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Limited number of arguments for queries #30

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153957 opened this issue Aug 10, 2014 · 8 comments
Open

Limited number of arguments for queries #30

153957 opened this issue Aug 10, 2014 · 8 comments

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@153957
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153957 commented Aug 10, 2014

Due to some (arbitrary?) limit in NumPy (numpy/numpy#4398) there is a limit to the number of inputs in a query in PyTables (PyTables/PyTables#286).

This mostly affects the coincidence_queries module where queries may include many station columns.
For instance, if looking for coincidences that include all stations in the Amsterdam cluster, more than 32 columns will be queried. This will fail.

Example using all station columns:

>>> coln = data.root.coincidences.coincidences.colnames
>>> c = data.root.coincidences.coincidences.read_where(' & '.join(coln[11:]))
........
ValueError: too many inputs

We could work around this by evaluating the query in python instead of in-kernel.
However that would slow execution..
Perhaps we can work around this limit or see if we can also vote for an increase in the limit.

@153957
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153957 commented May 11, 2015

Some more discussion at numpy/numpy#4840

@153957
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153957 commented Apr 18, 2016

See this as well numpy/numpy#4398

@tomkooij
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Won't fix.

@153957
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153957 commented Sep 12, 2016

Why not?

@153957 153957 reopened this Sep 12, 2016
@tomkooij
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@153957: We cannot change the numpy query limit and a work-around (in kernel query for large querys) is too much hassle.

I'm happy with leaving this open, but I was trying to close issues that seem too much hassle to fix. (Too low return on investment)

@153957
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153957 commented Sep 12, 2016

It would actually be nice if this is fixed, currently simply selecting 'Amsterdam' as cluster in the Coincidence download form returns the 'to many stations' error:
http://data.hisparc.nl/data/network/coincidences/?end=2016-09-11+00%3A01&start=2016-09-11&stations=None&n=2&cluster=Amsterdam&download=False

Is it to much trouble to convince NumPy to change it on their side? I know various attempts have been made, with little progress..

@tomkooij
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Ok, that make sense. Let's at least keep this open and hope numpy fixes this. (Or do the workaround thing...).

@153957
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153957 commented Sep 12, 2016

I think this should at least be kept open as long as the numpy issue above is still open.

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