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tyler
2025-08-19 09:13:22 -07:00
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mne/io/artemis123/__init__.py Normal file
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"""artemis123 module for conversion to FIF."""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from .artemis123 import read_raw_artemis123

530
mne/io/artemis123/artemis123.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import calendar
import datetime
import os.path as op
import numpy as np
from scipy.spatial.distance import cdist
from ..._fiff._digitization import DigPoint, _make_dig_points
from ..._fiff.constants import FIFF
from ..._fiff.meas_info import _empty_info
from ..._fiff.utils import _read_segments_file
from ...transforms import Transform, apply_trans, get_ras_to_neuromag_trans
from ...utils import _check_fname, logger, verbose, warn
from ..base import BaseRaw
from .utils import _load_mne_locs, _read_pos
@verbose
def read_raw_artemis123(
input_fname, preload=False, verbose=None, pos_fname=None, add_head_trans=True
) -> "RawArtemis123":
"""Read Artemis123 data as raw object.
Parameters
----------
input_fname : path-like
Path to the data file (extension ``.bin``). The header file with the
same file name stem and an extension ``.txt`` is expected to be found
in the same directory.
%(preload)s
%(verbose)s
pos_fname : path-like | None
If not None, load digitized head points from this file.
add_head_trans : bool (default True)
If True attempt to perform initial head localization. Compute initial
device to head coordinate transform using HPI coils. If no
HPI coils are in info['dig'] hpi coils are assumed to be in canonical
order of fiducial points (nas, rpa, lpa).
Returns
-------
raw : instance of Raw
A Raw object containing the data.
See Also
--------
mne.io.Raw : Documentation of attributes and methods.
"""
return RawArtemis123(
input_fname,
preload=preload,
verbose=verbose,
pos_fname=pos_fname,
add_head_trans=add_head_trans,
)
def _get_artemis123_info(fname, pos_fname=None):
"""Generate info struct from artemis123 header file."""
fname = op.splitext(fname)[0]
header = fname + ".txt"
logger.info("Reading header...")
# key names for artemis channel info...
chan_keys = [
"name",
"scaling",
"FLL_Gain",
"FLL_Mode",
"FLL_HighPass",
"FLL_AutoReset",
"FLL_ResetLock",
]
header_info = dict()
header_info["filter_hist"] = []
header_info["comments"] = ""
header_info["channels"] = []
with open(header) as fid:
# section flag
# 0 - None
# 1 - main header
# 2 - channel header
# 3 - comments
# 4 - length
# 5 - filtering History
sectionFlag = 0
for line in fid:
# skip emptylines or header line for channel info
if (not line.strip()) or (sectionFlag == 2 and line.startswith("DAQ Map")):
continue
# set sectionFlag
if line.startswith("<end"):
sectionFlag = 0
elif line.startswith("<start main header>"):
sectionFlag = 1
elif line.startswith("<start per channel header>"):
sectionFlag = 2
elif line.startswith("<start comments>"):
sectionFlag = 3
elif line.startswith("<start length>"):
sectionFlag = 4
elif line.startswith("<start filtering history>"):
sectionFlag = 5
else:
# parse header info lines
# part of main header - lines are name value pairs
if sectionFlag == 1:
values = line.strip().split("\t")
if len(values) == 1:
values.append("")
header_info[values[0]] = values[1]
# part of channel header - lines are Channel Info
elif sectionFlag == 2:
values = line.strip().split("\t")
if len(values) != 7:
raise OSError(
f"Error parsing line \n\t:{line}\nfrom file {header}"
)
tmp = dict()
for k, v in zip(chan_keys, values):
tmp[k] = v
header_info["channels"].append(tmp)
elif sectionFlag == 3:
header_info["comments"] = f"{header_info['comments']}{line.strip()}"
elif sectionFlag == 4:
header_info["num_samples"] = int(line.strip())
elif sectionFlag == 5:
header_info["filter_hist"].append(line.strip())
for k in [
"Temporal Filter Active?",
"Decimation Active?",
"Spatial Filter Active?",
]:
if header_info[k] != "FALSE":
warn(f"{k} - set to but is not supported")
if header_info["filter_hist"]:
warn("Non-Empty Filter history found, BUT is not supported")
# build mne info struct
info = _empty_info(float(header_info["DAQ Sample Rate"]))
# Attempt to get time/date from fname
# Artemis123 files saved from the scanner observe the following
# naming convention 'Artemis_Data_YYYY-MM-DD-HHh-MMm_[chosen by user].bin'
try:
date = datetime.datetime.strptime(
op.basename(fname).split("_")[2], "%Y-%m-%d-%Hh-%Mm"
)
meas_date = (calendar.timegm(date.utctimetuple()), 0)
except Exception:
meas_date = None
# build subject info must be an integer (as per FIFF)
try:
subject_info = {"id": int(header_info["Subject ID"])}
except ValueError:
subject_info = {"id": 0}
# build description
desc = ""
for k in ["Purpose", "Notes"]:
desc += f"{k} : {header_info[k]}\n"
desc += f"Comments : {header_info['comments']}"
info.update(
{
"meas_date": meas_date,
"description": desc,
"subject_info": subject_info,
"proj_name": header_info["Project Name"],
}
)
# Channel Names by type
ref_mag_names = ["REF_001", "REF_002", "REF_003", "REF_004", "REF_005", "REF_006"]
ref_grad_names = ["REF_007", "REF_008", "REF_009", "REF_010", "REF_011", "REF_012"]
# load mne loc dictionary
loc_dict = _load_mne_locs()
info["chs"] = []
bads = []
for i, chan in enumerate(header_info["channels"]):
# build chs struct
t = {
"cal": float(chan["scaling"]),
"ch_name": chan["name"],
"logno": i + 1,
"scanno": i + 1,
"range": 1.0,
"unit_mul": FIFF.FIFF_UNITM_NONE,
"coord_frame": FIFF.FIFFV_COORD_DEVICE,
}
# REF_018 has a zero cal which can cause problems. Let's set it to
# a value of another ref channel to make writers/readers happy.
if t["cal"] == 0:
t["cal"] = 4.716e-10
bads.append(t["ch_name"])
t["loc"] = loc_dict.get(chan["name"], np.zeros(12))
if chan["name"].startswith("MEG"):
t["coil_type"] = FIFF.FIFFV_COIL_ARTEMIS123_GRAD
t["kind"] = FIFF.FIFFV_MEG_CH
# While gradiometer units are T/m, the meg sensors referred to as
# gradiometers report the field difference between 2 pick-up coils.
# Therefore the units of the measurements should be T
# *AND* the baseline (difference between pickup coils)
# should not be used in leadfield / forwardfield computations.
t["unit"] = FIFF.FIFF_UNIT_T
t["unit_mul"] = FIFF.FIFF_UNITM_F
# 3 axis reference magnetometers
elif chan["name"] in ref_mag_names:
t["coil_type"] = FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG
t["kind"] = FIFF.FIFFV_REF_MEG_CH
t["unit"] = FIFF.FIFF_UNIT_T
t["unit_mul"] = FIFF.FIFF_UNITM_F
# reference gradiometers
elif chan["name"] in ref_grad_names:
t["coil_type"] = FIFF.FIFFV_COIL_ARTEMIS123_REF_GRAD
t["kind"] = FIFF.FIFFV_REF_MEG_CH
# While gradiometer units are T/m, the meg sensors referred to as
# gradiometers report the field difference between 2 pick-up coils.
# Therefore the units of the measurements should be T
# *AND* the baseline (difference between pickup coils)
# should not be used in leadfield / forwardfield computations.
t["unit"] = FIFF.FIFF_UNIT_T
t["unit_mul"] = FIFF.FIFF_UNITM_F
# other reference channels are unplugged and should be ignored.
elif chan["name"].startswith("REF"):
t["coil_type"] = FIFF.FIFFV_COIL_NONE
t["kind"] = FIFF.FIFFV_MISC_CH
t["unit"] = FIFF.FIFF_UNIT_V
bads.append(t["ch_name"])
elif chan["name"].startswith(("AUX", "TRG", "MIO")):
t["coil_type"] = FIFF.FIFFV_COIL_NONE
t["unit"] = FIFF.FIFF_UNIT_V
if chan["name"].startswith("TRG"):
t["kind"] = FIFF.FIFFV_STIM_CH
else:
t["kind"] = FIFF.FIFFV_MISC_CH
else:
raise ValueError(
f'Channel does not match expected channel Types:"{chan["name"]}"'
)
# incorporate multiplier (unit_mul) into calibration
t["cal"] *= 10 ** t["unit_mul"]
t["unit_mul"] = FIFF.FIFF_UNITM_NONE
# append this channel to the info
info["chs"].append(t)
if chan["FLL_ResetLock"] == "TRUE":
bads.append(t["ch_name"])
# HPI information
# print header_info.keys()
hpi_sub = dict()
# Don't know what event_channel is don't think we have it HPIs are either
# always on or always off.
# hpi_sub['event_channel'] = ???
hpi_sub["hpi_coils"] = [dict(), dict(), dict(), dict()]
hpi_coils = [dict(), dict(), dict(), dict()]
drive_channels = ["MIO_001", "MIO_003", "MIO_009", "MIO_011"]
key_base = "Head Tracking %s %d"
# set default HPI frequencies
if info["sfreq"] == 1000:
default_freqs = [140, 150, 160, 40]
else:
default_freqs = [700, 750, 800, 40]
for i in range(4):
# build coil structure
hpi_coils[i]["number"] = i + 1
hpi_coils[i]["drive_chan"] = drive_channels[i]
this_freq = header_info.pop(key_base % ("Frequency", i + 1), default_freqs[i])
hpi_coils[i]["coil_freq"] = this_freq
# check if coil is on
if header_info[key_base % ("Channel", i + 1)] == "OFF":
hpi_sub["hpi_coils"][i]["event_bits"] = [0]
else:
hpi_sub["hpi_coils"][i]["event_bits"] = [256]
info["hpi_subsystem"] = hpi_sub
info["hpi_meas"] = [{"hpi_coils": hpi_coils}]
# read in digitized points if supplied
if pos_fname is not None:
info["dig"] = _read_pos(pos_fname)
else:
info["dig"] = []
info._unlocked = False
info._update_redundant()
# reduce info['bads'] to unique set
info["bads"] = list(set(bads))
del bads
return info, header_info
class RawArtemis123(BaseRaw):
"""Raw object from Artemis123 file.
Parameters
----------
input_fname : path-like
Path to the Artemis123 data file (ending in ``'.bin'``).
%(preload)s
%(verbose)s
See Also
--------
mne.io.Raw : Documentation of attributes and methods.
"""
@verbose
def __init__(
self,
input_fname,
preload=False,
verbose=None,
pos_fname=None,
add_head_trans=True,
):
from ...chpi import (
_fit_coil_order_dev_head_trans,
compute_chpi_amplitudes,
compute_chpi_locs,
)
input_fname = str(_check_fname(input_fname, "read", True, "input_fname"))
fname, ext = op.splitext(input_fname)
if ext == ".txt":
input_fname = fname + ".bin"
elif ext != ".bin":
raise RuntimeError(
'Valid artemis123 files must end in "txt"' + ' or ".bin".'
