def csvToSignal(data, field):
while len(data["x-axis"]) > len(data[field]):
data["x-axis"].pop()
senial = Senial.Senial(data["x-axis"], data[field])
return senial
def addCSVQuotient(self,
filename,
name,
color,
fieldX,
fieldA,
fieldB,
mode=DB):
# get k such that kFieldA=fieldB
data = read_csv.read_csv_bode(filename)
signal1 = csvToSignal(data, fieldA, fieldX)
signal2 = csvToSignal(data, fieldB, fieldX)
if mode == DB:
yvarFinal = [
signal2.values[i] - signal1.values[i]
for i in range(len(signal1.values))
]
else:
yvarFinal = [
signal2.values[i] / signal1.values[i]
for i in range(len(signal1.values))
]
self.plotCount.append({
"signal": Senial.Senial(signal1.xvar, yvarFinal),
"color": color,
"name": name
})
return self
def addSpiceBodePlot(self,
filename,
name,
color,
mode,
color2=None,
name2=None):
spiceData = self.getSpiceData(filename)
suma = 0
for i in range(len(spiceData["pha"])):
if i > 0 and spiceData["pha"][i] + suma > 100 and spiceData["pha"][
i - 1] < -100:
suma += -360
spiceData["pha"][i] += suma
if mode == MAG:
xdata = spiceData["f"]
ydata = spiceData["abs"]
elif mode == PHA:
xdata = spiceData["f"]
ydata = spiceData["pha"]
elif mode == BOTH:
xdata = spiceData["f"]
ydata2 = spiceData["abs"]
ydata = spiceData["pha"]
else:
raise Exception("Invalid mode ", mode, " selected")
signal = Senial.Senial(xdata, ydata)
if mode != BOTH:
self.plotCount.append({
"signal": signal,
"color": color,
"name": name
})
else:
signal2 = Senial.Senial(xdata, ydata2)
self.plotCount.append({
"signal": signal,
"signal2": signal2,
"color": color2,
"name": name2,
"color2": color,
"name2": name
})
return self
def joaco_spectrum2():
fmuestra = 30000
dmuestra = 1 / fmuestra
xvar = arange(0, 2, dmuestra)
print(xvar)
# print(len(xvar))
periodSegundos = 0.2
period = periodSegundos / dmuestra
fc = 1000
fm = 200
yvar = np.zeros(len(xvar))
for i in range(len(yvar)):
if i % period <= period * 0.25:
yvar[i] = 1 #*np.sin(2*np.pi*fc)*np.sin(2*np.pi*fm)
else:
yvar[i] = 0
s1 = Senial.Senial(xvar, yvar)
s1f = FourierTransform.fourierTransform(s1)
xaux = s1f.xvar
tau = period * 0.25
print(1 / periodSegundos)
yaux = [abs(np.sinc(it * 0.25)) for it in xaux]
lxvar = len(s1f.xvar) / 2
lxvar2 = len(xaux) / 2
ExpressPlot.CombinedPlot()\
.setTitle("Espectro")\
.setXTitle("Frecuencia (Hz)")\
.setYTitle("Ampltiud (%)")\
.addSignalPlot(
signal=Senial.Senial(s1f.xvar, s1f.values), color="blue",name="espectro1")\
.addSignalPlot(
signal=Senial.Senial(xaux, yaux),
color="orange",
name="sinc"
)\
.plotAndSave("testjoaco.png")
# plt.plot(xaux,yaux,color="orange")
plt.show()
def csvToSignal(data, fieldY, fieldX="x_axis"):
try:
while len(data[fieldX]) > len(data[fieldY]):
data[fieldX].pop()
except KeyError:
pass
senial = Senial.Senial(data[fieldX], data[fieldY])
return senial
def addExcelPlot(self, filename, fieldX, fieldY, color, name):
info = pd.read_excel(filename)
self.plotCount.append({
"signal":
Senial.Senial(info[fieldX], info[fieldY]),
"color":
color,
"name":
