I need to calculate shortest paths with conditions (conditions may be very hard). I've tried next sample query but it takes me a lot of time
FOR p IN ANY K_SHORTEST_PATHS
'graph_vertices/1' TO 'graph_vertices/2'
graph_edges
OPTIONS {
weightAttribute: 'weight',
defaultWeight: 1
}
FILTER p.edges[*]._key NONE IN ['736023', '659112', '695090', '731912', '680907', '699903']
LIMIT 3
RETURN {name: CONCAT_SEPARATOR(' -> ', p.edges[*]._key), weight: sum(p.edges[*].weight)}
Is there any way to make pre filtration? I mean filter edges collection and then start finding shortest path. May be some other tips which can help me?
p.s. I can't use traversal because I don't know about length of path and also I need shortest paths according to weights.
Discussed in slack, arangodb team confirmed that this is currently not supported, created feature request in https://github.com/arangodb/arangodb/issues/10957
Related
I am trying to perform a basic merge operation to add nonexistent nodes and relationships to my graph by going through a csv file row by row. I'm using py2neo v4, and because there is basically no documentation or examples of how to use py2neo, I can't figure out how to actually get it done. This isn't my real code (it's very complicated to handle many different cases) but its structure is basically like this:
import py2neo as pn
graph = pn.Graph("bolt://localhost:###/", user="neo4j", password="py2neoSux")
matcher = pn.NodeMatcher(graph)
tx = graph.begin()
if (matcher.match("Prefecture", name="foo").first()) == None):
previousNode = pn.Node("Type1", name="fo0", yc=1)
else:
previousNode = matcher.match("Prefecture", name="foo").first())
thisNode = pn.Node("Type2", name="bar", yc=1)
tx.merge(previousNode)
tx.merge(thisNode)
theLink = pn.Relationship(thisNode, "PARTOF", previousNode)
tx.merge(theLink)
tx.commit()
Currently this throws the error
ValueError: Primary label and primary key are required for MERGE operation
the first time it needs to merge a node that it hasn't found (i.e., when creating a node). So then I change the line to:
tx.merge(thisNode,primary_label=list(thisNode.labels)[0], primary_key="name")
Which gives me the error IndexError: list index out of range from somewhere deep in the py2neo source code (....site-packages\py2neo\internal\operations.py", line 168, in merge_subgraph at node = nodes[i]). I tried to figure out what was going wrong there, but I couldn't decipher where the nodes list come from through various connections to other commands.
So, it currently matches and creates a few nodes without problem, but at some point it will match until it needs to create and then fails in trying to create that node (even though it is using the same code and doing the same thing under the same circumstances in a loop). It made it through all 20 rows in my sample once, but usually stops on the row 3-5.
I thought it had something to do with the transactions (see comments), but I get the same problem when I merge directly on the graph. Maybe it has to do with the py2neo merge function finding more identities for nodes than nodes. Maybe there is something wrong with how I specified my primarily label and/or key.
Because this error and code are opaque I have no idea how to move forward.
Anybody have any advice or instructions on merging nodes with py2neo?
Of course I'd like to know how to fix my current problem, but more generally I'd like to learn how to use this package. Examples, instructions, real documentation?
I am having a similar problem and just got done ripping my hair out to figure out what was wrong! SO! What I learned was that at least in my case.. and maybe yours too since we got similar error messages and were doing similar things. The problem lied for me in that I was trying to create a Node with a __primarykey__ field that had a different field name than the others.
PSEUDO EXAMPLE:
# in some for loop or complex code
node = Node("Example", name="Test",something="else")
node.__primarykey__ = "name"
<code merging or otherwise creating the node>
# later on in the loop you might have done something like this cause the field was null
node = Node("Example", something="new")
node.__primarykey__ = "something"
I hope this helps and was clear I'm still recovering from wrapping my head around things. If its not clear let me know and I'll revise.
Good luck.
