def run(args): if args.externals: print(mvpa2.wtf(include=['externals'])) elif args.debug: mvpa2.debug.print_registered() elif not args.learner_warehouse is False: from mvpa2.clfs.warehouse import clfswh clfswh.print_registered(*args.learner_warehouse) else: print(mvpa2.wtf())
def run(args): if args.externals: print mvpa2.wtf(include=['externals']) elif args.debug: mvpa2.debug.print_registered() elif not args.learner_warehouse is False: from mvpa2.clfs.warehouse import clfswh clfswh.print_registered(*args.learner_warehouse) else: print mvpa2.wtf()
def generate_testing_fmri_dataset(filename=None): """Helper to generate a dataset for regression testing of mvpa2/nibabel Parameters ---------- filename : str Filename of a dataset file to store. If not provided, it is composed using :func:`get_testing_fmri_dataset_filename` Returns ------- Dataset, string Generated dataset, filename to the HDF5 where it was stored """ import mvpa2 from mvpa2.base.hdf5 import h5save from mvpa2.datasets.sources import load_example_fmri_dataset # Load our sample dataset ds_full = load_example_fmri_dataset(name='1slice', literal=False) # Subselect a small "ROI" ds = ds_full[20:23, 10:14] # collect all versions/dependencies for possible need to troubleshoot later ds.a['wtf'] = mvpa2.wtf() ds.a['versions'] = mvpa2.externals.versions # save to a file identified by version of PyMVPA and nibabel and hash of # all other versions out_filename = filename or get_testing_fmri_dataset_filename() h5save(out_filename, ds, compression=9) # ATM it produces >700kB .hdf5 which is this large because of # the ds.a.mapper with both Flatten and StaticFeatureSelection occupying # more than 190kB each, with ds.a.mapper as a whole generating 570kB file # Among those .ca seems to occupy notable size, e.g. 130KB for the FlattenMapper # even though no heavy storage is really needed for any available value -- # primarily all is meta-information embedded into hdf5 to describe our things return ds, out_filename
def teardown_module(module, verbosity=None): "tear down test fixtures" verbosity = _get_verbosity(verbosity) # restore warning handlers warning.maxcount = _sys_settings['maxcount'] if verbosity < 3: # restore warning handlers warning.handlers = _sys_settings['handlers'] if verbosity < 4: # restore numpy settings np.seterr(**_sys_settings['np_errsettings']) if cfg.getboolean('tests', 'wtf', default='no'): sys.stderr.write(str(wtf()))