def __init__(self, parameters = None, **kwds):
super(DataWriterI3, self).__init__(**kwds)
raise Exception("Using the Insight3 format for analysis is deprecated!")
self.pixel_size = parameters.getAttr("pixel_size")
#
# If the i3 file already exists, read it in, write it
# out to prepare for starting the analysis from the
# end of what currently exists.
#
# FIXME: If the existing file is really large there
# could be problems here as we're going to load
# the whole thing into memory.
#
if(os.path.exists(self.filename)):
print("Found", self.filename)
i3data_in = readinsight3.loadI3File(self.filename)
if (i3data_in is None) or (i3data_in.size == 0):
self.start_frame = 0
else:
self.start_frame = int(numpy.max(i3data_in['fr']))
print(" Starting analysis at frame:", self.start_frame)
self.i3data = writeinsight3.I3Writer(self.filename)
if (self.start_frame > 0):
self.i3data.addMolecules(i3data_in)
self.total_peaks = i3data_in['x'].size
else:
self.start_frame = 0
self.i3data = writeinsight3.I3Writer(self.filename)
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 copyTracking(ref_track_filename, input_filename, output_filename):
i3_ref = readinsight3.I3Reader(ref_track_filename)
i3_in = readinsight3.I3Reader(input_filename)
# Verify that these two localization files are actually pairs.
assert (i3_ref.molecules == i3_in.molecules), "Input files do not match."
# # Verify that the output file doesn't already exist.
# assert not os.path.exists(output_filename), "Output file already exists."
i3_out = writeinsight3.I3Writer(output_filename)
i3_ref_data = i3_ref.nextBlock()
while (i3_ref_data is not False):
i3_in_data = i3_in.nextBlock()
# Copy tracking information from the reference data file.
for field in ["tl", "lk", "fi"]:
i3_in_data[field] = i3_ref_data[field]
# Save merge of reference (channel 0) and current channel.
i3_out.addMolecules(i3_in_data)
# Load the next block of reference data.
i3_ref_data = i3_ref.nextBlock()
# At least for now we are not bothering to copy the meta-data.
i3_out.close()
def __init__(self, **kwds):
super(MPDataWriter, self).__init__(**kwds)
parameters = kwds["parameters"]
self.offsets = []
# Figure out how many planes there are.
self.n_planes = len(mpUtilC.getExtAttrs(parameters))
# Save frame offsets for each plane.
for offset in mpUtilC.getOffsetAttrs(parameters):
self.offsets.append(parameters.getAttr(offset))
# Adjust starting frame based on channel 0 offset.
if (self.start_frame > 0) and (self.offsets[0] != 0):
self.start_frame += self.offsets[0]
print("Adjusted start frame to", self.start_frame,
"based on channel 0 offset.")
# Create writers for the other planes.
#
# FIXME: This won't work for existing I3 files.
#
assert (self.start_frame == 0)
self.i3_writers = [self.i3data]
for i in range(1, self.n_planes):
fname = self.filename[:-4] + "_ch" + str(i) + ".bin"
self.i3_writers.append(writeinsight3.I3Writer(fname))
def test_good_i3():
mlist_name = storm_analysis.getPathOutputTest("test_i3_io_mlist.bin")
# Create data.
locs = i3dtype.createDefaultI3Data(100)
# Save the data.
with writeinsight3.I3Writer(mlist_name) as i3w:
i3w.addMolecules(locs)
# Read the data.
locs = readinsight3.loadI3File(mlist_name)
assert(locs.shape[0] == 100)
def test_write_read_1():
"""
Test writing and reading.
"""
bin_name = storm_analysis.getPathOutputTest("test_insight3io.bin")
i3_locs = i3dtype.createDefaultI3Data(10)
i3dtype.posSet(i3_locs, 'x', 10.0)
with writeinsight3.I3Writer(bin_name) as i3:
i3.addMolecules(i3_locs)
i3_in = readinsight3.loadI3File(bin_name, verbose = False)
assert(numpy.allclose(i3_locs['x'], i3_in['x']))
def test_bad_i3():
mlist_name = storm_analysis.getPathOutputTest("test_i3_io_mlist.bin")
# Create data.
locs = i3dtype.createDefaultI3Data(100)
