optofluidics

Optofluidics is a Python library for the analysis of spectroscopic data stored in the HDF5 file format.

The HDF5 must contain measurement groups containing a set of timelapses. Each timelapse contains a collection of timestamped spectral data.

Each spectrum is expected to have a background spectrum stored as an attribute.

Absorbance calculations also assume a reference spectrum stored as an attribute.

Modelling functions assume a methyl viologen photoreduction process with carbon dots as the photosensitiser and EDTA as the sacrificial electron donor. Carbon dots are modelled with an error function to describe their delay/activation period. The bleaching of the photoreduced methyl viologen radical, and complexation of methyl viologen with EDTA, are both accounted for in the model.

HDF5 File Structure

• measurement ABC
  • timelapse_0
    • spectrum_0
    • spectrum_1
    • ...
  • timelapse_1
    • spectrum_0
    • spectrum_1
    • ...
• measurement XYZ

Installation

This package can be installed using pip.

pip install --index-url https://test.pypi.org/simple/ optofluidics

Usage

Optofluidics defines four object types: Datafiles, Datasets, Reactions and SV.

• A Datafile is the HDF5 file that may contain multiple Datasets.
• A Dataset is one timelapse containing multiple spectra at different times.
• A Reaction is a Dataset that has been processed to give concentration profiles.
• A SV is a Dataset that has been processed to give fluorescent counts.

import optofluidics as of
from lmfit import Parameters

datafile_1=of.Datafile('file_path') # loads the datafile
datafile_1.list_groups() # returns a list of measurements in the datafile

dataset_1=of.Dataset(datafile_1, group_key, timelapse_key) # loads a specific dataset
dataset_1.pre_process() # returns a Pandas DataFrame with background-correction
dataset_1.calculate_abs() # returns a Pandas DataFrame with absorbances (calculated from reference spectra)

reaction_1=of.Reaction(dataset_1,wav_centre,epsilon,path_length) # initialises concentration profile
reaction_1.calculate_conc(wav_range) # calculates concentration using absorbance values for wav_centre +- wav_range/2
reaction_1.linear_drift(times_arr) # applies a linear drift correction by fitting to nil absorption points specified in times_arr
reaction_1.find_turn_points(first_point, prominence) # finds turning points (except the first one which you must specify)
reaction_1.create_model(params) # returns a Pandas DataFrame with model
reaction_1.fit_model(params,method) # runs a lmfit fitting routine to optimise the model parameters
reaction_1.model2csv(file_name, file_path) # saves model to csv file

Model creation expects certain params (lmfit parameter object).

• t_erf representing the delay time in the error function model
• kc representing the complexation rate constant
• kbr representing the product bleaching rate constant
• k representing the saturation rate constant in the Erf model
• kr representing the back-reaction rate constant
• o representing the standard deviation in the Erf model
• c0 representing the initial concentration of reactant
• K representing the equilibrium constant for complexation
• end representing the total time for the model

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT