Purpose\n\nramanchada2 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\nFeatures
\n\nRead simulated data
\n\nProcess 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\nModels
\n\nLMFIT 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\nGenerate spectra
\n\nSpectra 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\nSpectrum manipulation
\n\nA 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\nConcept
\n\nThe 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\n\n.cha
\n\nramanchada software package introduced .cha file format, which is an HDF5\nwith a simple layout.
\n\nCache in .cha files
\n\nThe 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\n\nThe latest ramanchada2 package allows export of a spectrum to NeXus format.
\n\nDecorated Functions in Spectrum
\n\nFunction: __add__
\n\nDocstring: No docstring available
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\n\nFunction: __init__
\n\nDocstring: No docstring available
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\n\nFunction: __mul__
\n\nDocstring: No docstring available
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\n\nFunction: __sub__
\n\nDocstring: No docstring available
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\n\nFunction: __truediv__
\n\nDocstring: No docstring available
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\n\nFunction: abs_nm_to_shift_cm_1
\n\nDocstring: \n Convert wavelength to Ramanshift in wavenumber
\n\nArgs:\n spe: internal use only\n laser_wave_length_nm: Laser wave length\n\nReturns: Corrected x-values\n
\n\n
\n\nFunction: abs_nm_to_shift_cm_1_filter
\n\nDocstring: \n Convert wavelength to Ramanshift in wavenumber
\n\nArgs:\n spe: internal use only\n laser_wave_length_nm: Laser wave length\n\nReturns: Spectrum with corrected x-values\n
\n\n
\n\nFunction: add_baseline
\n\nDocstring: \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\nArgs:\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
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\n\nFunction: add_gaussian_noise
\n\nDocstring: \n Add gaussian noise to the spectrum.
\n\nRandom 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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\n\nFunction: add_gaussian_noise_drift
\n\nDocstring: \n Add cumulative gaussian noise to the spectrum.
\n\nExponential-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
\n\n
\n\nFunction: add_poisson_noise
\n\nDocstring: \n Add poisson noise to the spectrum.
\n\nFor 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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\n\nFunction: apply_processing
\n\nDocstring: No docstring available
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\n\nFunction: bayesian_gaussian_mixture
\n\nDocstring: \n Decompose the spectrum to Bayesian Gaussian Mixture
\n\nArgs:\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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\n\nFunction: calibrate_by_deltas_filter
\n\nDocstring: No docstring available
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\n\nFunction: calibrate_by_deltas_model
\n\nDocstring: \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\nThe 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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\n\nFunction: central_moments
\n\nDocstring: No docstring available
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\n\nFunction: convolve
\n\nDocstring: \n Convole spectrum with arbitrary lineshape.
\n\nArgs:\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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\n\nFunction: derivative_sharpening
\n\nDocstring: \n Derivative-based sharpening.
\n\nSharpen 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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\n\nFunction: drop_spikes
\n\nDocstring: \n Removes single-bin spikes.
\n\nRemove 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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\n\nFunction: dropna
\n\nDocstring: \n Remove non finite numbers on both axes
\n\nArgs:\n old_spe: internal use only\n new_spe: internal use only\n\nReturns: modified Spectrum\n
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\n\nFunction: find_peak_multipeak
\n\nDocstring: \n Find groups of peaks in spectrum.
\n\nArgs:\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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\n\nFunction: find_peak_multipeak_filter
\n\nDocstring: \n Same as find_peak_multipeak but the result is stored as metadata in the returned spectrum.
\n\nArgs:\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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\n\nFunction: fit_peak_multimodel
\n\nDocstring: No docstring available
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\n\nFunction: fit_peak_positions
\n\nDocstring: \n Calculate peak positions and amplitudes.
\n\nSequence 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
\n\n
\n\nFunction: fit_peaks_filter
\n\nDocstring: \n Same as fit_peak_multipeak but the result is stored as metadata in the returned spectrum.
\n\nArgs:\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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\n\nFunction: from_cache_or_calc
\n\nDocstring: \n Load spectrum from cache or calculate if needed.
