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from __future__ import absolute_import, division, print_function
import numbers
import operator
import numpy as np
from glue.external.six import PY3
from glue.core.roi import CategoricalROI
from glue.core.contracts import contract
from glue.core.util import split_component_view
from glue.core.registry import Registry
from glue.core.exceptions import IncompatibleAttribute
from glue.core.message import SubsetDeleteMessage, SubsetUpdateMessage
from glue.core.decorators import memoize
from glue.core.visual import VisualAttributes
from glue.config import settings
from glue.utils import view_shape, broadcast_to
__all__ = ['Subset', 'SubsetState', 'RoiSubsetState', 'CategoricalROISubsetState',
'RangeSubsetState', 'MultiRangeSubsetState', 'CompositeSubsetState',
'OrState', 'AndState', 'XorState', 'InvertState', 'MaskSubsetState', 'CategorySubsetState',
'ElementSubsetState', 'InequalitySubsetState', 'combine_multiple',
'CategoricalMultiRangeSubsetState', 'CategoricalROISubsetState2D']
OPSYM = {operator.ge: '>=', operator.gt: '>',
operator.le: '<=', operator.lt: '<',
operator.and_: '&', operator.or_: '|',
operator.xor: '^', operator.eq: '==',
operator.ne: '!='}
SYMOP = dict((v, k) for k, v in OPSYM.items())
class Subset(object):
"""Base class to handle subsets of data.
These objects both describe subsets of a dataset, and relay any
state changes to the hub that their parent data are assigned to.
This base class only directly impements the logic that relays
state changes back to the hub. Subclasses implement the actual
description and manipulation of data subsets
:param data:
The dataset that this subset describes
:type data: :class:`~glue.core.data.Data`
"""
@contract(data='isinstance(Data)|None',
color='color',
alpha=float,
label='string|None')
def __init__(self, data, color=settings.SUBSET_COLORS[0], alpha=0.5, label=None):
""" Create a new subset object.
Note: the preferred way for creating subsets is
via DataCollection.new_subset_group. Manually-instantiated
subsets will probably *not* be represented properly by the UI
"""
self._broadcasting = False # must be first def
self.data = data
self._subset_state = None
self._label = None
self._style = None
self._setup(color, alpha, label)
@contract(color='color', alpha='float', label='string|None')
def _setup(self, color, alpha, label):
self.color = color
self.label = label # trigger disambiguation
self.style = VisualAttributes(parent=self)
self.style.markersize *= 1.5
self.style.color = color
self.style.alpha = alpha
self.subset_state = SubsetState() # calls proper setter method
@property
def subset_state(self):
return self._subset_state
@subset_state.setter
def subset_state(self, state):
if isinstance(state, np.ndarray):
if self.data.shape != state.shape:
raise ValueError("Shape of mask doesn't match shape of data")
cids = self.data.pixel_component_ids
state = MaskSubsetState(state, cids)
if not isinstance(state, SubsetState):
raise TypeError("State must be a SubsetState instance or array")
self._subset_state = state
@property
def style(self):
return self._style
@style.setter
@contract(value=VisualAttributes)
def style(self, value):
value.parent = self
self._style = value
@property
def label(self):
""" Convenience access to subset's label """
return self._label
@label.setter
def label(self, value):
"""Set the subset's label
Subset labels within a data object must be unique. The input
will be auto-disambiguated if necessary
"""
value = Registry().register(self, value, group=self.data)
self._label = value
@property
def attributes(self):
"""
Returns a tuple of the ComponentIDs that this subset
depends upon
"""
return self.subset_state.attributes
def register(self):
""" Register a subset to its data, and start broadcasting
state changes
"""
self.data.add_subset(self)
self.do_broadcast(True)
@contract(returns='array[N]')
def to_index_list(self):
"""
Convert the current subset to a list of indices. These index
the elements in the (flattened) data object that belong to the subset.
If x is the numpy array corresponding to some component.data,
the two following statements are equivalent::
x.flat[subset.to_index_list()]
x[subset.to_mask()]
Returns:
A numpy array, giving the indices of elements in the data that
belong to this subset.
Raises:
IncompatibleDataException: if an index list cannot be created
for the requested data set.
"""
try:
return self.subset_state.to_index_list(self.data)
except IncompatibleAttribute as exc:
try:
return self._to_index_list_join()
except IncompatibleAttribute:
raise exc
def _to_index_list_join(self):
return np.where(self._to_mask_join(None).flat)[0]
def _to_mask_join(self, view):
"""
Convert the subset to a mask through an entity join to another
dataset.
"""
for other, (cid1, cid2) in self.data._key_joins.items():
if getattr(other, '_recursing', False):
continue
try:
self.data._recursing = True
s2 = Subset(other)
s2.subset_state = self.subset_state
mask_right = s2.to_mask()
except IncompatibleAttribute:
continue
finally:
self.data._recursing = False
if len(cid1) == 1 and len(cid2) == 1:
key_left = self.data[cid1[0], view]
key_right = other[cid2[0], mask_right]
mask = np.in1d(key_left.ravel(), key_right.ravel())
return mask.reshape(key_left.shape)
elif len(cid1) == len(cid2):
key_left_all = []
key_right_all = []
for cid1_i, cid2_i in zip(cid1, cid2):
key_left_all.append(self.data[cid1_i, view].ravel())
key_right_all.append(other[cid2_i, mask_right].ravel())
# TODO: The following is slow because we are looping in Python.
# This could be made significantly faster by switching to
# C/Cython.
key_left_all = zip(*key_left_all)
key_right_all = set(zip(*key_right_all))
result = [key in key_right_all for key in key_left_all]
result = np.array(result)
return result.reshape(self.data[cid1_i, view].shape)
elif len(cid1) == 1:
key_left = self.data[cid1[0], view].ravel()
mask = np.zeros_like(key_left, dtype=bool)
for cid2_i in cid2:
key_right = other[cid2_i, mask_right].ravel()
mask |= np.in1d(key_left, key_right)
return mask.reshape(self.data[cid1[0], view].shape)
elif len(cid2) == 1:
key_right = other[cid2[0], mask_right].ravel()
mask = np.zeros_like(self.data[cid1[0], view].ravel(), dtype=bool)
for cid1_i in cid1:
key_left = self.data[cid1_i, view].ravel()
mask |= np.in1d(key_left, key_right)
return mask.reshape(self.data[cid1[0], view].shape)
else:
raise Exception("Either the number of components in the key join sets "
"should match, or one of the component sets should ",
"contain a single component.")
raise IncompatibleAttribute
@contract(view='array_view', returns='array')
def to_mask(self, view=None):
"""
Convert the current subset to a mask.
:param view: An optional view into the dataset (e.g. a slice)
If present, the mask will pertain to the view and not the
entire dataset.
Returns:
A boolean numpy array, the same shape as the data, that
defines whether each element belongs to the subset.
"""
try:
return self.subset_state.to_mask(self.data, view)
except IncompatibleAttribute as exc:
try:
return self._to_mask_join(view)
except IncompatibleAttribute:
raise exc
@contract(value=bool)
def do_broadcast(self, value):
"""
Set whether state changes to the subset are relayed to a hub.
It can be useful to turn off broadcasting, when modifying the
subset in ways that don't impact any of the clients.
Attributes:
value: Whether the subset should broadcast state changes (True/False)
"""
object.__setattr__(self, '_broadcasting', value)
@contract(attribute='string')
def broadcast(self, attribute):
"""
Explicitly broadcast a SubsetUpdateMessage to the hub
:param attribute:
The name of the attribute (if any) that should be
broadcast as updated.
