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1018 lines (850 loc) · 32.6 KB
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from PYME.Analysis.piecewise import piecewiseLinear
import matplotlib.pyplot as plt
import numpy as np
import math
# should we do this with f_globals instead of f_locals?
# function to be used at shell prompt in PYME GUI shells
# can be used to search through the variable names available in the shell
def grepglobals2(expr, keyonly = False):
# instead of passing globals can we get this via inspect?
import inspect
frame = inspect.currentframe()
gframe = frame.f_back
res = filter(lambda x: expr in x, gframe.f_locals)
if len(res)> 0:
if keyonly:
return res
else:
return { key: gframe.f_locals[key] for key in res }
else:
return None
# convert paste board to UTF-16 little endian
# this is what pasting into the shell tab appears to require
def convertpb2utf16le():
import os
from sys import platform
if platform == "darwin":
os.system("pbpaste | iconv -f ascii -t utf-16le | pbcopy")
else:
raise RuntimeError('function only available on mac')
def getvar(varname, inmodule = False):
import inspect
frame = inspect.currentframe()
gframe = frame.f_back
# go up one level further if called within module
if inmodule:
gframe = gframe.f_back
var = None
try: # first for dh5view
var = gframe.f_locals[varname]
except:
pass
return var
def getmdh(inmodule = False):
import inspect
frame = inspect.currentframe()
gframe = frame.f_back
# go up one level further if called within module
if inmodule:
gframe = gframe.f_back
mp = None
try: # first for dh5view
mp = gframe.f_locals['mdv']
except:
try: # alternatively VisGui
mp = gframe.f_locals['mdp']
except:
pass
finally:
del frame
if mp is not None:
return mp.mdh
else:
return None
from PYME.IO import MetaDataHandler
def mdhnogui(filename):
import tables
h5f = tables.openFile(filename)
mdh = MetaDataHandler.HDFMDHandler(h5f)
return {'h5f': h5f, 'mdh': mdh}
def _mcheck(mdh,key):
if key in mdh.keys():
return mdh[key]
else:
return None
def _tformat(timeval):
import time
if timeval < 946684800: # timestamp for year 2000 as heuristic
return timeval
else:
return "%s (%s)" % (timeval,time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timeval)))
def seriestiming(mdh=None):
if mdh is None:
mdh = getmdh(inmodule=True)
tinfo = {}
if _mcheck(mdh,'StartTime'):
tinfo['start'] = mdh['StartTime']
tinfo['end'] = _mcheck(mdh,'EndTime')
elif _mcheck(mdh,'Source.StartTime'):
tinfo['start'] = mdh['Source.StartTime']
tinfo['end'] = _mcheck(mdh,'Source.EndTime')
else:
print("no timing info found")
return
print("Start\t\t%s" % _tformat(tinfo['start']))
if tinfo['end']:
print("End\t\t\t%s" % _tformat(tinfo['end']))
print("Duration\t%.2f s (%.1f min)" % (tinfo['end']-tinfo['start'],(tinfo['end']-tinfo['start'])/60.0))
def getDriftPars(mdh=None):
if mdh is None:
mdh = getmdh(inmodule=True)
try:
dc = mdh['DriftCorrection']
except:
print('could not find DriftCorrection info')
return None
else:
print('found drift correction info')
# estimate the number of frames or fall back to default
try:
frames = (mdh['Source.EndTime']-mdh['Source.StartTime'])/mdh['Source.Camera.CycleTime']
except:
frames = 2e4
exec('pars = %s' % dc['Parameters'])
a0,a1,a2,a3,a4 = [pars[v] for v in ['a0','a1','a2','a3','a4']]
b0,b1,b2,b3,b4 = [pars[v] for v in ['b0','b1','b2','b3','b4']]
t = np.arange(frames)
x = 0
y = 0
exec ('x = %s' % dc['ExprX'])
exec ('y = %s' % dc['ExprY'])
plt.plot(t,x)
plt.plot(t,y)
plt.show()
return pars
def visguiDriftPlot(driftpane):
parameterNames, indepVarsUsed, xCode, yCode, zCode , parameterNamesZ, varExpandCode, varExpandCodeZ = driftpane.dp.driftCorrFcn
