def get_subrectangles(m, n):
m, n = (max(m, n), min(m, n))
num_rectangles = 0
if m <= 0 or n <= 0:
num_rectangles = 0
elif m == 1:
num_rectangles = sum([math.ceil(n / (n - j)) for j in xrange(n)])
elif n == 1:
num_rectangles = sum([math.ciel(m / (m - i)) for i in xrange(m)])
else:
# number of subrectangles in (m * n) =
# number of unique unit rectangles in this level: m + n - 1
# number of row-wise: m
# number of col-wise: n
#
parts = (
# number of subrectangles in smaller problem: get_subrectangles(m - 1, n - 1)
get_subrectangles(m - 1, n - 1),
# number of unique unit rectangles in this level: m + n - 1
m + n - 1,
# number of new subrectangles formed by (m * (n - j)) for j = [0, n)
m * sum([math.ceil(n / (n - j)) for j in xrange(n)]),
# number of new subrectangles formed by (n * (m - i)) for i = [0, m)
sum([math.ciel(m / (m - i)) for i in xrange(m)]),
# number of new subrectangles sized (m - 1, n - 1): 3
3,
)
num_rectangles = sum(parts)
return num_rectangles
def event2LED(events):
if not events:
return None, None
else:
ledColor = np.zeros(48)
ledEvent = [''] * 48
for event in events:
start = parse(event['start'].get('dateTime',
event['start'].get('dateTime')))
startTime = start.hour + start.minute / 60.
end = parse(event['end'].get('dateTime',
event['start'].get('dateTime')))
endTime = end.hour + end.minute / 60.
# print (startTime, endTime)
color = event['colorId'] if 'colorId' in event.keys() else 1
if startTime < endTime:
for x in range(int(math.floor(startTime * 2)),
int(math.ceil(endTime * 2))):
ledColor[x] = color
ledEvent[x] = event['summary']
else:
for x in range(int(math.floor(startTime * 2)), 48):
ledColor[x] = color
ledEvent[x] = event['summary']
for y in range(0, int(math.ciel(endTime * 2))):
ledColor[y] = color
ledEvent[y] = event['summary']
return ledColor, ledEvent
def fish_eye_transofrm(self, point, screen_dist):
p_x, p_y, _ = point
c_x, c_y, c_z = self.screen_center
dx, dy = abs(c_x - p_x), abs(c_y - p_y)
theta = math.atan(dy / dx)
R = alg.calc_effective_radius(k,theta,screen_dist)
n_x = R*math.cos(theta)/self.pixel_size + c_x
n_y = R*math.sin(theta)/self.pixel_size + c_y
bl_x, bl_w, _ = bottom_left
ni, nj = math.ceil((n_x - bl_x) / self.pixel_size), math.ciel((n_y - bl_y) / self.pixel_size)
def median_group_views(self):
'''
Get the median number of views in this group
'''
medianIndex = len(self.videos) / 2
median = int(medianIndex)
if ((medianIndex % 2) == 1):
median = int((int(math.floor(medianIndex)) +
int(math.ciel(medianIndex))) / 2)
return self.get_video_views(self.videos[median])
def spacy_parse_chunks(text, nlp, parsefuncs=list(), chunk_size=10):
'''Parses document in sentence chunks to reduce memory use.
'''
# sentence is smallest unit spacy analyzes
# multiply by two since split includes punctuation too
sents = re.split(r'[\?\!\.]', text)
n_chunks = math.ciel(len(sents)/(chunk_size*2))
sent_chunks = [sents[i*chunk_size*2:(i+1)*chunk_size*2]
for i in range(n_chunks)]
for sent_chunk in sent_chunks:
subdoc = nlp(''.join(sent_chunk))
def hunger_games(tensors, k, alpha):
"""Takes a layer of a neural network and applies the tanh activation function; it
then applies KATE style competition and returns the result"""
with tf.variable_scope("KATE Competition"):
tan = tf.tanh(tensors, name="K-Activated")
pos = zero_if_false(tf.greater(tan, 0), tan)
neg = zero_if_false(tf.less(tan, 0), tan)
pos_adder = tf.reduce_sum(pos)
neg_adder = tf.reduce_sum(neg)
pos_val, _ = tf.nn.top_k(pos, math.ciel(k / 2)) + alpha * pos_adder
neg_val, _ = -tf.nn.top_k(-neg, math.floor(k / 2)) + alpha * neg_adder
return tf.concat([pos_val, neg_val], 0)
def median(alist):
"""Numpy can perform this task. But that is the only
numpy functionality we need. So I createed it and
removed the dependency.
Args:
:list alist: a list of float compatible values.
