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KarenCrawford - 10 Jul 2009
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AmitFriedman - 04 Aug 2009
TRYING DIFFERENT STAT ROI WEIGHTING APPROACHES TO REDUCE THE N80 FOR AD AND MCI
ATROPHY WEIGHTING APPROACHES:
One approach we tried is to use the atrophy weights themselves as weights. The intuition behind this approach is that voxels with greater atrophic rates show greater tissue loss and degeneration. Therefore we can argue that the detrimental effects of the disease are more pronounced across these voxels and should be given greater importance.
This approach introduces a variety of possibilities to us.
We can choose to use contraction voxels (<256) and expansion voxesls (>256) separately, and give two different numerical summaries for each Jacobian map. However the drug industry dislikes this approach of only using contraction or expansion voxels separately. This is because they reasonably argue that it is not justified and legitimate to only look at some part of our data; if nothing else, simply because different subjects have different regions of contraction and expansion and we cannot allow our method to be so subject specific. Indeed, after running this method on our MCI subjects, we learned that one of the subjects does not even have any expansion voxels in the stat ROI, and so we cannot even apply this algorithm on that given subject. A good method should not be so subject specific. Nonetheless, we still tested this method to see what results we get.
Alternatively, we can take all voxel values, and use all of our data to yield the numerical summary. If we suspected that expansion voxels in the ROI actually represented expansion of brain tissue, then the former approach might have had a better logical foundation to support it (only use contraction voxels for the numerical summary). However we know that the expansion voxels in the ROI most likely represent ventricular expansion which is correlated with tissue contraction. Therefore separating contraction and expansion voxels might not be the best approach. Indeed, the latter method, the one in which we use all voxel values proved to perform much better than the former one. Note however that if we take all voxel values and average them out, many of the contraction and expansion voxels are going to cancel out, and give us an average that is closer to 256. This is undesired for a couple of reasons. First, when terms cancel, we are essentially loosing data. But since both contraction and expansion voxels store data about tissue loss (remember that the expansion is really ventricular expansion), we should really try to avoid this. Second, our formula for n80 is inversely proportional to the square of (256 – average of all numerical summaries). This means that the larger the difference between our average of all numerical summaries and 256, the larger the denominator gets, and so we get a smaller n80. That is why we do not want voxels values to cancel. This cancellation will give us a more “moderate” result, closer to 256, which will tend to increase our n80 (smaller denominator). We can overcome this problem by reflecting all values greater or smaller then 256 about 256 to yield only voxel values which are all smaller or all bigger than 256 (or equal to). Since the n80 formula only takes into account the distance of the average of our numerical summaries from 256, it does not matter in which direction we reflect our voxel values about. I chose to reflect all the voxel values down, so that all values are smaller than or equal to 256. For example, the value 260 will be reflected about 256 to become 252. This will give us a numerical summary that is smaller than 256. Note that this downward reflection will align our voxel values. By aligning them, the values are no longer centered about 256, but are instead ordered linearly. Values much smaller than 256 stand for greater atrophy, whereas values closer to 256 (but still smaller) stand for less extreme atrophy. The value 256 is mapped to itself and still represents no change. Just reflecting all the voxel values about 256 and then taking the strait average across the stat ROI improved our previous n80 for AD by 12 (48 -> 36), and our n80 for MCI by 29 (85->56). This should not be a surprise at all because when we reflect all the values about 256 then the numerical summary will be more farther away from 256 because we will not have voxel-wise cancellation of values that will pull our numerical summary closer to 256 (as discussed above). In addition, note that by reflecting down we are essentially decreasing the voxel-wise variance because the values are no longer spread above and below 256, but rather only above or below 256. This should not necessarily decrease the variance between the numerical summaries however it is definitely possible given that now all the numerical summaries are all smaller or all bigger than 256 (depending on whether we reflect up or down); which is not the case if we do not reflect the voxel values.
The fact that our voxels are centered about 256 also affects our weights. If we use a weighting approach that is directly proportional to the voxel values, then larger values (above 256) will be assigned more weight. If our weighting approach is inversely proportional to the voxel values then the smaller voxel values (less than 256) will be assigned more weight. Note however that this type of bias can only take place when we use both contraction and expansion voxels together because when we separate them, they are already aligned so no bias can occur. We can eliminate this bias by implementing the same reflection approach. We can either make all voxel values smaller/bigger than or equal to 256 (align them) and by doing so eliminate the bias, or by just accounting for the distance from 256 which ignores whether the value is bigger or smaller than 256 (only accounts for how far the value is from 256). I will elaborate on this further when I describe the actual methods in the next several paragraphs.
If we want to use the atrophy values as weights, we can try a few different methods. We can use the actual values themselves, we can use their reciprocals, we can use their difference from 256, their difference from some max amount like the maximum atrophy rate in the whole Jacobian map, the maximum atrophy rate in the ROI, etc. We can also raise the weights to different powers and see how that affects the numerical summary.
USING THE ACTUAL VOXEL VALUES OR THEIR RECIPROCALS AS WEIGHTS
If we choose to separate the expansion (>256) and contraction (<256) values from each other, then our method would output two numerical summaries. We want smaller contraction and larger “expansion” values get greater weights in our numerical summaries. The farther a value is from 256 (above or below), the more weights we give it. Therefore we tried to use the reciprocals of the contraction values as weights for the numerical summary of all the voxel values smaller than 256, and the actual expansion values themselves as weights for the numerical summary of all the voxel values greater than 256. We also tried (just for the purpose of exploration) to give the contraction values weights that are equal to the actual contraction values after they are reflected about 256 (so values smaller than 256 are mapped to larger values greater than 256). Similarly, we also gave the expansion values weights that are equal to reciprocals of the expansion values reflected about 256 (larger values greater than 256 are mapped to smaller values less than 256, and then we take their reciprocal). Obviously this would not make a big difference in our end result, because mathematically it does not really matter that much if we use the reciprocal of the contraction values as weights or the their actual reflected values; or alternatively if we use the expansion values as weights or if we use the reciprocals of their reflected values. In any case, we use reciprocals for values smaller than 256 and the actual values for the values larger than 256 (remember, the distance from 256 is what counts).
If we did not want to separate contraction and expansion voxels, we could apply all of these weighting approaches (reciprocals of voxel values, actual voxel values) to all of the voxels together. We could either take the voxel values as they are, noting of course that the expansion and contraction ones will cancel each other out, or we can reflect all of them to be smaller or larger than 256. In addition, another factor we can vary when we use all voxel values together is whether or not weights are biased towards contraction or expansion voxels. If we use the reciprocals of the voxel values as weights then the weighting method is inversely proportional to the atrophy rates, and so smaller voxel values will be given greater weight (so in general, contraction values will be more important than expansion values). If we use the actual voxel values as weights, then our weighting approach is directly proportional to the atrophy rates, and so larger voxel values will be given greater weight (so in general, expansion values will be more important than contraction values). As I explained above, if we do not want our method to be biased, then we should invert all voxel values about 256 first (align them) and only then use the reciprocals/actual atrophy rates as weights. This way there is no bias towards contraction or expansion values. It is the values that are far apart from 256 that get more weight.
Pretty much all of the methods improved the previous results for n80 using the normal average over the stat ROI. The best of this set of methods for AD was the method where we use all voxel values, but did not let them cancel (reflect them up or down about 256), and used the actual voxel values as weights (did not reflect the weights), which gives us a bias towards expansion voxels (this method gave us the third best results for AD overall). Interestingly enough, almost no methods improved the n80 for greater powers of the weights. But this method’s performance improved as we raised the weights to greater and greater powers. The best result for AD was obtained empirically with power =18. It produced: n80 = 31, n90 = 41 (for AD). The 18th power gave the smallest number for n80/90. However since we round n80/90 up to get the number of people required for the drug trial (can’t have fractions of people), the 17th power is also sufficient to yield n80 = 31 and n90 = 41 (larger values than the 18th power, but still below 31 and 41, so we still round up to get the same results). This method yielded the fourth best results for MCI (second best out of this set of methods) with n80 =40, n90 = 53. The 25th power was also sufficient. The best of this set of methods for MCI (third best overall) is the method where we only use expansion voxels, and use the actual values as weights (unbiased weights because we are using contraction and expansion voxels separately). This method yielded n80=37, n90=49. Note that this method gave a smaller n80/90 for MCI, than it did for AD (n80 = 52, n90=70). We expect the MCI patients to have an average of the numerical summaries that is closer to 256 because they have more moderate atrophy rates. However they can also have much smaller standard deviation and so their n80 might not necessarily be smaller. That is the situation with this method. In any case, we would probably not use this method because the industry frowns upon methods that use expansion and contraction voxels separately. Furthermore, MCI patient MCI_033_S_1309_MIs6L8.img does not even have any expansion voxels, and so this method is pretty much invalid. Finally, note that this set of methods did not perform as well as the set of methods to be discussed next.
