def test_prime_function():
# run the "checks" below using the following list of (delta,q) pairs
half = QQ(1)/2
delta_q_pairs = [
# ([0], [half]),
([half], [half/4]),
([-half, half], [half/2, half/2]),
]
product_threshold = 10
for delta, q in delta_q_pairs:
genus = len(delta)
omega = build_prime_function(delta, q, product_threshold)
phi_j = [
delta[j] + q[j]**2*z/(1-z*delta[j].conjugate())
for j in range(genus)
]
yield check_vanishing_gamma, omega
yield check_vanishing_phij_gamma, omega, phi_j
yield check_pole_fixed_points, omega, delta, q, phi_j, genus
# yield check_equation_5_17, omega
yield check_symmetric, omega
def test_prime_function():
# run the "checks" below using the following list of (delta,q) pairs
half = QQ(1) / 2
delta_q_pairs = [
# ([0], [half]),
([half], [half / 4]),
([-half, half], [half / 2, half / 2]),
]
product_threshold = 10
for delta, q in delta_q_pairs:
genus = len(delta)
omega = build_prime_function(delta, q, product_threshold)
phi_j = [
delta[j] + q[j]**2 * z / (1 - z * delta[j].conjugate())
for j in range(genus)
]
yield check_vanishing_gamma, omega
yield check_vanishing_phij_gamma, omega, phi_j
yield check_pole_fixed_points, omega, delta, q, phi_j, genus
# yield check_equation_5_17, omega
yield check_symmetric, omega
def backward_problem(branch_pts, prime_function_tests=False,
slitmap_tests=False, slitmap_full=False, plot_circles=False, plot_F=False,
plot_branch_pts=False, prec='double', product_threshold=5, max_time=200):
# input:
# branch_pts = list of branch points
# prime_function_tests = Check to see if the prime function passes some
# tests
# slitmap_tests = Check to see if the slitmap passes some tests
# slitmap_full = Plot each component of the slitmap, for diagnostic
# reasons
# plot_circles = circle plot, unit circle and the Cj excised
# plot_F = Plots the whole fundamental domain, F, with shading
# plot_branch_pts = Plots the branch points with red xs
# prec = precision of group data. Double or infinite. Double is faster.
# product_threshold = determines the max number of terms in the prime \
# max_time = max time for prime function computation before timeout
# function product
#
# output:
# delta = list of centers of circles
# q = list of radii of circles
z = var('z') # complex variable not specified by x,y
gamma = var('gamma') #base point for 'abelmap' or prime function
# Make sure we are in the hyperbollic case.
if len(branch_pts)%2 != 0:
raise ValueError("Right now this module only works for "
"hyperelliptic curves with an even number of branch points. Try again "
"with len(branch_pts)%2=0")
# Force the list to be monotonically increasing.
branch_pts = sorted(branch_pts) # the lists are short enough rn
# this probably isn't too slow.
# Make sure no two branch points coincide
if duplicates_in_list(branch_pts):
raise ValueError("The list branch_pts cannot have duplicaties. This "
"program does not yet support branch_pts of multiplicity greater than "
" 1")
genus = len(branch_pts)/2 # There are 2g branch_pts
# Change branch point data to double or infinite precision
if prec == 'double':
branch_pts = map(CDF, branch_pts) # Complex double field
elif (prec == 'infinity' or prec == 'inf'):
branch_pts = map(CC, branch_pts) # infinite precision
else:
raise TypeError("Either 'double' or 'infinite' precision must be "
"entered for 'prec'")
# Plot the branch points if you want, not really necessary here.
if plot_branch_pts:
branch_point_plot(branch_pts) ## This can and should be fixed to just
## use the plot_points function
# Define the Cj algebraically
[delta,q] = define_group_data(genus) # We have delta[0] = delta_1, delta[1]
