add zou table

logm,low_3sigma,low_2sigma,low_1sigma,median,upp_1sigma,upp_2sigma,upp_3sigma

7.0,0.002,0.014,0.046,0.106,0.218,0.425,0.695

7.1,0.003,0.017,0.052,0.115,0.230,0.436,0.701

7.2,0.005,0.021,0.059,0.125,0.242,0.447,0.705

7.3,0.007,0.026,0.066,0.136,0.255,0.459,0.712

7.4,0.010,0.032,0.075,0.147,0.269,0.471,0.718

7.5,0.014,0.039,0.085,0.159,0.283,0.484,0.724

7.6,0.018,0.047,0.096,0.173,0.299,0.496,0.732

7.7,0.025,0.056,0.108,0.187,0.315,0.510,0.737

7.8,0.033,0.067,0.121,0.203,0.331,0.524,0.742

7.9,0.042,0.079,0.136,0.220,0.349,0.538,0.749

8.0,0.053,0.093,0.152,0.239,0.368,0.552,0.756

8.1,0.066,0.109,0.170,0.258,0.388,0.568,0.763

8.2,0.081,0.126,0.190,0.280,0.408,0.583,0.770

8.3,0.098,0.146,0.211,0.302,0.430,0.598,0.776

8.4,0.118,0.167,0.235,0.327,0.453,0.615,0.782

8.5,0.139,0.190,0.260,0.352,0.476,0.632,0.790

8.6,0.163,0.216,0.287,0.380,0.501,0.649,0.797

8.7,0.189,0.244,0.316,0.409,0.527,0.668,0.805

8.8,0.216,0.275,0.348,0.440,0.554,0.687,0.814

8.9,0.245,0.307,0.381,0.472,0.582,0.706,0.825

9.0,0.279,0.341,0.416,0.506,0.610,0.726,0.835

9.1,0.316,0.379,0.453,0.540,0.640,0.747,0.847

9.2,0.355,0.418,0.491,0.576,0.670,0.768,0.859

9.3,0.397,0.459,0.531,0.612,0.700,0.791,0.875

9.4,0.441,0.502,0.572,0.649,0.731,0.814,0.891

9.5,0.484,0.545,0.613,0.685,0.762,0.838,0.908

9.6,0.528,0.588,0.653,0.722,0.793,0.863,0.927

9.7,0.569,0.629,0.692,0.757,0.823,0.887,0.943

9.8,0.608,0.668,0.729,0.792,0.853,0.910,0.957

9.9,0.643,0.703,0.763,0.824,0.882,0.931,0.969

10.0,0.678,0.734,0.793,0.854,0.908,0.951,0.979

10.1,0.705,0.762,0.821,0.880,0.932,0.967,0.987

10.2,0.729,0.785,0.845,0.903,0.951,0.980,0.993

10.3,0.748,0.806,0.867,0.923,0.966,0.988,0.996

10.4,0.767,0.824,0.885,0.940,0.977,0.994,0.998

10.5,0.782,0.841,0.901,0.954,0.985,0.997,0.999

10.6,0.797,0.855,0.915,0.965,0.990,0.998,1.000

10.7,0.808,0.867,0.928,0.973,0.994,0.999,1.000

10.8,0.818,0.879,0.938,0.980,0.996,1.000,1.000

10.9,0.827,0.890,0.948,0.985,0.998,1.000,1.000

11.0,0.836,0.899,0.955,0.988,0.998,1.000,1.000

11.1,0.843,0.907,0.962,0.991,0.999,1.000,1.000

11.2,0.851,0.915,0.968,0.994,0.999,1.000,1.000

11.3,0.858,0.922,0.972,0.995,1.000,1.000,1.000

11.4,0.864,0.929,0.977,0.996,1.000,1.000,1.000

11.5,0.870,0.935,0.980,0.997,1.000,1.000,1.000

11.6,0.876,0.940,0.983,0.998,1.000,1.000,1.000

11.7,0.881,0.945,0.986,0.999,1.000,1.000,1.000

11.8,0.886,0.950,0.988,0.999,1.000,1.000,1.000

11.9,0.890,0.954,0.990,0.999,1.000,1.000,1.000

12.0,0.895,0.958,0.991,0.999,1.000,1.000,1.000

# NOTE: git clone 'https://github.com/lsst/throughputs.git'

if __name__ == "__main__":

    filenames = glob.glob("data/egg/share/filter-db/inaf/throughputs/baseline/hardware_*.fits")

    filenames = glob.glob("data/egg/share/filter-db/inaf/throughputs/baseline/hardware_*.dat")

    for filename_in in filenames:

        x, y = np.loadtxt(filename_in).T

        x *= u.nm.to(u.um)

        norm = np.trapz(y, x)

        norm = np.trapezoid(y, x)

        y /= norm

        assert np.isclose(np.trapz(y, x), 1.0)

        assert np.isclose(np.trapezoid(y, x), 1.0)

        filename_out = filename_in.replace(".dat", ".fits")

        fitsio.write(filename_out, {"LAM": np.array([x]), "RES": np.array([y])})