)
if not op.exists(input_fname):
raise RuntimeError(f"{input_fname} - Not Found")
info, header_info = _get_artemis123_info(input_fname, pos_fname=pos_fname)
last_samps = [header_info.get("num_samples", 1) - 1]
super().__init__(
info,
preload,
filenames=[input_fname],
raw_extras=[header_info],
last_samps=last_samps,
orig_format="single",
verbose=verbose,
)
if add_head_trans:
n_hpis = 0
for d in info["hpi_subsystem"]["hpi_coils"]:
if d["event_bits"] == [256]:
n_hpis += 1
if n_hpis < 3:
warn(
f"{n_hpis:d} HPIs active. At least 3 needed to perform"
"head localization\n *NO* head localization performed"
)
else:
# Localized HPIs using the 1st 250 milliseconds of data.
with info._unlock():
info["hpi_results"] = [
dict(
dig_points=[
dict(
r=np.zeros(3),
coord_frame=FIFF.FIFFV_COORD_DEVICE,
ident=ii + 1,
)
for ii in range(n_hpis)
],
coord_trans=Transform("meg", "head"),
)
]
coil_amplitudes = compute_chpi_amplitudes(
self, tmin=0, tmax=0.25, t_window=0.25, t_step_min=0.25
)
assert len(coil_amplitudes["times"]) == 1
coil_locs = compute_chpi_locs(self.info, coil_amplitudes)
with info._unlock():
info["hpi_results"] = None
hpi_g = coil_locs["gofs"][0]
hpi_dev = coil_locs["rrs"][0]
# only use HPI coils with localizaton goodness_of_fit > 0.98
bad_idx = []
for i, g in enumerate(hpi_g):
msg = f"HPI coil {i + 1} - location goodness of fit ({g:0.3f})"
if g < 0.98:
bad_idx.append(i)
msg += " *Removed from coregistration*"
logger.info(msg)
hpi_dev = np.delete(hpi_dev, bad_idx, axis=0)
hpi_g = np.delete(hpi_g, bad_idx, axis=0)
if pos_fname is not None:
# Digitized HPI points are needed.
hpi_head = np.array(
[
d["r"]
for d in self.info.get("dig", [])
if d["kind"] == FIFF.FIFFV_POINT_HPI
]
)
if len(hpi_head) != len(hpi_dev):
raise RuntimeError(
f"number of digitized ({len(hpi_head)}) and active "
f"({len(hpi_dev)}) HPI coils are not the same."
)
# compute initial head to dev transform and hpi ordering
head_to_dev_t, order, trans_g = _fit_coil_order_dev_head_trans(
hpi_dev, hpi_head
)
# set the device to head transform
self.info["dev_head_t"] = Transform(
FIFF.FIFFV_COORD_DEVICE, FIFF.FIFFV_COORD_HEAD, head_to_dev_t
)
# add hpi_meg_dev to dig...
for idx, point in enumerate(hpi_dev):
d = {
"r": point,
"ident": idx + 1,
"kind": FIFF.FIFFV_POINT_HPI,
"coord_frame": FIFF.FIFFV_COORD_DEVICE,
}
self.info["dig"].append(DigPoint(d))
dig_dists = cdist(hpi_head[order], hpi_head[order])
dev_dists = cdist(hpi_dev, hpi_dev)
tmp_dists = np.abs(dig_dists - dev_dists)
dist_limit = tmp_dists.max() * 1.1
logger.info(
"HPI-Dig corrregsitration\n"
f"\tGOF : {trans_g:0.3f}\n"
f"\tMax Coil Error : {100 * tmp_dists.max():0.3f} cm\n"
)
else:
logger.info("Assuming Cardinal HPIs")
nas = hpi_dev[0]
lpa = hpi_dev[2]
rpa = hpi_dev[1]
t = get_ras_to_neuromag_trans(nas, lpa, rpa)
with self.info._unlock():
self.info["dev_head_t"] = Transform(
FIFF.FIFFV_COORD_DEVICE, FIFF.FIFFV_COORD_HEAD, t
)
# transform fiducial points
nas = apply_trans(t, nas)
lpa = apply_trans(t, lpa)
rpa = apply_trans(t, rpa)
hpi = apply_trans(self.info["dev_head_t"], hpi_dev)
with self.info._unlock():
self.info["dig"] = _make_dig_points(
nasion=nas, lpa=lpa, rpa=rpa, hpi=hpi
)
order = np.array([0, 1, 2])
dist_limit = 0.005
# fill in hpi_results
hpi_result = dict()
# add HPI points in device coords...
dig = []
for idx, point in enumerate(hpi_dev):
dig.append(
{
"r": point,
"ident": idx + 1,
"kind": FIFF.FIFFV_POINT_HPI,
"coord_frame": FIFF.FIFFV_COORD_DEVICE,
}
)
hpi_result["dig_points"] = dig
# attach Transform
hpi_result["coord_trans"] = self.info["dev_head_t"]
# 1 based indexing
hpi_result["order"] = order + 1
hpi_result["used"] = np.arange(3) + 1
hpi_result["dist_limit"] = dist_limit
hpi_result["good_limit"] = 0.98
# Warn for large discrepancies between digitized and fit
# cHPI locations
if hpi_result["dist_limit"] > 0.005:
warn(
"Large difference between digitized geometry"
" and HPI geometry. Max coil to coil difference"
f" is {100.0 * tmp_dists.max():0.2f} cm\n"
"beware of *POOR* head localization"
)
# store it
with self.info._unlock():
self.info["hpi_results"] = [hpi_result]
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
"""Read a chunk of raw data."""
_read_segments_file(self, data, idx, fi, start, stop, cals, mult, dtype=">f4")

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mne/io/artemis123/resources/Artemis123_ChannelMap.csv Normal file
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name,Channel Type,CAD X+ (INCH),CAD Y+ (INCH),CAD Z+ (INCH),CAD X- (INCH),CAD Y- (INCH),CAD Z- (INCH)
Derived from '90-0395 Channel Map for 6th cooldown 2-01-13.xls',,,,,,,
MEG_059,MEG_GRAD,-1.97677,1.56552,2.91489,-4.18768,2.50074,5.40664
MEG_045,MEG_GRAD,-1.61144,0.93037,3.41137,-3.33479,1.92534,6.24186
MEG_029,MEG_GRAD,-0.91075,1.72387,3.473,-1.93587,2.72988,6.62081
MEG_073,MEG_GRAD,-2.38955,0.86972,2.76491,-4.94504,1.79985,4.90406
MEG_043,MEG_GRAD,-1.59926,2.33243,2.93122,-3.46787,3.39595,5.64209
MEG_085,MEG_GRAD,-2.78631,1.40783,1.84839,-5.89386,2.21359,3.13893
REF_013,UNUSED,,,,,,
MEG_071,MEG_GRAD,-2.43321,2.17533,2.12153,-5.27622,3.05529,3.88634
MEG_032,MEG_GRAD,0.93037,-1.61144,3.41137,1.92534,-3.33479,6.24186
MEG_048,MEG_GRAD,1.27145,-2.20222,2.76491,2.6312,-4.55737,4.90406
MEG_018,MEG_GRAD,0.44157,-2.50427,2.76491,0.91381,-5.18245,4.90406
MEG_006,MEG_GRAD,0,-3.0105,1.94967,0,-6.23006,3.21696
MEG_005,MEG_GRAD,0,-1.86073,3.41137,0,-3.85068,6.24186
MEG_049,MEG_GRAD,-1.27145,-2.20222,2.76491,-2.6312,-4.55737,4.90406
MEG_019,MEG_GRAD,-0.44157,-2.50427,2.76491,-0.91381,-5.18245,4.90406
MEG_033,MEG_GRAD,-0.93037,-1.61144,3.41137,-1.92534,-3.33479,6.24186
MEG_021,MEG_GRAD,-0.56074,-3.168,1.10519,-1.13708,-6.39559,2.21066
MEG_020,MEG_GRAD,0.56022,-3.16809,1.10519,1.13604,-6.39578,2.21066
MEG_034,MEG_GRAD,1.02965,-2.82894,1.94967,2.13081,-5.85434,3.21696
MEG_077,MEG_GRAD,-2.47272,-2.0647,1.06346,-5.01426,-4.15829,2.12604
MEG_035,MEG_GRAD,-1.02965,-2.82894,1.94967,-2.13081,-5.85434,3.21696
MEG_007,MEG_GRAD,0,-3.27147,0.25764,0,-6.63351,1.0751
MEG_023,MEG_GRAD,-0.576,-3.27431,-0.5962,-1.16503,-6.58484,0.21931
MEG_022,MEG_GRAD,0.56022,-3.27709,-0.59609,1.14872,-6.58771,0.21942
MEG_047,MEG_GRAD,-1.61144,-0.93037,3.41137,-3.33479,-1.92534,6.24186
MEG_061,MEG_GRAD,-1.86073,0,3.41137,-3.85068,0,6.24186
MEG_087,MEG_GRAD,-2.5429,0,2.76491,-5.2624,0,4.90406
MEG_113,MEG_GRAD,-3.22769,0.0086,0.98505,-6.5452,0.046,1.96703
MEG_101,MEG_GRAD,-2.96476,-0.52277,1.94967,-6.13541,-1.08184,3.21696
MEG_099,MEG_GRAD,-2.96476,0.52277,1.94967,-6.13541,1.08184,3.21696
MEG_063,MEG_GRAD,-1.94798,-1.63455,2.76491,-4.03123,-3.38261,4.90406
MEG_075,MEG_GRAD,-2.38955,-0.86972,2.76491,-4.94504,-1.79985,4.90406
MEG_089,MEG_GRAD,-2.60717,-1.50525,1.94967,-5.39539,-3.11503,3.21696
MEG_123,MEG_GRAD,-3.24454,-0.65992,-1.54654,-6.63007,-1.24165,-1.13258
MEG_103,MEG_GRAD,-3.03312,-1.09456,1.02677,-6.15066,-2.19102,2.05164
MEG_119,MEG_GRAD,-3.27163,-0.04807,-0.71822,-6.66217,-0.02172,-0.02891
MEG_121,MEG_GRAD,-3.24454,0.48346,-1.58979,-6.63007,1.0948,-1.22095
MEG_105,MEG_GRAD,-3.07707,-1.16672,-0.67591,-6.26323,-2.29919,0.05723
MEG_091,MEG_GRAD,-2.81455,-1.64764,0.19622,-5.75085,-3.31563,0.94961
MEG_115,MEG_GRAD,-3.20059,-0.58777,0.15614,-6.53962,-1.15004,0.86771
MEG_037,MEG_GRAD,-1.11155,-3.07561,0.25023,-2.27119,-6.23333,1.05996
MEG_067,MEG_GRAD,-2.08904,-2.51166,0.2289,-4.26844,-5.08104,1.01638
MEG_079,MEG_GRAD,-2.51137,-2.1514,-0.63867,-5.10885,-4.30296,0.13301
MEG_093,MEG_GRAD,-2.8532,-1.73435,-1.50591,-5.7542,-3.37531,-0.57691
MEG_051,MEG_GRAD,-1.61407,-2.7848,1.0907,-3.27306,-5.61852,2.18127
MEG_065,MEG_GRAD,-1.93511,-2.30617,1.94967,-4.00461,-4.7725,3.21696
REF_014,UNUSED,,,,,,
MEG_053,MEG_GRAD,-1.64275,-2.88336,-0.61098,-3.33826,-5.79135,0.1893
MEG_039,MEG_GRAD,-1.37821,4.03301,0.38766,-2.98972,7.09471,0.3625
MEG_041,MEG_GRAD,-1.59926,3.67934,1.66789,-3.46787,6.31662,2.90266
MEG_055,MEG_GRAD,-2.06278,3.53364,0.8475,-4.47296,6.00069,1.12372
MEG_069,MEG_GRAD,-2.43321,2.88136,1.45931,-5.27622,4.58626,2.45038
MEG_027,MEG_GRAD,-1.02514,3.32279,2.63742,-2.22293,5.54346,5.00502
MEG_025,MEG_GRAD,-0.92333,4.17235,1.20548,-2.00217,7.38566,1.89996
MEG_057,MEG_GRAD,-1.84667,3.00588,2.29955,-4.00435,4.85628,4.27238
REF_015,UNUSED,,,,,,
MEG_083,MEG_GRAD,-2.81067,2.32514,1.52142,-6.13327,3.15736,2.01067
MEG_095,MEG_GRAD,-2.85632,2.16654,0.82155,-6.24599,2.85761,0.88605
MEG_117,MEG_GRAD,-3.14455,0.87829,-0.52294,-6.53422,1.56936,-0.45844
MEG_109,MEG_GRAD,-3.0226,1.3925,0.37679,-6.41227,2.08357,0.44129
MEG_107,MEG_GRAD,-2.7791,2.44789,0.19401,-6.01824,3.66345,0.23867
