def findClusters(mlist_name, clist_name, eps, mc, ignore_z = True, ignore_category = True):
# Load the data.
pix_to_nm = 160.0
i3_data_in = readinsight3.loadI3GoodOnly(mlist_name)
c = i3_data_in['c']
x = i3_data_in['xc']*pix_to_nm
y = i3_data_in['yc']*pix_to_nm
if ignore_z:
print("Warning! Clustering without using localization z value!")
z = numpy.zeros(x.size)
else:
z = i3_data_in['zc']
# Perform analysis without regard to category.
if ignore_category:
print("Warning! Clustering without regard to category!")
c = numpy.zeros(c.size)
# Cluster the data.
labels = dbscanC.dbscan(x, y, z, c, eps, mc, z_factor=1.0)
# Save the data.
i3_data_out = writeinsight3.I3Writer(clist_name)
i3dtype.setI3Field(i3_data_in, 'lk', labels)
i3_data_out.addMolecules(i3_data_in)
i3_data_out.close()
def clusterStats(clist_name, min_size):
# Load the data.
pix_to_nm = 160.0
i3_data_in = readinsight3.loadI3GoodOnly(clist_name)
stats_fp = open(clist_name[:-8] + "stats.txt", "w")
header = [
"cluster",
"cat",
"size",
"x-center(nm)",
"y-center(nm)",
"z-center(nm)",
"size-x(nm)",
"size-y(nm)",
"size-z(nm)",
"rg",
]
stats_fp.write(" ".join(header) + "\n")
# Remove category zero localizations.
i3_data = i3dtype.maskData(i3_data_in, (i3_data_in["c"] != 0))
# Calculate cluster stats.
labels = i3_data["lk"]
for k in set(labels):
if k >= 2:
mask = labels == k
csize = mask.sum()
if csize > min_size:
x = pix_to_nm * i3_data["xc"][mask]
y = pix_to_nm * i3_data["yc"][mask]
z = i3_data["zc"][mask]
c = i3_data["c"][mask]
# Calculate size in x, y, z.
sx = numpy.max(x) - numpy.min(x)
sy = numpy.max(y) - numpy.min(y)
sz = numpy.max(z) - numpy.min(z)
# Calculate radius of gyration.
cx = numpy.mean(x)
cy = numpy.mean(y)
rx = x - cx
ry = y - cy
if 0:
cz = numpy.mean(z)
rz = z - cz
rg = math.sqrt(numpy.sum(rx * rx + ry * ry + rz * rz) / float(x.size))
else:
rg = math.sqrt(numpy.sum(rx * rx + ry * ry) / float(x.size))
print("Cluster:", k, x.size, "localizations")
stats = map(str, [k, c[0], csize, numpy.mean(x), numpy.mean(y), numpy.mean(z), sx, sy, sz, rg])
stats_fp.write(" ".join(stats) + "\n")
stats_fp.close()
def findClusters(mlist_name, clist_name, eps, mc, ignore_z = True, ignore_category = True):
# Load the data.
pix_to_nm = 160.0
i3_data_in = readinsight3.loadI3GoodOnly(mlist_name)
c = i3_data_in['c']
x = i3_data_in['xc']*pix_to_nm
y = i3_data_in['yc']*pix_to_nm
if ignore_z:
print("Warning! Clustering without using localization z value!")
z = numpy.zeros(x.size)
else:
z = i3_data_in['zc']
# Perform analysis without regard to category.
if ignore_category:
print("Warning! Clustering without regard to category!")
c = numpy.zeros(c.size)
# Cluster the data.
labels = dbscanC.dbscan(x, y, z, c, eps, mc, z_factor=1.0)
# Save the data.
i3_data_out = writeinsight3.I3Writer(clist_name)
i3dtype.setI3Field(i3_data_in, 'lk', labels)
i3_data_out.addMolecules(i3_data_in)
i3_data_out.close()
def clusterStats(clist_name, min_size):
# Load the data.
pix_to_nm = 160.0
i3_data_in = readinsight3.loadI3GoodOnly(clist_name)
stats_fp = open(clist_name[:-8] + "stats.txt", "w")
header = ["cluster", "cat", "size", "x-center(nm)", "y-center(nm)", "z-center(nm)", "size-x(nm)", "size-y(nm)", "size-z(nm)", "rg"]
stats_fp.write(" ".join(header) + "\n")
# Remove category zero localizations.
i3_data = i3dtype.maskData(i3_data_in, (i3_data_in['c'] != 0))
