lsst.afw.table._syntax Namespace Reference

Functions
def	KeyBaseCov_subfields
def	KeyBaseCov_subkeys
def	Schema_extract
def	BaseRecord_extract
def	BaseColumnView_extract

Function Documentation

def lsst.afw.table._syntax.BaseColumnView_extract	(		self,
			patterns,
			kwds
	)

Extract a dictionary of {<name>: <column-array>} in which the field names
match the given shell-style glob pattern(s).

Any number of glob patterns may be passed; the result will be the union of all
the result of each glob considered separately.

Note that extract("*", copy=True) provides an easy way to transform a row-major
ColumnView into a possibly more efficient set of contiguous NumPy arrays.

This routines unpacks Flag columns into full boolean arrays and covariances into dense
(i.e. non-triangular packed) arrays with dimension (N,M,M), where N is the number of
records and M is the dimension of the covariance matrix.  Fields with named subfields
(e.g. points) are always split into separate dictionary items, as is done in
BaseRecord.extract(..., split=True).  String fields are silently ignored.

Additional optional arguments may be passed as keywords:

  items ------ The result of a call to self.schema.extract(); this will be used instead
               of doing any new matching, and allows the pattern matching to be reused
               to extract values from multiple records.  This keyword is incompatible
               with any position arguments and the regex, sub, and ordered keyword
               arguments.

  where ------ Any expression that can be passed as indices to a NumPy array, including
               slices, boolean arrays, and index arrays, that will be used to index
               each column array.  This is applied before arrays are copied when
               copy is True, so if the indexing results in an implicit copy no
               unnecessary second copy is performed.

  copy ------- If True, the returned arrays will be contiguous copies rather than strided
               views into the catalog.  This ensures that the lifetime of the catalog is
               not tied to the lifetime of a particular catalog, and it also may improve
               the performance if the array is used repeatedly.
               Default is False.

  regex ------ A regular expression to be used in addition to any glob patterns passed
               as positional arguments.  Note that this will be compared with re.match,
               not re.search.

  sub -------- A replacement string (see re.MatchObject.expand) used to set the
               dictionary keys of any fields matched by regex.

  ordered----- If True, a collections.OrderedDict will be returned instead of a standard
               dict, with the order corresponding to the definition order of the Schema.
               Default is False.

Definition at line 147 of file _syntax.py.

def BaseColumnView_extract(self, *patterns, **kwds):
    """
    Extract a dictionary of {<name>: <column-array>} in which the field names
    match the given shell-style glob pattern(s).

    Any number of glob patterns may be passed; the result will be the union of all
    the result of each glob considered separately.

    Note that extract("*", copy=True) provides an easy way to transform a row-major
    ColumnView into a possibly more efficient set of contiguous NumPy arrays.

    This routines unpacks Flag columns into full boolean arrays and covariances into dense
    (i.e. non-triangular packed) arrays with dimension (N,M,M), where N is the number of
    records and M is the dimension of the covariance matrix.  Fields with named subfields
    (e.g. points) are always split into separate dictionary items, as is done in
    BaseRecord.extract(..., split=True).  String fields are silently ignored.

    Additional optional arguments may be passed as keywords:

      items ------ The result of a call to self.schema.extract(); this will be used instead
                   of doing any new matching, and allows the pattern matching to be reused
                   to extract values from multiple records.  This keyword is incompatible
                   with any position arguments and the regex, sub, and ordered keyword
                   arguments.

      where ------ Any expression that can be passed as indices to a NumPy array, including
                   slices, boolean arrays, and index arrays, that will be used to index
                   each column array.  This is applied before arrays are copied when
                   copy is True, so if the indexing results in an implicit copy no
                   unnecessary second copy is performed.

      copy ------- If True, the returned arrays will be contiguous copies rather than strided
                   views into the catalog.  This ensures that the lifetime of the catalog is
                   not tied to the lifetime of a particular catalog, and it also may improve
                   the performance if the array is used repeatedly.
                   Default is False.

      regex ------ A regular expression to be used in addition to any glob patterns passed
                   as positional arguments.  Note that this will be compared with re.match,
                   not re.search.

      sub -------- A replacement string (see re.MatchObject.expand) used to set the
                   dictionary keys of any fields matched by regex.

      ordered----- If True, a collections.OrderedDict will be returned instead of a standard
                   dict, with the order corresponding to the definition order of the Schema.
                   Default is False.

