def fold(rows, period, number_bins=20, flatten=True):
row_tree = Tree()
for row in rows:
mod_mjd = row["MJD"] % period
lst = row_tree.get(mod_mjd, [])
lst.append(row)
row_tree[mod_mjd] = lst
bins = np.linspace(0, period, number_bins + 1)
for i in range(len(bins) - 1):
v = bins[i]
biggest = bins[i + 1]
value = list(itertools.chain.from_iterable(row_tree[v:biggest].values()))
if len(value):
if not flatten:
yield (v, bin_fluxes(value))
else:
flux, error = rebin_error(value)
yield (v, flux, error)
def cbintree_build():
tree = FastBinaryTree.from_keys(keys)
def cbintree_build_delete():
tree = FastBinaryTree.from_keys(keys)
for key in keys:
del tree[key]
"""
def random_keys():
import random
return random.sample(range(KEYRANGE), KEYS)
try:
with open('testkeys.txt') as fp:
keys = eval(fp.read())
except IOError:
keys = random_keys()
py_searchtree = BinaryTree.from_keys(keys)
cy_searchtree = FastBinaryTree.from_keys(keys)
def bintree_build_delete():
tree = BinaryTree.from_keys(keys)
for key in keys:
del tree[key]
def cbintree_build_delete():
tree = FastBinaryTree.from_keys(keys)
for key in keys:
del tree[key]
def bintree_build():
class TDigest(object):
def __init__(self, delta=0.01, K=100):
self.C = BinaryTree()
self.n = 0
self.delta = delta
self.K = K
def __add__(self, other_digest):
C1 = list(self.C.values())
C2 = list(other_digest.C.values())
shuffle(C1)
shuffle(C2)
data = C1 + C2
new_digest = TDigest(self.delta, self.K)
for c in data:
new_digest.update((c.mean, c.count))
return new_digest
def __len__(self):
return len(self.C)
def __repr__(self):
return """<T-Digest: n=%d, centroids=%d>""" % (self.n, len(self))
def _add_centroid(self, centroid):
self.C.insert(centroid.mean, centroid)
return
def _compute_centroid_quantile(self, centroid):
denom = self.n
cumulative_sum = sum(
c_i.count for c_i in self.C.value_slice(-float('Inf'), centroid.mean))
return (centroid.count / 2. + cumulative_sum) / denom
def batch_update(self, values):
"""
Update the t-digest with an iterable of values. This API assumes all weights are equal
to 1.
"""
w = 1
for x in values:
self.update((x, w))
self.compress()
return
def _get_closest_centroids(self, x):
try:
ceil_key = self.C.ceiling_key(x)
except KeyError:
floor_key = self.C.floor_key(x)
return [self.C[floor_key]]
try:
floor_key = self.C.floor_key(x)
except KeyError:
ceil_key = self.C.ceiling_key(x)
return [self.C[ceil_key]]
if abs(floor_key - x) < abs(ceil_key - x):
return [self.C[floor_key]]
elif abs(floor_key - x) == abs(ceil_key - x) and (ceil_key != floor_key):
return [self.C[ceil_key], self.C[floor_key]]
else:
return [self.C[ceil_key]]
def update(self, (x, w)):
"""
Update the t-digest with value x and weight w.
"""
self.n += w
if len(self) == 0:
self._add_centroid(Centroid(x, w))
return
S = self._get_closest_centroids(x)
while len(S) != 0 and w > 0:
j = choice(range(len(S)))
c_j = S[j]
q = self._compute_centroid_quantile(c_j)
if c_j.count + w > 4 * self.n * self.delta * q * (1 - q):
S.pop(j)
continue
delta_w = min(4 * self.n * self.delta * q * (1 - q) - c_j.count, w)
self._update_centroid(c_j, x, delta_w)
w -= delta_w
S.pop(j)
if w > 0:
self._add_centroid(Centroid(x, w))
if len(self) > self.K / self.delta:
self.compress()
self.K *= 2
return
def __init__(self, delta=0.01, K=100):
self.C = BinaryTree()
self.n = 0
self.delta = delta
self.K = K
def __init__(self, delta=0.01, K=100):
self.C = BinaryTree()
self.n = 0
self.delta = delta
self.K = K
class TDigest(object):
def __init__(self, delta=0.01, K=100):
self.C = BinaryTree()
self.n = 0
self.delta = delta
self.K = K
def __add__(self, other_digest):
C1 = list(self.C.values())
C2 = list(other_digest.C.values())
shuffle(C1)
shuffle(C2)
data = C1 + C2
new_digest = TDigest(self.delta, self.K)
for c in data:
new_digest.update((c.mean, c.count))
return new_digest
def __len__(self):
return len(self.C)
def __repr__(self):
return """<T-Digest: n=%d, centroids=%d>""" % (self.n, len(self))
def _add_centroid(self, centroid):
self.C.insert(centroid.mean, centroid)
return
def _compute_centroid_quantile(self, centroid):
denom = self.n
cumulative_sum = sum(
c_i.count
for c_i in self.C.value_slice(-float('Inf'), centroid.mean))
return (centroid.count / 2. + cumulative_sum) / denom
def batch_update(self, values):
"""
Update the t-digest with an iterable of values. This API assumes all weights are equal
to 1.
"""
w = 1
for x in values:
self.update((x, w))
self.compress()
return
def _get_closest_centroids(self, x):
try:
ceil_key = self.C.ceiling_key(x)
except KeyError:
floor_key = self.C.floor_key(x)
return [self.C[floor_key]]
try:
floor_key = self.C.floor_key(x)
except KeyError:
ceil_key = self.C.ceiling_key(x)
return [self.C[ceil_key]]
if abs(floor_key - x) < abs(ceil_key - x):
return [self.C[floor_key]]
elif abs(floor_key - x) == abs(ceil_key -
x) and (ceil_key != floor_key):
return [self.C[ceil_key], self.C[floor_key]]
else:
return [self.C[ceil_key]]
def update(self, (x, w)):
"""
Update the t-digest with value x and weight w.
"""
self.n += w
if len(self) == 0:
self._add_centroid(Centroid(x, w))
return
S = self._get_closest_centroids(x)
while len(S) != 0 and w > 0:
j = choice(range(len(S)))
c_j = S[j]
q = self._compute_centroid_quantile(c_j)
if c_j.count + w > 4 * self.n * self.delta * q * (1 - q):
S.pop(j)
continue
delta_w = min(4 * self.n * self.delta * q * (1 - q) - c_j.count, w)
self._update_centroid(c_j, x, delta_w)
w -= delta_w
S.pop(j)
if w > 0:
self._add_centroid(Centroid(x, w))
if len(self) > self.K / self.delta:
self.compress()
self.K *= 2
return
def cbintree_build():
tree = FastBinaryTree.from_keys(keys)
def cbintree_build_delete():
tree = FastBinaryTree.from_keys(keys)
for key in keys:
del tree[key]
setup_FastBinaryTree = """
from __main__ import cbintree_build_delete, cbintree_build, cbintree_search, itercbintree
"""
def random_keys():
import random
return random.sample(range(KEYRANGE), KEYS)
try:
with open('testkeys.txt') as fp:
keys = eval(fp.read())
except IOError:
keys = random_keys()
py_searchtree = BinaryTree.from_keys(keys)
cy_searchtree = FastBinaryTree.from_keys(keys)
def bintree_build_delete():
tree = BinaryTree.from_keys(keys)
for key in keys:
del tree[key]
def cbintree_build_delete():
tree = FastBinaryTree.from_keys(keys)
for key in keys:
del tree[key]
def bintree_build():