USING THE DISTANCE FROM 256/MAX VALUE AS WEIGHT
If we assume that all the voxels in the ROI give us atrophy rates for tissue loss, whether the values are greater or smaller than 256, then we can just take the difference between the voxel value and 256 as an indicator of how severe the atrophy at that voxels is. Essentially, reflecting all the values so they are smaller than 256 and taking their reciprocals as weights, or reflecting all values so they are bigger than 256 and taking those reflected values as weights is the same concept as taking the distance from 256 itself as weight. As before, we can separate our contraction and expansion values, use them together and let them cancel each other out, or reflect them about 256 so they are all smaller or greater (or equal) to 256. Note however that this method is different from the previous one in the fact that this method cannot have biased weights. In the previous method, we could choose to let our weights be biased by taking the voxel values as they are, which will result in a bias toward contraction values (when we use the reciprocals), and in a bias toward expansion values (when we use the actual values themselves). We eliminated this bias by letting the weights be the aligned (reflected) values. However for this method, we are using the (absolute) distance from 256 as our weight. Hence it does not matter if the given voxel value is larger or smaller than 256, and so by default this method is unbiased.
We also tried to reflect the voxel values down about 256 and then subtract from some max amount like the largest atrophy rate in the ROI, or the largest atrophy rate in the whole Jacobian map. These approached did not perform as well as the one in which we find the distance from 256. Note that finding the distance from 256 is equivalent to reflecting down about 256 and then subtracting from 256. These methods have the disadvantage that each different Jacobian map has a different max value. Hence it might be a little problematic to compare numerical summaries of different Jacobian maps. For example, let us look at two maps, one with an incredibly large max value and one with a more moderate maximum value. Now let’s examine the weights that will be assigned to two arbitrary voxels at each of the two maps. In the first map, the maximum value is so incredibly large, that the two voxels’ distance from the max value will dwarf the difference in atrophic rates between those two voxels. Therefore they will be assigned approximately the same weight. In contrast, in the second map the distance from the max value is not that big, and so the difference in atrophic rates between the two voxels will be taken into more account and so they will be assigned relatively different weights based on their atrophic rates. This shows that this method will assign different weights based on different max values. On the other hand, the method in which we always subtract from 256 is a consistent approach that always assigns the same weights to equivalent voxels in different maps (holding the sum of all the weights which we divide by in order to normalize our weights constant). Hence it is more intuitive to compare numerical summaries of different Jacobian maps using this approach.
The method where we use the distance from the max value in the whole Jacobian map did not do that well. In essence, there isn’t an important mathematical difference between subtracting from the max value of the whole map, and the max value of the ROI. However when we subtract from the max of the whole map, we get a clumsier, not as neat of an answer because we are only dealing with values in the ROI anyway, so why subtract them from the max of the whole map? It is also possible that this method didn’t do as well because the max value in the whole map is equal to or greater than the max value in the ROI. As discussed in the previous paragraph, since the distance from the max value of the whole map is equal to or greater than the distance from the max value of the ROI, this means that this method will tend to overlook and downplay voxel-wise differences and will assign approximately the same weights to voxels with similar atrophic rates (more moderate weighting). The other method will not downplay the atrophic rate difference between voxels as much because the distance from the max in the ROI is not as big. The same principal can be applied to the difference between the method that takes the distance from 256 and the method that takes the distance from the max ROI. The distance from 256<distance from max ROI and so the distance from 256 method will give more importance to the voxel-wise atrophy differences than the distance from max ROI method.
The method in which we subtract from the max value in the ROI performed much better than the previous method discussed, and its performance improved for greater powers of the weights. The 15th power was the optimal power for AD, yielding n80 = 26 and n90 = 35, which has the second best performance overall. The 14th power was also sufficient to yield the same results (larger n80/90, but still below 26,35). This method performs almost as good as our best method for AD, but is more computationally costly. Again I will repeat that there isn’t a big mathematical difference between subtracting from 256 and subtracting from the max in the ROI. However the 256 method is consistent across subjects, which we cannot say about the max value approach. For MCI, just like for AD, this method’s performance improved for greater powers of the weights. However, we did not get one best power that outperforms the other ones. As we increase the power more and more, the n80/90 keeps going down. I stopped computing the n80/n90 at the 65th power, with n80 = 34, n90=45. The 42nd power was also sufficient to yield the same results (33<n80<34 and 44<n90<45). This is actually the best result for MCI out of all the methods. It even outperforms the distance from 256 approach.
The fact that the n80/90 keep improving for greater powers is worth discussing. The n80 will decrease for smaller variance of the numerical summaries and average of the numerical summaries that is farther away from 256. We can argue that we can just use the smallest voxel value in the stat ROI of each Jacobian map for its numerical summary. This will definitely move the average of the numerical summaries farther away from 256. However this can also potentially increase our variance if the different Jacobian maps have min values that are very spread apart from each other, thus increasing our n80. We can manipulate the numerical summaries so that we’ll get a smaller average of the numerical summaries, but we cannot really control the resulting standard deviation. As we increase the powers of the weights for a certain method, we are basically giving more weights to the most important voxel values (based on how that method determines what is an important voxel). For example, for the distance from 256 or the distance from the max ROI the smallest value gets the largest weight. Thus increasing the powers of the weights will give the smallest voxel value more and more weight compared to the other voxels. Therefore if we find that the nth power of the weights gives us the best n80 results, that means that the nth power gave the optimal possible ratio between the variance and average of the numerical summaries so that the n80/90 turned out to be the smallest. However if there is no one single optimal power, and n80/90 just keep decreasing for greater powers, that means that the given method pretty much wants us to only use the most important voxel value for each Jacobian map for the numerical summary. Apparently, for the distance from max ROI method in the MCI, we should only use the smallest voxel value (or a group of the smallest voxel values) from each map. This is apparently OK because the variance of the MCI subjects does not increase by that much as we assign more weight to the smaller voxel values. For the distance fro 256 on the other hand, the variance increases faster than the average deviates from 256 as we assign more weight to the smaller voxel values, and so the n80 actually increased for powers larger than 1. Note however that it would not be wise to use only one voxel for a numerical summary. This is because however a certain drug affects one single voxel (or just a few of them) is not a reliable measure of the slowdown of the progress of the disease. That specific voxel might have a smaller atrophic rate after the drug treatment, whereas at the same time, the atrophic rates in the rest of the voxels could have worsened. Thus, we want to use information about as many voxels as we can in order to determine whether the drug was helpful or not.
The distance from 256 approach (for our weights) yielded the best results overall for AD when we used all voxel values together, and did not let them cancel each other out (reflected them about 256). Voxels much smaller than 256 get greater weight, and values closer to 256 get smaller weights. Note again that after the reflection, values which were previously very large, will become very small and so they do not loose significance whatsoever. Raising the weights to greater powers (greater than the first power) did not improve our results. This is a very satisfying result because even though other methods perform almost as well as this one for AD, they required computing greater powers of the weights. This method on the other hand does not raise the weights to a power and it is therefore much more computationally fast and efficient. For the AD group, we got n80 = 25, n90 = 34 (best overall), and for MCI we have n80 = 36, n90 = 49 (second best overall, right after the distance from max ROI approach). This is a significant improvement compared to ADNI’s second stage method of using the average over the stat ROI. The n80 for AD is about half of what it was initially (48 -> 25), and is less than half of what it was initially for MCI (85->36). Note that although this method has the second best performance for MCI overall, it is much faster to compute, consistent across subjects (always subtract from the value 256 that does not change), uses all values instead of just expansion or contraction, and strives to use all the voxels for the numerical summary (whereas the distance from the max ROI method strives to use only one or only few of the several of the voxels with the smallest values for the numerical summary). Furthermore, one can say that this method is by construction perfect to give a very small n80/90 because the n80/90 will decrease for an average of the numerical summaries that is farther away from 256; and this method gives greater weight to voxel values that are farther away from 256. Therefore we crown this method as the best one.
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Change password
Please only use ResetPassword in case you really forgot your password. Thank you.
Follow this link to change your password.
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--
KristiClark - 21 Feb 2008
combine_diff
Purpose
Combine two .diff files into one.
Help Menu
Usage: ./combine_diff input1.diff input2.diff output.diff [options]
Options
-s Swap bytes
More Detailed Information
--
KristiClark - 20 Feb 2008
compute_single_tensor
Purpose
This program estimates a single diffusion tensor at each voxel.
Help Menu
Usage: ./compute_single_tensor input4D.img input.diff output_prefix [options]
Note: Overwrite permission for the output is assumed.
