diff --git a/swash/processing/zero_cross.py b/swash/processing/zero_cross.py index dcfb46a..5d5c0e5 100644 --- a/swash/processing/zero_cross.py +++ b/swash/processing/zero_cross.py @@ -19,6 +19,7 @@ config = configparser.ConfigParser() config.read(args.config) inp = pathlib.Path(config.get("post", "inp")) +inp_comp = pathlib.Path(config.get("post", "compare")) root = pathlib.Path(config.get("swash", "out")) log.info(f"Reading data from '{inp}'") @@ -26,13 +27,16 @@ x = np.load(inp.joinpath("x.npy")) t = np.load(inp.joinpath("t.npy")) watl = np.load(inp.joinpath("watl.npy")) +watl_comp = np.load(inp_comp.joinpath("watl.npy")) # Cospectral calculations x0 = config.getint("post", "x0") arg_x0 = np.abs(x - x0).argmin() w0 = watl[:, arg_x0] +w0_comp = watl_comp[:, arg_x0] cr0 = np.where(np.diff(np.sign(w0)))[0] +cr0_comp = np.where(np.diff(np.sign(w0_comp)))[0] wave = np.fromiter( ( @@ -44,6 +48,16 @@ wave = np.fromiter( ), dtype=np.single, ) +wave_comp = np.fromiter( + ( + np.abs( + np.max(np.abs(w0_comp[cr0_comp[i - 1] : cr0_comp[i]])) + + np.max(np.abs(w0_comp[cr0_comp[i] : cr0_comp[i + 1]])) + ) + for i in range(1, len(cr0) - 1) + ), + dtype=np.single, +) i0 = np.argmax(wave) @@ -53,8 +67,22 @@ out.mkdir(parents=True, exist_ok=True) fig, ax = plt.subplots() ax.plot(t[cr0[1:-1]] * 1e-3, wave) +ax.set(xlabel="t (s)", ylabel="z (m)") +ax.autoscale(True, "x", True) +ax.grid() fig.savefig(out.joinpath("wsize.pdf")) -fig2, ax2 = plt.subplots() -ax2.plot(t[cr0[i0 - 5] : cr0[i0 + 7]], w0[cr0[i0 - 5] : cr0[i0 + 7]]) +fig2, ax2 = plt.subplots(figsize=(10/2.54, 2/3*10/2.54), constrained_layout=True) +ax2.plot(t[cr0[i0 - 5] : cr0[i0 + 7]] * 1e-3, w0[cr0[i0 - 5] : cr0[i0 + 7]], color="k", label="Case 1") +ax2.plot(t[cr0[i0 - 5] : cr0[i0 + 7]] * 1e-3, w0_comp[cr0[i0 - 5] : cr0[i0 + 7]], color="k", ls="-.", label="Case 2") +ax2.set(xlabel="t (s)", ylabel="z (m)") +ax2.autoscale(True, "x", True) +ax2.grid() +ax2.legend() fig2.savefig(out.joinpath("maxw.pdf")) +fig2.savefig(out.joinpath("maxw.jpg"), dpi=200) + +log.info(f"RMS difference: {np.sqrt(np.mean((w0_comp-w0)**2))}m ; {np.sqrt(np.mean((w0_comp-w0)**2))/(w0.max()-w0.min()):%}") +log.info(f"Bias: {np.mean(w0_comp-w0)}m") +log.info(f"Maximum wave size: {wave.max()}m ; {wave_comp.max()}m") +log.info(f"Maximum wave size difference: {abs(wave_comp.max()-wave.max())/wave.max():%}")