Which is the better way to parse this file?

[Roberto A. F. De Almeida]

Hi,

I'm interested in parsing a file containing this "structure" :

"""dataset {
int catalog_number;
sequence {
string experimenter;
int32 time;
structure {
float64 latitude;
float64 longitude;
} location;
sequence {
float depth;
float temperature;
} xbt;
} casts;
} data;"""

I want to obtain a dictionary like this:

pprint.pprint(d ata)

{'casts': {'experimenter' : None,
'location': {'latitude': None, 'longitude': None},
'time': None,
'xbt': {'depth': None, 'temperature': None}},
'catalog_number ': None}

The values ('None') will be filled later. I tried to do the parsing
using regular expressions, but things became too complicated. I had
more success using SimpleParse, but I'm interested in more insights on
different ways of parsing this file.

TIA,

Roberto

[Terry Reedy]

"Roberto A. F. De Almeida" <ro*****@dealme ida.net> wrote in message
news:10******** *************** ***@posting.goo gle.com...

I'm interested in parsing a file containing this "structure" :

"""dataset {
int catalog_number;
sequence {
string experimenter;
int32 time;
structure {
float64 latitude;
float64 longitude;
} location;
sequence {
float depth;
float temperature;
} xbt;
} casts;
} data;"""
I suspect that what you actually want to do is parse structures 'like'
the above, as defined be a grammar not shown ;-)

You did not specify whether you will get such files from an
uncontrolable external source or whether you control the input format.
If the later, there is no obvious reason for separate database,
sequence, and structure productions since all three result in
dictionaries with no functional difference.
I want to obtain a dictionary like this:

pprint.pprint(d ata)

{'casts': {'experimenter' : None,
'location': {'latitude': None, 'longitude': None},
'time': None,
'xbt': {'depth': None, 'temperature': None}},
'catalog_number ': None}
The values ('None') will be filled later.

Using None as placeholders either tosses the type information or
requires that it be recorded elsewhere. Use the int and float type
objects instead. Note that standard Python cannot differentiate
between float and float64.
I tried to do the parsing
using regular expressions, but things became too complicated.
REs are great for linear repetition but not for indefinite nesting.
I had
more success using SimpleParse, but I'm interested in more insights on different ways of parsing this file.

I know nothing of SimpleParse (and therefore, of what would be
different). If the grammar is as simple as I infer from the sample --
dataset and sequences containing sequences, structures, and types -- I
would reread about recursive-descent parsing and maybe try that. The
type_entry function would return a (name, typeobject) pair and the
structure, sequence, and database functions a (name, dict) pair.

But as hinted above, I would think about simplifying the grammar
before worryinng about parsing. If you only have sequences of
sequences and type entries, parsing is trivial.

Terry J. Reedy

[Roberto A. F. De Almeida]

"Terry Reedy" <tj*****@udel.e du> wrote in message news:<au******* *************@c omcast.com>...

I suspect that what you actually want to do is parse structures 'like'
the above, as defined be a grammar not shown ;-)
Yes, you're right. :)

The grammar is not complex, but I'm still struggling to process the
result tree.
You did not specify whether you will get such files from an
uncontrolable external source or whether you control the input format.
If the later, there is no obvious reason for separate database,
sequence, and structure productions since all three result in
dictionaries with no functional difference.
This is a Dataset Descriptor for the Data Access Protocol
(http://www.unidata.ucar.edu/packages...dap-rfc-html/), an
API to access remote datasets. DAP servers describe their datasets
using this grammar, and I'm developing a module to access DAP servers.

I want to obtain a dictionary like this:

>> pprint.pprint(d ata)

{'casts': {'experimenter' : None,
'location': {'latitude': None, 'longitude': None},
'time': None,
'xbt': {'depth': None, 'temperature': None}},
'catalog_number ': None}
The values ('None') will be filled later.

Using None as placeholders either tosses the type information or
requires that it be recorded elsewhere. Use the int and float type
objects instead. Note that standard Python cannot differentiate
between float and float64.

Ok. One of the strong points of DAP is that data is retrieved only for
your region/period of interest. I created a class and redefined
__getitem__ so that data is only retrieved from the server when the
object is sliced.

data = file("http://dods.gso.uri.ed u/cgi-bin/nph-nc/data/fnoc1.nc")
print data.variables['lat'].shape (17,) print data.variables['lat'][1:4] # only this subset is retrieved

[ 47.5 45. 42.5 40. ]
I know nothing of SimpleParse (and therefore, of what would be
different). If the grammar is as simple as I infer from the sample --
dataset and sequences containing sequences, structures, and types -- I
would reread about recursive-descent parsing and maybe try that. The
type_entry function would return a (name, typeobject) pair and the
structure, sequence, and database functions a (name, dict) pair.
Yes, it's very simple. As you see, even a structure is identical to a
sequence. The declarations are basically "types" or declarations
containing "types". Do you think it can be done without 3rd party
modules?
But as hinted above, I would think about simplifying the grammar
before worryinng about parsing. If you only have sequences of
sequences and type entries, parsing is trivial.

I'll take a look in that. Thanks very much for the insights.

Regards,

Roberto