def train(self, training_set_inputs, training_set_outputs,
number_of_training_iterations):
for iteration in xrange(number_of_training_iterations):
# Pass the training set through our neural network (a single neuron).
output = self.think(training_set_inputs)
# Calculate the error (The difference between the desired output
# and the predicted output).
error = training_set_outputs - output
# Multiply the error by the input and again by the gradient of the Sigmoid curve.
# This means less confident weights are adjusted more.
# This means inputs, which are zero, do not cause changes to the weights.
adjustment = dot(training_set_inputs.T,
error * self.__sigmoid_derivative(output))
# Adjust the weights.
self.synaptic_weights += adjustment
def read_partitions(fp, header, lba_size=512):
fp.seek(header.part_entry_start_lba * lba_size)
fmt, GPTPartition = _make_fmt('GPTPartition',
GPT_PARTITION_FORMAT,
extras=['index'])
for idx in xrange(1, 1 + header.num_part_entries):
data = fp.read(header.part_entry_size)
if len(data) < struct.calcsize(fmt):
raise GPTError('Short partition entry')
part = GPTPartition._make(struct.unpack(fmt, data) + (idx, ))
if part.type == 16 * '\x00':
continue
part = part._replace(
type=str(uuid.UUID(bytes_le=part.type)),
unique=str(uuid.UUID(bytes_le=part.unique)),
# do C-style string termination; otherwise you'll see a
# long row of NILs for most names
name=part.name.decode('utf-16').split('\0', 1)[0],
)
yield part
def insertionSort(self, array, model):
counter = 0
shift = 0
unsortedArray = array
model.append(str("Изначальный массив:"))
model.append(str(unsortedArray))
model.append(str("Сортировка:"))
for i in xrange(1, len(array)):
j = i - 1
value = array.pop(i)
model.append(str(array))
shift += 1
while (j >= 0) and (array[j] > value):
j -= 1
counter += 1
array.insert(j + 1, value)
counter += 1
array.insert(len(array), counter)
array.insert(len(array), shift)
return array
def arePermutation(str1, str2):
len_str1 = len(str1)
len_str2 = len(str2)
if len_str1 != len_str2:
return 0
count1 = [0] * NO_CHARS
count2 = [0] * NO_CHARS
for i in str1:
count1[ord(
i
)] += 1 # return an integer representing the Unicode code point of the character
for i in str2:
count2[ord(i)] += 1
for i in xrange(NO_CHARS):
if count1[i] != count2[i]:
return 0
return 1
def flatten(self, root):
"""
:type root: TreeNode
:rtype: None Do not return anything, modify root in-place instead.
"""
nodes = []
def getNodes(node):
if node:
nodes.append(node)
if node.left:
getNodes(node.left)
if node.right:
getNodes(node.right)
getNodes(root)
for i in xrange(len(nodes) - 1):
node = nodes[i]
node.right = nodes[i + 1]
node.left = None
"""
def middle_cod(geneList, genLength):
onestr = ''.join(geneList)
amount = (genLength - 6) / 3
codons = (onestr[n:n + 3]
for n in xrange(3,
len(onestr) - 3, 3)) # creates generator
dict_codons = {}
for i in codons:
if dict_codons.get(i) is not None:
dict_codons[i] += 1
else:
dict_codons[i] = 1
# sum = 0
for i in dict_codons:
dict_codons[i] /= amount
dict_codons['TAA'] = dict_codons['TGA'] = dict_codons['TAG'] = 0
dict_codons[i] = round(dict_codons[i], 3)
# sum += dict_codons[i]
# print("total sum of frequency=: " + str(sumfre))
pd.reset_option('display.max_rows')
df = pd.DataFrame(dict_codons.items(), columns=["codon", "frequency"])
return df
def delete_entry(entry_id):
user = mongo.db.users.find_one({'username': session["user"]})
entry = mongo.db.entries.find_one({"_id": ObjectId(entry_id)})
# Deduct from Vacation days if Regular Vacation
if entry['entry_type'] == "Regular Vacation":
start_date_from_entry = datetime.strptime(entry["start_date"], '%d %b, %Y')
end_date_from_entry = datetime.strptime(entry["end_date"], '%d %b, %Y')
day_generator = (start_date_from_entry + timedelta(x + 1) for x in xrange(
(end_date_from_entry - start_date_from_entry).days))
how_many_days_to_add = sum(1 for day in day_generator if day.weekday() < 5) + 1
update_user_days_before_new_deduction = user['vacation_days'] + how_many_days_to_add
mongo.db.users.update_one({
"username": user['username']}, {
"$set": {
"vacation_days": update_user_days_before_new_deduction
}})
# Next Lets delete the user entry
mongo.db.entries.remove({"_id": ObjectId(entry_id)})
flash("Task Successfully Deleted!")
