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curry.py
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# -*- coding: UTF-8 -*-
#
# Authors: Dirk Gütlin <dirk.guetlin@stud.sbg.ac.at>
#
#
# License: BSD-3-Clause
import os.path as op
from collections import namedtuple
import re
import numpy as np
from datetime import datetime, timezone
from ..base import BaseRaw
from ..meas_info import create_info
from ..tag import _coil_trans_to_loc
from ..utils import _read_segments_file, _mult_cal_one
from ..constants import FIFF
from ..ctf.trans import _quaternion_align
from ...surface import _normal_orth
from ...transforms import (apply_trans, Transform, get_ras_to_neuromag_trans,
combine_transforms, invert_transform,
_angle_between_quats, rot_to_quat)
from ...utils import (check_fname, check_version, logger, verbose, warn,
_check_fname)
from ...annotations import Annotations
FILE_EXTENSIONS = {
"Curry 7": {
"info": ".dap",
"data": ".dat",
"labels": ".rs3",
"events_cef": ".cef",
"events_ceo": ".ceo",
"hpi": ".hpi",
},
"Curry 8": {
"info": ".cdt.dpa",
"data": ".cdt",
"labels": ".cdt.dpa",
"events_cef": ".cdt.cef",
"events_ceo": ".cdt.ceo",
"hpi": ".cdt.hpi",
}
}
CHANTYPES = {"meg": "_MAG1", "eeg": "", "misc": "_OTHERS"}
FIFFV_CHANTYPES = {"meg": FIFF.FIFFV_MEG_CH, "eeg": FIFF.FIFFV_EEG_CH,
"misc": FIFF.FIFFV_MISC_CH}
FIFFV_COILTYPES = {"meg": FIFF.FIFFV_COIL_CTF_GRAD, "eeg": FIFF.FIFFV_COIL_EEG,
"misc": FIFF.FIFFV_COIL_NONE}
SI_UNITS = dict(V=FIFF.FIFF_UNIT_V, T=FIFF.FIFF_UNIT_T)
SI_UNIT_SCALE = dict(c=1e-2, m=1e-3, u=1e-6, µ=1e-6, n=1e-9, p=1e-12, f=1e-15)
CurryParameters = namedtuple('CurryParameters',
'n_samples, sfreq, is_ascii, unit_dict, '
'n_chans, dt_start, chanidx_in_file')
def _get_curry_version(file_extension):
"""Check out the curry file version."""
return "Curry 8" if "cdt" in file_extension else "Curry 7"
def _get_curry_file_structure(fname, required=()):
"""Store paths to a dict and check for required files."""
_msg = "The following required files cannot be found: {0}.\nPlease make " \
"sure all required files are located in the same directory as {1}."
fname = _check_fname(fname, 'read', True, 'fname')
# we don't use os.path.splitext to also handle extensions like .cdt.dpa
fname_base, ext = fname.split(".", maxsplit=1)
version = _get_curry_version(ext)
my_curry = dict()
for key in ('info', 'data', 'labels', 'events_cef', 'events_ceo', 'hpi'):
fname = fname_base + FILE_EXTENSIONS[version][key]
if op.isfile(fname):
_key = 'events' if key.startswith('events') else key
my_curry[_key] = fname
missing = [field for field in required if field not in my_curry]
if missing:
raise FileNotFoundError(_msg.format(np.unique(missing), fname))
return my_curry
def _read_curry_lines(fname, regex_list):
"""Read through the lines of a curry parameter files and save data.
Parameters
----------
fname : str
Path to a curry file.
regex_list : list of str
A list of strings or regular expressions to search within the file.
Each element `regex` in `regex_list` must be formulated so that
`regex + " START_LIST"` initiates the start and `regex + " END_LIST"`
initiates the end of the elements that should be saved.
Returns
-------
data_dict : dict
A dictionary containing the extracted data. For each element `regex`
in `regex_list` a dictionary key `data_dict[regex]` is created, which
contains a list of the according data.