# = delta_2 etc. Kind of messy but it works out.
# There are g of each delta_j, q_j
# Define the location of the intersection of the C_j with the real axis. We
# have C_j = delta_j + q_j e^(it) centered on the real-axis. Therefore, for
# the hyperelliptic case we just take one pre-branch point (i.e. the
# preimage of the branchpoint under the slitmap) to be delta_j + q_j and the
# other to be delta_j - q_j.
# -------!!!!----!!!-- Question for non hyperelliptic case: What is
# important about taking the intersectin of C_j with the real-axis? Are we
# guaranteed that -- These are defined algebraically.
pre_branch_pts = [None]*2*len(xrange(genus))
pre_branch_pts[::2] = [ delta[j]-q[j] for j in xrange(genus) ]
pre_branch_pts[1::2] = [ delta[j]+q[j] for j in xrange(genus) ]
# There are 2g pre_branch_pts. We interlace them in this way to help with
# the algebraic problem below.
# Define the prime function algebraically, make sure it doesn't take too
# long!
signal.signal(signal.SIGALRM, handler)
signal.alarm(max_time) #Let it take max_time seconds at most!
try:
omega = build_prime_function(delta, q, product_threshold)
except Exception, exc: #stop if it takes too long
print exc
def backward_problem(branch_pts,
prime_function_tests=False,
slitmap_tests=False,
slitmap_full=False,
plot_circles=False,
plot_F=False,
plot_branch_pts=False,
prec='double',
product_threshold=5,
max_time=200):
# input:
# branch_pts = list of branch points
# prime_function_tests = Check to see if the prime function passes some
# tests
# slitmap_tests = Check to see if the slitmap passes some tests
# slitmap_full = Plot each component of the slitmap, for diagnostic
# reasons
# plot_circles = circle plot, unit circle and the Cj excised
# plot_F = Plots the whole fundamental domain, F, with shading
# plot_branch_pts = Plots the branch points with red xs
# prec = precision of group data. Double or infinite. Double is faster.
# product_threshold = determines the max number of terms in the prime \
# max_time = max time for prime function computation before timeout
# function product
#
# output:
# delta = list of centers of circles
# q = list of radii of circles
z = var('z') # complex variable not specified by x,y
gamma = var('gamma') #base point for 'abelmap' or prime function
# Make sure we are in the hyperbollic case.
if len(branch_pts) % 2 != 0:
raise ValueError(
"Right now this module only works for "
"hyperelliptic curves with an even number of branch points. Try again "
"with len(branch_pts)%2=0")
# Force the list to be monotonically increasing.
branch_pts = sorted(branch_pts) # the lists are short enough rn
# this probably isn't too slow.
# Make sure no two branch points coincide
if duplicates_in_list(branch_pts):
raise ValueError(
"The list branch_pts cannot have duplicaties. This "
"program does not yet support branch_pts of multiplicity greater than "
" 1")
genus = len(branch_pts) / 2 # There are 2g branch_pts
# Change branch point data to double or infinite precision
if prec == 'double':
branch_pts = map(CDF, branch_pts) # Complex double field
elif (prec == 'infinity' or prec == 'inf'):
branch_pts = map(CC, branch_pts) # infinite precision
else:
raise TypeError("Either 'double' or 'infinite' precision must be "
"entered for 'prec'")
# Plot the branch points if you want, not really necessary here.
if plot_branch_pts:
branch_point_plot(branch_pts) ## This can and should be fixed to just
## use the plot_points function
# Define the Cj algebraically
[delta,
q] = define_group_data(genus) # We have delta[0] = delta_1, delta[1]
# = delta_2 etc. Kind of messy but it works out.
# There are g of each delta_j, q_j
# Define the location of the intersection of the C_j with the real axis. We
# have C_j = delta_j + q_j e^(it) centered on the real-axis. Therefore, for
# the hyperelliptic case we just take one pre-branch point (i.e. the
# preimage of the branchpoint under the slitmap) to be delta_j + q_j and the
# other to be delta_j - q_j.
# -------!!!!----!!!-- Question for non hyperelliptic case: What is
# important about taking the intersectin of C_j with the real-axis? Are we
# guaranteed that -- These are defined algebraically.
pre_branch_pts = [None] * 2 * len(xrange(genus))
pre_branch_pts[::2] = [delta[j] - q[j] for j in xrange(genus)]
pre_branch_pts[1::2] = [delta[j] + q[j] for j in xrange(genus)]
# There are 2g pre_branch_pts. We interlace them in this way to help with
# the algebraic problem below.
# Define the prime function algebraically, make sure it doesn't take too
# long!
signal.signal(signal.SIGALRM, handler)
signal.alarm(max_time) #Let it take max_time seconds at most!