# Save the data.
i3w = writeinsight3.I3Writer(mlist_name)
i3w.addMolecules(locs)
i3w.fp.close()
# Read the data.
locs = readinsight3.loadI3File(mlist_name)
assert(locs is None)
def splitPeaks(mlist_filename, params_filename):
parameters = params.ParametersMultiplane().initFromFile(params_filename)
# Load the plane to plane mapping data.
mappings = {}
if parameters.hasAttr("mapping"):
if os.path.exists(parameters.getAttr("mapping")):
with open(parameters.getAttr("mapping"), 'rb') as fp:
mappings = pickle.load(fp)
else:
print("No mapping file parameter, nothing to do")
# Load frame offset information.
frame_offsets = list(
map(parameters.getAttr, mpUtilC.getOffsetAttrs(parameters)))
print(frame_offsets)
# Load the molecule list.
ch0_data = readinsight3.loadI3File(mlist_filename)
# Map to other channels.
basename = mlist_filename[:-4]
channel = 1
m_key = "0_1_"
while (m_key + "x") in mappings:
chn_data = ch0_data.copy()
# Map x.
tx = mappings[m_key + "x"]
chn_data['x'] = tx[0] + ch0_data['x'] * tx[1] + ch0_data['y'] * tx[2]
# Map y.
ty = mappings[m_key + "y"]
chn_data['y'] = ty[0] + ch0_data['x'] * ty[1] + ch0_data['y'] * ty[2]
# Map frame.
chn_data[
'fr'] = ch0_data['fr'] - frame_offsets[0] + frame_offsets[channel]
with writeinsight3.I3Writer(basename + "_ch" + str(channel) +
".bin") as i3w:
i3w.addMolecules(chn_data)
channel += 1
m_key = "0_" + str(channel) + "_"
def test_write_read_3():
"""
Test I3Reader on an empty file.
"""
bin_name = storm_analysis.getPathOutputTest("test_insight3io.bin")
i3_locs = i3dtype.createDefaultI3Data(10)
i3dtype.posSet(i3_locs, 'x', 10.0)
with writeinsight3.I3Writer(bin_name) as i3:
pass
i3_reader = readinsight3.I3Reader(bin_name)
# Read localizations.
i3_in = i3_reader.nextBlock()
assert(i3_in is False)
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 test_good_i3_metadata():
mlist_name = storm_analysis.getPathOutputTest("test_i3_io_mlist.bin")
# Create data.
locs = i3dtype.createDefaultI3Data(100)
# Save data and metadata.
i3w = writeinsight3.I3Writer(mlist_name)
i3w.addMolecules(locs)
etree = ElementTree.Element("xml")
test = ElementTree.SubElement(etree, "test")
test.text = "test"
i3w.closeWithMetadata(ElementTree.tostring(etree, 'ISO-8859-1'))
# Read the data.
locs = readinsight3.loadI3File(mlist_name)
assert(locs.shape[0] == 100)
# Read the metadata.
metadata = readinsight3.loadI3Metadata(mlist_name)
assert(metadata.find("test").text == "test")
def hdf5ToBin(hdf5_name, bin_name):
with saH5Py.SAH5Py(hdf5_name) as h5:
nm_per_pixel = h5.getPixelSize()
[movie_x, movie_y, movie_l, hash_value] = h5.getMovieInformation()
# Create Insight3 file for writing.
i3 = i3w.I3Writer(bin_name)
# Convert tracks.
if h5.hasTracks():
print("Converting tracks.")
for tracks in h5.tracksIterator():
i3.addMultiFitMolecules(tracks, 1, nm_per_pixel)
# Convert localizations.
else:
print("Converting localizations.")
for fnum, locs in h5.localizationsIterator(drift_corrected=False):
i3.addMultiFitMolecules(locs, fnum + 1, nm_per_pixel)
# Add metadata.
etree = ElementTree.Element("xml")
# Analysis parameters.
h5_metadata = h5.getMetadata()
etree.append(ElementTree.fromstring(h5_metadata))
# Movie properties.
movie_props = ElementTree.SubElement(etree, "movie")
field = ElementTree.SubElement(movie_props, "hash_value")
field.text = hash_value
for elt in [["movie_x", movie_x], ["movie_y", movie_y],
["movie_l", movie_l]]:
field = ElementTree.SubElement(movie_props, elt[0])
field.text = str(elt[1])
metadata = ElementTree.tostring(etree, 'ISO-8859-1')
# Close i3 file with metadata.
i3.closeWithMetadata(metadata)
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 test_write_read_2():
"""
Test I3Reader.