\n\nThe 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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\n\nFunction: from_chada
\n\nDocstring: No docstring available
\n\n
\n\nFunction: from_delta_lines
\n\nDocstring: \n Generate Spectrum with delta lines.
\n\nArgs:\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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\n\nFunction: from_local_file
\n\nDocstring: \n Read experimental spectrum from a local file.
\n\nArgs:\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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\n\nFunction: from_simulation
\n\nDocstring: \n Generate spectrum from simulation file.
\n\nThe 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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\n\nFunction: from_spectral_component_collection
\n\nDocstring: \n from_spectral_component_collection
\n\nArgs:\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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\n\nFunction: from_stream
\n\nDocstring: No docstring available
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\n\nFunction: from_test_spe
\n\nDocstring: Create new spectrum from test data.
\n\nArgs:\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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\n\nFunction: from_theoretical_lines
\n\nDocstring: \n Generate spectrum from lmfit shapes.
\n\nArgs:\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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\n\nFunction: gen_samples
\n\nDocstring: No docstring available
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\n\nFunction: get_spikes
\n\nDocstring: \n Get single-bin spikes only.
\n\nGet 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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\n\nFunction: hdr_from_multi_exposure
\n\nDocstring: Create an HDR spectrum from several spectra with different exposures.
\n\nThe 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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\n\nFunction: hht_sharpening
\n\nDocstring: \n Hilbert-Huang based sharpening.
\n\nIn 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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\n\nFunction: hht_sharpening_chain
\n\nDocstring: \n Hilbert-Huang based chain sharpening.
\n\nSequence 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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\n\nFunction: moving_average
\n\nDocstring: \n Moving average filter.
\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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\n\nFunction: moving_average_convolve
\n\nDocstring: \n Moving average filter.
\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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\n\n\n\nDocstring: \n Moving median filter.
\n\nThe 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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\n\nFunction: moving_minimum
\n\nDocstring: \n Moving minimum baseline estimator.\n Successive values are calculated as minima of rolling rectangular window.
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\n\nFunction: normalize
\n\nDocstring: \n Normalize the spectrum.
\n\nArgs:\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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\n\nFunction: pad_zeros
\n\nDocstring: \n Extend x-axis by 100% in both directions.
\n\nThe 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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\n\nFunction: recover_spikes
\n\nDocstring: \n Recover single-bin spikes.
\n\nRecover 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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\n\nFunction: resample_NUDFT
\n\nDocstring: \n Resample the spectrum using Non-uniform discrete fourier transform.
\n\nThe 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
\n\n
\n\nFunction: resample_NUDFT_filter
\n\nDocstring: \n Resample the spectrum using Non-uniform discrete fourier transform.
\n\nThe 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
\n\n
\n\nFunction: resample_spline
\n\nDocstring: \n Resample the spectrum using spline interpolation.
\n\nThe 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
\n\n
\n\nFunction: resample_spline_filter
\n\nDocstring: \n Resample the spectrum using spline interpolation.
\n\nThe 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
\n\n
\n\nFunction: scale_xaxis_fun
\n\nDocstring: \n Apply arbitrary calibration function to the x-axis values.
\n\nArgs:\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
\n\n
\n\nFunction: scale_xaxis_linear
\n\nDocstring: \n Scale x-axis using a factor.
\n\nArgs:\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
\n\n
\n\nFunction: scale_yaxis_linear
\n\nDocstring: \n Scale y-axis values
\n\nThis 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
\n\n
\n\nFunction: set_new_xaxis
\n\nDocstring: \n Substitute x-axis values with new ones
\n\nArgs:\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
\n\n
\n\nFunction: shift_cm_1_to_abs_nm
\n\nDocstring: \n Convert Ramanshift in wavenumber to wavelength
\n\nArgs:\n spe: internal use only\n laser_wave_length_nm: Laser wave length\n\nReturns: Corrected x-values\n
\n\n
\n\nFunction: shift_cm_1_to_abs_nm_filter
\n\nDocstring: \n Convert Ramanshift in wavenumber to wavelength
\n\nArgs:\n spe: internal use only\n laser_wave_length_nm: Laser wave length\n\nReturns: Spectrum with corrected x-values\n
\n\n
\n\nFunction: smoothing_RC1
\n\nDocstring: \n Smooth the spectrum.