MEG_111,MEG_GRAD,-3.20059,0.54013,0.11348,-6.53962,1.15454,0.78055
MEG_097,MEG_GRAD,-3.04326,1.22292,1.10768,-6.3884,1.94226,1.62169
MEG_081,MEG_GRAD,-2.54021,2.92425,0.68688,-5.5195,4.68347,0.71098
REF_001,REF_MAG,-2.26079604,3.98626183,5.04439808,-2.20703425,3.92437924,4.93090704
REF_002,REF_MAG,1.93013445,4.03046866,5.17689263,1.8763992,3.96852956,5.06341985
REF_004,REF_MAG,1.70031266,4.21202221,5.57217923,1.57144014,4.22797498,5.62449924
REF_012,REF_GRAD,4.64675,-0.89642,-0.43802,6.03162,-1.01804,-0.22614
REF_006,REF_MAG,2.07781,3.83073028,5.60154279,2.08802749,3.70619491,5.66468189
REF_008,REF_GRAD,4.50056,0.78066,1.76423,5.88573,0.92199,1.96135
REF_010,REF_GRAD,4.31926,2.18698,-0.37055,5.69806,2.46181,-0.34022
MEG_094,REF_GRAD,2.85632,2.16654,0.82155,6.24599,2.85761,0.88605
REF_016,UNUSED,,,,,,
REF_003,REF_MAG,-2.73073962,4.07852721,5.1569653,-2.8596759,4.06162797,5.1051015
REF_017,UNUSED,,,,,,
REF_011,REF_GRAD,-4.64675,-0.89642,-0.43802,-6.03162,-1.01804,-0.22614
REF_009,REF_GRAD,-4.31926,2.18698,-0.37055,-5.69806,2.46181,-0.34022
REF_007,REF_GRAD,-4.50056,0.78066,1.76423,-5.88573,0.92199,1.96135
REF_018,UNUSED,,,,,,
REF_005,REF_MAG,-2.4058382,3.78665997,5.47001894,-2.41506358,3.66222139,5.53350068
MEG_090,MEG_GRAD,2.81455,-1.64764,0.19622,5.75085,-3.31563,0.94961
MEG_088,MEG_GRAD,2.60717,-1.50525,1.94967,5.39539,-3.11503,3.21696
MEG_102,MEG_GRAD,3.03294,-1.09506,1.02679,6.1503,-2.19202,2.05167
MEG_122,MEG_GRAD,3.24454,-0.65992,-1.54654,6.63007,-1.24165,-1.13258
MEG_114,MEG_GRAD,3.20059,-0.58777,0.15614,6.53962,-1.15004,0.86771
MEG_104,MEG_GRAD,3.07159,-1.18176,-0.67534,6.25756,-2.31475,0.05782
MEG_120,MEG_GRAD,3.24454,0.48346,-1.58979,6.63007,1.0948,-1.22094
MEG_118,MEG_GRAD,3.27163,-0.06408,-0.71761,6.66217,-0.03828,-0.02828
MEG_106,MEG_GRAD,2.7791,2.44789,0.19401,6.01824,3.66345,0.23867
MEG_082,MEG_GRAD,2.81067,2.32514,1.52142,6.13327,3.15736,2.01067
MEG_110,MEG_GRAD,3.20059,0.54013,0.11348,6.53962,1.15454,0.78055
MEG_116,MEG_GRAD,3.14455,0.87829,-0.52294,6.53422,1.56936,-0.45844
MEG_096,MEG_GRAD,3.04326,1.22292,1.10768,6.3884,1.94226,1.62169
MEG_080,MEG_GRAD,2.54021,2.92425,0.68688,5.5195,4.68347,0.71098
MEG_108,MEG_GRAD,3.0226,1.3925,0.37679,6.41227,2.08357,0.44129
REF_019,UNUSED,,,,,,
MEG_009,MEG_GRAD,-0.48824,4.32904,0.13976,-1.05817,7.74156,0.10133
MEG_003,MEG_GRAD,0,3.44805,2.77097,0,5.81508,5.29461
MEG_010,MEG_GRAD,0.51257,3.97032,2.03007,1.11147,6.94759,3.68802
MEG_012,MEG_GRAD,0.51257,2.67525,3.24478,1.11147,4.13933,6.32201
MEG_004,MEG_GRAD,0,4.3528,1.03622,0,7.77696,1.53295
MEG_011,MEG_GRAD,-0.51257,3.97032,2.03007,-1.11147,6.94759,3.68802
MEG_008,MEG_GRAD,0.48824,4.32904,0.13976,1.05817,7.74156,0.10133
MEG_013,MEG_GRAD,-0.51257,2.67525,3.24478,-1.11147,4.13933,6.32201
MEG_024,MEG_GRAD,0.92333,4.17235,1.20548,2.00217,7.38566,1.89996
REF_020,UNUSED,,,,,,
MEG_068,MEG_GRAD,2.43321,2.88136,1.45931,5.27622,4.58626,2.45038
MEG_026,MEG_GRAD,1.02514,3.32279,2.63742,2.22293,5.54346,5.00502
MEG_038,MEG_GRAD,1.37821,4.03301,0.38766,2.98972,7.09471,0.3625
MEG_040,MEG_GRAD,1.59926,3.67934,1.66789,3.46787,6.31662,2.90266
MEG_054,MEG_GRAD,2.06278,3.53364,0.8475,4.47296,6.00069,1.12372
MEG_056,MEG_GRAD,1.84667,3.00588,2.29955,4.00435,4.85628,4.27238
MEG_058,MEG_GRAD,2.00892,1.56358,2.88668,4.25593,2.49543,5.34722
MEG_042,MEG_GRAD,1.59926,2.33243,2.93122,3.46787,3.39595,5.64209
MEG_028,MEG_GRAD,0.90968,1.7238,3.47337,1.93358,2.72985,6.62156
MEG_070,MEG_GRAD,2.43321,2.17533,2.12153,5.27622,3.05529,3.88634
REF_021,UNUSED,,,,,,
MEG_072,MEG_GRAD,2.38955,0.86972,2.76491,4.94504,1.79985,4.90406
MEG_044,MEG_GRAD,1.61144,0.93037,3.41137,3.33479,1.92534,6.24186
MEG_084,MEG_GRAD,2.78632,1.40783,1.84839,5.89386,2.21359,3.13893
MEG_046,MEG_GRAD,1.61144,-0.93037,3.41137,3.33479,-1.92534,6.24186
MEG_098,MEG_GRAD,2.96476,0.52277,1.94967,6.13541,1.08184,3.21696
MEG_060,MEG_GRAD,1.8607,0,3.41137,3.85068,0,6.24186
MEG_100,MEG_GRAD,2.96476,-0.52277,1.94967,6.13541,-1.08184,3.21696
MEG_074,MEG_GRAD,2.38955,-0.86972,2.76491,4.94504,-1.79985,4.90406
MEG_086,MEG_GRAD,2.5429,0,2.76491,5.2624,0,4.90406
MEG_062,MEG_GRAD,1.94798,-1.63455,2.76491,4.03123,-3.38261,4.90406
MEG_112,MEG_GRAD,3.22769,0.00807,0.98507,6.5452,0.04494,1.96707
MEG_016,MEG_GRAD,0.50538,-0.87535,3.83752,0.89368,-1.5479,7.20924
MEG_031,MEG_GRAD,-1.01076,0,3.83752,-1.78736,0,7.20924
MEG_015,MEG_GRAD,-0.50538,0.87535,3.83752,-0.89368,1.5479,7.20924
MEG_001,MEG_GRAD,0,0,4,0,0,7.46
MEG_002,MEG_GRAD,0,1.80611,3.59215,0,2.82922,6.89743
MEG_017,MEG_GRAD,-0.50538,-0.87535,3.83752,-0.89368,-1.5479,7.20924
MEG_014,MEG_GRAD,0.50538,0.87535,3.83752,0.89368,1.5479,7.20924
MEG_030,MEG_GRAD,1.01076,0,3.83752,1.78736,0,7.20924
MEG_050,MEG_GRAD,1.61362,-2.78506,1.09071,3.27214,-5.61905,2.18129
MEG_064,MEG_GRAD,1.93511,-2.30617,1.94967,4.00461,-4.7725,3.21696
MEG_076,MEG_GRAD,2.47238,-2.0651,1.06348,5.01358,-4.1591,2.12607
MEG_078,MEG_GRAD,2.50107,-2.16367,-0.6382,5.0982,-4.31565,0.13349
MEG_066,MEG_GRAD,2.08904,-2.51166,0.2289,4.26844,-5.08104,1.01638
MEG_036,MEG_GRAD,1.11155,-3.07561,0.25023,2.27119,-6.23333,1.05996
MEG_052,MEG_GRAD,1.62888,-2.89137,-0.61068,3.32391,-5.79963,0.18962
MEG_092,MEG_GRAD,2.8532,-1.73435,-1.50591,5.7542,-3.37531,-0.57691
1 name Channel Type CAD X+ (INCH) CAD Y+ (INCH) CAD Z+ (INCH) CAD X- (INCH) CAD Y- (INCH) CAD Z- (INCH)
2 Derived from '90-0395 Channel Map for 6th cooldown 2-01-13.xls'
3 MEG_059 MEG_GRAD -1.97677 1.56552 2.91489 -4.18768 2.50074 5.40664
4 MEG_045 MEG_GRAD -1.61144 0.93037 3.41137 -3.33479 1.92534 6.24186
5 MEG_029 MEG_GRAD -0.91075 1.72387 3.473 -1.93587 2.72988 6.62081
6 MEG_073 MEG_GRAD -2.38955 0.86972 2.76491 -4.94504 1.79985 4.90406
7 MEG_043 MEG_GRAD -1.59926 2.33243 2.93122 -3.46787 3.39595 5.64209
8 MEG_085 MEG_GRAD -2.78631 1.40783 1.84839 -5.89386 2.21359 3.13893
9 REF_013 UNUSED
10 MEG_071 MEG_GRAD -2.43321 2.17533 2.12153 -5.27622 3.05529 3.88634
11 MEG_032 MEG_GRAD 0.93037 -1.61144 3.41137 1.92534 -3.33479 6.24186
12 MEG_048 MEG_GRAD 1.27145 -2.20222 2.76491 2.6312 -4.55737 4.90406
13 MEG_018 MEG_GRAD 0.44157 -2.50427 2.76491 0.91381 -5.18245 4.90406
14 MEG_006 MEG_GRAD 0 -3.0105 1.94967 0 -6.23006 3.21696
15 MEG_005 MEG_GRAD 0 -1.86073 3.41137 0 -3.85068 6.24186
16 MEG_049 MEG_GRAD -1.27145 -2.20222 2.76491 -2.6312 -4.55737 4.90406
17 MEG_019 MEG_GRAD -0.44157 -2.50427 2.76491 -0.91381 -5.18245 4.90406
18 MEG_033 MEG_GRAD -0.93037 -1.61144 3.41137 -1.92534 -3.33479 6.24186
19 MEG_021 MEG_GRAD -0.56074 -3.168 1.10519 -1.13708 -6.39559 2.21066
20 MEG_020 MEG_GRAD 0.56022 -3.16809 1.10519 1.13604 -6.39578 2.21066
21 MEG_034 MEG_GRAD 1.02965 -2.82894 1.94967 2.13081 -5.85434 3.21696
22 MEG_077 MEG_GRAD -2.47272 -2.0647 1.06346 -5.01426 -4.15829 2.12604
23 MEG_035 MEG_GRAD -1.02965 -2.82894 1.94967 -2.13081 -5.85434 3.21696
24 MEG_007 MEG_GRAD 0 -3.27147 0.25764 0 -6.63351 1.0751
25 MEG_023 MEG_GRAD -0.576 -3.27431 -0.5962 -1.16503 -6.58484 0.21931
26 MEG_022 MEG_GRAD 0.56022 -3.27709 -0.59609 1.14872 -6.58771 0.21942
27 MEG_047 MEG_GRAD -1.61144 -0.93037 3.41137 -3.33479 -1.92534 6.24186
28 MEG_061 MEG_GRAD -1.86073 0 3.41137 -3.85068 0 6.24186
29 MEG_087 MEG_GRAD -2.5429 0 2.76491 -5.2624 0 4.90406
30 MEG_113 MEG_GRAD -3.22769 0.0086 0.98505 -6.5452 0.046 1.96703
31 MEG_101 MEG_GRAD -2.96476 -0.52277 1.94967 -6.13541 -1.08184 3.21696
32 MEG_099 MEG_GRAD -2.96476 0.52277 1.94967 -6.13541 1.08184 3.21696
33 MEG_063 MEG_GRAD -1.94798 -1.63455 2.76491 -4.03123 -3.38261 4.90406
34 MEG_075 MEG_GRAD -2.38955 -0.86972 2.76491 -4.94504 -1.79985 4.90406
35 MEG_089 MEG_GRAD -2.60717 -1.50525 1.94967 -5.39539 -3.11503 3.21696
36 MEG_123 MEG_GRAD -3.24454 -0.65992 -1.54654 -6.63007 -1.24165 -1.13258
37 MEG_103 MEG_GRAD -3.03312 -1.09456 1.02677 -6.15066 -2.19102 2.05164
38 MEG_119 MEG_GRAD -3.27163 -0.04807 -0.71822 -6.66217 -0.02172 -0.02891
39 MEG_121 MEG_GRAD -3.24454 0.48346 -1.58979 -6.63007 1.0948 -1.22095
40 MEG_105 MEG_GRAD -3.07707 -1.16672 -0.67591 -6.26323 -2.29919 0.05723
41 MEG_091 MEG_GRAD -2.81455 -1.64764 0.19622 -5.75085 -3.31563 0.94961
42 MEG_115 MEG_GRAD -3.20059 -0.58777 0.15614 -6.53962 -1.15004 0.86771
43 MEG_037 MEG_GRAD -1.11155 -3.07561 0.25023 -2.27119 -6.23333 1.05996
44 MEG_067 MEG_GRAD -2.08904 -2.51166 0.2289 -4.26844 -5.08104 1.01638
45 MEG_079 MEG_GRAD -2.51137 -2.1514 -0.63867 -5.10885 -4.30296 0.13301
46 MEG_093 MEG_GRAD -2.8532 -1.73435 -1.50591 -5.7542 -3.37531 -0.57691
47 MEG_051 MEG_GRAD -1.61407 -2.7848 1.0907 -3.27306 -5.61852 2.18127
48 MEG_065 MEG_GRAD -1.93511 -2.30617 1.94967 -4.00461 -4.7725 3.21696
49 REF_014 UNUSED
