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PicoQuantUtils.py
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PicoQuantUtils.py
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# PicoHarp 300 File Access Utility
# ported from the PicoQuant matlab demo to python by KJR, Oct 2010
# (for python version 2.6)
# This script reads a binary PicoHarp 300 data file (*.phd)
# and returns its contents. Works with file format version 2.0 only!
# Original Matlab code disclaimer:
# Peter Kapusta, PicoQuant GmbH, September 2006
# This is demo code. Use at your own risk. No warranties.
# Make sure you have enough memory when loading large files!
import sys
import os.path
import struct # deal with binary data
import pylab
import numpy
from scipy.optimize import curve_fit, leastsq
from scipy.signal import cspline1d, cspline1d_eval
class Trace():
""" A class for holding lifetime data. You pass
it the name of the phdfile (including .phd)
and it will load it and offer various methods
for plotting, wrapping (removing time offset)
and fitting exponentials.
"""
def __init__( self, phdfile ):
self.fname = phdfile
self.has_fit = False
self.ax = None
self.irf = None
self.wraptime = None
with open( phdfile, 'rb' ) as self.fobj:
self.readasciiheader( verbose=False )
self.readbinaryheader( verbose=False )
self.readboardheader()
self.readcurveheaders( verbose=False )
self.readhistograms()
self.resolution = self.curveheaders[0]['Resolution'] # curve 0 resolution, actually (ns)
self.t = []
for i, curve in enumerate( self.curves ):
self.t.append( pylab.arange(len(curve))*self.curveheaders[i]['Resolution'] )# this is in ns
self.raw_t = self.t[:]
def fit_exponential(
self,
tstart = 0.0,
tend = None,
guess = dict( l0=5.0, a0=1.0, b=0.0 ),
num_exp = None,
verbose = True,
deconvolve = False,
fixed_params = [None],
curve_num=0 ):
"""
fit a function of exponentials to a single curve of the file
(my files only have one curve at this point anyway,
curve 0).
The parameter num_exp (default is 1, max is 3) defines the number of
exponentials in the funtion to be fitted.
num_exp=1 yields:
f(t) = a0*exp(-t/l0) + b
where l0 is the lifetime and a0 and b are constants,
and we fit over the range from tstart to tend.
You don't have to pass this parameter anymore; just pass an initial guess and
the number of parameters passed will determine the type of model used.
If tend==None, we fit until the end of the curve.
If num_exp > 1, you will need to modify the initial
parameters for the fit (i.e. pass the method an explicit `guess`
parameter) because the default has only three parameters
but you will need two additional parameters for each additional
exponential (another lifetime and another amplitude) to describe
a multi-exponential fit.
For num_exp=2:
f(t) = a1*exp(-t/l1) + a0*exp(-t/l0) + b
and for num_exp=3:
f(t) = a2*exp(-t/l2) + a1*exp(-t/l1) + a0*exp(-t/l0) + b
verbose=True (default) results in printing of fitting results to terminal.
"""
self.fitstart = tstart
self.deconvolved = deconvolve
tpulse = 1.0e9/self.curveheaders[0]['InpRate0'] # avg. time between pulses, in ns
if num_exp is None:
num_exp = (1 + int(guess.has_key('l1')) +
int(guess.has_key('l2')) +
int(guess.has_key('l3')) +
int(guess.has_key('l4')))
num_a = (1 + int(guess.has_key('a1')) +
int(guess.has_key('a2')) +
int(guess.has_key('a3')) +
int(guess.has_key('a4')))
if num_exp != num_a:
raise ValueError("Missing a parameter! Unequal number of lifetimes and amplitudes.")
keylist = [ "l0", "a0", "b" ]
errlist = [ "l0_err", "a0_err" ]
if num_exp == 2:
keylist = [ "l1", "a1", "l0", "a0", "b" ]
errlist = [ "l1_err", "a1_err", "l0_err", "a0_err" ]
elif num_exp == 3 and not guess.has_key('t_ag') and not guess.has_key('t_d3'):
keylist = [ "l2", "a2", "l1", "a1", "l0", "a0", "b" ]
errlist = [ "l2_err", "a2_err", "l1_err", "a1_err", "l0_err", "a0_err" ]
elif num_exp == 3 and guess.has_key('t_ag'):
keylist = [ "l2", "a2", "l1", "a1", "l0", "a0", "t_ag" ]
errlist = [ "l2_err", "a2_err", "l1_err", "a1_err", "l0_err", "a0_err" ]
elif num_exp == 3 and guess.has_key('t_d3'):
keylist = [ "l2", "a2", "l1", "a1", "l0", "a0", "t_d3" ]
errlist = [ "l2_err", "a2_err", "l1_err", "a1_err", "l0_err", "a0_err" ]
elif num_exp == 4:
keylist = [ "l3", "a3", "l2", "a2", "l1", "a1", "l0", "a0", "b" ]
errlist = [ "l3_err", "a3_err", "l2_err", "a2_err", "l1_err", "a1_err", "l0_err", "a0_err" ]
elif num_exp == 5:
keylist = [ "l4", "a4", "l3", "a3", "l2", "a2", "l1", "a1", "l0", "a0", "b" ]
errlist = [ "l4_err", "a4_err", "l3_err", "a3_err", "l2_err", "a2_err", "l1_err", "a1_err", "l0_err", "a0_err" ]
if deconvolve==False:
params = [ guess[key] for key in keylist ]
free_params = [ i for i,key in enumerate(keylist) if not key in fixed_params ]
initparams = [ guess[key] for key in keylist if not key in fixed_params ]
def f(t, *args ):
for i,arg in enumerate(args): params[ free_params[i] ] = arg
local_params = params[:]
b = local_params.pop(-1)
result = pylab.zeros(len(t))
for l,a in zip(params[::2],params[1::2]):
result += abs(a)*pylab.exp(-(t-tstart)/abs(l))
return result+b
else:
raise NameError("Deconvolution with this module is not kept current. Use FastFit module from fit directory instead.")
if self.irf == None: raise AttributeError("No detector trace!!! Use self.set_detector() method.")
t0 = tstart
tstart = 0.0
keylist.append( "tshift" )
params = [ guess[key] for key in keylist ]
free_params = [ i for i,key in enumerate(keylist) if not key in fixed_params ]
initparams = [ guess[key] for key in keylist if not key in fixed_params ]
def f( t, *args ):
for i,arg in enumerate(args): params[ free_params[i] ] = arg
tshift = params[-1]
ideal = fmodel( t, *args )
irf = cspline1d_eval( self.irf_generator, t-tshift, dx=self.irf_dt, x0=self.irf_t0 )
convoluted = pylab.real(pylab.ifft( pylab.fft(ideal)*pylab.fft(irf) )) # very small imaginary anyway
return convoluted
def fmodel( t, *args ):
for i,arg in enumerate(args): params[ free_params[i] ] = arg
local_params = params[:]
tshift = local_params.pop(-1)
if guess.has_key('t_ag'):
t_ag = abs(local_params.pop(-1))
elif guess.has_key('t_d3'):
t_d3 = abs(local_params.pop(-1))
elif guess.has_key('a_fix'):
scale = local_params.pop(-1)
else:
b = local_params.pop(-1)
result = pylab.zeros(len(t))
for l,a in zip(local_params[::2],local_params[1::2]):
if guess.has_key('t_ag'): l = 1.0/(1.0/l + 1.0/t_ag)
if guess.has_key('t_d3'): l *= t_d3
if guess.has_key('a_fix'): a *= scale
result += abs(a)*pylab.exp(-t/abs(l))/(1.0-pylab.exp(-tpulse/abs(l)))
return result
istart = pylab.find( self.t[curve_num] >= tstart )[0]
if tend is not None:
iend = pylab.find( self.t[curve_num] <= tend )[-1]
else:
iend = len(self.t[curve_num])
# sigma (std dev.) is equal to sqrt of intensity, see
# Lakowicz, principles of fluorescence spectroscopy (2006)
# sigma gets inverted to find a weight for leastsq, so avoid zero
# and imaginary weight doesn't make sense.
trace_scaling = self.curves[0].max()/self.raw_curves[0].max()
sigma = pylab.sqrt(self.raw_curves[curve_num][istart:iend]*trace_scaling) # use raw curves for actual noise, scale properly
self.bestparams, self.pcov = curve_fit( f, self.t[curve_num][istart:iend],
self.curves[curve_num][istart:iend],
p0=initparams,
sigma=sigma)
if pylab.size(self.pcov) > 1 and len(pylab.find(self.pcov == pylab.inf))==0:
self.stderr = pylab.sqrt( pylab.diag(self.pcov) ) # is this true?
else:
self.stderr = [pylab.inf]*len(guess)
stderr = [numpy.NaN]*len(params)
for i,p in enumerate(self.bestparams):
params[ free_params[i] ] = p
stderr[ free_params[i] ] = self.stderr[i]
self.stderr = stderr
self.fitresults = dict()
keys = keylist[:]
stderr = stderr[:]
p = params[:]
if deconvolve:
tshift = p.pop(-1)
self.fitresults['tshift'] = tshift
tshift_err = stderr.pop(-1)
self.fitresults['tshift_err'] = tshift_err
keys.pop(-1)
self.fitresults['irf_dispersion'] = self.irf_dispersion
b = p.pop(-1)
self.fitresults['b'] = b
b_err = stderr.pop(-1)
self.fitresults['b_err'] = b_err
keys.pop(-1)
self.lifetime = [ abs(l) for l in p[::2] ]
for l,a,lkey,akey in zip(p[::2],p[1::2],keys[::2],keys[1::2]):
if guess.has_key('t_ag'): l = 1.0/(1.0/l + 1.0/b)
if guess.has_key('t_d3'): l *= b
if guess.has_key('a_fix'): a *= b
self.fitresults[lkey] = abs(l)
self.fitresults[akey] = abs(a)
for l,a,lkey,akey in zip(stderr[::2],stderr[1::2],errlist[::2],errlist[1::2]):
self.fitresults[lkey] = l
self.fitresults[akey] = a
self.fitresults['l0_int'] = self.fitresults['l0']*self.fitresults['a0']
if num_exp > 1: self.fitresults['l1_int'] = self.fitresults['l1']*self.fitresults['a1']
if num_exp > 2: self.fitresults['l2_int'] = self.fitresults['l2']*self.fitresults['a2']
if num_exp > 3: self.fitresults['l3_int'] = self.fitresults['l3']*self.fitresults['a3']
if num_exp > 4: self.fitresults['l4_int'] = self.fitresults['l4']*self.fitresults['a4']
self.bestfit = f( self.t[curve_num][istart:iend], *self.bestparams )
if deconvolve: self.model = fmodel( self.t[curve_num][istart:iend], *self.bestparams )
Chi2 = pylab.sum( (self.bestfit - self.curves[0][istart:iend])**2 / sigma**2 )
#Chi2 *= self.raw_curves[0].max()/self.curves[0].max() # undo any scaling
mean_squares = pylab.mean( (self.bestfit - self.curves[0][istart:iend])**2 )
degrees_of_freedom = len(self.bestfit) - len(free_params)
self.fitresults['MSE'] = mean_squares/degrees_of_freedom
self.fitresults['ReducedChi2'] = Chi2/degrees_of_freedom
if verbose:
print "Fit results: (Reduced Chi2 = %.3E)" % (self.fitresults['ReducedChi2'])
print " (MSE = %.3E)" % (self.fitresults['MSE'])
print " Offset/t_ag/scale = %.3f +-%.3e" % (self.fitresults['b'], self.fitresults['b_err'])
print " l0=%.3f +-%.3f ns, a0=%.3e +-%.3e" % (self.fitresults['l0'],
self.fitresults['l0_err'],
self.fitresults['a0'],
self.fitresults['a0_err'])
if num_exp > 1:
print " l1=%.3f +-%.3f ns, a1=%.3e +-%.3e" % (self.fitresults['l1'],
self.fitresults['l1_err'],
self.fitresults['a1'],
self.fitresults['a1_err'])
if num_exp > 2:
print " l2=%.3f +-%.3f ns, a2=%.3e +-%.3e" % (self.fitresults['l2'],
self.fitresults['l2_err'],
self.fitresults['a2'],
self.fitresults['a2_err'])
if num_exp > 3:
print " l3=%.3f +-%.3f ns, a3=%.3e +-%.3e" % (self.fitresults['l3'],
self.fitresults['l3_err'],
self.fitresults['a3'],
self.fitresults['a3_err'])
if num_exp > 4:
print " l4=%.3f +-%.3f ns, a4=%.3e +-%.3e" % (self.fitresults['l4'],
self.fitresults['l4_err'],
self.fitresults['a4'],
self.fitresults['a4_err'])
print " "
self.has_fit = True
def autocorr( self ):
x = self.residuals()
result = pylab.correlate( x, x, mode='full' )
return result[result.size/2+1:]/sum(x**2)
def residuals( self ):
return self.bestfit - self.curves[0]
def counts_per_second( self ):
"""
Divide by acquisition time (self.Tacq). Note that Tacq is in milliseconds.
"""
for i, curve in enumerate( self.curves ):
curve = pylab.np.array( curve, dtype=pylab.np.float )
curve /= self.Tacq/1000.0
self.curves[i] = curve
def get_max( self ):
""" return a tuple containing the time and height of
the maximum of curve[0]:
t_max, cts_max = self.get_max()
"""
cts_max = self.curves[0].max()
t_max = self.t[0][ pylab.where( self.curves[0]==cts_max )[0][0] ]
return t_max, cts_max
def normalize( self, value=None ):
# just a wrapper around normalize_curves
self.normalize_curves( value=value )
def normalize_curves( self, value=None ):
"""
Normalize the curve to its maximum value (default),
or normalize to some arbitrary value (if value != None).
"""
for i, curve in enumerate( self.curves ):
curve = pylab.np.array( curve, dtype=pylab.np.float )
if value is None:
curve /= curve.max()
else:
curve /= pylab.np.float(value)
self.curves[i] = curve
def plot( self, *args, **kwargs ):
kwargs['type'] = "trace"
self.plot_misc( *args, **kwargs )
def plotfit( self, *args, **kwargs):
kwargs['type'] = "fit"
self.plot_misc( *args, **kwargs )
def plotmodel( self, *args, **kwargs):
kwargs['type'] = "model"
self.plot_misc( *args, **kwargs )
def plotresiduals( self, *args, **kwargs):
kwargs['type'] = "residuals"
if not 'weighted' in kwargs.keys(): kwargs['weighted']=False
self.plot_misc( *args, **kwargs )
def plot_misc( self, *args, **kwargs ):
if kwargs['type'] == "trace":
data = self.curves[0]
t = self.t[0]
elif kwargs['type'] == "fit":
t = self.t[0].copy()
if self.deconvolved:
data=self.bestfit
else:
data = self.bestfit
t = t[pylab.find(t>=self.fitstart)]
elif kwargs['type'] == "model":
data = self.model
t = self.t[0]
elif kwargs['type'] == "residuals":
data = self.residuals()
if kwargs['weighted']:
trace_scaling = self.curves[0].max()/self.raw_curves[0].max()
data /= pylab.sqrt(self.raw_curves[0]*trace_scaling) # use raw curves in case BG was subtracted
del kwargs['weighted']
t = self.t[0]
del kwargs['type']
if 't0' in kwargs.keys():
t0 = kwargs['t0']
del kwargs['t0']
else:
t0 = 0.0
if 'semilogy' in kwargs.keys():
semilogy = kwargs['semilogy']
del kwargs['semilogy']
else:
semilogy = False
if 'fill' in kwargs.keys():
fill = kwargs['fill']
del kwargs['fill']
else:
fill = False
if 'yoffset' in kwargs.keys():
voffset = kwargs['yoffset']
del kwargs['yoffset']
else:
voffset = 1.0e-5 # makes toggling to semilog okay with fill
if self.ax is None:
try:
self.ax = pylab.gca()
except AttributeError:
f = pylab.figure(1)
self.ax = f.add_subplot(111)
if semilogy:
self.ax.plot( t+t0, data, *args, **kwargs )
self.ax.set_yscale('log')
else:
if fill:
self.ax.fill_between( t+t0, data, y2=voffset, *args, **kwargs )
else:
self.ax.plot( t+t0, data, *args, **kwargs )
self.ax.set_xlabel( 'Time (ns)' )
self.ax.set_ylabel( 'Intensity (arb. units)' )
pylab.show()
def readasciiheader( self, verbose=False ):
""" read this first. """
##################################################################################
#
# ASCII file header
#
##################################################################################
self.Ident = self.fobj.read(16).split('\x00')[0]
if verbose:
print self.Ident
self.FormatVersion = "".join( self.fobj.read(6).split(" ") ).split('\x00')[0] # the join/split deblanks the string
if verbose:
print "PHD file format version:", self.FormatVersion
if self.FormatVersion != '2.0':
raise TypeError("PicoQuantUtils.py is only able to load phd file version 2.0. Quitting.")
self.CreatorName = self.fobj.read(18).split('\x00')[0]
if verbose:
print "File created by:", self.CreatorName
self.CreatorVersion = self.fobj.read(12).split('\x00')[0]
if verbose:
print "Program version:", self.CreatorVersion
self.fobjTime = self.fobj.read(18).split('\x00')[0]
if verbose:
print "Time of creation:", self.fobjTime
self.CRLF = self.fobj.read(2)
if verbose:
print self.CRLF
self.Comment = self.fobj.read(256)
if verbose:
print "Comment:", self.Comment
def readbinaryheader( self, verbose=False ):
""" read this after the ascii header. """
##################################################################################
#
# Binary file header
#
##################################################################################
"""
I use the struct module to unpack the binary SPE data.
Some useful formats for struct.unpack_from() include:
fmt c type python
c char string of length 1
s char[] string (Ns is a string N characters long)
h short integer
H unsigned short integer
l long integer
f float float
d double float
precede these with '=' to force usage of standard python sizes,
not native sizes (to make usage the same on 32 and 64-bit systems)
"""
binheader = self.fobj.read( 208 ) # should take up 208 bytes in memory...
self.NumberOfCurves = struct.unpack_from( "=l", binheader, offset=0 )[0]
if verbose:
print "Number of curves:", self.NumberOfCurves
self.BitsPerHistogramBin = struct.unpack_from( "=l", binheader, offset=4 )[0]
if verbose:
print "Bits per histogram bin:", self.BitsPerHistogramBin
self.RoutingChannels = struct.unpack_from( "=l", binheader, offset=8 )[0]
if verbose:
print "Number of routing channels:", self.RoutingChannels
self.NumberOfBoards = struct.unpack_from( "=l", binheader, offset=12 )[0]
if verbose:
print "Number of boards:", self.NumberOfBoards
self.ActiveCurve = struct.unpack_from( "=l", binheader, offset=16 )[0]
if verbose:
print "Active curve:", self.ActiveCurve
self.MeasurementMode = struct.unpack_from( "=l", binheader, offset=20 )[0]
if verbose:
print "Measurement mode:", self.MeasurementMode
self.SubMode = struct.unpack_from( "=l", binheader, offset=24 )[0]
if verbose:
print "Sub mode:", self.SubMode
self.RangeNo = struct.unpack_from( "=l", binheader, offset=28 )[0]
if verbose:
print "Range number:", self.RangeNo
self.Offset = struct.unpack_from( "=l", binheader, offset=32 )[0]
if verbose:
print "Offset:", self.Offset
self.Tacq = struct.unpack_from( "=l", binheader, offset=36 )[0]
if verbose:
print "Acquisition time (ms):", self.Tacq
self.StopAt = struct.unpack_from( "=l", binheader, offset=40 )[0]
if verbose:
print "Stop at (counts):", self.StopAt
self.StopOnOverflow = struct.unpack_from( "=l", binheader, offset=44 )[0]
if verbose:
print "Stop on overflow:", self.StopOnOverflow
self.Restart = struct.unpack_from( "=l", binheader, offset=48 )[0]
if verbose:
print "Restart:", self.Restart
self.DispLinLog = struct.unpack_from( "=l", binheader, offset=52 )[0]
if verbose:
print "Display lin/log:", self.DispLinLog
self.DispTimeAxisFrom = struct.unpack_from( "=l", binheader, offset=56 )[0]
if verbose:
print "Time axis from (ns):", self.DispTimeAxisFrom
self.DispTimeAxisTo = struct.unpack_from( "=l", binheader, offset=60 )[0]
if verbose:
print "Time axis to (ns):", self.DispTimeAxisTo
self.DispCountAxisFrom = struct.unpack_from( "=l", binheader, offset=64 )[0]
if verbose:
print "Count Axis From:", self.DispCountAxisFrom
offset = 68
self.DispCountAxisTo = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Count Axis To:", self.DispCountAxisTo
self.DispCurveMapTo = []
self.DispCurveShow = []
for i in range(8):
offset += 4
self.DispCurveMapTo.append( struct.unpack_from( "=l", binheader, offset=offset )[0] )
offset += 4
self.DispCurveShow.append( struct.unpack_from( "=l", binheader, offset=offset+4 )[0] )
self.ParamStart = []
self.ParamStep = []
self.ParamEnd = []
for i in range(3):
offset += 4
self.ParamStart.append( struct.unpack_from( "=f", binheader, offset=offset )[0] )
offset += 4
self.ParamStep.append( struct.unpack_from( "=f", binheader, offset=offset )[0] )
offset += 4
self.ParamEnd.append( struct.unpack_from( "=f", binheader, offset=offset )[0] )
offset += 4
self.RepeatMode = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Repeat Mode:", self.RepeatMode
offset += 4
self.RepeatsPerCurve = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Repeat / Curve:", self.RepeatsPerCurve
offset += 4
self.RepeatTime = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Repeat Time:", self.RepeatTime
offset += 4
self.RepeatWaitTime = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Repeat Wait Time:", self.RepeatWaitTime
offset += 4
self.ScriptName = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Script Name:", self.ScriptName
def readboardheader( self, verbose=False ):
""" read this after the ascii header and the binary header. """
##################################################################################
#
# Header for each board
#
##################################################################################
boardheader = self.fobj.read( 156 )
# I don't actually do anything with this at this point. Just get it out of the way...
"""
for i = 1:NumberOfBoards
fprintf(1,'-------------------------------------\n')
fprintf(1,' Board No: #d\n', i-1)
HardwareIdent(:,i) = char(fread(fid, 16, 'char'))
fprintf(1,' Hardware Identifier: #s\n', HardwareIdent(:,i))
HardwareVersion(:,i) = char(fread(fid, 8, 'char'))
fprintf(1,' Hardware Version: #s\n', HardwareVersion(:,i))
HardwareSerial(i) = fread(fid, 1, 'int32')
fprintf(1,' HW Serial Number: #d\n', HardwareSerial(i))
SyncDivider(i) = fread(fid, 1, 'int32')
fprintf(1,' Sync divider: #d \n', SyncDivider(i))
CFDZeroCross0(i) = fread(fid, 1, 'int32')
fprintf(1,' CFD 0 ZeroCross: #3i mV\n', CFDZeroCross0(i))
CFDLevel0(i) = fread(fid, 1, 'int32')
fprintf(1,' CFD 0 Discr. : #3i mV\n', CFDLevel0(i))
CFDZeroCross1(i) = fread(fid, 1, 'int32')
fprintf(1,' CFD 1 ZeroCross: #3i mV\n', CFDZeroCross1(i))
CFDLevel1(i) = fread(fid, 1, 'int32')
fprintf(1,' CFD 1 Discr. : #3i mV\n', CFDLevel1(i))
Resolution(i) = fread(fid, 1, 'float')
fprintf(1,' Resolution: #2.6g ns\n', Resolution(i))
# below is new in format version 2.0
RouterModelCode(i) = fread(fid, 1, 'int32')
RouterEnabled(i) = fread(fid, 1, 'int32')
# Router Ch1
RtChan1_InputType(i) = fread(fid, 1, 'int32')
RtChan1_InputLevel(i) = fread(fid, 1, 'int32')
RtChan1_InputEdge(i) = fread(fid, 1, 'int32')
RtChan1_CFDPresent(i) = fread(fid, 1, 'int32')
RtChan1_CFDLevel(i) = fread(fid, 1, 'int32')
RtChan1_CFDZeroCross(i) = fread(fid, 1, 'int32')
# Router Ch2
RtChan2_InputType(i) = fread(fid, 1, 'int32')
RtChan2_InputLevel(i) = fread(fid, 1, 'int32')
RtChan2_InputEdge(i) = fread(fid, 1, 'int32')
RtChan2_CFDPresent(i) = fread(fid, 1, 'int32')
RtChan2_CFDLevel(i) = fread(fid, 1, 'int32')
RtChan2_CFDZeroCross(i) = fread(fid, 1, 'int32')
# Router Ch3
RtChan3_InputType(i) = fread(fid, 1, 'int32')
RtChan3_InputLevel(i) = fread(fid, 1, 'int32')
RtChan3_InputEdge(i) = fread(fid, 1, 'int32')
RtChan3_CFDPresent(i) = fread(fid, 1, 'int32')
RtChan3_CFDLevel(i) = fread(fid, 1, 'int32')
RtChan3_CFDZeroCross(i) = fread(fid, 1, 'int32')
# Router Ch4
RtChan4_InputType(i) = fread(fid, 1, 'int32')
RtChan4_InputLevel(i) = fread(fid, 1, 'int32')
RtChan4_InputEdge(i) = fread(fid, 1, 'int32')
RtChan4_CFDPresent(i) = fread(fid, 1, 'int32')
RtChan4_CFDLevel(i) = fread(fid, 1, 'int32')
RtChan4_CFDZeroCross(i) = fread(fid, 1, 'int32')
# Router settings are meaningful only for an existing router:
if RouterModelCode(i)>0
fprintf(1,'-------------------------------------\n')
fprintf(1,' Router Model Code: #d \n', RouterModelCode(i))
fprintf(1,' Router Enabled: #d \n', RouterEnabled(i))
fprintf(1,'-------------------------------------\n')
# Router Ch1
fprintf(1,'RtChan1 InputType : #d \n', RtChan1_InputType(i))
fprintf(1,'RtChan1 InputLevel : #4i mV\n', RtChan1_InputLevel(i))
fprintf(1,'RtChan1 InputEdge : #d \n', RtChan1_InputEdge(i))
fprintf(1,'RtChan1 CFDPresent : #d \n', RtChan1_CFDPresent(i))
fprintf(1,'RtChan1 CFDLevel : #4i mV\n', RtChan1_CFDLevel(i))
fprintf(1,'RtChan1 CFDZeroCross: #4i mV\n', RtChan1_CFDZeroCross(i))
fprintf(1,'-------------------------------------\n')
# Router Ch2
fprintf(1,'RtChan2 InputType : #d \n', RtChan2_InputType(i))
fprintf(1,'RtChan2 InputLevel : #4i mV\n', RtChan2_InputLevel(i))
fprintf(1,'RtChan2 InputEdge : #d \n', RtChan2_InputEdge(i))
fprintf(1,'RtChan2 CFDPresent : #d \n', RtChan2_CFDPresent(i))
fprintf(1,'RtChan2 CFDLevel : #4i mV\n', RtChan2_CFDLevel(i))
fprintf(1,'RtChan2 CFDZeroCross: #4i mV\n', RtChan2_CFDZeroCross(i))
fprintf(1,'-------------------------------------\n')
# Router Ch3
fprintf(1,'RtChan3 InputType : #d \n', RtChan3_InputType(i))
fprintf(1,'RtChan3 InputLevel : #4i mV\n', RtChan3_InputLevel(i))
fprintf(1,'RtChan3 InputEdge : #d \n', RtChan3_InputEdge(i))
fprintf(1,'RtChan3 CFDPresent : #d \n', RtChan3_CFDPresent(i))
fprintf(1,'RtChan3 CFDLevel : #4i mV\n', RtChan3_CFDLevel(i))
fprintf(1,'RtChan3 CFDZeroCross: #4i mV\n', RtChan3_CFDZeroCross(i))
fprintf(1,'-------------------------------------\n')
# Router Ch4
fprintf(1,'RtChan4 InputType : #d \n', RtChan4_InputType(i))
fprintf(1,'RtChan4 InputLevel : #4i mV\n', RtChan4_InputLevel(i))
fprintf(1,'RtChan4 InputEdge : #d \n', RtChan4_InputEdge(i))
