Develop Reference

Querying objects using attribute of member of many-to-many

I have the following models:
class Member(models.Model):
ref = models.CharField(max_length=200)
# some other stuff
def __str__(self):
return self.ref
class Feature(models.Model):
feature_id = models.BigIntegerField(default=0)
members = models.ManyToManyField(Member)
# some other stuff
A Member is basically just a pointer to a Feature. So let's say I have Features:
feature_id = 2, members = 1, 2
feature_id = 4
feature_id = 3
Then the members would be:
id = 1, ref = 4
id = 2, ref = 3
I want to find all of the Features which contain one or more Members from a list of "ok members." Currently my query looks like this:
# ndtmp is a query set of member-less Features which Members can point to
sids = [str(i) for i in list(ndtmp.values('feature_id'))]
# now make a query set that contains all rels and ways with at least one member with an id in sids
okmems = Member.objects.filter(ref__in=sids)
relsways = Feature.geoobjects.filter(members__in=okmems)
# now combine with nodes
op = relsways | ndtmp
This is enormously slow, and I'm not even sure if it's working. I've tried using print statements to debug, just to make sure anything is actually being parsed, and I get the following:
print(ndtmp.count())
>>> 12747
print(len(sids))
>>> 12747
print(okmems.count())
... and then the code just hangs for minutes, and eventually I quit it. I think that I just overcomplicated the query, but I'm not sure how best to simplify it. Should I:
Migrate Feature to use a CharField instead of a BigIntegerField? There is no real reason for me to use a BigIntegerField, I just did so because I was following a tutorial when I began this project. I tried a simple migration by just changing it in models.py and I got a "numeric" value in the column in PostgreSQL with format 'Decimal:( the id )', but there's probably some way around that that would force it to just shove the id into a string.
Use some feature of Many-To-Many Fields which I don't know abut to more efficiently check for matches
Calculate the bounding box of each Feature and store it in another column so that I don't have to do this calculation every time I query the database (so just the single fixed cost of calculation upon Migration + the cost of calculating whenever I add a new Feature or modify an existing one)?
Or something else? In case it helps, this is for a server-side script for an ongoing OpenStreetMap related project of mine, and you can see the work in progress here.
EDIT - I think a much faster way to get ndids is like this:
ndids = ndtmp.values_list('feature_id', flat=True)
This works, producing a non-empty set of ids.
Unfortunately, I am still at a loss as to how to get okmems. I tried:
okmems = Member.objects.filter(ref__in=str(ndids))
But it returns an empty query set. And I can confirm that the ref points are correct, via the following test:
Member.objects.values('ref')[:1]
>>> [{'ref': '2286047272'}]
Feature.objects.filter(feature_id='2286047272').values('feature_id')[:1]
>>> [{'feature_id': '2286047272'}]

You should take a look at annotate:
okmems = Member.objects.annotate(
feat_count=models.Count('feature')).filter(feat_count__gte=1)
relsways = Feature.geoobjects.filter(members__in=okmems)

Ultimately, I was wrong to set up the database using a numeric id in one table and a text-type id in the other. I am not very familiar with migrations yet, but as some point I'll have to take a deep dive into that world and figure out how to migrate my database to use numerics on both. For now, this works:
# ndtmp is a query set of member-less Features which Members can point to
# get the unique ids from ndtmp as strings
strids = ndtmp.extra({'feature_id_str':"CAST( \
feature_id AS VARCHAR)"}).order_by( \
'-feature_id_str').values_list('feature_id_str',flat=True).distinct()
# find all members whose ref values can be found in stride
okmems = Member.objects.filter(ref__in=strids)
# find all features containing one or more members in the accepted members list
relsways = Feature.geoobjects.filter(members__in=okmems)
# combine that with my existing list of allowed member-less features
op = relsways | ndtmp
# prove that this set is not empty
op.count()
# takes about 10 seconds
>>> 8997148 # looks like it worked!
Basically, I am making a query set of feature_ids (numerics) and casting it to be a query set of text-type (varchar) field values. I am then using values_list to make it only contain these string id values, and then I am finding all of the members whose ref ids are in that list of allowed Features. Now I know which members are allowed, so I can filter out all the Features which contain one or more members in that allowed list. Finally, I combine this query set of allowed Features which contain members with ndtmp, my original query set of allowed Features which do not contain members.

