def transform_bezier_simplex(k, degree, M):
print("------------------------------------------------------------")
print("k={}, degree={}".format(k, degree))
print("M =")
print_matrix(M)
assert (M.rows == k and M.cols == k)
rank = degree
control_point_symbols = sym.symbols(" ".join(
"P_" + "".join(str(i) for i in index)
for index in stensor_indices(k, rank)))
control_points = stensor(k, rank, control_point_symbols)
masks = stensor(k, rank)
for mask_index in masks.indices():
mask_tensor_index = masks.to_tensor_index(mask_index)
masks.set(
mask_index,
sum(
sum(
prod(M[tensor_index[j], mask_tensor_index[j]]
for j in range(rank))
for tensor_index in symmetric_tensor_indices(index)) *
control_points[index] for index in control_points.indices()))
print(masks[mask_index])
transformation = []
for mask_index in masks.indices():
transformation.append([
masks[mask_index].coeff(symbol) for symbol in control_point_symbols
])
transformation = sym.Matrix(transformation)
print_matrix(transformation)
return transformation
def general_discriminant(degree):
assert (degree <= 25)
variables = sym.symbols(" ".join(string.ascii_lowercase[:degree + 1]))
p = variables[::-1]
pp = [i * p[i] for i in range(1, len(p))]
print_matrix(sylvester_matrix(p, pp))
return (-(1 / variables[0]) * resultant(p, pp)).expand()
def bezier_basis(degree):
n = degree + 1
x = Sym('x')
polys = [
p.as_poly() for p in
[sym.binomial(n, k) * x**k * (1 - x)**(n - k) for k in range(n + 1)]
]
print_matrix(coefficient_matrix(polys))
def finite_differences(samples, order):
assert (len(set(samples)) == len(samples))
assert (order < len(samples))
print("finite_differences {}".format(", ".join(str(x) for x in samples)))
x = sym.symbols("x")
h = sym.symbols("h")
n = len(samples)
power_matrix = []
for i in range(n):
power_matrix.append([(samples[i] * h)**j / factorial(j)
for j in range(n)])
power_matrix = sym.Matrix(power_matrix).T
A = power_matrix.inv()
print_matrix(A)
print(sym.latex(power_matrix.T.subs(h, 1)))
print(sym.latex(A.T.subs(h, 1)))
weights = A.col(order).T
return [weights[0, i] for i in range(weights.cols)]
def bezier_curve_degree_elevation(m, n):
# Multiply the Bezier curve polynomial of degree m by (u+v)^(n-m) and regroup terms
# to get Bezier coefficients for the equivalent polynomial of degree n.
assert (n > m)
u, v = sym.symbols("u v")
ps = sym.symbols(" ".join("p_{}".format(i) for i in range(m + 1)))
f = 0
for i in range(m + 1):
f += ps[i] * sym.binomial(m, i) * u**i * v**(m - i)
f = f.as_poly()
print(f)
g = (u + v)**(n - m) * f
print(g)
M = []
for i in range(n + 1):
coeffs = []
for j in range(m + 1):
coeffs.append(
g.coeff_monomial(ps[j] * u**i * v**(n - i)) /
sym.binomial(n, i))
print(coeffs)
M.append(coeffs)
M = sym.Matrix(M)
print_matrix(M)
def bezier_triangle_subpatch(degree, a0, b0, c0, a1, b1, c1, a2, b2, c2):
x, y, z = Sym("x y z")
X, Y, Z = Sym("X Y Z")
monomials = [X**i * Y**j * Z**k for i, j, k in ordered_sums(3, degree)]
M = []
for mono in monomials:
p = mono.subs({
X: a0 * x + a1 * y + a2 * z,
Y: b0 * x + b1 * y + b2 * z,
Z: c0 * x + c1 * y + c2 * z,
}).expand().subs({
x: X,
y: Y,
z: Z,
}).as_poly()
# print(mono, "|->", p.as_expr())
coeffs = []
for mono2 in monomials:
try:
coeffs.append(p.coeff_monomial(mono2))
except:
coeffs.append(0)
M.append(coeffs)
M = Mat(M)
A = sym.diag(
*[trinomial(degree, i, j, k) for i, j, k in ordered_sums(3, degree)])
K = A * M * A.inv()
print_matrix(K)
for col, i, j, k in ordered_sums_indexed(3, degree):
string = "Q_{}{}{} = ".format(i, j, k)
string += " +\n ".join(
"({})P_{}{}{}".format(K[row, col], ii, jj, kk)
for row, ii, jj, kk in ordered_sums_indexed(3, degree)
if K[row, col] != 0)
print(string + "\n")
n = degp + degq
M = [[0 for _ in range(n)] for __ in range(n)]
for i in range(degq):
for j in range(degp + 1):
M[i + j][i] = p[j]
for i in range(degp):
