"""Some utility functions."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import json
import logging
from collections import OrderedDict
from copy import deepcopy
import numpy as np
from ._logging import verbose, warn
from ._typing import Self
from .check import _check_pandas_installed, _check_preload, _validate_type
from .numerics import _time_mask, object_hash, object_size
logger = logging.getLogger("mne") # one selection here used across mne-python
logger.propagate = False # don't propagate (in case of multiple imports)
class SizeMixin:
"""Estimate MNE object sizes."""
def __eq__(self, other):
"""Compare self to other.
Parameters
----------
other : object
The object to compare to.
Returns
-------
eq : bool
True if the two objects are equal.
"""
return isinstance(other, type(self)) and hash(self) == hash(other)
@property
def _size(self):
"""Estimate the object size."""
try:
size = object_size(self.info)
except Exception:
warn("Could not get size for self.info")
return -1
if hasattr(self, "data"):
size += object_size(self.data)
elif hasattr(self, "_data"):
size += object_size(self._data)
return size
def __hash__(self):
"""Hash the object.
Returns
-------
hash : int
The hash
"""
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..io import BaseRaw
if isinstance(self, Evoked):
return object_hash(dict(info=self.info, data=self.data))
elif isinstance(self, BaseEpochs | BaseRaw):
_check_preload(self, "Hashing ")
return object_hash(dict(info=self.info, data=self._data))
else:
raise RuntimeError(f"Hashing unknown object type: {type(self)}")
class GetEpochsMixin:
"""Class to add epoch selection and metadata to certain classes."""
def __getitem__(
self: Self,
item,
) -> Self:
"""Return an Epochs object with a copied subset of epochs.
Parameters
----------
item : int | slice | array-like | str
See Notes for use cases.
Returns
-------
epochs : instance of Epochs
The subset of epochs.
Notes
-----
Epochs can be accessed as ``epochs[...]`` in several ways:
1. **Integer or slice:** ``epochs[idx]`` will return an `~mne.Epochs`
object with a subset of epochs chosen by index (supports single
index and Python-style slicing).
2. **String:** ``epochs['name']`` will return an `~mne.Epochs` object
comprising only the epochs labeled ``'name'`` (i.e., epochs created
around events with the label ``'name'``).
If there are no epochs labeled ``'name'`` but there are epochs
labeled with /-separated tags (e.g. ``'name/left'``,
``'name/right'``), then ``epochs['name']`` will select the epochs
with labels that contain that tag (e.g., ``epochs['left']`` selects
epochs labeled ``'audio/left'`` and ``'visual/left'``, but not
``'audio_left'``).
If multiple tags are provided *as a single string* (e.g.,
``epochs['name_1/name_2']``), this selects epochs containing *all*
provided tags. For example, ``epochs['audio/left']`` selects
``'audio/left'`` and ``'audio/quiet/left'``, but not
``'audio/right'``. Note that tag-based selection is insensitive to
order: tags like ``'audio/left'`` and ``'left/audio'`` will be
treated the same way when selecting via tag.
3. **List of strings:** ``epochs[['name_1', 'name_2', ... ]]`` will
return an `~mne.Epochs` object comprising epochs that match *any* of
the provided names (i.e., the list of names is treated as an
inclusive-or condition). If *none* of the provided names match any
epoch labels, a ``KeyError`` will be raised.
If epoch labels are /-separated tags, then providing multiple tags
*as separate list entries* will likewise act as an inclusive-or
filter. For example, ``epochs[['audio', 'left']]`` would select
``'audio/left'``, ``'audio/right'``, and ``'visual/left'``, but not
``'visual/right'``.
4. **Pandas query:** ``epochs['pandas query']`` will return an
`~mne.Epochs` object with a subset of epochs (and matching
metadata) selected by the query called with
``self.metadata.eval``, e.g.::
epochs["col_a > 2 and col_b == 'foo'"]
would return all epochs whose associated ``col_a`` metadata was
greater than two, and whose ``col_b`` metadata was the string 'foo'.
Query-based indexing only works if Pandas is installed and
``self.metadata`` is a :class:`pandas.DataFrame`.
