def get_spikes(session,area,unit,trial):
if dowarn(): print 'NOTE UNIT IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
if dowarn(): print 'NOTE TRIAL IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
if dowarn(): print 'NOTE EVENT IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
assert trial>0
ByTrialSpikesMS = cgid.data_loader.metaloadvariable(session,area,ByTrialSpikesMS)
return ByTrialSpikesMS[unit-1,trial-1]
def get_trial_event(session, area, trial, event):
if dowarn():
print("TRIAL IS 1 INDEXED FOR MATLAB COMPATIBILITY")
if dowarn():
print("EVENT IS 1 INDEXED FOR MATLAB COMPATIBILITY")
assert trial > 0
debug(trial, event)
return metaloadvariable(session, area, "eventsByTrial")[trial - 1, event - 1]
def get_spikes_event(session,area,unit,trial,event,start,stop):
if dowarn(): print 'NOTE UNIT IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
if dowarn(): print 'NOTE TRIAL IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
if dowarn(): print 'NOTE EVENT IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
assert trial>0
ByTrialSpikesMS = cgid.data_loader.metaloadvariable(session,area,'ByTrialSpikesMS')
spikems = ByTrialSpikesMS[unit-1,trial-1]
te = cgid.data_loader.get_trial_event(session,area,trial,event)
start += te
stop += te
if len(spikems)==0: return np.array(spikems)
spikems = spikems[spikems>=start]
spikems = spikems[spikems<stop]
return np.unique(np.array(spikems)-start)
def get_available_channels(session, area):
"""
Gets the list of channels that are available for a sessiona and area
"""
if dowarn():
print("CHANNEL IS 1 INDEXED FOR MATLAB COMPATIBILITY")
return find(metaloadvariable(session, area, "availableChannels")[:, 0]) + 1
def get_good_channels(session, area, keepBad=False, rule="liberal"):
if dowarn():
print("CHANNEL IS 1 INDEXED FOR MATLAB COMPATIBILITY")
warn("CALLING SEMANTICS CHANGE")
good = get_available_channels(session, area)
if not keepBad:
for bad in get_bad_channels(session, area, rule=rule):
good = np.delete(good, find(good == bad))
return good
def get_spikes_session(session,area,unit,Fs=1000):
'''
spikeTimes:
1xNUNITS cell array of spike times in seconds. These are raw spike times
for the whole session and have not been segmented into individual trials.
'''
if dowarn(): print 'NOTE UNIT IS 1 INDEXED FOR MATLAB COMPATIBILITY CONVENTIONS'
spikeTimes = cgid.data_loader.metaloadvariable(session,area,'spikeTimes')
return int32(spikeTimes[0,unit-1][:,0]*Fs)
def get_valid_trials(session, area=None):
if dowarn():
print("TRIAL IS 1 INDEXED FOR MATLAB COMPATIBILITY")
if area is None:
trials = set(find(metaloadvariable(session, "M1", "eventsByTrial")[:, 0]) + 1)
trials &= set(find(metaloadvariable(session, "PMv", "eventsByTrial")[:, 0]) + 1)
trials &= set(find(metaloadvariable(session, "PMd", "eventsByTrial")[:, 0]) + 1)
return np.array(list(trials))
else:
return np.array(find(1 == metaloadvariable(session, area, "eventsByTrial")[:, 0]))
def metaloaddata(session, area):
"""
Loads a matfile from the provided path, caching it in the global dict
"matfilecache" using path as the lookup key.
Parameters
----------
path : string
unique absolute path to matfile to be loaded
"""
global cgid_matfilecache
path = archive_name(session, area)
if path in cgid_matfilecache:
return cgid_matfilecache[path]
print("caching", path)
if dowarn():
print("loading data...")
data = loadmat(path)
cgid_matfilecache[path] = data
if dowarn():
print("loaded")
return data
def get_data(session, area, trial, event, start, stop, lowf, highf, params):
"""
Need to abstract loading on neural data from the CGID arrays -- this is
getting too complicated.
Interface
request:
session
area
trial
reference event
start time offset
stop time offset
filter start frequency
filter stop frequency
filter parameter codes
returns:
times: len nt trial times in ms for the data relative to reference marker
xys: nelectrode-x-2 x,y positions of electrodes in array map space
data: a nt-x-nelectrode filtered neural data snippit
To do this, we will need to
-- identify and locate relevant data archive
-- import data archive
-- locate relevant trial, possibly in the raw LFP if we need to use padding
-- perform requested filtering on all channels
-- generate time and space bases
-- extract and pack data
Note: trial, event, start, stop are using the matlab convention, 1-index
Extracting the cortical locations of the electrodes is very very very
tricky. We will use the same coordinate system as in the videos, where Y is
the distance from ventral to dorsal along central sulcus from M1 array in
mm. and X is the distance from caudal to rostral from M1 implant in mm
"""
"""
#Test code
session = 'RUS120521'
area = 'PMv'
trial = 10
event = 6
start = -1000
stop = 0
lowf = 15
highf = 30
params = 0
times,xys,data = get_data(session,area,trial,event,start,stop,lowf,highf,params)
"""
if dowarn():
print("TRIAL IS 1 INDEXED FOR MATLAB COMPATIBILITY")
print("loading data..."),
availableChannels = metaloadvariable(session, area, "availableChannels")
eventsByTrial = metaloadvariable(session, area, "eventsByTrial")
print("done")
trialEvents = eventsByTrial[trial - 1]
trialStart = trialEvents[3]
eventCode = trialEvents[event - 1]
dataStart = eventCode + trialStart + start
dataStop = eventCode + trialStart + stop
# extracting and filtering the data is the easy part
times = arange(start, stop)
data = []
for ch in get_good_channels(session, area):
clipped = get_analytic_lfp(session, area, ch, trial, (event, start, stop), lowf, highf, Fs=1000)
data.append(clipped)
# compile the list of spatial locations
box, positions = getElectrodePositions(session, area)
xys = arr([positions[ch] for ch in sorted(list(positions.keys()))])
return times, xys, np.array(data).T, squeeze(availableChannels)