indepVars = driftpane.visFr.filter
#t = np.linspace(indepVars['t'].min(), indepVars['t'].max())
x = 0
y = 0
driftx=driftpane.visFr['driftx']
drifty=driftpane.visFr['drifty']
x_raw=driftpane.visFr['x_raw']
y_raw=driftpane.visFr['y_raw']
t = driftpane.visFr['t']
p = [driftpane.dp.driftCorrParams[pn] for pn in parameterNames]
exec(varExpandCode)
x1 = eval(xCode)
y1 = eval(yCode)
x = driftpane.visFr['x']
y = driftpane.visFr['y']
xs = np.mean(x[0:50])
ys = np.mean(y[0:50])
plt.figure(1)
plt.clf()
#plt.plot(t,-x1)
plt.plot(t,x-xs)
plt.figure(2)
plt.clf()
#plt.plot(t,-y1)
plt.plot(t,y-ys)
plt.show()
return (x1,y1,x,y)
def findSlide(mdh=None):
if mdh is None:
mdh = getmdh(inmodule=True)
try:
slideref = mdh['Source.Sample.SlideRef']
except:
return None
from PYME.Acquire import sampleInformationDjangoDirect as sampleInformation
from PYME.SampleDB2.samples import models
matches = models.Slide.objects.filter(reference__contains=slideref)
slide = matches[0]
return slide
# from PYME.SampleDB.samples import models
# qs3 = models.Slide.objects.filter(reference__contains='22_7_10_C')
# qs2 = models.Slide.objects.filter(slideID__exact=-1394421344L)
# sample=qs2[0].sample
# sample.sampleType
# sample.species
def imagestats():
import math
import scipy.ndimage as nd
image = getvar('image',inmodule=True)
if image is None:
print('could not find image')
return
do = getvar('do',inmodule=True)
if do is None:
print('could not find display object')
return
data = image.data[:,:,do.zp].squeeze()
dmed = nd.median(data)
print("mean:\t\t%f" % data.mean())
print("variance:\t%f" % data.var())
print("std dev:\t%f" % data.std())
print("median:\t\t%f" % dmed)
print("med-sqrt:\t%f" % math.sqrt(dmed))
# generate a default basename
def defaultbase():
import os.path
image = getvar('image',inmodule=True)
if image is None:
print('could not find image')
return
return os.path.splitext(os.path.basename(image.filename))[0]
def saveSelection(fname):
do = getvar('do',inmodule=True)
if do is None:
print('could not find display object')
return
lx, ly, hx, hy = do.GetSliceSelection()
image = getvar('image',inmodule=True)
if image is None:
print('could not find image')
return
filen = image.filename
print('source file %s' % (filen))
print('selection ', (lx,ly,hx,hy))
f = open(fname,'w')
f.write("%s\n" % filen)
for item in (lx,ly,hx,hy):
f.write("%d\t" % item)
f.write("\n")
f.flush()
f.close
def writecoords(filename,pipeline):
n = pipeline['x'].shape[0]
f = open(filename,'w')
px = pipeline['x']
py = pipeline['y']
pt = pipeline['t']
minx = px.min()
miny = py.min()
for i in range(n):
f.write("%.3f %.3f %d\n" % (px[i]-minx,py[i]-miny,pt[i]))
f.close()
def csvcoords(filename,pipeline,keys,fieldnames=None):
import csv
if fieldnames is None:
fieldnames = keys
with open(filename, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',',
quotechar='#', quoting=csv.QUOTE_MINIMAL)
writer.writerow(fieldnames)
pkeys = [pipeline[key] for key in keys] # cache the pipelines as these calls may be costly
n = pipeline['x'].shape[0]
for i in range(n):
writer.writerow([pkey[i] for pkey in pkeys])
def randmapping(pipeline):
pipeline.selectedDataSource.setMapping('rand1','0*x+np.random.rand(x.size)')
def binSum(binVar, indepVar, bins):
bm = np.zeros(len(bins) - 1,dtype = indepVar.dtype)
bs = np.zeros(len(bins) - 1)
bn = np.zeros(len(bins) - 1, dtype='i')
for i, el, er in zip(range(len(bm)), bins[:-1], bins[1:]):
v = indepVar[(binVar >= el)*(binVar < er)]
bn[i] = len(v)
if bn[i] == 0:
bm[i] = 0
bs[i] = 0
else:
bm[i] = v.sum()
bs[i] = v.std()
return bn, bm, bs
def frc(image):
from PYME.Analysis import binAvg
import numpy as np
# import pylab
import matplotlib.pyplot as plt