\n\nThe 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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\n\nFunction: spe_distribution
\n\nDocstring: No docstring available
\n\n
\n\nFunction: spike_indices
\n\nDocstring: \n Find spikes in spectrum
\n\nSingle-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
\n\n
\n\nFunction: subtract_baseline_rc1_als
\n\nDocstring: No docstring available
\n\n
\n\nFunction: subtract_baseline_rc1_snip
\n\nDocstring: No docstring available
\n\n
\n\n\n\nDocstring: \n Subtract moving median filter.
\n\nThe 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
\n\n
\n\nFunction: subtract_moving_minimum
\n\nDocstring: No docstring available
\n\n
\n\nFunction: trim_axes
\n\nDocstring: \n Trim axes of the spectrum.
\n\nArgs:\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
\n\n
\n\nFunction: xcal_argmin2d_iter_lowpass
\n\nDocstring: No docstring available
\n\n
\n\nFunction: xcal_fine
\n\nDocstring: No docstring available
\n\n
\n\nFunction: xcal_fine_RBF
\n\nDocstring: Wavelength calibration using Radial basis fuction interpolation
\n\nPlease be cautious! Interpolation might not be the most appropriate\napproach for this type of calibration.\n\n**kwargs are passed to RBFInterpolator\n
\n\n
\n\nFunction: y_noise_savgol
\n\nDocstring: No docstring available
\n\n
\n\nFunction: y_noise_savgol_DL
\n\nDocstring: 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': './FMNT-M_Ho785/S0B10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0B'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0N10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0N'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S0P10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S0P'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/S1N10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'S1N'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/Sil10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'Sil'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/nCAL10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'nCAL'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/sCAL10_iR785_OP03_8000msx2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'sCAL'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/LED785_Lens_1000x10_2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'NIR785_EL0-9002'}, {'OP': '03', 'device': 'Horiba', 'filename': './FMNT-M_Ho785/NIST785_Lens_80000x5_2.txt', 'laser_wl': '785', 'provider': 'FNMT', 'sample': 'NIST785_SRM2241'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/Ne_532nm_x50_800ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'Neon'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/Ne_532nm_x50_25ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'Neon'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/PST02_iRPlus532_Z050_100_2500msx5.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'PST'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/S0B02_iRPlus532_Z050_100_30000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S0B'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/S0N02_iRPlus532_Z050_100_40000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S0N'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/S0P02_iRPlus532_Z050_100_30000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S0P'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/S1N02_iRPlus532_Z050_100_12000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S1N'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/nCAL02_iRPlus532_Z050_100_9000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'nCAL'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW532/sCAL02_iRPlus532_Z050_100_20000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'sCAL'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/Ne_532nm_x100_2000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'Neon'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/Ne_532nm_x100_110ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'Neon'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/PST02_iRPlus532_Z100_100_50000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'PST'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/S0B02_iRPlus532_Z100_100_22000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S0B'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/S0N02_iRPlus532_Z100_100_28000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S0N'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/S0P02_iRPlus532_Z100_100_20000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S0P'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/S1N02_iRPlus532_Z100_100_6500ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'S1N'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/nCAL02_iRPlus532_Z100_100_30000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'nCAL'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/sCAL02_iRPlus532_Z100_100_65000ms.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'sCAL'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/Visible_532nm_130ms_100X_2.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'LED532_EL0-9001'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW532/NISTSRM2242aC_BW532_100x_800msx10.txt', 'laser_wl': '532', 'provider': 'ICV', 'sample': 'NIST532_SRM2242a'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/PST02_iRPlus785_Z020_100_1300ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'PST'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/S0B02_iRPlus785_Z020_100_full.