def test_ccor(self):
t0 = ts(times=[0,1,2,3],values=[1,2,3,4])
t0_stand = distances.stand(t0,t0.mean(),t0.std())
t1 = ts(times=[0,1,2,3],values=[-1,2,1,-1])
t1_stand = distances.stand(t1,t1.mean(), t1.std())
d = distances.ccor(t0_stand,t1_stand)
assert (str(d) == str(np.array([0.25819889,-0.94672926,-0.0860663,0.77459667])))
def test_kernelcorr(self):
"""tests that the kernelized cross correlation is 1 when
the two timeseries are identical"""
t0 = ts(times=[0,1,2,4,5,6],values=[3,4,5,6,7,8])
t0_stand = distances.stand(t0,t0.mean(),t0.std())
t1 = ts(times=[0,1,2,4,5,6],values=[3,4,5,6,7,8])
t1_stand = distances.stand(t1,t1.mean(), t1.std())
assert distances.kernel_corr(t1_stand, t0_stand) == 1
t3 = ts(times=[0,1,2,4,5,6],values=[3,7,9,10,16,20])
t3_stand = distances.stand(t3,t3.mean(), t3.std())
assert (distances.kernel_corr(t1_stand, t3_stand) < 1)
def random_ts(a):
"""Creates a TimeSeries with random values
a: scaling term to generate random values for time series
"""
t = np.arange(0.0, 1.0, 0.01)
v = a * np.random.random(100)
return ts(t, v)
def test_standardize(self):
t0 = ts(times=[0,1,2,4,5,6],values=[3,4,5,6,7,8])
assert t0.mean() == 5.5 #check mean
assert t0.std() == np.sqrt(17.5/6.0) #check sqrt
standardized_values = distances.stand(t0,t0.mean(),t0.std()).values()
assert (str(standardized_values) == str(np.array([-1.46385011,-0.87831007,-0.29277002,0.29277002,0.87831007,1.46385011]))) #check that standardized values are correct
def decode(json_object):
with open(json_object, 'r') as infile:
try:
d = json.load(infile)
return ts(d['times'], d['values'])
except json.JSONDecodeError:
raise TSDBInputError('Invalid JSON object received\n')
def tsmaker(m, s, j):
"""Makes a TimeSeries whose values are approximately normally distributed
m: location parameter for normal pdf
s: scale parameter for normal pdf
j: coefficient for extra randomness added to normally distributed values
"""
t = np.arange(0.0, 1.0, 0.01)
v = norm.pdf(t, m, s) + j * np.random.randn(100)
return ts(t, v)
def interpolate_to_match_input(ts_database, ts_input):
"""
interpolate the generated timeseries in the database to match the timeseries
input from the client
Parameter
----------------
ts_input: timeseries input by the client
ts_database: the timeseries generated in the database
"""
ts_input_times = ts_input.times()
ts_database_times = ts_database.times()
interpolated_values = ts_database.interpolate(ts_input_times)
interpolated_ts = ts(ts_input_times, interpolated_values)
return interpolated_ts
"compute a kernelized correlation so that we can get a real distance"
num = np.sum(np.exp(mult * ccor(ts1, ts2)))
denom1 = np.sqrt(np.sum(np.exp(mult * ccor(ts1, ts1))))
denom2 = np.sqrt(np.sum(np.exp(mult * ccor(ts2, ts2))))
return (num / denom1) / denom2
def distance(ts1, ts2, mult=1):
"""Calculates the distance metric using the kernal coefficient"""
return 2 * (1 - kernel_corr(ts1, ts2, mult))
##this is for a quick and dirty test of these functions
if __name__ == "__main__":
t0 = ts(times=[0, 1, 2, 4, 5, 6], values=[3, 4, 20, 6, 7, 8])
t0_stand = stand(t0, t0.mean(), t0.std())
t1 = ts(times=[0, 1, 2, 4, 5, 6], values=[3, 4, 5, 6, 7, 8])
t1_stand = stand(t1, t1.mean(), t1.std())
print(distance(t0_stand, t1_stand))
t1 = tsmaker(0.5, 0.1, 0.01)
t2 = tsmaker(0.5, 0.1, 0.01)
import matplotlib.pyplot as plt
plt.plot(t1)
plt.plot(t2)
plt.show()
standts1 = stand(t1, t1.mean(), t1.std())
standts2 = stand(t2, t2.mean(), t2.std())
def test_distance(self):
t0 = ts(times=[0,1,2,4,5,6],values=[3,4,5,6,7,8])
t0_stand = distances.stand(t0,t0.mean(),t0.std())
t1 = ts(times=[0,1,2,4,5,6],values=[3,4,5,6,7,8])
t1_stand = distances.stand(t1,t1.mean(), t1.std())
assert distances.distance(t0_stand, t1_stand) == 0
def test_maxcorratphase(self):
t0 = ts(times=[0,1,2,3],values=[1,2,3,4])
t0_stand = distances.stand(t0,t0.mean(),t0.std())
t1 = ts(times=[0,1,2,3],values=[-1,2,1,-1])
t1_stand = distances.stand(t1,t1.mean(), t1.std())
assert (distances.max_corr_at_phase(t0_stand,t1_stand)) == (3, 0.77459666924148329)
def test_standardizeConstant(self):
t0 = ts(times=[0,1,2,4,5,6],values=[3,3, 3, 3, 3, 3])
standardized_values = distances.stand(t0,t0.mean(),t0.std()).values()
assert (str(standardized_values) == str(np.array([0.,0.,,0.,0.,0.]))) #check that standardize a series of constant return a series of zeros
def sdecode(json_string):
try:
d = json.loads(json_string)
return ts(d['times'], d['values'])
except json.JSONDecodeError:
raise TSDBInputError('Invalid JSON object received:\n'+str(json_str))
