Python Parallel.flatten方法代碼示例

# 需要導入模塊: from joblib import Parallel [as 別名]
# 或者: from joblib.Parallel import flatten [as 別名]
def anisotropic_smooth(inpd, fiber_distance_threshold, points_per_fiber=30, n_jobs=2, cluster_max = 10):
    """ Average nearby fibers.
    
    The pairwise fiber distance matrix is computed, then fibers
    are averaged with their neighbors until an edge (>max_fiber_distance) is encountered.

    """

    # polydata to array conversion, fixed-length fiber representation
    current_fiber_array = fibers.FiberArray()
    current_fiber_array.points_per_fiber = points_per_fiber
    current_fiber_array.convert_from_polydata(inpd)
    original_number_of_fibers = current_fiber_array.number_of_fibers
    
    # fiber list data structure initialization for easy fiber averaging
    curr_count = list()
    curr_fibers = list()
    curr_indices = list()
    for lidx in range(0, current_fiber_array.number_of_fibers):
        curr_fibers.append(current_fiber_array.get_fiber(lidx))
        curr_count.append(1)
        curr_indices.append(list([lidx]))
        
    converged = False
    iteration_count = 0
    
    while not converged:
        print "<filter.py> ITERATION:", iteration_count, "SUM FIBER COUNTS:", numpy.sum(numpy.array(curr_count))
        print "<filter.py> number indices", len(curr_indices)
        
        # fiber data structures for output of this iteration
        next_fibers = list()
        next_count = list()
        next_indices = list()
        
        # information for this iteration
        done = numpy.zeros(current_fiber_array.number_of_fibers)
        fiber_indices = range(0, current_fiber_array.number_of_fibers)

        # if the maximum number of fibers have been combined, stop averaging this fiber
        done[numpy.nonzero(numpy.array(curr_count) >= cluster_max)] = 1
        
        # pairwise distance matrix
        if USE_PARALLEL:
            distances = Parallel(n_jobs=n_jobs, verbose=1)(
                delayed(similarity.fiber_distance)(
                current_fiber_array.get_fiber(lidx),
                current_fiber_array,
                0, 'Hausdorff')
                for lidx in fiber_indices)
            distances = numpy.array(distances)
        else:
            distances = \
                numpy.zeros(
                (current_fiber_array.number_of_fibers,
                 current_fiber_array.number_of_fibers))
            for lidx in fiber_indices:
                distances[lidx, :] = \
                    similarity.fiber_distance(
                        current_fiber_array.get_fiber(lidx),
                        current_fiber_array, 0, 'Hausdorff')

        # distances to self are not of interest
        for lidx in fiber_indices:
            distances[lidx,lidx] = numpy.inf
        
        # sort the pairwise distances. 
        distances_flat = distances.flatten()
        pair_order = numpy.argsort(distances_flat)

        print "<filter.py> DISTANCE MIN:", distances_flat[pair_order[0]], \
            "DISTANCE COUNT:", distances.shape

        # if the smallest distance is greater or equal to the
        # threshold, we have converged
        if distances_flat[pair_order[0]] >= fiber_distance_threshold:
            converged = True
            print "<filter.py> CONVERGED"
            break
        else:
            print "<filter.py> NOT CONVERGED"
            
        # loop variables
        idx = 0
        pair_idx = pair_order[idx]
        number_of_fibers = distances.shape[0]
        number_averages = 0
        
        # combine nearest neighbors unless done, until hit threshold
        while distances_flat[pair_idx] < fiber_distance_threshold:
            # find the fiber indices corresponding to this pairwise distance
            # use div and mod
            f_row = pair_idx / number_of_fibers
            f_col = pair_idx % number_of_fibers

            # check if this neighbor pair can be combined
            combine = (not done[f_row]) and (not done[f_col])
            if combine :
                done[f_row] += 1
                done[f_col] += 1
#.........這裏部分代碼省略.........