Python numpy.recarray方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def __init__(self,fileobj, hdr):
        """ gets list of frames and subheaders in pet file

        Parameters
        -----------
        fileobj : ECAT file <filename>.v  fileholder or file object
                  with read, seek methods

        Returns
        -------
        mlist : numpy recarray  nframes X 4 columns
        1 - Matrix identifier.
        2 - subheader record number
        3 - Last record number of matrix data block.
        4 - Matrix status:
            1 - exists - rw
            2 - exists - ro
            3 - matrix deleted
        """
        self.hdr = hdr
        self._mlist = self.get_mlist(fileobj) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def _keep_fields(base, keep_names, usemask=True, asrecarray=False):
    """
    Return a new array keeping only the fields in `keep_names`,
    and preserving the order of those fields.

    Parameters
    ----------
    base : array
        Input array
    keep_names : string or sequence
        String or sequence of strings corresponding to the names of the
        fields to keep. Order of the names will be preserved.
    usemask : {False, True}, optional
        Whether to return a masked array or not.
    asrecarray : string or sequence, optional
        Whether to return a recarray or a mrecarray (`asrecarray=True`) or
        a plain ndarray or masked array with flexible dtype. The default
        is False.
    """
    newdtype = [(n, base.dtype[n]) for n in keep_names]
    output = np.empty(base.shape, dtype=newdtype)
    output = recursive_fill_fields(base, output)
    return _fix_output(output, usemask=usemask, asrecarray=asrecarray) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def test_recfromtxt(self):
        #
        data = TextIO('A,B\n0,1\n2,3')
        kwargs = dict(delimiter=",", missing_values="N/A", names=True)
        test = np.recfromtxt(data, **kwargs)
        control = np.array([(0, 1), (2, 3)],
                           dtype=[('A', int), ('B', int)])
        assert_(isinstance(test, np.recarray))
        assert_equal(test, control)
        #
        data = TextIO('A,B\n0,1\n2,N/A')
        test = np.recfromtxt(data, dtype=None, usemask=True, **kwargs)
        control = ma.array([(0, 1), (2, -1)],
                           mask=[(False, False), (False, True)],
                           dtype=[('A', int), ('B', int)])
        assert_equal(test, control)
        assert_equal(test.mask, control.mask)
        assert_equal(test.A, [0, 2]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def test_recfromtxt(self):
        #
        data = TextIO('A,B\n0,1\n2,3')
        kwargs = dict(delimiter=",", missing_values="N/A", names=True)
        test = np.recfromtxt(data, **kwargs)
        control = np.array([(0, 1), (2, 3)],
                           dtype=[('A', np.int), ('B', np.int)])
        self.assertTrue(isinstance(test, np.recarray))
        assert_equal(test, control)
        #
        data = TextIO('A,B\n0,1\n2,N/A')
        test = np.recfromtxt(data, dtype=None, usemask=True, **kwargs)
        control = ma.array([(0, 1), (2, -1)],
                           mask=[(False, False), (False, True)],
                           dtype=[('A', np.int), ('B', np.int)])
        assert_equal(test, control)
        assert_equal(test.mask, control.mask)
        assert_equal(test.A, [0, 2]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def test_recarray_from_repr(self):
        a = np.array([(1,'ABC'), (2, "DEF")],
                     dtype=[('foo', int), ('bar', 'S4')])
        recordarr = np.rec.array(a)
        recarr = a.view(np.recarray)
        recordview = a.view(np.dtype((np.record, a.dtype)))

        recordarr_r = eval("numpy." + repr(recordarr), {'numpy': np})
        recarr_r = eval("numpy." + repr(recarr), {'numpy': np})
        recordview_r = eval("numpy." + repr(recordview), {'numpy': np})

        assert_equal(type(recordarr_r), np.recarray)
        assert_equal(recordarr_r.dtype.type, np.record)
        assert_equal(recordarr, recordarr_r)

        assert_equal(type(recarr_r), np.recarray)
        assert_equal(recarr_r.dtype.type, np.record)
        assert_equal(recarr, recarr_r)

        assert_equal(type(recordview_r), np.ndarray)
        assert_equal(recordview.dtype.type, np.record)
        assert_equal(recordview, recordview_r) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def get_mlist(self, fileobj):
        fileobj.seek(512)
        dat=fileobj.read(128*32)