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0B'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/S0B02_iRPlus785_Z020_100_12000ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0B'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/S0N02_iRPlus785_Z020_100_3800ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0N'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/S0P02_iRPlus785_Z020_100_12000ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0P'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/S1N02_iRPlus785_Z020_100_3800ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S1N'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/nCAL02_iRPlus785_Z020_100_1100ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'nCAL'}, {'OP': '020', 'device': 'BWtek', 'filename': './ICV_BW785/sCAL02_iRPlus785_Z020_100_2400ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'sCAL'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/PST02_iRPlus785_Z050_100_3200ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'PST'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/S0B02_iRPlus785_Z050_100_5500ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0B'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/S0N02_iRPlus785_Z050_100_3200ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0N'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/S0P02_iRPlus785_Z050_100_5700ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0P'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/S1N02_iRPlus785_Z050_100_2100ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S1N'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/nCAL02_iRPlus785_Z050_100_2100ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'nCAL'}, {'OP': '050', 'device': 'BWtek', 'filename': './ICV_BW785/sCAL02_iRPlus785_Z050_100_3800ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'sCAL'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/PST02_iRPlus785_Z100_100_9000ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'PST'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/S0B02_iRPlus785_Z100_100_4700ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0B'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/S0N02_iRPlus785_Z100_100_3100ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0N'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/S0P02_iRPlus785_Z100_100_4800ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S0P'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/S1N02_iRPlus785_Z100_100_2000ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'S1N'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/nCAL02_iRPlus785_Z100_100_6000ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'nCAL'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/sCAL02_iRPlus785_Z100_100_9500ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'sCAL'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/NIR_785nm_2000ms_100X_2.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'NIR785_EL0-9002'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/NISTSRM2241_BW785_100x_25sx5.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'NIST785_SRM2241'}, {'OP': '100', 'device': 'BWtek', 'filename': './ICV_BW785/Ne_785nm_x20_50ms.txt', 'laser_wl': '785', 'provider': 'ICV', 'sample': 'Neon'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/neon_new2_Z010.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'Neon'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/Pol_HLR633_Z010_100_15sx5.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'PST'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/S0B_HLR633_Z010_100_40sx5.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'S0B'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/S0N_HLR633_Z010_100_15sx5.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'S0N'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/S0P_HLR633_Z010_100_40sx5.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'S0P'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/S1N_HLR633_Z010_100_10sx5.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'S1N'}, {'OP': '010', 'device': 'Horiba', 'filename': './TOP_Ho633/Si_HLR633_Z010_100_40sx5.txt', 'laser_wl': '633', 'provider': 'TOP', 'sample': 'Sil'}, {'OP': '010', 'device': 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\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.
\n", "bases": "abc.ABC"}, "ramanchada2.misc.plottable.Plottable.plot": {"fullname": "ramanchada2.misc.plottable.Plottable.plot", "modulename": "ramanchada2.misc.plottable", "qualname": "Plottable.plot", "kind": "function", "doc": "\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": "\n"}, "ramanchada2.misc.spectrum_deco.dynamically_added.dynamically_added_filters": {"fullname": "ramanchada2.misc.spectrum_deco.dynamically_added.dynamically_added_filters", "modulename": "ramanchada2.misc.spectrum_deco.dynamically_added", "qualname": "dynamically_added_filters", "kind": "variable", "doc": "\n", "annotation": ": Set[str]", "default_value": "{'convolve', 'set_new_xaxis', 'add_poisson_noise', 'scale_xaxis_linear', 'subtract_baseline_rc1_snip', 'dropna', 'moving_average', 'xcal_fine', '__mul__', 'hht_sharpening', 'trim_axes', 'xcal_argmin2d_iter_lowpass', 'resample_NUDFT_filter', 'moving_minimum', 'shift_cm_1_to_abs_nm_filter', 'subtract_baseline_rc1_als', 'xcal_fine_RBF', 'moving_median', 'add_baseline', 'drop_spikes', 'derivative_sharpening', 'normalize', 'subtract_moving_minimum', 'hht_sharpening_chain', 'resample_spline_filter', 'add_gaussian_noise', 'add_gaussian_noise_drift', 'subtract_moving_median', 'abs_nm_to_shift_cm_1_filter', 'recover_spikes', 'scale_yaxis_linear', 'smoothing_RC1', '__truediv__', 'get_spikes', 'fit_peaks_filter', '__sub__', 'calibrate_by_deltas_filter', 'scale_xaxis_fun', 'moving_average_convolve', '__add__', 'find_peak_multipeak_filter', 'pad_zeros'}"}, 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"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"}, 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\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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nA Pydantic BaseModel for the root object of the model.
