#cython: cdivision=True #cython: nonecheck=False """Cython implementation of Dijkstra's minimum cost path algorithm, for use with data on a n-dimensional lattice. Original author: Zachary Pincus Inspired by code from Almar Klein Later modifications by Almar Klein (Dec 2013) License: BSD Copyright 2009 Zachary Pincus Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import cython import numpy as np from . import heap from .._shared.utils import warn cimport numpy as cnp from . cimport heap cnp.import_array() OFFSET_D = np.int8 OFFSETS_INDEX_D = np.int16 EDGE_D = np.int8 INDEX_D = np.intp FLOAT_D = np.float64 @cython.boundscheck(False) @cython.wraparound(False) def _get_edge_map(shape): """Return an array with edge points/lines/planes/hyperplanes marked. Given a shape (of length n), return an edge_map array with a shape of original_shape + (n,), where, for each dimension, edge_map[...,dim] will have zeros at indices not along an edge in that dimension, -1s at indices along the lower boundary, and +1s on the upper boundary. This allows one to, given an nd index, calculate not only if the index is at the edge of the array, but if so, which edge(s) it lies along. """ d = len(shape) edges = np.zeros(shape+(d,), order='F', dtype=EDGE_D) for i in range(d): slices = [slice(None)] * (d+1) slices[d] = i slices[i] = 0 edges[tuple(slices)] = -1 slices[i] = -1 edges[tuple(slices)] = 1 return edges @cython.boundscheck(False) @cython.wraparound(False) def _offset_edge_map(shape, offsets): """Return an array with positions marked where offsets will step out of bounds. Given a shape (of length n) and a list of n-d offsets, return a two arrays of (n,) + shape: pos_edge_map and neg_edge_map. For each dimension xxx_edge_map[dim, ...] has zeros at indices at which none of the given offsets (in that dimension) of the given sign (positive or negative, respectively) will step out of bounds. If the value is nonzero, it gives the largest offset (in terms of absolute value) that will step out of bounds in that direction. An example will be explanatory: >>> offsets = [[-2,0], [1,1], [0,2]] >>> pos_edge_map, neg_edge_map = _offset_edge_map((4,4), offsets) >>> neg_edge_map[0] array([[-1, -1, -1, -1], [-2, -2, -2, -2], [ 0, 0, 0, 0], [ 0, 0, 0, 0]], dtype=int8) >>> pos_edge_map[1] array([[0, 0, 2, 1], [0, 0, 2, 1], [0, 0, 2, 1], [0, 0, 2, 1]], dtype=int8) """ indices = np.indices(shape) # indices.shape = (n,)+shape #get the distance from each index to the upper or lower edge in each dim pos_edges = (shape - indices.T).T neg_edges = -1 - indices # now set the distances to zero if none of the given offsets could reach offsets = np.asarray(offsets) maxes = offsets.max(axis=0) mins = offsets.min(axis=0) for pos, neg, mx, mn in zip(pos_edges, neg_edges, maxes, mins): pos[pos > mx] = 0 neg[neg < mn] = 0 return pos_edges.astype(EDGE_D), neg_edges.astype(EDGE_D) @cython.boundscheck(False) @cython.wraparound(False) def make_offsets(d, fully_connected): """Make a list of offsets from a center point defining a n-dim neighborhood. Parameters ---------- d : int dimension of the offsets to produce fully_connected : bool whether the neighborhood should be singly- of fully-connected Returns ------- offsets : list of tuples of length `d` Examples -------- The singly-connected 2-d neighborhood is four offsets: >>> make_offsets(2, False) [(-1,0), (1,0), (0,-1), (0,1)] While the fully-connected 2-d neighborhood is the full cartesian product of {-1, 0, 1} (less the origin (0,0)). """ if fully_connected: mask = np.ones([3]*d, dtype=np.uint8) mask[tuple([1]*d)] = 0 else: mask = np.zeros([3]*d, dtype=np.uint8) for i in range(d): indices = [1]*d indices[i] = (0, -1) mask[tuple(indices)] = 1 offsets = [] for indices, value in np.ndenumerate(mask): if value == 1: indices = np.array(indices) - 1 offsets.append(indices) return offsets @cython.boundscheck(True) @cython.wraparound(True) def _unravel_index_fortran(flat_indices, shape): """_unravel_index_fortran(flat_indices, shape) Given a flat index into an n-d fortran-strided array, return an index