def findTracks(pipeline, rad_var='error_x', multiplier='2.0', nFrames=20):
import PYME.Analysis.points.DeClump.deClump as deClump
import warnings
warnings.warn('deprecated, use findTracks2 instead', DeprecationWarning)
if rad_var == '1.0':
delta_x = 0*pipeline['x'] + multiplier
else:
delta_x = multiplier*pipeline[rad_var]
t = pipeline['t'].astype('i')
x = pipeline['x'].astype('f4')
y = pipeline['y'].astype('f4')
delta_x = delta_x.astype('f4')
I = np.argsort(t)
clumpIndices = np.zeros(len(x), dtype='i')
clumpIndices[I] = deClump.findClumpsN(t[I], x[I], y[I], delta_x[I], nFrames)
numPerClump, b = np.histogram(clumpIndices, np.arange(clumpIndices.max() + 1.5) + .5)
trackVelocities = 0*x
trackVelocities[I] = calcTrackVelocity(x[I], y[I], clumpIndices[I], t.astype('f')[I])
#print b
pipeline.addColumn('clumpIndex', clumpIndices, -1)
pipeline.addColumn('clumpSize', numPerClump[clumpIndices - 1])
pipeline.addColumn('trackVelocity', trackVelocities)
pipeline.clumps = ClumpManager(pipeline)
def find_clumps_within_channel(datasource, gap_tolerance, radius_scale, radius_offset, inject=False):
"""
Args:
datasource: PYME datasource object - dictionary-like object with addColumn method
gap_tolerance: number of frames acceptable for a molecule to go MIA and still be called the same molecule when
it returns
radius_scale: multiplicative factor applied to the error_x term in deciding search radius for pairing
radius_offset: term added to radius_scale*error_x to set search radius
Returns:
Nothing, but adds clumpIndex column to datasource input
FIXME: This function should probably not exist as channel handling should ideally only be in one place within the code base. A prefered solution would be to split using a colour filter, clump
each channel separately, and then merge channels.
"""
from PYME.Analysis.points.DeClump import deClump
from PYME.IO import tabular
t = datasource['t'] #OK as int
clumps = np.zeros(len(t), 'i')
I = np.argsort(t)
t = t[I].astype('i')
x = datasource['x'][I].astype('f4')
y = datasource['y'][I].astype('f4')
deltaX = (radius_scale*datasource['error_x'][I] + radius_offset).astype('f4')
# extract color channel information
uprobe = np.unique(datasource['probe'])
probe = datasource['probe'][I]
# only clump within color channels
assigned = np.zeros_like(clumps)
startAt = 0
for pi in uprobe:
pmask = probe == pi
pClumps = deClump.findClumpsN(t[pmask], x[pmask], y[pmask], deltaX, gap_tolerance) + startAt
# throw all unclumped into the 0th clumpID, and preserve pClumps[-1] of the last iteration
pClumps[pClumps == startAt] = 0
# patch in assignments for this color channel
assigned[pmask] = pClumps
startAt = np.max(assigned)
clumps[I] = assigned
if not inject:
datasource = tabular.MappingFilter(datasource)
datasource.addColumn('clumpIndex', clumps)
return datasource
def findTracks2(datasource, rad_var='error_x', multiplier='2.0', nFrames=20, minClumpSize=0):
import PYME.Analysis.points.DeClump.deClump as deClump
