def gridFn():
mask = (self.i3data['c'] == channel)
self.i3data = i3dtype.maskData(self.i3data, mask)
return self.i3To2DGridAllChannelsMerged(zmin = zmin,
zmax = zmax,
uncorrected = uncorrected,
verbose = verbose)
def gridFn():
mask = (self.i3data['c'] == channel)
self.i3data = i3dtype.maskData(self.i3data, mask)
return self.i3To2DGridAllChannelsMerged(zmin = zmin,
zmax = zmax,
uncorrected = uncorrected,
verbose = verbose)
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 getEmitters(self, frame):
integrated_on = numpy.zeros(self.n_emitters)
#
# This is a little complicated because we are trying to include accurate
# modeling of emitters that turned on/off more than once in a single frame.
#
for i in range(self.n_emitters):
# The easy case, no change in state in the current frame.
if (self.next_transistion[i] >= (frame + 1.0)):
if self.am_on[i]:
integrated_on[i] = 1.0
else:
last_transistion = frame
while (self.next_transistion[i] < (frame + 1.0)):
if self.am_on[i]:
integrated_on[i] += self.next_transistion[i] - last_transistion
self.am_on[i] = not self.am_on[i]
last_transistion = self.next_transistion[i]
if self.am_on[i]:
self.next_transistion[i] += numpy.random.exponential(self.on_time)
else:
self.next_transistion[i] += numpy.random.exponential(self.off_time)
# Turned on and not off again in the current frame.
if self.am_on[i]:
integrated_on[i] += (frame + 1.0) - last_transistion
# Set height and return only those emitters that have height > 0.
self.i3_data['a'][:] = integrated_on * self.photons
return i3dtype.maskData(self.i3_data, (self.i3_data['a'] > 0.0))
def render2DImage(i3_reader,
shape,
category=None,
offsets=None,
scale=2,
sigma=None):
"""
Create a grayscale image from a Insight3 format binary data.
i3_reader - A readinsight3.I3Reader object.
shape - The shape of the output image. This will be multiplied by scale.
category - Filter for localizations of this category. The default is all categories.
offsets - X,Y offset of the image origin. The default is 0.0.
scale - The 'zoom' level of the output image, i.e. if the original STORM movie was
256x256 and scale = 2 then the output image will be 512x512.
sigma - The sigma to use when rendering gaussians (pixels). If this is None then
the image will be a histogram.
"""
image = numpy.zeros((shape[0] * scale, shape[1] * scale))
# Make sure we are starting at the beginning.
i3_reader.resetFp()
# Load the first block of data.
i3_data = i3_reader.nextBlock()
while (i3_data is not False):
sys.stdout.write(".")
sys.stdout.flush()
# Filter by category, if requested.
if category is not None:
i3_data = i3dtype.maskData(i3_data, (i3_data['c'] == category))
# Adjust by offsets, if specified. Note, not adjusted for scale.
if offsets is not None:
i3_data['xc'] -= offsets[0]
i3_data['yc'] -= offsets[1]
# Adjust x,y by scale.
xc = i3_data['xc'] * scale
yc = i3_data['yc'] * scale
# Histogram.
if sigma is None:
image += gridC.grid2D(numpy.round(xc), numpy.round(yc),
image.shape)
# Gaussians.
else:
dg.drawGaussiansXYOnImage(image, xc, yc, sigma=sigma)
# Load next block of data.
i3_data = i3_reader.nextBlock()
print()
return image
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 getMoleculesInFrameRange(self, start, stop, good_only = True):
start_mol_num = self.findFrame(start)
stop_mol_num = self.findFrame(stop)
if (type(self.localizations) == type(numpy.array([]))):
data = self.localizations[start_mol_num:stop_mol_num]
else:
cur = self.fp.tell()
self.fp.seek(16 + start_mol_num*self.record_size)
size = stop_mol_num - start_mol_num
data = numpy.fromfile(self.fp,
dtype=i3dtype.i3DataType(),
count=size)
self.fp.seek(cur)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def getMoleculesInFrameRange(self, start, stop, good_only=True):
start_mol_num = self.findFrame(start)
stop_mol_num = self.findFrame(stop)
if (type(self.localizations) == type(numpy.array([]))):
data = self.localizations[start_mol_num:stop_mol_num]
else:
cur = self.fp.tell()
self.fp.seek(16 + start_mol_num * self.record_size)
size = stop_mol_num - start_mol_num
data = numpy.fromfile(self.fp,
dtype=i3dtype.i3DataType(),
count=size)
self.fp.seek(cur)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def reduceMList(i3_name_in,
i3_name_out,
xstart=None,
xstop=None,
ystart=None,
ystop=None,
min_frame=None,
max_frame=None):
meta_data = readinsight3.loadI3Metadata(i3_name_in)
i3_in = readinsight3.I3Reader(i3_name_in)
i3_out = writeinsight3.I3Writer(i3_name_out)
i3_data = i3_in.nextBlock()
while (i3_data is not False):
sys.stdout.write(".")
