python-scitools 0.9.0-1 / usr / share / pyshared / scitools / BoxGrid.py

#!/usr/bin/env python
"""
Class for uniform and non-uniform grid on an interval, rectangle, or box.
"""

from scitools.errorcheck import right_type, wrong_type
from scitools.numpyutils import ndgrid, ndarray, wrap2callable, array, \
     zeros, linspace

# constants for indexing the space directions:
X = X1 = 0
Y = X2 = 1
Z = X3 = 2


class UniformBoxGrid(object):
    """
    Simple uniform grid on an interval, rectangle, box, or hypercube.

    =============      ====================================================
      Attributes                           Description
    =============      ====================================================
    nsd                no of spatial dimensions in the grid
    min_coor           array of minimum coordinates
    max_coor           array of maximum coordinates
    division           array of cell divisions in the 
    delta              array of grid spacings
    dirnames           names of the space directions ('x', 'y', etc.)
    shape              (nx+1, ny+1, ...); dimension of array over grid
    coor               list of coordinates; self.coor[Y][j] is the
                       the j-th coordinate in direction Y (=1)
                       X, Y, Z are predefined constants 0, 1, 2
    coorv              expanded version of coor for vectorized expressions
                       (in 2D, self.coorv[0] = self.coor[0][:,newaxis])
    tolerance          small geometric tolerance based on grid coordinates
    npoints            total number of grid points
    =============      ====================================================

    """
    def __init__(self,
                 min=(0,0),                  # minimum coordinates
                 max=(1,1),                  # maximum coordinates
                 division=(4,4),             # cell divisions
                 dirnames=('x', 'y', 'z')):  # names of the directions
        """
        Initialize a BoxGrid by giving domain range (minimum and
        maximum coordinates: min and max tuples/lists/arrays)
        and number of cells in each space direction (division tuple/list/array).
        The dirnames tuple/list holds the names of the coordinates in
        the various spatial directions.

        >>> g = UniformBoxGrid(min=0, max=1, division=10)
        >>> g = UniformBoxGrid(min=(0,-1), max=(1,1), division=(10,4))
        >>> g = UniformBoxGrid(min=(0,0,-1), max=(2,1,1), division=(2,3,5))
        """
        # Allow int/float specifications in one-dimensional grids
        # (just turn to lists for later multi-dimensional processing)
        if isinstance(min, (int,float)):
            min = [min]
        if isinstance(max, (int,float)):
            max = [max]
        if isinstance(division, (int,float)):
            division = [division]
        if isinstance(dirnames, str):
            dirnames = [dirnames]
        
        self.nsd = len(min)
        # strip dirnames down to right space dim (in case the default
        # with three components were unchanged by the user):
        dirnames = dirnames[:self.nsd]

        # check consistent lengths:
        for a in max, division:
            if len(a) != self.nsd:
                raise ValueError(
                    'Incompatible lengths of arguments to constructor'\
                    ' (%d != %d)' % (len(a), self.nsd))

        self.min_coor = array(min, float)
        self.max_coor = array(max, float)
        self.dirnames = dirnames
        self.division = division
        self.coor = [None]*self.nsd
        self.shape = [0]*self.nsd
        self.delta = zeros(self.nsd)

        for i in range(self.nsd):
            self.delta[i] = \
                 (self.max_coor[i] -  self.min_coor[i])/float(self.division[i])
            self.shape[i] = self.division[i] + 1  # no of grid points
            self.coor[i] = \
                 linspace(self.min_coor[i], self.max_coor[i], self.shape[i])
        self._more_init()

    def _more_init(self):
        self.shape = tuple(self.shape)
        self.coorv = ndgrid(*self.coor)
        if not isinstance(self.coorv, (list,tuple)):
            # 1D grid, wrap self.coorv as list:
            self.coorv = [self.coorv]

        self.npoints = 1
        for i in range(len(self.shape)):
            self.npoints *= self.shape[i]
            
        self.tolerance = (max(self.max_coor) - min(self.min_coor))*1E-14

        # nicknames: xcoor, ycoor, xcoorv, ycoorv, etc
        for i in range(self.nsd):
            self.__dict__[self.dirnames[i]+'coor'] = self.coor[i]
            self.__dict__[self.dirnames[i]+'coorv'] = self.coorv[i]

        if self.nsd == 3:
            # make boundary coordinates for vectorization:
            xdummy, \
            self.ycoorv_xfixed_boundary, \
            self.zcoorv_xfixed_boundary = ndgrid(0, self.ycoor, self.zcoor)
            
            self.xcoorv_yfixed_boundary, \
            ydummy, \
            self.zcoorv_yfixed_boundary = ndgrid(self.xcoor, 0, self.zcoor)
            
            self.xcoorv_yfixed_boundary, \
            self.zcoorv_yfixed_boundary, \
            zdummy = ndgrid(self.xcoor, self.ycoor, 0)
            