from PYME.IO import tabular
with_clumps = tabular.MappingFilter(datasource)
if rad_var == '1.0':
delta_x = 0 * datasource['x'] + multiplier
else:
delta_x = multiplier * datasource[rad_var]
t = datasource['t'].astype('i')
x = datasource['x'].astype('f4')
y = datasource['y'].astype('f4')
delta_x = delta_x.astype('f4')
I = np.argsort(t)
clumpIndices = np.zeros(len(x), dtype='i')
clumpIndices[I] = deClump.findClumpsN(t[I], x[I], y[I], delta_x[I], nFrames)
numPerClump, b = np.histogram(clumpIndices, np.arange(clumpIndices.max() + 1.5) + .5)
trackVelocities = 0 * x
trackVelocities[I] = calcTrackVelocity(x[I], y[I], clumpIndices[I], t.astype('f')[I])
#print b
with_clumps.addColumn('clumpIndex', clumpIndices)
with_clumps.addColumn('clumpSize', numPerClump[clumpIndices - 1])
with_clumps.addColumn('trackVelocity', trackVelocities)
if minClumpSize > 0:
filt = tabular.ResultsFilter(with_clumps, clumpSize=[minClumpSize, 1e6])
else:
filt = with_clumps
try:
filt.mdh = datasource.mdh
except AttributeError:
pass
return with_clumps, ClumpManager(filt)
def find_clumps(datasource, gap_tolerance, radius_scale, radius_offset, inject=False):
from PYME.Analysis.points.DeClump import deClump
from PYME.IO import tabular
t = datasource['t'] #OK as int
clumps = np.zeros(len(t), 'i')
I = np.argsort(t)
t = t[I].astype('i')
x = datasource['x'][I].astype('f4')
y = datasource['y'][I].astype('f4')
deltaX = (radius_scale*datasource['error_x'][I] + radius_offset).astype('f4')
assigned = deClump.findClumpsN(t, x, y, deltaX, gap_tolerance)
clumps[I] = assigned
if not inject:
datasource = tabular.MappingFilter(datasource)
datasource.addColumn('clumpIndex', clumps)
return datasource
def _extractAverageTrajectory(pipeline,
clumpRadiusVar='error_x',
clumpRadiusMultiplier=5.0,
timeWindow=25,
filter='Gaussian',
filterScale=10.0):
#import PYME.Analysis.trackUtils as trackUtils
import PYME.Analysis.points.DeClump.deClump as deClump
from scipy.optimize import fmin
#track beads through frames
if clumpRadiusVar == '1.0':
delta_x = 0 * pipeline['x'] + clumpRadiusMultiplier
else:
delta_x = clumpRadiusMultiplier * pipeline[clumpRadiusVar]
t = pipeline['t'].astype('i')
x = pipeline['x'].astype('f4')
y = pipeline['y'].astype('f4')
delta_x = delta_x.astype('f4')
I = np.argsort(t)
clumpIndex = np.zeros(len(x), dtype='i')
clumpIndex[I] = deClump.findClumpsN(t[I], x[I], y[I], delta_x[I],
timeWindow)
#trackUtils.findTracks(pipeline, clumpRadiusVar,clumpRadiusMultiplier, timeWindow)
#longTracks = pipeline['clumpSize'] > 50
#x = x[longTracks].copy()
#y = pipeline['y_raw'][longTracks].copy()
#t = pipeline['t'][longTracks].copy() #.astype('i')
#clumpIndex = pipeline['clumpIndex'][longTracks].copy()
tMax = t.max()
clumpIndices = list(set(clumpIndex))
x_f = []
y_f = []
clump_sizes = []
t_f = np.arange(0, tMax + 1, dtype='i')
#loop over all our clumps and extract trajectories
for ci in clumpIndices:
if ci > 0:
clump_mask = (clumpIndex == ci)
x_i = x[clump_mask]