from numpy.fft import fft2, fftshift
voxelsize = image.voxelsize
shape = image.data.shape[0:2]
hwin = np.sqrt(np.outer(np.hanning(shape[0]),np.hanning(shape[1])))
#assume we have exactly 2 channels #FIXME - add a selector
#grab image data
imA = hwin * image.data[:,:,:,0].squeeze()
imB = hwin * image.data[:,:,:,1].squeeze()
X, Y = np.mgrid[0:float(imA.shape[0]), 0:float(imA.shape[1])]
X = X/X.shape[0]
Y = Y/X.shape[1]
X = X - .5
Y = Y - .5
R = np.sqrt(X**2 + Y**2)
H1 = fft2(imA)
H2 = fft2(imB)
ringwidth = 1 # in pixels
rB = np.linspace(0,0.5,0.5*imA.shape[0]/ringwidth)
bn, bm, bs = binSum(R, fftshift(H1*H2.conjugate()), rB)
bn1, bm1, bs1 = binSum(R, fftshift(abs(H1*H1.conjugate())), rB)
bn2, bm2, bs2 = binSum(R, fftshift(abs(H2*H2.conjugate())), rB)
bmr = np.real(bm)
plt.figure()
ax = plt.gca()
freqpnm = rB/voxelsize[0]
ax.plot(freqpnm[:-1], bmr/np.sqrt(bm1*bm2))
ax.plot(freqpnm[:-1], 2./np.sqrt(bn/2))
ax.plot(freqpnm[:-1], 0*bmr + 1.0/7)
ax.plot(freqpnm[:-1], 0*bmr, '--')
xt = np.array([10., 15, 20, 30, 50, 80, 100, 150])
rt = 1.0/xt
plt.xticks(rt[::-1],['%d' % xi for xi in xt[::-1]])
plt.show()
return H1, H2, R, bmr/np.sqrt(bm1*bm2), bn, bm, bm1, bm2, rB
def abscorrel(a,b):
from numpy.fft import fftn, ifftn, fftshift, ifftshift
# from pylab import fftshift, ifftshift
import numpy as np
F0 = fftn(a)
Fi = ifftn(b)
corr = abs(fftshift(ifftn(F0*Fi)))
return corr
def cent2d(im,usefrac=0.25):
im -= im.min()
im = np.maximum(im - im.max()*(1.0-usefrac), 0)
xi, yi = np.where(im)
im_s = im[im>0]
im_s/= im_s.sum()
dxi = ((xi*im_s).sum() - im.shape[0]/2.)
dyi = ((yi*im_s).sum() - im.shape[1]/2.)
return [dxi,dyi]
def trackser(ref, series, frange=None, usefrac=0.25):
if frange is None:
frange = range(series.shape[2])
nframes = len(frange)
dx = np.zeros(nframes)
dy = np.zeros(nframes)
for i in range(nframes):
corr = abscorrel(ref,series[:,:,frange[i]].squeeze())
dxi, dyi = cent2d(corr,usefrac=usefrac)
dx[i] = dxi
dy[i] = dyi
return [dx,dy]
import matplotlib.pyplot as plt
def savitzky_golay(y, window_size, order, deriv=0):
r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
The Savitzky-Golay filter removes high frequency noise from data.
It has the advantage of preserving the original shape and
features of the signal better than other types of filtering
approaches, such as moving averages techhniques.
This code has been taken from http://www.scipy.org/Cookbook/SavitzkyGolay
Parameters
----------
y : array_like, shape (N,)
the values of the time history of the signal.
window_size : int
the length of the window. Must be an odd integer number.
order : int
the order of the polynomial used in the filtering.
Must be less then `window_size` - 1.
deriv: int
the order of the derivative to compute (default = 0 means only smoothing)
Returns
-------
ys : ndarray, shape (N)
the smoothed signal (or it's n-th derivative).
Notes
-----
The Savitzky-Golay is a type of low-pass filter, particularly
suited for smoothing noisy data. The main idea behind this
approach is to make for each point a least-square fit with a
polynomial of high order over a odd-sized window centered at
the point.
Examples
--------
t = np.linspace(-4, 4, 500)
y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
ysg = savitzky_golay(y, window_size=31, order=4)
import matplotlib.pyplot as plt
plt.plot(t, y, label='Noisy signal')
plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
plt.plot(t, ysg, 'r', label='Filtered signal')
plt.legend()
plt.savefig('images/golay.png')
#plt.show()
References
----------
.. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
Data by Simplified Least Squares Procedures. Analytical
Chemistry, 1964, 36 (8), pp 1627-1639.
.. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
Cambridge University Press ISBN-13: 9780521880688
"""
try:
window_size = np.abs(np.int(window_size))
order = np.abs(np.int(order))
except ValueError:
raise ValueError("window_size and order have to be of type int")
if window_size % 2 != 1 or window_size < 1:
raise TypeError("window_size size must be a positive odd number")
if window_size < order + 2:
raise TypeError("window_size is too small for the polynomials order")
order_range = range(order+1)
half_window = (window_size -1) // 2
# precompute coefficients
b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
m = np.linalg.pinv(b).A[deriv]
# pad the signal at the extremes with
# values taken from the signal itself
firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
y = np.concatenate((firstvals, y, lastvals))
return np.convolve( m, y, mode='valid')
from PYME.Analysis.BleachProfile import kinModels
def gphotons(pipeline):
colourFilter = pipeline.colourFilter
metadata = pipeline.mdh
chans = colourFilter.getColourChans()
channame = ''
if len(chans) == 0:
nph = kinModels.getPhotonNums(colourFilter, metadata)
merr = colourFilter['error_x']
return [channame, nph.mean(), merr.mean()]
ret = []
curcol = colourFilter.currentColour
for chan in chans:
channame = pipeline.fluorSpeciesDyes[chan]
colourFilter.setColour(chan)
nph = kinModels.getPhotonNums(colourFilter, metadata)
merr = colourFilter['error_x']
ret.append([channame,nph.mean(),merr.mean()])
colourFilter.setColour(curcol)
return ret
import PYME.Analysis.BleachProfile.kinModels as km
def plotphotons(pipeline,color='red'):
nph = km.getPhotonNums(pipeline.colourFilter,pipeline.mdh)
ph_range = 6*nph.mean()
n, bins = np.histogram(nph, np.linspace(0, ph_range, 100))
plt.bar(bins[:-1], n, width=bins[1]-bins[0], alpha=0.4, color=color)
return nph
def photonconvert(data,mdh=None):
if mdh is None:
mdh = getmdh(inmodule=True)
return (data-mdh['Camera.ADOffset'])*mdh['Camera.ElectronsPerCount']/mdh['Camera.TrueEMGain']
def gmesig(sig,N,Nb,voxelsize):
siga = np.sqrt(sig*sig+voxelsize*voxelsize/12.0)
return siga*siga/N*(16.0/9+8*math.pi*siga*siga*Nb/(N*voxelsize*voxelsize))
def gmestd(sig,N,Nb,voxelsize,mdh=None):
return np.sqrt(gmesig(sig,N,Nb,voxelsize))
# histogram with binwidth guaranteed to be one
def histone(data,binwidth=1):
d=data.squeeze()
plt.hist(d, bins=range(int(min(d)), int(max(d)) + binwidth, binwidth))
# this routine is designed for correlative tracking using the first (potentially averaged) image
# as reference and then determines displacement of later images aginst that one
import scipy.ndimage
from numpy.fft import *
def correltrack(data,start=0,avgover=10,pixelsize=70.0,centersize=7,centroidfrac=1.5):
cs = centersize
shp = [d for d in data.shape if d > 1]
nsteps = long((shp[2]-start)/avgover)
shh = (shp[0]/2,shp[1]/2)
xctw=np.zeros((2*centersize+1,2*centersize+1,nsteps))
shifts = []
i1 = data[:,:,start:start+avgover].squeeze().mean(axis=2)
I1 = fftn(i1)
for i in range(nsteps):
xc = abs(ifftshift(ifftn(I1*ifftn(data[:,:,start+i*avgover:start+(i+1)*avgover].squeeze().mean(axis=2)))))
xct = xc-xc.min()
xct = (xct-xct.max()/centroidfrac)*(xct > xct.max()/centroidfrac)
xctw[:,:,i] = xct[shh[0]-cs:shh[0]+cs+1,shh[1]-cs:shh[1]+cs+1]
shifts.append(scipy.ndimage.measurements.center_of_mass(xctw[:,:,i]))
sh = np.array(shifts)
t = start + np.arange(nsteps)*avgover
sh = pixelsize*(sh-sh[0])
return t, sh, xctw
# we ignore centroidfrac by default
def correltrack2(data,start=0,avgover=10,pixelsize=70.0,centersize=15,centroidfac=0.6,roi=[0,None,0,None]):
cs = centersize
shp = [d for d in data.shape if d > 1]
nsteps = long((shp[2]-start)/avgover)
xctw=np.zeros((2*centersize+1,2*centersize+1,nsteps))
shifts = []
if avgover > 1:
ref = data[:,:,start:start+avgover].squeeze().mean(axis=2)
