def test_simple():
if not os.path.exists('./noaa_data'):
p = params(clevel=5, storage='./noaa_data')
t = Table([], dshape='{f0: int, f1:int, f2:int, f3:float}', params=p)
# TODO: chunkwise copy
t.append(adapter[:])
t.commit()
else:
t = open('ctable://noaa_data')
print '--------------------------------------'
print 'mean', mean(t, 'f3')
print 'std', std(t, 'f2')
print '--------------------------------------'
qs1 = select(t, lambda x: x > 80000, 'f0')
qs2 = select2(t, lambda x,y: x > y, ['f0', 'f1'])
result = t[qs1]
def test_simple():
if not os.path.exists('./noaa_data'):
p = params(clevel=5, storage='./noaa_data')
t = Table([], dshape='{f0: int, f1:int, f2:int, f3:float}', params=p)
# TODO: chunkwise copy
t.append(adapter[:])
t.commit()
else:
t = open('ctable://noaa_data')
print '--------------------------------------'
print 'mean', mean(t, 'f3')
print 'std', std(t, 'f2')
print '--------------------------------------'
qs1 = select(t, lambda x: x > 80000, 'f0')
qs2 = select2(t, lambda x, y: x > y, ['f0', 'f1'])
result = t[qs1]
t = blaze.open('ctable://example1')
# Using chunked blaze array we can optimize for IO doing the sum
# operations chunkwise from disk.
t0 = time()
print blaze.mean(t, 'f0')
print "Chunked mean", round(time()-t0, 6)
# Using NumPy is just going to through the iterator protocol on
# carray which isn't going to efficient.
t0 = time()
print np.mean(t.data.ca['f0'])
print "NumPy mean", round(time()-t0, 6)
print '==================='
t0 = time()
#assert blaze.std(t, 'f0') == 28867.513458037913
print blaze.std(t, 'f0')
print "Chunked std", round(time()-t0, 6)
print '-------------------'
t0 = time()
print np.std(t.data.ca['f0'])
print "NumPy std", round(time()-t0, 6)
#blaze.generic_loop(t, 'f0')