clump_size = len(x_i)
if clump_size > 50:
y_i = y[clump_mask]
t_i = t[clump_mask].astype('i')
x_i_f = np.NaN * np.ones_like(t_f)
x_i_f[t_i] = x_i - x_i.mean()
y_i_f = np.NaN * np.ones_like(t_f)
y_i_f[t_i] = y_i - y_i.mean()
#clumps.append((x_i_f, y_i_f))
x_f.append(x_i_f)
y_f.append(y_i_f)
clump_sizes.append(len(x_i))
#re-order to start with the largest clump
clumpOrder = np.argsort(clump_sizes)[::-1]
x_f = np.array(x_f)[clumpOrder, :]
y_f = np.array(y_f)[clumpOrder, :]
def _mf(p, meas):
'''calculate the offset between trajectories'''
m_adj = meas + np.hstack([[0], p])[:, None]
return np.nansum(np.nanvar(m_adj, axis=0))
#print x_f.shape, np.hstack([[0], np.random.randn(x_f.shape[0]-1)]).shape
def _align(meas, tol=.1):
n_iters = 0
dm_old = 5e12
dm = 4e12
mm = np.nanmean(meas, 0)
while ((dm_old - dm) > tol) and (n_iters < 50):
dm_old = dm
mm = np.nanmean(meas, 0)
d = np.nanmean(meas - mm, 1)
dm = sum(d**2)
meas = meas - d[:, None]
n_iters += 1
print(n_iters, dm)
mm = np.nanmean(meas, 0)
print('Finished:', n_iters, dm)
return mm
x_corr = _align(x_f)
y_corr = _align(y_f)
filtered_corr = FILTER_FUNCS[filter](t_f, {
'x': x_corr,
'y': y_corr
}, filterScale)
return t_f, filtered_corr
def extractTrajectoriesClump(ds,
clumpRadiusVar='error_x',
clumpRadiusMultiplier=5.0,
timeWindow=25,
clumpMinSize=50,
align=True):
import PYME.Analysis.points.DeClump.deClump as deClump
#track beads through frames
if clumpRadiusVar == '1.0':
delta_x = 0 * ds['x'] + clumpRadiusMultiplier
else:
delta_x = clumpRadiusMultiplier * ds[clumpRadiusVar]
t = ds['t'].astype('i')
x = ds['x'].astype('f4')
y = ds['y'].astype('f4')
z = ds['z'].astype('f4')
delta_x = delta_x.astype('f4')
I = np.argsort(t)
clumpIndex = np.zeros(len(x), dtype='i')
isFiducial = np.zeros(len(x), dtype='i')
clumpIndex[I] = deClump.findClumpsN(t[I], x[I], y[I], delta_x[I],
timeWindow)
tMax = t.max()
clumpIndices = list(set(clumpIndex))
x_f = []
y_f = []
z_f = []
clump_sizes = []
t_f = np.arange(0, tMax + 1, dtype='i')
#loop over all our clumps and extract trajectories
for ci in clumpIndices:
if ci > 0:
clump_mask = (clumpIndex == ci)
x_i = x[clump_mask]
clump_size = len(x_i)
if clump_size > clumpMinSize:
y_i = y[clump_mask]
z_i = z[clump_mask]
t_i = t[clump_mask].astype('i')
isFiducial[
clump_mask] = 1 # mark the event mask that this is a fiducial
x_i_f = np.NaN * np.ones_like(t_f)
if align:
x_i_f[t_i] = x_i - x_i.mean()
else:
x_i_f[t_i] = x_i
y_i_f = np.NaN * np.ones_like(t_f)
if align:
y_i_f[t_i] = y_i - y_i.mean()
else:
y_i_f[t_i] = y_i
z_i_f = np.NaN * np.ones_like(t_f)
if align:
z_i_f[t_i] = z_i - z_i.mean()
else:
z_i_f[t_i] = z_i
#clumps.append((x_i_f, y_i_f))
x_f.append(x_i_f)
y_f.append(y_i_f)
z_f.append(z_i_f)
clump_sizes.append(len(x_i))
#re-order to start with the largest clump
clumpOrder = np.argsort(clump_sizes)[::-1]
x_f = np.array(x_f)[clumpOrder, :]
y_f = np.array(y_f)[clumpOrder, :]
z_f = np.array(z_f)[clumpOrder, :]
return (t_f, x_f, y_f, z_f, isFiducial)