Examples
Example validphys runcards can be found here. These are useful when trying to gain familiarity with how to produce results with validphys or when you want to carry out a common task, e.g. plotting some PDFs, and you do not want to write the runcard yourself.
It is strongly encouraged to capitalise namespaces, e.g. PDFscalespecs
rather than pdfscalespecs
.
This is to avoid confusion between namespaces, which are relevant only to that runcard, and any actions
within validphys
, which by convention are lower case.
Here we detail the examples that already exist and list the resources which it is recommended that you use when writing a new example runcard.
Existing examples
Runcard/folder name |
Tutorial |
What it does |
---|---|---|
API_example.ipynb |
Jupyter notebook example with API |
|
closure_templates/ |
Running closure tests |
|
cuts_options.yaml |
N/A |
Shows results for different cuts policites |
dataspecs.yaml |
N/A |
Shows how to use |
data_theory_comparison.yaml |
Data theory comparison |
|
export_data.yaml |
N/A |
Makes tables of experimental data and covariance matrices |
generate_a_report.yaml |
Shows how to generate a report |
|
kiplot.yaml |
N/A |
Plot kinematic coverage of data |
looping_example.yaml |
N/A |
Shows how to do actions in a loop over resources |
mc_gen_example.yaml |
N/A |
Analysis of pseudodata generation |
new_data_specification.yaml |
N/A |
Shows how to specify data in runcards |
pdfdistanceplots.yaml |
How to plot PDFs |
Distance PDF plots |
simple_runcard.yaml |
N/A |
Simple runcard example |
spiderplots.yaml |
N/A |
Plot spider/radar diagram for χ2 and ϕ |
taking_data_from_fit.yaml |
N/A |
Shows how to take |
theory_covariance/ |
Runcards for the |
Recommended resources
The resources that should be used in example runcards, where possible, match the defaults used in the tests. It is recommended that these are used to avoid situations when the user has to download different resources, which can be costly in terms of time and memory, to run each example runcard.
The recommended resources are:
Resource |
ID |
Description |
---|---|---|
NLO theoryid |
208 |
NNPDF4.0 NLO theory predictions with central scales |
NNLO theoryid |
162 |
Low precision NNLO theory predictions with central scales |
NLO theoryid for scale variations 1 |
163 |
Central scales, \(k_F = 1, k_R = 1\) |
NLO theoryid for scale variations 2 |
173 |
\(k_F = 0.5, k_R = 0.5\) |
NLO theoryid for scale variations 3 |
174 |
\(k_F = 1, k_R = 0.5\) |
NLO theoryid for scale variations 4 |
175 |
\(k_F = 2, k_R = 0.5\) |
NLO theoryid for scale variations 5 |
176 |
\(k_F = 0.5, k_R = 1\) |
NLO theoryid for scale variations 6 |
177 |
\(k_F = 2, k_R = 1\) |
NLO theoryid for scale variations 7 |
178 |
\(k_F = 0.5, k_R = 2\) |
NLO theoryid for scale variations 8 |
179 |
\(k_F = 1, k_R = 2\) |
NLO theoryid for scale variations 9 |
180 |
\(k_F = 2, k_R = 2\) |
NLO pdf |
NNPDF40_nlo_as_01180 |
NNPDF4.0 NLO PDF set with 100 replicas (+ central replica) |
NNLO pdf |
NNPDF40_nnlo_as_01180 |
NNPDF4.0 NNLO PDF set with 100 replicas (+ central replica) |
NNLO pdf hessian |
NNPDF40_nnlo_as_01180_hessian |
NNPDF4.0 NNLO hessian PDF set generated from replicas |
NLO fit |
NNPDF40_nlo_as_01180 |
NNPDF4.0 NLO fit with 100 replicas (+ central replica) |
NNLO fit |
NNPDF40_nnlo_lowprecision |
NNPDF4.0 NNLO low precision fit (theory 162) with 50 replicas (+ central replica) |
NNLO fit (iterated) |
NNPDF40_nnlo_low_precision_iterated |
Iteration of NNPDF40_nnlo_lowprecision |
fit |
NNPDF40_example_closure_test |
|