Consultation about IRR, metrics in uncertainty analysis

Hello, I have learned the relevant code, some places can understand, but some places still do not understand, may I ask you the following questions. Thanks for you help. Wish you a good day.

a) What does it mean ```to ensure Monte Carlo results will be at 10% IRR```? It seems that the IRR is changing, not 10% (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/models.py#L145-L151). Why is it converted to a list (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/models.py#L149), and why is this piece of code still used in HP while it is no longer used in lactic acid (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/HP/analyses/models_2015.py#L867-L875)?

b) Is ```model_dct['index_IRR']``` (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L80) relevant to code that ensures IRR is at 10% (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/models.py#L145-L151)?

c) For ```index_TEA = index_parameters + model_dct['index_TEA']``` and ```index_IRR = index_parameters + model_dct['index_IRR']``` (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L74-L80), so should the value of ```model_dct['index_IRR']``` be greater than that of ```model_dct['index_TEA']```, otherwise it seems impossible to extract the IRR result after the TEA index.

d) What it wants to express here (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L73-L88) is that the results of LCA, IRR and TEA are extracted according to the categories of LCA, IRR and TEA, and after the TEA results in raw data are extracted, the following results in raw data are the results of IRR, and after the IRR results in raw data are the results of LCA, right?

e) I changed ```model_dct['index_TEA']``` and ```model_dct['index_IRR']``` to numbers and deleted the code here (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L176-L185) to make the code work, but there is no IRR related result in raw data in excel results? It's all about TEA and LCA.

f) Can ```model.table = model.table.dropna()``` be omitted (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L94)? What is its purpose? Why delete null values?
Could I consult you why does HP have NAN in its results (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/HP/data/TRY_productivity_0.152/FEC_33_TRY_lowpH_0.152_glH_06.24.2021.csv)?

g) In lactic acid, there are six metrics according to ```spearman_metrics = model.metrics[0:2] + model.metrics[6:8] + \ model.metrics[index_IRR:index_IRR+2]``` (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L91-92), but why are there only 3 metrics in the article (Figure 3)?
May I ask what the metrics you mentioned here specifically refer to (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L91-92)?
Figure 3 in ```Sustainable Lactic Acid Production from Lignocellulosic Biomass``` is as follows:

h) What does it mean that ```Spearman's rho``` is empty in Excel results?

i) May I ask you the meaning of the ```probability``` calculated by ```cdf``` (https://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park/blob/master/biorefineries/lactic/analyses/1_uncertainties.py#L104)?

Start by reading biorefineries/lactic/models.py and biorefineries/lactic/analyses/1_uncertainties.py, then inspect the referenced HP models, CSV data, and Excel output. Trace the model metrics, index calculations, dropped values, Spearman results, and CDF output; done means the behavior and metric ordering are documented clearly enough to answer each question.

Written by the indexing model from the issue text.