def FindClusterPosition(self, clusterID):
""" this will find the weighted average of the cluster position
"""
# compute the imageArray of just the cluster
clusterArray = self.imageArray * self.clusterMask[:, :, clusterID]
# now use the cluster_position class to do the computation
positionInfo = cluster_position.cluster_position(clusterArray)
# and get the info
mean = positionInfo.mean
variance = positionInfo.variance
# done, return
return (mean, variance)
def FindClusterPosition(self,clusterID):
""" this will find the weighted average of the cluster position
"""
# compute the imageArray of just the cluster
clusterArray=self.imageArray*self.clusterMask[:,:,clusterID]
# now use the cluster_position class to do the computation
positionInfo=cluster_position.cluster_position(clusterArray)
# and get the info
mean = positionInfo.mean
variance = positionInfo.variance
# done, return
return (mean,variance)
def DoCluster(self,data):
#extract data from picture into numpy array
x_size = 512
y_size = 512
X = range(x_size)
Y = range(y_size)
size = x_size*y_size
Z = range(size)
# This is where the new code begins
# data = background subtracted dat
out = ndimage.gaussian_filter(data, 7)
threshold = get_threshold(out)
data -= threshold[0]+3*threshold[1]
out -= threshold[0]+3*threshold[1]
out[out<0] = 0
data[data<0] = 0 # also zero out these pixels, to avoid problems with cluster position finding
clusters = get_clusters(out,0.5*threshold[1])
for cluster in clusters:
integral = 0
count = 0
smooth_integral = 0
maximum = 0
maximum_smooth = 0
pixels = cluster.size
for coord in cluster:
smooth_integral+=out[coord[0]][coord[1]]
if maximum < data[coord[0]][coord[1]]:
maximum = data[coord[0]][coord[1]]
if maximum_smooth < out[coord[0]][coord[1]]:
maximum_smooth = out[coord[0]][coord[1]]
if data[coord[0]][coord[1]]>0.5*threshold[1]:
integral+=data[coord[0]][coord[1]]
count+=1
self.cluster_integral.append(integral)
self.cluster_pixels.append(count)
self.cluster_smooth_integral.append(smooth_integral)
self.cluster_smooth_pixels.append(pixels)
self.cluster_max.append(maximum)
self.cluster_smooth_max.append(maximum_smooth)
self.threshold.append(threshold[1])
self.threshold_mean.append(threshold[0])
# add in a few more variables
self.clusterFrac.append(integral/data.sum())
self.avgPixelCount.append(integral/count)
# also determine the position of the clusters (new code -MCR)
for cluster in clusters:
# first setup a clusterArray by zeroing out the data array
clusterArray=data*0.
# loop over all points in the cluster
for coord in cluster:
clusterArray[coord[0]][coord[1]]=data[coord[0]][coord[1]] # pull value from data array
# now use clusterArray to compute position and other info
positionInfo=cluster_position.cluster_position(clusterArray)
# and get the info
mean = positionInfo.mean
variance = positionInfo.variance
self.clusterPosition.append(mean)
self.clusterPositionVariance.append(variance)
return
