The collect function

In the user interface we have the possibility to perform a computation looping over a list of namespaces. In the code, we can define providers that collect the results of such computations with the collect function.

The signature is:

collect('provider_name', fuzzyspec)

This will expand the fuzzyspec relative to the current namespace and compute the function once for each frame. Then it will put all the results in a list (to be iterated in the same order as the fuzzyspec) and set that list as the result of the provider. The provider in the first argument is found following the standard reportengine rules. It can be a function defined in a provider module, a configuration input or a production rule, as well as another collect provider. As a special case, one can pass functions directly (defined with the def keyword). For example

possets_predictions = collect(positivity_predictions, ('posdatasets',))

Compared to a simple for loop, the collect function has the advantages that the computations are appropriately reused and several results could be computed simultaneously in the parallel mode.

We can use the output of collect as input to other providers. For example:

def count_negative_points(possets_predictions):
    return np.sum([(r.rawdata < 0).sum(axis=1) for r in
            possets_predictions], axis=0)

collect can be used to appropriately group nested inputs. For example, here is how to obtain a list of the experiments for each fit.

fits_experiments = collect('experiments', ('fits',))

Note that collect always returns a flat list with the provider evaluated for each of the namespaces spanned by the fuzzyspec. For example

fits_experiments_chi2_flat = collect(abs_chi2_data_experiment,
    ('fits', 'fitcontext', 'experiments',))

results in a flat list containing the result of abs_chi2_data_experiment resolved for each experiment in each fit. One can instead retain the structure by chaining several collect providers. For instance, the code

experiments_chi2 = collect(abs_chi2_data_experiment, ('experiments',))
fits_experiment_chi2_data = collect('experiments_chi2', ('fits', 'fitcontext'))

will result in fits_experiment_chi2_data producing one result for each fit, where each of them is itself a list where each item result of abs_chi2_data_experiment is evaluated for each experiment in a given fit.

Standard iteration techniques can be used to process the results of collect. For example, here is how we would print the χ² for each experiment in each fit:

def print_fits_experiments_chi2(
        fits, fits_experiments, fits_experiment_chi2_data):
    for fit, fit_experiments, experiments_data in zip(
            fits, fits_experiments, fits_experiment_chi2_data):
         print(f"Printing results for {fit}")
         for experiment, chi2data in zip(fit_experiments, experiments_data):
             print(f"χ² for {experiment} is ",

A minimal runcard to use the action above is:

  - NNPDF40_nlo_as_01180
  - NNPDF40_nnlo_as_01180

use_cuts: "fromfit"

  - print_fits_experiments_chi2