Options:
-3 Write out the 3D outputs also (default is 4D only)
-s Swap bytes in the .diff file
-g Ignore all b0 images
-m mask Do not compute for voxels with a value of 0 in the mask.img
-r # Number of restarts to be used with any nonlinear fitting.
Default is 6.
-p # Perturbations of the parameter estimates in nonlinear fitting
(default is 0.1 which means to perturb estimates by 10%)
-x # Only compute for the #th slice in the x dimension
-y # Only compute for the #th slice in the y dimension
-z # Only compute for the #th slice in the z dimension
-e # # # Constrain the eigenvalues
-n # Noise model
0 No noise modeled (default)
3 Rician noise (most accurate, maybe unstable)
Constrain the signal intensity associated with the b0 image
(default is to estimate simultaneously with the tensor)
-b Method [OutlierCriteria]
1 Mean [std dev (default is 100)]
2 Median [num IQRs (default is 100)]
Options for estimating the ADC profile (def is to estimate from signal intensities)
-a Method
1 Linear Least Squares, monoexpential
2 Nonlinear LS, monoexpential
3 Nonlinear LS, monoexpential with Rician noise
4 Nonlinear LS, biexponential
5 Nonlinear LS, biexponential with Rician noise
Options for estimating the single, second rank tensor
-c Constrain the eigenvalues to be positive.
-u Constrain the 2nd eigenvalue to be equal to the 3rd eigenvalue
-t Method
1 Linear Least Squares
4 Nonlinear Least Squares
More Detailed Information
Fitting the equation
The diffusion tensor has 6 unique elements, which are treated as unknowns (variables) in the equation. These variables are estimated based on the known values of the b-matrix, which includes the bvalues and the unit gradient vectors, in addition to the measured signal intensities.
Linear least squares vs. nonlinear least squares
The simplest (computationally fastest) method uses linear least squares. For this method, the signal intensities are log-transformed. This means that this model assumes that the log transform of the random noise in the imaging data is normally (Gaussian) distributed.
The nonlinear least squares fit (NLS) does not log-transform the data. In this model, it is assumed that the random noise in the imaging data (not the log-transform) is normally distributed.
Actually it is known that the noise is Rician distributed, due to the process of reconstructing the data from k-space to signal intensities. In many imaging experiments, it is often assumed that modeling the noise as Gaussian (NLS method) is not that big of an error compared to estimating the noise as Rician distributed. However, it is an even larger assumption to assume that the log-transform of the noise is Gaussian (LS method).
The NLS estimates used in this program are based on the Levenberg-Marquadt optimization algorithm, which can be viewed here:
http://www.ics.forth.gr/~lourakis/levmar/
Note: Currently using version 2.1
Because NLS is an iterative process, this can be computationally intensive. Also the estimates are sensitive to how many times the algorithm restarts (-r) at each voxel, and the amount by which the estimates are perturbed (-p).
Note: If data only has 6 directions, it is pointless to do anything other than LS.
Given no time restraints and adequate data acquisition (many directions), the optimal fitting includes NLS & estimating the noise as Rician.
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http://www.loni.ucla.edu/twiki/bin/view/CCB/AllHands2007Notes
Lesson's Learned
Project Management tools
Programmer guide notes
- Name: Craig Schwartz
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Current Projects
Current Projects RACI
- R = Responsible. Person owns the problem / project.
- A = Accountable. to whom "R" is Accountable. He or she must sign or approve the work before it is OK.
- S = can be Supportive. Can provide resources or can play a supporting role in implementation.
- C = should be Consulted. Has information and/or capability necessary to complete the work.
- I = should be Informed. Must be notified of results, but need not be consulted.
Staff Management
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--
KristiClark - 21 Feb 2008
create_diff_file.pl
Purpose
Generate a .diff file from textfile(s) that describe how the diffusion data was collected.
Help Menu
-o outputfile Output file, default is S001.diff
-t total.txt Single textfile that describes all diffusion info.
-n num_images Number of images = Number of diffusion-weighted directions + Number of non-diffusion-weighted images. NOTE: start counting with 1, not 0
-g grads.txt Gradients only textfile with exactly 3 columns for x, y, and z gradients. Each row represents an image.
-b value b value
More Detailed Information
.diff files are only used by LONI DTI Suite. They are binary files that describe the b values and gradient directions that correspond with images. .diff files can support any number of directions and b values.
If all of the data has a single non-zero b value, then you can use -b and -g. If, however, your data has multiple non-zero b values, then you should use -t.
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Csh Programming Considered Harmful
Taken from [http://www.faqs.org/faqs/unix-faq/shell/csh-whynot/].
From: Tom Christiansen <tchrist@mox.perl.com>
Newsgroups: comp.unix.shell, comp.unix.questions, comp.unix.programmer,
comp.infosystems.www.authoring.cgi
Subject: Csh Programming Considered Harmful
Date: 6 Oct 1996 14:03:18 GMT
Message-ID: <538e76$8uq$1@csnews.cs.colorado.edu>
Archive-name: unix-faq/shell/csh-whynot
Version: $Id: CshConsideredHarmful.txt,v 1.1 2007/10/03 16:09:27 dhasson Exp wwwprod $
The following periodic article answers in excruciating detail
the frequently asked question "Why shouldn't I program in csh?".
It is available for anon FTP from perl.com in /pub/perl/versus/csh.whynot.gz
*** CSH PROGRAMMING CONSIDERED HARMFUL ***
Resolved: The csh is a tool utterly inadequate for programming,
and its use for such purposes should be strictly banned!
I am continually shocked and dismayed to see people write test cases,
install scripts, and other random hackery using the csh. Lack of
proficiency in the Bourne shell has been known to cause errors in /etc/rc
and .cronrc files, which is a problem, because you *must* write these files
in that language.
The csh is seductive because the conditionals are more C-like, so the path
of least resistance is chosen and a csh script is written. Sadly, this is
a lost cause, and the programmer seldom even realizes it, even when they
find that many simple things they wish to do range from cumbersome to
impossible in the csh.
1. FILE DESCRIPTORS
The most common problem encountered in csh programming is that
you can't do file-descriptor manipulation. All you are able to
do is redirect stdin, or stdout, or dup stderr into stdout.
Bourne-compatible shells offer you an abundance of more exotic
possibilities.
1a. Writing Files
In the Bourne shell, you can open or dup arbitrary file descriptors.
For example,
exec 2>errs.out
means that from then on, stderr goes into errs file.
Or what if you just want to throw away stderr and leave stdout
alone? Pretty simple operation, eh?
cmd 2>/dev/null
Works in the Bourne shell. In the csh, you can only make a pitiful
attempt like this:
(cmd > /dev/tty) >& /dev/null
But who said that stdout was my tty? So it's wrong. This simple
operation *CANNOT BE DONE* in the csh.
Along these same lines, you can't direct error messages in csh scripts
out stderr as is considered proper. In the Bourne shell, you might say:
echo "$0: cannot find $file" 1>&2
but in the csh, you can't redirect stdout out stderr, so you end
up doing something silly like this:
sh -c 'echo "$0: cannot find $file" 1>&2'
1b. Reading Files
In the csh, all you've got is $<, which reads a line from your tty. What
if you've redirected stdin? Tough noogies, you still get your tty, which
you really can't redirect. Now, the read statement
in the Bourne shell allows you to read from stdin, which catches
redirection. It also means that you can do things like this:
exec 3<file1
exec 4<file2
Now you can read from fd 3 and get lines from file1, or from file2 through
fd 4. In modern, Bourne-like shells, this suffices:
read some_var 0<&3
read another_var 0<&4
Although in older ones where read only goes from 0, you trick it:
exec 5<&0 # save old stdin
exec 0<&3; read some_var
exec 0<&4; read another_var
exec 0<&5 # restore it
1c. Closing FDs
In the Bourne shell, you can close file descriptors you don't
want open, like 2>&-, which isn't the same as redirecting it
to /dev/null.
1d. More Elaborate Combinations
Maybe you want to pipe stderr to a command and leave stdout alone.
Not too hard an idea, right? You can't do this in the csh as I
mentioned in 1a. In a Bourne shell, you can do things like this:
exec 3>&1; grep yyy xxx 2>&1 1>&3 3>&- | sed s/file/foobar/ 1>&2 3>&-
grep: xxx: No such foobar or directory
Normal output would be unaffected. The closes there were in case
something really cared about all its FDs. We send stderr to sed,
and then put it back out 2.
