Is it just me or does this advert say Monday 28th April??
http://www.kriging.com/whereisshe.htm
From: Marcus Mattos Riether marcus.riet...@caixaseguros.com.br
To: Lisa Solomon li...@salford-systems.com; ai-geostats@jrc.it
ai-geostats@jrc.it
Sent: Tuesday, April 28, 2015 11:43 AM
Sorry to see you go.
Isobel
From: Younes Fadakar yfa.st...@ymail.com
To: Ask Geostatisticians ai-geostats@jrc.it
Cc: alghalan...@ymail.com alghalan...@ymail.com
Sent: Wednesday, February 6, 2013 6:48 AM
Subject: AI-GEOSTATS: The END of alghalandis.com
Hi
Some of my own thoughts on backtransforming the variance go as follows:
the backtransform for the variance in lognormal theory is exp{logarithmic
variance-1} times the square of the mean. In kriging this would adapt to
exp{logarithmic kriging variance-1} times the estimated value squared.
Younes
You can try what we used to do in the bad old days when it took 20 minutes to
calculate a semi-variogram on 1,000 samples -- moving windows.
Choose a sub-region size which includes about 1,000 samples. Calculate and
graph from the samples in this window. Shift half-a-window in one
Can I offer a couple of rough attempts from our web collections:
http://www.kriging.com/whatiskriging.html
is a short description for those coming to geostats cold and
http://www.kriging.com/RSMA1978/
is a 500 word article I was persuaded to write for the student magazine at the
Royal Scool
Yang
Yes the lagrangian multipier is subtracted, assuming you used the
semi-variogram in your kriging equations. If you use the covariance, it is
added.
The extra terms in the back transform are to correct for the difference between
the variance of the true values and the variance of the
Nick
The simplest way would be to do a aussian simulation and then do a rank
transfrom on the results, I think.
Isobel
http://www.kriging.com
--- On Tue, 17/11/09, Nick Hamm n...@hamm.org wrote:
From: Nick Hamm n...@hamm.org
Subject: AI-GEOSTATS: Unconditional simulation
To:
Meng
Your question sounds very complicated, so forgive me if I give a simplistic
answer. Read our 1987 paper called a novel approach to co-kriging which
explains what is now known as the non-co-located cross semi-variogram. You
can download a copy from my personal website at:
Greg
The answers to your questions depend heavily on what sort of data you have and
what software you are using.
If you are using borehole or other drilling data, sections of core down a hole
will tend to get very similar weights. Most mining packages recommend
compositing up into lengths of
Hi
I tackled a similar problem back in the early 80s on a South-African Pb-Zn
project where percussion holes had been used to infill a previous diamond
drilling campaign. The company allowed me to publish the results. The reference
is:
Clark, I, 1983: Reserve estimation -- a
Pedro
Why don't you work with the original co-ordinates? X in cm Y in metres. So
long as you do not expect the semi-variogram to be isotropic, it does not
matter what units you use. So long as you know, the computer does not need to!
Isobel
http://www.kriging.com
Pedro Mardones
Tomas
This is probably the model also known as the generalised linear:
gamma(h)=nugget effect + slope x distance-to-a-power
parameters are slope and power for distance.
I may be wrong!!
Isobel
http://www.kriging.com
tomas hlasny [EMAIL PROTECTED] wrote:
Dear all,
I
Fernando
Thank you for your email. I do not know much about variowin and am not up to
speed on semi-variograms in Surfer so I am posting your query on the
ai-geostats site. I am sure that some of our members can help you out.
Email me again if you get no help ;-)
Isobel
Adrian
It is a common misconception that using the covariance (total sill -
semi-variogram) rather than the semi-variogram brings more robust solutions.
You get exactly the same answer either way since one is just a constant minus
the other.
You can avoid solution problems by simple
Andrea
In theory kriging will honour the sample values provided your semi-variogram
model takes the value zero at zero distance.
Whether the data are honoured or not depends on which computer package you
use and what it does with the semi-variogram at zero. You can force this
Gregoire
The correlation between actual value and error of estimation is always
present to some extent and is simply due to the estimation process. High values
will b eunderestimated from neighbouring samples. Low values will be
overestimated from neighbouring samples. The only way you
Jamina
Different software packages have different requirements for defining
anisotropy. Some will allow you to define completely a model for each major
axis of the anisotropy ellipse. The simplest (geometric anisotropy) just accept
anisotropy 'factors' for the range of influence.
In
,with the help of
vertical variogram)?
I would appreciate to have your idea about it.
Kind Regards
E. R. Khojasteh
Isobel Clark [EMAIL PROTECTED] wrote:Jamina
Different software packages have different requirements for defining
anisotropy. Some will allow you to define completely a model
Why, thank you, Adrian. I like to strike a happy balance between sticking with
what I know and being open to new ideas ;-)
If it is good enough for NASA.
Isobel
Adrián Martínez Vargas [EMAIL PROTECTED] wrote:
Isobel Clark I apologise about Fortran
Hi Michael
Nice to see someone comfortable with rambling. I think we should have more of
that in the list!
Being an old warhorse and too far gone to change, I still use Fortran. My
excuse is always if it's good enough for NASA..
Visual Basic is pretty good too but
Hi Abani
You need my 1983 Mathematical Geology paper, Regression Revisited which can
be downloaded by folloing the publications link at
http://uk.geocities.com/drisobelclark/resume
Or, with less math, A simple alternative to Disjunctive Kriging written
with Flemming Clausen in 1981
Andrea
We use Cressie's goodness of fit statistic which allows for number of pairs
and other factors in semi-veriogram fitting. You can find a paper of his in
Methematical Geology around 1992, or in his book. It is also illustrated in our
free tutorial material at www.kriging.com
Olumide
I would think what they mean is that each order of polynomial has to be
balanced between the 'drift' at the actual estimated point and the weighted
average of the samples which proovides the estimator. For this you have to
introduce an extra lamda and an extra equation on the
equal zero but it makes more sense to make the trend from the samples honour
the trend at the point being estimated.
Isobel
http://www.kriging.com
Olumide [EMAIL PROTECTED] wrote:
Isobel Clark wrote:
I would think what they mean is that each order of polynomial has to be
balanced
Hi Olumide
You will find the basic kriging system for Multi-variable Universal
Co-kriging in our definitive 1987 paper which can be viewed or downloaded from
the web at:
http://www.kriging.com/publications/Battelle1987
Our thought was that interdependent trends would be
Hi
You will find drift also referred to as trend, generally understood as a
change in the 'expected' value from place to place within your study area. For
example, an airborne pollutant with a single source will show higher values
close to the source tending to 'thin out' as the distance
The ones I have are:
Applied Mineral Inventory Estimation by Alastair
J. Sinclair and Garston H. Blackwell
and
Case Histories and Methods in Mineral Resource
Evaluation (Geological Society Special Publication) by Alwyne E.
the data and from the moments, respectively.)
Peter
Isobel Clark writes:
Hi Peter
Sorry about the addendum. You are quite correct, none of the addenda seem
to have made it onto the web page!
Yes, but the problem with averaging the data in the cell is that the average
has a different standard deviation, depending on the layout of the sampling
within each cell.
So, if you decluster by averaging each cell you can end up with a set of
cells which all come from different
The later papers discuss the variations on the lognormal prompted by Sichel's
revivavl of interest in the late 1980s. The actual lognormal basis is not
discussed in those papers.
I am tracking down a copy of my original paper to add the mathematical
addenda on the 1987 paper and will
Mehari
SURFER will be giving you the arithmetic mean of the samples which fall
inside your search radius, not all possible samples. Effectively, you are
getting a moving average.
Isobel
http://www.kriging.com
Mehari
If you use a semi-variogram which is just nugget, the kriging estimate will
be the arithmetic mean of the sample values and the standard error will be the
standard sigma/root n of classical statistics.