"""
bin_name = storm_analysis.getPathOutputTest("test_insight3io.bin")
i3_locs = i3dtype.createDefaultI3Data(10)
i3dtype.posSet(i3_locs, 'x', 10.0)
with writeinsight3.I3Writer(bin_name) as i3:
i3.addMolecules(i3_locs)
i3_reader = readinsight3.I3Reader(bin_name)
# Read localizations.
i3_in = i3_reader.nextBlock()
assert(numpy.allclose(i3_locs['x'], i3_in['x']))
assert(i3_in is not False)
# This should return False because there are no more localizations.
i3_in = i3_reader.nextBlock()
assert(i3_in is False)
def test_sa_h5py_5():
"""
Test querying if the HDF5 file is a storm-analysis file.
"""
filename = "test_sa_hdf5.hdf5"
h5_name = storm_analysis.getPathOutputTest(filename)
storm_analysis.removeFile(h5_name)
# Open empty file.
with saH5Py.SAH5Py(h5_name, is_existing=False) as h5:
pass
assert (saH5Py.isSAHDF5(h5_name))
# Create generic HDF5 file.
f = h5py.File(h5_name, "w")
f.close()
assert (not saH5Py.isSAHDF5(h5_name))
# Create Insight3 file.
with i3w.I3Writer(h5_name) as i3:
pass
assert not (saH5Py.isSAHDF5(h5_name))
def MergeBin(bin_files, results_file):
assert not os.path.exists(results_file)
# Load meta data.
metadata = readinsight3.loadI3Metadata(bin_files[0])
# Create I3 writer.
i3w = writeinsight3.I3Writer(results_file)
# Sequentially read input files and copy into output file.
for b_file in bin_files:
print("Merging", b_file)
i3_reader = readinsight3.I3Reader(b_file)
i3_data = i3_reader.nextBlock()
while i3_data is not False:
print(" working..")
i3w.addMolecules(i3_data)
i3_data = i3_reader.nextBlock()
# Close i3 output file.
if metadata is None:
i3w.close()
else:
i3w.closeWithMetadata(ElementTree.tostring(metadata, 'ISO-8859-1'))
import storm_analysis.sa_library.i3dtype as i3dtype
import storm_analysis.sa_library.writeinsight3 as writeinsight3
background = 5
clusters = 100
length = 20000
p_on = 0.1
numpy.random.seed(0)
cx = numpy.random.uniform(low = 20.0, high = 236.0, size = clusters)
cy = numpy.random.uniform(low = 20.0, high = 236.0, size = clusters)
cz = numpy.random.uniform(low = -300.0, high = 300.0, size = clusters)
with writeinsight3.I3Writer("../data/test_clustering_list.bin") as i3_fp:
for i in range(length):
if((i % 500) == 0):
print("Creating frame", i)
on = (numpy.random.uniform(size = clusters) < p_on)
number_on = numpy.count_nonzero(on)
x = cx + numpy.random.normal(scale = 0.5, size = clusters)
y = cy + numpy.random.normal(scale = 0.5, size = clusters)
z = cz + numpy.random.normal(scale = 50.0, size = clusters)
# Add background
x = numpy.concatenate((x[on], numpy.random.uniform(high = 256.0, size = background)))
y = numpy.concatenate((y[on], numpy.random.uniform(high = 256.0, size = background)))
# Save results.
fx = fdecon.getXVector()
print(numpy.min(fx), numpy.max(fx))
decon_data = daxwriter.DaxWriter(sys.argv[3], fx.shape[0], fx.shape[1])
for i in range(fx.shape[2]):
decon_data.addFrame(fx[:, :, i])
decon_data.close()