"""
save_lines = {}
data_dict = {}
for regex in regex_list:
save_lines[regex] = False
data_dict[regex] = []
with open(fname) as fid:
for line in fid:
for regex in regex_list:
if re.match(regex + " END_LIST", line):
save_lines[regex] = False
if save_lines[regex] and line != "\n":
result = line.replace("\n", "")
if "\t" in result:
result = result.split("\t")
data_dict[regex].append(result)
if re.match(regex + " START_LIST", line):
save_lines[regex] = True
return data_dict
def _read_curry_parameters(fname):
"""Extract Curry params from a Curry info file."""
_msg_match = "The sampling frequency and the time steps extracted from " \
"the parameter file do not match."
_msg_invalid = "sfreq must be greater than 0. Got sfreq = {0}"
var_names = ['NumSamples', 'SampleFreqHz',
'DataFormat', 'SampleTimeUsec',
'NumChannels',
'StartYear', 'StartMonth', 'StartDay', 'StartHour',
'StartMin', 'StartSec', 'StartMillisec',
'NUM_SAMPLES', 'SAMPLE_FREQ_HZ',
'DATA_FORMAT', 'SAMPLE_TIME_USEC',
'NUM_CHANNELS',
'START_YEAR', 'START_MONTH', 'START_DAY', 'START_HOUR',
'START_MIN', 'START_SEC', 'START_MILLISEC']
param_dict = dict()
unit_dict = dict()
with open(fname) as fid:
for line in iter(fid):
if any(var_name in line for var_name in var_names):
key, val = line.replace(" ", "").replace("\n", "").split("=")
param_dict[key.lower().replace("_", "")] = val
for type in CHANTYPES:
if "DEVICE_PARAMETERS" + CHANTYPES[type] + " START" in line:
data_unit = next(fid)
unit_dict[type] = data_unit.replace(" ", "") \
.replace("\n", "").split("=")[-1]
# look for CHAN_IN_FILE sections, which may or may not exist; issue #8391
types = ["meg", "eeg", "misc"]
chanidx_in_file = _read_curry_lines(fname,
["CHAN_IN_FILE" +
CHANTYPES[key] for key in types])
n_samples = int(param_dict["numsamples"])
sfreq = float(param_dict["samplefreqhz"])
time_step = float(param_dict["sampletimeusec"]) * 1e-6
is_ascii = param_dict["dataformat"] == "ASCII"
n_channels = int(param_dict["numchannels"])
try:
dt_start = datetime(int(param_dict["startyear"]),
int(param_dict["startmonth"]),
int(param_dict["startday"]),
int(param_dict["starthour"]),
int(param_dict["startmin"]),
int(param_dict["startsec"]),
int(param_dict["startmillisec"]) * 1000,
timezone.utc)
# Note that the time zone information is not stored in the Curry info
# file, and it seems the start time info is in the local timezone
# of the acquisition system (which is unknown); therefore, just set
# the timezone to be UTC. If the user knows otherwise, they can
# change it later. (Some Curry files might include StartOffsetUTCMin,
# but its presence is unpredictable, so we won't rely on it.)
except (ValueError, KeyError):
dt_start = None # if missing keywords or illegal values, don't set
if time_step == 0:
true_sfreq = sfreq
elif sfreq == 0:
true_sfreq = 1 / time_step
elif not np.isclose(sfreq, 1 / time_step):
raise ValueError(_msg_match)
else: # they're equal and != 0
true_sfreq = sfreq
if true_sfreq <= 0:
raise ValueError(_msg_invalid.format(true_sfreq))
return CurryParameters(n_samples, true_sfreq, is_ascii, unit_dict,
n_channels, dt_start, chanidx_in_file)
def _read_curry_info(curry_paths):
"""Extract info from curry parameter files."""