Isobel
Mehari Tekeste [EMAIL PROTECTED] wrote:
Can I get some suggestion
NjeriThe full _expression_ for the estimation variance conains three terms:1) twice the weighted average of the semi-variograms between each sample and the point to be estimated2) the doubly weighted average of all the semi-variograms between every possible pair of samples used in the
PeijunI presume by the "pseudo" cross semi-variogram, you mean the 'non co-located' cross semi-variogram as opposed to the more traditional co-located cross semi-variogram?If so, the difference between the sill of your model and the nugget effect at zero is simply the classical covariance
DigbyThe variance of the residuals (whether regression or kriging) is the sum of the squared residuals divided by the degrees of freedom. Since the "degrees of freedom" is a fixed number, minimising the variance is identical to minimising the sum of squared residuals.IsobelDigby Millikan
PierreIf the relationship between your two variables is negative, the "pseudo" cross semi-variogram will start high and drop off, just like the co-located one. Difference is, the former doesn't go negative, the latter starts at zero and is all negative.One other feature of the "pseudo"
variogram to characterize the spatial cross correlation between two variables.Peijun From: Isobel Clark [mailto:[EMAIL PROTECTED] Sent: Thursday, September 21, 2006 10:28 PMTo: Peijun LiCc: ai-geostats@jrc.itSubject: RE: pseudo cross variogram: h=0 PeijunI presume by t
No, average of (Z*-Z) is zero average of (sum wZi - Z i)s zero sum wi times average of Z - average if Z =0 if sum w = 1 then this is true, otherwise notSays nothing at all about the average of Z.OK? IsobelDigby Millikan [EMAIL PROTECTED] wrote:BLUE : Best Linear Unbiased
nal Message- From: [EMAIL PROTECTED] [ mailto:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>Behalf Of sebastiano trevisani Sent: Monday, August 28, 2006 9:57 AM To: Isobel Clark Cc: ai-geostats@jrc.it Subject: Re: AI-GEOSTATS: Re:
standardized anomaly Hi Isobel I would like to use
SebastianoYour standardisation produces a mean of zero and a standard deviation of 1, without changing the characteristics of the semi-variogram (range, relative nugget effect etc.)I presume you will standardise each 'layer' separately? Then use a 3D search which does not include samples
SebastianoYou will be fine so long as you actually have a "stationary" phenomenon. That is, there is a constant mean and standard deviation over your study area -- no trends, no discontinuities, no changes of behaviour. Such a transformation also assumes that your data follow a fairly
RajniYou could download our free tutorial from the site http://www.kriging.comThere are lots of ways to detrend the data, this illustrates one of the simpler ways.IsobelRajni Gaur [EMAIL PROTECTED] wrote: Dear List members,I am working on the kriging of piezometric head data using the
YettaIf you have sub-populations, the lognormal backtransform probably wouldn't work very well -- this is one place where cross validation is extremely valuable. There are many methods of 'decomposing' mixed distributions. P.D.M. Macdonald has a nice shareware program using a maximum
OriolDownload for free, my old book Practical Geostatistics. Chapter 4 tells you all about calculating the variance for any weighted average estimator. Follow links from http://www.kriging.comIsobelOriol Falivene [EMAIL PROTECTED] wrote: Dear Colleagues,Im a PhD student working on
Hi MaartenShort answer is simply, No. If both variables are sampled at exactly the same location, introducing the secondary variable isprobably introducing more variation into your kriging rather than more information.IsobelMaarten De Boever [EMAIL PROTECTED] wrote: Dear all,The
ChaoshengIf you are only describing your samples, such concepts as random and independent are irrelevant. They apply to the use of your sample statistics to estimate population parameters. If all you want to do is describe your samples, you can calculate any statistics you like.However,
ChaoshengSome thoughts in response toyour questions:1: "Spatially correlated data provide redundant information for thecalculation of mean" I would not say "redundant". Even if information is correlated, the correlation is not perfect (=1) which wouldbe "redundant". If the data is
DigbyIf your distribution has a positive skewness as calculated (bulks towards zero with long tail into high value) the proportion below the mean will be significantly higher than 50%. If negatively skewed -- e.g. Calcium in limestone, iron in iron ore -- more than 50% will be above the mean.
Tom Would it be wise to state that if you only want the mean and variance = use block kriging, if you want a pdf = use conditional simulation? Oh, yes, please do. There are two ways to apply "discretisation". One is to estimate each of the fine grid of points and store the weights
Hello allThe real issue here is not what your philosophy is but what your software does with the semi-variogram model at zero distance.There are (to my knowledge) two possibilities in current software packages: (a) force the model to go through zero at zero distance, that is gamma(0)=0
. Behrang.- Original Message - From: Isobel Clark To: Behrang Kushavand Cc: AI Geostats mailing list Sent: Tuesday, February 28, 2006 9:53 PM Subject: [ai-geostats] Re: Software for Automatic Semivariogram EstimationBehrangWhat weighting do you use in the weighted least
Hi AllIt is difficult to have an automaticbest fit semi-variogram until you define what you mean by "best fit". Noel Cressie's goodness of fit statistic goes a long way towards the ideal, but is very insensitive to changes in nugget effect and pretty insensitive to fairly large changes in the
EdI use the Cressie statistic to four significant figures as a guide in the interactive fitting, but generally end up using a visual judgement. It tracks as you drag the model around, so you can watch it change.I think the 'real' visual objective function is probably the perpendicular (to
Jan, you sent this to me personally not to the list - although you may have posted it earlier to the list and I din't see it.You lost the right to my response when you turned down my invitation 12 years ago. Your relentless attempts to denigrate a subject simply because you do not understand
Hi, I do not know whether you received any answers off-list, so here goes.The "spherical" model of geostatistics was so-named by Matheron and is sometimes also known as the Matheron model. His idea was that a sample has a 'sphere of influence' around it. Potential (or actual) samples within
O boy, I wish my world included the kind of data which would allow modelling of anisotropy on a 10m scale! I am full of envy.Isobel http://www.stokos.demon.co.ukEdward Isaaks [EMAIL PROTECTED] wrote: Hello ListFYI, a few comments related to the ongoing discussion re Geostats Scam.Stephen
Jim (cc Fran!)Thanks for the long email. I think grandmother-hood must be scrambling my brains because I am not following some of your logic. Or maybe it is the after-effects of trying to thump sense into the heads of those shareholders ;-)It is most probable that Jan Merks got involved in
the meantime? Learn what you can and judge for yourself whether the ideas of geostatistics make sense in practice and could be applicable to your own problems. Isobel Clark http://www.kriging.com/courses * By using the ai-geostats mailing list you agree to follow its rules
( see http:/
PaulHave you considered doing your analyses in two stages:(a) presence/absence indicator where all values other than zero become '1' and you are effectively analysing the probability of presence (or absence) at your estimated or simulated points?(b) normal score transform or whatever
PerryI don't know about the fancy title, but theoretical change of support for Gaussian (Normal) distributions can be found in Chapter 3 of Practical Geostatistics, which is freely downloadable in lots of formats from http://www.kriging.com/pg1979_download.htmlIsobel* By using the
ErcanI have a full copy of the BromsBarn data which includes K and P values as well as pH.We got it direct from Dick Webster, but I can supply as text file in CSV or Geo-EAS format.Isobel http://www.kriging.com/whereisshe.htmlercan yesilirmak [EMAIL PROTECTED] wrote:Dear list members
How many simulations did you do?
IsobelAbhijith Titus D'souza [EMAIL PROTECTED] wrote:
Hello List:First of all I would like to thank all of you for yourfeedback on my earlier question.Just as an exercise I conducted conditional simulationon my dataset. I used the turning bands algorithmmethod and
lognormal kriging also solves the problem, where it is appropriate. That is, if your logarithms are close to Normal and cross validation shows that the backtransform is working.
with lognormal kriging, you can happily have negative weights and negative values on the logarithms. The backtransform
1963 Georges Matheron. Principles of geostatistics. Economic Geology, Vol. 58: p1246--1266
From Statistics for Spatial Data, Noel A.C. Cressie and a google search on "Georges Matheron Economic Geology" ;-)
Isobel
http://www.kriging.comRajni Gaur [EMAIL PROTECTED] wrote:
Dear List members,
Can
David
You seem to have two problems:
(1) the Vulcan answer does not match your hand calculation for the same weights and values.
(2) you have negative weights.
I would think that (1) was of far more concern than two simply because it suggests that the software is not performing the correct
What back-transform would you use for (1)? I use Sichel's theory, which produces prediction intervals for the lognormal back transform. Download any one of my lognormal kriging papers from http://uk.geocities.com/drisobelclark/resume (late 1990s, various audiences).
IsobelRecep kantarci [EMAIL
Hi Eric
What complications! You should find, in any basic statistical inference that the correlation is divided by (n-1) and has (n-2) degrees of freedom.
The logic behind this is because the correlation is actually calculated as the covariance divided by the two standard deviations.
