Python numpy.bitwise_and方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def test_values(self):
        for dt in self.bitwise_types:
            zeros = np.array([0], dtype=dt)
            ones = np.array([-1], dtype=dt)
            msg = "dt = '%s'" % dt.char

            assert_equal(np.bitwise_not(zeros), ones, err_msg=msg)
            assert_equal(np.bitwise_not(ones), zeros, err_msg=msg)

            assert_equal(np.bitwise_or(zeros, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_or(zeros, ones), ones, err_msg=msg)
            assert_equal(np.bitwise_or(ones, zeros), ones, err_msg=msg)
            assert_equal(np.bitwise_or(ones, ones), ones, err_msg=msg)

            assert_equal(np.bitwise_xor(zeros, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_xor(zeros, ones), ones, err_msg=msg)
            assert_equal(np.bitwise_xor(ones, zeros), ones, err_msg=msg)
            assert_equal(np.bitwise_xor(ones, ones), zeros, err_msg=msg)

            assert_equal(np.bitwise_and(zeros, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_and(zeros, ones), zeros, err_msg=msg)
            assert_equal(np.bitwise_and(ones, zeros), zeros, err_msg=msg)
            assert_equal(np.bitwise_and(ones, ones), ones, err_msg=msg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def _freq_vector(f, b, typ='lp'):
    """
        Returns a frequency modulated vector for filtering

        :param f: frequency vector, uniform and monotonic
        :param b: 2 bounds array
        :return: amplitude modulated frequency vector
    """
    filc = ((f <= b[0]).astype(float) +
            np.bitwise_and(f > b[0], f < b[1]).astype(float) *
            (0.5 * (1 + np.sin(np.pi * (f - ((b[0] + b[1]) / 2)) /
             (b[0] - b[1])))))
    if typ == 'hp':
        return 1 - filc
    elif typ == 'lp':
        return filc 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def set_ufunc(self, scalar_op):
        # This is probably a speed up of the implementation
        if isinstance(scalar_op, theano.scalar.basic.Add):
            self.ufunc = numpy.add
        elif isinstance(scalar_op, theano.scalar.basic.Mul):
            self.ufunc = numpy.multiply
        elif isinstance(scalar_op, theano.scalar.basic.Maximum):
            self.ufunc = numpy.maximum
        elif isinstance(scalar_op, theano.scalar.basic.Minimum):
            self.ufunc = numpy.minimum
        elif isinstance(scalar_op, theano.scalar.basic.AND):
            self.ufunc = numpy.bitwise_and
        elif isinstance(scalar_op, theano.scalar.basic.OR):
            self.ufunc = numpy.bitwise_or
        elif isinstance(scalar_op, theano.scalar.basic.XOR):
            self.ufunc = numpy.bitwise_xor
        else:
            self.ufunc = numpy.frompyfunc(scalar_op.impl, 2, 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def agent_start(self, observation):

        # Get intensity from current observation array
        tmp = np.bitwise_and(np.asarray(observation.intArray[128:]).reshape([210, 160]), 0b0001111)  # Get Intensity from the observation
        obs_array = (spm.imresize(tmp, (110, 84)))[110-84-8:110-8, :]  # Scaling

        # Initialize State
        self.state = np.zeros((4, 84, 84), dtype=np.uint8)
        self.state[0] = obs_array
        state_ = cuda.to_gpu(np.asanyarray(self.state.reshape(1, 4, 84, 84), dtype=np.float32))

        # Generate an Action e-greedy
        returnAction = Action()
        action, Q_now = self.DQN.e_greedy(state_, self.epsilon)
        returnAction.intArray = [action]

        # Update for next step
        self.lastAction = copy.deepcopy(returnAction)
        self.last_state = self.state.copy()
        self.last_observation = obs_array

        return returnAction 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def agent_start(self, observation):

        # Preprocess
        tmp = np.bitwise_and(np.asarray(observation.intArray[128:]).reshape([210, 160]), 0b0001111)  # Get Intensity from the observation
        obs_array = (spm.imresize(tmp, (110, 84)))[110-84-8:110-8, :]  # Scaling

