def extractSignalsForOperation(values, storedSignals):
selectedGraphs = values['selectedGraphs']
if len(selectedGraphs) != 2:
sg.Popup('Error!', 'Select 2 graphs to add')
return (None, None)
return storedSignals[getSelectedGraphIndex(
selectedGraphs[0])], storedSignals[getSelectedGraphIndex(
selectedGraphs[1])]
def onShowGraph(window, values, storedSignals):
if (len(values['selectedGraphs']) == 0):
sg.Popup('Error!', "Select at least one signal")
return
selectedGraphs = values["selectedGraphs"]
data = []
layout = plotly_layout
for x in selectedGraphs:
graph = storedSignals[getSelectedGraphIndex(x)]
isCoercedToContinuous = None
try:
isCoercedToContinuous = graph['displayContinuous']
except KeyError:
isCoercedToContinuous = False
data.append(
go.Scatter(
x=graph['x'],
y=graph['y'],
mode="markers" if graph['isDiscrete']
and not isCoercedToContinuous else 'lines',
))
figure = go.Figure(data=data, layout=layout)
py.plot(figure, filename='graph')
def onComputeErrorParameters(window, values, storedSignals):
selectedGraphs = values['selectedGraphs']
if len(selectedGraphs) != 1:
sg.Popup('Error!', 'Select 1 graph for error parameters computation!')
return None
selectedSignal = storedSignals[getSelectedGraphIndex(selectedGraphs[0])]
name, x, y, params = pluck(selectedSignal, 'name', 'x', 'y', 'params')
actualValuesForSignal = computeSignal(selectedSignal, x)
#test
newSignal = {
**selectedSignal, 'displayName':
f"computed: {selectedSignal['displayName']}",
'y': actualValuesForSignal
}
addToSelectionList(window, newSignal, storedSignals)
#test
errors = calculateErrorStatistics(selectedSignal['y'],
actualValuesForSignal)
MSE, SNR, PSNR, MD, ENOB = pluck(errors, 'MSE', 'SNR', 'PSNR', 'MD',
'ENOB')
sg.Popup(
'Signal properties', f"""
MSE: {MSE}
SNR: {SNR}
PSNR: {PSNR}
MD: {MD}
ENOB: {ENOB}
""")
def onGenerateSpectrum(window, selectedSignals, storedSignals):
if (len(selectedSignals) != 1):
sg.Popup('Error!', "Select single signal")
else:
selectedSignal = storedSignals[getSelectedGraphIndex(
selectedSignals[0])]
spectrum = ops.calculateSpectrum(selectedSignal)
addToSelectionList(window, spectrum, storedSignals)
def onReconstructSignal(window, values, storedSignals):
selectedGraphs = values['selectedGraphs']
if len(selectedGraphs) != 1:
sg.Popup('Error!', 'Select 1 graph to reconstruct!')
return None
reconstructed = reconstruct(storedSignals[getSelectedGraphIndex(
selectedGraphs[0])],
params=values)
addToSelectionList(window, reconstructed, storedSignals)
def saveFile(window, values, storedSignals):
selected = values["selectedGraphs"]
fileName = values['saveFile']
saveToBin = values['saveToBin']
if len(selected) != 1:
sg.Popup("Error!", "You have to select 1 signal to save.")
return
signalToSave = storedSignals[getSelectedGraphIndex(selected[0])]
fileOps.saveFile(signalToSave, fileName, saveToBin)
def removeFromSelectionList(window, selectedToRemove, storedSignals):
selectionList = window.FindElement('selectedGraphs')
alreadyRemoved = 0
for x in selectedToRemove:
number = getSelectedGraphIndex(x)
storedSignals.pop(number - alreadyRemoved)
alreadyRemoved += 1
currentList = []
for i in range(len(storedSignals)):
currentList.append(
f"{i}. {storedSignals[i]['displayName']} {storedSignals[i]['params']}"
)
selectionList.Update(currentList)
def onSignalProperties(window, selectedSignals, storedSignals):
if (len(selectedSignals) != 1):
sg.Popup('Error!', "Select single signal")
else:
selectedSignal = storedSignals[getSelectedGraphIndex(
selectedSignals[0])]
data = calculateStatistics(selectedSignal)
sg.Popup(
'Signal properties', f"""
Average value: {data['average']}
Average absolute value: {data['averageAbsolute']}
Average power: {data['averagePower']}
Standard deviation: {data['standardDeviation']}
Effective value: {data['effectiveValue']}
""")
def onQuantizeSignal(window, values, storedSignals):
selectedGraphs = values['selectedGraphs']
if len(selectedGraphs) != 1:
sg.Popup('Error!', 'Select 1 graph to quantize!')
return None
steps = try_int(values['quantizationSteps'])
if steps == None:
sg.Popup('Error!', 'Specify amount of quantization levels!')
return None
selectedGraph = storedSignals[getSelectedGraphIndex(selectedGraphs[0])]
quantized = quantize(selectedGraph, steps)
addToSelectionList(window, quantized, storedSignals)
def onSampleSignal(window, values, storedSignals):
selectedGraphs = values['selectedGraphs']
if len(selectedGraphs) != 1:
sg.Popup('Error!', 'Select 1 graph to sample!')
return None
fp = try_float(values['samplingFrequency'])
if fp == None or fp == 0:
sg.Popup('Error!', 'Specify correct sampling frequency!')
return None
selectedGraph = storedSignals[getSelectedGraphIndex(selectedGraphs[0])]
sampled = sample(selectedGraph, fp)
addToSelectionList(window, sampled, storedSignals)
def onShowHistogram(window, values, storedSignals):
if (len(values['selectedGraphs']) != 1):
sg.Popup('Error!', "Select only one signal")
return
selectedGraphs = values["selectedGraphs"]
ranges = int(values['ranges'])
data = []
for x in selectedGraphs:
graph = storedSignals[getSelectedGraphIndex(x)]
data.append(
go.Histogram(
x=graph['y'],
xbins={
'size': (max(graph['y']) - min(graph['y'])) / (ranges - 1)
},
))
py.plot(data, filename='graph')
def onFilter(window, values, storedSignals):
errors, params = assembleFilterParams(values)
if errors != "":
sg.Popup('Invalid parameters', errors)
return None
selectedSignal = values['selectedGraphs']
if len(selectedSignal) != 1:
sg.Popup('Error!', 'Select 1 graph to filter!')
return None
selectedSignal = storedSignals[getSelectedGraphIndex(selectedSignal[0])]
if not values["useK?"]:
params["K"] = selectedSignal["params"]["fp"] / params["K"]
filter, filteredSignal = filterSignal(selectedSignal, **params)
filteredSignal = {
'name': selectedSignal["name"],
'displayName': f"filtered {selectedSignal['displayName']}",
'isDiscrete': False,
'isComplex': False,
'isPeriodic': False,
'x': selectedSignal["x"],
'y': filteredSignal,
"params": selectedSignal['params']
}
filterTransmittance = operations.calculateFilterTransmittance(
selectedSignal, filteredSignal)
impulseResponse = {
'name': selectedSignal["name"],
'displayName': f"filter",
'isDiscrete': False,
'isComplex': False,
'isPeriodic': False,
'x': np.linspace(0,
len(filter) - 1, len(filter)),
'y': filter,
"params": selectedSignal['params']
}
addToSelectionList(window, filteredSignal, storedSignals)
addToSelectionList(window, impulseResponse, storedSignals)
addToSelectionList(window, filterTransmittance, storedSignals)