tuple. """ strides = np.multiply.accumulate([1] + list(shape[:-1])) indices = [tuple((idx // strides) % shape) for idx in flat_indices] return indices @cython.boundscheck(True) @cython.wraparound(True) def _ravel_index_fortran(indices, shape): """_ravel_index_fortran(flat_indices, shape) Given an index tuple into an n-d fortran-strided array, return a flat index. """ strides = np.multiply.accumulate([1] + list(shape[:-1])) flat_indices = [np.sum(strides * idx) for idx in indices] return flat_indices @cython.boundscheck(False) @cython.wraparound(False) def _normalize_indices(indices, shape): """_normalize_indices(indices, shape) Make all indices positive. If an index is out-of-bounds, return None. """ new_indices = [] for index in indices: if len(index) != len(shape): return None new_index = [] for i, s in zip(index, shape): i = int(i) if i < 0: i = s + i if not (0 <= i < s): return None new_index.append(i) new_indices.append(new_index) return new_indices @cython.boundscheck(True) @cython.wraparound(True) def _reverse(arr): """Reverse index an array safely, with bounds/wraparound checks on. """ return arr[::-1] @cython.boundscheck(False) @cython.wraparound(False) cdef class MCP: """MCP(costs, offsets=None, fully_connected=True, sampling=None) A class for finding the minimum cost path through a given n-d costs array. Given an n-d costs array, this class can be used to find the minimum-cost path through that array from any set of points to any other set of points. Basic usage is to initialize the class and call find_costs() with a one or more starting indices (and an optional list of end indices). After that, call traceback() one or more times to find the path from any given end-position to the closest starting index. New paths through the same costs array can be found by calling find_costs() repeatedly. The cost of a path is calculated simply as the sum of the values of the `costs` array at each point on the path. The class MCP_Geometric, on the other hand, accounts for the fact that diagonal vs. axial moves are of different lengths, and weights the path cost accordingly. Array elements with infinite or negative costs will simply be ignored, as will paths whose cumulative cost overflows to infinite. Parameters ---------- costs : ndarray offsets : iterable, optional A list of offset tuples: each offset specifies a valid move from a given n-d position. If not provided, offsets corresponding to a singly- or fully-connected n-d neighborhood will be constructed with make_offsets(), using the `fully_connected` parameter value. fully_connected : bool, optional If no `offsets` are provided, this determines the connectivity of the generated neighborhood. If true, the path may go along diagonals between elements of the `costs` array; otherwise only axial moves are permitted. sampling : tuple, optional For each dimension, specifies the distance between two cells/voxels. If not given or None, the distance is assumed unit. Attributes ---------- offsets : ndarray Equivalent to the `offsets` provided to the constructor, or if none were so provided, the offsets created for the requested n-d neighborhood. These are useful for interpreting the `traceback` array returned by the find_costs() method. """ def __init__(self, costs, offsets=None, fully_connected=True, sampling=None): """__init__(costs, offsets=None, fully_connected=True, sampling=None) See class documentation. """ costs = np.asarray(costs) if not np.can_cast(costs.dtype, FLOAT_D): raise TypeError('cannot cast costs array to ' + str(FLOAT_D)) # Check sampling if sampling is None: sampling = np.array([1.0 for s in costs.shape], FLOAT_D) elif isinstance(sampling, (list, tuple)): sampling = np.array(sampling, FLOAT_D) if sampling.ndim != 1 or len(sampling) != costs.ndim: raise ValueError('Need one sampling element per dimension.') else: raise ValueError('Invalid type for sampling: %r.' % type(sampling)) # We use flat, fortran-style indexing here (could use C-style, # but this is my code and I like fortran-style! Also, it's # faster when working with image