def process_tree(row, main_file, err_del_nodes_old, del_nodes_old, is_holl):
# находим ноды для нового дерева
row_float = float(row['x1'])
all_x2 = [float(row['x2']), ]
print 'STAAAAAAAAAAAAAAAAAAAAAAAAAAAAAART'
print 'is_holl', is_holl
try:
next_line = main_file.next()
next_row = get_row_dict(next_line)
print 'next_row', next_row
next_float = float(next_row['x1'])
except StopIteration:
return None, 1, 1, 1
add_nodes = [row, ]
while next_float == row_float:
add_nodes.append(next_row)
all_x2.append(float(next_row['x2']))
try:
next_line = main_file.next()
next_row = get_row_dict(next_line)
next_float = float(next_row['x1'])
except StopIteration:
next_float = 181
break
min_x2_float = min(all_x2)
max_x2_float = max(all_x2)
x_middle = (min_x2_float + row_float) / 2
# print 'all_x2', all_x2
# print 'x_middle', x_middle, 'prev_row', row_float, 'next_row', next_float
prev_tree = ALL_XS[-1][1]
next_tree = AVLTree()
# l()
# print 'prev_tree'
# prev_tree.show()
# l()
print 'err_del_nodes_old', err_del_nodes_old
l()
print 'prev_tree'
prev_tree.show()
# l()
print 'next_tree'
next_tree.show()
# актуализируем все значения новых нодов для добавления
update_dict_vals(add_nodes, x_middle)
# если предыдущее дерево пусто или между полигонами дыра
if is_holl or prev_tree.root.val is None:
for a in add_nodes:
# print 'add', float(a['val'])
next_tree.add(next_tree.root, float(a['val']), a['a'], a['b'], a['pol_id'], a['x2'], a['y2'])
else:
# удаляем ноды битые, у которых х2 меньше, чем следующий х1
for err_d in err_del_nodes_old:
print 'delete err_del_node', float(err_d['val'])
next_tree.delete_versionly(prev_tree, float(err_d['val'])) # 45.7427962225
print 'del_nodes_old', del_nodes_old
# обработка нодов на замену/добавление/удаление
to_replace, proc_del, proc_add = treatment_add_del(del_nodes_old, add_nodes)
print 'proc_del', proc_del
for d in proc_del:
next_tree.delete_versionly(prev_tree, float(d['val']))
print 'to_replace', to_replace
for (d, a) in to_replace:
next_tree.replace_versionly(prev_tree, float(d['val']), a)
# актуализируем все значения нодов в дереве
next_tree.update_vals(x_middle)
# актуализируем все значения новых нодов для добавления
# update_dict_vals(proc_add, x_middle)
print 'proc_add', proc_add
# предыдущее дерево непусто, но новое дерево пусто,
# и если мы удаляли уже, то без версионности
if next_tree.root.val is None and (del_nodes_old or err_del_nodes_old):
l()
print 'next_tree.root.val is None and (del_nodes_old or err_del_nodes_old)!!!'
for a in proc_add:
# print 'add', a['val']
next_tree.add(next_tree.root, float(a['val']), a['a'], a['b'], a['pol_id'], a['x2'], a['y2'])
# новое просто пусто(т.к. ничего не удадяли) или непусто, то версионность
else:
l()
print 'next_tree.root.val is None or is not empty!!!'
for a in proc_add:
# print 'add_versionly', a['val']
next_tree.add_versionly(prev_tree, a)
# обнуляем версионность дерева next_tree
next_tree.remove_update_flags(next_tree.root)
ALL_XS.append([float(row_float), next_tree])
err_del_nodes = [node for node in add_nodes if float(node['x2']) < next_float]
print 'err_del_nodes', err_del_nodes
# if err_del_nodes:
# print 'Err_del_nodes exists', row_float, err_del_nodes
del_nodes = [node for node in add_nodes if float(node['x2']) == next_float]
print 'next_tree'
next_tree.show()
print 'EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEND'
print 'max_x2_float', max_x2_float, 'next_float', next_float
l()
return next_row, err_del_nodes, del_nodes, max_x2_float < next_float
#
# ALL_XS[-1][1] = ref_to_tree
# # следующее значение Х
# ALL_XS.append([n_x, None])
#
# return n_x if not is_end else None
if __name__ == "__main__":
file_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'outer_sort', 'cut')
with open(file_path) as main_file:
line = main_file.next()
row = get_row_dict(line)
new_row, err_del_nodes, del_nodes, is_holl = process_tree(
row, main_file, [], [], False)
while new_row is not None:
new_row, err_del_nodes, del_nodes, is_holl = process_tree(
new_row, main_file, err_del_nodes, del_nodes, is_holl)
for (x, tree) in ALL_XS:
l()
print x
print '\n'
tree.show()
print len(ALL_XS)