Python numpy.full方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def query(self, coords, order=1):
        """
        Returns the map value at the specified location(s) on the sky.

        Args:
            coords (`astropy.coordinates.SkyCoord`): The coordinates to query.
            order (Optional[int]): Interpolation order to use. Defaults to `1`,
                for linear interpolation.

        Returns:
            A float array containing the map value at every input coordinate.
            The shape of the output will be the same as the shape of the
            coordinates stored by `coords`.
        """
        out = np.full(len(coords.l.deg), np.nan, dtype='f4')

        for pole in self.poles:
            m = (coords.b.deg >= 0) if pole == 'ngp' else (coords.b.deg < 0)

            if np.any(m):
                data, w = self._data[pole]
                x, y = w.wcs_world2pix(coords.l.deg[m], coords.b.deg[m], 0)
                out[m] = map_coordinates(data, [y, x], order=order, mode='nearest')

        return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def find_subject_path(sid, check_path=True):
    '''
    find_subject_path(sub) yields the full path of a HCP subject with the name given by the string
      sub, if such a subject can be found in the HCP search paths. See also add_subject_path.

    If no subject is found, then None is returned.
    '''
    # if it's a full/relative path already, use it:
    sub = str(sid)
    if ((not check_path or is_hcp_subject_path(sub)) and
        (check_path is None or os.path.isdir(sub))):
        return sub
    # check the subject directories:
    sdirs = config['hcp_subject_paths']
    return next((os.path.abspath(p) for sd in sdirs
                 for p in [os.path.join(sd, sub)]
                 if ((not check_path or is_hcp_subject_path(p)) and
                     (check_path is None or os.path.isdir(p)))),
                None) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def cos_edge(f=Ellipsis, width=np.pi, offset=0, scale=1):
    '''
    cos_edge() yields a potential function g(x) that calculates 0 for x < pi/2, 1 for x > pi/2, and
      0.5*(1 + cos(pi/2*(1 - x))) for x between -pi/2 and pi/2.
    
    The full formulat of the cosine well is, including optional arguments:
      scale/2 * (1 + cos(pi*(0.5 - (x - offset)/width)

    The following optional arguments may be given:
      * width (default: pi) specifies that the frequency of the cos-curve should be pi/width; the
        width is the distance between the points on the cos-curve with the value of 1.
      * offset (default: 0) specifies the offset of the minimum value of the coine curve on the
        x-axis.
      * scale (default: 1) specifies the height of the cosine well.
    '''
    f = to_potential(f)
    freq = np.pi/2
    (xmn,xmx) = (offset - width/2, offset + width/2)
    F = piecewise(scale,
                  ((-np.inf, xmn), 0),
                  ((xmn,xmx), scale/2 * (1 + cos(np.pi*(0.5 - (identity - offset)/width)))))
    if   is_const_potential(f):    return const_potential(F.value(f.c))
    elif is_identity_potential(f): return F
    else:                          return compose(F, f) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def signed_face_areas(faces, axis=1):
    '''
    signed_face_areas(faces) yields a potential function f(x) that calculates the signed area of
      each face represented by the simplices matrix faces.

    If faces is None, then the parameters must arrive in the form of a flattened (n x 3 x 2) matrix
    where n is the number of triangles. Otherwise, the faces matrix must be either (n x 3) or (n x 3
    x s); if the former, each row must list the vertex indices for the faces where the vertex matrix
    is presumed to be shaped (V x 2). Alternately, faces may be a full (n x 3 x 2) simplex array of
    the indices into the parameters.

    The optional argument axis (default: 1) may be set to 0 if the faces argument is a matrix but
    the coordinate matrix will be (2 x V) instead of (V x 2).
    '''
    faces = np.asarray(faces)
    if len(faces.shape) == 2:
        if faces.shape[1] != 3: faces = faces.T
        n = 2 * (np.max(faces) + 1)
        if axis == 0: tmp = np.reshape(np.arange(n), (2,-1)).T
        else:         tmp = np.reshape(np.arange(n), (-1,2))
        faces = np.reshape(tmp[faces.flat], (-1,3,2))
    faces = faces.flatten()
    return compose(TriangleSignedArea2DPotential(), part(Ellipsis, faces)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def face_areas(faces, axis=1):
    '''
    face_areas(faces) yields a potential function f(x) that calculates the unsigned area of each
      faces represented by the simplices matrix faces.

