[ncl-talk] confused about plotting t-values (manually)
Hoffman Cheung
hoffmancheung at gmail.com
Fri Oct 21 09:14:37 MDT 2016
Hi Noelia,
You are weclome.
"res" in my reply just corresponds to the resource that you want to
highlight the significant level. Sorry for causing any confusion.
Cheers,
Hoffman
2016-10-21 15:32 GMT+02:00 Noelia otero <noeli1680 at gmail.com>:
> Hi Hoffman,
>
> Thanks for your answer, I think I get it, however I would have to set the cnLevelSelectionMode
> for the second plot (res2, in my case), wouldn't I?
> I just realised that if I don't specify any LevelSelectionMode (just
> leaving it as automatically), it seems to work..
>
> Anyway, many thanks again,
>
> Best
>
> Noelia
>
> 2016-10-20 16:26 GMT+02:00 Hoffman Cheung <hoffmancheung at gmail.com>:
>
>> Hi Noelia,
>>
>> Let me try to answer this question briefly.
>>
>> If you use gsn_contour_shade, you have to make sure the "contour" you are
>> plotting is there, i.e. 0.05 and 1.960 in your case. Otherwise, the program
>> may not work "properly". Say, when you want to shade the parts smaller than
>> 0.05, but
>>
>> case 1: if the program only generates contours greater than 0.05, the
>> plot will not be shaded;
>> case 2: if the program generates contours smaller than 0.05, then only
>> part (not all) of the values smaller than 0.05 is shaded.
>>
>> By default, the contour levels are set automatically, such that the 0.05
>> might not be there. To solve the problem, you have to set
>> cnLevelSelectionMode, e.g.
>> res at cnLevelSelectionMode = "ExplicitLevels"
>> res at cnLevels = (/0., 0.05/) ; depending on which contour intervals want
>> to draw
>> res at cnLinesOn = False ; turn the resource off if you do not want to draw
>> the line (similar for the two resources below)
>> res at cnLineLabelsOn = False
>> res at cnInfoLabelOn = False
>>
>> Hope my answer helps you solve the problem.
>>
>> Best,
>> Hoffman
>>
>>
>>
>>
>> 2016-10-20 9:26 GMT+02:00 Noelia otero <noeli1680 at gmail.com>:
>>
>>> Hi Mary,
>>>
>>>
>>>
>>> I sent a couple of questions few days ago (from gmail) , and I was
>>> reading about some problems regarding the mail list, so I thought that
>>> maybe my mail has not been received …(I can not see it in the mail list).
>>> That's why I also added your email addressee (sorry for the
>>> inconvenience).
>>>
>>>
>>> I just wanted to re-send my question (see below), in case you can give
>>> me a suggestion.
>>>
>>>
>>>
>>> Thanks in advance!
>>>
>>>
>>>
>>>
>>>
>>> I'm doing a composite and I'm plotting significant areas (of anomalies).
>>> For that, I computed the t-values manually, and then I was trying to add
>>> contour for those significant values. However, I'm a bit confused..since if
>>> I used gsn_contour_shade for the values greater that the critical value (in
>>> my case) it seems not to work well...so, I 'd like to ask if anyone have a
>>> suggestion. This is part of my code:
>>>
>>>
>>>
>>>
>>>
>>> ;Average over the period
>>>
>>> compos_mera = dim_avg(compos_era(lat|:,lon|:,time|:)) ;reordering
>>>
>>> compos_mera!0 = "lat"
>>>
>>> compos_mera!1 = "lon"
>>>
>>> compos_mera&lat = lat
>>>
>>> compos_mera&lon = lon
>>>
>>>
>>>
>>> ;std
>>>
>>> composStd = dim_stddev_Wrap(compos_era(lat|:, lon|:, time|:)) ;
>>>
>>> composStd at _FillValue = -999
>>>
>>> composVar = dim_variance_n (compos_era,0)
>>>
>>> composVar at _FillValue = -999
>>>
>>> n = dimsizes(timeE) ;number of observations
>>>
>>>
>>>
