def as_tuple(
self
):
"""
Return indices as tuple.
"""
return cube.xyzarr_to_tuple(self.indices, coordaxis=1)
def clumpfind(
self,
levels=None,
corners=False,
seeded=False,
allow_new_peaks=True,
timer=True
):
"""
Generate a clumpfind assignment mask.
"""
# ...................................................
# Check user options
# ...................................................
if self.linked_data == None:
print "Clumpfind assignment requires data."
return
if seeded == True:
if self.linked_lmax == None:
print "Seeded clumpfind assignment requires local maxima."
return
if seeded == False and allow_new_peaks == False:
print "Cannot run an unseeded (classic) clumpfind without being able to add seeds."
return
# ...................................................
# Get data to use
# ...................................................
# Get the data and set the values we will not use to a low
# number that will be ignored by the algorithm.
data = copy.deepcopy(self.linked_data.data)
if self.linked_mask != None:
use = self.linked_mask.data*self.linked_data.valid
else:
use = self.linked_data.valid
min_use = np.min(self.linked_data.data[use])
max_use = np.max(self.linked_data.data[use])
low_value = min_use-1.
data[(use==False)] = low_value
# ...................................................
# Calculate contour levels
# ...................................................
if levels == None:
if self.linked_data.noise != None:
print "Defaulting to 2 sigma spacing."
levels = contour_values(
linspace = True,
maxval = max_use,
minval = min_use,
spacing = 2.0*self.linked_data.noise.scale
)
else:
print "Need a noise estimate."
return
self.levels = levels
# ...................................................
# Build the structuring element
# ...................................................
structure = (Struct(
"simple",
ndim=self.linked_data.data.ndim,
corners=corners)).struct
# ...................................................
# Initialize the output
# ...................................................
# ... data
self.data = np.zeros_like(data, dtype=np.int)
# ... local maxima
if seeded == False:
print "Initializing a new set of local maxima"
self.linked_lmax = \
lmax.Lmax(self.linked_data, self.linked_mask)
# ...................................................
# Loop over levels (from high to low)
# ...................................................
nlev = len(levels)
count = 0
for level in levels:
# ........................
# Print a counter
# ........................
perc = count*1./nlev
sys.stdout.write('\r')
sys.stdout.write("Clumpfind level %d out of %d" % (count, nlev))
sys.stdout.flush()
count += 1
# ............................
# Label regions for this level
# ............................
thresh = (data >= level)
labels, ncolors = ndimage.label(
thresh,
structure=structure)
# ...........................
# Vectorize the labeled data
# ...........................
# This gives a big speedup for sparse data.
ind = np.where(thresh)
val = self.linked_data.data[ind]
ind_arr = cube.xyztup_to_array(ind, coordaxis=1)
label_vec = labels[ind]
# Get the assignments for the current seeds
if self.linked_lmax.num > 0:
seed_labels = labels[self.linked_lmax.as_tuple()]
# ........................................
# Loop over discrete regions at this level
# ........................................
for label in range(1,ncolors+1):
# ........................................
# Get the indices for this region
# ........................................
this_color = np.where(label_vec == label)
this_val = val[this_color]
this_ind_arr = ind_arr[this_color[0],:]
this_ind = cube.xyzarr_to_tuple(this_ind_arr,coordaxis=1)
# ........................................
# Check if we should add a new peak
# ........................................
# If there are no peaks or if there are no peaks in
# this region, we want to add a new one --- but only
# if that's allowed!
# A future extension is to add additional criteria
# that must be met to add a peak (volume, area, etc.)
if self.linked_lmax.num == 0:
if allow_new_peaks:
add_a_new_peak = True
else:
continue
elif np.sum(seed_labels == label) == 0:
if allow_new_peaks:
add_a_new_peak = True
else:
continue
else:
add_a_new_peak = False
# ........................................
# Add a new peak
# ........................................
if add_a_new_peak:
# Find the location of the maximum value
maxind = np.argmax(this_val)
# Get the corresponding coordinates
peak_index = this_ind_arr[maxind,:]
# Add a local maximum
new_name = self.linked_lmax.add_local_max(peak_index)
# Label these data in the assignment cube
self.data[this_ind] = new_name
continue
# ........................................
# Deal with the case of a signle seed
# ........................................
if np.sum(seed_labels == label) == 1:
maxind = np.where((seed_labels == label))
self.data[this_ind] = self.linked_lmax.name[maxind]
continue
# ........................................
# Deal with the case of competing seeds
# ........................................
# Several matching labels
if np.sum(seed_labels == label) > 1:
# Initialize an assignment vector
this_assign = np.zeros_like(this_val)
best_dist = np.zeros_like(this_val)
# Identify the competing seeds
maxind = np.where((seed_labels == label))
n_max = len(maxind[0])
for i in range(n_max):
this_max_name = self.linked_lmax.name[maxind[0][i]]
this_max_coord = self.linked_lmax.indices[this_max_name-1]
dist_to_this_max = \
np.sum((this_ind_arr - this_max_coord)**2,axis=1)
if i == 0:
# ... all true for the first test
is_closest = (dist_to_this_max == dist_to_this_max)
else:
is_closest = (dist_to_this_max < best_dist)
this_assign[is_closest] = this_max_name
best_dist[is_closest] = dist_to_this_max[is_closest]
self.data[this_ind] = this_assign
def as_tuple(self):
"""
Return indices as tuple.
"""
return cube.xyzarr_to_tuple(self.indices, coordaxis=1)