# Calculate cluster stats.
labels = i3_data['lk']
for k in set(labels):
if (k>=2):
mask = (labels == k)
csize = mask.sum()
if (csize > min_size):
x = pix_to_nm*i3_data['xc'][mask]
y = pix_to_nm*i3_data['yc'][mask]
z = i3_data['zc'][mask]
c = i3_data['c'][mask]
# Calculate size in x, y, z.
sx = numpy.max(x) - numpy.min(x)
sy = numpy.max(y) - numpy.min(y)
sz = numpy.max(z) - numpy.min(z)
# Calculate radius of gyration.
cx = numpy.mean(x)
cy = numpy.mean(y)
rx = x - cx
ry = y - cy
if 0:
cz = numpy.mean(z)
rz = z - cz
rg = math.sqrt(numpy.sum(rx*rx + ry*ry + rz*rz) / float(x.size))
else:
rg = math.sqrt(numpy.sum(rx*rx + ry*ry) / float(x.size))
print("Cluster:", k, x.size, "localizations")
stats = map(str, [k, c[0], csize, numpy.mean(x), numpy.mean(y), numpy.mean(z), sx, sy, sz, rg])
stats_fp.write(" ".join(stats) + "\n")
stats_fp.close()
def __init__(self, filename, scale = 4, verbose = True):
I3GGeneric.__init__(self,
filename,
scale = scale,
verbose = verbose)
self.i3data = readinsight3.loadI3GoodOnly(filename, verbose = verbose)
self.i3data['fr'] -= 1
# Determine film size.
[image_x, image_y, self.film_l] = getFilmSize(filename, self.i3data)
self.im_size = [image_x, image_y]
# Determine what channels the image has.
self.channels = []
for i in range(10):
mask = (self.i3data['c'] == i)
if mask.sum() > 0:
self.channels.append(i)
def __init__(self, filename, scale = 4, verbose = True):
I3GGeneric.__init__(self,
filename,
scale = scale,
verbose = verbose)
self.i3data = readinsight3.loadI3GoodOnly(filename, verbose = verbose)
self.i3data['fr'] -= 1
# Determine film size.
[image_x, image_y, self.film_l] = getFilmSize(filename, self.i3data)
self.im_size = [image_x, image_y]
# Determine what channels the image has.
self.channels = []
for i in range(10):
mask = (self.i3data['c'] == i)
if mask.sum() > 0:
self.channels.append(i)
def clusterSize(clist_name, clist_size_name, remove_cat0 = False):
# Load the data.
i3_data_in = readinsight3.loadI3GoodOnly(clist_name)
# Remove category zero localizations.
if remove_cat0:
print("warning, removing category zero localizations!")
i3_data = i3dtype.maskData(i3_data_in, (i3_data_in['c'] != 0))
else:
i3_data = i3_data_in
# Record cluster localization numbers in the fit area field.
i3_data['a'] = dbscanC.localizationClusterSize(i3_data['lk'])+1
# Copy cluster id into the frame field.
i3_data['fr'] = i3_data['lk']
# Save the data.
i3_data_out = writeinsight3.I3Writer(clist_size_name)
i3_data_out.addMolecules(i3_data)
i3_data_out.close()
def clusterSize(clist_name, clist_size_name, remove_cat0=False):
# Load the data.
i3_data_in = readinsight3.loadI3GoodOnly(clist_name)
# Remove category zero localizations.
if remove_cat0:
print("warning, removing category zero localizations!")
i3_data = i3dtype.maskData(i3_data_in, (i3_data_in['c'] != 0))
else:
i3_data = i3_data_in
# Record cluster localization numbers in the fit area field.
i3_data['a'] = dbscanC.localizationClusterSize(i3_data['lk']) + 1
# Copy cluster id into the frame field.
i3_data['fr'] = i3_data['lk']
# Save the data.
i3_data_out = writeinsight3.I3Writer(clist_size_name)
i3_data_out.addMolecules(i3_data)
i3_data_out.close()
#
# Records the cluster size associated with
# a localization in its fit area field.
#
# Hazen 11/11
#
import sys
import storm_analysis.dbscan.dbscan_c as dbscanC
import storm_analysis.sa_library.i3dtype as i3dtype
import storm_analysis.sa_library.readinsight3 as readinsight3
import storm_analysis.sa_library.writeinsight3 as writeinsight3
# Load the data.
i3_data_in = readinsight3.loadI3GoodOnly(sys.argv[1])
# Remove category zero localizations.
if False:
print "warning, removing category zero localizations!"