>>> ;Compute t-student val for significant
>>>
>>> Scom = composStd/(sqrt(n))
>>>
>>> Scom at _FillValue=-999
>>>
>>> mu = 0 ; set the value to see how anomalies are significantly
>>> different from zero
>>>
>>> tval = (compos_mera - mu)/where(Scom.ne.0, Scom, Scom at _FillValue)
>>> ;avoid diving by zero
>>>
>>> abstval= abs(tval)
>>>
>>> ;see table ttest 0.05 2-tailed
>>>
>>> cv = 1.960 ;critival value according to the tables
>>>
>>>
>>>
>>>
>>>
>>> ;-------Starting plot------
>>>
>>>
>>>
>>> res = True ; plot mods desired
>>>
>>> res at mpProjection = "LambertConformal"; choose projection
>>>
>>> res at mpFillOn = False ; turn off map fill
>>>
>>> res at cnFillOn = True ; turn on color
>>>
>>> res at cnLinesOn = False ; turn off contour lines
>>>
>>> res at lbLabelBarOn = True
>>>
>>> res at gsnDraw = False
>>>
>>> res at gsnFrame = False
>>>
>>> res at gsnAddCyclic = False ; regional plot
>>>
>>> res at gsnMaximize = True ; enlarge plot
>>>
>>> res at mpMinLatF = 30 ; min lat to mask
>>>
>>> res at mpMaxLatF = 75 ; max lat to mask
>>>
>>> res at mpMinLonF = -20 ; min lon to mask
>>>
>>> res at mpMaxLonF = 40 ; max lon to mask
>>>
>>> res at gsnMaskLambertConformal = True ; turn on lc masking
>>>
>>> res at gsnMaskLambertConformalOutlineOn = False ; turns off outline
>>>
>>>
>>>
>>> ;----Plot 2plot (significant values )----
>>>
>>> ; generate shadow plot to overlay
>>>
>>> res2 = True
>>>
>>> res2 at gsnAddCyclic = False
>>>
>>> res2 at gsnDraw = False
>>>
>>> res2 at gsnFrame = False
>>>
>>> res2 at cnInfoLabelOn = False
>>>
>>> res2 at cnLinesOn = False ; do not draw contour lines
>>>
>>> res2 at cnFillScaleF = 0.9
>>>
>>>
>>>
>>> opt = True
>>>
>>> opt at gsnShadeFillType = "pattern"
>>>
>>> opt at gsnShadeHigh = 10
>>>
>>>
>>>
>>>
>>>
>>> plot= gsn_csm_contour_map(wks, compos_mera,res)
>>>
>>> plot2 = gsn_csm_contour(wks,abstval,res2)
>>>
>>> plot2= gsn_contour_shade(plot2,-999,cv,opt)
>>>
>>>
>>>
>>> overlay (plot, plot2)
>>>
>>>
>>>
>>>
>>>
>>> I also tried to computed the p-values and then make the plot, as follows:
>>>
>>>
>>>
>>> dim_s = dimsizes(tval) ;lat x lon
>>>
>>> df = new((/dim_s(0),dim_s(1)/),"integer")
>>>
>>> df = n-1
>>>
>>> beta_b = new((/dim_s(0),dim_s(1)/),"float") ; preallocate
>>> space for beta_b
>>>
>>> beta_b = 0.5 ;
>>> set entire beta_b array to 0.5, the suggested value of beta_b
>>>
>>> ; according to betainc documentation
>>>
>>>
>>>
>>> prob = betainc( df/(df+tval^2), df/2.0, beta_b)
>>>
>>> ; Now set up for significant at 0.05
>>>
>>>
>>>
>>>
>>>
>>> opt = True
>>>
>>> opt at gsnShadeFillType = "pattern"
>>>
>>> opt at gsnShadeLow = 10
>>>
>>>
>>>
>>> plot= gsn_csm_contour_map(wks,compos_mera,res)
>>>
>>> plot2 = gsn_csm_contour(wks,prob,res2)
>>>
>>> plot2= gsn_contour_shade(plot2,0.05,-999,opt)
>>>
>>>
>>>
>>> Both ways should be OK, am I wrong? What am I missing here, because
>>> looking at the values, there are significant values, but I don't know why I
>>> can't see it.
>>>
>>>
>>>
>>>
>>>
>>> Also, regarding my other question, if I want to use bootstrap for
>>> showing significant anomalies…anyone could give an idea about how to use
>>> it? I was wondering if there would be any example about that.
>>>
>>>
>>>
>>>
>>> Thanks again!
>>>
>>>
>>>
>>> Best
>>>
>>>
>>>
>>> Noelia.
>>>
>>>
>>>
>>> _______________________________________________
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>>>
>>>
>>
>
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