for j in range(degq + 1):
M[i + j][degq + i] = q[j]
return sym.Matrix(M)
def resultant(p, q):
return sylvester_matrix(p, q).det()
def general_discriminant(degree):
assert (degree <= 25)
variables = sym.symbols(" ".join(string.ascii_lowercase[:degree + 1]))
p = variables[::-1]
pp = [i * p[i] for i in range(1, len(p))]
print_matrix(sylvester_matrix(p, pp))
return (-(1 / variables[0]) * resultant(p, pp)).expand()
S = sylvester_matrix([1, -1, 3, -3], [3, -1, -2])
print_matrix(S)
print(S.det())
print(general_discriminant(2))
print(general_discriminant(3))
def Rec(n):
return Rat(1, n)
from sympy.diffgeom.rn import R2_r
from sympy.diffgeom import WedgeProduct
ex, ey = R2_r.base_vectors()
dx, dy = R2_r.base_oneforms()
print(WedgeProduct(dx, dy))
print(WedgeProduct(dx, dy)(ex, ey))
J = Mat([[Sym("J_{}{}".format(i, j)) for j in range(1, 3)]
for i in range(1, 4)])
print_matrix(J)
print_matrix(J.T * J)
class k_vector:
def __init__(self, n):
self.n = n
def E(n, *vecs):
assert (len(vecs) <= n)
k = len(vecs)
if len(set(vecs)) < k:
return 0
v = k_vector(n)
def solve_linear_dirichlet_bvp(coefficients, boundary, boundary_values,
intervals, order_of_accuracy):
datatype = float
assert (len(boundary) == 2 and len(boundary_values) == 2
and boundary[0] < boundary[1])
n = len(coefficients)
# Central finite differences.
# todo: Choose simplest central differences for the given accuracy.
system = np.zeros((intervals + 1, intervals + 1), dtype=datatype)
system[0, 0] = 1
system[intervals, intervals] = 1
dt = 1 / intervals
b = np.zeros(intervals + 1, dtype=datatype)
b[0] = boundary_values[0]
b[intervals] = boundary_values[1]
for i in range(1, intervals):
b[i] = -coefficients[0](boundary[0] +
(boundary[1] - boundary[0]) * i * dt)
h = sym.symbols('h')
for derivative_degree in range(
1, n): # skip 0'th order term as it will be in b.
samples = list(range(-1, 1 + 1)) # only up to order 2
num_samples = len(samples)
stencil = [
x.subs(h, dt)
for x in finite_differences(samples, derivative_degree)
]
stencil_radius = (len(stencil) - 1) // 2
M = np.zeros((intervals + 1, intervals + 1), dtype=datatype)
# Left boundary stencils.
for index, shift in enumerate(range(-stencil_radius + 1, 0)):
shifted_samples = [s - shift for s in samples]
shifted_weights = finite_differences(shifted_samples,
derivative_degree)
for i in range(num_samples):
M[index + 1, i] = shifted_weights[i].subs(h, dt)
# Right boundary stencils.
for index, shift in enumerate(range(1, stencil_radius)):
shifted_samples = [s - shift for s in samples]
shifted_weights = finite_differences(shifted_samples,
derivative_degree)
for i in range(num_samples):
M[intervals - index - 1,
intervals + 1 - num_samples + i] = shifted_weights[i].subs(
h, dt)
# Middle stencils.
for i in range(max(1, stencil_radius),
min(intervals, intervals - stencil_radius + 1)):
for j in range(num_samples):
M[i, j + i - stencil_radius] = stencil[j]
# Coefficients are functions of x, so discretize this function and use it to weight the rows.
row_multipliers = np.array([
coefficients[derivative_degree]
(boundary[0] + (boundary[1] - boundary[0]) * i * dt)
for i in range(intervals + 1)
],
dtype=datatype)
M = np.multiply(M, row_multipliers[:, np.newaxis])
print_matrix(Mat(M))
# Add this matrix to the total system.
system = np.add(system, M)
print_matrix(Mat(system))
print_matrix(Mat(b))
x = linalg.solve(system, b)
plt.plot(np.linspace(boundary[0], boundary[1], intervals + 1), x, "k")
def regular_triangular_bspline(continuity):
# Lots of printouts for debugging.
VERBOSE = False
# Test with integral values to see if it matches the Sabin paper.
TEST_INTEGRAL_VALUES = False
if continuity % 2 == 0:
patch_degree = 1
patches_width = 3
grid = stensor(3, 3, sym_rational=True)
grid.set((1, 1, 1), 1)
num_iterations = continuity // 2
else:
print("Odd continuities not yet supported.")