fprintf(1,'RtChan4 CFDPresent : #d \n', RtChan4_CFDPresent(i))
fprintf(1,'RtChan4 CFDLevel : #4i mV\n', RtChan4_CFDLevel(i))
fprintf(1,'RtChan4 CFDZeroCross: #4i mV\n', RtChan4_CFDZeroCross(i))
fprintf(1,'-------------------------------------\n')
end
end
"""
def readcurveheaders( self, verbose=False ):
""" read this after the ascii header, the binary header, and the boards header. """
##################################################################################
#
# Headers for each histogram (curve)
#
##################################################################################
MAXCURVES = 512
binheader = self.fobj.read( 150*MAXCURVES )
offset = 0
self.curveheaders = []
for i in range( self.NumberOfCurves ):
ch = dict()
ch['CurveIndex'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Curve index:", ch['CurveIndex']
offset += 4
ch['TimeOfRecording'] = struct.unpack_from( "=l", binheader, offset=offset )[0] # supposed to be unsigned long...
if verbose:
print "Repeat Wait Time:", ch['TimeOfRecording']
# The PicoHarp software saves the time of recording
# in a 32 bit serial time value as defined in all C libraries.
# This equals the number of seconds elapsed since midnight
# (00:00:00), January 1, 1970, coordinated universal time.
# The conversion to normal date and time strings is tricky...
# In matlab: (but we need it to be a uint32, which it's not, so I won't implement the conversion here)
# TimeOfRecording(i) = TimeOfRecording(i)/24/60/60+25569+693960
# fprintf(1,' Time of Recording: #s \n', datestr(TimeOfRecording(i),'dd-mmm-yyyy HH:MM:SS'))
offset += 4
ch['HardwareIdent'] = struct.unpack_from( "=16s", binheader, offset=offset )[0]
if verbose:
print "Repeat Wait Time:", ch['HardwareIdent']
offset += 16
ch['HardwareVersion'] = struct.unpack_from( "=8s", binheader, offset=offset )[0]
if verbose:
print "Hardware Version:", ch['HardwareVersion']
offset += 8
ch['HardwareSerial'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "HW Serial Number:", ch['HardwareSerial']
offset += 4
ch['SyncDivider'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Sync divider:", ch['SyncDivider']
offset += 4
ch['CFDZeroCross0'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "CFD 0 ZeroCross (mV):", ch['CFDZeroCross0']
offset += 4
ch['CFDLevel0'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "CFD 0 Discr. (mV):", ch['CFDLevel0']
offset += 4
ch['CFDZeroCross1'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "CFD 1 ZeroCross (mV):", ch['CFDZeroCross1']
offset += 4
ch['CFDLevel1'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "CFD 1 Discr. (mV):", ch['CFDLevel1']
offset += 4
ch['Offset'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Offset:", ch['Offset']
offset += 4
ch['RoutingChannel'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Routing Channel:", ch['RoutingChannel']
offset += 4
ch['ExtDevices'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "External Devices:", ch['ExtDevices']
offset += 4
ch['MeasMode'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Measure mode:", ch['MeasMode']
offset += 4
ch['SubMode'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Sub-Mode:", ch['SubMode']
offset += 4
ch['P1'] = struct.unpack_from( "=f", binheader, offset=offset )[0]
if verbose:
print "P1:", ch['P1']
offset += 4
ch['P2'] = struct.unpack_from( "=f", binheader, offset=offset )[0]
if verbose:
print "P2:", ch['P2']
offset += 4
ch['P3'] = struct.unpack_from( "=f", binheader, offset=offset )[0]
if verbose:
print "P3:", ch['P3']
offset += 4
ch['RangeNo'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Range No.:", ch['RangeNo']
offset += 4
ch['Resolution'] = struct.unpack_from( "=f", binheader, offset=offset )[0]
if verbose:
print "Resolution (ns):", ch['Resolution']
offset += 4
ch['Channels'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Channels:", ch['Channels']
offset += 4
ch['Tacq'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Acquisition Time (ms):", ch['Tacq']
offset += 4
ch['StopAfter'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Stop After (ms):", ch['StopAfter']
offset += 4
ch['StopReason'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Stop Reason (ms):", ch['StopReason']
