spectrum.baseline.add_baseline.generate_baseline", "modulename": "ramanchada2.spectrum.baseline.add_baseline", "qualname": "generate_baseline", "kind": "function", "doc": "

\n", "signature": "(\tn_freq: int = FieldInfo(annotation=int, required=True, metadata=[Gt(gt=2)]),\tsize: int = FieldInfo(annotation=int, required=True, metadata=[Gt(gt=2)]),\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.add_baseline.add_baseline": {"fullname": "ramanchada2.spectrum.baseline.add_baseline.add_baseline", "modulename": "ramanchada2.spectrum.baseline.add_baseline", "qualname": "add_baseline", "kind": "function", "doc": "

Add artificial baseline to the spectrum.\nA random baseline is generated in frequency domain using uniform random numbers.\nThe baseline in frequency domain is tapered with bohman window to reduce the bandwidth\nof the baseline to first n_freq frequencies and is transformed to \"time\" domain.\nAdditionaly by using func parameter the user can define arbitrary function\nto be added as baseline.

\n\n
Arguments:
\n\n
    \n
  • n_freq: Must be > 2. Number of lowest frequency bins distinct from zero.
    • \n
  • amplitude: Upper boundary for the uniform random generator.
    • \n
  • pedestal: Additive constant pedestal to the spectrum.
    • \n
  • func: Callable. User-defined function to be added as baseline. Example: func = lambda x: x*.01 + x**2*.0001.
    • \n
  • rng_seed: int, optional. Seed for the random generator.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_freq: int,\tamplitude: float,\tpedestal: float = 0,\tfunc: Optional[Callable] = None,\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.baseline_rc1": {"fullname": "ramanchada2.spectrum.baseline.baseline_rc1", "modulename": "ramanchada2.spectrum.baseline.baseline_rc1", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.baseline.baseline_rc1.baseline_als": {"fullname": "ramanchada2.spectrum.baseline.baseline_rc1.baseline_als", "modulename": "ramanchada2.spectrum.baseline.baseline_rc1", "qualname": "baseline_als", "kind": "function", "doc": "

\n", "signature": "(\ty,\tlam: float = 100000.0,\tp: float = 0.001,\tniter: typing.Annotated[int, Gt(gt=0)] = 100,\tsmooth: Union[Annotated[int, Gt(gt=0)], Literal[0]] = 7):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.baseline_rc1.baseline_snip": {"fullname": "ramanchada2.spectrum.baseline.baseline_rc1.baseline_snip", "modulename": "ramanchada2.spectrum.baseline.baseline_rc1", "qualname": "baseline_snip", "kind": "function", "doc": "

\n", "signature": "(y0, niter: typing.Annotated[int, Gt(gt=0)] = 30):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.baseline_rc1.subtract_baseline_rc1_als": {"fullname": "ramanchada2.spectrum.baseline.baseline_rc1.subtract_baseline_rc1_als", "modulename": "ramanchada2.spectrum.baseline.baseline_rc1", "qualname": "subtract_baseline_rc1_als", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\tlam=100000.0,\tp=0.001,\tniter=100,\tsmooth=7):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.baseline_rc1.subtract_baseline_rc1_snip": {"fullname": "ramanchada2.spectrum.baseline.baseline_rc1.subtract_baseline_rc1_snip", "modulename": "ramanchada2.spectrum.baseline.baseline_rc1", "qualname": "subtract_baseline_rc1_snip", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\tniter=30):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.moving_minimum": {"fullname": "ramanchada2.spectrum.baseline.moving_minimum", "modulename": "ramanchada2.spectrum.baseline.moving_minimum", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.baseline.moving_minimum.moving_minimum": {"fullname": "ramanchada2.spectrum.baseline.moving_minimum.moving_minimum", "modulename": "ramanchada2.spectrum.baseline.moving_minimum", "qualname": "moving_minimum", "kind": "function", "doc": "

Moving minimum baseline estimator.\nSuccessive values are calculated as minima of rolling rectangular window.

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\twindow_size: int):", "funcdef": "def"}, "ramanchada2.spectrum.baseline.moving_minimum.subtract_moving_minimum": {"fullname": "ramanchada2.spectrum.baseline.moving_minimum.subtract_moving_minimum", "modulename": "ramanchada2.spectrum.baseline.moving_minimum", "qualname": "subtract_moving_minimum", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\twindow_size: int):", "funcdef": "def"}, "ramanchada2.spectrum.calc": {"fullname": "ramanchada2.spectrum.calc", "modulename": "ramanchada2.spectrum.calc", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calc.central_moments": {"fullname": "ramanchada2.spectrum.calc.central_moments", "modulename": "ramanchada2.spectrum.calc.central_moments", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calc.central_moments.central_moments": {"fullname": "ramanchada2.spectrum.calc.central_moments.central_moments", "modulename": "ramanchada2.spectrum.calc.central_moments", "qualname": "central_moments", "kind": "function", "doc": "

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tboundaries=(-inf, inf),\tmoments=[1, 2, 3, 4],\tnormalize=False):", "funcdef": "def"}, "ramanchada2.spectrum.calibration": {"fullname": "ramanchada2.spectrum.calibration", "modulename": "ramanchada2.spectrum.calibration", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas": {"fullname": "ramanchada2.spectrum.calibration.by_deltas", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel", "kind": "class", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.__init__": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.__init__", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel.__init__", "kind": "function", "doc": "

\n", "signature": "(deltas: Dict[float, float], shift=0, scale=1)"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.params": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.params", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel.params", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.minx": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.minx", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel.minx", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.maxx": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.maxx", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel.maxx", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.model": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.model", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel.model", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.fit": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.DeltaSpeModel.fit", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "DeltaSpeModel.fit", "kind": "function", "doc": "

\n", "signature": "(self, spe, sigma, ax=None, no_fit=False):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.by_deltas.ParamBounds": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.ParamBounds", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "ParamBounds", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.min": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.min", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "ParamBounds.min", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.max": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.max", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "ParamBounds.max", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.model_config": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.model_config", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "ParamBounds.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.model_fields": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.model_fields", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "ParamBounds.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'min': FieldInfo(annotation=float, required=False, default=-inf), 'max': FieldInfo(annotation=float, required=False, default=inf)}"}, "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.model_computed_fields": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.ParamBounds.model_computed_fields", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "ParamBounds.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.shift": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.shift", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.shift", "kind": "variable", "doc": "

\n", "annotation": ": ramanchada2.spectrum.calibration.by_deltas.ParamBounds"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.scale": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.scale", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.scale", "kind": "variable", "doc": "

\n", "annotation": ": ramanchada2.spectrum.calibration.by_deltas.ParamBounds"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.scale2": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.scale2", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.scale2", "kind": "variable", "doc": "

\n", "annotation": ": ramanchada2.spectrum.calibration.by_deltas.ParamBounds"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.scale3": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.scale3", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.scale3", "kind": "variable", "doc": "

\n", "annotation": ": ramanchada2.spectrum.calibration.by_deltas.ParamBounds"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.model_config": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.model_config", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.model_fields": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.model_fields", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'shift': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf)), 'scale': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf)), 'scale2': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf)), 'scale3': FieldInfo(annotation=ParamBounds, required=False, default=ParamBounds(min=-inf, max=inf))}"}, "ramanchada2.spectrum.calibration.by_deltas.FitBounds.model_computed_fields": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.FitBounds.model_computed_fields", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "FitBounds.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.spectrum.calibration.by_deltas.calibrate_by_deltas_model": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.calibrate_by_deltas_model", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "calibrate_by_deltas_model", "kind": "function", "doc": "
    \n
  • Builds a composite model based on a set of user specified delta lines.
    • \n
  • Initial guess is calculated based on 10-th and 90-th percentiles of\nthe distributions.
    • \n
\n\n

The phasespace of the model is flat with big amount of narrow minima.\nIn order to find the best fit, the experimental data are successively\nconvolved with gaussians with different widths startign from wide to\nnarrow. The model for the calibration is 3-th order polynomial, which\npotentialy can be changed for higher order polynomial. In order to avoid\nsolving the inverse of the calibration function, the result is tabulated\nand interpolated linarly for each bin of the spectrum.\nThis alogrithm is useful for corse calibration.

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tdeltas: Dict[float, float],\tbounds: Optional[ramanchada2.spectrum.calibration.by_deltas.FitBounds] = None,\tconvolution_steps: Optional[List[float]] = [15, 1],\tscale2=True,\tscale3=False,\tinit_guess: Literal[None, 'cumulative'] = None,\tax=None,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.by_deltas.calibrate_by_deltas_filter": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.calibrate_by_deltas_filter", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "calibrate_by_deltas_filter", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tdeltas: Dict[float, float],\tconvolution_steps,\tinit_guess=None,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.by_deltas.xcal_fine": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.xcal_fine", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "xcal_fine", "kind": "function", "doc": "

Iterative calibration with provided reference based on ~ramanchada2.misc.utils.argmin2d.align()

\n\n

Iteratively apply polynomial of poly_order degree to match\nthe found peaks to the reference locations. The pairs are created\nusing ~ramanchada2.misc.utils.argmin2d.align() algorithm.

\n\n
Arguments:
\n\n
    \n
  • old_spe (Spectrum): internal use only
    • \n
  • new_spe (Spectrum): internal use only
    • \n
  • ref (Union[Dict[float, float], List[float]]): _description_
    • \n
  • ref (Dict[float, float]): If a dict is provided - wavenumber - amplitude pairs.\nIf a list is provided - wavenumbers only.
    • \n
  • poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
    • \n
  • should_fit (bool, optional): Whether the peaks should be fit or to\nassociate the positions with the maxima. Defaults to False.
    • \n
  • find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tref: Union[Dict[float, float], List[float]],\tshould_fit=False,\tpoly_order: typing.Annotated[int, Ge(ge=0)],\tfind_peaks_kw={}):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.by_deltas.xcal_fine_RBF": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.xcal_fine_RBF", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "xcal_fine_RBF", "kind": "function", "doc": "

Wavelength calibration using Radial basis fuction interpolation

\n\n

Please be cautious! Interpolation might not be the most appropriate\napproach for this type of calibration.

\n\n

**kwargs are passed to RBFInterpolator

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tref: Union[Dict[float, float], List[float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]],\tshould_fit=False,\tkernel: Literal['thin_plate_spline', 'cubic', 'quintic', 'multiquadric', 'inverse_multiquadric', 'inverse_quadratic', 'gaussian'] = 'thin_plate_spline',\tfind_peaks_kw={},\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.by_deltas.xcal_argmin2d_iter_lowpass": {"fullname": "ramanchada2.spectrum.calibration.by_deltas.xcal_argmin2d_iter_lowpass", "modulename": "ramanchada2.spectrum.calibration.by_deltas", "qualname": "xcal_argmin2d_iter_lowpass", "kind": "function", "doc": "

Calibrate spectrum

\n\n

The calibration is done in multiple steps. Both the spectrum and the reference\nare passed through a low-pass filter to preserve only general structure of the\nspectrum. low_pass_nfreqs defines the number of frequencies to be preserved in\neach step. Once all steps with low-pass filter a final step without a low-pass\nfilter is performed. Each calibration step is performed using\n~ramanchada2.spectrum.calibration.by_deltas.xcal_fine() algorithm.

