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scrapbook.py
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scrapbook.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from scipy import signal
from scipy import linalg
from matplotlib import pyplot as plt
from numpy import matlib
import control as ct
from quantiser_configurations import quantiser_configurations
from balreal import balreal, balreal_ct
%reload_ext autoreload
%autoreload 2
# Fc_lp = 10e3
# N_lp = 3
# Wn = 2*np.pi*Fc_lp
# b1, a1 = signal.butter(N_lp, Wn, 'lowpass', analog=True)
# Wlp = signal.TransferFunction(b1, a1) # filter LTI system instance
# Wlp_ss = Wlp.to_ss() # controllable canonical form
# A = Wlp_ss.A
# B = Wlp_ss.B
# C = Wlp_ss.C
# D = Wlp_ss.D
# A_, B_, C_, D_ = balreal_ct(A, B, C, D)
# Wlp_ss_d = signal.cont2discrete((A_, B_, C_, D_), dt=1e-6, method='zoh')
Wn = 100e3/(25e6/2)
bd, ad = signal.butter(3, Wn)
Wlpd = signal.dlti(bd, ad, dt=1/25e6)
Nsamp = int(1e6)
y = np.random.uniform(low=-1.0, high=1.0, size=[1,Nsamp])
y = y.reshape(-1, 1) # ensure the vector is a column vector
#y_avg_out = signal.lfilter(bd, ad, y)
y_avg_out = signal.dlsim(Wlpd, y) # filter the output
y_avg = y_avg_out[1] # extract the filtered data; lsim returns (T, y, x) tuple, want output y
"""
case 3: # zoh interp. matches physics, SciPi impl. causes numerical problems??
Wn = 2*np.pi*Fc_lp
b1, a1 = signal.butter(N_lp, Wn, 'lowpass', analog=True)
Wlp = signal.TransferFunction(b1, a1) # filter LTI system instance
Wlp_ss = Wlp.to_ss() # controllable canonical form
Ac = Wlp_ss.A
Bc = Wlp_ss.B
Cc = Wlp_ss.C
Dc = Wlp_ss.D
A_, B_, C_, D_ = balreal_ct(Ac, Bc, Cc, Dc)
Wlp_ss_d = signal.cont2discrete((A_, B_, C_, D_), dt=1e-6, method='zoh')
A1 = Wlp_ss_d.A
B1 = Wlp_ss_d.B
C1 = Wlp_ss_d.C
D1 = Wlp_ss_d.D
"""
#dt = 1e-6
#A = np.eye(3, k=-1)
#A[0,:] = a1[1:4]
#B = np.eye(3,1, k=0)
#C = np.zeros((1,3))
#C[0,2] = b1[-1]
#D = np.array([0])
#Wlp_ss = Wlp.to_ss()
#Wlp_ss_d = signal.cont2discrete((Wlp_ss.A, Wlp_ss.B, Wlp_ss.C, Wlp_ss.D), dt, method='zoh')
#Ad = Wlp_ss_d[0]
#Bd = Wlp_ss_d[1]
#Cd = Wlp_ss_d[2]
#Dd = Wlp_ss_d[3]
#A, B, C, D = balreal(Ad, Bd, Cd, Dd)
# Wn = 2*np.pi*Fc_lp
# b, a = signal.butter(N_lp, Wn, 'lowpass', analog=True)
# Wlp = signal.lti(b, a) # filter LTI system instance
# Wlp_ss = Wlp.to_ss()
# dt = 1e-3
# Wlp_ss_d = signal.cont2discrete((Wlp_ss.A, Wlp_ss.B, Wlp_ss.C, Wlp_ss.D), dt, method='zoh')
# Ad = Wlp_ss_d[0]
# Bd = Wlp_ss_d[1]
# Cd = Wlp_ss_d[2]
# Dd = Wlp_ss_d[3]
# G = Wlp.to_discrete(dt, method='zoh')
#Ad, Bd, Cd, Dd = balreal(Wlp_ss_d[0], Wlp_ss_d[1], Wlp_ss_d[2], Wlp_ss_d[3])
# import tkinter as tk
# root = tk.Tk()
# root.configure(bg='red')
# root.overrideredirect(True)
# root.state('normal')
# root.after(100, root.destroy) # set the flash time to 100 milliseconds
# root.mainloop()
#N = 1
#for k in range(0,N):
# print(k)
#QConfig = 4
#Nb, Mq, Vmin, Vmax, Rng, Qstep, YQ, Qtype = quantiser_configurations(QConfig)
# DEM code input range
#M = 2*(2**Nb - 1)
#cmin = 2**(Nb-1) - 1
#cmax = M - 2**(Nb-1) + 1
#Qseg = Rng/(cmax-cmin) # segmented step-size (LSB)
#if RUN_LIN_METHOD == lin_method.DEM:
# SIGNAL_OFFSET = Rng/2# - Qstep/2
#else:
"""
Ks =
1 1
2 2
4 4
8 8
16 16
32 32
64 64
128 128
256 256
512 512
1024 1024
2048 2048
4096 4096
8192 8192
16384 16384
32768 32768
"""
# DEM mapping from output segment weights to codes
#Ks = 2**np.arange(0,Nb)
#Ks = matlib.repmat(Ks.reshape(-1, 1),1,2)
# from balreal import balreal
# Hns_tf = signal.TransferFunction([1, -2, 1], [1, 0, 0], dt=1)
# Mns_tf = signal.TransferFunction([2, -1], [1, 0, 0], dt=1) # Mns = 1 - Hns
# Mns = Mns_tf.to_ss()
# A = Mns.A
# B = Mns.B
# C = Mns.C
# D = Mns.D
# A_, B_, C_, D_ = balreal(A, B, C, D)
# %%
#M = np.random.normal(0, 2.5, size=(4,10))
#x = np.array([1, 2, 3, 4, 3, 2, 1, 0, 1, 1, 1], np.int32)
#for k in range(0,M.shape[0]):
# print(k)
# print(M[k,x])
#M[x]
# w, h = signal.freqz(b, a)
# h[0] = h[1]
# f = Fs*w/(2*np.pi)
# fig, ax1 = plt.subplots()
# ax1.set_title('Digital filter frequency response')
# ax1.semilogx(f, 20*np.log10(abs(h)), 'b')
# ax1.set_ylabel('Amplitude (dB)', color='b')
# ax1.set_xlabel('Frequency (Hz)')
# ax1.grid(True)
# fig, ax2 = plt.subplots()
# angles = (180/np.pi)*np.unwrap(np.angle(h))
# ax2.semilogx(f, angles, 'g')
# ax2.set_ylabel('Angle (degrees)', color='g')
# ax2.set_xlabel('Frequency (Hz)')
# ax2.grid(True)
# plt.show()
# dsf = Dsf[1, :]
# f, Pxx_den = signal.welch(dsf, Fs)
# fig, ax1 = plt.subplots()
# ax1.loglog(f, Pxx_den)
# ax1.set_xlabel('frequency [Hz]')
# ax1.set_ylabel('PSD [V**2/Hz]')
# plt.show()