def smart_zoom(fractal, maxiter, data, pool=3, resx=256, resy=256):
subdata = sub(data)
dif_list = np.empty(16, dtype=object)
for i, x in enumerate(subdata):
dif_list[i] = diff(x, maxiter)
print(dif_list)
"""Gives index table"""
k = sorted(range(len(dif_list)), key=lambda x: dif_list[x])
val = k[len(k) - 1 - random.randint(0, pool - 1)]
print(str(k) + " chose val: " + str(val))
"""val, value=max(enumerate(dif_list), key=operator.itemgetter(1)) """
value = dif_list[val]
"""x=, y=0..3"""
x = (val) % 4
y = (val - x) / 4
print("Interesting x: " + str(x))
print("Interesting y: " + str(y))
print(fractal.w)
fractal_rec = fractal5.mandelbrot()
fractal_rec.x = fractal.x + Decimal((-3.0 + 2.0 * x) / 8.0) * fractal.w
fractal_rec.y = fractal.y + Decimal((-3.0 + 2.0 * y) / 8.0) * fractal.h
fractal_rec.w = fractal.w / Decimal(4.0)
fractal_rec.h = fractal.h / Decimal(4.0)
print("x: " + str(fractal_rec.x))
print("y: " + str(fractal_rec.y))
print("w: " + str(fractal_rec.w))
return fractal_rec
def rand_zoom(fractal,
maxiter,
data,
pool=3,
subx=8.0,
suby=16.0,
resx=256,
resy=256):
subdata = sub(data, incc=subx, incr=suby)
dif_list = []
nonNullData = []
for i, data in enumerate(subdata):
if np.var(data) >= 0.1:
nonNullData.append({"data": data, "indexVal": i})
val = nonNullData[random.randint(0, len(nonNullData))]["indexVal"]
"""Finds x and y"""
x = (val) % subx
y = (val - x) / subx
"""print("x: "+str(x)+"; y: "+str(y))"""
fractal_rec = fractal5.mandelbrot()
fractal_rec.x = fractal.x + Decimal(
(-(subx - 1) + 2.0 * x) / (subx * 2)) * fractal.w
fractal_rec.y = fractal.y + Decimal(
(-(suby - 1) + 2.0 * y) / (suby * 2)) * fractal.h
fractal_rec.w = fractal.w / Decimal(4)
fractal_rec.h = fractal.h / Decimal(4)
return fractal_rec
def mask_zoom(fractal,
maxiter,
data,
pool=3,
subx=8.0,
suby=16.0,
resx=256,
resy=256):
subdata = sub(data, incc=subx, incr=suby)
dif_list = np.empty(int(subx * suby), dtype=object)
mask = mask_hill(int(resx / subx), int(resy / suby), maxiter)
"""print(subdata[0].shape);
print(mask.shape);"""
for i, x in enumerate(subdata):
dif_list[i] = mask_fit(x, mask)
"""print(dif_list)"""
"""Gives index table"""
k = sorted(range(len(dif_list)), key=lambda x: dif_list[x])
val = k[len(k) - 1 - random.randint(0, pool - 1)]
"""print(str(k) + " chose val: "+str(val))"""
value = dif_list[val]
"""Finds x and y"""
x = (val) % subx
y = (val - x) / subx
"""print("x: "+str(x)+"; y: "+str(y))"""
fractal_rec = fractal5.mandelbrot()
fractal_rec.x = fractal.x + Decimal(
(-(subx - 1) + 2.0 * x) / (subx * 2)) * fractal.w
fractal_rec.y = fractal.y + Decimal(
(-(suby - 1) + 2.0 * y) / (suby * 2)) * fractal.h
fractal_rec.w = fractal.w / Decimal(4)
fractal_rec.h = fractal.h / Decimal(4)
return fractal_rec
fractal_rec.y=fractal.y+Decimal((-3.0+2.0*y)/8.0) * fractal.h
fractal_rec.w=fractal.w/Decimal(4.0)
fractal_rec.h=fractal.h/Decimal(4.0)
print("x: "+str(fractal_rec.x))
print("y: "+str(fractal_rec.y))
print("w: "+str(fractal_rec.w))
return fractal_rec
if __name__=="__main__":
print("How many threads to use?")
i_threads=int(input())
a=fractal5.mandelbrot()
a.x=a.x
a.y=a.y
a.w=Decimal(8.0)
a.h=Decimal(16.0)
getcontext().prec=6
for i in range(1,2048):
dbx=str(a.x)
dby=str(a.y)
dbw=str(a.w)
dbh=str(a.h)
r=a.render(i*80,resx=108*2,resy=216*2,threadCount=i_threads)
a=smart_zoom(a,i*80*1000,(r*10000).astype(int),resx=108*2,resy=216*2)
sn=sinemap(r,randOFF(),2,randOFF(),3,randOFF(),4)