arrays, which are often # already fortran-strided.) try: self.flat_costs = costs.astype(FLOAT_D, copy=False).ravel('F') except TypeError: self.flat_costs = costs.astype(FLOAT_D).flatten('F') warn('Upgrading NumPy should decrease memory usage and increase' ' speed.') size = self.flat_costs.shape[0] self.flat_cumulative_costs = np.empty(size, dtype=FLOAT_D) self.dim = len(costs.shape) self.costs_shape = costs.shape self.costs_heap = heap.FastUpdateBinaryHeap(initial_capacity=128, max_reference=size-1) # This array stores, for each point, the index into the offset # array (see below) that leads to that point from the # predecessor point. self.traceback_offsets = np.empty(size, dtype=OFFSETS_INDEX_D) # The offsets are a list of relative offsets from a central # point to each point in the relevant neighborhood. (e.g. (-1, # 0) might be a 2d offset). # These offsets are raveled to provide flat, 1d offsets that can be # used in the same way for flat indices to move to neighboring points. if offsets is None: offsets = make_offsets(self.dim, fully_connected) self.offsets = np.array(offsets, dtype=OFFSET_D) self.flat_offsets = np.array( _ravel_index_fortran(self.offsets, self.costs_shape), dtype=INDEX_D) # Instead of unraveling each index during the pathfinding algorithm, we # will use a pre-computed "edge map" that specifies for each dimension # whether a given index is on a lower or upper boundary (or none at # all). Flatten this map to get something that can be indexed as by the # same flat indices as elsewhere. # The edge map stores more than a boolean "on some edge" flag so as to # allow us to examine the non-out-of-bounds neighbors for a given edge # point while excluding the neighbors which are outside the array. pos, neg = _offset_edge_map(costs.shape, self.offsets) self.flat_pos_edge_map = pos.reshape((self.dim, size), order='F') self.flat_neg_edge_map = neg.reshape((self.dim, size), order='F') # The offset lengths are the distances traveled along each offset self.offset_lengths = np.sqrt(np.sum((sampling * self.offsets)**2, axis=1)).astype(FLOAT_D) self.dirty = 0 self.use_start_cost = 1 def _reset(self): """_reset() Clears paths found by find_costs(). """ cdef INDEX_T start self.costs_heap.reset() self.traceback_offsets[...] = -2 # -2 is not reached, -1 is start self.flat_cumulative_costs[...] = np.inf self.dirty = 0 # Get starts and ends # We do not pass them in as arguments for backwards compat starts, ends = self._starts, self._ends # push each start point into the heap. Note that we use flat indexing! for start in _ravel_index_fortran(starts, self.costs_shape): self.traceback_offsets[start] = -1 if self.use_start_cost: self.costs_heap.push_fast(self.flat_costs[start], start) else: self.costs_heap.push_fast(0, start) cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): """ float _travel_cost(float old_cost, float new_cost, float offset_length) The travel cost for going from the current node to the next. Default is simply the cost of the next node. """ return new_cost cpdef int goal_reached(self, INDEX_T index, FLOAT_T cumcost): """ int goal_reached(int index, float cumcost) This method is called each iteration after popping an index from the heap, before examining the neighbours. This method can be overloaded to modify the behavior of the MCP algorithm. An example might be to stop the algorithm when a certain cumulative cost is reached, or when the front is a certain distance away from the seed point. This method should return 1 if the algorithm should not check the current point's neighbours and 2 if the algorithm is now done. """ return 0 cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ _examine_neighbor(int index, int new_index, float offset_length) This method is called once for every pair of neighboring nodes, as soon as both nodes become frozen. """ pass cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ _update_node(int index, int new_index, float offset_length) This method is called when a node is updated. """ pass def