The fit error determination in the PDC depends on the method the user has chosen. Under the
tag Fit parameter Error estimation method: the options are
- from fit using arbitray y uncertainties
- from fit using calculated y uncertainties
- from Monte Carlo simulations
Which of these does the "SECTION: used integral errors" belong to?
This section appears to be errors associated with each peak intensity
from "SECTION: used integrals". I simply took these to be individual
peak errors and then performed standard Monte Carlo simulations on
these. Each peak appears to have a different error, but does "Fit
parameter Error estimation method: from fit using arbitray y
uncertainties" means one error for all peaks?
Perhaps the label "used integral errors" is a misleading. We could
just leave out the word "used".
If these errors are used or not during the fit depends on the chosen
fit error calculation. The way you
used the error would be "- from fit using calculated y
uncertainties" but the sample output files I had
sent so far used the option "-from fit using arbitray y
uncertainties". In the mean I made a comparison
and the differences in these methods is about those you see. I have
a bug somewhere else in the code
which I must fix first, then I will send new output files that all
used "- from fit using calculated y uncertainties".
The first option (~ one error for all peaks) is normally not
recommended but described in most books
and there may be cases to use, e.g. if there is no reasonable y
error calculation from the data.
Hi,
Ah, so these are measured errors and not back-calculated errors from the fit. And they are not used when fitting to a single error value. How do these numbers arise? The one error for all peaks is required for duplicated spectra. And if not all are duplicated, then it needs to be one error for all peaks for all spectra. If there is an RMSD measurement, then you have the same error for all peaks in one spectrum. The example files contain different errors for every single peak, but where does this come from and how are they calculated?
The first 2 are offered by the our standard Marquardt implementation.
The Levenberg-Marquardt optimisation and Monte Carlo simulations are
two unrelated techniques, one is for optimisation, the other for error
propagation. A very low quality error estimation method comes from
the covariance matrix. So one can use Levenberg-Marquardt (or any
other optimisation algorithm) and then determine the errors using the
covariance matrix defined at the solution. I don't understand the
differences in the error estimation methods in the PDC :S
The first option assumes the same
error for all y values. This then cancales out of the formula, therefore we call it arbitrary.
But this could be used for either the Monte Carlo simulations or the
covariance matrix error estimation methods. In relax, this is used in
combination with MC simulations.
The second option uses the y errors as determined before. As we discussed earlier the base plane RMS
is one source for these errors the other is from optional repetition experiments. If repetition experiments
are available the systematic error can be calculated as:
- worst case per peak scenario
- average variance calculation
As I showed using theoretical examples in
https://mail.gna.org/public/relax-devel/2010-11/msg00027.html (data,
python and _javascript_s are attached to https://gna.org/task/?7180),
the 2 worst case per peak methods on average significantly
overestimate the errors, and have huge variances meaning that the real
errors compared to the estimates are multiplied by a normal
distribution of 1.594+/-0.852. So 95% of the estimates from
duplicated spectra (assuming a normal distribution) will be scaled by
a factor between -0.11 and 3.298! This type of error estimate would
be fatal for subsequent model-free analysis.
Therefore in relax we will need to catch "Systematic error estimation
of data: worst case per peak scenario" and re-perform the relaxation
curve-fitting and Monte Carlo simulations. Or maybe throw an in relax
error and tell the user that they must go back to the PDC and use
"average variance calculation" with "Monte Carlo simlautions". In the
example file you sent
(http://svn.gna.org/viewcvs/relax/1.3/test_suite/shared_data/pdc_files/testT1.txt?revision=12551&view=markup),
it says "Fit parameter Error estimation method: from fit using
arbitray y uncertainties". Above you said that this corresponds to 1
error for all peaks. But you described in
https://mail.gna.org/public/relax-devel/2010-11/msg00021.html that the
current "worst case per peak scenario" method in the PDC uses a
different estimate for each peak. The file also says "Random error
estimation of data: RMS per spectrum (or trace/plane)". So I am quite
confused as to how to interpret the data. Where do I read the peak
intensity errors from?
We had the discussion before and as a result I implemented the
average variance method as you
suggested. We can The default value is however set to the first
option since in older PDC versions
this was the only option and I did not want that users suddenty get
different results if they overlook
this detail. However, in the documentation we can more explicitly
describe this and recommend the
average variance option.
Maybe I'll add a catch to relax then, telling the user to switch to this method in the PDC rather than using the "worst case per peak scenario" option. This will prevent them from pushing data into relax with too big errors - something which will really change the optimised values and also the model-free models selected. The result of this would be catastrophic - it will hide real molecular motions.
The test output files I have sent so far calculated the fit parameter errors via from fit using arbitray y uncertainties.
This option was chosen accidentally. The error there is different from the other 2 methods. Perhaps it is
a better idea to generate a new set of output files using the option "from fit using calculated y uncertainties".
That usually compares well with MC simulations.
What is the "from fit using calculated y uncertainties"? Is this the
covariance matrix technique for error estimation? For simple single
exponentials, the covariance matrix technique and Monte Carlo
simulations should not be too different, I hope. How does this
combine with "worst case per peak scenario" and "average variance
calculation"?
Marquardt internally uses a covariance matrix technique. I do not
fully understand the problem. One thing
is to get y values and errors of y values.
The y values are used to fit a function. When doing this the
determined y errors can be used or not.
Are we both talking about this technique
http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm ? In this method, you use the Jacobian matrix during optimisation and not the covariance matrix. The covariance matrix is estimated using this Jacobian matrix, but only at the end. The covariance matrix is not used at any point during optimisation as it is not part of the algorithm.
The matrix J^T.J is used as part of the algorithm and then if you invert this, you have the covariance matrix:
covar = (J^T.J)^-1
The elements of this estimated covariance matrix can then be used as a very rough estimate of the fitted parameter uncertainties. Is this what "from fit using calculated y uncertainties" means?
In the PDC I do 1000 MC simulations such that a vary the
y values based on a gaussian random distribution, the mean is the original y, the width is the error of y.
You mean you take the back-calculated y value and randomise this 1000
times with a Gaussian distribution centred at the back-calculated y
with an error set to the peak intensity error? If you take the
original y, this is not Monte Carlo simulations but Bootstrapping - a
technique which should never be used for error estimation. Well, you
can use bootstrapping for a direct calculation of values, but never
for optimised values.
No, we do not take back-calculated y values and randomise these. We
take the original y
values, randomise them and use them as input for the fit. I
I'm now confused by myself about what you write but you are
certainly more experienced in
these fields.
I would suggest having a look at Numerical Recipes in C (or in fortran, etc.). For example in the newer version, see section 15.6 "Confidence limits on estimated model parameters". This also has a good section on the Levenberg-Marquardt algorithm and also describes how bad the covariance matrix is for error estimation, and has a section about bootstrapping directly after the Monte Carlo section. Things are explained very nicely in this book. But taking the original y values should be considered a bug.
I am quite confused. The way I see the analysis, using the notations
from the statistics and optimisation fields, there are the following
categories:
Peak intensity notation:
- Height (the physical height of the Lorentizian peak at its mean,
or maximum point)
- Volume (sum of all points, i.e. the discrete integration of the Lorentizian)
- Height (as above but via fitting peak shape to a function)
- Volume (as above but via fitting peak shape to a function)
Peak errors:
- 1 average error for all peaks (from some spectra duplicated,
triplicated, etc.)
- 1 max error for all peaks (largest ever occurring difference)
- 1 average error for all peaks in one spectrum (only if all spectra
are duplicated)
- 1 max error for each peak across all spectra (current "worst case
per peak scenario"?).
- Each peak has a different error (from the baseplane RMSD)
Optimisation methods:
- Levenberg-Marquardt
- Simplex
- Newton
- etc.
Error estimation methods:
- Covariance matrix (bad)
- Jackknife (also bad)
- Bootstrapping (never to be used with optimisation)
- Monte Carlo simulations (considered the gold standard)
- etc.
I am having trouble classifying the PDC file
http://svn.gna.org/viewcvs/relax/1.3/test_suite/shared_data/pdc_files/testT1.txt?revision=12551&view=markup
In Summary the PDC does:
1. Determine all y (4 options available)
2. Determine all y errors. RMS calculation can always be done. If
repetition experiments are available
they are used and errors are calculated from those (2 options
available) and added. The errors coming
from the RMS calculation and the ones coming from the repetitions
were assumed to be independent.
The latter is usually much larger.
The output contains only the total error not the individual
contributions. If no repetition experiments are
available the error came from RMS only.
This is where I am lost. At no point is there 1 error value per peak per spectrum. But this is what is in the example PDC file in the "SECTION: used integral errors". From the RMSD, there is 1 error for all peaks per spectra. For replicated spectra there is 1 error for all peaks per spectra (unless not all are replicated, in which case there is 1 error for all peaks for all spectra). Am I missing something?
3. The y values are used for fitting. The y errors can be used by
Marquardt which internally does a covariance
type analysis but they may also be replaced by a "unique" error
used for all peaks. As a third option the
Marquardt can be run N times with randomised y values and using
the "unique" error for all peaks.
The first and second are simply the errors used in the optimisation target function, which should be a chi-squared statistic. This is part of the Levenberg-Marquardt algorithm.
This third option is technically Bootstrapping. For fitting, it gives neither the parameter values nor the parameter errors.
I would suggest the following:
Below is a testT1_rel.txt made this morning. It used the errors of y
as given in the section "used integral errors".
Perhaps you can re-run your script and check the ratio of your and
our errors.
I have re-run this, and the errors are very similar to what relax determines using Monte Carlo simulations. The results are at the very end. However I have used the 1 error per peak per spectrum as given in the PDC file, but I don't know the source of this error.
If the differences in the errors remain I have to talk to some
people here because I cannot just change major
things in the code. I have to report to a responsible person and he
finally tells me what I should do. There are
some people here with "simulation experience". What really
surprises me at the moment is that several people
used the PDC (and its newer sections) and compared fits and errors
to other programs and did not report
problems to me.
I've noticed this reporting issue too ;) It can take a while for bugs to surface in relax. To counter this, we have built in a test suite (thanks to Gary Thompson) which includes tests comparing to other programs. Luckily relax can control both Modelfree4 from Palmer and Dasha, so comparing model-free results can all be done internally in relax. It might be worth setting up a test data set an push this though both relax (
http://nmr-relax.com) and Art Palmer's curvefit (
http://biochemistry.hs.columbia.edu/labs/palmer/software/curvefit.html). Both relax and curvefit can use the Levenberg-Marquardt optimisation algorithm for finding the parameter values and the Monte Carlo simulation method for determining the parameter errors. These results could be used as a baseline and internal check for the PDC. I would not call these changes major though, more a refinement and improvements to the error estimates. The fitted values are not changing, only the error estimates are being improved.
Cheers,
Edward
[edward@localhost pdc_files]$ relax convert_data.py
relax repository checkout
Molecular dynamics by NMR data analysis
Copyright (C) 2001-2006 Edward d'Auvergne
Copyright (C) 2006-2011 the relax development team
This is free software which you are welcome to modify and redistribute under the conditions of the
GNU General Public License (GPL). This program, including all modules, is licensed under the GPL
and comes with absolutely no warranty. For details type 'GPL' within the relax prompt.
Assistance in using the relax prompt and scripting interface can be accessed by typing 'help' within
the prompt.
script = 'convert_data.py'
----------------------------------------------------------------------------------------------------
