[SciPy-user] [Timeseries] Linux installation error
Thu Apr 2 20:04:35 CDT 2009
On Apr 2, 2009, at 8:21 PM, Christiaan Putter wrote:
> Ok, just tested the code you sent me and it works just fine (had to
> kill numpy 1.2 though)
> Some simple copy / pastable examples would be great to get new users
> (that's me) going :-)
FYI, you can find some examples of application at http://hydroclimpy.sourceforge.net/
. That's a package of extensions for timeseries focused on
environmental series. That should get you started. Once again, we'll
soon put an Example section online.
For financial applications, check with Matt Knox, the co-author of the
package. I'm the hydrologist of the duo.
> Quick question:
> Is there any possibility to use python's decimal data type? I saw you
> used it in your sql example on the database side, but I'm guessing
> numpy doesn't allow for this?
I don't think that numpy can interact w/ Decimal, but I never actually
> I sometimes have problems with equality
> testing after several divisions / multiplications, so at the moment I
> revert to the "is kind of" instead of the "is equal to" approach of
You can use assert_almost_equal from the testing modules.
> I'll test h5py
> some time next week and if it performs better (which they claim on
> their site :-) I'll see if I can cook something up similar to what you
> did for integrating timeseries and pytables. I'll send it along to
> you once it's usable.
Please do, it's always useful indeed.
> 2009/4/3 Christiaan Putter <email@example.com>:
>> Hi Pierre,
>> Thanks for your swift reply, I'll test the code tomorrow.
>> I'm looking forward to using your timeseries module. I'm writing a
>> finance app using Enthought's tool suite and it seems timeseries will
>> come in quite handy. Up until now I've been using normal numpy
>> with pytables for storing actual historical data and postgres (with
>> SQLAlchemy) for storing some 'higher' level information about stocks
>> and the results of analysis done on said historical data. I've found
>> it's a pretty good combination since hdf5 compression keeps the data
>> size down to only a few hundred megs and SQLAlchemy is simply awesome
>> for running queries.
>> Pytables doesn't play well with threading though even though I'm
>> locks in any block of code that so much as sniffs at the hdf5 file
>> (strangely though it's rather stable on linux but crashes horribly on
>> windows without even the courtesy of a trace back). I'll test h5py
>> some time next week and if it performs better (which they claim on
>> their site :-) I'll see if I can cook something up similar to what
>> did for integrating timeseries and pytables. I'll send it along to
>> you once it's usable.
>> In case that doesn't work I'll probably resort to storing the
>> historical data in sql as well. Can someone give me some pointers on
>> how I would go about that perhaps? It's about 20 000 - 30 000 stocks
>> with on average about a decades worth of daily data. Would I dump
>> of that into a single table? 20 000 tables? hdf5 certainly is much
>> better suited for something like that...
>> Hope everyone is having a great day.
>> 2009/4/3 Pierre GM <firstname.lastname@example.org>:
>>> On Apr 2, 2009, at 6:43 PM, Christopher Barker wrote:
>>>> Pierre GM wrote:
>>>>> On Apr 2, 2009, at 6:19 PM, Tim Michelsen wrote:
>>>>>> Indeed, the newly organised documentation with its logo is just
>>>> where to I find these impressive docs?
>>> (or google "scikits timeseries" and feel lucky)
>>> SciPy-user mailing list
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