def get_response_content(fs):
locations = get_locations()
np_locs = [np.array(p) for p in locations]
edges = get_edges()
npoints = len(locations)
# start writing the response
np.set_printoptions(linewidth=200)
out = StringIO()
# print the layout data
print >> out, 'POINTS'
for i, (x, y) in enumerate(locations):
print >> out, i, x, y
print >> out, 'EDGES'
for i, j in edges:
print >> out, i, j
print >> out
# show the unweighted adjacency matrix
UA = np.zeros((npoints, npoints))
for i, j in edges:
UA[i, j] = 1
UA[j, i] = 1
print >> out, 'unweighted adjacency matrix:'
print >> out, UA
print >> out
# show the unweighted laplacian matrix
UL = Euclid.adjacency_to_laplacian(UA)
print >> out, 'unweighted laplacian matrix:'
print >> out, UL
print >> out
# show the weighted adjacency matrix
WA = np.zeros((npoints, npoints))
for i, j in edges:
d = np.linalg.norm(np_locs[i] - np_locs[j]) / math.sqrt(2.0)
w = 1.0 / d
WA[i, j] = w
WA[j, i] = w
print >> out, 'weighted adjacency matrix:'
print >> out, WA
print >> out
# show the weighted laplacian matrix
WL = Euclid.adjacency_to_laplacian(WA)
print >> out, 'weighted laplacian matrix:'
print >> out, WL
print >> out
# remove the two internal nodes by schur complementation
ntips = 4
schur_L = SchurAlgebra.schur_helper(WL, 2)
X = Euclid.dccov_to_points(np.linalg.pinv(schur_L))
print >> out, 'schur graph layout:'
print >> out, 'POINTS'
for i, v in enumerate(X):
print >> out, i, v[0], v[1]
print >> out, 'EDGES'
for i in range(ntips):
for j in range(i+1, ntips):
print >> out, i, j
# return the response
return out.getvalue()
def get_response_content(fs):
locations = get_locations()
np_locs = [np.array(p) for p in locations]
edges = get_edges()
npoints = len(locations)
# start writing the response
np.set_printoptions(linewidth=200)
out = StringIO()
# print the layout data
print >> out, 'POINTS'
for i, (x, y) in enumerate(locations):
print >> out, i, x, y
print >> out, 'EDGES'
for i, j in edges:
print >> out, i, j
print >> out
# show the unweighted adjacency matrix
UA = np.zeros((npoints, npoints))
for i, j in edges:
UA[i, j] = 1
UA[j, i] = 1
print >> out, 'unweighted adjacency matrix:'
print >> out, UA
print >> out
# show the unweighted laplacian matrix
UL = Euclid.adjacency_to_laplacian(UA)
print >> out, 'unweighted laplacian matrix:'
print >> out, UL
print >> out
# show the weighted adjacency matrix
WA = np.zeros((npoints, npoints))
for i, j in edges:
d = np.linalg.norm(np_locs[i] - np_locs[j]) / math.sqrt(2.0)
w = 1.0 / d
WA[i, j] = w
WA[j, i] = w
print >> out, 'weighted adjacency matrix:'
print >> out, WA
print >> out
# show the weighted laplacian matrix
WL = Euclid.adjacency_to_laplacian(WA)
print >> out, 'weighted laplacian matrix:'
print >> out, WL
print >> out
# remove the two internal nodes by schur complementation
ntips = 4
schur_L = SchurAlgebra.schur_helper(WL, 2)
X = Euclid.dccov_to_points(np.linalg.pinv(schur_L))
print >> out, 'schur graph layout:'
print >> out, 'POINTS'
for i, v in enumerate(X):
print >> out, i, v[0], v[1]
print >> out, 'EDGES'
for i in range(ntips):
for j in range(i + 1, ntips):
print >> out, i, j
# return the response
return out.getvalue()