        # Initialize State
        self.state = np.zeros((4, 84, 84), dtype=np.uint8)
        self.state[0] = obs_array
        state_ = cuda.to_gpu(np.asanyarray(self.state.reshape(1, 4, 84, 84), dtype=np.float32))

        # Generate an Action e-greedy
        returnAction = Action()
        action, Q_now = self.DQN.e_greedy(state_, self.epsilon)
        returnAction.intArray = [action]

        # Update for next step
        self.lastAction = copy.deepcopy(returnAction)
        self.last_state = self.state.copy()
        self.last_observation = obs_array

        return returnAction 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def guess_wfnsym(self, norb, nelec, fcivec=None, orbsym=None, wfnsym=None,
                     **kwargs):
        if orbsym is None:
            orbsym = self.orbsym
        if fcivec is None:
            wfnsym = direct_spin1_symm._id_wfnsym(self, norb, nelec, orbsym, wfnsym)

        elif wfnsym is None:
            strsa, strsb = getattr(fcivec, '_strs', self._strs)
            if isinstance(fcivec, numpy.ndarray) and fcivec.ndim <= 2:
                wfnsym = addons._guess_wfnsym(fcivec, strsa, strsb, orbsym)
            else:
                wfnsym = [addons._guess_wfnsym(c, strsa, strsb, orbsym)
                          for c in fcivec]
                if any(wfnsym[0] != x for x in wfnsym):
                    warnings.warn('Different wfnsym %s found in different CI vecotrs' % wfnsym)
                wfnsym = wfnsym[0]

        else:
            strsa, strsb = getattr(fcivec, '_strs', self._strs)
            na, nb = strsa.size, strsb.size

            airreps = numpy.zeros(na, dtype=numpy.int32)
            birreps = numpy.zeros(nb, dtype=numpy.int32)
            for i, ir in enumerate(orbsym):
                airreps[numpy.bitwise_and(strsa, 1<<i) > 0] ^= ir
                birreps[numpy.bitwise_and(strsb, 1<<i) > 0] ^= ir

            wfnsym = direct_spin1_symm._id_wfnsym(self, norb, nelec, orbsym, wfnsym)
            mask = (airreps.reshape(-1,1) ^ birreps) == wfnsym

            if isinstance(fcivec, numpy.ndarray) and fcivec.ndim <= 2:
                fcivec = [fcivec]
            if all(abs(c.reshape(na, nb)[mask]).max() < 1e-5 for c in fcivec):
                raise RuntimeError('Input wfnsym is not consistent with fcivec coefficients')

        verbose = kwargs.get('verbose', None)
        log = logger.new_logger(self, verbose)
        log.debug('Guessing CI wfn symmetry = %s', wfnsym)
        return wfnsym 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def _symmetrize_wfn(ci, strsa, strsb, orbsym, wfnsym=0):
    ci = ci.reshape(strsa.size,strsb.size)
    airreps = numpy.zeros(strsa.size, dtype=numpy.int32)
    birreps = numpy.zeros(strsb.size, dtype=numpy.int32)
    for i, ir in enumerate(orbsym):
        airreps[numpy.bitwise_and(strsa, 1<<i) > 0] ^= ir
        birreps[numpy.bitwise_and(strsb, 1<<i) > 0] ^= ir
    mask = (airreps.reshape(-1,1) ^ birreps) == wfnsym
    ci1 = numpy.zeros_like(ci)
    ci1[mask] = ci[mask]
    ci1 *= 1/numpy.linalg.norm(ci1)
    return ci1 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def _gen_strs_irrep(strs, orbsym):
    orbsym = numpy.asarray(orbsym) % 10
    irreps = numpy.zeros(len(strs), dtype=numpy.int32)
    if isinstance(strs, cistring.OIndexList):
        nocc = strs.shape[1]
        for i in range(nocc):
            irreps ^= orbsym[strs[:,i]]
    else:
        for i, ir in enumerate(orbsym):
            irreps[numpy.bitwise_and(strs, 1<<i) > 0] ^= ir
    return irreps 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def exprsco_to_rawsco(exprsco, clock=1789773.):
  rate, nsamps, exprsco = exprsco