#! /usr/bin/env python
# The data - PDC T2, PDC T2 err, PDC scale factor, relax R2, relax R2 err (MC sim).
data = ""
['Gln', 2, 0.4560, 0.012326 , 2.22814 , 2.19316068443, 0.0264822775112],
['Ile', 3, 0.4289, 0.0089800 , 2.22814 , 2.33150942395, 0.0218282475286],
['Phe', 4, 0.4259, 0.015866 , 2.22814 , 2.34809773828, 0.0393993956573],
['Val', 5, 0.4446, 0.0084807 , 2.22814 , 2.24905317514, 0.0190416227806],
['Lys', 6, 0.4386, 0.0032737 , 2.22814 , 2.27991249993, 0.0073387122964],
['Thr', 7, 0.4505, 0.012887 , 2.22814 , 2.21988325334, 0.0289980021014],
['Leu', 8, 0.4464, 0.017357 , 2.22814, 2.23992585719, 0.0394637142884],
['Thr', 9, 0.5081, 0.016893 , 2.22814, 1.96827404586, 0.0285585555608],
['Gly', 10, 0.4789, 0.0045383 , 2.22814, 2.08825352037, 0.00856203608884],
['Lys', 11, 0.5006, 0.0087386 , 2.22814, 1.99766965295, 0.0154821098023],
['Thr', 12, 0.4621, 0.028354 , 2.22814, 2.16418266589, 0.0589894950385],
['Ile', 13, 0.4400, 0.014724 , 2.22814 , 2.27249997894, 0.0323797055027],
['Thr', 14, 0.4547, 0.011743 , 2.22814 , 2.19913359459, 0.0261282530191],
['Leu', 15, 0.4387, 0.033559 , 2.22814 , 2.27947735732, 0.0804008170395],
['Glu', 16, 0.4792, 0.038163 , 2.22814, 2.08663871613, 0.0742976416989],
['Val', 17, 0.4377, 0.013371 , 2.22814 , 2.28454679926, 0.0319478148557],
['Glu', 18, 0.4744, 0.025534 , 2.22814 , 2.10794161516, 0.0491390514111],
['Ser', 20, 0.4458, 0.0075985 , 2.22814 , 2.2432376545, 0.0174075720472],
['Asp', 21, 0.4257, 0.0036873 , 2.22814 , 2.34905882789, 0.00896916086091],
['Thr', 22, 0.4396, 0.012438 , 2.22814 , 2.27492782819, 0.0283784241729],
['Ile', 23, 0.4054, 0.018904 , 2.22814, 2.46692508258, 0.0512139594604],
['Asn', 25, 0.4174, 0.010689 , 2.22814 , 2.39550908267, 0.028141856637],
['Val', 26, 0.4688, 0.035345 , 2.22814, 2.13297271051, 0.0738836932379],
['Lys', 27, 0.4233, 0.013666 , 2.22814 , 2.36245343286, 0.034473000886],
['Ala', 28, 0.4193, 0.014360 , 2.22814 , 2.38481618154, 0.0369276054641],
['Lys', 29, 0.4298, 0.011476 , 2.22814 , 2.32688199592, 0.0267154381038],
['Thr', 30, 0.4132, 0.026029 , 2.22814, 2.42020223098, 0.0641345037416],
['Gln', 31, 0.4216, 0.023199 , 2.22814 , 2.37198006924, 0.0607157552927],
['Asp', 32, 0.4268, 0.011464 , 2.22814 , 2.343030317, 0.0287089229986],
['Lys', 33, 0.4560, 0.014005 , 2.22814 , 2.19288519999, 0.0322404521468],
['Glu', 34, 0.4468, 0.0067758 , 2.22814 , 2.23809730593, 0.016155226898],
['Gly', 35, 0.4452, 0.0072146 , 2.22814 , 2.24610605696, 0.0166231784999],
['Ile', 36, 0.5023, 0.011122 , 2.22814 , 1.99097003714, 0.018556395961],
['Asp', 39, 0.4360, 0.0041799 , 2.22814 , 2.29368308937, 0.0107946336402],
['Gln', 40, 0.4435, 0.0097303 , 2.22814 , 2.25465715695, 0.0219579437623],
['Gln', 41, 0.4496, 0.010974 , 2.22814 , 2.22439489827, 0.0246838738103],
['Arg', 42, 0.4386, 0.060259 , 2.22814, 2.27979894813, 0.14609662846],
['Leu', 43, 0.4603, 0.027054 , 2.22814 , 2.17239195077, 0.0562093749792],
['Ile', 44, 0.4387, 0.012389 , 2.22814 , 2.27944810393, 0.0274015962376],
['Phe', 45, 0.4413, 0.014084 , 2.22814 , 2.26621382042, 0.0325598012054],
['Ala', 46, 0.4601, 0.017401 , 2.22814, 2.17327103743, 0.0366263114163],
['Gly', 47, 0.4563, 0.0095167 , 2.22814, 2.19159971705, 0.0200974396044],
['Lys', 48, 0.4543, 0.034380 , 2.22814, 2.20125816954, 0.0769594227925],