I'm currently working on a behavioral targeting application and I need a considerably large keyword database/tool/provider that enables applications to reach to the similar keywords via given keyword for my app. I've recently found that Freebase, which had been providing a similar service before Google acquired them and then integrated to their Knowledge Graph. I was wondering if it's possible to have a list of related topics/keywords for the given entity.
import json
import urllib
api_key = 'API_KEY_HERE'
query = 'Yoga'
service_url = 'https://kgsearch.googleapis.com/v1/entities:search'
params = {
'query': query,
'limit': 10,
'indent': True,
'key': api_key,
}
url = service_url + '?' + urllib.urlencode(params)
response = json.loads(urllib.urlopen(url).read())
for element in response['itemListElement']:
print element['result']['name'] + ' (' + str(element['resultScore']) + ')'
The script above returns the queries below, though I'd like to receive related topics to yoga, such as health, fitness, gym and so on, rather than the things that has the word "Yoga" in their name.
Yoga Sutras of Patanjali (71.245544)
Yōga, Tokyo (28.808222)
Sri Aurobindo (28.727333)
Yoga Vasistha (28.637642)
Yoga Hosers (28.253984)
Yoga Lin (27.524054)
Patanjali (27.061115)
Yoga Journal (26.635073)
Kripalu Center (26.074436)
Yōga Station (25.10318)
I'd really appreciate any suggestions, and I'm also open to using any other API if there is any that I could make use of. Cheers.
See your point:) So here's the script I use for that using Serpstat's API. Here's how it works:
Script collects the keywords from Serpstat's database
Then, collects search suggestions from Serpstat's database
Finally, collects search suggestions from Google's suggestions
Note that to make script work correctly, it's preferable to fill all input boxes. But not all of them are required.
Keyword — required keyword
Search Engine — a search engine for which the analysis will be carried out. For example, for the US Google, you need to set the g_us. The entire list of available search engines can be found here.
Limit the maximum number of phrases from the organic issue, which will participate in the analysis. You cannot set more than 1000 here.
Default keys — list of two-word keywords. You should give each of them some "weight" to receive some kind of result if something goes wrong.
Format: type, keyword, "weight". Every keyword should be written from a new line.
Types:
w — one word
p — two words
Examples:
"w; bottle; 50" — initial weight of word bottle is 50.
"p; plastic bottle; 30" — initial weight of phrase plastic bottle is 30.
"w; plastic bottle; 20" — incorrect. You cannot use a two-word phrase for the "w" type.
Bad words — comma-separated list of words you want the script to exclude from the results.
Token — here you need to enter your token for API access. It can be found on your profile page.
You can download the source code for script here
Currently, I made a tool to rename view numbers (“Detail Number”) on a sheet based on their location on the sheet. Where this is breaking is the transactions. Im trying to do two transactions sequentially in Revit Python Shell. I also did this originally in dynamo, and that had a similar fail , so I know its something to do with transactions.
Transaction #1: Add a suffix (“-x”) to each detail number to ensure the new numbers won’t conflict (1 will be 1-x, 4 will be 4-x, etc)
Transaction #2: Change detail numbers with calculated new number based on viewport location (1-x will be 3, 4-x will be 2, etc)
Better visual explanation here: https://www.docdroid.net/EP1K9Di/161115-viewport-diagram-.pdf.html
Py File here: http://pastebin.com/7PyWA0gV
Attached is the python file, but essentially what im trying to do is:
# <---- Make unique numbers
t = Transaction(doc, 'Rename Detail Numbers')
t.Start()
for i, viewport in enumerate(viewports):
setParam(viewport, "Detail Number",getParam(viewport,"Detail Number")+"x")
t.Commit()
# <---- Do the thang
t2 = Transaction(doc, 'Rename Detail Numbers')
t2.Start()
for i, viewport in enumerate(viewports):
setParam(viewport, "Detail Number",detailViewNumberData[i])
t2.Commit()
Attached is py file
As I explained in my answer to your comment in the Revit API discussion forum, the behaviour you describe may well be caused by a need to regenerate between the transactions. The first modification does something, and the model needs to be regenerated before the modifications take full effect and are reflected in the parameter values that you query in the second transaction. You are accessing stale data. The Building Coder provides all the nitty gritty details and numerous examples on the need to regenerate.
