Hello,
I have a function that I fitting to a curve via scipy.optimize.leastsq. The
function has 4 parameters and this is all working fine.
For a site, I have a number of curves (n=10 in the example below). I would
like to some of the parameters to be the best fit across all curves (best fit
Pierre GM-2 wrote:
>
> Mmh, probably a bug, I'd say.
> Mind opening a ticket ?
> Thanks in advance
> P.
>
>
Done - Ticket #1657
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quent setting. Generating
masked arrays does not seem to have this behaviour. Is this a bug or am I
doing it wrong?
Thanks,
Bevan
In [1]: eg = np.random.random(1)
In [2]: eg_mask = np.ma.masked_array(eg)
In [3]: eg_mask_invalid = np.ma.masked_invalid(eg)
In [4]: eg
Out[4]: array([ 0.78592569])
Pierre GM gmail.com> writes:
>
> I just pushed the weekend's changes, including a fix for your problem.
> Now it should be possible to define an array of dates w/ a timestep
different from 1:
> >>> date_array(start_date=Date("T", "2010-06-21 00:00"), length=4,
timestep=15)
> DateArray([21-Jun
Pierre GM gmail.com> writes:
>
> On Jun 10, 2010, at 7:16 PM, Bevan Jenkins wrote:
> > Hello,
> >
> > I have posted previously about dates prior to 1900 but this seems to be a
> > seperate issue. The error message is definitley different.
> > I can not
Hello,
I have posted previously about dates prior to 1900 but this seems to be a
seperate issue. The error message is definitley different.
I can not seem to convert a timseseries from one frequency ('D') to another
('H') when i use dates prior to 1970 as shown below. This works fine when I
u
Stéfan van der Walt sun.ac.za> writes:
>
> Hi Bevan
>
> Since the number of output elements are unknown, I don't think you can
> implement this efficiently using arrays. If your dataset isn't too
> large, a for-loop should do the trick. Otherwise, you may
is appreciated,
Bevan Jenkins
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hour =0)
In [5]: print (E)
<>
I was wondering if there is anyway to use timeseries with dates <1900?
If not I will forgo timeseries for the select few datasets I have with data
<1900.
Thanks for the v. useful scikit,
Bevan Jenkins
_
Anne Archibald gmail.com> writes:
>
> 2008/9/30 bevan gmail.com>:
> > Hello,
> >
> > I have some XY data. I would like to generate the equations for an upper
and
> > lower envelope that excludes a percentage of the data points.
> >
> > I wou
Hello,
I have some XY data. I would like to generate the equations for an upper and
lower envelope that excludes a percentage of the data points.
I would like to define the slope of the envelope line (say 3) and then have my
code find the intercept that fits my requirements (say 5% of data below
ray(Flow[1:],boolFlowdiff)
print MaskFlow
[-- 19.4 18.7 18.6 -- 34.8 25.1 --]
The output I would like is
[-- 19.4 18.7 18.6 -- -- -- --]
Where the second groups is blanked because the sequence only has 2 members.
thanks for your time,
bevan
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David Huard gmail.com> writes:
>
>
> Look at the timeseries package in scikits (only on svn i'm afraid). You'll
find exactly what you're looking for. Conversion from daily to monthly or
yearly time series is a breeze. Cheers, David
>
> 2008/3/13, Joris De Ridder ster.kuleuven.be>:
>
>
> I
Hello,
I am new to the world of Python and numpy but am very excited by what I have
seen so far.
I have been playing around with some rainfall data. The data is daily rainfall
for a period, say 30 years in the form:
Year Month JulianDay Rain (mm)
1970 1 1 0.0
1970 1 2
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