try:
omega = build_prime_function(delta, q, product_threshold)
except Exception, exc: #stop if it takes too long
print exc
def forward_problem(delta,
q,
prime_function_tests=False,
slitmap_tests=False,
slitmap_full=False,
plot_circles=False,
plot_F=False,
plot_branch_pts=False,
prec='double',
product_threshold=5,
max_time=200):
#
# This module does the full forward problem. Starting with group data,
# construct the SK prime function, the slitmap, and hence the branch points
# of an algebraic curve.
#
# input:
# delta = list of centers of circles
# q = list of radii of circles
# prime_function_tests = Check to see if the prime function passes some
# tests
# slitmap_tests = Check to see if the slitmap passes some tests
# slitmap_full = Plot each component of the slitmap, for diagnostic
# reasons
# plot_circles = circle plot, unit circle and the Cj excised
# plot_F = Plots the whole fundamental domain, F, with shading
# plot_branch_pts = Plots the branch points with red xs
# prec = precision of group data. Double or infinite. Double is faster.
# product_threshold = determines the max number of terms in the prime \
# function product
# max_time = max time for prime function computation before timeout
#
# output:
# branch_pts = branch points obtained.
z = var('z') # complex variable
gamma = var('gamma') #base point for 'abelmap' or prime function
genus = len(q)
# Change group data to double or infinite precision
if prec == 'double':
delta, q = map(CDF, delta), map(CDF, q) # Complex double
elif (prec == 'infinite' or prec == 'inf'):
delta, q = map(CC, delta), map(CC, q) #infinite precision
else:
raise TypeError("Either 'double' or 'infinite' precision must be "
"entered for 'prec'.")
# Before plotting, define a uniform color scheme to be used throughout all
# plots so we can keep track of which circle is which.
circle_colors = [(0.6 * random(), random(), random())
for k in xrange(genus)]
# Plot some stuff about the region if we want.
if plot_circles:
D_zeta = circle_plots(delta, q,
colors=circle_colors) #returns plot data
D_zeta.show(axes=True, title='$D_{\zeta}$', aspect_ratio=1)
# show and put on an equal-axis plot
if plot_F:
D_zeta = F_plot(delta, q, colors=circle_colors) #returns plot data
D_zeta.show(axes=True, title='$D_{\zeta}$')
# show, but maybe not on equal-axis plot.
# Build the prime function, but make sure it doesn't take too long
signal.signal(signal.SIGALRM, handler)
signal.alarm(max_time) #Let it take max_time seconds at most!
try:
omega = build_prime_function(delta, q, product_threshold)
except Exception, exc: #stop if it takes too long.
print exc
def forward_problem_on_Points_and_Lines(delta,
q,
Points,
Lines,
plot_circles=True,
plot_F=False,
slitmap_full=True,
slitmap_direct=False,
prec='double',
product_threshold=5,
max_time=200,
prime_function_tests=False,
slitmap_tests=False,
point_size=80,
save_plots=False):