    """
    copy = kwds.pop("copy", False)
    where = kwds.pop("where", None)
    d = kwds.pop("items", None)
    if d is None:
        d = self.schema.extract(*patterns, **kwds).copy()
    elif kwds:
        raise ValueError("Unrecognized keyword arguments for extract: %s" % ", ".join(kwds.keys()))
    def processArray(a):
        if where is not None:
            a = a[where]
        if copy:
            a = numpy.ascontiguousarray(a)
        return a
    for name, schemaItem in d.items(): # can't use iteritems because we might be adding/deleting elements
        key = schemaItem.key
        if key.HAS_NAMED_SUBFIELDS:
            for subname, subkey in zip(key.subfields, key.subkeys):
                d["%s.%s" % (name, subname)] = processArray(self.get(subkey))
            del d[name]
        elif key.getTypeString().startswith("Cov"):
            unpacked = None
            for idx, subkey in zip(key.subfields, key.subkeys):
                i, j = idx
                array = processArray(self.get(subkey))
                if unpacked is None:
                    unpacked = numpy.zeros((array.size, key.getSize(), key.getSize()), dtype=array.dtype)
                unpacked[:,i,j] = array
                if i != j:
                    unpacked[:,j,i] = array
            d[name] = unpacked
        elif key.getTypeString() == "String":
            del d[name]
        else:
            d[name] = processArray(self.get(schemaItem.key))
    return d

def lsst.afw.table._syntax.BaseRecord_extract	(		self,
			patterns,
			kwds
	)

Extract a dictionary of {<name>: <field-value>} in which the field names
match the given shell-style glob pattern(s).

Any number of glob patterns may be passed; the result will be the union of all
the result of each glob considered separately.

Additional optional arguments may be passed as keywords:

  items ------ The result of a call to self.schema.extract(); this will be used instead
               of doing any new matching, and allows the pattern matching to be reused
               to extract values from multiple records.  This keyword is incompatible
               with any position arguments and the regex, sub, and ordered keyword
               arguments.

  split ------ If True, fields with named subfields (e.g. points) will be split into
               separate items in the dict; instead of {"point": lsst.afw.geom.Point2I(2,3)},
               for instance, you'd get {"point.x": 2, "point.y": 3}.
               Default is False.

  regex ------ A regular expression to be used in addition to any glob patterns passed
               as positional arguments.  Note that this will be compared with re.match,
               not re.search.

  sub -------- A replacement string (see re.MatchObject.expand) used to set the
               dictionary keys of any fields matched by regex.

  ordered----- If True, a collections.OrderedDict will be returned instead of a standard
               dict, with the order corresponding to the definition order of the Schema.
               Default is False.

Definition at line 98 of file _syntax.py.

def BaseRecord_extract(self, *patterns, **kwds):
    """
    Extract a dictionary of {<name>: <field-value>} in which the field names
    match the given shell-style glob pattern(s).

    Any number of glob patterns may be passed; the result will be the union of all
    the result of each glob considered separately.

    Additional optional arguments may be passed as keywords:

      items ------ The result of a call to self.schema.extract(); this will be used instead
                   of doing any new matching, and allows the pattern matching to be reused
                   to extract values from multiple records.  This keyword is incompatible
                   with any position arguments and the regex, sub, and ordered keyword
                   arguments.

      split ------ If True, fields with named subfields (e.g. points) will be split into
                   separate items in the dict; instead of {"point": lsst.afw.geom.Point2I(2,3)},
                   for instance, you'd get {"point.x": 2, "point.y": 3}.
                   Default is False.

      regex ------ A regular expression to be used in addition to any glob patterns passed
                   as positional arguments.  Note that this will be compared with re.match,
                   not re.search.

      sub -------- A replacement string (see re.MatchObject.expand) used to set the
                   dictionary keys of any fields matched by regex.

      ordered----- If True, a collections.OrderedDict will be returned instead of a standard
                   dict, with the order corresponding to the definition order of the Schema.
                   Default is False.

    """
    d = kwds.pop("items", None)
    split = kwds.pop("split", False)
    if d is None:
        d = self.schema.extract(*patterns, **kwds).copy()
    elif kwds:
        raise ValueError("Unrecognized keyword arguments for extract: %s" % ", ".join(kwds.keys()))
    for name, schemaItem in d.items():  # can't use iteritems because we might be adding/deleting elements
        key = schemaItem.key
        if split and key.HAS_NAMED_SUBFIELDS:
            for subname, subkey in zip(key.subfields, key.subkeys):
                d["%s.%s" % (name, subname)] = self.get(subkey)
            del d[name]
        else:
            d[name] = self.get(schemaItem.key)
    return d