['Gln', 49, 0.4730, 0.0099851 , 2.22814 , 2.11409184343, 0.0211565921351],
['Leu', 50, 0.4455, 0.020395 , 2.22814 , 2.24441747047, 0.0466054926297],
['Glu', 51, 0.4687, 0.018214 , 2.22814 , 2.13351495913, 0.0369010861993],
['Asp', 52, 0.4888, 0.017327 , 2.22814 , 2.04583349976, 0.0309251585403],
['Arg', 54, 0.4561, 0.0059463 , 2.22814 , 2.19228267695, 0.0125176652031],
['Thr', 55, 0.4396, 0.013766 , 2.22814 , 2.27502074947, 0.0319568936121],
['Leu', 56, 0.4221, 0.0064286 , 2.22814 , 2.36913113746, 0.0158844775147],
['Ser', 57, 0.4351, 0.0057569 , 2.22814 , 2.29814478201, 0.0142640516301],
['Asp', 58, 0.4207, 0.010105 , 2.22814 , 2.37720483191, 0.0272911357828],
['Tyr', 59, 0.4493, 0.0055646 , 2.22814 , 2.22553611629, 0.0125933167265],
['Asn', 60, 0.4377, 0.0058785 , 2.22814 , 2.28459962433, 0.013370924242],
['Ile', 61, 0.4270, 0.010546 , 2.22814 , 2.34213728573, 0.0264153868176],
['Gln', 62, 0.5067, 0.012662 , 2.22814 , 1.97367886288, 0.0217945020797],
['Lys', 63, 0.4619, 0.0094808 , 2.22814 , 2.16500024708, 0.0195312534223],
['Glu', 64, 0.4312, 0.0095453 , 2.22814 , 2.31913930394, 0.0223294049604],
['Ser', 65, 0.4568, 0.0043694 , 2.22814 , 2.18918681844, 0.00908363177241],
['Thr', 66, 0.4584, 0.0054046 , 2.22814 , 2.18167008464, 0.0112902195625],
['Leu', 67, 0.4410, 0.018436 , 2.22814, 2.26772345178, 0.0444564014823],
['His', 68, 0.4451, 0.0043067 , 2.22814 , 2.24680733455, 0.00926065649886],
['Leu', 69, 0.4447, 0.015813 , 2.22814 , 2.24851949133, 0.0379125750722],
['Val', 70, 0.4319, 0.011793 , 2.22814 , 2.31543911345, 0.0279226422469],
['Leu', 71, 0.4596, 0.0046414 , 2.22814 , 2.1756894166, 0.00987045835498],
['Arg', 72, 0.4622, 0.012209 , 2.22814 , 2.16361975928, 0.0253849160526],
['Leu', 73, 0.5386, 0.016241 , 2.22814, 1.85657597782, 0.0255460422485],
['Arg', 74, 0.6202, 0.020482 , 2.22814, 1.6122677679, 0.0231001217925],
['Gly', 75, 0.8603, 0.038094 , 2.22814, 1.16243924266, 0.023448186193],
['Gly', 76, 1.311, 0.011609 , 2.22814, 0.763064480891, 0.0030557315479]]
r1 = []
r1_err = []
print("%-4s %-3s %-15s %-15s %-15s %-15s" % ('Name', 'Num', 'r1', 'err', 'r1_relax_ratio', 'err_relax_ratio'))
for i in range(len(data)):
r1.append(1.0/data[i][2])
r1_err.append((data[i][3] / data[i][4]) / data[i][2]**2)
print("%-4s %-3s %15.10f %15.10f %15.10f %15.10f" % (data[i][0], data[i][1], r1[-1], r1_err[-1], r1[-1]/data[i][5], r1_err[-1]/data[i][6]))
print("\nr1 = %s" % r1)
print("\nr1_err = %s" % r1_err)
----------------------------------------------------------------------------------------------------
Name Num r1 err r1_relax_ratio err_relax_ratio
Gln 2 2.1929824561 0.0266041877 0.9999187345 1.0046034622
Ile 3 2.3315458149 0.0219089603 1.0000156083 1.0036976307
Phe 4 2.3479690068 0.0392563299 0.9999451763 0.9963688334
Val 5 2.2492127755 0.0192553009 1.0000709634 1.0112216314
Lys 6 2.2799817601 0.0076376394 1.0000303785 1.0407329060
Thr 7 2.2197558269 0.0284983531 0.9999425977 0.9827695359
Leu 8 2.2401433692 0.0390916333 1.0000971068 0.9905715673
Thr 9 1.9681165125 0.0293674283 0.9999199637 1.0283233068
Gly 10 2.0881186051 0.0088809833 0.9999353933 1.0372513237
Lys 11 1.9976028765 0.0156501208 0.9999665729 1.0108519458
Thr 12 2.1640337589 0.0595936413 0.9999311948 1.0102415911