List Matching in Python using nested for loops

I have three lists, (1) treatments (2) medicine name and (3) medicine code symbol. I am trying to identify the respective medicine code symbol for each of 14,700 treatments. My current approach is to identify if any name in (2) is "in" (1), and then return the corresponding (3). However, I am returned an abitrary list (correct length) of medicine code symbols corresponding to the 14,700 treatments. Code for the method I've written is below:
codes = pandas.read_csv('Codes.csv', dtype=str)
codes_list = _codes.values.tolist()
names = pandas.read_csv('Names.csv', dtype=str)
names_list = names.values.tolist()
treatments = pandas.read_csv('Treatments.csv', dtype=str)
treatments_list = treatments.values.tolist()
matched_codes_list = range(len(treatments_list))
for i in range(len(treatments_list)):
for j in range(len(names_list)):
if names_list[j] in treatments_list[i]:
matched_codes_list[i]=codes_list_text[j]
print matched_codes_list
Any suggestions for where I am going wrong would be much appreciated!

I can't tell what you are expecting. You should replace the xxx_list code with examples instead, since you don't seem to have any problems with the csv reading.
Let's suppose you did that, and your result looks like this.
codes_list = ['shark', 'panda', 'horse']
names_list = ['fin', 'paw', 'hoof']
assert len(codes_list) == len(names_list)
treatments_list = ['tape up fin', 'reverse paw', 'stand on one hoof', 'pawn affinity maneuver', 'alert wing patrol']
it sounds like you are trying to determine the 'code' for each 'treatment', assuming that the number of codes and names are the same (and indicate some mapping). You plan to use the presence of the name to determine the code.
we can zip together the name and codes list to avoid using indexes there, and we can use iteration over the treatment list instead of indexes for pythonic readability
matched_codes_list = []
for treatment in treatment:
matched_codes = []
for name, code in zip(names_list, codes_list):
if name in treatment:
matched_codes.append(code)
matched_codes_list.append(matched_codes)
this would give something like
assert matched_codes_list == [
['shark'], # 'tape up fin'
['panda'], # 'reverse paw'
['horse'], # 'stand on one hoof'
['shark', 'panda', 'horse'], # 'pawn affinity maneuver'
[], # 'alert wing patrol'
]
note that the method used to do this is quite slow (and probably will give false positives, see 4th entry). You will traverse the text of all treatment descriptions once for each name/code pair.
You can use a dictionary like 'lookup = {name: code for name, code in zip(names_list, codes_list)}, or itertools.izip for minor gains. Otherwise something more clever might be needed, perhaps splitting treatments into a set containing words, or mapping words into multiple codes.

py2neo how to retrieve a node based on node's property?

I've found related methods:
find - doesn't work because this version of neo4j doesn't support labels.
match - doesn't work because I cannot specify a relation, because the node has no relations yet.
match_one - same as match.
node - doesn't work because I don't know the id of the node.
I need an equivalent of:
start n = node(*) where n.name? = "wvxvw" return n;
Cypher query. Seems like it should be basic, but it really isn't...
PS. I'm opposed to using Cypher for too many reasons to mention. So that's not an option either.

Well, you should create indexes so that your start nodes are reduced. This will be automatically taken care of with the use of labels, but in the meantime, there can be a work around.
Create an index, say "label", which will have keys pointing to the different types of nodes you will have (in your case, say 'Person')
Now while searching you can write the following query :
START n = node:label(key_name='Person') WHERE n.name = 'wvxvw' RETURN n; //key_name is the key's name you will assign while creating the node.

user797257 seems to be out of the game, but I think this could still be useful:
If you want to get nodes, you need to create an index. An index in Neo4j is the same as in MySQL or any other database (If I understand correctly). Labels are basically auto-indexes, but an index offers additional speed. (I use both).
somewhere on top, or in neo4j itself create an index:
index = graph_db.get_or_create_index(neo4j.Node, "index_name")
Then, create your node as usual, but do add it to the index:
new_node = batch.create(node({"key":"value"}))
batch.add_indexed_node(index, "key", "value", new_node)
Now, if you need to find your new_node, execute this:
new_node_ref = index.get("key", "value")
This returns a list. new_node_ref[0] has the top item, in case you want/expect a single node.

use selector to obtain node from the graph
The following code fetches the first node from list of nodes matching the search
selector = NodeSelector(graph)
node = selector.select("Label",key='value')
nodelist=list(node)
m_node=node.first()

using py2neo, this hacky function will iterate through the properties and values and labels gradually eliminating all nodes that don't match each criteria submitted. The final result will be a list of all (if any) nodes that match all the properties and labels supplied.
def find_multiProp(graph, *labels, **properties):
results = None
for l in labels:
for k,v in properties.iteritems():
if results == None:
genNodes = lambda l,k,v: graph.find(l, property_key=k, property_value=v)
results = [r for r in genNodes(l,k,v)]
continue
prevResults = results
results = [n for n in genNodes(l,k,v) if n in prevResults]
return results
see my other answer for creating a merge_one() that will accept multiple properties...

