numpy.asanyarray.ravel - python examples

Here are the examples of the python api numpy.asanyarray.ravel taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

8 Examples 7

0 View Complete Implementation : recfunctions.py
Copyright MIT License
Author : abhisuri97
def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False,
                 autoconvert=False):
    """
    Superposes arrays fields by fields

    Parameters
    ----------
    arrays : array or sequence
        Sequence of input arrays.
    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.
    autoconvert : {False, True}, optional
        Whether automatically cast the type of the field to the maximum.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> x = np.array([1, 2,])
    >>> rfn.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 = rfn.stack_arrays((z,zz))
    >>> test
    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),
                dtype = [('A', '|S3'), ('B', '<f8'), ('C', '<f8')])

    """
    if isinstance(arrays, ndarray):
        return arrays
    elif len(arrays) == 1:
        return arrays[0]
    seqarrays = [np.asanyarray(a).ravel() for a in arrays]
    nrecords = [len(a) for a in seqarrays]
    ndtype = [a.dtype for a in seqarrays]
    fldnames = [d.names for d in ndtype]
    #
    dtype_l = ndtype[0]
    newdescr = dtype_l.descr
    names = [_[0] for _ in newdescr]
    for dtype_n in ndtype[1:]:
        for descr in dtype_n.descr:
            name = descr[0] or ''
            if name not in names:
                newdescr.append(descr)
                names.append(name)
            else:
                nameidx = names.index(name)
                current_descr = newdescr[nameidx]
                if autoconvert:
                    if np.dtype(descr[1]) > np.dtype(current_descr[-1]):
                        current_descr = list(current_descr)
                        current_descr[-1] = descr[1]
                        newdescr[nameidx] = tuple(current_descr)
                elif descr[1] != current_descr[-1]:
                    raise TypeError("Incompatible type '%s' <> '%s'" %
                                    (dict(newdescr)[name], descr[1]))
    # Only one field: use concatenate
    if len(newdescr) == 1:
        output = ma.concatenate(seqarrays)
    else:
        #
        output = ma.masked_all((np.sum(nrecords),), newdescr)
        offset = np.camesum(np.r_[0, nrecords])
        seen = []
        for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]):
            names = a.dtype.names
            if names is None:
                output['f%i' % len(seen)][i:j] = a
            else:
                for name in n:
                    output[name][i:j] = a[name]
                    if name not in seen:
                        seen.append(name)
    #
    return _fix_output(_fix_defaults(output, defaults),
                       usemask=usemask, asrecarray=asrecarray)

0 View Complete Implementation : recfunctions.py
Copyright MIT License
Author : abhisuri97
def find_duplicates(a, key=None, ignoremask=True, return_index=False):
    """
    Find the duplicates in a structured array along a given key

    Parameters
    ----------
    a : array-like
        Input array
    key : {string, None}, optional
        Name of the fields along which to check the duplicates.
        If None, the search is performed by records
    ignoremask : {True, False}, optional
        Whether masked data should be discarded or considered as duplicates.
    return_index : {False, True}, optional
        Whether to return the indices of the duplicated values.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> ndtype = [('a', int)]
    >>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
    ...         mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
    >>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
    ... # yyy: judging by the output, the ignoremask flag has no effect
    """
    a = np.asanyarray(a).ravel()
    # Get a dictionary of fields
    fields = get_fieldstructure(a.dtype)
    # Get the sorting data (by selecting the corresponding field)
    base = a
    if key:
        for f in fields[key]:
            base = base[f]
        base = base[key]
    # Get the sorting indices and the sorted data
    sortidx = base.argsort()
    sortedbase = base[sortidx]
    sorteddata = sortedbase.filled()
    # Compare the sorting data
    flag = (sorteddata[:-1] == sorteddata[1:])
    # If masked data must be ignored, set the flag to false where needed
    if ignoremask:
        sortedmask = sortedbase.recordmask
        flag[sortedmask[1:]] = False
    flag = np.concatenate(([False], flag))
    # We need to take the point on the left as well (else we're missing it)
    flag[:-1] = flag[:-1] + flag[1:]
    duplicates = a[sortidx][flag]
    if return_index:
        return (duplicates, sortidx[flag])
    else:
        return duplicates

0 View Complete Implementation : arraysetops.py
Copyright MIT License
Author : alvarob96
def ediff1d(ary, to_end=None, to_begin=None):
    """
    The differences between consecutive elements of an array.

    Parameters
    ----------
    ary : array_like
        If necessary, will be flattened before the differences are taken.
    to_end : array_like, optional
        Number(s) to append at the end of the returned differences.
    to_begin : array_like, optional
        Number(s) to prepend at the beginning of the returned differences.

