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# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, division
from contextlib import contextmanager
from petl.compat import string_types
from petl.errors import ArgumentError
from petl.util.base import Table, iterpeek, data
from petl.io.numpy import infer_dtype
def fromhdf5(source, where=None, name=None, condition=None,
condvars=None, start=None, stop=None, step=None):
"""
Provides access to an HDF5 table. E.g.::
>>> import petl as etl
>>>
>>> # set up a new hdf5 table to demonstrate with
>>> class FooBar(tables.IsDescription): # doctest: +SKIP
... foo = tables.Int32Col(pos=0) # doctest: +SKIP
... bar = tables.StringCol(6, pos=2) # doctest: +SKIP
>>> #
>>> def setup_hdf5_table():
... import tables
... h5file = tables.open_file('example.h5', mode='w',
... title='Example file')
... h5file.create_group('/', 'testgroup', 'Test Group')
... h5table = h5file.create_table('/testgroup', 'testtable', FooBar,
... 'Test Table')
... # load some data into the table
... table1 = (('foo', 'bar'),
... (1, b'asdfgh'),
... (2, b'qwerty'),
... (3, b'zxcvbn'))
... for row in table1[1:]:
... for i, f in enumerate(table1[0]):
... h5table.row[f] = row[i]
... h5table.row.append()
... h5file.flush()
... h5file.close()
>>>
>>> setup_hdf5_table() # doctest: +SKIP
>>>
>>> # now demonstrate use of fromhdf5
>>> table1 = etl.fromhdf5('example.h5', '/testgroup', 'testtable') # doctest: +SKIP
>>> table1 # doctest: +SKIP
+-----+-----------+
| foo | bar |
+=====+===========+
| 1 | b'asdfgh' |
+-----+-----------+
| 2 | b'qwerty' |
+-----+-----------+
| 3 | b'zxcvbn' |
+-----+-----------+
>>> # alternatively just specify path to table node
... table1 = etl.fromhdf5('example.h5', '/testgroup/testtable') # doctest: +SKIP
>>> # ...or use an existing tables.File object
... h5file = tables.open_file('example.h5') # doctest: +SKIP
>>> table1 = etl.fromhdf5(h5file, '/testgroup/testtable') # doctest: +SKIP
>>> # ...or use an existing tables.Table object
... h5tbl = h5file.get_node('/testgroup/testtable') # doctest: +SKIP
>>> table1 = etl.fromhdf5(h5tbl) # doctest: +SKIP
>>> # use a condition to filter data
... table2 = etl.fromhdf5(h5tbl, condition='foo < 3') # doctest: +SKIP
>>> table2 # doctest: +SKIP
+-----+-----------+
| foo | bar |
+=====+===========+
| 1 | b'asdfgh' |
+-----+-----------+
| 2 | b'qwerty' |
+-----+-----------+
>>> h5file.close() # doctest: +SKIP
"""
return HDF5View(source, where=where, name=name,
condition=condition, condvars=condvars,
start=start, stop=stop, step=step)
class HDF5View(Table):
def __init__(self, source, where=None, name=None, condition=None,
condvars=None, start=None, stop=None, step=None):
self.source = source
self.where = where
self.name = name
self.condition = condition
self.condvars = condvars
self.start = start
self.stop = stop
self.step = step
def __iter__(self):
return iterhdf5(self.source, self.where, self.name, self.condition,
self.condvars, self.start, self.stop, self.step)
@contextmanager
def _get_hdf5_table(source, where, name, mode='r'):
import tables
needs_closing = False
h5file = None
# allow for polymorphic args
if isinstance(source, tables.Table):
# source is a table
h5tbl = source
elif isinstance(source, string_types):
# assume source is the name of an HDF5 file, try to open it
h5file = tables.open_file(source, mode=mode)
needs_closing = True
h5tbl = h5file.get_node(where, name=name)
elif isinstance(source, tables.File):
# source is an HDF5 file object
h5file = source
h5tbl = h5file.get_node(where, name=name)
else:
# invalid source
raise ArgumentError('invalid source argument, expected file name or '
'tables.File or tables.Table object, found: %r'
% source)
try:
yield h5tbl
finally:
# tidy up
if needs_closing:
h5file.close()
@contextmanager
def _get_hdf5_file(source, mode='r'):
import tables
needs_closing = False
# allow for polymorphic args
if isinstance(source, string_types):
# assume source is the name of an HDF5 file, try to open it
h5file = tables.open_file(source, mode=mode)
needs_closing = True
elif isinstance(source, tables.File):
# source is an HDF5 file object
h5file = source
else:
# invalid source
raise ArgumentError('invalid source argument, expected file name or '
'tables.File object, found: %r' % source)
try:
yield h5file
finally:
if needs_closing:
h5file.close()
def iterhdf5(source, where, name, condition, condvars, start, stop, step):
with _get_hdf5_table(source, where, name) as h5tbl:
# header row
hdr = tuple(h5tbl.colnames)
yield hdr
# determine how to iterate over the table
if condition is not None:
it = h5tbl.where(condition, condvars=condvars,
start=start, stop=stop, step=step)
else:
it = h5tbl.iterrows(start=start, stop=stop, step=step)
# data rows
for row in it:
yield row[:] # access row as a tuple
def fromhdf5sorted(source, where=None, name=None, sortby=None, checkCSI=False,
start=None, stop=None, step=None):
"""
Provides access to an HDF5 table, sorted by an indexed column, e.g.::
>>> import petl as etl
>>>
>>> # set up a new hdf5 table to demonstrate with
>>> class FooBar(tables.IsDescription): # doctest: +SKIP
... foo = tables.Int32Col(pos=0) # doctest: +SKIP
... bar = tables.StringCol(6, pos=2) # doctest: +SKIP
>>>
>>> def setup_hdf5_index():
... import tables
... h5file = tables.open_file('example.h5', mode='w',
... title='Example file')
... h5file.create_group('/', 'testgroup', 'Test Group')
... h5table = h5file.create_table('/testgroup', 'testtable', FooBar,
... 'Test Table')
... # load some data into the table
... table1 = (('foo', 'bar'),
... (1, b'asdfgh'),
... (2, b'qwerty'),
... (3, b'zxcvbn'))
... for row in table1[1:]:
... for i, f in enumerate(table1[0]):
... h5table.row[f] = row[i]
... h5table.row.append()
... h5table.cols.foo.create_csindex() # CS index is required
... h5file.flush()
... h5file.close()
>>>
>>> setup_hdf5_index() # doctest: +SKIP
>>>
... # access the data, sorted by the indexed column
... table2 = etl.fromhdf5sorted('example.h5', '/testgroup', 'testtable', sortby='foo') # doctest: +SKIP
>>> table2 # doctest: +SKIP
+-----+-----------+
| foo | bar |
+=====+===========+
| 1 | b'zxcvbn' |
+-----+-----------+
| 2 | b'qwerty' |
+-----+-----------+
| 3 | b'asdfgh' |
+-----+-----------+
"""
assert sortby is not None, 'no column specified to sort by'
return HDF5SortedView(source, where=where, name=name,
sortby=sortby, checkCSI=checkCSI,
start=start, stop=stop, step=step)
class HDF5SortedView(Table):
def __init__(self, source, where=None, name=None, sortby=None,
checkCSI=False, start=None, stop=None, step=None):
self.source = source
self.where = where
self.name = name
self.sortby = sortby
self.checkCSI = checkCSI
self.start = start
self.stop = stop
self.step = step
def __iter__(self):
return iterhdf5sorted(self.source, self.where, self.name, self.sortby,
self.checkCSI, self.start, self.stop, self.step)
def iterhdf5sorted(source, where, name, sortby, checkCSI, start, stop, step):
with _get_hdf5_table(source, where, name) as h5tbl:
# header row
hdr = tuple(h5tbl.colnames)
yield hdr
it = h5tbl.itersorted(sortby,
checkCSI=checkCSI,
start=start,
stop=stop,
step=step)
for row in it:
yield row[:] # access row as a tuple
def tohdf5(table, source, where=None, name=None, create=False, drop=False,
description=None, title='', filters=None, expectedrows=10000,
chunkshape=None, byteorder=None, createparents=False,
sample=1000):
"""
Write to an HDF5 table. If `create` is `False`, assumes the table
already exists, and attempts to truncate it before loading. If `create`
is `True`, a new table will be created, and if `drop` is True,
any existing table will be dropped first. If `description` is `None`,
the description will be guessed. E.g.::
>>> import petl as etl
>>> table1 = (('foo', 'bar'),
... (1, b'asdfgh'),
... (2, b'qwerty'),
... (3, b'zxcvbn'))
>>> etl.tohdf5(table1, 'example.h5', '/testgroup', 'testtable',
... drop=True, create=True, createparents=True) # doctest: +SKIP
>>> etl.fromhdf5('example.h5', '/testgroup', 'testtable') # doctest: +SKIP
+-----+-----------+
| foo | bar |
+=====+===========+
| 1 | b'asdfgh' |
+-----+-----------+
| 2 | b'qwerty' |
+-----+-----------+
| 3 | b'zxcvbn' |
+-----+-----------+
"""
import tables
it = iter(table)
if create:
with _get_hdf5_file(source, mode='a') as h5file:
if drop:
try:
h5file.get_node(where, name)
except tables.NoSuchNodeError:
pass
else:
h5file.remove_node(where, name)
# determine datatype
if description is None:
peek, it = iterpeek(it, sample)
# use a numpy dtype
description = infer_dtype(peek)
# create the table
h5file.create_table(where, name, description,
title=title,
filters=filters,
expectedrows=expectedrows,
chunkshape=chunkshape,
byteorder=byteorder,
createparents=createparents)
with _get_hdf5_table(source, where, name, mode='a') as h5table:
# truncate the existing table
h5table.truncate(0)
# load the data
_insert(it, h5table)
Table.tohdf5 = tohdf5
def appendhdf5(table, source, where=None, name=None):
"""
As :func:`petl.io.hdf5.tohdf5` but don't truncate the target table before
loading.
"""
with _get_hdf5_table(source, where, name, mode='a') as h5table:
# load the data
_insert(table, h5table)
Table.appendhdf5 = appendhdf5
def _insert(table, h5table):
it = data(table) # don't need header
for row in it:
for i, f in enumerate(h5table.colnames):
# depends on order of fields being the same in input table
# and hd5 table, but field names don't need to match
h5table.row[f] = row[i]
h5table.row.append()
h5table.flush()
|