# Find peaks in the decon data.
peaks = fdecon.getPeaks(parameters.getAttr("threshold"), 5)
zci = utilC.getZCenterIndex()
z_min, z_max = fdecon.getZRange()
peaks[:, zci] = 1.0e-3 * ((z_max - z_min) * peaks[:, zci] + z_min)
i3_writer = writeinsight3.I3Writer(sys.argv[3][:-4] + "_flist.bin")
i3_writer.addMultiFitMolecules(peaks, x_size, y_size, 1,
parameters.getAttr("pixel_size"))
i3_writer.close()
#
# The MIT License
#
# Copyright (c) 2016 Zhuang Lab, Harvard University
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
x_size = 300
y_size = 310
z_planes = [0.0]
#z_planes = [-250.0, 250.0]
#z_planes = [-250.0, 250.0]
#z_planes = [-750.0, -250.0, 250.0, 750.0]
z_value = -500.0
# Load emitter locations.
i3_locs = readinsight3.loadI3File("emitters.bin")
if False:
i3_locs = i3_locs[0]
# Make a bin file with emitter locations for each frame.
with writeinsight3.I3Writer("test_olist.bin") as i3w:
for i in range(frames):
i3_temp = i3_locs.copy()
i3dtype.setI3Field(i3_temp, "fr", i + 1)
i3dtype.posSet(i3_temp, "z", z_value)
i3w.addMolecules(i3_temp)
# Load channel to channel mapping file.
with open("map.map", 'rb') as fp:
mappings = pickle.load(fp)
# Create bin files for each plane.
for i, z_plane in enumerate(z_planes):
cx = mappings["0_" + str(i) + "_x"]
cy = mappings["0_" + str(i) + "_y"]
i3_temp = i3_locs.copy()
#
# Create a test .bin file for evaluating RCC correction.
#
# Hazen 10/14
#
import numpy
import sys
import storm_analysis.sa_library.writeinsight3 as writeinsight3
if (len(sys.argv) != 2):
print "usage: <out.bin>"
exit()
i3_out = writeinsight3.I3Writer(sys.argv[1])
n_locs = 100
def addMol(x, y, frame):
x += numpy.random.normal(scale=0.05, size=n_locs)
y += numpy.random.normal(scale=0.05, size=n_locs)
i3_out.addMoleculesWithXYFrame(x, y, i)
x1 = 255.0 * numpy.random.uniform(size=n_locs)
y1 = 255.0 * numpy.random.uniform(size=n_locs)
x2 = 255.0 * numpy.random.uniform(size=n_locs)
y2 = 255.0 * numpy.random.uniform(size=n_locs)
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 analyze(movie_name, settings_name, hres_name, bin_name):
movie_data = datareader.inferReader(movie_name)
#
# FIXME:
#
# This should also start at the same frame as hres in the event of a restart.
#
i3_file = writeinsight3.I3Writer(bin_name)
params = parameters.ParametersL1H().initFromFile(settings_name)
#
# Load the a matrix and setup the homotopy image analysis class.
#
a_mat_file = params.getAttr("a_matrix")
print("Using A matrix file:", a_mat_file)
a_mat = setup_A_matrix.loadAMatrix(a_mat_file)
image = movie_data.loadAFrame(0)
htia = homotopy_imagea_c.HomotopyIA(a_mat, params.getAttr("epsilon"),
image.shape)
#
# This opens the file. If it already exists, then it sets the file pointer
# to the end of the file & returns the number of the last frame analyzed.
#
curf = htia.openHRDataFile(hres_name)
#
# Figure out which frame to start & stop at.
#
[dax_x, dax_y, dax_l] = movie_data.filmSize()
if params.hasAttr("start_frame"):
if (params.getAttr("start_frame") >=
curf) and (params.getAttr("start_frame") < dax_l):
curf = params.getAttr("start_frame")
if params.hasAttr("max_frame"):
if (params.getAttr("max_frame") > 0) and (params.getAttr("max_frame") <
dax_l):
dax_l = params.getAttr("max_frame")
print("Starting analysis at frame", curf)
#
# Analyze the dax data.
#
total_peaks = 0
try:
while (curf < dax_l):
# Load image, subtract baseline & remove negative values.
image = movie_data.loadAFrame(curf).astype(numpy.float)
image -= params.getAttr("baseline")
mask = (image < 0)
image[mask] = 0
# Analyze image.
hres_image = htia.analyzeImage(image)
peaks = htia.saveHRFrame(hres_image, curf + 1)
[cs_x, cs_y, cs_a, cs_i] = htia.getPeaks(hres_image)
i3_file.addMoleculesWithXYAItersFrame(cs_x, cs_y, cs_a, cs_i,
curf + 1)
peaks = cs_x.size
total_peaks += peaks
print("Frame:", curf, peaks, total_peaks)
curf += 1
except KeyboardInterrupt:
print("Analysis stopped.")
