def getMetamapResults(sentences,metaMapBinDir):
metaMapExec = metaMapBinDir + 'metamap'
fileExists = False
input_file = None
if sentences is not None:
input_file = open("temp_input", "w")
if sentences is not None:
ids = range(1,len(sentences)+1)
for identifier, sentence in zip(ids, sentences):
input_file.write('%r|%r\n' % (identifier, sentence))
input_file.flush()
command = [metaMapExec,'--sldiID','--silent','--prune 2']
command.append(input_file.name)
#command.append(output_file.name)
# ['/home/shukla/Documents/WMC/backendStuff/MetaMap/public_mm/bin/metamap', '--sldiID', '/tmp/tmp3grliz', '/tmp/tmpoC_O9s']
metamap_process = subprocess.Popen(command, stdout=subprocess.PIPE)
while fileExists == False:
if os.path.isfile("temp_input.out"):
fileExists = os.stat("temp_input.out").st_size != 0
findings = phrases("temp_input.out")
os.remove(input_file.name)
os.remove("temp_input.out")
return findings
def getMetamapResults(sentences,metaMapBinDir):
metaMapExec = metaMapBinDir + 'metamap'
fileExists = False
input_file = None
RANDOM_SUFFIX_RANGE = 1000000
SUFFIX = random.randint(0, 1000000)
if sentences is not None:
input_file = open("/tmp/sunburst_metamap_%d" % SUFFIX, "w")
if sentences is not None:
ids = range(1,len(sentences)+1)
for identifier, sentence in zip(ids, sentences):
input_file.write('%r|%r\n' % (identifier, sentence))
input_file.flush()
command = [metaMapExec,'--sldiID','--silent']
command.append(input_file.name)
metamap_process = subprocess.Popen(command, stdout=subprocess.PIPE)
output_file_name = "/tmp/sunburst_metamap_%d.out" % SUFFIX
while fileExists == False:
if os.path.isfile(output_file_name):
fileExists = os.stat(output_file_name).st_size != 0
findings = phrases(output_file_name)
os.remove(input_file.name)
os.remove(output_file_name)
return findings
top = options.top
trj = options.traj
b = options.begin
e = options.end
skip = options.skip
rec_str = options.receptor
lig_str = options.ligand
lig_dist_cutoff = options.dist_cutoff
min_phrase_len = 3
u = MD.Universe(top, trj)
receptor = u.select_atoms(rec_str)
ligand = u.select_atoms(lig_str)
P = phrases.phrases(u, receptor, ligand, lig_dist_cutoff, min_phrase_len)
P.find_phrases(b, e, skip)
with open(options.out + '-phrases.dat', 'wb') as output:
pickle.dump(P.phrases, output, pickle.HIGHEST_PROTOCOL)
P.calc_dist()
np.savez(options.out + "-distance.npz", P.D)
exit()
elif options.program == "anal-phrases":
res0 = options.res0
threshold = options.jac
if options.dist != None:
P = phrases.read_phrases(options.phrases, min_len=3, dist=options.dist)
skip = options.skip
cutoff = options.cutoff
rec_str = options.receptor
lig_str = options.ligand
res0 = options.res0
threshold = options.jac
lig_dist_cutoff = options.lig_dist
min_phrase_len = 3
u = MD.Universe(top,trj)
receptor = u.select_atoms(rec_str)
ligand = u.select_atoms(lig_str)
P = phrases.phrases(u,receptor,ligand,lig_dist_cutoff,min_phrase_len)
P.find_phrases(b,e,skip)
with open(options.out+'-phrases.dat', 'wb') as output:
pickle.dump(P.phrases, output, pickle.HIGHEST_PROTOCOL)
P.calc_dist()
np.savez(options.out+"-distance.npz",P.D)
p = np.linspace(0,100,1002)
perc = np.percentile(P.D,p)
if cutoff==None:
n_val = 551
with open('sword.set', 'rb') as f:
sword_list = load(f)
with open('phrase.set','rb') as f:
phrase_list = load(f)
problem = lil_matrix((5000, 17173))
n = 0
for i, tfile in enumerate(train_files):
if i < 2500:
fdir = pos_dir
else:
fdir = neg_dir
with open(fdir+tfile) as f:
text = f.read()
tokens = tokenize(text)
fphrases = phrases(tokens)
for token in tokens:
if token in sword_list:
ind = sword_list.index(token)
problem[i, ind] = 1
for p in fphrases:
if p in phrase_list:
ind = phrase_list.index(p) + 3111
problem[i, ind] = 1
with open('problem.matrix', 'wb') as f:
dump(problem, f)
