def addMoleculesWithXYCatF(self, x, y, cat,f):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'c', cat)
i3dtype.setI3Field(i3data, 'fr', f)
self.addMolecules(i3data)
def addMoleculesWithXYAFrame(self, x, y, pa, frame):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'a', pa)
i3dtype.setI3Field(i3data, 'fr', frame)
self.addMolecules(i3data)
def addMoleculesWithXYAFrame(self, x, y, pa, frame):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'a', pa)
i3dtype.setI3Field(i3data, 'fr', frame)
self.addMolecules(i3data)
def addMoleculesWithXYZCat(self, x, y, z, cat):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.posSet(i3data, 'z', z)
i3dtype.setI3Field(i3data, 'c', cat)
self.addMolecules(i3data)
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 addMoleculesWithXYCatF(self, x, y, cat, f):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'c', cat)
i3dtype.setI3Field(i3data, 'fr', f)
self.addMolecules(i3data)
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 addMoleculesWithXYZCat(self, x, y, z, cat):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.posSet(i3data, 'z', z)
i3dtype.setI3Field(i3data, 'c', cat)
self.addMolecules(i3data)
def addMoleculesWithXYZIFrame(self, x, y, z, pi, f):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.posSet(i3data, 'z', z)
i3dtype.setI3Field(i3data, 'i', pi)
i3dtype.setI3Field(i3data, 'fr', f)
self.addMolecules(i3data)
def addMoleculesWithXYZIFrame(self, x, y, z, pi, f):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.posSet(i3data, 'z', z)
i3dtype.setI3Field(i3data, 'i', pi)
i3dtype.setI3Field(i3data, 'fr', f)
self.addMolecules(i3data)
def test_i3dtype_1():
"""
Test conversion to and from the fitter format.
"""
x_size = 100
y_size = 100
frame = 10
nm_per_pixel = 100.0
data_in = i3dtype.createDefaultI3Data(10)
i3dtype.posSet(data_in, 'x', numpy.arange(10))
i3dtype.posSet(data_in, 'y', numpy.arange(10) + 30.0)
i3dtype.posSet(data_in, 'z', numpy.arange(10) + 60.0)
i3dtype.setI3Field(data_in, 'fr', frame)
peaks = i3dtype.convertToMultiFit(data_in, frame, nm_per_pixel)
data_out = i3dtype.createFromMultiFit(peaks, frame, nm_per_pixel)
fields = ['x', 'ax', 'w']
for i in range(10):
for field in fields:
assert(abs(data_in[field][i] - data_out[field][i]) < 1.0e-6)
def addMoleculesWithXYIWFrame(self, x, y, pi, width, frame):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'i', pi)
i3dtype.setI3Field(i3data, 'w', width)
i3dtype.setI3Field(i3data, 'fr', frame)
self.addMolecules(i3data)
def addMoleculesWithXYIWFrame(self, x, y, pi, width, frame):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'i', pi)
i3dtype.setI3Field(i3data, 'w', width)
i3dtype.setI3Field(i3data, 'fr', frame)
self.addMolecules(i3data)
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 addMoleculesWithXYI(self, x, y, pi):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'i', pi)
self.addMolecules(i3data)
def addMoleculesWithXYI(self, x, y, pi):
i3data = i3dtype.createDefaultI3Data(x.size)
i3dtype.posSet(i3data, 'x', x)
i3dtype.posSet(i3data, 'y', y)
i3dtype.setI3Field(i3data, 'i', pi)
self.addMolecules(i3data)
def addDAOSTORMMolecules(self, frame, xc, yc, br, be, msky, niter, sharp,
chi, err):
#
# DAOSTORM -> Insight3 format mapping.
#
# xc - xcenter
# yc - ycenter
# br - brightness -> peak height
# be - brightness error (?) -> peak area
# msky - background -> peak background
# niter - fit iterations
# sharp - sharpness (?) -> peak angle
# chi - fit quality -> peak width
# err - error flag -> link
#
i3data = i3dtype.createDefaultI3Data(xc.size)
i3dtype.posSet(i3data, 'x', xc)
i3dtype.posSet(i3data, 'y', yc)
i3dtype.setI3Field(i3data, 'h', br)
i3dtype.setI3Field(i3data, 'a', be)
i3dtype.setI3Field(i3data, 'bg', msky)
i3dtype.setI3Field(i3data, 'fi', niter)
i3dtype.setI3Field(i3data, 'phi', sharp)
i3dtype.setI3Field(i3data, 'w', chi)
i3dtype.setI3Field(i3data, 'lk', err)
self.addMolecules(i3data)
def addDAOSTORMMolecules(self, frame, xc, yc, br, be, msky, niter, sharp, chi, err):
