Re: significance in correlation plots

I have already figured out what was going on, now it works.
Thanks!

Cheers


2014-07-11 17:51 GMT+02:00 Noelia otero <noeli1680@gmail.com>:

> Hi
>
> Thanks for the suggestion. Actually, I realized comparing with another
> plot in grads that the dimensions could be changed (it seems to me that the
> latitude need to be change). I did the same plot by using R and I had to
> reverse the latitude (increasing) to get the same plot that in grads, so my
> guess is that the difference could come from that.
> Is there any way to change that in ncl? I know that I can reorder them or
> transpose them..but my worries is that I will have the same problem with
> the coordinates).
>
> Thanks in advance,
>
> Cheers
>
>
> 2014-07-10 23:53 GMT+02:00 Mary Haley <haley@ucar.edu>:
>
> Hi,
>>
>> I don't think you are calling ShadeLtContour correctly, as it expects
>> five input arguments and not three. Is this just a typo, or did you get any
>> errors when you ran your script?
>>
>> I suggest using gsn_contour_shade in place of ShadeLtContour, which has
>> been deprecated. Your code would look like this:
>>
>> opt = True
>> opt@gsnShadeFillType = "pattern" ; pattern fill
>> opt@gsnShadeLow = 2 ; use pattern #2
>> plot2 = gsn_contour_shade(wks,0.05,0.75,opt)
>>
>> The plots should look the same regardless of which function you use, but
>> gsn_contour_shade is a bit more versatile.
>>
>> The two numeric arguments to gsn_contour_shade are the "low" and "high"
>> values that you want to shade based on. In the example above, then, you
>> could specify shading below 0.05, between 0.05 and 0.75, and/or above 0.75,
>> using the gsnShadeLow, gsnShadeMid, and gsnShadeHigh options. If you only
>> use the Low or High options, then the corresponding high or low value will
>> just be ignored.
>>
>> The important thing to note about ShadeLtContour and gsn_contour_shade is
>> that if you indicate a shade value that falls *between* the contour levels
>> of your plot, then these routines will only shade below the next closest
>> contour level that is less than this value. This may be the difference
>> that you are seeing between the grads and NCL plots?
>>
>> If you continue to have problems, it would help to see a sample image.
>>
>> Thanks,
>>
>> --Mary
>>
>>
>> On Thu, Jul 10, 2014 at 9:46 AM, Noelia otero <noeli1680@gmail.com>
>> wrote:
>>
>>> Hi,
>>>
>>> I was trying to plot the correlations and adding a contour effect in the
>>> 95% significant areas. I follow some examples but I am not confident with
>>> my result. This why I would like to know if I am in the right way to do
>>> that:
>>>
>>> load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"
>>> load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"
>>> load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/contributed.ncl"
>>> load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/shea_util.ncl"
>>>
>>> begin
>>>
>>> a =
>>> addfile("/home/nof/pruebas/ensem_means/pr_Amon_CanCM4_historical_ensmean_t42_westA_meanJAS.nc","r")
>>> b =
>>> addfile("/home/nof/pruebas/WAMI_CEOF/WAMI_Amon_CanCM4_historical_ensmean.nc","r")
>>>
>>>
>>> pr =a->pr
>>> wami =b->WAMI(:,0,0,0)
>>> lat = a->lat
>>> lon = a->lonlat@units="degrees_north"
>>>
>>> correl=escorc(wami,pr(lat|:,lon|:,time|:))
>>> correl!0="lat"
>>> correl!1="lon"
>>> correl&lat=lat
>>> correl&lon=lon
>>>
>>> ;***significant values
>>> sig = 0.05
>>> um = rtest(correl(lat|:,lon|:),45,0)
>>> um!0="lat"
>>> um!1="lon"
>>> um&lat=lat
>>> um&lon=lon
>>>
>>> wks = gsn_open_wks("pdf","example")
>>>
>>> res = True
>>> res@gsnAddCyclic = False
>>> res@cnFillOn = True
>>> res@cnLinesOn = True
>>> res@mpMinLong = min(correl&lon)
>>> res@mpMinLatF = min(correl&lat)
>>> res@mpMaxLonF = max(correl&lon)
>>> res@mpMaxLatF = max(correl&lat)
>>> ;plotting first
>>> plot = gsn_csm_contour_map_ce(wks,correl,res)
>>>
>>> res2 = True ; res2 probability plots
>>> res2@gsnAddCyclic = False
>>>
>>> res2@gsnDraw = False ; Do not draw plot
>>> res2@gsnFrame = False ; Do not advance frome
>>>
>>> res2@cnLevelSelectionMode = "ManualLevels" ; set manual contour
>>> levels
>>> res2@cnMinLevelValF = 0.00 ; set min contour
>>> level
>>> res2@cnMaxLevelValF = 1.05 ; set max contour
>>> level
>>> res2@cnLevelSpacingF = 0.05 ; set contour
>>> spacing
>>>
>>> res2@cnInfoLabelOn = False ; turn off info label
>>>
>>> res2@cnLinesOn = False ; do not draw contour
>>> lines
>>> res2@cnLineLabelsOn = False ; do not draw contour
>>> labels
>>>
>>> res2@cnFillScaleF = 0.6 ; add extra density
>>> map_ce(wks,correl,res)
>>> plot2 = gsn_csm_contour(wks,um, res2) ;
>>> plot2 = ShadeLtContour(plot2, 0.05, 2) ; plotting significant
>>> areas
>>>
>>> overlay (plot, plot2)
>>>
>>> draw (plot)
>>> frame(wks)
>>>
>>>
>>> end
>>>
>>> My doubts come from plot2 = ShadeLtContour(plot2, 0.05, 2) , would it
>>> be OK to show the 95% significance?? because I did the same plot in grads
>>> (just defining a threshold based on t-student), but the plots looks like a
>>> little bit different..
>>>
>>> Thanks in advance!
>>>
>>> Cheers
>>>
>>>
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>>>
>>>
>>
>

Received on Tue Jul 15 09:46:35 2014