\n\nAttributes:
\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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nA Pydantic BaseModel for the root object of the model.
\n\nAttributes:
\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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nA Pydantic BaseModel for the root object of the model.
\n\nAttributes:
\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\nA Pydantic BaseModel for the root object of the model.
\n\nAttributes:
\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\nA Pydantic BaseModel for the root object of the model.
\n\nAttributes:
\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.\nmin((lambda(x, p)-y)*2 | *p)\nFinds best parameters *p that minimise L2 distance between scaled x and original y
\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\norder_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.
\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\nUses 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\nParameters:
\n\nspectrum_a_dict : dict\n A dictionary representing the first spectrum, where keys are peak\n positions (float) and values are peak intensities (float).
\n\nspectrum_b_dict : dict\n A dictionary representing the second spectrum, where keys are peak\n positions (float) and values are peak intensities (float).
\n\nthreshold_max_distance : float, optional\n The maximum allowed distance for two peaks to be considered a match.\n Default is 8.
\n\ndf : bool, optional\n If True, return a DataFrame with matched peaks and their respective\n intensities; if False, return None
\n\nReturns:
\n\nmatched_peaks : (matched_peaks_a,matched_peaks_b,matched_distances, df)
\n\nExamples:
\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
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": "\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\nArguments:
\n\n\n- si_nm: Spectrum\nSilicon spectrum
\n- wl: Literal[514, 532, 633, 785]\nLaser 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\nReturns:
\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": "\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\nArguments:
\n\n\n- laser_wl: The wavelength of the laser used for calibration.
\n
\n\nExample:
\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
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\nParameters
\n\ny : (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\nepsilon : 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\nNotes
\n\nAn 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\nAn 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\nwhere \\( 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\nand
\n\n$$P(y)^T a = 0,$$
\n\nwhere \\( \\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\nThe 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\nWhen 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\nThe 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\nNew in version 1.7.0.
\n\nSee Also
\n\nNearestNDInterpolator\nLinearNDInterpolator\nCloughTocher2DInterpolator
\n\nReferences
\n\nExamples
\n\nDemonstrate 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
>>> 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
>>> 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
>>> 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
PCHIP 1-D monotonic cubic interpolation.
\n\nx 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\nParameters
\n\nx : 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\naxis : 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\nMethods
\n\n__call__\nderivative\nantiderivative\nroots
\n\nSee Also
\n\nCubicHermiteSpline : Piecewise-cubic interpolator.\nAkima1DInterpolator : Akima 1D interpolator.\nCubicSpline : Cubic spline data interpolator.\nPPoly : Piecewise polynomial in terms of coefficients and breakpoints.
\n\nNotes
\n\nThe interpolator preserves monotonicity in the interpolation data and does\nnot overshoot if the data is not smooth.
\n\nThe first derivatives are guaranteed to be continuous, but the second\nderivatives may jump at \\( x_k \\).
\n\nDetermines the derivatives at the points \\( x_k \\), \\( f'_k \\),\nby using PCHIP algorithm .
\n\nLet \\( 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\nwhere \\( w_1 = 2 h_k + h_{k-1} \\) and \\( w_2 = h_k + 2 h_{k-1} \\).
\n\nThe end slopes are set using a one-sided scheme .