:type attribute: ``str``
"""
if not hasattr(self, 'data') or not hasattr(self.data, 'hub'):
return
if self._broadcasting and self.data.hub:
msg = SubsetUpdateMessage(self, attribute=attribute)
self.data.hub.broadcast(msg)
def delete(self):
"""Broadcast a SubsetDeleteMessage to the hub, and stop broadcasting
Also removes subset reference from parent data's subsets list
"""
dobroad = self._broadcasting and self.data is not None and \
self.data.hub is not None
self.do_broadcast(False)
if self.data is not None and self in self.data.subsets:
self.data._subsets.remove(self)
if dobroad:
msg = SubsetDeleteMessage(self)
self.data.hub.broadcast(msg)
Registry().unregister(self, group=self.data)
@contract(file_name='string')
def write_mask(self, file_name, format="fits"):
""" Write a subset mask out to file
:param file_name: name of file to write to
:param format:
Name of format to write to. Currently, only "fits" is
supported
"""
mask = np.short(self.to_mask())
if format == 'fits':
try:
from astropy.io import fits
fits.writeto(file_name, mask, clobber=True)
except ImportError:
raise ImportError("Cannot write mask -- requires astropy")
else:
raise AttributeError("format not supported: %s" % format)
@contract(file_name='string')
def read_mask(self, file_name):
try:
from astropy.io import fits
mask = fits.open(file_name)[0].data
except ImportError:
raise ImportError("Cannot read mask -- requires astropy")
except IOError:
raise IOError("Could not read %s (not a fits file?)" % file_name)
ind = np.where(mask.flat)[0]
state = ElementSubsetState(indices=ind)
self.subset_state = state
def __del__(self):
self.delete()
def __setattr__(self, attribute, value):
object.__setattr__(self, attribute, value)
if not attribute.startswith('_'):
self.broadcast(attribute)
def __getitem__(self, view):
""" Retrieve the elements from a data view within the subset
:param view: View of the data. See data.__getitem__ for detils
"""
c, v = split_component_view(view)
ma = self.to_mask(v)
return self.data[view][ma]
@contract(other_subset='isinstance(Subset)')
def paste(self, other_subset):
"""paste subset state from other_subset onto self """
state = other_subset.subset_state.copy()
self.subset_state = state
def __str__(self):
dlabel = "(no data)"
if self.data is not None:
dlabel = "(data: %s)" % self.data.label
slabel = "Subset: (no label)"
if self.label:
slabel = "Subset: %s" % self.label
return "%s %s" % (slabel, dlabel)
def __repr__(self):
return self.__str__()
@contract(other='isinstance(Subset)', returns='isinstance(Subset)')
def __or__(self, other):
return _combine([self, other], operator.or_)
@contract(other='isinstance(Subset)', returns='isinstance(Subset)')
def __and__(self, other):
return _combine([self, other], operator.and_)
@contract(returns='isinstance(Subset)')
def __invert__(self):
return _combine([self], operator.invert)
@contract(other='isinstance(Subset)', returns='isinstance(Subset)')
def __xor__(self, other):
return _combine([self, other], operator.xor)
def __eq__(self, other):
if not isinstance(other, Subset):
return False
# XXX need to add equality specification for subset states
return (self.subset_state == other.subset_state and
self.style == other.style)
def state_as_mask(self):
"""
Convert the current SubsetState to a MaskSubsetState
"""
try:
m = self.to_mask()
except IncompatibleAttribute:
m = np.zeros(self.data.shape, dtype=np.bool)
cids = self.data.pixel_component_ids
return MaskSubsetState(m, cids)
# In Python 2 we need to do this explicitly
def __ne__(self, other):
return not self.__eq__(other)
# In Python 3, if __eq__ is defined, then __hash__ has to be re-defined
if PY3:
__hash__ = object.__hash__
class SubsetState(object):
def __init__(self):
pass
@property
def attributes(self):
return tuple()
@property
def subset_state(self): # convenience method, mimic interface of Subset
return self
@contract(data='isinstance(Data)')
def to_index_list(self, data):
return np.where(self.to_mask(data).flat)[0]
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
shp = view_shape(data.shape, view)
return np.zeros(shp, dtype=bool)
@contract(returns='isinstance(SubsetState)')
def copy(self):
return SubsetState()
@contract(other_state='isinstance(SubsetState)',
returns='isinstance(SubsetState)')
def __or__(self, other_state):
return OrState(self, other_state)
@contract(other_state='isinstance(SubsetState)',
returns='isinstance(SubsetState)')
def __and__(self, other_state):
return AndState(self, other_state)
@contract(returns='isinstance(SubsetState)')
def __invert__(self):
return InvertState(self)
@contract(other_state='isinstance(SubsetState)',
returns='isinstance(SubsetState)')
def __xor__(self, other_state):
return XorState(self, other_state)
class RoiSubsetState(SubsetState):
def __init__(self, xatt=None, yatt=None, roi=None):
super(RoiSubsetState, self).__init__()
self.xatt = xatt
self.yatt = yatt
self.roi = roi
@property
def attributes(self):
return (self.xatt, self.yatt)
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
# TODO: make sure that pixel components don't actually take up much
# memory and are just views
x = data[self.xatt, view]
y = data[self.yatt, view]
if (x.ndim == data.ndim and
self.xatt in data.pixel_component_ids and
self.yatt in data.pixel_component_ids):
# This is a special case - the ROI is defined in pixel space, so we
# can apply it to a single slice and then broadcast it to all other
# dimensions. We start off by extracting a slice which takes only
# the first elements of all dimensions except the attributes in
# question, for which we take all the elements. We need to preserve
# the dimensionality of the array, hence the use of slice(0, 1).
# Note that we can only do this if the view (if present) preserved
# the dimensionality, which is why we checked that x.ndim == data.ndim
subset = []
for i in range(data.ndim):
if i == self.xatt.axis or i == self.yatt.axis:
subset.append(slice(None))
else:
subset.append(slice(0, 1))
x_slice = x[subset]
y_slice = y[subset]
if self.roi.defined():
result = self.roi.contains(x_slice, y_slice)
else:
result = np.zeros(x_slice.shape, dtype=bool)
result = broadcast_to(result, x.shape)
else:
if self.roi.defined():
result = self.roi.contains(x, y)
else:
result = np.zeros(x.shape, dtype=bool)
if result.shape != x.shape:
raise ValueError("Unexpected error: boolean mask has incorrect dimensions")
return result
def copy(self):
result = RoiSubsetState()
result.xatt = self.xatt
result.yatt = self.yatt
result.roi = self.roi
return result
class CategoricalROISubsetState(SubsetState):
def __init__(self, att=None, roi=None):
super(CategoricalROISubsetState, self).__init__()
self.att = att
self.roi = roi
@property
def attributes(self):
return self.att,
@memoize
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
x = data.get_component(self.att)._categorical_data[view]
result = self.roi.contains(x, None)
assert x.shape == result.shape
return result.ravel()
def copy(self):
result = CategoricalROISubsetState()
result.att = self.att
result.roi = self.roi
return result
@staticmethod
def from_range(component, att, lo, hi):
roi = CategoricalROI.from_range(component, lo, hi)
subset = CategoricalROISubsetState(roi=roi,
att=att)
return subset
class RangeSubsetState(SubsetState):
def __init__(self, lo, hi, att=None):
super(RangeSubsetState, self).__init__()
self.lo = lo
self.hi = hi
self.att = att
@property
def attributes(self):
return (self.att,)
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
x = data[self.att, view]
result = (x >= self.lo) & (x <= self.hi)
return result
def copy(self):
return RangeSubsetState(self.lo, self.hi, self.att)
class MultiRangeSubsetState(SubsetState):
"""
A subset state defined by multiple discontinuous ranges
Parameters
----------
pairs : list
A list of (lo, hi) tuples
"""
def __init__(self, pairs, att=None):
super(MultiRangeSubsetState, self).__init__()
self.pairs = pairs
self.att = att
@property
def attributes(self):
return (self.att,)
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
x = data[self.att, view]
result = np.zeros_like(x, dtype=bool)
for lo, hi in self.pairs:
result |= (x >= lo) & (x <= hi)
return result
def copy(self):
return MultiRangeSubsetState(self.pairs, self.att)
class CategoricalROISubsetState2D(object):
"""
A 2D subset state where both attributes are categorical.