name
})
return self
def addSpiceBodePlot(self, filename, name, color, mode):
spiceData = self.getSpiceData(filename)
if mode == MAG:
xdata = spiceData["f"]
ydata = spiceData["abs"]
elif mode == PHA:
xdata = spiceData["f"]
ydata = spiceData["abs"]
else:
raise Exception("Invalid mode ", mode, " selected")
signal = Senial.Senial(xdata, ydata)
self.plotCount.append({"signal": signal, "color": color, "name": name})
return self
def fourierTransform(senial):
# Hacemos una aproximacion discreta muy buena de la transformada de fourier bilateral de la senial
frecuencyStep = 1 / (
(len(senial.xvar) - 1) * senial.separation
) #la senial debe estar mostrada de manera equiespaciada
fftOutput = 1 / len(senial.values) * abs(fft(senial.values))
iValsPositive = []
ampPositive = []
iValsNegative = []
ampNegative = []
for i in range(len(fftOutput) // 4):
iValsPositive.append(i)
ampPositive.append(fftOutput[i])
i = -1
while len(iValsNegative) + len(iValsPositive) < len(fftOutput) // 2:
iValsNegative.append(i)
ampNegative.append(fftOutput[i])
i = i - 1
iValsNegative.reverse()
ampNegative.reverse()
iVals = iValsNegative + iValsPositive
amplitud = ampNegative + ampPositive
frecuencies = [frecuencyStep * i for i in iVals]
final_freq = []
final_amp = []
for i in range(len(frecuencies)):
if abs(frecuencies[i]) < 10 * 1e3:
final_freq.append(frecuencies[i])
final_amp.append(amplitud[i])
senial = Senial.Senial(final_freq, final_amp)
return senial
def makeBodes():
config.GetConfigData().FAAfreq = 1600
f = logspace(2.3, 3.5, 200000)
wrange = [fi * 2 * pi for fi in f]
w, mag, pha = FiltroLP.getFiltroLP().getBode(wrange)
frange = [wi / 2 / pi for wi in w]
senialTeorica = Senial.Senial(frange, mag)
ExpressPlot.CombinedPlot()\
.setTitle("Respuesta en frecuencia")\
.setXTitle("Frecuencia (Hz)")\
.setYTitle("Amplitud (dB)")\
.setLogarithmic()\
.addSignalPlot(
signal=senialTeorica,
color="blue",
name="Teórica"
)\
.addSpiceBodePlot(
filename="ExpressInput/SimulacionV2/bode_Modulo_Sim.txt",
color="red",
name="Simulación",
mode=ExpressPlot.MAG
)\
.addCSVQuotient(
filename="ExpressInput/Mediciones/Filtro FAA/bode_moduloFAA.csv",
fieldX="Frequency (Hz)",
fieldA="Channel 1 Magnitude (dB)",
fieldB="Channel 2 Magnitude (dB)",
color="green",
name="Medición FR",
)\
.plotAndSave(filename="ExpressOutput/bodeFiltrosCaso3.png")
plt.show()
from math import cos, pi
import numpy as np
from util_python import Senial
# 0.0022
# 0.00002 ok
f = 440
xvar = np.linspace(0, 2, 100000)
yvar = [cos(2 * pi * f * x) for x in xvar]
Senial.Senial(xvar, yvar).writeCSV("Signals/cos_f_440.csv")
#s2 = Senial.Senial().loadFromCSV(
# "Signals/cos_f_440.csv"
#)
from util_python import Senial
from Systems import Ej5SystemC
from ExpressPlot import ExpressPlot
from numpy import pi
fs = 44100
sys = Ej5SystemC.getSystem(rl=1, fc=250, fs=fs)
w, H = sys.freqresp()
w = w / 2 / pi * fs
ExpressPlot.CombinedPlot()\
.setXTitle("Frecuencia (Hz)")\
.setYTitle("Amplitud (veces)")\
.addSignalPlot(
