def get_taxo_by_name():
data = request.get_json()
pid = data["pid"]
uid = data["uid"]
sid = data["sid"]
tid = data["tid"]
lid = data["lid"]
pip = data["pip"]
dbname = data["rid"]
pipeline = pip
# dbname="isuezrouja"
analysis = "taxonomy"
x = sql.SQL(main_db)
rf = rootvar.result_files(pid, analysis, pip, sid, dbname)
samples = x.exe('select * from samples where project_id="' +
pid+'" and sample_id="'+sid+'"')
xpath = x.project(pid)[0][4]
sample = rootvar.samples(samples[0], xpath)
x.close()
view = rootvar.ViewSampleResults(sample, pipeline, dbname, analysis, rf)
try:
# return jsonify(x=pip)
return jsonify(matrix=view.level(lid, type="matches"))
except Exception as inst:
return "ERROR: "+str(inst)
def get_tree():
try:
data = request.get_json()
pid = data["pid"]
uid = data["uid"]
sid = data["sid"]
selval = data["value"]
pipeline = data["pip"]
dbname = data["rid"]
# dbname="isuezrouja"
analysis = "taxonomy"
rf = rootvar.result_files(pid, analysis, pipeline, sid, dbname)
x = sql.SQL(main_db)
samples = x.exe('select * from samples where project_id="' +
pid+'" and sample_id="'+sid+'"')
xpath = x.project(pid)[0][4]
sample = rootvar.samples(samples[0], xpath)
with open(rf.json) as data_file:
data = json.load(data_file)
x.close()
###print data
view = rootvar.ViewSampleResults(
sample, pipeline, dbname, analysis, rf)
return jsonify(tree=[data], range=view.range(selval)[0], pip=pipeline)
except Exception as inst:
return "ERROR: "+str(inst)
def functional(data):
pid = data["pid"]
uid = data["uid"]
sids = data["sid"]
pipeline = data["pip"]
rid = data["rid"]
edges = []
FULL_MATRIX = []
#first see if the data set contains taxonomy, function or both annotations
analysis = "function"
#print rid
#
#
os.system("mkdir " + rootvar.__ROOTPRO__ + "/" + pid + "/" + pipeline +
"/RESULTS/ >> " + rootvar.log + " 2>&1")
all_samples_tree_file = rootvar.__ROOTPRO__ + "/" + pid + "/" + pipeline + "/RESULTS/" + rid + ".all_samples_tree.pk"
log = open(
rootvar.__ROOTPRO__ + "/" + pid + "/" + pipeline + "/RESULTS/" + rid +
".log", "w")
#
# if os.path.isfile(all_samples_tree_file):
# x=sql.SQL(all_samples_tree_file+".db")
# val=x.exe("select distinct sample_name from full_matrix")
# stored_samples=len(val)
#
# x=sql.SQL(main_db)
# # load all the samples that have been finalized to run
# sids=x.exe('select c.sample_id from (select * from samples a inner join (select * from sample_status where pip="'+str(pipeline)+'") b on a.sample_id==b.sid) c where c.project_id=="'+pid+'" and c.status="Done" and c.rid="'+rid+'"')
# x.close()
# if os.path.isfile(all_samples_tree_file) and stored_samples == len(sids):
# x=sql.SQL(all_samples_tree_file+".db")
# FULL_MATRIX=x.exe("select * from full_matrix")
# return ["function",FULL_MATRIX,all_samples_tree_file+".json"]
# nodes={}
x = sql.SQL(main_db)
log.write("echo #LOG for Project ID: " + str(pid) + "\n")
for sid in sids:
samples = x.exe('select * from samples where project_id="' + pid +
'" and sample_id="' + sid + '"')
# log.write(str(sid)+"\n")
xpath = x.project(pid)[0][4]
sample = rootvar.samples(samples[0], xpath)
#print sample.name
# log.write(str(samples[0])+"\n")
rf = rootvar.result_files(pid, analysis, pipeline, sample.id, rid)
view = rootvar.ViewSampleResults(sample, pipeline, rid, analysis, rf)
FULL_MATRIX += view.all_func()
#print sample.name
x.close()
# log.write(str(FULL_MATRIX))
#print FULL_MATRIX[0]
#rootvar.full_matrix_function(all_samples_tree_file+".db", FULL_MATRIX)
#print "matrix done"
# tree=["none"]
return ["function", FULL_MATRIX, all_samples_tree_file + ".json"]
def get_childs_of_taxonomy():
data = request.get_json()
pid = data["pid"]
uid = data["uid"]
sid = data["sid"]
tid = data["tid"]
lid = data["lid"]
cond = data["cond"]
pip = data["pip"]
rid = data["rid"]
dbname = data["rid"]
analysis = "taxonomy"
##print cond, pip, rid, pid, uid, sid, tid, lid
if cond == "one": # this is if I am analyzing one or multiple samples
rf = rootvar.result_files(pid, analysis, pip, sid, dbname)
x = sql.SQL(main_db)
samples = x.exe('select * from samples where project_id="' +
pid+'" and sample_id="'+sid+'"')
xpath = x.project(pid)[0][4]
sample = rootvar.samples(samples[0], xpath)
x.close()