.. versionadded:: 0.16
"""
return self._getitem(item)
def _item_to_select(self, item):
if isinstance(item, str):
item = [item]
# Convert string to indices
if (
isinstance(item, list | tuple)
and len(item) > 0
and isinstance(item[0], str)
):
select = self._keys_to_idx(item)
elif isinstance(item, slice):
select = item
else:
select = np.atleast_1d(item)
if len(select) == 0:
select = np.array([], int)
return select
def _getitem(
self,
item,
reason="IGNORED",
copy=True,
drop_event_id=True,
select_data=True,
return_indices=False,
):
"""
Select epochs from current object.
Parameters
----------
item: slice, array-like, str, or list
see `__getitem__` for details.
reason: str, list/tuple of str
entry in `drop_log` for unselected epochs
copy: bool
return a copy of the current object
drop_event_id: bool
remove non-existing event-ids after selection
select_data: bool
apply selection to data
(use `select_data=False` if subclasses do not have a
valid `_data` field, or data has already been subselected)
return_indices: bool
return the indices of selected epochs from the original object
in addition to the new `Epochs` objects
Returns
-------
`Epochs` or tuple(Epochs, np.ndarray) if `return_indices` is True
subset of epochs (and optionally array with kept epoch indices)
"""
inst = self.copy() if copy else self
if self._data is not None:
np.copyto(inst._data, self._data, casting="no")
del self
select = inst._item_to_select(item)
has_selection = hasattr(inst, "selection")
if has_selection:
key_selection = inst.selection[select]
drop_log = list(inst.drop_log)
if reason is not None:
_validate_type(reason, (list, tuple, str), "reason")
if isinstance(reason, list | tuple):
for r in reason:
_validate_type(r, str, r)
if isinstance(reason, str):
reason = (reason,)
reason = tuple(reason)
for idx in np.setdiff1d(inst.selection, key_selection):
drop_log[idx] = reason
inst.drop_log = tuple(drop_log)
inst.selection = key_selection
del drop_log
inst.events = np.atleast_2d(inst.events[select])
if inst.metadata is not None:
pd = _check_pandas_installed(strict=False)
if pd:
metadata = inst.metadata.iloc[select]
if has_selection:
metadata.index = inst.selection
else:
metadata = np.array(inst.metadata, "object")[select].tolist()
# will reset the index for us
GetEpochsMixin.metadata.fset(inst, metadata, verbose=False)
if inst.preload and select_data:
# ensure that each Epochs instance owns its own data so we can
# resize later if necessary
inst._data = np.require(inst._data[select], requirements=["O"])
if drop_event_id:
# update event id to reflect new content of inst
inst.event_id = {
k: v for k, v in inst.event_id.items() if v in inst.events[:, 2]
}
if return_indices:
return inst, select
else:
return inst
def _keys_to_idx(self, keys):
"""Find entries in event dict."""
from ..event import match_event_names # avoid circular import
keys = keys if isinstance(keys, list | tuple) else [keys]
try:
# Assume it's a condition name
return np.where(
np.any(
np.array(
[
self.events[:, 2] == self.event_id[k]
for k in match_event_names(self.event_id, keys)
]
),
axis=0,
)
)[0]
except KeyError as err:
# Could we in principle use metadata with these Epochs and keys?
if len(keys) != 1 or self.metadata is None:
# If not, raise original error
raise
msg = str(err.args[0]) # message for KeyError
pd = _check_pandas_installed(strict=False)
# See if the query can be done
if pd:
md = self.metadata if hasattr(self, "_metadata") else None
self._check_metadata(metadata=md)
try:
# Try metadata
vals = (
self.metadata.reset_index()
.query(keys[0], engine="python")
.index.values
)
except Exception as exp:
msg += (
" The epochs.metadata Pandas query did not "
f"yield any results: {exp.args[0]}"
)
else:
return vals
else:
# If not, warn this might be a problem
msg += (
" The epochs.metadata Pandas query could not "
"be performed, consider installing Pandas."
)
raise KeyError(msg)
def __len__(self):
"""Return the number of epochs.
Returns
-------
n_epochs : int
The number of remaining epochs.
Notes
-----
This function only works if bad epochs have been dropped.
