/usr/lib64/python2.6/site-packages/numpy/lib
Edit: /usr/lib64/python2.6/site-packages/numpy/lib/recfunctions.pyo (29451B)
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Collection of utilities to manipulate structured arrays.
Most of these functions were initially implemented by John Hunter for matplotlib.
They have been rewritten and extended for convenience.
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Fills fields from output with fields from input,
with support for nested structures.
Parameters
----------
input : ndarray
Input array.
output : ndarray
Output array.
Notes
-----
* `output` should be at least the same size as `input`
Examples
--------
>>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', int), ('B', float)])
>>> b = np.zeros((3,), dtype=a.dtype)
>>> recursive_fill_fields(a, b)
np.array([(1, 10.), (2, 20.), (0, 0.)], dtype=[('A', int), ('B', float)])
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Returns the field names of the input datatype as a tuple.
Parameters
----------
adtype : dtype
Input datatype
Examples
--------
>>> get_names(np.empty((1,), dtype=int)) is None
True
>>> get_names(np.empty((1,), dtype=[('A',int), ('B', float)]))
('A', 'B')
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> get_names(adtype)
('a', ('b', ('ba', 'bb')))
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Returns the field names of the input datatype as a tuple. Nested structure
are flattend beforehand.
Parameters
----------
adtype : dtype
Input datatype
Examples
--------
>>> get_names_flat(np.empty((1,), dtype=int)) is None
True
>>> get_names_flat(np.empty((1,), dtype=[('A',int), ('B', float)]))
('A', 'B')
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> get_names_flat(adtype)
('a', 'b', 'ba', 'bb')
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Flatten a structured data-type description.
Examples
--------
>>> ndtype = np.dtype([('a', '
>> flatten_descr(ndtype)
(('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32')))
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Combine the dtype description of a series of arrays.
Parameters
----------
seqarrays : sequence of arrays
Sequence of arrays
flatten : {boolean}, optional
Whether to collapse nested descriptions.
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... ('B', [('BA', int),
... ('BB', [('BBA', int), ('BBB', int)])])])
>>> get_fieldstructure(ndtype)
{'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'],
'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}
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Returns an iterator of concatenated fields from a sequence of arrays,
collapsing any nested structure.
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Returns an iterator of concatenated fields from a sequence of arrays.
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Returns an iterator of concatenated items from a sequence of arrays.
Parameters
----------
seqarray : sequence of arrays
Sequence of arrays.
fill_value : {None, integer}
Value used to pad shorter iterables.
flatten : {True, False},
Whether to
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Private function: return a recarray, a ndarray, a MaskedArray
or a MaskedRecords depending on the input parameters
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masked_array(data = [(1, 10.0) (2, 20.0) (--, 30.0)],
mask = [(False, False) (False, False) (True, False)],
fill_value=(999999, 1e+20)
dtype=[('f0', '>> merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])),
... usemask=False)
array(data = [(1, 10.0) (2, 20.0) (-1, 30.0)],
dtype=[('f0', '>> merge_arrays((np.array([1, 2]).view([('a', int)]),
np.array([10., 20., 30.])),
usemask=False, asrecarray=True)
rec.array(data = [(1, 10.0) (2, 20.0) (-1, 30.0)],
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Return a new array with fields in `drop_names` dropped.
Nested fields are supported.
Parameters
----------
base : array
Input array
drop_names : string or sequence
String or sequence of strings corresponding to the names of the fields
to drop.
usemask : {False, True}, optional
Whether to return a masked array or not.
asrecarray : string or sequence
Whether to return a recarray or a mrecarray (`asrecarray=True`) or
a plain ndarray or masked array with flexible dtype (`asrecarray=False`)
Examples
--------
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
dtype=[('a', int), ('b', [('ba', float), ('bb', int)])])
>>> drop_fields(a, 'a')
array([((2.0, 3),), ((5.0, 6),)],
dtype=[('b', [('ba', '>> drop_fields(a, 'ba')
array([(1, (3,)), (4, (6,))],
dtype=[('a', '>> drop_fields(a, ['ba', 'bb'])
array([(1,), (4,)],
dtype=[('a', '>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
dtype=[('a', int),
('b', [('ba', float), ('bb', (float, 2))])])
>>> rename_fields(a, {'a':'A', 'bb':'BB'})
array([(1, (2.0, 3)), (4, (5.0, 6))],
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Add new fields to an existing array.
The names of the fields are given with the `names` arguments,
the corresponding values with the `data` arguments.
If a single field is appended, `names`, `data` and `dtypes` do not have
to be lists but just values.
Parameters
----------
base : array
Input array to extend.
names : string, sequence
String or sequence of strings corresponding to the names
of the new fields.
data : array or sequence of arrays
Array or sequence of arrays storing the fields to add to the base.
dtypes : sequence of datatypes, optional
Datatype or sequence of datatypes.
If None, the datatypes are estimated from the `data`.
See Also
--------
append_fields
Returns
-------
appended_array : np.recarray
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>>> stack_arrays(x) is x
True
>>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
>>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
dtype=[('A', '|S3'), ('B', float), ('C', float)])
>>> test = stack_arrays((z,zz))
>>> masked_array(data = [('A', 1.0, --) ('B', 2.0, --) ('a', 10.0, 100.0)
... ('b', 20.0, 200.0) ('c', 30.0, 300.0)],
... mask = [(False, False, True) (False, False, True) (False, False, False)
... (False, False, False) (False, False, False)],
... fill_value=('N/A', 1e+20, 1e+20)
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>>> a = ma.array([1, 1, 1, 2, 2, 3, 3],
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>>> find_duplicates(a, ignoremask=True, return_index=True)
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Join arrays `r1` and `r2` on key `key`.
The key should be either a string or a sequence of string corresponding
to the fields used to join the array.
An exception is raised if the `key` field cannot be found in the two input
arrays.
Neither `r1` nor `r2` should have any duplicates along `key`: the presence
of duplicates will make the output quite unreliable. Note that duplicates
are not looked for by the algorithm.
Parameters
----------
key : {string, sequence}
A string or a sequence of strings corresponding to the fields used
for comparison.
r1, r2 : arrays
Structured arrays.
jointype : {'inner', 'outer', 'leftouter'}, optional
If 'inner', returns the elements common to both r1 and r2.
If 'outer', returns the common elements as well as the elements of r1
not in r2 and the elements of not in r2.
If 'leftouter', returns the common elements and the elements of r1 not
in r2.
r1postfix : string, optional
String appended to the names of the fields of r1 that are present in r2
but absent of the key.
r2postfix : string, optional
String appended to the names of the fields of r2 that are present in r1
but absent of the key.
defaults : {dictionary}, optional
Dictionary mapping field names to the corresponding default values.
usemask : {True, False}, optional
Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`)
or a ndarray.
asrecarray : {False, True}, optional
Whether to return a recarray (or MaskedRecords if `usemask==True`) or
just a flexible-type ndarray.
Notes
-----
* The output is sorted along the key.
* A temporary array is formed by dropping the fields not in the key for the
two arrays and concatenating the result. This array is then sorted, and
the common entries selected. The output is constructed by filling the fields
with the selected entries. Matching is not preserved if there are some
duplicates...
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Join arrays `r1` and `r2` on keys.
Alternative to join_by, that always returns a np.recarray.
See Also
--------
join_by : equivalent function
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