    Returns
    -------
    ediff1d : ndarray
        The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.

    See Also
    --------
    diff, gradient

    Notes
    -----
    When applied to masked arrays, this function drops the mask information
    if the `to_begin` and/or `to_end` parameters are used.

    Examples
    --------
    >>> x = np.array([1, 2, 4, 7, 0])
    >>> np.ediff1d(x)
    array([ 1,  2,  3, -7])

    >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
    array([-99,   1,   2,   3,  -7,  88,  99])

    The returned array is always 1D.

    >>> y = [[1, 2, 4], [1, 6, 24]]
    >>> np.ediff1d(y)
    array([ 1,  2, -3,  5, 18])

    """
    # force a 1d array
    ary = np.asanyarray(ary).ravel()

    # fast track default case
    if to_begin is None and to_end is None:
        return ary[1:] - ary[:-1]

    if to_begin is None:
        l_begin = 0
    else:
        to_begin = np.asanyarray(to_begin).ravel()
        l_begin = len(to_begin)

    if to_end is None:
        l_end = 0
    else:
        to_end = np.asanyarray(to_end).ravel()
        l_end = len(to_end)

    # do the calculation in place and copy to_begin and to_end
    l_diff = max(len(ary) - 1, 0)
    result = np.empty(l_diff + l_begin + l_end, dtype=ary.dtype)
    result = ary.__array_wrap__(result)
    if l_begin > 0:
        result[:l_begin] = to_begin
    if l_end > 0:
        result[l_begin + l_diff:] = to_end
    np.subtract(ary[1:], ary[:-1], result[l_begin:l_begin + l_diff])
    return result

0 View Complete Implementation : recfunctions.py
Copyright MIT License
Author : alvarob96
def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False,
                 autoconvert=False):
    """
    Superposes arrays fields by fields

    Parameters
    ----------
    arrays : array or sequence
        Sequence of input arrays.
    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.
    autoconvert : {False, True}, optional
        Whether automatically cast the type of the field to the maximum.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> x = np.array([1, 2,])
    >>> rfn.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 = rfn.stack_arrays((z,zz))
    >>> test
    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),
                dtype = [('A', '|S3'), ('B', '<f8'), ('C', '<f8')])

    """
    if isinstance(arrays, ndarray):
        return arrays
    elif len(arrays) == 1:
        return arrays[0]
    seqarrays = [np.asanyarray(a).ravel() for a in arrays]
    nrecords = [len(a) for a in seqarrays]
    ndtype = [a.dtype for a in seqarrays]
    fldnames = [d.names for d in ndtype]
    #
    dtype_l = ndtype[0]
    newdescr = get_fieldspec(dtype_l)
    names = [n for n, d in newdescr]
    for dtype_n in ndtype[1:]:
        for fname, fdtype in get_fieldspec(dtype_n):
            if fname not in names:
                newdescr.append((fname, fdtype))
                names.append(fname)
            else:
                nameidx = names.index(fname)
                _, cdtype = newdescr[nameidx]
                if autoconvert:
                    newdescr[nameidx] = (fname, max(fdtype, cdtype))
                elif fdtype != cdtype:
                    raise TypeError("Incompatible type '%s' <> '%s'" %
                                    (cdtype, fdtype))
    # Only one field: use concatenate
    if len(newdescr) == 1:
        output = ma.concatenate(seqarrays)
    else:
        #
        output = ma.masked_all((np.sum(nrecords),), newdescr)
        offset = np.camesum(np.r_[0, nrecords])
        seen = []
        for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]):
            names = a.dtype.names
            if names is None:
                output['f%i' % len(seen)][i:j] = a
            else:
                for name in n:
                    output[name][i:j] = a[name]
                    if name not in seen:
                        seen.append(name)
    #
    return _fix_output(_fix_defaults(output, defaults),
                       usemask=usemask, asrecarray=asrecarray)

0 View Complete Implementation : arraysetops.py
Copyright Apache License 2.0
Author : awslabs
@array_function_dispatch(_ediff1d_dispatcher)
def ediff1d(ary, to_end=None, to_begin=None):
    """
    The differences between consecutive elements of an array.

    Parameters
    ----------
    ary : array_like
        If necessary, will be flattened before the differences are taken.
    to_end : array_like, optional
        Number(s) to append at the end of the returned differences.
    to_begin : array_like, optional
        Number(s) to prepend at the beginning of the returned differences.

    Returns
    -------
    ediff1d : ndarray
        The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.

    See Also
    --------
    diff, gradient

    Notes
    -----
    When applied to masked arrays, this function drops the mask information
    if the `to_begin` and/or `to_end` parameters are used.

