Python如何插入2D数据的大点云

Question

So let's say I have a point cloud of data in the form of Z = f(X, Y)

The problem is that I have millions of points, with data that is extremely fine in some (X,Y) regions and extremely sparse in other regions.

Ideally the interpolated solution needs to be continuous, and as smooth as possible. The application is for finite element analysis.

I've tried:

• Instead of interpolating I use a KDTree to average nearest nodes. This works very well for points in the fine region but not so well at sparse regions, because discontinuities in the result may arise.
• scipy.interpolate.XXX - 2d functions run into memory error. scipy libraries aren't capable of interpolating large numbers of points.

I'm thinking the best way is some sort of hacked up combination of KDTree average nearest nodes and then some sort of interpolation for far away points, but I'm thinking that interpolating millions of points ought to be solved problem...

Anybody have any good ideas on what to do?

Answer 1

So to interpolate arbitrarily large point clouds I wrote a piece of code to partition data into smaller chunks. It's not the best piece of code but will be available for those too lazy to write their own.

import scipy.interpolate
from scipy.interpolate import griddata
from scipy.spatial.qhull import QhullError



class Interp2P(object):
    """
    Reconstruction of interpolation for 2d applications.
    This class is used to avoid any memory errors due to interpolation 
    of large numbers of points.

    Built for use for extremely large point clouds. Interpolation
    is partitioned into automatic control parameters px, py, pe, blockpts. 
    The scipy implementation of interpolation functions has memory problems
    for large point clouds. This class divides the problem into several
    smaller partitions.


    Parameters
    ----------
    points : array shape (a, 2)
        table of point coordinates describing z = f(x,y) where

        - column 0 = x
        - column 1 = y

    values : array of shape (a, b)
        Corresponding values z = f(x, y)

        values may possibly have multiple columns,
        depending on the interpolator kind used. 

    kind : str
        Interpolation method. Can be

        - 'nearest'
        - 'linear'
        - 'cubic'

    px : int or None
        Number of partitions in x-direction. If None, a default is calculated
        according to the number of blockpts
    py : int or None
        Number of partitions in y-direction. If None, a default is calculated
        according to the number of blockpts.
    pe : scalar 
        Proportion of block length to overlap on other blocks. 
        For example, if pe=0.25, the block will be extended 25% on both the 
        left and right sides of px to overlap on successive blocks. 

    blockpts : int
        Approximate number of interpolation points within each partition block.
        Defaults to 300*300. blockpts is used to automatically size either
        px or py if these are set to None. 

    """    
    def __init__(self, points, values, kind='linear', 
                 px = None, py = None, pe = 0.5, blockpts = 300*300,
                 **kwargs):
        points = np.array(points)
        self.x = points[:, 0]
        self.y = points[:, 1]
        self.z = np.array(values)
        self.points = points
        self.values = np.array(self.z)

        self.kind = kind
        self.kwargs = kwargs
        self.px = px
        self.py = py
        self.pe = pe
        self.blockpts = blockpts
        self._set_partitions()
        return


    def _set_partitions(self):
        """ Calculate the number of partitions to use in data set"""
        ptnum = len(self.x)
        blockpts = self.blockpts

        blocknum = ptnum / blockpts + 1
        if self.px is None:
            if self.py is None:
                self.px = int(np.sqrt(blocknum))
                self.py = int(blocknum / self.px)
            else:
                self.px = int(blocknum / self.py)

        if self.py is None:
            self.py = int(blocknum / self.px)

        self.px = max(self.px, 1)
        self.py = max(self.py, 1)

        self.xmax = np.max(self.x)
        self.xmin = np.min(self.x)
        self.xlen = self.xmax - self.xmin
        self.xp = self.xlen / self.px       # block x length
        self.xe = self.xp * self.pe         # block x overlap length

        self.ymax = np.max(self.y)
        self.ymin = np.min(self.y)
        self.ylen = self.ymax - self.ymin
        self.yp = self.ylen / self.py       # block y length    
        self.ye = self.yp * self.pe         # block y overlap length


        xfudge = (self.xmax - self.xmin) / 1000.
        yfudge = (self.ymax - self.ymin) / 1000.

