# Node organization

This module is about ordering nodes in terms of the slot nodes they are attached to.

## Canonical Order

Nodes are linked to subsets of slots, and there is a canonical ordering on subsets of integers that is inherited by the nodes.

The canonical order is a way to sort the nodes in your corpus in such a way that you can enumerate all nodes in the order you encounter them if you walk through your corpus.

Formally
A node A comes before a node B if A contains the smallest slot that occurs in only one of A and B.

Briefly this means:

• embedder nodes come before the nodes that lie embedded in them;
• earlier stuff comes before later stuff,
• if a verse coincides with a sentence, the verse comes before the sentence, because verses generally contain sentences and not the other way round;
• if two objects are intersecting, but none embeds the other, the one with the smallest slot that does not occur in the other, comes first.

first things first, big things first

That means, roughly, that you start with a book node (Genesis), then a chapter node (Genesis 1), then a verse node, Genesis 1:1, then a sentence node, then a clause node, a phrase node, and the first word node. Then follow all word nodes in the first phrase, then the phrase node of the second phrase, followed by the word nodes in that phrase. When ever you enter a higher structure, you will first get the node corresponding to that structure, and after that the nodes corresponding to the building blocks of that structure.

This concept follows the intuition that slot sets with smaller elements come before slot set with bigger elements, and embedding slot sets come before embedded slot sets. Hence, if you enumerate a set of nodes that happens to constitute a tree hierarchy based on slot set embedding, and you enumerate those nodes in the slot set order, you will walk the tree in pre-order.

This order is a modification of the one as described in (Doedens 1994, 3.6.3).

Doedens, Crist-Jan (1994), Text Databases. One Database Model and Several Retrieval Languages, number 14 in Language and Computers, Editions Rodopi, Amsterdam, Netherlands and Atlanta, USA. ISBN: 90-5183-729-1, https://books.google.nl/books?id=9ggOBRz1dO4C. The order as defined by Doedens corresponds to walking trees in post-order.

For a lot of processing, it is handy to have a the stack of embedding elements available when working with an element. That is the advantage of pre-order over post-order. It is very much like SAX parsing in the XML world.

Expand source code Browse git
"""
# Node organization

This module is about ordering nodes in terms of the slot nodes they are attached to.

## Canonical Order

Nodes are linked to subsets of slots, and there is a canonical ordering
on subsets of integers that is inherited by the nodes.

The canonical order is a way to sort the nodes in your corpus in such a way
that you can enumerate all nodes in the order you encounter them if you

Formally
:   A node *A* comes before a node *B* if *A* contains the smallest slot
that occurs in only one of *A* and *B*.

Briefly this means:

*   embedder nodes come before the nodes that lie embedded in them;
*   earlier stuff comes before later stuff,
*   if a verse coincides with a sentence, the verse comes before the sentence,
because verses generally contain sentences and not the other way round;
*   if two objects are intersecting, but none embeds the other, the one with the
smallest slot that does not occur in the other, comes first.

!!! note "first things first, big things first"
book node (Genesis), then a chapter node (Genesis 1), then a verse node, Genesis
1:1, then a sentence node, then a clause node, a phrase node, and the first word
node. Then follow all word nodes in the first phrase, then the phrase node of
the second phrase, followed by the word nodes in that phrase. When ever you
enter a higher structure, you will first get the node corresponding to that
structure, and after that the nodes corresponding to the building blocks of that
structure.

This concept follows the intuition that slot sets with smaller elements come
before slot set with bigger elements, and embedding slot sets come before
embedded slot sets. Hence, if you enumerate a set of nodes that happens to
constitute a tree hierarchy based on slot set embedding, and you enumerate those
nodes in the slot set order, you will walk the tree in pre-order.

This order is a modification of the one as described in (Doedens 1994, 3.6.3).

![fabric](../images/DoedensLO.png)

> Doedens, Crist-Jan (1994), *Text Databases. One Database Model and Several
> Retrieval Languages*, number 14 in Language and Computers, Editions Rodopi,
> Amsterdam, Netherlands and Atlanta, USA. ISBN: 90-5183-729-1,
> https://books.google.nl/books?id=9ggOBRz1dO4C. The order as defined by
> Doedens corresponds to walking trees in post-order.

For a lot of processing, it is handy to have a the stack of embedding elements
available when working with an element. That is the advantage of pre-order over
post-order. It is very much like SAX parsing in the XML world.
"""

import functools

class Nodes:
def __init__(self, api):
self.api = api
C = api.C
Crank = C.rank.data

self.otypeRank = {d[0]: i for (i, d) in enumerate(reversed(C.levels.data))}
"""Dictionary that provides a ranking of the node types.

The node types are ordered in C.levels.data, and if you reverse that list,
you get the rank of a type by looking at the position in which that type occurs.

The *slotType* has rank 0 (otypeRank[F.otype.slotType] == 0),
and the more comprehensive a type is, the higher its rank.
"""

self.sortKey = lambda n: Crank[n - 1]
"""Sort key function for the canonical ordering between nodes.

!!! hint "usage"
The following two pieces of code do the same thing:
sortNodes(nodeSet) and sorted(nodeSet, key=sortKey).

--------
tf.core.nodes: canonical ordering
tf.core.nodes.Nodes.sortNodes: sorting nodes
"""

self.sortKeyTuple = lambda tup: tuple(Crank[n - 1] for n in tup)
"""Sort key function for the canonical ordering between tuples of nodes.
It applies sortKey to each member of the tuple.
Handy to sort search results. We can sort them in canonical order like this:

sorted(results, key=lambda tup: tuple(sortKey(n) for n in tup))

This is exactly what sortKeyTuple does, but then a bit more efficient:

sorted(results, key=sortKeyTuple)

--------
tf.core.nodes: canonical ordering
"""

(sortKeyChunk, sortKeyChunkLength) = self.makeSortKeyChunk()

self.sortKeyChunk = sortKeyChunk
"""Sort key function for the canonical ordering between chunks of nodes.

sorted(chunks, key=sortKeyChunk)

A chunk is a tuple consisting of a node and a subset of its slots.
Mostly, this subset of slots is contiguous (no gaps), and mostly it is
maximal: the slots immediately before and after the chunk do not belong to the node.

But the sortkey also works if these conditions are not met.

Notes
-----
The use case for this function is that we have a bunch of nodes,
each linked to a set of slots.
For each node, we have split its slot set in maximal contiguous parts, its chunks.
Now we want to order those chunks in the canonical ordering.

--------
tf.core.nodes: canonical ordering
"""

self.sortKeyChunkLength = sortKeyChunkLength

def makeSortKeyChunk(self):
api = self.api

fOtype = api.F.otype
otypeRank = self.otypeRank
fOtypev = fOtype.v

def beforePosition(chunk1, chunk2):
(n1, (b1, e1)) = chunk1
(n2, (b2, e2)) = chunk2
if b1 < b2:
return -1
elif b1 > b2:
return 1

r1 = otypeRank[fOtypev(n1)]
r2 = otypeRank[fOtypev(n2)]

if r1 > r2:
return -1
elif r1 < r2:
return 1

return (
-1
if e1 > e2
else 1
if e1 < e2
else -1
if n1 < n2
else 1
if n1 > n2
else 0
)

def beforeLength(chunk1, chunk2):
(n1, (b1, e1)) = chunk1
(n2, (b2, e2)) = chunk2

size1 = e1 - b1
size2 = e2 - b2

if size1 > size2:
return -1
elif size2 > size1:
return 1
elif b1 < b2:
return -1
elif b1 > b2:
return 1

r1 = otypeRank[fOtypev(n1)]
r2 = otypeRank[fOtypev(n2)]

if r2 > r1:
return -1
elif r1 > r2:
return 1

return (
-1
if n1 < n2
else 1
if n1 > n2
else 0
)

return (
functools.cmp_to_key(beforePosition),
functools.cmp_to_key(beforeLength),
)

def sortNodes(self, nodeSet):
"""Delivers a tuple of nodes sorted by the *canonical ordering*.

nodeSet: iterable
An iterable of nodes to be sorted.

--------
tf.core.nodes: canonical ordering
"""

api = self.api

Crank = api.C.rank.data
return sorted(nodeSet, key=lambda n: Crank[n - 1])

def walk(self, events=False):
"""Generates all nodes in the *canonical order*.
(tf.core.nodes)

By walk() you traverse all nodes of your corpus
in a very natural order. See tf.core.nodes.

