add some filter stuff

plots/04_filters.py

@@ -0,0 +1,85 @@
+# %%
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Tuple, Union
+
+import numpy as np
+from matplotlib import pyplot as plt
+from matplotlib.animation import FuncAnimation
+from matplotlib.axes import Axes
+from matplotlib.figure import Figure
+from matplotlib.gridspec import GridSpec
+from scipy import signal
+
+
+# %%
+def db20(x):
+    return 20 * np.log10(x)
+
+
+# %%
+wpass = 20 * 2 * np.pi
+wstop = 30 * 2 * np.pi
+gpass = 2
+gstop = 30
+
+o, wn = signal.cheb1ord(
+    wpass,
+    wstop,
+    2,
+    30,
+    analog=True,
+)
+zpk_cheb1 = signal.cheby1(o, gpass, wn, analog=True, output="zpk", btype="lowpass")
+
+o, wn = signal.cheb2ord(
+    wpass,
+    wstop,
+    gpass,
+    gstop,
+    analog=True,
+)
+zpk_cheb2 = signal.cheby2(o, gpass, wn, analog=True, output="zpk", btype="lowpass")
+
+o, wn = signal.buttord(wpass, wstop, gpass, gstop, analog=True)
+zpk_butter = signal.butter(o, wn, btype="lowpass", analog=True, output="zpk")
+
+o, wn = signal.ellipord(wpass, wstop, gpass, gstop, analog=True)
+zpk_ellip = signal.ellip(o, gpass, gstop, wn, btype="lowpass", analog=True, output="zpk")
+
+# %%
+fig = plt.figure(layout="constrained")
+gs = GridSpec(2, 2, figure=fig)
+ax_zp = fig.add_subplot(gs[:, 0])
+ax_f = fig.add_subplot(gs[0, 1])
+ax_t = fig.add_subplot(gs[1, 1])
+
+for z, p, k in [
+    zpk_cheb1,
+    # zpk_cheb2,
+    zpk_butter,
+    zpk_ellip,
+]:
+    lines_z = ax_zp.plot(np.real(z), np.imag(z), "o", mfc="none")
+    lines_p = ax_zp.plot(np.real(p), np.imag(p), "x", color=lines_z[0].get_color())
+    # plt.xlim(-150, 150)
+    ax_zp.grid(True)
+    ax_zp.set_xlabel("Real(s)")
+    ax_zp.set_ylabel("Imag(s)")
+
+    f = np.linspace(0.1 * np.max([wpass, wstop]), 10 * np.max([wpass, wstop]), 1001) / (2 * np.pi)
+    ax_f.semilogx(f, db20(signal.freqs_zpk(z, p, k, f * 2 * np.pi)[1]))
+    ax_f.grid(True)
+    ax_f.set_xlabel("Frequnecy [Hz]")
+    ax_f.set_ylabel("Gain [dB]")
+    ax_f.set_xlim(f[0], f[-1])
+
+    # t = np.arange(101)
+    # x = np.zeros(len(t))
+    # x[int(len(x) / 2)] = 1
+    # ax_t.plot(t, signal.lfilter(*ba, x))
+    # ax_t.set_ylim(-10, 100)
+    # ax_t.grid(True)
+
+# %%