find_costs(self, starts, ends=None, find_all_ends=True, max_coverage=1.0, max_cumulative_cost=None, max_cost=None): """ Find the minimum-cost path from the given starting points. This method finds the minimum-cost path to the specified ending indices from any one of the specified starting indices. If no end positions are given, then the minimum-cost path to every position in the costs array will be found. Parameters ---------- starts : iterable A list of n-d starting indices (where n is the dimension of the `costs` array). The minimum cost path to the closest/cheapest starting point will be found. ends : iterable, optional A list of n-d ending indices. find_all_ends : bool, optional If 'True' (default), the minimum-cost-path to every specified end-position will be found; otherwise the algorithm will stop when a a path is found to any end-position. (If no `ends` were specified, then this parameter has no effect.) Returns ------- cumulative_costs : ndarray Same shape as the `costs` array; this array records the minimum cost path from the nearest/cheapest starting index to each index considered. (If `ends` were specified, not all elements in the array will necessarily be considered: positions not evaluated will have a cumulative cost of inf. If `find_all_ends` is 'False', only one of the specified end-positions will have a finite cumulative cost.) traceback : ndarray Same shape as the `costs` array; this array contains the offset to any given index from its predecessor index. The offset indices index into the `offsets` attribute, which is a array of n-d offsets. In the 2-d case, if offsets[traceback[x, y]] is (-1, -1), that means that the predecessor of [x, y] in the minimum cost path to some start position is [x+1, y+1]. Note that if the offset_index is -1, then the given index was not considered. """ # basic variables to use for end-finding; also fix up the start and end # lists cdef BOOL_T use_ends = 0 cdef INDEX_T num_ends cdef BOOL_T all_ends = find_all_ends cdef INDEX_T[:] flat_ends starts = _normalize_indices(starts, self.costs_shape) if starts is None: raise ValueError('start points must all be within the costs array') elif not starts: raise ValueError('no valid start points to start front' + 'propagation') if ends is not None: ends = _normalize_indices(ends, self.costs_shape) if ends is None: raise ValueError('end points must all be within ' 'the costs array') use_ends = 1 num_ends = len(ends) flat_ends = np.array(_ravel_index_fortran( ends, self.costs_shape), dtype=INDEX_D) # Always perform a reset to (re)initialize our arrays and start # positions self._starts, self._ends = starts, ends self._reset() # Get shorter names for arrays cdef FLOAT_T[:] flat_costs = self.flat_costs cdef FLOAT_T[:] flat_cumulative_costs = self.flat_cumulative_costs cdef OFFSETS_INDEX_T[:] traceback_offsets = self.traceback_offsets cdef EDGE_T[:, :] flat_pos_edge_map = self.flat_pos_edge_map cdef EDGE_T[:, :] flat_neg_edge_map = self.flat_neg_edge_map cdef OFFSET_T[:, :] offsets = self.offsets cdef INDEX_T[:] flat_offsets = self.flat_offsets cdef FLOAT_T[:] offset_lengths = self.offset_lengths # Short names for other attributes cdef heap.FastUpdateBinaryHeap costs_heap = self.costs_heap cdef DIM_T dim = self.dim cdef int num_offsets = len(flat_offsets) # Variables used during front propagation cdef FLOAT_T cost, new_cost, cumcost, new_cumcost, offset_length cdef INDEX_T index, new_index cdef BOOL_T is_at_edge, use_offset cdef INDEX_T d, i, iter cdef OFFSET_T offset cdef EDGE_T pos_edge_val, neg_edge_val cdef int num_ends_found = 0 cdef FLOAT_T inf = np.inf cdef int goal_reached cdef INDEX_T maxiter = int(max_coverage * flat_costs.size) for iter in range(maxiter): # This is rather like a while loop, except we are guaranteed to # exit, which is nice during developing to prevent eternal loops. # Find the point with the minimum cost in the heap. Once # popped, this point's minimum cost path has been found. if costs_heap.count == 0: # nothing in the heap: we've found paths to every # point in the array break # Get current cumulative cost and index from the heap cumcost = costs_heap.pop_fast() index = costs_heap._popped_ref # Record the cost we found to this point flat_cumulative_costs[index] = cumcost # Check if goal is reached goal_reached = self.goal_reached(index, cumcost) if goal_reached > 0: if goal_reached == 1: continue # Skip neighbours else: break # Done completely if use_ends: # If we're only tracing out a path to one or more # endpoints, check to see if this is an endpoint, and # if so, if we're done pathfinding. for i in range(num_ends): if index == flat_ends[i]: num_ends_found += 1 break if (num_ends_found and not all_ends) or \ num_ends_found == num_ends: # if we've found one or all of the end points (as # requested), stop searching break # Look into the edge map to see if this point is at an # edge along any axis is_at_edge = 0 for d in range(dim): if (flat_pos_edge_map[d, index] != 0 or flat_neg_edge_map[d, index] != 0): is_at_edge = 1 break # Now examine the points neighboring the given point for i in range(num_offsets): # First, if we're at some edge, scrutinize the offset # to ensure that it won't put us out-of-bounds. If, # for example, the edge_map at (x, y) is (-1, 0) -- # though of course we use flat indexing below -- that # means that (x, y) is along the lower edge of the # array; thus offsets with -1 or more negative in the # x-dimension should not be used! use_offset = 1 if is_at_edge: for d in range(dim): offset = offsets[i, d] pos_edge_val = flat_pos_edge_map[d, index] neg_edge_val = flat_neg_edge_map[d, index] if (pos_edge_val > 0 and offset >= pos_edge_val) or \ (neg_edge_val < 0 and offset <= neg_edge_val): # the offset puts us out of bounds... use_offset = 0 break # If not at an edge, or the specific offset doesn't # push over the edge, then we go on. if not use_offset: continue # using the flat offsets, calculate the new flat index new_index = index + flat_offsets[i] # Get offset length offset_length = offset_lengths[i] # If we have already found the best path here then # ignore this point if flat_cumulative_costs[new_index] != inf: # Give subclass the opportunity to examine these two nodes # Note that only when both nodes are "frozen" their # cumulative cost is set. By doing the check here, each # pair of nodes is checked exactly once. self._examine_neighbor(index, new_index, offset_length) continue # Get cost and new cost cost = flat_costs[index] new_cost = flat_costs[new_index] # If the cost at this point is negative or infinite, ignore it if new_cost < 0 or new_cost == inf: continue # Calculate new cumulative cost new_cumcost = cumcost + self._travel_cost(cost, new_cost, offset_length) # Now we ask the heap to append or update the cost to # this new point, but only if that point isn't already # in the heap, or it is but the new cost is lower. # don't push infs into the heap though! if new_cumcost != inf: costs_heap.push_if_lower_fast(new_cumcost, new_index) # If we did perform an append or update, we should # record the offset from the predecessor to this new # point if costs_heap._pushed: traceback_offsets[new_index] = i self._update_node(index, new_index, offset_length) # Un-flatten the costs and traceback arrays for human consumption. cumulative_costs = np.asarray(flat_cumulative_costs) cumulative_costs = cumulative_costs.reshape(self.costs_shape, order='F') traceback = np.asarray(traceback_offsets) traceback = traceback.reshape(self.costs_shape, order='F') self.dirty = 1 return cumulative_costs, traceback def traceback(self, end): """traceback(end) Trace a minimum cost path through the pre-calculated traceback array. This convenience function reconstructs the the minimum cost path to a given end position from one of the starting indices provided to find_costs(), which must have been called previously. This function can be called as many times as desired after find_costs() has been run. Parameters ---------- end : iterable An n-d index into the `costs` array. Returns ------- traceback : list of n-d tuples A list of indices into the `costs` array, starting with one of the start positions passed to find_costs(), and ending