def returnIntervals(self):
topN = [int(arg) for arg in sys.argv[4].split(",")]
#print(topN)
path = sys.argv[1]
data = csvToJson.parseCsv(path)
gap = sys.argv[2].split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in sys.argv[3].split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = 0.1
if len(sys.argv) == 6:
threshold = float(sys.argv[5])
return self.combineProduceIntervals(data, topN, gap, intervalIdx, threshold)
def sendIntervals(self, data, gap, intervalIdx, topN, threshold="0.1"):
topN = [int(arg) for arg in topN.split(",")]
data = loads(data)
gap = gap.split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in intervalIdx.split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = float(threshold)
res = self.combineProduceIntervals(data, topN, gap, intervalIdx, threshold)
print("res", [len(x) for x in res])
# print("res",flattenInts(res))
# print("res",[x.toList() for x in res])
res = [x.tolist() for x in res]
return dumps(res)
def returnIntervals(self,
data,
topN,
gap,
intervalIdx,
threshold="0.1"):
topN = [int(arg) for arg in topN.split(",")]
data = loads(data)
gap = gap.split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in intervalIdx.split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = float(threshold)
return dumps(
combineProduceIntervals(data, topN, gap, intervalIdx,
threshold))
def sendIntervals(self, data, gap, intervalIdx, topN, threshold="0.1"):
# print("gap", gap)
# print("intervalIdx", intervalIdx)
# print("topN", topN)
topN = [int(arg) for arg in topN.split(",")]
data = loads(data)
#print(data)
gap = gap.split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in intervalIdx.split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = float(threshold)
intervals = self.combineProduceIntervals(data, topN, gap, intervalIdx, threshold)
print("intervals",intervals)
intsFlat = []
for intLst in intervals:
intsFlat += intLst.flatten().astype(int).tolist()
return dumps(intsFlat)
def parseElapsedTime(self, col): newCol = np.zeros(len(col)) for i in range(len(newCol)): newCol[i] = csvToJson.elapsedTimeToSec(col[i]) return newCol[:]
def combineProduceIntervals(self, data, topN, gap, intervalIdx, threshold):
topN = [int(arg) for arg in topN.split(",")]
data = loads(data)
gap = gap.split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in intervalIdx.split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = float(threshold)
self.reformatArray(data)
data["data"][13] = self.parseColumn(data["data"][13], int)
data["data"][9] = self.parseColumn(data["data"][9], int)
data["data"][1] = self.parseColumn(data["data"][1], float)
data["data"][3] = self.parseElapsedTime(data["data"][3])
#note to self, I think there are cases where I am zeroing a "---" for power and it ruins the variance
#also look into an alternative for variance that allows for one or two out liers without skewing the result?
power = data["data"][13]
filtPower = filterData(power)
rises = getStep(1, filtPower, 0.15)
falls = getStep(-1, filtPower, 0.2) #- 1
steps = np.hstack((rises, falls))
steps = np.sort(steps)
steps[steps >= len(power)] = len(power) - 1
groupings = []
for i in range(len(intervalIdx)):
groupings.append([])
sorted_orderings = []
for i in range(len(groupings)):
cur_sorted_orderings, cur_groupings = self.getSortedOrderings(
falls, rises, intervalIdx[i], gap[i], data, filtPower,
threshold, topN[i], i)
groupings[i] = cur_groupings
sorted_orderings += cur_sorted_orderings
groupsToUse = []
sorted_on_finish, opt = self.getBestSchedule(sorted_orderings)
for i in range(len(opt[1])):
interval = sorted_on_finish[opt[1][i]]
grouping_idx = interval[3]
groupsToUse += groupings[grouping_idx][interval[2]].tolist()
return groupsToUse
def printIntervals():
topN = [int(arg) for arg in sys.argv[4].split(",")]
print(topN)
path = sys.argv[1]
data = csvToJson.parseCsv(path)
gap = sys.argv[2].split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in sys.argv[3].split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = 0.1
if len(sys.argv) == 6:
threshold = float(sys.argv[5])
return combineProduceIntervals(data, topN, gap, intervalIdx,
threshold)
def returnIntervals(self,
data,
topN,
gap,
intervalIdx,
threshold="0.1"):
topN = [int(arg) for arg in topN.split(",")]
data = loads(data)
gap = gap.split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in intervalIdx.split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = float(threshold)
return dumps(
combineProduceIntervals(data, topN, gap, intervalIdx,
threshold))
def main():
printIntervals()
def combineProduceIntervals(self, data, topN, gap, intervalIdx, threshold="0.1"):
topN = [int(arg) for arg in topN.split(",")]
data = loads(data)
gap = gap.split(",")
intervalIdxs = {"strokes": 9, "distance": 1, "time": 3}
intervalIdx = [intervalIdxs[arg] for arg in intervalIdx.split(",")]
for i in range(len(intervalIdx)):
if intervalIdx[i] == 3:
gap[i] = csvToJson.elapsedTimeToSec("00:" + gap[i] + ".0")
else:
gap[i] = int(gap[i])
threshold = float(threshold)
self.reformatArray(data)
data["data"][13] = self.parseColumn(data["data"][13], int)
data["data"][9] = self.parseColumn(data["data"][9], int)
data["data"][1] = self.parseColumn(data["data"][1], float)
data["data"][3] = self.parseElapsedTime(data["data"][3])
# start_time = time.time()
#note to self, I think there are cases where I am zeroing a "---" for power and it ruins the variance
#also look into an alternative for variance that allows for one or two out liers without skewing the result?
power = data["data"][13]
# sys.stdout.write(json.dumps(power.tolist()))
# sys.stdout.flush()
# return;
filtPower = filterData(power)
rises = getStep(1, filtPower, 0.15)
falls = getStep(-1, filtPower, 0.2) #- 1
steps = np.hstack((rises,falls))
steps = np.sort(steps)
steps[steps >= len(power)] = len(power) - 1
# print(len(data["data"][9]))
# print(len(data["data"][1]))
# print(len(data["data"][3]))
# print(len(data["data"][13]))
# print(len(filtPower))
# print("rises",rises)
# print("falls",len(falls),falls)
groupings = []
for i in range(len(intervalIdx)):
groupings.append([])
# [[]] * len(intervalIdx)
sorted_orderings = []
for i in range(len(groupings)):
cur_sorted_orderings, cur_groupings = self.getSortedOrderings(falls, rises, intervalIdx[i], gap[i], data, filtPower,threshold, topN[i], i)
groupings[i] = cur_groupings
# print("sorted_orderings", i, cur_sorted_orderings)
sorted_orderings += cur_sorted_orderings
groupsToUse = []
sorted_on_finish, opt = self.getBestSchedule(sorted_orderings)
# print(opt)
# print(sorted_on_finish[opt[1][0]])
# print(sorted_on_finish[opt[1][1]])
# print()
for i in range(len(opt[1])):
interval = sorted_on_finish[opt[1][i]]
grouping_idx = interval[3]
groupsToUse += groupings[grouping_idx][interval[2]].tolist()
# print("flattened groupings",np.array(groupsToUse).flatten())
# print("--- %s seconds ---" % (time.time() - start_time))
# plt.subplot(211)
# plt.plot(data["data"][13],'-bD', markevery=np.array(groupsToUse).flatten().tolist())
# plt.show()
return dumps(groupsToUse)