    Examples
    --------
    >>> x = np.array([1, 2, 4, 7, 0])
    >>> np.ediff1d(x)
    array([ 1,  2,  3, -7])

    >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
    array([-99,   1,   2,   3,  -7,  88,  99])

    The returned array is always 1D.

    >>> y = [[1, 2, 4], [1, 6, 24]]
    >>> np.ediff1d(y)
    array([ 1,  2, -3,  5, 18])

    """
    # force a 1d array
    ary = np.asanyarray(ary).ravel()

    # enforce propagation of the dtype of input
    # ary to returned result
    dtype_req = ary.dtype

    # fast track default case
    if to_begin is None and to_end is None:
        return ary[1:] - ary[:-1]

    if to_begin is None:
        l_begin = 0
    else:
        _to_begin = np.asanyarray(to_begin, dtype=dtype_req)
        if not np.all(_to_begin == to_begin):
            raise ValueError("cannot convert 'to_begin' to array with dtype "
                            "'%r' as required for input ary" % dtype_req)
        to_begin = _to_begin.ravel()
        l_begin = len(to_begin)

    if to_end is None:
        l_end = 0
    else:
        _to_end = np.asanyarray(to_end, dtype=dtype_req)
        # check that casting has not overflowed
        if not np.all(_to_end == to_end):
            raise ValueError("cannot convert 'to_end' to array with dtype "
                             "'%r' as required for input ary" % dtype_req)
        to_end = _to_end.ravel()
        l_end = len(to_end)

    # do the calculation in place and copy to_begin and to_end
    l_diff = max(len(ary) - 1, 0)
    result = np.empty(l_diff + l_begin + l_end, dtype=ary.dtype)
    result = ary.__array_wrap__(result)
    if l_begin > 0:
        result[:l_begin] = to_begin
    if l_end > 0:
        result[l_begin + l_diff:] = to_end
    np.subtract(ary[1:], ary[:-1], result[l_begin:l_begin + l_diff])
    return result

0 View Complete Implementation : recfunctions.py
Copyright Apache License 2.0
Author : dnanexus
def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False,
                 autoconvert=False):
    """
    Superposes arrays fields by fields

    Parameters
    ----------
    seqarrays : array or sequence
        Sequence of input arrays.
    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.
    autoconvert : {False, True}, optional
        Whether automatically cast the type of the field to the maximum.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> x = np.array([1, 2,])
    >>> rfn.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 = rfn.stack_arrays((z,zz))
    >>> test
    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),
                dtype = [('A', '|S3'), ('B', '<f8'), ('C', '<f8')])

    """
    if isinstance(arrays, ndarray):
        return arrays
    elif len(arrays) == 1:
        return arrays[0]
    seqarrays = [np.asanyarray(a).ravel() for a in arrays]
    nrecords = [len(a) for a in seqarrays]
    ndtype = [a.dtype for a in seqarrays]
    fldnames = [d.names for d in ndtype]
    #
    dtype_l = ndtype[0]
    newdescr = dtype_l.descr
    names = [_[0] for _ in newdescr]
    for dtype_n in ndtype[1:]:
        for descr in dtype_n.descr:
            name = descr[0] or ''
            if name not in names:
                newdescr.append(descr)
                names.append(name)
            else:
                nameidx = names.index(name)
                current_descr = newdescr[nameidx]
                if autoconvert:
                    if np.dtype(descr[1]) > np.dtype(current_descr[-1]):
                        current_descr = list(current_descr)
                        current_descr[-1] = descr[1]
                        newdescr[nameidx] = tuple(current_descr)
                elif descr[1] != current_descr[-1]:
                    raise TypeError("Incompatible type '%s' <> '%s'" % \
                                    (dict(newdescr)[name], descr[1]))
    # Only one field: use concatenate
    if len(newdescr) == 1:
        output = ma.concatenate(seqarrays)
    else:
        #
        output = ma.masked_all((np.sum(nrecords),), newdescr)
        offset = np.camesum(np.r_[0, nrecords])
        seen = []
        for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]):
            names = a.dtype.names
            if names is None:
                output['f%i' % len(seen)][i:j] = a
            else:
                for name in n:
                    output[name][i:j] = a[name]
                    if name not in seen:
                        seen.append(name)
    #
    return _fix_output(_fix_defaults(output, defaults),
                       usemask=usemask, asrecarray=asrecarray)

0 View Complete Implementation : arraysetops.py
Copyright BSD 3-Clause "New" or "Revised" License
Author : holzschu
@array_function_dispatch(_ediff1d_dispatcher)
def ediff1d(ary, to_end=None, to_begin=None):
    """
    The differences between consecutive elements of an array.