with the given `end` index. These indices specify the minimum-cost path from any given start index to the `end` index. (The total cost of that path can be read out from the `cumulative_costs` array returned by find_costs().) """ if not self.dirty: raise Exception('find_costs() must be run before traceback()') ends = _normalize_indices([end], self.costs_shape) if ends is None: raise ValueError('the specified end point must be ' 'within the costs array') traceback = [tuple(ends[0])] cdef INDEX_T flat_position =\ _ravel_index_fortran(ends, self.costs_shape)[0] if self.flat_cumulative_costs[flat_position] == np.inf: raise ValueError('no minimum-cost path was found ' 'to the specified end point') # Short names for arrays cdef OFFSETS_INDEX_T [:] traceback_offsets = self.traceback_offsets cdef OFFSET_T [:,:] offsets = self.offsets cdef INDEX_T [:] flat_offsets = self.flat_offsets # New array cdef INDEX_T [:] position = np.array(ends[0], dtype=INDEX_D) cdef OFFSETS_INDEX_T offset cdef DIM_T d cdef DIM_T dim = self.dim while 1: offset = traceback_offsets[flat_position] if offset == -1: # At a point where we can go no further: probably a start point break flat_position -= flat_offsets[offset] for d in range(dim): position[d] -= offsets[offset, d] traceback.append(tuple(position)) return _reverse(traceback) @cython.boundscheck(False) @cython.wraparound(False) cdef class MCP_Geometric(MCP): """MCP_Geometric(costs, offsets=None, fully_connected=True) Find distance-weighted minimum cost paths through an n-d costs array. See the documentation for MCP for full details. This class differs from MCP in that the cost of a path is not simply the sum of the costs along that path. This class instead assumes that the costs array contains at each position the "cost" of a unit distance of travel through that position. For example, a move (in 2-d) from (1, 1) to (1, 2) is assumed to originate in the center of the pixel (1, 1) and terminate in the center of (1, 2). The entire move is of distance 1, half through (1, 1) and half through (1, 2); thus the cost of that move is `(1/2)*costs[1,1] + (1/2)*costs[1,2]`. On the other hand, a move from (1, 1) to (2, 2) is along the diagonal and is sqrt(2) in length. Half of this move is within the pixel (1, 1) and the other half in (2, 2), so the cost of this move is calculated as `(sqrt(2)/2)*costs[1,1] + (sqrt(2)/2)*costs[2,2]`. These calculations don't make a lot of sense with offsets of magnitude greater than 1. Use the `sampling` argument in order to deal with anisotropic data. """ def __init__(self, costs, offsets=None, fully_connected=True, sampling=None): """__init__(costs, offsets=None, fully_connected=True, sampling=None) See class documentation. """ MCP.__init__(self, costs, offsets, fully_connected, sampling) if np.absolute(self.offsets).max() > 1: raise ValueError('all offset components must be 0, 1, or -1') self.use_start_cost = 0 cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): return offset_length * 0.5 * (old_cost + new_cost) @cython.boundscheck(True) @cython.wraparound(True) cdef class MCP_Connect(MCP): """MCP_Connect(costs, offsets=None, fully_connected=True) Connect source points using the distance-weighted minimum cost function. A front is grown from each seed point simultaneously, while the origin of the front is tracked as well. When two fronts meet, create_connection() is called. This method must be overloaded to deal with the found edges in a way that is appropriate for the application. """ cdef INDEX_T [:] flat_idmap def __init__(self, costs, offsets=None, fully_connected=True, sampling=None): MCP.