alfa_max = 3 # dB Máxima Atenuación en la Banda de Paso.
alfa_min = 40 #dB Mínima Atenuación en la Banda de Stop.
eps = 1 # Ripple
my_N = [2, 3, 4] #Orden de mis Filtros.
# Diseño de los Filtros:
# ---------------------------------------------------------------------------
# N = 2:
# Filtro Chebyshev Tipo I:
z1, p1, k1 = sig.cheb1ap(my_N[0], eps)
NUM1, DEN1 = sig.zpk2tf(z1, p1, k1)
tf_ch1 = tf(NUM1, DEN1)
# Filtro Chebyshev Tipo II o Chebyshev Inverso:
z2, p2, k2 = sig.cheb2ap(my_N[0], alfa_min)
NUM2, DEN2 = sig.zpk2tf(z2, p2, k2)
tf_ch2 = tf(NUM2, DEN2)
analyze_sys([tf_ch1, tf_ch2],
['Cheby Tipo I Orden 2', 'Cheby Tipo II o Inverso Orden 2'])
# ---------------------------------------------------------------------------
# N = 3:
for (this_order, this_ripple, this_att) in zip(orders2analyze, ripple,
attenuation):
if aprox_name == 'Butterworth':
z, p, k = sig.buttap(this_order)
eps = np.sqrt(10**(this_ripple / 10) - 1)
num, den = sig.zpk2tf(z, p, k)
num, den = sig.lp2lp(num, den, eps**(-1 / this_order))
z, p, k = sig.tf2zpk(num, den)
elif aprox_name == 'Chebyshev1':
z, p, k = sig.cheb1ap(this_order, this_ripple)
elif aprox_name == 'Chebyshev2':
z, p, k = sig.cheb2ap(this_order, this_ripple)
elif aprox_name == 'Bessel':
z, p, k = sig.besselap(this_order, norm='mag')
elif aprox_name == 'Cauer':
z, p, k = sig.ellipap(this_order, this_ripple, this_att)
num, den = sig.zpk2tf(z, p, k)
p1_lp = np.roots ( den1_lp )
p2_lp = np.roots ( den2_lp )
my_z_lp = np.array ([])
my_p_lp = np.concatenate ( (p1_lp, p2_lp), axis = None )
my_k_lp = 1
NUM, DEN = sig.zpk2tf ( my_z_lp, my_p_lp, my_k_lp )
NUM_lp, DEN_lp = sig.lp2lp ( NUM, DEN, w0 )
my_tf_lp = transf_f (NUM_lp,DEN_lp)
# ---------------------------------------------------------------------------
# Ahora Utilizando la función Cheby Tipo 1 dentro del módulo scipy:
z, p, k = sig.cheb1ap (orden_filtro, eps)
NUM_ch, DEN_ch = sig.zpk2tf ( z, p, k )
# ---------------------------------------------------------------------------
# Filtro Destino - Filtro High-Pass:
# Calculo w0:
NUM_hp, DEN_hp = sig.lp2hp ( NUM, DEN, w0 )
my_tf_hp = transf_f ( NUM_hp, DEN_hp )
my_z_hp, my_p_hp, my_k_hp = sig.tf2zpk (NUM_hp, DEN_hp )
print('\n', my_tf_bw)
#pretty_print_lti(my_tf_bw)
print('\nDenominador Factorizado de la Transferencia Normalizada:',
convert2SOS(my_tf_bw))
NUM1, DEN1 = sig.lp2lp(NUM1, DEN1, wb)
my_tf_bw = tfunction(NUM1, DEN1)
# ----------------------------------------------------------------------------
print('\n\nFiltro Chebyshev de Orden 5:\n')
# Filtro de Chebyshev: Uso de los Métodos dentro del Paquete signal de Scipy.
z2, p2, k2 = sig.cheb1ap(orden_filtro, eps)