\n\n
Arguments:
\n\n
    \n
  • old_spe (Spectrum): internal use only
    • \n
  • new_spe (Spectrum): internal use only
    • \n
  • ref (Dict[float, float]): wavenumber - amplitude pairs
    • \n
  • low_pass_nfreqs (List[int], optional): The number of elements defines the\nnumber of low-pass steps and their values define the amount of frequencies\nto keep. Defaults to [100, 500].
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tref: Dict[float, float],\tlow_pass_nfreqs: List[int] = [100, 500]):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.change_x_units": {"fullname": "ramanchada2.spectrum.calibration.change_x_units", "modulename": "ramanchada2.spectrum.calibration.change_x_units", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.change_x_units.abs_nm_to_shift_cm_1": {"fullname": "ramanchada2.spectrum.calibration.change_x_units.abs_nm_to_shift_cm_1", "modulename": "ramanchada2.spectrum.calibration.change_x_units", "qualname": "abs_nm_to_shift_cm_1", "kind": "function", "doc": "

Convert wavelength to Ramanshift in wavenumber

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Corrected x-values

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.change_x_units.shift_cm_1_to_abs_nm": {"fullname": "ramanchada2.spectrum.calibration.change_x_units.shift_cm_1_to_abs_nm", "modulename": "ramanchada2.spectrum.calibration.change_x_units", "qualname": "shift_cm_1_to_abs_nm", "kind": "function", "doc": "

Convert Ramanshift in wavenumber to wavelength

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Corrected x-values

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.change_x_units.abs_nm_to_shift_cm_1_filter": {"fullname": "ramanchada2.spectrum.calibration.change_x_units.abs_nm_to_shift_cm_1_filter", "modulename": "ramanchada2.spectrum.calibration.change_x_units", "qualname": "abs_nm_to_shift_cm_1_filter", "kind": "function", "doc": "

Convert wavelength to Ramanshift in wavenumber

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Spectrum with corrected x-values

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.change_x_units.shift_cm_1_to_abs_nm_filter": {"fullname": "ramanchada2.spectrum.calibration.change_x_units.shift_cm_1_to_abs_nm_filter", "modulename": "ramanchada2.spectrum.calibration.change_x_units", "qualname": "shift_cm_1_to_abs_nm_filter", "kind": "function", "doc": "

Convert Ramanshift in wavenumber to wavelength

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Spectrum with corrected x-values

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.normalize": {"fullname": "ramanchada2.spectrum.calibration.normalize", "modulename": "ramanchada2.spectrum.calibration.normalize", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.normalize.normalize": {"fullname": "ramanchada2.spectrum.calibration.normalize.normalize", "modulename": "ramanchada2.spectrum.calibration.normalize", "qualname": "normalize", "kind": "function", "doc": "

Normalize the spectrum.

\n\n
Arguments:
\n\n
    \n
  • strategy: If unity: normalize to sum(y). If min_unity: subtract the minimum and normalize to 'unity'. If\nunity_density: normalize to \u03a3(y_i*\u0394x_i). If unity_area: same as unity_density. If minmax: scale\namplitudes in range [0, 1]. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tstrategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax', 'L1', 'L2'] = 'minmax'):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.scale_xaxis": {"fullname": "ramanchada2.spectrum.calibration.scale_xaxis", "modulename": "ramanchada2.spectrum.calibration.scale_xaxis", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.scale_xaxis.scale_xaxis_linear": {"fullname": "ramanchada2.spectrum.calibration.scale_xaxis.scale_xaxis_linear", "modulename": "ramanchada2.spectrum.calibration.scale_xaxis", "qualname": "scale_xaxis_linear", "kind": "function", "doc": "

Scale x-axis using a factor.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • factor: Defaults to 1.\nMultiply x-axis values with factor
    • \n
  • preserve_integral: optional. Defaults to False.\nIf True, preserves the integral in sence\n$\\sum y_{orig;\\,i}*{\\Delta x_{orig}}_i = \\sum y_{new;\\,i}*{\\Delta x_{new}}_i = $
    • \n
\n\n

Returns: Corrected spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tfactor: float = 1,\tpreserve_integral: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.scale_xaxis.scale_xaxis_fun": {"fullname": "ramanchada2.spectrum.calibration.scale_xaxis.scale_xaxis_fun", "modulename": "ramanchada2.spectrum.calibration.scale_xaxis", "qualname": "scale_xaxis_fun", "kind": "function", "doc": "

Apply arbitrary calibration function to the x-axis values.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • fun: function to be applied
    • \n
  • args: Additional arguments to the provided functions
    • \n
\n\n

Returns: Corrected spectrum

\n\n
Raises:
\n\n
    \n
  • ValueError: If the new x-values are not strictly monotonically increasing.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tfun: Callable[[Union[int, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float],\targs=[]):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.scale_yaxis": {"fullname": "ramanchada2.spectrum.calibration.scale_yaxis", "modulename": "ramanchada2.spectrum.calibration.scale_yaxis", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.scale_yaxis.scale_yaxis_linear": {"fullname": "ramanchada2.spectrum.calibration.scale_yaxis.scale_yaxis_linear", "modulename": "ramanchada2.spectrum.calibration.scale_yaxis", "qualname": "scale_yaxis_linear", "kind": "function", "doc": "

Scale y-axis values

\n\n

This function provides the same result as spe*const

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • factor optional. Defaults to 1.\nY-values scaling factor
    • \n
\n\n

Returns: corrected spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\tfactor: float = 1):", "funcdef": "def"}, "ramanchada2.spectrum.calibration.set_new_xaxis": {"fullname": "ramanchada2.spectrum.calibration.set_new_xaxis", "modulename": "ramanchada2.spectrum.calibration.set_new_xaxis", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.calibration.set_new_xaxis.set_new_xaxis": {"fullname": "ramanchada2.spectrum.calibration.set_new_xaxis.set_new_xaxis", "modulename": "ramanchada2.spectrum.calibration.set_new_xaxis", "qualname": "set_new_xaxis", "kind": "function", "doc": "

Substitute x-axis values with new ones

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • xaxis: new x-axis values
    • \n
\n\n

Returns: corrected spectrum

\n\n
Raises:
\n\n
    \n
  • ValueError: If the provided array does not match the shape of the spectrum.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\txaxis: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]):", "funcdef": "def"}, "ramanchada2.spectrum.creators": {"fullname": "ramanchada2.spectrum.creators", "modulename": "ramanchada2.spectrum.creators", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_cache_or_calc": {"fullname": "ramanchada2.spectrum.creators.from_cache_or_calc", "modulename": "ramanchada2.spectrum.creators.from_cache_or_calc", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_cache_or_calc.logger": {"fullname": "ramanchada2.spectrum.creators.from_cache_or_calc.logger", "modulename": "ramanchada2.spectrum.creators.from_cache_or_calc", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.spectrum.creators.from_cache_or_calc (WARNING)>"}, "ramanchada2.spectrum.creators.from_cache_or_calc.from_cache_or_calc": {"fullname": "ramanchada2.spectrum.creators.from_cache_or_calc.from_cache_or_calc", "modulename": "ramanchada2.spectrum.creators.from_cache_or_calc", "qualname": "from_cache_or_calc", "kind": "function", "doc": "

Load spectrum from cache or calculate if needed.

\n\n

The cache is a nested structure of spectra. All processings applied to\na spectrum result to spectra of the initial one. If part of the requred\nprocessings are available, only the needed steps are calculated and added\nto the cache.

\n\n
Arguments:
\n\n
    \n
  • required_steps: List of required steps in the form\n[{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
    • \n
  • cachefile: optional. Defaults to None.\nFilename of the cache. If None no cache is used
    • \n
\n", "signature": "(\trequired_steps: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel,\tcachefile: Optional[str] = None):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_chada": {"fullname": "ramanchada2.spectrum.creators.from_chada", "modulename": "ramanchada2.spectrum.creators.from_chada", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_chada.from_chada": {"fullname": "ramanchada2.spectrum.creators.from_chada.from_chada", "modulename": "ramanchada2.spectrum.creators.from_chada", "qualname": "from_chada", "kind": "function", "doc": "

\n", "signature": "(filename: str, dataset: str = '/raw', h5module=None):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_delta_lines": {"fullname": "ramanchada2.spectrum.creators.from_delta_lines", "modulename": "ramanchada2.spectrum.creators.from_delta_lines", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_delta_lines.from_delta_lines": {"fullname": "ramanchada2.spectrum.creators.from_delta_lines.from_delta_lines", "modulename": "ramanchada2.spectrum.creators.from_delta_lines", "qualname": "from_delta_lines", "kind": "function", "doc": "

Generate Spectrum with delta lines.

\n\n
Arguments:
\n\n
    \n
  • deltas: Keys of the dictionary are the x positions of the deltas; values are the amplitudes of the corresponding\ndeltas.
    • \n
  • xcal: Callable, optional. x axis calibration function.
    • \n
  • nbins: int, optional. Number of bins in the spectrum.
    • \n
  • xaxis: Array-like, optional. The xaxis of the new spectrum. If xaxis is provided,\nxcal should be None and nbins is ignored.
    • \n
\n\n

Example:

\n\n

This will produce spectrum with 1000 bins in the range [-1000, 2000):

\n\n
\n
xcal = lambda x: x*3 -1000, nbins=1000\n
\n
\n", "signature": "(\tdeltas: Dict[float, float],\txcal: Optional[Callable[[float], float]] = None,\tnbins: typing.Annotated[int, Gt(gt=0)] = 2000,\txaxis: Optional[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = None,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_local_file": {"fullname": "ramanchada2.spectrum.creators.from_local_file", "modulename": "ramanchada2.spectrum.creators.from_local_file", "kind": "module", "doc": "

Create spectrum from local files.

\n"}, "ramanchada2.spectrum.creators.from_local_file.from_local_file": {"fullname": "ramanchada2.spectrum.creators.from_local_file.from_local_file", "modulename": "ramanchada2.spectrum.creators.from_local_file", "qualname": "from_local_file", "kind": "function", "doc": "

Read experimental spectrum from a local file.

\n\n
Arguments:
\n\n
    \n
  • in_file_name: Path to a local file containing a spectrum.
    • \n
  • filetype: Specify the filetype. Filetype can be any of: spc, sp, spa, 0, 1, 2, wdf, ngs, jdx, dx,\ntxt, txtr, csv, prn, rruf, spe (Princeton Instruments) or None.\nNone used to determine by extension of the file.
    • \n
  • backend: native, rc1_parser or None. None means both.
    • \n
\n\n
Raises:
\n\n
    \n
  • ValueError: When called with unsupported file formats.
    • \n
\n", "signature": "(\tin_file_name: str,\tfiletype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None,\tbackend: Optional[Literal['native', 'rc1_parser']] = None):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_simulation": {"fullname": "ramanchada2.spectrum.creators.from_simulation", "modulename": "ramanchada2.spectrum.creators.from_simulation", "kind": "module", "doc": "

Create spectrum from simulation output files.

\n"}, "ramanchada2.spectrum.creators.from_simulation.from_simulation": {"fullname": "ramanchada2.spectrum.creators.from_simulation.from_simulation", "modulename": "ramanchada2.spectrum.creators.from_simulation", "qualname": "from_simulation", "kind": "function", "doc": "

Generate spectrum from simulation file.

\n\n

The returned spectrum has only few x/y pairs -- one for each simulated line. Values along\nthe x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.

\n\n
Arguments:
\n\n
    \n
  • in_file: Path to a local file, or file-like object.
    • \n
  • sim_type: If vasp: .dat file from VASP simulation. If crystal_out: .out file from CRYSTAL simulation, not\npreferred. If crystal_dat: .dat file from CRYSTAL simulation.
    • \n
  • use: One of the directions I_tot, I_perp, I_par, I_xx, I_xy,\nI_xz, I_yy, I_yz, I_zz, I_tot, I_perp, I_par are\navailable for both CRYSTAL and VASP. I_xx, I_xy, I_xz,\nI_yy, I_yz, I_zz are available only for CRYSTAL. If a Dict is\npassed, the key should be directions and values should be weighting factor.\nFor example, use={'I_perp': .1, 'I_par': .9}
    • \n
\n", "signature": "(\tin_file: Union[str, io.TextIOBase],\tsim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],\tuse: Union[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Dict[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Annotated[float, Gt(gt=0)]]] = 'I_tot',\tnbins: typing.Annotated[int, Gt(gt=0)] = 2000):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_spectral_component_collection": {"fullname": "ramanchada2.spectrum.creators.from_spectral_component_collection", "modulename": "ramanchada2.spectrum.creators.from_spectral_component_collection", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_spectral_component_collection.from_spectral_component_collection": {"fullname": "ramanchada2.spectrum.creators.from_spectral_component_collection.from_spectral_component_collection", "modulename": "ramanchada2.spectrum.creators.from_spectral_component_collection", "qualname": "from_spectral_component_collection", "kind": "function", "doc": "

from_spectral_component_collection

\n\n
Arguments:
\n\n
    \n
  • spe_components: SpectralComponentCollection
    • \n
  • x: int or array-like, optional, default 2000. x axis of the spectrum.
    • \n
\n", "signature": "(\tspe_components: ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection,\tx=2000):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_stream": {"fullname": "ramanchada2.spectrum.creators.from_stream", "modulename": "ramanchada2.spectrum.creators.from_stream", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_stream.from_stream": {"fullname": "ramanchada2.spectrum.creators.from_stream.from_stream", "modulename": "ramanchada2.spectrum.creators.from_stream", "qualname": "from_stream", "kind": "function", "doc": "

\n", "signature": "(\tin_stream: Union[io.TextIOBase, _io.BytesIO, _io.BufferedReader],\tfiletype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']],\tfilename: Optional[str] = None,\tbackend: Optional[Literal['native', 'rc1_parser']] = None):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_test_spe": {"fullname": "ramanchada2.spectrum.creators.from_test_spe", "modulename": "ramanchada2.spectrum.creators.from_test_spe", "kind": "module", "doc": "

Create spectrum from local files.