Examples
--------
This can be used as::
>>> epochs.drop_bad() # doctest: +SKIP
>>> len(epochs) # doctest: +SKIP
43
>>> len(epochs.events) # doctest: +SKIP
43
"""
from ..epochs import BaseEpochs
if isinstance(self, BaseEpochs) and not self._bad_dropped:
raise RuntimeError(
"Since bad epochs have not been dropped, the "
"length of the Epochs is not known. Load the "
"Epochs with preload=True, or call "
"Epochs.drop_bad(). To find the number "
"of events in the Epochs, use "
"len(Epochs.events)."
)
return len(self.events)
def __iter__(self):
"""Facilitate iteration over epochs.
This method resets the object iteration state to the first epoch.
Notes
-----
This enables the use of this Python pattern::
>>> for epoch in epochs: # doctest: +SKIP
>>> print(epoch) # doctest: +SKIP
Where ``epoch`` is given by successive outputs of
:meth:`mne.Epochs.next`.
"""
self._current = 0
self._current_detrend_picks = self._detrend_picks
return self
def __next__(self, return_event_id=False):
"""Iterate over epoch data.
Parameters
----------
return_event_id : bool
If True, return both the epoch data and an event_id.
Returns
-------
epoch : array of shape (n_channels, n_times)
The epoch data.
event_id : int
The event id. Only returned if ``return_event_id`` is ``True``.
"""
if not hasattr(self, "_current_detrend_picks"):
self.__iter__() # ensure we're ready to iterate
if self.preload:
if self._current >= len(self._data):
self._stop_iter()
epoch = self._data[self._current]
self._current += 1
else:
is_good = False
while not is_good:
if self._current >= len(self.events):
self._stop_iter()
epoch_noproj = self._get_epoch_from_raw(self._current)
epoch_noproj = self._detrend_offset_decim(
epoch_noproj, self._current_detrend_picks
)
epoch = self._project_epoch(epoch_noproj)
self._current += 1
is_good, _ = self._is_good_epoch(epoch)
# If delayed-ssp mode, pass 'virgin' data after rejection decision.
if self._do_delayed_proj:
epoch = epoch_noproj
if not return_event_id:
return epoch
else:
return epoch, self.events[self._current - 1][-1]
def _stop_iter(self):
del self._current
del self._current_detrend_picks
raise StopIteration # signal the end
next = __next__ # originally for Python2, now b/c public
def _check_metadata(self, metadata=None, reset_index=False):
"""Check metadata consistency."""
# reset_index=False will not copy!
if metadata is None:
return
else:
pd = _check_pandas_installed(strict=False)
if pd:
_validate_type(metadata, types=pd.DataFrame, item_name="metadata")
if len(metadata) != len(self.events):
raise ValueError(
"metadata must have the same number of "
f"rows ({len(metadata)}) as events ({len(self.events)})"
)
if reset_index:
if hasattr(self, "selection"):
# makes a copy
metadata = metadata.reset_index(drop=True)
metadata.index = self.selection
else:
metadata = deepcopy(metadata)
else:
_validate_type(metadata, types=list, item_name="metadata")
if reset_index:
metadata = deepcopy(metadata)
return metadata
@property
def metadata(self):
"""Get the metadata."""
return self._metadata
@metadata.setter
@verbose
def metadata(self, metadata, verbose=None):
metadata = self._check_metadata(metadata, reset_index=True)
if metadata is not None:
if _check_pandas_installed(strict=False):
n_col = metadata.shape[1]
else:
n_col = len(metadata[0])
n_col = f" with {n_col} columns"
else:
n_col = ""
if hasattr(self, "_metadata") and self._metadata is not None:
action = "Removing" if metadata is None else "Replacing"
action += " existing"
else:
action = "Not setting" if metadata is None else "Adding"
logger.info(f"{action} metadata{n_col}")
self._metadata = metadata
def _check_decim(info, decim, offset, check_filter=True):
"""Check decimation parameters."""
if decim < 1 or decim != int(decim):
raise ValueError("decim must be an integer > 0")
decim = int(decim)
new_sfreq = info["sfreq"] / float(decim)
offset = int(offset)
if not 0 <= offset < decim:
raise ValueError(
f"decim must be at least 0 and less than {decim}, got {offset}"
)
if check_filter:
lowpass = info["lowpass"]
if decim > 1 and lowpass is None:
warn(
"The measurement information indicates data is not low-pass "
f"filtered. The decim={decim} parameter will result in a "
f"sampling frequency of {new_sfreq} Hz, which can cause "
"aliasing artifacts."