Ile 13 2.2727272727 0.0341332766 1.0001000193 1.0541564881
Thr 14 2.1992522542 0.0254909885 1.0000539575 0.9756101371
Leu 15 2.2794620470 0.0782584545 0.9999932834 0.9733539709
Glu 16 2.0868113523 0.0745875623 1.0000827341 1.0039021502
Val 17 2.2846698652 0.0313233584 1.0000538689 0.9804538617
Glu 18 2.1079258010 0.0509198386 0.9999924978 1.0362397543
Ser 20 2.2431583670 0.0171595251 0.9999646549 0.9857506292
Asp 21 2.3490721165 0.0091318485 1.0000056570 1.0181385574
Thr 22 2.2747952684 0.0288863527 0.9999417301 1.0178984051
Ile 23 2.4666995560 0.0516230601 0.9999085799 1.0079880682
Asn 25 2.3957834212 0.0275352936 1.0001145220 0.9784462342
Val 26 2.1331058020 0.0721789076 1.0000623972 0.9769260903
Lys 27 2.3623907394 0.0342296457 0.9999734626 0.9929407026
Ala 28 2.3849272597 0.0366574491 1.0000465772 0.9926841616
Lys 29 2.3266635644 0.0278814427 0.9999061270 1.0436453477
Thr 30 2.4201355276 0.0684217822 0.9999724389 1.0668482370
Gln 31 2.3719165085 0.0585767922 0.9999732035 0.9647708728
Asp 32 2.3430178069 0.0282452147 0.9999946607 0.9838479390
Lys 33 2.1929824561 0.0302281071 1.0000443508 0.9375832257
Glu 34 2.2381378693 0.0152332221 1.0000181240 0.9429283912
Gly 35 2.2461814915 0.0163365171 1.0000335846 0.9827553178
Ile 36 1.9908421262 0.0197839980 0.9999357545 1.0661551968
Asp 39 2.2935779817 0.0098684839 0.9999541751 0.9142027654
Gln 40 2.2547914318 0.0222022254 1.0000595544 1.0111249767
Gln 41 2.2241992883 0.0243651941 0.9999120615 0.9870895586
Arg 42 2.2799817601 0.1405860375 1.0000801878 0.9622811903
Leu 43 2.1724961981 0.0573069191 1.0000479874 1.0195259972
Ile 44 2.2794620470 0.0288907296 1.0000061168 1.0543447654
Phe 45 2.2660321777 0.0324575464 0.9999198475 0.9968594762
Ala 46 2.1734405564 0.0368915808 1.0000780018 1.0072425911
Gly 47 2.1915406531 0.0205136512 0.9999730498 1.0207096813
Lys 48 2.2011886419 0.0747614857 0.9999684146 0.9714403125
Gln 49 2.1141649049 0.0200303096 1.0000345593 0.9467644631
Leu 50 2.2446689113 0.0461196303 1.0001120295 0.9895749994
Glu 51 2.1335609132 0.0372111378 1.0000215391 1.0084022359
Asp 52 2.0458265139 0.0325475652 0.9999965853 1.0524623555
Arg 54 2.1925016444 0.0128287457 1.0000998810 1.0248513199
Thr 55 2.2747952684 0.0319705364 0.9999008884 1.0004269112
Leu 56 2.3691068467 0.0161935932 0.9999897470 1.0194602345
Ser 57 2.2983222248 0.0136479695 1.0000772113 0.9568087574
Asp 58 2.3769907297 0.0256241117 0.9999099353 0.9389170128
Tyr 59 2.2256843980 0.0123713940 1.0000666274 0.9823777394
Asn 60 2.2846698652 0.0137711736 1.0000307454 1.0299343099
Ile 61 2.3419203747 0.0259590946 0.9999073876 0.9827262730
Gln 62 1.9735543714 0.0221339042 0.9999369242 1.0155728308
Lys 63 2.1649707729 0.0199437392 0.9999863861 1.0211192658
Glu 64 2.3191094620 0.0230403782 0.9999871323 1.0318402214
Ser 65 2.1891418564 0.0093978203 0.9999794618 1.0345884315
Thr 66 2.1815008726 0.0115433500 0.9999224392 1.0224203287
Leu 67 2.2675736961 0.0425448547 0.9999339621 0.9570017664
His 68 2.2466861379 0.0097563407 0.9999460583 1.0535258134
Leu 69 2.2487069935 0.0358870316 1.0000833892 0.9465733073
Val 70 2.3153507756 0.0283736636 0.9999618484 1.0161525308
Leu 71 2.1758050479 0.0098615796 1.0000531470 0.9991004674
Arg 72 2.1635655560 0.0256494311 0.9999749479 1.0104201645