Python sorting question - given list of ['url', 'tag1', 'tag2',..]s and search specification ['tag3', 'tag1',...], return relevant url list

I'm quite new to programming so I'm sure there's a terser way to pose this, but I'm trying to create a personal bookmarking program. Given multiple urls each with a list of tags ordered by relevance, I want to be able to create a search consisting of a list of tags that returns a list of most relevant urls. My first solution, below, is to give the first tag a value of 1, the second 2, and so on & let the python list sort function do the rest. 2 questions:
1) Is there a much more elegant/efficient way of doing this (embarrass me!)
2) Any other general approaches to the sorting by relevance given the inputs above problem?
Much obliged.
# Given a list of saved urls each with a corresponding user-generated taglist
# (ordered by relevance), the user enters a "search" list-of-tags, and is
# returned a sorted list of urls.
# Generate sample "content" linked-list-dictionary. The rationale is to
# be able to add things like 'title' etc at later stages and to
# treat each url/note as in independent entity. But a single dictionary
# approach like "note['url1']=['b','a','c','d']" might work better?
content = []
note = {'url':'url1', 'taglist':['b','a','c','d']}
content.append(note)
note = {'url':'url2', 'taglist':['c','a','b','d']}
content.append(note)
note = {'url':'url3', 'taglist':['a','b','c','d']}
content.append(note)
note = {'url':'url4', 'taglist':['a','b','d','c']}
content.append(note)
note = {'url':'url5', 'taglist':['d','a','c','b']}
content.append(note)
# An example search term of tags, ordered by importance
# I'm using a dictionary with an ordinal number system
# This seems clumsy
search = {'d':1,'a':2,'b':3}
# Create a tagCloud with one entry for each tag that occurs
tagCloud = []
for note in content:
for tag in note['taglist']:
if tagCloud.count(tag) == 0:
tagCloud.append(tag)
# Create a dictionary that associates an integer value denoting
# relevance (1 is most relevant etc) for each existing tag
d={}
for tag in tagCloud:
try:
d[tag]=search[tag]
except KeyError:
d[tag]=100
# Create a [[relevance, tag],[],[],...] result list & sort
result=[]
for note in content:
resultNote=[]
for tag in note['taglist']:
resultNote.append([d[tag],tag])
resultNote.append(note['url'])
result.append(resultNote)
result.sort()
# Remove the relevance values & recreate a list containing
# the url string followed by corresponding tags.
# Its so hacky i've forgotten how it works!
# It's mostly for display, but suggestions on "best-practice"
# intermediate-form data storage?
finalResult=[]
for note in result:
temp=[]
temp.append(note.pop())
for tag in note:
temp.append(tag[1])
finalResult.append(temp)
print "Content: ", content
print "Search: ", search
print "Final Result: ", finalResult

1) Is there a much more elegant/efficient way of doing this (embarrass me!)
Sure thing. The basic idea: quit trying to tell Python what to do, and just ask it for what you want.
content = [
{'url':'url1', 'taglist':['b','a','c','d']},
{'url':'url2', 'taglist':['c','a','b','d']},
{'url':'url3', 'taglist':['a','b','c','d']},
{'url':'url4', 'taglist':['a','b','d','c']},
{'url':'url5', 'taglist':['d','a','c','b']}
]
search = {'d' : 1, 'a' : 2, 'b' : 3}
# We can create the tag cloud like this:
# tagCloud = set(sum((note['taglist'] for note in content), []))
# But we don't actually need it: instead, we'll just use a default value
# when looking things up in the 'search' dict.
# Create a [[relevance, tag],[],[],...] result list & sort
result = sorted(
[
[search.get(tag, 100), tag]
for tag in note['taglist']
] + [[note['url']]]
# The result will look like [ [relevance, tag],... , [url] ]
# Note that the url is wrapped in a list too. This makes the
# last processing step easier: we just take the last element of
# each nested list.
for note in content
)
# Remove the relevance values & recreate a list containing
# the url string followed by corresponding tags.
finalResult = [
[x[-1] for x in note]
for note in result
]
print "Content: ", content
print "Search: ", search
print "Final Result: ", finalResult

I suggest you also give a weight to each tag, depending on how rare it is (e.g. a “tarantula” tag would weigh more than a “nature” tag¹). For a given URL, rare tags that are common with other URLs should mark a stronger relevance, while frequently used tags of the given URL not existing in another URL should mark down the relevance.
It's easy to convert the rules I describe above as calculations of a numerical relevance for every other URL.
¹ unless all your URLs are related to “tarantulas”, of course :)

Best practices for manipulating database result sets in Python?