i3_data = i3dtype.maskData(i3_data_in, (i3_data_in['c'] != 0))
else:
i3_data = i3_data_in
# Record cluster localization numbers in the fit area field.
i3_data['a'] = dbscanC.localizationClusterSize(i3_data['lk']) + 1
# Copy cluster id into the frame field.
i3_data['fr'] = i3_data['lk']
# Save the data.
def voronoi(mlist_name, clist_name, density_factor, min_size, verbose=True):
i3_data_in = readinsight3.loadI3GoodOnly(mlist_name)
n_locs = i3_data_in['xc'].size
points = numpy.column_stack((i3_data_in['xc'], i3_data_in['yc']))
print("Creating Voronoi object.")
vor = Voronoi(points)
print("Calculating 2D region sizes.")
for i, region_index in enumerate(vor.point_region):
if ((i % 10000) == 0):
print("Processing point", i)
vertices = []
for vertex in vor.regions[region_index]:
# I think these are edge regions?
if (vertex == -1):
vertices = []
break
vertices.append(vor.vertices[vertex])
if (len(vertices) > 0):
area = Polygon(vertices).area
i3_data_in['a'][i] = 1.0 / area
# Used median density based threshold.
ave_density = numpy.median(i3_data_in['a'])
if verbose:
print("Min density", numpy.min(i3_data_in['a']))
print("Max density", numpy.max(i3_data_in['a']))
print("Median density", ave_density)
# Record the neighbors of each point.
max_neighbors = 40
neighbors = numpy.zeros((n_locs, max_neighbors), dtype=numpy.int32) - 1
neighbors_counts = numpy.zeros((n_locs), dtype=numpy.int32)
print("Calculating neighbors")
for ridge_p in vor.ridge_points:
p1 = ridge_p[0]
p2 = ridge_p[1]
# Add p2 to the list for p1
neighbors[p1, neighbors_counts[p1]] = p2
neighbors_counts[p1] += 1
# Add p1 to the list for p2
neighbors[p2, neighbors_counts[p2]] = p1
neighbors_counts[p2] += 1
if False:
n1 = neighbors[0, :]
print(n1)
print(neighbors[n1[0], :])
# Mark connected points that meet the minimum density criteria.
print("Marking connected regions")
i3_data_in['lk'] = -1
min_density = density_factor * ave_density
visited = numpy.zeros((n_locs), dtype=numpy.int32)
def neighborsList(index):
nlist = []
for i in range(neighbors_counts[index]):
loc_index = neighbors[index, i]
if (visited[loc_index] == 0):
nlist.append(neighbors[index, i])
visited[loc_index] = 1
return nlist
cluster_id = 2
for i in range(n_locs):
if (visited[i] == 0):
if (i3_data_in['a'][i] > min_density):
cluster_elt = [i]
c_size = 1
visited[i] = 1
to_check = neighborsList(i)
while (len(to_check) > 0):
# Remove last localization from the list.
loc_index = to_check[-1]
to_check = to_check[:-1]
# If the localization has sufficient density add to cluster and check neighbors.
if (i3_data_in['a'][loc_index] > min_density):
to_check += neighborsList(loc_index)
cluster_elt.append(loc_index)
c_size += 1
# Mark as visited.
visited[loc_index] = 1
# Mark the cluster if there are enough localizations in the cluster.
if (c_size > min_size):
print("cluster", cluster_id, "size", c_size)
for elt in cluster_elt:
i3_data_in['lk'][elt] = cluster_id
cluster_id += 1
visited[i] = 1
print(cluster_id, "clusters")
# Save the data.
print("Saving results")
i3_data_out = writeinsight3.I3Writer(clist_name)
i3_data_out.addMolecules(i3_data_in)
i3_data_out.close()
def clusterImages(mlist_name, title, min_size, image_max, output, image_size):
i3_data = readinsight3.loadI3GoodOnly(mlist_name)
print("Only coloring clusters with at least", min_size, "localizations.")