The order is much like walking a tree in pre-order: first parents,
then children from left to right.

The thing is: in general the nodes do not form a tree, but a more
liberal structure: a graph.

But even then we can order the nodes in such a way that nodes that embed
slots from other nodes come before those other nodes, provided those other
nodes start later.

When we generate those nodes and consume them, we now when each node starts,
but we loose track of where exactly they end.

To remedy that, you can call this function with events=True.
In that case, a stream of events is generated, where each event has the
form (node, False) or (node, True), where False means: beginning of
node and True means: end of node.

In case of slot nodes, only one event per slot is generated: (node, None).

!!! hint "More ways of walking"
Under tf.core.nodefeature.NodeFeatures there is another convenient way
to walk through subsets of nodes.

Parameters
----------
events: boolean, optional False
If True, wraps the generated nodes in event tuples as described above.

Returns
-------
nodes: int
One at a time.
"""

api = self.api

if events:
C = api.C
endSlots = C.boundary.data[1]

otype = api.F.otype
Fotypev = otype.v
slotType = otype.slotType

for n in C.order.data:
if Fotypev(n) == slotType:
yield (n, None)
for m in reversed(endSlots[n - 1]):
yield(m, True)
else:
yield(n, False)

else:
for n in api.C.order.data:
yield n

## Classes

 class Nodes (api) 
Expand source code Browse git
class Nodes:
def __init__(self, api):
self.api = api
C = api.C
Crank = C.rank.data

self.otypeRank = {d[0]: i for (i, d) in enumerate(reversed(C.levels.data))}
"""Dictionary that provides a ranking of the node types.

The node types are ordered in C.levels.data, and if you reverse that list,
you get the rank of a type by looking at the position in which that type occurs.

The *slotType* has rank 0 (otypeRank[F.otype.slotType] == 0),
and the more comprehensive a type is, the higher its rank.
"""

self.sortKey = lambda n: Crank[n - 1]
"""Sort key function for the canonical ordering between nodes.

!!! hint "usage"
The following two pieces of code do the same thing:
sortNodes(nodeSet) and sorted(nodeSet, key=sortKey).

--------
tf.core.nodes: canonical ordering
tf.core.nodes.Nodes.sortNodes: sorting nodes
"""

self.sortKeyTuple = lambda tup: tuple(Crank[n - 1] for n in tup)
"""Sort key function for the canonical ordering between tuples of nodes.
It applies sortKey to each member of the tuple.
Handy to sort search results. We can sort them in canonical order like this:

sorted(results, key=lambda tup: tuple(sortKey(n) for n in tup))

This is exactly what sortKeyTuple does, but then a bit more efficient:

sorted(results, key=sortKeyTuple)

--------
tf.core.nodes: canonical ordering
"""

(sortKeyChunk, sortKeyChunkLength) = self.makeSortKeyChunk()

self.sortKeyChunk = sortKeyChunk
"""Sort key function for the canonical ordering between chunks of nodes.

sorted(chunks, key=sortKeyChunk)

A chunk is a tuple consisting of a node and a subset of its slots.
Mostly, this subset of slots is contiguous (no gaps), and mostly it is
maximal: the slots immediately before and after the chunk do not belong to the node.

But the sortkey also works if these conditions are not met.

Notes
-----
The use case for this function is that we have a bunch of nodes,
each linked to a set of slots.
For each node, we have split its slot set in maximal contiguous parts, its chunks.
Now we want to order those chunks in the canonical ordering.

--------
tf.core.nodes: canonical ordering
"""

self.sortKeyChunkLength = sortKeyChunkLength

def makeSortKeyChunk(self):
api = self.api

fOtype = api.F.otype
otypeRank = self.otypeRank
fOtypev = fOtype.v

def beforePosition(chunk1, chunk2):
(n1, (b1, e1)) = chunk1
(n2, (b2, e2)) = chunk2
if b1 < b2:
return -1
elif b1 > b2:
return 1

r1 = otypeRank[fOtypev(n1)]
r2 = otypeRank[fOtypev(n2)]

if r1 > r2:
return -1
elif r1 < r2:
return 1

return (
-1
if e1 > e2
else 1
if e1 < e2
else -1
if n1 < n2
else 1
if n1 > n2
else 0
)

def beforeLength(chunk1, chunk2):
(n1, (b1, e1)) = chunk1
(n2, (b2, e2)) = chunk2

