def _save_output(output_path, name, fig, fig_ext, datasets):
"""
Save figure and data output.
Parameters
----------
output_path : str
The output_path to save to.
name : str
The 'fixed' part of the file name saved to.
fig : matplotlib.figure.Figure
The figure instance to save.
fig_ext : str
The file extension to use for the saved figure.
datasets : dict
The dict of dicts for datasets to save in a HDF database.
"""
@_decorate_validation
def validate_input():
_generic('output_path', 'string')
_generic('name', 'string')
_generic('fig', mpl.figure.Figure)
_generic('fig_ext', 'string')
_levels('datasets', (_generic(None, 'mapping'),
_generic(None, 'mapping')))
validate_input()
if output_path[-1] == os.sep:
path = output_path
prefix = ''
else:
path, prefix, no_ext = _split_path(output_path)
prefix = prefix + '_'
fig.savefig(path + prefix + name + os.path.extsep + fig_ext)
db_path = path + prefix + name + '.hdf5'
_io.create_database(db_path)
with _File(db_path, mode='a') as h5file:
data_group = h5file.create_group('/', 'data', __name__ + ': ' + name)
for dataset in datasets:
set_group = h5file.create_group(data_group, dataset, dataset)
for array in datasets[dataset]:
h5file.create_array(set_group, array, datasets[dataset][array])
def test_create_database(self):
io.create_database(self.h5_name)
with tb.File(self.h5_name, mode='a') as h5_file:
annotation_keys = sorted(io.read_annotations(h5_file).keys())
self.assertEqual(annotation_keys, self.ref_annotation_keys)
chase_keys = sorted(io.read_chases(h5_file).keys())
self.assertEqual(chase_keys, self.ref_chase_keys)
# Overwrite test
io.create_database(self.h5_name)
with self.assertRaises(IOError):
io.create_database(self.h5_name, overwrite=False)