rand_color = randomcolor.RandomColor()
scale = 4
image_size[0] = scale * image_size[0]
image_size[1] = scale * image_size[1]
red_image = numpy.zeros(image_size)
grn_image = numpy.zeros(image_size)
blu_image = numpy.zeros(image_size)
sum_image = numpy.zeros(image_size)
labels = i3_data['lk']
start = int(numpy.min(labels))
stop = int(numpy.max(labels)) + 1
num_clusters = 0
for k in range(start,stop):
if ((k%200)==0):
print("Processing cluster", k)
mask = (labels == k)
csize = mask.sum()
x = scale * i3_data['xc'][mask]
y = scale * i3_data['yc'][mask]
if (k == -1) or (csize < min_size):
r = 1.0
g = 1.0
b = 1.0
else:
num_clusters += 1
color = rand_color.generate()
r = int(color[0][1:3], 16)/255.0
g = int(color[0][3:5], 16)/255.0
b = int(color[0][5:7], 16)/255.0
if False:
temp = numpy.zeros(image_size)
dg.drawGaussiansXYOnImage(temp, x, y, sigma = 1.5)
red_image += r * temp
grn_image += g * temp
blu_image += b * temp
sum_image += temp
else:
for i in range(x.size):
yi = int(x[i])
xi = int(y[i])
if (xi >= 0) and (xi < image_size[0]) and (yi >= 0) and (yi < image_size[1]):
red_image[xi,yi] += r
grn_image[xi,yi] += g
blu_image[xi,yi] += b
sum_image[xi,yi] += 1
# Some wacky normalization scheme..
mask = (sum_image > image_max)
red_image[mask] = (red_image[mask]/sum_image[mask]) * image_max
grn_image[mask] = (grn_image[mask]/sum_image[mask]) * image_max
blu_image[mask] = (blu_image[mask]/sum_image[mask]) * image_max
sum_image[mask] = image_max
rgb_image = numpy.zeros((image_size[0], image_size[1], 3))
rgb_image[:,:,0] = red_image
rgb_image[:,:,1] = grn_image
rgb_image[:,:,2] = blu_image
rgb_image = (255.0/image_max)*rgb_image
sum_image = (255.0/image_max)*sum_image
rgb_image = rgb_image.astype(numpy.uint8)
sum_image = sum_image.astype(numpy.uint8)
title += " (" + str(num_clusters) + ")"
# Save RGB image.
img1 = Image.fromarray(rgb_image, "RGB")
try:
draw1 = ImageDraw.Draw(img1)
font1 = ImageFont.truetype("FreeMono.ttf", 24)
draw1.text((2,2), title, (255,255,255), font = font1)
except IOError:
print("Text drawing disabled, true type font file may be missing?")
img1.save(output + "_01.png")
# Save grayscale image.
img2 = Image.fromarray(sum_image, "L")
try:
img2 = img2.convert("RGB")
draw2 = ImageDraw.Draw(img2)
font2 = ImageFont.truetype("FreeMono.ttf", 24)
draw2.text((2,2), title, (255,255,255), font = font2)
except IOError:
print("Text drawing disabled, true type font file may be missing?")
img2.save(output + "_02.png")
def voronoi(mlist_name, clist_name, density_factor, min_size, verbose = True):
i3_data_in = readinsight3.loadI3GoodOnly(mlist_name)
n_locs = i3_data_in['xc'].size
points = numpy.column_stack((i3_data_in['xc'], i3_data_in['yc']))
print("Creating Voronoi object.")
vor = Voronoi(points)
print("Calculating 2D region sizes.")
for i, region_index in enumerate(vor.point_region):
if ((i%10000) == 0):
print("Processing point", i)
vertices = []
for vertex in vor.regions[region_index]:
# I think these are edge regions?
if (vertex == -1):
vertices = []
break
vertices.append(vor.vertices[vertex])
if (len(vertices) > 0):
area = Polygon(vertices).area
i3_data_in['a'][i] = 1.0/area
# Used median density based threshold.
ave_density = numpy.median(i3_data_in['a'])
if verbose:
print("Min density", numpy.min(i3_data_in['a']))
print("Max density", numpy.max(i3_data_in['a']))
print("Median density", ave_density)
# Record the neighbors of each point.
max_neighbors = 40
neighbors = numpy.zeros((n_locs, max_neighbors), dtype = numpy.int32) - 1
neighbors_counts = numpy.zeros((n_locs), dtype = numpy.int32)
print("Calculating neighbors")
for ridge_p in vor.ridge_points:
p1 = ridge_p[0]
p2 = ridge_p[1]
# Add p2 to the list for p1
neighbors[p1,neighbors_counts[p1]] = p2
neighbors_counts[p1] += 1
# Add p1 to the list for p2
neighbors[p2,neighbors_counts[p2]] = p1
neighbors_counts[p2] += 1
if False:
n1 = neighbors[0,:]
print(n1)
print(neighbors[n1[0],:])
