def test_adds():
"""Tests methods that add data to FOOOF objects.
Checks: add_data, add_settings, add_results.
"""
# Note: uses it's own tfm, to not add stuff to the global one
tfm = get_tfm()
# Test adding data
freqs, pows = np.array([1, 2, 3]), np.array([10, 10, 10])
tfm.add_data(freqs, pows)
assert np.all(tfm.freqs == freqs)
assert np.all(tfm.power_spectrum == np.log10(pows))
# Test adding settings
fooof_settings = FOOOFSettings([1, 4], 6, 0, 2, 'fixed')
tfm.add_settings(fooof_settings)
for setting in get_description()['settings']:
assert getattr(tfm, setting) == getattr(fooof_settings, setting)
# Test adding meta data
fooof_meta_data = FOOOFMetaData([3, 40], 0.5)
tfm.add_meta_data(fooof_meta_data)
for meta_dat in get_description()['meta_data']:
assert getattr(tfm, meta_dat) == getattr(fooof_meta_data, meta_dat)
# Test adding results
fooof_results = FOOOFResults([1, 1], [10, 0.5, 0.5], 0.95, 0.02,
[10, 0.5, 0.25])
tfm.add_results(fooof_results)
for setting in get_description()['results']:
assert getattr(tfm, setting) == getattr(fooof_results,
setting.strip('_'))
def test_fg_get_fooof(tfg):
"""Check return of an individual model fit to a FOOOF object from FOOOFGroup."""
desc = get_description()
# Check without regenerating
tfm0 = tfg.get_fooof(0, False)
assert tfm0
# Check that settings are copied over properly
for setting in desc['settings']:
assert getattr(tfg, setting) == getattr(tfm0, setting)
# Check with regenerating
tfm1 = tfg.get_fooof(1, True)
assert tfm1
# Check that regenerated model is created
for result in desc['results']:
assert np.all(getattr(tfm1, result))
# Test when object has no data (clear a copy of tfg)
new_tfg = tfg.copy()
new_tfg._reset_data_results(False, True, True, True)
tfm2 = new_tfg.get_fooof(0, True)
assert tfm2
# Check that data info is copied over properly
for meta_dat in desc['meta_data']:
assert getattr(tfm2, meta_dat)
def load_json(file_name, file_path):
"""Load json file.
Parameters
----------
file_name : str or FileObject, optional
File from which to load data.
file_path : str
Path to directory from which to load.
Returns
-------
dat : dict
Dictionary of data loaded from file.
"""
# Load data from file
if isinstance(file_name, str):
with open(fpath(file_path, fname(file_name, 'json')), 'r') as infile:
dat = json.load(infile)
elif isinstance(file_name, io.IOBase):
dat = json.loads(file_name.readline())
# Get dictionary of available attributes, and convert specified lists back into arrays
dat = dict_lst_to_array(dat, get_description()['arrays'])
return dat
def save_fm(fm,
file_name,
file_path=None,
append=False,
save_results=False,
save_settings=False,
save_data=False):
"""Save out data, results and/or settings from FOOOF object. Saves out to a JSON file.
Parameters
----------
fm : FOOOF() object
FOOOF object from which to save data.
file_name : str or FileObject
File to which to save data.
file_path : str, optional
Path to directory in which to save. If not provided, saves to current directory.
append : bool, optional, default: False
Whether to append to an existing file, if available.
This option is only valid (and only used) if file_name is a str.
save_results : bool, optional
Whether to save out FOOOF model fit results.
save_settings : bool, optional
Whether to save out FOOOF settings.
save_data : bool, optional
Whether to save out input data.
"""
# Convert object to dictionary & convert all arrays to lists - for JSON serializing
obj_dict = dict_array_to_lst(fm.__dict__)
# Set and select which variables to keep. Use a set to drop any potential overlap
# Note that results also saves frequency information to be able to recreate freq vector
attributes = get_description()
keep = set((attributes['results'] + attributes['meta_data'] if save_results else []) + \
(attributes['settings'] if save_settings else []) + \
(attributes['data'] if save_data else []))
obj_dict = dict_select_keys(obj_dict, keep)
# Save out - create new file, (creates a JSON file)
if isinstance(file_name, str) and not append:
with open(fpath(file_path, fname(file_name, 'json')), 'w') as outfile:
json.dump(obj_dict, outfile)
# Save out - append to file_name (appends to a JSONlines file)
elif isinstance(file_name, str) and append:
with open(fpath(file_path, fname(file_name, 'json')), 'a') as outfile:
json.dump(obj_dict, outfile)
outfile.write('\n')
# Save out - append to given file object (appends to a JSONlines file)
elif isinstance(file_name, io.IOBase):
json.dump(obj_dict, file_name)
file_name.write('\n')
else:
raise ValueError('Save file not understood.')
def test_fooof_results():
results = FOOOFResults([], [], None, None, [])
assert results
# Check that the object has the correct fields, given the object description
results_fields = get_description()['results']
for field in results_fields:
getattr(results, field.strip('_'))
assert True
def test_fooof_settings():
settings = FOOOFSettings([], None, None, None, None)
assert settings
# Check that the object has the correct fields, given the object description
settings_fields = get_description()['settings']
for field in settings_fields:
getattr(settings, field)
assert True
def get_results(self):
"""Return model fit parameters and goodness of fit metrics in a FOOOFResults object.
