So i've been looking around on the pytransitions github and SO and it seems after 0.8 the way you could use macro-states (or super state with substates in it) has change. I would like to know if it's still possible to create such a machine with pytransition (the blue square is suppose to be a macro-state that has 2 states in it, one of them, the green one, being another macro) :
Or do I have to follow the workflow suggested here : https://github.com/pytransitions/transitions/issues/332 ?
Thx a lot for any info !
I would like to know if it's still possible to create such a machine with pytransition.
The way HSMs are created and managed has changed in 0.8 but you can of course use (deeply) nested states. For a state to have substates, you need to pass the states (or children) parameter with the state definitions/objects you'd like to nest. Furthermore, you can pass transitions for that particular scope. I am using HierarchicalGraphMachine since this allows me to create a graph right away.
from transitions.extensions.factory import HierarchicalGraphMachine
states = [
# create a state named A
{"name": "A",
# with the following children
"states":
# a state named '1' which will be accessible as 'A_1'
["1", {
# and a state '2' with its own children ...
"name": "2",
# ... 'a' and 'b'
"states": ["a", "b"],
"transitions": [["go", "a", "b"],["go", "b", "a"]],
# when '2' is entered, 'a' should be entered automatically.
"initial": "a"
}],
# we could also pass [["go", "A_1", "A_2"]] to the machine constructor
"transitions": [["go", "1", "2"]],
"initial": "1"
}]
m = HierarchicalGraphMachine(states=states, initial="A")
m.go()
m.get_graph().draw("foo.png", prog="dot") # [1]
Output of 1:
I have the following example, where I create a graph programmetically, write it to a GML file and read the file into a graph again.
I want to be able to use the graph loaded from file in place of the programmatically created one:
import networkx as nx
g = nx.Graph()
g.add_edge(1,4)
nx.write_gml(g, "test.gml")
gg = nx.read_gml("test.gml", label="label")
print(gg.edges(data=True))
The contents of test.gml is a follows:
graph [
node [
id 0
label "1"
]
node [
id 1
label "4"
]
edge [
source 0
target 1
]
]
Nodes 1 and 4 from the python code are now represented by two nodes with ID 0 and 1 and labels "1" and "4"
After reading the file, I now have to access node 4 as follows:
gg['4']
Instead of
g[4]
for the original graph.
I could of course make sure to cast every node to string before looking up the node, but this is not practical for huge graphs.
An alternative would be to programmatically create (yet another) graph that is identical to g but with integer keys, but this is even more cumbersome.
What should I do?
Try:
nx.read_gml(fpath, destringizer=int)
Ref:
https://networkx.org/documentation/stable/reference/readwrite/generated/networkx.readwrite.gml.read_gml.html
We are using the MSSQL module with Node.js.
I am running the following query:
SELECT AVG((RAT_VALUE * 1.0)) FROM RAT WHERE RAT_PER_ID_FROM IS NOT NULL AND RAT_PER_ID_ABOUT = 139 AND RAT_USE = 'Y' AND RAT_ABOUT_ROLE = 'RS' AND RAT_DATE_INSERTED >= '10/1/2018' AND RAT_DATE_INSERTED < '10/1/2019'
If I run this against the database directly, it returns:
4.45
The output from MSSQL is:
4
The exact resultset returned is:
results { recordsets: [ [ [Object] ] ],
recordset: [ { '': 4 } ],
output: {},
rowsAffected: [ 1 ] }
In other words, MSSQL is always returning the value 4, instead of 4.45.
The column type od RAT_VALUE is INT in the database but I've tried changing it to DECIMAL(5, 2) without any luck.
I've tried explicitly returning a DECIMAL from the query like:
SELECT CAST(AVG((RAT_VALUE * 1.0)) AS DECIMAL(5, 2)) ...
But no luck there either.
It seems MSSQL is simply clipping and dropping the decimal part of any number, even numbers of Decimal types.
I even set the value as 4.75 in the database and returned it directly and it still returns 4.
Any ideas out there?
Background
I have a rocksdb collection that contains three fields: _id, author, subreddit.
Problem
I would like to create a Arango graph that creates a graph connecting these two existing columns. But the examples and the drivers seem to only accept collections as its edge definitions.
Issue
The ArangoDb documentation is lacking information on how I can create a graph using edges and nodes pulled from the same collection.
EDIT:
Solution
This was fixed with a code change at this Arangodb issues ticket.
Here's one way to do it using jq, a JSON-oriented command-line tool.
First, an outline of the steps:
1) Use arangoexport to export your author/subredit collection to a file, say, exported.json;
2) Run the jq script, nodes_and_edges.jq, shown below;
3) Use arangoimp to import the JSON produced in (2) into ArangoDB.
There are several ways the graph can be stored in ArangoDB, so ultimately you might wish to tweak nodes_and_edges.jq accordingly (e.g. to generate the nodes first, and then the edges).