__init__(self, costs, offsets, fully_connected, sampling) # Create id map to keep track of origin of nodes self.flat_idmap = np.zeros(self.costs_shape, INDEX_D).ravel('F') def _reset(self): """ Reset the id map. """ cdef INDEX_T start MCP._reset(self) starts, ends = self._starts, self._ends # Reset idmap self.flat_idmap[...] = -1 id = 0 for start in _ravel_index_fortran(starts, self.costs_shape): self.flat_idmap[start] = id id += 1 cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): """ Equivalent to MCP_Geometric. """ return offset_length * 0.5 * (old_cost + new_cost) cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ Check whether two fronts are meeting. If so, the flat_traceback is obtained and a connection is created. """ # Short names cdef INDEX_T [:] flat_idmap = self.flat_idmap cdef FLOAT_T [:] flat_cumulative_costs = self.flat_cumulative_costs # Get ids cdef INDEX_T id1 = flat_idmap[index] cdef INDEX_T id2 = flat_idmap[new_index] if id2 < 0 or id1 < 0: pass elif id2 != id1: # We reached the 'front' of another seed point! # Get position/coordinates pos1, pos2 = _unravel_index_fortran([index, new_index], self.costs_shape) # Also get the costs, so we can keep the path with the least cost cost1 = flat_cumulative_costs[index] cost2 = flat_cumulative_costs[new_index] # Create connection self.create_connection(id1, id2, pos1, pos2, cost1, cost2) def create_connection(self, id1, id2, tb1, tb2, cost1, cost2): """ create_connection id1, id2, pos1, pos2, cost1, cost2) Overload this method to keep track of the connections that are found during MCP processing. Note that a connection with the same ids can be found multiple times (but with different positions and costs). At the time that this method is called, both points are "frozen" and will not be visited again by the MCP algorithm. Parameters ---------- id1 : int The seed point id where the first neighbor originated from. id2 : int The seed point id where the second neighbor originated from. pos1 : tuple The index of of the first neighbour in the connection. pos2 : tuple The index of of the second neighbour in the connection. cost1 : float The cumulative cost at `pos1`. cost2 : float The cumulative costs at `pos2`. """ pass cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ Keep track of the id map so that we know which seed point a certain front originates from. """ self.flat_idmap[new_index] = self.flat_idmap[index] @cython.boundscheck(False) @cython.wraparound(False) cdef class MCP_Flexible(MCP): """MCP_Flexible(costs, offsets=None, fully_connected=True) Find minimum cost paths through an N-d costs array. See the documentation for MCP for full details. This class differs from MCP in that several methods can be overloaded (from pure Python) to modify the behavior of the algorithm and/or create custom algorithms based on MCP. Note that goal_reached can also be overloaded in the MCP class. """ def travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): """ travel_cost(old_cost, new_cost, offset_length) This method calculates the travel cost for going from the current node to the next. The default implementation returns new_cost. Overload this method to adapt the behaviour of the algorithm. """ return new_cost def examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ examine_neighbor(index, new_index, offset_length) This method is called once for every pair of neighboring nodes, as soon as both nodes are frozen. This method can be overloaded to obtain information about neightboring nodes, and/or to modify the behavior of the MCP algorithm. One example is the MCP_Connect class, which checks for meeting fronts using this hook. """ pass def update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): """ update_node(index, new_index, offset_length) This method is called when a node is updated, right after new_index is pushed onto the heap and the traceback map is updated. This method can be overloaded to keep track of other arrays that are used by a specific implementation of the algorithm. For instance the MCP_Connect class uses it to update an id map. """ pass cdef FLOAT_T _travel_cost(self, FLOAT_T old_cost, FLOAT_T new_cost, FLOAT_T offset_length): return self.travel_cost(old_cost, new_cost, offset_length) cdef void _examine_neighbor(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): self.examine_neighbor(index, new_index, offset_length) cdef void _update_node(self, INDEX_T index, INDEX_T new_index, FLOAT_T offset_length): self.update_node(index, new_index, offset_length)