def run(*dataspec, **query):
f = urllib.urlopen("http://api.census.gov/data/" + "/".join(dataspec) + "?" + urllib.urlencode(query))
response = tangelo.empty_response()
response['result'] = f.read()
return response
def run(database, table, start_time, end_time, center, degree, host="mongo", port="21000", fields="true"):
response = tangelo.empty_response()
try:
degree = int(degree)
except ValueError:
response["error"] = "argument 'degree' must be an integer"
return response
client = impala.ImpalaBeeswaxClient(host + ':' + port)
client.connect()
talkers = set([center])
distance = {center: 0}
current_talkers = list(talkers)
all_results = []
for i in range(degree):
query = build_query(database, table, start_time, end_time, current_talkers)
qResults = client.execute(query)
results = convert_results(qResults, "true")
current_talkers = list(itertools.chain(*map(lambda x: [x["emailto"], x["emailfrom"]], results)))
current_talkers = list(set(current_talkers))
talkers = talkers.union(current_talkers)
for t in current_talkers:
if t not in distance:
distance[t] = i+1
all_results.append(results)
talkers = list(talkers)
talker_index = {name: index for (index, name) in enumerate(talkers)}
all_results = itertools.chain(*all_results)
edges = []
ident = 0
for result in all_results:
source = result["emailfrom"]
target = result["emailto"]
ident += 1
rec = { "source": talker_index[source],
"target": talker_index[target],
"id": str(ident) }
edges.append(rec)
talkers = [{"email": n, "distance": distance[n]} for n in talkers]
response["result"] = { "nodes": talkers,
"edges": edges }
return response
def run(*dataspec, **query):
f = urllib.urlopen("http://api.census.gov/data/" + "/".join(dataspec) +
"?" + urllib.urlencode(query))
response = tangelo.empty_response()
response['result'] = f.read()
return response
def run(servername, dbname, datatype, by=None, datemin=None, datemax=None, charity=None):
# Construct an empty response object.
response = tangelo.empty_response()
# Establish a connection to the MongoDB server.
try:
conn = pymongo.Connection(servername)
except pymongo.errors.AutoReconnect as e:
response["error"] = "error: %s" % (e.message)
return bson.json_util.dumps(response)
# Extract the requested database and collection.
db = conn[dbname]
if datatype == "transactions":
coll = db["charitynet.normalized.transactions"]
conditions = [{"date": {"$ne": None}}]
if datemin != None and datemax != None:
date_min = datetime.datetime.strptime(datemin, "%Y-%m-%d")
date_max = datetime.datetime.strptime(datemax, "%Y-%m-%d")
conditions.append({"date": {"$gte": date_min}})
conditions.append({"date": {"$lt": date_max}})
if charity != None:
conditions.append({"charity_id": int(charity)})
pipeline = []
if len(conditions) > 0:
pipeline.append({"$match": {"$and": conditions}})
if by == "month":
group = {"year": {"$year": "$date"}, "month": {"$month": "$date"}}
else:
group = "$county"
pipeline.append({"$group": {"_id": group, "amount": {"$sum": "$amount"}}})
result = coll.aggregate(pipeline)
if by == "month":
response = [[d["_id"], float(d["amount"])] for d in result["result"] if d["_id"] != None]
else:
response = [["%05d" % d["_id"], float(d["amount"])] for d in result["result"] if d["_id"] != None]
elif datatype == "population":
coll = db["census"]
response = [[d["_id"], int(d["pop2010"])] for d in coll.find()]
elif datatype == "charities":
coll = db["charitynet.normalized.transactions"]
result = coll.aggregate([{"$group": {"_id": "$charity_id", "count": {"$sum": 1}}}, {"$sort": {"count": -1}}])
response = [[d["_id"], d["_id"], d["count"]] for d in result["result"]]
else:
response["error"] = "error: unknown datatype requested"
# Convert to JSON and return the result.
return bson.json_util.dumps(response)
def stream(self, key=None, action="next"):
if action != "show":
# Check for key parameter.
if key is None:
raise cherrypy.HTTPError("400 Required Query Parameter Missing", "The streaming API requires a 'key' query parameter")
# Check that the key actually exists.
if key not in self.streams:
raise cherrypy.HTTPError("404 Key Not Found", "The key '%s' does not reference any existing stream" % (key))
# Construct a container object.
result = tangelo.empty_response()
# Perform the requested action.
actions = ["next", "delete", "show"]
if action == "next":
# Grab the stream in preparation for running it.
stream = self.streams[key]
# Attempt to run the stream via its next() method - if this yields a
# result, then continue; if the next() method raises StopIteration,
# then there are no more results to retrieve; if any other exception
# is raised, this is treated as an error.
try:
result["stream_finished"] = False
result["result"] = stream.next()
except StopIteration:
result["stream_finished"] = True
del self.streams[key]
except:
del self.streams[key]
raise cherrypy.HTTPError("501 Error in Python Service", "Caught exception while executing stream service keyed by %s:<br><pre>%s</pre>" % (key, traceback.format_exc()))
elif action == "delete":
del self.streams[key]
result["result"] = "OK"
elif action == "show":
raise cherrypy.HTTPError("501 Unimplemented", "The 'show' action in the Tangelo streaming API has not yet been implemented")
else:
raise cherrypy.HTTPError("400 Bad Query Parameter", "The 'action' parameter must be one of: %s" % (", ".join(actions)))
try:
result = json.dumps(result)
except TypeError:
raise cherrypy.HTTPError("501 Bad Response from Python Service", "The stream keyed by %s returned a non JSON-seriazable result: %s" % (key, result["result"]))
return result
def run(servername, dbname, collname, file_hash=None, data=None):
# Construct an empty response object.
response = tangelo.empty_response();
# If no schema was passed in, give an error.
#
# TODO(choudhury): see comment below about error codes, etc.
if file_hash == None:
response['error'] = "no file hash"
return bson.json_util.dumps(response)
# Establish a connection to the MongoDB server.
try:
conn = pymongo.Connection(servername)
except pymongo.errors.AutoReconnect as e:
response['error'] = "error: %s" % (e.message)
return bson.json_util.dumps(response)
# Extract the requested database and collection.
db = conn[dbname]
coll = db[collname]
# If no data field was specified, treat this as a read request;
# otherwise, write the data to the database.
if data == None:
# Create a search schema for finding the record with the appropriate
# hash.
schema = {'file_hash' : file_hash}
# Apply the schema to retrieve documents.
response['result'] = [d for d in coll.find(schema)]
else:
# Convert the JSON object "data" to a Python object.
try:
pydata = bson.json_util.loads(data)
except ValueError as e:
response['error'] = e.message
return bson.json_util.dumps(response)
# Apply the schema to an insert request.
coll.insert({'file_hash': file_hash, 'data': data})
# Return a success code.
response['result'] = "ok"
# Convert to JSON and return the result.
return bson.json_util.dumps(response)
def run(servername, dbname, datatype, datemin = "2012-01-01", datemax = "2012-02-01"):
# Construct an empty response object.
response = tangelo.empty_response();
# Establish a connection to the MongoDB server.
try:
conn = pymongo.Connection(servername)
except pymongo.errors.AutoReconnect as e:
response['error'] = "error: %s" % (e.message)
return bson.json_util.dumps(response)
# Extract the requested database and collection.
db = conn[dbname]
if datatype == "full":
# Output number of donors per county. Use a string prefixed by "0" if needed
# for the county code to output state codes less than 10 correctly.
coll = db["charitynet.normalized.donors.counties"]
result = coll.find()
# Do not use normal 'result' field so the URL be used directly in a vega
# specification.
response = [["%05d" % d['_id'], int(d['value'])] for d in result if d['_id'] != None]
elif datatype == "bycounty":
coll = db["charitynet.normalized.transactions"]
result = coll.aggregate([{"$group": {"_id": "$county", "amount": {"$sum": "$amount"}}}])
response = [["%05d" % d['_id'], float(d['amount'])] for d in result["result"] if d["_id"] != None]
elif datatype == "bydate":
date_min = datetime.datetime.strptime(datemin, "%Y-%m-%d")
date_max = datetime.datetime.strptime(datemax, "%Y-%m-%d")
coll = db["charitynet.normalized.transactions"]
query = {"$and": [{"date": {"$gte": date_min}}, {"date": {"$lt": date_max}}]}
group = {"_id": "$county", "amount": {"$sum": "$amount"}}
result = coll.aggregate([{"$match": query}, {"$group": group}])
response = [["%05d" % d['_id'], float(d['amount'])] for d in result["result"] if d["_id"] != None]
elif datatype == "population":
coll = db["census"]
response = [[d["_id"], int(d["pop2010"])] for d in coll.find()]
else:
response['error'] = "error: unknown datatype requested"
# Convert to JSON and return the result.
return bson.json_util.dumps(response)
def run(text=""):
# Create an empty result container.
response = empty_response();
response['result'] = [];
# If nothing passed in, return an empty result.
if text == "":
return response
# Otherwise, perform named entity recognition.
sentences = nltk.sent_tokenize(text)
chunks = [nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(s))) for s in sentences]
# Now find all tagged chunks that are not whole sentences - gather the leaves of such
# chunks into strings, and place them in the list of named entities.
for c in chunks:
for subtree in filter(lambda x: x.node != 'S', c.subtrees()):
response['result'].append( (subtree.node, ' '.join(map(lambda x: x[0], subtree.leaves())) ) )
return response
def run(servername, dbname, collname, name=None, data=None, code=None):
# Construct an empty response object.
response = tangelo.empty_response()
# If no schema was passed in, give an error.
#
# TODO(choudhury): see comment below about error codes, etc.
if name == None:
response['error'] = "no name"
return bson.json_util.dumps(response)
# Establish a connection to the MongoDB server.
try:
conn = pymongo.Connection(servername)
except pymongo.errors.AutoReconnect as e:
response['error'] = "error: %s" % (e.message)
return bson.json_util.dumps(response)
# Extract the requested database and collection.
db = conn[dbname]
coll = db[collname]
# If no data field was specified, treat this as a read request;
# otherwise, write the data to the database.
if data == None and code == None:
# Create a search schema for finding the record with the appropriate
# hash.
schema = {'_id': name}
# Apply the schema to retrieve documents.
response['result'] = [d for d in coll.find(schema)]
else:
# Apply the schema to an insert request.
coll.save({'_id': name, 'data': data, 'code': code})
# Return a success code.
response['result'] = "ok"
# Convert to JSON and return the result.
return bson.json_util.dumps(response)
def run(server, db, coll, method="find", query=None, limit=1000, fields=None, sort=None, fill=None):
# Create an empty response object.
response = tangelo.empty_response()
# Check the requested method.
if method not in ["find", "insert"]:
response["error"] = "Unsupported MongoDB operation '%s'" % (method)
return bson.json_util.dumps(response)
# Decode the query strings into Python objects.
try:
if query is not None:
query = decode(query, "query", response)
if fields is not None:
fields = decode(fields, "fields", response)
if sort is not None:
sort = decode(sort, "sort", response)
if fill is not None:
fill = decode(fill, "fill", response)
else:
fill = True
except ValueError:
return bson.json_util.dumps(response)
# Cast the limit value to an int.
try:
limit = int(limit)
except ValueError:
response["error"] = "Argument 'limit' ('%s') could not be converted to int." % (limit)
return bson.json_util.dumps(response)
# Create database connection.
try:
c = pymongo.Connection(server)[db][coll]
except pymongo.errors.AutoReconnect:
response["error"] = "Could not connect to MongoDB server '%s'" % (server)
return bson.json_util.dumps(response)
# Perform the requested action.
if method == "find":
# Do a find operation with the passed arguments.
it = c.find(spec=query, fields=fields, limit=limit, sort=sort)
# Create a list of the results.
if fill:
results = [x for x in it]
else:
results = []
# Create an object to structure the results.
retobj = {}
retobj["count"] = it.count()
retobj["data"] = results
# Pack the results into the response object, and return it.
response["result"] = retobj
else:
raise RuntimeError("illegal method '%s' in module 'mongo'")
# Return the response object.
return bson.json_util.dumps(response)
def run(servername,
dbname,
data_coll,
name=None,
objectid=None,
_id=None,
accession=None,
scientific_name=None,
noid=False,
noloc=False,
maxdepth=100):
def recursiveHelper(child, depth=0):
it = c.find({'_id': child})
phylo = it[0]
if 'clades' in phylo:
counter = 0
for child in phylo['clades']:
if depth >= maxdepth:
phylo['clades'][counter] = str(child)
else:
phylo['clades'][counter] = recursiveHelper(
child, depth + 1)
counter += 1
if noid:
del phylo['_id']
else:
phylo['_id'] = str(phylo['_id'])
if 'loc' in phylo:
if noloc:
del phylo['loc']
return phylo
# Construct an empty response object.
response = tangelo.empty_response()
query = dict()
# Decode the query strings into Python objects.
try:
if name is not None:
decodeAndAdd(name, query, 'sequences.name', response)
if objectid is not None:
decodeAndAdd(ObjectId(objectid), query, 'objectid', response)
if _id is not None: decodeAndAdd(_id, query, '_id', response)
if accession is not None:
decodeAndAdd(accession, query, 'sequences.accession.source',
response)
if scientific_name is not None:
decodeAndAdd(scientific_name, query, 'taxonomies.scientific_name',
response)
except ValueError:
return bson.json_util.dumps(response)
# Cast the maxdepth value to an int.
try:
maxdepth = int(maxdepth)
except ValueError:
response[
'error'] = "Argument 'limit' ('%s') could not be converted to int." % (
maxdepth)
return bson.json_util.dumps(response)
# Create database connection.
try:
c = pymongo.Connection(servername)[dbname][data_coll]
except pymongo.errors.AutoReconnect:
response['error'] = "Could not connect to MongoDB server '%s'" % (
servername)
return bson.json_util.dumps(response)
# if no arguments given just search from root
if not query:
query['rooted'] = True
it = c.find(query)
# create a new tree for results
if it.count() == 1:
phylo = it[0]
phylotree = recursiveHelper(phylo['_id'])
# Convert to JSON and return the result.
return bson.json_util.dumps(phylotree, sort_keys=True)
else:
response['error'] = "Search returned %s object(s) to root the tree" % (
it.count())
response['error'] += "| %s" % (str(query))
return bson.json_util.dumps(response)
def run(start_date=None, end_date=None, omit_countries=None, omit_diseases=None):
# Check for required arguments.
if start_date is None:
return tangelo.HTTPStatusCode("422 Missing Parameter", "Required parameter <i>start_date</i> missing.");
elif end_date is None:
return tangelo.HTTPStatusCode("422 Missing Parameter", "Required parameter <i>end_date</i> missing.");
# Convert arguments to date objects.
try:
start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d")
except ValueError:
return tangelo.HTTPStatusCode("422 Bad Parameter", "Parameter <i>start_date</i> ('%s') was not in YYYY-MM-DD form." % (start_date))
try:
end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d")
except ValueError:
return tangelo.HTTPStatusCode("422 Bad Parameter", "Parameter <i>end_date</i> ('%s') was not in YYYY-MM-DD form." % (end_date))
# See if there are any countries or diseases to omit.
if omit_countries is None:
omit_countries = []
else:
try:
omit_countries = bson.json_util.loads(omit_countries)
except ValueError:
return tangelo.HTTPStatusCode("422 Bad Parameter", "Parameter <i>omit_countries</i> ('%s') was not JSON-deserializable." % (omit_countries))
if omit_diseases is None:
omit_diseases = []
else:
try:
omit_diseases = bson.json_util.loads(omit_diseases)
except ValueError:
return tangelo.HTTPStatusCode("422 Bad Parameter", "Parameter <i>omit_diseases</i> ('%s') was not JSON-deserializable." % (omit_diseases))
# Perform the lookup.
coll = pymongo.Connection("mongo").canepi.alerts
query = coll.find({"$and": [{"date": {"$gte": start_date} },
{"date": {"$lt": end_date} },
{"disease": {"$not": {"$in": omit_diseases}}},
{"country": {"$not": {"$in": omit_countries}}}]
},
fields = ["_id", "date", "rating.rating", "disease", "country"])
# Compute the graph structure.
nodes = []
links = []
diseases = {}
countries = {}
for q in query:
# For each result record, construct an "alert type" node and store it in
# the node list.
alert = {"id": str(q["_id"]), "date": q["date"], "rating": q["rating"]["rating"], "type": "alert"}
nodes.append(alert)
# Extract the disease and country, and create nodes for them if they
# don't already exist.
if q["country"] not in countries:
countries[q["country"]] = {"id": q["country"], "type": "country"}
country = countries[q["country"]]
if q["disease"] not in diseases:
diseases[q["disease"]] = {"id": q["disease"], "type": "disease"}
disease = diseases[q["disease"]]
# Create links between the alert and its country and its disease.
links += [{"source": alert, "target": country}, {"source": alert, "target": disease}]
# Add the disease and country nodes to the node list.
nodes += countries.values() + diseases.values()
# Create an index map of the nodes.
nodemap = {value["id"]: index for (index, value) in enumerate(nodes)}
# Replace the raw entries in the links list with indices into the node
# array.
for i, v in enumerate(links):
links[i]["source"] = nodemap[v["source"]["id"]]
links[i]["target"] = nodemap[v["target"]["id"]]
# Create a response object and pack the graph structure into it.
r = tangelo.empty_response()
r["result"] = {"nodes": nodes, "links": links}
# Use the special bson encoder and return the result.
return bson.json_util.dumps(r)
def run(host, database, collection, start_time=None, end_time=None, center=None, degree=None):
response = tangelo.empty_response()
# Bail with error if any of the required arguments is missing.
missing = map(lambda x: x[0], filter(lambda x: x[1] is None, zip(["start_time", "end_time", "center", "degree"], [start_time, end_time, center, degree])))
if len(missing) > 0:
response["error"] = "missing required arguments: %s" % (", ".join(missing))
return response
# Cast the arguments to the right types.
#
# The degree is the degree of separation between the center element and the
# retrieved nodes - an integer.
try:
degree = int(degree)
except ValueError:
response["error"] = "argument 'degree' must be an integer"
return response
# The start time is the number of milliseconds since the epoch (which is how
# JavaScript dates are constructed, and therefore how dates are stored in
# MongoDB) - an integer.
try:
start_time = datetime.datetime.strptime(start_time, "%Y-%m-%d")
except ValueError:
response["error"] = "argument 'start_time' must be in YYYY-MM-DD format"
return response
# The end time is another date - an integer.
try:
end_time = datetime.datetime.strptime(end_time, "%Y-%m-%d")
except ValueError:
response["error"] = "argument 'end_time' must be in YYYY-MM-DD format"
return response
# Get a handle to the database collection.
if SparkContext._active_spark_context == None:
sc = SparkContext('spark://impaladev.darpa.mil:7077', 'Enron Emailers')
else:
sc = SparkContext._active_spark_context
enronData = sc.textFile('hdfs://localhost:8020/user/bigdata/pgill/enron/email_graph_fixed.txt').map(lambda line: line.split('\t')).cache()
def withinTimespan(record):
recordDate = datetime.datetime.strptime(record[2], "%Y-%m-%d")
return recordDate >= start_time and recordDate < end_time
def emptyRecords(record):
return record[0] != "" and record[1] != ""
def orderRecord(record):
if record[1] < record[0]:
record[0], record[1] = record[1], record[0]
return record
enronSpan = enronData.filter(withinTimespan).filter(emptyRecords).map(orderRecord).map(lambda rec: (rec[0], rec[1])).distinct().cache()
# Start a set of all interlocutors we're interested in - that includes the
# center emailer.
talkers = set([center])
# Also start a table of distances from the center.
distance = {center: 0}
current_talkers = list(talkers)
all_results = []
for i in range(degree):
def emailsInvolved(record):
return any(keyword in record for keyword in current_talkers)
results = enronSpan.filter(emailsInvolved).collect()
# Collect the names.
current_talkers = list(itertools.chain(*map(lambda x: [x[1], x[0]], results)))
current_talkers = list(set(current_talkers))
talkers = talkers.union(current_talkers)
# Compute updates to everyone's distance from center.
for t in current_talkers:
if t not in distance:
distance[t] = i+1
# save the cursor.
all_results.append(results)
# Construct a canonical graph structure from the set of talkers and the list
# of emails.
#
# Start with an index map of the talkers.
talkers = list(talkers)
talker_index = {name: index for (index, name) in enumerate(talkers)}
# Create a chained iterable from all the rewound partial results.
all_results = itertools.chain(*all_results)
# Create a list of graph edges suitable for use by D3 - replace each record
# in the data with one that carries an index into the emailers list.
edges = []
ident = 0
for result in all_results:
source = result[0]
target = result[1]
ident += 1
rec = { "source": talker_index[source],
"target": talker_index[target],
"id": str(ident) }
edges.append(rec)
talkers = [{"email": n, "distance": distance[n]} for n in talkers]
# Stuff the graph data into the response object, and return it.
response["result"] = { "nodes": talkers,
"edges": edges }
return response
def run(host,
database,
collection,
start_time=None,
end_time=None,
center=None,
degree=None):
response = tangelo.empty_response()
# Bail with error if any of the required arguments is missing.
missing = map(
lambda x: x[0],
filter(
lambda x: x[1] is None,
zip(["start_time", "end_time", "center", "degree"],
[start_time, end_time, center, degree])))
if len(missing) > 0:
response["error"] = "missing required arguments: %s" % (
", ".join(missing))
return response
# Cast the arguments to the right types.
#
# The degree is the degree of separation between the center element and the
# retrieved nodes - an integer.
try:
degree = int(degree)
except ValueError:
response["error"] = "argument 'degree' must be an integer"
return response
# The start time is the number of milliseconds since the epoch (which is how
# JavaScript dates are constructed, and therefore how dates are stored in
# MongoDB) - an integer.
try:
start_time = datetime.datetime.strptime(start_time, "%Y-%m-%d")
except ValueError:
response[
"error"] = "argument 'start_time' must be in YYYY-MM-DD format"
return response
# The end time is another date - an integer.
try:
end_time = datetime.datetime.strptime(end_time, "%Y-%m-%d")
except ValueError:
response["error"] = "argument 'end_time' must be in YYYY-MM-DD format"
return response
# Get a handle to the database collection.
try:
c = pymongo.Connection(host)[database][collection]
except pymongo.errors.AutoReconnect as e:
response["error"] = "database error: %s" % (e.message)
return response
# Start a set of all interlocutors we're interested in - that includes the
# center emailer.
talkers = set([center])
# Also start a table of distances from the center.
distance = {center: 0}
current_talkers = list(talkers)
all_results = []
for i in range(degree):
# Construct and send a query to retrieve all records involving the
# current talkers, occurring within the time bounds specified, and
# involving two known addresses.
query = {
"$and": [{
"date": {
"$gte": start_time
}
}, {
"date": {
"$lt": end_time
}
}, {
"source": {
"$ne": ""
}
}, {
"target": {
"$ne": ""
}
}, {
"$or": [{
"source": {
"$in": current_talkers
}
}, {
"target": {
"$in": current_talkers
}
}]
}]
}
results = c.find(query, fields=["target", "source"])
# Collect the names.
#current_talkers = list(set(map(lambda x: x["target"] if x["source"] == center else x["source"], results)))
current_talkers = list(
itertools.chain(
*map(lambda x: [x["target"], x["source"]], results)))
talkers = talkers.union(current_talkers)
# Compute updates to everyone's distance from center.
for t in current_talkers:
if t not in distance:
distance[t] = i + 1
# Rewind and save the cursor.
results.rewind()
all_results.append(results)
# Construct a canonical graph structure from the set of talkers and the list
# of emails.
#
# Start with an index map of the talkers.
talkers = list(talkers)
talker_index = {name: index for (index, name) in enumerate(talkers)}
# Create a chained iterable from all the rewound partial results.
all_results = itertools.chain(*all_results)
# Create a list of graph edges suitable for use by D3 - replace each record
# in the data with one that carries an index into the emailers list.
edges = []
for result in all_results:
source = result["source"]
target = result["target"]
ident = str(result["_id"])
rec = {
"source": talker_index[source],
"target": talker_index[target],
"id": ident
}
edges.append(rec)
talkers = [{"email": n, "distance": distance[n]} for n in talkers]
# Stuff the graph data into the response object, and return it.
response["result"] = {"nodes": talkers, "edges": edges}
return response
def run(host, database, collection, start_time=None, end_time=None, center=None, degree=None):
response = tangelo.empty_response()
# Bail with error if any of the required arguments is missing.
missing = map(lambda x: x[0], filter(lambda x: x[1] is None, zip(["start_time", "end_time", "center", "degree"], [start_time, end_time, center, degree])))
if len(missing) > 0:
response["error"] = "missing required arguments: %s" % (", ".join(missing))
return response
# Cast the arguments to the right types.
#
# The degree is the degree of separation between the center element and the
# retrieved nodes - an integer.
try:
degree = int(degree)
except ValueError:
response["error"] = "argument 'degree' must be an integer"
return response
# The start time is the number of milliseconds since the epoch (which is how
# JavaScript dates are constructed, and therefore how dates are stored in
# MongoDB) - an integer.
try:
start_time = datetime.datetime.strptime(start_time, "%Y-%m-%d")
except ValueError:
response["error"] = "argument 'start_time' must be in YYYY-MM-DD format"
return response
# The end time is another date - an integer.
try:
end_time = datetime.datetime.strptime(end_time, "%Y-%m-%d")
except ValueError:
response["error"] = "argument 'end_time' must be in YYYY-MM-DD format"
return response
# Get a handle to the database collection.
try:
c = pymongo.Connection(host)[database][collection]
except pymongo.errors.AutoReconnect as e:
response["error"] = "database error: %s" % (e.message)
return response
# Start a set of all interlocutors we're interested in - that includes the
# center emailer.
talkers = set([center])
# Also start a table of distances from the center.
distance = {center: 0}
current_talkers = list(talkers)
all_results = []
for i in range(degree):
# Construct and send a query to retrieve all records involving the
# current talkers, occurring within the time bounds specified, and
# involving two known addresses.
query = {"$and": [ {"date": {"$gte": start_time} },
{"date": {"$lt": end_time} },
{"source": {"$ne": ""} },
{"target": {"$ne": ""} },
{"$or": [
{"source": {"$in": current_talkers} },
{"target": {"$in": current_talkers} }
]
}
]
}
results = c.find(query, fields=["target", "source"])
# Collect the names.
#current_talkers = list(set(map(lambda x: x["target"] if x["source"] == center else x["source"], results)))
current_talkers = list(itertools.chain(*map(lambda x: [x["target"], x["source"]], results)))
talkers = talkers.union(current_talkers)
# Compute updates to everyone's distance from center.
for t in current_talkers:
if t not in distance:
distance[t] = i+1
# Rewind and save the cursor.
results.rewind()
all_results.append(results)
# Construct a canonical graph structure from the set of talkers and the list
# of emails.
#
# Start with an index map of the talkers.
talkers = list(talkers)
talker_index = {name: index for (index, name) in enumerate(talkers)}
# Create a chained iterable from all the rewound partial results.
all_results = itertools.chain(*all_results)
# Create a list of graph edges suitable for use by D3 - replace each record
# in the data with one that carries an index into the emailers list.
edges = []
for result in all_results:
source = result["source"]
target = result["target"]
ident = str(result["_id"])
rec = { "source": talker_index[source],
"target": talker_index[target],
"id": ident }
edges.append(rec)
talkers = [{"email": n, "distance": distance[n]} for n in talkers]
# Stuff the graph data into the response object, and return it.
response["result"] = { "nodes": talkers,
"edges": edges }
return response
def run(servername, projectName,datasetName, name=None, objectid=None, _id=None, accession=None, scientific_name=None, noid=False, noloc=False, nobranchlength=False, maxdepth=1000):
def recursiveHelper(child, depth = 0):
it = c.find({'_id':child})
phylo = it[0]
if 'clades' in phylo:
counter = 0
for child in phylo['clades']:
if depth >= maxdepth:
phylo['clades'][counter] = str(child)
else:
phylo['clades'][counter] = recursiveHelper(child, depth + 1)
counter += 1
if noid:
del phylo['_id']
else:
phylo['_id'] = str(phylo['_id'])
if nobranchlength:
if 'branch_length' in phylo:
del phylo['branch_length']
if 'loc' in phylo:
if noloc:
del phylo['loc']
return phylo
# lookup the data collection
data_coll = api.returnCollectionForObjectByName(projectName,'PhyloTree',datasetName)
dbname = api.getMongoDatabase()
print "initializing to db: ",dbname, "collection: ",data_coll
# Construct an empty response object.
response = tangelo.empty_response();
query = dict()
# Decode the query strings into Python objects.
try:
if name is not None: decodeAndAdd(name, query, 'sequences.name', response)
if objectid is not None: decodeAndAdd(ObjectId(objectid), query, 'objectid', response)
if _id is not None: decodeAndAdd(_id, query, '_id', response)
if accession is not None: decodeAndAdd(accession, query, 'sequences.accession.source', response)
if scientific_name is not None: decodeAndAdd(scientific_name, query, 'taxonomies.scientific_name', response)
except ValueError:
return bson.json_util.dumps(response)
# Cast the maxdepth value to an int.
try:
maxdepth = int(maxdepth)
except ValueError:
response['error'] = "Argument 'limit' ('%s') could not be converted to int." % (maxdepth)
return bson.json_util.dumps(response)
# Create database connection.
try:
c = pymongo.Connection(servername)[dbname][data_coll]
except pymongo.errors.AutoReconnect:
response['error'] = "Could not connect to MongoDB server '%s'" % (servername)
return bson.json_util.dumps(response)
# if no arguments given just search from root
if not query:
query['rooted'] = True
it = c.find(query)
# create a new tree for results
if it.count() == 1:
phylo = it[0]
phylotree = recursiveHelper(phylo['_id'])
