def simulateSpatialCorr(tau, rho, c, D, w0, z0):
w0 = np.resize(w0, (5, 5))
z0 = np.resize(z0, (5, 5))
G = correlations()
if type(tau) == float:
tau = [tau]
Nt = len(tau)
Gall = np.zeros((9, 9, Nt))
for i in range(Nt):
Gsinglet = np.zeros((9, 9))
for shifty in np.arange(-4, 5):
print('shifty: ' + str(shifty))
for shiftx in np.arange(-4, 5):
print(' shiftx: ' + str(shiftx))
# go through all detector elements
n = 0 # number of overlapping detector elements
Gtemp = 0
for detx in np.arange(np.max((0, shiftx)),
np.min((5, 5 + shiftx))):
print(' detx: ' + str(detx))
for dety in np.arange(np.max((0, shifty)),
np.min((5, 5 + shifty))):
print(' dety: ' + str(dety))
dummy, Gout = twoFocusFCS(
tau[i], shiftx * rho, shifty * rho, 0, c, D,
w0[dety, detx], w0[dety - shifty, detx - shiftx],
z0[dety, detx], z0[dety - shifty, detx - shiftx])
Gtemp += Gout[0, 1]
n += 1
Gtemp /= n
Gsinglet[shifty + 4, shiftx + 4] = Gtemp
Gall[:, :, i] = Gsinglet
G.autoSpatial = Gall
G.dwellTime = tau[0]
return G
def FCSvectors2G(tau, G, fieldName='central'):
Gmatrix = np.zeros((np.size(tau, 0), 2))
Gmatrix[:, 0] = tau
Gmatrix[:, 1] = G
G = correlations()
setattr(G, fieldName, Gmatrix)
return G
def FCSLoadG(fnameRoot, folderName="", printFileNames=True):
G = correlations()
files = listFiles(folderName, "csv", fnameRoot)
for file in files:
setattr(G, stripGfname(file, fnameRoot, printFileNames), csv2array(file, ','))
G.dwellTime = 1e6 * csv2array(file, ',')[1, 0] # in µs
print('--------------------------')
print(str(len(files)) + ' files found.')
print('--------------------------')
return G
def simulateTwoFocusCrossCenter(tau, c, D, w, z, shift):
G = correlations()
N = np.size(w, 0)
for i in range(N):
rhoy = np.floor(i / 5)
rhox = np.mod(i, 5)
[xx, Gtemp] = twoFocusFCS(tau, (rhox - 2) * shift, (rhoy - 2) * shift,
0, c, D, w[12], w[i], z[12], z[i])
setattr(G, 'det12x' + str(i), Gtemp)
return G
def twoFocusFCS(tau, rhox, rhoy, rhoz, c, D, w0, w1, z0, z1):
"""
Calculate the FCS cross correlation between two PSFs
========== ===============================================================
Input Meaning
tau Lag time [s]
rhox Spatial shift in the x, y, and z direction between two detector
rhoy elements [m]. Usually, rhoz = 0. Rhox and rhoy correspond to
rhoz the spatial shifts in the sample space
c Concentration of fluorophores/particles [/m^3]
D Diffusion coefficient of the fluorophores/particles [µm^2/s]
w0 Lateral 1/e^2 radius of the first PSF
w1 Lateral 1/e^2 radius of the second PSF
z0 Axial 1/e^2 value of the first PSF
z1 Axial 1/e^2 value of the second PSF
========== ===============================================================
========== ===============================================================
Output Meaning
---------- ---------------------------------------------------------------
G Object with all autocorrelations
========== ===============================================================
"""
factorW = 8 * D * tau + w0**2 + w1**2
factorZ = 8 * D * tau + z0**2 + z1**2
G0 = 2 * np.sqrt(2) / np.pi**(3 / 2) / c / factorW / np.sqrt(factorZ)
expTerm = 16 * D * tau * (rhox**2 + rhoy**2)
expTerm = expTerm + 2 * (z0**2 + z1**2) * (rhox**2 + rhoy**2)
expTerm = expTerm + 2 * (w0**2 + w1**2) * rhoz**2
expTerm = -1 * expTerm / factorW / factorZ
expTerm = np.exp(expTerm)
Gy = G0 * expTerm
if type(Gy) == np.float64:
Garray = np.zeros((1, 2))
else:
Garray = np.zeros((np.size(Gy, 0), 2))
Garray[:, 0] = tau
Garray[:, 1] = Gy
G = correlations()
setattr(G, 'theory', Garray)
return G, Garray
def aTimes2CorrsParallel(data, listOfCorr, accuracy=50, taumax="auto", performCoarsening=True, split=10):
"""
Calculate correlations between several photon streams with arrival times
stored in macrotimes, using parallel computing to speed up the process
========== ===============================================================
Input Meaning
---------- ---------------------------------------------------------------
data Object having fields det0, det1, ..., det24 which contain
the macrotimes of the photon arrivals [in a.u.]