    # could have _ in all variable names and define read-only
    # access via properties

    def string2griddata(s):
        """
        Turn a text specification of a grid into a dictionary
        with the grid data.
        For example,

        >>> s = "domain=[0,10] indices=[0:11]"
        >>> data = BoxGrid.string2griddata(s)
        >>> data
        {'dirnames': ('x', 'y'), 'division': [10], 'max': [10], 'min': [0]}

        >>> s = "domain=[0.2,0.5]x[0,2E+00] indices=[0:20]x[0:100]"
        >>> data = BoxGrid.string2griddata(s)
        >>> data
        {'dirnames': ('x', 'y', 'z'),
         'division': [19, 99],
         'max': [0.5, 2],
         'min': [0.2, 0]}

        >>> s = "[0,1]x[0,2]x[-1,1.5] [0:25]x[0:10]x[0:16]"
        >>> data = BoxGrid.string2griddata(s)
        >>> data
        {'dirnames': ('x', 'y', 'z'),
         'division': [24, 9, 15],
         'max': [1.0, 2.0, 1.5],
         'min': [0.0, 0.0, -1.0]}

        The data dictionary can be used as keyword arguments to the
        class UniformBoxGrid constructor.
        """
        
        domain  = r'\[([^,]*),([^\]]*)\]'
        indices = r'\[([^:,]*):([^\]]*)\]'
        import re
        d = re.findall(domain, s)
        i = re.findall(indices, s)
        nsd = len(d)
        if nsd != len(i):
            raise ValueError('could not parse "%s"' % s)
        kwargs = {}
        dirnames = ('x', 'y', 'z')
        for dir in range(nsd):
            if not isinstance(d[dir], (list,tuple)) or len(d[dir]) != 2 or \
               not isinstance(i[dir], (list,tuple)) or len(i[dir]) != 2:
                raise ValueError('syntax error in "%s"' % s)

            # old syntax (nx, xmin, xmax, ny, ymin, etc.):
            #kwargs[dirnames[dir]] = (float(d[dir][0]), float(d[dir][1]))
            #kwargs['n'+dirnames[dir]] = int(i[dir][1]) - int(i[dir][0]) # no of cells!
            kwargs['min'] = [float(d[dir][0]) for dir in range(nsd)]
            kwargs['max'] = [float(d[dir][1]) for dir in range(nsd)]
            kwargs['division'] = [int(i[dir][1]) - int(i[dir][0]) \
                                  for dir in range(nsd)]
            kwargs['dirnames'] = dirnames[:nsd]
        return kwargs
    string2griddata = staticmethod(string2griddata)

    def __getitem__(self, i):
        """
        Return access to coordinate array in direction no i, or direction
        name i, or return the coordinate of a point if i is an nsd-tuple.
        
        >>> g = UniformBoxGrid(x=(0,1), y=(-1,1), nx=2, ny=4)  # xy grid
        >>> g[0][0] == g.min[0]   # min coor in direction 0
        True
        >>> g['x'][0] == g.min[0]   # min coor in direction 'x'
        True
        >>> g[0,4]
        (0.0, 1.0)
        >>> g = UniformBoxGrid(min=(0,-1), max=(1,1), division=(2,4), dirnames=('y', 'z'))
        >>> g[1][0] == g.min[1]
        True
        >>> g['z'][0] == g.min[1]   # min coor in direction 'z'
        True
        """
        if isinstance(i, str):
            return self.coor[self.name2dirindex(i)]
        elif isinstance(i, int):
            if self.nsd > 1:
                return self.coor[i]     # coordinate array
            else:
                return self.coor[0][i]  # coordinate itself in 1D
        elif isinstance(i, (list,tuple)):
            return tuple([self.coor[k][i[k]] for k in range(len(i))])
        else:
            wrong_type(i, 'i', 'Must be str, int, tuple')
            