)
elif decim > 1 and new_sfreq < 3 * lowpass:
warn(
"The measurement information indicates a low-pass frequency "
f"of {lowpass} Hz. The decim={decim} parameter will result "
f"in a sampling frequency of {new_sfreq} Hz, which can "
"cause aliasing artifacts."
) # > 50% nyquist lim
return decim, offset, new_sfreq
class TimeMixin:
"""Class for time operations on any MNE object that has a time axis."""
def time_as_index(self, times, use_rounding=False):
"""Convert time to indices.
Parameters
----------
times : list-like | float | int
List of numbers or a number representing points in time.
use_rounding : bool
If True, use rounding (instead of truncation) when converting
times to indices. This can help avoid non-unique indices.
Returns
-------
index : ndarray
Indices corresponding to the times supplied.
"""
from ..source_estimate import _BaseSourceEstimate
if isinstance(self, _BaseSourceEstimate):
sfreq = 1.0 / self.tstep
else:
sfreq = self.info["sfreq"]
index = (np.atleast_1d(times) - self.times[0]) * sfreq
if use_rounding:
index = np.round(index)
return index.astype(int)
def _handle_tmin_tmax(self, tmin, tmax):
"""Convert seconds to index into data.
Parameters
----------
tmin : int | float | None
Start time of data to get in seconds.
tmax : int | float | None
End time of data to get in seconds.
Returns
-------
start : int
Integer index into data corresponding to tmin.
stop : int
Integer index into data corresponding to tmax.
"""
_validate_type(
tmin,
types=("numeric", None),
item_name="tmin",
type_name="int, float, None",
)
_validate_type(
tmax,
types=("numeric", None),
item_name="tmax",
type_name="int, float, None",
)
# handle tmin/tmax as start and stop indices into data array
n_times = self.times.size
start = 0 if tmin is None else self.time_as_index(tmin)[0]
stop = n_times if tmax is None else self.time_as_index(tmax)[0]
# truncate start/stop to the open interval [0, n_times]
start = min(max(0, start), n_times)
stop = min(max(0, stop), n_times)
return start, stop
@property
def times(self):
"""Time vector in seconds."""
return self._times_readonly
def _set_times(self, times):
"""Set self._times_readonly (and make it read only)."""
# naming used to indicate that it shouldn't be
# changed directly, but rather via this method
self._times_readonly = times.copy()
self._times_readonly.flags["WRITEABLE"] = False
class ExtendedTimeMixin(TimeMixin):
"""Class for time operations on epochs/evoked-like MNE objects."""
@property
def tmin(self):
"""First time point."""
return self.times[0]
@property
def tmax(self):
"""Last time point."""
return self.times[-1]
@verbose
def crop(self, tmin=None, tmax=None, include_tmax=True, verbose=None):
"""Crop data to a given time interval.
Parameters
----------
tmin : float | None
Start time of selection in seconds.
tmax : float | None
End time of selection in seconds.
%(include_tmax)s
%(verbose)s
Returns
-------
inst : instance of Raw, Epochs, Evoked, AverageTFR, or SourceEstimate
The cropped time-series object, modified in-place.
Notes
-----
%(notes_tmax_included_by_default)s
"""
t_vars = dict(tmin=tmin, tmax=tmax)
for name, t_var in t_vars.items():
_validate_type(
t_var,
types=("numeric", None),
item_name=name,
)
if tmin is None:
tmin = self.tmin
elif tmin < self.tmin:
warn(
f"tmin is not in time interval. tmin is set to "
f"{type(self)}.tmin ({self.tmin:g} s)"
)
tmin = self.tmin
if tmax is None:
tmax = self.tmax
elif tmax > self.tmax:
warn(
f"tmax is not in time interval. tmax is set to "
f"{type(self)}.tmax ({self.tmax:g} s)"
)
tmax = self.tmax
include_tmax = True
mask = _time_mask(
self.times, tmin, tmax, sfreq=self.info["sfreq"], include_tmax=include_tmax
)
self._set_times(self.times[mask])
self._raw_times = self._raw_times[mask]
self._update_first_last()
self._data = self._data[..., mask]
return self
@verbose
def decimate(self, decim, offset=0, *, verbose=None):
"""Decimate the time-series data.
