def calculate(path_data, path_artists, talky=False):
"""
Reads all connected components from the given dataset and computes
measures for this graph. For each connected component
'calculate_connected_component' is called - see this method for
documentation.
"""
top_artists = get_top_artists(path_artists)
for i, graph in enumerate(iterator.components(path_data)):
calculate_connected_component(i, graph, top_artists, talky)
def calculate_concurrent(path_data, path_artists, num_threads=4, talky=False):
"""
Same as `calculate`, but uses multiple threads to accelerate the computation
process. Threading is only applied the calculation of the measures - the
input data is still read sequentially.
Note: this is even more memory consuming than `calculate`.
Further note: the real bottleneck seems to be reading the data from disk.
"""
from threading import Thread
from Queue import Queue
from sys import stdout
import time
def worker(id):
while True:
index, graph = queue.get()
# filling the queue might take longer than processing (due to file reads)
# thus we tell the queue that we are done; queue is not involved later on so it should be ok
queue.task_done()
do_work(index, graph)
status[id] += 1
def do_work(index, graph):
calculate_connected_component(index, graph, top_artists)
def print_stati():
# fancy output looks ugly...
if talky:
# print table head
stdout.write(
"Progress:"
+ "all".rjust(11)
+ " || "
+ " | ".join([("T%s" % (i + 1)).rjust(5) for i in range(num_threads)])
+ "\n"
)
while do_the_print:
# reprint table body
stdout.write(
"\r" + str(sum(status)).rjust(20) + " || " + " | ".join([str(i).rjust(5) for i in status])
)
stdout.flush()
time.sleep(0.75)
stdout.write("\n")
num_threads = max(num_threads, 1) # stupid user might be stupid
queue = Queue(maxsize=num_threads * 4)
top_artists = get_top_artists(path_artists)
status = [0 for i in range(num_threads)]
do_the_print = True
# create workers
for i in range(num_threads):
t = Thread(target=worker, args=(i,))
t.daemon = True
t.start()
# for status information
status_thread = Thread(target=print_stati)
status_thread.daemon = True
status_thread.start()
# load data
for tupel in enumerate(iterator.components(path_data)):
queue.put(tupel)
# wait until all threads are finished
queue.join()
do_the_print = False
status_thread.join() # let it write a newline