return
print("regular_triangular_bspline, continuity={}".format(continuity))
print("============================================================")
print("Initial grid")
print_triangle_stensor(grid)
print("------------------------------------------------------------")
# Each repetition gives 2 more degrees of continuity.
for iteration in range(num_iterations):
print(
"================================================================================"
)
print("ITERATION", iteration + 1)
print(
"================================================================================"
)
# Do the same shift-subtract-integrate convolution symmetrically in each direction.
# Directions of convolution: i->j, j->k, k->i.
# The below code is written w/r/t i->j, and the other directions are accounted for by permuting the multi-indices used.
for perm_number, perm in enumerate(cyclic_permutations(3)):
print(
"--------------------------------------------------------------------------------"
)
print("DIRECTION", ", ".join(str(i) for i in perm((1, 2, 3))))
if perm_number == 1:
print(" --------")
elif perm_number == 2:
print(""" \\
\\
\\
\\
\\""")
elif perm_number == 3:
print(""" /
/
/
/
/ """)
print(
"--------------------------------------------------------------------------------"
)
# Shift the patch coefficients over one patch in the direction of integration,
# and subtract this from the patch coefficients.
shift_subtract_grid = stensor(3,
patch_degree * (patches_width + 1),
sym_rational=True)
for index in grid.indices():
new_index = add_indices(index, perm((patch_degree, 0, 0)))
shift_index = relative_index(
new_index, perm((-patch_degree, patch_degree, 0)))
shift_subtract_grid.set(
new_index, shift_subtract_grid[new_index] + grid[index])
shift_subtract_grid.set(
shift_index,
shift_subtract_grid[shift_index] - grid[index])
print("Shift and subtract")
print_triangle_stensor(grid)
print("------->")
print_triangle_stensor(shift_subtract_grid)
# Integrate, raising the degree of each patch by one.
integrand_grid = shift_subtract_grid
integral_grid = stensor(3,
(patch_degree + 1) * (patches_width + 1))
# Go over each strip of patches. In each strip, there are two stages of integration, for each orientation of triangle.
# /\
# /__\ <-- Each quadrilateral: ,---------
# /__/ \ /\ /
# /__/ \ / \ /
# /__/ \ / \ /
# /__/ \ / \ /
# /__/ \ /________\/
# /__/ \
# /__/_____________\
new_patch_degree = patch_degree + 1
patches_width += 1 # Now that shift-subtract is done, the grid is wider.
for depth in range(patches_width):
if VERBOSE: print("~~~~~~ DEPTH {} ~~~~~~".format(depth))
# Depth increases from the top-left strip to the bottom-right corner.
for height in range(patches_width - depth):
if VERBOSE:
print("~~~~~~ DEPTH {}: HEIGHT {} ~~~~~~".format(
depth, height))
# Height ranges from the bottom-most part of the strip to the top-most.
#
# Each quadrilateral is formed by two patches with shared coefficients.
# For example, below is the structure of coefficients for a quadrilateral of degree 1 and 2.
#
# o---------o
# /\ /
# / \ /
# / \ /
# / \ /
# o________o/
# Degree 1 patches integrate to
# *---o-----o
# /\ / \ /
# / \/ \ /
# *---o-----o
# / \ / \ /
# *___o____o/ The *s are constants of integration, assumed already computed when integrating previous strips.
# Compute important corresponding indices for the patch/pair in both the integrand grid and the integral grid.
integrand_lower_left_index = perm(
((patches_width - height - depth) * patch_degree,
depth * patch_degree, height * patch_degree))
integrand_lower_right_index = relative_index(
integrand_lower_left_index,
perm((-patch_degree, patch_degree, 0)))
integral_lower_left_index = perm(
((patches_width - height - depth) * new_patch_degree -
1, depth * new_patch_degree + 1,
height * new_patch_degree))
integral_lower_right_index = relative_index(
integral_lower_left_index,
perm((-patch_degree - 1, patch_degree, 1)))
if VERBOSE:
print("integrand_lower_left_index:",
integrand_lower_left_index)
print("integrand_lower_right_index:",
integrand_lower_right_index)
print("integral_lower_left_index:",
integral_lower_left_index)
print("integral_lower_right_index:",
integral_lower_right_index)
