model_components

The model components used in KIPET are maintained here.

component_expression

data_component

element_blocks

expression

model_components

objectives

This file contains the object functions used throughout Kipet modules in one place. This should reduce the redundancy in using objective terms

Note

These methods are not complete in all cases and may not be used in future versions of KIPET.

kipet.model_components.objectives.get_objective(model, *args, **kwargs)

Main method to gather the objective terms

Parameters

  • model (ConcreteModel) – The model for which the objective will be built

  • args (tuple) – The arguments to be passed

  • kwargs (dict) – The arguments to be passed

Returns

An objective component for the model

Return type

Objective

kipet.model_components.objectives.conc_objective(model, *args, **kwargs)

Method to build concentration terms in the objective function

Parameters

  • ConcreteModel model (Pyomo) – This is the current model used in parameter fitting

  • args (tuple) – Arguments to be passed

  • kwargs (dict) – Arguments to be passed

Returns

obj

Return type

expression

kipet.model_components.objectives.comp_objective(model, *args, **kwargs)

Method to build individual complementary state terms in the objective function

Parameters

  • ConcreteModel model (Pyomo) – This is the current model used in parameter fitting

  • args (tuple) – Arguments to be passed

  • kwargs (dict) – Arguments to be passed

Returns

obj

Return type

expression

kipet.model_components.objectives.spectra_objective(model, *args, **kwargs)

Method to build individual spectral terms in the objective function

Parameters

  • ConcreteModel model (Pyomo) – This is the current model used in parameter fitting

  • args (tuple) – Arguments to be passed

  • kwargs (dict) – Arguments to be passed

Returns

obj

Return type

expression

kipet.model_components.objectives.absorption_objective(model, *args, **kwargs)

Method to build individual absorption terms in the objective function

Parameters

  • ConcreteModel model (Pyomo) – This is the current model used in parameter fitting

  • args (tuple) – Arguments to be passed

  • kwargs (dict) – Arguments to be passed

Returns

obj

Return type

expression

spectral_handler

Spectral Data Handling for Kipet

class kipet.model_components.spectral_handler.SpectralData(name, data=None, file=None, remove_negatives=False)

This class is used to handle the spectral data used in a ReactionModel

Since spectral data is different from the state data and requires different methods to modify the data, a separate class was designed to house all of the spectra specific methods.

Parameters

  • name (str) – The name for the data set

  • data (pandas.DataFrame) – The spectral data (D matrix)

  • remove_negatives (bool) – Option to set negative values to zero

Methods
__init__(name, data=None, file=None, remove_negatives=False)

Initialize a SpectralData instance

Parameters

  • name (str) – The name for the data set

  • data (pandas.DataFrame) – The spectral data (D matrix)

  • remove_negatives (bool) – Option to set negative values to zero

add_data(data)

Adds a dataset to a SpectralData instance.

This is used only if the SpectralData instance is created without a data attribute (not being None). This handles setting up the data_orig attribute as well and is therefore better than simply using setattr.

Parameters

data (pandas.DataFrame) – The spectral data (D matrix)

Returns

None

reset()

Resets the data back to the originally supplied data

Returns

None

plot(data_set='data')

Plots spectral data in 3D plot.

Plots the modified or original data sets.

Parameters

data_set (pandas.DataFrame) – attribute name of the spectral data

Returns

None

savitzky_golay(window=3, poly=2, deriv=0, in_place=True)

Implementation of the Savitzky-Golay filter for Kipet. Used for smoothing data, with the option to also differentiate the data. Can be used to remove high-frequency noise. Creates a least-squares fit of data within each time window with a high order polynomial centered centered at the middle of the window of points.

Parameters

  • window (int) – The length of the window. Must be an odd integer number

  • poly (int) – Order of the polynoial used in the filter. Should be less than window_size-1

  • deriv (int) – (optional) The order of the derivative to compute (default = 0 means only smoothing)

  • in_place (bool) – Option to update the data in place

Returns

DataFrame containing the smoothed data

Return type

pandas.DataFrame

References

This code is an amalgamation of those developed in the scipy.org cookbook and that employed in Matlab by WeiFeng Chen. Original paper: A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 1964, 36 (8), pp 1627-1639.

snv(offset=0, in_place=True)

Implementation of the Standard Normal Variate (SNV) filter for Kipet which is a weighted normalization method that is commonly used to remove scatter effects in spectroscopic data, this pre-processing step can be applied before the SG filter or used on its own. SNV can be sensitive to noisy entries in the spectra and can increase nonlinear behaviour between S and C as it is not a linear transformation.

Parameters

  • offset (float) – User-defined offset which can be used to avoid over-normalization for samples with near-zero standard deviation. Guide for choosing this value is for something near the expected noise level to be specified. Default value is zero.

  • in_place (bool) – Option to update the data in place

Returns

DataFrame containing pre-processed data

Return type

pandas.DataFrame

msc(reference_spectra=None, in_place=True)

Implementation of the Multiplicative Scatter Correction (MSC) filter for Kipet which is simple pre-processing method that attempts to remove scaling effects and offset effects in spectroscopic data. This pre-processing step can be applied before the SG filter or used on its own. This approach requires a reference spectrum which must be determined beforehand. In this implementation, the default reference spectrum is the average spectrum of the dataset provided, however an optional argument exists for user-defined reference spectra to be provided.

Parameters

  • reference_spectra (pandas.DataFrame) – Optional user-provided reference spectra argument. Default is to automatically determine this using the average spectra values

  • in_place (bool) – Option to update the data in place

Returns

DataFrame pre-processed data

Return type

pandas.DataFrame

baseline_shift(shift=None, in_place=True)

Implementation of basic baseline shift. 2 modes are avaliable: 1. Automatic mode that requires no user arguments. The method identifies the lowest value (NOTE THAT THIS ONLY WORKS IF LOWEST VALUE IS NEGATIVE) and shifts the spectra up until this value is at zero. 2. Baseline shift provided by user. User provides the number that is added to every wavelength value in the full spectral dataset.

Parameters

  • shift (float) – user-defined baseline shift

  • in_place (bool) – Option to update the data in place

Returns

DataFrame containing pre-processed data

Return type

pandas.DataFrame

decrease_wavelengths(A_set=2, specific_subset=None, in_place=True)

Takes in the original, full dataset and removes specific wavelengths, or only keeps every multiple of A_set. Returns a new, smaller dataset that should be easier to solve

Parameters

  • A_set (int) – optional user-provided multiple of wavelengths to keep. i.e. if 3, every third value is kept. Default is 2.

  • specific_subset (array-like) – (optional) If the user already knows which wavelengths they would like to remove, then a list containing these can be included.

param bool in_place: Option to update the data in place

Returns

DataFrame with the smaller dataset

Return type

decrease_times(A_set=2, in_place=True)

Takes in the original, full dataset and removes specific wavelengths, or only keeps every multiple of A_set. Returns a new, smaller dataset that should be easier to solve

Parameters

A_set (array-like) – optional user-provided multiple of wavelengths to keep. i.e. if 3, every third value is kept. Default is 2.

param bool in_place: Option to update the data in place

Returns

DataFrame with the smaller dataset

Return type

pandas.DataFrame

units_handler