    def __setitem__(self, i, value):
        raise AttributeError('subscript assignment is not valid for '\
                             '%s instances' % self.__class__.__name__)

    def ncells(self, i):
        """Return no of cells in direction i."""
        # i has the meaning as in __getitem__. May be removed if not much used
        return len(self.coor[i])-1

    def name2dirindex(self, name):
        """
        Return direction index corresponding to direction name.
        In an xyz-grid, 'x' is 0, 'y' is 1, and 'z' is 2.
        In an yz-grid, 'x' is not defined, 'y' is 0, and 'z' is 1.
        """
        try:
            return self.dirnames.index(name)
        except ValueError:
            print name, 'is not defined'
            return None

    def dirindex2name(self, i):
        """Inverse of name2dirindex."""
        try:
            return self.dirnames[i]
        except IndexError:
            print i, 'is not a valid index'
            return None
    
    def ok(self):
        return True  # constructor init only => always ok

    def __len__(self):
        """Total number of grid points."""
        n = 1
        for dir in self.coor:
            n *= len(dir)
        return n

    def __repr__(self):
        s = self.__class__.__name__ + \
            '(min=%s, max=%s, division=%s, dirnames=%s)' % \
            (self.min_coor.tolist(),
             self.max_coor.tolist(),
             self.division, self.dirnames)
        return s

    def __str__(self):
        """Pretty print, using the syntax of init_fromstring."""
        domain = 'x'.join(['[%g,%g]' % (min_, max_) \
                           for min_, max_ in zip(self.min_coor, self.max_coor)])
        indices = 'x'.join(['[0:%d]' % div for div in self.division])
        return 'domain=%s  indices=%s' % (domain, indices)
                      
    def interpolator(self, point_values):
        """
        Given a self.nsd dimension array point_values with
        values at each grid point, this method returns a function
        for interpolating the scalar field defined by point_values
        at an arbitrary point.

        2D Example:
        given a filled array point_values[i,j], compute
        interpolator = grid.interpolator(point_values)
        v = interpolator(0.1243, 9.231)  # interpolate point_values
        
        >>> g=UniformBoxGrid(x=(0,2), nx=2, y=(-1,1), ny=2)
        >>> g
        UniformBoxGrid(x=(0,2), nx=2, y=(-1,1), ny=2)
        >>> def f(x,y): return 2+2*x-y

        >>> f=g.vectorized_eval(f)
        >>> f
        array([[ 3.,  2.,  1.],
               [ 5.,  4.,  3.],
               [ 7.,  6.,  5.]])
        >>> i=g.interpolator(f)
        >>> i(0.1,0.234)        # interpolate (not a grid point)
        1.9660000000000002
        >>> f(0.1,0.234)        # exact answer
        1.9660000000000002
        """
        args = self.coor
        args.append(point_values)
        # make use of wrap2callable, which applies ScientificPython
        return wrap2callable(args)

    def vectorized_eval(self, f):
        """
        Evaluate a function f (of the space directions) over a grid.
        f is supposed to be vectorized.

        >>> g = BoxGrid(x=(0,1), y=(0,1), nx=3, ny=3)
        >>> # f(x,y) = sin(x)*exp(x-y):
        >>> a = g.vectorized_eval(lambda x,y: sin(x)*exp(y-x))
        >>> print a
        [[ 0.          0.          0.          0.        ]
         [ 0.23444524  0.3271947   0.45663698  0.63728825]
         [ 0.31748164  0.44308133  0.6183698   0.86300458]
         [ 0.30955988  0.43202561  0.60294031  0.84147098]]

        >>> # f(x,y) = 2: (requires special consideration)
        >>> a = g.vectorized_eval(lambda x,y: zeros(g.shape)+2)
        >>> print a
        [[ 2.  2.  2.  2.]
         [ 2.  2.  2.  2.]
         [ 2.  2.  2.  2.]
         [ 2.  2.  2.  2.]]
        """
        a = f(*self.coorv)