I am writing a simple Python web application that consists of several pages of business data formatted for the iPhone. I'm comfortable programming Python, but I'm not very familiar with Python "idiom," especially regarding classes and objects. Python's object oriented design differs somewhat from other languages I've worked with. So, even though my application is working, I'm curious whether there is a better way to accomplish my goals.
Specifics: How does one typically implement the request-transform-render database workflow in Python? Currently, I am using pyodbc to fetch data, copying the results into attributes on an object, performing some calculations and merges using a list of these objects, then rendering the output from the list of objects. (Sample code below, SQL queries redacted.) Is this sane? Is there a better way? Are there any specific "gotchas" I've stumbled into in my relative ignorance of Python? I'm particularly concerned about how I've implemented the list of rows using the empty "Record" class.
class Record(object):
pass
def calculate_pnl(records, node_prices):
for record in records:
try:
# fill RT and DA prices from the hash retrieved above
if hasattr(record, 'sink') and record.sink:
record.da = node_prices[record.sink][0] - node_prices[record.id][0]
record.rt = node_prices[record.sink][1] - node_prices[record.id][1]
else:
record.da = node_prices[record.id][0]
record.rt = node_prices[record.id][1]
# calculate dependent values: RT-DA and PNL
record.rtda = record.rt - record.da
record.pnl = record.rtda * record.mw
except:
print sys.exc_info()
def map_rows(cursor, mappings, callback=None):
records = []
for row in cursor:
record = Record()
for field, attr in mappings.iteritems():
setattr(record, attr, getattr(row, field, None))
if not callback or callback(record):
records.append(record)
return records
def get_positions(cursor):
# get the latest position time
cursor.execute("SELECT latest data time")
time = cursor.fetchone().time
hour = eelib.util.get_hour_ending(time)
# fetch the current positions
cursor.execute("SELECT stuff FROM atable", (hour))
# read the rows
nodes = {}
def record_callback(record):
if abs(record.mw) > 0:
if record.id: nodes[record.id] = None
return True
else:
return False
records = util.map_rows(cursor, {
'id': 'id',
'name': 'name',
'mw': 'mw'
}, record_callback)
# query prices
for node_id in nodes:
# RT price
row = cursor.execute("SELECT price WHERE ? ? ?", (node_id, time, time)).fetchone()
rt5 = row.lmp if row else None
# DA price
row = cursor.execute("SELECT price WHERE ? ? ?", (node_id, hour, hour)).fetchone()
da = row.da_lmp if row else None
# update the hash value
nodes[node_id] = (da, rt5)
# calculate the position pricing
calculate_pnl(records, nodes)
# sort
records.sort(key=lambda r: r.name)
# return the records
return records

The empty Record class and the free-floating function that (generally) applies to an individual Record is a hint that you haven't designed your class properly.
class Record( object ):
"""Assuming rtda and pnl must exist."""
def __init__( self ):
self.da= 0
self.rt= 0
self.rtda= 0 # or whatever
self.pnl= None #
self.sink = None # Not clear what this is
def setPnl( self, node_prices ):
# fill RT and DA prices from the hash retrieved above
# calculate dependent values: RT-DA and PNL
Now, your calculate_pnl( records, node_prices ) is simpler and uses the object properly.
def calculate_pnl( records, node_prices ):
for record in records:
record.setPnl( node_prices )
The point isn't to trivially refactor the code in small ways.
The point is this: A Class Encapsulates Responsibility.
Yes, an empty-looking class is usually a problem. It means the responsibilities are scattered somewhere else.
A similar analysis holds for the collection of records. This is more than a simple list, since the collection -- as a whole -- has operations it performs.
The "Request-Transform-Render" isn't quite right. You have a Model (the Record class). Instances of the Model get built (possibly because of a Request.) The Model objects are responsible for their own state transformations and updates. Perhaps they get displayed (or rendered) by some object that examines their state.
It's that "Transform" step that often violates good design by scattering responsibility all over the place. "Transform" is a hold-over from non-object design, where responsibility was a nebulous concept.

Have you considered using an ORM? SQLAlchemy is pretty good, and Elixir makes it beautiful. It can really reduce the ammount of boilerplate code needed to deal with databases. Also, a lot of the gotchas mentioned have already shown up and the SQLAlchemy developers dealt with them.

Depending on how much you want to do with the data you may not need to populate an intermediate object. The cursor's header data structure will let you get the column names - a bit of introspection will let you make a dictionary with col-name:value pairs for the row.
You can pass the dictionary to the % operator. The docs for the odbc module will explain how to get at the column metadata.
This snippet of code to shows the application of the % operator in this manner.
>>> a={'col1': 'foo', 'col2': 'bar', 'col3': 'wibble'}
>>> 'Col1=%(col1)s, Col2=%(col2)s, Col3=%(col3)s' % a
'Col1=foo, Col2=bar, Col3=wibble'
>>>

Using a ORM for an iPhone app might be a bad idea because of performance issues, you want your code to be as fast as possible. So you can't avoid boilerplate code. If you are considering a ORM, besides SQLAlchemy I'd recommend Storm.