else:
ref = data[:,:,start].squeeze()
ref = ref[roi[0]:roi[3],roi[1]:roi[3]]
refn = ref/ref.mean() - 1
Frefn = fftn(refn)
shh = (ref.shape[0]/2,ref.shape[1]/2)
for i in range(nsteps):
comp = data[:,:,start+i*avgover:start+(i+1)*avgover].squeeze()
if len(comp.shape) > 2:
comp = comp.mean(axis=2)
comp = comp[roi[0]:roi[3],roi[1]:roi[3]]
compn = comp/comp.mean() - 1
xc = ifftshift(np.abs(ifftn(Frefn*ifftn(compn))))
xcm = xc.max()
xcp = np.maximum(xc - centroidfac*xcm, 0)
xctw[:,:,i] = xcp[shh[0]-cs:shh[0]+cs+1,shh[1]-cs:shh[1]+cs+1]
shifts.append(scipy.ndimage.measurements.center_of_mass(xctw[:,:,i]))
sh = np.array(shifts)
t = start + np.arange(nsteps)*avgover
sh = pixelsize*(sh-sh[0])
return t, sh, xctw
def meanvards(dataSource, start=0, end=-1):
nslices = dataSource.getNumSlices()
if end < 0:
end = nslices + end
nframes = end - start
xSize, ySize = dataSource.getSliceShape()
m = np.zeros((xSize,ySize),dtype='float64')
for frameN in range(start,end):
m += dataSource.getSlice(frameN)
m = m / nframes
v = np.zeros((xSize,ySize),dtype='float64')
for frameN in range(start,end):
v += (dataSource.getSlice(frameN)-m)**2
v = v / (nframes-1)
return (m,v)
def darkCal(dataSource, integrationTimes,transitionTimes):
ms = []
vs = []
endTimes = transitionTimes[1:]+[-1]
for istart, istop in zip(transitionTimes, endTimes):
print("starting at %d, using %d frames..." % (istart,istop-istart))
m, v = meanvards(dataSource,istart,istop)
ms.append(m)
vs.append(v)
return (ms,vs)
def darkCalfromMetadata(dataSource,mdh=None):
if mdh is None:
mdh = getmdh(inmodule=True)
it,tt = (mdh['Protocol.IntegrationTimes'],mdh['Protocol.Transitions'])
ms, vs = darkCal(dataSource,it,tt)
return (ms,vs,it)
from scipy import stats
def isnparray(a):
return type(a).__module__ == np.__name__
def dcfit(ms,integrationTimes):
import sys
if not isnparray(ms):
ofs = np.dstack(ms) # offsets
else:
ofs = ms
itimes = np.asarray(integrationTimes)
sz = ofs.shape
sz2d = sz[0:2]
def z2d():
return np.zeros(sz2d,dtype = 'float32')
dc = z2d()
offs = z2d()
r_value = z2d()
p_value = z2d()
std_err = z2d()
for x in range(sz[0]):
print("line %d" % (x) + '\r',)
sys.stdout.flush()
for y in range(sz[1]):
dc[x,y], offs[x,y], r_value[x,y], p_value[x,y], std_err[x,y] = \
stats.linregress(itimes,ofs[x,y,:])
return (dc,offs,r_value,p_value,std_err)
def subsampidx(arraylen, percentage=10):
newlen = percentage*1e-2*arraylen
idx = np.random.choice(arraylen,newlen)
return idx
from scipy.stats import gaussian_kde
def scatterdens(x,y,subsample=1.0, s=40, xlabel=None, ylabel=None, **kwargs):
xf = x.flatten()
yf = y.flatten()
if subsample < 1.0:
idx = subsampidx(xf.size,percentage = 100*subsample)
xs = xf[idx]
ys = yf[idx]
else:
xs = xf
ys = yf
estimator = gaussian_kde([xs,ys])
density = estimator.evaluate([xf,yf])
print("density min, max: %f, %f" % (density.min(), density.max()))
plt.scatter(xf,yf,c=density,marker='o',linewidth='0',zorder=3,s=s,**kwargs)
if xlabel is not None:
plt.xlabel(xlabel)
if ylabel is not None:
plt.ylabel(ylabel)
return estimator
def multicolcheck(pipeline,subsample=0.03,dA=20,xrange=[-1000,3000],yrange=[-1000,6000]):
p = pipeline
plt.figure()
plt.subplot(1, 2, 1)
estimator = scatterdens(p['fitResults_Ag'],p['fitResults_Ar'],subsample=subsample,s=10)
plt.xlim(xrange)
plt.ylim(yrange)
x1d = np.arange(xrange[0],xrange[1],dA)
y1d = np.arange(yrange[0],yrange[1],dA)
x2d = x1d[:,None] * np.ones_like(y1d)[None,:]
y2d = np.ones_like(x1d)[:,None] * y1d[None,:]
imd = estimator.evaluate([x2d.flatten(),y2d.flatten()])
imd2d = imd.reshape(x2d.shape)
imd2d /= imd2d.max()
#plt.figure()
plt.subplot(1, 2, 2)
plt.imshow(imd2d[:,::-1].transpose(),cmap=plt.get_cmap('jet'),extent=[xrange[0],xrange[1],yrange[0],yrange[1]])
plt.grid(True)
return imd2d
def intdens(image,framenum=0):
