Commit d4d41fb2 authored by csongor's avatar csongor
Browse files

WIP - simplifications

parent 8e865a13
......@@ -41,31 +41,23 @@ def smooth_power_2s(power, k, exclude=1, smooth_length=None):
print "p_smooth3", p_smooth
print "p_smooth length ", len(p_smooth)
# dk = 0.5*(k[2:] - k[:-2])
# dk = np.r_[0.5*(k[1]-k[0]),dk]
# dk = np.r_[dk,0.5*(k[-1]-k[-2])]
# if (smooth_length is None) or (smooth_length < 0):
# smooth_length = k[1]-k[0]
#
# p_smooth = np.empty(power.shape)
# for i in xrange(len(p_smooth)):
# l = i-int(2*smooth_length/dk[i])-1
# l = max(l,0)
# u = i+int(2*smooth_length/dk[i])+2
# u = min(u,len(p_smooth))
# C = np.exp(-(k[l:u]-k[i])**2/(2.*smooth_length**2))*dk[l:u]
# p_smooth[i] = np.sum(C*power[l:u])/np.sum(C)
if (exclude > 0):
p_smooth = np.r_[excluded_power,p_smooth]
return p_smooth
def smoothie(power, startindex, endindex, k, exclude=1, smooth_length=None):
def GaussianKernel(mpower, mk, mu,smooth_length):
C = np.exp(-(mk - mu) ** 2 / (2. * smooth_length ** 2))
return np.sum(C * mpower) / np.sum(C)
def smoothie(power, startindex, endindex, k, kernelfunction, exclude=1,
smooth_length=None):
if smooth_length == 0:
# No smoothing requested, just return the input array.
return power
excluded_power = []
if (exclude > 0):
k = k[exclude:]
excluded_power = np.copy(power[:exclude])
......@@ -74,44 +66,27 @@ def smoothie(power, startindex, endindex, k, exclude=1, smooth_length=None):
if (smooth_length is None) or (smooth_length < 0):
smooth_length = k[1]-k[0]
nmirror = int(5*smooth_length/(k[1]-k[0]))+2
# print "nmirror", nmirror
mpower = np.r_[np.exp(2*np.log(power[0])-np.log(power[1:nmirror][::-1])),power,np.exp(2*np.log(power[-1])-np.log(power[-nmirror:-1][::-1]))]
mk = np.r_[(2*k[0]-k[1:nmirror][::-1]),k,(2*k[-1]-k[-nmirror:-1][::-1])]
mdk = np.r_[0.5*(mk[1]-mk[0]),0.5*(mk[2:]-mk[:-2]),0.5*(mk[-1]-mk[-2])]
# print "mpower", mpower
p_smooth = np.empty(mpower.shape)
# print "p_smooth", p_smooth
for i in xrange(len(p_smooth)):
l = i-int(2*smooth_length/mdk[i])-1
l = max(l,0)
u = i+int(2*smooth_length/mdk[i])+2
u = min(u,len(p_smooth))
#print "i", i, "l", l, "u", u
C = np.exp(-(mk[l:u]-mk[i])**2/(2.*smooth_length**2))*mdk[l:u]
p_smooth[i] = np.sum(C*mpower[l:u])/np.sum(C)
# print "p_smooth[",i,"] = ",p_smooth[i]
p_smooth = np.empty(endindex-startindex)
for i in xrange(startindex, endindex):
l = max(i-int(2*smooth_length)-1,0)
u = min(i+int(2*smooth_length)+2,len(p_smooth))
p_smooth[i-startindex] = kernelfunction(power[l:u], k[l:u], k[i],
smooth_length)
# print "p_smooth2", " all ", p_smooth
p_smooth = p_smooth[nmirror - 1:-nmirror + 1]
# print "p_smooth3", p_smooth
# print "p_smooth length ", len(p_smooth)
if (exclude > 0):
p_smooth = np.r_[excluded_power,p_smooth]
return p_smooth
def smooth_something(datablock, axis=None, startindex=None, endindex=None, kernelfunction=lambda x:x, k=None):
if axis == None:
axis = size(datablock)
def smooth_something(datablock, axis=0, startindex=None, endindex=None,
kernelfunction=lambda x:x, k=None, sigma=None):
if startindex == None:
startindex=0
if endindex == None:
endindex=len(datablock)
print kernelfunction
return np.apply_along_axis(kernelfunction,axis,datablock, startindex=startindex, endindex=endindex, k=k)
return np.apply_along_axis(smoothie, axis, datablock,
startindex=startindex, endindex=endindex, k=k,
smooth_length=sigma, kernelfunction=kernelfunction)
\ No newline at end of file
......@@ -6,21 +6,45 @@ import numpy as np
print "///////////////////////////////////////First thing ////////////////////////"
ksq=np.sqrt(np.arange(8))
k=np.arange(8)
power = np.ones(512).reshape((8,8,8))
power[0][4][4]=1000
power[1][4][4]=1000
power[2][4][4]=1000
power[3][4][4]=1000
power[4][4][4]=1000
power[5][4][4]=1000
power[6][4][4]=1000
power[7][4][4]=1000
n=8
ksq=np.sqrt(np.arange(n))
kk=np.arange(n)
power = np.ones(n**3).reshape((n,n,n))
# power[0][4][4]=1000
# power[1][4][4]=1000
# power[2][4][4]=1000
# power[3][4][4]=1000
power[n/2][n/2][n/2]=10000
# power[5][4][4]=1000
# power[6][4][4]=1000
# power[7][4][4]=1000
k = kk
sigma=k[1]-k[0]
mirrorsize=7
startindex=mirrorsize/2
endindex=n-mirrorsize/2
print power, k, power.shape
smooth = extended.smooth_something(datablock=power, axis=(2), startindex=None, endindex=None, kernelfunction=extended.smoothie, k=ksq)
smooth = extended.smooth_something(datablock=power, axis=(2),
startindex=startindex, endindex=endindex,
kernelfunction=extended.GaussianKernel, k=k,
sigma=sigma)
print "Smoooooth", smooth
doublesmooth = extended.smooth_something(datablock=smooth, axis=(1),
startindex=startindex, endindex=endindex,
kernelfunction=extended.GaussianKernel,
k=k, sigma=sigma)
print "DoubleSmooth", doublesmooth
tripplesmooth = extended.smooth_something(datablock=doublesmooth, axis=(0),
startindex=startindex, endindex=endindex,
kernelfunction=extended.GaussianKernel,
k=k, sigma=sigma)
print "TrippleSmooth", tripplesmooth
print "///////////////////////////////////////Final thing ////////////////////////"
print "smooth.len == power.len" , smooth.shape, power.shape, power.shape==smooth.shape
\ No newline at end of file
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