    Parameters
    ----------
    ary : array_like
        If necessary, will be flattened before the differences are taken.
    to_end : array_like, optional
        Number(s) to append at the end of the returned differences.
    to_begin : array_like, optional
        Number(s) to prepend at the beginning of the returned differences.

    Returns
    -------
    ediff1d : ndarray
        The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.

    See Also
    --------
    diff, gradient

    Notes
    -----
    When applied to masked arrays, this function drops the mask information
    if the `to_begin` and/or `to_end` parameters are used.

    Examples
    --------
    >>> x = np.array([1, 2, 4, 7, 0])
    >>> np.ediff1d(x)
    array([ 1,  2,  3, -7])

    >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
    array([-99,   1,   2,   3,  -7,  88,  99])

    The returned array is always 1D.

    >>> y = [[1, 2, 4], [1, 6, 24]]
    >>> np.ediff1d(y)
    array([ 1,  2, -3,  5, 18])

    """
    # force a 1d array
    ary = np.asanyarray(ary).ravel()

    # we have unit tests enforcing
    # propagation of the dtype of input
    # ary to returned result
    dtype_req = ary.dtype

    # fast track default case
    if to_begin is None and to_end is None:
        return ary[1:] - ary[:-1]

    if to_begin is None:
        l_begin = 0
    else:
        to_begin = np.asanyarray(to_begin)
        if not np.can_cast(to_begin, dtype_req):
            raise TypeError("dtype of to_begin must be compatible "
                            "with input ary")

        to_begin = to_begin.ravel()
        l_begin = len(to_begin)

    if to_end is None:
        l_end = 0
    else:
        to_end = np.asanyarray(to_end)
        if not np.can_cast(to_end, dtype_req):
            raise TypeError("dtype of to_end must be compatible "
                            "with input ary")

        to_end = to_end.ravel()
        l_end = len(to_end)

    # do the calculation in place and copy to_begin and to_end
    l_diff = max(len(ary) - 1, 0)
    result = np.empty(l_diff + l_begin + l_end, dtype=ary.dtype)
    result = ary.__array_wrap__(result)
    if l_begin > 0:
        result[:l_begin] = to_begin
    if l_end > 0:
        result[l_begin + l_diff:] = to_end
    np.subtract(ary[1:], ary[:-1], result[l_begin:l_begin + l_diff])
    return result

0 View Complete Implementation : recfunctions.py
Copyright GNU General Public License v3.0
Author : Microvellum
def stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False,
                 autoconvert=False):
    """
    Superposes arrays fields by fields

    Parameters
    ----------
    seqarrays : array or sequence
        Sequence of input arrays.
    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.
    autoconvert : {False, True}, optional
        Whether automatically cast the type of the field to the maximum.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> x = np.array([1, 2,])
    >>> rfn.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 = rfn.stack_arrays((z,zz))
    >>> test
    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),
                dtype = [('A', '|S3'), ('B', '<f8'), ('C', '<f8')])

    """
    if isinstance(arrays, ndarray):
        return arrays
    elif len(arrays) == 1:
        return arrays[0]
    seqarrays = [np.asanyarray(a).ravel() for a in arrays]
    nrecords = [len(a) for a in seqarrays]
    ndtype = [a.dtype for a in seqarrays]
    fldnames = [d.names for d in ndtype]
    #
    dtype_l = ndtype[0]
    newdescr = dtype_l.descr
    names = [_[0] for _ in newdescr]
    for dtype_n in ndtype[1:]:
        for descr in dtype_n.descr:
            name = descr[0] or ''
            if name not in names:
                newdescr.append(descr)
                names.append(name)
            else:
                nameidx = names.index(name)
                current_descr = newdescr[nameidx]
                if autoconvert:
                    if np.dtype(descr[1]) > np.dtype(current_descr[-1]):
                        current_descr = list(current_descr)
                        current_descr[-1] = descr[1]
                        newdescr[nameidx] = tuple(current_descr)
                elif descr[1] != current_descr[-1]:
                    raise TypeError("Incompatible type '%s' <> '%s'" %
                                    (dict(newdescr)[name], descr[1]))
    # Only one field: use concatenate
    if len(newdescr) == 1:
        output = ma.concatenate(seqarrays)
    else:
        #
        output = ma.masked_all((np.sum(nrecords),), newdescr)
        offset = np.camesum(np.r_[0, nrecords])
        seen = []
        for (a, n, i, j) in zip(seqarrays, fldnames, offset[:-1], offset[1:]):
            names = a.dtype.names
            if names is None:
                output['f%i' % len(seen)][i:j] = a
            else:
                for name in n:
                    output[name][i:j] = a[name]
                    if name not in seen:
                        seen.append(name)
    #
    return _fix_output(_fix_defaults(output, defaults),
                       usemask=usemask, asrecarray=asrecarray)