def _createFromExtension(self, extension, max_bins):
time = []
mag = []
err = []
error_occured = False
for line in extension.data:
try:
time.append(line[self.TIME_COL])
mag.append(line[self.MAG_COL])
err.append(line[self.ERR_COL] / self.ERR_MAG_RATIO)
# Case of no lc
except IndexError:
error_occured = True
if error_occured:
if len(time) == len(mag) and len(time) > 0:
if not err or len(err) != len(time):
err = np.zeros(len(time))
else:
return None
if len(time) > max_bins:
red_time, red_mag = to_ekvi_PAA(time, mag, bins=max_bins)
red_time, red_err = to_ekvi_PAA(time, err, bins=max_bins)
else:
return time, mag, err
return red_time, red_mag, red_err
def getFeatures(self, star):
"""
Get skewness
Parameters
-----------
star : lcc.entities.star.Star object
Star to process
Returns
-------
float
Kurtosis of investigated star
"""
lc = star.lightCurve
if self.bins:
_, mags = to_ekvi_PAA(lc.time, lc.mag, self.bins)
else:
mags = lc.mag
kurt = kurtosis(mags)
if self.absolute:
kurt = abs(kurt)
return kurt
def getSpaceCoords(self, stars):
"""
Get list of skewness values
Parameters
-----------
stars : list of Star objects
Stars which contain light curves
Returns
-------
list
List of list of floats
"""
kurtosis_list = []
for star in stars:
if star.lightCurve:
lc = star.lightCurve
if self.bins:
_, mags = to_ekvi_PAA(lc.time, lc.mag, self.bins)
else:
mags = lc.mag
kurt = kurtosis(mags)
if self.absolute:
kut = abs(kurt)
else:
kurt = None
kurtosis_list.append(kurt)
return kurtosis_list
def getFeatures(self, star):
"""
Get density of the star's light curve
Parameters
-----------
star : lcc.entities.star.Star object
Star to process
Returns
-------
list, iterable, int, float
Density (points per time lag) of the investigated star
"""
x, _ = to_ekvi_PAA(star.lightCurve.time, star.lightCurve.mag)
ren = x.max() - x.min()
return len(x) / ren
def test_to_ekvi_PAA1():
n = 100
x = np.linspace(0, 1, n)
y = np.random.random_sample(n)
bins = np.random.randint(5, 30)
x_ekv1, y_ekv1 = to_ekvi_PAA(x, y, bins)
x_ekv2, y_ekv2 = to_ekvi_PAA(x, y, len(x))
x_ekv3, y_ekv3 = to_ekvi_PAA(x, y, 3 * len(x))
assert len(x_ekv1) == bins
assert len(x_ekv2) == len(x)
assert len(x_ekv3) == len(x)
assert np.nan not in y_ekv1
assert np.nan not in y_ekv2
assert np.nan not in y_ekv3
assert (y_ekv3 == y).all()
assert (y_ekv2 == y).all()
for _ in range(100):
n = np.random.randint(30, 700)
x = np.random.random_sample(n)
x1 = np.linspace(0, 1, n)
y = np.random.random_sample(n)
bins = np.random.randint(5, 30)
x_ekv1, y_ekv1 = to_ekvi_PAA(x, y, bins)
x_ekv2, y_ekv2 = to_ekvi_PAA(x, y, len(x))
x_ekv3, y_ekv3 = to_ekvi_PAA(x, y, 3 * len(x))
x_ekv4, y_ekv4 = to_ekvi_PAA(x1, y, len(x1))
assert len(x_ekv1) == bins
assert len(x_ekv2) == len(x)
assert len(x_ekv3) == len(x)
assert np.nan not in y_ekv1
assert np.nan not in y_ekv2
assert np.nan not in y_ekv3
assert (y_ekv4 == y).all()
thr = 0.1
assert abs(y_ekv1.mean() - y.mean()) / (y_ekv1.mean() + y.mean()) < thr
assert abs(y_ekv2.mean() - y.mean()) / (y_ekv2.mean() + y.mean()) < thr
assert abs(y_ekv3.mean() - y.mean()) / (y_ekv3.mean() + y.mean()) < thr
def getAbbe(self, bins=None):
"""
Compute Abbe value of the light curve
Parameters
-----------
bins : int
Number of bins from original dimension
Returns
--------
float
Abbe value of the light curve
"""
if bins:
x = to_ekvi_PAA(self.time, self.mag, bins)[1]
else:
x = self.mag
return abbe(x, len(self.time))
def getSpaceCoords(self, stars):
"""
Get list of curve densities
Parameters
-----------
stars : list of Star objects
Stars with color magnitudes in their 'more' attribute
Returns
-------
list
Densities of points per time lag
"""
coo = []
for star in stars:
if star.lightCurve:
x, _ = to_ekvi_PAA(star.lightCurve.time, star.lightCurve.mag)
ren = x.max() - x.min()
coo.append(len(x) / ren)
else:
coo.append([None])
return coo
def test_fix_missing():
x = np.linspace(0, 10, 20)
y = x.copy()
x[:5] = np.linspace(-15, -10, 5)
xx, yy = to_ekvi_PAA(x, y)
yy[0] = np.nan
yy[-1] = np.nan
res1 = fix_missing(xx, yy, replace_at_borders=False)
assert len(res1[0]) == len(res1[1])
assert len(res1[0]) == len(xx) - 2
assert not np.isnan(res1[0]).any()
assert not np.isnan(res1[1]).any()
res2 = fix_missing(xx, yy, replace_at_borders=True)
assert len(res2[0]) == len(res2[1])
assert len(res2[0]) == len(xx)
assert not np.isnan(res2[0]).any()
assert not np.isnan(res2[1]).any()
assert res2[1][0] == res2[1][1]
assert res2[1][-2] == res2[1][-1]
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