Consider the pipeline:
A | B | C
You want to know the status of C, well, that's easy: it's in $?, or
$status in csh. But if you want it from A, you're out of luck -- if
you're in the csh, that is. In the Bourne shell, you can get it, although
doing so is a bit tricky. Here's something I had to do where I ran dd's
stderr into a grep -v pipe to get rid of the records in/out noise, but had
to return the dd's exit status, not the grep's:
device=/dev/rmt8
dd_noise='^[0-9]+\+[0-9]+ records (in|out)$'
exec 3>&1
status=`((dd if=$device ibs=64k 2>&1 1>&3 3>&- 4>&-; echo $? >&4) |
egrep -v "$dd_noise" 1>&2 3>&- 4>&-) 4>&1`
exit $status;
The csh has also been known to close all open file descriptors besides
the ones it knows about, making it unsuitable for applications that
intend to inherit open file descriptors.
2. COMMAND ORTHOGONALITY
2a. Built-ins
The csh is a horrid botch with its built-ins. You can't put them
together in many reasonable ways. Even simple little things like this:
% time | echo
which while nonsensical, shouldn't give me this message:
Reset tty pgrp from 9341 to 26678
Others are more fun:
% sleep 1 | while
while: Too few arguments.
[5] 9402
% jobs
[5] 9402 Done sleep |
Some can even hang your shell. Try typing ^Z while you're sourcing
something, or redirecting a source command. Just make sure you have
another window handy. Or try
% history | more
on some systems.
Aliases are not evaluated everywhere you would like them do be:
% alias lu 'ls -u'
% lu
HISTORY News bin fortran lib lyrics misc tex
Mail TEX dehnung hpview logs mbox netlib
% repeat 3 lu
lu: Command not found.
lu: Command not found.
lu: Command not found.
% time lu
lu: Command not found.
2b. Flow control
You can't mix flow-control and commands, like this:
who | while read line; do
echo "gotta $line"
done
You can't combine multiline constructs in a csh using semicolons.
There's no easy way to do this
alias cmd 'if (foo) then bar; else snark; endif'
You can't perform redirections with if statements that are
evaluated solely for their exit status:
if ( { grep vt100 /etc/termcap > /dev/null } ) echo ok
And even pipes don't work:
if ( { grep vt100 /etc/termcap | sed 's/$/###' } ) echo ok
But these work just fine in the Bourne shell:
if grep vt100 /etc/termcap > /dev/null ; then echo ok; fi
if grep vt100 /etc/termcap | sed 's/$/###/' ; then echo ok; fi
Consider the following reasonable construct:
if ( { command1 | command2 } ) then
...
endif
The output of command1 won't go into the input of command2. You will get
the output of both commands on standard output. No error is raised. In
the Bourne shell or its clones, you would say
if command1 | command2 ; then
...
fi
2c. Stupid parsing bugs
Certain reasonable things just don't work, like this:
% kill -1 `cat foo`
`cat foo`: Ambiguous.
But this is ok:
% /bin/kill -1 `cat foo`
If you have a stopped job:
[2] Stopped rlogin globhost
You should be able to kill it with
% kill %?glob
kill: No match
but
% fg %?glob
works.
White space can matter:
if(expr)
may fail on some versions of csh, while
if (expr)
works! Your vendor may have attempted to fix this bug, but odds are good
that their csh still won't be able to handle
if(0) then
if(1) then
echo A: got here
else
echo B: got here
endif
echo We should never execute this statement
endif
3. SIGNALS
In the csh, all you can do with signals is trap SIGINT. In the Bourne
shell, you can trap any signal, or the end-of-program exit. For example,
to blow away a tempfile on any of a variety of signals:
$ trap 'rm -f /usr/adm/tmp/i$$ ;
echo "ERROR: abnormal exit";
exit' 1 2 3 15
$ trap 'rm tmp.$$' 0 # on program exit
4. QUOTING
You can't quote things reasonably in the csh:
set foo = "Bill asked, \"How's tricks?\""
doesn't work. This makes it really hard to construct strings with
mixed quotes in them. In the Bourne shell, this works just fine.
In fact, so does this:
cd /mnt; /usr/ucb/finger -m -s `ls \`u\``
Dollar signs cannot be escaped in double quotes in the csh. Ug.
set foo = "this is a \$dollar quoted and this is $HOME not quoted"
dollar: Undefined variable.
You have to use backslashes for newlines, and it's just darn hard to
get them into strings sometimes.
set foo = "this and that";
echo $foo
this and that
echo "$foo"
Unmatched ".
Say what? You don't have these problems in the Bourne shell, where it's
just fine to write things like this:
echo 'This is
some text that contains
several newlines.'
As distributed, quoting history references is a challenge. Consider:
% mail adec23!alberta!pixel.Convex.COM!tchrist
alberta!pixel.Convex.COM!tchri: Event not found.
5. VARIABLE SYNTAX
There's this big difference between global (environment) and local
(shell) variables. In csh, you use a totally different syntax
to set one from the other.
In the Bourne shell, this
VAR=foo cmds args
is the same as
(export VAR; VAR=foo; cmd args)
or csh's
(setenv VAR; cmd args)
You can't use :t, :h, etc on envariables. Watch:
echo Try testing with $SHELL:t
It's really nice to be able to say
${PAGER-more}
or
FOO=${BAR:-${BAZ}}
to be able to run the user's PAGER if set, and more otherwise.
You can't do this in the csh. It takes more verbiage.
You can't get the process number of the last background command from the
csh, something you might like to do if you're starting up several jobs in
the background. In the Bourne shell, the pid of the last command put in
the background is available in $!.
The csh is also flaky about what it does when it imports an
environment variable into a local shell variable, as it does
with HOME, USER, PATH, and TERM. Consider this:
% setenv TERM '`/bin/ls -l / > /dev/tty`'
% csh -f
And watch the fun!
6. EXPRESSION EVALUATION
Consider this statement in the csh:
if ($?MANPAGER) setenv PAGER $MANPAGER
Despite your attempts to only set PAGER when you want
to, the csh aborts:
MANPAGER: Undefined variable.
That's because it parses the whole line anyway AND EVALUATES IT!
You have to write this:
if ($?MANPAGER) then
setenv PAGER $MANPAGER
endif
That's the same problem you have here:
if ($?X && $X == 'foo') echo ok
X: Undefined variable
This forces you to write a couple nested if statements. This is highly
undesirable because it renders short-circuit booleans useless in
situations like these. If the csh were the really C-like, you would
expect to be able to safely employ this kind of logic. Consider the
common C construct:
if (p && p->member)
Undefined variables are not fatal errors in the Bourne shell, so
this issue does not arise there.
While the csh does have built-in expression handling, it's not
what you might think. In fact, it's space sensitive. This is an
error
@ a = 4/2
but this is ok
@ a = 4 / 2
The ad hoc parsing csh employs fouls you up in other places
as well. Consider:
% alias foo 'echo hi' ; foo
foo: Command not found.
% foo
hi
7. ERROR HANDLING
Wouldn't it be nice to know you had an error in your script before
you ran it? That's what the -n flag is for: just check the syntax.
This is especially good to make sure seldom taken segments of code
code are correct. Alas, the csh implementation of this doesn't work.
Consider this statement:
exit (i)
Of course, they really meant
exit (1)
or just
exit 1
Either shell will complain about this. But if you hide this in an if
clause, like so:
#!/bin/csh -fn
if (1) then
exit (i)
endif
The csh tells you there's nothing wrong with this script. The equivalent
construct in the Bourne shell, on the other hand, tells you this:
#!/bin/sh -n
if (1) then
exit (i)
endif
/tmp/x: syntax error at line 3: `(' unexpected
RANDOM BUGS
Here's one:
fg %?string
^Z
kill %?string
No match.
Huh? Here's another
!%s%x%s
Coredump, or garbage.
If you have an alias with backquotes, and use that in backquotes in
another one, you get a coredump.
Try this:
% repeat 3 echo "/vmu*"
/vmu*
/vmunix
/vmunix
What???
Here's another one:
% mkdir tst
% cd tst
% touch '[foo]bar'
% foreach var ( * )
> echo "File named $var"
> end
foreach: No match.
8. SUMMARY
While some vendors have fixed some of the csh's bugs (the tcsh also does
much better here), many have added new ones. Most of its problems can
never be solved because they're not actually bugs per se, but rather the
direct consequences of braindead design decisions. It's inherently flawed.
Do yourself a favor, and if you *have* to write a shell script, do it in the
Bourne shell. It's on every UNIX system out there. However, behavior
can vary.
There are other possibilities.
The Korn shell is the preferred programming shell by many sh addicts,
but it still suffers from inherent problems in the Bourne shell's design,
such as parsing and evaluation horrors. The Korn shell or its
public-domain clones and supersets (like bash) aren't quite so ubiquitous
as sh, so it probably wouldn't be wise to write a sharchive in them that
you post to the net. When 1003.2 becomes a real standard that companies
are forced to adhere to, then we'll be in much better shape. Until
then, we'll be stuck with bug-incompatible versions of the sh lying about.
