👌 Use self-generated guidance spectra where possible

[s-weigand]

Currently the case studies use guidance spectra generated by TMP.
Those spectra can also be created with result.create_clp_guide_dataset or the function create_clp_guide_dataset and should be created with pyglotaran for transparency and demonstrating this feature.
Further more the TMP generated guidance spectra have values that are bigger by a factor of 1e3 which later requires to apply a weight of 1e-3 on them in the case studies that they are used in.

[jsnel]

@s-weigand can you update this ticket to reflect the current state of things?

There is now at least one case study (rc) where we generate our own guidance spectra (in target_rc_part1.ipynb).

for species in result.data["rc"].species.values:
    clp_guide = result.create_clp_guide_dataset(species, "rc")
    project.import_data(clp_guide, dataset_name=f"guide_rcg_{species}", allow_overwrite=True)

But we could streamline where and how it is used.

In another case study (dPSI) we show how one can read in externally obtained guidance spectra (in ex_four_datasets_two_guidance_with_area_irf-with_sum_plot_minimal.ipynb).

from pyglotaran_extras import plot_data_overview

experiment_data = {
    "DPSI400tr2": "experiment_data/RT400_590excDPSIjul30tr24KtargetPB10b.ascii",
    "DPSI590tr2": "experiment_data/RT400_590excDPSIjul30tr24KtargetPB10d.ascii",
    "DPSI400tr4": "experiment_data/RT400_590excDPSIjul30tr24KtargetPB10a.ascii",
    "DPSI590tr4": "experiment_data/RT400_590excDPSIjul30tr24KtargetPB10c.ascii",
    "data_guide_s1": "experiment_data/RT400_590excDPSIjul30tr24KtargetPB10e.ascii", # <- here
    "data_guide_s4": "experiment_data/RT400_590excDPSIjul30tr24KtargetPB10f.ascii", # <- here
}

plot_data_overview(experiment_data["DPSI400tr4"], linlog=True, linthresh=150);

But we could add some words about the origin of these guidance spectra.