        # Construct block upper/lower limits
        xl = self.xmin - xfudge
        xu = self.xmax + xfudge
        yl = self.ymin - yfudge
        yu = self.ymax + yfudge

        # Construct blocks        
        self.xblocks = np.linspace(xl, xu, self.px + 1)
        self.yblocks = np.linspace(yl, yu, self.py + 1)        
        return


    def _choose_block(self, x, y):
        """
        Calculate which interpolation block to use for the given 
        coordinates (x, y)

        Returns
        --------
        xindex : int array of shape (N,)
            index locations for x-dimension of blocks
        yindex : int array of shape (N,)
            index locations for y-dimension of blocks

        """
        xindex = np.searchsorted(self.xblocks, x) - 1
        yindex = np.searchsorted(self.yblocks, y) - 1
        return xindex, yindex


    @lazy_property
    def _template_interp(self):
        """
        Construct template interpolator function based on kind 
        """

        if self.kind == 'linear':
            template = scipy.interpolate.LinearNDInterpolator

        elif self.kind == 'cubic':
            template = scipy.interpolate.CloughTocher2DInterpolator

        elif self.kind == 'nearest':
            template = scipy.interpolate.NearestNDInterpolator

        elif self.kind == 'rbf':
            template = Rbf_wrapper
#            def func1(points, values, **kwargs):
#                args = np.column_stack((points, values))
#                f = scipy.interpolate.Rbf(args, **kwargs)
#                return f
#            template = func1

        return template


    @lazy_property
    def _interpolators(self):
        """
        Construct interpolators for every block.

        - 0 dimension corresponds to x data.
        - 1 dimension corresponds to y data.

        """

        # Bounds of block interpolation points
        xl_arr = self.xblocks[0:-1] - self.xe
        xu_arr = self.xblocks[1:]  + self.xe

        yl_arr = self.yblocks[0:-1] - self.ye
        yu_arr = self.yblocks[1:] + self.ye

        # Loop through all block boundaries and construct interpolators. 
        interpolators = []
        for (xl, xu) in zip(xl_arr, xu_arr):
            interpx = []
            for (yl, yu) in zip(yl_arr, yu_arr):

                #Set original data partition
                ix0 = np.logical_and(xl <= self.x, self.x <= xu)
                iy0 = np.logical_and(yl <= self.y, self.y <= yu)
                index1 = np.logical_and(ix0, iy0)
                x0 = self.x[index1]
                y0 = self.y[index1]
                z0 = self.z[index1]
                points = np.column_stack((x0, y0))
                try:
                    interp1 = self._template_interp(points, z0, **self.kwargs)
                    interpx.append(interp1)
                except ValueError:
                    interpx.append(None)
            interpolators.append(interpx)
        return interpolators


    def interpolate(self, x, y):
        """Interpolate points. 

        Parameters
        ----------
        x : array of shape (m,)
            x-coordinates of desired points to interpolate
        y : array of shape (m,)
            y-coordinates of desired points to interpolate

        Returns
        -------
        values : array of shape (m, n)
            interpolated values of points. 
        """
        x = np.atleast_1d(x)
        y = np.atleast_1d(y)
        xlen = len(x)

        # Property shape the result        
        shape = list(self.z.shape)
        shape[0] = xlen
        result = np.empty(shape)
        result[:] = np.nan

        # Loop through all block boundaries and send points to the block's
        # corresponding interpolator. 

        xindex, yindex = self._choose_block(x, y)
        for ix in range(self.px):
            for iy in range(self.py):
                index1 = xindex == ix
                index2 = yindex == iy
                index = np.logical_and(index1, index2)
                interp = self._interpolators[ix][iy]

                points = np.column_stack((x[index], y[index]))
                if len(points) > 0:
                    result[index] = interp(points)
        return result


    def __call__(self, points):
        """
        Interpolate in the style of LinearNDInterpolator.

        Parameters
        ----------
        points : array of shape (m, 2)
            coordinates of x (column 0) and y (column 1). 

        Returns
        -------
        values : array of shape (m, n)
            interpolated values of points. 
        """
        points = np.atleast_2d(points)
        x = points[:, 0]
        y = points[:, 1]
        return self.interpolate(x, y)





def lazy_property(fn):
    """
    Version of lazy_property by John Huang.

    Decorator used to cache property results into dictionary.
    The cache can be clered using clean_lazy_properties.
    """

    cache_name = _data_holder_attr
    attr_name = fn.__name__

    def get_cache(instance):
        if not hasattr(instance, cache_name):
            setattr(instance, cache_name, {})
        return getattr(instance, cache_name)

    @property
    @wraps(fn)
    def get_attr(self):
        cache = get_cache(self)
        if attr_name not in cache:
            cache[attr_name] = fn(self)
        return cache[attr_name]

    return get_attr