The Plan 9 shell, rc, is much cleaner in its parsing and evaluation; it is
not widely available, so you'd be significantly sacrificing portability.
No vendor is shipping it yet.
If you don't have to use a shell, but just want an interpreted language,
many other free possibilities present themselves, like Perl, REXX, TCL,
Scheme, or Python. Of these, Perl is probably the most widely available
on UNIX (and many other) systems and certainly comes with the most
extensive UNIX interface. Increasing numbers vendors ship Perl with
their standard systems. (See the comp.lang.perl FAQ for a list.)
If you have a problem that would ordinarily use sed or awk or sh, but it
exceeds their capabilities or must run a little faster, and you don't want
to write the silly thing in C, then Perl may be for you. You can get
at networking functions, binary data, and most of the C library. There
are also translators to turn your sed and awk scripts into Perl scripts,
as well as a symbolic debugger. Tchrist's personal rule of thumb is
that if it's the size that fits in a Makefile, it gets written in the
Bourne shell, but anything bigger gets written in Perl.
See the comp.lang.{perl,rexx,tcl} newsgroups for details about these
languages (including FAQs), or David Muir Sharnoff's comparison of
freely available languages and tools in comp.lang.misc and news.answers.
NOTE: Doug Hamilton <hamilton@bix.com> has a program that he sells for
profit for little toy non-UNIX systems. He calls it 'csh' or the
'hamilton csh', but it's not a csh as it's neither bug nor feature
compatible with the real csh. Actually, he's fixed a great deal, but
in doing so, has created a totally different shell.
--
Tom Christiansen Perl Consultant, Gamer, Hiker tchrist@mox.perl.com
"Espousing the eponymous /cgi-bin/perl.exe?FMH.pl execution model is like
reading a suicide note -- three days too late."
--Tom Christiansen <tchrist@mox.perl.com>
--
DavidHasson - 03 Oct 2007
- Set GROUP = IvoDinov, LindaCapetilloCunliffe, ScottNeu, RicoMagsipoc, AlanGRobinson, TerryNovorr, AlexBui, ChristinaWang, DeniAberle, JohnAdams, LillianGelberg, MartinShapiro, MaryKay, PaulMischel, RoseCai, KeithNorris, RogerLewis, TerenceDoherty, MatthewLee, AmandaHammond, LeslieRaffel, PollySegal, KatherineKahn, PamelaDavidson, AnneBerman, StevenDubinett, ArthurToga, RobertDennis, GraceLiang, PriscillaDeng, RicoMagsipoc, SandyLui, LoniAA, RobertKaplan, JonathanBraun, StanleyKorenman, JackNeedleman, BarbaraLevey, CaroleKlove, DeanYamaguchi, TomDrake, ElizabethWagar, LeslieIngramDrake, StottParker, DebbyPeterson, SallyOliver, RobertBilder, NatalieStCyr, LoniAA, LorenKarp, HelenWilliamsBayne
- Set ALLOWTOPICCHANGE = TWikiAdminGroup, CtsaGroup
--
LindaCapetilloCunliffe - 08 Jun 2006
- Name: caguilar
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width: 99% for full window width (default), width: auto to disable.
- Set EDITBOXSTYLE = width: 99%
- Optionally write protect your home page: (set it to your WikiName)
Related topics
DIRAC: Diffusion Imaging Reconstruction and Analysis Collection
Overview
DIRAC is a group of modules available on the
LONI Pipeline for analyzing diffusion-weighted MRI (DWI) datasets. The currently available modules are:
- DIRACUploader: uploads a DWI dataset for further processing with DIRAC
- DWISynthesizer: generates a simulated DWI dataset
- DWIDataPacker: converts a DWI dataset to the NIfTI MiND format
- DWITensorCalculator: calculates a 2nd-order diffusion tensor (DTI) reconstruction
- DWIQBallCalculator: calculates a discrete q-ball (ODF) reconstruction
- DWISpharmCalculator: calculates a continuous spherical harmonic (ODF) reconstruction
- DWIMeshDeconvolver: performs mesh-based spherical deconvolution for fiber orientation distribution (FOD) reconstruction
- DWITensorStatistician: calculates various metrics on diffusion tensors
- DWIODFStatistician: calculates various metrics on ODFs
Documentation
DIRACUploader
Uploads a DWI dataset for further processing with DIRAC
- Input
- Zipped Data: A
.zip archive containing ANALYZE format DWIs and a single data information .txt file containing one line for each DWI as: b-value|gx,gy,gz|filename.img (e.g. 1000.0|1,0,-1|epi1.img).
- Output
- Data Information File: A text file containing b-value and gradient direction information about the simulated DWIs. Suitable for input to DWIDataPacker.
- Notes
- The uploaded
.zip archive must not contain any directories or subdirectories. All .img, .hdr, and the .txt file must reside in the root of the zip archive.
- A properly-constructed data information
.txt file will contain as many lines as there are DWIs in the dataset.
- Images should be eddy-current and motion corrected before uploading to DIRAC. The b-value and gradient direction information should also be adjusted in accordance with these transforms.
DWISynthesizer
Generates a simulated DWI dataset
- Input
- Simulation Information File: A text file which defines the simulated dataset desired.
- Output
- Synthetic Data Information File: A text file containing b-value and gradient direction information about the simulated DWIs. Suitable for input to DWIDataPacker.
- Notes
- The simulation is performed using the standard multi-tensor model with complex Gaussian noise.
- The input text file has lines which specify the simulation parameters. These are best illustrated with examples.
-
b=3000: This will set the simulation b-value to 3000 mm/s^2
-
snr=30: This will set the simulation signal-to-noise ratio to 30
-
g=1,0,-1: This will add a diffusion-weighting gradient direction (1,0,-1) to the simulation. Note that the direction vector need not be normalized. Add one line like this for each gradient direction you desire.
-
f=1,0,0|0.5|8.5|1,1,1: Specifies that a fiber should be simulated in voxel (1,0,0). This fiber should contribute to 50% of the total signal in that voxel (useful for simulating multiple fibers in a voxel). The ratio of the largest eigenvalue to the second largest should be 8.5 (this defines fiber anisotropy; DWISynthesizer assumes that the second and third eigenvalues are equal, i.e. cylindrical tensors only). Finally, the fiber (and primary eigenvector) should be oriented along the (1,1,1) direction. Repeat lines like this for each fiber you need to simulate.
- The ordering of lines in the input file does not matter.
- A sample input file is available here.
DWIDataPacker
Converts a DWI dataset to the NIfTI MiND format
- Input
- Data Information File: A
.txt file describing one DWI per line as b-value|gx,gy,gz|/full/path/to/filename.img (e.g. 1000.0|1,0,-1|/tmp/epi1.img).
- Output Prefix: A filename prefix. No spaces.
- Crop Volume: A string of integers: x0,y0,z0,xN,yN,zN. Voxels outside this range will be discarded.
- Mask File: A binary mask ANALYZE image. Masked voxels will be packed with the value 0.
- Output
- Packed Data File: A NIfTI MiND file containing the raw DWI dataset,
- Notes
- Non-LONI users should always use the DIRACUploader module to create the input for DWIDataPacker.
- It's worth saving the packed data file. Nearly all DIRAC modules require it as input.
- Images should be eddy-current and motion corrected before this conversion to the MiND format. The b-value and gradient direction information should also be adjusted in accordance with these transforms.
DWITensorCalculator
Calculates a 2nd-order diffusion tensor (DTI) reconstruction
- Input
- Packed Data File: NIfTI MiND formatted file containing the raw DWI dataset. Generated by DWIDataPacker.
- Output
- Tensor Volume: NIfTI MiND formatted file containing the 2nd-order diffusion tensor reconstruction.
- Notes
- The tensor reconstruction is performed using a weighted linear least-squares fit.
DWIQBallCalculator
Calculates a discrete q-ball (ODF) reconstruction
- Input
- Packed Data File: NIfTI MiND formatted file containing the raw DWI dataset. Generated by DWIDataPacker.
- Reconstruction Directions: An icosahedral tessellation level for generating reconstruction directions.
- Output
- ODF Volume: NIfTI MiND formatted file containing the q-ball ODF reconstruction.
- Notes
- If you have very particular reconstruction directions in mind, you can change the reconstruction directions parameter to accept
.txt files and provide a file containing one x,y,z reconstruction direction per line.
DWISpharmCalculator
Calculates a continuous spherical harmonic (ODF) reconstruction
- Input
- Packed Data File: NIfTI MiND formatted file containing the raw DWI dataset. Generated by DWIDataPacker.
- Regularization: Regularization parameter value (controls ODF smoothness).
- Maximum Degree: The maximum degree spherical harmonic coefficients to estimate.