50 MEG_053 MEG_GRAD -1.64275 -2.88336 -0.61098 -3.33826 -5.79135 0.1893
51 MEG_039 MEG_GRAD -1.37821 4.03301 0.38766 -2.98972 7.09471 0.3625
52 MEG_041 MEG_GRAD -1.59926 3.67934 1.66789 -3.46787 6.31662 2.90266
53 MEG_055 MEG_GRAD -2.06278 3.53364 0.8475 -4.47296 6.00069 1.12372
54 MEG_069 MEG_GRAD -2.43321 2.88136 1.45931 -5.27622 4.58626 2.45038
55 MEG_027 MEG_GRAD -1.02514 3.32279 2.63742 -2.22293 5.54346 5.00502
56 MEG_025 MEG_GRAD -0.92333 4.17235 1.20548 -2.00217 7.38566 1.89996
57 MEG_057 MEG_GRAD -1.84667 3.00588 2.29955 -4.00435 4.85628 4.27238
58 REF_015 UNUSED
59 MEG_083 MEG_GRAD -2.81067 2.32514 1.52142 -6.13327 3.15736 2.01067
60 MEG_095 MEG_GRAD -2.85632 2.16654 0.82155 -6.24599 2.85761 0.88605
61 MEG_117 MEG_GRAD -3.14455 0.87829 -0.52294 -6.53422 1.56936 -0.45844
62 MEG_109 MEG_GRAD -3.0226 1.3925 0.37679 -6.41227 2.08357 0.44129
63 MEG_107 MEG_GRAD -2.7791 2.44789 0.19401 -6.01824 3.66345 0.23867
64 MEG_111 MEG_GRAD -3.20059 0.54013 0.11348 -6.53962 1.15454 0.78055
65 MEG_097 MEG_GRAD -3.04326 1.22292 1.10768 -6.3884 1.94226 1.62169
66 MEG_081 MEG_GRAD -2.54021 2.92425 0.68688 -5.5195 4.68347 0.71098
67 REF_001 REF_MAG -2.26079604 3.98626183 5.04439808 -2.20703425 3.92437924 4.93090704
68 REF_002 REF_MAG 1.93013445 4.03046866 5.17689263 1.8763992 3.96852956 5.06341985
69 REF_004 REF_MAG 1.70031266 4.21202221 5.57217923 1.57144014 4.22797498 5.62449924
70 REF_012 REF_GRAD 4.64675 -0.89642 -0.43802 6.03162 -1.01804 -0.22614
71 REF_006 REF_MAG 2.07781 3.83073028 5.60154279 2.08802749 3.70619491 5.66468189
72 REF_008 REF_GRAD 4.50056 0.78066 1.76423 5.88573 0.92199 1.96135
73 REF_010 REF_GRAD 4.31926 2.18698 -0.37055 5.69806 2.46181 -0.34022
74 MEG_094 REF_GRAD 2.85632 2.16654 0.82155 6.24599 2.85761 0.88605
75 REF_016 UNUSED
76 REF_003 REF_MAG -2.73073962 4.07852721 5.1569653 -2.8596759 4.06162797 5.1051015
77 REF_017 UNUSED
78 REF_011 REF_GRAD -4.64675 -0.89642 -0.43802 -6.03162 -1.01804 -0.22614
79 REF_009 REF_GRAD -4.31926 2.18698 -0.37055 -5.69806 2.46181 -0.34022
80 REF_007 REF_GRAD -4.50056 0.78066 1.76423 -5.88573 0.92199 1.96135
81 REF_018 UNUSED
82 REF_005 REF_MAG -2.4058382 3.78665997 5.47001894 -2.41506358 3.66222139 5.53350068
83 MEG_090 MEG_GRAD 2.81455 -1.64764 0.19622 5.75085 -3.31563 0.94961
84 MEG_088 MEG_GRAD 2.60717 -1.50525 1.94967 5.39539 -3.11503 3.21696
85 MEG_102 MEG_GRAD 3.03294 -1.09506 1.02679 6.1503 -2.19202 2.05167
86 MEG_122 MEG_GRAD 3.24454 -0.65992 -1.54654 6.63007 -1.24165 -1.13258
87 MEG_114 MEG_GRAD 3.20059 -0.58777 0.15614 6.53962 -1.15004 0.86771
88 MEG_104 MEG_GRAD 3.07159 -1.18176 -0.67534 6.25756 -2.31475 0.05782
89 MEG_120 MEG_GRAD 3.24454 0.48346 -1.58979 6.63007 1.0948 -1.22094
90 MEG_118 MEG_GRAD 3.27163 -0.06408 -0.71761 6.66217 -0.03828 -0.02828
91 MEG_106 MEG_GRAD 2.7791 2.44789 0.19401 6.01824 3.66345 0.23867
92 MEG_082 MEG_GRAD 2.81067 2.32514 1.52142 6.13327 3.15736 2.01067
93 MEG_110 MEG_GRAD 3.20059 0.54013 0.11348 6.53962 1.15454 0.78055
94 MEG_116 MEG_GRAD 3.14455 0.87829 -0.52294 6.53422 1.56936 -0.45844
95 MEG_096 MEG_GRAD 3.04326 1.22292 1.10768 6.3884 1.94226 1.62169
96 MEG_080 MEG_GRAD 2.54021 2.92425 0.68688 5.5195 4.68347 0.71098
97 MEG_108 MEG_GRAD 3.0226 1.3925 0.37679 6.41227 2.08357 0.44129
98 REF_019 UNUSED
99 MEG_009 MEG_GRAD -0.48824 4.32904 0.13976 -1.05817 7.74156 0.10133
100 MEG_003 MEG_GRAD 0 3.44805 2.77097 0 5.81508 5.29461
101 MEG_010 MEG_GRAD 0.51257 3.97032 2.03007 1.11147 6.94759 3.68802
102 MEG_012 MEG_GRAD 0.51257 2.67525 3.24478 1.11147 4.13933 6.32201
103 MEG_004 MEG_GRAD 0 4.3528 1.03622 0 7.77696 1.53295
104 MEG_011 MEG_GRAD -0.51257 3.97032 2.03007 -1.11147 6.94759 3.68802
105 MEG_008 MEG_GRAD 0.48824 4.32904 0.13976 1.05817 7.74156 0.10133
106 MEG_013 MEG_GRAD -0.51257 2.67525 3.24478 -1.11147 4.13933 6.32201
107 MEG_024 MEG_GRAD 0.92333 4.17235 1.20548 2.00217 7.38566 1.89996
108 REF_020 UNUSED
109 MEG_068 MEG_GRAD 2.43321 2.88136 1.45931 5.27622 4.58626 2.45038
110 MEG_026 MEG_GRAD 1.02514 3.32279 2.63742 2.22293 5.54346 5.00502
111 MEG_038 MEG_GRAD 1.37821 4.03301 0.38766 2.98972 7.09471 0.3625
112 MEG_040 MEG_GRAD 1.59926 3.67934 1.66789 3.46787 6.31662 2.90266
113 MEG_054 MEG_GRAD 2.06278 3.53364 0.8475 4.47296 6.00069 1.12372
114 MEG_056 MEG_GRAD 1.84667 3.00588 2.29955 4.00435 4.85628 4.27238
115 MEG_058 MEG_GRAD 2.00892 1.56358 2.88668 4.25593 2.49543 5.34722
116 MEG_042 MEG_GRAD 1.59926 2.33243 2.93122 3.46787 3.39595 5.64209
117 MEG_028 MEG_GRAD 0.90968 1.7238 3.47337 1.93358 2.72985 6.62156
118 MEG_070 MEG_GRAD 2.43321 2.17533 2.12153 5.27622 3.05529 3.88634
119 REF_021 UNUSED
120 MEG_072 MEG_GRAD 2.38955 0.86972 2.76491 4.94504 1.79985 4.90406
121 MEG_044 MEG_GRAD 1.61144 0.93037 3.41137 3.33479 1.92534 6.24186
122 MEG_084 MEG_GRAD 2.78632 1.40783 1.84839 5.89386 2.21359 3.13893
123 MEG_046 MEG_GRAD 1.61144 -0.93037 3.41137 3.33479 -1.92534 6.24186
124 MEG_098 MEG_GRAD 2.96476 0.52277 1.94967 6.13541 1.08184 3.21696
125 MEG_060 MEG_GRAD 1.8607 0 3.41137 3.85068 0 6.24186
126 MEG_100 MEG_GRAD 2.96476 -0.52277 1.94967 6.13541 -1.08184 3.21696
127 MEG_074 MEG_GRAD 2.38955 -0.86972 2.76491 4.94504 -1.79985 4.90406
128 MEG_086 MEG_GRAD 2.5429 0 2.76491 5.2624 0 4.90406
129 MEG_062 MEG_GRAD 1.94798 -1.63455 2.76491 4.03123 -3.38261 4.90406
130 MEG_112 MEG_GRAD 3.22769 0.00807 0.98507 6.5452 0.04494 1.96707
131 MEG_016 MEG_GRAD 0.50538 -0.87535 3.83752 0.89368 -1.5479 7.20924
132 MEG_031 MEG_GRAD -1.01076 0 3.83752 -1.78736 0 7.20924
133 MEG_015 MEG_GRAD -0.50538 0.87535 3.83752 -0.89368 1.5479 7.20924
134 MEG_001 MEG_GRAD 0 0 4 0 0 7.46
135 MEG_002 MEG_GRAD 0 1.80611 3.59215 0 2.82922 6.89743
136 MEG_017 MEG_GRAD -0.50538 -0.87535 3.83752 -0.89368 -1.5479 7.20924
137 MEG_014 MEG_GRAD 0.50538 0.87535 3.83752 0.89368 1.5479 7.20924
138 MEG_030 MEG_GRAD 1.01076 0 3.83752 1.78736 0 7.20924
139 MEG_050 MEG_GRAD 1.61362 -2.78506 1.09071 3.27214 -5.61905 2.18129
140 MEG_064 MEG_GRAD 1.93511 -2.30617 1.94967 4.00461 -4.7725 3.21696
141 MEG_076 MEG_GRAD 2.47238 -2.0651 1.06348 5.01358 -4.1591 2.12607
142 MEG_078 MEG_GRAD 2.50107 -2.16367 -0.6382 5.0982 -4.31565 0.13349
143 MEG_066 MEG_GRAD 2.08904 -2.51166 0.2289 4.26844 -5.08104 1.01638
144 MEG_036 MEG_GRAD 1.11155 -3.07561 0.25023 2.27119 -6.23333 1.05996
145 MEG_052 MEG_GRAD 1.62888 -2.89137 -0.61068 3.32391 -5.79963 0.18962
146 MEG_092 MEG_GRAD 2.8532 -1.73435 -1.50591 5.7542 -3.37531 -0.57691

144
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MEG_001,0.0,0.0,0.10160000191,1.0,-0.0,-0.0,-0.0,1.0,-0.0,0.0,0.0,1.0
MEG_002,0.0,0.0458751948625,0.0912406117153,1.0,-0.0,-0.0,-0.0,0.955282042035,-0.295696161906,0.0,0.295696161906,0.955282042035
MEG_003,0.0,0.0875804716465,0.0703826393232,1.0,-0.0,-0.0,-0.0,0.729376031116,-0.684113006186,0.0,0.684113006186,0.729376031116
MEG_004,0.0,0.110561122079,0.0263199884948,1.0,-0.0,-0.0,-0.0,0.143563509474,-0.989641106032,0.0,0.989641106032,0.143563509474
MEG_005,0.0,-0.0472625428885,0.086648799629,1.0,0.0,-0.0,0.0,0.818061560022,0.575130666904,0.0,-0.575130666904,0.818061560022
MEG_006,0.0,-0.0764667014376,0.049521618931,1.0,0.0,-0.0,0.0,0.366268930876,0.930509038255,0.0,-0.930509038255,0.366268930876
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MEG_010,0.0130192782448,0.100846129896,0.0515637789694,0.979744824976,-0.100693145673,-0.173092369408,-0.100693145673,0.499431154085,-0.860482081594,0.173092369408,0.860482081594,0.479175979061
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MEG_012,0.0130192782448,0.0679513512775,0.0824174135494,0.984142307767,-0.038765954324,-0.17309280415,-0.038765954324,0.905232161619,-0.423145287527,0.17309280415,0.423145287527,0.889374469386
MEG_013,-0.0130192782448,0.0679513512775,0.0824174135494,0.984142307767,0.038765954324,0.17309280415,0.038765954324,0.905232161619,-0.423145287527,-0.17309280415,0.423145287527,0.889374469386
MEG_014,0.0128366522413,0.022233890418,0.0974730098325,0.993621350642,-0.0110480572384,-0.112225451567,-0.0110480572384,0.980864355149,-0.194378643965,0.112225451567,0.194378643965,0.974485705791
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MEG_016,0.0128366522413,-0.022233890418,0.0974730098325,0.993621350642,0.0110480572384,-0.112225451567,0.0110480572384,0.980864355149,0.194378643965,0.112225451567,-0.194378643965,0.974485705791
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MEG_018,0.0112158782109,-0.0636084591958,0.0702287153203,0.988488638046,0.0652835409099,-0.136485426846,0.0652835409099,0.629762253104,0.774039768908,0.136485426846,-0.774039768908,0.61825089115