size1 = e1 - b1
size2 = e2 - b2

if size1 > size2:
return -1
elif size2 > size1:
return 1
elif b1 < b2:
return -1
elif b1 > b2:
return 1

r1 = otypeRank[fOtypev(n1)]
r2 = otypeRank[fOtypev(n2)]

if r2 > r1:
return -1
elif r1 > r2:
return 1

return (
-1
if n1 < n2
else 1
if n1 > n2
else 0
)

return (
functools.cmp_to_key(beforePosition),
functools.cmp_to_key(beforeLength),
)

def sortNodes(self, nodeSet):
"""Delivers a tuple of nodes sorted by the *canonical ordering*.

nodeSet: iterable
An iterable of nodes to be sorted.

--------
tf.core.nodes: canonical ordering
"""

api = self.api

Crank = api.C.rank.data
return sorted(nodeSet, key=lambda n: Crank[n - 1])

def walk(self, events=False):
"""Generates all nodes in the *canonical order*.
(tf.core.nodes)

By walk() you traverse all nodes of your corpus
in a very natural order. See tf.core.nodes.

The order is much like walking a tree in pre-order: first parents,
then children from left to right.

The thing is: in general the nodes do not form a tree, but a more
liberal structure: a graph.

But even then we can order the nodes in such a way that nodes that embed
slots from other nodes come before those other nodes, provided those other
nodes start later.

When we generate those nodes and consume them, we now when each node starts,
but we loose track of where exactly they end.

To remedy that, you can call this function with events=True.
In that case, a stream of events is generated, where each event has the
form (node, False) or (node, True), where False means: beginning of
node and True means: end of node.

In case of slot nodes, only one event per slot is generated: (node, None).

!!! hint "More ways of walking"
Under tf.core.nodefeature.NodeFeatures there is another convenient way
to walk through subsets of nodes.

Parameters
----------
events: boolean, optional False
If True, wraps the generated nodes in event tuples as described above.

Returns
-------
nodes: int
One at a time.
"""

api = self.api

if events:
C = api.C
endSlots = C.boundary.data[1]

otype = api.F.otype
Fotypev = otype.v
slotType = otype.slotType

for n in C.order.data:
if Fotypev(n) == slotType:
yield (n, None)
for m in reversed(endSlots[n - 1]):
yield(m, True)
else:
yield(n, False)

else:
for n in api.C.order.data:
yield n

### Instance variables

var otypeRank

Dictionary that provides a ranking of the node types.

The node types are ordered in C.levels.data, and if you reverse that list, you get the rank of a type by looking at the position in which that type occurs.

The slotType has rank 0 (otypeRank[F.otype.slotType] == 0), and the more comprehensive a type is, the higher its rank.

var sortKey

Sort key function for the canonical ordering between nodes.

usage

The following two pieces of code do the same thing: sortNodes(nodeSet) and sorted(nodeSet, key=sortKey).

tf.core.nodes
canonical ordering
Nodes.sortNodes()
sorting nodes
var sortKeyChunk

Sort key function for the canonical ordering between chunks of nodes.

sorted(chunks, key=sortKeyChunk)


A chunk is a tuple consisting of a node and a subset of its slots. Mostly, this subset of slots is contiguous (no gaps), and mostly it is maximal: the slots immediately before and after the chunk do not belong to the node.

But the sortkey also works if these conditions are not met.

## Notes

The use case for this function is that we have a bunch of nodes, each linked to a set of slots. For each node, we have split its slot set in maximal contiguous parts, its chunks. Now we want to order those chunks in the canonical ordering.

tf.core.nodes
canonical ordering
var sortKeyTuple

Sort key function for the canonical ordering between tuples of nodes. It applies sortKey to each member of the tuple. Handy to sort search results. We can sort them in canonical order like this:

sorted(results, key=lambda tup: tuple(sortKey(n) for n in tup))