Summary of this entire thread including both problems addressed:
http://thebuildingcoder.typepad.com/blog/2016/12/need-for-regen-and-parameter-display-name-confusion.html
So this issue actually had nothing to do with transactions or doc regeneration. I discovered (with some help :) ), that the problem lied in how I was setting/getting the parameter. "Detail Number", like a lot of parameters, has duplicate versions that share the same descriptive param Name in a viewport element.
Apparently the reason for this might be legacy issues, though im not sure. Thus, when I was trying to get/set detail number, it was somehow grabbing the incorrect read-only parameter occasionally, one that is called "VIEWER_DETAIL_NUMBER" as its builtIn Enumeration. The correct one is called "VIEWPORT_DETAIL_NUMBER". This was happening because I was trying to get the param just by passing the descriptive param name "Detail Number".Revising how i get/set parameters via builtIn enum resolved this issue. See images below.
Please see pdf for visual explanation: https://www.docdroid.net/WbAHBGj/161206-detail-number.pdf.html
I know there are a lot of questions about this issue, but I've reached a point where I can't really do anything else but to ask if somebody else has a solution for this issue...
Using the Foursquare api explorer to test out my query I can't seem to obtain an accurate or even good fit for the data I need to obtain.
It is quite simple. I need to obtain the closest venue from a set of coordinates. I don't mind not having results if nothing is found near by.
So, reading the API documentation (https://developer.foursquare.com/docs/venues/venues) I conclude that I need a search and not an explore because I don't want sugestions of recommended venues (and the results when I tested it proved that it wasn't what I was expecting).
So, using search api I want to find places (the place, but places would do...) close to these coordinates
ll=37.424782,-122.162989
considering that I want places close by, I add
radius=51
and I don't really want many results
limit=2
from the documentation I see that radius is
Only valid for requests with intent=browse, or requests with
intent=checkin and categoryId or query
so, I use
intent=browse
which concludes my query to:
venues/search?intent=browse&ll=37.424782,-122.162989&radius=51&limit=2
Query Result:
https://developer.foursquare.com/docs/explore#req=venues/search%3Fintent%3Dbrowse%26ll%3D37.424782,-122.162989%26radius%3D51%26limit%3D2
Here we can see that the first result is straight outside of the radius ... distance: 135
the second result however is cool ... distance: 50
What am I doing wrong to get these results? If I increase the limit all I get is more results that are also outside the radius, I could iterate through them and find the one with the smallest distance... but I have no guarantee that the closest result will be on the top X that I limit, even If I had that guarantee, it would be a tiresome solution to an apparently simple question...
Thanks for the help...
Marc
EDIT:
I managed to make have the query perform as I intended ... But I had to add all of the parent categories from:
https://developer.foursquare.com/categorytree
categoryId=
4d4b7104d754a06370d81259, Arts & Entertainment
4d4b7105d754a06372d81259, College & University
4d4b7105d754a06373d81259, Event
4d4b7105d754a06374d81259, Food
4d4b7105d754a06376d81259, Nightlife Spot
4d4b7105d754a06377d81259, Outdoors & Recreation
4d4b7105d754a06375d81259, Professional & Other Places
4e67e38e036454776db1fb3a, Residence
4d4b7105d754a06378d81259, Shop & Service
4d4b7105d754a06379d81259 Travel & Transport
making my query into:
venues/search?