  m = exprsco[:, :3, 0]
  m_zero = np.where(m == 0)

  m = m.astype(np.float32)
  f = 440 * np.power(2, ((m - 69) / 12))

  f_p, f_tr = f[:, :2], f[:, 2:]

  t_p = np.round((clock / (16 * f_p)) - 1)
  t_tr = np.round((clock / (32 * f_tr)) - 1)
  t = np.concatenate([t_p, t_tr], axis=1)

  t = t.astype(np.uint16)
  t[m_zero] = 0
  th = np.right_shift(np.bitwise_and(t, 0b11100000000), 8)
  tl = np.bitwise_and(t, 0b00011111111)

  rawsco = np.zeros((exprsco.shape[0], 4, 4), dtype=np.uint8)
  rawsco[:, :, 2:] = exprsco[:, :, 1:]
  rawsco[:, :3, 0] = th
  rawsco[:, :3, 1] = tl
  rawsco[:, 3, 1:] = exprsco[:, 3, :]

  return (clock, rate, nsamps, rawsco) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def test_truth_table_bitwise(self):
        arg1 = [False, False, True, True]
        arg2 = [False, True, False, True]

        out = [False, True, True, True]
        assert_equal(np.bitwise_or(arg1, arg2), out)

        out = [False, False, False, True]
        assert_equal(np.bitwise_and(arg1, arg2), out)

        out = [False, True, True, False]
        assert_equal(np.bitwise_xor(arg1, arg2), out) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def test_types(self):
        for dt in self.bitwise_types:
            zeros = np.array([0], dtype=dt)
            ones = np.array([-1], dtype=dt)
            msg = "dt = '%s'" % dt.char

            assert_(np.bitwise_not(zeros).dtype == dt, msg)
            assert_(np.bitwise_or(zeros, zeros).dtype == dt, msg)
            assert_(np.bitwise_xor(zeros, zeros).dtype == dt, msg)
            assert_(np.bitwise_and(zeros, zeros).dtype == dt, msg) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def test_identity(self):
        assert_(np.bitwise_or.identity == 0, 'bitwise_or')
        assert_(np.bitwise_xor.identity == 0, 'bitwise_xor')
        assert_(np.bitwise_and.identity == -1, 'bitwise_and') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import bitwise_and [as 別名]
def __init__(self, image, label, regions, dxyz, iorigin=[0, 0, 0],
                 dims2xyz=[0, 1, 2], xyz2dims=[0, 1, 2]):
        """
        self.image: image volume (ap, ml, dv)
        self.label: label volume (ap, ml, dv)
        self.bc: atlas.BrainCoordinate object
        self.regions: atlas.BrainRegions object
        self.top: 2d np array (ap, ml) containing the z-coordinate (m) of the surface of the brain
        self.dims2xyz and self.zyz2dims: map image axis order to xyz coordinates order
        """
        self.image = image
        self.label = label
        self.regions = regions
        self.dims2xyz = dims2xyz
        self.xyz2dims = xyz2dims
        assert(np.all(self.dims2xyz[self.xyz2dims] == np.array([0, 1, 2])))
        assert(np.all(self.xyz2dims[self.dims2xyz] == np.array([0, 1, 2])))
        # create the coordinate transform object that maps volume indices to real world coordinates
        nxyz = np.array(self.image.shape)[self.dims2xyz]
        bc = BrainCoordinates(nxyz=nxyz, xyz0=(0, 0, 0), dxyz=dxyz)
        self.bc = BrainCoordinates(nxyz=nxyz, xyz0=- bc.i2xyz(iorigin), dxyz=dxyz)
        """
        Get the volume top surface
        """
        l0 = self.label == 0
        s = np.zeros(self.label.shape[:2])
        s[np.all(l0, axis=2)] = np.nan
        iz = 0
        # not very elegant, but fast enough for our purposes
        while True:
            if iz >= l0.shape[2]:
                break
            inds = np.bitwise_and(s == 0, ~l0[:, :, iz])
            s[inds] = iz
            iz += 1
        self.top = self.bc.i2z(s)