    If faces is None, then the parameters must arrive in the form of a flattened (n x 3 x 2) matrix
    where n is the number of triangles. Otherwise, the faces matrix must be either (n x 3) or (n x 3
    x s); if the former, each row must list the vertex indices for the faces where the vertex matrix
    is presumed to be shaped (V x 2). Alternately, faces may be a full (n x 3 x 2) simplex array of
    the indices into the parameters.

    The optional argument axis (default: 1) may be set to 0 if the faces argument is a matrix but
    the coordinate matrix will be (2 x V) instead of (V x 2).
    '''
    faces = np.asarray(faces)
    if len(faces.shape) == 2:
        if faces.shape[1] != 3: faces = faces.T
        n = 2 * (np.max(faces) + 1)
        if axis == 0: tmp = np.reshape(np.arange(n), (2,-1)).T
        else:         tmp = np.reshape(np.arange(n), (-1,2))
        faces = np.reshape(tmp[faces.flat], (-1,3,2))
    faces = faces.flatten()
    return compose(TriangleArea2DPotential(), part(Ellipsis, faces)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def _retinotopic_field_sign_triangles(m, retinotopy):
    t = m.tess if isinstance(m, geo.Mesh) or isinstance(m, geo.Topology) else m
    # get the polar angle and eccen data as a complex number in degrees
    if pimms.is_str(retinotopy):
        (x,y) = as_retinotopy(retinotopy_data(m, retinotopy), 'geographical')
    elif retinotopy is Ellipsis:
        (x,y) = as_retinotopy(retinotopy_data(m, 'any'),      'geographical')
    else:
        (x,y) = as_retinotopy(retinotopy,                     'geographical')
    # Okay, now we want to make some coordinates...
    coords = np.asarray([x, y])
    us = coords[:, t.indexed_faces[1]] - coords[:, t.indexed_faces[0]]
    vs = coords[:, t.indexed_faces[2]] - coords[:, t.indexed_faces[0]]
    (us,vs) = [np.concatenate((xs, np.full((1, t.face_count), 0.0))) for xs in [us,vs]]
    xs = np.cross(us, vs, axis=0)[2]
    xs[np.isclose(xs, 0)] = 0
    return np.sign(xs) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def test_gluon_trainer_step():
    def check_trainer_step():
        ctx = mx.cpu(0)
        shape = (10, 1)
        x = mx.gluon.Parameter('x', shape=shape)
        x.initialize(ctx=ctx, init='ones')
        trainer = mx.gluon.Trainer([x], 'sgd', {'learning_rate': 1.0, 'multi_precision': False}, kvstore=kv)
        with mx.autograd.record():
            w = x.data(ctx)
            y = (my_rank + 1) * w
            y.backward()
        trainer.step(1)
        expected = 1 - (1 + nworker) * nworker / 2
        assert_almost_equal(x.data(ctx).asnumpy(), np.full(shape, expected))
    check_trainer_step()
    print('worker ' + str(my_rank) + ' passed test_gluon_trainer_step') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def test_gluon_trainer_sparse_step():
    def check_trainer_sparse_step():
        ctx = mx.cpu(0)
        shape = (2, 10)
        all_rows = mx.nd.arange(0, shape[0], ctx=ctx)
        x = mx.gluon.Parameter('x', shape=shape, stype='row_sparse', grad_stype='row_sparse')
        x.initialize(ctx=ctx, init='ones')
        trainer = mx.gluon.Trainer([x], 'sgd', {'learning_rate': 1.0}, kvstore=kv)
        with mx.autograd.record():
            w = x.row_sparse_data(all_rows)
            y = (my_rank + 1) * w
            y.backward()
        trainer.step(1)
        expected = 1 - (1 + nworker) * nworker / 2
        assert_almost_equal(x.row_sparse_data(all_rows).asnumpy(), np.full(shape, expected))
    check_trainer_sparse_step()
    print('worker ' + str(my_rank) + ' passed test_gluon_trainer_sparse_step') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def create_colorful_test_image(self):
    """This function creates an image that can be used to test vis functions.

    It makes an image composed of four colored rectangles.