intent=checkin&ll=37.424782,-122.162989&radius=60&categoryId=4d4b7104d754a06370d81259,4d4b7105d754a06372d81259,4d4b7105d754a06373d81259,4d4b7105d754a06374d81259,4d4b7105d754a06376d81259,4d4b7105d754a06377d81259,4d4b7105d754a06375d81259,4e67e38e036454776db1fb3a,4d4b7105d754a06378d81259,4d4b7105d754a06379d81259
Query Result:
https://developer.foursquare.com/docs/explore#req=venues/search%3Fintent%3Dcheckin%26ll%3D37.424782,-122.162989%26radius%3D60%26categoryId%3D4d4b7104d754a06370d81259,4d4b7105d754a06372d81259,4d4b7105d754a06373d81259,4d4b7105d754a06374d81259,4d4b7105d754a06376d81259,4d4b7105d754a06377d81259,4d4b7105d754a06375d81259,4e67e38e036454776db1fb3a,4d4b7105d754a06378d81259,4d4b7105d754a06379d81259
It still has results outside of my radius still ... but it's an acceptable error margin ... it is weird however.
I managed to make have the query perform as I intended ... But I had to add all of the parent categories from:
https://developer.foursquare.com/categorytree
categoryId=
4d4b7104d754a06370d81259, Arts & Entertainment
4d4b7105d754a06372d81259, College & University
4d4b7105d754a06373d81259, Event
4d4b7105d754a06374d81259, Food
4d4b7105d754a06376d81259, Nightlife Spot
4d4b7105d754a06377d81259, Outdoors & Recreation
4d4b7105d754a06375d81259, Professional & Other Places
4e67e38e036454776db1fb3a, Residence
4d4b7105d754a06378d81259, Shop & Service
4d4b7105d754a06379d81259 Travel & Transport
making my query into: venues/search?
intent=checkin&ll=37.424782,-122.162989&radius=60&categoryId=4d4b7104d754a06370d81259,4d4b7105d754a06372d81259,4d4b7105d754a06373d81259,4d4b7105d754a06374d81259,4d4b7105d754a06376d81259,4d4b7105d754a06377d81259,4d4b7105d754a06375d81259,4e67e38e036454776db1fb3a,4d4b7105d754a06378d81259,4d4b7105d754a06379d81259
Query Result: https://developer.foursquare.com/docs/explore#req=venues/search%3Fintent%3Dcheckin%26ll%3D37.424782,-122.162989%26radius%3D60%26categoryId%3D4d4b7104d754a06370d81259,4d4b7105d754a06372d81259,4d4b7105d754a06373d81259,4d4b7105d754a06374d81259,4d4b7105d754a06376d81259,4d4b7105d754a06377d81259,4d4b7105d754a06375d81259,4e67e38e036454776db1fb3a,4d4b7105d754a06378d81259,4d4b7105d754a06379d81259
It still has results outside of my radius still ... but it's an acceptable error margin ... it is weird however.
Although this question is old I'm responding for others. I was working on something similar to this recently and what I learned was that in order to use radius you also need to use the 'query' parameter. What I did was to use the star character '*' and it worked for me. I have to say though that the limit of 50 is something I haven't solved yet which I'm working on at the moment.
Here is the situation:
The first problem I'm having is with obtaining information from a CSV file. The purpose of the code I'm writing is to get a bunch of information on ZCTAs (zip codes), for a number of different cohorts (there are six currently being used, but the code is meant to be flexible to have any number of cohorts). One file contains the population, by cohort, for each ZCTA. Another file has the number of 'cases' (cases of cancer observed) for each cohort, for each ZCTA. Another file has the crude rate for each cohort, for the state of Iowa (the focus of this research), for the rate at which one can 'expect' to see the number of people who have cancer, for a population, by cohort. There are a couple of other files, but these are the focus, as this is where my issue is exhibited.
What my code does, initially, is to read the population file and get the population of each cohort by ZCTA. Each ZCTA, and the information, is stored in a list, which is then stored in a list of lists (nested), containing all of the ZCTAs. The code then gets the crude rate. Then, the crude rate is taken times the appropriate cohort, for each ZCTA and summed with all of the other cohorts within each ZCTA, to get the total number of people we can EXPECT to see having cancer, for each ZCTA. The population is also summed up. This information is stored in a another list, as well as a list containing all of the ZCTAs. This information will be the focus (The list of all of the ZCTAs, which each contain the total population and the total number of expected cases).