- Output
- ODF Volume: NIfTI MiND formatted file containing the spherical harmonic coefficients for the ODF reconstruction.
DWIMeshDeconvolver
Performs mesh-based spherical deconvolution for fiber orientation distribution (FOD) reconstruction
- Input
- Packed Data File: NIfTI MiND formatted file containing the raw DWI dataset. Generated by DWIDataPacker.
- Convolution Matrix: a
.txt file containing a pre-computed convolution matrix (e.g. one generated by a previous run on DWIMeshDeconvolver on a the same/similar dataset).
- White Matter Mask: An ANALYZE white matter mask.
- Regularization strength: Controls the weight of the data-driven and regularization terms in the deconvolution.
- p-Norm: p-norm tuning of the regularization term in the deconvolution.
- Mesh Density: An icosahedral tessellation level for generating reconstruction directions.
- Output
- FOD Volume: NIfTI MiND formatted file containing the mesh-based fiber orientation distribution reconstruction.
- Convolution Matrix: a
.txt file containing the convolution matrix used in this run.
- Notes
- The deconvolution is performed on a discrete hemispherical mesh using projected gradient descent to guarantee realness, non-negativity, and symmetry of the FOD solution.
- The deconvolution requires a convex optimization to be carried out at each voxel. This can be an order of magnitude slower than other DIRAC modules. You can speed things up significantly by using a smaller mesh density (but your FODs will be coarser).
DWITensorStatistician
Calculates various metrics on diffusion tensors
- Input
- Tensor Volume: NIfTI MiND formatted file containing the 2nd-order diffusion tensor reconstruction.
- Metric: Metric to calculate. Choose from:
- FA: fractional anisotropy
- SRA: scaled relative anisotropy
- VF: volume fraction
- LIN: lattice index
- CA: anisotropy index
- CL: linear anisotropy index
- CP: planar anisotropy index
- CS: spherical anisotropy index
- SI: secondary eigenvector index
- MD: mean diffusivity
- GA: geodesic anisotropy
- TGA: hyperbolic tangent of GA
- XX: xx component of the diffusion tensor
- YY: yy component of the diffusion tensor
- ZZ: zz component of the diffusion tensor
- XY: xy component of the diffusion tensor
- XZ: xz component of the diffusion tensor
- YZ: yz component of the diffusion tensor
- RGB: RGB color volume indicating the direction of the primary eigenvector
- RGBFA: RGB color volume scaled by the FA
- Output
- Metric Map: A NIfTI file containing a map of the metric specified.
DWIODFStatistician
Calculates various metrics on ODFs
- Input
- Test Volume: A NIfTI MiND file containing an ODF reconstruction (q-ball or spherical harmonic).
- Reference Volume: For two-sample metrics, a NIfTI MiND file containing an ODF reconstruction (q-ball or spherical harmonic).
- Metric: Metric to calculate. Choose from:
- GFA: generalized fractional anisotropy (one-sample)
- L1: L1 distance (two-sample)
- L2: L2 distance (two-sample)
- KS: Kolmogorov-Smirnov distance (two-sample)
- EMD: Earth mover's distance (two-sample)
- Output
- Metric Map: A NIfTI file containing a map of the metric specified.
Upcoming modules and tools
- DWIFastTract: Fast-marching tractography for DTI, ODF, or FOD reconstructions
Related projects
- MiND: Metadata in NIfTI for DWI
- DIVA: Diffusion Imaging Visualization Application
DIVA: Diffusion Imaging Visualization Application
Requirements
DIVA will not work unless you have the following installed:
Release
- diva_beta-0.0.1.zip – This is an incomplete beta version of the application. The only justification I can offer for unleashing it is that DIVA enables direct visualization of the reconstructions generated by DIRAC, which I think might be somewhat important for neuro imaging. I apologize in advance for the frustration you are about to experience.
Documentation
- For now, type
java -jar DIVA.jar for usage instructions. If you find them undecipherable, try something like java -jar DIVA.jar -i /path/to/my/results.nii -c 0,0,0,10,10,0 to render 100 voxels. If that fails, feel free to contact me. Sorry, I know this less than ideal, but this is unfinished code and it doesn't make much sense to spend a lot of time documenting things that might change. Do send me a note though; I'll point you in the right direction.
Related projects
- MiND: Metadata in NIfTI for DWI
- DIRAC: Diffusion Imaging Reconstruction and Analysis Collection
--
KristiClark - 21 Feb 2008
dti_regress_stats
Purpose
Computes statistics based on the linear least squares estimate of the single, second rank diffusion tensor.
Help Menu
Usage: ./dti_regress_stats input4D_rawdata.img input.diff input_Diff_tensor.img input_b0_estimated.img [optional outputs]
Note: Overwrite permission for the output is assumed.
-r Rsq.img Rsquared image
-c Corr.img Correlation coefficient (r) for the model
-f Fstat.img F statistic for the regression
-d Resid.img Output the residuals as a 4D image.
-t St_Res.img Output the standardized residuals as a 4D image.
-a AIC.img AIC computed for each voxel
-i AICc.img AIC computed with correction for small n
-j BIC.img BIC computed at each voxel
-k LL.img Log likelihood computed at each voxel
-x # Only compute for the #th slice in the x dimension
-y # Only compute for the #th slice in the y dimension
-z # Only compute for the #th slice in the z dimension
-s Swap bytes of the .diff file
-b # Indicate how the b0 data was used
0 b0 data was treated as any other measurement (default)
1 b0 signal was constrained, directly estimated from data
2 b0 data was ignored
-e # Indicate how many eigenvalues were constrained
0 Eigenvalues were all estimated (default)
1 1 eigenvalue was constrained
2 2 eigenvalues were constrained
3 3 eigenvalues were constrained
6 6 eigenvalues were constrained
9 9 eigenvalues were constrained
-m Model Menu
1 LS scalar ADC
2 NLS scalar ADC
3 NLS scalar ADC + noise
4 NLS scalar ADC mixture model
5 NLS scalar ADC mixture model + noise
6 Linear estimate of single tensor
7 NLS single tensor
8 NLS single tensor + noise
9 NLS single tensor + an isotropic compartment
10 NLS single tensor + an isotropic compartment + noise
11 NLS two tensors
12 NLS two tensors + noise
13 NLS two tensors + an isotropic compartment
14 NLS two tensors + isotropic compartment + noise
15 NLS three tensors
16 NLS three tensors + noise
17 NLS three tensors + an isotropic compartment
18 NLS three tensors + an isotropic compartment + noise
Note: For nonlinear models, the Rsquared can be negative.
More Detailed Information
- Name: Daniel Cohn
- Email: dan.cohn@cshs.org
- Department: Medical Genetics Institute
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David Hasson
My Links
Stuff
Hours
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- Occasionally 9:30am - 6:30pm
Out of Office
- Nov 19, 2008 - Nov 30, 2007
Current Wiki Projects_
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08.17.07:
- Serial Numbers from Cerebro Racks C-H
- Serial Numbers from TP9500, TP9400, Sun
StorEdge?
- CMS Aliasing
-MS Project for Move
- Sun Temp Script
- Get DIVE Laptop Info
- Order Laptop Memory
-Ektron Environment
-XP on
TestMe?
-Finish Ektron
-Mantis Refresh
-Polyvision Embed
ASUCLA
-Spark
-XP on VMWare
--
DavidHasson - 09 Oct 2007
1) Setup Hugo, Rico and KVM Workstations on Moog
2) Setup DHCP Fun
- include /usr/local/etc/dhcpd.static.conf
- database on mgmt-db
- web app
- cron scp, dhcpd restart
- web based push to restart
3) New print server on moog
4) Experiment with LVS virtual machines
- print services
- iis cms
5) Complete CMS VMs
- setup redundancy
6) CS3 on Bosch
7) DPH Roaming Profile
8) Thompson Mac w/ LDAP
9) Linda's Workstation
10) Visual
SourceSafe? for Web Devel
11) PDCs from cerebellum
- run into sw3, where v-test and d-test were
12) Windows AIK?
13) License servers
- matlab
- mathmatica
-
14) User web directories
- nessus
1) Address List
2) Web Site
3) Call Hotels for Guests
--
DavidHasson - 09 Oct 2007
1) Call Robere
2) Marketing Mtg, 1p
3) Plan TBS 'Versary
4) Invites for Thai peeps
5) X10 Info?
6) Target: Dust Buster, Sheets, Soap, anything for apt.
7) All addresses in Ruby
a
a) Mac - Root Passwd, Apps, Image, NIS
i) Matlab 2007b license?
ii) Tools requested by paul? install remotely.
iii) Check if smith is an artist
iv) Add smith info to DB, can be after deploy
v) get monitor and put in thompson's office on standing desk as you enter door
f) Try Mac VMWare again? If not get JP's laptop.
g) solaris VM start (try)
i) permanant location for DPH files.
j) Get JD and DPH sending encrypted / signed e-mail
n) Database!!!!