INDEX
If your jq does not have INDEX defined, then use this:
def INDEX(stream; idx_expr):
reduce stream as $row ({};
.[$row|idx_expr|
if type != "string" then tojson
else .
end] |= $row);
def INDEX(idx_expr): INDEX(.[]; idx_expr);
nodes_and_edges.jq
# This module is for generating JSON suitable for importing into ArangoDB.
### Generic Functions
# nodes/2
# $name must be the name of the ArangoDB collection of nodes corresponding to $key.
# The scheme for generating key names can be altered by changing the first
# argument of assign_keys, e.g. to "" if no prefix is wanted.
def nodes($key; $name):
map( {($key): .[$key]} ) | assign_keys($name[0:1] + "_"; 1);
def assign_keys(prefix; start):
. as $in
| reduce range(0;length) as $i ([];
. + [$in[$i] + {"_key": "\(prefix)\(start+$i)"}]);
# nodes_and_edges facilitates the normalization of an implicit graph
# in an ArangoDB "document" collection of objects having $from and $to keys.
# The input should be an array of JSON objects, as produced
# by arangoexport for a single collection.
# If $nodesq is truthy, then the JSON for both the nodes and edges is emitted,
# otherwise only the JSON for the edges is emitted.
#
# The first four arguments should be strings.
#
# $from and $to should be the key names in . to be used for the from-to edges;
# $name1 and $name2 should be the names of the corresponding collections of nodes.
def nodes_and_edges($from; $to; $name1; $name2; $nodesq ):
def dict($s): INDEX(.[$s]) | map_values(._key);
def objects: to_entries[] | {($from): .key, "_key": .value};
(nodes($from; $name1) | dict($from)) as $fdict
| (nodes($to; $name2) | dict($to) ) as $tdict
| (if $nodesq then $fdict, $tdict | objects
else empty end),
(.[] | {_from: "\($name1)/\($fdict[.[$from]])",
_to: "\($name2)/\($tdict[.[$to]])"} ) ;
### Problem-Specific Functions
# If you wish to generate the collections separately,
# then these will come in handy:
def authors: nodes("author"; "authors");
def subredits: nodes("subredit"; "subredits");
def nodes_and_edges:
nodes_and_edges("author"; "subredit"; "authors"; "subredits"; true);
nodes_and_edges
Invocation
jq -cf extract_nodes_edges.jq exported.json
This invocation will produce a set of JSONL (JSON-Lines) for "authors", one for "subredits" and an edge collection.
Example
exported.json
[
{"_id":"test/115159","_key":"115159","_rev":"_V8JSdTS---","author": "A", "subredit": "S1"},
{"_id":"test/145120","_key":"145120","_rev":"_V8ONdZa---","author": "B", "subredit": "S2"},
{"_id":"test/114474","_key":"114474","_rev":"_V8JZJJS---","author": "C", "subredit": "S3"}
]
Output
{"author":"A","_key":"name_1"}
{"author":"B","_key":"name_2"}
{"author":"C","_key":"name_3"}
{"subredit":"S1","_key":"sid_1"}
{"subredit":"S2","_key":"sid_2"}
{"subredit":"S3","_key":"sid_3"}
{"_from":"authors/name_1","_to":"subredits/sid_1"}
{"_from":"authors/name_2","_to":"subredits/sid_2"}
{"_from":"authors/name_3","_to":"subredits/sid_3"}
Please note that the following queries take a while to complete on this huge dataset, however they should complete sucessfully after some hours.
We start the arangoimp to import our base dataset:
arangoimp --create-collection true --collection RawSubReddits --type jsonl ./RC_2017-01
We use arangosh to create the collections where our final data is going to live in:
db._create("authors")
db._createEdgeCollection("authorsToSubreddits")
We fill the authors collection by simply ignoring any subsequently occuring duplicate authors;
We will calculate the _key of the author by using the MD5 function,
so it obeys the restrictions for allowed chars in _key, and we can know it later on by calling MD5() again on the author field:
db._query(`
FOR item IN RawSubReddits
INSERT {
_key: MD5(item.author),
author: item.author
} INTO authors
OPTIONS { ignoreErrors: true }`);
After the we have filled the second vertex collection (we will keep the imported collection as the first vertex collection) we have to calculate the edges.