mdh = image.mdh
pixarea = 1e6*mdh['voxelsize.x']*mdh['voxelsize.y']
data = image.data[:,:,framenum].squeeze()
intdens = float(pixarea*data.sum())
nevts = None
try:
nevts = int(mdh['Rendering.NEventsRendered'])
except:
pass
if nevts is not None:
print("Ratio Events/Intdens = %f" % (nevts/intdens))
return intdens
def px(p):
t = p['t']*p.mdh['Camera.CycleTime']
x = p['x']-p['x'][0:10].mean()
plt.plot(t,x)
def py(p):
t = p['t']*p.mdh['Camera.CycleTime']
y = p['y']-p['y'][0:10].mean()
plt.plot(t,y)
def cumuexpfit(t,tau):
return 1-np.exp(-t/tau)
from scipy.optimize import curve_fit
def darktimes(pipeline, mdh=None, plot=True, report=True):
if mdh is None:
mdh = getmdh(inmodule=True)
t = pipeline['t']
x = pipeline['x']
y = pipeline['y']
# determine darktime from gaps and reject zeros (no real gaps)
dts = t[1:]-t[0:-1]-1
dtg = dts[dts>0]
nts = dtg.shape[0]
# now make a cumulative histogram from these
cumux = np.sort(dtg+0.01*np.random.random(nts)) # hack: adding random noise helps us ensure uniqueness of x values
cumuy = (1.0+np.arange(nts))/np.float(nts)
bbx = (x.min(),x.max())
bby = (y.min(),y.max())
voxx = 1e3*mdh['voxelsize.x']
voxy = 1e3*mdh['voxelsize.y']
bbszx = bbx[1]-bbx[0]
bbszy = bby[1]-bby[0]
maxtd = dtg.max()
binedges = np.arange(0,maxtd,5)
binctrs = 0.5*(binedges[0:-1]+binedges[1:])
h,be2 = np.histogram(dtg,bins=binedges)
hc = np.cumsum(h)
hcg = hc[h>0]/float(nts) # only nonzero bins and normalise
binctrsg = binctrs[h>0]
popth,pcovh = curve_fit(cumuexpfit,binctrsg,hcg, p0=(300.0))
popt,pcov = curve_fit(cumuexpfit,cumux,cumuy, p0=(300.0))
if plot:
plt.subplot(211)
plt.plot(cumux,cumuy,'o')
plt.plot(cumux,cumuexpfit(cumux,popt[0]))
plt.plot(binctrs,hc/float(nts),'o')
plt.plot(binctrs,cumuexpfit(binctrs,popth[0]))
plt.ylim(-0.2,1.2)
plt.subplot(212)
plt.semilogx(cumux,cumuy,'o')
plt.semilogx(cumux,cumuexpfit(cumux,popt[0]))
plt.semilogx(binctrs,hc/float(nts),'o')
plt.semilogx(binctrs,cumuexpfit(binctrs,popth[0]))
plt.ylim(-0.2,1.2)
plt.show()
if report:
print("events: %d" % t.shape[0])
print("dark times: %d" % nts)
print("region: %d x %d nm (%d x %d pixel)" % (bbszx,bbszy,bbszx/voxx,bbszy/voxy))
print("centered at %d,%d (%d,%d pixels)" % (x.mean(),y.mean(),x.mean()/voxx,y.mean()/voxy))
print("darktime: %.1f (%.1f) frames" % (popt[0],popth[0]))
print("qunits: %.2f" % (200/(popt[0]+popth[0])))
return (cumux,cumuy,popt[0],pcov)
def darktimehist(ton):
# determine darktime from gaps and reject zeros (no real gaps)
dts = ton[1:]-ton[0:-1]-1
dtg = dts[dts>0]
nts = dtg.shape[0]
# now make a cumulative histogram from these
cumux = np.sort(dtg+0.01*np.random.random(nts)) # hack: adding random noise helps us ensure uniqueness of x values
cumuy = (1.0+np.arange(nts))/np.float(nts)
popt,pcov = curve_fit(cumuexpfit,cumux,cumuy, p0=(300.0))
return (cumux,cumuy,cumuexpfit(cumux,popt[0]),popt[0])
def analyze1dSeries(series,chunklength=500):
offset = series.mean()
chunks = int(len(series)/chunklength)
chunkmaxs = np.array([max(series[chunk*chunklength:(chunk+1)*chunklength]) for chunk in range(chunks)])
peakaverage = chunkmaxs.mean()
offset = series[series < (offset+0.5*(peakaverage-offset))].mean()
return (offset,peakaverage)
def datafrompipeline(datasource,pipeline, ctr, boxsize = 7):
tser = np.arange(min(datasource.shape[2],pipeline['t'].max()))
bszh = int(boxsize/2)
rawser = np.zeros((2*bszh+1,2*bszh+1,tser.shape[0]))
for t in range(len(tser)):
ctrx = ctr[0,t]
ctry = ctr[1,t]
rawser[:,:,t] = datasource[int(ctrx)-bszh:int(ctrx)+bszh+1,int(ctry)-bszh:int(ctry)+bszh+1,t].squeeze()
return (tser, rawser)
from io import StringIO
import sys
def darkAnalysisRawPlusPipeline(datasource, pipeline, driftPane=None, boxsize = 7, doplot = True,
threshfactor=0.45, mdh=None, debug=1):
xp = pipeline['x'] # in new code use 'x_raw' and 'y_raw'!
