python类choose()的实例源码

def test_broadcast2(self):
        A = np.choose(self.ind, (self.x, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def _read_particles(self):
        if not os.path.exists(self.particle_filename): return
        with open(self.particle_filename, 'r') as f:
            lines = f.readlines()
            self.num_stars = int(lines[0].strip().split(' ')[0])
            for num, line in enumerate(lines[1:]):
                particle_position_x = float(line.split(' ')[1])
                particle_position_y = float(line.split(' ')[2])
                particle_position_z = float(line.split(' ')[3])
                coord = [particle_position_x, particle_position_y, particle_position_z]
                # for each particle, determine which grids contain it
                # copied from object_finding_mixin.py
                mask = np.ones(self.num_grids)
                for i in range(len(coord)):
                    np.choose(np.greater(self.grid_left_edge.d[:,i],coord[i]), (mask,0), mask)
                    np.choose(np.greater(self.grid_right_edge.d[:,i],coord[i]), (0,mask), mask)
                ind = np.where(mask == 1)
                selected_grids = self.grids[ind]
                # in orion, particles always live on the finest level.
                # so, we want to assign the particle to the finest of
                # the grids we just found
                if len(selected_grids) != 0:
                    grid = sorted(selected_grids, key=lambda grid: grid.Level)[-1]
                    ind = np.where(self.grids == grid)[0][0]
                    self.grid_particle_count[ind] += 1
                    self.grids[ind].NumberOfParticles += 1

                    # store the position in the *.sink file for fast access.
                    try:
                        self.grids[ind]._particle_line_numbers.append(num + 1)
                    except AttributeError:
                        self.grids[ind]._particle_line_numbers = [num + 1]

def _read_particle_file(self, fn):
        """actually reads the orion particle data file itself.

        """
        if not os.path.exists(fn): return
        with open(fn, 'r') as f:
            lines = f.readlines()
            self.num_stars = int(lines[0].strip()[0])
            for num, line in enumerate(lines[1:]):
                particle_position_x = float(line.split(' ')[1])
                particle_position_y = float(line.split(' ')[2])
                particle_position_z = float(line.split(' ')[3])
                coord = [particle_position_x, particle_position_y, particle_position_z]
                # for each particle, determine which grids contain it
                # copied from object_finding_mixin.py
                mask = np.ones(self.num_grids)
                for i in range(len(coord)):
                    np.choose(np.greater(self.grid_left_edge.d[:,i],coord[i]), (mask,0), mask)
                    np.choose(np.greater(self.grid_right_edge.d[:,i],coord[i]), (0,mask), mask)
                ind = np.where(mask == 1)
                selected_grids = self.grids[ind]
                # in orion, particles always live on the finest level.
                # so, we want to assign the particle to the finest of
                # the grids we just found
                if len(selected_grids) != 0:
                    grid = sorted(selected_grids, key=lambda grid: grid.Level)[-1]
                    ind = np.where(self.grids == grid)[0][0]
                    self.grid_particle_count[ind] += 1
                    self.grids[ind].NumberOfParticles += 1

                    # store the position in the particle file for fast access.
                    try:
                        self.grids[ind]._particle_line_numbers.append(num + 1)
                    except AttributeError:
                        self.grids[ind]._particle_line_numbers = [num + 1]
        return True

def find_point(self, coord):
        """
        Returns the (objects, indices) of grids containing an (x,y,z) point
        """
        mask=np.ones(self.num_grids)
        for i in range(len(coord)):
            np.choose(np.greater(self.grid_left_edge[:,i],coord[i]), (mask,0), mask)
            np.choose(np.greater(self.grid_right_edge[:,i],coord[i]), (0,mask), mask)
        ind = np.where(mask == 1)
        return self.grids[ind], ind

def find_slice_grids(self, coord, axis):
        """
        Returns the (objects, indices) of grids that a slice intersects along
        *axis*
        """
        # Let's figure out which grids are on the slice
        mask = np.ones(self.num_grids)
        # So if gRE > coord, we get a mask, if not, we get a zero
        #    if gLE > coord, we get a zero, if not, mask
        # Thus, if the coordinate is between the edges, we win!
        np.choose(np.greater(self.grid_right_edge[:,axis],coord),(0,mask),mask)
        np.choose(np.greater(self.grid_left_edge[:,axis],coord),(mask,0),mask)
        ind = np.where(mask == 1)
        return self.grids[ind], ind

def forward_cpu(self, x):
        col = conv.im2col_cpu(
            x[0], self.kh, self.kw, self.sy, self.sx, self.ph, self.pw,
            pval=-float('inf'), cover_all=self.cover_all)
        n, c, kh, kw, out_h, out_w = col.shape
        col = col.reshape(n, c, kh * kw, out_h, out_w)

