The Datasets module

Calculation datasets.

This module deals with the handling of series of calculations. Classes and functions of this module are meant to simplify the approach to ensemble calculations with the code, and to deal with parallel executions of multiple instances of the code.

class BigDFT.Datasets.Dataset(label='BigDFT dataset', run_dir='runs', **kwargs)

Bases: BigDFT.Calculators.Runner

A set of calculations.

Such class contains the various instances of a set of calculations with the code. The different calculations are labelled by parameter values and information that might then be retrieved for inspection and plotting.

Parameters:

• label (str) – The label of the dataset. It will be needed to identify the instance for example in plot titles or in the running directory.
• run_dir (str) – path of the directory where the runs will be performed.
• input (dict) – Inputfile to be used for the runs as default, can be overridden by the specific inputs of the run

append_run(id, runner, **kwargs)

Add a run into the dataset.

Append to the list of runs to be performed the corresponding runner and the arguments which are associated to it.

Parameters:

• id (dict) – the id of the run, useful to identify the run in the dataset. It has to be a dictionary as it may contain different keyword. For example a run might be classified as id = {'hgrid':0.35, 'crmult': 5}.
• runner (Runner) – the runner class to which the remaining keyword arguments will be passed at the input.

Raises:

ValueError – if the provided id is identical to another previously appended run.

Todo

include id in the runs spcification

calculators = None

Calculators which will be used by the run method, useful to gather the inputs in the case of a multiple run.

fetch_results(id=None, attribute=None)

Retrieve some attribute from some of the results.

Selects out of the results the objects which have in their id at least the dictionary specified as input. May return an attribute of each result if needed.

Parameters:

• id (dict) – dictionary of the retrieved id. Return a list of the runs that have the id argument inside the provided id in the order provided by append_run().
• attribute (str) – if present, provide the attribute of each of the results instead of the result object

Example

>>> study=Dataset()
>>> study.append_run(id={'cr': 3},input={'dft':{'rmult':[3,8]}})
>>> study.append_run(id={'cr': 4},input={'dft':{'rmult':[4,8]}})
>>> study.append_run(id={'cr': 3, 'h': 0.5},
>>>                  input={'dft':{'hgrids': 0.5, 'rmult':[4,8]}})
>>> #append other runs if needed
>>> study.run()  #run the calculations
>>> # returns a list of the energies of first and the third result in this example
>>> data=study.fetch_results(id={'cr': 3},attribute='energy')

ids = None

List of run ids, to be used in order to classify and fetch the results

names = None

List of run names, needed for distinguishing the logfiles and input files. Eah name should be unique to correctly identify a run.

post_processing(**kwargs)

Calls the Dataset function with the results of the runs as arguments

process_run()

Run the dataset, by performing explicit run of each of the item of the runs_list.

results = None

Set of the results of each of the runs. The set is not ordered as the runs may be executed asynchronously.

runs = None

List of the runs which have to be treated by the dataset these runs contain the input parameter to be passed to the various runners.

set_postprocessing_function(func)

Set the callback of run.

Calls the function func after having performed the appended runs.

Parameters:func (func) – function that process the inputs results and returns the value of the run method of the dataset. The function is called as func(self).

BigDFT.Datasets.combine_datasets(*args)

Define a new instance of the dataset class that should provide as a result a list of the runs of the datasets