def _read_tsq(self, event_name):
"""Read the metadata (TSQ) file of a TDT Tank.
Returns
-------
b : pandas.DataFrame
Recording metadata
"""
# create the path name
tsq_name = self.path + os.extsep + self.header_ext
# read in the raw data as a numpy rec array and convert to DataFrame
b = DataFrame(np.fromfile(tsq_name, dtype=self.tsq_dtype))
# zero based indexing
b.channel -= 1
b.channel = b.channel.astype(f8)
# -1s are invalid
b.channel[b.channel == -1] = np.nan
b.type = EventTypes[b.type].reset_index(drop=True)
b.format = DataTypes[b.format].reset_index(drop=True)
b.timestamp[np.logical_not(b.timestamp)] = np.nan
b.fs[np.logical_not(b.fs)] = np.nan
# fragile subtraction (i.e., what if TDT changes this value?)
b.size -= 10
# create some new indices based on the electrode array
srt = Indexer.sort('channel').reset_index(drop=True)
shank = srt.shank[b.channel].reset_index(drop=True)
tsq = b.join(shank)
# convert the event_name to a number
name = name2num(event_name)
# get the row of the metadata where its value equals the name-number
row = tsq.name == name
# make sure there's at least one event
assert row.any(), 'no event named %s in tank: %s' % (event_name,
self.path)
# get all the metadata for those events
tsq = tsq[row]
# convert to integer where possible
tsq.channel = tsq.channel.astype(int)
tsq.shank = tsq.shank.astype(int)
return tsq, row
def test_name2num():
num_to_test = 10
str_len = 4
letters = string.ascii_letters
x = Series(dict(zip(letters, map(ord, letters))))
base = 256 ** np.arange(str_len)
mn = base.dot(np.repeat(x.min(), str_len))
mx = base.dot(np.repeat(x.max(), str_len))
for _ in xrange(num_to_test):
name = random.sample(letters, str_len)
num = name2num(name)
assert mn <= num <= mx