Parameters
----------
categories : dict
A dictionary containing for each label of one categorical component an
interable of labels for the other categorical component (using sets will
provide the best performance)
att1 : :class:`~glue.core.component_id.ComponentID`
The component ID matching the keys of the ``categories`` dictionary
att2 : :class:`~glue.core.component_id.ComponentID`
The component ID matching the values of the ``categories`` dictionary
"""
def __init__(self, categories, att1, att2):
self.categories = categories
self.att1 = att1
self.att2 = att2
@property
def attributes(self):
return (self.att1, self.att2)
@memoize
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
# Extract categories and numerical values
labels1 = data.get_component(self.att1).labels
labels2 = data.get_component(self.att2).labels
if view is not None:
labels1 = labels1[view]
labels2 = labels2[view]
# Initialize empty mask
mask = np.zeros(labels1.shape, dtype=bool)
# A loop over all values here is actually reasonably efficient compared
# to alternatives. Any improved implementation, even vectorized, should
# ensure that it is more efficient for large numbers of categories and
# values.
for i in range(len(labels1)):
if labels1[i] in self.categories:
if labels2[i] in self.categories[labels1[i]]:
mask[i] = True
return mask
def copy(self):
result = CategoricalROISubsetState2D(self.categories,
self.att1, self.att2)
return result
class CategoricalMultiRangeSubsetState(SubsetState):
"""
A 2D subset state where one attribute is categorical and the other is
numerical, and where for each category, there are multiple possible subset
ranges.
Parameters
----------
ranges : dict
A dictionary containing for each category (key), a list of tuples
giving the ranges of values for the numerical attribute.
cat_att : :class:`~glue.core.component_id.ComponentID`
The component ID for the categorical attribute
num_att : :class:`~glue.core.component_id.ComponentID`
The component ID for the numerical attribute
"""
def __init__(self, ranges, cat_att, num_att):
self.ranges = ranges
self.cat_att = cat_att
self.num_att = num_att
@property
def attributes(self):
return (self.cat_att, self._num_att)
@memoize
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
# Extract categories and numerical values
labels = data.get_component(self.cat_att).labels
values = data[self.num_att]
if view is not None:
labels = labels[view]
values = values[view]
# Initialize empty mask
mask = np.zeros(values.shape, dtype=bool)
# A loop over all values here is actually reasonably efficient compared
# to alternatives. Any improved implementation, even vectorized, should
# ensure that it is more efficient for large numbers of categories and
# values. For example, using 10000 categories and 1000000 data points
# takes 1.2 seconds on a laptop.
for i in range(len(values)):
if labels[i] in self.ranges:
for lo, hi in self.ranges[labels[i]]:
if values[i] >= lo and values[i] <= hi:
mask[i] = True
break
return mask
def copy(self):
result = CategoricalMultiRangeSubsetState(self.ranges,
self.cat_att,
self.num_att)
return result
class CompositeSubsetState(SubsetState):
op = None
def __init__(self, state1, state2=None):
super(CompositeSubsetState, self).__init__()
self.state1 = state1.copy()
if state2:
state2 = state2.copy()
self.state2 = state2
def copy(self):
return type(self)(self.state1, self.state2)
@property
def attributes(self):
att = self.state1.attributes
if self.state2 is not None:
att += self.state2.attributes
return tuple(sorted(set(att)))
@memoize
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
return self.op(self.state1.to_mask(data, view),
self.state2.to_mask(data, view))
def __str__(self):
sym = OPSYM.get(self.op, self.op)
return "(%s %s %s)" % (self.state1, sym, self.state2)
class OrState(CompositeSubsetState):
op = operator.or_
class AndState(CompositeSubsetState):
op = operator.and_
class XorState(CompositeSubsetState):
op = operator.xor
class InvertState(CompositeSubsetState):
@memoize
@contract(data='isinstance(Data)', view='array_view')
def to_mask(self, data, view=None):
return ~self.state1.to_mask(data, view)
def __str__(self):
return "(~%s)" % self.state1
class MaskSubsetState(SubsetState):
"""
A subset defined by boolean pixel mask
"""
def __init__(self, mask, cids):
"""
:param cids: List of ComponentIDs, defining the pixel coordinate space of the mask
:param mask: Boolean ndarray
"""
self.cids = cids
self.mask = mask
