def graphs_from_files():
filenames = ["Adam Smith Business School",
"Dental School",
"School of Chemistry",
"School of Critical Studies",
"School of Culture and Creative Arts",
"School of Education"
]
schools_tree = es.get_tree("http://www.gla.ac.uk/schools/")
ns = 'http://exslt.org/regular-expressions'
path = '//div[@class="row standardContent"]//a[re:match(@href, "schools/[A-Za-z]+/")]'
a_elems = schools_tree.xpath(path, namespaces={'re':ns})
base_url = "http://www.gla.ac.uk"
urls = []
names = []
for a in a_elems:
staff_page_url = base_url + a.get("href") + "staff/"
urls.append(staff_page_url)
school_name = a.text
names.append(school_name)
school_names_urls = zip(names, urls)
print school_names_urls
for name, url in school_names_urls:
if name in filenames:
with open("../coauthor_data/" + name + ".txt") as f:
d = json.load(f)
staff_names = es.get_names(url)
gm = gfd.GraphMaker(d, staff_names)
gm.write_to_file(name + " graph")
def scrape_and_make(data_dict):
#authorkw_dict = defaultdict(str)
authorkw_dict = {}
names = es.get_names("http://www.gla.ac.uk/schools/computing/staff/")
# TODO do we need to pass dd into gm?
gm = gfd.GraphMaker(data_dict, names)
# TODO adding of keywords has to be done somewhere else - text processing module...
#gm.add_kw_to_data()
for title, info in data_dict.items():
authors = info["authors"]
# At this point kws are in a list of phrases (returned that way from es and from kw extraction)
# First, stem or lemmatize each word in each phrase (or don't)
# To add to author kw, join on "|" so we have a full string which can be searched for substring
# Then we need to tokenise that into individual terms to put in inv index
# Use regex to split into terms.. take into account "|"
# Once tokenised, build inv index like in search.py
keyword_list = info["keywords"]
# TODO temporary fixes
keyword_list = [kw for kw in keyword_list if kw != ""]
stemmed_kw_list = stem_kwlist(keyword_list)
#stemmed_kw_list = keyword_list[:]
# TODO temporary fixes
stemmed_kw_list = [kw for kw in stemmed_kw_list if "abstract avail" not in kw]
# TODO do we need to check if authors exists?
for author in authors:
# TODO Add a node to collab graph here
gm.add_vertex(author)
# Make list of keywords into a single string, with phrases separated by a divisor
# TODO using kw list for now
#kw_string = "|".join(stemmed_kw_list)
author_name = author[0]
# TODO have to do this to work with shelve because loading from json for now
author_id = author[1].encode("utf-8")
# Add author to authorkw_dict / add keywords to existing author
# TODO using kw list but may become string
if author_id not in authorkw_dict:
authorkw_dict[author_id] = {"name": author_name, "keywords": stemmed_kw_list}
else:
authorkw_dict[author_id]["keywords"].extend(stemmed_kw_list)
# NB each author is a (name, unique id) pair; unique id is dict key, name goes as value
# Add edges to collab graph here
gm.add_links(title, authors)
# Potentially add to inv_index here as we are already looping through the data_dict
clean_authorkw = clean_keywords(authorkw_dict.copy())
# store collab graph (as json?)
gm.write_to_file("newcsgraph")
# store authorkw_dict (using shelve for now)
she = shelve.open("authorkw.db")
she.update(authorkw_dict)
she.close()
# TODO
return (clean_authorkw, gm.get_graph())
def graph_me_up():
with open("../coauthor_data/School of Computing Science.txt") as f:
d = json.load(f)
names = es.get_names("http://www.gla.ac.uk/schools/computing/staff/")
gm = gfd.GraphMaker(d, names)
g = gm.get_graph()
gm.write_to_file("cslatest")
return g