# Obtengo los Coeficientes de mi Transferencia.
NUM2, DEN2 = sig.zpk2tf(z2, p2, k2)
# Obtengo la Transferencia Normalizada
my_tf_ch = tfunction(NUM2, DEN2)
print('\n', my_tf_ch)
#pretty_print_lti(my_tf_ch)
print('\nDenominador Factorizado de la Transferencia Normalizada:',
convert2SOS(my_tf_ch))
# ----------------------------------------------------------------------------
all_sys = []
analog_filters = []
digital_filters = []
filter_names = []
analog_filters_names = []
digital_filters_names = []
if aprox_name == 'Butterworth':
z, p, k = sig.buttap(order2analyze)
elif aprox_name == 'Chebyshev1':
z, p, k = sig.cheb1ap(order2analyze, ripple)
elif aprox_name == 'Chebyshev2':
z, p, k = sig.cheb2ap(order2analyze, ripple)
elif aprox_name == 'Bessel':
z, p, k = sig.besselap(order2analyze, norm='mag')
elif aprox_name == 'Cauer':
z, p, k = sig.ellipap(order2analyze, ripple, attenuation)
num, den = sig.zpk2tf(z, p, k)
ripple = 0.5
attenuation = 40
orders2analyze = range(2,7)
all_sys = []
filter_names = []
for ii in orders2analyze:
if aprox_name == 'Butterworth':
z,p,k = sig.buttap(ii)
elif aprox_name == 'Chebyshev1':
z,p,k = sig.cheb1ap(ii, ripple)
elif aprox_name == 'Chebyshev2':
z,p,k = sig.cheb2ap(ii, ripple)
elif aprox_name == 'Bessel':
z,p,k = sig.besselap(ii, norm='mag')
elif aprox_name == 'Cauer':
z,p,k = sig.ellipap(ii, ripple, attenuation)
num, den = sig.zpk2tf(z,p,k)
def compute_normalised_by_order(self, ap, n, aa) -> ApproximationErrorCode:
""" Generates normalised transfer function prioritising the fixed order """
# Computing needed constants
zeros, poles, gain = ss.cheb1ap(n, ap)
self.h_aux = ss.ZerosPolesGain(zeros, poles, gain)
return ApproximationErrorCode.OK
eps = np.sqrt(10**(alfa_max / 10) - 1)
z, p, k = sig.lp2lp_zpk(z, p, k, omega_butter)
elif aprox_name == 'Chebyshev1':
if force_order > 0:
this_order = force_order
else:
this_order, _ = sig.cheb1ord(omega_p,
omega_s,
alfa_max,
alfa_min,
analog=True)
z, p, k = sig.cheb1ap(this_order, alfa_max)
elif aprox_name == 'Chebyshev2':
if force_order > 0:
this_order = force_order
else:
this_order, _ = sig.cheb2ord(omega_p,
omega_s,
alfa_max,
alfa_min,
analog=True)
z, p, k = sig.cheb2ap(this_order, alfa_min)
elif aprox_name == 'Bessel':
def filter_data():
a = sig.cheb1ap(6, 3)
plt.plot(a[1])
plt.show()
# Cálculo del Epsilon (Ripple)
eps = np.sqrt ( 10**(ripple_/10) - 1 )
wb = eps**(-1/order_filter) # Omega_Butter / Renormalización.
NUM, DEN = sig.zpk2tf (z, p, k)
NUM, DEN = sig.lp2lp ( NUM, DEN, wb )
z, p, k = sig.tf2zpk ( NUM, DEN )
elif (aprox_name == 'Chebyshev1'):
# Le paso Orden y ripple (alfa_máx)
z, p, k = sig.cheb1ap (order_filter, ripple_)
elif (aprox_name == 'Bessel'):
z, p, k = sig.besselap ( order_filter, norm = 'mag' )
else:
sys.exit(' No Existe esa Aproximación o Está mal Escrita.')
# Genero el Numerador y Denominador de mi Transferencia
NUM, DEN = sig.zpk2tf(z, p, k)
