[ncl-talk] stat_dispersion

[Mary Haley]

Hi Jonathan,

When you say dim_stat4 bombed, what exactly was the error message?  This is
a built-in function, so you don't need to load any NCL scripts to use it.

--Mary


On Sat, Nov 29, 2014 at 3:47 PM, jbuzan <jbuzan at purdue.edu> wrote:

> Hi Mary,
>
> Sorry for the delayed response!
>
> I realized there was a two problems:
> 1) I had an error in the original dataset.
> 2) I figured out how to use IND to feed only non-missing values to the
> exceedance (the original issue).
>
> I did have a problem with dim_stat4.   When it was trying to calculate
> kurtosis and skewness for stat_dispersion (dim_stat4 2,3), the contributed
> file would blow up saying it cannot find dim_stat4.  I turned it off, but I
> can’t seem to figure out why dim_stat4 bombed.
>
> -Jonathan
>
>
>
>
> On Oct 9, 2014, at 6:38 PM, Mary Haley <haley at ucar.edu> wrote:
>
> Jonathan,
>
> Did anybody ever respond to this one?
>
> If not, the code is quite complex, and it would be useful if you could
> provide a full script and data.
>
> --Mary
>
>
> On Fri, Sep 26, 2014 at 3:30 PM, jbuzan <jbuzan at purdue.edu> wrote:
>
>> I am having some issues with stat_dispersion.
>> Perhaps my code ‘fixes’ cause the issue.  I am not sure.
>>
>> I have 2 datasets:
>> time: 1460 lat: 192, lon 288
>> time: 2920 lat: 192, lon 288
>> I have added a second year to the original year (both 1460 time steps).
>> The stat_dispersion, in theory, should have the same minimum and maximum
>> values.
>>
>> The data is partitioned by Temperature and by a difference field.
>> Here is a snippet of the code that does the partitioning:
>> do k = 0, dimsizes(numvar)-1
>> ;do k = 16, 16
>>    thresholdstart = k*5.d + 200.d
>>    print(thresholdstart)
>>    thresholdend = k*5.d + 205.d
>>    print(thresholdend)
>> ;   location = ind(temperature1d.lt.thresholdend)
>>    location =
>> ind(temperature1d.lt.thresholdend.and.temperature1d.ge.thresholdstart)
>> ;   printVarSummary(location)
>> ;   print(location)
>>    if(.not.all(ismissing(location)))
>>         tbin = wetbulb1d(location)
>>         else
>>         tbin = value
>>         tbin at _FillValue = value
>>    end if
>>  ;  print(tbin)
>>
>>    if(.not.all(ismissing(tbin)))
>>         tbin at _FillValue = -999.d
>>         delete(tbin at _FillValue)
>>         numarray(:,k) = stat_dispersion(tbin,opt)
>>         else
>>         numarray(:,k) = value
>>    end if
>>    delete(tbin)
>>    delete(location)
>>   ; print(numarray)
>> end do
>> numarray = where(numarray.eq.-999.d,value,numarray)
>>
>> replace_ieeenan (numarray, value, 0)
>> numarray at _FillValue = value
>> numarray at missing_value = numarray at _FillValue
>> numarray!0 = "stat"
>> numarray!1 = "bin"
>> numarray&bin = numvar
>> printVarSummary(numarray)
>>
>> Here is a snippet of the code that grabs the values:
>> box_var = numvar
>> box_var at long_name = longnamea
>> box_var at units = unitsa
>> ;box_var(:,0) = field1c(22,:)
>> box_var(:,0) = field1c(2,:)
>> box_var(:,1) = field1c(24,:)
>> box_var(:,2) = field1c(8,:)
>> box_var(:,3) = field1c(25,:)
>> box_var(:,4) = field1c(14,:)
>> ;box_var(:,4) = field1c(26,:)
>>
>> field1c of 14 and 2 are the maximum and minimums from Stat_Dispersion.
>> I think the issue is that stat_dispersion field becomes too large, and
>> drops something.  It might be how I characterize the missing values.
>>
>> Any help would be appreciated.
>>
>> -Jonathan
>>
>>
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>
>
>
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