# -*- coding: utf-8 -*-
# written by Ralf Biehl at the Forschungszentrum Jülich ,
# Jülich Center for Neutron Science 1 and Institute of Complex Systems 1
# Jscatter is a program to read, analyse and plot data
# Copyright (C) 2015 Ralf Biehl
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
"""
Functions to read DLS data, apply the CONTIN analysis and plot the CONTIN results.
This is a wrapper around the original Fortran code of S. Provencher.
As the license is unclear please download and install if needed.
For CONTIN refer to http://s-provencher.com/contin.shtml
**For using CONTIN and the wrapper you need a working CONTIN executable in your path.**
::
# Download the FORTRAN source code from his web page in a browser or
wget http://s-provencher.com/pub/contin/contin.for.gz
gunzip contin.for.gz
# compile with gfortran
gfortran contin.for -o contin
# move to a place in your PATH eg $HOME/local/bin
mv contin $HOME/local/bin/contin
# check if it is executable, open a new shell
which contin
# if not found check if the path is in your PATH variable and set it in .bashrc or .profile
# if still not found may be its not executable; so make it
chmod u+x $HOME/local/bin/contin
# reload the dls module
reload(js.dls)
References
.. [1] S.W. Provencher:
Inverse problems in polymer characterization: Direct analysis of polydispersity with photon correlation
spectroscopy.
Makromol. Chem. 180, 201 (1979).
.. [2] S.W. Provencher:
A constrained regularization method for inverting data represented by linear algebraic or integral equations.
Comput. Phys. Commun. 27, 213 (1982).
.. [3] S.W. Provencher:
CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral
equations.
Comput. Phys. Commun. 27, 229 (1982).
---------------
"""
from __future__ import print_function
from __future__ import division
import io
import codecs
import collections
import locale
import numpy as np
import os
import sys
import time
import subprocess
from math import pi
from . import formel
from . import GracePlot
from .dataarray import dataArray as dA
from .dataarray import dataList as dL
# As CONTIN uses cgs units we use these here too
kb = 1.3806505e-16 # cm2 g s-2 K-1
path = os.path.realpath(os.path.dirname(__file__))
# http://bugs.pythoorg/issue1731717
# this workaround at http://bugs.pythoorg/issue1236
# subprocess._cleanup = lambda: None
subprocess._cleanup = lambda: None
# find continexe
# noinspection PyBroadException
try:
p = subprocess.Popen('which contin', shell=True,
bufsize=0,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
continexe = p.communicate()[0].strip()
except:
continexe = ''
[docs]def w2f(word):
"""converts if possible to float"""
try:
return float(word)
except ValueError:
return word
def _visc(T, **kw):
# T in degrees
# a viscosity calculation based on temperature
# calc viscosity in cPoise=1e3*[Pa*s]
if 'v' in kw and kw['v'][0] == 'd':
v = 1e3 * formel.viscosity(mat='d2o', T=T) # heavy water
elif 'v' in kw and kw['v'][0] == 'h':
v = 1e3 * formel.viscosity(mat='h2o', T=T)
elif 'v' in kw:
v = kw['v']
else:
v = 1e3 * formel.viscosity(mat='h2o', T=T) # default to normal water
return v # in cPoise
# noinspection PyUnusedLocal,PyUnusedLocal, PyIncorrectDocstring,PyBroadException
[docs]def contin(datalist, Ngrid=256, tmin=-2, tmax=-2, bgr=0, distribution='dls', RDG=-1, timescale=1e-6, **kw):
"""
Inverse Laplace transform known as CONTIN analysis developed by Steven Provencher [1,2]_ .
This is a wrapper for the original FORTRAN program from Steven Provencher.
The CONTIN analysis is developed for heterodisperse, polydisperse, and multimodal systems that cannot
be resolved with the cumulant method. The resolution for separating two different particle
populations is approximately a factor of five or higher and the difference in relative
intensities between two different populations should be less than 1:10−5. (from Wikipedia and own experience).
Iuser[i] and Rxx are defined in the original Fortran code.
Parameters
----------
datalist : dataList or single dataArray
Correlation data as measured e.g. by DLS
Check type parameter dependent on input data g1 or g2!!!!
Each dataArray is processed.
timescale : float; default 1e-6
Timescale factor. CONTIN uses internal units of seconds.
As typical instruments use microseconds the time axis needs to be scaled by this factor.
Ngrid : int
Number of points between min and max.
tmin,tmax : float,int
First/last time value to use in fit
If tmin/tmax are negative the number of points are dropped at begin/end.
eg min=-5 drops the 5 shortest time points; for max the 5 last points
gmin/gmax : float
First/last time value in distribution
If not given calculated from tmin/tmax according to used kernel
qgmin/qgmax : float
Only for 'relax'. Same as gmin,gmax except that gmin=qgmin/q^2
qtmin/qtmax : float
q^2*t scaled times
bgr : float; default 0
if >0:assume background and fit it
typ : int; default 0
| 0 input is g1 (Field correlation)
| -> means dont change input in datalist
| 1 input is intensity correlation g2 as measured by DLS;
| => field correlation is calculated as g1= sign(g2/R21-1)*abs(g2/R21-1)^0.5
| -1 input is g2-1 of DLS, takes only the square root; sign preserved as for 1
| ALV and Malvern Zetasizer save g2-1
distribution : string, default='x',
| selects Iuser(10) as {'m':1,'D':2,'L':2,'r':3,'u':4,'x':4,'d':4,'T':4} according to first character of
| distribution and RUSER(15,16,17,19). The kernel g1=l^R23*exp(-R21*t*l^R22) describes
| always the field correlation.
| 'molweight' weight fraction molecular weight (mw) distribution l=mw -> mw*exp(-q**2*R18**t*mw**R22)
| or 'mass' R18 to be set in input: relates diffusion coefficient to mol weight
| R22 to be set in input: exponent eg for polymers IN THETA solvent Diffusion=R18*l^0.5
| R21 = R20**2*R18=q**2*R18
| R23=1
| l=mw and field(0)~ mw for weight fraction (intensity ~ mw**2)
| 'Diff' intensity weight diffusion coefficient distribution with l=Diff -> exp(-q**2*t*Diff);
| R23 =0
| R22 =1
| R21 =R20^2=q^2
| l is therefore Diffusion coefficient
| 'Laplace' Laplace transform with exp(-t*Gamma)
| R23=0
| R22=1
| sets R16=0 (wavelength) => R20=undefined and R21=1
| l is therefore relaxation rate with transform exp(-t*l)
| 'radius' weight fraction radius distribution of spheres satisfying stokes law
| with l=rh -> rh**3*exp(-q**2* [k_B*T*/(0.06*pi*visc*rh)] * t)
| R23=3
| R22=-1
| R21=k_B*R18*R20^2/(0.06pi*R19)
| and l=rh is a the hydrodynamic radius
| weight fraction as field(0) ~ V ~ rh**3 for spheres
| 'x' or intensity weight relaxation times distribution l=T -> exp(-t/T)
| R21=1, R22=-1, R23=0
| 'T1' intensity weight relaxation times distribution l=T1 -> T1*exp(-t/T1)
| for nmr rotational diffusion T1 with R23=-1 R21=1, R22=-1 neglecting T2
| intensity ~ number of contributing atoms ~M ~ V ~ Rh**3
| rot correlation time tc=(4pi*visc/kT)*Rh**3 -> intensity(0) ~ tc -> R23=1
| 'dls' intensity weight radius distribution of spheres satisfying stokes law
| with l=rh -> exp(-q**2* [k_B*T*/(0.06*pi*visc*rh)] * t)
| R21 = k_B*T*/(0.06*pi*visc)
| R22=-1, R23=0
| 'user' general case RUSER(15,16,17,19,21,22,23) all user specified
| eg R16=0,R23=0,R22=-1,R21=0 is laplace with Gamma^-1
RDG : 1,-1, default = -1
| 1 use rayleighDebyeGans formfactor with wall thickness WALL [cm] of hollow sphere (default WALL =0)
| distribution needs to be the 'radius' distribution
l : float , R16 overrides value in dataArray if given
| overrides wavelength in data and set it to 632 nm
| (except for Laplace and relaxation then l=0 overrides this because no wavelength is used)
n : float, R15, overrides value in dataArray if given
refractive index of solvent, default 1.333
a : float, R17, overrides value in dataArray if given
scattering angle in degrees , default 90°
T : float, R18, overrides value in dataArray if given
absolute Temperature in K or factor ; default is 293K
WALL : float, R24, overrides value in dataArray if given
| wall thickness in cm for simulation of hollow spheres =0 normal sphere as default
| if WALL<=0 will cause the hard sphere to be calculated wall =0
v : float, R19, overrides value in dataArray if given
| viscosity in centipoise; 1cPoise=1e-3 Pa*s
| if v='d2o' or 'h2o' the viscosity of d2o or h2o at the given Temperature T is used
| default is h2o at T
write : any
if write is given as keyword argument (write=1) the contin output is writen to a file with the '.con' as ending
Returns
-------
| input dataList with best solution added as parameters with name 'contin_'::
| .contin_bestFit best fit result see below!!!!!!!!!!!!!!!!!
| .contin_result_fit fitted correlation function
| .contin_fits sequence of fits in CONTIN same structure as best Fit
| .contin_alpha ALPHA of best solution
| .contin_alphalist list of ALPHA in fits
|
| dataArray with best solution distribution
| .contin_bestFit [relaxation times,intensity weight ci, errors, hydrodynamic radii,
| mass weight ci , number weight ci]
| .contin_bestFit.attr() shows all available parameters from CONTIN fit and other parameters
| .contin_bestFit.fitquality measure for the fit quality ALPHA and ...
| .contin_bestFit.peaks original CONTIN results
| as intensity weight relaxation time distribution
| .contin_bestFit.ipeaks data extracted from the peaks as list for all peaks in a solution
| it is a intensity weight relaxation time distribution
| [weight peak weight
| mean peak mean
| std peak standard deviation
| mean_err error of mean
| imean index of peak mean
| 1/(q**2*mean) diffusion constant in cm**2/s
| Rh hydrodynamic radius in cm
| q wavevector in 1/cm
| 1/(q**2*mean) diffusion constant in nm**2/ns
| Rh hydrodynamic radius in nm
| q wavevector in 1/nm
| ]
| .contin_bestFit.mpeaks same for 2 strongest peaks mass weight
| .contin_bestFit.npeaks same for 2 strongest peaks number weight
| .contin_bestFit.ipeaks_name' content of ipeaks
| .contin_bestFit.info'
| .contin_bestFit.baseline' baseline + error
| .contin_bestFit.momentEntireSolution' all peaks together
| .contin_bestFit.maximum' maximum value
| .contin_bestFit.filename'
| .contin_bestFit.imaximum' index maximum value
Notes
-----
CONTIN source and executable can be downloaded from http://s-provencher.com/pages/contin.shtml.
Download and compile as (Linux and MacOS)
::
wget http://s-provencher.com/pub/contin/contin.for.gz
gunzip contin.for.gz
gfortran contin.f -o contin
# move to a place in your PATH that this module can find it
If Peaks seem small please integrate them as the peak area determines the contributed intensity.
Peak area is strongly dependent on the peak point separation P(i)*(x[i]-x[i-1]) in particular on a log scale.
See [4]_ for details.
::
ff=result.contin_bestFit
p1=(10<ff.X) &(ff.X<100) # peak borders 10 and 100
fp1=np.trapz(ff.Y[p1],ff.X[p1])/np.trapz(ff.Y,ff.X) # fraction of full signal
**An example dataset with noise**
Please test noise
::
import jscatter as js
import numpy as np
t=js.loglist(1,100000,1000) #times in microseconds
q=4*np.pi/1.333/632*np.sin(np.pi/2) # 90 degrees for 632 nm , unit is 1/nm**2
D=0.05*1000 # nm**2/ns * 1000 = units nm**2/microseconds
noise=0.0001 # typical < 1e-3
data=js.dA(np.c_[t,0.95*np.exp(-q**2*D*t)+noise*np.random.randn(len(t))].T)
#Add attributes to overwrite defaults
data.Angle =90 # scattering angle in degrees
data.Temperature=293 # Temperature of measurement in K
data.Viscosity =1 # viscosity cPoise
data.Refractive =1.333 # refractive index
data.Wavelength =632 # wavelength
p=js.grace()
p.plot(data,legend='simulated data')
p.xaxis(scale='l')
# do CONTIN
dr=js.dls.contin(data,distribution='x')
js.dls.contin_display(dr) # display overview
# same but do it yourself
p.plot(dr,li=1,sy=0,le='contin')
p.plot(dr.contin_bestFit[0].X,dr.contin_bestFit[0].Y*100,sy=2)
p.legend()
# access peak values by
dr[0].contin_bestFit.ipeaks_name
dr[0].contin_bestFit.ipeaks
R20 (scattering vector) is calculated as R20= 4e-7*pi*R15/R16*sin(R17/2), if R16!=0. else R20=0
References
----------
.. [1] CONTIN: A general purpose constrained regularization program for inverting
noisy linear algebraic and integral equations
Provencher, S Computer Physics Communications 27: 229.(1982)
doi:10.1016/0010-4655(82)90174-6.
.. [2] http://s-provencher.com/pub/contin/cpc2.pdf.
.. [3] A constrained regularization method for inverting data represented by linear algebraic or integral equations
Provencher, S. W. Comp. Phys. Commu 27: 213–227. (1982)
doi:10.1016/0010-4655(82)90173-4
.. [4] Transformation Properties of Probability Density Functions
Stanislav Sýkora
Permalink via DOI: 10.3247/SL1Math04.001
Original code description in CONTIN
::
C THE FOLLOWING ARE THE NECESSARY INPUT - 0460
C 0461
C DOUSIN = T (DOUSIN MUST ALWAYS BE .TRUE..) 0462
C 0463
C LUSER(3) = T, TO HAVE FORMF2, THE SQUARED FORM FACTORS, COMPUTED IN 0464
C USERK. 0465
C = F, TO SET ALL THE FORMF2 TO 1. (AS WOULD BE APPROPRIATE 0466
C WITH LAPLACE TRANSFORMS). 0467
C RUSER(24) MAY BE NECESSARY INPUT TO SPECIFY THE FORM FACTOR (E.G., 0468
C THE WALL THICKNESS OF A HOLLOW SPHERE) IF LUSER(3)=T. SEE 0469
C COMMENTS IN USERK. 0470
C IUSER(18) MAY BE NECESSARY INPUT IF LUSER(3)=T (E.G., TO SPECIFY THE 0471
C NUMBER OF POINTS OVER WHICH THE SQUARED FORM FACTORS WILL 0472
C BE AVERAGED). SEE COMMENTS IN USERK. 0473
C 0474
C RUSER(16) = WAVELENGTH OF INCIDENT LIGHT (IN NANOMETERS), 0475
C = 0, IF RUSER(20), THE MAGNITUDE OF THE SCATTERING VECTOR 0476
C (IN CM**-1), IS NOT TO BE COMPUTED. WHEN 0477
C RUSER(16)=0, RUSER(15) AND RUSER(17) NEED NOT BE 0478
C INPUT, AND CONTIN WILL SET RUSER(21)=1 0479
C (AS APPROPRIATE WITH LAPLACE TRANSFORMS). 0480
C 0481
C RUSER(15) = REFRACTIVE INDEX. 0482
C RUSER(17) = SCATTERING ANGLE (IN DEGREES). 0483
C 0484
C 0485
C IUSER(10) SELECTS SPECIAL CASES OF USERK FOR MORE CONVENIENT USE. 0486
C 0487
C IUSER(10) = 1, FOR MOLECULAR WEIGHT DISTRIBUTIONS FROM PCS 0488
C (WHERE THE SOLUTION, S(G), IS SUCH THAT S(G)DG IS 0489
C THE WEIGHT FRACTION WITH MOLECULAR WEIGHT BETWEEN 0490
C G AND G+DG). 0491
C CONTIN SETS - 0492
C RUSER(23) = 1., 0493
C RUSER(21) = RUSER(18)*RUSER(20)**2. 0494
C (SEE ABOVE DISCUSSION OF RUSER(16).) 0495
C YOU MUST INPUT - 0496
C RUSER(18) TO SATISFY THE EQUATION (IN CGS UNITS) - 0497
C (DIFFUSION COEFF.)=RUSER(18)*(MOL. WT.)**RUSER(22). 0498
C RUSER(22) (MUST ALSO BE INPUT, TYPICALLY ABOUT -.5). 0499
C 0500
C IUSER(10) = 2, FOR DIFFUSION-COEFFICIENT DISTRIBUTONS OR LAPLACE 0501
C TRANSFORMS (WHERE G IS DIFF. COEFF. IN CM**2/SEC 0502
C OR, E.G., TIME CONSTANT). 0503
C CONTIN SETS - 0504
C RUSER(23) = 0., 0505
C RUSER(22) = 1., 0506
C RUSER(21) = RUSER(20)**2 (SEE ABOVE DISCUSSION 0507
C OF RUSER(16).). 0508
C 0509
C IUSER(10) = 3, FOR SPHERICAL-RADIUS DISTRIBUTIONS, ASSUMING THE 0510
C EINSTEIN-STOKES RELATION (WHERE THE SOLUTION, S(G), 0511
C IS SUCH THAT S(G)DG IS THE WEIGHT FRACTION OF 0512
C PARTICLES WITH RADIUS (IN CM) BETWEEN G AND G+DG. 0513
C WEIGHT-FRACTION DISTRIBUTIONS YIELD BETTER SCALED 0514
C PROBLEMS THAN NUMBER-FRACTION DISTRIBUTIONS, WHICH 0515
C WOULD REQUIRE RUSER(23)=6.) 0516
C CONTIN SETS - 0517
C RUSER(23) = 3., 0518
C RUSER(22) = -1., 0519
C RUSER(21) = RUSER(20)**2*(BOLTZMANN CONST.)* 0520
C RUSER(18)/(.06*PI*RUSER(19)). 0521
C (SEE ABOVE DISCUSSION OF RUSER(16).) 0522
C YOU MUST HAVE INPUT - 0523
C RUSER(18) = TEMPERATURE (IN DEGREES KELVIN), 0524
C RUSER(19) = VISCOSITY (IN CENTIPOISE). 0525
C 0526
C IUSER(10) = 4, FOR GENERAL CASE, WHERE YOU MUST HAVE INPUT - 0527
C RUSER(J), J = 21, 22, 23. 0528
C 0529
C 0530
"""
# contin is called in a shell like: contin <input.txt >output.txt
# we mimic this by subprocess.Popen
# create a pipe to CONTIN with the input to stdin and stdout to
# outputpath = os.path.realpath(os.path.dirname(__file__))
if continexe == '':
raise Exception('There is no contin executable found. ' +
'Please compile and place executable at callable path. ' +
'See documentation of DLS module.')
if datalist._isdataArray:
datalist = dL(datalist)
elif isinstance(datalist, str):
datalist = dL(datalist)
if len(datalist) == 0:
raise NameError('There are no data in datalist')
# some consistency tests
if 'typ' in kw:
typ = kw['typ']
else:
typ = 0
if 'R22' in kw:
if kw['R22'] == 0:
raise ValueError('R22 is not allowed to be equal zero if given')
# some defaults and functions #######################################################
if distribution[0] == 'L':
# set R16 =0 to get a real laplace l is otherwise the wavelength
l = 0
elif 'l' in kw:
l = kw['l'] # which is set with a default here
else:
l = 632. # in nm
if 'T' in kw:
T = kw['T']
del kw['T']
if T < 273:
print('Warning: temperature below ZERO!! ')
elif 'R18' in kw:
T = kw['R18'] # here we mis use the T as the proportionality constant for kernel molweight
else:
T = 293. # in K
if 'n' in kw:
n = kw['n'] # default refractive index
else:
n = 1.333
if 'a' in kw:
a = kw['a'] # default angle
else:
a = 90. # in degrees
if bgr != 0:
bgr = 1 # no background
# wavevector in 1/cm
qq = lambda n, ll, theta: 4. * pi * n / (ll * 1e-7) * np.sin(np.deg2rad(theta) / 2.)
# hydrodynamic radius Rh(relaxationtime) in cm, gamma is relaxation time visc in cPoise
Rh = lambda gamma, q, T, visc: kb * T / (pi * 0.06 * visc) * q ** 2 * gamma
# mass_weight(t)
massweight = lambda ci, qq, Ri: ci * Ri ** 3 * qq ** 6 / (np.sin(qq * Ri) - qq * Ri * np.cos(qq * Ri)) ** 2
# number weight
numberweight = lambda ci, qq, Ri: ci * qq ** 6 / (np.sin(qq * Ri) - qq * Ri * np.cos(qq * Ri)) ** 2
##########################################################
# DEFINES THE kind of the distribution kernel
distr = {'m': 1,
'D': 2, 'L': 2,
'r': 3,
'u': 4, 'x': 4, 'd': 4, 'T': 4}
if distribution[0] == 'm' and 'R18' not in kw.keys() and 'R21' not in kw.keys():
print('please provide R18 and R22 in input to function')
print("to describe D=R18*molWeight^R22 eg polymer R22=0.5")
return
if 'R22' in kw:
R22 = float(kw['R22'])
else:
R22 = 0
if 'R21' in kw:
R21 = float(kw['R21'])
else:
R21 = 0
if 'R23' in kw:
R23 = float(kw['R23'])
else:
R23 = 0
edist = ('x', 'T', 'd') # here we set R21-R23 explicitly in the later header part
if distribution[0] == 'x': # l^R23*exp(-R21*t*l^R22) with R21=1,R22=-1,R23=0 ==> l=1/T -> exp(-t/T)
R21 = 1
R22 = -1
R23 = 0
if distribution[0] == 'T': # l^R23*exp(-R21*t*l^R22) with R21=1,R22=-1,R23=1 ==> l=T -> T*exp(-t/T)
R21 = 1
R22 = -1
R23 = 1
if distribution[0] == 'd': # l^R23*exp(-R21*t*l^R22) with R22=-1,R23=0 ==> l=Rh -> 1*exp(-R21*t/Rh)
# I(t)=exp(- [ q**2*k_B*T*/(0.06*pi*visc)] * t/Rh )
# l-> rh ; t=t ; R21-> q**2* [k_B*T*/(0.06*pi*visc)]
R21 = kb * T / (0.06 * pi * _visc(T, **kw)) * qq(n, l, a) ** 2 # D*Rh * Q**2
R22 = -1
R23 = 0
# write header für CONTIN als ASCII inputfile #######################################
last = 1 # for multi dataset evaluation this is -1 (false) except for the last one
# we process here ONLY single files
elements = 40 # just to have an array for it; last 2 lines are "end" and NY
header = np.array([''] * elements, dtype='|S70') # a single fortran line
header[0] = 'filename'.ljust(70) # the loaded file
header[1] = 'LAST'.ljust(6) + str().rjust(5) + ('%15.4E' % last) # see above
# header[2]='GMNMX'.ljust(6)+str(1).rjust(5)+('%15.4E' % gmin) # first point of the distribution to fit
# header[3]='GMNMX'.ljust(6)+str(2).rjust(5)+('%15.4E' % gmax) # last point of the distribution to fit
header[4] = 'IWT'.ljust(6) + str().rjust(5) + (
'%15.4E' % 5) # fit strategy how to determine errors -> 5 from a prefit, results in 2 fits but good errors
# unweighted fit IWT=1 ->errors equal; IWT=4 direct input of errors not implemented
header[5] = 'NERFIT'.ljust(6) + str().rjust(5) + (
'%15.4E' % 0) # number of points around a point to determine error; safety margin default 10; we use 0
header[6] = 'NINTT'.ljust(6) + str().rjust(5) + (
'%15.4E' % -1) # number of equally spaced sets in tk; <0 means direct input as used here
header[7] = 'IFORMT'.ljust(6) + str().rjust(20) # format of time variable for direct input
header[8] = '(1E12.5)'.ljust(26) # 1 in a row
header[9] = 'IFORMY'.ljust(6) + str().rjust(20) # format of y variable for direct input correlation
header[10] = '(1E12.5)'.ljust(26) # 1 in a row
if 'IGRID' in kw.keys():
# Grid=2 is log grid ; 1 is equally spaced grid; default 2= log grid
header[11] = 'IGRID'.ljust(6) + str().rjust(5) + ('%15.4E' % float(kw['IGRID']))
header[12] = 'NLINF'.ljust(6) + str().rjust(5) + ('%15.4E' % bgr) # allows a single const background , 0 no bgr
header[13] = 'NG'.ljust(6) + str().rjust(5) + ('%15.4E' % Ngrid) # Ngrid points between gmin,gmax
header[14] = 'DOUSIN'.ljust(6) + str().rjust(5) + (
'%15.4E' % 1) # Do User INPUT ; to use the below given values anyway this is the default
header[15] = 'IUSER'.ljust(6) + str(10).rjust(5) + (
'%15.4E' % distr[distribution[0]]) # selects the kernel see help above
header[16] = 'RUSER'.ljust(6) + str(15).rjust(5) + ('%15.4E' % n) # refractive index
header[17] = 'RUSER'.ljust(6) + str(16).rjust(5) + ('%15.4E' % l) # wavelength in nm
header[18] = 'RUSER'.ljust(6) + str(17).rjust(5) + ('%15.4E' % a) # scattering angle in degrees
header[19] = 'RUSER'.ljust(6) + str(18).rjust(5) + (
'%15.4E' % T) # absolute Temperature in K or proportionality constant
header[20] = 'RUSER'.ljust(6) + str(19).rjust(5) + ('%15.4E' % _visc(T, **kw)) # viscosity in centipoise
header[25] = 'RUSER'.ljust(6) + str(10).rjust(5) + ('%15.4E' % typ) # (0) means dont change; input is g1;
# (1) input is intensity correlation g2; => calculate (g2/R21-1)^0.5
# (-1) input is g2-1, takes only the square root
# ALV and Zetasizer Data are g2-1 -> -1
header[26] = 'LUSER'.ljust(6) + str(3).rjust(5) + (
'%15.4E' % RDG) # use rayleighDebyeGans formfactor (set to true => 1) or const 1 (set to false => -1 )
if 'R16' in kw.keys(): header[17] = 'RUSER'.ljust(6) + str(16).rjust(5) + ('%15.4E' % float(kw['R16']))
if 'WALL' in kw.keys(): header[24] = 'RUSER'.ljust(6) + str(24).rjust(5) + ('%15.4E' % float(
kw['WALL'])) # wall thickness in cm in RDG for simulating hollow spheres =0 normal sphere
if 'ALPS1' in kw.keys(): header[27] = 'ALPST'.ljust(6) + str(1).rjust(5) + (
'%15.4E' % float(kw['ALPS1'])) # take this alpha in preliminary error analysis
if 'ALPS2' in kw.keys(): header[28] = 'ALPST'.ljust(6) + str(2).rjust(5) + (
'%15.4E' % float(kw['ALPS2'])) # take this alpha in final analysis and as choosen solution
if 'I18' in kw.keys(): header[29] = 'IUSER'.ljust(6) + str(18).rjust(5) + (
'%15.4E' % float(kw['I18'])) # formfactor average over 2 I18+1 points
if distribution[0] in edist or 'R21' in kw.keys(): header[22] = 'RUSER'.ljust(6) + str(21).rjust(5) + (
'%15.4E' % R21)
if distribution[0] in edist or 'R23' in kw.keys(): header[23] = 'RUSER'.ljust(6) + str(23).rjust(5) + (
'%15.4E' % R23)
if distribution[0] in edist or 'R22' in kw.keys(): header[21] = 'RUSER'.ljust(6) + str(22).rjust(5) + (
'%15.4E' % R22)
if 'IQUAD' in kw.keys(): header[30] = 'IQUAD'.ljust(6) + str().rjust(5) + (
'%15.4E' % float(kw['IQUAD'])) # quadrature default=3 Simpson 1 is direct; 2 is trapezoidal
if 'NORDER' in kw.keys(): header[30] = 'NORDER'.ljust(6) + str().rjust(5) + (
'%15.4E' % float(kw['NORDER'])) # order regularization; default 2
if 'PLEVEL' in kw.keys():
word = ('%5.2f' % float(kw['PLEVEL']))
header[31] = 'PLEVEL'.ljust(6)
header[32] = (word * 4) # 0.1<PLEVEL<0.9 best is not to use it, default =0.5
if 'NONNEG' in kw.keys():
header[33] = 'NONNEG'.ljust(6) + str().rjust(5) + (
'%15.4E' % float(kw['NONNEG'])) # no negative values in the solution default is 1;
else:
# default is 1 as a distribution has no negative values
header[33] = 'NONNEG'.ljust(6) + str().rjust(5) + ('%15.4E' % 1)
header[-2] = 'END'.ljust(26)
header[-1] = 'NY'.ljust(6) + str(0).rjust(5) # Number of datapoints is set per file later
# ende header für CONTIN als ASCII inputfile ##################################
# a default grid min max is needed if it is not given explicitly
# to transform tmin and tmax according to this function from kernel exp 1=t*R21*l**R22 -> l=(t*R21)**-1/R22
if distribution[0] == 'L':
transk = lambda t, n, l, a, T, v, R22, R21: 1 / t
elif distribution[0] == 'm':
transk = lambda t, n, l, a, T, v, R22, R21: (t * T * qq(n, l, a) ** 2) ** (-1 / R22)
elif distribution[0] == 'D':
transk = lambda t, n, l, a, T, v, R22, R21: (t * qq(n, l, a) ** 2) ** (-1)
elif distribution[0] == 'r':
transk = lambda t, n, l, a, T, v, R22, R21: (t * kb * T * qq(n, l, a) ** 2 / (0.06 * pi * v))
elif distribution[0] == 'd':
transk = lambda t, n, l, a, T, v, R22, R21: (t * kb * T * qq(n, l, a) ** 2 / (0.06 * pi * v))
elif distribution[0] == 'u' or distribution[0] == 'x':
transk = lambda t, n, l, a, T, v, R22, R21: (t * R21) ** (-1 / R22)
elif distribution[0] == 'T':
transk = lambda t, n, l, a, T, v, R22, R21: (t * R21) ** (-1 / R22)
###################################
# now look at the data
idata = 0
print('processing %i datasets ' % len(datalist))
for data in datalist:
idata += 1
print(idata)
try:
file = getattr(data, '@name')[0]
except AttributeError:
file = time.strftime("%y%m%d%H%M%S", time.localtime())
header[0] = file
data.extract_comm(deletechars=':[]()"') # find extra parameters in comments
# data[0]*=0.001 # have all in s not in ms from ALV
# extract datetime from comments from ALV instrument
timestr = ''
for line in data.comment:
if line[:4] == 'Time' or line[:4] == 'Date':
try:
timestr += line.translate(None, '"').split()[2]
datetime = time.mktime(time.strptime(timestr, "%d.%m.%Y%H:%M:%S"))
except:
pass
# noinspection PyBroadException
try:
data.datetime = datetime
except:
data.datetime = 0
# take values from data
# this allows to have float or list of float (take first)
_getfirst = lambda a: a if isinstance(data.Angle, (float, int)) else a[0]
if 'n' not in kw:
try:
n = _getfirst(data.Refractive)
except:
pass
if 'l' not in kw and distribution[0] != 'L' and 'R16' not in kw:
# noinspection PyBroadException
try:
l = _getfirst(data.Wavelength)
except:
pass
if 'a' not in kw:
try:
a = _getfirst(data.Angle)
except:
pass
if 'T' not in kw:
try:
T = _getfirst(data.Temperature)
except:
pass
if 'v' not in kw:
try:
v = _getfirst(data.Viscosity)
except:
v = _visc(T, **kw)
else:
v = _visc(T, **kw)
# or override it
if l != 0:
contin_wavevector = qq(n, l, a) # in 1/cm
else:
contin_wavevector = 0
# create header for contin with parameters from datafile
try:
if 'qtmin' in kw:
tmin = float(kw['qtmin']) / contin_wavevector ** 2
if 'qtmax' in kw:
tmax = float(kw['qtmax']) / contin_wavevector ** 2
except AttributeError:
raise Exception('dont use qtmin / qtmax with Laplace option. wavevector is zero')
itmin, itmax = data.X.searchsorted((tmin, tmax)) # searches where tmin,tmax fit into list and outputs the place
if tmin < 0:
itmin = -tmin # if negative purge first points
if tmax < 0:
itmax = tmax # same but if negative count from behind
try:
if 'gmin' not in kw and 'qgmin' not in kw:
gmin = transk(data.X[itmin], n, l, a, T, _visc(T, **kw), R22,
R21) # calculate min max of the grid if not given
elif 'qgmin' in kw:
gmin = float(kw['qgmin']) / contin_wavevector ** 2
else:
gmin = float(kw['gmin'])
if 'gmax' not in kw and 'qgmax' not in kw:
gmax = transk(data.X[itmax], n, l, a, T, _visc(T, **kw), R22, R21)
elif 'qgmax' in kw:
gmax = float(kw['qgmax']) / contin_wavevector ** 2
else:
gmax = float(kw['gmax'])
except ZeroDivisionError:
print('wavevector is zero; use qgmax/qgmin only with non laplace option')
header[2] = 'GMNMX'.ljust(6) + str(1).rjust(5) + ('%15.4E' % min(gmin, gmax)) # fit interval min
header[3] = 'GMNMX'.ljust(6) + str(2).rjust(5) + ('%15.4E' % max(gmin, gmax)) # fit interval max
if 'T' not in kw and 'R18' not in kw:
try:
T = data.Temperature[0]
except:
T = 273.15 + 20
header[19] = 'RUSER'.ljust(6) + str(18).rjust(5) + ('%15.4E' % T)
header[16] = 'RUSER'.ljust(6) + str(15).rjust(5) + ('%15.4E' % n)
header[17] = 'RUSER'.ljust(6) + str(16).rjust(5) + ('%15.4E' % l)
header[18] = 'RUSER'.ljust(6) + str(17).rjust(5) + ('%15.4E' % a)
header[20] = 'RUSER'.ljust(6) + str(19).rjust(5) + ('%15.4E' % v)
p = subprocess.Popen(continexe, shell=True,
bufsize=0,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
# set the number of values
lenX = len(data.X[itmin:itmax])
header[-1] = 'NY'.ljust(6) + str(lenX).rjust(5)
# now write the header and the data to a input buffer
input = io.BytesIO()
input.writelines([b' ' + line + b'\n' for line in header if line != b''])
input.writelines([b' ' + (b'%8.5E' % line) + b'\n' for line in data.X[itmin:itmax]])
if 'test' in kw: # just as a test with simple exponential relax=valuefloat(kw['test'])
input.writelines(
[' ' + ('%8.5E' % line) + '\n' for line in 1e-3 + np.exp(-data.X[itmin:itmax] / float(kw['test']) * 2)])
else:
input.writelines([b' ' + (b'%8.5E' % line) + b'\n' for line in data.Y[itmin:itmax]])
# to check input
if 'write' in kw or 'w' in kw:
with open('./' + 'input.con', 'w') as f:
zeilen = f.writelines(input.getvalue().decode('utf-8'))
# now run contin in a shell like environment with pipes
# with input in stdin and collect the stout and error in output and error
(output, error) = p.communicate(input.getvalue())
output = output.decode('utf-8')
error = error.decode('utf-8')
input.close()
if error != '':
print(file, ' :', error)
if len(output) == 0:
print('there was nothing in output yet')
return
# to check output
if 'write' in kw or 'w' in kw:
with open('./' + file + '.con', 'w') as f:
zeilen = f.writelines(output)
# CONTIN finished and we look at the output ------------------------------------
# sort the output to the data
outblocks = output.split('UNREGULARIZED VARIABLES')[-1].split(file)
if len(outblocks) < 3:
print('last lines of CONTIN output')
print(outblocks[-1])
raise Exception('CONTIN ended with no result; use w=1 to get output for analysis')
# blocks of different alpha in result (without prefit results);
# first repeat input ; then preliminary res; last two choosen solution
# second last is fit ; last is result distribution
# take the fit block after Abscissa;splitlines and take only lenX;split line to words and convert to float
temp = np.r_[[[float(vv) for vv in line[:22].split()] for line in
outblocks[-2].split('ABSCISSA\n')[-1].splitlines()[:lenX]]].T
data.contin_result_fit = temp[[1, 0]] # resort to have xtime in 0 and fit_y in 1
data.contin_fits = []
for k in range(len(outblocks))[1:-2]: # all solutions with different alpha
chosen = outblocks[k].splitlines()
# take the chosen block line 6 to 6+Ngrid;split into words and convert to float;
# sometimes D is used instead of E for float 1E5
# order of temp -> [y, error_y, t]
temp = np.r_[[[float(vv) for vv in line[:31].replace('D', 'E').split()] for line in chosen[6:Ngrid + 6]]].T
# fit quality in 3rd line of chosen last chosen block
try:
RR = Rh(temp[2] * timescale, contin_wavevector, T,
v) # hydrodynamic radius temp[2] is correlation time]
ci_massw = massweight(temp[0], contin_wavevector,
RR) # mass weighted contribution ci temp[0] is fraction
ci_numw = numberweight(temp[0], contin_wavevector, RR) # number weighted contribution
# tohave [t,y,error_y] and hydrodyn Radius, mass weight and number weighting the result output
data.contin_fits.append(dA(np.c_[temp[[2, 0, 1]].T, RR, ci_massw, ci_numw].T))
except:
data.contin_fits.append(dA(np.c_[temp[[2, 0, 1]].T].T))
# fitquality from contin output
data.contin_fits[-1].fitquality = {}
name = [aa.lstrip() for aa in chosen[2].lstrip().split(' ')]
line = chosen[3].split()
if line[0] == '*':
value = [float(li) for li in line[1:]]
else:
value = [float(li) for li in line[:]]
for i in range(len(name)):
data.contin_fits[-1].fitquality[name[i]] = value[i]
# look at the peaks found at the end of solution block
lines = chosen[Ngrid + 6:]
linesl = []
data.contin_fits[-1].peaks = []
entireSol = -1
for i in range(len(lines)):
if lines[i][:5] == '0PEAK':
linesl.append(i) # first peak line number
if lines[i].strip()[:7] == 'MOMENTS':
entireSol = i # entire solution line number
# append if entire solution exists (does not for single peaks)
if entireSol != -1:
data.contin_fits[-1].momentEntireSolution = {}
for j in 1, 2, 3, 4, 5:
val = lines[entireSol + j][44:].replace('X (10**', '').replace(')', '').split()
data.contin_fits[-1].momentEntireSolution['mom_' + val[0]] = \
[w2f(vv) for vv in [val[0], val[1] + 'E' + val[2]] + val[3:-1]]
data.contin_fits[-1].momentEntireSolution['std/mean'] = w2f(lines[entireSol + 4][:45].split()[-1])
data.contin_fits[-1].baseline = [w2f(vv) for vv in np.r_[lines[0][22:].replace('D', 'E').split()][[0, 2]]]
for i in linesl:
data.contin_fits[-1].peaks.append({}) # append a new peak
words = lines[i].split()
data.contin_fits[-1].peaks[-1]['nr'] = w2f(words[1])
data.contin_fits[-1].peaks[-1]['minmax'] = [w2f(words[4]), w2f(words[6])]
data.contin_fits[-1].peaks[-1]['iminmax'] = [
abs(data.contin_fits[-1][0] - data.contin_fits[-1].peaks[-1]['minmax'][0]).argmin(),
abs(data.contin_fits[-1][0] - data.contin_fits[-1].peaks[-1]['minmax'][1]).argmin()]
for j in 1, 2, 3, 4, 5:
val = lines[i + j][44:].replace('X (10**', '').replace(')', '').split()
data.contin_fits[-1].peaks[-1]['mom_' + val[0]] = [w2f(vv) for vv in
[val[0], val[1] + 'E' + val[2]] + val[3:-1]]
data.contin_fits[-1].peaks[-1]['std/mean'] = w2f(lines[i + 4][:45].split()[-1])
data.contin_fits[-1].peaks[-1]['weight'] = data.contin_fits[-1].peaks[-1]['mom_0'][1]
data.contin_fits[-1].peaks[-1]['mean'] = data.contin_fits[-1].peaks[-1]['mom_1'][3]
data.contin_fits[-1].peaks[-1]['imean'] = data.contin_fits[-1][0].searchsorted(
data.contin_fits[-1].peaks[-1]['mean'])
data.contin_fits[-1].peaks[-1]['mean_errproz'] = data.contin_fits[-1].peaks[-1]['mom_1'][4]
data.contin_fits[-1].peaks[-1]['mean_err'] = data.contin_fits[-1].peaks[-1]['mom_1'][4] / 100. * \
data.contin_fits[-1].peaks[-1]['mom_1'][3]
# standard deviation is std=sqrt(mom2/mom1- mean**2)
data.contin_fits[-1].peaks[-1]['std'] = abs(
data.contin_fits[-1].peaks[-1]['mom_2'][1] / data.contin_fits[-1].peaks[-1]['mom_0'][1] -
data.contin_fits[-1].peaks[-1]['mom_1'][3] ** 2) ** 0.5
data.contin_fits[-1].peaks[-1]['mean_num'] = data.contin_fits[-1].peaks[-1]['mom_0'][3]
if distribution[0] == 'm': # mass fraction mol weight see provencher
data.contin_fits[-1].peaks[-1]['mean_mass'] = data.contin_fits[-1].peaks[-1]['mom_1'][3]
data.contin_fits[-1].peaks[-1]['mean_z'] = data.contin_fits[-1].peaks[-1]['mom_1'][3]
data.contin_fits[-1].peaks[-1]['mean_z+1'] = data.contin_fits[-1].peaks[-1]['mom_1'][3]
# now the chosen solution from CONTIN, which is the last given (repeated)
chosen = outblocks[-1].splitlines()
data.contin_alpha = float(chosen[3].split()[0]) # this is the choosen solution alpha
data.contin_alphalist = [f.fitquality['ALPHA'] for f in data.contin_fits] #
data.contin_bestFit = data.contin_fits[
data.contin_alphalist.index(data.contin_alpha)] # this is the choosen solution
data.contin_bestFit.imaximum = data.contin_bestFit[0].searchsorted(data.contin_bestFit[1].max())
data.contin_bestFit.columnname = \
'relaxationtimes;intensityweightci; errors; hydrodynamicradii; massweightci;numberweightci'
# a short info about the peaks
if 1: # try:
ipeaks = np.c_[[[p['weight'],
p['mean'],
p['std'],
p['mean_err'],
p['imean'],
(1 / (contin_wavevector ** 2 * p['mean'] * timescale) if contin_wavevector != 0 else 0),
Rh(p['mean'] * timescale, contin_wavevector, T, v),
contin_wavevector,
(1e5 / (contin_wavevector ** 2 * p['mean'] * timescale) if contin_wavevector != 0 else 0),
Rh(p['mean'] * timescale, contin_wavevector, T, v) * 1e7,
contin_wavevector * 1e-7]
for p in data.contin_bestFit.peaks]]
# sort for largest weight but take only npeak strongest
sequence = ipeaks[:, 0].argsort()[::-1] # [:3]
ipeaks = ipeaks[sequence, :]
# and sort the remaining for peak position in wavevector contin_wavevector
sequence = ipeaks[:, 5].argsort()
data.contin_bestFit.ipeaks = ipeaks[sequence, :]
data.contin_bestFit.ipeaks_name = ['weight',
'mean',
'std',
'mean_err',
'imean',
'mean_Deff',
'Rh',
'wavevector',
'mean_Deff_nmns',
'Rh_nm',
'wavevector_nm',
]
else: # except:
ipeaks = np.c_[[[p['weight'],
p['mean'],
p['std'],
p['mean_err'],
p['imean']]
for p in data.contin_bestFit.peaks]]
# sort for largest weight but take only npeak strongest
sequence = ipeaks[:, 0].argsort()[::-1][:3]
ipeaks = ipeaks[sequence, :]
data.contin_bestFit.ipeaks = ipeaks
data.contin_bestFit.ipeaks_name = ['weight',
'mean',
'std',
'mean_err',
'imean']
try:
# calculate the mass weight properties
temp = []
for p in data.contin_bestFit.peaks:
imin = max(0, p['iminmax'][0] - 1)
imax = min(len(data.contin_bestFit[0]), p['iminmax'][1] + 1)
weight = np.trapz(data.contin_bestFit[4, imin:imax], data.contin_bestFit[0, imin:imax])
mmean = np.trapz(data.contin_bestFit[4, imin:imax] * data.contin_bestFit[0, imin:imax],
data.contin_bestFit[0, imin:imax]) / weight
std = np.sqrt(np.trapz((data.contin_bestFit[4, imin:imax] * data.contin_bestFit[0, imin:imax] ** 2),
data.contin_bestFit[0, imin:imax]) / weight - mmean ** 2)
temp.append([weight,
mmean,
std,
p['mean_err'],
min(data.contin_bestFit[0].searchsorted(mmean), len(data.contin_bestFit[0])),
(1 / (contin_wavevector ** 2 * mmean * timescale) if contin_wavevector != 0 else 0),
Rh(mmean * timescale, contin_wavevector, T, v),
contin_wavevector,
(1e5 / (contin_wavevector ** 2 * mmean * timescale) if contin_wavevector != 0 else 0),
Rh(mmean * timescale, contin_wavevector, T, v) * 1e7,
contin_wavevector * 1e-7])
mpeaks = np.c_[temp]
mpeaks[:, 0] = mpeaks[:, 0] / mpeaks[:, 0].sum()
mpeaks = mpeaks[sequence, :]
data.contin_bestFit.mpeaks = mpeaks
# calculate the number weight properties
temp = []
for p in data.contin_bestFit.peaks:
imin = max(0, p['iminmax'][0] - 1)
imax = min(len(data.contin_bestFit[0]), p['iminmax'][1] + 1)
weight = np.trapz(data.contin_bestFit[5, imin:imax], data.contin_bestFit[0, imin:imax])
nmean = np.trapz(data.contin_bestFit[5, imin:imax] * data.contin_bestFit[0, imin:imax],
data.contin_bestFit[0, imin:imax]) / weight
std = np.sqrt(np.trapz((data.contin_bestFit[5, imin:imax] * data.contin_bestFit[0, imin:imax] ** 2),
data.contin_bestFit[0, imin:imax]) / weight - nmean ** 2)
temp.append([weight,
nmean,
std,
p['mean_err'],
min(data.contin_bestFit[0].searchsorted(nmean), len(data.contin_bestFit[0])),
(1 / (contin_wavevector ** 2 * nmean * timescale) if contin_wavevector != 0 else 0),
Rh(nmean * timescale, contin_wavevector, T, v),
contin_wavevector,
(1e5 / (contin_wavevector ** 2 * nmean * timescale) if contin_wavevector != 0 else 0),
Rh(nmean * timescale, contin_wavevector, T, v) * 1e7,
contin_wavevector * 1e-7])
npeaks = np.c_[temp]
npeaks[:, 0] = npeaks[:, 0] / npeaks[:, 0].sum()
npeaks = npeaks[sequence, :]
data.contin_bestFit.npeaks = npeaks
except:
pass
return datalist
# noinspection PyIncorrectDocstring
[docs]def contin_display(result_list, select=None, npeak=2, *args, **kw):
"""
A routine to plot CONTIN results to get an overview over CONTIN output.
Parameters
----------
result_list : dataList
Output of dls.contin.
select : list of int
Sequence of integers in result_list to select for output.
npeak : int
Number of peaks in output default 2.
dlogy :
shows distribution in y logscale
Notes
-----
| access diffusion of all first peaks by output[:,1,6]
| mean and std as
| output[:,1,6].mean()
| output[:,1,6].std()
"""
if select is None:
select = []
p = GracePlot()
p.multi(1, 3)
p1 = p.g[0]
p1.subtitle('correlation')
p2 = p.g[1]
if 'mass' in kw.keys():
p2title = 'mass weighted \\ndistribution'
elif 'number' in kw.keys():
p2title = 'number weighted \\ndistribution'
else: # intensity
p2title = 'intensity weighted \\ndistribution'
p2.subtitle(p2title)
p3 = p.g[2]
p3.subtitle('relaxation times')
if isinstance(select, int):
select = [select]
if not select:
select = range(len(result_list))
for i in select:
data = result_list[i]
p1.plot(data[0], data[1], line=0, symbol=-1)
p1.plot(data.contin_result_fit[0], data.contin_result_fit[1], line=-1, symbol=0)
p1.yaxis(min=0.0001, scale='n')
p1.xaxis(min=0.0001, scale='l')
p1.ylabel('g1(t)')
p1.xlabel(r't')
if 'mass' in args:
YY = data.contin_bestFit[4] / data.contin_bestFit[4].max()
peaks = data.contin_bestFit.mpeaks
elif 'number' in args:
YY = data.contin_bestFit[5] / data.contin_bestFit[5].max()
peaks = data.contin_bestFit.npeaks
else: # intensity
YY = data.contin_bestFit[1] / data.contin_bestFit[1].max()
peaks = data.contin_bestFit.ipeaks
if 'dlogy' in args:
p2.plot(data.contin_bestFit[0], YY,
legend='%.3g a=%.3g PROB1 %.3g' % (data.contin_alpha,
data.contin_bestFit.fitquality['ALPHA'],
data.contin_bestFit.fitquality['PROB1 TO REJECT']),
symbol=-1)
p2.xaxis(min=0.0001, scale='l')
p2.yaxis(min=0.0001, scale='l')
else:
# try:
p2.plot(data.contin_bestFit[0], YY,
legend='%.3g a=%.3g PROB1 %.3g' % (data.contin_alpha,
data.contin_bestFit.fitquality['ALPHA'],
data.contin_bestFit.fitquality['PROB1 TO REJECT']),
symbol=-1)
p2.xaxis(min=0.0001, scale='l')
# except: pass
p2.ylabel('distr(t)')
p2.xlabel(r't')
p3.ylabel(r'peak\smean\N ')
p3.xlabel(r'q / nm\S-1')
for pp in peaks[:npeak]:
p3.plot([pp[10]], [pp[1]], [pp[3] / pp[1]], symbol=-1, line=0)
return
_mode = 'r'
if sys.version_info < (3, 0):
# older version uses universal newline as 'U', from python 3 it is supported by default in 'r' mode
_mode += 'U'
# noinspection PyBroadException
[docs]def readZetasizerNano(filename, NANOcoding="cp1257", NANOlocale=None):
"""
Read ascii file data exported from Malvern Zetasizer Nano.
Format of Zetasizer is one measurement per line with content defined in export macro.
