This is a PyTables generated file with data collected from vehicle
(bicycle) dynamics measurements. Meta data are in tables and time series
are stored in array objects.
http://mae.ucdavis.edu/~biosport/InstrumentedBicycleData/InstrumentedBicycleData.h5.bz2
It is about 308 mb compressed and 610 mb uncompressed.
Jason
On Sun, Oct 21, 2012 at 1:01 PM, Andy Wilson <wilson.andre...@gmail.com>wrote:
> On Sun, Oct 21, 2012 at 10:41 AM, Francesc Alted <fal...@pytables.org>
> wrote:
>
> > Hi,
> >
> > I'm going to give a tutorial on PyTables next Thursday during the PyData
> > conference in New York (http://nyc2012.pydata.org/) and I'd like to use
> > some real life data files. So, if you have some public repository with
> > data generated with PyTables, please tell me. I'm looking for files
> > that are not very large (< 1GB), and that use the Table object
> > significantly. A small description of the data included will be more
> > that welcome too!
> >
> > Thanks!
> >
> > --
> > Francesc Alted
>
>
>
> Hi Francesc.
>
> I've been working on a library for accessing climatology data that
> uses pytables to cache data from the USGS. It could easily be used to
> create a sample dataset for some area of interest. File size is
> determined by how much data gets queried.
>
>
> The general layout is:
>
> /usgs/sites
> - the sites table contains information and metadata about a site
>
>
> /usgs/values/<AGENCY>/<SITE_CODE>/<PARAMETER_CODE>
> - a table containing all the timeseries data for each site and
> parameter is created as data are queried
> - parameter codes are a bit obscure but a dict with descriptive
> metadata stashed at table.attrs.variable
> - the datetime column has a CSIndex on it and stored as as a string
> because some sites have data prior to the year 1901
> - pretty inefficient in terms of disk space (lots of large-ish string
> columns) because it handles a very general class of data types
>
>
> Here's what the code would look like to download and create the hdf5
> file for 10 random sites in New York:
>
> import ulmo
>
> # the default location for the hdf5 file is OS dependent, so provide
> the path you want to use
> hdf5_file_path = './usgs_data.h5'
>
> # get list of sites in NY
> ulmo.usgs.pytables.update_site_list(state_code='NY', path=hdf5_file_path)
> sites = ulmo.usgs.pytables.get_sites(path=hdf5_file_path)
>
> # download data for a few random sites
> for site in sites.keys()[:10]:
> ulmo.usgs.pytables.update_site_data(site, path=hdf5_file_path)
>
>
>
> The project is on github: https://github.com/swtools/ulmo
> and the code that does all the pytables stuff (including the table
> descriptions) is here:
> https://github.com/swtools/ulmo/blob/master/ulmo/usgs/pytables.py
>
> -andy
>
>
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--
Jason K. Moore, Ph.D.
Personal Website <http://biosport.ucdavis.edu/lab-members/jason-moore>
Sports Biomechanics Lab <http://biosport.ucdavis.edu>, UC Davis
Davis Open Science <http://daviswiki.org/Davis_Open_Science>
Google Voice: +01 530-601-9791
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