# The integrand patch corresponds to the bottom-right sub-triangle in this integrated patch:
# *
# /\
# / \
# *---o\ e.g. The integrand here is of degree 1.
# / \ / \
# *___o____o
# ^
# lower_left_index into the integral grid.
# *----o---o
# \ / \ /
# *----o <- lower_right_index into the integral grid.
# \ /
# \*
if VERBOSE:
print(
"------------------------------------------------------------"
)
print("Lower-left triangle")
print(
"------------------------------------------------------------"
)
print("Integrand:")
print_triangle_stensor(integrand_grid)
for patch_depth in range(patch_degree + 1):
for patch_height in range(patch_degree + 1 -
patch_depth):
integrand_index = relative_index(
integrand_lower_left_index,
perm((-patch_depth - patch_height, patch_depth,
patch_height)))
integral_index = relative_index(
integral_lower_left_index,
perm((-patch_depth - patch_height, patch_depth,
patch_height)))
left_integral_index = relative_index(
integral_index, perm((1, -1, 0)))
if VERBOSE:
print("integrand_index:", integrand_index)
print("integral_index:", integral_index)
print("left_integral_index:",
left_integral_index)
if TEST_INTEGRAL_VALUES:
integral_grid.set(
integral_index,
integrand_grid[integrand_index] +
integral_grid[left_integral_index])
else:
integral_grid.set(
integral_index,
integrand_grid[integrand_index] /
(patch_degree + 1) +
integral_grid[left_integral_index])
# When height is maximal, there is no adjacent triangle to the top-right, so
# those coefficients aren't computed.
if height == patches_width - depth - 1:
continue
# Otherwise, do effectively the same thing for the second triangle. The previous triangle has been integrated,
# so the initial conditions are already provided.
# *----o---o
# \ / \ / Now, patch_depth increases from the bottom-left strip to the top-right corner,
# *----o and patch_height increases from the bottom-right to the top-left part of the strip.
# \ /
# \*
if VERBOSE:
print(
"------------------------------------------------------------"
)
print("Top-right triangle")
print(
"------------------------------------------------------------"
)
for patch_depth in range(patch_degree + 1):
for patch_height in range(patch_degree + 1 -
patch_depth):
integrand_index = relative_index(
integrand_lower_right_index,
perm((-patch_depth, -patch_height,
patch_depth + patch_height)))
integral_index = relative_index(
integral_lower_right_index,
perm((-patch_depth, -patch_height,
patch_depth + patch_height)))
left_integral_index = relative_index(
integral_index, perm((1, -1, 0)))
if VERBOSE:
print("integrand_index:", integrand_index)
print("integral_index:", integral_index)
print("left_integral_index:",
left_integral_index)
if TEST_INTEGRAL_VALUES:
integral_grid.set(
integral_index,
integrand_grid[integrand_index] +
integral_grid[left_integral_index])
else:
integral_grid.set(
integral_index,
integrand_grid[integrand_index] /
(patch_degree + 1) +
integral_grid[left_integral_index])
# Continue for permutation ...
# The grid has been integrated.
grid = integral_grid
patch_degree += 1
print("Integrated")
print_triangle_stensor(grid)
print("patch_degree:", patch_degree)
print("patches_width:", patches_width)
print("COMPLETE")
print("patch_degree:", patch_degree)
print("patches_width:", patches_width)
print("grid_width:", patch_degree * patches_width)
# Convert this raw triangle of Bezier coefficients into a triangle of triangles, each triangle being
# the Bezier coefficients of a patch (polynomial piece of the basis function).
# These patches are of /\-oriented triangles.
patches = stensor(3, patches_width - 1)
for patch_index in patches.indices():
lower_left_index = (patch_index[0] * patch_degree + patch_degree,
patch_index[1] * patch_degree,
patch_index[2] * patch_degree)
print(patch_index, "-->", lower_left_index)
bezier_coefficients = stensor(3, patch_degree, sym_rational=True)
for bezier_coefficient_index in bezier_coefficients.indices():
grid_index = (lower_left_index[0] - bezier_coefficient_index[1] -
bezier_coefficient_index[2],
lower_left_index[1] + bezier_coefficient_index[1],
lower_left_index[2] + bezier_coefficient_index[2])
bezier_coefficients.set(bezier_coefficient_index, grid[grid_index])
print_triangle_stensor(bezier_coefficients)
# if all(bezier_coefficients[i] == 0 for i in bezier_coefficients.indices()):
# # This patch is zero everywhere. Signify this by giving it the value None.
# patches.set(patch_index, None)
# else:
# patches.set(patch_index, bezier_coefficients)
#------------------------------------------------------------
patches.set(patch_index, bezier_coefficients)
# Now the basis function is represented by a bounding triangle of polynomial patches.
# Each patch in this triangle stores a triangle of Bezier coefficients, or is None if this patch is outside the support of the basis function.
