!sudo pip install PyMySQL

!sudo pip install rdflib

#!/usr/bin/env python

import pymysql

#conn = pymysql.connect(host='127.0.0.1', unix_socket='/tmp/mysql.sock', user='root', passwd=None, db='mysql')

conn = pymysql.connect(host='my01.winhost.com', port=3306, user='openworm', passwd='openworm', db='mysql_31129_celegans')
   
cur = conn.cursor()

#let's look at IDs and names coming from the database
cur.execute("SELECT DISTINCT ID, Entity FROM tblentity LIMIT 10")

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
   print(r)

cur.close()
conn.close()

from rdflib import Graph
from rdflib import Namespace
from rdflib.namespace import RDF
from rdflib import URIRef, BNode, Literal

g = Graph()

n = Namespace("http://openworm.org/entities/")

print(n['worm'])

#!/usr/bin/env python

import pymysql
from rdflib import Graph
from rdflib import Namespace
from rdflib.namespace import RDF, RDFS
from rdflib import URIRef, BNode, Literal
import urllib

#conn = pymysql.connect(host='127.0.0.1', unix_socket='/tmp/mysql.sock', user='root', passwd=None, db='mysql')

conn = pymysql.connect(host='my01.winhost.com', port=3306, user='openworm', passwd='openworm', db='mysql_31129_celegans')
   
cur = conn.cursor()

cur.execute("SELECT DISTINCT ID, Entity FROM tblentity LIMIT 10")

n = Namespace("http://openworm.org/entities/")

# print cur.description

g = Graph()

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    #first item is a number -- needs to be converted to a string
    first = str(r[0])
    #second item is text 
    second = str(r[1])
    
    # This is the backbone of any RDF graph.  The unique
    # ID for each entity is encoded as a URI and every other piece of 
    # knowledge about that entity is connected via triples to that URI
    # In this case, we connect the common name of that entity to the 
    # root URI via the RDFS label property.
    g.add( (n[first], RDFS.label, Literal(second)) )

cur.close()
conn.close()

print g.serialize(format='turtle')

#!/usr/bin/env python

import pymysql
from rdflib import Graph
from rdflib import Namespace
from rdflib.namespace import RDF, RDFS
from rdflib import URIRef, BNode, Literal
import urllib

#conn = pymysql.connect(host='127.0.0.1', unix_socket='/tmp/mysql.sock', user='root', passwd=None, db='mysql')

conn = pymysql.connect(host='my01.winhost.com', port=3306, user='openworm', passwd='openworm', db='mysql_31129_celegans')
   
cur = conn.cursor()

#first step, grab all entities and add them to the graph

cur.execute("SELECT DISTINCT ID, Entity FROM tblentity LIMIT 10")

n = Namespace("http://openworm.org/entities/")

# print cur.description

g = Graph()

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    #first item is a number -- needs to be converted to a string
    first = str(r[0])
    #second item is text 
    second = str(r[1])
    
    # This is the backbone of any RDF graph.  The unique
    # ID for each entity is encoded as a URI and every other piece of 
    # knowledge about that entity is connected via triples to that URI
    # In this case, we connect the common name of that entity to the 
    # root URI via the RDFS label property.
    g.add( (n[first], RDFS.label, Literal(second)) )


#second stem, get the relationships between them and add them to the graph
cur.execute("SELECT DISTINCT EnID1, Relation, EnID2 FROM tblrelationship LIMIT 10")

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    print r
    #all items are numbers -- need to be converted to a string
    first = str(r[0])
    second = str(r[1])
    third = str(r[2])
    
    g.add( (n[first], n[second], n[third]) )

cur.close()
conn.close()

print g.serialize(format='turtle')

#!/usr/bin/env python

import pymysql
from rdflib import Graph
from rdflib import Namespace
from rdflib.namespace import RDF, RDFS
from rdflib import URIRef, BNode, Literal
import urllib

#conn = pymysql.connect(host='127.0.0.1', unix_socket='/tmp/mysql.sock', user='root', passwd=None, db='mysql')

conn = pymysql.connect(host='my01.winhost.com', port=3306, user='openworm', passwd='openworm', db='mysql_31129_celegans')
   
cur = conn.cursor()

#first step, grab all entities and add them to the graph

cur.execute("SELECT DISTINCT ID, Entity FROM tblentity")

n = Namespace("http://openworm.org/entities/")

# print cur.description

g = Graph()

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    #first item is a number -- needs to be converted to a string
    first = str(r[0])
    #second item is text 
    second = str(r[1])
    
    # This is the backbone of any RDF graph.  The unique
    # ID for each entity is encoded as a URI and every other piece of 
    # knowledge about that entity is connected via triples to that URI
    # In this case, we connect the common name of that entity to the 
    # root URI via the RDFS label property.
    g.add( (n[first], RDFS.label, Literal(second)) )


#second stem, get the relationships between them and add them to the graph
cur.execute("SELECT DISTINCT EnID1, Relation, EnID2 FROM tblrelationship")

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    #print r
    #all items are numbers -- need to be converted to a string
    first = str(r[0])
    second = str(r[1])
    third = str(r[2])
    
    g.add( (n[first], n[second], n[third]) )

cur.close()
conn.close()

print("going to get results...")

qres = g.query(
     """SELECT ?subject ?predicate ?object
       WHERE {
          ?subject ?predicate ?object.
    	} LIMIT 5""")

print("printing results")


print("The graph has " + str(len(g)) + " items in it")

print("Name--not necessarily in strign format: ")
print(qres.result[0])

# when done!
g.close()

#!/usr/bin/env python

import pymysql
from rdflib import Graph
from rdflib import Namespace
from rdflib.namespace import RDF, RDFS
from rdflib import URIRef, BNode, Literal
import urllib

#conn = pymysql.connect(host='127.0.0.1', unix_socket='/tmp/mysql.sock', user='root', passwd=None, db='mysql')

conn = pymysql.connect(host='my01.winhost.com', port=3306, user='openworm', passwd='openworm', db='mysql_31129_celegans')
   
cur = conn.cursor()

#first step, grab all entities and add them to the graph

cur.execute("SELECT DISTINCT ID, Entity FROM tblentity")

n = Namespace("http://openworm.org/entities/")

# print cur.description

g = Graph()

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    #first item is a number -- needs to be converted to a string
    first = str(r[0])
    #second item is text 
    second = str(r[1])
    
    # This is the backbone of any RDF graph.  The unique
    # ID for each entity is encoded as a URI and every other piece of 
    # knowledge about that entity is connected via triples to that URI
    # In this case, we connect the common name of that entity to the 
    # root URI via the RDFS label property.
    g.add( (n[first], RDFS.label, Literal(second)) )


#second stem, get the relationships between them and add them to the graph
cur.execute("SELECT DISTINCT EnID1, Relation, EnID2 FROM tblrelationship")

# r = cur.fetchall()
# print r
# ...or...
for r in cur.fetchall():
    #print r
    #all items are numbers -- need to be converted to a string
    first = str(r[0])
    second = str(r[1])
    third = str(r[2])
    
    g.add( (n[first], n[second], n[third]) )

cur.close()
conn.close()

print("going to get results...")

qres = g.query(
     """SELECT ?predLabel ?objLabel     #we want to get out the labels associated with the predicates and the objects
       WHERE {
          ?AVALnode ?p "AVAL".          #we are looking first for the 'AVALnode' that is the anchor of all information about the AVAL neuron
          ?AVALnode ?predicate ?object .# having identified that node, here we match any predicate and object associated with the AVALnode
          ?predicate rdfs:label ?predLabel .#for the predicates, look up their plain text label (because otherwise we only have URIs)
          ?object rdfs:label ?objLabel  #for the object, look up their plain text label.
    	} ORDER BY ?predLabel           #sort by the predicate""")       

print("printing results")


print("The graph has " + str(len(g)) + " items in it\n\n")

print "AVAL has the following information stored about it: \n"
for r in qres.result:
    print str(r[0]), str(r[1])

# when done!
g.close()