def findAvgSlope(data):
slopes = []
newData = Rescale.rescaleX(
data) #Normalize x so it can be used to compare data sets
y1 = newData[0, 1]
x1 = newData[0, 0]
y2 = 0.0
x2 = 0.0
for i in range(1, newData.shape[0]):
x2 = newData[i, 0]
y2 = newData[i, 1]
if not ((x2 - x1) == 0):
slopes.append(
abs((y2 - y1) / (x2 - x1))
) #Abs so the value doesn't even out - might change later, if there is a zero in denom - invalid - don't add
y1 = y2
x1 = x2
return (float)(stats._sum(slopes)[1] / len(slopes))
def findPeakInfo(data):
time = 0.0
times = []
min = 999999999.9
max = 0.0
average = 0.0
newData = Rescale.rescaleX(
data) #When we subtract, we want a standard unit of time
peaks = sig.find_peaks(newData[:, 1])[
0] #grab the indices of the peaks, which are located at index 0
if len(peaks) > 0:
for i in range(
0, len(peaks)
): #The indices are in the array contained in element
time = newData[peaks[i]][
0] - time #Add the differences in time for each peak
times.append(time)
peakHeight = newData[peaks[i]][1]
if (
peakHeight > max
): #Designwise - should be in their own methods, but I really don't want to write this loop 4 times
max = peakHeight
if (peakHeight < min):
min = peakHeight
average += peakHeight
timeToPeak = (float)(stats._sum(times)[1] / len(times))
average /= len(peaks)
else:
min = 0
max = 0.0
timeToPeak = 0
peakFeatures = {
"ttp": timeToPeak,
"min": min,
"max": max,
"avg": average
}
return peakFeatures #Return the average time (in normalized x-axis units)
def findAverage(resultArray):
numResults = len(resultArray)
return (float)(stats._sum(resultArray)[1] / numResults)
def findSum(data):
return (float)(stats._sum(data[:, 1])[1])
def findMode(data):
return (float)(stats._sum(data[:, 1])[1] / data.shape[0])
def update_event(self, inp=-1):
self.set_output_val(0, statistics._sum(self.input(0), self.input(1)))