offset += 4
ch['InpRate0'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Input Rate 0 (Hz):", ch['InpRate0']
offset += 4
ch['InpRate1'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Input Rate 1 (Hz):", ch['InpRate1']
offset += 4
ch['HistCountRate'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Hist. Count Rate (cps):", ch['HistCountRate']
offset += 4
ch['IntegralCount'] = struct.unpack_from( "=2l", binheader, offset=offset )[0]
if verbose:
print "Integral Count:", ch['IntegralCount']
offset += 8
ch['Reserved'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Reserved:", ch['Reserved']
offset += 4
ch['DataOffset'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
if verbose:
print "Data Offset relative to the start of the file:", ch['DataOffset']
offset += 4
# below is new in format version 2.0
ch['RouterModelCode'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RouterEnabled'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RtChan_InputType'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RtChan_InputLevel'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RtChan_InputEdge'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RtChan_CFDPresent'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RtChan_CFDLevel'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
ch['RtChan_CFDZeroCross'] = struct.unpack_from( "=l", binheader, offset=offset )[0]
offset += 4
self.curveheaders.append( ch )
def readhistograms( self ):
##################################################################################
#
# Reads all histograms into a list of numpy arrays
#
##################################################################################
self.fobj.seek(0) # go back to beginning because 'DataOffset' is measured from there.
binarydata = self.fobj.read()
self.curves = []
for i,curve in enumerate( self.curveheaders ):
# This will typically be waaay too long and will be padded with zeros because it
# can accomodate up to 2**16 histogram bins but our laser rep rate is ~76MHz, so
# if you were set at 4ps resolution, that would only require 1/76MHz/4ps ~ 3281 bins
#
# It's worth noting that when we wrap the curve (move data from before laser to after),
# there is going to be some small error in the timing because the number of bins will
# not be an exact integer. This error will be at most the resolution, which is likely
# to be either really small compared to the lifetime of the emitter or, if the lifetime
# is really short, the fluorescence will have decayed before the end of the un-wrapped
# curve. The only time this could be an issue is if the lifetime is really short and
# the peak in lifetime occurrs at the very end of the un-wrapped curve. Then you should
# insert enough BNC cable to bring the peak back toward the front of the un-wrapped curve.
zeropadded = pylab.np.array(
struct.unpack_from( "="+str(curve['Channels'])+"l", binarydata, offset=curve['DataOffset'] ),
dtype=pylab.np.int
)
nbins = 1.0/self.curveheaders[0]['InpRate0']/self.curveheaders[0]['Resolution']/1.0e-9
nfullbins = pylab.floor( nbins )
npartialbins = pylab.mod( nbins, 1 )
if npartialbins > 0.0:
# I think in this case (which is almost always the case), the first bin is sometimes
# the 'partial' bin, and the last bin is sometimes the 'partial' bin. So we'll delete them.
# (or uncomment other line to add them.)
###zeropadded[0] += zeropadded[ nfullbins ] # add the partial bin to the first bin
self.curves.append( zeropadded[1:nfullbins] )
else:
self.curves.append( zeropadded[:nfullbins] )
self.raw_curves = self.curves[:]
def set_axes( self, axes ):
""" this allows you to plot the data to a particular axes
"""
self.ax = axes
def zero_except( self, tstart=None, tend=None):
""" blank curve[0] of a trace except within the given window.
Useful for removing background and reflections from IRF.
"""
self.curves[0][pylab.find(self.t[0]<tstart)] = 0.0
self.curves[0][pylab.find(self.t[0]>tend)] = 0.0
def set_irf( self, irf=None, wraptime=None, dispersion=None ):
"""
The detector response isn't a delta-function, meaning that what
you measure isn't the true time-dependence of the system you are
measuring. It's the time dependence of the system convolved with