MEG_019,-0.0112158782109,-0.0636084591958,0.0702287153203,0.988488638046,-0.0652835409099,0.136485426846,-0.0652835409099,0.629762253104,0.774039768908,-0.136485426846,-0.774039768908,0.61825089115
MEG_020,0.0142295882675,-0.0804694875128,0.0280718265278,0.979010049739,0.117656650617,-0.166421858768,0.117656650617,0.340489745704,0.93285778425,0.166421858768,-0.93285778425,0.319499795443
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MEG_022,0.0142295882675,-0.0832380875649,-0.0151406862846,0.976588506526,0.131701883642,-0.170086750705,0.131701883642,0.259108088321,0.956826845574,0.170086750705,-0.956826845574,0.235696594848
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MEG_024,0.0234525824409,0.105977691992,0.0306191925756,0.919030275794,-0.241167202261,-0.311803997292,-0.241167202261,0.281686827798,-0.928703888007,0.311803997292,0.928703888007,0.200717103592
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MEG_026,0.0260385564895,0.0843988675867,0.0669904692594,0.928846648352,-0.131916373824,-0.346181995721,-0.131916373824,0.755430639878,-0.641811980763,0.346181995721,0.641811980763,0.68427728823
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MEG_028,0.0231058724344,0.0437845208231,0.0882235996586,0.954148215535,-0.0450524345745,-0.295924755521,-0.0450524345745,0.955732979975,-0.290765797726,0.295924755521,0.290765797726,0.90988119551
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MEG_030,0.0256733044827,0.0,0.0974730098325,0.974485414074,-0.0,-0.224450835944,-0.0,1.0,-0.0,0.224450835944,0.0,0.974485414074
MEG_031,-0.0256733044827,0.0,0.0974730098325,0.974485414074,0.0,0.224450835944,0.0,1.0,-0.0,-0.224450835944,0.0,0.974485414074
MEG_032,0.0236313984443,-0.0409305767695,0.086648799629,0.954515845737,0.078781387629,-0.287563894118,0.078781387629,0.863545730655,0.498078572146,0.287563894118,-0.498078572146,0.818061576392
MEG_033,-0.0236313984443,-0.0409305767695,0.086648799629,0.954515845737,-0.078781387629,0.287563894118,-0.078781387629,0.863545730655,0.498078572146,-0.287563894118,-0.498078572146,0.818061576392
MEG_034,0.0261531104917,-0.0718550773509,0.049521618931,0.925866960833,0.203678027441,-0.318254036858,0.203678027441,0.440401481873,0.874392243734,0.318254036858,-0.874392243734,0.366268442706
MEG_035,-0.0261531104917,-0.0718550773509,0.049521618931,0.925866960833,-0.203678027441,0.318254036858,-0.203678027441,0.440401481873,0.874392243734,-0.318254036858,-0.874392243734,0.366268442706
MEG_036,0.0282333705308,-0.0781204954687,0.00635584211949,0.908973236603,0.247867468623,-0.335155744599,0.247867468623,0.325052548187,0.91263495381,0.335155744599,-0.91263495381,0.23402578479
MEG_037,-0.0282333705308,-0.0781204954687,0.00635584211949,0.908973236603,-0.247867468623,0.335155744599,-0.247867468623,0.325052548187,0.91263495381,-0.335155744599,-0.91263495381,0.23402578479
MEG_038,0.0350065346581,0.102438455926,0.00984656418512,0.781484001521,-0.415157481208,-0.465754249753,-0.415157481208,0.211244323513,-0.884884230609,0.465754249753,0.884884230609,-0.00727167496558
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MEG_040,0.0406212047637,0.093455237757,0.0423644067965,0.785045408535,-0.303378150058,-0.540060556425,-0.303378150058,0.57182444299,-0.762219459517,0.540060556425,0.762219459517,0.356869851524
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MEG_042,0.0406212047637,0.0592437231138,0.0744529893997,0.836463385382,-0.0930769183398,-0.540060822675,-0.0930769183398,0.947025241119,-0.307375795983,0.540060822675,0.307375795983,0.7834886265
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MEG_044,0.0409305767695,0.0236313984443,0.086648799629,0.863545730655,-0.078781387629,-0.498078572146,-0.078781387629,0.954515845737,-0.287563894118,0.498078572146,0.287563894118,0.818061576392
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MEG_112,0.0819833275413,0.000204978003854,0.0250207784704,0.283903999524,-0.00795851694118,-0.958819681203,-0.00795851694118,0.999911550977,-0.010656088948,0.958819681203,0.010656088948,0.283815550501
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1 MEG_001 0.0 0.0 0.10160000191 1.0 -0.0 -0.0 -0.0 1.0 -0.0 0.0 0.0 1.0
2 MEG_002 0.0 0.0458751948625 0.0912406117153 1.0 -0.0 -0.0 -0.0 0.955282042035 -0.295696161906 0.0 0.295696161906 0.955282042035
3 MEG_003 0.0 0.0875804716465 0.0703826393232 1.0 -0.0 -0.0 -0.0 0.729376031116 -0.684113006186 0.0 0.684113006186 0.729376031116
4 MEG_004 0.0 0.110561122079 0.0263199884948 1.0 -0.0 -0.0 -0.0 0.143563509474 -0.989641106032 0.0 0.989641106032 0.143563509474
5 MEG_005 0.0 -0.0472625428885 0.086648799629 1.0 0.0 -0.0 0.0 0.818061560022 0.575130666904 0.0 -0.575130666904 0.818061560022
6 MEG_006 0.0 -0.0764667014376 0.049521618931 1.0 0.0 -0.0 0.0 0.366268930876 0.930509038255 0.0 -0.930509038255 0.366268930876
7 MEG_007 0.0 -0.0830953395622 0.00654405612303 1.0 0.0 -0.0 0.0 0.236260571358 0.971689735678 0.0 -0.971689735678 0.236260571358
8 MEG_008 0.0124012962331 0.109957618067 0.00354990406674 0.972562667953 -0.164284112711 -0.164719723212 -0.164284112711 0.0163303909087 -0.986277875978 0.164719723212 0.986277875978 -0.0111069411385
9 MEG_009 -0.0124012962331 0.109957618067 0.00354990406674 0.972562667953 0.164284112711 0.164719723212 0.164284112711 0.0163303909087 -0.986277875978 -0.164719723212 0.986277875978 -0.0111069411385
10 MEG_010 0.0130192782448 0.100846129896 0.0515637789694 0.979744824976 -0.100693145673 -0.173092369408 -0.100693145673 0.499431154085 -0.860482081594 0.173092369408 0.860482081594 0.479175979061
11 MEG_011 -0.0130192782448 0.100846129896 0.0515637789694 0.979744824976 0.100693145673 0.173092369408 0.100693145673 0.499431154085 -0.860482081594 -0.173092369408 0.860482081594 0.479175979061
12 MEG_012 0.0130192782448 0.0679513512775 0.0824174135494 0.984142307767 -0.038765954324 -0.17309280415 -0.038765954324 0.905232161619 -0.423145287527 0.17309280415 0.423145287527 0.889374469386
13 MEG_013 -0.0130192782448 0.0679513512775 0.0824174135494 0.984142307767 0.038765954324 0.17309280415 0.038765954324 0.905232161619 -0.423145287527 -0.17309280415 0.423145287527 0.889374469386
14 MEG_014 0.0128366522413 0.022233890418 0.0974730098325 0.993621350642 -0.0110480572384 -0.112225451567 -0.0110480572384 0.980864355149 -0.194378643965 0.112225451567 0.194378643965 0.974485705791
15 MEG_015 -0.0128366522413 0.022233890418 0.0974730098325 0.993621350642 0.0110480572384 0.112225451567 0.0110480572384 0.980864355149 -0.194378643965 -0.112225451567 0.194378643965 0.974485705791
16 MEG_016 0.0128366522413 -0.022233890418 0.0974730098325 0.993621350642 0.0110480572384 -0.112225451567 0.0110480572384 0.980864355149 0.194378643965 0.112225451567 -0.194378643965 0.974485705791
17 MEG_017 -0.0128366522413 -0.022233890418 0.0974730098325 0.993621350642 -0.0110480572384 0.112225451567 -0.0110480572384 0.980864355149 0.194378643965 -0.112225451567 -0.194378643965 0.974485705791
18 MEG_018 0.0112158782109 -0.0636084591958 0.0702287153203 0.988488638046 0.0652835409099 -0.136485426846 0.0652835409099 0.629762253104 0.774039768908 0.136485426846 -0.774039768908 0.61825089115
19 MEG_019 -0.0112158782109 -0.0636084591958 0.0702287153203 0.988488638046 -0.0652835409099 0.136485426846 -0.0652835409099 0.629762253104 0.774039768908 -0.136485426846 -0.774039768908 0.61825089115
20 MEG_020 0.0142295882675 -0.0804694875128 0.0280718265278 0.979010049739 0.117656650617 -0.166421858768 0.117656650617 0.340489745704 0.93285778425 0.166421858768 -0.93285778425 0.319499795443
21 MEG_021 -0.0142427962678 -0.0804672015128 0.0280718265278 0.978972050612 -0.117759654311 0.166572470526 -0.117759654311 0.340528364062 0.932830690472 -0.166572470526 -0.932830690472 0.319500414673
22 MEG_022 0.0142295882675 -0.0832380875649 -0.0151406862846 0.976588506526 0.131701883642 -0.170086750705 0.131701883642 0.259108088321 0.956826845574 0.170086750705 -0.956826845574 0.235696594848
23 MEG_023 -0.0146304002751 -0.0831674755635 -0.0151434802847 0.976546368958 -0.131816629327 0.170239729521 -0.131816629327 0.259149948413 0.956799707604 -0.170239729521 -0.956799707604 0.235696317371
24 MEG_024 0.0234525824409 0.105977691992 0.0306191925756 0.919030275794 -0.241167202261 -0.311803997292 -0.241167202261 0.281686827798 -0.928703888007 0.311803997292 0.928703888007 0.200717103592
25 MEG_025 -0.0234525824409 0.105977691992 0.0306191925756 0.919030275794 0.241167202261 0.311803997292 0.241167202261 0.281686827798 -0.928703888007 -0.311803997292 0.928703888007 0.200717103592