# cleanup
htia.closeHRDataFile()
i3_file.close()
if False:
z = i3_data_in['zc']
else:
print "Warning! Clustering without using localization z value!"
z = numpy.zeros(x.size)
# Perform analysis without regard to category.
if True:
print "Warning! Clustering without regard to category!"
c = numpy.zeros(c.size)
# Cluster the data.
if (len(sys.argv) == 4):
print "Using eps =", sys.argv[2], "mc =", sys.argv[3]
labels = dbscanC.dbscan(x,
y,
z,
c,
float(sys.argv[2]),
int(sys.argv[3]),
z_factor=1.0)
else:
print "Using eps = 80, mc = 5"
labels = dbscanC.dbscan(x, y, z, c, 80.0, 5, z_factor=1.0)
# Save the data.
i3_data_out = writeinsight3.I3Writer(sys.argv[1][:-8] + "clusters_list.bin")
i3dtype.setI3Field(i3_data_in, 'lk', labels)
i3_data_out.addMolecules(i3_data_in)
i3_data_out.close()
def psfLocalizations(i3_filename, mapping_filename, frame = 1, aoi_size = 8, movie_filename = None):
# Load localizations.
i3_reader = readinsight3.I3Reader(i3_filename)
# Load mapping.
mappings = {}
if os.path.exists(mapping_filename):
with open(mapping_filename, 'rb') as fp:
mappings = pickle.load(fp)
else:
print("Mapping file not found, single channel data?")
# Try and determine movie frame size.
i3_metadata = readinsight3.loadI3Metadata(i3_filename)
if i3_metadata is None:
if movie_filename is None:
raise Exception("I3 metadata not found and movie filename is not specified.")
else:
movie_fp = datareader.inferReader(movie_filename)
[movie_y, movie_x] = movie_fp.filmSize()[:2]
else:
movie_data = i3_metadata.find("movie")
# FIXME: These may be transposed?
movie_x = int(movie_data.find("movie_x").text)
movie_y = int(movie_data.find("movie_y").text)
# Load localizations in the requested frame.
locs = i3_reader.getMoleculesInFrame(frame)
print("Loaded", locs.size, "localizations.")
# Remove localizations that are too close to each other.
in_locs = numpy.zeros((locs["x"].size, util_c.getNPeakPar()))
in_locs[:,util_c.getXCenterIndex()] = locs["x"]
in_locs[:,util_c.getYCenterIndex()] = locs["y"]
out_locs = util_c.removeNeighbors(in_locs, 2 * aoi_size)
xf = out_locs[:,util_c.getXCenterIndex()]
yf = out_locs[:,util_c.getYCenterIndex()]
#
# Remove localizations that are too close to the edge or
# outside of the image in any of the channels.
#
is_good = numpy.ones(xf.size, dtype = numpy.bool)
for i in range(xf.size):
# Check in Channel 0.
if (xf[i] < aoi_size) or (xf[i] + aoi_size >= movie_x):
is_good[i] = False
continue
if (yf[i] < aoi_size) or (yf[i] + aoi_size >= movie_y):
is_good[i] = False
continue
# Check other channels.
for key in mappings:
if not is_good[i]:
break
coeffs = mappings[key]
[ch1, ch2, axis] = key.split("_")
if (ch1 == "0"):
if (axis == "x"):
xm = coeffs[0] + coeffs[1]*xf[i] + coeffs[2]*yf[i]
if (xm < aoi_size) or (xm + aoi_size >= movie_x):
is_good[i] = False
break
elif (axis == "y"):
ym = coeffs[0] + coeffs[1]*xf[i] + coeffs[2]*yf[i]
if (ym < aoi_size) or (ym + aoi_size >= movie_y):