yp = pipeline['y']
if mdh is None: # there may be other ways to get at the mdh, e.g. via pipeline?
mdh = pipeline.mdh
xpix = 1e3*mdh['voxelsize.x']
ypix = 1e3*mdh['voxelsize.y']
# we need a new strategy for the pixel center selection
# and inclusion of drift
# strategy:
# 1. if we have filterkeys x and y (look up where to find these!) use the center of that ROI
# 2. if we have a drift time course calculate a centerpix(t), i.e. centerpix as a function of x
# for 1: use pipeline.filterKeys['x'] and pipeline.filterKeys['y']
# for 2: for a given xctr and yctr find the x_raw and y_raw; question: how to do that?
# for 2: we will have to get timecourse of shift as (1) x = x_raw + dx(t)
# for 2: if we manage to get (1) we will get (2) xctr_raw(t) = xctr-dx(t)
# for 2: (2) needs texting with bead sample
# for 2: once we have xctr_raw(t), yctr_raw(t) we need to modify datafrompipeline
# for 2: make datafrompipeline so that it accepts ctr(t) = [xctr(t),yctr(t)]!!
try:
bbox = [pipeline.filterkeys['x'][0],pipeline.filterkeys['x'][1],pipeline.filterkeys['y'][0],pipeline.filterkeys['y'][1]]
except:
bbox = [xp.min(),xp.max(),yp.min(),yp.max()]
bboxpix = [bbox[0]/xpix, bbox[1]/xpix, bbox[2]/ypix, bbox[3]/ypix] # only for diagnosis
bbctr = 0.5*np.array([bbox[0]+bbox[1],bbox[2]+bbox[3]])
t = np.arange(0,pipeline['t'].max())
if driftPane is None:
bbctrt = bbctr[:,None]*(np.ones((t.shape))[None,:])
else:
dx,dy,tt = getdriftcurves(driftPane,pipeline,t) # this should now return the desired times in all cases
bbctrt = np.zeros((2,t.shape[0]))
bbctrt[0,:] = bbctr[0]-dx
bbctrt[1,:] = bbctr[1]-dy
ctrpix = np.rint(bbctrt / np.array(xpix,ypix))
if debug: # FIXME - use logging module
print('BBox (nm): ',bbox)
print('BBox (pix): ',bboxpix)
print('Ctr (pix): ',ctrpix[:,0])
sys.stdout.flush()
# return (bbox, bbctrt,ctrpix,t)
print('extracting region from data...')
sys.stdout.flush()
tser, rawser = datafrompipeline(datasource,pipeline,ctrpix,boxsize = boxsize)
print('analyzing data...')
