Some observations about allclose I have a test case where I would like to impose absolute (logical_)and relative errors (Pdb) pdf_r array([ 2.97341655e-90, 3.68756271e-24, 2.01840159e-07, 3.98942280e-01, 4.83941449e-01, 1.07981933e-01]) (Pdb) pdf_st array([ 3.09721604e-90, 3.69697466e-24, 2.01872148e-07, 3.98942181e-01, 4.83941207e-01, 1.07982122e-01]) (Pdb) pdf_st - pdf_r array([ 1.23799491e-91, 9.41194691e-27, 3.19891525e-11, -9.95821123e-08, -2.41764579e-07, 1.89015140e-07]) (Pdb) (pdf_st - pdf_r) / pdf_r array([ 4.16354347e-02, 2.55234897e-03, 1.58487551e-04, -2.49615338e-07, -4.99574028e-07, 1.75043301e-06]) allclose just uses the sum of abolute and relative errors, which I think is binding in the wrong way, for what I want. (Pdb) np.allclose(pdf_st, pdf_r, rtol=1e-4, atol=1e-8) True the relative and absolute errors are to tight on one side and too loose on the other side (Pdb) np.allclose(pdf_st, pdf_r, rtol=1e-2, atol=0) False (Pdb) np.allclose(pdf_st, pdf_r, rtol=0, atol=1e-8) False tighter bounds for both relative and absolute errors if I impose both separately (Pdb) np.allclose(pdf_st, pdf_r, rtol=0, atol=1e-6) True (Pdb) np.allclose(pdf_st, pdf_r, rtol=1e-1, atol=0) True Are there any alternatives to this? (or am I too tired) (test cases are for statistical distributions, where all values are smaller than one, and some values can be very small) Josef