26 MEG_026 0.0260385564895 0.0843988675867 0.0669904692594 0.928846648352 -0.131916373824 -0.346181995721 -0.131916373824 0.755430639878 -0.641811980763 0.346181995721 0.641811980763 0.68427728823
27 MEG_027 -0.0260385564895 0.0843988675867 0.0669904692594 0.928846648352 0.131916373824 0.346181995721 0.131916373824 0.755430639878 -0.641811980763 -0.346181995721 0.641811980763 0.68427728823
28 MEG_028 0.0231058724344 0.0437845208231 0.0882235996586 0.954148215535 -0.0450524345745 -0.295924755521 -0.0450524345745 0.955732979975 -0.290765797726 0.295924755521 0.290765797726 0.90988119551
29 MEG_029 -0.0231330504349 0.0437862988232 0.0882142016584 0.954036318954 0.0451068389737 0.296277024411 0.0451068389737 0.955734030088 -0.290753911081 -0.296277024411 0.290753911081 0.909770349043
30 MEG_030 0.0256733044827 0.0 0.0974730098325 0.974485414074 -0.0 -0.224450835944 -0.0 1.0 -0.0 0.224450835944 0.0 0.974485414074
31 MEG_031 -0.0256733044827 0.0 0.0974730098325 0.974485414074 0.0 0.224450835944 0.0 1.0 -0.0 -0.224450835944 0.0 0.974485414074
32 MEG_032 0.0236313984443 -0.0409305767695 0.086648799629 0.954515845737 0.078781387629 -0.287563894118 0.078781387629 0.863545730655 0.498078572146 0.287563894118 -0.498078572146 0.818061576392
33 MEG_033 -0.0236313984443 -0.0409305767695 0.086648799629 0.954515845737 -0.078781387629 0.287563894118 -0.078781387629 0.863545730655 0.498078572146 -0.287563894118 -0.498078572146 0.818061576392
34 MEG_034 0.0261531104917 -0.0718550773509 0.049521618931 0.925866960833 0.203678027441 -0.318254036858 0.203678027441 0.440401481873 0.874392243734 0.318254036858 -0.874392243734 0.366268442706
35 MEG_035 -0.0261531104917 -0.0718550773509 0.049521618931 0.925866960833 -0.203678027441 0.318254036858 -0.203678027441 0.440401481873 0.874392243734 -0.318254036858 -0.874392243734 0.366268442706
36 MEG_036 0.0282333705308 -0.0781204954687 0.00635584211949 0.908973236603 0.247867468623 -0.335155744599 0.247867468623 0.325052548187 0.91263495381 0.335155744599 -0.91263495381 0.23402578479
37 MEG_037 -0.0282333705308 -0.0781204954687 0.00635584211949 0.908973236603 -0.247867468623 0.335155744599 -0.247867468623 0.325052548187 0.91263495381 -0.335155744599 -0.91263495381 0.23402578479
38 MEG_038 0.0350065346581 0.102438455926 0.00984656418512 0.781484001521 -0.415157481208 -0.465754249753 -0.415157481208 0.211244323513 -0.884884230609 0.465754249753 0.884884230609 -0.00727167496558
39 MEG_039 -0.0350065346581 0.102438455926 0.00984656418512 0.781484001521 0.415157481208 0.465754249753 0.415157481208 0.211244323513 -0.884884230609 -0.465754249753 0.884884230609 -0.00727167496558
40 MEG_040 0.0406212047637 0.093455237757 0.0423644067965 0.785045408535 -0.303378150058 -0.540060556425 -0.303378150058 0.57182444299 -0.762219459517 0.540060556425 0.762219459517 0.356869851524
41 MEG_041 -0.0406212047637 0.093455237757 0.0423644067965 0.785045408535 0.303378150058 0.540060556425 0.303378150058 0.57182444299 -0.762219459517 -0.540060556425 0.762219459517 0.356869851524
42 MEG_042 0.0406212047637 0.0592437231138 0.0744529893997 0.836463385382 -0.0930769183398 -0.540060822675 -0.0930769183398 0.947025241119 -0.307375795983 0.540060822675 0.307375795983 0.7834886265
43 MEG_043 -0.0406212047637 0.0592437231138 0.0744529893997 0.836463385382 0.0930769183398 0.540060822675 0.0930769183398 0.947025241119 -0.307375795983 -0.540060822675 0.307375795983 0.7834886265
44 MEG_044 0.0409305767695 0.0236313984443 0.086648799629 0.863545730655 -0.078781387629 -0.498078572146 -0.078781387629 0.954515845737 -0.287563894118 0.498078572146 0.287563894118 0.818061576392
45 MEG_045 -0.0409305767695 0.0236313984443 0.086648799629 0.863545730655 0.078781387629 0.498078572146 0.078781387629 0.954515845737 -0.287563894118 -0.498078572146 0.287563894118 0.818061576392
46 MEG_046 0.0409305767695 -0.0236313984443 0.086648799629 0.863545730655 0.078781387629 -0.498078572146 0.078781387629 0.954515845737 0.287563894118 0.498078572146 -0.287563894118 0.818061576392
47 MEG_047 -0.0409305767695 -0.0236313984443 0.086648799629 0.863545730655 -0.078781387629 0.498078572146 -0.078781387629 0.954515845737 0.287563894118 -0.498078572146 -0.287563894118 0.818061576392
48 MEG_048 0.0322948306071 -0.0559363890516 0.0702287153203 0.904562399003 0.165302346745 -0.392991094644 0.165302346745 0.713688676641 0.680678784005 0.392991094644 -0.680678784005 0.618251075645
49 MEG_049 -0.0322948306071 -0.0559363890516 0.0702287153203 0.904562399003 -0.165302346745 0.392991094644 -0.165302346745 0.713688676641 0.680678784005 -0.392991094644 -0.680678784005 0.618251075645
50 MEG_050 0.0409859487705 -0.0707405253299 0.0277040345208 0.825297111533 0.298522923381 -0.479341988471 0.298522923381 0.489900043633 0.819073874241 0.479341988471 -0.819073874241 0.315197155166
51 MEG_051 -0.0409973787708 -0.0707339213298 0.0277037805208 0.825197788666 -0.298579570884 0.479477683976 -0.298579570884 0.489996382374 0.818995595294 -0.479477683976 -0.818995595294 0.31519417104
52 MEG_052 0.0413735527778 -0.0734407993807 -0.0155112722916 0.805087785644 0.334422043575 -0.489893411036 0.334422043575 0.426212956438 0.840538168399 0.489893411036 -0.840538168399 0.231300742083
53 MEG_053 -0.0417258507844 -0.0732373453769 -0.0155188922918 0.804976833568 -0.334486625117 0.490031626568 -0.334486625117 0.426317886079 0.840459253996 -0.490031626568 -0.840459253996 0.231294719647
54 MEG_054 0.052394612985 0.0897544576874 0.0215265004047 0.550644162251 -0.459958724875 -0.696583791076 -0.459958724875 0.529188204946 -0.713020206696 0.696583791076 0.713020206696 0.0798323671971
55 MEG_055 -0.052394612985 0.0897544576874 0.0215265004047 0.550644162251 0.459958724875 0.696583791076 0.459958724875 0.529188204946 -0.713020206696 -0.696583791076 0.713020206696 0.0798323671971
56 MEG_056 0.0469054188818 0.0763493534354 0.0584085710981 0.752331243475 -0.212397698952 -0.623606380317 -0.212397698952 0.817850328992 -0.534797210957 0.623606380317 0.534797210957 0.570181572467
57 MEG_057 -0.0469054188818 0.0763493534354 0.0584085710981 0.752331243475 0.212397698952 0.623606380317 0.212397698952 0.817850328992 -0.534797210957 -0.623606380317 0.534797210957 0.570181572467
58 MEG_058 0.0510265689593 0.0397149327466 0.0733216733784 0.75352606525 -0.102214380931 -0.649423351381 -0.102214380931 0.95761101603 -0.269320185484 0.649423351381 0.269320185484 0.71113708128
59 MEG_059 -0.0502099589439 0.0397642087476 0.0740382073919 0.762632097113 0.100407167247 0.638991928915 0.100407167247 0.957527538003 -0.27029505125 -0.638991928915 0.27029505125 0.720159635116
60 MEG_060 0.0472617808885 0.0 0.086648799629 0.818057480605 -0.0 -0.575136469394 -0.0 1.0 -0.0 0.575136469394 0.0 0.818057480605
61 MEG_061 -0.0472625428885 0.0 0.086648799629 0.818061560022 0.0 0.575130666904 0.0 1.0 -0.0 -0.575130666904 0.0 0.818061560022
62 MEG_062 0.0494786929302 -0.0415175707805 0.0702287153203 0.775981248219 0.187974664221 -0.602095198473 0.187974664221 0.842270014862 0.505219504448 0.602095198473 -0.505219504448 0.618251263081
63 MEG_063 -0.0494786929302 -0.0415175707805 0.0702287153203 0.775981248219 -0.187974664221 0.602095198473 -0.187974664221 0.842270014862 0.505219504448 -0.602095198473 -0.505219504448 0.618251263081
64 MEG_064 0.0491517949241 -0.0585767191012 0.049521618931 0.738156783089 0.312052080775 -0.598120441436 0.312052080775 0.628111423833 0.712811011513 0.598120441436 -0.712811011513 0.366268206923
65 MEG_065 -0.0491517949241 -0.0585767191012 0.049521618931 0.738156783089 -0.312052080775 0.598120441436 -0.312052080775 0.628111423833 0.712811011513 -0.598120441436 -0.712811011513 0.366268206923
66 MEG_066 0.0530616169976 -0.0637961651994 0.0058140601093 0.676804202704 0.381028180993 -0.629883796022 0.381028180993 0.550790957291 0.742594671847 0.629883796022 -0.742594671847 0.227595159994
67 MEG_067 -0.0530616169976 -0.0637961651994 0.0058140601093 0.676804202704 -0.381028180993 0.629883796022 -0.381028180993 0.550790957291 0.742594671847 -0.629883796022 -0.742594671847 0.227595159994
68 MEG_068 0.0618035351619 0.0731865453759 0.0370664746968 0.475173275464 -0.314728784866 -0.821678860785 -0.314728784866 0.811263025695 -0.492745466865 0.821678860785 0.492745466865 0.286436301159
69 MEG_069 -0.0618035351619 0.0731865453759 0.0370664746968 0.475173275464 0.314728784866 0.821678860785 0.314728784866 0.811263025695 -0.492745466865 -0.821678860785 0.492745466865 0.286436301159
70 MEG_070 0.0618035351619 0.0552533830388 0.0538868630131 0.552894017073 -0.138386914129 -0.821679540869 -0.138386914129 0.957166893906 -0.254323807789 0.821679540869 0.254323807789 0.510060910979
