def test_check_generation_number(self):
'''
Load hutterites pedigree from file including the old_generation numbers
given in that file. Check if each child's parents have the same old_generation #. Such
an ordering cannot be guaranteed to exist for any DAG, but maybe here.
'''
file_name = itu.HUTT_PED
p = PedigreeOldStudyReader().read(file_name)
# Check that generation order from study is consistent
self.__check_generation_order_consistent(p, p.old_generation)
self.__assert_max_generation_gap_equals(p, p.old_generation, 7)
# Test our algorithm vs. old study algorithm without alien node alignment. Only
# a single node should be different (an alien, 999999981)
depth = pt.node_depth(p.graph, max_generation_gap=0)
self.__test_generation_order_differs_by(p, depth, 1)
self.__assert_max_generation_gap_equals(p, depth, 10)
# Test depth calculation with a sufficiently enough max generation gap between parent
# and child, as is implicitly assumed in Gaixin's and Mark's old study code
depth = pt.node_depth(p.graph, max_generation_gap=8)
self.__test_generation_order_differs_by(p, depth, 0)
self.__assert_max_generation_gap_equals(p, depth, 7)
# With alien alignment and our default, smaller max generation gap, check that our
# generation order is consistent, but it should be quite different than the old study's
depth = pt.node_depth(p.graph)
self.__test_generation_order_differs_by(p, depth, 19)
# Our order almost attained the requested (default) max generation depth of 3
self.__assert_max_generation_gap_equals(p, depth, 4)
'''
def test_check_generation_number(self):
'''
Load hutterites pedigree from file including the old_generation numbers
given in that file. Check if each child's parents have the same old_generation #. Such
an ordering cannot be guaranteed to exist for any DAG, but maybe here.
'''
file_name = itu.HUTT_PED
p = PedigreeOldStudyReader().read(file_name)
# Check that generation order from study is consistent
self.__check_generation_order_consistent(p, p.old_generation)
self.__assert_max_generation_gap_equals(p, p.old_generation, 7)
# Test our algorithm vs. old study algorithm without alien node alignment. Only
# a single node should be different (an alien, 999999981)
depth = pt.node_depth(p.graph, max_generation_gap=0)
self.__test_generation_order_differs_by(p, depth, 1)
self.__assert_max_generation_gap_equals(p, depth, 10)
# Test depth calculation with a sufficiently enough max generation gap between parent
# and child, as is implicitly assumed in Gaixin's and Mark's old study code
depth = pt.node_depth(p.graph, max_generation_gap=8)
self.__test_generation_order_differs_by(p, depth, 0)
self.__assert_max_generation_gap_equals(p, depth, 7)
# With alien alignment and our default, smaller max generation gap, check that our
# generation order is consistent, but it should be quite different than the old study's
depth = pt.node_depth(p.graph)
self.__test_generation_order_differs_by(p, depth, 19)
# Our order almost attained the requested (default) max generation depth of 3
self.__assert_max_generation_gap_equals(p, depth, 4)
'''
def _layout_positions(g, g_extended=None):
'''Return a dictionary that maps pedigree nodes to graph layout positions (x,y). Uses
a layered spring model. Capable of handling both normal and extended pedigree graphs.'''
g_extended = g_extended if g_extended else g
# x-positions: spring model
L = laplacian(g_extended, dtype=np.float)
[_, v] = eigsh(L, 2, which='SM')
x = v[:, 1]
# Compute depths based on -original- graph
y = pt.node_depth(g)
for node in g_extended.nodes_iter():
# Marriage node: 0.5 above child depth. Note that marriage nodes must have children
if node < 0:
y[node] = y[g_extended.successors_iter(node).next()] - 0.5
ymax = max(d for d in y.itervalues())
# Reverse the y-axis (generation 0 on top)
return dict(zip(g_extended.nodes_iter(), zip(x, ((ymax - y[node]) for node in g_extended.nodes_iter()))))
def _layout_positions(g, g_extended=None):
'''Return a dictionary that maps pedigree nodes to graph layout positions (x,y). Uses
a layered spring model. Capable of handling both normal and extended pedigree graphs.'''
g_extended = g_extended if g_extended else g
# x-positions: spring model
L = laplacian(g_extended, dtype=np.float)
[_, v] = eigsh(L, 2, which='SM')
x = v[:, 1]
# Compute depths based on -original- graph
y = pt.node_depth(g)
for node in g_extended.nodes_iter():
# Marriage node: 0.5 above child depth. Note that marriage nodes must have children
if node < 0:
y[node] = y[g_extended.successors_iter(node).next()] - 0.5
ymax = max(d for d in y.itervalues())
# Reverse the y-axis (generation 0 on top)
return dict(
zip(g_extended.nodes_iter(),
zip(x, ((ymax - y[node]) for node in g_extended.nodes_iter()))))