So, the problem is that I then need to take this newly acquired list and get the number of OBSERVED cases, for each cohort, sum those together, append it to the appropriate ZCTA and write it to a new file. I have code implemented that does this fine, EXCEPT that the bottom 22 or so ZCTAs don't get the number of observed cases. I don't know if it is the code, or what, but it works for all of the other 906, but doesn't get the bottom 22.
The reader will find sample data for the files I've discussed (the observed case file, and the output file) at: Gist
Here is the code I'm using:
`expectedcsv = open('ExpectedCases.csv', 'w', newline= '')
expectedwriter = csv.writer(expectedcsv, delimiter = ',')
expectedHeader = ['zcta', 'expected', 'pop', 'observed']
thecasesreader = csv.reader(thecasescsv, delimiter = ',')
for zcta in zctaPop:
caseCounter = 0
thecasescsv = open('NewCaseFile.csv', 'r', newline = '')
thecasesreader = csv.reader(thecasescsv, delimiter = ',')
for case in thecasesreader:
if case[0] == zcta[0]:
for i in range(3, len(case)):
caseCounter += int(case[i])
zcta.append(caseCounter)
expectedwriter.writerow(zcta)
expectedcsv.close()
thecasescsv.close()`
Something else I would also like to bring up is that later on in the code, the actual purpose for all of this, is to create an SMR filter, for each grid point. The grid points are somewhat arbitrary they have been placed (via coordinates) over the entire state of Iowa. The SMR is the number of observed divided by the number of expected cases. The threshold, that is, how many expected cases for a particular filter, is set by the user. So, if a user wants a filter created on 150 expected cases (for each grid point), the code goes through each ZCTA, summing up the expected cases until greater than 150 are found. The distance to this last ZCTA is the 'radius' of the filter.
To do this, I built a distance matrix (the distance from each grid point to every ZCTA) and then sorted it, nearest to furthest. Because of the size of the file (2300 X 930), I have to read this file line by line and get all of the information from other files. So, starting with the nearest ZCTA, I get the population, expected cases, and observed cases (the problem with this file was discussed above) and add these each to their respective counter (one for population, one for observed and one for expected). Then it goes to the next closest ZCTA and does the same, until the the threshold is exceeded.
The problem here is that I couldn't use the CSV Module to read these files, as I was already reading from another file and the index would be lost. So, I had to use just the regular filename.read(), which then required some interesting use of maketrans and .translate. I'm not sure its efficient or works great. Everything seems to be fine, but without the above problem being fixed, it's impossible to tell. I have included the code below, but was wondering if anybody had any better ideas/suggestions?
`expectedCSV = open('ExpectedCases.csv', 'r', newline = '')
table = str.maketrans('\r', ' ')
content = expectedCSV.read()
expectedCSV.close()
content = content.translate(table)
content = content.split(sep = '\n')
newContent = []
for item in content:
newContent.append((item.split(sep= ',')))
content = ' '
for item in newContent:
if item[0] == currentZcta:
expectedTotal += (float(item[1]))
totalPop += (float(item[2]))
totalObservedCount += (float(item[3]))`
Also, I couldn't figure out how to color the methods blue and the variables red, as some of the more awesome users of this site do. I would be very much interested in learning how to do that for future posts.
If anybody needs more info or anything clarified to help answer/formulate a solution, please, by all means, ask! Thanks for taking the time to read!
So, I ended up "solving" this by computing the observed along with the expected and population, by opening the file for each ZCTA computed. This did not really solve the issue I was dealing with, but rather found a way around it. I'm somewhat disappointed that more people didn't view and/or respond to this. If someone comes up with an answer to the actual problem, by all means, post it here. -Mike