This is exactly what sortKeyTuple does, but then a bit more efficient:

sorted(results, key=sortKeyTuple)


tf.core.nodes
canonical ordering

### Methods

 def makeSortKeyChunk(self) 
Expand source code Browse git
def makeSortKeyChunk(self):
api = self.api

fOtype = api.F.otype
otypeRank = self.otypeRank
fOtypev = fOtype.v

def beforePosition(chunk1, chunk2):
(n1, (b1, e1)) = chunk1
(n2, (b2, e2)) = chunk2
if b1 < b2:
return -1
elif b1 > b2:
return 1

r1 = otypeRank[fOtypev(n1)]
r2 = otypeRank[fOtypev(n2)]

if r1 > r2:
return -1
elif r1 < r2:
return 1

return (
-1
if e1 > e2
else 1
if e1 < e2
else -1
if n1 < n2
else 1
if n1 > n2
else 0
)

def beforeLength(chunk1, chunk2):
(n1, (b1, e1)) = chunk1
(n2, (b2, e2)) = chunk2

size1 = e1 - b1
size2 = e2 - b2

if size1 > size2:
return -1
elif size2 > size1:
return 1
elif b1 < b2:
return -1
elif b1 > b2:
return 1

r1 = otypeRank[fOtypev(n1)]
r2 = otypeRank[fOtypev(n2)]

if r2 > r1:
return -1
elif r1 > r2:
return 1

return (
-1
if n1 < n2
else 1
if n1 > n2
else 0
)

return (
functools.cmp_to_key(beforePosition),
functools.cmp_to_key(beforeLength),
)
 def sortNodes(self, nodeSet) 

Delivers a tuple of nodes sorted by the canonical ordering.

nodeSet: iterable An iterable of nodes to be sorted.

tf.core.nodes
canonical ordering
Expand source code Browse git
def sortNodes(self, nodeSet):
"""Delivers a tuple of nodes sorted by the *canonical ordering*.

nodeSet: iterable
An iterable of nodes to be sorted.

--------
tf.core.nodes: canonical ordering
"""

api = self.api

Crank = api.C.rank.data
return sorted(nodeSet, key=lambda n: Crank[n - 1])
 def walk(self, events=False) 

Generates all nodes in the canonical order. (tf.core.nodes)

By walk() you traverse all nodes of your corpus in a very natural order. See tf.core.nodes.

The order is much like walking a tree in pre-order: first parents, then children from left to right.

The thing is: in general the nodes do not form a tree, but a more liberal structure: a graph.

But even then we can order the nodes in such a way that nodes that embed slots from other nodes come before those other nodes, provided those other nodes start later.

When we generate those nodes and consume them, we now when each node starts, but we loose track of where exactly they end.

To remedy that, you can call this function with events=True. In that case, a stream of events is generated, where each event has the form (node, False) or (node, True), where False means: beginning of node and True means: end of node.

In case of slot nodes, only one event per slot is generated: (node, None).

More ways of walking

Under NodeFeatures there is another convenient way to walk through subsets of nodes.

## Parameters

events : boolean, optional False
If True, wraps the generated nodes in event tuples as described above.

## Returns

nodes : int
One at a time.
Expand source code Browse git
def walk(self, events=False):
"""Generates all nodes in the *canonical order*.
(tf.core.nodes)

By walk() you traverse all nodes of your corpus
in a very natural order. See tf.core.nodes.

The order is much like walking a tree in pre-order: first parents,
then children from left to right.

The thing is: in general the nodes do not form a tree, but a more
liberal structure: a graph.

But even then we can order the nodes in such a way that nodes that embed
slots from other nodes come before those other nodes, provided those other
nodes start later.

When we generate those nodes and consume them, we now when each node starts,
but we loose track of where exactly they end.

To remedy that, you can call this function with events=True.
In that case, a stream of events is generated, where each event has the
form (node, False) or (node, True), where False means: beginning of
node and True means: end of node.

In case of slot nodes, only one event per slot is generated: (node, None).

!!! hint "More ways of walking"
Under tf.core.nodefeature.NodeFeatures there is another convenient way
to walk through subsets of nodes.

Parameters
----------
events: boolean, optional False
If True, wraps the generated nodes in event tuples as described above.

Returns
-------
nodes: int
One at a time.
"""

api = self.api

if events:
C = api.C
endSlots = C.boundary.data[1]

otype = api.F.otype
Fotypev = otype.v
slotType = otype.slotType

for n in C.order.data:
if Fotypev(n) == slotType:
yield (n, None)
for m in reversed(endSlots[n - 1]):
yield(m, True)
else:
yield(n, False)

else:
for n in api.C.order.data:
yield n