signal=Senial.Senial(w, abs(H)),
color="blue",
name="Amplitud"
)\
.plot()\
.show()
def calcularPlotMatchedZ(f0, fs, mode="butter", n=4, filename="out.png", title="noTitle"):
w0 = 2 * pi * f0
if mode == "butter":
b, a = signal.butter(n, w0, 'low', analog=True)
elif mode == "cheby":
b, a = signal.cheby1(n, 1, w0, 'low', analog=True)
sys = signal.lti(b, a)
w_range = linspace(0, fs, 100000) * 2 * pi
w, h = signal.freqresp(sys, w_range) # signal.freqs(b, a, 100000)
w_m2 = -1
for i in range(len(w)):
if 20 * log10(abs(h[i])) <= -2 and w_m2 == -1:
w_m2 = w[i]
if mode == "butter":
b, a = signal.butter(n, w0 * (w0 / w_m2), 'low', analog=True)
elif mode == "cheby":
b, a = signal.cheby1(n, 1, w0 * (w0 / w_m2), 'low', analog=True)
sys = signal.lti(b, a)
w, h = signal.freqresp(sys, w_range)
f = w / 2 / pi
poles = sys.poles
zeros = sys.zeros
new_poles = exp(poles / fs)
new_zeros = exp(zeros / fs)
new_zeros = np.hstack([new_zeros, [-1] * n])
# print(new_poles, new_zeros)
sys = signal.dlti(new_zeros, new_poles, 1)
w2, h2 = signal.dfreqresp(sys, w_range / fs)
f = w / 2 / pi
factor = h[0] / h2[0]
f2 = w2 / 2 / pi * fs
CombinedPlot() \
.setTitle(title) \
.setXTitle("Frecuencia (hz)") \
.setYTitle("Amplitud (Db)") \
.addSignalPlot(
signal=Senial.Senial(
f, 20 * log10(abs(h))
),
color="red",
name="Analógica"
) \
.addSignalPlot(
signal=Senial.Senial(
f2, 20 * log10(abs(h2) * factor)
),
color="blue",
name="Digital método invariante al impulso"
).plot().save("output/" + filename)
def spectra_plot(medcsv, simxml, out, fmax=None):
fmed = []
pmed = []
with open(medcsv, 'rt') as csvfile:
reader = list(csv.reader(csvfile))
# hola = csv.reader(csvfile)
reader.pop(0) # saco el header, me quedo solo con las mediciones
if fmax is None:
fmax = float(reader[-1][0])
for row in reader:
f = float(row[0])
if f > fmax:
break
fmed.append(f)
pmed.append((10**(float(row[1]) / 20))**2)
#plt.plot(fmed, pmed)
#plt.show()
s1 = Senial.Senial(fmed, pmed)
s_xml = SignalsReadWrite.readSignal(simxml)
s2 = FourierTransform.fourierTransform(s_xml)
fsim = []
psim = []
for f in s2.xvar:
if f < 0:
continue
if f > fmax:
break
fsim.append(f)
psim.append(s2.values[s2.xvar.index(f)])
s2.xvar = fsim
s2.values = psim
psum = sum(s2.values)
s2.values = [v / psum * 100 for v in s2.values]
psum = sum(pmed)
s1.values = [v / psum * 100 for v in s1.values]
ExpressPlot.CombinedPlot()\
.setTitle(" ")\
.setXTitle("Frecuencia (Hz)")\
.setYTitle("Potencia (% de total)")\
.addSignalPlot(
signal=s1,
color="orange",
name="Medido"
)\
.addSignalPlot(
signal=s2,
color="blue",
name="Simulado"
)\
.plotAndSave(
filename=out
)
plt.show()
w_range = linspace(0, fs, 100000) * 2 * pi
w, h = signal.freqresp(tf, w_range)
w2, h2 = signal.dfreqresp(tf2, w_range / fs)
f = w / 2 / pi
f2 = w2 / 2 / pi
CombinedPlot() \
.setTitle("Legendre") \
.setXTitle("Frecuencia (hz)") \
.setYTitle("Amplitud (Db)") \
.addSignalPlot(
signal=Senial.Senial(
f, 20 * log10(abs(h))
),
color="red",
name="Analógica"
) \
