remove rough script

MDANSE/Src/MDANSE/Mathematics/Signal.py

@@ -932,130 +932,3 @@ def power_spectrum(
 
     # Adjust to atom selection
     return (output["romega"], output["pps_total"])
-
-if __name__=="__main__":
-    filter_class = filter_map["ChebyshevTypeI"]
-    filter_class.set_defaults()
-
-    import os
-    os.chdir('..\\..\\..\\')
-
-    data = numpy.loadtxt("Tests\\UnitTests\\TrajectoryFilter\\methane_hydrogen_position.csv")
-    ps = ()
-    ps_raw = ()
-
-    N = 10000
-    fs = 200.0
-
-    pre_filter_freqs = {
-         "h": fftpack.fft(data),
-         "w": fftpack.fftfreq(N, d=1/fs)
-    }
-
-    pre_w = pre_filter_freqs["w"][:np.int32(N/2)]
-    dw = pre_w[1] - pre_w[0]
-    pre_amplitudes = (2 * (np.abs(pre_filter_freqs["h"])) / N)[:np.int32(N/2)]
-
-    import matplotlib.pyplot as plt
-
-    x_raw = np.linspace(0, 646, 160)
-    x = np.linspace(0, 646, 1000)
-
-    plt.figure(figsize=(10, 6))
-    plt.plot(x_raw, ps_raw, label="RAW POWER SPECTRUM")
-    plt.show()
-
-    f = filter_class(**{
-        "n_steps": 10000,
-        "time_step_ps": 0.005,
-        "order": 2,
-        "attenuation_type": "highpass",
-        "cutoff_freq": 25.0,
-        "max_ripple": 1.0
-    })
-
-    data1 = f.apply(data)
-
-    post_filter_freqs = {
-        "h": fftpack.fft(data1),
-        "w": fftpack.fftfreq(N, d=1/fs)
-    }
-
-    post_w = post_filter_freqs["w"][:np.int32(N/2)]
-    post_amplitudes = (2 * (np.abs(post_filter_freqs["h"])) / N)[:np.int32(N/2)]
-
-    plt.figure(figsize=(10, 6))
-    plt.plot(x, ps, label="UPSAMPLED POWER SPECTRUM")
-    plt.show()
-
-    # w0 = 2 * np.pi * 1.5
-    # a0 = 20.0
-    #
-    # w1 = 2 * np.pi * 60.0
-    # a1 = 5.0
-    #
-    # N = 10000
-    # t = np.linspace(0, 20, N)
-    # fs = (t[1] - t[0])**(-1)
-    # x0 = a0 * np.sin(w0*t) + a1 * np.sin(w1*t)
-    #
-    # pre_filter_freqs = {
-    #     "h": fftpack.fft(x0),
-    #     "w": fftpack.fftfreq(N, d=1/fs)
-    # }
-    #
-    # pre_w = pre_filter_freqs["w"][:np.int32(N/2)]
-    # dw = pre_w[1] - pre_w[0]
-    # pre_amplitudes = (2 * (np.abs(pre_filter_freqs["h"])) / N)[:np.int32(N/2)]
-
-    import matplotlib.pyplot as plt
-    plt.figure(figsize=(10, 6))
-    #plt.plot(pre_w, pre_amplitudes, label="pre-filter")
-    plt.show()
-
-    #import matplotlib.pyplot as plt
-    #plt.figure(figsize=(10, 6))
-    #plt.plot(t, x, label="pre-filter")
-    #plt.show()
-
-    #f = filter_class(**{
-    #    "attenuation_type": "lowpass",
-    #    "order": 1,
-    #    "cutoff_freq": 60.0,
-    #    "time_step_ps": 1/fs,
-    #    "n_steps": N}
-    #)
-
-    #x1 = f.apply(x0)
-
-    #post_filter_freqs = {
-    #    "h": fftpack.fft(x1),
-    #    "w": fftpack.fftfreq(N, d=1/fs)
-    #}
-
-#    post_w = post_filter_freqs["w"][:np.int32(N/2)]
-#    post_amplitudes = (2 * (np.abs(post_filter_freqs["h"])) / N)[:np.int32(N/2)]
-
-    import matplotlib.pyplot as plt
-    plt.figure(figsize=(10, 6))
-    #plt.plot(post_w, post_amplitudes, label="post-filter")
-    plt.show()
-
-    #s = f.__str__()
-    #r = f.__repr__()
-
-
-
-    #plt.figure(figsize=(10, 6))
-    #plt.plot(t, f.apply(x), label="post-filter")
-    #plt.show()
-
-    #string_representation = [
-    #    f"Trajectory filter of type {1.0} implemented with the following parameters:\n\n",
-    #    f"  # sample_freq\n  Reciprocal of the molecular dynamics time step, in picohertz\n      {2.0}\n\n",
-    #    f"  # Sample frequency\n  Reciprocal of the molecular dynamics time step in picohertz\n      {3.0}\n\n",
-    #]
-
-    #string_representation.append(f"  # Cutoff frequency\n  Reciprocal of the molecular dynamics time step in picohertz\n      {4.0}\n\n")
-
-    #print("".join(string_representation))