        dt = np.dtype([('matlist',np.int32)])
        if not self.hdr.endianness is native_code:
            dt = dt.newbyteorder(self.hdr.endianness)
        nframes = self.hdr['num_frames']
        mlist = np.zeros((nframes,4), dtype='uint32')
        record_count = 0
        done = False

        while not done: #mats['matlist'][0,1] == 2:

            mats = np.recarray(shape=(32,4), dtype=dt,  buf=dat)
            if not (mats['matlist'][0,0] +  mats['matlist'][0,3]) == 31:
                mlist = []
                return mlist

            nrecords = mats['matlist'][0,3]
            mlist[record_count:nrecords+record_count,:] = mats['matlist'][1:nrecords+1,:]
            record_count+= nrecords
            if mats['matlist'][0,1] == 2 or mats['matlist'][0,1] == 0:
                done = True
            else:
                # Find next subheader
                tmp = int(mats['matlist'][0,1]-1)#cast to int
                fileobj.seek(0)
                fileobj.seek(tmp*512)
                dat = fileobj.read(128*32)

        return mlist 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def _get_subheaders(self):
        """retreive all subheaders and return list of subheader recarrays
        """
        subheaders = []
        header = self._header
        endianness = self.endianness
        dt = self._subhdrdtype
        if not self.endianness is native_code:
            dt = self._subhdrdtype.newbyteorder(self.endianness)
        if self._header['num_frames'] > 1:
            for item in self._mlist._mlist:
                if item[1] == 0:
                    break
                self.fileobj.seek(0)
                offset = (int(item[1])-1)*512
                self.fileobj.seek(offset)
                tmpdat = self.fileobj.read(512)
                sh = (np.recarray(shape=(), dtype=dt,
                                  buf=tmpdat))
                subheaders.append(sh.copy())
        else:
            self.fileobj.seek(0)
            offset = (int(self._mlist._mlist[0][1])-1)*512
            self.fileobj.seek(offset)
            tmpdat = self.fileobj.read(512)
            sh = (np.recarray(shape=(), dtype=dt,
                              buf=tmpdat))
            subheaders.append(sh)
        return subheaders 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def test_mlist(self):
        fid = open(self.example_file, 'rb')
        hdr = self.header_class.from_fileobj(fid)
        mlist =  self.mlist_class(fid, hdr)
        fid.seek(0)
        fid.seek(512)
        dat=fid.read(128*32)
        dt = np.dtype([('matlist',np.int32)])
        dt = dt.newbyteorder('>')
        mats = np.recarray(shape=(32,4), dtype=dt,  buf=dat)
        fid.close()
        #tests
        assert_true(mats['matlist'][0,0] +  mats['matlist'][0,3] == 31)
        assert_true(mlist.get_frame_order()[0][0] == 0)
        assert_true(mlist.get_frame_order()[0][1] == 16842758.0)
        # test badly ordered mlist
        badordermlist = mlist
        badordermlist._mlist = np.array([[  1.68427540e+07,   3.00000000e+00,
                                            1.20350000e+04,   1.00000000e+00],
                                         [  1.68427530e+07,   1.20360000e+04,
                                            2.40680000e+04,   1.00000000e+00],
                                         [  1.68427550e+07,   2.40690000e+04,
                                            3.61010000e+04,   1.00000000e+00],
                                         [  1.68427560e+07,   3.61020000e+04,
                                            4.81340000e+04,   1.00000000e+00],
                                         [  1.68427570e+07,   4.81350000e+04,
                                            6.01670000e+04,   1.00000000e+00],
                                         [  1.68427580e+07,   6.01680000e+04,
                                            7.22000000e+04,   1.00000000e+00]])
        assert_true(badordermlist.get_frame_order()[0][0] == 1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def _datetime64_index(self, recarr):
        """ Given a np.recarray find the first datetime64 column """
        # TODO: Handle multi-indexes
        names = recarr.dtype.names
        for name in names:
            if recarr[name].dtype == DTN64_DTYPE:
                return name
        return None 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def _fix_output(output, usemask=True, asrecarray=False):
    """
    Private function: return a recarray, a ndarray, a MaskedArray
    or a MaskedRecords depending on the input parameters
    """
    if not isinstance(output, MaskedArray):
        usemask = False
    if usemask:
        if asrecarray:
            output = output.view(MaskedRecords)
    else:
        output = ma.filled(output)
        if asrecarray:
            output = output.view(recarray)
    return output 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def rec_drop_fields(base, drop_names):
    """
    Returns a new numpy.recarray with fields in `drop_names` dropped.
    """
    return drop_fields(base, drop_names, usemask=False, asrecarray=True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def rename_fields(base, namemapper):
    """
    Rename the fields from a flexible-datatype ndarray or recarray.