def copy(self):
return MaskSubsetState(self.mask, self.cids)
def to_mask(self, data, view=None):
view = view or slice(None)
# shortcut for data on the same pixel grid
if data.pixel_component_ids == self.cids:
return self.mask[view].copy()
# locate each element of data in the coordinate system of the mask
vals = [data[c, view].astype(np.int) for c in self.cids]
result = self.mask[vals]
for v, n in zip(vals, data.shape):
result &= ((v >= 0) & (v < n))
return result
def __gluestate__(self, context):
return dict(cids=[context.id(c) for c in self.cids],
mask=context.do(self.mask))
@classmethod
def __setgluestate__(cls, rec, context):
return cls(context.object(rec['mask']),
[context.object(c) for c in rec['cids']])
class CategorySubsetState(SubsetState):
def __init__(self, attribute, values):
super(CategorySubsetState, self).__init__()
self._attribute = attribute
self._values = np.asarray(values).ravel()
@memoize
def to_mask(self, data, view=None):
vals = data[self._attribute, view]
result = np.in1d(vals.ravel(), self._values)
return result.reshape(vals.shape)
def copy(self):
return CategorySubsetState(self._attribute, self._values.copy())
def __gluestate__(self, context):
return dict(att=context.id(self._attribute),
vals=context.do(self._values))
@classmethod
def __setgluestate__(cls, rec, context):
return cls(context.object(rec['att']),
context.object(rec['vals']))
class ElementSubsetState(SubsetState):
def __init__(self, indices=None, data=None):
super(ElementSubsetState, self).__init__()
self._indices = indices
if data is None:
self._data_uuid = None
else:
self._data_uuid = data.uuid
@memoize
def to_mask(self, data, view=None):
if data.uuid == self._data_uuid or self._data_uuid is None:
# XXX this is inefficient for views
result = np.zeros(data.shape, dtype=bool)
if self._indices is not None:
try:
result.flat[self._indices] = True
except IndexError:
if self._data_uuid is None:
raise IncompatibleAttribute()
else:
raise
if view is not None:
result = result[view]
return result
else:
raise IncompatibleAttribute()
def copy(self):
state = ElementSubsetState(indices=self._indices)
state._data_uuid = self._data_uuid
return state
def __gluestate__(self, context):
return dict(indices=context.do(self._indices),
data_uuid=self._data_uuid)
@classmethod
def __setgluestate__(cls, rec, context):
state = cls(indices=context.object(rec['indices']))
try:
state._data_uuid = rec['data_uuid']
except KeyError: # BACKCOMPAT
pass
return state
class InequalitySubsetState(SubsetState):
def __init__(self, left, right, op):
from glue.core.component_link import ComponentLink
super(InequalitySubsetState, self).__init__()
from glue.core.data import ComponentID
valid_ops = [operator.gt, operator.ge,
operator.lt, operator.le,
operator.eq, operator.ne]
if op not in valid_ops:
raise TypeError("Invalid boolean operator: %s" % op)
if not isinstance(left, ComponentID) and not \
isinstance(left, numbers.Number) and not \
isinstance(left, ComponentLink):
raise TypeError("Input must be ComponenID or NumberType: %s"
% type(left))
if not isinstance(right, ComponentID) and not \
isinstance(right, numbers.Number) and not \
isinstance(right, ComponentLink):
raise TypeError("Input must be ComponenID or NumberType: %s"
% type(right))
self._left = left
self._right = right
self._operator = op
@property
def left(self):
return self._left
@property
def right(self):
return self._right
@property
def operator(self):
return self._operator
@memoize
def to_mask(self, data, view=None):
left = self._left
if not isinstance(self._left, numbers.Number):
left = data[self._left, view]
right = self._right
if not isinstance(self._right, numbers.Number):
right = data[self._right, view]
return self._operator(left, right)
def copy(self):
return InequalitySubsetState(self._left, self._right, self._operator)
def __str__(self):
sym = OPSYM.get(self._operator, self._operator)
return "(%s %s %s)" % (self._left, sym, self._right)
def __repr__(self):
return '<%s: %s>' % (self.__class__.__name__, self)
@contract(subsets='list(isinstance(Subset))', returns=Subset)
def _combine(subsets, operator):
state = operator(*[s.subset_state for s in subsets])
result = Subset(None)
result.subset_state = state
return result
def combine_multiple(subsets, operator):
if len(subsets) == 0:
return SubsetState()
else:
combined = subsets[0]
for subset in subsets[1:]:
combined = operator(combined, subset)
return combined
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