def check_orth(seq, tol=1.0e-04, verbose=True):
"""Check the orthogonality of the basis sequence *seq*."""
orth = True
if verbose: iprint('[I] Orthonormality check:')
i = 0
rowvec = np.zeros(len(seq))
for s1 in seq:
if verbose:
with ui.context(ui.colors.OKGREEN) as c:
print('[I] row {}:'.format(i), end="")
j = 0
for s2 in seq:
v = np.dot(s1, s2)
rowvec[j] = v
if i == j and abs(v - 1.0) > tol:
orth = False
if i != j and abs(v) > tol:
orth = False
j += 1
if verbose:
with ui.context(ui.colors.OKGREEN) as c:
for v in rowvec:
print("{0:5.2f} ".format(v), end="")
print(' ')
i += 1
if verbose:
if orth: iprint("Basis is orthonormal.")
else: eprint("Basis is NOT orthonormal")
return orth
def create_feature(c, name, units=None):
"""Experimental: Creates a new feature table that can be used to store user-defined features."""
try:
c.execute('CREATE TABLE ' + name +
' (timestamp integer primary key, value real)')
except s3.OperationalError as e:
eprint("{}".format(e))
finally:
return True
def get_spike_basisid(self, spike_id):
"""Returns the basis_id associated with the given spike_id."""
self.cur.execute(
'SELECT basisID FROM spikebasis where spikeID == {};'.format(
spike_id))
result = self.cur.fetchone()
if result is None:
eprint('Spike id {} has no basis!'.format(spike_id))
return result[0]
def get_functions(self, basis_id, nfuncs=None, width=None):
if nfuncs is None:
self.cur.execute(
'select max(component) from functions where basisID={};'.
format(basis_id))
result = self.fetchone()
if result is not None:
nfuncs = result[0] + 1
else:
eprint("Error: Could not infer nfuncs from functions table!")
return None
if width is None:
self.cur.execute(
'select max(k) from functions where basisID={};'.format(
basis_id))
result = self.fetchone()
if result is not None:
width = result[0] + 1
else:
eprint("Error: Could not infer width from functions table!")
return None
width = int(width)
basis = []
for i in range(nfuncs):
ker = numpy.zeros(width)
basis.append(ker)
# Now fill in the vectors from the functions table - these
# have to be rectangular (i.e., same number of samples for
# each function):
self.cur.execute(
'select component,k,value from functions where basisID={};'.format(
basis_id))
result = self.fetchall()
for i in range(len(result)):
r = result[i]
c = r[0]
k = r[1]
v = r[2]
vec = basis[c]
vec[k] = v
return basis
def get_db_coefs(self, which, into=None):
"""Deprecated."""
meta = self.get_metadata()
nchannels = meta[1]
eprint("get_db_coefs has been DEPRECATED!")
if into != None:
r = into
else:
self.execute('select count(*) from spikecoefs where channel == 0;')
result = self.fetchone()
nspikes = result[0]
r = numpy.zeros((nspikes, nchannels))
for k in range(nchannels):
self.execute(
'SELECT coef from spikecoefs where cindex={} and channel=={} order by spikeID;'
.format(which, k))
result = self.fetchall()
for j in range(nspikes):
r[j][k] = result[j][0]
return r
def make_meta_table(self, samprate, nchannels, fname, verbose=False):
"""Make the metadata table for this event store, specifying the sampling rate, number of channels, and full name of the raw data source."""
ret = True
self.sample_rate = samprate
self.nchannels = nchannels
self.recording = fname
self.schema.create_table(self, 'meta')
self.execute('SELECT * from meta;')
result = self.fetchone()
iprint("{}".format(result))
if result is None:
self.execute('INSERT INTO meta values ({}, {}, "{}");'.format(
samprate, nchannels, fname))
ret = True
else:
if result[0] != samprate or result[1] != nchannels or result[
2] != fname:
eprint(
"You supplied arguments that are inconsistent with this db file!"
)
eprint("db file says: {}".format(result))
eprint("args say: {} {} {}".format(samprate, nchannels, fname))
ret = False
else:
if verbose:
iprint("Valid db file for these arguments.")
ret = True
return ret