M = M * (1 + .4j)
# Category IV: binary or text
if np.random.random() < 0.5: # binary
# if True:
cat_4 = 'binary'
digits = 0
else: # text
cat_4 = 'text'
digits = 4 + np.random.randint(22)
print(
'Test %3d/%d %-11s %-6s (rho=%6.4f) %-7s %-6s %4d x %4d' %
(iSet + 1, nTests, cat_1, cat_2, density, cat_3, cat_4, nRows, nCols))
cop4.Save(filename, M, digits=digits)
fh = cop4.OP4(filename, 'r')
L = fh.Load(nmat=1)
error = np.abs(np.max(M - L))
if cat_2 == 'dense':
pass
else:
error = np.abs(np.max(M - L))
if type(error) == np.float64:
# happens when input is 1x1, or error is exactly zero
pass
else:
if error.getnnz() == 0:
error = 0.0
else:
def dense_square_test(dimension, is_binary=True):
filenames = []
cop4_save = []
cop4_load = []
pop4_load = []
# generate the file list
for i,n in enumerate(dimension):
if is_cyfiles:
if is_binary:
filenames.append( 'cop4_%dx%d_binary.op4' % (n, n) )
else:
filenames.append( 'cop4_%dx%d_ascii.op4' % (n, n) )
else:
if is_binary:
filenames.append( 'pyop4_%dx%d_binary.op4' % (n, n) )
else:
filenames.append( 'pyop4_%dx%d_ascii.op4' % (n, n) )
print("is_cyfiles=%s is_binary=%s" % (is_cyfiles, is_binary))
# create dense matrices using cop4
pyop4 = OP4()
for i,n in enumerate(dimension):
if not os.path.exists(filenames[i]):
print('%s does not exist, will create.' % filenames[i])
A = np.random.randn(n,n) #.astype(np.float32)
#print('*****\n', A)
mat_name = 'matrix%-3i' % i
if is_cyfiles:
start_elapsed = time.time()
kwargs = {
mat_name : A,
'digits' : 5,
}
if is_binary:
del kwargs['digits']
else:
print('digits =', kwargs['digits'])
cop4.Save(filenames[i], **kwargs)
#cop4.Save(File[i], A=A, digits=5) # ascii
#cop4.Save(File[i], A=A) # binary
end_elapsed = time.time()
else:
start_elapsed = time.time()
matrices = {'A' : (1, A) } # 1 is the form -> square
names = None # name order -> sorted
pyop4.write_op4(filenames[i], matrices, names, precision='default', is_binary=is_binary)
end_elapsed = time.time()
print('wrote %-20s in %8.3f s' % (filenames[i], end_elapsed-start_elapsed))
print("filenames =", filenames)
# Method 1: cop4
for i in xrange(len(dimension)):
start_elapsed = time.time()
B = cop4.Load(filenames[i], '*') # dictionary-based
end_elapsed = time.time()
#print(B.keys())
key0 = B.keys()[0]
#print(B[key0])
del B
cop4_load.append(end_elapsed - start_elapsed)
print('cop4 read %-20s in %8.3f s' % (filenames[i], cop4_load[i]))
# Method 2: pure Python op4
for i in xrange(len(dimension)):
start_elapsed = time.time()
B = pyop4.read_op4(filenames[i])
end_elapsed = time.time()
#print("keys = %s" % B.keys())
key0 = B.keys()[0]
#print("form = %s" % B[key0][0])
#print(B[key0][1], '\n')
del B
pop4_load.append(end_elapsed - start_elapsed)
print('pop4 read %-20s in %8.3f s' % (filenames[i], pop4_load[i]))
print('Dimension C op4 Python op4')
print('--------- --------- ----------')
for i,n in enumerate(dimension):
print('%6d %8.3f %8.3f' % (n, cop4_load[i], pop4_load[i]))
if make_plot:
plt.semilogy(dimension, cop4_load, color='green')
plt.semilogy(dimension, pop4_load, color='blue' )
plt.legend(['cop4', 'op4'], 'upper left')
plt.grid(True)
plt.xlabel('Matrix Dimension')
plt.ylabel('Time [seconds]')
plt.title('Dense Square Matrix Load Performance')
# be 1.5 GB.
dimension = range(1000, 5000, 1000)
#print dimension
File = []
cop4_save = []
cop4_load = []
pop4_load = []
for i,n in enumerate(dimension): File.append( 'A_%dx%d.op4' % (n, n) )
for i,n in enumerate(dimension):
if not os.path.exists(File[i]):
print('%s does not exist, will create.' % (File[i]))
A = np.random.rand(n,n).astype(np.float32)
start_elapsed = time.time()
cop4.Save(File[i], A=A)
end_elapsed = time.time()
print('wrote %-20s in %8.3f s' % (File[i], end_elapsed-start_elapsed))
# Method 1: cop4
for i in range(len(dimension)):
start_elapsed = time.time()
B = cop4.Load(File[i])
end_elapsed = time.time()
cop4_load.append(end_elapsed - start_elapsed)
print('cop4 read %-20s in %8.3f s' % (File[i], cop4_load[i]))
# Method 2: pure Python op4
for i in range(len(dimension)):
start_elapsed = time.time()
op4 = OP4()