view = rootvar.ViewSampleResults(sample, pip, dbname, analysis, rf)
matrix = view.childs_of(tid, 'rpkm')
###print matrix
return jsonify(matrix=matrix)
else:
# load big tree
all_samples_tree_file = rootvar.__ROOTPRO__+"/" + \
pid+"/"+pip+"/RESULTS/"+rid+".all_samples_tree.pk"
G = nx.read_gpickle(all_samples_tree_file)
tree = json_graph.tree_data(G, root='R')
data = []
for i in G.successors(tid):
data.append(i)
x = sql.SQL(all_samples_tree_file+".db")
matrix = rootvar.get_matrix_level_childs(x, data)
samples_sel = list(set([str(i[0]) for i in matrix]))
M, N = rootvar.v2m(matrix, samples_sel, 0, 0)
##print M
heatmap = 'none' # heatmap=iclust.main(M,None)
return jsonify(data=matrix[0], heatmap=heatmap)
def get_functional_counts():
try:
data = request.get_json()
pid = data["pid"]
uid = data["uid"]
sid = data["sid"]
pip = data["pip"]
rid = data["rid"]
pip = data['pip']
analysis = "function"
# lid="assembly"
# file=rootvar.__ROOTPRO__+"/"+pid+"/assembly/idba_ud/"+sid+"/pred.genes."+rid+".matches.function.abundance.results.sqlite3.db"
x = sql.SQL(main_db)
rf = rootvar.result_files(pid, "function", pip, sid, rid)
samples = x.exe('select * from samples where project_id="' +
pid+'" and sample_id="'+sid+'"')
xpath = x.project(pid)[0][4]
sample = rootvar.samples(samples[0], xpath)
x.close()
view = rootvar.ViewSampleResults(sample, pip, rid, analysis, rf)
return jsonify(matrix=view.func_one_sample(), m2=view.func_structure(pid, pip, sid))
except Exception as inst:
return "ERROR: "+str(inst)
def taxonomy(data):
pid = data["pid"]
uid = data["uid"]
sids = data["sid"]
pipeline = data["pip"]
rid = data["rid"]
edges = []
FULL_MATRIX = []
#first see if the data set contains taxonomy, function or both annotations
analysis = "taxonomy"
#print '\n\n\n here we goo!!!\n\n\n', pipeline
rootvar.mkdir(rootvar.__ROOTPRO__ + "/" + pid + "/" + pipeline +
"/RESULTS/")
all_samples_tree_file = rootvar.__ROOTPRO__ + "/" + pid + "/" + pipeline + "/RESULTS/" + rid + ".all_samples_tree.pk"
#
stored_samples = 0
if os.path.isfile(all_samples_tree_file):
#print '\n\n\n if the X file has been created'
x = sql.SQL(all_samples_tree_file + ".db")
val = x.exe("select distinct sample_name from full_matrix")
stored_samples = len(val)
#
x = sql.SQL(main_db)
sids = x.exe(
'select c.sample_id from (select * from samples a inner join (select * from sample_status where pip="'
+ str(pipeline) +
'") b on a.sample_id==b.sid) c where c.project_id=="' + pid +
'" and c.status="Done" and c.rid="' + rid + '"')
#
#
#print '\n\n\n\n super important \n', sids
#print '\n\n\n get all the samples from the X file'
#
if os.path.isfile(all_samples_tree_file) and stored_samples == len(sids):
#print 'both have the same length'
G = nx.read_gpickle(all_samples_tree_file)
tree = json_graph.tree_data(G, root='R')
return ["taxonomy", tree]
#section
#print '\n\n\n SO the file has not been created, because the matrix of abundances has not been fetched for those samples'
nodes = {}
#print '\n\n\n So for each sample I get all the information from the sql tables. And create the tree \n\n\n\n\n\n'
#
for sid in sids:
samples = x.exe('select * from samples where project_id="' + pid +
'" and sample_id="' + sid[0] + '"')
xpath = x.project(pid)[0][4]
sample = rootvar.samples(samples[0], xpath)
rf = rootvar.result_files(pid, analysis, pipeline, sample.id, rid)
view = rootvar.ViewSampleResults(sample, pipeline, rid, analysis, rf)
FULL_MATRIX += view.all()
Gp = nx.read_gpickle(rf.pk)
edges += Gp.edges()
for node in Gp.nodes():
if not node in nodes:
nodes[node] = Gp.node[node]['level']
x.close()
rootvar.full_matrix_sql(all_samples_tree_file + ".db", FULL_MATRIX)
G = nx.DiGraph()
for i in edges:
if not i in G.edges():
if not i[1] in G.nodes():
G.add_node(i[1], samples=1, level=nodes[i[1]])
else:
G.node[i[1]]['samples'] += 1
if not i[0] in G.nodes():
G.add_node(i[0], samples=1, level=nodes[i[0]])
else:
G.node[i[0]]['samples'] += 1
if not G.predecessors(i[1]):
G.add_edge(i[0], i[1])
else:
G.node[i[0]]['samples'] += 1
G.node[i[1]]['samples'] += 1
tree = json_graph.tree_data(G, root='R')
nx.write_gpickle(G, all_samples_tree_file)
return ["taxonomy", tree, all_samples_tree_file + ".json"]