\n"}, "ramanchada2.spectrum.creators.from_test_spe.from_test_spe": {"fullname": "ramanchada2.spectrum.creators.from_test_spe.from_test_spe", "modulename": "ramanchada2.spectrum.creators.from_test_spe", "qualname": "from_test_spe", "kind": "function", "doc": "

Create new spectrum from test data.

\n\n
Arguments:
\n\n
    \n
  • index: int or None, optional, default is None. If int: will be used as an index of filtered list. If\nNone: a random spectrum will be taken.
    • \n
  • **kwargs: The rest of the parameters will be used as filter.
    • \n
\n", "signature": "(index=None, **kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.creators.from_theoretical_lines": {"fullname": "ramanchada2.spectrum.creators.from_theoretical_lines", "modulename": "ramanchada2.spectrum.creators.from_theoretical_lines", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.from_theoretical_lines.from_theoretical_lines": {"fullname": "ramanchada2.spectrum.creators.from_theoretical_lines.from_theoretical_lines", "modulename": "ramanchada2.spectrum.creators.from_theoretical_lines", "qualname": "from_theoretical_lines", "kind": "function", "doc": "

Generate spectrum from lmfit shapes.

\n\n
Arguments:
\n\n
    \n
  • shapes: The shapes to be used for spectrum generation.
    • \n
  • params: Shape parameters to be applied to be used with shapes.
    • \n
  • x: Array with x values, by default np.array(2000).
    • \n
\n", "signature": "(\tshapes: List[Literal['gaussian', 'gaussian2d', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7', 'breit_wigner', 'damped_oscillator', 'dho', 'logistic', 'lognormal', 'students_t', 'expgaussian', 'doniach', 'skewed_gaussian', 'skewed_voigt', 'thermal_distribution', 'step', 'rectangle', 'exponential', 'powerlaw', 'linear', 'parabolic', 'sine', 'expsine', 'split_lorentzian']],\tparams: List[Dict],\tx: Union[int, numpy.ndarray[Any, numpy.dtype[numpy.float64]]] = 2000):", "funcdef": "def"}, "ramanchada2.spectrum.creators.hdr_from_multi_exposure": {"fullname": "ramanchada2.spectrum.creators.hdr_from_multi_exposure", "modulename": "ramanchada2.spectrum.creators.hdr_from_multi_exposure", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.creators.hdr_from_multi_exposure.hdr_from_multi_exposure": {"fullname": "ramanchada2.spectrum.creators.hdr_from_multi_exposure.hdr_from_multi_exposure", "modulename": "ramanchada2.spectrum.creators.hdr_from_multi_exposure", "qualname": "hdr_from_multi_exposure", "kind": "function", "doc": "

Create an HDR spectrum from several spectra with different exposures.

\n\n

The resulting spectrum will have the details in low-intensity peaks\nfrom long-exposure-time spectrum. As long-exposure-time\nspectrum might be sturated, the information for high-intensity\npeaks will be taken from short-exposure-time spectrum.\nThis function will work on a very limited number of spectra,\nbecause we still do not have standardized metadata.

\n", "signature": "(spes_in: List[ramanchada2.spectrum.spectrum.Spectrum]):", "funcdef": "def"}, "ramanchada2.spectrum.filters": {"fullname": "ramanchada2.spectrum.filters", "modulename": "ramanchada2.spectrum.filters", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.add_gaussian_noise": {"fullname": "ramanchada2.spectrum.filters.add_gaussian_noise", "modulename": "ramanchada2.spectrum.filters.add_gaussian_noise", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.add_gaussian_noise.add_gaussian_noise": {"fullname": "ramanchada2.spectrum.filters.add_gaussian_noise.add_gaussian_noise", "modulename": "ramanchada2.spectrum.filters.add_gaussian_noise", "qualname": "add_gaussian_noise", "kind": "function", "doc": "

Add gaussian noise to the spectrum.

\n\n

Random number i.i.d. $N(0, \\sigma)$ is added to every sample

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • sigma: Sigma of the gaussian noise.
    • \n
  • rng_seed: int or rng state, optional, seed for the random generator.\nIf a state is provided, it is updated in-place.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tsigma: typing.Annotated[float, Gt(gt=0)],\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.filters.add_gaussian_noise_drift": {"fullname": "ramanchada2.spectrum.filters.add_gaussian_noise_drift", "modulename": "ramanchada2.spectrum.filters.add_gaussian_noise_drift", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.add_gaussian_noise_drift.generate_add_gaussian_noise_drift": {"fullname": "ramanchada2.spectrum.filters.add_gaussian_noise_drift.generate_add_gaussian_noise_drift", "modulename": "ramanchada2.spectrum.filters.add_gaussian_noise_drift", "qualname": "generate_add_gaussian_noise_drift", "kind": "function", "doc": "

\n", "signature": "(\ty,\t/,\tsigma: typing.Annotated[float, Gt(gt=0)],\tcoef: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=1), None, None],\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.filters.add_gaussian_noise_drift.add_gaussian_noise_drift": {"fullname": "ramanchada2.spectrum.filters.add_gaussian_noise_drift.add_gaussian_noise_drift", "modulename": "ramanchada2.spectrum.filters.add_gaussian_noise_drift", "qualname": "add_gaussian_noise_drift", "kind": "function", "doc": "

Add cumulative gaussian noise to the spectrum.

\n\n

Exponential-moving-average-like gaussian noise is added\nto each sample. The goal is to mimic the low-frequency noise\n(or random substructures in spectra).\nThe additive noise is\n$$a_i = coef*\\sum_{j=0}^{i-1}g_j + g_i,$$

\n\n

where\n$$g_i = \\mathcal{N}(0, 1+\\frac{coef}{\\sqrt 2}).$$

\n\n

This way drifting is possible while keeping the\n$$\\sigma(\\Delta(a)) \\approx 1.$$

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • sigma: Sigma of the gaussian noise.
    • \n
  • coef: float in [0, 1], drifting coefficient. If coef == 0,\nthe result is identical to add_gaussian_noise().
    • \n
  • rng_seed: int or rng state, optional. Seed for the random generator.\nIf a state is provided, it is updated in-place.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tsigma: typing.Annotated[float, Gt(gt=0)],\tcoef: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=1), None, None],\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.filters.add_poisson_noise": {"fullname": "ramanchada2.spectrum.filters.add_poisson_noise", "modulename": "ramanchada2.spectrum.filters.add_poisson_noise", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.add_poisson_noise.add_poisson_noise": {"fullname": "ramanchada2.spectrum.filters.add_poisson_noise.add_poisson_noise", "modulename": "ramanchada2.spectrum.filters.add_poisson_noise", "qualname": "add_poisson_noise", "kind": "function", "doc": "

Add poisson noise to the spectrum.

\n\n

For each particular sample the noise is proportional to $\\sqrt{scale*a_i}$.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • scale: float, optional, default is 1. Scale the amplitude of the noise.
    • \n
  • rng_seed: int or rng state, optional. Seed for the random generator.\nIf a state is provided, it is updated in-place.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tscale: float = 1,\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.filters.convolve": {"fullname": "ramanchada2.spectrum.filters.convolve", "modulename": "ramanchada2.spectrum.filters.convolve", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.convolve.convolve": {"fullname": "ramanchada2.spectrum.filters.convolve.convolve", "modulename": "ramanchada2.spectrum.filters.convolve", "qualname": "convolve", "kind": "function", "doc": "

Convole spectrum with arbitrary lineshape.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • lineshape: callable, str or np.ndarray.\n If callable: should have a single positional argument x, e.g.\nlambda x: np.exp((x/5)**2).\nIf predefined peak profile: can be gaussian, lorentzian, voigt,\npvoigt, moffat or pearson4.\nIf np.ndarray: lineshape in samples.
    • \n
  • **kwargs: Additional kwargs will be passed to lineshape function.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlineshape: Union[Callable[[Union[float, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Literal['gaussian', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7']],\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.filters.drop_spikes": {"fullname": "ramanchada2.spectrum.filters.drop_spikes", "modulename": "ramanchada2.spectrum.filters.drop_spikes", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.drop_spikes.spike_indices": {"fullname": "ramanchada2.spectrum.filters.drop_spikes.spike_indices", "modulename": "ramanchada2.spectrum.filters.drop_spikes", "qualname": "spike_indices", "kind": "function", "doc": "

Find spikes in spectrum

\n\n

Single-bin spikes are located using left and right successive\ndifferences. The threshold is based on the standart deviation\nof the metric which makes this algorithm less optimal.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • n_sigma: Threshold value should be n_sigma times the standart\ndeviation of the metric.
    • \n
\n\n

Returns: List of spike indices

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)]) -> numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]:", "funcdef": "def"}, "ramanchada2.spectrum.filters.drop_spikes.drop_spikes": {"fullname": "ramanchada2.spectrum.filters.drop_spikes.drop_spikes", "modulename": "ramanchada2.spectrum.filters.drop_spikes", "qualname": "drop_spikes", "kind": "function", "doc": "

Removes single-bin spikes.

\n\n

Remove x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • n_sigma: optional, default is 10.\nThreshold is n_sigma times the standard deviation.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.filters.drop_spikes.recover_spikes": {"fullname": "ramanchada2.spectrum.filters.drop_spikes.recover_spikes", "modulename": "ramanchada2.spectrum.filters.drop_spikes", "qualname": "recover_spikes", "kind": "function", "doc": "

Recover single-bin spikes.

\n\n

Recover x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • n_sigma: optional, default is 10.\nThreshold is n_sigma times the standard deviation.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.filters.drop_spikes.get_spikes": {"fullname": "ramanchada2.spectrum.filters.drop_spikes.get_spikes", "modulename": "ramanchada2.spectrum.filters.drop_spikes", "qualname": "get_spikes", "kind": "function", "doc": "

Get single-bin spikes only.

\n\n

Get x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • n_sigma: optional, default is 10.\nThreshold is n_sigma times the standard deviation.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.filters.dropna": {"fullname": "ramanchada2.spectrum.filters.dropna", "modulename": "ramanchada2.spectrum.filters.dropna", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.dropna.dropna": {"fullname": "ramanchada2.spectrum.filters.dropna.dropna", "modulename": "ramanchada2.spectrum.filters.dropna", "qualname": "dropna", "kind": "function", "doc": "

Remove non finite numbers on both axes

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum):", "funcdef": "def"}, "ramanchada2.spectrum.filters.moving_average": {"fullname": "ramanchada2.spectrum.filters.moving_average", "modulename": "ramanchada2.spectrum.filters.moving_average", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.moving_average.moving_average": {"fullname": "ramanchada2.spectrum.filters.moving_average.moving_average", "modulename": "ramanchada2.spectrum.filters.moving_average", "qualname": "moving_average", "kind": "function", "doc": "

Moving average filter.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\twindow_size: typing.Annotated[int, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.filters.moving_average.moving_average_convolve": {"fullname": "ramanchada2.spectrum.filters.moving_average.moving_average_convolve", "modulename": "ramanchada2.spectrum.filters.moving_average", "qualname": "moving_average_convolve", "kind": "function", "doc": "

Moving average filter.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\twindow_size: typing.Annotated[int, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.filters.moving_median": {"fullname": "ramanchada2.spectrum.filters.moving_median", "modulename": "ramanchada2.spectrum.filters.moving_median", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.moving_median.moving_median": {"fullname": "ramanchada2.spectrum.filters.moving_median.moving_median", "modulename": "ramanchada2.spectrum.filters.moving_median", "qualname": "moving_median", "kind": "function", "doc": "

Moving median filter.

\n\n

The resultant spectrum is moving minimum of the input.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\twindow_size: typing.Annotated[int, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.filters.moving_median.subtract_moving_median": {"fullname": "ramanchada2.spectrum.filters.moving_median.subtract_moving_median", "modulename": "ramanchada2.spectrum.filters.moving_median", "qualname": "subtract_moving_median", "kind": "function", "doc": "

Subtract moving median filter.

\n\n

The resultant spectrum is moving minimum of the input subtracted from the input.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\twindow_size: int):", "funcdef": "def"}, "ramanchada2.spectrum.filters.pad_zeros": {"fullname": "ramanchada2.spectrum.filters.pad_zeros", "modulename": "ramanchada2.spectrum.filters.pad_zeros", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.pad_zeros.pad_zeros": {"fullname": "ramanchada2.spectrum.filters.pad_zeros.pad_zeros", "modulename": "ramanchada2.spectrum.filters.pad_zeros", "qualname": "pad_zeros", "kind": "function", "doc": "

Extend x-axis by 100% in both directions.

\n\n

The x-axis of resultant spectrum will be:\n$[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.\nThe length of the new spectrum is 3 times the original. The added values\nare with an uniform step. In the middle is the original spectrum with\noriginal x and y values. The coresponding y vallues for the newly added\nx-values are always zeros.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/):", "funcdef": "def"}, "ramanchada2.spectrum.filters.resampling": {"fullname": "ramanchada2.spectrum.filters.resampling", "modulename": "ramanchada2.spectrum.filters.resampling", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.resampling.resample_NUDFT": {"fullname": "ramanchada2.spectrum.filters.resampling.resample_NUDFT", "modulename": "ramanchada2.spectrum.filters.resampling", "qualname": "resample_NUDFT", "kind": "function", "doc": "

Resample the spectrum using Non-uniform discrete fourier transform.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • window: optional, Defaults to None.\nThe window to be used for lowpass filter. If None 'blackmanharris' is used.\nIf no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n
Returns:
\n\n
\n

(x_values, y_values)

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\twindow: Union[Callable, Tuple[Any, ...], Literal['barthann', 'bartlett', 'blackman', 'blackmanharris', 'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss', 'exponential', 'flattop', 'gaussian', 'general_cosine', 'general_gaussian', 'general_hamming', 'hamming', 'hann', 'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey'], NoneType] = None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.filters.resampling.resample_NUDFT_filter": {"fullname": "ramanchada2.spectrum.filters.resampling.resample_NUDFT_filter", "modulename": "ramanchada2.spectrum.filters.resampling", "qualname": "resample_NUDFT_filter", "kind": "function", "doc": "

Resample the spectrum using Non-uniform discrete fourier transform.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • window: optional, Defaults to None.\nThe window to be used for lowpass filter. If None 'blackmanharris' is used.\nIf no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\twindow=None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.filters.resampling.resample_spline": {"fullname": "ramanchada2.spectrum.filters.resampling.resample_spline", "modulename": "ramanchada2.spectrum.filters.resampling", "qualname": "resample_spline", "kind": "function", "doc": "

Resample the spectrum using spline interpolation.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • spline: optional, Defaults to 'pchip'.\nName of the spline funcion to be used.
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n
Returns:
\n\n
\n

(x_values, y_values)

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\tspline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',\tinterp_kw_args: Optional[Dict] = None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.filters.resampling.resample_spline_filter": {"fullname": "ramanchada2.spectrum.filters.resampling.resample_spline_filter", "modulename": "ramanchada2.spectrum.filters.resampling", "qualname": "resample_spline_filter", "kind": "function", "doc": "

Resample the spectrum using spline interpolation.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • spline: optional, Defaults to 'pchip'.\nName of the spline funcion to be used.
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\tspline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',\tinterp_kw_args: Optional[Dict] = None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.filters.sharpen_lines": {"fullname": "ramanchada2.spectrum.filters.sharpen_lines", "modulename": "ramanchada2.spectrum.filters.sharpen_lines", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.sharpen_lines.derivative_sharpening": {"fullname": "ramanchada2.spectrum.filters.sharpen_lines.derivative_sharpening", "modulename": "ramanchada2.spectrum.filters.sharpen_lines", "qualname": "derivative_sharpening", "kind": "function", "doc": "

Derivative-based sharpening.

\n\n

Sharpen the spectrum subtracting second derivative and add fourth derivative.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • filter_fraction float in (0; 1]: Default is 0.6\nDepth of filtration
    • \n
  • signal_width: The width of features to be enhanced in sample count
    • \n
  • der2_factor: Second derivative scaling factor
    • \n
  • der4_factor: Fourth derivative scaling factor
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tfilter_fraction: typing.Annotated[float, None, Interval(gt=0, ge=None, lt=None, le=1), None, None] = 0.6,\tsig_width: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=None), None, None] = 0.25,\tder2_factor: float = 1,\tder4_factor: float = 0.1):", "funcdef": "def"}, "ramanchada2.spectrum.filters.sharpen_lines.hht_sharpening": {"fullname": "ramanchada2.spectrum.filters.sharpen_lines.hht_sharpening", "modulename": "ramanchada2.spectrum.filters.sharpen_lines", "qualname": "hht_sharpening", "kind": "function", "doc": "

Hilbert-Huang based sharpening.

\n\n

In order to reduce the overshooting, moving minimum is subtracted from the result

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • movmin: optional. Default is 100\nWindow size for moving minimum
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tmovmin=100):", "funcdef": "def"}, "ramanchada2.spectrum.filters.sharpen_lines.hht_sharpening_chain": {"fullname": "ramanchada2.spectrum.filters.sharpen_lines.hht_sharpening_chain", "modulename": "ramanchada2.spectrum.filters.sharpen_lines", "qualname": "hht_sharpening_chain", "kind": "function", "doc": "

Hilbert-Huang based chain sharpening.

\n\n

Sequence of Hilbert-Huang sharpening procedures are performed.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • movmin: List[int], optional. Default is [150, 50]\nThe numer of values in the list defines how many iterations\nof HHT_sharpening will be performed and the values define\nthe moving minimum window sizes for the corresponding operations.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tmovmin: List[Annotated[int, Gt(gt=0)]] = [150, 50]):", "funcdef": "def"}, "ramanchada2.spectrum.filters.smoothing": {"fullname": "ramanchada2.spectrum.filters.smoothing", "modulename": "ramanchada2.spectrum.filters.smoothing", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.smoothing.smoothing_RC1": {"fullname": "ramanchada2.spectrum.filters.smoothing.smoothing_RC1", "modulename": "ramanchada2.spectrum.filters.smoothing", "qualname": "smoothing_RC1", "kind": "function", "doc": "

Smooth the spectrum.

\n\n

The spectrum will be smoothed using the specified filter.\nThis method is inherited from ramanchada1 for compatibility reasons.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • method: method to be used
    • \n
  • **kwargs: keyword arguments to be passed to the selected method
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*args,\tmethod: Literal['savgol', 'sg', 'wiener', 'median', 'gauss', 'gaussian', 'lowess', 'boxcar'],\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.filters.trim_axes": {"fullname": "ramanchada2.spectrum.filters.trim_axes", "modulename": "ramanchada2.spectrum.filters.trim_axes", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.filters.trim_axes.trim_axes": {"fullname": "ramanchada2.spectrum.filters.trim_axes.trim_axes", "modulename": "ramanchada2.spectrum.filters.trim_axes", "qualname": "trim_axes", "kind": "function", "doc": "

Trim axes of the spectrum.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • method: 'x-axis' or 'bins'\nIf 'x-axis' boundaries will be interpreted as x-axis values.\nIf 'bins' boundaries will be interpreted as indices.
    • \n
  • boundaries: lower and upper boundary for the trimming.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tmethod: Literal['x-axis', 'bins'],\tboundaries: Tuple[float, float]):", "funcdef": "def"}, "ramanchada2.spectrum.multimap": {"fullname": "ramanchada2.spectrum.multimap", "modulename": "ramanchada2.spectrum.multimap", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.multimap.spc": {"fullname": "ramanchada2.spectrum.multimap.spc", "modulename": "ramanchada2.spectrum.multimap.spc", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates": {"fullname": "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates", "modulename": "ramanchada2.spectrum.multimap.spc", "qualname": "SPCMapCoordinates", "kind": "class", "doc": "

SPCMapCoordinates(z, w)

\n", "bases": "builtins.tuple"}, "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates.__init__": {"fullname": "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates.__init__", "modulename": "ramanchada2.spectrum.multimap.spc", "qualname": "SPCMapCoordinates.__init__", "kind": "function", "doc": "

Create new instance of SPCMapCoordinates(z, w)

\n", "signature": "(z, w)"}, "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates.z": {"fullname": "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates.z", "modulename": "ramanchada2.spectrum.multimap.spc", "qualname": "SPCMapCoordinates.z", "kind": "variable", "doc": "

Alias for field number 0

\n"}, "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates.w": {"fullname": "ramanchada2.spectrum.multimap.spc.SPCMapCoordinates.w", "modulename": "ramanchada2.spectrum.multimap.spc", "qualname": "SPCMapCoordinates.w", "kind": "variable", "doc": "

Alias for field number 1

\n"}, "ramanchada2.spectrum.multimap.spc.read_map_spc": {"fullname": "ramanchada2.spectrum.multimap.spc.read_map_spc", "modulename": "ramanchada2.spectrum.multimap.spc", "qualname": "read_map_spc", "kind": "function", "doc": "

\n", "signature": "(\tfilename: str) -> Dict[ramanchada2.spectrum.multimap.spc.SPCMapCoordinates, ramanchada2.spectrum.spectrum.Spectrum]:", "funcdef": "def"}, "ramanchada2.spectrum.peaks": {"fullname": "ramanchada2.spectrum.peaks", "modulename": "ramanchada2.spectrum.peaks", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.peaks.find_peaks": {"fullname": "ramanchada2.spectrum.peaks.find_peaks", "modulename": "ramanchada2.spectrum.peaks.find_peaks", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.peaks.find_peaks.peak_boundaries": {"fullname": "ramanchada2.spectrum.peaks.find_peaks.peak_boundaries", "modulename": "ramanchada2.spectrum.peaks.find_peaks", "qualname": "peak_boundaries", "kind": "function", "doc": "

\n", "signature": "(spe, wlen, width, prominence):", "funcdef": "def"}, "ramanchada2.spectrum.peaks.find_peaks.find_peak_multipeak": {"fullname": "ramanchada2.spectrum.peaks.find_peaks.find_peak_multipeak", "modulename": "ramanchada2.spectrum.peaks.find_peaks", "qualname": "find_peak_multipeak", "kind": "function", "doc": "

Find groups of peaks in spectrum.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • prominence: Optional. Defaults to None\nIf None the prominence value will be spe.y_nose. Reasonable value for\npromience is const * spe.y_noise_MAD.
    • \n
  • wlen: optional. Defaults to None.\nwlen value used in scipy.signal.find_peaks. If wlen is None, 200 will be used.
    • \n
  • width: optional. Defaults to None.\nwidth value used in scipy.signal.find_peaks. If width is None, 2 will be used.
    • \n
  • hht_chain: optional. Defaults to None.\nList of hht_chain window sizes. If None, no hht sharpening is performed.
    • \n
  • bgm_kwargs: kwargs for bayesian_gaussian_mixture
    • \n
  • sharpening 'hht' or None. Defaults to None.\nIf 'hht' hht sharpening will be performed before finding peaks.
    • \n
  • strategy: optional. Defauts to 'topo'.\nPeakfinding method
    • \n
\n\n
Returns:
\n\n
\n

ListPeakCandidateMultiModel: Located peak groups

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tprominence: Optional[Annotated[float, Ge(ge=0)]] = None,\twlen: Optional[Annotated[int, Ge(ge=0)]] = None,\twidth: Union[int, Tuple[int, int], NoneType] = None,\thht_chain: Optional[List[Annotated[int, Gt(gt=0)]]] = None,\tbgm_kwargs={},\tsharpening: Optional[Literal['hht']] = None,\tstrategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo') -> ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel:", "funcdef": "def"}, "ramanchada2.spectrum.peaks.find_peaks.find_peak_multipeak_filter": {"fullname": "ramanchada2.spectrum.peaks.find_peaks.find_peak_multipeak_filter", "modulename": "ramanchada2.spectrum.peaks.find_peaks", "qualname": "find_peak_multipeak_filter", "kind": "function", "doc": "

Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • args, *kwargs: same as find_peak_multipeak
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*args,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture": {"fullname": "ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture", "modulename": "ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture.bayesian_gaussian_mixture": {"fullname": "ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture.bayesian_gaussian_mixture", "modulename": "ramanchada2.spectrum.peaks.find_peaks_BayesianGaussianMixture", "qualname": "bayesian_gaussian_mixture", "kind": "function", "doc": "

Decompose the spectrum to Bayesian Gaussian Mixture

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • n_samples: optional. Defaults to 5000.\nResampled dataset size
    • \n
  • n_components: optional. Defaults to 50.\nNumber of expected gaussian components
    • \n
  • max_iter: optional. Defaults to 100.\nMaximal number of iterations.
    • \n
  • moving_minimum_window: optional. Defaults to None.\nIf None no moving minimum is subtracted, otherwise as specified.
    • \n
  • random_state: optional. Defaults to None.\nRandom generator seed to be used.
    • \n
  • trim_range: optional. Defaults to None:\nIf None ignore trimming, otherwise trim range is in x-axis values.
    • \n
\n\n
Returns:
\n\n
\n

BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_samples: typing.Annotated[int, Gt(gt=0)] = 5000,\tn_components: typing.Annotated[int, Gt(gt=0)] = 50,\tmax_iter: typing.Annotated[int, Gt(gt=0)] = 100,\tmoving_minimum_window: Optional[Annotated[int, Gt(gt=0)]] = None,\trandom_state=None,\ttrim_range: Optional[Tuple[float, float]] = None) -> sklearn.mixture._bayesian_mixture.BayesianGaussianMixture:", "funcdef": "def"}, "ramanchada2.spectrum.peaks.fit_peaks": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.peaks.fit_peaks.logger": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks.logger", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.spectrum.peaks.fit_peaks (WARNING)>"}, "ramanchada2.spectrum.peaks.fit_peaks.available_models": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks.available_models", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "qualname": "available_models", "kind": "variable", "doc": "

\n", "default_value": "['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']"}, "ramanchada2.spectrum.peaks.fit_peaks.available_models_type": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks.available_models_type", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "qualname": "available_models_type", "kind": "variable", "doc": "

\n", "default_value": "typing.Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']"}, "ramanchada2.spectrum.peaks.fit_peaks.build_multipeak_model_params": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks.build_multipeak_model_params", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "qualname": "build_multipeak_model_params", "kind": "function", "doc": "

\n", "signature": "(\tprofile: Union[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'], List[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']]],\tcandidates: ramanchada2.misc.types.peak_candidates.PeakCandidateMultiModel,\tbaseline_model: Literal['linear', None] = 'linear'):", "funcdef": "def"}, "ramanchada2.spectrum.peaks.fit_peaks.fit_peak_multimodel": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks.fit_peak_multimodel", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "qualname": "fit_peak_multimodel", "kind": "function", "doc": "

\n", "signature": "(\tspe,\t/,\t*,\tprofile: Union[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'], List[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']]],\tcandidates: ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel,\tno_fit=False,\tshould_break=[False],\tkwargs_fit={},\tvary_baseline: bool = False,\tbound_centers_to_group: bool = False) -> ramanchada2.misc.types.fit_peaks_result.FitPeaksResult:", "funcdef": "def"}, "ramanchada2.spectrum.peaks.fit_peaks.fit_peaks_filter": {"fullname": "ramanchada2.spectrum.peaks.fit_peaks.fit_peaks_filter", "modulename": "ramanchada2.spectrum.peaks.fit_peaks", "qualname": "fit_peaks_filter", "kind": "function", "doc": "

Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • should_break: same as in fit_peaks_multipeak
    • \n
  • args, *kwargs: same as fit_peaks_multipeak
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*args,\tshould_break=[False],\tkwargs_fit={},\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.peaks.get_fitted_peaks": {"fullname": "ramanchada2.spectrum.peaks.get_fitted_peaks", "modulename": "ramanchada2.spectrum.peaks.get_fitted_peaks", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.peaks.get_fitted_peaks.logger": {"fullname": "ramanchada2.spectrum.peaks.get_fitted_peaks.logger", "modulename": "ramanchada2.spectrum.peaks.get_fitted_peaks", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.spectrum.peaks.get_fitted_peaks (WARNING)>"}, "ramanchada2.spectrum.peaks.get_fitted_peaks.fit_peak_positions": {"fullname": "ramanchada2.spectrum.peaks.get_fitted_peaks.fit_peak_positions", "modulename": "ramanchada2.spectrum.peaks.get_fitted_peaks", "qualname": "fit_peak_positions", "kind": "function", "doc": "

Calculate peak positions and amplitudes.

\n\n

Sequence of multiple processings:

\n\n
    \n
  • subtract_moving_minimum
    • \n
  • find_peak_multipeak
    • \n
  • filter peaks with x-location better than threshold
    • \n
\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • mov_min: optional. Defaults to 40\nsubtract moving_minimum with the specified window.
    • \n
  • center_err_threshold: optional. Defaults to 0.5.\nthreshold for centroid standard deviation. Only peaks\nwith better uncertainty will be returned.
    • \n
  • find_peaks_kw: optional\nkeyword arguments to be used with find_peak_multipeak
    • \n
  • fit_peaks_kw: optional\nkeyword arguments to be used with fit_peaks_multipeak
    • \n
\n\n
Returns:
\n\n
\n

Dict[float, float]: {positions: amplitudes}

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tmov_min=40,\tcenter_err_threshold=0.5,\tfind_peaks_kw={},\tfit_peaks_kw={}) -> Dict[float, float]:", "funcdef": "def"}, "ramanchada2.spectrum.spectrum": {"fullname": "ramanchada2.spectrum.spectrum", "modulename": "ramanchada2.spectrum.spectrum", "kind": "module", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.logger": {"fullname": "ramanchada2.spectrum.spectrum.logger", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger ramanchada2.spectrum.spectrum (WARNING)>"}, "ramanchada2.spectrum.spectrum.Spectrum": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum", "kind": "class", "doc": "

Helper class that provides a standard way to create an ABC using\ninheritance.

\n", "bases": "ramanchada2.misc.plottable.Plottable"}, "ramanchada2.spectrum.spectrum.Spectrum.__init__": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.__init__", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.__init__", "kind": "function", "doc": "

\n", "signature": "(\tx: Union[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], int, NoneType] = None,\ty: Optional[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = None,\tcachefile: Optional[str] = None,\tmetadata: Optional[ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel] = None,\tapplied_processings: Optional[ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel] = None)"}, "ramanchada2.spectrum.spectrum.Spectrum.applied_processings_dict": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.applied_processings_dict", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.applied_processings_dict", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.write_csv": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.write_csv", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.write_csv", "kind": "function", "doc": "

\n", "signature": "(self, filename, delimiter=',', newline='\\n'):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.write_cha": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.write_cha", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.write_cha", "kind": "function", "doc": "

\n", "signature": "(self, chafile, dataset):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.write_nexus": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.write_nexus", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.write_nexus", "kind": "function", "doc": "

\n", "signature": "(self, chafile, dataset):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.write_cache": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.write_cache", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.write_cache", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.process": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.process", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.process", "kind": "function", "doc": "

\n", "signature": "(self, algorithm: str, **kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.apply_creator": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.apply_creator", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.apply_creator", "kind": "function", "doc": "

\n", "signature": "(\tcls,\tstep: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel,\tcachefile_=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.apply_processing": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.apply_processing", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.apply_processing", "kind": "function", "doc": "

\n", "signature": "(\tself,\tstep: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingModel):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.x": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.x", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.x", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.Spectrum.x_bin_boundaries": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.x_bin_boundaries", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.x_bin_boundaries", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.Spectrum.y": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.y", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.y", "kind": "variable", "doc": "

\n", "annotation": ": numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]"}, "ramanchada2.spectrum.spectrum.Spectrum.y_noise": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.y_noise", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.y_noise", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.Spectrum.y_noise_MAD": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.y_noise_MAD", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.y_noise_MAD", "kind": "function", "doc": "

\n", "signature": "(self):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.y_noise_savgol_DL": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.y_noise_savgol_DL", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.y_noise_savgol_DL", "kind": "function", "doc": "

\n", "signature": "(self, order: typing.Annotated[int, Gt(gt=0)] = 1):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.y_noise_savgol": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.y_noise_savgol", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.y_noise_savgol", "kind": "function", "doc": "

\n", "signature": "(self, order: typing.Annotated[int, Gt(gt=0)] = 1):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.x_err": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.x_err", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.x_err", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.Spectrum.y_err": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.y_err", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.y_err", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.Spectrum.meta": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.meta", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.meta", "kind": "variable", "doc": "

\n", "annotation": ": ramanchada2.misc.types.spectrum.metadata.SpeMetadataModel"}, "ramanchada2.spectrum.spectrum.Spectrum.result": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.result", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.result", "kind": "variable", "doc": "

\n"}, "ramanchada2.spectrum.spectrum.Spectrum.spe_distribution": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.spe_distribution", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.spe_distribution", "kind": "function", "doc": "

\n", "signature": "(self, trim_range: Optional[Tuple[float, float]] = None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.gen_samples": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.gen_samples", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.gen_samples", "kind": "function", "doc": "

\n", "signature": "(self, size: typing.Annotated[int, Gt(gt=0)], trim_range=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.moving_minimum": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.moving_minimum", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.moving_minimum", "kind": "function", "doc": "

Moving minimum baseline estimator.\nSuccessive values are calculated as minima of rolling rectangular window.

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\twindow_size: int):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.subtract_moving_minimum": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.subtract_moving_minimum", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.subtract_moving_minimum", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\twindow_size: int):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.subtract_baseline_rc1_als": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.subtract_baseline_rc1_als", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.subtract_baseline_rc1_als", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\tlam=100000.0,\tp=0.001,\tniter=100,\tsmooth=7):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.subtract_baseline_rc1_snip": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.subtract_baseline_rc1_snip", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.subtract_baseline_rc1_snip", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\tniter=30):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.add_baseline": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.add_baseline", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.add_baseline", "kind": "function", "doc": "

Add artificial baseline to the spectrum.\nA random baseline is generated in frequency domain using uniform random numbers.\nThe baseline in frequency domain is tapered with bohman window to reduce the bandwidth\nof the baseline to first n_freq frequencies and is transformed to \"time\" domain.\nAdditionaly by using func parameter the user can define arbitrary function\nto be added as baseline.

\n\n
Arguments:
\n\n
    \n
  • n_freq: Must be > 2. Number of lowest frequency bins distinct from zero.
    • \n
  • amplitude: Upper boundary for the uniform random generator.
    • \n
  • pedestal: Additive constant pedestal to the spectrum.
    • \n
  • func: Callable. User-defined function to be added as baseline. Example: func = lambda x: x*.01 + x**2*.0001.
    • \n
  • rng_seed: int, optional. Seed for the random generator.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_freq: int,\tamplitude: float,\tpedestal: float = 0,\tfunc: Optional[Callable] = None,\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.central_moments": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.central_moments", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.central_moments", "kind": "function", "doc": "

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tboundaries=(-inf, inf),\tmoments=[1, 2, 3, 4],\tnormalize=False):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.scale_yaxis_linear": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.scale_yaxis_linear", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.scale_yaxis_linear", "kind": "function", "doc": "

Scale y-axis values

\n\n

This function provides the same result as spe*const

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • factor optional. Defaults to 1.\nY-values scaling factor
    • \n
\n\n

Returns: corrected spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\tfactor: float = 1):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.normalize": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.normalize", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.normalize", "kind": "function", "doc": "

Normalize the spectrum.

\n\n
Arguments:
\n\n
    \n
  • strategy: If unity: normalize to sum(y). If min_unity: subtract the minimum and normalize to 'unity'. If\nunity_density: normalize to \u03a3(y_i*\u0394x_i). If unity_area: same as unity_density. If minmax: scale\namplitudes in range [0, 1]. If 'L1' or 'L2': L1 or L2 norm without subtracting the pedestal.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tstrategy: Literal['unity', 'min_unity', 'unity_density', 'unity_area', 'minmax', 'L1', 'L2'] = 'minmax'):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.abs_nm_to_shift_cm_1": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.abs_nm_to_shift_cm_1", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.abs_nm_to_shift_cm_1", "kind": "function", "doc": "

Convert wavelength to Ramanshift in wavenumber

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Corrected x-values

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.shift_cm_1_to_abs_nm": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.shift_cm_1_to_abs_nm", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.shift_cm_1_to_abs_nm", "kind": "function", "doc": "

Convert Ramanshift in wavenumber to wavelength

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Corrected x-values

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.abs_nm_to_shift_cm_1_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.abs_nm_to_shift_cm_1_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.abs_nm_to_shift_cm_1_filter", "kind": "function", "doc": "

Convert wavelength to Ramanshift in wavenumber

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Spectrum with corrected x-values

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.shift_cm_1_to_abs_nm_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.shift_cm_1_to_abs_nm_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.shift_cm_1_to_abs_nm_filter", "kind": "function", "doc": "

Convert Ramanshift in wavenumber to wavelength

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • laser_wave_length_nm: Laser wave length
    • \n
\n\n

Returns: Spectrum with corrected x-values

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlaser_wave_length_nm: float):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.calibrate_by_deltas_model": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.calibrate_by_deltas_model", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.calibrate_by_deltas_model", "kind": "function", "doc": "
    \n
  • Builds a composite model based on a set of user specified delta lines.
    • \n
  • Initial guess is calculated based on 10-th and 90-th percentiles of\nthe distributions.
    • \n
\n\n

The phasespace of the model is flat with big amount of narrow minima.\nIn order to find the best fit, the experimental data are successively\nconvolved with gaussians with different widths startign from wide to\nnarrow. The model for the calibration is 3-th order polynomial, which\npotentialy can be changed for higher order polynomial. In order to avoid\nsolving the inverse of the calibration function, the result is tabulated\nand interpolated linarly for each bin of the spectrum.\nThis alogrithm is useful for corse calibration.

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tdeltas: Dict[float, float],\tbounds: Optional[ramanchada2.spectrum.calibration.by_deltas.FitBounds] = None,\tconvolution_steps: Optional[List[float]] = [15, 1],\tscale2=True,\tscale3=False,\tinit_guess: Literal[None, 'cumulative'] = None,\tax=None,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.calibrate_by_deltas_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.calibrate_by_deltas_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.calibrate_by_deltas_filter", "kind": "function", "doc": "

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tdeltas: Dict[float, float],\tconvolution_steps,\tinit_guess=None,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.xcal_fine": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.xcal_fine", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.xcal_fine", "kind": "function", "doc": "

Iterative calibration with provided reference based on ~ramanchada2.misc.utils.argmin2d.align()

\n\n

Iteratively apply polynomial of poly_order degree to match\nthe found peaks to the reference locations. The pairs are created\nusing ~ramanchada2.misc.utils.argmin2d.align() algorithm.

\n\n
Arguments:
\n\n
    \n
  • old_spe (Spectrum): internal use only
    • \n
  • new_spe (Spectrum): internal use only
    • \n
  • ref (Union[Dict[float, float], List[float]]): _description_
    • \n
  • ref (Dict[float, float]): If a dict is provided - wavenumber - amplitude pairs.\nIf a list is provided - wavenumbers only.
    • \n
  • poly_order (NonNegativeInt): polynomial degree to be used usualy 2 or 3
    • \n
  • should_fit (bool, optional): Whether the peaks should be fit or to\nassociate the positions with the maxima. Defaults to False.
    • \n
  • find_peaks_kw (dict, optional): kwargs to be used in find_peaks. Defaults to {}.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tref: Union[Dict[float, float], List[float]],\tshould_fit=False,\tpoly_order: typing.Annotated[int, Ge(ge=0)],\tfind_peaks_kw={}):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.xcal_fine_RBF": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.xcal_fine_RBF", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.xcal_fine_RBF", "kind": "function", "doc": "

Wavelength calibration using Radial basis fuction interpolation

\n\n

Please be cautious! Interpolation might not be the most appropriate\napproach for this type of calibration.

\n\n

**kwargs are passed to RBFInterpolator

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tref: Union[Dict[float, float], List[float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]],\tshould_fit=False,\tkernel: Literal['thin_plate_spline', 'cubic', 'quintic', 'multiquadric', 'inverse_multiquadric', 'inverse_quadratic', 'gaussian'] = 'thin_plate_spline',\tfind_peaks_kw={},\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.xcal_argmin2d_iter_lowpass": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.xcal_argmin2d_iter_lowpass", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.xcal_argmin2d_iter_lowpass", "kind": "function", "doc": "

Calibrate spectrum

\n\n

The calibration is done in multiple steps. Both the spectrum and the reference\nare passed through a low-pass filter to preserve only general structure of the\nspectrum. low_pass_nfreqs defines the number of frequencies to be preserved in\neach step. Once all steps with low-pass filter a final step without a low-pass\nfilter is performed. Each calibration step is performed using\n~ramanchada2.spectrum.calibration.by_deltas.xcal_fine() algorithm.

\n\n
Arguments:
\n\n
    \n
  • old_spe (Spectrum): internal use only
    • \n
  • new_spe (Spectrum): internal use only
    • \n
  • ref (Dict[float, float]): wavenumber - amplitude pairs
    • \n
  • low_pass_nfreqs (List[int], optional): The number of elements defines the\nnumber of low-pass steps and their values define the amount of frequencies\nto keep. Defaults to [100, 500].
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tref: Dict[float, float],\tlow_pass_nfreqs: List[int] = [100, 500]):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.set_new_xaxis": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.set_new_xaxis", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.set_new_xaxis", "kind": "function", "doc": "

Substitute x-axis values with new ones

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • xaxis: new x-axis values
    • \n
\n\n

Returns: corrected spectrum

\n\n
Raises:
\n\n
    \n
  • ValueError: If the provided array does not match the shape of the spectrum.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\txaxis: numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.scale_xaxis_linear": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.scale_xaxis_linear", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.scale_xaxis_linear", "kind": "function", "doc": "

Scale x-axis using a factor.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • factor: Defaults to 1.\nMultiply x-axis values with factor
    • \n
  • preserve_integral: optional. Defaults to False.\nIf True, preserves the integral in sence\n$\\sum y_{orig;\\,i}*{\\Delta x_{orig}}_i = \\sum y_{new;\\,i}*{\\Delta x_{new}}_i = $
    • \n
\n\n

Returns: Corrected spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tfactor: float = 1,\tpreserve_integral: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.scale_xaxis_fun": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.scale_xaxis_fun", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.scale_xaxis_fun", "kind": "function", "doc": "

Apply arbitrary calibration function to the x-axis values.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • fun: function to be applied
    • \n
  • args: Additional arguments to the provided functions
    • \n
\n\n

Returns: Corrected spectrum

\n\n
Raises:
\n\n
    \n
  • ValueError: If the new x-values are not strictly monotonically increasing.
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tfun: Callable[[Union[int, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float],\targs=[]):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_cache_or_calc": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_cache_or_calc", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_cache_or_calc", "kind": "function", "doc": "

Load spectrum from cache or calculate if needed.

\n\n

The cache is a nested structure of spectra. All processings applied to\na spectrum result to spectra of the initial one. If part of the requred\nprocessings are available, only the needed steps are calculated and added\nto the cache.

\n\n
Arguments:
\n\n
    \n
  • required_steps: List of required steps in the form\n[{'proc': str, 'args': List[Any], 'kwargs': Dict[str, Any]}, ...]
    • \n
  • cachefile: optional. Defaults to None.\nFilename of the cache. If None no cache is used
    • \n
\n", "signature": "(\trequired_steps: ramanchada2.misc.types.spectrum.applied_processings.SpeProcessingListModel,\tcachefile: Optional[str] = None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_chada": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_chada", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_chada", "kind": "function", "doc": "

\n", "signature": "(filename: str, dataset: str = '/raw', h5module=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_delta_lines": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_delta_lines", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_delta_lines", "kind": "function", "doc": "

Generate Spectrum with delta lines.

\n\n
Arguments:
\n\n
    \n
  • deltas: Keys of the dictionary are the x positions of the deltas; values are the amplitudes of the corresponding\ndeltas.
    • \n
  • xcal: Callable, optional. x axis calibration function.
    • \n
  • nbins: int, optional. Number of bins in the spectrum.
    • \n
  • xaxis: Array-like, optional. The xaxis of the new spectrum. If xaxis is provided,\nxcal should be None and nbins is ignored.
    • \n
\n\n

Example:

\n\n

This will produce spectrum with 1000 bins in the range [-1000, 2000):

\n\n
\n
xcal = lambda x: x*3 -1000, nbins=1000\n
\n
\n", "signature": "(\tdeltas: Dict[float, float],\txcal: Optional[Callable[[float], float]] = None,\tnbins: typing.Annotated[int, Gt(gt=0)] = 2000,\txaxis: Optional[numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]] = None,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_local_file": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_local_file", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_local_file", "kind": "function", "doc": "

Read experimental spectrum from a local file.

\n\n
Arguments:
\n\n
    \n
  • in_file_name: Path to a local file containing a spectrum.
    • \n
  • filetype: Specify the filetype. Filetype can be any of: spc, sp, spa, 0, 1, 2, wdf, ngs, jdx, dx,\ntxt, txtr, csv, prn, rruf, spe (Princeton Instruments) or None.\nNone used to determine by extension of the file.
    • \n
  • backend: native, rc1_parser or None. None means both.
    • \n
\n\n
Raises:
\n\n
    \n
  • ValueError: When called with unsupported file formats.
    • \n
\n", "signature": "(\tin_file_name: str,\tfiletype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe', 'cha']] = None,\tbackend: Optional[Literal['native', 'rc1_parser']] = None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_simulation": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_simulation", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_simulation", "kind": "function", "doc": "

Generate spectrum from simulation file.

\n\n

The returned spectrum has only few x/y pairs -- one for each simulated line. Values along\nthe x-axis will not be uniform. To make it uniform, one needs to resample the spectrum.

\n\n
Arguments:
\n\n
    \n
  • in_file: Path to a local file, or file-like object.
    • \n
  • sim_type: If vasp: .dat file from VASP simulation. If crystal_out: .out file from CRYSTAL simulation, not\npreferred. If crystal_dat: .dat file from CRYSTAL simulation.
    • \n
  • use: One of the directions I_tot, I_perp, I_par, I_xx, I_xy,\nI_xz, I_yy, I_yz, I_zz, I_tot, I_perp, I_par are\navailable for both CRYSTAL and VASP. I_xx, I_xy, I_xz,\nI_yy, I_yz, I_zz are available only for CRYSTAL. If a Dict is\npassed, the key should be directions and values should be weighting factor.\nFor example, use={'I_perp': .1, 'I_par': .9}
    • \n
\n", "signature": "(\tin_file: Union[str, io.TextIOBase],\tsim_type: Literal['vasp', 'crystal_out', 'crystal_dat', 'raw_dat'],\tuse: Union[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Dict[Literal['I_tot', 'I_perp', 'I_par', 'I_xx', 'I_xy', 'I_xz', 'I_yy', 'I_yz', 'I_zz'], Annotated[float, Gt(gt=0)]]] = 'I_tot',\tnbins: typing.Annotated[int, Gt(gt=0)] = 2000):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_spectral_component_collection": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_spectral_component_collection", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_spectral_component_collection", "kind": "function", "doc": "

from_spectral_component_collection

\n\n
Arguments:
\n\n
    \n
  • spe_components: SpectralComponentCollection
    • \n
  • x: int or array-like, optional, default 2000. x axis of the spectrum.
    • \n
\n", "signature": "(\tspe_components: ramanchada2.spectral_components.spectral_component_collection.SpectralComponentCollection,\tx=2000):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_stream": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_stream", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_stream", "kind": "function", "doc": "

\n", "signature": "(\tin_stream: Union[io.TextIOBase, _io.BytesIO, _io.BufferedReader],\tfiletype: Optional[Literal['spc', 'sp', 'spa', '0', '1', '2', 'wdf', 'ngs', 'jdx', 'dx', 'txt', 'txtr', 'csv', 'prn', 'rruf', 'spe']],\tfilename: Optional[str] = None,\tbackend: Optional[Literal['native', 'rc1_parser']] = None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_test_spe": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_test_spe", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_test_spe", "kind": "function", "doc": "

Create new spectrum from test data.