#
# DAOSTORM -> Insight3 format mapping.
#
# xc - xcenter
# yc - ycenter
# br - brightness -> peak height
# be - brightness error (?) -> peak area
# msky - background -> peak background
# niter - fit iterations
# sharp - sharpness (?) -> peak angle
# chi - fit quality -> peak width
# err - error flag -> link
#
i3data = i3dtype.createDefaultI3Data(xc.size)
i3dtype.posSet(i3data, 'x', xc)
i3dtype.posSet(i3data, 'y', yc)
i3dtype.setI3Field(i3data, 'h', br)
i3dtype.setI3Field(i3data, 'a', be)
i3dtype.setI3Field(i3data, 'bg', msky)
i3dtype.setI3Field(i3data, 'fi', niter)
i3dtype.setI3Field(i3data, 'phi', sharp)
i3dtype.setI3Field(i3data, 'w', chi)
i3dtype.setI3Field(i3data, 'lk', err)
self.addMolecules(i3data)
def addDAOSTORMMolecules(self, frame, xc, yc, br, be, msky, niter, sharp,
chi, err):
"""
This is for localizations identified by the original DAOSTORM
algorithm, not the 3D-DAOSTORM algorithm.
DAOSTORM -> Insight3 format mapping.
xc - xcenter
yc - ycenter
br - brightness -> peak height
be - brightness error (?) -> peak area
msky - background -> peak background
niter - fit iterations
sharp - sharpness (?) -> peak angle
chi - fit quality -> peak width
err - error flag -> link
"""
i3data = i3dtype.createDefaultI3Data(xc.size)
i3dtype.posSet(i3data, 'x', xc)
i3dtype.posSet(i3data, 'y', yc)
i3dtype.setI3Field(i3data, 'h', br)
i3dtype.setI3Field(i3data, 'a', be)
i3dtype.setI3Field(i3data, 'bg', msky)
i3dtype.setI3Field(i3data, 'fi', niter)
i3dtype.setI3Field(i3data, 'phi', sharp)
i3dtype.setI3Field(i3data, 'w', chi)
i3dtype.setI3Field(i3data, 'lk', err)
self.addMolecules(i3data)
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)))
z = numpy.concatenate((z[on], numpy.random.uniform(low = -500.0, high = 500.0, size = background)))
i3d = i3dtype.createDefaultI3Data(number_on + background)
i3dtype.posSet(i3d, "x", x)
i3dtype.posSet(i3d, "y", y)
i3dtype.posSet(i3d, "z", z)
i3dtype.setI3Field(i3d, "fr", i+1)
i3_fp.addMolecules(i3d)
def addDAOSTORMMolecules(self, frame, xc, yc, br, be, msky, niter, sharp, chi, err):
"""
This is for localizations identified by the original DAOSTORM
algorithm, not the 3D-DAOSTORM algorithm.
DAOSTORM -> Insight3 format mapping.
xc - xcenter
yc - ycenter
br - brightness -> peak height
be - brightness error (?) -> peak area
msky - background -> peak background
niter - fit iterations
sharp - sharpness (?) -> peak angle
chi - fit quality -> peak width
err - error flag -> link
"""
i3data = i3dtype.createDefaultI3Data(xc.size)
i3dtype.posSet(i3data, 'x', xc)
i3dtype.posSet(i3data, 'y', yc)
i3dtype.setI3Field(i3data, 'h', br)
i3dtype.setI3Field(i3data, 'a', be)
i3dtype.setI3Field(i3data, 'bg', msky)
i3dtype.setI3Field(i3data, 'fi', niter)
i3dtype.setI3Field(i3data, 'phi', sharp)
i3dtype.setI3Field(i3data, 'w', chi)
i3dtype.setI3Field(i3data, 'lk', err)
self.addMolecules(i3data)
#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()
xi = i3_temp["x"]
yi = i3_temp["y"]