is_good[i] = False
break
#
# Save localizations for each channel.
#
gx = xf[is_good]
gy = yf[is_good]
basename = os.path.splitext(i3_filename)[0]
with writeinsight3.I3Writer(basename + "_c1_psf.bin") as w3:
w3.addMoleculesWithXY(gx, gy)
index = 1
while ("0_" + str(index) + "_x" in mappings):
cx = mappings["0_" + str(index) + "_x"]
cy = mappings["0_" + str(index) + "_y"]
#cx = mappings[str(index) + "_0" + "_x"]
#cy = mappings[str(index) + "_0" + "_y"]
xm = cx[0] + cx[1] * gx + cx[2] * gy
ym = cy[0] + cy[1] * gx + cy[2] * gy
with writeinsight3.I3Writer(basename + "_c" + str(index+1) + "_psf.bin") as w3:
w3.addMoleculesWithXY(xm, ym)
index += 1
#
# Print localizations that were kept.
#
print(gx.size, "localizations were kept:")
for i in range(gx.size):
print("ch0: {0:.2f} {1:.2f}".format(gx[i], gy[i]))
index = 1
while ("0_" + str(index) + "_x" in mappings):
cx = mappings["0_" + str(index) + "_x"]
cy = mappings["0_" + str(index) + "_y"]
xm = cx[0] + cx[1] * gx[i] + cx[2] * gy[i]
ym = cy[0] + cy[1] * gx[i] + cy[2] * gy[i]
print("ch" + str(index) + ": {0:.2f} {1:.2f}".format(xm, ym))
index += 1
print("")
print("")
def alignAndMerge(file1, file2, results_file, scale = 2, dx = 0, dy = 0, z_min = -500.0, z_max = 500.0):
assert not os.path.exists(results_file)
z_bins = int((z_max - z_min)/50)
# Load meta data.
metadata1 = readinsight3.loadI3Metadata(file1)
metadata2 = readinsight3.loadI3Metadata(file1)
# If meta data is available, update the film length
# field to be which ever data set is longer.
#
# Note that the merged file will still be messy in that the
# frame numbers for the second movie are not changed, so they
# will likely overlap with those of the first movie and break
# the assumption that frame number always increases as you
# go through the file.
#
if (metadata1 is not None) and (metadata2 is not None):
f1_length = int(metadata1.find("movie").find("movie_l").text)
f2_length = int(metadata2.find("movie").find("movie_l").text)
if (f2_length > f1_length):
metadata1.find("movie").find("movie_l").text = str(f2_length)
i3_data1 = i3togrid.I3GData(file1, scale = scale)
i3_data2 = i3togrid.I3GData(file2, scale = scale)
# Determine x,y offsets.
xy_data1 = i3_data1.i3To2DGridAllChannelsMerged()
xy_data2 = i3_data2.i3To2DGridAllChannelsMerged()
[corr, offx, offy, xy_success] = imagecorrelation.xyOffset(xy_data1,
xy_data2,
scale,
center = [dx * scale,
dy * scale])
assert(xy_success)
# Update x,y positions in file2.
offx = offx/float(scale)
offy = offy/float(scale)
print("x,y offsets", offx, offy)
i3_data2.i3data['xc'] += offx
i3_data2.i3data['yc'] += offy
# Determine z offsets.
xyz_data1 = i3_data1.i3To3DGridAllChannelsMerged(z_bins,
zmin = z_min,
zmax = z_max)
xyz_data2 = i3_data2.i3To3DGridAllChannelsMerged(z_bins,
zmin = z_min,
zmax = z_max)
[corr, fit, dz, z_success] = imagecorrelation.zOffset(xyz_data1, xyz_data2)
assert(z_success)
dz = dz * (z_max - z_min)/float(z_bins)
print("z offset", dz)
# Update z positions in file2.
i3_data2.i3data['zc'] -= dz
i3w = writeinsight3.I3Writer(results_file)
i3w.addMolecules(i3_data1.getData())
i3w.addMolecules(i3_data2.getData())
if metadata1 is None:
i3w.close()
else:
i3w.closeWithMetadata(ElementTree.tostring(metadata1, 'ISO-8859-1'))
def mergeAnalysis(dir_name, bin_base_name, extensions=[".bin"]):
