def normalize(self):
param = TemplateParameters(wa=self.denormalized_template.wp /
self.denormalized_template.wa,
wp=1,
alpha_p=self.denormalized_template.alpha_p,
alpha_a=self.denormalized_template.alpha_a)
self.normalized_template = \
LowPassTemplate(param=param,
n_min=self.denormalized_template.n_min, n_max=self.denormalized_template.n_max,
q_max=self.denormalized_template.q_max,
denorm_degree=self.denormalized_template.denorm_degree)
def calculate_new_filter_cb(self):
if (not self.is_filter_type_set
) or self.approximation_type_name == "Approximation type":
return
nmin = 0
nmax = 25
if self.n_options_name == "N min y max":
nmin = int(self.nmin_value.get())
nmax = int(self.nmin_value.get())
elif self.n_options_name == "N fijo":
nmin = int(self.nfijo_value.get())
nmax = int(self.nfijo_value.get())
Qmax = 200
if len(self.qmax_value.get()):
Qmax = float(self.qmax_value.get())
template_parameters_list = []
for key in self.template_parameters_input:
if self.template_parameters_input[key][0].get():
template_parameters_list.append(
float(self.template_parameters_input[key][0].get()))
else:
template_parameters_list.append(None)
print(self.template_parameters_input[key][0].get())
template_parameters_list_to_send = TemplateParameters(
*template_parameters_list)
normal = 1
dn = self.normalizacion.get()
if (dn == "Desnormalizado"):
normal = 0
auxTup = self.m.add_filter(self.filter_type_name.get(), self.approximation_type_name.get(), \
template_parameters_list_to_send, nmin, nmax, Qmax, normal)
if auxTup[1] == -1: #si hubo error
messagebox.showinfo("Error", auxTup[0])
else:
new_filter_input = []
new_filter_input.append(
IntVar()) # guarda si esta seleccionado o no
new_filter_input.append(auxTup[0]) # nombre
new_filter_input.append(auxTup[1]) # filter index
new_filter_input.append(
Checkbutton(self.existing_filters_frame,
variable=new_filter_input[0],
text=new_filter_input[1],
state='normal'))
new_filter_input[3].pack(side=TOP, fill='x')
self.filters_input.append(new_filter_input)
def get_parameter_list():
return TemplateParameters(wa=True, wp=True, alpha_a=True, alpha_p=True)
def get_parameter_list():
return TemplateParameters(wrg=True, tau=True, tol=True)
import numpy
import math
from group_delay import group_delay
# m = Model()
# s, id = m.add_filter("Band-pass", "Inverse Chebyshev", [500, 100, 200, 1, 20], 1, 25, 100, 50)
# #m.f.auto_stage_decomposition(.1, 15)
# print(s)
# m.get_plot(id, "Step response", False)
tau = 10e-3
w_rg = 600
tolerance = 0.20
params = TemplateParameters(wrg=w_rg, tau=tau, tol=tolerance)
template = GroupDelayTemplate(param=params, q_max=100, n_min=1, n_max=25)
f = GroupDelay(template=template, approx="Bessel")
f.calculate_pzkn()
tf = f.calculate_tf(f.denormalized_poles, f.denormalized_zeros, f.denormalized_k)
[w, mag, pha] = signal.bode(tf)
plt.semilogx(w, -mag)
plt.grid(b=True)
plt.show()
print(tf.num/tf.den[0])
print(tf.den/tf.den[0])
gd = -numpy.diff(pha)/numpy.diff(w)*math.pi/180
w = numpy.delete(w, 0)