sys.stdout.flush()
tevts = pipeline['t'].copy()
rawm, peakav, fitev, fitr, rawthresh = analyzeDataPlusEvents(tser, rawser, tevts, doplot = doplot,
threshfactor=threshfactor, debug=debug)
return (tser, rawser, rawm, peakav, tevts, fitev, fitr, rawthresh)
def analyzeDataPlusEvents(tser, rawser, tevts, doplot = True,
threshfactor=0.45, debug=1, rawthresh=None, size=6):
rawm = rawser.mean(axis=0).mean(axis=0)
offset, peakav = analyze1dSeries(rawm,chunklength=500)
rawm = rawm-offset
peakav = peakav-offset
tp = tevts
if rawthresh is None:
rawthresh = threshfactor * peakav
th = tser[rawm > (rawthresh)]
ctp, chip, chipfit, taup = darktimehist(tp)
ctr, chir, chirfit, taur = darktimehist(th)
outstr = StringIO()
outstr.write("events: %d (%d raw) \n" % (tp.shape[0],th.shape[0]))
outstr.write("dark times: %d (%d raw) \n" % (ctp.shape[0],ctr.shape[0]))
#print >>outstr, "region: %d x %d nm (%d x %d pixel)" % (bbszx,bbszy,bbszx/voxx,bbszy/voxy)
#print >>outstr, "centered at %d,%d (%d,%d pixels)" % (x.mean(),y.mean(),x.mean()/voxx,y.mean()/voxy)
outstr.write("darktime: ev %.1f (raw %.1f) frames \n" % (taup,taur))
outstr.write("qunits: ev %.2f (raw %.2f), eunits: %.2f \n" % (200.0/taup,200.0/taur,tp.shape[0]/500.0))
labelstr = str(outstr.getvalue())
if debug: # FIXME - use logging module
print(labelstr)
if doplot:
plt.figure()
plt.plot(tser, rawm)
peaklevel = plt.plot(tser, peakav*np.ones(tser.shape), '--', label = 'median peak')
events_h5r = plt.plot(tp, 1.2*rawthresh*np.ones(tp.shape),'o',c='red', label='events')
events_raw = plt.plot(th, rawthresh * np.ones(th.shape),'o',c='blue', label='raw detected')
plt.legend(handles=[events_raw[0], events_h5r[0], peaklevel[0]])
plt.figure()
events = plt.semilogx(ctp, chip, 'o', c='red', alpha=.5, markersize = size, label = 'events')
eventfit = plt.semilogx(ctp, chipfit, label='event fit')
raw = plt.semilogx(ctr, chir, 'o', c='blue', alpha=.5, markersize = size, label='raw')
rawfit = plt.semilogx(ctr, chirfit, label='raw data fit')
plt.ylim(-0.2,1.2)
plt.annotate(labelstr, xy=(0.5, 0.1), xycoords='axes fraction',
fontsize=10)
plt.legend(handles=[events[0],raw[0],eventfit[0],rawfit[0]],loc=4)
return (rawm, peakav, (ctp, chip, chipfit, taup), (ctr, chir, chirfit, taur), rawthresh)
import pickle
def savepickled(object,fname):
fi = open(fname,'wb')
pickle.dump(object,fi)
fi.close()
def loadpickled(fname):
fi = open(fname,'r')
return pickle.load(fi)
from PYME.DSView import dsviewer
def setdriftparsFromImg(driftPane,img = None):
if img is None:
img = dsviewer.openViewers[dsviewer.openViewers.keys()[0]].image
driftPane.tXExpr.SetValue(img.mdh['DriftCorrection.ExprX'])
driftPane.tYExpr.SetValue(img.mdh['DriftCorrection.ExprY'])
driftPane.tZExpr.SetValue(img.mdh['DriftCorrection.ExprZ'])
driftPane.OnDriftExprChange(None)
destp = driftPane.dp.driftCorrParams
srcp = img.mdh['DriftCorrection.Parameters']
for key in destp.keys():
if key.startswith(('a','b')):
destp[key] = srcp[key]
driftPane.OnDriftExprChange(None)
return destp
def getOpenImages():
img = dsviewer.openViewers
return img
def setSelectionFromFilterKeys(visFr,img):
glcv = visFr.glCanvas
fk = img.mdh['Filter.Keys']
x0,x1 = fk['x']
y0,y1 = fk['y']
glcv.selectionStart = (x0,y0)
glcv.selectionFinish = (x1,y1)
import PYMEnf.DriftCorrection.compactFit as cf
def getdriftcurves(driftPane,pipeline,t=None):
if t is None:
t = pipeline['t']
if 'driftx' in driftPane.dp.driftExprX:
tt, dx = getdriftxyzFromEvts(pipeline,t,coordpos=0)
tt, dy = getdriftxyzFromEvts(pipeline,t,coordpos=1)
indepVars = { 't': tt, 'driftx': dx, 'drifty': dy }
else:
indepVars = pipeline.filter
dx,dy,tt = cf.xyDriftCurves(driftPane.dp.driftCorrFcn,driftPane.dp.driftCorrParams,indepVars,t)
return (dx,dy,tt)
from PYME.Analysis import piecewiseMapping
from scipy.interpolate import interp1d
def getdriftxyzFromEvts(pipeline, tframes=None, coordpos=0):
ts = []
cs = []
for e in pipeline.events[pipeline.events['EventName'] == 'ShiftMeasure']:
ts.append(e['Time'])
cs.append(float(e['EventDescr'].split(', ')[coordpos]))
if len(ts) > 0: