def interface():
args = argparse.ArgumentParser()
args.add_argument("-i", "--inst-file", help="Institution profiles")
args.add_argument("-f", "--faculty-file", help="Faculty profiles")
args.add_argument("-g", "--gs-dir", help="Directory of GS profiles")
args.add_argument("-d", "--dblp-dir", help="Directory of DBLP profiles")
args = args.parse_args()
return args
if __name__ == "__main__":
args = interface()
inst = institution_parser.parse_institution_records(open(args.inst_file, "rU"))
faculty = load_assistant_profs(open(args.faculty_file, "rU"), inst)
# gs_prefix = os.path.join(args.gs_dir, 'GSP_')
dblp_prefix = os.path.join(args.dblp_dir, "DBLP_")
for f in faculty:
# Check for each profile, download if missing
"""
if 'gs' in f:
gs_file = os.path.join(args.gs_dir, GS_FILE % f['gs'])
if not os.path.isfile(gs_file):
print 'GS -> ', f['facultyName']
download_all_gs_pages(f['gs'], gs_prefix)
"""
if "dblp" in f:
dblp_file = os.path.join(args.dblp_dir, DBLP_FILE % f["dblp"])
name = line.split(':', 1)[-1].strip()
if name == next_name:
output.write('# dblp_n : %d\n' % num_papers[next_ind])
output.write('# dblp_n_2011 : %d\n' %
num_papers_2011[next_ind])
next_ind += 1
if next_ind < max_ind:
next_name = names[next_ind]
else:
done = True
if not done:
print 'WARNING: failed to link all z-scores!'
output.close()
if __name__ == "__main__":
args = interface()
inst = institution_parser.parse_institution_records(open(args.inst_file))
faculty = load_assistant_profs(open(args.faculty_file, 'rU'), inst)
load.load_all_publications(faculty, args.dblp_dir, gs_dir=None)
dists, tots = get_paper_counts_by_topic(faculty)
means, stds = get_topic_means_stds(dists, tots)
print means
print stds
set_zscores(faculty, means, stds)
#add_zscores_to_file(faculty, args.faculty_file, args.output_file)
add_counts_to_file(faculty, args.faculty_file, args.output_file)
if len(w) < 3:
return None
return w
def add_words_from_title(words, title, stop_words, lem):
for word in title.split():
w = word_filter(word, stop_words, lem)
if w:
words.append(w)
if __name__ == "__main__":
args = interface()
faculty = load.load_assistant_profs(open(args.input_file))
load.load_all_publications(faculty, args.dblp_dir, args.gs_dir)
lem = WordNetLemmatizer()
stop_words = stopwords.words('english')
if args.custom_stops:
custom_words = [
word_filter(w.strip(), [], lem)
for w in open(args.custom_stops, 'rU')
]
stop_words += custom_words
for f in faculty:
tag = None
if 'dblp_pubs' in f:
# Finish writing the last record(s)
if line: # Didn't reach the end yet, so grab the next line
line = in_fp.readline()
while line:
output.write(line)
line = in_fp.readline()
print 'Linked %d DBLP profiles and %d GS profiles in new output file' % (dblp_linked, gs_linked)
output.close()
if __name__=="__main__":
args = interface()
faculty = load_assistant_profs(open(args.faculty_file, 'rU'))
link_gs_profiles(faculty, args.gs_file)
link_dblp_profiles(faculty, args.dblp_file)
add_links_to_file(faculty, args.faculty_file, args.output_file)
covered = 0
both = 0
for f in faculty:
if 'gs' in f or 'dblp' in f:
covered += 1
else:
print f['facultyName']
if 'gs' in f and 'dblp' in f:
both += 1
print '%d of %d have at least one of the two profiles.' % (covered, len(faculty))
print '%d of %d have both profiles.' % (both, len(faculty))
if len(w) < 3:
return None
return w
def add_words_from_title(words, title, stop_words, lem):
for word in title.split():
w = word_filter(word, stop_words, lem)
if w:
words.append(w)
if __name__=="__main__":
args = interface()
inst = institution_parser.parse_institution_records(open(args.inst_file))
faculty = load.load_assistant_profs(open(args.fac_file), inst)
load.load_all_publications(faculty, args.dblp_dir, args.gs_dir)
lem = WordNetLemmatizer()
stop_words = stopwords.words('english')
if args.custom_stops:
custom_words = [word_filter(w.strip(), [], lem) for w in open(args.custom_stops, 'rU')]
stop_words += custom_words
written = 0
for f in faculty:
tag = None
words = []
if 'dblp_pubs' in f:
tag = f['dblp']
if len(w) < 3:
return None
return w
def add_words_from_title(words, title, stop_words, lem):
for word in title.split():
w = word_filter(word, stop_words, lem)
if w:
words.append(w)
if __name__ == "__main__":
args = interface()
inst = institution_parser.parse_institution_records(open(args.inst_file))
faculty = load.load_assistant_profs(open(args.fac_file), inst)
load.load_all_publications(faculty, args.dblp_dir, args.gs_dir)
lem = WordNetLemmatizer()
stop_words = stopwords.words('english')
if args.custom_stops:
custom_words = [
word_filter(w.strip(), [], lem)
for w in open(args.custom_stops, 'rU')
]
stop_words += custom_words
written = 0
for f in faculty:
tag = None
words = []
HS_INST_FILE = os.path.join(DATA_DIR, 'inst_hs_CURRENT.txt')
# Colors
ACCENT_COLOR_1 = np.array([176., 116., 232.]) / 255.
# Load the standard set of files
# Business
bs_inst = institution_parser.parse_institution_records(open(
BS_INST_FILE, 'rU'))
all_bs_faculty = [
person for person in faculty_parser.parse_faculty_records(
open(BS_FACULTY_FILE, 'rU'), school_info=bs_inst, ranking='pi')
]
bs_faculty = load.load_assistant_profs(open(BS_FACULTY_FILE, 'rU'),
school_info=bs_inst,
ranking='pi',
year_start=1970,
year_stop=2012)
# bs_faculty_df = convert_faculty_list_to_df(bs_faculty)
# Computer Science
cs_inst = institution_parser.parse_institution_records(open(
CS_INST_FILE, 'rU'))
all_cs_faculty = [
person for person in faculty_parser.parse_faculty_records(
open(CS_FACULTY_FILE, 'rU'), school_info=cs_inst, ranking='pi')
]
cs_faculty = load.load_assistant_profs(open(CS_FACULTY_FILE, 'rU'),
school_info=cs_inst,
ranking='pi',
year_start=1970,
if len(w) < 3:
return None
return w
def add_words_from_title(words, title, stop_words, lem):
for word in title.split():
w = word_filter(word, stop_words, lem)
if w:
words.append(w)
if __name__=="__main__":
args = interface()
faculty = load.load_assistant_profs(open(args.input_file))
load.load_all_publications(faculty, args.dblp_dir, args.gs_dir)
lem = WordNetLemmatizer()
stop_words = stopwords.words('english')
if args.custom_stops:
custom_words = [word_filter(w.strip(), [], lem) for w in open(args.custom_stops, 'rU')]
stop_words += custom_words
for f in faculty:
tag = None
if 'dblp_pubs' in f:
tag = f['dblp']
try:
title = np.random.choice(f['dblp_pubs'])['title']