# Consider a regular isometric grid in the domain.
# For each point, there is a basis function that corresponds to a control point.
# Consider a /\-oriented triangle in the domain tessellation.
# This triangle is in the support of a certain number of basis functions, each corresponding to a control point.
# The image of this triangle is a polynomial piece of the surface. This polynomial piece has Bezier points dependent on
# the control points of those basis functions.
#
# Initialize a triangle of points symmetrically bounding a central triangle in the domain.
# Go over each point, and check whether the support of that point's basis function reaches the central triangle.
#
# The triangle patch has Bezier points. Each Bezier point is a combination of the relevant control points weighted by
# the corresponding Bezier coefficient in the relevant basis function patch of that control point.
patches_triangle_corners = [
(patches_width // 3 - 1, patches_width // 3, patches_width // 3),
(patches_width // 3, patches_width // 3 - 1, patches_width // 3),
(patches_width // 3, patches_width // 3, patches_width // 3 - 1)
]
patches_triangle_corners_matrix = sym.Matrix(
[[i / (patches_width - 1) for i in corner]
for corner in patches_triangle_corners])
bezier_masks = stensor(3, patch_degree)
for index in bezier_masks.indices():
# Each Bezier point has a mask of weight contributions from the bounding triangle of relevant control points.
bezier_masks.set(
index, stensor(3, 1 + 3 * (continuity // 2), sym_rational=True))
print("Organizing coefficients...")
print("patch_degree:", patch_degree)
print("patches_width:", patches_width)
print("grid_width:", patch_degree * patches_width)
control_points_width = 1 + 3 * (continuity // 2)
for control_point_index in stensor_indices(3, control_points_width):
# Convert to central-triangle coordinates.
central_triangle_index = [
i - continuity // 2 for i in control_point_index
] #---I think this is correct.
patches_barycentric = patches_triangle_corners_matrix * sym.Matrix(
central_triangle_index)
patches_barycentric = [
patches_barycentric[i, 0] for i in range(patches_barycentric.rows)
]
patches_index = [
round((patches_width - 1) * x) for x in patches_barycentric
]
if any(i < 0 for i in patches_index):
# This patch does not exist in the bounding triangle of the patches.
continue
patch = patches[patches_index]
for bezier_index in bezier_masks.indices():
bezier_masks[bezier_index].set(
control_point_index,
bezier_masks[bezier_index][control_point_index] +
patch[bezier_index])
for index in bezier_masks.indices():
print_triangle_stensor(bezier_masks[index])
print(
"sum =",
sum(bezier_masks[index][i] for i in bezier_masks[index].indices()))
de_boor_to_bezier_matrix = []
# Print out the de-Boor-to-Bezier matrix in C/C++ syntax.
string = "float ____[({0}*({0}+1))/2 * ({1}*({1}+1))/2] = ".format(
patch_degree + 1, control_points_width + 1) + "{\n"
for bezier_index in bezier_masks.indices():
mask = bezier_masks[bezier_index]
string += " "
weights = []
for control_point_index in mask.indices():
weight = mask[control_point_index]
weights.append(weight)
if weight == 0:
string += "0, "
else:
string += "{}.f/{}.f, ".format(weight.p, weight.q)
string += "\n"
de_boor_to_bezier_matrix.append(weights)
string += "};"
print(string)
f = open(
"data/triangular_bspline_coefficients_C_{}.txt".format(continuity),
"w+")
f.write(string)
f.close()
#================================================================================
# Subdivision
#================================================================================
# A full rank (possibly overdetermined) system is wanted, so that the de Boor window can
# be transformed to Bezier points, those Bezier points subdivided, then an inversion attempt made
# with the Moore-Penrose pseudoinverse, to find de Boor points which transform to the subdivided Bezier points.
#
# It is necessary to remove columns of zeroes. These correspond to points in the bounding triangle which are not actually in the
# support of the basis function.
M = []
M_extracted_columns = [
] # The relevant columns correspond to named points in the bounding triangle, so tracking which points are considered will be useful.
for col in range(len(de_boor_to_bezier_matrix[0])):
c = [
de_boor_to_bezier_matrix[row][col]
for row in range(len(de_boor_to_bezier_matrix))
]
if not all(x == 0 for x in c):
M_extracted_columns.append(col)
M.append(c)
M = sym.Matrix(M).T
print_matrix(M)
print("Computing pseudo-inverse...")
try:
M_pseudoinverse = (M.T * M).inv() * M.T
except:
print("Failed to compute pseudo-inverse. M is not full rank.")
# The de Boor subdivision matrix is computed:
# - The window of de Boor points is transformed by M.
# - The resulting Bezier points are transformed by T to new Bezier points based on a certain affine parameter transformation.
# - The pseudoinverse of M, M+, is used in an attempt to find de Boor points which would generate these new Bezier points.