curry_params = _read_curry_parameters(curry_paths['info'])
R = np.eye(4)
R[[0, 1], [0, 1]] = -1 # rotate 180 deg
# shift down and back
# (chosen by eyeballing to make the CTF helmet look roughly correct)
R[:3, 3] = [0., -0.015, -0.12]
curry_dev_dev_t = Transform('ctf_meg', 'meg', R)
# read labels from label files
label_fname = curry_paths['labels']
types = ["meg", "eeg", "misc"]
labels = _read_curry_lines(label_fname,
["LABELS" + CHANTYPES[key] for key in types])
sensors = _read_curry_lines(label_fname,
["SENSORS" + CHANTYPES[key] for key in types])
normals = _read_curry_lines(label_fname,
['NORMALS' + CHANTYPES[key] for key in types])
assert len(labels) == len(sensors) == len(normals)
all_chans = list()
for key in ["meg", "eeg", "misc"]:
chanidx_is_explicit = (len(curry_params.chanidx_in_file["CHAN_IN_FILE"
+ CHANTYPES[key]]) > 0) # channel index
# position in the datafile may or may not be explicitly declared,
# based on the CHAN_IN_FILE section in info file
for ind, chan in enumerate(labels["LABELS" + CHANTYPES[key]]):
chanidx = len(all_chans) + 1 # by default, just assume the
# channel index in the datafile is in order of the channel
# names as we found them in the labels file
if chanidx_is_explicit: # but, if explicitly declared, use
# that index number
chanidx = int(curry_params.chanidx_in_file["CHAN_IN_FILE"
+ CHANTYPES[key]][ind])
if chanidx <= 0: # if chanidx was explicitly declared to be ' 0',
# it means the channel is not actually saved in the data file
# (e.g. the "Ref" channel), so don't add it to our list.
# Git issue #8391
continue
ch = {"ch_name": chan,
"unit": curry_params.unit_dict[key],
"kind": FIFFV_CHANTYPES[key],
"coil_type": FIFFV_COILTYPES[key],
"ch_idx": chanidx
}
if key == "eeg":
loc = np.array(sensors["SENSORS" + CHANTYPES[key]][ind], float)
# XXX just the sensor, where is ref (next 3)?
assert loc.shape == (3,)
loc /= 1000. # to meters
loc = np.concatenate([loc, np.zeros(9)])
ch['loc'] = loc
# XXX need to check/ensure this
ch['coord_frame'] = FIFF.FIFFV_COORD_HEAD
elif key == 'meg':
pos = np.array(sensors["SENSORS" + CHANTYPES[key]][ind], float)
pos /= 1000. # to meters
pos = pos[:3] # just the inner coil
pos = apply_trans(curry_dev_dev_t, pos)
nn = np.array(normals["NORMALS" + CHANTYPES[key]][ind], float)
assert np.isclose(np.linalg.norm(nn), 1., atol=1e-4)
nn /= np.linalg.norm(nn)
nn = apply_trans(curry_dev_dev_t, nn, move=False)
trans = np.eye(4)
trans[:3, 3] = pos
trans[:3, :3] = _normal_orth(nn).T
ch['loc'] = _coil_trans_to_loc(trans)
ch['coord_frame'] = FIFF.FIFFV_COORD_DEVICE
all_chans.append(ch)
ch_count = len(all_chans)