#
# This module does the forward problem with test point(s) and line(s) in
# place. I.e. it places a point somewhere in the fundamental domain, F,
# then tracks it as we do the slitmap. Usually we do this using
# slitmap_full which will be the default, however, there is a flag for
# this.
#
# input:
# delta = centers of circles, list
# q = radii of circles, list
# Points = points to plot in the domain, list
# Lines = lines to plot in the domain, list
# plot_circles = plot the circles from the group data and the
# points and lines in the fundamental domain together.
# plot_F = plot the fundamental domain. Lines are filled in as well.
# slitmap_full = build the full slitmap, from zeta1, zeta2, z
# slitmap_direct = build the slitmap all in one step. Minimum output.
# prec = double or infinite precision of the group data?
# product_threshold = determines the max number of terms in the prime \
# function product
# max_time = max time for prime function computation before timeout
# prime_function_tests = Check to see if the prime function passes some
# tests
# slitmap_tests = Check to see if the slitmap passes some tests
# point_size = marker size for point_plot
# save_plots = save the plots or just display them?
#
# output:
# only plots
#
z = var('z') # complex variable
gamma = var('gamma') #base point for 'abelmap' or prime function
genus = len(q)
# Generate colors for plotting here since we want them to be uniform across
# plot_circles and slitmap plots
circle_colors = [(0.6 * random(), random(), random())
for k in xrange(genus)]
point_colors = [(0.6 * random(), random(), random())
for k in xrange(len(Points))]
line_colors = [(0.6 * random(), random(), random())
for k in xrange(len(Lines))]
# We multiply the "R" by 0.6 so nothing is too red.
# Change group data to double or infinite precision
if prec == 'double':
delta, q = map(CDF, delta), map(CDF, q) # Complex double
Points = map(CDF, Points)
Lines = [map(CDF, line) for line in Lines]
elif (prec == 'infinite' or prec == 'inf'):
delta, q = map(CC, delta), map(CC, q) #infinite precision
Points = map(CC, Points)
Lines = [map(CC, line) for line in Lines]
else:
raise TypeError("Either 'double' or 'infinite' precision must be "
"entered for 'prec'.")
# Plot some stuff about the region if we want.
if plot_circles:
D_zeta = circle_plots(delta, q, colors=circle_colors)
D_zeta += plot_points(Points,
colors=point_colors,
mark_size=point_size)
D_zeta += plot_lines(Lines, colors=line_colors, thickness=2)
if save_plots == False:
D_zeta.show(axes=True, title='$D_{\zeta}$', aspect_ratio=1)
# show and put on an equal-axis plot
else:
D_zeta.save('circle_plot.eps')
# Plot F, the fundamental domain
if plot_F:
D_zeta = F_plot(delta, q, colors=circle_colors)
D_zeta += plot_points(Points,
colors=point_colors,
mark_size=point_size)
D_zeta += plot_lines(Lines, colors=line_colors, thickness=2)
if save_plots == False:
D_zeta.show(axes=True, title='$F$, the Fundamental Domain')
else:
D_zeta.save('Fundamental_domain.eps')
# Build the prime function, but make sure it doesn't take too long
signal.signal(signal.SIGALRM, handler)
signal.alarm(max_time) #Let it take max_time seconds at most!
try:
omega = build_prime_function(delta, q, product_threshold)
except Exception, exc: #stop if it takes too long.
print exc
def forward_problem(delta, q, prime_function_tests=False,
slitmap_tests=False, slitmap_full=False, plot_circles=False, plot_F=False,
plot_branch_pts=False, prec='double', product_threshold=5, max_time=200):
#
# This module does the full forward problem. Starting with group data,
# construct the SK prime function, the slitmap, and hence the branch points
# of an algebraic curve.
#
# input:
# delta = list of centers of circles
# q = list of radii of circles
# prime_function_tests = Check to see if the prime function passes some
# tests
# slitmap_tests = Check to see if the slitmap passes some tests
# slitmap_full = Plot each component of the slitmap, for diagnostic
# reasons
# plot_circles = circle plot, unit circle and the Cj excised
# plot_F = Plots the whole fundamental domain, F, with shading
# plot_branch_pts = Plots the branch points with red xs
# prec = precision of group data. Double or infinite. Double is faster.
# product_threshold = determines the max number of terms in the prime \
# function product
# max_time = max time for prime function computation before timeout
#
# output:
# branch_pts = branch points obtained.
z = var('z') # complex variable
gamma = var('gamma') #base point for 'abelmap' or prime function
genus = len(q)
# Change group data to double or infinite precision
if prec == 'double':
delta, q = map(CDF, delta), map(CDF, q) # Complex double
elif (prec=='infinite' or prec=='inf'):
delta, q = map(CC, delta), map(CC, q) #infinite precision
else:
raise TypeError("Either 'double' or 'infinite' precision must be "
"entered for 'prec'.")