sys.stdout.flush()
# Create mask.
mask = numpy.full(i3_data.size, True, dtype=bool)
if xstart is not None:
mask = mask & (i3_data['xc'] > xstart)
if xstop is not None:
mask = mask & (i3_data['xc'] < xstop)
if ystart is not None:
mask = mask & (i3_data['yc'] > ystart)
if ystop is not None:
mask = mask & (i3_data['yc'] < ystop)
if min_frame is not None:
mask = mask & (i3_data['fr'] > min_frame)
if max_frame is not None:
mask = mask & (i3_data['fr'] < max_frame)
i3_data = i3dtype.maskData(i3_data, mask)
if (i3_data.size > 0):
i3_out.addMolecules(i3_data)
i3_data = i3_in.nextBlock()
print()
if meta_data is not None:
i3_out.closeWithMetadata(ElementTree.tostring(meta_data, 'ISO-8859-1'))
else:
i3_out.close()
def nextBlock(self, block_size=400000, good_only=True):
# check if we have read all the molecules.
if (self.cur_molecule >= self.molecules):
return False
size = block_size
# adjust size if we'll read past the end of the file.
if ((self.cur_molecule + size) > self.molecules):
size = self.molecules - self.cur_molecule
self.cur_molecule += size
# read the data
data = numpy.fromfile(self.fp, dtype=i3dtype.i3DataType(), count=size)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
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()
def nextBlock(self, block_size = 400000, good_only = True):
# check if we have read all the molecules.
if(self.cur_molecule>=self.molecules):
return False
size = block_size
# adjust size if we'll read past the end of the file.
if((self.cur_molecule+size)>self.molecules):
size = self.molecules - self.cur_molecule
self.cur_molecule += size
# read the data
data = numpy.fromfile(self.fp,
dtype=i3dtype.i3DataType(),
count=size)
if good_only:
return i3dtype.maskData(data, (data['c'] != 9))
else:
return data
def getEmitters(self, frame):
integrated_on = numpy.zeros(self.n_emitters)
#
# This is a little complicated because we are trying to include accurate
# modeling of emitters that turned on/off more than once in a single frame.
#
for i in range(self.n_emitters):
# The easy case, no change in state in the current frame.
if (self.next_transistion[i] >= (frame + 1.0)):
if self.am_on[i]:
integrated_on[i] = 1.0
else:
last_transistion = frame
while (self.next_transistion[i] < (frame + 1.0)):
if self.am_on[i]:
integrated_on[
i] += self.next_transistion[i] - last_transistion
self.am_on[i] = not self.am_on[i]
last_transistion = self.next_transistion[i]
if self.am_on[i]:
self.next_transistion[i] += numpy.random.exponential(
self.on_time)
else:
self.next_transistion[i] += numpy.random.exponential(
self.off_time)
# Turned on and not off again in the current frame.
if self.am_on[i]:
integrated_on[i] += (frame + 1.0) - last_transistion
# Set height and return only those emitters that have height > 0.
self.i3_data['a'][:] = integrated_on * self.photons
return i3dtype.maskData(self.i3_data, (self.i3_data['a'] > 0.0))
# 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.
i3_data_out = writeinsight3.I3Writer(sys.argv[2])
i3_data_out.addMolecules(i3_data)
i3_data_out.close()
def loadI3NumpyGoodOnly(filename, verbose=1):
data = loadI3FileNumpy(filename, verbose=verbose)
return i3dtype.maskData(data, (data['c'] != 9))
def loadI3NumpyGoodOnly(filename, verbose = True):
"""
This filters out molecules with poor z fits (daostorm "3D" fit mode).