    Nested fields are supported.

    Parameters
    ----------
    base : ndarray
        Input array whose fields must be modified.
    namemapper : dictionary
        Dictionary mapping old field names to their new version.

    Examples
    --------
    >>> from numpy.lib import recfunctions as rfn
    >>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
    ...   dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
    >>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
    array([(1, (2.0, [3.0, 30.0])), (4, (5.0, [6.0, 60.0]))],
          dtype=[('A', '<i4'), ('b', [('ba', '<f8'), ('BB', '<f8', 2)])])

    """
    def _recursive_rename_fields(ndtype, namemapper):
        newdtype = []
        for name in ndtype.names:
            newname = namemapper.get(name, name)
            current = ndtype[name]
            if current.names:
                newdtype.append(
                    (newname, _recursive_rename_fields(current, namemapper))
                    )
            else:
                newdtype.append((newname, current))
        return newdtype
    newdtype = _recursive_rename_fields(base.dtype, namemapper)
    return base.view(newdtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def rec_append_fields(base, names, data, dtypes=None):
    """
    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
    """
    return append_fields(base, names, data=data, dtypes=dtypes,
                         asrecarray=True, usemask=False) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def test_recfromcsv(self):
        #
        data = TextIO('A,B\n0,1\n2,3')
        kwargs = dict(missing_values="N/A", names=True, case_sensitive=True)
        test = np.recfromcsv(data, dtype=None, **kwargs)
        control = np.array([(0, 1), (2, 3)],
                           dtype=[('A', int), ('B', int)])
        assert_(isinstance(test, np.recarray))
        assert_equal(test, control)
        #
        data = TextIO('A,B\n0,1\n2,N/A')
        test = np.recfromcsv(data, dtype=None, usemask=True, **kwargs)
        control = ma.array([(0, 1), (2, -1)],
                           mask=[(False, False), (False, True)],
                           dtype=[('A', int), ('B', int)])
        assert_equal(test, control)
        assert_equal(test.mask, control.mask)
        assert_equal(test.A, [0, 2])
        #
        data = TextIO('A,B\n0,1\n2,3')
        test = np.recfromcsv(data, missing_values='N/A',)
        control = np.array([(0, 1), (2, 3)],
                           dtype=[('a', int), ('b', int)])
        assert_(isinstance(test, np.recarray))
        assert_equal(test, control)
        #
        data = TextIO('A,B\n0,1\n2,3')
        dtype = [('a', int), ('b', float)]
        test = np.recfromcsv(data, missing_values='N/A', dtype=dtype)
        control = np.array([(0, 1), (2, 3)],
                           dtype=dtype)
        assert_(isinstance(test, np.recarray))
        assert_equal(test, control)

        #gh-10394
        data = TextIO('color\n"red"\n"blue"')
        test = np.recfromcsv(data, converters={0: lambda x: x.strip(b'\"')})
        control = np.array([('red',), ('blue',)], dtype=[('color', (bytes, 4))])
        assert_equal(test.dtype, control.dtype)
        assert_equal(test, control) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import recarray [as 別名]
def recfromtxt(fname, **kwargs):
    """
    Load ASCII data from a file and return it in a record array.

    If ``usemask=False`` a standard `recarray` is returned,
    if ``usemask=True`` a MaskedRecords array is returned.

    Parameters
    ----------
    fname, kwargs : For a description of input parameters, see `genfromtxt`.

    See Also
    --------
    numpy.genfromtxt : generic function

    Notes
    -----
    By default, `dtype` is None, which means that the data-type of the output
    array will be determined from the data.

    """
    kwargs.setdefault("dtype", None)
    usemask = kwargs.get('usemask', False)
    output = genfromtxt(fname, **kwargs)
    if usemask:
        from numpy.ma.mrecords import MaskedRecords
        output = output.view(MaskedRecords)
    else:
        output = output.view(np.recarray)
    return output