The
some Geostatistical Books. I need them for self training and need to contain numerical examples and practices. I have already ordered Practical Geostatistics written by Isobel Clark. It would be highly appreciated.if you could please advise me more specially on books with trend in Oil
Perry
Your basic semi-variogram graph has a parabola added to it. Shoots off upwards (usually) at some distance. If the distance is large (past the range of influence) you can ignore it. See some of our mid-80s papers on the Wolfcamp data whichlots ofpeople use as a teaching set now. Or read my
I am a little worried by the statements:" As you point out, the sub-sample values should have a normal distribution. Increasing the number of samples (n) would help. "
Averages of lognormal (or other highly skewed) data are not Normal. The lognormal, in particular, does not conform to the
Hi Kevin
Can I refer you to the works of Herbert Sichel which was developed exactly for this problem, earliest paper Trans Inst Min Metall 1949. Or you can download my 1987 SAIMMpaper from http://uk.geocities.com/drisobelclark/resume which describes Sichel's work.
Isobel
Simone
Under the intrinsic hypothesis you can have a semi-variogram (bounded or unbounded) if the data is non-stationary.
Generally we assume a stationary mean when calculating a semi-variogram to simplify the calculation. If the mean is not stationary, you have to include a drift or 'trend' in
Simone
Not so banal a question. 34 years ago my supervisor
gave me some papers to read which said exactly that.
Even with a Master's in applied statistics, I could
not make head-nor-tail of the explanation. So I went
on a three week short course at Fontainebleau and they
explained it around the
to change that. It was several years
before an editor pointed out to him that there is no
'e' on the end of Isobel Clark.
Isobel
http://uk.geocities.com/drisobelclark/practica.htm
* By using the ai-geostats mailing list you agree to follow its rules
( see http://www.ai-geostats.org/help_ai-geostats.htm
Hello people
Thank you for your swift responses, especially on the
weekend. This turned out to be a long reply, so feel
free to read the next paragraph and skip on to the
last one.
I think we should be fair to Jan Merks. He got a bee
in his bonnet over an issue which is less than well
explained
Dutch-fashion, where the g is a kind of
throat-clearing sound,
More like the ch in the Scottish loch or like the
greek letter chi which forms the first letter in
Christos.
If you want to be pedantic, the technique was not
named kriging by Matheron but krigeage - a attempt
to turn krige into a
Colin
As a personal style, I tend to use a capital when
referring to (say) Ordinary Kriging, Indicator Kriging
and so on and a small letter when used as a noun or
verb: the area was kriged
Isobel
http://uk.geocities.com/drisobelclark
* By using the ai-geostats mailing list you agree
Even stranger when you consider that the Rev Bayes
refused to have his work published during his
lifetime.
Isobel
http://geoecosse.bizland.com/whatsnew
--- Wilmer Rivers [EMAIL PROTECTED] wrote:
In reports, should kriging, kriged, and krige be
written with an uppercase
K, or lowercase as
Gregoire
Michel David coined the term relative semi-variogram
back in the 70s for what I think you mean by general
relative -- that is, each lag is divided by the square
of the mean of the samples used at that lag.
Gary Raymond proposed the pairwise relative soon
after. I used the type you are
What I don't understand is, how to, automatically,
calculate all the parameters (Sill, Range, Nugget)
and
fit the perfect model so it produce a as sharp as
possible result.
All you need to do is be able to define perfect!
Please please let me know if you do - then I can
finally retire ;-)
Ruben (et al)
It is true that Matheron's theory is based on no
distributional assumptions. In fact, there is no
requirement for the distribution to be the same at
every location in the study area.
The necessity for using traditional geostatistical
theory is that the 'difference between two
Marek
Although theoretically non-point support has no reason
to be lognormal, in practice it very often is. We have
had good results in estimating areas and volumes,
although we have limited experience with non-point
support of any significance.
You can test the persistency of lognormality by
/seasonsgreetings.htm
--- Annelies Govaerts
[EMAIL PROTECTED] wrote:
I have a question about an excercise I found in
Practical
geostatistics (Isobel Clark).
In chapter 4 they look at the estimation variance of
some, theoretical,
examples.
One of the examples is a 2D panel (30m at 40m). They
use
Jack
I find Edzer notation confusing, since evryone I know
uses C0 for the bugget effect not the total sill of
the semi-variogram model.
