API reference

This module serves as reference for the different classes and associated methods of the PhysioFit package.

fitter.py

PhysioFit software main module

class physiofit.base.fitter.PhysioFitter(data: DataFrame, model: Model, mc: bool = True, iterations: int = 100, sd=None, debug_mode: bool = False)

Bases: object

This class is responsible for most of Physiofit’s heavy lifting. Features included are:

• loading of data from csv or tsv file * **equation system

initialization** using the following analytical functions (in absence of lag and degradation):

X(t) = X0 * exp(mu * t) Mi(t) = qMi * (X0 / mu) * (exp(mu * t) - 1) + Mi0

• simulation of data points from given initial parameters

• cost calculation using the equation:

  residuum = sum((sim - meas) / sd)²

• optimization of the initial parameters using

scipy.optimize.minimize (‘Differential evolution’, with polish with ‘L-BFGS-B’ method) * sensitivity analysis, khi2 tests and plotting

Parameters:

data (class: pandas.DataFrame :param model: Model to initialize) – DataFrame containing data and passed by IOstream object

parameters and optimize :type model: physiofit.models.base_model.Model :param mc: Should Monte-Carlo sensitivity analysis be performed ( default=True) :type mc: Boolean :param iterations: number of iterations for Monte-Carlo simulation (default=50) :type iterations: int :param sd: sd matrix used for residuum calculations. Can be:

• a matrix with the same dimensions as the measurements

matrix (but without the time column) * a named vector containing sds for all the metabolites provided in the input file * 0 in which case the matrix is automatically constructed from default values * a dictionary with the data column headers as keys and the associated value as a scalar or list

aic_test()

Calculate the Akaike Information Criterion (AIC) for the model

initialize_sd_matrix()

Initialize the sd matrix from different types of inputs: single value, vector or matrix.

Returns:

None

khi2_test()

This method performs a chi-squared test to evaluate the goodness of fit of the model. It calculates the chi-squared statistic and the p-value and logs the results.

The chi-squared test is a statistical hypothesis test that is valid to perform when the test statistic is chi-squared distributed under the null hypothesis.

monte_carlo_analysis()

Run a monte carlo analysis to calculate optimization standard deviations on parameters and simulated data points

optimize()

Run optimization and build the simulated matrix from the optimized parameters

verify_attrs()

Check that attributes are valid

io.py

Module to handle inputs and outputs for PhysioFit

class physiofit.base.io.ConfigParser(selected_model, sds, mc, iterations, path_to_data=None)

Bases: object

The ConfigParser class is used to parse configuration files for the PhysioFit package. It reads a YAML file and extracts the necessary parameters for the model fitting process. It also includes methods to update the model with the parsed parameters and export the run parameters back to a yaml config file.

AUTHORIZED_KEYS = ['path_to_data', 'model', 'sds', 'mc', 'iterations']

check_data_path()

Check if the data path is valid :return: True if the path is valid, False otherwise, None if no path

export_config(export_path)

classmethod from_file(yaml_file)

classmethod from_galaxy(galaxy_yaml)

get_kwargs()

update_model(model)

class physiofit.base.io.IoHandler

Bases: object

Input/Output class that handles the former and initializes the PhysioFitter component object. It is the preferred interface for interacting with the PhysioFit package.

static add_model(model_file)

allowed_keys = {'debug_mode', 'iterations', 'mc', 'model', 'sd'}

static get_local_model_folder() → str

Return the path towards the actual environment’s used models folder

static get_model_list()

get_models(data: DataFrame = None)

Read modules containing the different models and add them to models attribute

Returns:

list containing the different model objects

initialize_fitter(data: DataFrame, **kwargs) → PhysioFitter

Initialize a PhysioFitter object

Parameters:

• data – input data

• kwargs – Keyword arguments for fitter initialization

Returns:

None

output_pdf(fitter: PhysioFitter, export_path: str | Path = None)

Handle the creation and output of a pdf file containing fit results as a plot

Parameters:

• fitter

• export_path – Path to exported pdf. In local mode, it is sent to the _res directory

Returns:

None

output_plots(fitter, export_path)

Handle the creation and export of the different plots in svg format :return: None

output_recap(export_path: str, galaxy=False)

static output_report(fitter, export_path: str | list = None)

Handle creation and export of the report containing stats from monte carlo analysis of optimization parameters

Parameters:

• export_path – Path to export the report

• fitter – PhysioFitter object containing results from the optimization of parameters

• export_path – list of paths to export the stats and fluxes. [ 0] is for stats and [1] for fluxes.

plot_data(fitter, display: bool = False)

Plot the data

Parameters:

• display – Should plots be displayed or not on creation

• fitter – PhysioFitter object after optimization of parameters

has been executed

static read_data(data: str) → DataFrame

Read initial data file (csv or tsv)

Parameters:

data – str containing the relative or absolute path to the data

Returns:

pandas DataFrame containing the data

static read_model(model_file: str)

Import and return the model class from .py file containing the model.

code from untrusted sources can lead to propagation of viruses and compromised security.

Parameters:

model_file – path to the model.py file to import

static read_yaml(yaml_file: str | bytes) → ConfigParser

Import yaml configuration file and parse keyword arguments

Parameters:

yaml_file – path to the yaml file or json file :return

config_parser: Dictionary containing arguments parsed from yaml file

select_model(name: str, data: DataFrame = None) → Model

Select a model from the list of models in the model folder of the package src directory

gui.py

class physiofit.ui.gui.App

Bases: object

Physiofit Graphical User Interface

check_uptodate()

Compare installed and most recent Physiofit versions.

static clear_input_from_session_state()

silent_sim()

start_app()

Launch the application

base_model.py

class physiofit.models.base_model.Bounds(mapping=None, **kwargs)

Bases: dict

class physiofit.models.base_model.Model(data: DataFrame)

Bases: ABC

abstract get_params()

Return params_to_estimate:

List of parameters to estimate

Return fixed_parameters:

dict of constant parameters

Return bounds:

dict of upper and lower bounds

Return default_init_values:

dict containing default initial values for params

abstract static simulate(parameters: list, data_matrix: ndarray | DataFrame, time_vector: ndarray, args: dict | list)

exception physiofit.models.base_model.ModelError

Bases: Exception

class physiofit.models.base_model.StandardDevs(mapping=None, **kwargs)

Bases: dict

property vector