        # check if f is really vectorized:
        try:
            msg = 'calling %s, which is supposed to be vectorized' % f.__name__
        except AttributeError:  # if __name__ is missing
            msg = 'calling a function, which is supposed to be vectorized'
        try:
            self.compatible(a)
        except (IndexError,TypeError), e:
            print 'e=',e, type(e), e.__class__.__name__
            raise e.__class__('BoxGrid.vectorized_eval(f):\n%s, BUT:\n%s' % \
                              (msg, e))
        return a
        
    def init_fromstring(s):
        data = UniformBoxGrid.string2griddata(s)
        return UniformBoxGrid(**data)
    init_fromstring = staticmethod(init_fromstring)

    def compatible(self, data_array, name_of_data_array=''):
        """
        Check that data_array is a NumPy array with dimensions
        compatible with the grid.
        """
        if not isinstance(data_array, ndarray):
            raise TypeError('data %s is %s, not NumPy array' % \
                            (name_of_data_array, type(data_array)))
        else:
            if data_array.shape != self.shape:
                raise IndexError("data %s of shape %s is not "\
                                 "compatible with the grid's shape %s" % \
                                 (name_of_data_array, data_array.shape,
                                  self.shape))
        return True # if we haven't raised any exceptions

    def iter(self, domain_part='all', vectorized_version=True):
        """
        Return iterator over grid points.
        domain_part = 'all':  all grid points
        domain_part = 'interior':  interior grid points
        domain_part = 'all_boundary':  all boundary points
        domain_part = 'interior_boundary':  interior boundary points
        domain_part = 'corners':  all corner points
        domain_part = 'all_edges':  all points along edges in 3D grids
        domain_part = 'interior_edges':  interior points along edges

        vectorized_version is true if the iterator returns slice
        objects for the index slice in each direction.
        vectorized_version is false if the iterator visits each point
        at a time (scalar version).
        """
        self.iterator_domain = domain_part
        self.vectorized_iter = vectorized_version
        return self

    def __iter__(self):
        # Idea: set up slices for the various self.iterator_domain
        # values. In scalar mode, make a loop over the slices and
        # yield the scalar value. In vectorized mode, return the
        # appropriate slices.
        
        self._slices = []  # elements meant to be slice objects
                    
        if self.iterator_domain == 'all':
            self._slices.append([])
            for i in range(self.nsd):
                self._slices[-1].append((i, slice(0, len(self.coor[i]), 1)))

        elif self.iterator_domain == 'interior':
            self._slices.append([])
            for i in range(self.nsd):
                self._slices[-1].append((i, slice(1, len(self.coor[i])-1, 1)))

        elif self.iterator_domain == 'all_boundary':
            for i in range(self.nsd):
                self._slices.append([])
                # boundary i fixed at 0:
                for j in range(self.nsd):
                    if j != i:
                        self._slices[-1].\
                           append((j, slice(0, len(self.coor[j]), 1)))
                    else:
                        self._slices[-1].append((i, slice(0, 1, 1)))
                # boundary i fixed at its max value:
                for j in range(self.nsd):
                    if j != i:
                        self._slices[-1].\
                           append((j, slice(0, len(self.coor[j]), 1)))
                    else:
                        n = len(self.coor[i])
                        self._slices[-1].append((i, slice(n-1, n, 1)))
                        
        elif self.iterator_domain == 'interior_boundary':
            for i in range(self.nsd):
                self._slices.append([])
                # boundary i fixed at 0:
                for j in range(self.nsd):
                    if j != i:
                        self._slices[-1].\
                           append((j, slice(1, len(self.coor[j])-1, 1)))
                    else:
                        self._slices[-1].append((i, slice(0, 1, 1)))
                # boundary i fixed at its max value:
                for j in range(self.nsd):
                    if j != i:
                        self._slices[-1].\
                           append((j, slice(1, len(self.coor[j])-1, 1)))
                    else:
                        n = len(self.coor[i])
                        self._slices[-1].append((i, slice(n-1, n, 1)))

