def test_interpolate1(self):
a = TimeSeries([0, 5, 10], [1, 2, 3])
self.assertEqual(a.interpolate([-100, 100]),
TimeSeries([-100, 100], [1, 3]))
with self.assertRaises(TypeError):
a.interpolate({'a': 1, 'b': 2})
def test_interpolate(self):
""" test TimeSeries.interpolate """
tzstr = "US/Eastern"
tz = pytz.timezone(tzstr)
h = TimeSeries.hour
n = datetime.datetime.now()
times1 = [n, n+h, n+2*h, n+3*h]
times2 = [n, n+3*h]
times2 = TimeSeries.makeAware(times2, tz)
values = [6.0, 8.0]
ts = TimeSeries("test", times2, values, tzstr)
res = ts.interpolate(times1)
target = [6.0, 6.66667, 7.33333, 8.0]
for i in range(len(res)):
self.failUnlessAlmostEqual(res[i], target[i], places=4)
times3 = [n-h, n+2*h, n+4*h]
res = ts.interpolate(times3)
target = [None, 7.3333333, None]
for i in range(len(res)):
self.failUnlessAlmostEqual(res[i], target[i], places=4)
def load_ts_file(filepath):
'''
Takes in file and reads time series from it
Parameters
----------
filepath: path of the file
Returns
-------
timeSeries object : TimeSeries class
>>> ts=load_ts_file('169975.dat_folded.txt')
>>> ts._values[0]
15.137
'''
#Only considers the first two columns of the text file (other columns are discarded)
#Only evaluates time values between 0 and 1
#First column is presumed to be times and second column is presumed to be light curve values.
data = np.loadtxt(filepath, delimiter=' ', dtype=str)
clean_input = []
for i in range(len(data)):
row = data[i].split("\\t")
clean_input.append([float(row[0][2:]), float(row[1])])
data = np.array(clean_input)
_, indices = np.unique(data[:, 0], return_index=True)
data = data[indices, :]
times, values = data.T
full_ts = TimeSeries(times=list(times), values=list(values))
interpolated_ts = full_ts.interpolate(
list(np.arange(0.0, 1.0, (1.0 / 100))))
full_ts_interpolated = TimeSeries(times=list(
np.arange(0.0, 1.0, (1.0 / 100))),
values=list(interpolated_ts))
return full_ts_interpolated
def test_lazy_interpolate():
ts = TimeSeries([1, 2, 3, 4, 5], [0, 5, 10, 15, 20])
assert ts.interpolate([-100, 100]).lazy.eval() == TimeSeries([1, 5],
[-100, 100])
def test_interpolate3(self):
a = TimeSeries([0, 5, 10], [1, 2, 3])
b = TimeSeries([2.5, 7.5], [100, -100])
self.assertEqual(a.interpolate([1]), TimeSeries([1], [1.2]))
def test_interpolate2(self):
a = TimeSeries([0, 5, 10], [1, 2, 3])
b = TimeSeries([2.5, 7.5], [100, -100])
self.assertEqual(a.interpolate(b.times),
TimeSeries([2.5, 7.5], [1.5, 2.5]))
def test_interpolate3(self):
a = TimeSeries([0,5,10], [1,2,3])
b = TimeSeries([2.5,7.5], [100, -100])
self.assertEqual(a.interpolate([1]),TimeSeries([1], [1.2]))
def test_interpolate2(self):
a = TimeSeries([0,5,10], [1,2,3])
b = TimeSeries([2.5,7.5], [100, -100])
self.assertEqual(a.interpolate(b.times),TimeSeries([2.5, 7.5], [1.5, 2.5]))
def test_interpolate1(self):
a = TimeSeries([0,5,10], [1,2,3])
self.assertEqual(a.interpolate([-100,100]),TimeSeries([-100, 100], [1, 3]))
with self.assertRaises(TypeError):
a.interpolate({'a':1,'b':2})
def test_interpolate(self):
a = TimeSeries([0, 5, 10], [ 1, 2, 3])
b = TimeSeries([2.5,7.5], [100, -100])
self.assertTrue ( a.interpolate([1]) == TimeSeries([1],[1.2]) )
self.assertTrue ( a.interpolate(b.times) == TimeSeries([2.5,7.5], [1.5, 2.5]))
self.assertTrue ( a.interpolate([-100, 100]) == TimeSeries([-100,100], [1,3]))