.addSignalPlot(
signal=Senial.Senial(
f2, 20 * log10(abs(h2))
),
color="blue",
name="Digital método invariante al impulso"
).plot().show()#save("output/" + filename)
# .setFs(44100)\
# .plotAndSave(
# "Output/polosSysA.png"
# )
w, H = sys.freqresp()
w = w/2/np.pi * fs/1000
ExpressPlot.CombinedPlot()\
.addSignalPlot(
Senial.Senial(w, H),
color="blue",
name="Magnitud"
)\
.setTitle("Respuesta en frecuencia")\
.setXTitle("Frecuencia (kHz)")\
.setYTitle("Amplitud (veces)")\
.plotAndSave(
"Output/rtaFreq.png"
)
sys = Ej5SystemA.getSystem(
rl = 1,
l = 100
)
def calcularPlotImpinvar(f0,
fs,
mode="butter",
n=4,
filename="out.png",
title="noTitle"):
w0 = 2 * pi * f0
print("f0 = ", f0)
if mode == "butter":
b, a = signal.butter(n, w0, 'low', analog=True)
elif mode == "cheby":
b, a = signal.cheby1(n, 1, w0, 'low', analog=True)
sys = signal.lti(b, a)
w_range = linspace(0, fs, 100000) * 2 * pi
w, h = signal.freqresp(sys, w_range) # signal.freqs(b, a, 100000)
w_m2 = -1
for i in range(len(w)):
if 20 * log10(abs(h[i])) <= -2 and w_m2 == -1:
w_m2 = w[i]
if mode == "butter":
b, a = signal.butter(n, w0 * (w0 / w_m2), 'low', analog=True)
elif mode == "cheby":
b, a = signal.cheby1(n, 1, w0 * (w0 / w_m2), 'low', analog=True)
sys = signal.lti(b, a)
sys_original = sys
w, h = signal.freqresp(sys, w_range)
f = w / 2 / pi
b2, a2 = impinvar_causal(b, a, fs=fs, tol=0.0001)
sys = signal.dlti(b2, a2)
w2, h2 = signal.dfreqresp(sys, w_range / fs)
factor = h[0] / h2[0]
sys = signal.dlti(b2 / factor, a2)
f2 = w2 / 2 / pi * fs
CombinedPlot() \
.setTitle(title) \
.setXTitle("Frecuencia (hz)") \
.setYTitle("Amplitud (Db)") \
.addSignalPlot(
signal=Senial.Senial(
f, 20 * log10(abs(h))
),
color="red",
name="Analógica"
).addSignalPlot(
signal=Senial.Senial(
f2, 20 * log10(abs(h2) * factor)
),
color="blue",
name="Digital método invariante al impulso"
).plot().save("output/" + filename)
l = 50
t = np.arange(0, 1, 1 / fs)
noise = np.random.normal(0, 1, l) #2 * np.random.random_sample(l) - 1
empty = [0] * (len(t) - len(noise))
input = np.hstack([noise, empty])
rl = 1
y = Ej5SystemB.processSystemB(input, rl, l, b=0.5)
t *= 1000
ExpressPlot.CombinedPlot()\
.addSignalPlot(
Senial.Senial(t, y),
color="blue",
name="Rta a ruido uniforme"
)\
.setTitle(
"Respuesta a ruido uniforme longitud L=50"
)\
.setXTitle("Tiempo (ms)")\
.setYTitle("Amplitud")\
.plotAndSave(
"Output/rtaRuidoGauss2.png"
)\
y = np.array(y)
#PlaySound.playSound(y)
from util_python import Senial
from ExpressPlot import ExpressPlot
import matplotlib.pyplot as plt
import mido
from IPython.display import Audio
import numpy as np
from numpy import cos, sin, pi
import simpleaudio as sa
def synthetize(fs, wavetable, nsamples):
samples = []
current_sample = 0
s1 = Senial.Senial().loadFromCSV(
filename="Signals/cos_f_440.csv"
)
do = 261.626
dob = 277.1826
re = 293.665
reb = 311.1270
mi = 329.628
fa = 349.228
fab = 369.9944
sol = 391.99
solb = 415.3047
la = 440.000
si = 493.883
ExpressPlot.CombinedPlot()\
