TGA-FTIR-hyphenation-tool-kit

A package for handling hyphenated TGA and FTIR data. Includes basic plotting as well as as advanced deconvolution of FTIR data. For installation with conda and pip:

First create fresh environment with name :

conda create -n <YOUR_ENV> python

conda activate <YOUR_ENV>

Then run:

pip install git+https://github.com/LeonSaal/TGA-FTIR-hyphenation-tool-kit.git

To run GUI, download run.py, navigate to folder in your command prompt and run:

python run.py

Main Usage

Core classes of the package are Sample and Worklist, where a Worklist object holds multiple Sample objects.

1. Initialization\ 2. Correction\ 3. Fitting\ 4. Robustness of fit\ 5. Plotting\ 6. Saving\ 7. Loading

1. Initialization

obj = Sample(<SAMPLE_NAME>) Optional arguments:

alias           alias to use in e.g. plots
mode            one of ["construct", "pickle"] to either:
                - construct Sample from raw data
                - load Sample from pickle
profile         name of data loading profile. Can be set in
                settings.ini

For more info on how to save as pickle, see 6. Saving.

Class attributes:

name            name of Sample, default is filename used for initialization
alias           alias of Sample, defaults to Sample.name
sample          sample type used for grouping, defauls to 
                Sample.name
info = None     contains sample information e.g. initial_mass
tga = None      contains tga data
ir = None       contains ir data
linreg = None   contains calibration data
raw             conatins raw data from first initialization,
                remains unchanged after e.g. correction. Is of 
                type Sample    

after correction:

baseline        contains data of baseline used for correction. 
                Is of type Baseline which inherits from Sample

wl = Worklist([obj], name = <WORKLIST_NAME>)

Class attributes:

samples         List of Sample objects
name            name of Worklist

For overview on Worklist use print(Worklist). Worklists can be added to yield a new Worklist with combined samples. To modify inplace you can Worklist.append(Sample | Worklist)

2. Correction

obj.corr(<BASELINE_NAME> | None)

wl.corr(<BASELINE_NAME> | [<BASELINE_NAMES>] | None)

If argument = None, it is lookes for a refernece given in the samplelog.

Optional arguments:

plot = False        plot corrections

3. Fitting

obj.fit(<REFERENCE_NAME>)

wl.fit(<REFERENCE_NAME>)

Valid values for <REFERENCE_NAME> can be found and/ or set in Fitting_parameter.xlsx which is accessible trough TGA_FTIR_tools.fit_preferences().

Optional arguments:

T_max = None        limit fit interval
T_max_tol = 50      tolerance of center value
save = True         save results
plot = True         plot results
presets = None      pass presets other than those specified by <REFERENCE_NAME>
mod_sample = True   modify object during fitting

4. Robustness of fit

wl.robustness(<REFERENCE_NAME>)

Optional arguments:

T_max = None        limit fit interval
save = True         save results
var_T = 10          absolute variance of HWHM_max and center_0
var_rel = 0.3       relative variance of HWHM_0 and height_0

5. Plotting

obj.plot(<OPTION>) options = ["TG", "heat_flow", "IR", "DIR", "cumsum", "IR_to_DTG", "fit"]

wl.plot(<OPTION>) options = ["TG", "heat_flow", "IR", "DIR", "cumsum", "IR_to_DTG", "fit", "robustness", "results"]

6. Saving

obj.save()

Optional arguments:

how        one of ["samplelog", "excel", "pickle"] to save    
            - obj.info in the samplelog
            - obj.info, obj.ir, obj.tga to excel-file
            - obj as pickle-file

wl.save()

Optional arguments:

fname       filename, if None: wl.name

7. Loading

wl.load(<FNAME>)

Load Worklist from pickle-file as produced by 6. Saving.

For Sample, see 1. Initialization and the use of the mode-argument.