# Convert to JSON and return the result.
return bson.json_util.dumps(phylotree, sort_keys=True)
else:
response['error'] = "Search returned %s object(s) to root the tree" % (it.count())
response['error'] += "| %s" %(str(query))
return bson.json_util.dumps(response)
def run(servername,
dbname,
datatype,
by=None,
datemin=None,
datemax=None,
charity=None):
# Construct an empty response object.
response = tangelo.empty_response()
# Establish a connection to the MongoDB server.
try:
conn = pymongo.Connection(servername)
except pymongo.errors.AutoReconnect as e:
response['error'] = "error: %s" % (e.message)
return bson.json_util.dumps(response)
# Extract the requested database and collection.
db = conn[dbname]
if datatype == "transactions":
coll = db["charitynet.normalized.transactions"]
conditions = [{
"date": {
"$ne": None
}
}]
if datemin != None and datemax != None:
date_min = datetime.datetime.strptime(datemin, "%Y-%m-%d")
date_max = datetime.datetime.strptime(datemax, "%Y-%m-%d")
conditions.append({"date": {"$gte": date_min}})
conditions.append({"date": {"$lt": date_max}})
if charity != None:
conditions.append({"charity_id": int(charity)})
pipeline = []
if len(conditions) > 0:
pipeline.append({"$match": {"$and": conditions}})
if by == "month":
group = {"year": {"$year": "$date"}, "month": {"$month": "$date"}}
else:
group = "$county"
pipeline.append(
{"$group": {
"_id": group,
"amount": {
"$sum": "$amount"
}
}})
result = coll.aggregate(pipeline)
if by == "month":
response = [[d["_id"], float(d["amount"])]
for d in result["result"] if d["_id"] != None]
else:
response = [["%05d" % d["_id"],
float(d["amount"])] for d in result["result"]
if d["_id"] != None]
elif datatype == "population":
coll = db["census"]
response = [[d["_id"], int(d["pop2010"])] for d in coll.find()]
elif datatype == "charities":
coll = db["charitynet.normalized.transactions"]
result = coll.aggregate([{
"$group": {
"_id": "$charity_id",
"count": {
"$sum": 1
}
}
}, {
"$sort": {
"count": -1
}
}])
response = [[d["_id"], d["_id"], d["count"]] for d in result["result"]]
else:
response['error'] = "error: unknown datatype requested"
# Convert to JSON and return the result.
return bson.json_util.dumps(response)
def run(server,
db,
coll,
method='find',
query=None,
limit=1000,
fields=None,
sort=None,
fill=None):
# Create an empty response object.
response = tangelo.empty_response()
# Check the requested method.
if method not in ['find', 'insert']:
response['error'] = "Unsupported MongoDB operation '%s'" % (method)
return bson.json_util.dumps(response)
# Decode the query strings into Python objects.
try:
if query is not None: query = decode(query, 'query', response)
if fields is not None: fields = decode(fields, 'fields', response)
if sort is not None: sort = decode(sort, 'sort', response)
if fill is not None:
fill = decode(fill, 'fill', response)
else:
fill = True
except ValueError:
return bson.json_util.dumps(response)
# Cast the limit value to an int.
try:
limit = int(limit)
except ValueError:
response[
'error'] = "Argument 'limit' ('%s') could not be converted to int." % (
limit)
return bson.json_util.dumps(response)
# Create database connection.
try:
c = pymongo.Connection(server)[db][coll]
except pymongo.errors.AutoReconnect:
response['error'] = "Could not connect to MongoDB server '%s'" % (
server)
return bson.json_util.dumps(response)
# Perform the requested action.
if method == 'find':
# Do a find operation with the passed arguments.
it = c.find(spec=query, fields=fields, limit=limit, sort=sort)
# Create a list of the results.
if fill:
results = [x for x in it]
else:
results = []
# Create an object to structure the results.
retobj = {}
retobj['count'] = it.count()
retobj['data'] = results
# Pack the results into the response object, and return it.
response['result'] = retobj
else:
raise RuntimeError("illegal method '%s' in module 'mongo'")
# Return the response object.
return bson.json_util.dumps(response)
def invoke_service(self, module, *pargs, **kwargs):
# TODO(choudhury): This method should attempt to load the named module,
# then invoke it with the given arguments. However, if the named module
# is "config" or something similar, the method should instead launch a
# special "config" app, which lists the available app modules, along
# with docstrings or similar. It should also allow the user to
# add/delete search paths for other modules.
tangelo.content_type("text/plain")
# Save the system path (be sure to *make a copy* using the list()
# function) - it will be modified before invoking the service, and must
# be restored afterwards.
origpath = list(sys.path)
# By default, the result should be a bare response that we will place an
# error message in if something goes wrong; if nothing goes wrong this
# will be replaced with some other object.
result = tangelo.empty_response()
# Store the modpath in the thread-local storage (tangelo.paths() makes
# use of this per-thread data, so this is the way to get the data across
# the "module boundary" properly).
modpath = os.path.dirname(module)
cherrypy.thread_data.modulepath = modpath
cherrypy.thread_data.modulename = module
# Extend the system path with the module's home path.
sys.path.insert(0, modpath)
# Import the module if not already imported previously (or if the module
# to import, or its configuration file, has been updated since the last
# import).
try:
stamp = self.modules.get(module)
mtime = os.path.getmtime(module)
config_file = module[:-2] + "json"
config_mtime = None
if os.path.exists(config_file):
config_mtime = os.path.getmtime(config_file)
if stamp is None or mtime > stamp["mtime"] or (config_mtime is not None and config_mtime > stamp["mtime"]):
if stamp is None:
tangelo.log("loading new module: " + module)
else:
tangelo.log("reloading module: " + module)
# Load any configuration the module might carry with it.
if config_mtime is not None:
try:
with open(config_file) as f:
config = json.loads(json_minify(f.read()))
if type(config) != dict:
msg = "Service module configuration file does not contain a key-value store (i.e., a JSON Object)"
tangelo.log(msg)
raise TypeError(msg)
except IOError:
tangelo.log("Could not open config file %s" % (config_file))
raise
except ValueError as e:
tangelo.log("Error reading config file %s: %s" % (config_file, e))
raise
else:
config = {}
cherrypy.config["module-config"][module] = config
# Remove .py to get the module name
name = module[:-3]
# Load the module.
service = imp.load_source(name, module)
self.modules[module] = { "module": service,
"mtime": max(mtime, config_mtime) }
else:
service = stamp["module"]
except:
bt = traceback.format_exc()
tangelo.log("Error importing module %s" % (tangelo.request_path()), "SERVICE")
tangelo.log(bt, "SERVICE")
result = tangelo.HTTPStatusCode("501 Error in Python Service", "There was an error while trying to import module %s:<br><pre>%s</pre>" % (tangelo.request_path(), bt))
else:
# Try to run the service - either it's in a function called "run()",
# or else it's in a REST API consisting of at least one of "get()",
# "put()", "post()", or "delete()".
#
# Collect the result in a variable - depending on its type, it will be
# transformed in some way below (by default, to JSON, but may also raise
# a cherrypy exception, log itself in a streaming table, etc.).
#
try:
if 'run' in dir(service):
# Call the module's run() method, passing it the positional and
# keyword args that came into this method.
result = service.run(*pargs, **kwargs)
else:
# Reaching here means it's a REST API. Check for the
# requested method, ensure that it was marked as being part
# of the API, and call it; or give a 405 error.
method = cherrypy.request.method
restfunc = service.__dict__[method.lower()]
if restfunc is not None and hasattr(restfunc, "restful") and restfunc.restful:
result = restfunc(*pargs, **kwargs)
else:
result = tangelo.HTTPStatusCode(405, "Method not allowed")
except Exception as e:
bt = traceback.format_exc()
tangelo.log("Caught exception while executing service %s" % (tangelo.request_path()), "SERVICE")
tangelo.log(bt, "SERVICE")
result = tangelo.HTTPStatusCode("501 Error in Python Service", "There was an error executing service %s:<br><pre>%s</pre>" % (tangelo.request_path(), bt))
# Restore the path to what it was originally.
sys.path = origpath
# Check the type of the result to decide what result to finally return:
#
# 1. If it is an HTTPStatusCode object, raise a cherrypy HTTPError
# exception, which will cause the browser to do the right thing.
#
# 2. TODO: If it's a Python generator object, log it with the Tangelo
# streaming API.
#
# 3. If it's a Python dictionary, convert it to JSON.
#
# 4. If it's a string, don't do anything to it.
#
# This allows the services to return a Python object if they wish, or to
# perform custom serialization (such as for MongoDB results, etc.).
if isinstance(result, tangelo.HTTPStatusCode):
if result.msg:
raise cherrypy.HTTPError(result.code, result.msg)
else:
raise cherrypy.HTTPError(result.code)
elif "next" in dir(result):
# Generate a key corresponding to this object, using 100 random
# bytes from the system - ensure the random key is not already in
# the table (even though it would be crazy to wind up with a
# collision).
#
# TODO(choudhury): replace this with a call to generate_key().
# Move the comment above into the generate_key() function.
key = md5.md5(os.urandom(100)).hexdigest()
while key in self.streams:
key = md5.md5(os.urandom(100)).hexdigest()
# Log the object in the streaming table.
self.streams[key] = result
# Create an object describing the logging of the generator object.
result = tangelo.empty_response()
result["stream_key"] = key
# Serialize it to JSON.
result = json.dumps(result)
elif not isinstance(result, types.StringTypes):
try:
result = json.dumps(result)
except TypeError as e:
t = e.message.split("<service.")[1].split()[0]
msg = "Service %s returned an object of type %s that could not be serialized to JSON" % (tangelo.request_path(), t)
tangelo.log("Error: %s" % (msg), "SERVICE")
raise cherrypy.HTTPError("501 Error in Python Service", msg)
return result
def run(database, table, start_time, end_time, center, degree, host="localhost", port=10000, fields="true"):
response = tangelo.empty_response()
try:
degree = int(degree)
except ValueError:
response["error"] = "argument 'degree' must be an integer"
return response
client = init_shark(host, port, database)
talkers = set([center])
distance = {center: 0}
current_talkers = list(talkers)
all_results = []
for i in range(degree):
query = build_query(database, table, start_time, end_time, current_talkers)
client.execute(query)
results = client.fetchAll()
current_talkers = list(itertools.chain(*map(lambda x: [x.split("\t")[0], x.split("\t")[1]], results)))
current_talkers = list(set(current_talkers))
talkers = talkers.union(current_talkers)
for t in current_talkers:
if t not in distance:
distance[t] = i+1
all_results.append(results)
talkers = list(talkers)
talker_index = {name: index for (index, name) in enumerate(talkers)}
all_results = itertools.chain(*all_results)
edges = []
ident = 0;
for result in all_results:
resultArray = result.split("\t")
source = resultArray[1]
target = resultArray[0]
ident += 1
rec = { "source": talker_index[source],
"target": talker_index[target],
"id": str(ident) }
edges.append(rec)
talkers = [{"email": n, "distance": distance[n]} for n in talkers]
response["result"] = { "nodes": talkers,
"edges": edges }
return response
def run(host, port, graph, start_time=None, days=1, center=None, degree=None):
response = tangelo.empty_response()
# Bail with error if any of the required arguments is missing.
missing = map(lambda x: x[0], filter(lambda x: x[1] is None, zip(["start_time", "days", "center", "degree"], [start_time, days, center, degree])))
if len(missing) > 0:
response["error"] = "missing required arguments: %s" % (", ".join(missing))
