def stand(x, m, s):
'''Standardize timeseries x by mean m and std deviation s
Args:
x: Timeseries that is beign standardized
m: Mean of the timeseries after standardization
s: Standard deviation of the timeseries after standardization
Output:
A timeseries with mean 0 and standard deviation 1
'''
vals = np.array(list(iter(x)))
vals = (vals - m) / s
return ts.TimeSeries(vals, list(x.itertimes()))
Returns (Prints):
#### Nearest Timeseries ####
tsdata/ts398.dat
Requires folder:
tsdb
'''
if __name__ == "__main__":
# Load in the TS to Evaluate
filename = sys.argv[1]
x = np.loadtxt(filename, delimiter=' ')
origTs = ts.TimeSeries(x[:,1],x[:,0])
time = np.arange(0.0, 1.0, 0.01)
testTs = origTs.interpolate(time)
# Find the Nearest vantagePt
minDist = float('inf')
for j in range(20):
dbName = "tsdb/db"+str(j)+".dbdb"
db = lab10.connect(dbName)
vantagePtFile = db.get(0)
x = np.loadtxt(vantagePtFile, delimiter=' ')
comparePt = ts.TimeSeries(x[:,1],x[:,0])
dist = 2*(1-ss.kernel_corr(comparePt,testTs))
if dist < minDist:
minDist = dist
minDbName = dbName
'''
if __name__ == "__main__":
indexes = np.random.choice(1000, 20, replace=False)
vantagePtList = []
dbList = []
#Create TS Referencing
#The 20 randomally selected vantagePtFiles
for j in range(20):
fileName = 'tsdata/ts' + str(indexes[j]) + '.dat'
dbName = "tsdb/db" + str(j) + ".dbdb"
x = np.loadtxt(fileName, delimiter=' ')
vantagePt = ts.TimeSeries(x[:, 1], x[:, 0])
vantagePtList.append(vantagePt)
##Remove DB if it has previously been created
if os.path.exists(dbName):
os.remove(dbName)
# Connect to Databses
db = lab10.connect(dbName)
dbList.append(db)
#For all 20 Databases
#Loop through 1000 TimeSeries
#Add Key = Distance(vantagePt, comparePt)
#Value = comparePT's fileName
for i in range(1000):
fileName = 'tsdata/ts' + str(i) + '.dat'
def test_kernel_corr():
ts1 = ts.TimeSeries([0, 1, 2, 3, 4, 3, 2, 1, 0],
[1, 2, 3, 4, 5, 6, 7, 8, 9])
ts2 = ts.TimeSeries([1, 2, 3, 4, 3, 2, 1, 0, 0],
[1, 2, 3, 4, 5, 6, 7, 8, 9])
assert abs(kernel_corr(ts1, ts2, 10) - 1) < 1e-3
def test_maxccor():
ts1 = ts.TimeSeries([0, 1, 2, 3, 4, 3, 2, 1, 0],
[1, 2, 3, 4, 5, 6, 7, 8, 9])
ts2 = ts.TimeSeries([1, 2, 3, 4, 3, 2, 1, 0, 0],
[1, 2, 3, 4, 5, 6, 7, 8, 9])
assert abs(max_corr_at_phase(ts1, ts2)[1] - 1.0) < 1e-5
def test_self_maxccor():
ts1 = ts.TimeSeries([100, 101, 102, 103], [1, 2, 3, 4])
assert max_corr_at_phase(ts1, ts1)[1] == 1.0
def test_stand():
ts1 = ts.TimeSeries([100, 101, 102, 103], [1, 2, 3, 4])
ts1_stand = stand(ts1, np.mean([100, 101, 102, 103]),
np.std([100, 101, 102, 103]))
assert np.std(list(iter(ts1_stand))) == 1.0
def random_ts(a):
t = np.arange(0.0, 1.0, 0.01)
v = a * np.random.random(100)
return ts.TimeSeries(v, t)
def tsmaker(m, s, j):
t = np.arange(0.0, 1.0, 0.01)
v = norm.pdf(t, m, s) + j * np.random.randn(100)
return ts.TimeSeries(v, t)
