def test_spiral_even(self):
self.assertListEqual([
[1, 2, 3, 4],
[12, 13, 14, 5],
[11, 16, 15, 6],
[10, 9, 8, 7]
], spiral(4))
def test_spiral():
gen = spiral()
assert next(gen) == (0, 0)
assert next(gen) == (1, 0)
assert next(gen) == (1, -1)
assert next(gen) == (0, -1)
assert next(gen) == (-1, -1)
assert next(gen) == (-1, 0)
def generate_spiral(gridsize, aperture, r_max, r_min, splits, settings):
split_odd = splits[0]
split_even = splits[1]
first = settings[0]
second = settings[1]
third = settings[2]
fourth = settings[3]
sampling = aperture / (gridsize // 2)
wfarr = np.zeros([gridsize, gridsize], dtype=np.complex128)
c = gridsize // 2
for i in range(gridsize):
for j in range(gridsize):
x = i - c
y = j - c
phi = math.atan2(y, x)
r = sampling * math.hypot(x, y)
wfarr[i][j] = spiral(r, phi, aperture, r_max, r_min, split_odd,
split_even, first, second, third, fourth)
return wfarr
group2.add_argument('-s', '--solvable', action='store_true', help='Generate only solvable puzzle')
group2.add_argument('-u', '--unsolvable', action='store_false', help='Generate only unsolvable puzzle')
parser.add_argument('-a', '--anim', action='store_true' , help='Launch an animation of the resolution of the puzzle')
return parser.parse_args(arg)
if __name__ == '__main__':
arg = parse_argument(sys.argv[1:])
if (arg.file):
try:
fd = open(arg.file, "r")
parsing = Parsing(fd)
except:
sys.exit("Error - Openning error")
start = parsing.puzzle
goal = printspiral(spiral(int(sqrt(len(start)))))
if (my_resolvable(parsing, start, goal) == 1):
exit
else:
if arg.length < 71 and arg.length > 2:
goal = printspiral(spiral(arg.length))
solvable = None
if (arg.solvable == True):
solvable = True
elif (arg.unsolvable == False):
solvable = False
start = get_puzzle(arg.length, goal, solvable)
else:
sys.exit('Wrong size')
def create_dataset(N, data_type, noise,
sm=0.1, d=1, angle=3*np.pi,
ndist=1, means=np.array([[-0.25, 0.0],[0.25, 0.0]]), sigmas=np.array([0.1, 0.1]),
ml=1, b=0.2, sl=0.05,
ssin=0.02,
sspi=1, wrappings = 4, mspi=0.5):
#Sample a dataset from different distributions
# X, Y = create_dataset(N, type, noise)
#
# INPUT
# N Number of samples
# data_type Type of distribution used. It must be one from
# 'Moons' 'Gaussian' 'Linear' 'Sinusoidal' 'Spiral'
# noise probability to have a wrong label in the dataset.
#
# 'Moons' parameters:
# 1- sm: standard deviation of the gaussian noise. Default is 0.1
# 2- d: 1X2 translation vector between the two classes. With d = 0
# the classes are placed on a circle. Default is 1.
# 3- angle: rotation angle of the moons in (radians). Default is 3*np.pi.
#
# 'Gaussian' parameters:
# 1- ndist: number of gaussians for each class. Default is 1.
# 2- means: vector of size(2*ndist X 2) with the means of each gaussian.
# Default is np.array([[-0.25, 0.0],[0.25, 0.0]]).
# 3- sigmas: A sequence of covariance matrices of size (2*ndist, 2).
# Default is np.array([0.1, 0.1]).
#
# 'Linear' parameters:
# 1- ml: slope of the separating line. Default is 1.
# 2- b: bias of the line. Default is 0.2.
# 3- sl: standard deviation of the gaussian noise. Default is 0.05.
#
# 'Sinusoidal' parameters:
# 1- ssin: standard deviation of the gaussian noise. Default is 0.02.
#
# 'Spiral' parameters:
# 1- sspi: standard deviation of the gaussian noise. Default is 1.
# 2- wrappings: wrappings number of wrappings of each spiral. Default is 4.
# 3- mspi: multiplier m of x * sin(m * x) for the second spiral. Default is 0.5.
#
# OUTPUT
# X data matrix with a sample for each row
# Y vector with the labels
#
# EXAMPLE:
# X, Y = create_dataset(100, 'SPIRAL', 0.01)
# X, Y = create_dataset(100, 'SPIRAL', 0, sspi=0.1, wrappings=2, m=2);
NN = [int(math.floor(float(N) / 2.0)), int(math.ceil(float(N) / 2.0))]
if data_type=='Moons':
X, Y = moons(NN, sm, angle, d)
elif data_type=='Gaussian':
X, Y = gaussian(NN, ndist, means, sigmas)
elif data_type=='Linear':
X, Y = linear_data(NN, ml, b, sl)
elif data_type=='Sinusoidal':
X, Y = sinusoidal(NN, ssin)
elif data_type=='Spiral':
X, Y = spiral(NN, sspi, wrappings, mspi)
else:
print 'Specified dataset type is not correct. It must be one of "Moons", "Gaussian", "Linear", "Sinusoidal", "Spiral"'
swap=np.random.random((np.size(Y, axis=0), np.size(Y, axis=1)))<=noise
Y[swap]=Y[swap]*-1
return X, Y
from spiral import spiral
import numpy as np
import matplotlib.pyplot as plt
s = spiral(0, 1)
x1, y1 = s.out()
z1 = np.zeros(*x1.shape)
d1 = np.vstack((x1, y1, z1))
np.save('s1.npy', d1)
x2, y2 = -x1, -y1
z2 = np.ones(*x2.shape)
d2 = np.vstack((x2, y2, z2))
np.save('s2.npy', d2)
plt.plot(x1, y1, 'bo')
plt.plot(x2, y2, 'ro')
plt.show()
+ str(objectPrint.position) + '}')
def changePosition(positionBox, positionChange):
positionBox.position = positionChange
fin = open('base.pov')
sdl = fin.read()
fin.close()
#regular expression for camera
findStr = r"camera\s*{\s*location\s*<\s*\-*\d+,\-*\d+,\-*\d+\s*>"
listForCam = range(0, 10)
#lists for x, y, and z cordinates
listOfX, listOfY = spiral.spiral()
listOfZ = range(0, 50)
num = 1
#in this for loop we change the camera a bit
for i in range(0, 10):
#finding and changing in sdl the camera line
if i % 2 == 0:
sdl = re.sub(
findStr, "camera{location < " + str(0) + ' , ' +
str(listForCam[num]) + ' , ' + str(-7) + " >", sdl)
num = num + 1
outfile = 'temp.pov'
fout = open(outfile, 'w')
fout.write(sdl)
fout.close()
def test_spiral_stop():
assert len(list(spiral(10))) == 10
def test_spiral_odd(self):
self.assertListEqual([
[1, 2, 3],
[8, 9, 4],
[7, 6, 5]
], spiral(3))
def test_spiral_one(self):
self.assertListEqual([[1]], spiral(1))
def test_spiral_5(self):
result = spiral.diagonalSum(spiral.spiral(5))
self.assertEqual(result, 101)
def test_spiral_1001(self):
result = spiral.diagonalSum(spiral.spiral(1001))
self.assertEqual(result, 669171001)
print("Result: {0}".format(result))