smbinterp/interp/grid/__init__.py

from collections import defaultdict
import pickle

from xml.dom.minidom import Document

import numpy as np
from scipy.spatial import KDTree

from interp.baker import run_baker
from interp.baker import get_phis
import interp

import logging
log = logging.getLogger("interp")

MAX_SEARCH_COUNT = 256
TOL = 1e-8

__version__ = interp.__version__


class grid(object):
    def __init__(self, verts=None, q=None):
        """
            verts = array of arrays (if passed in, will convert to numpy.array)
                            [
                                [x0,y0 <, z0>],
                                [x1,y1 <, z1>],
                                ...
                            ]

            q = array (1D) of physical values
        """

        if verts != None:
            self.verts = np.array(verts)
            self.tree = KDTree(self.verts)

        if q != None:
            self.q = np.array(q)

        self.cells = {}
        self.cells_for_vert = defaultdict(list)

    def get_containing_simplex(self, X):
        if not self.cells:
            raise Exception("cell connectivity is not set up")

        # get closest point
        (dist, indicies) = self.tree.query(X, 2)
        closest_point = indicies[0]

        log.debug('X: %s' % X)
        log.debug('point index: %d' % closest_point)
        log.debug('actual point %s' % self.verts[closest_point])
        log.debug('distance = %0.4f' % dist[0])

        simplex = None
        checked_cells = []
        cells_to_check = list(self.cells_for_vert[closest_point])

        attempts = 0
        while not simplex and cells_to_check:
            attempts += 1

            if attempts > MAX_SEARCH_COUNT:
                raise Exception("Is the search becoming exhaustive?'\
                                    '(%d attempts)" % attempts)

            cur_cell = cells_to_check.pop(0)
            checked_cells.append(cur_cell)

            if cur_cell.contains(X, self):
                simplex = cur_cell
                continue

            for neighbor in cur_cell.neighbors:
                if (neighbor not in checked_cells) \
                        and (neighbor not in cells_to_check):
                    cells_to_check.append(neighbor)

        if not simplex:
            raise Exception('no containing simplex found')

        log.debug("simplex vert indicies: %s" % simplex.verts)
        R = self.create_mesh(simplex.verts)
        log.debug("R:\n%s", R)

        log.debug('total attempts before finding simplex: %d' % attempts)
        return R

    def create_mesh(self, indicies):
        """
            this function takes a list of indicies, and then creates and
            returns a grid object (collection of verts and q).

            note: the input is indicies, the grid contains verts
        """

        return grid(self.verts[indicies], self.q[indicies])

    def get_simplex_and_nearest_points(self, X, extra_points=3):
        """
            this returns two grid objects: R and S.

            R is a grid object that is a containing simplex around point X

            S : some verts from all points that are not the simplex
        """
        simplex_size = self.dim + 1
        log.debug("extra verts: %d" % extra_points)
        log.debug("simplex size: %d" % simplex_size)

        r_mesh = self.get_containing_simplex(X)

        # and some UNIQUE extra verts
        (dist, indicies) = self.tree.query(X, simplex_size + extra_points)
        log.debug("extra indicies: %s" % indicies)

        unique_indicies = []
        for index in indicies:
            close_point_in_R = False
            for rvert in r_mesh.verts:
                if all(rvert == self.verts[index]):
                    close_point_in_R = True
                    break

            if not close_point_in_R:
                unique_indicies.append(index)
            else:
                log.debug('throwing out %s: %s' % (index, self.verts[index]))

        log.debug("indicies: %s" % indicies)
        log.debug("unique indicies: %s" % unique_indicies)
        s_mesh = self.create_mesh(unique_indicies)

        return (r_mesh, s_mesh)

    def run_baker(self, X, order=2, extra_points=3):
        (R, S) = self.get_simplex_and_nearest_points(X, extra_points)
        answer = run_baker(X, R, S, order)
        return answer

    def for_qhull_generator(self):
        """
            this returns a generator that should be fed into qdelaunay
        """

        yield str(len(self.verts[0]))
        yield '%d' % len(self.verts)

        for p in self.verts:
            yield "%f %f %f" % tuple(p)

    def for_qhull(self):
        """
            this returns a single string that should be fed into qdelaunay
        """
        r = '%d\n' % len(self.verts[0])
        r += '%d\n' % len(self.verts)
        for p in self.verts:
            # r += "%f %f %f\n" % tuple(p)
            r += "%s\n" % " ".join("%f" % i for i in p)
        return r

    def __str__(self):
        r = ''
        assert(len(self.verts) == len(self.q))
        for c, i in enumerate(zip(self.verts, self.q)):
            r += "%d vert(%s): q(%0.4f)" % (c, i[0], i[1])
            cell_str = ", ".join([str(f.name) for f in self.cells_for_vert[c]])
            r += "    cells: [%s]" % cell_str
            r += "\n"
        if self.cells:
            for v in self.cells.itervalues():
                r += "%s\n" % v
        return r

    def normalize_q(self, new_max=0.1):
        largest_number = np.max(np.abs(self.q))
        self.q *= new_max / largest_number

    def dump_to_blender_files(self,
                    pfile='/tmp/points.p', cfile='/tmp/cells.p'):
        if len(self.verts[0]) == 2:
            pickle.dump([(p[0], p[1], 0.0) for p in self.verts],
                                open(pfile, 'w'))
        else:
            pickle.dump([(p[0], p[1], p[2]) for p in self.verts],
                                open(pfile, 'w'))

        pickle.dump([f.verts for f in self.cells.itervalues()],
                                open(cfile, 'w'))

    def get_xml(self):
        doc = Document()
        ps = doc.createElement("points")
        doc.appendChild(ps)
        for i in zip(self.verts, self.q):
            p = doc.createElement("point")

            p.setAttribute("x", str(i[0][0]))
            p.setAttribute('y', str(i[0][1]))
            p.setAttribute('z', str(i[0][2]))
            p.setAttribute('q', str(i[1]))
            ps.appendChild(p)

        return doc

    def toxml(self):
        return self.get_xml().toxml()

    def toprettyxml(self):
        return self.get_xml().toprettyxml()


class cell(object):
    def __init__(self, name):
        self.name = name
        self.verts = []
        self.neighbors = []

    def add_vert(self, v):
        """
             v should be an index into grid.verts
        """
        self.verts.append(v)

    def add_neighbor(self, n):
        """
             reference to another cell object
        """
        self.neighbors.append(n)

    def contains(self, X, G):
        """
            X = point of interest
            G = corrensponding grid object (G.verts)

            because of the way i'm storing things, a cell simply stores
            indicies, and so one must pass in a reference to the grid object
            containing real verts.

            this simply calls grid.simplex.contains
        """
        return contains(X, [G.verts[i] for i in self.verts])

    def __str__(self):
        # neighbors = [str(i.name) for i in self.neighbors]
        return '<cell %s: verts: %s neighbor count: %s>' %\
                     (
                         self.name,
                         self.verts,
                         len(self.neighbors),
                         # ", ".join(neighbors)
                     )

    __repr__ = __str__


def contains(X, R):
    """
        tests if X (point) is in R

        R is a simplex, represented by a list of n-degree coordinates
    """
    phis = get_phis(X, R)

    r = True
    if [i for i in phis if i < 0.0 - TOL]:
        r = False
    return r