Nov-05-2019, 05:41 AM
is this what you are looking for?
>>> import numpy as np >>> delta = 0.025 >>> x = y = np.arange(-3.0, 3.0, delta) >>> x array([-3.0000000e+00, -2.9750000e+00, -2.9500000e+00, -2.9250000e+00, -2.9000000e+00, -2.8750000e+00, -2.8500000e+00, -2.8250000e+00, -2.8000000e+00, -2.7750000e+00, -2.7500000e+00, -2.7250000e+00, -2.7000000e+00, -2.6750000e+00, -2.6500000e+00, -2.6250000e+00, -2.6000000e+00, -2.5750000e+00, -2.5500000e+00, -2.5250000e+00, -2.5000000e+00, -2.4750000e+00, -2.4500000e+00, -2.4250000e+00, -2.4000000e+00, -2.3750000e+00, -2.3500000e+00, -2.3250000e+00, -2.3000000e+00, -2.2750000e+00, -2.2500000e+00, -2.2250000e+00, -2.2000000e+00, -2.1750000e+00, -2.1500000e+00, -2.1250000e+00, -2.1000000e+00, -2.0750000e+00, -2.0500000e+00, -2.0250000e+00, -2.0000000e+00, -1.9750000e+00, -1.9500000e+00, -1.9250000e+00, -1.9000000e+00, -1.8750000e+00, -1.8500000e+00, -1.8250000e+00, -1.8000000e+00, -1.7750000e+00, -1.7500000e+00, -1.7250000e+00, -1.7000000e+00, -1.6750000e+00, -1.6500000e+00, -1.6250000e+00, -1.6000000e+00, -1.5750000e+00, -1.5500000e+00, -1.5250000e+00, -1.5000000e+00, -1.4750000e+00, -1.4500000e+00, -1.4250000e+00, -1.4000000e+00, -1.3750000e+00, -1.3500000e+00, -1.3250000e+00, -1.3000000e+00, -1.2750000e+00, -1.2500000e+00, -1.2250000e+00, -1.2000000e+00, -1.1750000e+00, -1.1500000e+00, -1.1250000e+00, -1.1000000e+00, -1.0750000e+00, -1.0500000e+00, -1.0250000e+00, -1.0000000e+00, -9.7500000e-01, -9.5000000e-01, -9.2500000e-01, -9.0000000e-01, -8.7500000e-01, -8.5000000e-01, -8.2500000e-01, -8.0000000e-01, -7.7500000e-01, -7.5000000e-01, -7.2500000e-01, -7.0000000e-01, -6.7500000e-01, -6.5000000e-01, -6.2500000e-01, -6.0000000e-01, -5.7500000e-01, -5.5000000e-01, -5.2500000e-01, -5.0000000e-01, -4.7500000e-01, -4.5000000e-01, -4.2500000e-01, -4.0000000e-01, -3.7500000e-01, -3.5000000e-01, -3.2500000e-01, -3.0000000e-01, -2.7500000e-01, -2.5000000e-01, -2.2500000e-01, -2.0000000e-01, -1.7500000e-01, -1.5000000e-01, -1.2500000e-01, -1.0000000e-01, -7.5000000e-02, -5.0000000e-02, -2.5000000e-02, -1.0658141e-14, 2.5000000e-02, 5.0000000e-02, 7.5000000e-02, 1.0000000e-01, 1.2500000e-01, 1.5000000e-01, 1.7500000e-01, 2.0000000e-01, 2.2500000e-01, 2.5000000e-01, 2.7500000e-01, 3.0000000e-01, 3.2500000e-01, 3.5000000e-01, 3.7500000e-01, 4.0000000e-01, 4.2500000e-01, 4.5000000e-01, 4.7500000e-01, 5.0000000e-01, 5.2500000e-01, 5.5000000e-01, 5.7500000e-01, 6.0000000e-01, 6.2500000e-01, 6.5000000e-01, 6.7500000e-01, 7.0000000e-01, 7.2500000e-01, 7.5000000e-01, 7.7500000e-01, 8.0000000e-01, 8.2500000e-01, 8.5000000e-01, 8.7500000e-01, 9.0000000e-01, 9.2500000e-01, 9.5000000e-01, 9.7500000e-01, 1.0000000e+00, 1.0250000e+00, 1.0500000e+00, 1.0750000e+00, 1.1000000e+00, 1.1250000e+00, 1.1500000e+00, 1.1750000e+00, 1.2000000e+00, 1.2250000e+00, 1.2500000e+00, 1.2750000e+00, 1.3000000e+00, 1.3250000e+00, 1.3500000e+00, 1.3750000e+00, 1.4000000e+00, 1.4250000e+00, 1.4500000e+00, 1.4750000e+00, 1.5000000e+00, 1.5250000e+00, 1.5500000e+00, 1.5750000e+00, 1.6000000e+00, 1.6250000e+00, 1.6500000e+00, 1.6750000e+00, 1.7000000e+00, 1.7250000e+00, 1.7500000e+00, 1.7750000e+00, 1.8000000e+00, 1.8250000e+00, 1.8500000e+00, 1.8750000e+00, 1.9000000e+00, 1.9250000e+00, 1.9500000e+00, 1.9750000e+00, 2.0000000e+00, 2.0250000e+00, 2.0500000e+00, 2.0750000e+00, 2.1000000e+00, 2.1250000e+00, 2.1500000e+00, 2.1750000e+00, 2.2000000e+00, 2.2250000e+00, 2.2500000e+00, 2.2750000e+00, 2.3000000e+00, 2.3250000e+00, 2.3500000e+00, 2.3750000e+00, 2.4000000e+00, 2.4250000e+00, 2.4500000e+00, 2.4750000e+00, 2.5000000e+00, 2.5250000e+00, 2.5500000e+00, 2.5750000e+00, 2.6000000e+00, 2.6250000e+00, 2.6500000e+00, 2.6750000e+00, 2.7000000e+00, 2.7250000e+00, 2.7500000e+00, 2.7750000e+00, 2.8000000e+00, 2.8250000e+00, 2.8500000e+00, 2.8750000e+00, 2.9000000e+00, 2.9250000e+00, 2.9500000e+00, 2.9750000e+00])