On Aug 14, 2:20 pm, Chris <beaum...@ifa.hawaii.edu> wrote:
> On Aug 14, 9:56 am, mbwel...@gmail.com wrote:
>
>
>
>> On Aug 14, 11:50 am, Brian Larsen <balar...@gmail.com> wrote:
>
>>> Matt,
>
>>> this isn't anywhere near enough information to provide a coherent and
>>> meaningful answer.
>
>>> - What exactly are you trying to do?
>>> - What have you tried?
>>> - What bits of code are working and not?
>
>>> Cheers,
>
>>> Brian
>
>>> ------------------------------------------------------------ --------------
>>> Brian Larsen
>>> Boston University
>>> Center for Space Physicshttp://people.bu.edu/balarsen/Home/IDL
>
>> Guess I should be more specific then :)
>
>> Here is my code (non iterative):
>> a= 3.6e007 ; area of region in meters^2
>> o= (60*!pi/180) ; fault dip angle in degrees
>> c= 6e-003 ; scaling factor
>> t= 50e003 ; elastic lithosphere thickness in meters
>> v= (a*t) ; volume of region in meters^3
>> x= 5e003 ; depth of faulting in meters, 5-7km for normal
>> faults, ~30km for thrust faults
>
>> h= (x/sin(o)) ; depth of faulting in meters
>> u= 3 ; fault aspect ratio: Length/Height(down dip)
>> = 2 or 3
>> kns=(sin(o)*cos(o)/v) ; horizontal normal strain constant for small
>> faults
>> knl=(c*cos(o)*x^2/v/sin(o)) ; horizontal normal strain
>> constant for large faults
>> kvs=(-sin(o)*cos(o)/v) ; vertical normal strain constant for small
>> faults
>> kvl=(-cos(o)/v) ; vertical normal strain constant for large
>> faults
>
>> ind_small = where(ar_plan[1,*] lt 2*x) ; select faults such that L
>> < 2x
>> ind_large = where(ar_plan[1,*] ge 2*x) ; select faults such that L> 2x
>
>> ar_plan_small = ar_plan[*,ind_small] ; place in matrice with
>> identifer
>> ar_plan_large = ar_plan[*,ind_large] ; place in matrice with
>> identifer
>> lc_small= ar_plan_small[1,*] ; select only lengths to sum for
>> small faults
>> lc_large= ar_plan_large[1,*] ; select only lengths to sum for
>> large faults
>> tl_small = total(lc_small^3) ; sum lengths according to
>> kostrov summation, small faults
>> tl_large = total(lc_large) ; sum lengths according to kostrov
>> summation, large faults
>
>> ens= (kns*c/u)*tl_small ; horizontal normal strain
>> for small faults
>> enl= knl*tl_large ; horizontal normal strain for large
>> faults
>> e_t= ens+enl ; total horizontal normal strain
>
>> I need to vary the parameters o,c,t,x and u with in a certain range
>> (e.g. o= 50-80 degrees) in order to reproduce e_t (total horizontal
>> normal strain) to within ~ +-10% and I need all the possible
>> combintation saved to an ascii file, or some other output. Where
>> ar_plan is a FLOAT = Array[2, 129], different arrays have different
>> dimensions and I have multiple arrays, but # of columns [2] should
>> remain constant at this stage.
>
>> I'm having some trouble getting started, but will probably have some
>> issues in the implementation as well :)
>
>> As an aside, I have another issue where, for example, ind_small = -1
>> for no returned results instead of 0. This causes:
>> % Attempt to subscript AR_PLAN with IND_SMALL is out of range and the
>> program stops running.
>> I would like this to run even with no returned results. Does anyone
>> know how to do this?
>
>> ~Matt
>
> I think the main difficulty you are going to run into is that, with 5
> independent variables, exhaustively searching the entire search space
> for solutions may not feasible. The most straightforward approach, of
> course, is to have five nested loops over each of your variables and
> checking to see if that combination of variables satisfies your
> constraint of reproducing e_t. However, even if you just tested 100
> values for each variable, that would be 10^10 total steps in the loop.
> Furthermore, such an approach is extremely inefficient because it has
> no sense of 'how close' a given combination of variables are- it will
> spend the vast majority of the time checking ridiculous candidates.
>
> There are a number of search algorithms that you could look into.
> Probably the easiest is some sort of monte carlo search like the
> following: Define a 'fitness function' for a combination of
> independent variables to be how far off the calculated e_t is from the
> goal e_t. You now want to minimize this error. Start with some random
> values for each of your variables, and use some local minimum finding
> algorithm (there is a built in amoeba function for 1 variable, but
> look into algorithms like steepest ascent hill climbing, downhill
> simplex, etc) to find a local error minimum. If the error is small
> enough, count that as an acceptable solution. If not, throw it away.
> Now start with new random values for the variables, and repeat. A book
> like Numerical Recipes by Press et al describes such algorithms.
>
> The problem with this approach is that it is not guaranteed to find
> ALL acceptable combinations of values - that is only possible with an
> exhaustive search which is probably not feasible.
>
> As for your problem of WHERE returning -1, use the count keyword in
> where. Then, test for whether or not that count is zero and, if it is,
> skip that case.
>
> chris
I'm trying to fix the where statement returning -1, here is what I've
tried thus far:
ind_small = where(ar_plan[1,*] lt 2*x,count) ; select faults such
that L < 2x
if count ge 0 then ar_plan_small=ar_plan[*,ind_small] else
ar_plan_small=0
ar_plan_small
but I'm still getting the same error, I'm sure I have the syntax
wrong. Unfortunately I'm not quite at the level to trouble shoot this
myself, confidently.
I have ordered the book suggested, I would imagine that it would come
in handy very soon, but for the shear learning experience of it I
would like to try it in IDL first (plus research waits for no amazon
order). I can limit the increments for each variable to make it more
manageable (less than 10^10 total steps), I just need some help and/or
examples to illustrate how to create five nested loops for each
variable, with each bounded condition and set increment that satisfy
e_t that are recorded to an ASCII file. e.g. o = 50-80, del o = 5;
t=5-100, del t = 10; etc...
Thanks,
~Matt
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