\n\nReferences
\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\nInterpolate data with a piecewise cubic polynomial which is twice\ncontinuously differentiable . The result is represented as a PPoly\ninstance with breakpoints matching the given data.
\n\nParameters
\n\nx : 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 .
\n\nIf `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\nextrapolate : {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\nAttributes
\n\nx : 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\nMethods
\n\n__call__\nderivative\nantiderivative\nintegrate\nroots
\n\nSee Also
\n\nAkima1DInterpolator : Akima 1D interpolator.\nPchipInterpolator : PCHIP 1-D monotonic cubic interpolator.\nPPoly : Piecewise polynomial in terms of coefficients and breakpoints.
\n\nNotes
\n\nParameters bc_type and extrapolate work independently, i.e. the\nformer controls only construction of a spline, and the latter only\nevaluation.
\n\nWhen 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\nWhen '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\nNew in version 0.18.0.
\n\nExamples
\n\nIn 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
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
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
References
\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\nUsage:
\n\n\n \n - 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",\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
\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\nThis 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\nUsage:
\n\n\n Load single certificate
\n \n \n
>>> cert = CertificatesDict.load(wavelength="785", key="NIST785_SRM2241")\n>>> cert.plot()\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"}, "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\nUsage:
\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", "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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nData 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\nParameters
\n\ndata : 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\nIf data is a list of dicts, column order follows insertion-order.\n
\n\nindex : 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\nSee Also
\n\nDataFrame.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\nNotes
\n\nPlease reference the :ref:User Guide <basics.dataframe> for more information.
\n\nExamples
\n\nConstructing 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
Notice that the inferred dtype is int64.
\n\n\n
>>> df.dtypes\ncol1 int64\ncol2 int64\ndtype: object\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
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
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
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
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
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
>>> 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
Validate a DataFrame against the schema with dynamic column mapping.
\n\nArguments:
\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\nArguments:
\n\n\n- df (pd.DataFrame): The input DataFrame.
\n- column_mapping (Dict[str, str]): The dynamic mapping for column names.
\n
\n\nReturns:
\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\nEquivalent to dataframe * other, but with support to substitute a fill_value\nfor missing data in one of the inputs. With reverse version, rmul.
\n\nAmong flexible wrappers (add, sub, mul, div, floordiv, mod, pow) to\narithmetic operators: +, -, *, /, //, %, **.
\n\nParameters
\n\nother : 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\nReturns
\n\nDataFrame\n Result of the arithmetic operation.
\n\nSee Also
\n\nDataFrame.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\nNotes
\n\nMismatched indices will be unioned together.
\n\nExamples
\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
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
>>> df.add(1)\n angles degrees\ncircle 1 361\ntriangle 4 181\nrectangle 5 361\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
>>> df.rdiv(10)\n angles degrees\ncircle inf 0.027778\ntriangle 3.333333 0.055556\nrectangle 2.500000 0.027778\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
>>> df.sub([1, 2], axis='columns')\n angles degrees\ncircle -1 358\ntriangle 2 178\nrectangle 3 358\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
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
>>> df.mul({'circle': 0, 'triangle': 2, 'rectangle': 3}, axis='index')\n angles degrees\ncircle 0 0\ntriangle 6 360\nrectangle 12 1080\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
>>> df * other\n angles degrees\ncircle 0 NaN\ntriangle 9 NaN\nrectangle 16 NaN\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
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
>>> 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
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\nAttributes:
\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\nMethods:
\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\nArguments:
\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": 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"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": 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"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\nArguments:
\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\nArguments:
\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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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\nThe 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": "\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\nPlease 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": "\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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\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\nArguments:
\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\nReturns: 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\nArguments:
\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\nReturns: Corrected spectrum
\n\nRaises:
\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\nThis function provides the same result as spe*const
\n\nArguments:
\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\nReturns: 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- xaxis: new x-axis values
\n
\n\nReturns: corrected spectrum
\n\nRaises:
\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\nThe 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\nArguments:
\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\nArguments:
\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\nExample:
\n\nThis will produce spectrum with 1000 bins in the range [-1000, 2000):
\n\n\n
xcal = lambda x: x*3 -1000, nbins=1000\n
\n
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\nArguments:
\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\nRaises:
\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']] = 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\nThe 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\nArguments:
\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\nArguments:
\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\nArguments:
\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\nArguments:
\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\nThe 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\nRandom number i.i.d. $N(0, \\sigma)$ is added to every sample
\n\nArguments:
\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\nReturns: 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\nExponential-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\nArguments:
\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\nReturns: 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\nFor each particular sample the noise is proportional to $\\sqrt{scale*a_i}$.
