[ncl-talk] Grainy image

[Mary Haley]

Hi Tim,

I'm a little puzzled as to how changing the resolution of the map outlines
would affect the smoothness of the contours. Out of curiosity, could you
send the new image? You can send it to me directly if you want.

Thanks,

--Mary


On Fri, Jan 11, 2019 at 6:37 AM Tim Risner <trisner at awis.com> wrote:

> Easiest solution beyond manipulating data is setting the mpDataResolution
> to "FinestResolution"
> This gets rid of most of the graininess, as well as smooths the RasterFill
> a little more.
>
> *Tim Risner*
> *Operational Meteorologist*
> *(888) 798-9955 ext 1006*
>
> <http://www.awis.com>[image:
> https://www.facebook.com/AWISWeatherServices/]
> <https://www.facebook.com/AWISWeatherServices/>
> <https://twitter.com/awisweather>
>
>
> On Thu, Jan 10, 2019 at 1:01 PM Tim Risner <trisner at awis.com> wrote:
>
>> Thanks Mary and Rick. I'll work with the examples you mentioned and see
>> what I can do to correct it.
>>
>> Attached is a google maps image showing the data and placement, to answer
>> your questions.
>>
>>
>>
>> *Tim Risner*
>> *Operational Meteorologist*
>> *(888) 798-9955 ext 1006*
>>
>> <http://www.awis.com>[image:
>> https://www.facebook.com/AWISWeatherServices/]
>> <https://www.facebook.com/AWISWeatherServices/>
>> <https://twitter.com/awisweather>
>>
>>
>> On Thu, Jan 10, 2019 at 11:32 AM Mary Haley <haley at ucar.edu> wrote:
>>
>>> Hi Tim,
>>>
>>> I echo what Rick says about not being certain without seeing the data.
>>>
>>> Is your data a set of random points, or on a gridded structure, or
>>> something else? If it's on a gridded structure, then what happens if you
>>> don't set trGridType at all?
>>>
>>> I'm also interested to know the structure of your lat/lon data and how
>>> dense the values are. Does your data contain any missing values? I don't
>>> think this is the issue here, but it is important to be aware of this.
>>>
>>> One thing I like to do for problem plots like this is to zoom in on the
>>> area with the problem, and then plot dots where the lat/lon locations are
>>> just to see if there's anything going on with regard to the density of the
>>> locations and/or potential missing values.
>>>
>>> If you look at this page:
>>>
>>> http://www.ncl.ucar.edu/Applications/datagrid.shtml
>>>
>>> you will see some examples of using gsn_coordinates to plot the lat/lon
>>> locations of your data, and also use different colors for where the data is
>>> missing or not.
>>>
>>> In particular, datagrid_6.ncl might be useful if you have curvilinear
>>> data.
>>>
>>> http://www.ncl.ucar.edu/Applications/datagrid.shtml#ex6
>>>
>>> If you have random data, then contour1d.ncl on the same same might be
>>> useful.
>>>
>>> --Mary
>>>
>>>
>>> On Thu, Jan 10, 2019 at 10:07 AM Rick Brownrigg <brownrig at ucar.edu>
>>> wrote:
>>>
>>>> Hi Tim,
>>>>
>>>> I don't know for certain without looking at the data, but I have to
>>>> imagine its manifest in the data itself, and that in those regions you
>>>> mentioned the probability is hovering right around 50%.  The color contrast
>>>> between 40% and 50% is rather abrupt, relative to the rest of the
>>>> successive colors, which I think draws attention to the fluctuations. There
>>>> also appears to be similar fluctuations going on in Montana and Minnesota,
>>>> but its harder to see because those colors "closer" to each other,
>>>> perceptually.
>>>>
>>>> FWIW...
>>>> Rick
>>>>
>>>>
>>>> On Thu, Jan 10, 2019 at 9:52 AM Tim Risner <trisner at awis.com> wrote:
>>>>
>>>>> I'm curious as to what may be causing a grainy section in Central
>>>>> Mississippi and SE Ohio of the attached image.
>>>>> I'm using "RasterFill" and cnRasterSmoothingOn is set to True.
>>>>> trGridType of all types causes it.
>>>>> Data is pretty uniform in those regions.
>>>>>
>>>>>
>>>>> *Tim Risner*
>>>>> *Operational Meteorologist*
>>>>> *(888) 798-9955 ext 1006*
>>>>>
>>>>> <http://www.awis.com>[image:
>>>>> https://www.facebook.com/AWISWeatherServices/]
>>>>> <https://www.facebook.com/AWISWeatherServices/>
>>>>> <https://twitter.com/awisweather>
>>>>> _______________________________________________
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