Leu 73 1.8566654289 0.0251268237 1.0000481807 0.9835896879
Arg 74 1.6123831022 0.0238982645 1.0000715355 1.0345514485
Gly 75 1.1623852145 0.0231001211 0.9999535217 0.9851559902
Gly 76 0.7627765065 0.0030314259 0.9996226080 0.9920458727
r1 = [2.1929824561403506, 2.3315458148752621, 2.3479690068091101, 2.2492127755285649, 2.2799817601459189, 2.2197558268590454, 2.2401433691756272, 1.9681165124975399, 2.0881186051367719, 1.9976028765481419, 2.1640337589266392, 2.2727272727272729, 2.1992522542335604, 2.2794620469569185, 2.0868113522537564, 2.2846698652044779, 2.1079258010118043, 2.2431583669807091, 2.3490721165139767, 2.2747952684258417, 2.4666995559940799, 2.3957834211787254, 2.1331058020477816, 2.3623907394283012, 2.3849272597185784, 2.3266635644485807, 2.4201355275895451, 2.3719165085388996, 2.3430178069353325, 2.1929824561403506, 2.2381378692927485, 2.2461814914645104, 1.9908421262193909, 2.2935779816513762, 2.254791431792559, 2.2241992882562278, 2.2799817601459189, 2.1724961981316535, 2.2794620469569185, 2.2660321776569226, 2.1734405564007826, 2.1915406530791146, 2.201188641866608, 2.1141649048625792, 2.244668911335578, 2.1335609131640707, 2.0458265139116203, 2.1925016443762333, 2.2747952684258417, 2.369106846718787, 2.2983222247759136, 2.3769907297361539, 2.2256843979523704, 2.2846698652044779, 2.3419203747072599, 1.9735543714229324, 2.164970772894566, 2.3191094619666046, 2.1891418563922942, 2.1815008726003491, 2.2675736961451247, 2.2466861379465288, 2.2487069934787498, 2.3153507756425098, 2.1758050478677111, 2.1635655560363478, 1.8566654288897142, 1.6123831022250887, 1.1623852144600721, 0.76277650648360029]
r1_err = [0.026604187674262224, 0.021908960327458739, 0.039256329888494042, 0.019255300851873068, 0.0076376393742018656, 0.028498353066316331, 0.039091633313933949, 0.029367428292696705, 0.0088809832670172228, 0.015650120819053141, 0.059593641324394583, 0.03413327663803193, 0.025490988510701602, 0.078258454528893073, 0.074587562253744674, 0.031323358447156807, 0.050919838563005242, 0.017159525098024916, 0.0091318484997306177, 0.028886352705770644, 0.051623060062711716, 0.027535293649668169, 0.072178907573441836, 0.034229645721721408, 0.036657449069796476, 0.02788144268955875, 0.068421782245556562, 0.058576792228853564, 0.028245214723318316, 0.030228107121372902, 0.015233222109109863, 0.016336517069652536, 0.019783997987628171, 0.0098684839256478925, 0.022202225374497418, 0.024365194102863259, 0.14058603752635557, 0.057306919080342175, 0.028890729555661861, 0.032457546373300998, 0.036891580812815593, 0.020513651173707466, 0.074761485726614368, 0.020030309594628797, 0.046119630340251502, 0.03721113783141361, 0.03254756520159726, 0.012828745705261387, 0.031970536368197362, 0.016193593172273917, 0.013647969516280896, 0.025624111685121922, 0.012371394017083925, 0.013771173631860843, 0.025959094636482449, 0.022133904172475703, 0.019943739155053694, 0.023040378158236934, 0.009397820347615311, 0.011543349996187752, 0.042544854747859025, 0.0097563406703048468, 0.035887031574619428, 0.028373663586608974, 0.0098615795560442095, 0.025649431054374158, 0.02512682372176913, 0.02389826446011489, 0.023100121087805757, 0.0030314258702649616]
Re: Addition of PDC support to relax 1.3.10 and an inconsistency in the error calculation.