Returns
-------
FOOOFResults
Object containing the FOOOF model fit results from the current FOOOF object.
"""
return FOOOFResults(**{key.strip('_') : getattr(self, key) \
for key in get_description()['results']})
def get_settings(self):
"""Return user defined settings of the FOOOF object.
Returns
-------
FOOOFSettings
Object containing the settings from the current FOOOF object.
"""
return FOOOFSettings(
**
{key: getattr(self, key)
for key in get_description()['settings']})
def add_meta_data(self, fooof_meta_data):
"""Add data information into object from a FOOOFMetaData object.
Parameters
----------
fooof_meta_data : FOOOFMetaData
A FOOOF meta data object containing meta data information.
"""
for meta_dat in get_description()['meta_data']:
setattr(self, meta_dat, getattr(fooof_meta_data, meta_dat))
self._regenerate_freqs()
def add_settings(self, fooof_settings):
"""Add settings into object from a FOOOFSettings object.
Parameters
----------
fooof_settings : FOOOFSettings
A FOOOF data object containing the settings for a FOOOF model.
"""
for setting in get_description()['settings']:
setattr(self, setting, getattr(fooof_settings, setting))
self._check_loaded_settings(fooof_settings._asdict())
def get_meta_data(self):
"""Return data information from the FOOOF object.
Returns
-------
FOOOFMetaData
Object containing meta data from the current FOOOF object.
"""
return FOOOFMetaData(**{
key: getattr(self, key)
for key in get_description()['meta_data']
})
def _check_loaded_results(self, data):
"""Check if results have been added and check data.
Parameters
----------
data : dict
A dictionary of data that has been added to the object.
"""
# If results loaded, check dimensions of peak parameters
# This fixes an issue where they end up the wrong shape if they are empty (no peaks)
if set(get_description()['results']).issubset(set(data.keys())):
self.peak_params_ = check_array_dim(self.peak_params_)
self.gaussian_params_ = check_array_dim(self.gaussian_params_)
def test_fooof_resets():
"""Check that all relevant data is cleared in the resest method."""
# Note: uses it's own tfm, to not clear the global one
tfm = get_tfm()
tfm._reset_data_results()
tfm._reset_internal_settings()
desc = get_description()
for data in ['data', 'results', 'model_components']:
for field in desc[data]:
assert getattr(tfm, field) == None
assert tfm.freqs == None and tfm.fooofed_spectrum_ == None
def _check_loaded_settings(self, data):
"""Check if settings added, and update the object as needed.
Parameters
----------
data : dict
A dictionary of data that has been added to the object.
"""
# If settings not loaded from file, clear from object, so that default
# settings, which are potentially wrong for loaded data, aren't kept
if not set(get_description()['settings']).issubset(set(data.keys())):
# Reset all public settings to None
for setting in get_description()['settings']:
setattr(self, setting, None)
# Infer whether knee fitting was used, if aperiodic params have been loaded
if np.all(self.aperiodic_params_):
self.aperiodic_mode = infer_ap_func(self.aperiodic_params_)
# Reset internal settings so that they are consistent with what was loaded
# Note that this will set internal settings to None, if public settings unavailable
self._reset_internal_settings()
def get_info(f_obj, aspect):
"""Get a specified selection of information from a FOOOF derived object.
Parameters
----------
f_obj : FOOOF or FOOOFGroup
FOOOF derived object to get attributes from.
aspect : {'settings', 'meta_data', 'results'}
Which set of attributes to compare the objects across.
Returns
-------
dict
The set of specified info from the FOOOF derived object.
"""
return {key : getattr(f_obj, key) for key in get_description()[aspect]}
def test_fooof_fit_failure():
"""Test that fit handles a failure."""
# Use a new FOOOF, that is monkey-patched to raise an error
# This mimicks the main fit-failure, without requiring bad data / waiting for it to fail.
tfm = FOOOF(verbose=False)
def raise_runtime_error(*args, **kwargs):
raise RuntimeError('Test-MonkeyPatch')
tfm._fit_peaks = raise_runtime_error
# Run a FOOOF fit - this should raise an error, but continue in try/except
tfm.fit(*gen_power_spectrum([3, 50], [50, 2], [10, 0.5, 2, 20, 0.3, 4]))
# Check after failing out of fit, all results are reset
for result in get_description()['results']:
cur_res = getattr(tfm, result)
assert cur_res is None or np.all(np.isnan(cur_res))
def test_load_file_contents():
"""Check that loaded files contain the contents they should.
Note that is this test fails, it likely stems from an issue from saving.
"""
file_name = 'test_fooof_str_all'
file_path = pkg.resource_filename(__name__, 'test_files')
loaded_data = load_json(file_name, file_path)
desc = get_description()
# Check settings
for setting in desc['settings']:
assert setting in loaded_data.keys()
# Check results
for result in desc['results']:
assert result in loaded_data.keys()
# Check results
for datum in desc['data']:
assert datum in loaded_data.keys()
"""Instantiated objects that can be used as 'helper items' for working with FOOOF.""" from fooof.core.info import get_description, get_peak_indices ################################################################################################### ################################################################################################### OBJ_DESC = get_description() PEAK_INDS = get_peak_indices()