1) loni-sw-3 labels
y) sophos smbmount on abi-vi, symlink from software (or maybe a windows .lnk!)
Done:
m) Call Kevin at Facman - x92667 - about AC duct
t) make login script to install printers. (JD)
q) send Linh-ham to configure $1200 machine for Jet Jiang
s) JD to fix GPO to allow users to install printers
b) Maya LS - E-mail Andrew Lee
h) rh5 -> mario!!! (think about asking rico for esx on xanto) (JD)
e) E-mail Rico about AD & Bansky
x) tiger CD -> ekan (mantis)
Notes:
1) Changing the "admin" group to be named "ladmin" broke administrator functionality. This implies that the name, not just the uid, is important! Accordingly, we can imply that the group names are indeed important, and conflicting AD and local names could potentially pose a problem.
Contact Support:
1) APC -> Certificate on Weatherboy
2) APC -> Remote racks? (try direct cord to rack g first)
3) Apple -> AD integration group mapping (memberd)
Todo Tomorrow:
w) setup dhcp statics for the 13 net
r) tell Yoav to fix the computer on Jet Jiang's desk
d) KVM Plan, no execute today
j) artist name database (excel)
k) iTerm?
l) File Ultramon in Software
k) JD to send rico info on getting secure key?
l) twiki write up on abdiesus NFS stuff?
m) laminate stuff?
n) get msi's for loni software and push instead.
o) close mantis case about Tiger CD
u) e3k can be powered down in reed
v) twiki: vlan/subnet + dhcp pages
o) httpd.conf on cerebro-scs, try to do isilon proxy w/ mod_rewrite; implement on viola if works, e-mail all about easier access if successful.
p) leonardo in Reed; labeling of interface on core2?
x) Call Ed Bowman at x54408 about ac duct
z) ask grace about ucla extension dialing scheme
Personal Notes:
Was really stressed coming in, then got more excited writing a todo list. At a certain point, got overwhelmed and wanted to move everyhing to tomorrow's list!
When get into the 'jist' of things, very content doing whatever. Just like getting things done. Thinking though that when the day starts to end I get upset because I didn't get enough done. I am very productive though, in general, I just feel I need to take care of stuff as it comes up so diversions can be 75% of my time.
What actually did:
1) Mac Setup
- Reset to NIS
- (diversion) Fixed and sorted tWiki System Setup Pages
- (support) Showed JJiang how to print (needed to login to a machine before he was auth'd)
- (support) Alee needed to replace her net cable, used black 25ft from dell PEM kit
- (support) JJiang needed to clear a paper jam on toad, was printing duplex
- (email) Replied to SMadsen's request to have printers taken care of on the mac
- (email) Added Sarah Madsen (External) to AD because her name was showing up incorrectly
- (diversion) more tWiki (Windows setup) fixing
- (diversion) e-mailed Rico about patchlink / sophos on dahn
- (diversion) called Kevin at x231 (VRU Pager Gateway) - x92667, told me to call Ed
- didn't know number, call Trouble Desk at x68352, they said don't give out numbers, but that it would be taken care of (10:09am)
added 192.168.14.1 to
--
DavidHasson - 09 Oct 2007
Personal
At Work
- Redesign invites w/ Maynee's suggestions
- Print out all main invitations & cut
- E-mail Dad w/ missing addresses
- E-mail Warittha with addresses so far
Slightly less boring
- Get Wiki on funtaff.com? Use for wedding & life planning?
- Look at money software?
At Home
- Get main page up
- Picture
- Registry
- Placeholder for engagement story
- Links with stuff that is "us"
- Gallery with us, kitty cats, family stuff
- Page w/ mini-bios to update peeps
- Contact info
- Pay Wen w/ Paypal and Amex
- Work on a songlist, if tentative, e-mail to peeps
- Update Kate Kinney on stuff, ask about credit card payment
- Drop Wells Fargo contract in mail
LONI
Today
- Cerebellum-b, top 8 nodes analog.
- Denes, Mario
- Viola-test
- Cshrc -> perl (spontaneous)
- Finish Viola-cms
- Setup SSH for sftp
- Make sure Don is up and running
- Create VM for VSS05 (Setup later)
- Forward funtaff mail -> loni, setup filter
- Spend some time on the Wiki
- Look at the licensing servers
- Cygwin?
Tomorrow & Later
- Cerebellum-b, next 16 nodes analog.
- titan, sargant, third node (for vmware) -> prod
- matisse -> workstation attached to rm kb/mouse
- Windows XP
- Dell KVM Software
- Apex KVM so it can be used manually if needed
- Power up VMs for viola-cms and sql
- Work on infrastructure home page
- GSSAPI w/ Apache
- Seating chart + schedule for students
TODO: Move to a seperate page w/ all projects
Under the radar
-
Back burner
- Database / Rule world
Other things remembered
- Corbu needs to be done in VM, would be better done by JD or DPH to see how Linh Nam's writeup is
Notes
- Started doing Todo lists on the Wiki instead, liking that
- Can't really type because AC vent is so cold, trying to fix
- Decided to move some stuff in my .cshrc from csh to perl, given this link:
--
DavidHasson - 03 Oct 2007
- Queenie's genious idea of having a machine with all the OS's virtualized
- Rule in outlook to flag messages to funtaff
--
DavidHasson - 05 Oct 2007
General outline for the day:
- Prepare information for today's meeting
- Get database into MySQL?
- Database dreaming and planning
- Finish KVM Project
- IPMI Project:
- DNS for all mgmt nodes that are up
- Make executive decision for shared LAN configs
- .cshrc in perl
- Label loni-sw-3
- Realign invitations
- Look for Gothic fonts in ASUCLA archive
- Deal with people / bills
- E-mail ASUCLA IT Guy w/ login information
- Pay Wen
- Transfer $5k
- Call Printer
- JSP Stuff
Tomorrow
- People / bills
- Pay DJ
- Pay Kate
- Call Calamigos
Dreaming / Planning
- Nessie
- Migrate to Mario
- Install Tomcat
- Test mod_rewrite on dev-mgmt-db
- Implement mod_rewrite on viola-rs1/2
- Regulus @ Mario
- License servers!
--
DavidHasson - 08 Oct 2007
David's Daily Notes
2007
December
October
September
Older
--
DavidHasson - 03 Oct 2007
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--
KristiClark - 21 Feb 2008
fiber_track
Purpose
Streamline tractography.
Help Menu
Usage: ./fiber_track input.img [options]
-r ROI.img Fiber tracking will start with all non-zero voxels
in this image. Default is to track from any voxel
above the FA threshold. However this can
lead to a segmentation fault from lack of memory.
-a avoid.img Any tracts that pass through any non-zero voxels
in this image will be removed.
-i incl.img Tracts must pass through at least one non-zero
voxel in this image in order to be included.
-u output.ucf Output the tracts in UCF format.
-o output.img Output the tracts in analyze format.
-w X Y Z Output image dimensions, not used in region growing alg
(default is to match the input image dims
-c file.txt Comprehensive output text: coordinates and FA
-t Angle Turning angle threshold, must be less than 90
Default is 70 degrees.
-f FA Fractional anisotropy threshold, must be less than 1
Default is 0.25.
-s mm Step size (mm) to be used
Default is voxel size in the x dimension
-d # Seed density (mm) to be used
Default is voxel size in the x dimension
-p # # Min & max number of points allowed per tract
(default 1 10000)
-g # Maximum iterations for the region growing algorithm
(default 1000) accuracy-time tradeoff
-x Flip the sign of the x component of the primary eigenvector
-y Flip the sign of the y component of the primary eigenvector
-z Flip the sign of the z component of the primary eigenvector
-k Slice number
-e Input type menu number
1 Eigenvectors and eigenvalues (default)
2 Coeff of the sph harmonics for the ODF
3 Single vectors (e.g. from odf_vectors)
4 Tensors (can be mixture models)
-m Menu_number FT Model
1 Streamline algorithm, Euler method (default)
2 Streamline algorithm, Runge-Kutta 2nd order
3 Streamline algorithm, Runge-Kutta 4th order
-n Menu_number Vector/tensor/ODF field interpolation model
1 Nearest neighbor (default)
2 Trilinear interpolation
Example: ./fiber_track input_eigs.img output_prefix
-r roi.img -t 65 -f 0.45 -s 0.5 -m 3 -i 2
Note: the input image should be single vectors or
an eigenvolume (t_dim=12), e.g. the output from tensor_eig.
Note: Overwrite permission for the output is assumed.