\n\nArguments:
\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\nReturns: 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\nArguments:
\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\nReturns: 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\nSingle-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\nArguments:
\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\nReturns: 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\nRemove x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold.
\n\nArguments:
\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\nReturns: 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\nRecover x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.
\n\nArguments:
\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\nReturns: 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\nGet x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.
\n\nArguments:
\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\nReturns: 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n
\n\nReturns: 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nThe resultant spectrum is moving minimum of the input.
\n\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nThe resultant spectrum is moving minimum of the input subtracted from the input.
\n\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nThe 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n
\n\nReturns: 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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.
\n\nArguments:
\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\nReturns:
\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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.
\n\nArguments:
\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\nReturns: 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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.
\n\nArguments:
\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\nReturns:
\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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.
\n\nArguments:
\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\nReturns: 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\nSharpen the spectrum subtracting second derivative and add fourth derivative.
\n\nArguments:
\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\nReturns: 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\nIn order to reduce the overshooting, moving minimum is subtracted from the result
\n\nArguments:
\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\nReturns: 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\nSequence of Hilbert-Huang sharpening procedures are performed.
\n\nArguments:
\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\nReturns: 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\nThe spectrum will be smoothed using the specified filter.\nThis method is inherited from ramanchada1 for compatibility reasons.
\n\nArguments:
\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\nReturns: 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\nArguments:
\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\nReturns: 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.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\nArguments:
\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\nReturns:
\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\nArguments:
\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\nArguments:
\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\nReturns:
\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\nArguments:
\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\nSequence of multiple processings:
\n\n\nsubtract_moving_minimumfind_peak_multipeak- filter peaks with x-location better than threshold
\n
\n\nArguments:
\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\nReturns:
\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\nArguments:
\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\nThis function provides the same result as spe*const
\n\nArguments:
\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\nReturns: 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\nArguments:
\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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nArguments:
\n\n\n- spe: internal use only
\n- laser_wave_length_nm: Laser wave length
\n
\n\nReturns: 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\nThe 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": "\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\nPlease 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": "\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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- xaxis: new x-axis values
\n
\n\nReturns: corrected spectrum
\n\nRaises:
\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\nArguments:
\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\nReturns: 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\nArguments:
\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\nReturns: Corrected spectrum
\n\nRaises:
\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\nThe 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\nArguments:
\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\nArguments:
\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\nExample:
\n\nThis will produce spectrum with 1000 bins in the range [-1000, 2000):
\n\n\n
xcal = lambda x: x*3 -1000, nbins=1000\n
\n
Read experimental spectrum from a local file.
\n\nArguments:
\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\nRaises:
\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']] = 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\nThe 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\nArguments:
\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\nArguments:
\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\nArguments:
\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\nArguments:
\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\nThe 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nRandom number i.i.d. $N(0, \\sigma)$ is added to every sample
\n\nArguments:
\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\nReturns: 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\nFor each particular sample the noise is proportional to $\\sqrt{scale*a_i}$.
\n\nArguments:
\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\nReturns: 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\nArguments:
\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\nReturns: 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\nSharpen the spectrum subtracting second derivative and add fourth derivative.
\n\nArguments:
\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\nReturns: 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\nIn order to reduce the overshooting, moving minimum is subtracted from the result
\n\nArguments:
\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\nReturns: 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\nSequence of Hilbert-Huang sharpening procedures are performed.
