def get_params(self, name, col=None):
"""Return model fit parameters for specified feature(s).
Parameters
----------
name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared'}
Name of the data field to extract across the group.
col : {'CF', 'PW', 'BW', 'offset', 'knee', 'exponent'} or int, optional
Column name / index to extract from selected data, if requested.
Only used for name of {'aperiodic_params', 'peak_params', 'gaussian_params'}.
Returns
-------
out : ndarray
Requested data.
Notes
-----
For further description of the data you can extract, check the FOOOFResults documentation.
"""
if not self.group_results:
raise RuntimeError('No model fit data is available to extract - can not proceed.')
# If col specified as string, get mapping back to integer
if isinstance(col, str):
col = get_indices(self.aperiodic_mode)[col]
# Pull out the requested data field from the group data
# As a special case, peak_params are pulled out in a way that appends
# an extra column, indicating from which FOOOF run each peak comes from
if name in ('peak_params', 'gaussian_params'):
out = np.array([np.insert(getattr(data, name), 3, index, axis=1)
for index, data in enumerate(self.group_results)])
# This updates index to grab selected column, and the last colum
# This last column is the 'index' column (FOOOF object source)
if col is not None:
col = [col, -1]
else:
out = np.array([getattr(data, name) for data in self.group_results])
# Some data can end up as a list of separate arrays.
# If so, concatenate it all into one 2d array
if isinstance(out[0], np.ndarray):
out = np.concatenate([arr.reshape(1, len(arr)) \
if arr.ndim == 1 else arr for arr in out], 0)
# Select out a specific column, if requested
if col is not None:
out = out[:, col]
return out
def update_sim_ap_params(sim_params, delta, field=None):
"""Update the aperiodic parameter definition in a SimParams object.
Parameters
----------
sim_params : SimParams
Object storing the current parameter definition.
delta : float or list of float
Value(s) by which to update the parameters.
field : {'offset', 'knee', 'exponent'} or list of string
Field of the aperiodic parameter(s) to update.
Returns
-------
new_sim_params : SimParams
Updated object storing the new parameter definition.
Raises
------
InconsistentDataError
If the input parameters and update values are inconsistent.
"""
# Grab the aperiodic parameters that need updating
ap_params = sim_params.aperiodic_params.copy()
# If field isn't specified, check shapes line up and update across parameters
if not field:
if not len(ap_params) == len(delta):
raise InconsistentDataError(
"The number of items to update and "
"number of new values is inconsistent.")
ap_params = [param + update for param, update in zip(ap_params, delta)]
# If labels are given, update deltas according to their labels
else:
# This loop checks & casts to list, to work for single or multiple passed in values
for cur_field, cur_delta in zip(
list([field]) if not isinstance(field, list) else field,
list([delta]) if not isinstance(delta, list) else delta):
data_ind = get_indices(infer_ap_func(ap_params))[cur_field]
ap_params[data_ind] = ap_params[data_ind] + cur_delta
# Replace parameters. Note that this copies a new object, as data objects are immutable
new_sim_params = sim_params._replace(aperiodic_params=ap_params)
return new_sim_params
def get_params(self, name, col=None):
"""Return model fit parameters for specified feature(s).
Parameters
----------
name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared'}
Name of the data field to extract.
col : {'CF', 'PW', 'BW', 'offset', 'knee', 'exponent'} or int, optional
Column name / index to extract from selected data, if requested.
Only used for name of {'aperiodic_params', 'peak_params', 'gaussian_params'}.
Returns
-------
out : float or 1d array
Requested data.
Notes
-----
For further description of the data you can extract, check the FOOOFResults documentation.
If there is no data on periodic features, this method will return NaN.
"""
if self.aperiodic_params_ is None:
raise RuntimeError(
'No model fit data is available to extract - can not proceed.')
# If col specified as string, get mapping back to integer
if isinstance(col, str):
col = get_indices(self.aperiodic_mode)[col]
# Extract the request data field from object
out = getattr(self, name + '_')
# Periodic values can be empty arrays - if so replace with NaN array
if isinstance(out, np.ndarray) and out.size == 0:
out = np.array([np.nan, np.nan, np.nan])
# Select out a specific column, if requested
if col is not None:
# Extract column, & if result is a single value in an array, unpack from array
out = out[col] if out.ndim == 1 else out[:, col]
out = out[0] if isinstance(out,
np.ndarray) and out.size == 1 else out
return out
def update_sim_ap_params(sim_params, delta, field=None):
"""Update the aperiodic parameter definition in a SimParams object.
Parameters
----------
sim_params : SimParams object
Object storing the current parameter definitions.
delta : float or list
Value(s) by which to update the parameters.
field : {'offset', 'knee', 'exponent'} or list of string
Field of the aperiodic parameters to update.
Returns
-------
new_sim_params : SimParams object
Updated object storing the new parameter definitions.
Notes
-----
SimParams is a `namedtuple`, which is immutable.
Therefore, this function constructs and returns a new `SimParams` object.
"""
ap_params = sim_params.aperiodic_params.copy()
if not field:
if not len(ap_params) == len(delta):
raise ValueError('')
ap_params = [ii + jj for ii, jj in zip(ap_params, delta)]
else:
field = list([field]) if not isinstance(field, list) else field
delta = list([delta]) if not isinstance(delta, list) else delta
for cur_field, cur_delta in zip(field, delta):
dat_ind = get_indices(infer_ap_func(sim_params.aperiodic_params))[cur_field]
ap_params[dat_ind] = ap_params[dat_ind] + cur_delta
new_sim_params = SimParams(ap_params, *sim_params[1:])
return new_sim_params
def get_params(self, name, col=None):
"""Return model fit parameters for specified feature(s).
Parameters
----------
name : {'aperiodic_params', 'peak_params', 'gaussian_params', 'error', 'r_squared'}
Name of the data field to extract across the group.
col : {'CF', 'PW', 'BW', 'offset', 'knee', 'exponent'} or int, optional
Column name / index to extract from selected data, if requested.
Only used for name of {'aperiodic_params', 'peak_params', 'gaussian_params'}.
Returns
-------
out : ndarray
Requested data.
Raises
------
NoModelError
If there are no model fit results available.
ValueError
If the input for the `col` input is not understood.
Notes
-----
When extracting peak information ('peak_params' or 'gaussian_params'), an additional column
is appended to the returned array, indicating the index of the model that the peak came from.
"""
if not self.has_model:
raise NoModelError(
"No model fit results are available, can not proceed.")
# Allow for shortcut alias, without adding `_params`
if name in ['aperiodic', 'peak', 'gaussian']:
name = name + '_params'
# If col specified as string, get mapping back to integer
if isinstance(col, str):
col = get_indices(self.aperiodic_mode)[col]
elif isinstance(col, int):
if col not in [0, 1, 2]:
raise ValueError("Input value for `col` not valid.")
# Pull out the requested data field from the group data
# As a special case, peak_params are pulled out in a way that appends
# an extra column, indicating which FOOOF run each peak comes from
if name in ('peak_params', 'gaussian_params'):
# Collect peak data, appending the index of the model it comes from
out = np.vstack([
np.insert(getattr(data, name), 3, index, axis=1)
for index, data in enumerate(self.group_results)
])
# This updates index to grab selected column, and the last column
# This last column is the 'index' column (FOOOF object source)
if col is not None:
col = [col, -1]
else:
out = np.array(
[getattr(data, name) for data in self.group_results])
# Select out a specific column, if requested
if col is not None:
out = out[:, col]
return out