# Create Insight3 file writers.
i3_out = []
for ext in extensions:
i3_out.append(writeinsight3.I3Writer(bin_base_name + ext))
# Find all the job*.xml files.
job_xml_files = glob.glob(dir_name + "job*.xml")
# Sort job files.
job_xml_files = sorted(
job_xml_files,
key=lambda x: int(
os.path.splitext(os.path.basename(x))[0].split("_")[1]))
# Check for corresponding mlist.bin files.
metadata = None
last_frame = 0
for i in range(len(job_xml_files)):
job_complete = True
for j, ext in enumerate(extensions):
mlist_name = dir_name + "p_" + str(i + 1) + "_mlist" + ext
if os.path.exists(mlist_name) and readinsight3.checkStatus(
mlist_name):
# Load metadata from the first file.
if (i == 0) and (j == 0):
metadata = readinsight3.loadI3Metadata(mlist_name)
# Read localizations.
i3_data = readinsight3.loadI3File(mlist_name, verbose=False)
# Check for empty file.
if (i3_data.size == 0):
print("No localizations found in", mlist_name)
else:
# Print frame range covered.
if (j == 0):
last_frame = i3_data["fr"][-1]
print(i3_data["fr"][0], last_frame, mlist_name)
# Add localizations to the output file.
i3_out[j].addMolecules(i3_data)
else:
job_complete = False
break
if not job_complete:
print("Merge failed because", job_xml_files[i], "is incomplete.")
for j, ext in enumerate(extensions):
i3_out[j].close()
os.remove(bin_base_name + ext)
assert (False)
if metadata is None:
print("No metadata found.")
for i3w in i3_out:
i3w.close()
else:
# Fix movie length node based on the last frame of the last molecule.
metadata.find("movie").find("movie_l").text = str(last_frame)
# Also need to fix analysis end points. We are assuming that the
# entire movie was analyzed.
metadata.find("settings").find("start_frame").text = "-1"
metadata.find("settings").find("max_frame").text = "-1"
for i3w in i3_out:
i3w.closeWithMetadata(ElementTree.tostring(metadata, 'ISO-8859-1'))
def peakFinding(find_peaks, movie_file, mlist_file, parameters):
# open files for input & output
movie_data = datareader.inferReader(movie_file)
[movie_x, movie_y, movie_l] = movie_data.filmSize()
# if the i3 file already exists, read it in,
# write it out & start the analysis from the
# end.
total_peaks = 0
if (os.path.exists(mlist_file)):
print("Found", mlist_file)
i3data_in = readinsight3.loadI3File(mlist_file)
try:
curf = int(numpy.max(i3data_in['fr']))
except ValueError:
curf = 0
print(" Starting analysis at frame:", curf)
i3data = writeinsight3.I3Writer(mlist_file)
if (curf > 0):
i3data.addMolecules(i3data_in)
total_peaks = i3data_in['x'].size
else:
curf = 0
i3data = writeinsight3.I3Writer(mlist_file)
# process parameters
if hasattr(parameters, "start_frame"):
if (parameters.start_frame >= curf) and (parameters.start_frame <
movie_l):
curf = parameters.start_frame
if hasattr(parameters, "max_frame"):
if (parameters.max_frame > 0) and (parameters.max_frame < movie_l):
movie_l = parameters.max_frame
static_bg_estimator = None
if hasattr(parameters, "static_background_estimate"):
if (parameters.static_background_estimate > 0):
print("Using static background estimator.")
static_bg_estimator = static_background.StaticBGEstimator(
movie_data,
start_frame=curf,
sample_size=parameters.static_background_estimate)
# analyze the movie
# catch keyboard interrupts & "gracefully" exit.
try:
while (curf < movie_l):
#for j in range(l):
# Set up the analysis.
image = movie_data.loadAFrame(curf) - parameters.baseline
mask = (image < 1.0)
if (numpy.sum(mask) > 0):
print(" Removing negative values in frame", curf)
