def calc_flatten_matrix(flatten: FlattenOrStr,
stereo_nchan: int) -> np.ndarray:
"""Raises CorrError on invalid input.
If flatten is Flatten.Stereo, returns shape=(nchan,nchan) identity matrix.
- (N,nchan) @ (nchan,nchan) = (N,nchan).
Otherwise, returns shape=(nchan) flattening matrix.
- (N,nchan) @ (nchan) = (N)
https://docs.scipy.org/doc/numpy/reference/generated/numpy.matmul.html#numpy.matmul
'''
If the second argument is 1-D,
it is promoted to a matrix by appending a 1 to its dimensions.
After matrix multiplication the appended 1 is removed."
'''
"""
if flatten is Flatten.Stereo:
# 2D identity (results in 2-dim data)
flatten_matrix = np.eye(stereo_nchan, dtype=FLOAT)
# 1D (results in 1-dim data)
elif flatten is Flatten.SumAvg:
flatten_matrix = np.ones(stereo_nchan, dtype=FLOAT) / stereo_nchan
elif flatten is Flatten.DiffAvg:
flatten_matrix = calc_flatten_matrix(str(flatten), stereo_nchan)
flatten_matrix = rightpad(flatten_matrix, stereo_nchan, 0)
else:
words = flatten.replace(",", " ").split()
try:
flatten_matrix = np.array([FLOAT(word) for word in words])
except ValueError as e:
raise CorrError("Invalid stereo flattening matrix") from e
flatten_abs_sum = np.sum(np.abs(flatten_matrix))
if flatten_abs_sum == 0:
raise CorrError(
"Stereo flattening matrix must have nonzero elements")
flatten_matrix /= flatten_abs_sum
assert flatten_matrix.dtype == FLOAT, flatten_matrix.dtype
return flatten_matrix
def _calc_lag_prevention(self) -> np.ndarray:
""" Returns input-data window,
which zeroes out all data older than 1-ish frame old.
See https://github.com/jimbo1qaz/corrscope/wiki/Correlation-Trigger
"""
N = self._buffer_nsamp
halfN = N // 2
# - Create a cosine taper of `width` <= 1 frame
# - Right-pad(value=1, len=1 frame)
# - Place in left half of N-sample buffer.
# To avoid cutting off data, use a narrow transition zone (invariant to stride).
lag_prevention = self.cfg.lag_prevention
tsamp_frame = self._tsamp_frame
transition_nsamp = round(tsamp_frame *
lag_prevention.transition_frames)
# Left half of a Hann cosine taper
# Width (type=subsample) = min(frame * lag_prevention, 1 frame)
assert transition_nsamp <= tsamp_frame
width = transition_nsamp
taper = windows.hann(width * 2)[:width]
# Right-pad=1 taper to lag_prevention.max_frames long [t-#*f, t]
taper = rightpad(taper,
iround(tsamp_frame * lag_prevention.max_frames))
# Left-pad=0 taper to left `halfN` of data_taper [t-halfN, t]
taper = leftpad(taper, halfN)
# Generate left half-taper to prevent correlating with 1-frame-old data.
# Right-pad=1 taper to [t-halfN, t-halfN+N]
# TODO switch to rightpad()? Does it return FLOAT or not?
data_taper = np.ones(N, dtype=FLOAT)
data_taper[:halfN] = np.minimum(data_taper[:halfN], taper)
return data_taper