"""
data = loadI3FileNumpy(filename, verbose = verbose)
return i3dtype.maskData(data, (data['c'] != 9))
def loadI3NumpyGoodOnly(filename, verbose = 1):
data = loadI3FileNumpy(filename, verbose = verbose)
return i3dtype.maskData(data, (data['c'] != 9))
def loadI3NumpyGoodOnly(filename, verbose = True):
"""
This filters out molecules with poor z fits (daostorm "3D" fit mode).
"""
data = loadI3FileNumpy(filename, verbose = verbose)
return i3dtype.maskData(data, (data['c'] != 9))
args.maxi = numpy.max(i3_data['i']) + 1
if (args.maxi > (numpy.max(i3_data['i']) + 1)):
args.maxi = numpy.max(i3_data['i']) + 1
max_frame = numpy.max(i3_data['fr'])
on_times = []
off_times = []
for i in range(int(args.maxi)):
if ((i % 100) == 0):
print("Analyzing emitter", i)
# We are counting on getting the frames where the emitter was on in order.
emitter_i3 = i3dtype.maskData(i3_data, (i3_data['i'] == i))
frames_on = emitter_i3['fr']
#print(frames_on, frames_on.size)
# Check if the emitter was never on.
if (frames_on.size == 0):
continue
# Check if off in the first frame.
if (frames_on[0] != 1):
off_times.append(frames_on[0])
# Check if only a single frame.
if (frames_on.size == 1):
on_times.append(1)
continue
args.maxi = numpy.max(i3_data['i']) + 1
if (args.maxi > (numpy.max(i3_data['i']) + 1)):
args.maxi = numpy.max(i3_data['i']) + 1
max_frame = numpy.max(i3_data['fr'])
on_times = []
off_times = []
for i in range(int(args.maxi)):
if ((i % 100) == 0):
print("Analyzing emitter", i)
# We are counting on getting the frames where the emitter was on in order.
emitter_i3 = i3dtype.maskData(i3_data, (i3_data['i'] == i))
frames_on = emitter_i3['fr']
#print(frames_on, frames_on.size)
# Check if the emitter was never on.
if (frames_on.size == 0):
continue
# Check if off in the first frame.
if (frames_on[0] != 1):
off_times.append(frames_on[0])
# Check if only a single frame.
if (frames_on.size == 1):
on_times.append(1)
continue
def render3DImage(i3_reader,
shape,
category=None,
offsets=None,
scale=2,
sigma=None,
z_edges=None):
"""
Create a stack of grayscale images from a Insight3 format binary data.
i3_reader - A readinsight3.I3Reader object.
shape - The shape of the output image. This will be multiplied by scale.
category - Filter for localizations of this category. The default is all categories.
offsets - X,Y offset of the image origin. The default is 0.0.
scale - The 'zoom' level of the output image, i.e. if the original STORM movie was
256x256 and scale = 2 then the output image will be 512x512.
sigma - The sigma to use when rendering gaussians (pixels). If this is None then
the image will be a histogram.
z_edges - A list of z values specifying the z range for each image. This should be
in nanometers.
"""
num_z = len(z_edges) - 1
images = []
for i in range(num_z):
images.append(numpy.zeros((shape[0] * scale, shape[1] * scale)))
# Make sure we are starting at the beginning.
i3_reader.resetFp()
# Load the first block of data.
i3_data = i3_reader.nextBlock()
while (i3_data is not False):
sys.stdout.write(".")
sys.stdout.flush()
# Filter by category, if requested.
if category is not None:
i3_data = i3dtype.maskData(i3_data, (i3_data['c'] == category))
# Adjust by offsets, if specified. Note, not adjusted for scale.
if offsets is not None:
i3_data['xc'] -= offsets[0]
i3_data['yc'] -= offsets[1]
# Adjust x,y by scale.
xc = i3_data['xc'] * scale
yc = i3_data['yc'] * scale
# Iterate through z ranges.
for i in range(num_z):
z_mask = (i3_data['zc'] > z_edges[i]) & (i3_data['zc'] <
z_edges[i + 1])
xc_z = xc[z_mask]
yc_z = yc[z_mask]
# Histogram.
if sigma is None:
images[i] += gridC.grid2D(numpy.round(xc_z), numpy.round(yc_z),
images[0].shape)
# Gaussians.
else:
dg.drawGaussiansXYOnImage(images[i], xc_z, yc_z, sigma=sigma)
# Load next block of data.
i3_data = i3_reader.nextBlock()
print()
return images
