def _1d_histogram_tester(binlowhighs, x, weights=1):
h = Histogram(binlowhighs)
h.fill(x, weights=weights)
if np.isscalar(weights):
ynp = np.histogram(x, h.edges[0])[0]
else:
ynp = np.histogram(x, h.edges[0], weights=weights)[0]
assert_array_almost_equal(ynp, h.values)
h.reset()
h._always_use_fillnd = True
h.fill(x, weights=weights)
assert_array_almost_equal(ynp, h.values)
import itertools
if __name__ == '__main__':
x = [1000, 0, 10.01]
y = [1000, 0, 9.01]
xf = np.random.random(1000000)*10*4
yf = np.random.random(1000000)*9*15
xi = xf.astype(int)
yi = yf.astype(int)
wf = np.linspace(1, 10, len(xf))
wi = wf.copy()
times = []
print("Testing 2D histogram timings")
for xvals, yvals, weights in itertools.product([xf, xi], [yf, yi], [wf, wi]):
t0 = time()
h = Histogram(x, y)
h.fill(xvals, yvals, weights=weights)
skxray_time = time() - t0
edges = h.edges
t0 = time()
ynp = np.histogram2d(xvals, yvals, bins=edges, weights=weights)[0]
numpy_time = time() - t0
times.append(numpy_time / skxray_time)
assert_almost_equal(np.sum(h.values), np.sum(ynp))
print('skxray is %s times faster than numpy, on average' % np.average(times))
#
# test_1d_histogram()
# test_2d_histogram()
#TODO do a better job sampling the variable space