assert (ch_count == curry_params.n_chans) # ensure that we have assembled
# the same number of channels as declared in the info (.DAP) file in the
# DATA_PARAMETERS section. Git issue #8391
# sort the channels to assure they are in the order that matches how
# recorded in the datafile. In general they most likely are already in
# the correct order, but if the channel index in the data file was
# explicitly declared we might as well use it.
all_chans = sorted(all_chans, key=lambda ch: ch['ch_idx'])
ch_names = [chan["ch_name"] for chan in all_chans]
info = create_info(ch_names, curry_params.sfreq)
with info._unlock():
info['meas_date'] = curry_params.dt_start # for Git issue #8398
_make_trans_dig(curry_paths, info, curry_dev_dev_t)
for ind, ch_dict in enumerate(info["chs"]):
all_chans[ind].pop('ch_idx')
ch_dict.update(all_chans[ind])
assert ch_dict['loc'].shape == (12,)
ch_dict['unit'] = SI_UNITS[all_chans[ind]['unit'][1]]
ch_dict['cal'] = SI_UNIT_SCALE[all_chans[ind]['unit'][0]]
return info, curry_params.n_samples, curry_params.is_ascii
_card_dict = {'Left ear': FIFF.FIFFV_POINT_LPA,
'Nasion': FIFF.FIFFV_POINT_NASION,
'Right ear': FIFF.FIFFV_POINT_RPA}
def _make_trans_dig(curry_paths, info, curry_dev_dev_t):
# Coordinate frame transformations and definitions
no_msg = 'Leaving device<->head transform as None'
info['dev_head_t'] = None
label_fname = curry_paths['labels']
key = 'LANDMARKS' + CHANTYPES['meg']
lm = _read_curry_lines(label_fname, [key])[key]
lm = np.array(lm, float)
lm.shape = (-1, 3)
if len(lm) == 0:
# no dig
logger.info(no_msg + ' (no landmarks found)')
return
lm /= 1000.
key = 'LM_REMARKS' + CHANTYPES['meg']
remarks = _read_curry_lines(label_fname, [key])[key]
assert len(remarks) == len(lm)
with info._unlock():
info['dig'] = list()
cards = dict()
for remark, r in zip(remarks, lm):
kind = ident = None
if remark in _card_dict:
kind = FIFF.FIFFV_POINT_CARDINAL
ident = _card_dict[remark]
cards[ident] = r
elif remark.startswith('HPI'):
kind = FIFF.FIFFV_POINT_HPI
ident = int(remark[3:]) - 1
if kind is not None:
info['dig'].append(dict(
kind=kind, ident=ident, r=r,
coord_frame=FIFF.FIFFV_COORD_UNKNOWN))
with info._unlock():
info['dig'].sort(key=lambda x: (x['kind'], x['ident']))
has_cards = len(cards) == 3
has_hpi = 'hpi' in curry_paths
if has_cards and has_hpi: # have all three
logger.info('Composing device<->head transformation from dig points')
hpi_u = np.array([d['r'] for d in info['dig']
if d['kind'] == FIFF.FIFFV_POINT_HPI], float)
hpi_c = np.ascontiguousarray(
_first_hpi(curry_paths['hpi'])[:len(hpi_u), 1:4])
unknown_curry_t = _quaternion_align(
'unknown', 'ctf_meg', hpi_u, hpi_c, 1e-2)
angle = np.rad2deg(_angle_between_quats(
np.zeros(3), rot_to_quat(unknown_curry_t['trans'][:3, :3])))
dist = 1000 * np.linalg.norm(unknown_curry_t['trans'][:3, 3])
logger.info(' Fit a %0.1f° rotation, %0.1f mm translation'
% (angle, dist))
unknown_dev_t = combine_transforms(
unknown_curry_t, curry_dev_dev_t, 'unknown', 'meg')
unknown_head_t = Transform(
'unknown', 'head',
get_ras_to_neuromag_trans(
*(cards[key] for key in (FIFF.FIFFV_POINT_NASION,
FIFF.FIFFV_POINT_LPA,
FIFF.FIFFV_POINT_RPA))))
with info._unlock():
info['dev_head_t'] = combine_transforms(
invert_transform(unknown_dev_t), unknown_head_t, 'meg', 'head')
for d in info['dig']:
d.update(coord_frame=FIFF.FIFFV_COORD_HEAD,
r=apply_trans(unknown_head_t, d['r']))
else:
if has_cards:
no_msg += ' (no .hpi file found)'
elif has_hpi:
no_msg += ' (not all cardinal points found)'
else:
no_msg += ' (neither cardinal points nor .hpi file found)'
logger.info(no_msg)
def _first_hpi(fname):
# Get the first HPI result
with open(fname, 'r') as fid:
for line in fid:
line = line.strip()
if any(x in line for x in ('FileVersion', 'NumCoils')) or not line:
continue
hpi = np.array(line.split(), float)
break
else:
raise RuntimeError('Could not find valid HPI in %s' % (fname,))
# t is the first entry
assert hpi.ndim == 1
hpi = hpi[1:]
hpi.shape = (-1, 5)
hpi /= 1000.
return hpi
def _read_events_curry(fname):
"""Read events from Curry event files.