xf = cx[0] + cx[1] * xi + cx[2] * yi
# Save the molecule locations.
a_vals = PSF.PSFIntegral(z_vals, h_vals)
ax = numpy.ones(num_objects)
ww = 2.0 * 160.0 * numpy.ones(num_objects)
if (PSF.psf_type == "astigmatic"):
sx_vals = objects[:,3]
sy_vals = objects[:,4]
ax = sy_vals/sx_vals
ww = 2.0*160.0*numpy.sqrt(sx_vals*sy_vals)
mols = i3dtype.createDefaultI3Data(num_objects)
i3dtype.posSet(mols, 'x', x_vals + 1.0)
i3dtype.posSet(mols, 'y', y_vals + 1.0)
i3dtype.posSet(mols, 'z', z_vals)
i3dtype.setI3Field(mols, 'a', a_vals)
i3dtype.setI3Field(mols, 'h', h_vals)
i3dtype.setI3Field(mols, 'w', ww)
i3dtype.setI3Field(mols, 'ax', ax)
i3dtype.setI3Field(mols, 'fr', i+1)
i3_data.addMolecules(mols)
dax_data.close()
i3_data.close()
#
# The MIT License
#
# Copyright (c) 2016 Zhuang Lab, Harvard University
#
# Save the molecule locations.
a_vals = PSF.PSFIntegral(z_vals, h_vals)
ax = numpy.ones(num_objects)
ww = 2.0 * 160.0 * numpy.ones(num_objects)
if (PSF.psf_type == "astigmatic"):
sx_vals = objects[:,3]
sy_vals = objects[:,4]
ax = sy_vals/sx_vals
ww = 2.0*160.0*numpy.sqrt(sx_vals*sy_vals)
mols = i3dtype.createDefaultI3Data(num_objects)
i3dtype.posSet(mols, 'x', x_vals + 1.0)
i3dtype.posSet(mols, 'y', y_vals + 1.0)
i3dtype.posSet(mols, 'z', z_vals)
i3dtype.setI3Field(mols, 'a', a_vals)
i3dtype.setI3Field(mols, 'h', h_vals)
i3dtype.setI3Field(mols, 'w', ww)
i3dtype.setI3Field(mols, 'ax', ax)
i3dtype.setI3Field(mols, 'fr', i+1)
i3_data.addMolecules(mols)
dax_data.close()
i3_data.close()
#
# The MIT License
#
# Copyright (c) 2016 Zhuang Lab, Harvard University
#
def addMultiFitMolecules(self,
molecules,
x_size,
y_size,
frame,
nm_per_pixel,
inverted=False):
n_molecules = molecules.shape[0]
h = molecules[:, 0]
if inverted:
xc = y_size - molecules[:, 1]
yc = x_size - molecules[:, 3]
wx = 2.0 * molecules[:, 2] * nm_per_pixel
wy = 2.0 * molecules[:, 4] * nm_per_pixel
else:
xc = molecules[:, 3] + 1
yc = molecules[:, 1] + 1
wx = 2.0 * molecules[:, 4] * nm_per_pixel
wy = 2.0 * molecules[:, 2] * nm_per_pixel
bg = molecules[:, 5]
zc = molecules[:,
6] * 1000.0 # fitting is done in um, insight works in nm
st = numpy.round(molecules[:, 7])
err = molecules[:, 8]
# calculate peak area, which is saved in the "a" field.
parea = 2.0 * 3.14159 * h * molecules[:, 2] * molecules[:, 4]
ax = wy / wx
ww = numpy.sqrt(wx * wy)
i3data = i3dtype.createDefaultI3Data(xc.size)
i3dtype.posSet(i3data, 'x', xc)
i3dtype.posSet(i3data, 'y', yc)
i3dtype.posSet(i3data, 'z', zc)
i3dtype.setI3Field(i3data, 'h', h)
i3dtype.setI3Field(i3data, 'bg', bg)
i3dtype.setI3Field(i3data, 'fi', st)
i3dtype.setI3Field(i3data, 'a', parea)
i3dtype.setI3Field(i3data, 'w', ww)
i3dtype.setI3Field(i3data, 'ax', ax)
i3dtype.setI3Field(i3data, 'fr', frame)
i3dtype.setI3Field(i3data, 'i', err)
self.addMolecules(i3data)