return response
# Cast the arguments to the right types.
#
# The degree is the degree of separation between the center element and the
# retrieved nodes - an integer.
try:
degree = int(degree)
except ValueError:
response["error"] = "argument 'degree' must be an integer"
return response
# The start time is the number of milliseconds since the epoch (which is how
# JavaScript dates are constructed, and therefore how dates are stored in
# MongoDB) - an integer.
try:
start_time = datetime.datetime.strptime(start_time, "%Y-%m-%d")
except ValueError:
response["error"] = "argument 'start_time' must be in YYYY-MM-DD format"
return response
try:
days = int(days)
except ValueError:
response["error"] = "argument 'days' must be an integer"
return response
dateList = [ start_time + datetime.timedelta(days=x) for x in range(0,days) ]
config = Config("http://"+host+":"+port+"/graphs/"+graph)
config.set_logger(DEBUG)
#client = RexsterClient(config)
g = Graph(config)
# Start a set of all interlocutors we're interested in - that includes the
# center emailer.
talkers = set([center])
# Also start a table of distances from the center.
distance = {center: 0}
current_talkers = list(talkers)
center_vertex = g.vertices.index.lookup(email=center).next()
edgeId = 0;
edges = []
for i in range(degree):
new_talkers = []
for talker_email in current_talkers:
current_vertex = g.vertices.index.lookup(email=talker_email).next()
for day in dateList:
dayString = day.strftime('%m/%d/%Y')
adjacent = current_vertex.bothV(dayString)
if adjacent != None:
adjacent_talkers = list(set(itertools.chain(*map(lambda x: [x.email], adjacent))))
if '' in adjacent_talkers:
adjacent_talkers.remove('')
for this_talker in adjacent_talkers:
newEdge = { "source": this_talker,
"target": talker_email,
"id": edgeId }
edges.append(newEdge)
edgeId += 1
new_talkers.extend(adjacent_talkers)
current_talkers.extend(new_talkers)
current_talkers = list(set(current_talkers))
talkers = talkers.union(current_talkers)
# Compute updates to everyone's distance from center.
for t in current_talkers:
if t not in distance:
distance[t] = i+1
# Construct a canonical graph structure from the set of talkers and the list
# of emails.
#
# Start with an index map of the talkers.
talkers = list(set(talkers))
talker_index = {name: index for (index, name) in enumerate(talkers)}
for edge in edges:
edge["source"] = talker_index[edge["source"]]
edge["target"] = talker_index[edge["target"]]
talkers = [{"email": n, "distance": distance[n]} for n in talkers]
# Stuff the graph data into the response object, and return it.
response["result"] = { "nodes": talkers,
"edges": edges }
return response
def run_on_collection(servername, dbname, data_coll, boundary_type, _id=None, lng=-1.0, lat=-1.0, radius=0.0, swlng=-1.0, swlat=-1.0, nelng=-1.0, nelat=-1.0, limit=1000, _filter='true'):
earthRadius = 6378137 #meters
# Construct an empty response object.
response = tangelo.empty_response();
try:
limit = int(limit)
except ValueError:
response['error'] = "Argument (%s), value (%s) could not be converted to int" % ('limit', limit)
if limit > 1000 or limit < 1:
limit = 1000
if boundary_type == 'circle':
# convert types to floats
try:
lng = float(lng)
lat = float(lat)
radius = float(radius)
except ValueError as e:
response['error'] = e.message + " Argument could not be converted to float."
return bson.json_util.dumps(response)
#check bounds
if lng > 180.0 or lng < -180.0:
response['error'] = "Longitude out of bounds: %s" % (lng)
return bson.json_util.dumps(response)
if lat > 90.0 or lat < -90.0:
response['error'] = "Latitude out of bounds: %s" % (lat)
return bson.json_util.dumps(response)
if radius < 0.0:
response['error'] = "Radius cannot be negative: %s" % (radius)
return bson.json_util.dumps(response)
# convert radius from meters to percentage of earth
radius = radius / earthRadius
container = [[lng, lat], radius]
query = {'loc' : {'$within' : { '$centerSphere' : container }}}
elif boundary_type == 'rect':
try:
swlng = float(swlng)
swlat = float(swlat)
nelng = float(nelng)
nelat = float(nelat)
except ValueError as e:
response['error'] = e.message + " Argument could not be converted to float."
return bson.json_util.dumps(response)
container = [[swlng, swlat], [nelng, nelat]]
query = {'loc' : {'$within' : { '$box' : container }}}
elif boundary_type == 'id':
True
else:
response['error'] = "Invalid geometery type: %s" % (boundary_type)
return bson.json_util.dumps(response)
# Create database connection.
try:
c = pymongo.Connection(servername)[dbname][data_coll]
except pymongo.errors.AutoReconnect:
response['error'] = "Could not connect to MongoDB server '%s'" % (servername)
return bson.json_util.dumps(response)
# Perform the query
if boundary_type == 'id':
mpath = materializedPaths.materializedPaths(servername,dbname,data_coll,data_coll)
if mpath.checkIfPresent():
it = mpath.getDescendantsCriteria(_id, "loc")
else:
# materialized paths not present, create it!
mpath.generateFromChildTree()
it = mpath.getDescendantsCriteria(_id, "loc")
#response['error'] = "Materialized Paths not present in dataset"
#return bson.json_util.dumps(response)
else:
it = c.find(spec=query, limit=limit)
#response['error'] = "we made it here" + " " + str(_id) + " " + str(it.count())
#return bson.json_util.dumps(response)
# Create a list of the results
results = list()
# if we want to filter by only those locations of items in our range
if _filter == 'true':
count = 0
try:
for item in it:
# for each location of the item
for location in item['loc']:
# if it's in bounds create new object, add to results
if boundary_type == 'id' or isInBounds(float(location[0]), float(location[1]), container, boundary_type, response):
marker = dict()
marker['name'] = item['taxonomies'][0]['scientific_name']
marker['ID'] = item['_id']
marker['lng'] = location[0]
marker['lat'] = location[1]
results.append(marker)
count += 1
# else: don't add point
except ValueError:
return bson.json_util.dumps(response)
except KeyError:
return bson.json_util.dumps(response)
# otherwise return document containing all locations of item in range
else:
results = [x for x in it]
# Create an object to structure the results
retobj = dict()
retobj['count'] = count if _filter == 'true' else it.count()
retobj['data'] = results
# Pack the results in the response object, and return it
response['result'] = retobj
return bson.json_util.dumps(response)