# Mark connected points that meet the minimum density criteria.
print("Marking connected regions")
i3_data_in['lk'] = -1
min_density = density_factor * ave_density
visited = numpy.zeros((n_locs), dtype = numpy.int32)
def neighborsList(index):
nlist = []
for i in range(neighbors_counts[index]):
loc_index = neighbors[index,i]
if (visited[loc_index] == 0):
nlist.append(neighbors[index,i])
visited[loc_index] = 1
return nlist
cluster_id = 2
for i in range(n_locs):
if (visited[i] == 0):
if (i3_data_in['a'][i] > min_density):
cluster_elt = [i]
c_size = 1
visited[i] = 1
to_check = neighborsList(i)
while (len(to_check) > 0):
# Remove last localization from the list.
loc_index = to_check[-1]
to_check = to_check[:-1]
# If the localization has sufficient density add to cluster and check neighbors.
if (i3_data_in['a'][loc_index] > min_density):
to_check += neighborsList(loc_index)
cluster_elt.append(loc_index)
c_size += 1
# Mark as visited.
visited[loc_index] = 1
# Mark the cluster if there are enough localizations in the cluster.
if (c_size > min_size):
print("cluster", cluster_id, "size", c_size)
for elt in cluster_elt:
i3_data_in['lk'][elt] = cluster_id
cluster_id += 1
visited[i] = 1
print(cluster_id, "clusters")
# Save the data.
print("Saving results")
i3_data_out = writeinsight3.I3Writer(clist_name)
i3_data_out.addMolecules(i3_data_in)
i3_data_out.close()
def clusterImages(mlist_name, title, min_size, image_max, output, image_size):
i3_data = readinsight3.loadI3GoodOnly(mlist_name)
print("Only coloring clusters with at least", min_size, "localizations.")
rand_color = randomcolor.RandomColor()
scale = 4
image_size[0] = scale * image_size[0]
image_size[1] = scale * image_size[1]
red_image = numpy.zeros(image_size)
grn_image = numpy.zeros(image_size)
blu_image = numpy.zeros(image_size)
sum_image = numpy.zeros(image_size)
labels = i3_data['lk']
start = int(numpy.min(labels))
stop = int(numpy.max(labels)) + 1
num_clusters = 0
for k in range(start,stop):
if ((k%200)==0):
print("Processing cluster", k)
mask = (labels == k)
csize = mask.sum()
x = scale * i3_data['xc'][mask]
y = scale * i3_data['yc'][mask]
if (k == -1) or (csize < min_size):
r = 1.0
g = 1.0
b = 1.0
else:
num_clusters += 1
color = rand_color.generate()
r = int(color[0][1:3], 16)/255.0
g = int(color[0][3:5], 16)/255.0
b = int(color[0][5:7], 16)/255.0
if False:
temp = numpy.zeros(image_size)
dg.drawGaussiansXYOnImage(temp, x, y, sigma = 1.5)
red_image += r * temp
grn_image += g * temp
blu_image += b * temp
sum_image += temp
else:
for i in range(x.size):
yi = int(x[i])
xi = int(y[i])
if (xi >= 0) and (xi < image_size[0]) and (yi >= 0) and (yi < image_size[1]):
red_image[xi,yi] += r
grn_image[xi,yi] += g
blu_image[xi,yi] += b
sum_image[xi,yi] += 1
# Some wacky normalization scheme..
mask = (sum_image > image_max)
red_image[mask] = (red_image[mask]/sum_image[mask]) * image_max
grn_image[mask] = (grn_image[mask]/sum_image[mask]) * image_max
blu_image[mask] = (blu_image[mask]/sum_image[mask]) * image_max
sum_image[mask] = image_max
rgb_image = numpy.zeros((image_size[0], image_size[1], 3))
rgb_image[:,:,0] = red_image
rgb_image[:,:,1] = grn_image
rgb_image[:,:,2] = blu_image
rgb_image = (255.0/image_max)*rgb_image
sum_image = (255.0/image_max)*sum_image
rgb_image = rgb_image.astype(numpy.uint8)
sum_image = sum_image.astype(numpy.uint8)
title += " (" + str(num_clusters) + ")"
# Save RGB image.
img1 = Image.fromarray(rgb_image, "RGB")
try:
draw1 = ImageDraw.Draw(img1)
font1 = ImageFont.truetype("FreeMono.ttf", 24)
draw1.text((2,2), title, (255,255,255), font = font1)
except IOError:
print("Text drawing disabled, true type font file may be missing?")
img1.save(output + "_01.png")
# Save grayscale image.
img2 = Image.fromarray(sum_image, "L")
try:
img2 = img2.convert("RGB")
draw2 = ImageDraw.Draw(img2)
font2 = ImageFont.truetype("FreeMono.ttf", 24)
draw2.text((2,2), title, (255,255,255), font = font2)
except IOError:
print("Text drawing disabled, true type font file may be missing?")
img2.save(output + "_02.png")