# So, de_boor_subdiv = M+ T M.
bottom_left_affine_parameter_transform = Mat([[1, Rat(1, 2),
Rat(1, 2)],
[0, Rat(1, 2), 0],
[0, 0, Rat(1, 2)]])
middle_affine_parameter_transform = Mat([[Rat(1, 2),
Rat(1, 2), 0],
[0, Rat(1, 2),
Rat(1, 2)],
[Rat(1, 2), 0,
Rat(1, 2)]])
T = transform_bezier_simplex(3, patch_degree,
bottom_left_affine_parameter_transform)
deboor_subdiv = M_pseudoinverse * T * M
# Check that all rows sum to 1.
assert (all(
sum(deboor_subdiv[row, col] for col in range(deboor_subdiv.cols)) == 1
for row in range(deboor_subdiv.rows)))
for i, index in enumerate(stensor_indices(3, control_points_width)):
if i in M_extracted_columns: # do not consider points not in the support.
print(index)
print_matrix(deboor_subdiv)
def deboor_to_bezier(domain_simplex_dim, continuity, knots=None):
#----- This only works for domain_simplex_dim == 2. The attempt of generalization to higher simplex dimension did not work or really make sense.
print("============================================================")
print("deboor_to_bezier, domain_simplex_dim={}, continuity={}".format(
domain_simplex_dim, continuity))
print("------------------------------------------------------------")
# Compute the de Boor net width as a figurate number, then check
# that the knot tensor has the right shape.
# ------------------------------------------------------------
deboor_net_width = figurate_number(domain_simplex_dim - 1,
continuity + 1) + 1 #---
num_deboor_points = figurate_number(domain_simplex_dim - 1,
deboor_net_width)
degree = deboor_net_width - 1
knots_width = deboor_net_width + continuity
num_knots = figurate_number(domain_simplex_dim - 1, knots_width)
print("deboor_net_width:", deboor_net_width)
print("num_deboor_points:", num_deboor_points)
print("degree:", degree)
print("knots_width:", knots_width)
print("num_knots:", num_knots)
if knots == None:
knots = stensor(domain_simplex_dim, knots_width - 1)
for index in knots.indices():
knots.set(index, index) # just some distinct value for now
elif type(knots) == list:
knots = stensor(domain_simplex_dim, knots_width - 1, knots)
assert (knots.k == domain_simplex_dim)
assert (knots.rank == knots_width - 1)
deboor_weights_matrix = []
for deboor_index in stensor_indices(domain_simplex_dim, degree):
# For each de Boor net index, get a list of corresponding knot
# indices into the knot tensor.
# ------------------------------------------------------------
print("------------------------------------------------------------")
print("deboor_index:", deboor_index)
print("---")
top_knot_mask_index = list(deboor_index)
top_knot_mask_index[0] += continuity
knot_mask_indices = deboor_to_bezier_knot_mask(top_knot_mask_index,
continuity)
print("knot_mask_indices:", knot_mask_indices)
knot_mask = [knots[knot_index] for knot_index in knot_mask_indices]
print("knot_mask:", knot_mask)
# The values in the knot tensor are points in the affine domain. The de Boor net point p (of index deboor_index) is
# p = f(...) where f is multi-affine and symmetric and the inputs are the knots in the knot mask of p, knot_mask.
# Expanding p = f(...) gives p as an affine combination of Bezier points f(e_i,...), which are multiset inputs
# of the affine domain basis simplex that the knots are expressed in.
affine_weights = [0 for _ in range(num_deboor_points)
] # one for each Bezier point.
for multiindex in itertools.product(range(domain_simplex_dim),
repeat=degree):
flat_index = tensor_to_flat_index(domain_simplex_dim, degree,
multiindex)
affine_weights[flat_index] += prod(
knot_mask[position][i]
for position, i in enumerate(multiindex))
print("weights:", affine_weights)
deboor_weights_matrix.append(affine_weights)
print(knot_mask)