Note: By setting a minimum number of points per tract, you can reduce the number of contours in the UCF that do not go anywhere (helps with memory issues in displaying the UCF). However, if you want the UCF to show the original ROI, leave the minimum number at 1. Also, if you are getting segmentation fault (memory errors), try reducing the maximum number of points.
The -k option can be used to only start tracking from a single slice. This can be used if doing whole brain fiber tracking to help parallelize the processing, i.e. if it takes hours to run this algorithm on the whole brain, then this option can be used multiple times with one number per slice to divide the time it takes to compute by the number of slices.
More Detailed Information
File Attachments
Each topic can have one or more files of any type attached to it by using the Attach screen to upload (or download) files from your local PC. Attachments are stored under revision control: uploads are automatically backed up; all previous versions of a modified file can be retrieved.
What Are Attachments Good For?
File Attachments can be used to create powerful customized groupware solutions, like file sharing and document management systems, and quick Web page authoring.
Document Management System
- You can use Attachments to store and retrieve documents (in any format, with associated graphics, and other media files); attach documents to specific TWiki topics; collaborate on documents with full revision control; distribute documents on a need-to-know basis using web and topic-level access control; create a central reference library that's easy to share with an user group spread around the world.
File Sharing
- For file sharing, FileAttachments on a series of topics can be used to quickly create a well-documented, categorized digital download center for all types of files: documents; graphics and other media; drivers and patches; applications; anything you can safely upload!
Web Authoring
- Through your Web browser, you can easily upload graphics (or sound files, or anything else you want to link to on a page) and place them on a single page, or use them across a web, or site-wide.
- NOTE: You can also add graphics - any files - directly, typically by FTP upload. This requires FTP access, and may be more convenient if you have a large number of files to load. FTP-ed files can't be managed using browser-based Attachment controls. You can use your browser to create TWikiVariables shortcuts, like this %H% =
.
Uploading Files
- Click on the
Attach link at the bottom of the page. The Attach screen lets you browse for a file, add a comment, and upload it. The uploaded file will show up in the File Attachment table.
- NOTE: The topic must already exist. It is a two step process if you want to attach a file to a non-existing topic; first create the topic, then add the file attachment.
- Any type of file can be uploaded. Some files that might pose a security risk are renamed, ex:
*.php files are renamed to *.php.txt so that no one can place code that would be read in a .php file.
- The previous upload path is retained for convenience. In case you make some changes to the local file and want to upload it, again you can copy the previous upload path into the Local file field.
- TWiki can limit the file size. This is defined by the
%ATTACHFILESIZELIMIT% variable of the TWikiPreferences, currently set at 30000 KB.
-
It's not recommended to upload files greater than a few hundred K through a browser. Large files can be extremely slow-loading, and often time out. Use an FTP site for large file uploads.
Downloading Files
- NOTE: There is no access control on individual attachments. If you need control over single files, create a separate topic per file and set topic-level access restrictions for each.
Moving Attachment Files
An attachment can be moved between topics.
- Click
Manage on the Attachment to be moved.
- On the control screen, select the new web and/or topic.
- Click
Move. The attachment and its version history are moved. The original location is stored as topic Meta Data.
Deleting Attachments
Move unwanted Attachments to web
Trash, topic
TrashAttachment.
Linking to Attached Files
- To reference an attachment located in another topic, enter:
-
%PUBURL%/%WEB%/OtherTopic/Sample.txt (if it's within the same web)
-
%PUBURL%/Otherweb/OtherTopic/Sample.txt (if it's in a different web)
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Attach file: Sample.txt
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Edit topic and write text: %INCLUDE{"%ATTACHURL%/Sample.txt"}%
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Attach file: Smile.gif
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Edit topic and write text: %ATTACHURL%/Smile.gif
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Preview: text appears as 
, an image.
File Attachment Contents Table
Files attached to a topic are displayed in a directory table, displayed at the bottom of the page, or optionally, hidden and accessed when you click
Attach.
File Attachment Controls
Clicking on a
Manage link takes you to a new page that looks like this:
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h means the attachment is hidden, it isn't listed when viewing a topic.
- The second table is all the versions of the attachment. Click on View to see that version. If it's the most recent version, you'll be taken to an URL that always displays the latest version, which is usually what you want.
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Hide file checkbox, then click Change properties.
Known Issues
- Unlike topics, attachments are not locked during editing. As a workaround, you can change the comment to indicate an attachment file is being worked on - the comment on the specific version isn't lost, it's there when you list all versions of the attachment.
Set of tags that handle html form rendering. They're meant to work together with the
BeanUtils?.populate method but this is not a requirement. The tags make it easy to populate form fields, taking on the burden of properly escaping fields, selecting selections, coordinating radio choice selection, checking selected checkboxes, etc.
Here's how a form would be populated using JSTL 2.1 (with ELs allowed in all attributes):
<form action=”submit.jsp”>
<input type=”text” name=”email” value=”${formBean.email}” />
<select name=”site”>
<c:forEach items=”${formBean.sites}” var=”site”>
<c:choose>
<c:when test=”${formBean.selectedSiteId eq site.id}”>
<option value=”${site.id}” SELECTED>
</c:when>
<c:otherwise>
<option value=”${site.id}”>
</c:otherwise>
</c:choose>
<c:out value=”${site.name}” />
</option>
</c:forEach>
</select>
</form>
Here's the same form using the form taglib:
<form:form action=”submit.jsp” bean=”formBean”>
<form:text name=”email” />
<form:select name=”sites” selected=”selectedSite” />
</form:form>
Both will render the same html.
| HTML | Form Taglib Element Name | Description |
|---|
| <form> ... </form> | form | Form Tag |
| <input type="text"> | text | Text Input field |
| <input type="checkbox"> | checkbox | Checkbox Input Tag |
| <input type="radio"> | radio_choice | Radio button |
| N/A | radio_group | Provides child radio_choice tags with a place to coordinate the selection. |
| <select> | select | Combo dropdown select or multi-select boxes. |
| <textarea> | textarea | Text area tag for large text entry fields - lots of text. |
| <input type="hidden"> | hidden | Hidden input field |
| <input type="password"> | password | Password input field |
| Range Input Widget | range | Renders a set of html form fields that permit number range entry. |
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My Links
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Classes and static methods.
Name: BeanUtils?
Description: Populates a
JavaBean? using the keys and values in a Map object.
Useful when you need to populate a Form bean from a query. Just get the query's
ParameterMap? and feed it to populate along with your bean. You'll get your form fully populated.
Name: IdaUtils?
Description:
- Returns “value_number” or “value_string” based on the data_type
- Returns a nicely formatted value for the value of the image protocol based on the value's class.
- Tries to find a Java class that best matches a term's data_type.
Name: Services
Description: Like the existing Utilities class.
- Authenticates a user
- Returns a User object based on an email
Name: Logging
Description: Logging configuration that extends sun's logging configuration to allow for per-logger handler/formatter configuration.
Name: DatabaseUtils?
Description:
- Connection factory class for retrieving jdbc connections from named DataSources?.
- Convenience methods for commonly used programming constructs when dealing with connections.
Name: ConnectionProxy?
Description: Profiling proxy used by the
DatabaseUtils? to wrap an existing concrete connection for profiling and connection pool leak detection.
Name: Collection of VOs
Description: Value Objects that map to entities of the IDA. Default constructors, accessors for all properties. public data members for populating them without the need for methods.
--
RobertCadena - 03 Jan 2006
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General Responsibility Assignment Software Patterns (GRASP)
Expert
Who, in the general case, is responsible ?
Assign a responsibility to the information expert - the class that
has the information necessary to fulfill the responsibility.
Creator
Who Creates ?
Assign class B the responsibility to create an instance of class A if one of the following
is true:
- ) B contains A
- ) B aggregates A
- ) B has the initializing data for A
- ) B records A
- ) B closely uses A
Controller
Who handles a system event ?
Assign the responsibility for handling a system event message to a
class representing one of these choices:
- ) The busines or overall organization (a facade controller)
- ) The overall "system" (a facade cont4roller).
- ) An animate thing in the domain that woudl perform the work (a role controller)
- ) An artifical class (Pure Fabrication) representing the use case (a use case controller).
Low Coupling
How to support low dependency and increased reuse ?
Assign responsibilities so that coupling remains low.
High Cohesion
How to keep complexity manageable ?
Assign responsibilities so that cohesion remains high.
Polymorphism
Wo, when behavior varies by type ?
When related alternatives or behaviors vary by type(class), assign responsibility for the
behavior - using polymorphic operations - to the
types for which the behavior varies.
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No permission to read topic
HCP - perhaps you need to log in?
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)
See home page at TWiki:Main/AndreaSterbini
-- AndreaSterbini - 13 Dec 2001
Sample.txt
Smile.gif
