Python interpolate.NearestNDInterpolator方法代码示例

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def _interpolator(points, coord_source, coord_target, method="idw"):

    coord_source_i, coord_source_j = coord_source
    coord_target_i, coord_target_j = coord_target

    # reshape
    trg = np.vstack((coord_source_i.ravel(), coord_source_j.ravel())).T
    src = np.vstack((coord_target_i.ravel(), coord_target_j.ravel())).T

    if method == "nearest":
        interpolator = NearestNDInterpolator(src, points.ravel(),
                                             tree_options={"balanced_tree": False})
        points_interpolated = interpolator(trg)
    elif method == "linear":
        interpolator = LinearNDInterpolator(src, points.ravel(), fill_value=0)
        points_interpolated = interpolator(trg)
    elif method == "idw":
        interpolator = ipol.Idw(src, trg)
        points_interpolated = interpolator(points.ravel())

    # reshape output
    points_interpolated = points_interpolated.reshape(points.shape)

    return points_interpolated.astype(points.dtype) 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def inpaint(img, threshold=1):
  h, w = img.shape[:2]

  if len(img.shape) == 3:  # RGB
    mask = np.all(img == 0, axis=2).astype(np.uint8)
    img = cv2.inpaint(img, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)

  else:  # depth
    mask = np.where(img > threshold)
    xx, yy = np.meshgrid(np.arange(w), np.arange(h))
    xym = np.vstack((np.ravel(xx[mask]), np.ravel(yy[mask]))).T
    img = np.ravel(img[mask])
    interp = interpolate.NearestNDInterpolator(xym, img)
    img = interp(np.ravel(xx), np.ravel(yy)).reshape(xx.shape)

  return img 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def _build_vertex_values(self, items, area_func, value_func):
        """
        Interpolate vertice with known altitudes to get altitudes for the remaining ones.
        """
        vertex_values = np.empty(self.vertices.shape[:1], dtype=np.int32)
        if not vertex_values.size:
            return vertex_values
        vertex_value_mask = np.full(self.vertices.shape[:1], fill_value=False, dtype=np.bool)

        for item in items:
            faces = area_func(item).faces
            if not faces:
                continue
            i_vertices = np.unique(self.faces[np.array(tuple(chain(*faces)))].flatten())
            vertex_values[i_vertices] = value_func(item, i_vertices)
            vertex_value_mask[i_vertices] = True

        if np.any(vertex_value_mask) and not np.all(vertex_value_mask):
            interpolate = NearestNDInterpolator(self.vertices[vertex_value_mask],
                                                vertex_values[vertex_value_mask])
            vertex_values[np.logical_not(vertex_value_mask)] = interpolate(
                *np.transpose(self.vertices[np.logical_not(vertex_value_mask)])
            )

        return vertex_values 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def define_claret_filter_tables(self):
        """
            Define the filter_claret table. For more details, see " Gravity and limb-darkening coefficients for
            the Kepler, CoRoT,
            Spitzer, uvby,   UBVRIJHK,
            and Sloan photometric systems"
            Claret, A. and Bloemen, S. 2011A&A...529A..75C.
        """
        all_filters = np.unique(self.claret_table[:,-1])
        self.filter_claret_table = {}

        for filter in all_filters:

            good_filter  = np.where(self.claret_table[:,-1] == filter)[0]

            subclaret = self.claret_table[good_filter,:-1].astype(float)

            grid_interpolation = si.NearestNDInterpolator(subclaret[:,:-1],subclaret[:,-1])

            self.filter_claret_table[filter] = grid_interpolation 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def make_nearest_interpolator_unstructured(field, grid=None):
	'''Make a nearest interpolator for an unstructured grid.

	Parameters
	----------
	field : Field or array_like
		The field to interpolate.
	grid : Grid or None
		The grid of the field. If it is given, the grid of `field` is replaced by this grid.

	Returns
	-------
	Field generator
		The interpolator as a Field generator. The grid on which this field generator will be evaluated does
		not need to have any structure.
	'''
	if grid is None:
		grid = field.grid
	else:
		field = Field(field, grid)

	interp = NearestNDInterpolator(grid.points, field)

	def interpolator(evaluated_grid):
		res = interp(grid.points)
		return Field(res, evaluated_grid)

	return interpolator 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def test_nearest_options():
    # smoke test that NearestNDInterpolator accept cKDTree options
    npts, nd = 4, 3
    x = np.arange(npts*nd).reshape((npts, nd))
    y = np.arange(npts)
    nndi = NearestNDInterpolator(x, y)

    opts = {'balanced_tree': False, 'compact_nodes': False}
    nndi_o = NearestNDInterpolator(x, y, tree_options=opts)
    assert_allclose(nndi(x), nndi_o(x), atol=1e-14) 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def build_interpolator(self, dem_proc):
        # Build an interpolator
        gc = dem_proc.elev.grid_coordinates
#       points = np.meshgrid(gc.x_axis, gc.y_axis)
#       points = np.column_stack([pts.ravel() for pts in points])
#       interp = spinterp.NearestNDInterpolator(points, dem_proc.data.ravel())
#       interp = spinterp.LinearNDInterpolator(points, np.ravel(dem_proc.data),
#                                               fill_value=np.nan)
        interp = spinterp.interpolate.RegularGridInterpolator(
            points=(gc.y_axis[::-1], gc.x_axis),
            values=dem_proc.data[::-1, :].astype(float),
            method='nearest', fill_value=np.nan, bounds_error=False)
        return interp 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def __init__(self, filename, verbose=1):
        """
        Initialisation of class to load templates from a file and create the interpolation
        objects

        Parameters
        ----------
        filename: string
            Location of Template file
        verbose: int
            Verbosity level,
            0 = no logging
            1 = File + interpolation point information
            2 = Detailed description of interpolation points
        """
        self.verbose = verbose
        if self.verbose:
            print("Loading lookup tables from", filename)

        grid, bins, template = self.parse_fits_table(filename)
        x_bins, y_bins = bins

        self.interpolator = interpolate.LinearNDInterpolator(
            grid, template, fill_value=0
        )
        self.nearest_interpolator = interpolate.NearestNDInterpolator(grid, template)

        self.grid_interp = interpolate.RegularGridInterpolator(
            (x_bins, y_bins),
            np.zeros([x_bins.shape[0], y_bins.shape[0]]),
            method="linear",
            bounds_error=False,
            fill_value=0,
        ) 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def lin_interp(shape, xyd):
    # taken from https://github.com/hunse/kitti
    m, n = shape
    ij, d = xyd[:, 1::-1], xyd[:, 2]
    f = LinearNDInterpolator(ij, d, fill_value=0)
    # f = NearestNDInterpolator(ij, d)
    J, I = np.meshgrid(np.arange(n), np.arange(m))
    IJ = np.vstack([I.flatten(), J.flatten()]).T
    disparity = f(IJ).reshape(shape)
    return disparity 

# 需要导入模块: from scipy import interpolate [as 别名]
# 或者: from scipy.interpolate import NearestNDInterpolator [as 别名]
def image_inpaint_pixels(img, valid_mask):
    assert img.shape == valid_mask.shape, \
        'image size %r and mask size %r should be equal' \
        % (img.shape, valid_mask.shape)
    coords = np.array(np.nonzero(valid_mask)).T
    values = img[valid_mask]
    it = interpolate.NearestNDInterpolator(coords, values)
    img_paint = it(list(np.ndindex(img.shape))).reshape(img.shape)
    return img_paint