image[mask] = 1.0
# Find and fit the peaks.
if static_bg_estimator is not None:
bg_estimate = static_bg_estimator.estimateBG(
curf) - parameters.baseline
[peaks,
residual] = find_peaks.analyzeImage(image,
bg_estimate=bg_estimate)
else:
[peaks, residual] = find_peaks.analyzeImage(image)
# Save the peaks.
if (type(peaks) == type(numpy.array([]))):
# remove unconverged peaks
peaks = find_peaks.getConvergedPeaks(peaks)
# save results
if (parameters.orientation == "inverted"):
i3data.addMultiFitMolecules(peaks,
movie_x,
movie_y,
curf + 1,
parameters.pixel_size,
inverted=True)
else:
i3data.addMultiFitMolecules(peaks,
movie_x,
movie_y,
curf + 1,
parameters.pixel_size,
inverted=False)
total_peaks += peaks.shape[0]
print("Frame:", curf, peaks.shape[0], total_peaks)
else:
print("Frame:", curf, 0, total_peaks)
curf += 1
print("")
i3data.close()
find_peaks.cleanUp()
return 0
except KeyboardInterrupt:
print("Analysis stopped.")
i3data.close()
find_peaks.cleanUp()
return 1
i3dtype.posSet(m_data, "x", numpy.random.uniform(high=im_size,
size=total_match))
i3dtype.posSet(m_data, "y", numpy.random.uniform(high=im_size,
size=total_match))
# Create noise 1.
total_noise = args.total - total_match
n1_data = i3dtype.createDefaultI3Data(total_noise)
i3dtype.posSet(n1_data, "x",
numpy.random.uniform(high=im_size, size=total_noise))
i3dtype.posSet(n1_data, "y",
numpy.random.uniform(high=im_size, size=total_noise))
# Create noise 2.
n2_data = i3dtype.createDefaultI3Data(total_noise)
i3dtype.posSet(n2_data, "x",
numpy.random.uniform(high=im_size, size=total_noise))
i3dtype.posSet(n2_data, "y",
numpy.random.uniform(high=im_size, size=total_noise))
# Save data sets.
with writeinsight3.I3Writer("locs1.bin") as i3w:
i3w.addMolecules(m_data)
i3w.addMolecules(n1_data)
with writeinsight3.I3Writer("locs2.bin") as i3w:
i3w.addMolecules(m_data)
i3w.addMolecules(n2_data)
# 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 KMeansClassifier(codebook, input_basename, output_name, extensions = [".bin", "_ch1.bin", "_ch2.bin", "_ch3.bin"], max_distance = 80):
"""
Note:
1. The default is that there are 4 color channels / cameras.
2. The maximum distance is in percent, so '80' means that the 20%
of the localizations that most distant from a cluster center
will put in category 9.
"""
n_channels = codebook.shape[1]
assert (n_channels == len(extensions)), "Codebook size does not match data."
# Create a reader for each file.
i3_readers = []
for ext in extensions:
i3_name = input_basename + ext
print(i3_name)
i3_readers.append(readinsight3.I3Reader(i3_name))
# Create writer for the results.
i3_out = writeinsight3.I3Writer(output_name)
# Read first block of the first channel data.
i3_data = [i3_readers[0].nextBlock()]
while (i3_data[0] is not False):
print("working..")
# Read the data from the other channels.
for i in range(1,len(i3_readers)):
i3_data.append(i3_readers[i].nextBlock())
# Load height data for each channel.
features = numpy.zeros((i3_data[0].size, n_channels))
for i in range(len(i3_readers)):
features[:,i] = i3_data[i]['h']
# Normalize by total height.
total = numpy.sum(features, axis = 1)
for i in range(features.shape[0]):
features[i,:] = features[i,:]/total[i]
# Whiten the features as recommended by Scipy.
features = scipy.cluster.vq.whiten(features)
# Classify using codebook.
[category, distance] = scipy.cluster.vq.vq(features, codebook)
dist_max = numpy.percentile(distance, max_distance)
# Put top XX% in distance in category 9 (the discard category).
mask = (distance > dist_max)
category[mask] = 9
#
# Store category and distance in the 'c' and 'i' field respectively.
#
i3_data[0]['c'] = category
i3_data[0]['i'] = distance
i3_out.addMolecules(i3_data[0])
# Load the next block of data.
i3_data = [i3_readers[0].nextBlock()]
# Close output file
i3_out.close()