listOfCorr List of correlations to calculate
split Chunk size [s]
========== ===============================================================
Output Meaning
---------- ---------------------------------------------------------------
G [N x 2] matrix with tau and G values
========== ===============================================================
"""
if taumax == "auto":
taumax = 1 / data.macrotime
G = correlations()
Ndet = 21
calcAv = False
if 'av' in listOfCorr:
# calculate the correlations of all channels and calculate average
listOfCorr.remove('av')
listOfCorr += list(range(Ndet))
calcAv = True
for corr in listOfCorr:
print("Calculating correlation " + str(corr))
# EXTRACT DATA
if type(corr) == int:
dataExtr = getattr(data, 'det' + str(corr))
t0 = dataExtr[:, 0]
corrname = 'det' + str(corr)
elif corr == "sum5" or corr == "sum3":
print("Extracting and sorting photons")
dataExtr = extractSpadPhotonStreams(data, corr)
t0 = dataExtr[:, 0]
corrname = corr
# CALCULATE CORRELATIONS
duration = t0[-1] * data.macrotime
Nchunks = int(np.floor(duration / split))
# go over all filters
for j in range(np.shape(dataExtr)[1] - 1):
print(" Filter " + str(j))
if j == 0:
Processed_list = Parallel(n_jobs=multiprocessing.cpu_count() - 1)(delayed(parallelG)(t0, [1], data.macrotime, j, split, accuracy, taumax, performCoarsening, chunk) for chunk in list(range(Nchunks)))
else:
w0 = dataExtr[:, j+1]
Processed_list = Parallel(n_jobs=multiprocessing.cpu_count() - 1)(delayed(parallelG)(t0, w0, data.macrotime, j, split, accuracy, taumax, performCoarsening, chunk) for chunk in list(range(Nchunks)))
for chunk in range(Nchunks):
setattr(G, corrname + "F" + str(j) + '_chunk' + str(chunk), Processed_list[chunk])
# average over all chunks
listOfFields = list(G.__dict__.keys())
listOfFields = [i for i in listOfFields if i.startswith(corrname + "F" + str(j) + "_chunk")]
Gav = sum(getattr(G, i) for i in listOfFields) / len(listOfFields)
setattr(G, corrname + "F" + str(j) + '_average', Gav)
if calcAv:
# calculate average correlation of all detector elements
for f in range(np.shape(dataExtr)[1] - 1):
# start with correlation of detector 20 (last one)
Gav = getattr(G, 'det' + str(Ndet-1) + 'F' + str(f) + '_average')
# add correlations detector elements 0-19
for det in range(Ndet - 1):
Gav += getattr(G, 'det' + str(det) + 'F' + str(f) + '_average')
# divide by the number of detector elements to get the average
Gav = Gav / Ndet
# store average in G
setattr(G, 'F' + str(f) + '_average', Gav)
return G