\n\nArguments:
\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\nReturns: 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\nSingle-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\nArguments:
\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\nReturns: 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\nRemove x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold.
\n\nArguments:
\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\nReturns: 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\nRecover x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.
\n\nArguments:
\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\nReturns: 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\nGet x, y pairs recognised as spikes using left and right\nsuccessive differences and standard-deviation-based threshold\nand linear interpolation.
\n\nArguments:
\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\nReturns: 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\nThe resultant spectrum is moving minimum of the input.
\n\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nThe resultant spectrum is moving minimum of the input subtracted from the input.
\n\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n- window_size:
int, optional, default is 10. \n
\n\nReturns: 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\nArguments:
\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\nReturns: 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\nExponential-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\nArguments:
\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\nReturns: 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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.
\n\nArguments:
\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\nReturns:
\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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with NUDFT and inverse FFT.
\n\nArguments:
\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\nReturns: 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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.
\n\nArguments:
\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\nReturns:
\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\nThe x-axis of the result will be uniform. The corresponding y-values\nwill be calculated with spline interpolation.
\n\nArguments:
\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\nReturns: 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\nThe 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n
\n\nReturns: 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\nArguments:
\n\n\n- old_spe: internal use only
\n- new_spe: internal use only
\n
\n\nReturns: 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\nThe spectrum will be smoothed using the specified filter.\nThis method is inherited from ramanchada1 for compatibility reasons.
\n\nArguments:
\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\nReturns: 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\nArguments:
\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\nReturns:
\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\nSequence of multiple processings:
\n\n\nsubtract_moving_minimumfind_peak_multipeak- filter peaks with x-location better than threshold
\n
\n\nArguments:
\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\nReturns:
\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\nArguments:
\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\nReturns:
\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\nArguments:
\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\nArguments:
\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\nA base class for creating Pydantic models.
\n\nAttributes:
\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\nThis 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": "\n", "signature": "(\tpeaks_list: List[ramanchada2.theoretical_lines.model_from_lines.PydPeakModel]) -> Tuple[lmfit.model.Model, lmfit.parameter.Parameters]:", "funcdef": "def"}}, "docInfo": {"ramanchada2": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 5124}, "ramanchada2.auxiliary": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, "ramanchada2.auxiliary.spectra": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 3}, 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1.4142135623730951}, "ramanchada2.spectrum.filters.add_gaussian_noise_drift.add_gaussian_noise_drift": {"tf": 1.4142135623730951}, "ramanchada2.spectrum.spectrum.Spectrum.add_gaussian_noise_drift": {"tf": 1.4142135623730951}}, "df": 5, "d": {"docs": {}, "df": 0, "x": {"docs": {"ramanchada2": {"tf": 1}, "ramanchada2.spectrum.creators.from_local_file.from_local_file": {"tf": 1}, "ramanchada2.spectrum.spectrum.Spectrum.from_local_file": {"tf": 1}}, "df": 3}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {"ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.save_coefficients": {"tf": 1}, "ramanchada2.protocols.calibration.xcalibration.CustomPChipInterpolator.load_coefficients": {"tf": 1}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent": {"tf": 1}}, "df": 3}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"ramanchada2.protocols.calibration.calibration_model.CalibrationModel.__init__": {"tf": 2.449489742783178}, "ramanchada2.protocols.calibration.xcalibration.CustomCubicSplineInterpolator": {"tf": 3.1622776601683795}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationCertificate": {"tf": 3.1622776601683795}, "ramanchada2.protocols.calibration.ycalibration.CertificatesDict": {"tf": 2}, "ramanchada2.protocols.calibration.ycalibration.YCalibrationComponent": {"tf": 2.449489742783178}, "ramanchada2.protocols.spectraframe.SpectraFrame": {"tf": 6.324555320336759}}, "df": 6}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator": {"tf": 2}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator": {"tf": 1.4142135623730951}}, "df": 1, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"ramanchada2.protocols.calibration.xcalibration.CustomRBFInterpolator": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "c": {"docs": {"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