    Returns:
      colorful test numpy array image.
    """
    ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)
    ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)
    ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)
    imr = np.concatenate((ch255, ch128, ch128), axis=2)
    img = np.concatenate((ch255, ch255, ch0), axis=2)
    imb = np.concatenate((ch255, ch0, ch255), axis=2)
    imw = np.concatenate((ch128, ch128, ch128), axis=2)
    imu = np.concatenate((imr, img), axis=1)
    imd = np.concatenate((imb, imw), axis=1)
    image = np.concatenate((imu, imd), axis=0)
    return image 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def make_source_target_alignment(self, sequence):
        space_char_code = self._char_map_inverse[' ']
        unknown_word_code = self._word_map_inverse['<unknown>']

        source = []
        target = []
        length = 0

        for word in sequence.split(' '):
            source.append(
                np.array([space_char_code] + self.encode_source(word),
                         dtype='int32')
            )
            target.append(
                np.full(len(word) + 1, self.encode_target([word])[0],
                        dtype='int32')
            )
            length += 1 + len(word)

        # concatenate data
        return (
            length,
            np.concatenate(source),
            np.concatenate(target)
        ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def __init__(self, epsilon=1e-4, shape=(), scope=''):
        sess = get_session()

        self._new_mean = tf.placeholder(shape=shape, dtype=tf.float64)
        self._new_var = tf.placeholder(shape=shape, dtype=tf.float64)
        self._new_count = tf.placeholder(shape=(), dtype=tf.float64)

        
        with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
            self._mean  = tf.get_variable('mean',  initializer=np.zeros(shape, 'float64'),      dtype=tf.float64)
            self._var   = tf.get_variable('std',   initializer=np.ones(shape, 'float64'),       dtype=tf.float64)    
            self._count = tf.get_variable('count', initializer=np.full((), epsilon, 'float64'), dtype=tf.float64)

        self.update_ops = tf.group([
            self._var.assign(self._new_var),
            self._mean.assign(self._new_mean),
            self._count.assign(self._new_count)
        ])

        sess.run(tf.variables_initializer([self._mean, self._var, self._count]))
        self.sess = sess
        self._set_mean_var_count() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def _positional_to_optimal(self, K):
        k, l = self.k, self.l

        suffix = np.full((len(K), self.l), 0.0)
        X = np.column_stack([K, suffix])
        X[:, self.k + self.l - 1] = 0.35

        for i in range(self.k + self.l - 2, self.k - 1, -1):
            m = X[:, i + 1:k + l]
            val = m.sum(axis=1) / m.shape[1]
            X[:, i] = 0.35 ** ((0.02 + 1.96 * val) ** -1)

        ret = X * (2 * (np.arange(self.n_var) + 1))
        return ret


# ---------------------------------------------------------------------------------------------------------
# TRANSFORMATIONS
# --------------------------------------------------------------------------------------------------------- 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def _do(self, problem, pop, off, **kwargs):
        ret = np.full((len(pop), 1), False)

        pop_F, pop_CV, pop_feasible = pop.get("F", "CV", "feasible")
        off_F, off_CV, off_feasible = off.get("F", "CV", "feasible")

        if problem.n_constr > 0:

            # 1) Both infeasible and constraints have been improved
            ret[(~pop_feasible & ~off_feasible) & (off_CV < pop_CV)] = True

            # 2) A solution became feasible
            ret[~pop_feasible & off_feasible] = True

            # 3) Both feasible but objective space value has improved
            ret[(pop_feasible & off_feasible) & (off_F < pop_F)] = True

        else:
            ret[off_F < pop_F] = True

        return ret[:, 0] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def _do(self, problem, X, **kwargs):
        n_parents, n_matings, n_var = X.shape

        _X = np.full((self.n_offsprings, n_matings, problem.n_var), False)

        for k in range(n_matings):
            p1, p2 = X[0, k], X[1, k]

            both_are_true = np.logical_and(p1, p2)
            _X[0, k, both_are_true] = True

            n_remaining = problem.n_max - np.sum(both_are_true)

            I = np.where(np.logical_xor(p1, p2))[0]

            S = I[np.random.permutation(len(I))][:n_remaining]
            _X[0, k, S] = True

        return _X 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import full [as 別名]
def _do(self, problem, X, **kwargs):

        _, n_matings, n_var = X.shape

        def fun(mask, operator):
            return operator._do(problem, X[..., mask], **kwargs)

        ret = apply_mixed_variable_operation(problem, self.process, fun)

        # for the crossover the concatenation is different through the 3d arrays.
        X = np.full((self.n_offsprings, n_matings, n_var), np.nan, dtype=np.object)
        for i in range(len(self.process)):
            mask, _X = self.process[i]["mask"], ret[i]
            X[..., mask] = _X

        return X