def plot(cluster_file,
run_dir='../run/',
anim_path=None,
frame_path=None,
min_size=None):
# configure default animation and frame path
if anim_path is None:
anim_path = 'anim'
if frame_path is None:
frame_path = os.path.join(anim_path, 'frames')
# ensure paths exist
if not os.path.isdir(anim_path):
os.mkdir(anim_path)
if not os.path.isdir(frame_path):
os.mkdir(frame_path)
# load clusters
clusters = rc.load_clusters(cluster_file, sort='random')
# compress, if a threshold is set
if min_size is not None:
clusters = compress_clusters(clusters, min_size)
# calculate number of clusters
n_clusters = len(clusters)
agent_cluster = rc.agent_cluster(clusters)
# establish cluster object as a shared memory object.
#manager = Manager()
#cluster = manager.dict(agent_cluster)
start = 1
stop = 300
# grouping to keep memory footprint reasonably small.
# TODO: Fix this using iterators and multiprocessing.Pool(). Running into
# major issues with serialization of Agent class that is preventing
# plot_step from being picklable.
for begin in xrange(start, stop, 1500):
end = min(begin + 1499, stop)
p = get_population_during_time(begin, end)
for t in xrange(begin, end, 3):
plot_step(t,
p,
agent_cluster,
n_clusters,
frame_path,
cmap='Paired')
subprocess.call((
"mencoder mf://%s/*.png -o %s/output.avi -mf type=png:w=600:h=800:fps=30 -ovc x264 -x264encopts qp=20"
% (frame_path, anim_path)).split())
def plot(cluster_file, run_dir='../run/', anim_path=None, frame_path=None,
min_size=None):
# configure default animation and frame path
if anim_path is None:
anim_path = 'anim'
if frame_path is None:
frame_path = os.path.join(anim_path, 'frames')
# ensure paths exist
if not os.path.isdir(anim_path):
os.mkdir(anim_path)
if not os.path.isdir(frame_path):
os.mkdir(frame_path)
# load clusters
clusters = rc.load_clusters(cluster_file, sort='random')
# compress, if a threshold is set
if min_size is not None:
clusters = compress_clusters(clusters, min_size)
# calculate number of clusters
n_clusters = len(clusters)
agent_cluster = rc.agent_cluster(clusters)
# establish cluster object as a shared memory object.
#manager = Manager()
#cluster = manager.dict(agent_cluster)
start = 1
stop = 300
# grouping to keep memory footprint reasonably small.
# TODO: Fix this using iterators and multiprocessing.Pool(). Running into
# major issues with serialization of Agent class that is preventing
# plot_step from being picklable.
for begin in xrange(start, stop, 1500):
end = min(begin + 1499, stop)
p = get_population_during_time(begin, end)
for t in xrange(begin, end, 3):
plot_step(t, p, agent_cluster, n_clusters, frame_path, cmap='Paired')
subprocess.call(("mencoder mf://%s/*.png -o %s/output.avi -mf type=png:w=600:h=800:fps=30 -ovc x264 -x264encopts qp=20" % (frame_path, anim_path) ).split())
print cluster
exit()
'''
import readcluster
clusters = readcluster.load_clusters(sys.argv[-1])
new_clust = [clust for clust in clusters if len(clust) > 700]
small_clust = []
for clust in clusters:
if len(clust) <= 700:
small_clust.extend(clust)
new_clust.append(small_clust)
cluster = readcluster.agent_cluster(new_clust)
n_clusters = len(set(cluster.values()))
print n_clusters
print "processing..."
# establish cluster object as a shared memory object.
from multiprocessing import Pool, Manager
manager = Manager()
cluster = manager.dict(cluster)
#p = manager.list(p)
start = 1
stop = 30000
'''
# for testing purposes:
print cluster
exit()
'''
import readcluster
clusters = readcluster.load_clusters(sys.argv[-1])
new_clust = [clust for clust in clusters if len(clust) > 700]
small_clust = []
for clust in clusters:
if len(clust) <= 700:
small_clust.extend(clust)
new_clust.append(small_clust)
cluster = readcluster.agent_cluster(new_clust)
n_clusters = len(set(cluster.values()))
print n_clusters
print "processing..."
# establish cluster object as a shared memory object.
from multiprocessing import Pool, Manager
manager = Manager()
cluster = manager.dict(cluster)
#p = manager.list(p)
start = 1
stop = 30000
'''