\n\n
Arguments:
\n\n
    \n
  • index: int or None, optional, default is None. If int: will be used as an index of filtered list. If\nNone: a random spectrum will be taken.
    • \n
  • **kwargs: The rest of the parameters will be used as filter.
    • \n
\n", "signature": "(index=None, **kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.from_theoretical_lines": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.from_theoretical_lines", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.from_theoretical_lines", "kind": "function", "doc": "

Generate spectrum from lmfit shapes.

\n\n
Arguments:
\n\n
    \n
  • shapes: The shapes to be used for spectrum generation.
    • \n
  • params: Shape parameters to be applied to be used with shapes.
    • \n
  • x: Array with x values, by default np.array(2000).
    • \n
\n", "signature": "(\tshapes: List[Literal['gaussian', 'gaussian2d', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7', 'breit_wigner', 'damped_oscillator', 'dho', 'logistic', 'lognormal', 'students_t', 'expgaussian', 'doniach', 'skewed_gaussian', 'skewed_voigt', 'thermal_distribution', 'step', 'rectangle', 'exponential', 'powerlaw', 'linear', 'parabolic', 'sine', 'expsine', 'split_lorentzian']],\tparams: List[Dict],\tx: Union[int, numpy.ndarray[Any, numpy.dtype[numpy.float64]]] = 2000):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.hdr_from_multi_exposure": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.hdr_from_multi_exposure", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.hdr_from_multi_exposure", "kind": "function", "doc": "

Create an HDR spectrum from several spectra with different exposures.

\n\n

The resulting spectrum will have the details in low-intensity peaks\nfrom long-exposure-time spectrum. As long-exposure-time\nspectrum might be sturated, the information for high-intensity\npeaks will be taken from short-exposure-time spectrum.\nThis function will work on a very limited number of spectra,\nbecause we still do not have standardized metadata.

\n", "signature": "(spes_in: List[ramanchada2.spectrum.spectrum.Spectrum]):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.moving_average": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.moving_average", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.moving_average", "kind": "function", "doc": "

Moving average filter.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\twindow_size: typing.Annotated[int, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.moving_average_convolve": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.moving_average_convolve", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.moving_average_convolve", "kind": "function", "doc": "

Moving average filter.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\twindow_size: typing.Annotated[int, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.add_gaussian_noise": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.add_gaussian_noise", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.add_gaussian_noise", "kind": "function", "doc": "

Add gaussian noise to the spectrum.

\n\n

Random number i.i.d. $N(0, \\sigma)$ is added to every sample

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • sigma: Sigma of the gaussian noise.
    • \n
  • rng_seed: int or rng state, optional, seed for the random generator.\nIf a state is provided, it is updated in-place.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tsigma: typing.Annotated[float, Gt(gt=0)],\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.add_poisson_noise": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.add_poisson_noise", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.add_poisson_noise", "kind": "function", "doc": "

Add poisson noise to the spectrum.

\n\n

For each particular sample the noise is proportional to $\\sqrt{scale*a_i}$.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • scale: float, optional, default is 1. Scale the amplitude of the noise.
    • \n
  • rng_seed: int or rng state, optional. Seed for the random generator.\nIf a state is provided, it is updated in-place.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tscale: float = 1,\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.convolve": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.convolve", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.convolve", "kind": "function", "doc": "

Convole spectrum with arbitrary lineshape.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • lineshape: callable, str or np.ndarray.\n If callable: should have a single positional argument x, e.g.\nlambda x: np.exp((x/5)**2).\nIf predefined peak profile: can be gaussian, lorentzian, voigt,\npvoigt, moffat or pearson4.\nIf np.ndarray: lineshape in samples.
    • \n
  • **kwargs: Additional kwargs will be passed to lineshape function.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tlineshape: Union[Callable[[Union[float, numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]]]], float], numpy.ndarray[Any, numpy.dtype[+_ScalarType_co]], Literal['gaussian', 'lorentzian', 'voigt', 'pvoigt', 'moffat', 'pearson4', 'pearson7']],\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.derivative_sharpening": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.derivative_sharpening", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.derivative_sharpening", "kind": "function", "doc": "

Derivative-based sharpening.

\n\n

Sharpen the spectrum subtracting second derivative and add fourth derivative.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • filter_fraction float in (0; 1]: Default is 0.6\nDepth of filtration
    • \n
  • signal_width: The width of features to be enhanced in sample count
    • \n
  • der2_factor: Second derivative scaling factor
    • \n
  • der4_factor: Fourth derivative scaling factor
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tfilter_fraction: typing.Annotated[float, None, Interval(gt=0, ge=None, lt=None, le=1), None, None] = 0.6,\tsig_width: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=None), None, None] = 0.25,\tder2_factor: float = 1,\tder4_factor: float = 0.1):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.hht_sharpening": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.hht_sharpening", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.hht_sharpening", "kind": "function", "doc": "

Hilbert-Huang based sharpening.

\n\n

In order to reduce the overshooting, moving minimum is subtracted from the result

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • movmin: optional. Default is 100\nWindow size for moving minimum
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tmovmin=100):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.hht_sharpening_chain": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.hht_sharpening_chain", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.hht_sharpening_chain", "kind": "function", "doc": "

Hilbert-Huang based chain sharpening.

\n\n

Sequence of Hilbert-Huang sharpening procedures are performed.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • movmin: List[int], optional. Default is [150, 50]\nThe numer of values in the list defines how many iterations\nof HHT_sharpening will be performed and the values define\nthe moving minimum window sizes for the corresponding operations.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tmovmin: List[Annotated[int, Gt(gt=0)]] = [150, 50]):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.spike_indices": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.spike_indices", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.spike_indices", "kind": "function", "doc": "

Find spikes in spectrum

\n\n

Single-bin spikes are located using left and right successive\ndifferences. The threshold is based on the standart deviation\nof the metric which makes this algorithm less optimal.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • n_sigma: Threshold value should be n_sigma times the standart\ndeviation of the metric.
    • \n
\n\n

Returns: List of spike indices

\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)]) -> numpy.ndarray[typing.Any, numpy.dtype[+_ScalarType_co]]:", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.drop_spikes": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.drop_spikes", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.drop_spikes", "kind": "function", "doc": "

Removes single-bin spikes.

\n\n

Remove x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • n_sigma: optional, default is 10.\nThreshold is n_sigma times the standard deviation.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.recover_spikes": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.recover_spikes", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.recover_spikes", "kind": "function", "doc": "

Recover single-bin spikes.

\n\n

Recover x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • n_sigma: optional, default is 10.\nThreshold is n_sigma times the standard deviation.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.get_spikes": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.get_spikes", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.get_spikes", "kind": "function", "doc": "

Get single-bin spikes only.

\n\n

Get x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • n_sigma: optional, default is 10.\nThreshold is n_sigma times the standard deviation.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_sigma: typing.Annotated[float, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.moving_median": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.moving_median", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.moving_median", "kind": "function", "doc": "

Moving median filter.

\n\n

The resultant spectrum is moving minimum of the input.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\twindow_size: typing.Annotated[int, Gt(gt=0)] = 10):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.subtract_moving_median": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.subtract_moving_median", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.subtract_moving_median", "kind": "function", "doc": "

Subtract moving median filter.

\n\n

The resultant spectrum is moving minimum of the input subtracted from the input.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • window_size: int, optional, default is 10.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\twindow_size: int):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.trim_axes": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.trim_axes", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.trim_axes", "kind": "function", "doc": "

Trim axes of the spectrum.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • method: 'x-axis' or 'bins'\nIf 'x-axis' boundaries will be interpreted as x-axis values.\nIf 'bins' boundaries will be interpreted as indices.
    • \n
  • boundaries: lower and upper boundary for the trimming.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tmethod: Literal['x-axis', 'bins'],\tboundaries: Tuple[float, float]):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.add_gaussian_noise_drift": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.add_gaussian_noise_drift", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.add_gaussian_noise_drift", "kind": "function", "doc": "

Add cumulative gaussian noise to the spectrum.

\n\n

Exponential-moving-average-like gaussian noise is added\nto each sample. The goal is to mimic the low-frequency noise\n(or random substructures in spectra).\nThe additive noise is\n$$a_i = coef*\\sum_{j=0}^{i-1}g_j + g_i,$$

\n\n

where\n$$g_i = \\mathcal{N}(0, 1+\\frac{coef}{\\sqrt 2}).$$

\n\n

This way drifting is possible while keeping the\n$$\\sigma(\\Delta(a)) \\approx 1.$$

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • sigma: Sigma of the gaussian noise.
    • \n
  • coef: float in [0, 1], drifting coefficient. If coef == 0,\nthe result is identical to add_gaussian_noise().
    • \n
  • rng_seed: int or rng state, optional. Seed for the random generator.\nIf a state is provided, it is updated in-place.
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tsigma: typing.Annotated[float, Gt(gt=0)],\tcoef: typing.Annotated[float, None, Interval(gt=None, ge=0, lt=None, le=1), None, None],\trng_seed=None):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.resample_NUDFT": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.resample_NUDFT", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.resample_NUDFT", "kind": "function", "doc": "

Resample the spectrum using Non-uniform discrete fourier transform.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • window: optional, Defaults to None.\nThe window to be used for lowpass filter. If None 'blackmanharris' is used.\nIf no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n
Returns:
\n\n
\n

(x_values, y_values)

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\twindow: Union[Callable, Tuple[Any, ...], Literal['barthann', 'bartlett', 'blackman', 'blackmanharris', 'bohman', 'boxcar', 'chebwin', 'cosine', 'dpss', 'exponential', 'flattop', 'gaussian', 'general_cosine', 'general_gaussian', 'general_hamming', 'hamming', 'hann', 'kaiser', 'nuttall', 'parzen', 'taylor', 'triang', 'tukey'], NoneType] = None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.resample_NUDFT_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.resample_NUDFT_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.resample_NUDFT_filter", "kind": "function", "doc": "

Resample the spectrum using Non-uniform discrete fourier transform.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • window: optional, Defaults to None.\nThe window to be used for lowpass filter. If None 'blackmanharris' is used.\nIf no low-pass filter is required, one can use window=lambda x: [1]*len(x).
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\twindow=None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.resample_spline": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.resample_spline", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.resample_spline", "kind": "function", "doc": "

Resample the spectrum using spline interpolation.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • spline: optional, Defaults to 'pchip'.\nName of the spline funcion to be used.
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n
Returns:
\n\n
\n

(x_values, y_values)

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\tspline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',\tinterp_kw_args: Optional[Dict] = None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.resample_spline_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.resample_spline_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.resample_spline_filter", "kind": "function", "doc": "

Resample the spectrum using spline interpolation.

\n\n

The x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • x_range: optional. Defaults to (0, 4000).\nThe x_range of the new spectrum.
    • \n
  • xnew_bins: optional. Defaults to 100.\nNumber of bins of the new spectrum
    • \n
  • spline: optional, Defaults to 'pchip'.\nName of the spline funcion to be used.
    • \n
  • cumulative: optional. Defaults to False.\nIf True, the resultant spectrum will be cumulative and normalized\n(in analogy with CDF).
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tx_range: Tuple[float, float] = (0, 4000),\txnew_bins: typing.Annotated[int, Gt(gt=0)] = 100,\tspline: Literal['pchip', 'akima', 'makima', 'cubic_spline'] = 'pchip',\tinterp_kw_args: Optional[Dict] = None,\tcumulative: bool = False):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.pad_zeros": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.pad_zeros", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.pad_zeros", "kind": "function", "doc": "

Extend x-axis by 100% in both directions.