# Before plotting, define a uniform color scheme to be used throughout all
# plots so we can keep track of which circle is which.
circle_colors = [ (0.6*random(), random(), random()) for k in xrange(genus)
]
# Plot some stuff about the region if we want.
if plot_circles:
D_zeta=circle_plots(delta, q, colors=circle_colors) #returns plot data
D_zeta.show(axes = True, title='$D_{\zeta}$', aspect_ratio = 1)
# show and put on an equal-axis plot
if plot_F:
D_zeta=F_plot(delta, q, colors=circle_colors) #returns plot data
D_zeta.show(axes = True, title='$D_{\zeta}$')
# show, but maybe not on equal-axis plot.
# Build the prime function, but make sure it doesn't take too long
signal.signal(signal.SIGALRM, handler)
signal.alarm(max_time) #Let it take max_time seconds at most!
try:
omega = build_prime_function(delta, q, product_threshold)
except Exception, exc: #stop if it takes too long.
print exc
def forward_problem_on_Points_and_Lines(
delta, q, Points, Lines, plot_circles=True, plot_F=False,
slitmap_full=True, slitmap_direct=False, prec='double',
product_threshold=5, max_time=200, prime_function_tests=False,
slitmap_tests=False, point_size=80, save_plots=False
):
#
# This module does the forward problem with test point(s) and line(s) in
# place. I.e. it places a point somewhere in the fundamental domain, F,
# then tracks it as we do the slitmap. Usually we do this using
# slitmap_full which will be the default, however, there is a flag for
# this.
#
# input:
# delta = centers of circles, list
# q = radii of circles, list
# Points = points to plot in the domain, list
# Lines = lines to plot in the domain, list
# plot_circles = plot the circles from the group data and the
# points and lines in the fundamental domain together.
# plot_F = plot the fundamental domain. Lines are filled in as well.
# slitmap_full = build the full slitmap, from zeta1, zeta2, z
# slitmap_direct = build the slitmap all in one step. Minimum output.
# prec = double or infinite precision of the group data?
# product_threshold = determines the max number of terms in the prime \
# function product
# max_time = max time for prime function computation before timeout
# prime_function_tests = Check to see if the prime function passes some
# tests
# slitmap_tests = Check to see if the slitmap passes some tests
# point_size = marker size for point_plot
# save_plots = save the plots or just display them?
#
# output:
# only plots
#
z = var('z') # complex variable
gamma = var('gamma') #base point for 'abelmap' or prime function
genus = len(q)
# Generate colors for plotting here since we want them to be uniform across
# plot_circles and slitmap plots
circle_colors = [ (0.6*random(), random(), random()) for k in
xrange(genus) ]
point_colors = [ (0.6*random(), random(), random()) for k in
xrange(len(Points)) ]
line_colors = [ (0.6*random(), random(), random()) for k in
xrange(len(Lines)) ]
# We multiply the "R" by 0.6 so nothing is too red.
# Change group data to double or infinite precision
if prec == 'double':
delta, q = map(CDF, delta), map(CDF, q) # Complex double
Points = map(CDF, Points)
Lines = [ map(CDF, line) for line in Lines ]
elif (prec=='infinite' or prec=='inf'):
delta, q = map(CC, delta), map(CC, q) #infinite precision
Points = map(CC, Points)
Lines = [ map(CC, line) for line in Lines ]
else:
raise TypeError("Either 'double' or 'infinite' precision must be "
"entered for 'prec'.")
# Plot some stuff about the region if we want.
if plot_circles:
D_zeta = circle_plots(delta, q, colors=circle_colors)
D_zeta += plot_points(Points, colors=point_colors,
mark_size=point_size)
D_zeta += plot_lines(Lines, colors=line_colors, thickness=2)
if save_plots==False:
D_zeta.show(axes=True, title='$D_{\zeta}$', aspect_ratio=1)
# show and put on an equal-axis plot
else:
D_zeta.save('circle_plot.eps')
# Plot F, the fundamental domain
if plot_F:
D_zeta = F_plot(delta, q, colors=circle_colors)
D_zeta += plot_points(Points, colors=point_colors,
mark_size=point_size)
D_zeta += plot_lines(Lines, colors=line_colors, thickness=2)
if save_plots==False:
D_zeta.show(axes = True, title = '$F$, the Fundamental Domain')
else:
D_zeta.save('Fundamental_domain.eps')
# Build the prime function, but make sure it doesn't take too long
signal.signal(signal.SIGALRM, handler)
signal.alarm(max_time) #Let it take max_time seconds at most!
try:
omega = build_prime_function(delta, q, product_threshold)
except Exception, exc: #stop if it takes too long.
print exc