[med-svn] [python-mne] 100/353: added envelope parameter, renamed vars, more comments
Yaroslav Halchenko
debian at onerussian.com
Fri Nov 27 17:24:37 UTC 2015
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yoh pushed a commit to tag 0.4
in repository python-mne.
commit adfe663db1e6ea079a999cd6b8bc4796e22b536b
Author: Martin Luessi <mluessi at nmr.mgh.harvard.edu>
Date: Mon Mar 5 15:12:22 2012 -0500
added envelope parameter, renamed vars, more comments
---
mne/fiff/raw.py | 79 +++++++++++++++++++++++++++++++++++-----------
mne/fiff/tests/test_raw.py | 5 ++-
2 files changed, 65 insertions(+), 19 deletions(-)
diff --git a/mne/fiff/raw.py b/mne/fiff/raw.py
index 4fb12b0..e63db41 100644
--- a/mne/fiff/raw.py
+++ b/mne/fiff/raw.py
@@ -244,6 +244,13 @@ class Raw(object):
The Raw object has to be constructed using preload=True (or string).
+ Note: If n_jobs > 1, more memory is required as "len(picks) * n_times"
+ addtional time points need to be temporaily stored in memory.
+
+ Note: If the data type changes (dtype != None), more memory is required
+ since the original and the converted data needs to be stored in
+ memory.
+
Parameters
----------
fun : function
@@ -296,20 +303,38 @@ class Raw(object):
self._data[picks, :] = data_picks_new
- def apply_hilbert(self, picks, n_jobs=1, verbose=5):
- """ Compute analytic signal for a subset of channels.
+ def apply_hilbert(self, picks, envelope=False, n_jobs=1, verbose=5):
+ """ Compute analytic signal or envelope for a subset of channels.
- Compute analytic signal for the channels defined in "picks". The
- data of the Raw object is modified inplace and converted to a
- complex representation (the analytic signal is complex valued).
+ If envelope=False, the analytic signal for the channels defined in
+ "picks" is computed and the data of the Raw object is converted to
+ a complex representation (the analytic signal is complex valued).
- The Raw object has to be constructed using preload=True (or string).
+ If envelope=True, the absolute value of the analytic signal for the
+ channels defined in "picks" is computed, resulting in the envelope
+ signal.
+
+ Note: DO NOT use envelope=True if you intend to compute an inverse
+ solution from the raw data. If you want to compute the
+ envelope in source space, use envelope=False and compute the
+ envelope after the inverse solution has been obtained.
+
+ Note: If envelope=False, more memory is required since the original
+ raw data as well as the analytic signal have temporarily to
+ be stored in memory.
+
+ Note: If n_jobs > 1 and envelope=True, more memory is required as
+ "len(picks) * n_times" addtional time points need to be
+ temporaily stored in memory.
Parameters
----------
picks : list of int
Indices of channels to apply the function to.
+ envelope : bool (default: False)
+ Compute the envelope signal of each channel.
+
n_jobs: int
Number of jobs to run in parallel.
@@ -324,15 +349,18 @@ class Raw(object):
where "F" is the Fourier transform, "U" the unit step function,
and "y" the Hilbert transform of "x". One usage of the analytic
- signal is the computation of the enevelope signal, which is given by
+ signal is the computation of the envelope signal, which is given by
"e(t) = abs(x_a(t))". Due to the linearity of Hilbert transform and the
MNE inverse solution, the enevlope in source space can be obtained
by computing the analytic signal in sensor space, applying the MNE
inverse, and computing the envelope in source space.
"""
- self.apply_function(hilbert, picks, np.complex64, n_jobs, verbose)
+ if envelope:
+ self.apply_function(_envelope, picks, None, n_jobs, verbose)
+ else:
+ self.apply_function(hilbert, picks, np.complex64, n_jobs, verbose)
- def band_pass_filter(self, picks, f_low, f_high, filter_length=None,
+ def band_pass_filter(self, picks, l_freq, h_freq, filter_length=None,
n_jobs=1, verbose=5):
"""Band-pass filter a subset of channels.