# Check that the rows sum to 1. If not, something definitely went wrong.
for row in deboor_weights_matrix:
assert (sum(row) == 1)
deboor_weights_matrix = sym.Matrix(deboor_weights_matrix)
print_matrix(deboor_weights_matrix)
print_matrix(deboor_weights_matrix.inv())
deboor_to_bezier_matrix = deboor_weights_matrix.inv()
for i, index in enumerate(stensor_indices(domain_simplex_dim, degree)):
print("{}: {}".format(
index,
", ".join(str(c) for c in list(deboor_to_bezier_matrix.row(i)))))
quadratic_triangular_bspline_subdivision_scheme()
A = Mat([[0, 0, 0, Half, 0, 0, 0, 0, 0], [0, 0, 0, 0, Half, 0, 0, 0, 0],
[0, 0, 0, 0, 0, Half, 0, 0, 0], [Half, Half, Half, 0, 0, 0, 1, 1, 1]])
AM = Mat([[0, 0, 0, 0, Rat(1, 8),
Rat(1, 8), 0, 0, Quarter],
[0, 0, 0, Rat(1, 8), 0,
Rat(1, 8), 0, Quarter, 0],
[0, 0, 0, Rat(1, 8), Rat(1, 8), 0, Quarter, 0, 0],
[
Half, Half, Half, Quarter, Quarter, Quarter,
Rat(3, 4),
Rat(3, 4),
Rat(3, 4)
]])
print_matrix(AM * A.T * (A * A.T).inv())
A = Mat([[0, 0, Half, 0, 0], [0, 0, 0, Half, 0], [Half, Half, 0, 0, 1]])
AM = Mat([[0, 0, Half, 0, 0], [0, 0, Rat(1, 8),
Rat(1, 8), Quarter],
[Rat(3, 4), Rat(1, 4), Rat(1, 2), 0,
Rat(1, 2)]])
print_matrix(A)
print_matrix(AM)
print_matrix(AM * A.T * (A * A.T).inv())
x, y, z = Sym("x y z")
X, Y, Z = Sym("X Y Z")
print(((1 - x)**2 / 2).expand())
print((x / 2 + y / 2 + x * y).subs(y, 1 - x).expand())
def create_stochastic_blockmodel_graph(self, blocks=10, size=100, self_block_connectivity=0.9, other_block_connectivity=0.1, connectivity_matrix=None, directed=False,
self_edges=False, power_exp=None, scale=None, plot_stat=False):
size = size if isinstance(size, list) else [size]
self_block_connectivity = self_block_connectivity if isinstance(self_block_connectivity, list) else [self_block_connectivity]
other_block_connectivity = other_block_connectivity if isinstance(other_block_connectivity, list) else [other_block_connectivity]
num_nodes = sum([size[i % len(size)] for i in xrange(blocks)])
if power_exp is None:
self.print_f("Starting to create Stochastic Blockmodel Graph with {} nodes and {} blocks".format(num_nodes, blocks))
else:
self.print_f("Starting to create degree-corrected (alpha=" + str(power_exp) + ") Stochastic Blockmodel Graph with {} nodes and {} blocks".format(num_nodes, blocks))
self.print_f('convert/transform probabilities')
blocks_range = range(blocks)
block_sizes = np.array([size[i % len(size)] for i in blocks_range])
# create connectivity matrix of self- and other-block-connectivity
if connectivity_matrix is None:
connectivity_matrix = []
self.print_f('inner conn: ' + str(self_block_connectivity) + '\tother conn: ' + str(other_block_connectivity))
for idx in blocks_range:
row = []
for jdx in blocks_range:
if idx == jdx:
row.append(self_block_connectivity[idx % len(self_block_connectivity)])
else:
if scale is not None:
prob = other_block_connectivity[idx % len(other_block_connectivity)] / (num_nodes - block_sizes[idx]) * block_sizes[jdx]
if directed:
row.append(prob)
else:
row.append(prob / 2)
else:
row.append(other_block_connectivity[idx % len(other_block_connectivity)])
connectivity_matrix.append(row)
# convert con-matrix to np.array
if connectivity_matrix is not None and isinstance(connectivity_matrix, np.matrix):
connectivity_matrix = np.asarray(connectivity_matrix)
# convert con-matrix to np.array
if connectivity_matrix is not None and not isinstance(connectivity_matrix, np.ndarray):
connectivity_matrix = np.array(connectivity_matrix)
self.print_f('conn mat')
printing.print_matrix(connectivity_matrix)
if scale == 'relative' or scale == 'absolute':
new_connectivity_matrix = []
for i in blocks_range:
connectivity_row = connectivity_matrix[i, :] if connectivity_matrix is not None else None
nodes_in_src_block = block_sizes[i]
multp = 1 if scale == 'absolute' else (nodes_in_src_block * (nodes_in_src_block - 1))
row_prob = [(connectivity_row[idx] * multp) / (nodes_in_src_block * (nodes_in_block - 1)) for idx, nodes_in_block in enumerate(block_sizes)]
new_connectivity_matrix.append(np.array(row_prob))
connectivity_matrix = np.array(new_connectivity_matrix)
self.print_f(scale + ' scaled conn mat:')