Parameters
----------
fname : str
Path to a curry event file with extensions .cef, .ceo,
.cdt.cef, or .cdt.ceo
Returns
-------
events : ndarray, shape (n_events, 3)
The array of events.
"""
check_fname(fname, 'curry event', ('.cef', '.ceo', '.cdt.cef', '.cdt.ceo'),
endings_err=('.cef', '.ceo', '.cdt.cef', '.cdt.ceo'))
events_dict = _read_curry_lines(fname, ["NUMBER_LIST"])
# The first 3 column seem to contain the event information
curry_events = np.array(events_dict["NUMBER_LIST"], dtype=int)[:, 0:3]
return curry_events
def _read_annotations_curry(fname, sfreq='auto'):
r"""Read events from Curry event files.
Parameters
----------
fname : str
The filename.
sfreq : float | 'auto'
The sampling frequency in the file. If set to 'auto' then the
``sfreq`` is taken from the respective info file of the same name with
according file extension (\*.dap for Curry 7; \*.cdt.dpa for Curry8).
So data.cef looks in data.dap and data.cdt.cef looks in data.cdt.dpa.
Returns
-------
annot : instance of Annotations | None
The annotations.
"""
required = ["events", "info"] if sfreq == 'auto' else ["events"]
curry_paths = _get_curry_file_structure(fname, required)
events = _read_events_curry(curry_paths['events'])
if sfreq == 'auto':
sfreq = _read_curry_parameters(curry_paths['info']).sfreq
onset = events[:, 0] / sfreq
duration = np.zeros(events.shape[0])
description = events[:, 2]
return Annotations(onset, duration, description)
@verbose
def read_raw_curry(fname, preload=False, verbose=None):
"""Read raw data from Curry files.
Parameters
----------
fname : str
Path to a curry file with extensions .dat, .dap, .rs3, .cdt, cdt.dpa,
.cdt.cef or .cef.
%(preload)s
%(verbose)s
Returns
-------
raw : instance of RawCurry
A Raw object containing Curry data.
"""
return RawCurry(fname, preload, verbose)
class RawCurry(BaseRaw):
"""Raw object from Curry file.
Parameters
----------
fname : str
Path to a curry file with extensions .dat, .dap, .rs3, .cdt, cdt.dpa,
.cdt.cef or .cef.
%(preload)s
%(verbose)s
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
@verbose
def __init__(self, fname, preload=False, verbose=None):
curry_paths = _get_curry_file_structure(
fname, required=["info", "data", "labels"])
data_fname = op.abspath(curry_paths['data'])
info, n_samples, is_ascii = _read_curry_info(curry_paths)
last_samps = [n_samples - 1]
raw_extras = dict(is_ascii=is_ascii)
super(RawCurry, self).__init__(
info, preload, filenames=[data_fname], last_samps=last_samps,
orig_format='int', raw_extras=[raw_extras], verbose=verbose)
if 'events' in curry_paths:
logger.info('Event file found. Extracting Annotations from'
' %s...' % curry_paths['events'])
annots = _read_annotations_curry(curry_paths['events'],
sfreq=self.info["sfreq"])
self.set_annotations(annots)
else:
logger.info('Event file not found. No Annotations set.')
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
"""Read a chunk of raw data."""
if self._raw_extras[fi]['is_ascii']:
if isinstance(idx, slice):
idx = np.arange(idx.start, idx.stop)
kwargs = dict(skiprows=start, usecols=idx)
if check_version("numpy", "1.16.0"):
kwargs['max_rows'] = stop - start
else:
warn("Data reading might take longer for ASCII files. Update "
"numpy to version 1.16.0 or greater for more efficient "
"data reading.")
block = np.loadtxt(self._filenames[0], **kwargs)[:stop - start].T
data_view = data[:, :block.shape[1]]
_mult_cal_one(data_view, block, idx, cals, mult)
else:
_read_segments_file(
self, data, idx, fi, start, stop, cals, mult, dtype="<f4")