Return:
:float median: median of `alist`
"""
alist.sort()
n = len(alist)
mid = math.ciel(n/2)
if (n % 2) == 0:
mid2 = mid - 1
return (alist[mid] + alist[mid2])/2.0
return alist[mid]
def get(self, search):
firstString = []
secondString = []
thirdString = []
first = True
second = True
for i in range(0, len(search)):
if search[i] == ":":
first = False
continue
if first:
firstString.append(search[i])
elif not first:
if search[i] == ":":
second = False
if second:
if search[i] == "_":
secondString.append(" ")
continue
secondString.append(search[i])
elif not second:
thirdString.append(search[i])
f = ''.join(firstString) # 'all', 'author', or 'title'
t = ''.join(thirdString)
t = int(t) # page number
print(f)
if len(secondString) > 0:
s = ''.join(secondString) # name of title or name of author
print(s)
if f == "author":
data = BookModel.query.filter_by(author=s).all()
newdata =[]
of = math.ciel(len(data)/page_size) # total number of pages
page = t # page number requested by frontend
for book in range(page*page_size, (page+1)*page_size):
newdata.append(book.json_page(page, of))
return {"books": [book.json() for book in BookModel.query.filter_by(author=s).all()]}
#used to be return {"books": [book.json() for book in BookModel.query.filter_by(author=s).all()]}
elif f == "title":
return {"books": [book.json() for book in BookModel.query.filter_by(title=s).all()]}
else:
return{"message": "error, can only search by title or author: /booklist/author:Bill_Shakespeare"}
elif search != "all":
return {"message": "Please enter booklist/all or booklist/author:xxx or booklist/title:xxx"}
return {"books": [book.json() for book in BookModel.query.all()]}
def payouts(players, winningPlayer):
favor, multiplier = odds(players[1], players[2])
if winningPlayer == 1:
winners = players[1]
losers = players[2]
else:
winners = players[2]
losers = players[1]
for i in winners:
if favor == 1 and winners[i][0] != "NA":
if winningPlayer == 1:
userData = collection.find({"username": winners[i][0]})
winnerData = [{
"username":
winners[i][0],
"amount": (int(userData['amount']) +
(math.ciel(float(winners[i][1]) / multiplier)))
}]
collection.replace_one(winnerData, True)
else:
userData = collection.find({"username": winners[i][0]})
winnerData = [{
"username":
winners[i][0],
"amount": (int(userData['amount']) +
(math.ciel(float(winners[i][1]) * multiplier)))
}]
collection.replace_one(winnerData, True)
elif winners[i][0] != "NA":
if winningPlayer == 1:
userData = collection.find({"username": winners[i][0]})
winnerData = [{
"username":
winners[i][0],
"amount": (int(userData['amount']) +
(math.ciel(float(winners[i][1]) * multiplier)))
}]
collection.replace_one(winnerData, True)
else:
userData = collection.find({"username": winners[i][0]})
winnerData = [{
"username":
winners[i][0],
"amount": (int(userData['amount']) +
(math.ciel(float(winners[i][1]) / multiplier)))
}]
collection.replace_one(winnerData, True)
for i in losers:
if losers[i][0] != "NA":
userData = collection.find({"username": losers[i][0]})
loserData = [{
"username":
losers[i][0],
"amount":
(int(userData['amount']) - math.ciel(float(winners[i][1])))
}]
collection.replace_one(loserData, True)
import subprocess
import sys
import math
sys.path.append('/home/ubuntu/TOOLS/Scripts/utility')
from job_manager import job_manager
fn = sys.argv[1]
th = sys.argv[2]
# create sub_files to process
lc_info = subprocess.check_output('wc -l ' + fn, shell=True)
fh = open(fn, 'r')
lc = lc_info.split()
line_split = math.ciel(float(lc[0])/float(th))
cur = 1
fct = 1
flist = []
cur_file = fn + str(fct) + 'split'
out_pre = 'Gene_metrics' + str(fct)
out = open(cur_file, 'w')
job_list = []
cmd = '/home/ubuntu/TOOLS/dropseq/2_calc_mean_variance_bin.py '
job_list.append(cmd + cur_file + ' ' + out_pre + ' 0')
head = next(fh)
# out.write(head)
for line in fh:
if cur > line_split:
out.close()
fct += 1
def __init__(self, length=None): if length: length = math.ciel(length//8) # Convert bits to bytes. super().__init__(length)
def time(n): steps = 3 + 2*math.ciel(n/5)
(175, 10917280, <class 'int'>) >>> lng=5422222222222222222222222222222222222222222222222222 >>> lng,id(lng),type(lng) (5422222222222222222222222222222222222222222222222222, 139934208108128, <class 'int'>) >>> var=-5 >>> var -5 >>> abs(var) 5 >>> var=-0 >>> math.ciel(var) Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'math' is not defined >>> import math >>> math.ciel(var) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: module 'math' has no attribute 'ciel' >>> math.ceil(var) 0 >>> math.floor(var) 0 >>> var=56.89 >>> math.ceil(var) 57 >>> math.floor(var) 56 >>> math.expl(l) Traceback (most recent call last): File "<stdin>", line 1, in <module>
import subprocess
import sys
import math
sys.path.append('/home/ubuntu/TOOLS/Scripts/utility')
from job_manager import job_manager
fn = sys.argv[1]
th = sys.argv[2]
# create sub_files to process
lc_info = subprocess.check_output('wc -l ' + fn, shell=True)
fh = open(fn, 'r')
lc = lc_info.split()
line_split = math.ciel(float(lc[0]) / float(th))
cur = 1
fct = 1
flist = []
cur_file = fn + str(fct) + 'split'
out_pre = 'Gene_metrics' + str(fct)
out = open(cur_file, 'w')
job_list = []
cmd = '/home/ubuntu/TOOLS/dropseq/2_calc_mean_variance_bin.py '
job_list.append(cmd + cur_file + ' ' + out_pre + ' 0')
head = next(fh)
# out.write(head)
for line in fh:
if cur > line_split:
out.close()
fct += 1
Type "help", "copyright", "credits" or "license()" for more information.
>>> import math
>>>
>>> x = sqrt(25)
Traceback (most recent call last):
File "<pyshell#2>", line 1, in <module>
x = sqrt(25)
NameError: name 'sqrt' is not defined
>>> x = math.sqrt(25)
>>> x
5.0
>>> print(math.floor)
<built-in function floor>
>>> print(math.floor(4.9))
4
>>> print(math.ciel(4.9))
Traceback (most recent call last):
File "<pyshell#7>", line 1, in <module>
print(math.ciel(4.9))
AttributeError: module 'math' has no attribute 'ciel'
>>> print(math.ceil(4.9))
5
>>> print(math.ceil(4.1))
5
>>> print()
>>> print(math.pi)
3.141592653589793
>>> print(math.e)
2.718281828459045
>>> print(math.pow(11,6))