Since each author can have created several subreds, its most probably going to be several edges originating from each author. As previously mentioned,
we can use the MD5()-function again to reference the author previously created:
db._query(`
FOR onesubred IN RawSubReddits
INSERT {
_from: CONCAT('authors/', MD5(onesubred.author)),
_to: CONCAT('RawSubReddits/', onesubred._key)
} INTO authorsToSubreddits")
After the edge collection is filled (which may again take a while - we're talking about 40 million edges herer, right? - we create the graph description:
db._graphs.save({
"_key": "reddits",
"orphanCollections" : [ ],
"edgeDefinitions" : [
{
"collection": "authorsToSubreddits",
"from": ["authors"],
"to": ["RawSubReddits"]
}
]
})
We now can use the UI to browse the graphs, or use AQL queries to browse the graph. Lets pick the sort of random first author from that list:
db._query(`for author IN authors LIMIT 1 RETURN author`).toArray()
[
{
"_key" : "1cec812d4e44b95e5a11f3cbb15f7980",
"_id" : "authors/1cec812d4e44b95e5a11f3cbb15f7980",
"_rev" : "_W_Eu-----_",
"author" : "punchyourbuns"
}
]
We identified an author, and now run a graph query for him:
db._query(`FOR vertex, edge, path IN 0..1
OUTBOUND 'authors/1cec812d4e44b95e5a11f3cbb15f7980'
GRAPH 'reddits'
RETURN path`).toArray()
One of the resulting paths looks like that:
{
"edges" : [
{
"_key" : "128327199",
"_id" : "authorsToSubreddits/128327199",
"_from" : "authors/1cec812d4e44b95e5a11f3cbb15f7980",
"_to" : "RawSubReddits/38026350",
"_rev" : "_W_LOxgm--F"
}
],
"vertices" : [
{
"_key" : "1cec812d4e44b95e5a11f3cbb15f7980",
"_id" : "authors/1cec812d4e44b95e5a11f3cbb15f7980",
"_rev" : "_W_HAL-y--_",
"author" : "punchyourbuns"
},
{
"_key" : "38026350",
"_id" : "RawSubReddits/38026350",
"_rev" : "_W-JS0na--b",
"distinguished" : null,
"created_utc" : 1484537478,
"id" : "dchfe6e",
"edited" : false,
"parent_id" : "t1_dch51v3",
"body" : "I don't understand tension at all."
"Mine is set to auto."
"I'll replace the needle and rethread. Thanks!",
"stickied" : false,
"gilded" : 0,
"subreddit" : "sewing",
"author" : "punchyourbuns",
"score" : 3,
"link_id" : "t3_5o66d0",
"author_flair_text" : null,
"author_flair_css_class" : null,
"controversiality" : 0,
"retrieved_on" : 1486085797,
"subreddit_id" : "t5_2sczp"
}
]
}
For a graph you need an edge collection for the edges and vertex collections for the nodes. You can't create a graph using only one collection.
Maybe this topic in the documentations is helpful for you.
Here's an AQL solution, which however presupposes that all the referenced collections already exist, and that UPSERT is not necessary.
FOR v IN testcollection
LET a = v.author
LET s = v.subredit
FILTER a
FILTER s
LET fid = (INSERT {author: a} INTO authors RETURN NEW._id)[0]
LET tid = (INSERT {subredit: s} INTO subredits RETURN NEW._id)[0]
INSERT {_from: fid, _to: tid} INTO author_of
RETURN [fid, tid]
Trying to get some of my old code up and running in Pharo. Some method names are different but after some hardship I managed to find equivalents that work.
I am parsing my code and I'd like to check if the receiver or any of the arguments is aSymbol in an effort to match them to supported alternatives. I've managed to do this to selectors, by analysing RBMessageNode s
aNode selector == aSymbol ifTrue: [ aNode selector: replacementSymbol ].
How can this be done to arguments and receivers? Is there a comprehensive guide on RBParser somewhere?
By direct manipulation
Assuming that you are looking for cases like this:
aSymbol message: aSymbol message: aSymbol
For receiver you should do:
(aNode isMessage and: [
aNode receiver isVariable and: [
aNode receiver name = 'aSymbol' ]]) ifTrue: [
"do your job here" ]
Here is another example on how to replace #aSymbol arguments with #newSymbol:
messageNode arguments: (messageNode arguments collect: [ :arg |
(arg isLiteralNode and: [ arg value = #aSymbol ])
ifFalse: [ arg ]
ifTrue: [ | newNode |
newNode := #aNewSymbol asLiteralNode.
arg replaceSourceWith: newNode.
newNode ] ]).
methodClass compile: ast newSource
The replaceSourceWith: makes sure that just a source will be replaced, but for newSource to actually return a new source you also need to swap the nodes themselves, that's why I'm doing a collect on arguments and return the new ones where needed.
You can view help about RBParser in Word Menu > Help > Help Browser > Refactoring Framework.
You can also play around by inspecting
RBParser parseExpression: 'aSymbol message: aSymbol message: aSymbol'
and looking at its contents
By Parse Tree Transformation
You can use pattern code to match and replace certain code. For example to change the symbol argument of a perform: message you can do this:
ast := yourMethod parseTree.
rewriter := RBParseTreeRewriter new
replace: '`receiver perform: #aSymbol'
with: '`receiver perform: #newSelector'.
(rewriter executeTree: ast) ifTrue: [
yourMethod class compile: ast newSource ]
You can learn more about the pattern matching syntax in the help topic Word Menu > Help > Help Browser > Refactoring Framework > Refactoring Engine > RBPatternParser …. I thing that MatchTool from pharo catalog can greatly help you in testing the match expressions (it also has a dedicated help topic about the matching syntax) while RewriteTool can help you to preview how your code will be transformed.