71 MEG_071 -0.0618035351619 0.0552533830388 0.0538868630131 0.552894017073 0.138386914129 0.821679540869 0.138386914129 0.957166893906 -0.254323807789 -0.821679540869 0.254323807789 0.510060910979
72 MEG_072 0.0606945711411 0.0220908884153 0.0702287153203 0.662907428432 -0.12269267874 -0.738579885939 -0.12269267874 0.955343146999 -0.268823321284 0.738579885939 0.268823321284 0.61825057543
73 MEG_073 -0.0606945711411 0.0220908884153 0.0702287153203 0.662907428432 0.12269267874 0.738579885939 0.12269267874 0.955343146999 -0.268823321284 -0.738579885939 0.268823321284 0.61825057543
74 MEG_074 0.0606945711411 -0.0220908884153 0.0702287153203 0.662907428432 0.12269267874 -0.738579885939 0.12269267874 0.955343146999 0.268823321284 0.738579885939 -0.268823321284 0.61825057543
75 MEG_075 -0.0606945711411 -0.0220908884153 0.0702287153203 0.662907428432 -0.12269267874 0.738579885939 -0.12269267874 0.955343146999 0.268823321284 -0.738579885939 -0.268823321284 0.61825057543
76 MEG_076 0.0627984531806 -0.0524535409861 0.0270123925078 0.587320034467 0.340056606259 -0.73444991773 0.340056606259 0.719786504995 0.605201529878 0.73444991773 -0.605201529878 0.307106539462
77 MEG_077 -0.0628070891808 -0.0524433809859 0.0270118845078 0.587208379993 -0.340036516337 0.734548491268 -0.340036516337 0.719895397972 0.605083286446 -0.734548491268 -0.605083286446 0.307103777966
78 MEG_078 0.0635271791943 -0.0549572190332 -0.0162102803048 0.539322307512 0.381717195782 -0.750615368258 0.381717195782 0.683709413476 0.621959339803 0.750615368258 -0.621959339803 0.223031720988
79 MEG_079 -0.0637887991992 -0.0546455610273 -0.016222218305 0.539196876386 -0.381695169412 0.750716675014 -0.381695169412 0.683831999207 0.621838077403 -0.750716675014 -0.621838077403 0.223028875593
80 MEG_080 0.064521335213 0.0742759513964 0.017446752328 0.263693428198 -0.434776489447 -0.861066304154 -0.434776489447 0.743271888347 -0.508444986421 0.861066304154 0.508444986421 0.00696531654525
81 MEG_081 -0.064521335213 0.0742759513964 0.017446752328 0.263693428198 0.434776489447 0.861066304154 0.434776489447 0.743271888347 -0.508444986421 -0.861066304154 0.508444986421 0.00696531654525
82 MEG_082 0.0713910193422 0.0590585571103 0.0386440687265 0.192087187177 -0.202359959395 -0.960287956478 -0.202359959395 0.949314390716 -0.240525745844 0.960287956478 0.240525745844 0.141401577893
83 MEG_083 -0.0713910193422 0.0590585571103 0.0386440687265 0.192087187177 0.202359959395 0.960287956478 0.202359959395 0.949314390716 -0.240525745844 -0.960287956478 0.240525745844 0.141401577893
84 MEG_084 0.0707725293305 0.0357588826723 0.0469491068826 0.412488973038 -0.15233685973 -0.89813491653 -0.15233685973 0.960500283795 -0.232879123147 0.89813491653 0.232879123147 0.372989256833
85 MEG_085 -0.0707722753305 0.0357588826723 0.0469491068826 0.412487827635 0.152336666507 0.898135475354 0.152336666507 0.960500461005 -0.232878518647 -0.898135475354 0.232878518647 0.37298828864
86 MEG_086 0.0645896612143 0.0 0.0702287153203 0.618250335472 -0.0 -0.785981248306 -0.0 1.0 -0.0 0.785981248306 0.0 0.618250335472
87 MEG_087 -0.0645896612143 0.0 0.0702287153203 0.618250335472 0.0 0.785981248306 0.0 1.0 -0.0 -0.785981248306 0.0 0.618250335472
88 MEG_088 0.066222119245 -0.0382333507188 0.049521618931 0.524701809918 0.274413611706 -0.80584438968 0.274413611706 0.841567184852 0.46525460029 0.80584438968 -0.46525460029 0.36626899477
89 MEG_089 -0.066222119245 -0.0382333507188 0.049521618931 0.524701809918 -0.274413611706 0.80584438968 -0.274413611706 0.841567184852 0.46525460029 -0.80584438968 -0.46525460029 0.36626899477
90 MEG_090 0.071489571344 -0.0418500567868 0.0049839880937 0.408585986848 0.335957722235 -0.848640029826 0.335957722235 0.809156380101 0.482077132224 0.848640029826 -0.482077132224 0.217742366948
91 MEG_091 -0.071489571344 -0.0418500567868 0.0049839880937 0.408585986848 -0.335957722235 0.848640029826 -0.335957722235 0.809156380101 0.482077132224 -0.848640029826 -0.482077132224 0.217742366948
92 MEG_092 0.0724712813625 -0.0440524908282 -0.0382501147191 0.445815918958 0.313476011591 -0.83843959625 0.313476011591 0.822681283702 0.474266059932 0.83843959625 -0.474266059932 0.26849720266
93 MEG_093 -0.0724712813625 -0.0440524908282 -0.0382501147191 0.445815918958 -0.313476011591 0.83843959625 -0.313476011591 0.822681283702 0.474266059932 -0.83843959625 -0.474266059932 0.26849720266
94 MEG_094 0.0725505293639 0.0550301170346 0.0208673703923 0.0578041209796 -0.192090470788 -0.979673381608 -0.192090470788 0.96083749697 -0.199731208002 0.979673381608 0.199731208002 0.0186416179491
95 MEG_095 -0.0725505293639 0.0550301170346 0.0208673703923 0.0578041209796 0.192090470788 0.979673381608 0.192090470788 0.96083749697 -0.199731208002 -0.979673381608 0.199731208002 0.0186416179491
96 MEG_096 0.0772988054532 0.031062168584 0.0281350725289 0.186190165142 -0.175001933135 -0.966802743999 -0.175001933135 0.962367527045 -0.20790157837 0.966802743999 0.20790157837 0.148557692187
97 MEG_097 -0.0772988054532 0.031062168584 0.0281350725289 0.186190165142 0.175001933135 0.966802743999 0.175001933135 0.962367527045 -0.20790157837 -0.966802743999 0.20790157837 0.148557692187
98 MEG_098 0.0753049054157 0.0132783582496 0.049521618931 0.385377976058 -0.108374224505 -0.91637265511 -0.108374224505 0.980890739219 -0.1615808936 0.91637265511 0.1615808936 0.366268715277
99 MEG_099 -0.0753049054157 0.0132783582496 0.049521618931 0.385377976058 0.108374224505 0.91637265511 0.108374224505 0.980890739219 -0.1615808936 -0.91637265511 0.1615808936 0.366268715277
100 MEG_100 0.0753049054157 -0.0132783582496 0.049521618931 0.385377976058 0.108374224505 -0.91637265511 0.108374224505 0.980890739219 0.1615808936 0.91637265511 -0.1615808936 0.366268715277
101 MEG_101 -0.0753049054157 -0.0132783582496 0.049521618931 0.385377976058 -0.108374224505 0.91637265511 -0.108374224505 0.980890739219 0.1615808936 -0.91637265511 -0.1615808936 0.366268715277
102 MEG_102 0.0770366774483 -0.0278145245229 0.0260804664903 0.373752636547 0.220368615692 -0.900969832953 0.220368615692 0.922455039947 0.31704001718 0.900969832953 -0.31704001718 0.296207676495
103 MEG_103 -0.0770412494484 -0.0278018245227 0.0260799584903 0.373679152588 -0.220281297547 0.901021665041 -0.220281297547 0.92252557096 0.31689544155 -0.901021665041 -0.31689544155 0.296204723548
104 MEG_104 0.0780183874667 -0.0300167045643 -0.0171536363225 0.300373897506 0.24880001314 -0.920800779299 0.24880001314 0.911522102566 0.327453828799 0.920800779299 -0.327453828799 0.211896000073
105 MEG_105 -0.0781575794694 -0.0296346885571 -0.0171681143228 0.300287289279 -0.248701776907 0.920855564169 -0.248701776907 0.911602900892 0.327303494098 -0.920855564169 -0.327303494098 0.211890190171
106 MEG_106 0.0705891413271 0.0621764071689 0.00492785409264 0.134758903688 -0.324701145067 -0.936167295022 -0.324701145067 0.878148606143 -0.351317793345 0.936167295022 0.351317793345 0.0129075098315
107 MEG_107 -0.0705891413271 0.0621764071689 0.00492785409264 0.134758903688 0.324701145067 0.936167295022 0.324701145067 0.878148606143 -0.351317793345 -0.936167295022 0.351317793345 0.0129075098315
108 MEG_108 0.0767740414434 0.0353695006649 0.00957046617992 0.0578041209796 -0.192090470788 -0.979673381608 -0.192090470788 0.96083749697 -0.199731208002 0.979673381608 0.199731208002 0.0186416179491
109 MEG_109 -0.0767740414434 0.0353695006649 0.00957046617992 0.0578041209796 0.192090470788 0.979673381608 0.192090470788 0.96083749697 -0.199731208002 -0.979673381608 0.199731208002 0.0186416179491
110 MEG_110 0.0812949875283 0.0137193022579 0.00288239205419 0.219230938619 -0.143668166804 -0.965037436268 -0.143668166804 0.973563831901 -0.177575119486 0.965037436268 0.177575119486 0.192794770521
111 MEG_111 -0.0812949875283 0.0137193022579 0.00288239205419 0.219230938619 0.143668166804 0.965037436268 0.143668166804 0.973563831901 -0.177575119486 -0.965037436268 0.177575119486 0.192794770521
112 MEG_112 0.0819833275413 0.000204978003854 0.0250207784704 0.283903999524 -0.00795851694118 -0.958819681203 -0.00795851694118 0.999911550977 -0.010656088948 0.958819681203 0.010656088948 0.283815550501
113 MEG_113 -0.0819833275413 0.000218440004107 0.0250202704704 0.283900779742 0.00807295557139 0.958819677859 0.00807295557139 0.999908989411 -0.0108092683826 -0.958819677859 0.0108092683826 0.283809769153
