def harmonic_reference(f0, fs, Ns, Nh=1, standardise_out=False):
"""
Generate reference signals for canonical correlation analysis (CCA)
-based steady-state visual evoked potentials (SSVEPs) detection [1, 2].
function [ y_ref ] = cca_reference(listFreq, fs, Ns, Nh)
Input:
f0 : stimulus frequency
fs : Sampling frequency
Ns : # of samples in trial
Nh : # of harmonics
Output:
X : Generated reference signals with shape (Nf, Ns, 2*Nh)
"""
X = np.zeros((Nh * 2, Ns))
for harm_i in range(Nh):
# Sin and Cos
X[2 * harm_i, :] = np.sin(
np.arange(1, Ns + 1) * (1 / fs) * 2 * np.pi * (harm_i + 1) * f0)
gc.collect()
X[2 * harm_i + 1, :] = np.cos(
np.arange(1, Ns + 1) * (1 / fs) * 2 * np.pi * (harm_i + 1) * f0)
gc.collect()
# print(micropython.mem_info(1))
if standardise_out: # zero mean, unit std. dev
return standardise(X)
return X
ref_result = [0.81649658, 0.81649658, 0.81649658]
for p, q in zip(list(result), ref_result):
print(math.isclose(p, q, rel_tol=1e-06, abs_tol=1e-06))
print("Testing np.median:")
a = np.array([253, 254, 255], dtype=np.uint8)
print(np.median(a))
print(np.median(a, axis=0))
a = np.array([range(255 - 3, 255),
range(240 - 3, 240),
range(250 - 3, 250)],
dtype=np.float)
print(np.median(a))
print(np.median(a, axis=0))
print(np.median(a, axis=1))
print("Testing np.roll:") ## Here is problem
print(np.arange(10))
print(np.roll(np.arange(10), 2))
print(np.roll(np.arange(10), -2))
a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(np.roll(a, 2))
print(np.roll(a, -2))
print("Testing np.clip:")
print(np.clip(5, 3, 6)) ## Here is problem
print(np.clip(7, 3, 6))
print(np.clip(1, 3, 6))
a = np.array([1, 2, 3, 4, 5, 6, 7], dtype=np.int8)
print(np.clip(a, 3, 5))
a = np.array([1, 2, 3, 4, 5, 6, 7], dtype=np.float)
print(np.clip(a, 3, 5))
def __getitem__(self, key: Union[_RClassKeyType, Tuple[_RClassKeyType,
...]]):
if not isinstance(key, tuple):
key = (key, )
objs: List[np.ndarray] = []
scalars: List[int] = []
arraytypes: List[_DType] = []
scalartypes: List[_DType] = []
# these may get overridden in following loop
axis = 0
for idx, item in enumerate(key):
scalar = False
try:
if isinstance(item, np.ndarray):
newobj = item
elif isinstance(item, slice):
step = item.step
start = item.start
stop = item.stop
if start is None:
start = 0
if step is None:
step = 1
if isinstance(step, complex):
size = int(abs(step))
newobj: np.ndarray = np.linspace(start, stop, num=size)
else:
newobj = np.arange(start, stop, step)
# if is number
elif isinstance(item, (int, float, bool)):
newobj = np.array([item])
scalars.append(len(objs))
scalar = True
scalartypes.append(newobj.dtype())
else:
newobj = np.array(item)
except TypeError:
raise Exception(
"index object %s of type %s is not supported by r_[]" %
(str(item), type(item)))
objs.append(newobj)
if not scalar and isinstance(newobj, np.ndarray):
arraytypes.append(newobj.dtype())
# Ensure that scalars won't up-cast unless warranted
final_dtype = min(arraytypes + scalartypes)
for idx, obj in enumerate(objs):
if obj.dtype != final_dtype:
objs[idx] = np.array(objs[idx], dtype=final_dtype)
return np.concatenate(tuple(objs), axis=axis)
# Prior sample for pre-emphasis
self.prior = 0
self.alpha = alpha
# Build mel filter matrix
self.filters = numpy.zeros((nfft/2+1,nfilt), 'd')
dfreq = float(samprate) / nfft
if upperf > samprate/2:
raise(Exception,
"Upper frequency %f exceeds Nyquist %f" % (upperf, samprate/2))
melmax = mel(upperf)
melmin = mel(lowerf)
dmelbw = (melmax - melmin) / (nfilt + 1)
# Filter edges, in Hz
filt_edge = melinv(melmin + dmelbw * numpy.arange(nfilt + 2, dtype='d'))
for whichfilt in range(0, nfilt):
# Filter triangles, in DFT points
leftfr = round(filt_edge[whichfilt] / dfreq)
centerfr = round(filt_edge[whichfilt + 1] / dfreq)
rightfr = round(filt_edge[whichfilt + 2] / dfreq)
# For some reason this is calculated in Hz, though I think
# it doesn't really matter
fwidth = (rightfr - leftfr) * dfreq
height = 2. / fwidth
if centerfr != leftfr:
leftslope = height / (centerfr - leftfr)
else:
leftslope = 0
def hamm(M):
x = np.arange(M, dtype=np.float)
pi = np.array([2*np.pi/(M - 1)])
a_54, b_46 = np.array([0.54]), np.array([0.46])
return a_54 - b_46*np.cos(pi*x)
print(np.array(np.array(range(5), dtype=np.uint16), dtype=np.int8))
print(np.array(np.array(range(5), dtype=np.uint16), dtype=np.uint16))
print(np.array(np.array(range(5), dtype=np.uint16), dtype=np.int16))
print(np.array(np.array(range(5), dtype=np.uint16), dtype=np.float))
print(np.array(np.array(range(5), dtype=np.int16), dtype=np.uint8))
print(np.array(np.array(range(5), dtype=np.int16), dtype=np.int8))
print(np.array(np.array(range(5), dtype=np.int16), dtype=np.uint16))
print(np.array(np.array(range(5), dtype=np.int16), dtype=np.int16))
print(np.array(np.array(range(5), dtype=np.int16), dtype=np.float))
print(np.array(np.array(range(5), dtype=np.float), dtype=np.uint8))
print(np.array(np.array(range(5), dtype=np.float), dtype=np.int8))
print(np.array(np.array(range(5), dtype=np.float), dtype=np.uint16))
print(np.array(np.array(range(5), dtype=np.float), dtype=np.int16))
print(np.array(np.array(range(5), dtype=np.float), dtype=np.float))
print("Array creation using ARANGE:")
print(np.arange(10))
print(np.arange(2, 10))
print(np.arange(2, 10, 3))
print(np.arange(2, 10, 3, dtype=np.float))
print("Array concatenation:")
a = np.array([1,2,3], dtype=np.float)
b = np.array([4,5,6], dtype=np.float)
print(np.concatenate((a,b)))
print(np.concatenate((a,b), axis=0))
a = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype=np.float)
b = np.array([[1,2,3],[4,5,6],[7,8,9]], dtype=np.float)
print(np.concatenate((a,b), axis=0))
print(np.concatenate((a,b), axis=1))
print(np.concatenate((b,a), axis=0))
print(np.concatenate((b,a), axis=1))
print("Identity array creation:")
def resample(X, factor):
idx_rs = np.arange(0, len(X) - 1, factor)
return X[idx_rs]