return redirect(url_for("manage_entries"))
def parseData(log_file_path, export_file, regex, read_line=True):
with open(log_file_path, "r") as file:
match_list = []
if read_line == True:
for line in file:
for match in re.finditer(regex, line, re.S):
match_text = match.group()
match_list.append(match_text)
print(match_text)
else:
data = file.read()
for match in re.finditer(regex, data, re.S):
match_text = match.group();
match_list.append(match_text)
file.close()
with open("output", "w+") as file:
file.write("EXPORTED DATA:\n")
match_list_clean = list(set(match_list))
for item in xrange(0, len(match_list_clean)):
print(match_list_clean[item])
file.write(match_list_clean[item] + "\n")
file.close()
def rev():
if len(i) == 0:
return 0
else:
if len(sc) > 0:
if i[0] != sc[0]:
r.append(i[0])
i.pop(0)
else:
while len(sc) > 0:
for m in xrange(len(sc)):
if i[int(m)] == sc[int(m)]:
mp = True
if mp:
i.pop(0)
sc.pop(0)
else:
i.pop(0)
else:
r.append(i[0])
i.pop(0)
rev()
def frames_per_second(location):
"""
Calculate FPS
Inout: Path of the Video
Output: return list of fps(size 2).
"""
# video from path
video = cv2.VideoCapture(location)
# getting fps
fps1 = video.get(cv2.CAP_PROP_FPS)
# Number of frames to capture
num_frames = 100
start = time.time()
# Grab a few frames
for i in xrange(0, num_frames):
ret, frame = video.read()
end = time.time()
seconds = end - start # Time elapsed
fps = num_frames / seconds # Calculate frames per second
return [fps1, fps]
def markPlaceses(board, column, row):
# check row & columns
i = -1
while i > -n:
board[row][i] = 1
board[i][column] = 1
i -= 1
# check row
for i in xrange(n):
board[row][i] = 1
i = 1
# check diagonal
while 0 <= column + i <= n - 1 and 0 <= row + i <= n - 1:
board[row + i][column + i] = 1
i += 1
i = 1
# check other diagonal
while 0 <= column + i <= n - 1 and 0 <= row - i <= n - 1:
board[row - i][column + i] = 1
i += 1
def print_hi(name):
# Configure depth and color streams
pipeline = rs.pipeline()
colorizer = rs.colorizer()
threshold_distance = 0.4
tr1 = rs.threshold_filter(min_dist=0.15, max_dist=threshold_distance)
config = rs.config()
# Get device product line for setting a supporting resolution
pipeline_wrapper = rs.pipeline_wrapper(pipeline)
pipeline_profile = config.resolve(pipeline_wrapper)
device = pipeline_profile.get_device()
device_product_line = str(device.get_info(rs.camera_info.product_line))
depth_sensor = pipeline_profile.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
if device_product_line == 'L500':
config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30)
else:
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
clipping_distance_in_meters = 0.9
clipping_distance = clipping_distance_in_meters / depth_scale
align_to = rs.stream.color
align = rs.align(align_to)
pipeline.start(config)
#thresholds for detect skins
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
try:
while True:
frames = pipeline.wait_for_frames()
# Align the depth frame to color frame
aligned_frames = align.process(frames)
# Get aligned frames
aligned_depth_frame = aligned_frames.get_depth_frame(
) # aligned_depth_frame is a 640x480 depth image
color_frame = aligned_frames.get_color_frame()
