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ff.py
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import numpy as np
import math
from pyDOE import lhs as _lhs
def scatter_halftone(box, ninit, dotmax, radius):
lb = box[0]
rb = box[1]
db = box[2]
ub = box[3]
count=0
dotnr = -1
N_PDP_MAX = 100000
pdp_x = np.zeros(N_PDP_MAX)
pdp_y = np.zeros(N_PDP_MAX)
pdp_x[:ninit] = np.linspace(lb, rb, ninit)
pdp_y[:ninit] = np.random.rand(ninit) * 1e-4 + db
pdp_num = ninit
xy = np.zeros((dotmax, 2))
i = np.argmin(pdp_y[:ninit])
ym = pdp_y[i]
fan = np.linspace(0.1, 0.9, 5)
while ym <= ub and dotnr < dotmax:
dotnr += 1
xy[dotnr, 0] = pdp_x[i]
xy[dotnr, 1] = pdp_y[i]
r = radius(xy[dotnr, :])
dist2 = (pdp_x[:pdp_num] - pdp_x[i]) ** 2 + (pdp_y[:pdp_num] - pdp_y[i]) ** 2
ileft = np.where(dist2[:i] > r ** 2)
if len(ileft[0]) == 0:
ileft = -1
ang_left = np.pi
else:
ileft = max(ileft[0])
ang_left = np.arctan2(pdp_y[ileft] - pdp_y[i], pdp_x[ileft] - pdp_x[i])
iright = np.where(dist2[i:pdp_num] > r ** 2)
if len(iright[0]) == 0:
iright = -1
ang_right = 0
else:
iright = min(iright[0])
ang_right = np.arctan2(pdp_y[i + iright] - pdp_y[i], pdp_x[i + iright] - pdp_x[i])
ang = ang_left - fan * (ang_left - ang_right)
pdp_new_x = pdp_x[i] + r * np.cos(ang)
pdp_new_y = pdp_y[i] + r * np.sin(ang)
ind = np.logical_and(pdp_new_x <= rb, pdp_new_x >= lb)
pdp_new_x = pdp_new_x[ind]
pdp_new_y = pdp_new_y[ind]
new_add = len(pdp_new_x)
if iright ==-1 and ileft == -1:
removed = pdp_num
elif iright ==-1:
removed = pdp_num-ileft-1
elif ileft == -1:
removed = iright-1+i-ileft
else:
removed = i-ileft+iright-1
if iright!=-1:
pdp_x[iright + i + new_add - removed:pdp_num + new_add - removed] = pdp_x[iright + i:pdp_num]
pdp_y[iright + i + new_add - removed:pdp_num + new_add - removed] = pdp_y[iright + i:pdp_num]
pdp_x[ileft + 1:ileft + 1 + new_add] = pdp_new_x
pdp_y[ileft + 1:ileft + 1 + new_add] = pdp_new_y
pdp_num = pdp_num + new_add - removed
i = np.argmin(pdp_y[:pdp_num])
ym = pdp_y[i]
xy = xy[:dotnr, :]
return xy
def error_ff(target_num, error, max_min_density_ratio=20, box=None, sdf=None):
_N = len(error)
if box is None:
box = [0, 1, 0, 1]
if sdf is None:
sdf = lambda x: np.ones((len(x), 1))
error = -error
error_min = np.min(error)
error_max = np.max(error)
error = ((error - error_min) / ((error_max - error_min) + 1e-10))
min_scale = 0.02
max_scale = 1.
scale = (min_scale + max_scale) / 2
def r(xy):
ixy = np.asarray(np.round(xy * (_N - 1)), dtype=int)
return (error[ixy[1], ixy[0]] * (1 - 1 / math.sqrt(max_min_density_ratio)) + 1 / math.sqrt(
max_min_density_ratio)) * scale
xy = scatter_halftone(box, 100, 10000, r)
len_xy = len(xy)
while np.abs(len_xy - target_num) / target_num > 0.05 and max_scale - min_scale > 0.003:
if target_num > len_xy:
max_scale = scale
else:
min_scale = scale
scale = (max_scale + min_scale) / 2
def r(xy):
ixy = np.asarray(np.round(xy * (_N - 1)), dtype=int)
return (error[ixy[1], ixy[0]] * (1 - 1 / math.sqrt(max_min_density_ratio)) + 1 / math.sqrt(
max_min_density_ratio)) * scale
xy = scatter_halftone([0, 1, 0, 1], 100, 10000, r)
xy = xy[sdf(xy).ravel() > 0, :]
len_xy = len(xy)
return xy
def halton(b):
"""Generator function for Halton sequence."""
n, d = 0, 1
while True:
x = d - n
if x == 1:
n = 1
d *= b
else:
y = d // b
while x <= y:
y //= b
n = (b + 1) * y - x
yield n / d
def hammersely(Nsize, p=2):
y = []
for i, num in enumerate(halton(p)):
if i >= Nsize:
break
y.append(num)
x = np.arange(0, Nsize) / Nsize
return np.array([x, y]).T
def lhs(Nsize):
xy = _lhs(2, Nsize)
return xy