def clock(size=(256,256),angle=0,sharpEdge=False):
"""
:param sharpEdge: if true, 100% black ends at 100% white for a difinitive angle -- otherwise gives more of a lighting effect
"""
angle=0.75-angle/360.0
dc=deltaC(size)
img=np.arctan2(dc[:,:,0],dc[:,:,1])/math.pi
img=(normalize(img)+angle)%1.0
if not sharpEdge:
img=deltaFromGray(img)
return img
def clock2(size=(256,256),waveform="sine",frequency=(10,10),noise=0.2,noiseBasis="perlin",
noiseOctaves=4,noiseSoften=0.0,direction=0,invert=False,seed=None):
"""
a rotary clockface gradient
"""
noiseSize=max(size)/8 # TODO: pass this value in somehow
turb=turbulence(size,noiseSize,seed=seed)*noise
#points=np.ndarray((int(size[0]),int(size[1])))
angle=0.75-direction/360.0
dc=deltaC(size)
img=np.arctan2(dc[:,:,0],dc[:,:,1])/math.pi
img=np.abs(np.sin((((normalize(img)+angle)%1.0)+turb)*math.pi))
img=normalize(img)
if invert:
img=1.0-img
return img
def logpolar2cartesian(polar_data):
"""
From:
https://stackoverflow.com/questions/2164570/reprojecting-polar-to-cartesian-grid
"""
from scipy.ndimage.interpolation import map_coordinates
theta_step=1
range_step=500
x=np.arange(-100000, 100000, 1000)
y=x
order=3
# "x" and "y" are numpy arrays with the desired cartesian coordinates
# we make a meshgrid with them
X, Y = np.meshgrid(x, y)
# Now that we have the X and Y coordinates of each point in the output plane
# we can calculate their corresponding theta and range
Tc = np.degrees(np.arctan2(Y, X)).ravel()
Rc = np.ln(np.sqrt(X**2 + Y**2)).ravel()
# Negative angles are corrected
Tc[Tc < 0] = 360 + Tc[Tc < 0]
# Using the known theta and range steps, the coordinates are mapped to
# those of the data grid
Tc = Tc / theta_step
Rc = Rc / range_step
# An array of polar coordinates is created stacking the previous arrays
#coords = np.vstack((Ac, Sc))
coords = np.vstack((Tc, Rc))
# To avoid holes in the 360º - 0º boundary, the last column of the data
# copied in the begining
polar_data = np.vstack((polar_data, polar_data[-1,:]))
# The data is mapped to the new coordinates
# Values outside range are substituted with nans
cart_data = map_coordinates(polar_data, coords, order=order, mode='constant', cval=np.nan)
# The data is reshaped and returned
return cart_data.reshape(len(Y), len(X)).T
fig = plt.figure()
plt.xticks([])
plt.yticks([])
k = 5 # number of plane waves
stripes = 37 # number of stripes per wave
N = 512 # image size in pixels
ite = 30 # iterations
phases = np.arange(0, 2 * pi, 2 * pi / ite)
image = np.empty((N, N))
d = np.arange(-N / 2, N / 2, dtype=np.float64)
xv, yv = np.meshgrid(d, d)
theta = np.arctan2(yv, xv)
r = np.log(np.sqrt(xv * xv + yv * yv))
r[np.isinf(r) == True] = 0
tcos = theta * np.cos(np.arange(0, pi, pi / k))[:, np.newaxis, np.newaxis]
rsin = r * np.sin(np.arange(0, pi, pi / k))[:, np.newaxis, np.newaxis]
inner = (tcos - rsin) * stripes
cinner = np.cos(inner)
sinner = np.sin(inner)
i = 0
for phase in phases:
image[:] = np.sum(cinner * np.cos(phase) - sinner * np.sin(phase),
axis=0) + k
plt.imshow(image.copy2numpy(), cmap="RdGy")