# Validate that both frames are valid
if not aligned_depth_frame or not color_frame:
continue
depth_image = np.asanyarray(aligned_depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
# Remove background - Set pixels further than clipping_distance to grey
grey_color = 153
depth_image_3d = np.dstack(
(depth_image, depth_image,
depth_image)) # depth image is 1 channel, color is 3 channels
bg_removed = np.where(
(depth_image_3d > clipping_distance) | (depth_image_3d <= 0),
grey_color, color_image)
# Render images
depth_colormap = cv2.applyColorMap(
cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
images = np.hstack((bg_removed, depth_colormap))
# cv2.namedWindow('Background Removed', cv2.WINDOW_AUTOSIZE)
cv2.imshow('Color+depth', images)
# images[270:, :] = [0, 0, 0]
# images[0:240,:] = [0,0,0]
# images[:, 0:50] = [0, 0, 0]
# images[:, 630:] = [0, 0, 0]
img_hsv = cv2.cvtColor(bg_removed, cv2.COLOR_BGR2HSV)
## Gen lower mask (0-5) and upper mask (175-180) of RED
lower_red = np.array([94, 80, 2], dtype="uint8")
upper_red = np.array([126, 255, 255], dtype="uint8")
mask = cv2.inRange(img_hsv, lower_red, upper_red)
bluepen = cv2.bitwise_and(bg_removed, bg_removed, mask=mask)
cv2.imshow('BluePen', bluepen)
bgModel = cv2.createBackgroundSubtractorMOG2(0, 50)
fgmask = bgModel.apply(bluepen)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
img = cv2.bitwise_and(bluepen, bluepen, mask=fgmask)
# Skin detect and thresholding
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower = np.array([94, 80, 2], dtype="uint8")
upper = np.array([126, 255, 255], dtype="uint8")
skinMask = cv2.inRange(hsv, lower, upper)
kernel = np.ones((3, 3), np.uint8)
skinMask = cv2.erode(skinMask, kernel, iterations=2)
skinMask = cv2.dilate(skinMask, kernel, iterations=1)
cv2.imshow('Threshold Hands', skinMask)
contours, hierarchy = cv2.findContours(skinMask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
length = len(contours)
maxArea = -1
drawing = np.zeros(img.shape, np.uint8)
if length > 0:
for i in xrange(length):
temp = contours[i]
area = cv2.contourArea(temp)
if area > maxArea:
maxArea = area
ci = i
res = contours[ci]
hull = cv2.convexHull(res)
# drawing = np.zeros(img.shape, np.uint8)
cx, cy = centroid(res)
print(aligned_depth_frame.get_distance(cx, cy))
cv2.drawContours(drawing, [res], 0, (0, 255, 0), 2)
else:
drawing = np.zeros(img.shape, np.uint8)
cv2.imshow('DRAWING', drawing)
# bluepen_gray = cv2.cvtColor(bluepen, cv2.COLOR_BGR2HSV)
# contours, hierarchy = cv2.findContours(bluepen_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# length = len(contours)
# drawing = np.zeros(bluepen_gray.shape, np.uint8)
# maxArea = -1
# if length > 0:
# for i in xrange(length):
# temp = contours[i]
# area = cv2.contourArea(temp)
# if area > maxArea:
# maxArea = area
# ci = i
# res1 = contours[ci]
#
# hull = cv2.convexHull(res1)
#
# cv2.drawContours(drawing, [res1], 0, (0, 255, 0), 2)
# cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 3)
# cv2.imshow('',drawing)
# length = len(contours)
# print(length)
# drawing = np.zeros(res.shape, np.uint8)
# maxArea = -1
# if length > 0:
# for i in xrange(length):
# temp = contours[i]
# area = cv2.contourArea(temp)
# if area > maxArea:
# maxArea = area
# ci = i
# res = contours[ci]