114 MEG_114 0.0812949875283 -0.0149293582807 0.00396595607456 0.227559595527 0.130073723873 -0.965037541675 0.130073723873 0.978096467321 0.162505775197 0.965037541675 -0.162505775197 0.205656062847
115 MEG_115 -0.0812949875283 -0.0149293582807 0.00396595607456 0.227559595527 -0.130073723873 0.965037541675 -0.130073723873 0.978096467321 0.162505775197 -0.965037541675 -0.162505775197 0.205656062847
116 MEG_116 0.0798715715016 0.0223085664194 -0.0132826762497 0.0578041209796 -0.192090470788 -0.979673381608 -0.192090470788 0.96083749697 -0.199731208002 0.979673381608 0.199731208002 0.0186416179491
117 MEG_117 -0.0798715715016 0.0223085664194 -0.0132826762497 0.0578041209796 0.192090470788 0.979673381608 0.192090470788 0.96083749697 -0.199731208002 -0.979673381608 0.199731208002 0.0186416179491
118 MEG_118 0.0830994035623 -0.0016276320306 -0.0182272943427 0.199274663362 -0.00609304526277 -0.979924733508 -0.00609304526277 0.999953635537 -0.00745664647062 0.979924733508 0.00745664647062 0.199228298899
119 MEG_119 -0.0830994035623 -0.00122097802295 -0.018242788343 0.199270871862 0.00622296522868 0.979924688091 0.00622296522868 0.999951637458 -0.00761560563545 -0.979924688091 0.00761560563545 0.19922250932
120 MEG_120 0.0824113175493 0.0122798842309 -0.0403806667592 0.134815219165 -0.156230210311 -0.978476866394 -0.156230210311 0.971788825746 -0.176687859065 0.978476866394 0.176687859065 0.106604044912
121 MEG_121 -0.0824113175493 0.0122798842309 -0.0403806667592 0.134812452063 0.156230709979 0.978477167862 0.156230709979 0.971788735519 -0.176687913503 -0.978477167862 0.176687913503 0.106601187582
122 MEG_122 0.0824113175493 -0.0167619683151 -0.0392821167385 0.144888827116 0.146932333372 -0.978477448481 0.146932333372 0.974752861061 0.168130155723 0.978477448481 -0.168130155723 0.119641688177
123 MEG_123 -0.0824113175493 -0.0167619683151 -0.0392821167385 0.144888827116 -0.146932333372 0.978477448481 -0.146932333372 0.974752861061 0.168130155723 -0.978477448481 -0.168130155723 0.119641688177
124 REF_001 -0.0574242204956 0.101251052386 0.128127713641 0.221198761686 0.896440347728 -0.384012774259 0.896440347728 -0.0318490232173 0.442018486814 0.384012774259 -0.442018486814 -0.810650261531
125 REF_002 0.0490254159517 0.102373905889 0.131493075274 0.222503034056 -0.896198721013 0.383823204472 -0.896198721013 -0.0330228704748 0.442422131545 -0.383823204472 -0.442422131545 -0.810519836419
126 REF_003 -0.069360787652 0.103594593082 0.130986921083 -0.347310972431 -0.17658747001 0.920973373049 -0.17658747001 0.976855280479 0.120708849866 -0.920973373049 -0.120708849866 -0.370455691952
127 REF_004 0.0431879423759 0.106985366145 0.141533355103 0.383166262537 0.0763561288473 0.920517982899 0.0763561288473 0.990548087664 -0.113948355026 -0.920517982899 0.113948355026 0.3737143502
128 REF_005 -0.0611082914288 0.0961811650462 0.138938483688 0.997012450802 -0.040298218601 0.0658955728709 -0.040298218601 0.456428559123 0.888847019455 -0.0658955728709 -0.888847019455 0.453441009925
129 REF_006 0.0527763749922 0.0973005509413 0.142279189541 0.99632914816 0.0447419955488 -0.072982068763 0.0447419955488 0.454664406796 0.889538324653 0.072982068763 -0.889538324653 0.450993554956
130 REF_007 -0.114314226149 0.0198287643728 0.0448114428425 0.149033474209 0.0868247934117 0.985012933323 0.0868247934117 0.991141197071 -0.100501655296 -0.985012933323 0.100501655296 0.14017467128
131 REF_008 0.114314226149 0.0198287643728 0.0448114428425 0.149033474209 -0.0868247934117 -0.985012933323 -0.0868247934117 0.991141197071 -0.100501655296 0.985012933323 0.100501655296 0.14017467128
132 REF_009 -0.109709206063 0.0555492930443 -0.00941197017695 0.0589562738411 0.187574011648 0.98047954998 0.187574011648 0.96261171626 -0.195434576966 -0.98047954998 0.195434576966 0.0215679901007
133 REF_010 0.109709206063 0.0555492930443 -0.00941197017695 0.0589562738411 -0.187574011648 -0.98047954998 -0.187574011648 0.96261171626 -0.195434576966 0.98047954998 0.195434576966 0.0215679901007
134 REF_011 -0.118027452219 -0.0227690684281 -0.0111257082092 0.157170103306 -0.0740177576494 0.984793851615 -0.0740177576494 0.993499722223 0.0864851056297 -0.984793851615 -0.0864851056297 0.150669825529
135 REF_012 0.118027452219 -0.0227690684281 -0.0111257082092 0.157170103306 0.0740177576494 -0.984793851615 0.0740177576494 0.993499722223 0.0864851056297 0.984793851615 -0.0864851056297 0.150669825529
136 REF_013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
137 REF_014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
138 REF_015 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
139 REF_016 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
140 REF_017 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
141 REF_018 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
142 REF_019 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
143 REF_020 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
144 REF_021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

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mne/io/artemis123/utils.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os.path as op
import numpy as np
from ..._fiff._digitization import _artemis123_read_pos
from ...transforms import rotation3d_align_z_axis
from ...utils import logger
def _load_mne_locs(fname=None):
"""Load MNE locs structure from file (if exists) or recreate it."""
if not fname:
# find input file
resource_dir = op.join(op.dirname(op.abspath(__file__)), "resources")
fname = op.join(resource_dir, "Artemis123_mneLoc.csv")
if not op.exists(fname):
raise OSError(f'MNE locs file "{fname}" does not exist')
logger.info(f"Loading mne loc file {fname}")
locs = dict()
with open(fname) as fid:
for line in fid:
vals = line.strip().split(",")
locs[vals[0]] = np.array(vals[1::], np.float64)
return locs
def _generate_mne_locs_file(output_fname):
"""Generate mne coil locs and save to supplied file."""
logger.info("Converting Tristan coil file to mne loc file...")
resource_dir = op.join(op.dirname(op.abspath(__file__)), "resources")
chan_fname = op.join(resource_dir, "Artemis123_ChannelMap.csv")
chans = _load_tristan_coil_locs(chan_fname)
# compute a dict of loc structs
locs = {n: _compute_mne_loc(cinfo) for n, cinfo in chans.items()}
# write it out to output_fname
with open(output_fname, "w") as fid:
for n in sorted(locs.keys()):
fid.write(f"{n},")
fid.write(",".join(locs[n].astype(str)))
fid.write("\n")
def _load_tristan_coil_locs(coil_loc_path):
"""Load the Coil locations from Tristan CAD drawings."""
channel_info = dict()
with open(coil_loc_path) as fid:
# skip 2 Header lines
fid.readline()
fid.readline()
for line in fid:
line = line.strip()
vals = line.split(",")
channel_info[vals[0]] = dict()
if vals[6]:
channel_info[vals[0]]["inner_coil"] = np.array(vals[2:5], np.float64)
channel_info[vals[0]]["outer_coil"] = np.array(vals[5:8], np.float64)
else: # nothing supplied
channel_info[vals[0]]["inner_coil"] = np.zeros(3)
channel_info[vals[0]]["outer_coil"] = np.zeros(3)
return channel_info
def _compute_mne_loc(coil_loc):
"""Convert a set of coils to an mne Struct.
Note input coil locations are in inches.
"""
loc = np.zeros(12)
if (np.linalg.norm(coil_loc["inner_coil"]) == 0) and (
np.linalg.norm(coil_loc["outer_coil"]) == 0
):
return loc
# channel location is inner coil location converted to meters From inches
loc[0:3] = coil_loc["inner_coil"] / 39.370078
# figure out rotation
z_axis = coil_loc["outer_coil"] - coil_loc["inner_coil"]
R = rotation3d_align_z_axis(z_axis)
loc[3:13] = R.T.reshape(9)
return loc
def _read_pos(fname):
"""Read the .pos file and return positions as dig points."""
nas, lpa, rpa, hpi, extra = None, None, None, None, None
with open(fname) as fid:
for line in fid:
line = line.strip()
if len(line) > 0:
parts = line.split()
# The lines can have 4 or 5 parts. First part is for the id,
# which can be an int or a string. The last three are for xyz
# coordinates. The extra part is for additional info
# (e.g. 'Pz', 'Cz') which is ignored.
if len(parts) not in [4, 5]:
continue
if parts[0].lower() == "nasion":
nas = np.array([float(p) for p in parts[-3:]]) / 100.0
elif parts[0].lower() == "left":
lpa = np.array([float(p) for p in parts[-3:]]) / 100.0
elif parts[0].lower() == "right":
rpa = np.array([float(p) for p in parts[-3:]]) / 100.0
elif "hpi" in parts[0].lower():
if hpi is None:
hpi = list()
hpi.append(np.array([float(p) for p in parts[-3:]]) / 100.0)
else:
if extra is None:
extra = list()
extra.append(np.array([float(p) for p in parts[-3:]]) / 100.0)
return _artemis123_read_pos(nas, lpa, rpa, hpi, extra)