Parameters
----------
%(decim)s
%(offset_decim)s
%(verbose)s
Returns
-------
inst : MNE-object
The decimated object.
See Also
--------
mne.Epochs.resample
mne.io.Raw.resample
Notes
-----
%(decim_notes)s
If ``decim`` is 1, this method does not copy the underlying data.
.. versionadded:: 0.10.0
References
----------
.. footbibliography::
"""
# if epochs have frequencies, they are not in time (EpochsTFR)
# and so do not need to be checked whether they have been
# appropriately filtered to avoid aliasing
from ..epochs import BaseEpochs
from ..evoked import Evoked
from ..time_frequency import BaseTFR
# This should be the list of classes that inherit
_validate_type(self, (BaseEpochs, Evoked, BaseTFR), "inst")
decim, offset, new_sfreq = _check_decim(
self.info, decim, offset, check_filter=not hasattr(self, "freqs")
)
start_idx = int(round(-self._raw_times[0] * (self.info["sfreq"] * self._decim)))
self._decim *= decim
i_start = start_idx % self._decim + offset
decim_slice = slice(i_start, None, self._decim)
with self.info._unlock():
self.info["sfreq"] = new_sfreq
if self.preload:
if decim != 1:
self._data = self._data[..., decim_slice].copy()
self._raw_times = self._raw_times[decim_slice].copy()
else:
self._data = np.ascontiguousarray(self._data)
self._decim_slice = slice(None)
self._decim = 1
else:
self._decim_slice = decim_slice
self._set_times(self._raw_times[self._decim_slice])
self._update_first_last()
return self
def shift_time(self, tshift, relative=True):
"""Shift time scale in epoched or evoked data.
Parameters
----------
tshift : float
The (absolute or relative) time shift in seconds. If ``relative``
is True, positive tshift increases the time value associated with
each sample, while negative tshift decreases it.
relative : bool
If True, increase or decrease time values by ``tshift`` seconds.
Otherwise, shift the time values such that the time of the first
sample equals ``tshift``.
Returns
-------
epochs : MNE-object
The modified instance.
Notes
-----
This method allows you to shift the *time* values associated with each
data sample by an arbitrary amount. It does *not* resample the signal
or change the *data* values in any way.
"""
_check_preload(self, "shift_time")
start = tshift + (self.times[0] if relative else 0.0)
new_times = start + np.arange(len(self.times)) / self.info["sfreq"]
self._set_times(new_times)
self._update_first_last()
return self
def _update_first_last(self):
"""Update self.first and self.last (sample indices)."""
from ..dipole import DipoleFixed
from ..evoked import Evoked
if isinstance(self, Evoked | DipoleFixed):
self.first = int(round(self.times[0] * self.info["sfreq"]))
self.last = len(self.times) + self.first - 1
def _prepare_write_metadata(metadata):
"""Convert metadata to JSON for saving."""
if metadata is not None:
if not isinstance(metadata, list):
metadata = metadata.reset_index().to_json(orient="records")
else: # Pandas DataFrame
metadata = json.dumps(metadata)
assert isinstance(metadata, str)
return metadata
def _prepare_read_metadata(metadata):
"""Convert saved metadata back from JSON."""
if metadata is not None:
pd = _check_pandas_installed(strict=False)
# use json.loads because this preserves ordering
# (which is necessary for round-trip equivalence)
metadata = json.loads(metadata, object_pairs_hook=OrderedDict)
assert isinstance(metadata, list)
if pd:
metadata = pd.DataFrame.from_records(metadata)
if "index" in metadata.columns:
metadata.set_index("index", inplace=True)
assert isinstance(metadata, pd.DataFrame)
return metadata
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