#
# hull = cv2.convexHull(res)
#
#
# cv2.drawContours(drawing, [res], 0, (0, 255, 0), 2)
# cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 3)
# cv2.imshow('Only contour for calibration', drawing)
# cv2.imshow('BluePen', res)
# converted = cv2.cvtColor(images, cv2.COLOR_BGR2HSV)
# skinMask = cv2.inRange(converted, lower, upper)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
# skinMask = cv2.erode(skinMask, kernel, iterations=2)
# skinMask = cv2.dilate(skinMask, kernel, iterations=1)
#
# skinMask = cv2.GaussianBlur(skinMask, (5, 5), 0)
# skin = cv2.bitwise_and(images, images, mask=skinMask)
cv2.waitKey(1)
# img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# ## Gen lower mask (0-5) and upper mask (175-180) of RED
# mask1 = cv2.inRange(img_hsv, (0, 50, 20), (5, 255, 255))
# mask2 = cv2.inRange(img_hsv, (175, 50, 20), (180, 255, 255))
#
# ## Merge the mask and crop the red regions
# mask = cv2.bitwise_or(mask1, mask2)
# croped = cv2.bitwise_and(img, img, mask=mask)
finally:
# Stop streaming
pipeline.stop()
# threshold_filter = rs.threshold_filter()
# threshold_filter.set_option(rs.option.max_distance, 1)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_default.xml')
from pip._vendor.msgpack.fallback import xrange
f = open('./dist/2.txt', 'r').readlines()
stringsArr = [line.rstrip() for line in f]
position = 0
string = []
for stringItemFirst in stringsArr:
for j in range(1, len(stringsArr)):
positions = [
i for i in xrange(len(stringsArr[j]))
if stringsArr[j][i] != stringItemFirst[i]
]
print(positions)
if len(positions) == 1:
position = positions.pop()
string = stringItemFirst
print(string[:position] + string[(position + 1):])
def double_click(self, pos=(-1, -1), button_name="left"):
"""Double click at the specifed placed"""
for i in xrange(2):
self.click(pos, button_name)
def result_plot(x,y,w_min):
x1 = p.arange(0, 0.8, 0.01)
y1 = [-(w_min[2] + w_min[0] * x1[k]) / w_min[1] for k in xrange(len(x1))]
color = ['r'] * y.count(1.) + ['b'] * y.count(0.)
plt.scatter(x[0], x[1], c=color)
plt.plot(x1, y1)
def dfunc(x, y, w):
# 一阶导数
df = p.zeros(len(x))
for i in xrange(len(x[0])):
df += x[:, i] * (y[i] - p1(x[:, i], w))
return -df
def p1(x, w):
# 后验概率估计
wx = 0
for i in xrange(len(x)):
wx += w[i] * x[i]
return p.exp(wx) / (1 + p.exp(wx))
def edit_entry(entry_id):
if request.method == "POST":
user = mongo.db.users.find_one({'username': session["user"]})
entry = mongo.db.entries.find_one({"_id": ObjectId(entry_id)})
username = user["username"]
entry_username = entry["created_by"]
if username != entry_username:
flash("""This is not your entry. You can edit only your own!
Please select only your entry!""")
return redirect(url_for("calendar_home"))
print("User: {} and Created By: {}".format(username, entry_username))
add_entry_department = request.form.get("category_name")
user_department = user["department"]
if user["department"] == "":
mongo.db.users.update_one(
{"username": user['username']},
{"$set": {
"department": request.form["category_name"]
}})
else:
if add_entry_department != user_department:
print(
"Add entry department: {} is not the same as user department: {}"
.format(add_entry_department, user_department))
flash("""Departments doesn't match.
