def GenerateAM(fp, fm, t0, tf, vp):
times = linspace(t0, tf, 100000)
values = [vp*(cos(2*pi*fp*t) + cos(2*pi*(fp-fm)*t)/2 + cos(2*pi*(fp+fm)*t)/2) for t in times]
SignalsReadWrite.writeSignal(
Senial.Senial(times, values),
"Signals/AM_" + str(fp / 1000) + "kHz.xml"
)
def GenerateSine(freq, a, b, vp):
#print(freq)
times = linspace(a, b, 100000)
values = [vp*sin(2*pi*freq*t) for t in times]
SignalsReadWrite.writeSignal(
Senial.Senial(times, values),
"Signals/Seno_"+str(freq/1000)+"k.xml"
)
def GenerateSquare(start, end, vp):
times = linspace(0, 0.005, 100000)
values = [vp*square(t, start, end) for t in times]
SignalsReadWrite.writeSignal(
Senial.Senial(times, values),
"Signals/Sqaure_" + str(start / 1000) + "_" + str(end/1000) + "ms.xml"
)
def Generate32Sine(freq, a, b, vp):
times = linspace(a, b, 100000)
T = 2/3/freq
values = [vp*Sine32(t, T) for t in times]
SignalsReadWrite.writeSignal(
Senial.Senial(times, values),
"Signals/Sine32_" + str(freq / 1000) + "k.xml"
)
def subniquistTest():
#m = 3
s1 = SignalsReadWrite.readSignal("Signals/AM_2.4Khz.xml")
s2 = SampleAndHold.SampleAndHold().processInput(s1, None, 1)
#s3 = SignalsReadWrite.readSignal("Signals/AM_0.36Khz.xml")
fs = 2040
lowcut = 2400 - 250
highcut = 2400 + 250
print(1 / s1.separation)
#print(s1.xvar[1] - s1.xvar[0])
values = butter_bandpass_filter(s2.values,
lowcut,
highcut,
1 / s1.separation,
order=2)
values = [v for v in values]
s3 = Senial.Senial(s2.xvar, values)
#print(s3.values)
CombinedPlot() \
.addSignalPlot(s1, "orange", "Señal AM 2.4Khz") \
.addSignalPlot(s2, "blue", "Salida LLave analogica") \
.addSignalPlot(s3, "green", "señal subniquist") \
.plotAndSave("IndividualTests/test_niquist.png")
plt.show()
def GenerateExp(f, t0, tf, vp):
times = linspace(t0, tf, 100000)
values = []
for t in times:
t = abs(t)
T = 1/f
if t > T/2:
t -= (ceil(t/T)-1)*T
if t > T/2:
t -= T
values.append(vp * exp(-abs(t * (10 * f))))
SignalsReadWrite.writeSignal(
Senial.Senial(times, values),
"Signals/Exp_" + str(f) + "Hz.xml"
)
def testsubplot():
s_in = SignalsReadWrite.readSignal("Signals/Coseno_0.5k_2Vp.xml")
config.GetConfigData().setFs(4000)
config.GetConfigData().setSampleCycle(50)
s_sh = SampleAndHold.getSampleAndHold().processInput(s_in, None, 1)
s_out = LlaveAnalogica.getLlaveAnalogica().processInput(s_sh, None, 1)
f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)
ax1.plot(s_in.xvar, s_in.values)
# ax1.set_title('Sharing both axes')
ax2.plot(s_sh.xvar, s_sh.values)
ax3.plot(s_out.xvar, s_out.values)
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
f.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
ticks = np.arange(0, 0.00401, 0.0005 / 4)
#ticks = ticks + [ticks[1]+ticks[-1]]
ax1.xaxis.set_ticks(ticks, minor=True)
ax2.xaxis.set_ticks(ticks, minor=True)
ax3.xaxis.set_ticks(ticks, minor=True)
ax1.grid(axis='x',
which='major',
linestyle='-',
linewidth=0.3,
color='black')
ax1.grid(axis='x',
which='minor',
linestyle=':',
linewidth=0.1,
color='black')
ax2.grid(axis='x',
which='major',
linestyle='-',
linewidth=0.3,
color='black')
ax2.grid(axis='x',
which='minor',
linestyle=':',
linewidth=0.1,
color='black')
ax3.grid(axis='x',
which='major',
linestyle='-',
linewidth=0.3,
color='black')
ax3.grid(axis='x',
which='minor',
linestyle=':',
linewidth=0.1,
color='black')
plt.show()
def addXMLPlot(self, filename, name, color):
signal = SignalsReadWrite.readSignal(inputDirectory + "/" + filename)
signal.mode = "teorica"
self.plotCount.append(
{
"signal": signal,
"color": color,
"name": name
}
)
return self
def processSignals(inputFile, modes, loadingModel):
signal = SignalsReadWrite.readSignal(inputFile)
PlotSignals.getSignalsData().setSignal("Entrada", signal)
total = modes["FAA"].get() + \
modes["Sample and Hold"].get() + \
modes["Llave analógica"].get() + \
modes["FR"].get()
if total != 0:
fraction = 100 / total
else:
fraction = 1
print("processing")
if modes["FAA"].get():
signal = FiltroLP.getFiltroLP().processInput(signal, loadingModel,
fraction)
PlotSignals.getSignalsData().setSignal("FAA", signal)
#loadingModel.update(fraction)
print("A")
if modes["Sample and Hold"].get():
signal = SampleAndHold.getSampleAndHold().processInput(
signal, loadingModel, fraction)
PlotSignals.getSignalsData().setSignal("Sample and Hold", signal)
#loadingModel.update(fraction)
print("B")
if modes["Llave analógica"].get():
signal = LlaveAnalogica.getLlaveAnalogica().processInput(
signal, loadingModel, fraction)
PlotSignals.getSignalsData().setSignal("Llave analógica", signal)
#loadingModel.update(fraction)
print("C")
if modes["FR"].get():
signal = FiltroLP.getFiltroLP().processInput(signal, loadingModel,
fraction)
PlotSignals.getSignalsData().setSignal("FR", signal)
#loadingModel.update(fraction)
print("D")
loadingModel.callOnFinished()
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()
def saveFile(self):
if self.currentSignal:
filename = filedialog.asksaveasfile(mode='w',
defaultextension=".xls")
if filename:
SignalsReadWrite.writeSignal(self.currentSignal, filename.name)