        elif self.iterator_domain == 'corners':
            if self.nsd == 1:
                for i0 in (0, len(self.coor[0])-1):
                    self._slices.append([])
                    self._slices[-1].append((0, slice(i0, i0+1, 1)))
            elif self.nsd == 2:
                for i0 in (0, len(self.coor[0])-1):
                    for i1 in (0, len(self.coor[1])-1):
                        self._slices.append([])
                        self._slices[-1].append((0, slice(i0, i0+1, 1)))
                        self._slices[-1].append((0, slice(i1, i1+1, 1)))
            elif self.nsd == 3:
                for i0 in (0, len(self.coor[0])-1):
                    for i1 in (0, len(self.coor[1])-1):
                        for i2 in (0, len(self.coor[2])-1):
                            self._slices.append([])
                            self._slices[-1].append((0, slice(i0, i0+1, 1)))
                            self._slices[-1].append((0, slice(i1, i1+1, 1)))
                            self._slices[-1].append((0, slice(i2, i2+1, 1)))

        elif self.iterator_domain == 'all_edges':
            print 'iterator over "all_edges" is not implemented'
        elif self.iterator_domain == 'interior_edges':
            print 'iterator over "interior_edges" is not implemented'
        else:
            raise ValueError('iterator over "%s" is not impl.' % \
                             self.iterator_domain)
        
#    "def __next__(self):"
        """
        If vectorized mode:
        Return list of slice instances, where the i-th element in the
        list represents the slice for the index in the i-th space
        direction (0,...,nsd-1).

        If scalar mode:
        Return list of indices (in multi-D) or the index (in 1D).
        """
        if self.vectorized_iter:
            for s in self._slices:
                yield [slice_in_dir for dir, slice_in_dir in s]
        else:
            # scalar version
            for s in self._slices:
                slices = [slice_in_dir for dir, slice_in_dir in s]
                if len(slices) == 1:
                    for i in xrange(slices[0].start, slices[0].stop):
                        yield i
                elif len(slices) == 2:
                    for i in xrange(slices[0].start, slices[0].stop):
                        for j in xrange(slices[1].start, slices[1].stop):
                            yield [i, j]
                elif len(slices) == 3:
                    for i in xrange(slices[0].start, slices[0].stop):
                        for j in xrange(slices[1].start, slices[1].stop):
                            for k in xrange(slices[2].start, slices[2].stop):
                                yield [i, j, k]
                             

    def locate_cell(self, point):
        """
        Given a point (x, (x,y), (x,y,z)), locate the cell in which
        the point is located, and return
        1) the (i,j,k) vertex index
        of the "lower-left" grid point in this cell,
        2) the distances (dx, (dx,dy), or (dx,dy,dz)) from this point to
        the given point,
        3) a boolean list if point matches the
        coordinates of any grid lines. If a point matches
        the last grid point in a direction, the cell index is
        set to the max index such that the (i,j,k) index can be used
        directly for look up in an array of values. The corresponding
        element in the distance array is then set 0.
        4) the indices of the nearest grid point.
        
        The method only works for uniform grid spacing.
        Used for interpolation.

        >>> g1 = UniformBoxGrid(min=0, max=1, division=4)
        >>> cell_index, distance, match, nearest = g1.locate_cell(0.7)
        >>> print cell_index
        [2]
        >>> print distance
        [ 0.2]
        >>> print match
        [False]
        >>> print nearest
        [3]
        >>>
        >>> g1.locate_cell(0.5)
        ([2], array([ 0.]), [True], [2])
        >>>
        >>> g2 = UniformBoxGrid.init_fromstring('[-1,1]x[-1,2] [0:3]x[0:4]')
        >>> print g2.coor
        [array([-1.        , -0.33333333,  0.33333333,  1.        ]), array([-1.  , -0.25,  0.5 ,  1.25,  2.  ])]
        >>> g2.locate_cell((0.2,0.2))
        ([1, 1], array([ 0.53333333,  0.45      ]), [False, False], [2, 2])
        >>> g2.locate_cell((1,2))
        ([3, 4], array([ 0.,  0.]), [True, True], [3, 4])
        >>>
        >>>
        >>>
        """
        if isinstance(point, (int,float)):
            point = [point]
        nsd = len(point)
        if nsd != self.nsd:
            raise ValueError('point=%s has wrong dimension (this is a %dD grid!)' % \
                             (point, self.nsd))
        #index = zeros(nsd, int)
        index = [0]*nsd
        distance = zeros(nsd)
        grid_point = [False]*nsd
        nearest_point = [0]*nsd
        for i, coor in enumerate(point):
            # is point inside the domain?
            if coor < self.min_coor[i] or coor > self.max_coor[i]:
                raise ValueError(
                    'locate_cell: point=%s is outside the domain [%s,%s]' % \
                    point, self.min_coor[i], self.max_coor[i])
            index[i] = int((coor - self.min_coor[i])//self.delta[i])  # (need integer division)
            distance[i] = coor - (self.min_coor[i] + index[i]*self.delta[i])
            if distance[i] > self.delta[i]/2:
                nearest_point[i] = index[i] + 1
            else:
                nearest_point[i] = index[i]
            if abs(distance[i]) < self.tolerance:
                grid_point[i] = True
                nearest_point[i] = index[i]
            if (abs(distance[i] - self.delta[i])) < self.tolerance:
                # last cell, update index such that it coincides with the point
                grid_point[i] = True
                index[i] += 1
                nearest_point[i] = index[i]
                distance[i] = 0.0
                
