Hi All,
I have a problem involving lat/lon data. Basically, I am evaluating
numerical weather model data against satellite data, and trying to produce
gridded plots of various statistics. There are various steps involved with
this, but basically, I get to the point where I have four arrays of th
On Sun, Jun 20, 2010 at 4:24 AM, Tom Durrant wrote:
> Hi All,
> I have a problem involving lat/lon data. Basically, I am evaluating
> numerical weather model data against satellite data, and trying to produce
> gridded plots of various statistics. There are various steps involved with
> this, bu
>
> are you doing something like np.polyfit(model, obs, 1) ?
>
> If you are using polyfit with deg=1, i.e. fitting a straight line,
> then this could be also calculated using the weights in histogram2d.
>
> histogram2d (histogramdd) uses np.digitize and np.bincount, so I'm
> surprised if the hi
On Sun, Jun 20, 2010 at 10:57 PM, Tom Durrant wrote:
>
>>
>> are you doing something like np.polyfit(model, obs, 1) ?
>>
>> If you are using polyfit with deg=1, i.e. fitting a straight line,
>> then this could be also calculated using the weights in histogram2d.
>>
>> histogram2d (histogramdd) use
On 06/20/2010 03:24 AM, Tom Durrant wrote:
> Hi All,
>
> I have a problem involving lat/lon data. Basically, I am evaluating
> numerical weather model data against satellite data, and trying to
> produce gridded plots of various statistics. There are various steps
> involved with this, but bas
>
>
> the basic idea is in "polyfit on multiple data points" on
> numpy-disscusion mailing list April 2009
>
> In this case, calculations have to be done by groups
>
> subtract mean (this needs to be replaced by group demeaning)
> modeldm = model - model.mean()
> obsdm = obs - obs.mean()
>
> xx =
>
>
> >
> What exactly are trying to fit because it is rather bad practice to fit
> a model to some summarized data as you lose the uncertainty in the
> original data?
> If you define your boxes, you can loop through directly on each box and
> even fit the equation:
>
> model=mu +beta1*obs
>
> The
On 06/22/2010 09:13 AM, Tom Durrant wrote:
>
What exactly are trying to fit because it is rather bad practice
to fit
a model to some summarized data as you lose the uncertainty in the
original data?
If you define your boxes, you can loop through directly on each
box
On Tue, Jun 22, 2010 at 10:09 AM, Tom Durrant wrote:
>>
>> the basic idea is in "polyfit on multiple data points" on
>> numpy-disscusion mailing list April 2009
>>
>> In this case, calculations have to be done by groups
>>
>> subtract mean (this needs to be replaced by group demeaning)
>> modeldm
On 06/22/2010 02:58 PM, josef.p...@gmail.com wrote:
> On Tue, Jun 22, 2010 at 10:09 AM, Tom Durrant wrote:
>
>>> the basic idea is in "polyfit on multiple data points" on
>>> numpy-disscusion mailing list April 2009
>>>
>>> In this case, calculations have to be done by groups
>>>
>>> subtract
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