If you want to select different department, update your department in Profile"""
)
return redirect(url_for("manage_entries"))
# Deduct from Vacation days if Regular Vacation
if entry['entry_type'] == "Regular Vacation":
start_date_from_entry = datetime.strptime(entry["start_date"],
'%d %b, %Y')
end_date_from_entry = datetime.strptime(entry["end_date"],
'%d %b, %Y')
day_generator = (start_date_from_entry + timedelta(x + 1)
for x in xrange((end_date_from_entry -
start_date_from_entry).days))
how_many_days_to_add = sum(
1 for day in day_generator if day.weekday() < 5) + 1
update_user_days_before_new_deduction = user[
'vacation_days'] + how_many_days_to_add
mongo.db.users.update_one({"username": user['username']}, {
"$set": {
"vacation_days": update_user_days_before_new_deduction
}
})
user = mongo.db.users.find_one({'username': session["user"]})
# Next we deduct new amount of vacation days
start_date = datetime.strptime(request.form.get("start_date"),
'%d %b, %Y')
end_date = datetime.strptime(request.form.get("end_date"),
'%d %b, %Y')
day_generator = (start_date + timedelta(x + 1)
for x in xrange((end_date - start_date).days))
how_many_days = sum(
1 for day in day_generator if day.weekday() < 5) + 1
update_user_days = user['vacation_days'] - how_many_days
mongo.db.users.update_one(
{"username": user['username']},
{"$set": {
"vacation_days": update_user_days
}})
# Check if End Date is Before Start Date
start_date = datetime.strptime(request.form.get("start_date"),
'%d %b, %Y')
end_date = datetime.strptime(request.form.get("end_date"), '%d %b, %Y')
if end_date < start_date:
flash("End Date is before start date. Please correct date entry")
return redirect(url_for("manage_entries"))
update = {
"category_name": request.form.get("category_name"),
"entry_type": request.form.get("entry_type"),
"entry_description": request.form.get("entry_description"),
"start_date": request.form.get("start_date"),
"end_date": request.form.get("end_date"),
"created_by": session["user"]
}
mongo.db.entries.update({"_id": ObjectId(entry_id)}, update)
flash("Entry successfully Updated!")
entry = mongo.db.entries.find_one({"_id": ObjectId(entry_id)})
categories = mongo.db.categories.find().sort("category_name", 1)
vacation_types = mongo.db.vacation_types.find().sort("entry_type", 1)
return render_template("edit_entry.html",
categories=categories,
vacation_types=vacation_types,
entry=entry)
from __future__ import division, print_function, unicode_literals
import math
import numpy as np
import matplotlib.pyplot as plt
from pip._vendor.msgpack.fallback import xrange
N = 100
d0 = 2
C = 3
X = np.zeros((d0, N * C))
y = np.zeros(N * C, dtype='uint8')
for j in xrange(C):
ix = range(N * j, N * (j + 1))
r = np.linspace(0.0, 1, N)
t = np.linspace(j * 4, (j + 1) * 4, N) + np.random.randn(N) * 0.2 # theta
X[:, ix] = np.c_[r * np.sin(t), r * np.cos(t)].T
y[ix] = j
#print(y[ix])
#print("==============")
# lets visualize the data:
# plt.scatter(X[:N, 0], X[:N, 1], c=y[:N], s=40, cmap=plt.cm.Spectral)
plt.plot(X[0, :N], X[1, :N], 'bs', markersize=5)
plt.plot(X[0, N:2 * N], X[1, N:2 * N], 'ro', markersize=5)
plt.plot(X[0, 2 * N:], X[1, 2 * N:], 'g^', markersize=5)
# plt.axis('off')
plt.xlim([-1.5, 1.5])
while not questionmark: # Checks to see if there is a question mark
question = input("Please ask a question:\n"
) # Allow the user to input their question.
if "?" in question:
questionmark = True
else:
print("This is not a question.")
# Show an in progress message
toolbar_width = 40
print("Magic 8 Balling in progress, do not disturb.")