        # We select maximum twice, since the implementation using numpy.choose
        # hits its bug when kh * kw >= 32.
        self.indexes = col.argmax(axis=2)
        y = col.max(axis=2)
        return y,

def select_item(x, t):
    """Select elements stored in given indices.

    This function returns ``t.choose(x.T)``, that means
    ``y[i] == x[i, t[i]]`` for all ``i``.

    Args:
        x (Variable): Variable storing arrays.
        t (Variable): Variable storing index numbers.

    Returns:
        ~chainer.Variable: Variable that holds ``t``-th element of ``x``.

    """
    return SelectItem()(x, t)

def test_mixed(self):
        c = np.array([True, True])
        a = np.array([True, True])
        assert_equal(np.choose(c, (a, 1)), np.array([1, 1]))

def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amax = a.max(i)
            aargmax = a.argmax(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amax == aargmax.choose(*a.transpose(i,*axes))))

def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amin = a.min(i)
            aargmin = a.argmin(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amin == aargmin.choose(*a.transpose(i,*axes))))

def test_basic(self):
        A = np.choose(self.ind, (self.x, self.y))
        assert_equal(A, [2, 2, 3])

def test_broadcast1(self):
        A = np.choose(self.ind, (self.x2, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def array_colorkey (surface):
    """pygame.numpyarray.array_colorkey (Surface): return array

    copy the colorkey values into a 2d array

    Create a new array with the colorkey transparency value from each
    pixel. If the pixel matches the colorkey it will be fully
    tranparent; otherwise it will be fully opaque.

    This will work on any type of Surface format. If the image has no
    colorkey a solid opaque array will be returned.

    This function will temporarily lock the Surface as pixels are
    copied.
    """
    colorkey = surface.get_colorkey ()
    if colorkey == None:
        # No colorkey, return a solid opaque array.
        array = numpy.empty (surface.get_width () * surface.get_height (),
                             numpy.uint8)
        array.fill (0xff)
        array.shape = surface.get_width (), surface.get_height ()
        return array

    # Taken from from Alex Holkner's pygame-ctypes package. Thanks a
    # lot.
    array = array2d (surface)
    # Check each pixel value for the colorkey and mark it as opaque or
    # transparent as needed.
    val = surface.map_rgb (colorkey)
    array = numpy.choose (numpy.equal (array, val),
                          (numpy.uint8 (0xff), numpy.uint8 (0)))
    array.shape = surface.get_width (), surface.get_height ()
    return array

def test_mixed(self):
        c = np.array([True, True])
        a = np.array([True, True])
        assert_equal(np.choose(c, (a, 1)), np.array([1, 1]))

def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amax = a.max(i)
            aargmax = a.argmax(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amax == aargmax.choose(*a.transpose(i,*axes))))

def test_all(self):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        for i in range(a.ndim):
            amin = a.min(i)
            aargmin = a.argmin(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(amin == aargmin.choose(*a.transpose(i,*axes))))

def test_basic(self):
        A = np.choose(self.ind, (self.x, self.y))
        assert_equal(A, [2, 2, 3])

def test_broadcast1(self):
        A = np.choose(self.ind, (self.x2, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def test_choose(self):
        choices = [[0, 1, 2],
                   [3, 4, 5],
                   [5, 6, 7]]
        tgt = [5, 1, 5]
        a = [2, 0, 1]

        out = np.choose(a, choices)
        assert_equal(out, tgt)

def clip(self, a, m, M, out=None):
        # use slow-clip
        selector = np.less(a, m) + 2*np.greater(a, M)
        return selector.choose((a, m, M), out=out)

    # Handy functions