def to_letter_rep(self, x):
"""
Function takes a series of data, x, and transforms it
to a string representation.
Parameters
----------
x : list, iterable
Data series
Returns
-------
str
SAX word
list
Indices
"""
paaX, indices = to_PAA(normalize(x), self.word_size)
self.scaling_factor = np.sqrt(len(x) / self.word_size)
return self.alphabetize(paaX), indices
def getSpaceCoords(self, stars):
"""
Get reduced light curve as coordinates
Parameters
-----------
stars : list of Star objects
Stars with color magnitudes in their 'more' attribute
Returns
-------
list
List of list of floats
"""
if not self.bins:
self.bins = np.min(
[len(st.lightCurve.mag) for st in stars if st.lightCurve])
self.LABEL = [
"Light curve point " + str(i + 1) for i in range(self.bins)
]
print "Setting bins as min: ", self.bins
coords = []
for star in stars:
if star.lightCurve:
x, y = to_ekvi_PAA(star.lightCurve.time, star.lightCurve.mag)
if len(y) > self.bins:
y, _ = to_PAA(y, self.bins)
else:
y, _ = to_PAA(star.lightCurve.mag, self.bins)
y = np.array(y)
if self.height:
y = self.height * y / (y.max() - y.min())
y = np.array([int(round(q)) for q in y])
else:
y = y / (y.max() - y.min())
y -= y.mean()
coords.append(y.tolist())
else:
coords.append(None)
if self.red_dim:
_coords = [c for c in coords if c]
if len(_coords[0]) > self.red_dim:
_coords = self._reduceDimension(_coords)
else:
QueryInputError(
"Number of samples have to be greater then reduced dimension"
)
k = 0
red_coo = []
for c in coords:
if c:
red_coo.append(_coords[k])
k += 1
else:
red_coo.append([np.NaN])
coords = red_coo
return coords
def test_to_PAA():
for _ in range(100):
x = np.random.random_sample(np.random.randint(30, 700))
bins = np.random.randint(5, 30)
assert len(to_PAA(x, bins)[0]) == bins