        return index, distance, grid_point, nearest_point

    def interpolate(v0, v1, x0, x1, x):
        return v0 + (v1-v0)/float(x1-x0)*(x-x0)

    def gridline_slice(self, start_coor, direction=0, end_coor=None):
        """
        Compute start and end indices of a line through the grid,
        and return a tuple that can be used as slice for the
        grid points along the line.
        
        The line must be in x, y or z direction (direction=0,1 or 2).
        If end_coor=None, the line ends where the grid ends.
        start_coor holds the coordinates of the start of the line.
        If start_coor does not coincide with one of the grid points,
        the line is snapped onto the grid (i.e., the line coincides with
        a grid line).

        Return: tuple with indices and slice describing the grid point
        indices that make up the line, plus a boolean "snapped" which is
        True if the original line did not coincide with any grid line,
        meaning that the returned line was snapped onto to the grid.

        >>> g2 = UniformBoxGrid.init_fromstring('[-1,1]x[-1,2] [0:3]x[0:4]')
        >>> print g2.coor
        [array([-1.        , -0.33333333,  0.33333333,  1.        ]),
         array([-1.  , -0.25,  0.5 ,  1.25,  2.  ])]

        >>> g2.gridline_slice((-1, 0.5), 0)
        ((slice(0, 4, 1), 2), False)

        >>> g2.gridline_slice((-0.9, 0.4), 0)
        ((slice(0, 4, 1), 2), True)

        >>> g2.gridline_slice((-0.2, -1), 1)
        ((1, slice(0, 5, 1)), True)

        """
        
        start_cell, start_distance, start_match, start_nearest = \
                    self.locate_cell(start_coor)
        # If snapping the line onto to the grid is not desired, the
        # start_cell and start_match lists must be used for interpolation
        # (i.e., interpolation is needed in the directions i where
        # start_match[i] is False).
        
        start_snapped = start_nearest[:]
        if end_coor is None:
            end_snapped = start_snapped[:]
            end_snapped[direction] = self.division[direction] # highest legal index
        else:
            end_cell, end_distance, end_match, end_nearest = \
                      self.locate_cell(end_coor)
            end_snapped = end_nearest[:]
        # recall that upper index limit must be +1 in a slice:
        line_slice = start_snapped[:]
        line_slice[direction] = \
            slice(start_snapped[direction], end_snapped[direction]+1, 1)
        # note that if all start_match are true, then the plane
        # was not snapped
        return tuple(line_slice), not array(start_match).all()

        
    def gridplane_slice(self, value, constant_coor=0):
        """
        Compute a slice for a plane through the grid,
        defined by coor[constant_coor]=value.
        
        Return a tuple that can be used as slice, plus a boolean
        parameter "snapped" reflecting if the plane was snapped
        onto a grid plane (i.e., value did not correspond to
        an existing grid plane).
        """
        start_coor = self.min_coor.copy()
        start_coor[constant_coor] = value
        start_cell, start_distance, start_match, start_nearest = \
                    self.locate_cell(start_coor)
        start_snapped = [0]*self.nsd
        start_snapped[constant_coor] = start_nearest[constant_coor]
        # recall that upper index limit must be +1 in a slice:
        end_snapped = [self.division[i] for i in range(self.nsd)]
        end_snapped[constant_coor] = start_snapped[constant_coor]
        plane_slice = [slice(start_snapped[i], end_snapped[i]+1, 1) \
                       for i in range(self.nsd)]
        plane_slice[constant_coor] = start_nearest[constant_coor]
        return tuple(plane_slice), not start_match[constant_coor]
        

        
class BoxGrid(UniformBoxGrid):
    """
    Extension of class UniformBoxGrid to non-uniform box grids.
    The coordinate vectors (in each space direction) can have
    arbitrarily spaced coordinate values.