sys.stdout.write("[%s]" % (" " * toolbar_width)) # setup toolbar
sys.stdout.flush()
sys.stdout.write("\b" *
(toolbar_width + 1)) # return to start of line, after '['
for i in xrange(toolbar_width):
time.sleep(0.2) # Amount of pause between update
# Updates the bar
sys.stdout.write("-")
sys.stdout.flush()
sys.stdout.write("]\n") # This ends the progress bar
print(choice(responses)) # Randomizes responses
while not quitresponse: # Allow the user to ask another question/advice or quit the game
quitinput = input(
'Would you like to ask the Magic 8 Ball again?\n [Yes] \n [No] \n')
if str(quitinput.lower()) in ('yes', 'no'):
if str(quitinput.lower()) in 'yes':
quit = False
quitresponse = True
else:
from pip._vendor.msgpack.fallback import xrange
n = int(input())
for i in xrange(n):
print(i + 1, end="")
import sys
from itertools import product
from pip._vendor.msgpack.fallback import xrange
width, height = 10, 5
coordinates = list(product(xrange(width), xrange(height)))
coordinates[0] = "Godzilla"
print(coordinates)
#second
counter = 0
for line in sys.stdin:
counter += 1
ab = line.split()
itemsCount = len(ab)
finalExpression = []
nextCounter = 0
for index in range(len(ab)):
if (index == nextCounter):
item = ab[index]
if item in ['+', '-', '*'] and index <= (itemsCount - 3):
if ab[index +
1].isnumeric() and ab[index + 2].isnumeric() and int(
ab[index + 1]) in range(-10, 10) and int(
ab[index + 2]) in range(-10, 10):
result = 0
if (item == '+'):
result = int(ab[index + 1]) + int(ab[index + 2])
elif (item == '-'):
result = int(ab[index + 1]) - int(ab[index + 2])
X = np.array([[0, 0, 1],
[0, 1, 1],
[1, 0, 1],
[1, 1, 1]])
# output dataset
y = np.array([[0, 0, 1, 1]]).T
# seed random numbers to make calculation
# deterministic (just a good practice)
np.random.seed(1)
# initialize weights randomly with mean 0
syn0 = 2 * np.random.random((3, 1)) - 1
for iter in xrange(10000):
# forward propagation
l0 = X
l1 = nonlin(np.dot(l0, syn0))
# how much did we miss?
l1_error = y - l1
# multiply how much we missed by the
# slope of the sigmoid at the values in l1
l1_delta = l1_error * nonlin(l1, True)
# update weights
syn0 += np.dot(l0.T, l1_delta)
sleep(0.001)
from PIL import Image
from pytesseract import *
img = cv2.imread('img/c1.jpg') #이미지 파일에서 영상을 읽어들인다. 각 픽셀정보
img = cv2.resize(img, (600, 420)) #img를 크기를 600*420으로 재조정
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #흑백처리
blur = cv2.GaussianBlur(gray, (3, 3), 0) #잡음제거
#threshold
canny = cv2.Canny(blur, 75, 200) #윤곽추축
#컨투어 작업
contours, hierarchy = cv2.findContours(canny, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for i in xrange(len(contours)):
cnt = contours[i]
print(i)
area = cv2.contourArea(cnt) #컨투어 영역의 넓이
rect = cv2.minAreaRect(cnt) #컨투어 영역을 포함한 최소의 사각형을 추출
box = cv2.boxPoints(rect) #위에서 추출한 사각형의 4개 좌표를 반환(좌표타입은 float)
box = np.int0(box) #위에서 추출한 4좌표의 타입을 int로 변환 == int()
h = box[0][1] - box[1][1]
w = box[2][0] - box[1][0]
if w == 0:
continue
if 1 / 6 <= h / w <= 1 / 4 and area >= 500: #번호판 추출 조건
img = cv2.drawContours(img, [box], 0, (0, 0, 255), 2)
roi = img[box[1][1]:box[0][1],
box[1][0]:box[2][0]] #조건에 맞는 컨투어 영역을 잘라서 roi에 저장
@author: Jacky
'''
from collections import OrderedDict
from time import time
from random import randint
from pip._vendor.distlib.compat import raw_input
from pip._vendor.msgpack.fallback import xrange
d = OrderedDict()
d['Jacky']=(1,20)
d['Tracy']=(1,22)
d['Dora']=(2,20)
for k in d:
print(k)
players = list('ABCDEFGH')
d = OrderedDict()
start = time()
for i in xrange(8):
raw_input()
p = players.pop(randint(0,7-i))
end = time()
print(i+1,p,end - start)
d[p]=(i+1,end - start)
print()
print('='*20)
for k in d:
print(k,d[k])
def add_entry():
if request.method == "POST":
user = mongo.db.users.find_one({'username': session["user"]})
start_date = datetime.strptime(request.form.get("start_date"),
'%d %b, %Y')
end_date = datetime.strptime(request.form.get("end_date"), '%d %b, %Y')
if end_date < start_date:
flash("End Date is before start date. Please correct date entry")
return redirect(url_for("add_entry"))
add_entry_department = request.form.get("category_name")
user_department = user["department"]
if user["department"] == "":
mongo.db.users.update_one(
{"username": user['username']},
{"$set": {
"department": request.form["category_name"]
}})
else:
if add_entry_department != user_department:
print(
"Add entry department: {} is not the same as user department: {}"
.format(add_entry_department, user_department))
flash("""Departments doesn't match.
If you want to select different department, update your department in Profile"""
)
return redirect(url_for("add_entry"))
# Deduct from Vacation days if Regular Vacation
entry_type = request.form.get("entry_type")
if entry_type == "Regular Vacation":
day_generator = (start_date + timedelta(x + 1)
for x in xrange((end_date - start_date).days))
how_many_days = sum(
1 for day in day_generator if day.weekday() < 5) + 1
update_user_days = user['vacation_days'] - how_many_days
if update_user_days <= 0:
flash(
"Current status of days left {}! You don't have enough days!"
.format(user['vacation_days']))
return redirect(url_for("manage_entries"))
else:
mongo.db.users.update_one(
{"username": user['username']},
{"$set": {
"vacation_days": update_user_days
}})
entry = {
"category_name": request.form.get("category_name"),
"entry_type": request.form.get("entry_type"),
"entry_description": request.form.get("entry_description"),
"start_date": request.form.get("start_date"),
"end_date": request.form.get("end_date"),
"created_by": session["user"]
}
mongo.db.entries.insert_one(entry)
flash("Entry successfully added")
categories = mongo.db.categories.find().sort("category_name", 1)
vacation_types = mongo.db.vacation_types.find().sort("entry_type", 1)
return render_template("add_entry.html",
categories=categories,
vacation_types=vacation_types)
if S['speedX']<10:
R['accel']+= 1/(S['speedX']+.1)
# Traction Control System
if ((S['wheelSpinVel'][2]+S['wheelSpinVel'][3]) -
(S['wheelSpinVel'][0]+S['wheelSpinVel'][1]) > 5):
R['accel']-= .2
R['accel']= clip(R['accel'], 0 ,1)
# Automatic Transmission
R['gear']=1
if S['speedX']>50:
R['gear']=2
if S['speedX']>80:
R['gear']=3
if S['speedX']>110:
R['gear']=4
if S['speedX']>140:
R['gear']=5
if S['speedX']>170:
R['gear']=6
return
# ================ MAIN ================
if __name__ == "__main__":
C= Client()
for step in xrange(C.maxSteps, 0, -1):
C.get_servers_input()
drive_example(C)
C.respond_to_server()
C.shutdown()
def largeSum(num):
listOf50 = []
for i in xrange(50, len(str(num))+1,50):
listOf50.append(long(str(num)[i-50:i]))
totalSum = reduce(lambda x , y : x + y, listOf50)
return int(str(totalSum)[0:10])
# | to stop (exclusive) by step. range(i, j) produces i, i+1, i+2, ..., j-1.
# | start defaults to 0, and stop is omitted! range(4) produces 0, 1, 2, 3.
# | These are exactly the valid indices for a list of 4 elements.
# | When step is given, it specifies the increment (or decrement).
# print(help(range(10)))
from pip._vendor.msgpack.fallback import xrange
for item in range(0, 10, 2):
print(item)
#result 0 2 4 6 8
#the xrange() is quit similar to range() except that xrange() releases or frees the memory when not in use .
#whereas range() doesn't release the memory .
x = 0
for item in xrange(20):
print("hello world !")
for char in "this is a sentence !!! ":
print(char)
list = [char for char in "this is a sentence ! "]
print(list)
#indefinite Loop
#try to get from user a valid integer
#
test = True
while test:
test = False
# Importamo modul random import random # Iz knjižnice pip._vendor.msgpack.fallback importamo xrange from pip._vendor.msgpack.fallback import xrange # Programu sporočimo naj izbere 10 števil od 1 do 100 array = random.sample(xrange(101),10) # Vpeljemo spremenljivko total in s funkcijo sum() seštejemo elemente "tabele" total = sum(array) # Izpišemo element print(array) # Izpišemo seštevek vsem elementov. print(total)