@@ -341,15 +369,19 @@ class Raw(object):
The Raw object has to be constructed using preload=True (or string).
+ Note: If n_jobs > 1, more memory is required as "len(picks) * n_times"
+ addtional time points need to be temporaily stored in memory.
+
+
Parameters
----------
picks : list of int
Indices of channels to filter.
- f_low : float
+ l_freq : float
Low cut-off frequency in Hz.
- f_high : float
+ h_freq : float
High cut-off frequency in Hz.
filter_length : int (default: None)
@@ -366,15 +398,18 @@ class Raw(object):
"""
fs = float(self.info['sfreq'])
self.apply_function(band_pass_filter, picks, None, n_jobs, verbose, fs,
- f_low, f_high, filter_length=filter_length)
+ l_freq, h_freq, filter_length=filter_length)
- def high_pass_filter(self, picks, fp, filter_length=None, n_jobs=1,
+ def high_pass_filter(self, picks, freq, filter_length=None, n_jobs=1,
verbose=5):
"""High-pass filter a subset of channels.
Applies a zero-phase high-pass filter to the channels selected by
"picks". The data of the Raw object is modified inplace.
+ Note: If n_jobs > 1, more memory is required as "len(picks) * n_times"
+ addtional time points need to be temporaily stored in memory.
+
The Raw object has to be constructed using preload=True (or string).
Parameters
@@ -382,7 +417,7 @@ class Raw(object):
picks : list of int
Indices of channels to filter.
- fp : float
+ freq : float
Cut-off frequency in Hz.
filter_length : int (default: None)
@@ -400,15 +435,18 @@ class Raw(object):
fs = float(self.info['sfreq'])
self.apply_function(high_pass_filter, picks, None, n_jobs, verbose,
- fs, fp, filter_length=filter_length)
+ fs, freq, filter_length=filter_length)
- def low_pass_filter(self, picks, fp, filter_length=None, n_jobs=1,
+ def low_pass_filter(self, picks, freq, filter_length=None, n_jobs=1,
verbose=5):
"""Low-pass filter a subset of channels.
Applies a zero-phase low-pass filter to the channels selected by
"picks". The data of the Raw object is modified in-place.
+ Note: If n_jobs > 1, more memory is required as "len(picks) * n_times"
+ addtional time points need to be temporaily stored in memory.
+
The Raw object has to be constructed using preload=True (or string).
Parameters
@@ -416,7 +454,7 @@ class Raw(object):
picks : list of int
Indices of channels to filter.
- fp : float
+ freq : float
Cut-off frequency in Hz.
filter_length : int (default: None)
@@ -433,7 +471,7 @@ class Raw(object):
"""
fs = float(self.info['sfreq'])
self.apply_function(low_pass_filter, picks, None, n_jobs, verbose,
- fs, fp, filter_length=filter_length)
+ fs, freq, filter_length=filter_length)
def save(self, fname, picks=None, tmin=0, tmax=None, buffer_size_sec=10,
drop_small_buffer=False):
@@ -859,3 +897,8 @@ def finish_writing_raw(fid):
end_block(fid, FIFF.FIFFB_RAW_DATA)
end_block(fid, FIFF.FIFFB_MEAS)
end_file(fid)
+
+
+def _envelope(x):
+ """ Compute envelope signal """
+ return np.abs(hilbert(x))
diff --git a/mne/fiff/tests/test_raw.py b/mne/fiff/tests/test_raw.py
index 744a36b..2321956 100644
--- a/mne/fiff/tests/test_raw.py
+++ b/mne/fiff/tests/test_raw.py
@@ -177,6 +177,9 @@ def test_hilbert():
picks_meg = pick_types(raw.info, meg=True)
picks = picks_meg[:4]
+ raw2 = deepcopy(raw)
raw.apply_hilbert(picks, verbose=0)
+ raw2.apply_hilbert(picks, envelope=True, n_jobs=2, verbose=0)
- #XXX what to test?
+ env = np.abs(raw._data[picks, :])
+ assert_array_almost_equal(env, raw2._data[picks, :])
--
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