if n_classes_hist < 2:
n_classes_hist = 20 # default value
print(
"n_classes_hist should not be inferior than 2. Assigned to default value (20)."
)
events = np.load('events_eruption_v8.npy', fix_imports=True, encoding='latin1')
#events=np.ones((7, 10),dtype=np.float32)
tiempos = np.zeros(1)
for i in range(1):
start = time.time()
xcm_pos, xclags_pos, xcm_neg, xclags_neg, max_hist, min_hist =\
compute_correlation(events, 250, num_threads, chunk_size, threshold, n_classes_hist)
diff_time = time.time() - start
print("Execution finished in: " + str(diff_time))
tiempos[i] = diff_time
print("Max: " + str(tiempos.max()) + " Mean: " + str(tiempos.mean()) +
" Min: " + str(tiempos.min()))
xcm_pos = csr_matrix(xcm_pos, dtype=np.float32)
xclags_pos = csr_matrix(xclags_pos, dtype=np.int32)
xcm_neg = csr_matrix(xcm_neg, dtype=np.float32)
xclags_neg = csr_matrix(xclags_neg, dtype=np.int32)
max_hist = csr_matrix(max_hist, dtype=np.int32)
min_hist = csr_matrix(min_hist, dtype=np.int32)
save_npz('out_xcm_pos.npz', xcm_pos, compressed=True)
num_threads = int(sys.argv[1])
threshold = float(sys.argv[2])
initialiseRunTime()
events = np.load('events_eruption_v8.npy', fix_imports=True, encoding='latin1')
#events=np.ones((7, 10),dtype=np.float32)
times_loop = 1
tiempos = np.zeros(times_loop)
print("The number of threads is: " + str(num_threads))
for i in range(times_loop):
start = time.time()
xcm_pos, xclags_pos, xcm_neg, xclags_neg = compute_correlation(
events, 256, num_threads, threshold)
diff_time = time.time() - start
print("Execution finished in: " + str(diff_time))
tiempos[i] = diff_time
print("Max: " + str(tiempos.max()) + " Mean: " + str(tiempos.mean()) +
" Min: " + str(tiempos.min()))
xcm_pos = csr_matrix(xcm_pos, dtype=np.float32)
xclags_pos = csr_matrix(xclags_pos, dtype=np.int32)
xcm_neg = csr_matrix(xcm_neg, dtype=np.float32)
xclags_neg = csr_matrix(xclags_neg, dtype=np.int32)
save_npz('out_xcm_pos.npz', xcm_pos, compressed=True)
save_npz('out_xcl_pos.npz', xclags_pos, compressed=True)
save_npz('out_xcm_neg.npz', xcm_neg, compressed=True)
nGPUS = int(sys.argv[1])
threshold = float(sys.argv[2])
initialiseRunTime(nGPUS)
events = np.load('events_eruption_v8.npy', fix_imports=True, encoding='latin1')
#events=np.ones((7, 10),dtype=np.float32)
times_loop = 1
tiempos = np.zeros(times_loop)
print("The number of gpus to use is: " + str(nGPUS))
for i in range(times_loop):
start = time.time()
xcm_pos, xclags_pos, xcm_neg, xclags_neg, python_time = compute_correlation(
events, 256, nGPUS, threshold)
diff_time = time.time() - start - python_time
print("Execution finished in: " + str(diff_time))
tiempos[i] = diff_time
print("Max: " + str(tiempos.max()) + " Mean: " + str(tiempos.mean()) +
" Min: " + str(tiempos.min()))
xcm_pos = csr_matrix(xcm_pos, dtype=np.float32)
xclags_pos = csr_matrix(xclags_pos, dtype=np.int32)
xcm_neg = csr_matrix(xcm_neg, dtype=np.float32)
xclags_neg = csr_matrix(xclags_neg, dtype=np.int32)
save_npz('out_xcm_pos.npz', xcm_pos, compressed=True)
save_npz('out_xcl_pos.npz', xclags_pos, compressed=True)
save_npz('out_xcm_neg.npz', xcm_neg, compressed=True)
import numpy as np
import time
from correlation_lib import compute_correlation
events = np.load('events_eruption_v8.npy', fix_imports=True, encoding='latin1')
#events=np.ones((128, 1500),dtype=np.float64)
num_threads = 4
tiempos = np.zeros(1)
for i in range(1):
start = time.time()
xcm_pos, xclags_pos, xcm_neg, xclags_neg = compute_correlation(
events, 250, num_threads)
diff_time = time.time() - start
print("Execution finished in: " + str(diff_time))
tiempos[i] = diff_time
print("Max: " + str(tiempos.max()) + " Mean: " + str(tiempos.mean()) +
" Min: " + str(tiempos.min()))
np.savetxt('xcm_v1_neg.txt', xcm_neg, delimiter='\t', fmt='%6.3f')
np.savetxt('xcl_v1_neg.txt', xclags_neg, delimiter='\t', fmt='%6.0f')
np.savetxt('xcm_v1_pos.txt', xcm_pos, delimiter='\t', fmt='%6.3f')
np.savetxt('xcl_v1_pos.txt', xclags_pos, delimiter='\t', fmt='%6.0f')
import numpy as np
import time
from correlation_lib import compute_correlation
events = np.load('events_eruption_v8.npy', fix_imports=True, encoding='latin1')
#events=np.ones((128, 1500),dtype=np.float64)
tiempos = np.zeros(1)
for i in range(1):
start = time.time()
xcm_pos, xclags_pos, xcm_neg, xclags_neg = compute_correlation(events, 250)
diff_time = time.time() - start
print("Execution finished in: " + str(diff_time))
tiempos[i] = diff_time
print("Max: " + str(tiempos.max()) + " Mean: " + str(tiempos.mean()) +
" Min: " + str(tiempos.min()))
np.savetxt('xcm_v1_neg.txt', xcm_neg, delimiter='\t', fmt='%6.3f')
np.savetxt('xcl_v1_neg.txt', xclags_neg, delimiter='\t', fmt='%6.0f')
np.savetxt('xcm_v1_pos.txt', xcm_pos, delimiter='\t', fmt='%6.3f')
np.savetxt('xcl_v1_pos.txt', xclags_pos, delimiter='\t', fmt='%6.0f')