printing.print_matrix(connectivity_matrix)
# create nodes and store corresponding block-id
self.print_f('insert nodes')
vertex_to_block = []
appender = vertex_to_block.append
colors = self.graph.new_vertex_property("float")
for i in xrange(blocks):
block_size = size[i % len(size)]
for j in xrange(block_size):
appender((self.graph.add_vertex(), i))
node = vertex_to_block[-1][0]
colors[node] = i
# create edges
get_rand = np.random.random
add_edge = self.graph.add_edge
self.print_f('create edge probs')
degree_probs = defaultdict(lambda: dict())
for vertex, block_id in vertex_to_block:
if power_exp is None:
degree_probs[block_id][vertex] = 1
else:
degree_probs[block_id][vertex] = math.exp(power_exp * np.random.random())
tmp = dict()
self.print_f('normalize edge probs')
all_prop = []
for block_id, node_to_prop in degree_probs.iteritems():
sum_of_block_norm = 1 / sum(node_to_prop.values())
tmp[block_id] = {key: val * sum_of_block_norm for key, val in node_to_prop.iteritems()}
all_prop.append(tmp[block_id].values())
degree_probs = tmp
if plot_stat:
plt.clf()
plt.hist(all_prop, bins=15)
plt.savefig("prop_dist.png")
plt.close('all')
self.print_f('count edges between blocks')
edges_between_blocks = defaultdict(lambda: defaultdict(int))
for idx, (src_node, src_block) in enumerate(vertex_to_block):
conn_mat_row = connectivity_matrix[src_block, :]
for dest_node, dest_block in vertex_to_block:
if get_rand() < conn_mat_row[dest_block]:
edges_between_blocks[src_block][dest_block] += 1
self.print_f('create edges')
for src_block, dest_dict in edges_between_blocks.iteritems():
self.print_f(' -- Processing Block {}. Creating links to: {}'.format(src_block, dest_dict))
for dest_block, num_edges in dest_dict.iteritems():
self.print_f(' ++ adding {} edges to {}'.format(num_edges, dest_block))
for i in xrange(num_edges):
# find src node
prob = np.random.random()
prob_sum = 0
src_node = None
for vertex, v_prob in degree_probs[src_block].iteritems():
prob_sum += v_prob
if prob_sum >= prob:
src_node = vertex
break
# find dest node
prob = np.random.random()
prob_sum = 0
dest_node = None
for vertex, v_prob in degree_probs[dest_block].iteritems():
prob_sum += v_prob
if prob_sum >= prob:
dest_node = vertex
break
if src_node is None or dest_node is None:
print 'Error selecting node:', src_node, dest_node
if self.graph.edge(src_node, dest_node) is None:
if self_edges or not src_node == dest_node:
add_edge(src_node, dest_node)
self.graph.vertex_properties["colorsComm"] = colors
return self.return_and_reset()
import sympy as sym
from printing import print_matrix
Rat = sym.Rational
Mat = sym.Matrix
Sym = sym.symbols
m = Mat([[Rat(1,2), Rat(1,2), 0],
[Rat(1,6), Rat(2,3), Rat(1,6)],
[0, Rat(1,2), Rat(1,2)]])
[P,D] = m.diagonalize()
print_matrix(P)
print_matrix(D)
K = P * Mat([[0,0,0],[0,0,0],[0,0,1]]) * P.inv()
print_matrix(K)
C = [1, 5, 9]
c = [1, 5, 9]
for i in range(100):
c = [(c[0]+c[1])/2, c[0]/6 + 2*c[1]/3 + c[2]/6, (c[1]+c[2])/2]
print(c[1])
print(C[0]/5 + 3*C[1]/5 + C[2]/5)
scale_dir *= 1 / sqrt(sum(x**2 for x in scale_dir))
scale = random.uniform(0, 0.5)
points = np.array([[scales[i] * np.random.normal(0, 1) for i in range(3)]
for __ in range(n)])
for row in range(points.shape[0]):
points[row, :] = points[
row, :] + (scale - 1) * scale_dir * points[row, :].dot(scale_dir)
# print_matrix(sym.Matrix(points))
mean = 1 / n * sum(points[row, :] for row in range(points.shape[0]))
# C = 1/n * sum(np.outer(points[row,:] - mean, points[row,:] - mean) for row in range(points.shape[0]))
centralized_points = np.array(
[points[row, :] - mean for row in range(points.shape[0])])
C = (1 / n) * centralized_points.T.dot(
centralized_points) # Easier way to write the above.
print_matrix(Mat(C))
print("det(C) =", linalg.det(C))
U, s, Vh = linalg.svd(C)
# Compute principal plane.
C2 = sum(s[i] * np.outer(U[:, i], Vh[i, :].T) for i in range(2))
print_matrix(Mat(C2))
print("det(C2) =", linalg.det(C2)) # Should be 0.
fig = plt.figure()
ax = plt.gca(projection="3d")
X = U[:, 0]
Y = U[:, 1]
plane_points = np.array([
x * X + y * Y + mean
for x, y in np.mgrid[-3:3:25j, -3:3:25j].reshape(2, -1).T
