Exemplo n.º 1
0
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
Exemplo n.º 2
0
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))
Exemplo n.º 3
0
    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)
Exemplo n.º 4
0
        # 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
Exemplo n.º 5
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)
Exemplo n.º 6
0
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:")
Exemplo n.º 7
0
def resample(X, factor):
    idx_rs = np.arange(0, len(X) - 1, factor)
    return X[idx_rs]