\n\n

The x-axis of resultant spectrum will be:\n$[x_{lower}-(x_{upper}-x_{lower})..(x_{upper}+(x_{upper}-x_{lower}))]$.\nThe length of the new spectrum is 3 times the original. The added values\nare with an uniform step. In the middle is the original spectrum with\noriginal x and y values. The coresponding y vallues for the newly added\nx-values are always zeros.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.dropna": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.dropna", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.dropna", "kind": "function", "doc": "

Remove non finite numbers on both axes

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.smoothing_RC1": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.smoothing_RC1", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.smoothing_RC1", "kind": "function", "doc": "

Smooth the spectrum.

\n\n

The spectrum will be smoothed using the specified filter.\nThis method is inherited from ramanchada1 for compatibility reasons.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • method: method to be used
    • \n
  • **kwargs: keyword arguments to be passed to the selected method
    • \n
\n\n

Returns: modified Spectrum

\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*args,\tmethod: Literal['savgol', 'sg', 'wiener', 'median', 'gauss', 'gaussian', 'lowess', 'boxcar'],\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.bayesian_gaussian_mixture": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.bayesian_gaussian_mixture", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.bayesian_gaussian_mixture", "kind": "function", "doc": "

Decompose the spectrum to Bayesian Gaussian Mixture

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • n_samples: optional. Defaults to 5000.\nResampled dataset size
    • \n
  • n_components: optional. Defaults to 50.\nNumber of expected gaussian components
    • \n
  • max_iter: optional. Defaults to 100.\nMaximal number of iterations.
    • \n
  • moving_minimum_window: optional. Defaults to None.\nIf None no moving minimum is subtracted, otherwise as specified.
    • \n
  • random_state: optional. Defaults to None.\nRandom generator seed to be used.
    • \n
  • trim_range: optional. Defaults to None:\nIf None ignore trimming, otherwise trim range is in x-axis values.
    • \n
\n\n
Returns:
\n\n
\n

BayesianGaussianMixture: Fitted Bayesian Gaussian Mixture

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tn_samples: typing.Annotated[int, Gt(gt=0)] = 5000,\tn_components: typing.Annotated[int, Gt(gt=0)] = 50,\tmax_iter: typing.Annotated[int, Gt(gt=0)] = 100,\tmoving_minimum_window: Optional[Annotated[int, Gt(gt=0)]] = None,\trandom_state=None,\ttrim_range: Optional[Tuple[float, float]] = None) -> sklearn.mixture._bayesian_mixture.BayesianGaussianMixture:", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.fit_peak_positions": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.fit_peak_positions", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.fit_peak_positions", "kind": "function", "doc": "

Calculate peak positions and amplitudes.

\n\n

Sequence of multiple processings:

\n\n
    \n
  • subtract_moving_minimum
    • \n
  • find_peak_multipeak
    • \n
  • filter peaks with x-location better than threshold
    • \n
\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • mov_min: optional. Defaults to 40\nsubtract moving_minimum with the specified window.
    • \n
  • center_err_threshold: optional. Defaults to 0.5.\nthreshold for centroid standard deviation. Only peaks\nwith better uncertainty will be returned.
    • \n
  • find_peaks_kw: optional\nkeyword arguments to be used with find_peak_multipeak
    • \n
  • fit_peaks_kw: optional\nkeyword arguments to be used with fit_peaks_multipeak
    • \n
\n\n
Returns:
\n\n
\n

Dict[float, float]: {positions: amplitudes}

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*,\tmov_min=40,\tcenter_err_threshold=0.5,\tfind_peaks_kw={},\tfit_peaks_kw={}) -> Dict[float, float]:", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.find_peak_multipeak": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.find_peak_multipeak", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.find_peak_multipeak", "kind": "function", "doc": "

Find groups of peaks in spectrum.

\n\n
Arguments:
\n\n
    \n
  • spe: internal use only
    • \n
  • prominence: Optional. Defaults to None\nIf None the prominence value will be spe.y_nose. Reasonable value for\npromience is const * spe.y_noise_MAD.
    • \n
  • wlen: optional. Defaults to None.\nwlen value used in scipy.signal.find_peaks. If wlen is None, 200 will be used.
    • \n
  • width: optional. Defaults to None.\nwidth value used in scipy.signal.find_peaks. If width is None, 2 will be used.
    • \n
  • hht_chain: optional. Defaults to None.\nList of hht_chain window sizes. If None, no hht sharpening is performed.
    • \n
  • bgm_kwargs: kwargs for bayesian_gaussian_mixture
    • \n
  • sharpening 'hht' or None. Defaults to None.\nIf 'hht' hht sharpening will be performed before finding peaks.
    • \n
  • strategy: optional. Defauts to 'topo'.\nPeakfinding method
    • \n
\n\n
Returns:
\n\n
\n

ListPeakCandidateMultiModel: Located peak groups

\n
\n", "signature": "(\tspe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\tprominence: Optional[Annotated[float, Ge(ge=0)]] = None,\twlen: Optional[Annotated[int, Ge(ge=0)]] = None,\twidth: Union[int, Tuple[int, int], NoneType] = None,\thht_chain: Optional[List[Annotated[int, Gt(gt=0)]]] = None,\tbgm_kwargs={},\tsharpening: Optional[Literal['hht']] = None,\tstrategy: Literal['topo', 'bayesian_gaussian_mixture', 'bgm', 'cwt'] = 'topo') -> ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel:", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.find_peak_multipeak_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.find_peak_multipeak_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.find_peak_multipeak_filter", "kind": "function", "doc": "

Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • args, *kwargs: same as find_peak_multipeak
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*args,\t**kwargs):", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.fit_peak_multimodel": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.fit_peak_multimodel", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.fit_peak_multimodel", "kind": "function", "doc": "

\n", "signature": "(\tspe,\t/,\t*,\tprofile: Union[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7'], List[Literal['Gaussian', 'Lorentzian', 'Moffat', 'Voigt', 'PseudoVoigt', 'Pearson4', 'Pearson7']]],\tcandidates: ramanchada2.misc.types.peak_candidates.ListPeakCandidateMultiModel,\tno_fit=False,\tshould_break=[False],\tkwargs_fit={},\tvary_baseline: bool = False,\tbound_centers_to_group: bool = False) -> ramanchada2.misc.types.fit_peaks_result.FitPeaksResult:", "funcdef": "def"}, "ramanchada2.spectrum.spectrum.Spectrum.fit_peaks_filter": {"fullname": "ramanchada2.spectrum.spectrum.Spectrum.fit_peaks_filter", "modulename": "ramanchada2.spectrum.spectrum", "qualname": "Spectrum.fit_peaks_filter", "kind": "function", "doc": "

Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.

\n\n
Arguments:
\n\n
    \n
  • old_spe: internal use only
    • \n
  • new_spe: internal use only
    • \n
  • should_break: same as in fit_peaks_multipeak
    • \n
  • args, *kwargs: same as fit_peaks_multipeak
    • \n
\n", "signature": "(\told_spe: ramanchada2.spectrum.spectrum.Spectrum,\tnew_spe: ramanchada2.spectrum.spectrum.Spectrum,\t/,\t*args,\tshould_break=[False],\tkwargs_fit={},\t**kwargs):", "funcdef": "def"}, "ramanchada2.theoretical_lines": {"fullname": "ramanchada2.theoretical_lines", "modulename": "ramanchada2.theoretical_lines", "kind": "module", "doc": "

\n"}, "ramanchada2.theoretical_lines.model_from_lines": {"fullname": "ramanchada2.theoretical_lines.model_from_lines", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "kind": "module", "doc": "

\n"}, "ramanchada2.theoretical_lines.model_from_lines.model_from_lines": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.model_from_lines", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "model_from_lines", "kind": "function", "doc": "

\n", "signature": "(\tnames: List[str],\tpositions: List[float],\tintensities: Dict[str, List[float]],\tmodel: Literal['gaussian', 'voigt'] = 'gaussian') -> Tuple[lmfit.model.Model, lmfit.parameter.Parameters]:", "funcdef": "def"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel", "kind": "class", "doc": "

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

\n\n

A base class for creating Pydantic models.

\n\n
Attributes:
\n\n
    \n
  • __class_vars__: The names of the class variables defined on the model.
    • \n
  • __private_attributes__: Metadata about the private attributes of the model.
    • \n
  • __signature__: The synthesized __init__ [Signature][inspect.Signature] of the model.
    • \n
  • __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
    • \n
  • __pydantic_core_schema__: The core schema of the model.
    • \n
  • __pydantic_custom_init__: Whether the model has a custom __init__ function.
    • \n
  • __pydantic_decorators__: Metadata containing the decorators defined on the model.\nThis replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.
    • \n
  • __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to\n__args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
    • \n
  • __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
    • \n
  • __pydantic_post_init__: The name of the post-init method for the model, if defined.
    • \n
  • __pydantic_root_model__: Whether the model is a [RootModel][pydantic.root_model.RootModel].
    • \n
  • __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the model.
    • \n
  • __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the model.
    • \n
  • __pydantic_extra__: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra]\nis set to 'allow'.
    • \n
  • __pydantic_fields_set__: The names of fields explicitly set during instantiation.
    • \n
  • __pydantic_private__: Values of private attributes set on the model instance.
    • \n
\n", "bases": "pydantic.main.BaseModel"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.model", "kind": "variable", "doc": "

\n", "annotation": ": Literal['gaussian', 'voigt']"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.position": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.position", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.position", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.inensity": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.inensity", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.inensity", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.sigma": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.sigma", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.sigma", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.name": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.name", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model_config": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model_config", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.model_config", "kind": "variable", "doc": "

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

\n", "annotation": ": ClassVar[pydantic.config.ConfigDict]", "default_value": "{}"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model_fields": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model_fields", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.model_fields", "kind": "variable", "doc": "

Metadata about the fields defined on the model,\nmapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

\n\n

This replaces Model.__fields__ from Pydantic V1.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.FieldInfo]]", "default_value": "{'model': FieldInfo(annotation=Literal['gaussian', 'voigt'], required=False, default='voigt'), 'position': FieldInfo(annotation=float, required=True), 'inensity': FieldInfo(annotation=float, required=False, default=1, metadata=[Gt(gt=0)]), 'sigma': FieldInfo(annotation=float, required=False, default=1, metadata=[Gt(gt=0)]), 'name': FieldInfo(annotation=str, required=False, default='')}"}, "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model_computed_fields": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.PydPeakModel.model_computed_fields", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "PydPeakModel.model_computed_fields", "kind": "variable", "doc": "

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

\n", "annotation": ": ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]", "default_value": "{}"}, "ramanchada2.theoretical_lines.model_from_lines.model_from_list": {"fullname": "ramanchada2.theoretical_lines.model_from_lines.model_from_list", "modulename": "ramanchada2.theoretical_lines.model_from_lines", "qualname": "model_from_list", "kind": "function", "doc": "

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{"ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator": {"tf": 1}}, "df": 1}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + + // mirrored in build-search-index.js (part 1) + // Also split on html tags. this is a cheap heuristic, but good enough. + elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); + + let searchIndex; + if (docs._isPrebuiltIndex) { + console.info("using precompiled search index"); + searchIndex = elasticlunr.Index.load(docs); + } else { + console.time("building search index"); + // mirrored in build-search-index.js (part 2) + searchIndex = elasticlunr(function () { + this.pipeline.remove(elasticlunr.stemmer); + this.pipeline.remove(elasticlunr.stopWordFilter); + this.addField("qualname"); + this.addField("fullname"); + this.addField("annotation"); + this.addField("default_value"); + this.addField("signature"); + this.addField("bases"); + this.addField("doc"); + this.setRef("fullname"); + }); + for (let doc of docs) { + searchIndex.addDoc(doc); + } + console.timeEnd("building search index"); + } + + return (term) => searchIndex.search(term, { + fields: { + qualname: {boost: 4}, + fullname: {boost: 2}, + annotation: {boost: 2}, + default_value: {boost: 2}, + signature: {boost: 2}, + bases: {boost: 2}, + doc: {boost: 1}, + }, + expand: true + }); +})(); \ No newline at end of file