    The coor argument must be a list of nsd (number of
    space dimension) components, each component contains the
    grid coordinates in that space direction (stored as an array).
    """
    def __init__(self, coor, dirnames=('x', 'y', 'z')):
        
        UniformBoxGrid.__init__(self,
                                min=[a[0] for a in coor],
                                max=[a[-1] for a in coor],
                                division=[len(a)-1 for a in coor],
                                dirnames=dirnames)
        # override:
        self.coor = coor

    def __repr__(self):
        s = self.__class__.__name__ + '(coor=%s)' % self.coor
        return s

    def locate_cell(self, point):
        raise NotImplementedError('Cannot locate point in cells in non-uniform grids')
    
        
def _test(g, points=None):
    print 'g=%s' % str(g)
    # dump all the contents of a grid object:
    import scitools.misc;  scitools.misc.dump(g, hide_nonpublic=False)
    from numpy import zeros
    def fv(*args):
        # vectorized evaluation function
        return zeros(g.shape)+2
    def fs(*args):
        # scalar version
        return 2
    fv_arr = g.vectorized_eval(fv)
    fs_arr = zeros(g.shape)

    coor = [0.0]*g.nsd
    itparts = ['all', 'interior', 'all_boundary', 'interior_boundary',
               'corners']
    if g.nsd == 3:
        itparts += ['all_edges', 'interior_edges']
    for domain_part in itparts:
        print '\niterator over "%s"' % domain_part
        for i in g.iter(domain_part, vectorized_version=False):
            if isinstance(i, int):  i = [i]  # wrap index as list (if 1D)
            for k in range(g.nsd):
                coor[k] = g.coor[k][i[k]]
            print i, coor
            if domain_part == 'all':  # fs_arr shape corresponds to all points
                fs_arr[i] = fs(*coor)
        print 'vectorized iterator over "%s":' % domain_part
        for slices in g.iter(domain_part, vectorized_version=True):
            if domain_part == 'all':
                fs_arr[slices] = fv(*g.coor)
            # else: more complicated
            print slices
    # boundary slices...

    if points is not None:
        print '\n\nInterpolation in', g, '\n', g.coor
        for p in points:
            index, distance = g.locate_cell(p)
            print 'point %s is in cell %s, distance=%s' % (p, index, distance)
    

def _test2():
    g1 = UniformBoxGrid(min=0, max=1, division=4)
    _test(g1, [0.7, 0.5])
    spec = '[0,1]x[-1,2] with indices [0:3]x[0:2]'
    g2 = UniformBoxGrid.init_fromstring(spec)
    _test(g2, [(0.2,0.2), (1,2)])
    g3 = UniformBoxGrid(min=(0,0,-1), max=(1,1,1), division=(4,1,2))
    _test(g3)
    print 'g3=\n%s' % str(g3)
    print 'g3[Z]=', g3[Z]
    print 'g3[Z][1] =', g3[Z][1]
    print 'dx, dy, dz spacings:', g3.delta
    g4 = UniformBoxGrid(min=(0,-1), max=(1,1),
                        division=(4,2), dirnames=('y','z'))
    _test(g4)
    print 'g4["y"][-1]:', g4["y"][-1]
    
def _test4():
    from numpy import sin, zeros, exp
    # check vectorization evaluation:
    g = UniformBoxGrid(min=(0,0), max=(1,1), division=(3,3))
    try:
        g.vectorized_eval(lambda x,y: 2)
    except TypeError, msg:
        # fine, expect to arrive here
        print '*** Expected error in this test:', msg
    try:
        g.vectorized_eval(lambda x,y: zeros((2,2))+2)
    except IndexError, msg:
        # fine, expect to arrive here
        print '*** Expected error in this test:', msg

    a = g.vectorized_eval(lambda x,y: sin(x)*exp(y-x))
    print a
    a = g.vectorized_eval(lambda x,y: zeros(g.shape)+2)
    print a

        
if __name__ == '__main__':
    _test2()
    #_test4()