First line gives names of columns as header line, so header line in NANO export is necessary.
Lists as Sizes, Volumes... need to be separated by "sample name" because of different length.
Parameters
----------
filename : string
Zetasizer filename
NANOcoding : string
UTF coding from Windows
NANOlocale : list of string
| encoding of weekday names in NAno textfile
| ['de_DE','UTF-8'] NANO set to German
| ['en_US','UTF-8'] NANO set to US
| ...
Returns
-------
dataList
X,Y columns are
* correlation data [CorrelationDelayTimes,sign(CorrData)*|CorrelationData|**0.5, CorrelationData] if present
* or RelaxationTimes, Sizes, Intensities, Volumes, Diffusions if present
If no correlation or sizes the data are zero and only parameters are stored.
No unit conversion is done.
Parameters contain any other information stored in line.
CorrelationData.Y contains 1-g2=g1**2 with 0 for negative 1-g2.
Notes
-----
The template for export in Zetasizer has some requirements:
- The header line should be included.
- Separator is the tab.
- First entries should be "Serial Number" "Type" "Sample Name" "Measurement Date and Time"
- Sequence data as "Sizes", "Intensities" should be separated by "Sample Name" as separator.
- Appending to same file is allowed if new header line is included.
Examples
--------
::
import jscatter as js
import numpy as np
import glob
files=glob.glob('../rawdata/PC4EG4/pc4eg4*.txt')
for file in files:
data=js.dls.readZetasizerNano(file)
p=js.grace()
p.plot(data) # plots correlation function
"""
if NANOlocale is None:
NANOlocale = ['en_US', 'UTF-8']
seqcolumnNames = ['CorrelationDelayTimes', 'CorrelationData',
'RelaxationTimes', 'Sizes', 'Intensities', 'Volumes', 'Diffusions',
'DistributionFitDelayTimes', 'DistributionFitData', 'DistributionFit']
# DSL data are writen on windows computer with specific encoding
# read it
# locale.resetlocale()
# get locale
old_loc = locale.getlocale(locale.LC_TIME)
# set it to german or english us dependent on DLS Computer language
locale.setlocale(locale.LC_TIME, NANOlocale)
#
with codecs.open(filename, _mode, NANOcoding) as f:
zeilen = f.readlines()
output = dL()
# scan each line
for izeile, zeile in enumerate(zeilen):
# encode in utf8 for correct use in datatime and split line
worte = zeile.rstrip().replace(',', '.').split('\t')
para = {} # single parameters
data = {} # sequence of data
next = 0
# detect a header line
if any(x in worte[:4] for x in ['Serial Number', 'Record', 'Type', 'Sample Name']):
headerline = worte
header = collections.OrderedDict() # the key order is preserved in OrderedDict
for i, field in enumerate(headerline):
# if multiple entries as sequence it can be identified by '['
key = field.split('[')[0].split('(')[0]
if key not in header.keys():
header[''.join(key.split())] = [(i, field, key)]
else:
header[''.join(key.split())] += [(i, field, key)]
continue
if 'Trend' in worte[:4]:
continue # skip Trend lines
if 'Aggregation Point' in worte[:4]:
continue # skip line
for key in header.keys():
if '[' in header[key][0][1] \
or key in seqcolumnNames: # belongs to a sequence of data as distribution fit
start = next
try:
# try to find index of next "SampleName" as separator
next = worte.index(para['SampleName'][0], start)
except (ValueError, KeyError):
print('in line ', izeile, '', key,
' is missing a "sample Name" after the sequence. Check your export template')
continue
data[''.join(key.split())] = worte[start:next]
next += 1
else:
try:
# not a sequence; is only a parameter
if ''.join(key.split()) in para.keys(): # already there
para[''.join(key.split())] += worte[next]
else: # new one
para[''.join(key.split())] = [worte[next]]
except:
print('in line ', izeile, '', key,
' is missing parameter value. Check your export template')
continue
next += 1
if data == {}:
print('in line ', izeile, ' is missing data at all. Check your export template')
continue
# append to output as new dataArray
names = [r'RelaxationTimes', r'Sizes', r'Intensities', r'Numbers', r'Volumes', r'Diffusions']
columnnames = list(set(names).intersection(set(data.keys())))
columnnames = [name for name in names if name in columnnames]
if 'CorrelationDelayTimes' in data.keys() and len(data['CorrelationData']) > 0:
# we save here g1 instead of 1-g2 => take root later to prevent negative roots
cor = np.array(data['CorrelationData'], dtype=float)
output.append(dA(np.c_[np.array(data['CorrelationDelayTimes'], dtype=float),
np.sign(cor) * np.sqrt(np.abs(cor)), cor].T,
XYeYeX=[0, 1]))
del data['CorrelationDelayTimes']
del data['CorrelationData']
output[-1].columnname = 'CorrelationDelayTimes; CorrelationData=g2-1'
elif len(columnnames) > 0:
output.append(dA(np.c_[[np.array(data[name], dtype=float) for name in columnnames]], XYeYeX=[0, 1]))
output[-1].columnname = ''.join(['%s; ' % name for name in columnnames])
for coln in names[2:]:
if coln in columnnames:
output[-1].setColumnIndex(ix=0, iy=columnnames.index(coln), iey=None)
break
for name in columnnames:
del data[name]
else: # only parameters
output.append(dA(np.zeros(3), XYeYeX=[0, 1]))
output[-1].comment += ['only parameters in file']
# append parameters to result
for key, item in para.items():
if key == 'MeasurementDateandTime':
# make a computational time
timestring = ' '.join(item[0].replace(',', '').replace('.', '').split('\t'))
tt = time.mktime(time.strptime(timestring, '%A %d %B %Y %H:%M:%S'))
setattr(output[-1], key.split('(')[0], time.ctime(tt))
setattr(output[-1], 'MeasurementTimeSeconds', tt)
elif key.split('(')[0] == u'T':
setattr(output[-1], 'Temperature', np.array(item[0], dtype=float))
else:
try:
setattr(output[-1], key.split('(')[0], np.array(item[0], dtype=float))
except:
setattr(output[-1], key.split('(')[0], item[0])
try:
# append distribution if not already above saved
distributions = dA(np.c_[[np.array(data[name], dtype=float) for name in columnnames]], XYeYeX=[0, 1])
distributions.columnname = ''.join(['%s; ' % name for name in columnnames])
for coln in names[2:]:
if coln in columnnames:
distributions.setColumnIndex(ix=0, iy=columnnames.index(coln), iey=None)
break
for name in columnnames:
del data[name]
output[-1].distributions = distributions
except:
pass
try:
distributions = []
columnnames = ''
for dist in ['DistributionFitDelayTimes', 'DistributionFitData', 'DistributionFit']:
try:
distributions.append(getattr(output[-1], dist))
columnnames += dist + ';'
delattr(output[-1], dist)
except:
pass
output[-1].distributionFit = dA(np.r_[distributions].squeeze())
output[-1].distributionFit.columnames = columnnames
output[-1].distributionFit.setColumnIndex(iey=None)
output[-1].distributionFit.comment = ['this is g1']
except:
pass
# append all remaining data to parameters ------------------------------------------------------
for key in data.keys():
try:
setattr(output[-1], key, np.array(data[key], dtype=float))
except:
pass
output[-1].filename = filename
# reset locale
locale.setlocale(locale.LC_TIME, old_loc)
return output