The correlogram relationship is a theoretical one but
should hold provided the paricular gamma(h) is
calculated using all the same samples at the total
sill.
Edzer
PS -- what was that bugget? :-)
Sorry, keyboard a bit congested ;-)
nugget effect C0, total sill C(0)!
Isobel
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Digby
The variance/sill relationship is theoretical and does
not depend on the layout of the samples, regular or
clustered. Since the sill only uses pairs where
samples are uncorrelated from one another, the
clustering is irrelevant.
It does depend on the distribution of the samples
values being
Meng-Ying
Assuming that you generated your line with a Spherical
model, range 3, 27 samples making 9 ranges the
variance within that line will (theoretically) be
0.9191 of the semi-variogram sill.
Of course this theory depends on you have every
possible sample in that length, not just 27 of
Rajive
I haven't read the other responses yet, so this may be
redundant.
Two possibilities:
(1) anisotropy: if this is shallow marine data there
should be a difference between longshore drift and
off-shore deepening of sea-bed. You have an
omni-directional semi-variogram. It is possible that
be able to
clarify the things you clarifies. You're good.
Meng-ying
On Wed, 8 Dec 2004, Isobel Clark wrote:
Meng-Ying
I don't know how to say this any other way. At
distances larger than the range of influence,
samples
are NOT SPATIALLY CORRELATED.
The variance
And just a personal opinion, I would like to think
geostatistic
theories apply to population of any size, as small
as 27, or as large as
1,000,000. If I'm making an example that
geostatistics doesn't apply, then
there's something to concern about in this approach.
Geostatistics applies to
Digby
I see where you are coming from on this, but in fact
the sill is composed of those pairs of samples which
are independent of one another - or, at least, have
reached some background correlation. This is why the
sill makes a better estimate of the variance than the
conventional statistical
Meng-Ying
We are talking about estimating the variance of a set
of samples where spatial dependence exists.
The classical statistical unbiassed estimator of the
population variance is s-squared which is the sum of
the squared deviations from the mean divided by the
relevant degrees of freedom.
Colin Daly
-Original Message-
From: Chaosheng Zhang
[mailto:[EMAIL PROTECTED]
Sent: Sun 12/5/2004 11:42 AM
To: [EMAIL PROTECTED]
Cc: Colin Badenhorst; Isobel Clark; Donald E. Myers
Subject: Re: [ai-geostats] F and T-test for samples
drawn from the same p
Dear all
Don
Thank you for the extended clarification of F and t
hypothesis test. For those unfamiliar with the
concept, it is worth noting that the F test for
multiple means may be more familiar under the title
Analysis of variance.
My own brief answer was in the context of Colin's
question, where it
Kai
I would suggest you take a look at:
Introduction to Geostatistics: Applications in
Hydrogeology (Stanford-Cambridge Program)
P. K. Kitanidis
which is a great base to work from.
Isobel
http:///geoecosse.bizland.com
* By using the ai-geostats mailing list you agree to follow its rules
(
Samuel
Practical Geostatistics (1979) Chapter 3. Get it for
free at
http://uk.geocities.com/drisobelclark/practica.htm
Isobel
http://geoecosse.bizland.com/books.htm
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*
Mark
We have about 13 data sets available on our free
download site, ranging from mining data to fisheries,
agriculture and environmental stuff. Number of data
ranges from 27 to 20,000.
Download from http://geoecosse.bizland.com/softwares
and find details and references for most of them at
xhy
your questions are long-standing and as yet unanswered
in general.
1. How to select the lag class and lag distance in
order to obtain a more reasonable experimental
variogram?
I always think of it as focussing a camera. Believe
there is a pattern in your data and our task is to
balance
Dear oh Dear, I am failing to communicate (again).
As far as I know, I didn't say you could not use
geostatistics when a trend is present! I regularly use
Universal Kriging for data with a trend and kriging
with an external drift when the trend is governed by
an outside factor (see free tutorial
Kevin
Sounds like an ideal case for Geographically Weighted
Regression.
You could use semi-variograms or spatial
auto-correlation to determine exactly how proximity
defines relationship. My only current beef with GWR is
the seemingly pre-defined distance weighting
functions. Not had much time
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