content stringlengths 7 1.05M | fixed_cases stringlengths 1 1.28M |
|---|---|
def slices(num_string,slice_size):
if slice_size>len(num_string):
raise ValueError
list_of_slices = []
for i in range(len(num_string)-slice_size+1):
temp_answer = []
for j in range(slice_size):
temp_answer.append(int(num_string[i+j]))
list_of_slices.append(temp_answer)
return list_of_slices
def largest_product(num_string,slice_size):
slices_to_test = slices(num_string,slice_size)
answer = 1
for i in range(len(slices_to_test)):
working_value = 1
for j in range(slice_size):
working_value*=slices_to_test[i][j]
if working_value > answer:
answer = working_value
return answer | def slices(num_string, slice_size):
if slice_size > len(num_string):
raise ValueError
list_of_slices = []
for i in range(len(num_string) - slice_size + 1):
temp_answer = []
for j in range(slice_size):
temp_answer.append(int(num_string[i + j]))
list_of_slices.append(temp_answer)
return list_of_slices
def largest_product(num_string, slice_size):
slices_to_test = slices(num_string, slice_size)
answer = 1
for i in range(len(slices_to_test)):
working_value = 1
for j in range(slice_size):
working_value *= slices_to_test[i][j]
if working_value > answer:
answer = working_value
return answer |
global LAYER_UNKNOWN
LAYER_UNKNOWN = 'unknown'
class Design(object):
def __init__(self, layers, smells) -> None:
self.layers = layers
self.smells = smells
super().__init__()
| global LAYER_UNKNOWN
layer_unknown = 'unknown'
class Design(object):
def __init__(self, layers, smells) -> None:
self.layers = layers
self.smells = smells
super().__init__() |
"""
Initialization file for tweets library module.
These exist here in lib as some of them are useful as help functions of other
scripts (such as getting available campaigns). However, these could be moved to
utils/reporting/ as individual scripts. And they could be called directly or
with make, to avoid having multiple ways of calling something.
"""
| """
Initialization file for tweets library module.
These exist here in lib as some of them are useful as help functions of other
scripts (such as getting available campaigns). However, these could be moved to
utils/reporting/ as individual scripts. And they could be called directly or
with make, to avoid having multiple ways of calling something.
""" |
"""
Coin change
given coins of different denominations and a total amount of money.
Write a function to compute the number of combinations that make up that amount.
You may assume that you have infinite number of each kind of coin.
Input: amount = 25, coins = [1, 2, 5]
"""
class Solution:
def change(self, amount: int, coins: List[int]) -> int:
dic = {0:1}
for coin in coins:
for i in range(amount+1):
dic[i] =dic.get(i,0) + dic.get(i-coin,0)
return dic.get(amount,0)
amount = 25
coins = [1, 2, 5]
a = Solution()
a.change(amount, coins)
# 42 | """
Coin change
given coins of different denominations and a total amount of money.
Write a function to compute the number of combinations that make up that amount.
You may assume that you have infinite number of each kind of coin.
Input: amount = 25, coins = [1, 2, 5]
"""
class Solution:
def change(self, amount: int, coins: List[int]) -> int:
dic = {0: 1}
for coin in coins:
for i in range(amount + 1):
dic[i] = dic.get(i, 0) + dic.get(i - coin, 0)
return dic.get(amount, 0)
amount = 25
coins = [1, 2, 5]
a = solution()
a.change(amount, coins) |
class RecentCounter:
def __init__(self):
self.slide_window = deque()
def ping(self, t: int) -> int:
self.slide_window.append(t)
# invalidate the outdated pings
while self.slide_window:
if self.slide_window[0] < t - 3000:
self.slide_window.popleft()
else:
break
return len(self.slide_window)
# Your RecentCounter object will be instantiated and called as such:
# obj = RecentCounter()
# param_1 = obj.ping(t) | class Recentcounter:
def __init__(self):
self.slide_window = deque()
def ping(self, t: int) -> int:
self.slide_window.append(t)
while self.slide_window:
if self.slide_window[0] < t - 3000:
self.slide_window.popleft()
else:
break
return len(self.slide_window) |
# --------------
# Code starts here
class_1=['Geoffrey Hinton','Andrew Ng','Sebastian Raschka','Yoshua Bengio']
class_2=['Hilary Mason','Carla Gentry','Corinna Cortes']
new_class=class_1+class_2
print(new_class)
new_class.append('Peter Warden')
print(new_class)
new_class.remove("Carla Gentry")
print(new_class)
# Code ends here
# --------------
# Code starts here
courses = {'Math':65,'English':70,'History':80,'French':70,'Science':60}
marks=[]
for i in courses:
marks.append(courses[i])
print(marks)
print(courses['Math'])
print(courses['English'])
print(courses['History'])
print(courses['French'])
print(courses['Science'])
total =courses['Math'] + courses['English'] +courses['History'] + courses['French'] +courses['Science']
print(total)
percentage=(total*100/500)
print(percentage)
# Code ends here
# --------------
# Code starts here
mathematics= {'Geoffrey Hinton': 78,'Andrew Ng':95,'Sebastian Raschka':65,'Yoshua Benjio':50,'Hilary Mason':70,'Corinna Cortes':66,'Peter Warden':75}
max_marks_scored =max(mathematics,key=mathematics.get)
topper=max_marks_scored
print(topper)
# Code ends here
# --------------
# Given string
topper = 'andrew ng'
# Code starts here
# Create variable first_name
first_name = (topper.split()[0])
print (first_name)
# Create variable Last_name and store last two element in the list
last_name = (topper.split()[1])
print (last_name)
# Concatenate the string
full_name = last_name + ' ' + first_name
# print the full_name
print (full_name)
# print the name in upper case
certificate_name = full_name.upper()
print (certificate_name)
# Code ends here
| class_1 = ['Geoffrey Hinton', 'Andrew Ng', 'Sebastian Raschka', 'Yoshua Bengio']
class_2 = ['Hilary Mason', 'Carla Gentry', 'Corinna Cortes']
new_class = class_1 + class_2
print(new_class)
new_class.append('Peter Warden')
print(new_class)
new_class.remove('Carla Gentry')
print(new_class)
courses = {'Math': 65, 'English': 70, 'History': 80, 'French': 70, 'Science': 60}
marks = []
for i in courses:
marks.append(courses[i])
print(marks)
print(courses['Math'])
print(courses['English'])
print(courses['History'])
print(courses['French'])
print(courses['Science'])
total = courses['Math'] + courses['English'] + courses['History'] + courses['French'] + courses['Science']
print(total)
percentage = total * 100 / 500
print(percentage)
mathematics = {'Geoffrey Hinton': 78, 'Andrew Ng': 95, 'Sebastian Raschka': 65, 'Yoshua Benjio': 50, 'Hilary Mason': 70, 'Corinna Cortes': 66, 'Peter Warden': 75}
max_marks_scored = max(mathematics, key=mathematics.get)
topper = max_marks_scored
print(topper)
topper = 'andrew ng'
first_name = topper.split()[0]
print(first_name)
last_name = topper.split()[1]
print(last_name)
full_name = last_name + ' ' + first_name
print(full_name)
certificate_name = full_name.upper()
print(certificate_name) |
widget = WidgetDefault()
widget.border = "None"
widget.background = "None"
commonDefaults["RadialMenuWidget"] = widget
def generateRadialMenuWidget(file, screen, menu, parentName):
name = menu.getName()
file.write(" %s = leRadialMenuWidget_New();" % (name))
generateBaseWidget(file, screen, menu)
writeSetInt(file, name, "NumberOfItemsShown", menu.getItemsShown(), 5)
writeSetBoolean(file, name, "HighlightProminent", menu.getHighlightProminent(), False)
writeSetInt(file, name, "Theta", menu.getTheta(), 0)
width = menu.getSize().width
height = menu.getSize().height
touchX = menu.getTouchX()
touchY = menu.getTouchY()
touchWidth = menu.getTouchWidth()
touchHeight = menu.getTouchHeight()
file.write(" %s->fn->setTouchArea(%s, %d, %d, %d, %d);" % (name, name, touchX, touchY, width * touchWidth / 100, height * touchHeight / 100))
ellipseType = menu.getEllipseType().toString()
if ellipseType == "Default":
ellipseType = "LE_RADIAL_MENU_ELLIPSE_TYPE_DEFAULT"
elif ellipseType == "Orbital":
ellipseType = "LE_RADIAL_MENU_ELLIPSE_TYPE_OBITAL"
else:
ellipseType = "LE_RADIAL_MENU_ELLIPSE_TYPE_ROLLODEX"
writeSetLiteralString(file, name, "EllipseType", ellipseType, "LE_RADIAL_MENU_ELLIPSE_TYPE_DEFAULT")
writeSetBoolean(file, name, "DrawEllipse", menu.getEllipseVisible(), True)
scaleMode = menu.getSizeScale().toString()
if scaleMode == "Off":
scaleMode = "LE_RADIAL_MENU_INTERPOLATE_OFF"
elif scaleMode == "Gradual":
scaleMode = "LE_RADIAL_MENU_INTERPOLATE_GRADUAL"
else:
scaleMode = "LE_RADIAL_MENU_INTERPOLATE_PROMINENT"
writeSetLiteralString(file, name, "ScaleMode", scaleMode, "LE_RADIAL_MENU_INTERPOLATE_GRADUAL")
blendMode = menu.getAlphaScale().toString()
if blendMode == "Off":
blendMode = "LE_RADIAL_MENU_INTERPOLATE_OFF"
elif blendMode == "Gradual":
blendMode = "LE_RADIAL_MENU_INTERPOLATE_GRADUAL"
else:
blendMode = "LE_RADIAL_MENU_INTERPOLATE_PROMINENT"
writeSetLiteralString(file, name, "BlendMode", blendMode, "LE_RADIAL_MENU_INTERPOLATE_GRADUAL")
min = menu.getMinSizePercent()
max = menu.getMaxSizePercent()
if min != 30 or max != 100:
file.write(" %s->fn->setScaleRange(%s, %d, %d);" % (name, name, min, max))
min = menu.getMinAlphaAmount()
max = menu.getMaxAlphaAmount()
if min != 128 or max != 255:
file.write(" %s->fn->setBlendRange(%s, %d, %d);" % (name, name, min, max))
touchX = menu.getTouchX()
touchY = menu.getTouchY()
touchWidth = menu.getTouchWidth()
touchHeight = menu.getTouchHeight()
if touchX != 0 or touchY != 75 or touchWidth != 100 or touchHeight != 50:
file.write(" %s->fn->setTouchArea(%s, %d, %d, %d, %d);" % (name, name, touchX, touchY, touchWidth, touchHeight))
x = menu.getLocation(False).x
y = menu.getLocation(False).y
width = menu.getSize().width
height = menu.getSize().height
xp = x + width / 2;
yp = y + height / 2;
items = menu.getItemList()
if len(items) > 0:
for idx, item in enumerate(items):
varName = "%s_image_%d" % (name, idx)
file.write(" %s = leImageScaleWidget_New();" % (varName))
imageName = craftAssetName(item.image)
if imageName != "NULL":
file.write(" %s->fn->setImage(%s, %s);" % (varName, varName, imageName))
file.write(" %s->fn->setTransformWidth(%s, %d);" % (varName, varName, item.currentSize.width))
file.write(" %s->fn->setTransformHeight(%s, %s);" % (varName, varName, item.currentSize.height))
file.write(" %s->fn->setStretchEnabled(%s, LE_TRUE);" % (varName, varName))
file.write(" %s->fn->setPreserveAspectEnabled(%s, LE_TRUE);" % (varName, varName))
else:
file.write(" %s->fn->setBackgroundType(%s, LE_WIDGET_BACKGROUND_FILL);" % (varName, varName))
file.write(" %s->fn->setBorderType(%s, LE_WIDGET_BORDER_LINE);" % (varName, varName))
if not (item.t == 270 and menu.getItemsShown() < len(items)):
file.write(" %s->fn->setVisible(%s, LE_FALSE);" % (varName, varName))
file.write(" %s->fn->setPosition(%s, %d, %d);" % (varName, varName, xp, yp))
file.write(" %s->fn->setSize(%s, %d, %d);" % (varName, varName, item.originalSize.width, item.originalSize.height))
if item.originalAlphaAmount != 255:
file.write(" %s->fn->setAlphaAmount(%s, %d);" % (varName, varName, item.originalAlphaAmount));
file.write(" %s->fn->addWidget(%s, (leWidget*)%s);" % (name, name, varName))
writeEvent(file, name, menu, "ItemSelectedEvent", "ItemSelectedEventCallback", "OnItemSelected")
writeEvent(file, name, menu, "ItemProminenceChangedEvent", "ItemProminenceChangedEvent", "OnItemProminenceChanged")
file.write(" %s->fn->addChild(%s, (leWidget*)%s);" % (parentName, parentName, name))
file.writeNewLine()
def generateRadialMenuEvent(screen, widget, event, genActions):
text = ""
if event.name == "ItemSelectedEvent":
text += "void %s_OnItemSelected(%s)\n" % (widget.getName(), getWidgetVariableName(widget))
if event.name == "ItemProminenceChangedEvent":
text += "void %s_OnItemProminenceChanged(%s)\n" % (widget.getName(), getWidgetVariableName(widget))
text += generateActions(widget, event, genActions, None, None)
return text
def generateRadialMenuAction(text, variables, owner, event, action):
i = 0 | widget = widget_default()
widget.border = 'None'
widget.background = 'None'
commonDefaults['RadialMenuWidget'] = widget
def generate_radial_menu_widget(file, screen, menu, parentName):
name = menu.getName()
file.write(' %s = leRadialMenuWidget_New();' % name)
generate_base_widget(file, screen, menu)
write_set_int(file, name, 'NumberOfItemsShown', menu.getItemsShown(), 5)
write_set_boolean(file, name, 'HighlightProminent', menu.getHighlightProminent(), False)
write_set_int(file, name, 'Theta', menu.getTheta(), 0)
width = menu.getSize().width
height = menu.getSize().height
touch_x = menu.getTouchX()
touch_y = menu.getTouchY()
touch_width = menu.getTouchWidth()
touch_height = menu.getTouchHeight()
file.write(' %s->fn->setTouchArea(%s, %d, %d, %d, %d);' % (name, name, touchX, touchY, width * touchWidth / 100, height * touchHeight / 100))
ellipse_type = menu.getEllipseType().toString()
if ellipseType == 'Default':
ellipse_type = 'LE_RADIAL_MENU_ELLIPSE_TYPE_DEFAULT'
elif ellipseType == 'Orbital':
ellipse_type = 'LE_RADIAL_MENU_ELLIPSE_TYPE_OBITAL'
else:
ellipse_type = 'LE_RADIAL_MENU_ELLIPSE_TYPE_ROLLODEX'
write_set_literal_string(file, name, 'EllipseType', ellipseType, 'LE_RADIAL_MENU_ELLIPSE_TYPE_DEFAULT')
write_set_boolean(file, name, 'DrawEllipse', menu.getEllipseVisible(), True)
scale_mode = menu.getSizeScale().toString()
if scaleMode == 'Off':
scale_mode = 'LE_RADIAL_MENU_INTERPOLATE_OFF'
elif scaleMode == 'Gradual':
scale_mode = 'LE_RADIAL_MENU_INTERPOLATE_GRADUAL'
else:
scale_mode = 'LE_RADIAL_MENU_INTERPOLATE_PROMINENT'
write_set_literal_string(file, name, 'ScaleMode', scaleMode, 'LE_RADIAL_MENU_INTERPOLATE_GRADUAL')
blend_mode = menu.getAlphaScale().toString()
if blendMode == 'Off':
blend_mode = 'LE_RADIAL_MENU_INTERPOLATE_OFF'
elif blendMode == 'Gradual':
blend_mode = 'LE_RADIAL_MENU_INTERPOLATE_GRADUAL'
else:
blend_mode = 'LE_RADIAL_MENU_INTERPOLATE_PROMINENT'
write_set_literal_string(file, name, 'BlendMode', blendMode, 'LE_RADIAL_MENU_INTERPOLATE_GRADUAL')
min = menu.getMinSizePercent()
max = menu.getMaxSizePercent()
if min != 30 or max != 100:
file.write(' %s->fn->setScaleRange(%s, %d, %d);' % (name, name, min, max))
min = menu.getMinAlphaAmount()
max = menu.getMaxAlphaAmount()
if min != 128 or max != 255:
file.write(' %s->fn->setBlendRange(%s, %d, %d);' % (name, name, min, max))
touch_x = menu.getTouchX()
touch_y = menu.getTouchY()
touch_width = menu.getTouchWidth()
touch_height = menu.getTouchHeight()
if touchX != 0 or touchY != 75 or touchWidth != 100 or (touchHeight != 50):
file.write(' %s->fn->setTouchArea(%s, %d, %d, %d, %d);' % (name, name, touchX, touchY, touchWidth, touchHeight))
x = menu.getLocation(False).x
y = menu.getLocation(False).y
width = menu.getSize().width
height = menu.getSize().height
xp = x + width / 2
yp = y + height / 2
items = menu.getItemList()
if len(items) > 0:
for (idx, item) in enumerate(items):
var_name = '%s_image_%d' % (name, idx)
file.write(' %s = leImageScaleWidget_New();' % varName)
image_name = craft_asset_name(item.image)
if imageName != 'NULL':
file.write(' %s->fn->setImage(%s, %s);' % (varName, varName, imageName))
file.write(' %s->fn->setTransformWidth(%s, %d);' % (varName, varName, item.currentSize.width))
file.write(' %s->fn->setTransformHeight(%s, %s);' % (varName, varName, item.currentSize.height))
file.write(' %s->fn->setStretchEnabled(%s, LE_TRUE);' % (varName, varName))
file.write(' %s->fn->setPreserveAspectEnabled(%s, LE_TRUE);' % (varName, varName))
else:
file.write(' %s->fn->setBackgroundType(%s, LE_WIDGET_BACKGROUND_FILL);' % (varName, varName))
file.write(' %s->fn->setBorderType(%s, LE_WIDGET_BORDER_LINE);' % (varName, varName))
if not (item.t == 270 and menu.getItemsShown() < len(items)):
file.write(' %s->fn->setVisible(%s, LE_FALSE);' % (varName, varName))
file.write(' %s->fn->setPosition(%s, %d, %d);' % (varName, varName, xp, yp))
file.write(' %s->fn->setSize(%s, %d, %d);' % (varName, varName, item.originalSize.width, item.originalSize.height))
if item.originalAlphaAmount != 255:
file.write(' %s->fn->setAlphaAmount(%s, %d);' % (varName, varName, item.originalAlphaAmount))
file.write(' %s->fn->addWidget(%s, (leWidget*)%s);' % (name, name, varName))
write_event(file, name, menu, 'ItemSelectedEvent', 'ItemSelectedEventCallback', 'OnItemSelected')
write_event(file, name, menu, 'ItemProminenceChangedEvent', 'ItemProminenceChangedEvent', 'OnItemProminenceChanged')
file.write(' %s->fn->addChild(%s, (leWidget*)%s);' % (parentName, parentName, name))
file.writeNewLine()
def generate_radial_menu_event(screen, widget, event, genActions):
text = ''
if event.name == 'ItemSelectedEvent':
text += 'void %s_OnItemSelected(%s)\n' % (widget.getName(), get_widget_variable_name(widget))
if event.name == 'ItemProminenceChangedEvent':
text += 'void %s_OnItemProminenceChanged(%s)\n' % (widget.getName(), get_widget_variable_name(widget))
text += generate_actions(widget, event, genActions, None, None)
return text
def generate_radial_menu_action(text, variables, owner, event, action):
i = 0 |
def find_divisor(numbers):
for index, number in enumerate(numbers):
print("len", len(numbers[index + 1:]))
for divider in reversed(numbers[index + 1:]):
if number % divider == 0:
print("found {} and {}. Rest: {}".format(
number, divider, number % divider))
return int(number / divider)
return 0
with open("input", "r") as f:
input = f.read()
lines = input.split("\n")
sum = 0
for line in lines:
if not len(line):
continue
numbers = sorted([int(x) for x in line.split("\t")], reverse=True)
sum = sum + find_divisor(numbers)
print("CS", sum)
| def find_divisor(numbers):
for (index, number) in enumerate(numbers):
print('len', len(numbers[index + 1:]))
for divider in reversed(numbers[index + 1:]):
if number % divider == 0:
print('found {} and {}. Rest: {}'.format(number, divider, number % divider))
return int(number / divider)
return 0
with open('input', 'r') as f:
input = f.read()
lines = input.split('\n')
sum = 0
for line in lines:
if not len(line):
continue
numbers = sorted([int(x) for x in line.split('\t')], reverse=True)
sum = sum + find_divisor(numbers)
print('CS', sum) |
class University:
def __init__(self, name, country, world_rank):
self.name = name
self.country = country
self.world_rank = world_rank | class University:
def __init__(self, name, country, world_rank):
self.name = name
self.country = country
self.world_rank = world_rank |
#!/usr/bin/env python
class Solution:
def copyRandomList(self, head: 'Node') -> 'Node':
curr = head
while curr:
node = Node(curr.val, curr.next, None)
curr.next, curr = node, curr.next
curr = head
while curr:
copy = curr.next
copy.random = curr.random.next
curr.next = copy.next
ret = copy = head.next
while copy.next:
copy = copy.next = copy.next.next
return ret
| class Solution:
def copy_random_list(self, head: 'Node') -> 'Node':
curr = head
while curr:
node = node(curr.val, curr.next, None)
(curr.next, curr) = (node, curr.next)
curr = head
while curr:
copy = curr.next
copy.random = curr.random.next
curr.next = copy.next
ret = copy = head.next
while copy.next:
copy = copy.next = copy.next.next
return ret |
def _responses_path(
config: "Config",
sim_runner: "FEMRunner",
sim_params: "SimParams",
response_type: "ResponseType",
) -> str:
"""Path to fem that were generated with given parameters."""
return sim_runner.sim_out_path(
config=config, sim_params=sim_params, ext="npy", response_types=[response_type]
)
# determinant of matrix a
def det(a):
return (
a[0][0] * a[1][1] * a[2][2]
+ a[0][1] * a[1][2] * a[2][0]
+ a[0][2] * a[1][0] * a[2][1]
- a[0][2] * a[1][1] * a[2][0]
- a[0][1] * a[1][0] * a[2][2]
- a[0][0] * a[1][2] * a[2][1]
)
# unit normal vector of plane defined by points a, b, and c
def unit_normal(a, b, c):
x = det([[1, a[1], a[2]], [1, b[1], b[2]], [1, c[1], c[2]]])
y = det([[a[0], 1, a[2]], [b[0], 1, b[2]], [c[0], 1, c[2]]])
z = det([[a[0], a[1], 1], [b[0], b[1], 1], [c[0], c[1], 1]])
magnitude = (x ** 2 + y ** 2 + z ** 2) ** 0.5
return x / magnitude, y / magnitude, z / magnitude
# dot product of vectors a and b
def dot(a, b):
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]
# cross product of vectors a and b
def cross(a, b):
x = a[1] * b[2] - a[2] * b[1]
y = a[2] * b[0] - a[0] * b[2]
z = a[0] * b[1] - a[1] * b[0]
return x, y, z
# area of polygon poly
def poly_area(poly):
if len(poly) < 3: # not a plane - no area
raise ValueError("Not a plane, need >= 3 points")
total = [0, 0, 0]
for i in range(len(poly)):
vi1 = poly[i]
if i is len(poly) - 1:
vi2 = poly[0]
else:
vi2 = poly[i + 1]
prod = cross(vi1, vi2)
total[0] += prod[0]
total[1] += prod[1]
total[2] += prod[2]
result = dot(total, unit_normal(poly[0], poly[1], poly[2]))
return abs(result / 2)
| def _responses_path(config: 'Config', sim_runner: 'FEMRunner', sim_params: 'SimParams', response_type: 'ResponseType') -> str:
"""Path to fem that were generated with given parameters."""
return sim_runner.sim_out_path(config=config, sim_params=sim_params, ext='npy', response_types=[response_type])
def det(a):
return a[0][0] * a[1][1] * a[2][2] + a[0][1] * a[1][2] * a[2][0] + a[0][2] * a[1][0] * a[2][1] - a[0][2] * a[1][1] * a[2][0] - a[0][1] * a[1][0] * a[2][2] - a[0][0] * a[1][2] * a[2][1]
def unit_normal(a, b, c):
x = det([[1, a[1], a[2]], [1, b[1], b[2]], [1, c[1], c[2]]])
y = det([[a[0], 1, a[2]], [b[0], 1, b[2]], [c[0], 1, c[2]]])
z = det([[a[0], a[1], 1], [b[0], b[1], 1], [c[0], c[1], 1]])
magnitude = (x ** 2 + y ** 2 + z ** 2) ** 0.5
return (x / magnitude, y / magnitude, z / magnitude)
def dot(a, b):
return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]
def cross(a, b):
x = a[1] * b[2] - a[2] * b[1]
y = a[2] * b[0] - a[0] * b[2]
z = a[0] * b[1] - a[1] * b[0]
return (x, y, z)
def poly_area(poly):
if len(poly) < 3:
raise value_error('Not a plane, need >= 3 points')
total = [0, 0, 0]
for i in range(len(poly)):
vi1 = poly[i]
if i is len(poly) - 1:
vi2 = poly[0]
else:
vi2 = poly[i + 1]
prod = cross(vi1, vi2)
total[0] += prod[0]
total[1] += prod[1]
total[2] += prod[2]
result = dot(total, unit_normal(poly[0], poly[1], poly[2]))
return abs(result / 2) |
KEY_PRESS = 0
MOUSE_DOWN = 1
MOUSE_UP = 2
MOUSE_DOUBLE_CLICK = 3
MOUSE_MOVE = 5
SCROLL_DOWN = 6
SCROLL_UP = 7
SCROLL_STEP = 1
CTRL = 'ctrl'
SHIFT = 'shift'
ALT = 'alt'
MODIFIER_KEYS = (CTRL, SHIFT, ALT,)
MODIFIER_KEYS_PRESS_DELAY = .4
EVENTS_DELAY = .05
LEFT = "left"
MIDDLE = "middle"
RIGHT = "right"
HIGH_QUALITY = 75
MEDIUM_QUALITY = 60
LOW_QUALITY = 40
HIGH_SCALE = 70/100
MEDIUM_SCALE = 50/100
LOW_SCALE = 40/100
| key_press = 0
mouse_down = 1
mouse_up = 2
mouse_double_click = 3
mouse_move = 5
scroll_down = 6
scroll_up = 7
scroll_step = 1
ctrl = 'ctrl'
shift = 'shift'
alt = 'alt'
modifier_keys = (CTRL, SHIFT, ALT)
modifier_keys_press_delay = 0.4
events_delay = 0.05
left = 'left'
middle = 'middle'
right = 'right'
high_quality = 75
medium_quality = 60
low_quality = 40
high_scale = 70 / 100
medium_scale = 50 / 100
low_scale = 40 / 100 |
#Celsius to Fahrenheit conversion
#F = C *9/5 +32
F= 0
print("Give the Number of Celcius: ")
c=float(input())
print("The result is: ")
F=c*9/5+32
print(F)
| f = 0
print('Give the Number of Celcius: ')
c = float(input())
print('The result is: ')
f = c * 9 / 5 + 32
print(F) |
# -*- coding: utf-8 -*-
"""
ParaMol MM_engines subpackage.
Contains modules related to the ParaMol representation of MM engines.
"""
__all__ = ['openmm', 'resp'] | """
ParaMol MM_engines subpackage.
Contains modules related to the ParaMol representation of MM engines.
"""
__all__ = ['openmm', 'resp'] |
#Software By AwesomeWithRex
def read_file(filename):
with open(filename) as f:
filename = f.readlines()
return filename
def get_template():
template = ''
with open('template.html', 'r') as f:
template = f.readlines()
return template
def put_in_body(file, template):
count = 0
body_tag = 0
for i in template:
count += 1
if '|b|' in i:
body_tag = count - 1
text_to_append = ""
for line in file:
text_to_append += line
formatted_text = ""
for word in text_to_append:
formatted_text += word
if '\n' in word:
formatted_text += word.replace('\n', '<br/>\t')
template[body_tag] = template[body_tag].replace('|b|', formatted_text)
for i in template:
print(i)
return template
def save_template(name_of_doc, saved_doc_file):
with open(name_of_doc, 'w') as f:
f.writelines(saved_doc_file)
def put_in_title():
pass
def main():
content = read_file('text.txt')
template = get_template()
formatted_template = put_in_body(content, template)
save_template('the.html',formatted_template)
if __name__=='__main__':
main()
| def read_file(filename):
with open(filename) as f:
filename = f.readlines()
return filename
def get_template():
template = ''
with open('template.html', 'r') as f:
template = f.readlines()
return template
def put_in_body(file, template):
count = 0
body_tag = 0
for i in template:
count += 1
if '|b|' in i:
body_tag = count - 1
text_to_append = ''
for line in file:
text_to_append += line
formatted_text = ''
for word in text_to_append:
formatted_text += word
if '\n' in word:
formatted_text += word.replace('\n', '<br/>\t')
template[body_tag] = template[body_tag].replace('|b|', formatted_text)
for i in template:
print(i)
return template
def save_template(name_of_doc, saved_doc_file):
with open(name_of_doc, 'w') as f:
f.writelines(saved_doc_file)
def put_in_title():
pass
def main():
content = read_file('text.txt')
template = get_template()
formatted_template = put_in_body(content, template)
save_template('the.html', formatted_template)
if __name__ == '__main__':
main() |
class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def print_list(self):
cur_node=self.head
while cur_node:
print(cur_node.data)
cur_node = cur_node.next
def append(self, data):
new_node = Node(data)
if self.head is None:
self.head = new_node
return
last_node = self.head
while last_node.next:
last_node = last_node.next
last_node.next = new_node
def prepend(self,data):
new_node = Node(data)
new_node.next = self.head
self.head = new_node
def insert_after_node(self, prev_node, data):
if not prev_node:
print("previous Node not in the list")
return
new_node = Node(data)
new_node.next = prev_node.next
prev_node.next = new_node
def delete_node(self, key):
current_node = self.head
if current_node and current_node.data==key:
self.head = current_node.next
current_node = None
return
prev = None
while current_node and current_node.data != key:
prev = current_node
current_node = current_node.next
if current_node is None:
return
prev.next = current_node.next
current_node = None
llist = LinkedList()
llist.append("A")
llist.append("B")
llist.append("C")
llist.append("D")
#llist.prepend("E")
llist.delete_node("A")
llist.insert_after_node(llist.head.next,"E")
#print(llist.head.data)
llist.print_list()
| class Node:
def __init__(self, data):
self.data = data
self.next = None
class Linkedlist:
def __init__(self):
self.head = None
def print_list(self):
cur_node = self.head
while cur_node:
print(cur_node.data)
cur_node = cur_node.next
def append(self, data):
new_node = node(data)
if self.head is None:
self.head = new_node
return
last_node = self.head
while last_node.next:
last_node = last_node.next
last_node.next = new_node
def prepend(self, data):
new_node = node(data)
new_node.next = self.head
self.head = new_node
def insert_after_node(self, prev_node, data):
if not prev_node:
print('previous Node not in the list')
return
new_node = node(data)
new_node.next = prev_node.next
prev_node.next = new_node
def delete_node(self, key):
current_node = self.head
if current_node and current_node.data == key:
self.head = current_node.next
current_node = None
return
prev = None
while current_node and current_node.data != key:
prev = current_node
current_node = current_node.next
if current_node is None:
return
prev.next = current_node.next
current_node = None
llist = linked_list()
llist.append('A')
llist.append('B')
llist.append('C')
llist.append('D')
llist.delete_node('A')
llist.insert_after_node(llist.head.next, 'E')
llist.print_list() |
# -*- coding: utf-8 -*-
def in_segregation(x0, R, n, N=None):
"""
return the actual indium concentration
in th nth layer
Params
------
x0 : float
the indium concentration between 0 and 1
R : float
the segregation coefficient
n : int
the current layer
N : int
number of layers in the well
"""
if N:
return x0*(1-R**N)*R**(n-N)
return x0*(1-R**n)
| def in_segregation(x0, R, n, N=None):
"""
return the actual indium concentration
in th nth layer
Params
------
x0 : float
the indium concentration between 0 and 1
R : float
the segregation coefficient
n : int
the current layer
N : int
number of layers in the well
"""
if N:
return x0 * (1 - R ** N) * R ** (n - N)
return x0 * (1 - R ** n) |
class Solution:
def angleClock(self, hour: int, minutes: int) -> float:
hdeg = ((hour*30) + (minutes*0.5))%360
mdeg = (minutes * 6)
angle = abs(hdeg-mdeg)
return min(angle, 360-angle)
| class Solution:
def angle_clock(self, hour: int, minutes: int) -> float:
hdeg = (hour * 30 + minutes * 0.5) % 360
mdeg = minutes * 6
angle = abs(hdeg - mdeg)
return min(angle, 360 - angle) |
# Copyright (c) 2010 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'variables': {
'chromium_code': 1,
'protoc_out_dir': '<(SHARED_INTERMEDIATE_DIR)/protoc_out',
},
'targets': [
{
# Protobuf compiler / generate rule for sync.proto. This is used by
# test code in net, which is why it's isolated into its own .gyp file.
'target_name': 'sync_proto',
'type': 'none',
'sources': [
'sync.proto',
'encryption.proto',
'app_specifics.proto',
'autofill_specifics.proto',
'bookmark_specifics.proto',
'extension_specifics.proto',
'nigori_specifics.proto',
'password_specifics.proto',
'preference_specifics.proto',
'session_specifics.proto',
'test.proto',
'theme_specifics.proto',
'typed_url_specifics.proto',
],
'rules': [
{
'rule_name': 'genproto',
'extension': 'proto',
'inputs': [
'<(PRODUCT_DIR)/<(EXECUTABLE_PREFIX)protoc<(EXECUTABLE_SUFFIX)',
],
'outputs': [
'<(PRODUCT_DIR)/pyproto/sync_pb/<(RULE_INPUT_ROOT)_pb2.py',
'<(protoc_out_dir)/chrome/browser/sync/protocol/<(RULE_INPUT_ROOT).pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/<(RULE_INPUT_ROOT).pb.cc',
],
'action': [
'<(PRODUCT_DIR)/<(EXECUTABLE_PREFIX)protoc<(EXECUTABLE_SUFFIX)',
'--proto_path=.',
'./<(RULE_INPUT_ROOT)<(RULE_INPUT_EXT)',
'--cpp_out=<(protoc_out_dir)/chrome/browser/sync/protocol',
'--python_out=<(PRODUCT_DIR)/pyproto/sync_pb',
],
'message': 'Generating C++ and Python code from <(RULE_INPUT_PATH)',
},
],
'dependencies': [
'../../../../third_party/protobuf/protobuf.gyp:protoc#host',
],
},
{
'target_name': 'sync_proto_cpp',
'type': '<(library)',
'sources': [
'<(protoc_out_dir)/chrome/browser/sync/protocol/sync.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/sync.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/encryption.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/encryption.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/app_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/app_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/autofill_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/autofill_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/bookmark_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/bookmark_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/extension_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/extension_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/nigori_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/nigori_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/password_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/password_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/preference_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/preference_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/session_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/session_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/theme_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/theme_specifics.pb.h',
'<(protoc_out_dir)/chrome/browser/sync/protocol/typed_url_specifics.pb.cc',
'<(protoc_out_dir)/chrome/browser/sync/protocol/typed_url_specifics.pb.h',
],
'export_dependent_settings': [
'../../../../third_party/protobuf/protobuf.gyp:protobuf_lite',
'sync_proto',
],
'dependencies': [
'../../../../third_party/protobuf/protobuf.gyp:protobuf_lite',
'sync_proto',
],
'direct_dependent_settings': {
'include_dirs': [
'<(protoc_out_dir)',
],
},
# This target exports a hard dependency because it includes generated
# header files.
'hard_dependency': 1,
},
],
}
# Local Variables:
# tab-width:2
# indent-tabs-mode:nil
# End:
# vim: set expandtab tabstop=2 shiftwidth=2:
| {'variables': {'chromium_code': 1, 'protoc_out_dir': '<(SHARED_INTERMEDIATE_DIR)/protoc_out'}, 'targets': [{'target_name': 'sync_proto', 'type': 'none', 'sources': ['sync.proto', 'encryption.proto', 'app_specifics.proto', 'autofill_specifics.proto', 'bookmark_specifics.proto', 'extension_specifics.proto', 'nigori_specifics.proto', 'password_specifics.proto', 'preference_specifics.proto', 'session_specifics.proto', 'test.proto', 'theme_specifics.proto', 'typed_url_specifics.proto'], 'rules': [{'rule_name': 'genproto', 'extension': 'proto', 'inputs': ['<(PRODUCT_DIR)/<(EXECUTABLE_PREFIX)protoc<(EXECUTABLE_SUFFIX)'], 'outputs': ['<(PRODUCT_DIR)/pyproto/sync_pb/<(RULE_INPUT_ROOT)_pb2.py', '<(protoc_out_dir)/chrome/browser/sync/protocol/<(RULE_INPUT_ROOT).pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/<(RULE_INPUT_ROOT).pb.cc'], 'action': ['<(PRODUCT_DIR)/<(EXECUTABLE_PREFIX)protoc<(EXECUTABLE_SUFFIX)', '--proto_path=.', './<(RULE_INPUT_ROOT)<(RULE_INPUT_EXT)', '--cpp_out=<(protoc_out_dir)/chrome/browser/sync/protocol', '--python_out=<(PRODUCT_DIR)/pyproto/sync_pb'], 'message': 'Generating C++ and Python code from <(RULE_INPUT_PATH)'}], 'dependencies': ['../../../../third_party/protobuf/protobuf.gyp:protoc#host']}, {'target_name': 'sync_proto_cpp', 'type': '<(library)', 'sources': ['<(protoc_out_dir)/chrome/browser/sync/protocol/sync.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/sync.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/encryption.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/encryption.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/app_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/app_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/autofill_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/autofill_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/bookmark_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/bookmark_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/extension_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/extension_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/nigori_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/nigori_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/password_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/password_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/preference_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/preference_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/session_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/session_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/theme_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/theme_specifics.pb.h', '<(protoc_out_dir)/chrome/browser/sync/protocol/typed_url_specifics.pb.cc', '<(protoc_out_dir)/chrome/browser/sync/protocol/typed_url_specifics.pb.h'], 'export_dependent_settings': ['../../../../third_party/protobuf/protobuf.gyp:protobuf_lite', 'sync_proto'], 'dependencies': ['../../../../third_party/protobuf/protobuf.gyp:protobuf_lite', 'sync_proto'], 'direct_dependent_settings': {'include_dirs': ['<(protoc_out_dir)']}, 'hard_dependency': 1}]} |
#!/usr/bin/env python
# encoding: utf-8
def run(whatweb, pluginname):
whatweb.recog_from_file(pluginname, "sysImages/css/PagesCSS.css", "foosun")
whatweb.recog_from_file(pluginname, "Tags.html", "Foosun")
| def run(whatweb, pluginname):
whatweb.recog_from_file(pluginname, 'sysImages/css/PagesCSS.css', 'foosun')
whatweb.recog_from_file(pluginname, 'Tags.html', 'Foosun') |
# reading withdrawal amount and account balance
x,y=map(float,input().split())
# this will check if account balance is less than the withdrawal amount or
# withdrawal amount is multiple of 5 and print the current account balance
if(x+0.5>=y or x%5!=0 or y<=0):
# printing the result upto two decimals
print("%.2f"%y)
# otherwise transaction will take place and print updated account balance
else:
y=y-x-0.50
# printing the result upto two decimals
print("%.2f"%y)
| (x, y) = map(float, input().split())
if x + 0.5 >= y or x % 5 != 0 or y <= 0:
print('%.2f' % y)
else:
y = y - x - 0.5
print('%.2f' % y) |
#--------------------------------------
# Open and Parse BF File
#--------------------------------------
fileName = input("Enter name of Brainf*** file here: ")
file = open(fileName, "r")
programCode = []
validCommands = [">", "<", "+", "-", ".", ",", "[", "]"]
for x in file:
for y in x:
if y in validCommands:
programCode.append(y)
file.close()
#--------------------------------------
# Find Indexes of Matching Brackets
#--------------------------------------
bracketPositions = []
loopIndex = 0
openIndex = []
for x in programCode:
if x == "[":
openIndex.append(loopIndex)
if x == "]":
openPosition = openIndex.pop()
bracketPositions.append([openPosition, loopIndex])
loopIndex += 1
#--------------------------------------
# Set Up BF Cells and Pointers
#--------------------------------------
memCells = []
memPointer = 0
instructionPointer = 0
memCellsStepper = 0
maxCells = 5000
while memCellsStepper < maxCells:
memCells.append(0)
memCellsStepper += 1
#--------------------------------------
# Define BF Commands
#--------------------------------------
def moveRight():
global memPointer
memPointer += 1
if memPointer >= maxCells:
memPointer = 0
def moveLeft():
global memPointer
memPointer -= 1
if memPointer < 0:
memPointer = maxCells - 1
def incrementCell():
memCells[memPointer] += 1
def decrementCell():
memCells[memPointer] -= 1
def outputValue():
print(chr(memCells[memPointer]), end="")
def takeInput():
print()
value = input(">")
memCells[memPointer] = ord(value[0])
def openBracket():
global instructionPointer
if memCells[memPointer] == 0:
for x in bracketPositions:
if x[0] == instructionPointer:
instructionPointer = x[1]
def closeBracket():
global instructionPointer
if memCells[memPointer] != 0:
for x in bracketPositions:
if x[1] == instructionPointer:
instructionPointer = x[0]
#--------------------------------------
# Execute BF Code
#--------------------------------------
while instructionPointer != len(programCode):
x = programCode[instructionPointer]
if x == ">":
moveRight()
if x == "<":
moveLeft()
if x == "+":
incrementCell()
if x == "-":
decrementCell()
if x == ".":
outputValue()
if x == ",":
takeInput()
if x == "[":
openBracket()
if x == "]":
closeBracket()
instructionPointer += 1
| file_name = input('Enter name of Brainf*** file here: ')
file = open(fileName, 'r')
program_code = []
valid_commands = ['>', '<', '+', '-', '.', ',', '[', ']']
for x in file:
for y in x:
if y in validCommands:
programCode.append(y)
file.close()
bracket_positions = []
loop_index = 0
open_index = []
for x in programCode:
if x == '[':
openIndex.append(loopIndex)
if x == ']':
open_position = openIndex.pop()
bracketPositions.append([openPosition, loopIndex])
loop_index += 1
mem_cells = []
mem_pointer = 0
instruction_pointer = 0
mem_cells_stepper = 0
max_cells = 5000
while memCellsStepper < maxCells:
memCells.append(0)
mem_cells_stepper += 1
def move_right():
global memPointer
mem_pointer += 1
if memPointer >= maxCells:
mem_pointer = 0
def move_left():
global memPointer
mem_pointer -= 1
if memPointer < 0:
mem_pointer = maxCells - 1
def increment_cell():
memCells[memPointer] += 1
def decrement_cell():
memCells[memPointer] -= 1
def output_value():
print(chr(memCells[memPointer]), end='')
def take_input():
print()
value = input('>')
memCells[memPointer] = ord(value[0])
def open_bracket():
global instructionPointer
if memCells[memPointer] == 0:
for x in bracketPositions:
if x[0] == instructionPointer:
instruction_pointer = x[1]
def close_bracket():
global instructionPointer
if memCells[memPointer] != 0:
for x in bracketPositions:
if x[1] == instructionPointer:
instruction_pointer = x[0]
while instructionPointer != len(programCode):
x = programCode[instructionPointer]
if x == '>':
move_right()
if x == '<':
move_left()
if x == '+':
increment_cell()
if x == '-':
decrement_cell()
if x == '.':
output_value()
if x == ',':
take_input()
if x == '[':
open_bracket()
if x == ']':
close_bracket()
instruction_pointer += 1 |
_registered_input_modules_types = {}
def register(name, class_type):
if name in _registered_input_modules_types:
raise RuntimeError("Dublicate input module name: " + name)
_registered_input_modules_types[name] = class_type
def load_modules(agent, input_link_config):
input_modules = []
# get input modules configuration from Parameter Server
if not isinstance(input_link_config, dict):
raise RuntimeError("Input link configuration is not valid.")
# process configuration
for module_name, module_config in input_link_config.iteritems():
module_type = _registered_input_modules_types.get(module_name)
if module_type:
input_modules.append( module_type(agent, module_config) )
else:
raise RuntimeError("Input module {} type is unknown." % module_name)
return input_modules
| _registered_input_modules_types = {}
def register(name, class_type):
if name in _registered_input_modules_types:
raise runtime_error('Dublicate input module name: ' + name)
_registered_input_modules_types[name] = class_type
def load_modules(agent, input_link_config):
input_modules = []
if not isinstance(input_link_config, dict):
raise runtime_error('Input link configuration is not valid.')
for (module_name, module_config) in input_link_config.iteritems():
module_type = _registered_input_modules_types.get(module_name)
if module_type:
input_modules.append(module_type(agent, module_config))
else:
raise runtime_error('Input module {} type is unknown.' % module_name)
return input_modules |
def find_missing(array):
return [x for x in range(array[0], array[-1] + 1) if x not in array]
lst = [2, 4, 1, 7, 10]
print(find_missing(lst)) | def find_missing(array):
return [x for x in range(array[0], array[-1] + 1) if x not in array]
lst = [2, 4, 1, 7, 10]
print(find_missing(lst)) |
def trigger():
return """
CREATE OR REPLACE FUNCTION trg_mensagem_ticket_solucao()
RETURNS TRIGGER AS $$
BEGIN
IF (NEW.solucao) THEN
UPDATE ticket SET solucionado_id = NEW.id, data_solucao = NOW(), hora_solucao = NOW() WHERE id = NEW.ticket_id;
END IF;
RETURN NEW;
END
$$ LANGUAGE plpgsql;
DROP TRIGGER IF EXISTS trg_mensagem_ticket_solucao ON mensagem_ticket;
CREATE TRIGGER trg_mensagem_ticket_solucao
AFTER INSERT ON mensagem_ticket
FOR EACH ROW EXECUTE PROCEDURE trg_mensagem_ticket_solucao();
"""
| def trigger():
return '\n CREATE OR REPLACE FUNCTION trg_mensagem_ticket_solucao()\n RETURNS TRIGGER AS $$\n BEGIN\n IF (NEW.solucao) THEN\n UPDATE ticket SET solucionado_id = NEW.id, data_solucao = NOW(), hora_solucao = NOW() WHERE id = NEW.ticket_id;\n END IF;\n \n RETURN NEW;\n END\n $$ LANGUAGE plpgsql;\n \n DROP TRIGGER IF EXISTS trg_mensagem_ticket_solucao ON mensagem_ticket;\n CREATE TRIGGER trg_mensagem_ticket_solucao\n AFTER INSERT ON mensagem_ticket\n FOR EACH ROW EXECUTE PROCEDURE trg_mensagem_ticket_solucao();\n ' |
S1 = "Hello Python"
print(S1) # Prints complete string
print(S1[0]) # Prints first character of the string
print(S1[2:5]) # Prints character starting from 3rd t 5th
print(S1[2:]) # Prints string starting from 3rd character
print(S1 * 2) # Prints string two times
print(S1 + "Thanks") # Prints concatenated string
| s1 = 'Hello Python'
print(S1)
print(S1[0])
print(S1[2:5])
print(S1[2:])
print(S1 * 2)
print(S1 + 'Thanks') |
def onSpawn():
while True:
pet.moveXY(48, 8)
pet.moveXY(12, 8)
pet.on("spawn", onSpawn)
while True:
hero.say("Run!!!")
hero.say("Faster!")
| def on_spawn():
while True:
pet.moveXY(48, 8)
pet.moveXY(12, 8)
pet.on('spawn', onSpawn)
while True:
hero.say('Run!!!')
hero.say('Faster!') |
def valid_parentheses(parens):
"""Are the parentheses validly balanced?
>>> valid_parentheses("()")
True
>>> valid_parentheses("()()")
True
>>> valid_parentheses("(()())")
True
>>> valid_parentheses(")()")
False
>>> valid_parentheses("())")
False
>>> valid_parentheses("((())")
False
>>> valid_parentheses(")()(")
False
"""
d = {"(" : 1, ")" : -1}
s = 0
for c in parens:
s = s + d[c]
if s < 0:
return False
return s == 0
| def valid_parentheses(parens):
"""Are the parentheses validly balanced?
>>> valid_parentheses("()")
True
>>> valid_parentheses("()()")
True
>>> valid_parentheses("(()())")
True
>>> valid_parentheses(")()")
False
>>> valid_parentheses("())")
False
>>> valid_parentheses("((())")
False
>>> valid_parentheses(")()(")
False
"""
d = {'(': 1, ')': -1}
s = 0
for c in parens:
s = s + d[c]
if s < 0:
return False
return s == 0 |
# TO print Fibonacci Series upto n numbers and replace all prime numbers and multiples of 5 by 0
# Checking for prime numbers
def isprime(numb):
if numb == 2:
return True
elif numb == 3:
return True
else :
for i in range(2, numb // 2 + 1):
if (numb % i) == 0:
return False
else:
return True
# Finding out the fibonacci numbers
def fibonacci_series(n):
flag = 0
a,b = 1,1
if n == 1:
print(a)
else:
print(a, end = " ")
print(b, end = " ")
while flag <= n:
c = a + b
a,b = b,c
flag += 1
if c % 5 == 0 or isprime(c):
print(0, end = " ")
else:
print(c, end = " ")
# The number of fibonacci terms required
n1 = int(input("Enter the value of n: "))
n = n1 - 3
fibonacci_series(n) | def isprime(numb):
if numb == 2:
return True
elif numb == 3:
return True
else:
for i in range(2, numb // 2 + 1):
if numb % i == 0:
return False
else:
return True
def fibonacci_series(n):
flag = 0
(a, b) = (1, 1)
if n == 1:
print(a)
else:
print(a, end=' ')
print(b, end=' ')
while flag <= n:
c = a + b
(a, b) = (b, c)
flag += 1
if c % 5 == 0 or isprime(c):
print(0, end=' ')
else:
print(c, end=' ')
n1 = int(input('Enter the value of n: '))
n = n1 - 3
fibonacci_series(n) |
class Solution:
def reorderLogFiles(self, logs: List[str]) -> List[str]:
def corder(log):
identifier, detail = log.split(None, 1)
return (0, detail, identifier) if detail[0].isalpha() else (1,)
return sorted(logs, key=corder)
| class Solution:
def reorder_log_files(self, logs: List[str]) -> List[str]:
def corder(log):
(identifier, detail) = log.split(None, 1)
return (0, detail, identifier) if detail[0].isalpha() else (1,)
return sorted(logs, key=corder) |
# -*- coding: utf-8 -*-
"""
reV Econ utilities
"""
def lcoe_fcr(fixed_charge_rate, capital_cost, fixed_operating_cost,
annual_energy_production, variable_operating_cost):
"""Calculate the Levelized Cost of Electricity (LCOE) using the
fixed-charge-rate method:
LCOE = ((fixed_charge_rate * capital_cost + fixed_operating_cost)
/ annual_energy_production + variable_operating_cost)
Parameters
----------
fixed_charge_rate : float | np.ndarray
Fixed charge rage (unitless)
capital_cost : float | np.ndarray
Capital cost (aka Capital Expenditures) ($)
fixed_operating_cost : float | np.ndarray
Fixed annual operating cost ($/year)
annual_energy_production : float | np.ndarray
Annual energy production (kWh for year)
(can be calculated as capacity * cf * 8760)
variable_operating_cost : float | np.ndarray
Variable operating cost ($/kWh)
Returns
-------
lcoe : float | np.ndarray
LCOE in $/MWh
"""
lcoe = ((fixed_charge_rate * capital_cost + fixed_operating_cost)
/ annual_energy_production + variable_operating_cost)
lcoe *= 1000 # convert $/kWh to $/MWh
return lcoe
| """
reV Econ utilities
"""
def lcoe_fcr(fixed_charge_rate, capital_cost, fixed_operating_cost, annual_energy_production, variable_operating_cost):
"""Calculate the Levelized Cost of Electricity (LCOE) using the
fixed-charge-rate method:
LCOE = ((fixed_charge_rate * capital_cost + fixed_operating_cost)
/ annual_energy_production + variable_operating_cost)
Parameters
----------
fixed_charge_rate : float | np.ndarray
Fixed charge rage (unitless)
capital_cost : float | np.ndarray
Capital cost (aka Capital Expenditures) ($)
fixed_operating_cost : float | np.ndarray
Fixed annual operating cost ($/year)
annual_energy_production : float | np.ndarray
Annual energy production (kWh for year)
(can be calculated as capacity * cf * 8760)
variable_operating_cost : float | np.ndarray
Variable operating cost ($/kWh)
Returns
-------
lcoe : float | np.ndarray
LCOE in $/MWh
"""
lcoe = (fixed_charge_rate * capital_cost + fixed_operating_cost) / annual_energy_production + variable_operating_cost
lcoe *= 1000
return lcoe |
"""
Base Exception
MLApp Exception - inherit from Base Exception
"""
class MLAppBaseException(Exception):
def __init__(self, message):
self.message = message
class FrameworkException(MLAppBaseException):
def __init__(self, message=None):
if message is not None and isinstance(message, str):
self.message = message
def __str__(self):
return "[ML APP ERROR] %s\n" % str(self.message)
class UserException(MLAppBaseException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[USER ERROR] %s\n" % str(self.message)
class FlowManagerException(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[FLOW MANAGER ERROR] %s\n" % str(self.message)
class DataManagerException(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[DATA MANAGER ERROR] %s\n" % str(self.message)
class ModelManagerException(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[MODEL MANAGER ERROR] %s\n" % str(self.message)
class JobManagerException(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[JOB MANAGER ERROR] %s\n" % str(self.message)
class PipelineManagerException(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[PIPELINE MANAGER ERROR] %s\n" % str(self.message)
class EnvironmentException(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[ENVIRONMENT ERROR] %s\n" % str(self.message)
class IoManagerException(FlowManagerException, DataManagerException, ModelManagerException, JobManagerException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[IO MANAGER ERROR] %s\n" % str(self.message)
class ConfigError(FlowManagerException, DataManagerException, ModelManagerException, JobManagerException):
def __init__(self, message):
self.message = message
def __str__(self):
return "[CONFIG ERROR] %s\n" % str(self.message)
class ConfigKeyError(ConfigError):
def __init__(self, message):
self.message = message
def __str__(self):
return "[KEY ERROR] %s\n" % str(self.message)
class ConfigValueError(ConfigError):
def __init__(self, message):
self.message = message
def __str__(self):
return "[VALUE ERROR] %s\n" % str(self.message)
| """
Base Exception
MLApp Exception - inherit from Base Exception
"""
class Mlappbaseexception(Exception):
def __init__(self, message):
self.message = message
class Frameworkexception(MLAppBaseException):
def __init__(self, message=None):
if message is not None and isinstance(message, str):
self.message = message
def __str__(self):
return '[ML APP ERROR] %s\n' % str(self.message)
class Userexception(MLAppBaseException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[USER ERROR] %s\n' % str(self.message)
class Flowmanagerexception(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[FLOW MANAGER ERROR] %s\n' % str(self.message)
class Datamanagerexception(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[DATA MANAGER ERROR] %s\n' % str(self.message)
class Modelmanagerexception(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[MODEL MANAGER ERROR] %s\n' % str(self.message)
class Jobmanagerexception(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[JOB MANAGER ERROR] %s\n' % str(self.message)
class Pipelinemanagerexception(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[PIPELINE MANAGER ERROR] %s\n' % str(self.message)
class Environmentexception(UserException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[ENVIRONMENT ERROR] %s\n' % str(self.message)
class Iomanagerexception(FlowManagerException, DataManagerException, ModelManagerException, JobManagerException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[IO MANAGER ERROR] %s\n' % str(self.message)
class Configerror(FlowManagerException, DataManagerException, ModelManagerException, JobManagerException):
def __init__(self, message):
self.message = message
def __str__(self):
return '[CONFIG ERROR] %s\n' % str(self.message)
class Configkeyerror(ConfigError):
def __init__(self, message):
self.message = message
def __str__(self):
return '[KEY ERROR] %s\n' % str(self.message)
class Configvalueerror(ConfigError):
def __init__(self, message):
self.message = message
def __str__(self):
return '[VALUE ERROR] %s\n' % str(self.message) |
# initiate empty list to hold user input and sum value of zero
user_list = []
list_sum = 0
# seek user input for ten numbers
for i in range(10):
userInput = input("Enter any 2-digit number: ")
# check to see if number is even and if yes, add to list_sum
# print incorrect value warning when ValueError exception occurs
try:
number = int(userInput)
user_list.append(number)
if number % 2 == 0:
list_sum += number
except ValueError:
print("Incorrect value. That's not an int!")
print("user_list: {}".format(user_list))
print("The sum of the even numbers in user_list is: {}.".format(list_sum))
| user_list = []
list_sum = 0
for i in range(10):
user_input = input('Enter any 2-digit number: ')
try:
number = int(userInput)
user_list.append(number)
if number % 2 == 0:
list_sum += number
except ValueError:
print("Incorrect value. That's not an int!")
print('user_list: {}'.format(user_list))
print('The sum of the even numbers in user_list is: {}.'.format(list_sum)) |
load("@io_bazel_rules_docker//container:pull.bzl", "container_pull")
def containers():
container_pull(
name = "alpine_linux_amd64",
registry = "index.docker.io",
repository = "library/alpine",
tag = "3.14.2",
)
| load('@io_bazel_rules_docker//container:pull.bzl', 'container_pull')
def containers():
container_pull(name='alpine_linux_amd64', registry='index.docker.io', repository='library/alpine', tag='3.14.2') |
BUILD_STATE = (
('triggered', 'Triggered'),
('building', 'Building'),
('finished', 'Finished'),
)
BUILD_TYPES = (
('html', 'HTML'),
('pdf', 'PDF'),
('epub', 'Epub'),
('man', 'Manpage'),
)
| build_state = (('triggered', 'Triggered'), ('building', 'Building'), ('finished', 'Finished'))
build_types = (('html', 'HTML'), ('pdf', 'PDF'), ('epub', 'Epub'), ('man', 'Manpage')) |
class Solution:
def getDecimalValue(self, head: ListNode) -> int:
return self.getDecimalValueHelper(head)[0]
def getDecimalValueHelper(self, head: ListNode) -> int:
if head is None:
return (0, 0)
total, exp = self.getDecimalValueHelper(head.next)
currbit = head.val
total += currbit * (2**exp)
return (total, exp+1)
| class Solution:
def get_decimal_value(self, head: ListNode) -> int:
return self.getDecimalValueHelper(head)[0]
def get_decimal_value_helper(self, head: ListNode) -> int:
if head is None:
return (0, 0)
(total, exp) = self.getDecimalValueHelper(head.next)
currbit = head.val
total += currbit * 2 ** exp
return (total, exp + 1) |
# Copyright (C) 2009 Duncan McGreggor <duncan@canonical.com>
# Copyright (C) 2009 Robert Collins <robertc@robertcollins.net>
# Copyright (C) 2012 New Dream Network, LLC (DreamHost)
# Licenced under the txaws licence available at /LICENSE in the txaws source.
__all__ = ["REGION_US", "REGION_EU", "EC2_US_EAST", "EC2_US_WEST",
"EC2_ASIA_PACIFIC", "EC2_EU_WEST", "EC2_SOUTH_AMERICA_EAST", "EC2_ALL_REGIONS"]
# These old EC2 variable names are maintained for backwards compatibility.
REGION_US = "US"
REGION_EU = "EU"
EC2_ENDPOINT_US = "https://us-east-1.ec2.amazonaws.com/"
EC2_ENDPOINT_EU = "https://eu-west-1.ec2.amazonaws.com/"
SQS_ENDPOINT_US = "https://sqs.us-east-1.amazonaws.com/"
# These are the new EC2 variables.
EC2_US_EAST = [
{"region": "US East (Northern Virginia) Region",
"endpoint": "https://ec2.us-east-1.amazonaws.com"}]
EC2_US_WEST = [
{"region": "US West (Oregon) Region",
"endpoint": "https://ec2.us-west-2.amazonaws.com"},
{"region": "US West (Northern California) Region",
"endpoint": "https://ec2.us-west-1.amazonaws.com"}]
EC2_US = EC2_US_EAST + EC2_US_WEST
EC2_ASIA_PACIFIC = [
{"region": "Asia Pacific (Singapore) Region",
"endpoint": "https://ec2.ap-southeast-1.amazonaws.com"},
{"region": "Asia Pacific (Tokyo) Region",
"endpoint": "https://ec2.ap-northeast-1.amazonaws.com"}]
EC2_EU_WEST = [
{"region": "EU (Ireland) Region",
"endpoint": "https://ec2.eu-west-1.amazonaws.com"}]
EC2_EU = EC2_EU_WEST
EC2_SOUTH_AMERICA_EAST = [
{"region": "South America (Sao Paulo) Region",
"endpoint": "https://ec2.sa-east-1.amazonaws.com"}]
EC2_SOUTH_AMERICA = EC2_SOUTH_AMERICA_EAST
EC2_ALL_REGIONS = EC2_US + EC2_ASIA_PACIFIC + EC2_EU + EC2_SOUTH_AMERICA
# This old S3 variable is maintained for backwards compatibility.
S3_ENDPOINT = "https://s3.amazonaws.com/"
# These are the new S3 variables.
S3_US_DEFAULT = [
{"region": "US Standard *",
"endpoint": "https://s3.amazonaws.com"}]
S3_US_WEST = [
{"region": "US West (Oregon) Region",
"endpoint": "https://s3-us-west-2.amazonaws.com"},
{"region": "US West (Northern California) Region",
"endpoint": "https://s3-us-west-1.amazonaws.com"}]
S3_ASIA_PACIFIC = [
{"region": "Asia Pacific (Singapore) Region",
"endpoint": "https://s3-ap-southeast-1.amazonaws.com"},
{"region": "Asia Pacific (Tokyo) Region",
"endpoint": "https://s3-ap-northeast-1.amazonaws.com"}]
S3_US = S3_US_DEFAULT + S3_US_WEST
S3_EU_WEST = [
{"region": "EU (Ireland) Region",
"endpoint": "https://s3-eu-west-1.amazonaws.com"}]
S3_EU = S3_EU_WEST
S3_SOUTH_AMERICA_EAST = [
{"region": "South America (Sao Paulo) Region",
"endpoint": "s3-sa-east-1.amazonaws.com"}]
S3_SOUTH_AMERICA = S3_SOUTH_AMERICA_EAST
S3_ALL_REGIONS = S3_US + S3_ASIA_PACIFIC + S3_EU + S3_SOUTH_AMERICA
| __all__ = ['REGION_US', 'REGION_EU', 'EC2_US_EAST', 'EC2_US_WEST', 'EC2_ASIA_PACIFIC', 'EC2_EU_WEST', 'EC2_SOUTH_AMERICA_EAST', 'EC2_ALL_REGIONS']
region_us = 'US'
region_eu = 'EU'
ec2_endpoint_us = 'https://us-east-1.ec2.amazonaws.com/'
ec2_endpoint_eu = 'https://eu-west-1.ec2.amazonaws.com/'
sqs_endpoint_us = 'https://sqs.us-east-1.amazonaws.com/'
ec2_us_east = [{'region': 'US East (Northern Virginia) Region', 'endpoint': 'https://ec2.us-east-1.amazonaws.com'}]
ec2_us_west = [{'region': 'US West (Oregon) Region', 'endpoint': 'https://ec2.us-west-2.amazonaws.com'}, {'region': 'US West (Northern California) Region', 'endpoint': 'https://ec2.us-west-1.amazonaws.com'}]
ec2_us = EC2_US_EAST + EC2_US_WEST
ec2_asia_pacific = [{'region': 'Asia Pacific (Singapore) Region', 'endpoint': 'https://ec2.ap-southeast-1.amazonaws.com'}, {'region': 'Asia Pacific (Tokyo) Region', 'endpoint': 'https://ec2.ap-northeast-1.amazonaws.com'}]
ec2_eu_west = [{'region': 'EU (Ireland) Region', 'endpoint': 'https://ec2.eu-west-1.amazonaws.com'}]
ec2_eu = EC2_EU_WEST
ec2_south_america_east = [{'region': 'South America (Sao Paulo) Region', 'endpoint': 'https://ec2.sa-east-1.amazonaws.com'}]
ec2_south_america = EC2_SOUTH_AMERICA_EAST
ec2_all_regions = EC2_US + EC2_ASIA_PACIFIC + EC2_EU + EC2_SOUTH_AMERICA
s3_endpoint = 'https://s3.amazonaws.com/'
s3_us_default = [{'region': 'US Standard *', 'endpoint': 'https://s3.amazonaws.com'}]
s3_us_west = [{'region': 'US West (Oregon) Region', 'endpoint': 'https://s3-us-west-2.amazonaws.com'}, {'region': 'US West (Northern California) Region', 'endpoint': 'https://s3-us-west-1.amazonaws.com'}]
s3_asia_pacific = [{'region': 'Asia Pacific (Singapore) Region', 'endpoint': 'https://s3-ap-southeast-1.amazonaws.com'}, {'region': 'Asia Pacific (Tokyo) Region', 'endpoint': 'https://s3-ap-northeast-1.amazonaws.com'}]
s3_us = S3_US_DEFAULT + S3_US_WEST
s3_eu_west = [{'region': 'EU (Ireland) Region', 'endpoint': 'https://s3-eu-west-1.amazonaws.com'}]
s3_eu = S3_EU_WEST
s3_south_america_east = [{'region': 'South America (Sao Paulo) Region', 'endpoint': 's3-sa-east-1.amazonaws.com'}]
s3_south_america = S3_SOUTH_AMERICA_EAST
s3_all_regions = S3_US + S3_ASIA_PACIFIC + S3_EU + S3_SOUTH_AMERICA |
"""
224. Basic Calculator
Example 1:
Input: "1 + 1"
Output: 2
Example 2:
Input: " 2-1 + 2 "
Output: 3
Example 3:
Input: "(1+(4+5+2)-3)+(6+8)"
Output: 23
"""
class Solution:
def calculate(self, s):
"""
:type s: str
:rtype: int
"""
self.stack = []
i, res, n, sign = 0, 0, len(s), 1
while i < n:
if s[i] == '+' or s[i] == '-':
sign = 1 if s[i] == '+' else -1
elif s[i] == '(':
self.stack.append(res)
self.stack.append(sign)
sign, res = 1, 0
elif s[i] == ')':
res = self.stack.pop()*res
res += self.stack.pop()
elif s[i].isdigit():
val = 0
while i< n and s[i].isdigit():
val = val*10 + int(s[i])
i+=1
res += sign*val
i-=1
i+=1
return res
class Solution:
def calculate(self, s):
total = 0
i, signs, n = 0, [1,1], len(s)
while i < n:
if s[i].isdigit():
start = i
while i<n and s[i].isdigit():
i+=1
total += signs.pop()*int(s[start:i])
continue
if s[i] in '+-(':
signs.append(signs[-1]*(1,-1)[s[i] == '-'])
elif s[i] == ')':
signs.pop()
i += 1
return total | """
224. Basic Calculator
Example 1:
Input: "1 + 1"
Output: 2
Example 2:
Input: " 2-1 + 2 "
Output: 3
Example 3:
Input: "(1+(4+5+2)-3)+(6+8)"
Output: 23
"""
class Solution:
def calculate(self, s):
"""
:type s: str
:rtype: int
"""
self.stack = []
(i, res, n, sign) = (0, 0, len(s), 1)
while i < n:
if s[i] == '+' or s[i] == '-':
sign = 1 if s[i] == '+' else -1
elif s[i] == '(':
self.stack.append(res)
self.stack.append(sign)
(sign, res) = (1, 0)
elif s[i] == ')':
res = self.stack.pop() * res
res += self.stack.pop()
elif s[i].isdigit():
val = 0
while i < n and s[i].isdigit():
val = val * 10 + int(s[i])
i += 1
res += sign * val
i -= 1
i += 1
return res
class Solution:
def calculate(self, s):
total = 0
(i, signs, n) = (0, [1, 1], len(s))
while i < n:
if s[i].isdigit():
start = i
while i < n and s[i].isdigit():
i += 1
total += signs.pop() * int(s[start:i])
continue
if s[i] in '+-(':
signs.append(signs[-1] * (1, -1)[s[i] == '-'])
elif s[i] == ')':
signs.pop()
i += 1
return total |
n = int(input())
for i in range(0, n):
line = input()
b, p = line.split()
b = int(b)
p = float(p)
calc = (60 * b) / p
var = 60 / p
min = calc - var
max = calc + var
print(min, calc, max) | n = int(input())
for i in range(0, n):
line = input()
(b, p) = line.split()
b = int(b)
p = float(p)
calc = 60 * b / p
var = 60 / p
min = calc - var
max = calc + var
print(min, calc, max) |
a = int(input('First number'))
b = int(input('Second number'))
if a>b:
print(a)
else:
print(b)
| a = int(input('First number'))
b = int(input('Second number'))
if a > b:
print(a)
else:
print(b) |
# Copyright (c) Microsoft Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
async def test_should_clear_cookies(context, page, server):
await page.goto(server.EMPTY_PAGE)
await context.addCookies(
[{"url": server.EMPTY_PAGE, "name": "cookie1", "value": "1"}]
)
assert await page.evaluate("document.cookie") == "cookie1=1"
await context.clearCookies()
assert await context.cookies() == []
await page.reload()
assert await page.evaluate("document.cookie") == ""
async def test_should_isolate_cookies_when_clearing(context, server, browser):
another_context = await browser.newContext()
await context.addCookies(
[{"url": server.EMPTY_PAGE, "name": "page1cookie", "value": "page1value"}]
)
await another_context.addCookies(
[{"url": server.EMPTY_PAGE, "name": "page2cookie", "value": "page2value"}]
)
assert len(await context.cookies()) == 1
assert len(await another_context.cookies()) == 1
await context.clearCookies()
assert len(await context.cookies()) == 0
assert len(await another_context.cookies()) == 1
await another_context.clearCookies()
assert len(await context.cookies()) == 0
assert len(await another_context.cookies()) == 0
await another_context.close()
| async def test_should_clear_cookies(context, page, server):
await page.goto(server.EMPTY_PAGE)
await context.addCookies([{'url': server.EMPTY_PAGE, 'name': 'cookie1', 'value': '1'}])
assert await page.evaluate('document.cookie') == 'cookie1=1'
await context.clearCookies()
assert await context.cookies() == []
await page.reload()
assert await page.evaluate('document.cookie') == ''
async def test_should_isolate_cookies_when_clearing(context, server, browser):
another_context = await browser.newContext()
await context.addCookies([{'url': server.EMPTY_PAGE, 'name': 'page1cookie', 'value': 'page1value'}])
await another_context.addCookies([{'url': server.EMPTY_PAGE, 'name': 'page2cookie', 'value': 'page2value'}])
assert len(await context.cookies()) == 1
assert len(await another_context.cookies()) == 1
await context.clearCookies()
assert len(await context.cookies()) == 0
assert len(await another_context.cookies()) == 1
await another_context.clearCookies()
assert len(await context.cookies()) == 0
assert len(await another_context.cookies()) == 0
await another_context.close() |
"""
RedPocket Exceptions
"""
class RedPocketException(Exception):
"""Base API Exception"""
def __init__(self, message: str = ""):
self.message = message
class RedPocketAuthError(RedPocketException):
"""Invalid Account Credentials"""
class RedPocketAPIError(RedPocketException):
"""Error returned from API Call"""
def __init__(self, message: str = "", return_code: int = -1):
super().__init__(message=message)
self.return_code = return_code
| """
RedPocket Exceptions
"""
class Redpocketexception(Exception):
"""Base API Exception"""
def __init__(self, message: str=''):
self.message = message
class Redpocketautherror(RedPocketException):
"""Invalid Account Credentials"""
class Redpocketapierror(RedPocketException):
"""Error returned from API Call"""
def __init__(self, message: str='', return_code: int=-1):
super().__init__(message=message)
self.return_code = return_code |
# pylint: disable=C0111
__all__ = ["test_dataset",
"test_label_smoother",
"test_noam_optimizer",
"test_tokenizer",
"test_transformer",
"test_transformer_data_batching",
"test_transformer_dataset",
"test_transformer_positional_encoder",
"test_vocabulary",
"test_word2vec",
"test_data",
"test_cnn"]
| __all__ = ['test_dataset', 'test_label_smoother', 'test_noam_optimizer', 'test_tokenizer', 'test_transformer', 'test_transformer_data_batching', 'test_transformer_dataset', 'test_transformer_positional_encoder', 'test_vocabulary', 'test_word2vec', 'test_data', 'test_cnn'] |
distancia1: float; distancia2: float; distancia3: float; maiorD: float
print("Digite as tres distancias: ")
distancia1 = float(input())
distancia2 = float(input())
distancia3 = float(input())
if distancia1 > distancia2 and distancia1 > distancia3:
maiorD = distancia1
elif distancia2 > distancia3:
maiorD = distancia2
else:
maiorD = distancia3
print(f"MAIOR DISTANCIA = {maiorD:.2f}")
| distancia1: float
distancia2: float
distancia3: float
maior_d: float
print('Digite as tres distancias: ')
distancia1 = float(input())
distancia2 = float(input())
distancia3 = float(input())
if distancia1 > distancia2 and distancia1 > distancia3:
maior_d = distancia1
elif distancia2 > distancia3:
maior_d = distancia2
else:
maior_d = distancia3
print(f'MAIOR DISTANCIA = {maiorD:.2f}') |
#
# PySNMP MIB module ALTIGA-GLOBAL-REG (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ALTIGA-GLOBAL-REG
# Produced by pysmi-0.3.4 at Wed May 1 11:21:16 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
SingleValueConstraint, ValueSizeConstraint, ConstraintsUnion, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ValueSizeConstraint", "ConstraintsUnion", "ValueRangeConstraint", "ConstraintsIntersection")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
Gauge32, ModuleIdentity, Bits, NotificationType, ObjectIdentity, TimeTicks, MibIdentifier, iso, Integer32, Counter32, Counter64, Unsigned32, IpAddress, enterprises, MibScalar, MibTable, MibTableRow, MibTableColumn = mibBuilder.importSymbols("SNMPv2-SMI", "Gauge32", "ModuleIdentity", "Bits", "NotificationType", "ObjectIdentity", "TimeTicks", "MibIdentifier", "iso", "Integer32", "Counter32", "Counter64", "Unsigned32", "IpAddress", "enterprises", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
altigaGlobalRegModule = ModuleIdentity((1, 3, 6, 1, 4, 1, 3076, 1, 1, 1, 1))
altigaGlobalRegModule.setRevisions(('2005-01-05 00:00', '2003-10-20 00:00', '2003-04-25 00:00', '2002-07-10 00:00',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: altigaGlobalRegModule.setRevisionsDescriptions(('Added the new MIB Modules(65 to 67)', 'Added the new MIB Modules(58 to 64)', 'Added the new MIB Modules(54 to 57)', 'Updated with new header',))
if mibBuilder.loadTexts: altigaGlobalRegModule.setLastUpdated('200501050000Z')
if mibBuilder.loadTexts: altigaGlobalRegModule.setOrganization('Cisco Systems, Inc.')
if mibBuilder.loadTexts: altigaGlobalRegModule.setContactInfo('Cisco Systems 170 W Tasman Drive San Jose, CA 95134 USA Tel: +1 800 553-NETS E-mail: cs-cvpn3000@cisco.com')
if mibBuilder.loadTexts: altigaGlobalRegModule.setDescription('The Altiga Networks central registration module. Acronyms The following acronyms are used in this document: ACE: Access Control Encryption BwMgmt: Bandwidth Management CTCP: Cisco Transmission Control Protocol DHCP: Dynamic Host Configuration Protocol DNS: Domain Name Service FTP: File Transfer Protocol FW: Firewall HTTP: HyperText Transfer Protocol ICMP: Internet Control Message Protocol IKE: Internet Key Exchange IP: Internet Protocol LBSSF: Load Balance Secure Session Failover L2TP: Layer-2 Tunneling Protocol MIB: Management Information Base NAT: Network Address Translation NTP: Network Time Protocol PPP: Point-to-Point Protocol PPTP: Point-to-Point Tunneling Protocol SEP: Scalable Encryption Processor SNMP: Simple Network Management Protocol SSH: Secure Shell Protocol SSL: Secure Sockets Layer UDP: User Datagram Protocol VPN: Virtual Private Network NAC: Network Admission Control ')
altigaRoot = MibIdentifier((1, 3, 6, 1, 4, 1, 3076))
altigaReg = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1))
altigaModules = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1))
alGlobalRegModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 1))
alCapModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 2))
alMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 3))
alComplModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 4))
alVersionMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 6))
alAccessMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 7))
alEventMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 8))
alAuthMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 9))
alPptpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 10))
alPppMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 11))
alHttpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 12))
alIpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 13))
alFilterMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 14))
alUserMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 15))
alTelnetMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 16))
alFtpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 17))
alTftpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 18))
alSnmpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 19))
alIpSecMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 20))
alL2tpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 21))
alSessionMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 22))
alDnsMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 23))
alAddressMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 24))
alDhcpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 25))
alWatchdogMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 26))
alHardwareMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 27))
alNatMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 28))
alLan2LanMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 29))
alGeneralMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 30))
alSslMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 31))
alCertMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 32))
alNtpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 33))
alNetworkListMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 34))
alSepMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 35))
alIkeMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 36))
alSyncMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 37))
alT1E1MibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 38))
alMultiLinkMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 39))
alSshMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 40))
alLBSSFMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 41))
alDhcpServerMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 42))
alAutoUpdateMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 43))
alAdminAuthMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 44))
alPPPoEMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 45))
alXmlMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 46))
alCtcpMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 47))
alFwMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 48))
alGroupMatchMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 49))
alACEServerMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 50))
alNatTMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 51))
alBwMgmtMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 52))
alIpSecPreFragMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 53))
alFipsMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 54))
alBackupL2LMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 55))
alNotifyMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 56))
alRebootStatusMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 57))
alAuthorizationModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 58))
alWebPortalMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 59))
alWebEmailMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 60))
alPortForwardMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 61))
alRemoteServerMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 62))
alWebvpnAclMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 63))
alNbnsMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 64))
alSecureDesktopMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 65))
alSslTunnelClientMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 66))
alNacMibModule = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 67))
altigaGeneric = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 2))
altigaProducts = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 3))
altigaCaps = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 4))
altigaReqs = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 5))
altigaExpr = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 6))
altigaHw = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2))
altigaVpnHw = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1))
altigaVpnChassis = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1))
altigaVpnIntf = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 2))
altigaVpnEncrypt = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 3))
vpnConcentrator = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 1))
vpnRemote = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 2))
vpnClient = MibIdentifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 3))
vpnConcentratorRev1 = ObjectIdentity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 1, 1))
if mibBuilder.loadTexts: vpnConcentratorRev1.setStatus('current')
if mibBuilder.loadTexts: vpnConcentratorRev1.setDescription("The first revision of Altiga's VPN Concentrator hardware. 603e PPC processor. C10/15/20/30/50/60.")
vpnConcentratorRev2 = ObjectIdentity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 1, 2))
if mibBuilder.loadTexts: vpnConcentratorRev2.setStatus('current')
if mibBuilder.loadTexts: vpnConcentratorRev2.setDescription("The second revision of Altiga's VPN Concentrator hardware. 740 PPC processor. C10/15/20/30/50/60.")
vpnRemoteRev1 = ObjectIdentity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 2, 1))
if mibBuilder.loadTexts: vpnRemoteRev1.setStatus('current')
if mibBuilder.loadTexts: vpnRemoteRev1.setDescription("The first revision of Altiga's VPN Concentrator (Remote) hardware. 8240 PPC processor.")
vpnClientRev1 = ObjectIdentity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 3, 1))
if mibBuilder.loadTexts: vpnClientRev1.setStatus('current')
if mibBuilder.loadTexts: vpnClientRev1.setDescription("The first revision of Altiga's VPN Hardware Client hardware. 8260 PPC processor.")
mibBuilder.exportSymbols("ALTIGA-GLOBAL-REG", PYSNMP_MODULE_ID=altigaGlobalRegModule, alNatTMibModule=alNatTMibModule, alWebEmailMibModule=alWebEmailMibModule, alEventMibModule=alEventMibModule, alPptpMibModule=alPptpMibModule, alAccessMibModule=alAccessMibModule, alDhcpMibModule=alDhcpMibModule, alIkeMibModule=alIkeMibModule, alHttpMibModule=alHttpMibModule, alSepMibModule=alSepMibModule, alMibModule=alMibModule, altigaVpnHw=altigaVpnHw, altigaExpr=altigaExpr, alHardwareMibModule=alHardwareMibModule, altigaGeneric=altigaGeneric, alRebootStatusMibModule=alRebootStatusMibModule, alSslMibModule=alSslMibModule, alVersionMibModule=alVersionMibModule, altigaVpnChassis=altigaVpnChassis, alSyncMibModule=alSyncMibModule, altigaHw=altigaHw, alPppMibModule=alPppMibModule, vpnRemote=vpnRemote, alGroupMatchMibModule=alGroupMatchMibModule, alNotifyMibModule=alNotifyMibModule, alCapModule=alCapModule, altigaReg=altigaReg, altigaRoot=altigaRoot, altigaReqs=altigaReqs, vpnClient=vpnClient, alIpSecPreFragMibModule=alIpSecPreFragMibModule, alL2tpMibModule=alL2tpMibModule, alAutoUpdateMibModule=alAutoUpdateMibModule, alSshMibModule=alSshMibModule, alSslTunnelClientMibModule=alSslTunnelClientMibModule, alAddressMibModule=alAddressMibModule, alLan2LanMibModule=alLan2LanMibModule, alSecureDesktopMibModule=alSecureDesktopMibModule, alDhcpServerMibModule=alDhcpServerMibModule, altigaVpnEncrypt=altigaVpnEncrypt, alPortForwardMibModule=alPortForwardMibModule, alT1E1MibModule=alT1E1MibModule, alAuthorizationModule=alAuthorizationModule, vpnRemoteRev1=vpnRemoteRev1, vpnConcentratorRev1=vpnConcentratorRev1, alFwMibModule=alFwMibModule, altigaProducts=altigaProducts, alPPPoEMibModule=alPPPoEMibModule, alFilterMibModule=alFilterMibModule, alCertMibModule=alCertMibModule, alTelnetMibModule=alTelnetMibModule, alGlobalRegModule=alGlobalRegModule, alWebPortalMibModule=alWebPortalMibModule, alNacMibModule=alNacMibModule, alCtcpMibModule=alCtcpMibModule, vpnClientRev1=vpnClientRev1, vpnConcentrator=vpnConcentrator, alGeneralMibModule=alGeneralMibModule, alAuthMibModule=alAuthMibModule, alACEServerMibModule=alACEServerMibModule, alNetworkListMibModule=alNetworkListMibModule, altigaCaps=altigaCaps, alWebvpnAclMibModule=alWebvpnAclMibModule, altigaVpnIntf=altigaVpnIntf, alSessionMibModule=alSessionMibModule, alIpSecMibModule=alIpSecMibModule, alFipsMibModule=alFipsMibModule, alTftpMibModule=alTftpMibModule, vpnConcentratorRev2=vpnConcentratorRev2, alSnmpMibModule=alSnmpMibModule, alFtpMibModule=alFtpMibModule, alBackupL2LMibModule=alBackupL2LMibModule, alAdminAuthMibModule=alAdminAuthMibModule, alXmlMibModule=alXmlMibModule, alLBSSFMibModule=alLBSSFMibModule, alWatchdogMibModule=alWatchdogMibModule, alDnsMibModule=alDnsMibModule, alBwMgmtMibModule=alBwMgmtMibModule, altigaModules=altigaModules, alMultiLinkMibModule=alMultiLinkMibModule, alNtpMibModule=alNtpMibModule, alNbnsMibModule=alNbnsMibModule, alRemoteServerMibModule=alRemoteServerMibModule, alNatMibModule=alNatMibModule, altigaGlobalRegModule=altigaGlobalRegModule, alComplModule=alComplModule, alIpMibModule=alIpMibModule, alUserMibModule=alUserMibModule)
| (octet_string, integer, object_identifier) = mibBuilder.importSymbols('ASN1', 'OctetString', 'Integer', 'ObjectIdentifier')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(single_value_constraint, value_size_constraint, constraints_union, value_range_constraint, constraints_intersection) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'SingleValueConstraint', 'ValueSizeConstraint', 'ConstraintsUnion', 'ValueRangeConstraint', 'ConstraintsIntersection')
(notification_group, module_compliance) = mibBuilder.importSymbols('SNMPv2-CONF', 'NotificationGroup', 'ModuleCompliance')
(gauge32, module_identity, bits, notification_type, object_identity, time_ticks, mib_identifier, iso, integer32, counter32, counter64, unsigned32, ip_address, enterprises, mib_scalar, mib_table, mib_table_row, mib_table_column) = mibBuilder.importSymbols('SNMPv2-SMI', 'Gauge32', 'ModuleIdentity', 'Bits', 'NotificationType', 'ObjectIdentity', 'TimeTicks', 'MibIdentifier', 'iso', 'Integer32', 'Counter32', 'Counter64', 'Unsigned32', 'IpAddress', 'enterprises', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn')
(display_string, textual_convention) = mibBuilder.importSymbols('SNMPv2-TC', 'DisplayString', 'TextualConvention')
altiga_global_reg_module = module_identity((1, 3, 6, 1, 4, 1, 3076, 1, 1, 1, 1))
altigaGlobalRegModule.setRevisions(('2005-01-05 00:00', '2003-10-20 00:00', '2003-04-25 00:00', '2002-07-10 00:00'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts:
altigaGlobalRegModule.setRevisionsDescriptions(('Added the new MIB Modules(65 to 67)', 'Added the new MIB Modules(58 to 64)', 'Added the new MIB Modules(54 to 57)', 'Updated with new header'))
if mibBuilder.loadTexts:
altigaGlobalRegModule.setLastUpdated('200501050000Z')
if mibBuilder.loadTexts:
altigaGlobalRegModule.setOrganization('Cisco Systems, Inc.')
if mibBuilder.loadTexts:
altigaGlobalRegModule.setContactInfo('Cisco Systems 170 W Tasman Drive San Jose, CA 95134 USA Tel: +1 800 553-NETS E-mail: cs-cvpn3000@cisco.com')
if mibBuilder.loadTexts:
altigaGlobalRegModule.setDescription('The Altiga Networks central registration module. Acronyms The following acronyms are used in this document: ACE: Access Control Encryption BwMgmt: Bandwidth Management CTCP: Cisco Transmission Control Protocol DHCP: Dynamic Host Configuration Protocol DNS: Domain Name Service FTP: File Transfer Protocol FW: Firewall HTTP: HyperText Transfer Protocol ICMP: Internet Control Message Protocol IKE: Internet Key Exchange IP: Internet Protocol LBSSF: Load Balance Secure Session Failover L2TP: Layer-2 Tunneling Protocol MIB: Management Information Base NAT: Network Address Translation NTP: Network Time Protocol PPP: Point-to-Point Protocol PPTP: Point-to-Point Tunneling Protocol SEP: Scalable Encryption Processor SNMP: Simple Network Management Protocol SSH: Secure Shell Protocol SSL: Secure Sockets Layer UDP: User Datagram Protocol VPN: Virtual Private Network NAC: Network Admission Control ')
altiga_root = mib_identifier((1, 3, 6, 1, 4, 1, 3076))
altiga_reg = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1))
altiga_modules = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1))
al_global_reg_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 1))
al_cap_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 2))
al_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 3))
al_compl_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 4))
al_version_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 6))
al_access_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 7))
al_event_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 8))
al_auth_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 9))
al_pptp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 10))
al_ppp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 11))
al_http_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 12))
al_ip_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 13))
al_filter_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 14))
al_user_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 15))
al_telnet_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 16))
al_ftp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 17))
al_tftp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 18))
al_snmp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 19))
al_ip_sec_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 20))
al_l2tp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 21))
al_session_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 22))
al_dns_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 23))
al_address_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 24))
al_dhcp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 25))
al_watchdog_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 26))
al_hardware_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 27))
al_nat_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 28))
al_lan2_lan_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 29))
al_general_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 30))
al_ssl_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 31))
al_cert_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 32))
al_ntp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 33))
al_network_list_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 34))
al_sep_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 35))
al_ike_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 36))
al_sync_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 37))
al_t1_e1_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 38))
al_multi_link_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 39))
al_ssh_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 40))
al_lbssf_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 41))
al_dhcp_server_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 42))
al_auto_update_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 43))
al_admin_auth_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 44))
al_pp_po_e_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 45))
al_xml_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 46))
al_ctcp_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 47))
al_fw_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 48))
al_group_match_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 49))
al_ace_server_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 50))
al_nat_t_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 51))
al_bw_mgmt_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 52))
al_ip_sec_pre_frag_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 53))
al_fips_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 54))
al_backup_l2_l_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 55))
al_notify_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 56))
al_reboot_status_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 57))
al_authorization_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 58))
al_web_portal_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 59))
al_web_email_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 60))
al_port_forward_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 61))
al_remote_server_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 62))
al_webvpn_acl_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 63))
al_nbns_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 64))
al_secure_desktop_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 65))
al_ssl_tunnel_client_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 66))
al_nac_mib_module = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 1, 67))
altiga_generic = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 2))
altiga_products = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 3))
altiga_caps = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 4))
altiga_reqs = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 5))
altiga_expr = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 6))
altiga_hw = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2))
altiga_vpn_hw = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1))
altiga_vpn_chassis = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1))
altiga_vpn_intf = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 2))
altiga_vpn_encrypt = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 3))
vpn_concentrator = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 1))
vpn_remote = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 2))
vpn_client = mib_identifier((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 3))
vpn_concentrator_rev1 = object_identity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 1, 1))
if mibBuilder.loadTexts:
vpnConcentratorRev1.setStatus('current')
if mibBuilder.loadTexts:
vpnConcentratorRev1.setDescription("The first revision of Altiga's VPN Concentrator hardware. 603e PPC processor. C10/15/20/30/50/60.")
vpn_concentrator_rev2 = object_identity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 1, 2))
if mibBuilder.loadTexts:
vpnConcentratorRev2.setStatus('current')
if mibBuilder.loadTexts:
vpnConcentratorRev2.setDescription("The second revision of Altiga's VPN Concentrator hardware. 740 PPC processor. C10/15/20/30/50/60.")
vpn_remote_rev1 = object_identity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 2, 1))
if mibBuilder.loadTexts:
vpnRemoteRev1.setStatus('current')
if mibBuilder.loadTexts:
vpnRemoteRev1.setDescription("The first revision of Altiga's VPN Concentrator (Remote) hardware. 8240 PPC processor.")
vpn_client_rev1 = object_identity((1, 3, 6, 1, 4, 1, 3076, 1, 2, 1, 1, 3, 1))
if mibBuilder.loadTexts:
vpnClientRev1.setStatus('current')
if mibBuilder.loadTexts:
vpnClientRev1.setDescription("The first revision of Altiga's VPN Hardware Client hardware. 8260 PPC processor.")
mibBuilder.exportSymbols('ALTIGA-GLOBAL-REG', PYSNMP_MODULE_ID=altigaGlobalRegModule, alNatTMibModule=alNatTMibModule, alWebEmailMibModule=alWebEmailMibModule, alEventMibModule=alEventMibModule, alPptpMibModule=alPptpMibModule, alAccessMibModule=alAccessMibModule, alDhcpMibModule=alDhcpMibModule, alIkeMibModule=alIkeMibModule, alHttpMibModule=alHttpMibModule, alSepMibModule=alSepMibModule, alMibModule=alMibModule, altigaVpnHw=altigaVpnHw, altigaExpr=altigaExpr, alHardwareMibModule=alHardwareMibModule, altigaGeneric=altigaGeneric, alRebootStatusMibModule=alRebootStatusMibModule, alSslMibModule=alSslMibModule, alVersionMibModule=alVersionMibModule, altigaVpnChassis=altigaVpnChassis, alSyncMibModule=alSyncMibModule, altigaHw=altigaHw, alPppMibModule=alPppMibModule, vpnRemote=vpnRemote, alGroupMatchMibModule=alGroupMatchMibModule, alNotifyMibModule=alNotifyMibModule, alCapModule=alCapModule, altigaReg=altigaReg, altigaRoot=altigaRoot, altigaReqs=altigaReqs, vpnClient=vpnClient, alIpSecPreFragMibModule=alIpSecPreFragMibModule, alL2tpMibModule=alL2tpMibModule, alAutoUpdateMibModule=alAutoUpdateMibModule, alSshMibModule=alSshMibModule, alSslTunnelClientMibModule=alSslTunnelClientMibModule, alAddressMibModule=alAddressMibModule, alLan2LanMibModule=alLan2LanMibModule, alSecureDesktopMibModule=alSecureDesktopMibModule, alDhcpServerMibModule=alDhcpServerMibModule, altigaVpnEncrypt=altigaVpnEncrypt, alPortForwardMibModule=alPortForwardMibModule, alT1E1MibModule=alT1E1MibModule, alAuthorizationModule=alAuthorizationModule, vpnRemoteRev1=vpnRemoteRev1, vpnConcentratorRev1=vpnConcentratorRev1, alFwMibModule=alFwMibModule, altigaProducts=altigaProducts, alPPPoEMibModule=alPPPoEMibModule, alFilterMibModule=alFilterMibModule, alCertMibModule=alCertMibModule, alTelnetMibModule=alTelnetMibModule, alGlobalRegModule=alGlobalRegModule, alWebPortalMibModule=alWebPortalMibModule, alNacMibModule=alNacMibModule, alCtcpMibModule=alCtcpMibModule, vpnClientRev1=vpnClientRev1, vpnConcentrator=vpnConcentrator, alGeneralMibModule=alGeneralMibModule, alAuthMibModule=alAuthMibModule, alACEServerMibModule=alACEServerMibModule, alNetworkListMibModule=alNetworkListMibModule, altigaCaps=altigaCaps, alWebvpnAclMibModule=alWebvpnAclMibModule, altigaVpnIntf=altigaVpnIntf, alSessionMibModule=alSessionMibModule, alIpSecMibModule=alIpSecMibModule, alFipsMibModule=alFipsMibModule, alTftpMibModule=alTftpMibModule, vpnConcentratorRev2=vpnConcentratorRev2, alSnmpMibModule=alSnmpMibModule, alFtpMibModule=alFtpMibModule, alBackupL2LMibModule=alBackupL2LMibModule, alAdminAuthMibModule=alAdminAuthMibModule, alXmlMibModule=alXmlMibModule, alLBSSFMibModule=alLBSSFMibModule, alWatchdogMibModule=alWatchdogMibModule, alDnsMibModule=alDnsMibModule, alBwMgmtMibModule=alBwMgmtMibModule, altigaModules=altigaModules, alMultiLinkMibModule=alMultiLinkMibModule, alNtpMibModule=alNtpMibModule, alNbnsMibModule=alNbnsMibModule, alRemoteServerMibModule=alRemoteServerMibModule, alNatMibModule=alNatMibModule, altigaGlobalRegModule=altigaGlobalRegModule, alComplModule=alComplModule, alIpMibModule=alIpMibModule, alUserMibModule=alUserMibModule) |
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module is part of the nmeta2 suite
.
It defines a custom traffic classifier
.
To create your own custom classifier, copy this example to a new
file in the same directory and update the code as required.
Call it from nmeta by specifying the name of the file (without the
.py) in main_policy.yaml
.
Classifiers are called per packet, so performance is important
.
"""
class Classifier(object):
"""
A custom classifier module for import by nmeta2
"""
def __init__(self, logger):
"""
Initialise the classifier
"""
self.logger = logger
def classifier(self, flow):
"""
A really basic statistical classifier to demonstrate ability
to differentiate 'bandwidth hog' flows from ones that are
more interactive so that appropriate classification metadata
can be passed to QoS for differential treatment.
.
This method is passed a Flow class object that holds the
current context of the flow
.
It returns a dictionary specifying a key/value of QoS treatment to
take (or not if no classification determination made).
.
Only works on TCP.
"""
#*** Maximum packets to accumulate in a flow before making a
#*** classification:
_max_packets = 7
#*** Thresholds used in calculations:
_max_packet_size_threshold = 1200
_interpacket_ratio_threshold = 0.3
#*** Dictionary to hold classification results:
_results = {}
if flow.packet_count >= _max_packets and not flow.finalised:
#*** Reached our maximum packet count so do some classification:
self.logger.debug("Reached max packets count, finalising")
flow.finalised = 1
#*** Call functions to get statistics to make decisions on:
_max_packet_size = flow.max_packet_size()
_max_interpacket_interval = flow.max_interpacket_interval()
_min_interpacket_interval = flow.min_interpacket_interval()
#*** Avoid possible divide by zero error:
if _max_interpacket_interval and _min_interpacket_interval:
#*** Ratio between largest directional interpacket delta and
#*** smallest. Use a ratio as it accounts for base RTT:
_interpacket_ratio = float(_min_interpacket_interval) / \
float(_max_interpacket_interval)
else:
_interpacket_ratio = 0
self.logger.debug("max_packet_size=%s interpacket_ratio=%s",
_max_packet_size, _interpacket_ratio)
#*** Decide actions based on the statistics:
if (_max_packet_size > _max_packet_size_threshold and
_interpacket_ratio < _interpacket_ratio_threshold):
#*** This traffic looks like a bandwidth hog so constrain it:
_results['qos_treatment'] = 'constrained_bw'
else:
#*** Doesn't look like bandwidth hog so default priority:
_results['qos_treatment'] = 'default_priority'
self.logger.debug("Decided on results %s", _results)
return _results
| """
This module is part of the nmeta2 suite
.
It defines a custom traffic classifier
.
To create your own custom classifier, copy this example to a new
file in the same directory and update the code as required.
Call it from nmeta by specifying the name of the file (without the
.py) in main_policy.yaml
.
Classifiers are called per packet, so performance is important
.
"""
class Classifier(object):
"""
A custom classifier module for import by nmeta2
"""
def __init__(self, logger):
"""
Initialise the classifier
"""
self.logger = logger
def classifier(self, flow):
"""
A really basic statistical classifier to demonstrate ability
to differentiate 'bandwidth hog' flows from ones that are
more interactive so that appropriate classification metadata
can be passed to QoS for differential treatment.
.
This method is passed a Flow class object that holds the
current context of the flow
.
It returns a dictionary specifying a key/value of QoS treatment to
take (or not if no classification determination made).
.
Only works on TCP.
"""
_max_packets = 7
_max_packet_size_threshold = 1200
_interpacket_ratio_threshold = 0.3
_results = {}
if flow.packet_count >= _max_packets and (not flow.finalised):
self.logger.debug('Reached max packets count, finalising')
flow.finalised = 1
_max_packet_size = flow.max_packet_size()
_max_interpacket_interval = flow.max_interpacket_interval()
_min_interpacket_interval = flow.min_interpacket_interval()
if _max_interpacket_interval and _min_interpacket_interval:
_interpacket_ratio = float(_min_interpacket_interval) / float(_max_interpacket_interval)
else:
_interpacket_ratio = 0
self.logger.debug('max_packet_size=%s interpacket_ratio=%s', _max_packet_size, _interpacket_ratio)
if _max_packet_size > _max_packet_size_threshold and _interpacket_ratio < _interpacket_ratio_threshold:
_results['qos_treatment'] = 'constrained_bw'
else:
_results['qos_treatment'] = 'default_priority'
self.logger.debug('Decided on results %s', _results)
return _results |
# Since any modulus should lay between 0 and 101, we can record all
# possible modulus at any given point in the calculation. The possible
# set of values of next step can be calculated using the previous set.
# Since there's guaranteed to be an answer, we will eventually make
# modulus 0 possible. We then backtrack to fill in all these operators.
N = int(input())
A = list(map(int, input().split()))
op = ['*'] * (N - 1)
possible = [[None] * 101 for i in range(N)]
possible[0][A[0]] = True
end = N - 1
for i in range(N - 1):
if possible[i][0]:
end = i
break
for x in range(101):
if possible[i][x]:
possible[i + 1][(x + A[i + 1]) % 101] = ('+', x)
possible[i + 1][(x + 101 - A[i + 1]) % 101] = ('-', x)
possible[i + 1][(x * A[i + 1]) % 101] = ('*', x)
x = 0
for i in range(end, 0, -1):
op[i - 1] = possible[i][x][0]
x = possible[i][x][1]
print(''.join(str(x) for t in zip(A, op) for x in t) + str(A[-1]))
| n = int(input())
a = list(map(int, input().split()))
op = ['*'] * (N - 1)
possible = [[None] * 101 for i in range(N)]
possible[0][A[0]] = True
end = N - 1
for i in range(N - 1):
if possible[i][0]:
end = i
break
for x in range(101):
if possible[i][x]:
possible[i + 1][(x + A[i + 1]) % 101] = ('+', x)
possible[i + 1][(x + 101 - A[i + 1]) % 101] = ('-', x)
possible[i + 1][x * A[i + 1] % 101] = ('*', x)
x = 0
for i in range(end, 0, -1):
op[i - 1] = possible[i][x][0]
x = possible[i][x][1]
print(''.join((str(x) for t in zip(A, op) for x in t)) + str(A[-1])) |
# File: koodous_consts.py
#
# Copyright (c) 2018-2021 Splunk Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
# either express or implied. See the License for the specific language governing permissions
# and limitations under the License.
PHANTOM_ERR_CODE_UNAVAILABLE = "Error code unavailable"
PHANTOM_ERR_MSG_UNAVAILABLE = "Unknown error occurred. Please check the asset configuration and|or action parameters."
VAULT_ERR_INVALID_VAULT_ID = "Invalid Vault ID"
VAULT_ERR_FILE_NOT_FOUND = "Vault file could not be found with supplied Vault ID"
KOODOUS_BASE_URL = 'https://api.koodous.com'
KOODOUS_SUCC_TEST_CONNECTIVITY = "Test connectivity passed"
KOODOUS_ERR_TEST_CONNECTIVITY = "Test Connectivity Failed"
KOODOUS_ERR_INVALID_ATTEMPT_PARAM = "Attempts must be integer number. Error: {0}"
KOODOUS_ERR_GET_REPORT_PARAMS = "Must specify either 'sha256' or 'vault_id'"
KOODOUS_ERR_UPLOADING_URL = "Error retrieving upload URL"
| phantom_err_code_unavailable = 'Error code unavailable'
phantom_err_msg_unavailable = 'Unknown error occurred. Please check the asset configuration and|or action parameters.'
vault_err_invalid_vault_id = 'Invalid Vault ID'
vault_err_file_not_found = 'Vault file could not be found with supplied Vault ID'
koodous_base_url = 'https://api.koodous.com'
koodous_succ_test_connectivity = 'Test connectivity passed'
koodous_err_test_connectivity = 'Test Connectivity Failed'
koodous_err_invalid_attempt_param = 'Attempts must be integer number. Error: {0}'
koodous_err_get_report_params = "Must specify either 'sha256' or 'vault_id'"
koodous_err_uploading_url = 'Error retrieving upload URL' |
"""
Conditional expression Evaluated to one of two expressions depending on a boolean.
e.g: result = true_value if condition else false_value
"""
def sequence_class(immutable):
return tuple if immutable else list
seq = sequence_class(immutable=True)
t = seq("OrHasson")
print(t)
print(type(t))
| """
Conditional expression Evaluated to one of two expressions depending on a boolean.
e.g: result = true_value if condition else false_value
"""
def sequence_class(immutable):
return tuple if immutable else list
seq = sequence_class(immutable=True)
t = seq('OrHasson')
print(t)
print(type(t)) |
def print_two(*args):
arg1, arg2 =args
print(f"arg1 : {arg1},arg2 : {arg2}")
def print_two_again(arg1,arg2):
print(f"arg1:{arg1},arg2:{arg2}")
def print_one(arg1):
print(f"arg1:{arg1}")
def print_none():
print("I got nothing")
print_two("Zed","Shaw")
print_two_again("Zed","Shaw")
print_one("First!")
print_none()
| def print_two(*args):
(arg1, arg2) = args
print(f'arg1 : {arg1},arg2 : {arg2}')
def print_two_again(arg1, arg2):
print(f'arg1:{arg1},arg2:{arg2}')
def print_one(arg1):
print(f'arg1:{arg1}')
def print_none():
print('I got nothing')
print_two('Zed', 'Shaw')
print_two_again('Zed', 'Shaw')
print_one('First!')
print_none() |
'''
Provide transmission-daemon RPC credentials
'''
rpc_ip = ''
rpc_port = ''
rpc_username = ''
rpc_password = ''
| """
Provide transmission-daemon RPC credentials
"""
rpc_ip = ''
rpc_port = ''
rpc_username = ''
rpc_password = '' |
''' Kattis - secretchamber
Without much execution time pressure along with nodes being characters, we opt to use python with a
dict of dicts as our adjacency matrix. This is basically just floyd warshall transitive closure.
Time: O(V^3), Mem: O(V^2)
'''
n, q = input().split()
n = int(n)
q = int(q)
edges = []
node_names = set()
for i in range(n):
u, v = input().split()
edges.append((u,v))
node_names.add(u)
node_names.add(v)
adjmat = {}
for i in node_names:
adjmat[i] = {}
for j in node_names:
adjmat[i][j] = 0
for u, v in edges:
adjmat[u][v] = 1
for k in node_names:
for i in node_names:
for j in node_names:
adjmat[i][j] |= adjmat[i][k] & adjmat[k][j]
for _ in range(q):
a, b = input().split()
if len(a) != len(b):
print("no")
continue
no = 0
for i in range(len(a)):
if (a[i] == b[i]):
continue
if not(a[i] in node_names and b[i] in node_names):
no = 1
break
if (adjmat[a[i]][b[i]] == 0):
no = 1
break
if no:
print("no")
else:
print("yes")
| """ Kattis - secretchamber
Without much execution time pressure along with nodes being characters, we opt to use python with a
dict of dicts as our adjacency matrix. This is basically just floyd warshall transitive closure.
Time: O(V^3), Mem: O(V^2)
"""
(n, q) = input().split()
n = int(n)
q = int(q)
edges = []
node_names = set()
for i in range(n):
(u, v) = input().split()
edges.append((u, v))
node_names.add(u)
node_names.add(v)
adjmat = {}
for i in node_names:
adjmat[i] = {}
for j in node_names:
adjmat[i][j] = 0
for (u, v) in edges:
adjmat[u][v] = 1
for k in node_names:
for i in node_names:
for j in node_names:
adjmat[i][j] |= adjmat[i][k] & adjmat[k][j]
for _ in range(q):
(a, b) = input().split()
if len(a) != len(b):
print('no')
continue
no = 0
for i in range(len(a)):
if a[i] == b[i]:
continue
if not (a[i] in node_names and b[i] in node_names):
no = 1
break
if adjmat[a[i]][b[i]] == 0:
no = 1
break
if no:
print('no')
else:
print('yes') |
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
maria = Person("Maria Popova", 25)
print(hasattr(maria,"name"))
print(hasattr(maria,"surname"))
print(getattr(maria, "age"))
setattr(maria, "surname", "Popova")
print(getattr(maria, "surname"))
| class Person:
def __init__(self, name, age):
self.name = name
self.age = age
maria = person('Maria Popova', 25)
print(hasattr(maria, 'name'))
print(hasattr(maria, 'surname'))
print(getattr(maria, 'age'))
setattr(maria, 'surname', 'Popova')
print(getattr(maria, 'surname')) |
def spiral(steps):
dx = 1
dy = 0
dd = 1
x = 0
y = 0
d = 0
for _ in range(steps - 1):
x += dx
y += dy
d += 1
if d == dd:
d = 0
tmp = dx
dx = -dy
dy = tmp
if dy == 0:
dd += 1
yield x, y
def aoc(data):
*_, (x, y) = spiral(int(data))
return abs(x) + abs(y)
| def spiral(steps):
dx = 1
dy = 0
dd = 1
x = 0
y = 0
d = 0
for _ in range(steps - 1):
x += dx
y += dy
d += 1
if d == dd:
d = 0
tmp = dx
dx = -dy
dy = tmp
if dy == 0:
dd += 1
yield (x, y)
def aoc(data):
(*_, (x, y)) = spiral(int(data))
return abs(x) + abs(y) |
with open('do-plecaka.txt', 'r') as f:
dane = []
# getting and cleaning data
for line in f:
dane.append([int(x) for x in line.split()])
# printing
for x in dane:
print(x) | with open('do-plecaka.txt', 'r') as f:
dane = []
for line in f:
dane.append([int(x) for x in line.split()])
for x in dane:
print(x) |
# measurements in inches
ball_radius = 3
goal_top = 50
goal_width = 58
goal_half = 29
angle_threshold = .1
class L_params(object):
horizontal_offset = 14.5
vertical_offset = 18.5
min_y = ball_radius - vertical_offset+3 # in robot coords
max_y = goal_top - vertical_offset
min_x = -14.5
max_x = 14.0
l1 = 11
l2 = 11
shoulder_offset = -60
elbow_offset = 0
angle_threshold = angle_threshold
class R_params(object):
horizontal_offset = 43.5
vertical_offset = 18.5
min_y = ball_radius - vertical_offset+2 # in robot coords
max_y = goal_top - vertical_offset
min_x = -14.0
max_x = 14.5
l1 = 11
l2 = 11
shoulder_offset = 0
elbow_offset = 0
angle_threshold = angle_threshold
left_arm = L_params()
right_arm = R_params()
windows_port = "COM8"
unix_port = "/dev/tty.usbserial-A4012B2H"
ubuntu_port = "/dev/ttyUSB0"
num_servos = 4
servo_speed = 500
baudrate = 400000
| ball_radius = 3
goal_top = 50
goal_width = 58
goal_half = 29
angle_threshold = 0.1
class L_Params(object):
horizontal_offset = 14.5
vertical_offset = 18.5
min_y = ball_radius - vertical_offset + 3
max_y = goal_top - vertical_offset
min_x = -14.5
max_x = 14.0
l1 = 11
l2 = 11
shoulder_offset = -60
elbow_offset = 0
angle_threshold = angle_threshold
class R_Params(object):
horizontal_offset = 43.5
vertical_offset = 18.5
min_y = ball_radius - vertical_offset + 2
max_y = goal_top - vertical_offset
min_x = -14.0
max_x = 14.5
l1 = 11
l2 = 11
shoulder_offset = 0
elbow_offset = 0
angle_threshold = angle_threshold
left_arm = l_params()
right_arm = r_params()
windows_port = 'COM8'
unix_port = '/dev/tty.usbserial-A4012B2H'
ubuntu_port = '/dev/ttyUSB0'
num_servos = 4
servo_speed = 500
baudrate = 400000 |
class Solution:
def numDecodings(self, s: str) -> int:
if s[0] == '0' or '00' in s:
return 0
for idx, _ in enumerate(s):
if idx == 0:
pre, cur = 1, 1
else:
tmp = cur
if _ != '0':
if s[idx - 1] == '0':
cur = tmp
pre = tmp
elif 0 < int(s[idx - 1] + _) < 27:
cur = pre + tmp
pre = tmp
else:
cur = tmp
pre = tmp
else:
if s[idx - 1] > '2':
return 0
else:
cur = pre
pre = tmp
return cur
| class Solution:
def num_decodings(self, s: str) -> int:
if s[0] == '0' or '00' in s:
return 0
for (idx, _) in enumerate(s):
if idx == 0:
(pre, cur) = (1, 1)
else:
tmp = cur
if _ != '0':
if s[idx - 1] == '0':
cur = tmp
pre = tmp
elif 0 < int(s[idx - 1] + _) < 27:
cur = pre + tmp
pre = tmp
else:
cur = tmp
pre = tmp
elif s[idx - 1] > '2':
return 0
else:
cur = pre
pre = tmp
return cur |
def find_skew_value(text):
length_of_text = len(text)
skew_value = 0
skew_value_list = []
for i in range(0, length_of_text):
if text[i] == 'C':
skew_value = skew_value - 1
elif text[i] == 'G':
skew_value = skew_value + 1
skew_value_list.append(skew_value)
return text, skew_value_list
| def find_skew_value(text):
length_of_text = len(text)
skew_value = 0
skew_value_list = []
for i in range(0, length_of_text):
if text[i] == 'C':
skew_value = skew_value - 1
elif text[i] == 'G':
skew_value = skew_value + 1
skew_value_list.append(skew_value)
return (text, skew_value_list) |
# -*- coding: utf-8 -*-
"""
Created on Wed May 22 10:46:35 2019
@author: SPAD-FCS
"""
class correlations:
pass
def selectG(G, selection='average'):
"""
Return a selection of the autocorrelations
========== ===============================================================
Input Meaning
---------- ---------------------------------------------------------------
G Object with all autocorrelations, i.e. output of e.g.
FCS2CorrSplit
selection Default value 'average': select only the autocorrelations that
are averaged over multiple time traces.
E.g. if FCS2CorrSplit splits a time trace in 10 pieces,
calculates G for each trace and then calculates the average G,
all autocorrelations are stored in G. This function removes all
of them except for the average G.
========== ===============================================================
========== ===============================================================
Output Meaning
---------- ---------------------------------------------------------------
G Autocorrelation object with only the pixel dwell time and the
average autocorrelations stored. All other autocorrelations are
removed.
========== ===============================================================
"""
# get all attributes of G
Glist = list(G.__dict__.keys())
if selection == 'average':
# make a new list containing only 'average' attributes
Glist2 = [s for s in Glist if "average" in s]
else:
Glist2 = Glist
# make a new object with the average attributes
Gout = correlations()
for i in Glist2:
setattr(Gout, i, getattr(G, i))
# add dwell time
Gout.dwellTime = G.dwellTime
return(Gout)
| """
Created on Wed May 22 10:46:35 2019
@author: SPAD-FCS
"""
class Correlations:
pass
def select_g(G, selection='average'):
"""
Return a selection of the autocorrelations
========== ===============================================================
Input Meaning
---------- ---------------------------------------------------------------
G Object with all autocorrelations, i.e. output of e.g.
FCS2CorrSplit
selection Default value 'average': select only the autocorrelations that
are averaged over multiple time traces.
E.g. if FCS2CorrSplit splits a time trace in 10 pieces,
calculates G for each trace and then calculates the average G,
all autocorrelations are stored in G. This function removes all
of them except for the average G.
========== ===============================================================
========== ===============================================================
Output Meaning
---------- ---------------------------------------------------------------
G Autocorrelation object with only the pixel dwell time and the
average autocorrelations stored. All other autocorrelations are
removed.
========== ===============================================================
"""
glist = list(G.__dict__.keys())
if selection == 'average':
glist2 = [s for s in Glist if 'average' in s]
else:
glist2 = Glist
gout = correlations()
for i in Glist2:
setattr(Gout, i, getattr(G, i))
Gout.dwellTime = G.dwellTime
return Gout |
#!/usr/bin/env python3
etape = 1
compteur = 0
n = 0
while True:
print(f"{etape:4d} : {n:5d} { -2+(etape)*(etape+2):6d} ; ", end="")
for _ in range(3 + etape):
n += 1
print(n, end=" ")
compteur += 1
if compteur == 500000:
print(n)
exit()
print()
n += etape
etape += 1
| etape = 1
compteur = 0
n = 0
while True:
print(f'{etape:4d} : {n:5d} {-2 + etape * (etape + 2):6d} ; ', end='')
for _ in range(3 + etape):
n += 1
print(n, end=' ')
compteur += 1
if compteur == 500000:
print(n)
exit()
print()
n += etape
etape += 1 |
class EPIconst:
class FeatureName:
pseknc = "pseknc"
cksnap = "cksnap"
dpcp = "dpcp"
eiip = "eiip"
kmer = "kmer"
tpcp = "tpcp"
all = sorted([pseknc, cksnap, dpcp, eiip, kmer, tpcp])
class CellName:
K562 = "K562"
NHEK = "NHEK"
IMR90 = "IMR90"
HeLa_S3 = "HeLa-S3"
HUVEC = "HUVEC"
GM12878 = "GM12878"
all = sorted([GM12878, HeLa_S3, HUVEC, IMR90, K562, NHEK])
class MethodName:
ensemble = "meta"
xgboost = "xgboost"
svm = "svm"
deepforest = "deepforest"
lightgbm = "lightgbm"
rf = "rf"
all = sorted([lightgbm, rf, xgboost, svm, deepforest])
class ModelInitParams:
logistic = {"n_jobs": 13, }
mlp = {}
deepforest = {"n_jobs": 13, "use_predictor": False, "random_state": 1, "predictor": 'forest', "verbose": 0}
lightgbm = {"n_jobs": 13, 'max_depth': -1, 'num_leaves': 31,
'min_child_samples': 20,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0,
'reg_alpha': 0.0, 'reg_lambda': 0.0,
'min_split_gain': 0.0,
'objective': None,
'n_estimators': 100, 'learning_rate': 0.1,
'device': 'gpu', 'boosting_type': 'gbdt',
'class_weight': None, 'importance_type': 'split',
'min_child_weight': 0.001, 'random_state': None,
'subsample_for_bin': 200000, 'silent': True}
rf = {"n_jobs": 13, 'n_estimators': 100, "max_depth": None, 'min_samples_split': 2, "min_samples_leaf": 1,
'max_features': 'auto'}
svm = {"probability": True}
xgboost = {'learning_rate': 0.1, 'n_estimators': 500, 'max_depth': 5, 'min_child_weight': 1, 'seed': 0,
'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1,
'use_label_encoder': False, 'eval_metric': 'logloss', 'tree_method': 'gpu_hist'}
class BaseModelParams:
GM12878_cksnap_deepforest = {"max_layers": 20, "n_estimators": 5, "n_trees": 250}
GM12878_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 125, 'min_child_samples': 90,
'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1,
'n_estimators': 250}
GM12878_cksnap_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
GM12878_cksnap_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0,
'learning_rate': 0.1}
GM12878_cksnap_rf = {'n_estimators': 340, 'max_depth': 114, 'min_samples_leaf': 3, 'min_samples_split': 2,
'max_features': 'sqrt'}
"----------------------------------------------"
GM12878_dpcp_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 300}
GM12878_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 331, 'max_bin': 135, 'min_child_samples': 190,
'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.9,
'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
GM12878_dpcp_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'}
GM12878_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 3, 'reg_lambda': 3,
'learning_rate': 0.1}
GM12878_dpcp_rf = {'n_estimators': 150, 'max_depth': 88, 'min_samples_leaf': 1, 'min_samples_split': 3,
'max_features': "sqrt"}
"----------------------------------------------"
GM12878_eiip_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 300}
GM12878_eiip_lightgbm = {'max_depth': 12, 'num_leaves': 291, 'max_bin': 115, 'min_child_samples': 40,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
GM12878_eiip_rf = {'n_estimators': 280, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 7,
'max_features': "sqrt"}
GM12878_eiip_svm = {'C': 1.0, 'gamma': 2048.0, 'kernel': 'rbf'}
GM12878_eiip_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
GM12878_kmer_deepforest = {'max_layers': 25, 'n_estimators': 5,
'n_trees': 400}
GM12878_kmer_lightgbm = {'max_depth': 12, 'num_leaves': 291, 'max_bin': 115, 'min_child_samples': 40,
'colsample_bytree': 1.0, 'subsample': 0.8, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
GM12878_kmer_rf = {'n_estimators': 170, 'max_depth': 41, 'min_samples_leaf': 3, 'min_samples_split': 2,
'max_features': 'sqrt'}
GM12878_kmer_svm = {'C': 2.0, 'gamma': 128.0,
'kernel': 'rbf'}
GM12878_kmer_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
GM12878_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400}
GM12878_pseknc_lightgbm = {'max_depth': 11, 'num_leaves': 291, 'max_bin': 185, 'min_child_samples': 80,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 40, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
GM12878_pseknc_rf = {'n_estimators': 250, 'max_depth': 41, 'min_samples_leaf': 2, 'min_samples_split': 6,
'max_features': 'log2'}
GM12878_pseknc_svm = {'C': 0.5, 'gamma': 1024.0, 'kernel': 'rbf'}
GM12878_pseknc_xgboost = {'n_estimators': 950, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.1,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01,
'learning_rate': 0.1}
"----------------------------------------------"
GM12878_tpcp_deepforest = {'max_layers': 15, 'n_estimators': 2,
'n_trees': 100}
GM12878_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 321, 'max_bin': 175, 'min_child_samples': 80,
'colsample_bytree': 0.9, 'subsample': 1.0, 'subsample_freq': 20, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
GM12878_tpcp_rf = {'n_estimators': 250, 'max_depth': 89, 'min_samples_leaf': 2, 'min_samples_split': 9,
'max_features': "log2"}
GM12878_tpcp_svm = {'C': 16.0, 'gamma': 64.0,
'kernel': 'rbf'}
GM12878_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 6, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"=============================================="
HeLa_S3_cksnap_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 300}
HeLa_S3_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 341, 'max_bin': 105, 'min_child_samples': 80,
'colsample_bytree': 0.9, 'subsample': 0.9, 'subsample_freq': 40, 'reg_alpha': 0.1,
'reg_lambda': 0.1, 'min_split_gain': 0.4, 'learning_rate': 0.1, 'n_estimators': 150}
HeLa_S3_cksnap_svm = {'C': 128.0, 'gamma': 128.0,
'kernel': 'rbf'}
HeLa_S3_cksnap_rf = {'n_estimators': 340, 'max_depth': 44, 'min_samples_leaf': 1, 'min_samples_split': 5,
'max_features': 'sqrt'}
HeLa_S3_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 0,
'colsample_bytree': 0.7, 'subsample': 0.7, 'reg_alpha': 3, 'reg_lambda': 0.5,
'learning_rate': 0.1}
"----------------------------------------------"
HeLa_S3_dpcp_deepforest = {"max_layers": 10, "n_estimators": 2, "n_trees": 400}
HeLa_S3_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 221, 'max_bin': 155, 'min_child_samples': 180,
'colsample_bytree': 0.7, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 200}
HeLa_S3_dpcp_rf = {'n_estimators': 70, 'max_depth': 32, 'min_samples_leaf': 1, 'min_samples_split': 8,
'max_features': 'sqrt'}
HeLa_S3_dpcp_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'}
HeLa_S3_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
HeLa_S3_eiip_deepforest = {'max_layers': 10, 'n_estimators': 5,
'n_trees': 200}
HeLa_S3_eiip_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 5, 'min_child_samples': 110,
'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 100}
HeLa_S3_eiip_rf = {'n_estimators': 180, 'max_depth': 138, 'min_samples_leaf': 6, 'min_samples_split': 10,
'max_features': 'sqrt'}
HeLa_S3_eiip_svm = {'C': 2.0, 'gamma': 1024.0,
'kernel': 'rbf'}
HeLa_S3_eiip_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
HeLa_S3_kmer_deepforest = {'max_layers': 10, 'n_estimators': 5,
'n_trees': 200}
HeLa_S3_kmer_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 165, 'min_child_samples': 90,
'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 70, 'reg_alpha': 0.001,
'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 125}
HeLa_S3_kmer_rf = {'n_estimators': 240, 'max_depth': 77, 'min_samples_leaf': 2, 'min_samples_split': 2,
'max_features': 'sqrt'}
HeLa_S3_kmer_svm = {'C': 8.0, 'gamma': 128.0,
'kernel': 'rbf'}
HeLa_S3_kmer_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
HeLa_S3_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200}
HeLa_S3_pseknc_lightgbm = {'max_depth': 12, 'num_leaves': 261, 'max_bin': 25, 'min_child_samples': 90,
'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
HeLa_S3_pseknc_rf = {'n_estimators': 330, 'max_depth': 118, 'min_samples_leaf': 1, 'min_samples_split': 8,
'max_features': 'log2'}
HeLa_S3_pseknc_svm = {'C': 1.0, 'gamma': 256.0, 'kernel': 'rbf'}
HeLa_S3_pseknc_xgboost = {'n_estimators': 750, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.1, 'reg_lambda': 2,
'learning_rate': 0.1}
"----------------------------------------------"
HeLa_S3_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 250}
HeLa_S3_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 341, 'max_bin': 45, 'min_child_samples': 10,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 250}
HeLa_S3_tpcp_rf = {'n_estimators': 320, 'max_depth': 99, 'min_samples_leaf': 1, 'min_samples_split': 10,
'max_features': 'sqrt'}
HeLa_S3_tpcp_svm = {'C': 4.0, 'gamma': 32.0,
'kernel': 'rbf'}
HeLa_S3_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 7, 'min_child_weight': 4, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"=============================================="
HUVEC_cksnap_deepforest = {"max_layers": 10, "n_estimators": 2,
"n_trees": 200}
HUVEC_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 271, 'max_bin': 45, 'min_child_samples': 10,
'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 0.5,
'reg_lambda': 0.5, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 175}
HUVEC_cksnap_rf = {'n_estimators': 270, 'max_depth': 38, 'min_samples_leaf': 2, 'min_samples_split': 2,
'max_features': "auto"}
HUVEC_cksnap_svm = {'C': 8.0, 'gamma': 64.0, 'kernel': 'rbf'}
HUVEC_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.7, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
HUVEC_dpcp_deepforest = {"max_layers": 10, "n_estimators": 2, "n_trees": 400}
HUVEC_dpcp_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 245, 'min_child_samples': 30,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.5,
'reg_lambda': 0.3, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
HUVEC_dpcp_rf = {'n_estimators': 300, 'max_depth': 61, 'min_samples_leaf': 2, 'min_samples_split': 3,
'max_features': 'log2'}
HUVEC_dpcp_svm = {'C': 4.0, 'gamma': 16.0, 'kernel': 'rbf'}
HUVEC_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 3, 'reg_lambda': 3,
'learning_rate': 0.1}
"----------------------------------------------"
HUVEC_eiip_deepforest = {'max_layers': 15, 'n_estimators': 2,
'n_trees': 300}
HUVEC_eiip_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 25, 'min_child_samples': 80,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
HUVEC_eiip_rf = {'n_estimators': 310, 'max_depth': 28, 'min_samples_leaf': 1, 'min_samples_split': 2,
'max_features': 'sqrt'}
HUVEC_eiip_svm = {'C': 4.0, 'gamma': 512.0, 'kernel': 'rbf'}
HUVEC_eiip_xgboost = {'n_estimators': 600, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0,
'learning_rate': 0.1}
"----------------------------------------------"
HUVEC_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 300}
HUVEC_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 251, 'max_bin': 5, 'min_child_samples': 170,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 0.5,
'reg_lambda': 0.7, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 125}
HUVEC_kmer_rf = {'n_estimators': 230, 'max_depth': 59, 'min_samples_leaf': 1, 'min_samples_split': 4,
'max_features': 'auto'}
HUVEC_kmer_svm = {'C': 4.0, 'gamma': 64.0,
'kernel': 'rbf'}
HUVEC_kmer_xgboost = {'n_estimators': 600, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0,
'learning_rate': 0.1}
"----------------------------------------------"
HUVEC_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400}
HUVEC_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 311, 'max_bin': 115, 'min_child_samples': 190,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 175}
HUVEC_pseknc_rf = {'n_estimators': 310, 'max_depth': 42, 'min_samples_leaf': 2, 'min_samples_split': 7,
'max_features': 'sqrt'}
HUVEC_pseknc_svm = {'C': 1.0, 'gamma': 256.0, 'kernel': 'rbf'}
HUVEC_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
HUVEC_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 150}
HUVEC_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 251, 'max_bin': 35, 'min_child_samples': 190,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
HUVEC_tpcp_rf = {'n_estimators': 330, 'max_depth': 121, 'min_samples_leaf': 2, 'min_samples_split': 5,
'max_features': "sqrt"}
HUVEC_tpcp_svm = {'C': 2.0, 'gamma': 32.0, 'kernel': 'rbf'}
HUVEC_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.9, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"=============================================="
IMR90_cksnap_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 250}
IMR90_cksnap_lightgbm = {'max_depth': 0, 'num_leaves': 271, 'max_bin': 95, 'min_child_samples': 60,
'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.3, 'learning_rate': 0.1, 'n_estimators': 225}
IMR90_cksnap_rf = {'n_estimators': 280, 'max_depth': 124, 'min_samples_leaf': 1, 'min_samples_split': 2,
'max_features': 'auto'}
IMR90_cksnap_svm = {'C': 16.0, 'gamma': 16.0, 'kernel': 'rbf'}
IMR90_cksnap_xgboost = {'n_estimators': 900, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0.4,
'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0.1,
'learning_rate': 0.1}
"----------------------------------------------"
IMR90_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 200}
IMR90_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 281, 'max_bin': 115, 'min_child_samples': 20,
'colsample_bytree': 0.7, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.5, 'learning_rate': 0.1, 'n_estimators': 125}
IMR90_dpcp_rf = {'n_estimators': 70, 'max_depth': 116, 'min_samples_leaf': 1, 'min_samples_split': 9,
'max_features': 'log2'}
IMR90_dpcp_svm = {'C': 1.0, 'gamma': 32.0, 'kernel': 'rbf'}
IMR90_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.05, 'reg_lambda': 0.1,
'learning_rate': 0.1}
"----------------------------------------------"
IMR90_eiip_deepforest = {'max_layers': 15, 'n_estimators': 2,
'n_trees': 350}
IMR90_eiip_lightgbm = {'max_depth': 13, 'num_leaves': 331, 'max_bin': 55, 'min_child_samples': 50,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 80, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.4, 'learning_rate': 0.2, 'n_estimators': 200}
IMR90_eiip_rf = {'n_estimators': 240, 'max_depth': 78, 'min_samples_leaf': 1, 'min_samples_split': 2,
'max_features': 'auto'}
IMR90_eiip_svm = {'C': 4.0, 'gamma': 512.0, 'kernel': 'rbf'}
IMR90_eiip_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
IMR90_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 250}
IMR90_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 271, 'max_bin': 175, 'min_child_samples': 120,
'colsample_bytree': 0.8, 'subsample': 1.0, 'subsample_freq': 30, 'reg_alpha': 0.7,
'reg_lambda': 0.9, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
IMR90_kmer_rf = {'n_estimators': 280, 'max_depth': 79, 'min_samples_leaf': 2, 'min_samples_split': 3,
'max_features': 'auto'}
IMR90_kmer_svm = {'C': 2.0, 'gamma': 64.0,
'kernel': 'rbf'}
IMR90_kmer_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0.2,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
IMR90_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300}
IMR90_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 15, 'min_child_samples': 50,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
IMR90_pseknc_rf = {'n_estimators': 240, 'max_depth': 96, 'min_samples_leaf': 3, 'min_samples_split': 4,
'max_features': 'auto'}
IMR90_pseknc_svm = {'C': 4.0, 'gamma': 1024.0,
'kernel': 'rbf'}
IMR90_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.2,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
IMR90_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300}
IMR90_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 35, 'min_child_samples': 60,
'colsample_bytree': 0.6, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 0.5, 'min_split_gain': 0.1, 'learning_rate': 0.1, 'n_estimators': 100}
IMR90_tpcp_rf = {'n_estimators': 290, 'max_depth': 71, 'min_samples_leaf': 5, 'min_samples_split': 4,
'max_features': 'auto'}
IMR90_tpcp_svm = {'C': 1.0, 'gamma': 512.0, 'kernel': 'rbf'}
IMR90_tpcp_xgboost = {'n_estimators': 950, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.5,
'learning_rate': 0.1}
"=============================================="
K562_cksnap_deepforest = {"max_layers": 20, "n_estimators": 2, "n_trees": 400}
K562_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 311, 'max_bin': 225, 'min_child_samples': 60,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 250}
K562_cksnap_rf = {'n_estimators': 330, 'max_depth': 109, 'min_samples_leaf': 2, 'min_samples_split': 3,
'max_features': 'sqrt'}
K562_cksnap_svm = {'C': 16.0, 'gamma': 32.0, 'kernel': 'rbf'}
K562_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 2, 'reg_lambda': 0.05,
'learning_rate': 0.1}
"----------------------------------------------"
K562_dpcp_deepforest = {"max_layers": 10, "n_estimators": 2,
"n_trees": 150}
K562_dpcp_lightgbm = {'colsample_bytree': 0.7, 'subsample': 0.7, 'subsample_freq': 80, 'reg_alpha': 1e-05,
'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 225}
K562_dpcp_rf = {'n_estimators': 240, 'max_depth': 127, 'min_samples_leaf': 1, 'min_samples_split': 6,
'max_features': 'sqrt'}
K562_dpcp_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'}
K562_dpcp_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.05,
'learning_rate': 0.1}
"----------------------------------------------"
K562_eiip_deepforest = {'max_layers': 10, 'n_estimators': 5,
'n_trees': 150}
K562_eiip_lightgbm = {'max_depth': 0, 'num_leaves': 321, 'max_bin': 225, 'min_child_samples': 110,
'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.1, 'learning_rate': 0.1, 'n_estimators': 150}
K562_eiip_rf = {'n_estimators': 120, 'max_depth': 93, 'min_samples_leaf': 3, 'min_samples_split': 3,
'max_features': 'auto'}
K562_eiip_svm = {'C': 2.0, 'gamma': 1024.0, 'kernel': 'rbf'}
K562_eiip_xgboost = {'n_estimators': 650, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0,
'learning_rate': 0.1}
"----------------------------------------------"
K562_kmer_deepforest = {'max_layers': 15, 'n_estimators': 5,
'n_trees': 150}
K562_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 321, 'max_bin': 5, 'min_child_samples': 70,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
K562_kmer_rf = {'n_estimators': 290, 'max_depth': 137, 'min_samples_leaf': 10, 'min_samples_split': 7,
'max_features': "auto"}
K562_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
K562_kmer_xgboost = {'n_estimators': 650, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0,
'learning_rate': 0.1}
"----------------------------------------------"
K562_pseknc_deepforest = {'max_layers': 15, 'n_estimators': 2,
'n_trees': 300}
K562_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 241, 'max_bin': 65, 'min_child_samples': 200,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
K562_pseknc_rf = {'n_estimators': 250, 'max_depth': 50, 'min_samples_leaf': 1, 'min_samples_split': 6,
'max_features': 'log2'}
K562_pseknc_svm = {'C': 0.5, 'gamma': 512.0, 'kernel': 'rbf'}
K562_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.7, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1,
'learning_rate': 0.1}
"----------------------------------------------"
K562_tpcp_deepforest = {'max_layers': 20, 'n_estimators': 2,
'n_trees': 300}
K562_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 241, 'max_bin': 105, 'min_child_samples': 130,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
K562_tpcp_rf = {'n_estimators': 280, 'max_depth': 143, 'min_samples_leaf': 5, 'min_samples_split': 2,
'max_features': 'sqrt'}
K562_tpcp_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'}
K562_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 2, 'reg_lambda': 1,
'learning_rate': 0.1}
"=============================================="
NHEK_cksnap_deepforest = {"max_layers": 20, "n_estimators": 5, "n_trees": 400}
NHEK_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 205, 'min_child_samples': 90,
'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 75}
NHEK_cksnap_rf = {'n_estimators': 300, 'max_depth': 76, 'min_samples_leaf': 3, 'min_samples_split': 3,
'max_features': 'auto'}
NHEK_cksnap_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
NHEK_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
NHEK_dpcp_deepforest = {"max_layers": 10, "n_estimators": 8, "n_trees": 200}
NHEK_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 301, 'max_bin': 145, 'min_child_samples': 70,
'colsample_bytree': 0.7, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.9,
'reg_lambda': 1.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
NHEK_dpcp_rf = {'n_estimators': 300, 'max_depth': 138, 'min_samples_leaf': 1, 'min_samples_split': 5,
'max_features': 'auto'}
NHEK_dpcp_svm = {'C': 8.0, 'gamma': 16.0, 'kernel': 'rbf'}
NHEK_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.5,
'colsample_bytree': 0.7, 'subsample': 0.7, 'reg_alpha': 0, 'reg_lambda': 1,
'learning_rate': 0.1}
"----------------------------------------------"
NHEK_eiip_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 100}
NHEK_eiip_lightgbm = {'max_depth': 11, 'num_leaves': 231, 'max_bin': 255, 'min_child_samples': 70,
'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
NHEK_eiip_rf = {'n_estimators': 230, 'max_depth': 56, 'min_samples_leaf': 2, 'min_samples_split': 6,
'max_features': 'log2'}
NHEK_eiip_svm = {'C': 8.0, 'gamma': 512.0, 'kernel': 'rbf'}
NHEK_eiip_xgboost = {'n_estimators': 850, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1,
'learning_rate': 0.1}
"----------------------------------------------"
NHEK_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 200}
NHEK_kmer_lightgbm = {'max_depth': 13, 'num_leaves': 261, 'max_bin': 115, 'min_child_samples': 60,
'colsample_bytree': 0.9, 'subsample': 0.9, 'subsample_freq': 40, 'reg_alpha': 0.0,
'reg_lambda': 0.001, 'min_split_gain': 1.0, 'learning_rate': 0.1, 'n_estimators': 150}
NHEK_kmer_rf = {'n_estimators': 60, 'max_depth': 117, 'min_samples_leaf': 3, 'min_samples_split': 3,
'max_features': "auto"}
NHEK_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
NHEK_kmer_xgboost = {'n_estimators': 850, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1,
'learning_rate': 0.1}
"----------------------------------------------"
NHEK_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 150}
NHEK_pseknc_lightgbm = {'max_depth': 12, 'num_leaves': 271, 'max_bin': 155, 'min_child_samples': 20,
'colsample_bytree': 0.9, 'subsample': 0.8, 'subsample_freq': 60, 'reg_alpha': 0.1,
'reg_lambda': 1e-05, 'min_split_gain': 0.7, 'learning_rate': 0.1, 'n_estimators': 75}
NHEK_pseknc_rf = {'n_estimators': 190, 'max_depth': 85, 'min_samples_leaf': 1, 'min_samples_split': 10,
'max_features': 'auto'}
NHEK_pseknc_svm = {'C': 0.5, 'gamma': 512.0, 'kernel': 'rbf'}
NHEK_pseknc_xgboost = {'n_estimators': 950, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0.1, 'reg_lambda': 3,
'learning_rate': 0.1}
"----------------------------------------------"
NHEK_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2,
'n_trees': 200}
NHEK_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 241, 'max_bin': 15, 'min_child_samples': 90,
'colsample_bytree': 0.7, 'subsample': 0.8, 'subsample_freq': 40, 'reg_alpha': 0.001,
'reg_lambda': 0.001, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 100}
NHEK_tpcp_rf = {'n_estimators': 120, 'max_depth': 115, 'min_samples_leaf': 1, 'min_samples_split': 4,
'max_features': 'auto'}
NHEK_tpcp_svm = {'C': 1.0, 'gamma': 128.0, 'kernel': 'rbf'}
NHEK_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 0,
'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.01, 'reg_lambda': 0.01,
'learning_rate': 0.1}
class MetaModelParams:
################# GM12878 ######################
GM12878_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'identity', 'hidden_layer_sizes': 32}
GM12878_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'identity', 'hidden_layer_sizes': 8}
GM12878_6f5m_prob_logistic = {'C': 2.900000000000001}
GM12878_4f2m_prob_logistic = {'C': 0.9000000000000001}
GM12878_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 400}
GM12878_4f2m_prob_deepforest = {'max_layers': 20, 'n_estimators': 10, 'n_trees': 200}
GM12878_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 331, 'max_bin': 55, 'min_child_samples': 200,
'colsample_bytree': 0.7, 'subsample': 0.8, 'subsample_freq': 30, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1,
'n_estimators': 50}
GM12878_4f2m_prob_lightgbm = {'max_depth': 11, 'num_leaves': 311, 'max_bin': 85, 'min_child_samples': 150,
'colsample_bytree': 0.8, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1,
'n_estimators': 75}
GM12878_6f5m_prob_rf = {'n_estimators': 250, 'max_depth': 50, 'min_samples_leaf': 9, 'min_samples_split': 5,
'max_features': 'auto'}
GM12878_4f2m_prob_rf = {'n_estimators': 140, 'max_depth': 53, 'min_samples_leaf': 6, 'min_samples_split': 7,
'max_features': 'log2'}
GM12878_6f5m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'}
GM12878_4f2m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'}
GM12878_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0,
'learning_rate': 0.1}
GM12878_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 0.01,
'learning_rate': 0.05}
################# HeLa_S3 ######################
HeLa_S3_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'relu', 'hidden_layer_sizes': 32}
HeLa_S3_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd',
'activation': 'relu', 'hidden_layer_sizes': (16, 32)}
HeLa_S3_6f5m_prob_logistic = {'C': 1.9000000000000004}
HeLa_S3_4f2m_prob_logistic = {'C': 0.5000000000000001}
HeLa_S3_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 10, 'n_trees': 400}
HeLa_S3_4f2m_prob_deepforest = {'max_layers': 15, 'n_estimators': 13, 'n_trees': 400}
HeLa_S3_6f5m_prob_lightgbm = {'max_depth': 5, 'num_leaves': 281, 'max_bin': 175, 'min_child_samples': 180,
'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 80, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2,
'n_estimators': 150}
HeLa_S3_4f2m_prob_lightgbm = {'max_depth': 3, 'num_leaves': 311, 'max_bin': 35, 'min_child_samples': 20,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 1.0,
'n_estimators': 125}
HeLa_S3_6f5m_prob_rf = {'n_estimators': 130, 'max_depth': 20, 'min_samples_leaf': 2, 'min_samples_split': 3,
'max_features': 'sqrt'}
HeLa_S3_4f2m_prob_rf = {'n_estimators': 210, 'max_depth': 117, 'min_samples_leaf': 2, 'min_samples_split': 5,
'max_features': 'auto'}
HeLa_S3_6f5m_prob_svm = {'C': 0.125, 'gamma': 0.0625, 'kernel': 'rbf'}
HeLa_S3_4f2m_prob_svm = {'C': 0.25, 'gamma': 0.0625, 'kernel': 'rbf'}
HeLa_S3_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.7, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.05,
'learning_rate': 0.1}
HeLa_S3_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.05,
'learning_rate': 0.1}
################# HUVEC ########################
HUVEC_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd',
'activation': 'relu', 'hidden_layer_sizes': 8}
HUVEC_4f2m_prob_mlp = {'batch_size': 128, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'tanh', 'hidden_layer_sizes': (8, 16)}
HUVEC_6f5m_prob_logistic = {'C': 2.900000000000001}
HUVEC_4f2m_prob_logistic = {'C': 0.9000000000000001}
HUVEC_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 250}
HUVEC_4f2m_prob_deepforest = {'max_layers': 15, 'n_estimators': 13, 'n_trees': 400}
HUVEC_6f5m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 311, 'max_bin': 45, 'min_child_samples': 170,
'colsample_bytree': 0.7, 'subsample': 0.6, 'subsample_freq': 10, 'reg_alpha': 0.0,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1,
'n_estimators': 100}
HUVEC_4f2m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 261, 'max_bin': 45, 'min_child_samples': 180,
'colsample_bytree': 0.9, 'subsample': 0.8, 'subsample_freq': 10, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 200}
HUVEC_6f5m_prob_rf = {'n_estimators': 290, 'max_depth': 105, 'min_samples_leaf': 5, 'min_samples_split': 2,
'max_features': 'log2'}
HUVEC_4f2m_prob_rf = {'n_estimators': 140, 'max_depth': 76, 'min_samples_leaf': 3, 'min_samples_split': 2,
'max_features': 'log2'}
HUVEC_6f5m_prob_svm = {'C': 0.125, 'gamma': 0.0625, 'kernel': 'rbf'}
HUVEC_4f2m_prob_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'}
HUVEC_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.01, 'reg_lambda': 0.02,
'learning_rate': 0.05}
HUVEC_4f2m_prob_xgboost = {'n_estimators': 50, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.02,
'learning_rate': 0.01}
################# IMR90 ########################
IMR90_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd',
'activation': 'identity', 'hidden_layer_sizes': (16, 32)}
IMR90_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'tanh', 'hidden_layer_sizes': (8, 16)}
IMR90_6f5m_prob_logistic = {'C': 2.5000000000000004}
IMR90_4f2m_prob_logistic = {'C': 2.5000000000000004}
IMR90_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 8, 'n_trees': 300}
IMR90_4f2m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 200}
IMR90_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 341, 'max_bin': 85, 'min_child_samples': 70,
'colsample_bytree': 0.9, 'subsample': 1.0, 'subsample_freq': 40, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
IMR90_4f2m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 321, 'max_bin': 55, 'min_child_samples': 60,
'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 30, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 175}
IMR90_6f5m_prob_rf = {'n_estimators': 340, 'max_depth': 9, 'min_samples_leaf': 7, 'min_samples_split': 3,
'max_features': 'log2'}
IMR90_4f2m_prob_rf = {'n_estimators': 270, 'max_depth': 120, 'min_samples_leaf': 10, 'min_samples_split': 7,
'max_features': 'log2'}
IMR90_6f5m_prob_svm = {'C': 1.0, 'gamma': 32.0, 'kernel': 'rbf'}
IMR90_4f2m_prob_svm = {'C': 2.0, 'gamma': 32.0, 'kernel': 'rbf'}
IMR90_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.9, 'reg_alpha': 0, 'reg_lambda': 0,
'learning_rate': 0.05}
IMR90_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01,
'learning_rate': 0.07}
################# K562 #########################
K562_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd',
'activation': 'logistic', 'hidden_layer_sizes': (8, 16)}
K562_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'tanh', 'hidden_layer_sizes': 8}
K562_6f5m_prob_logistic = {'C': 2.900000000000001}
K562_4f2m_prob_logistic = {'C': 0.1}
K562_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 400}
K562_4f2m_prob_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 300}
K562_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 65, 'min_child_samples': 80,
'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 30, 'reg_alpha': 1e-05,
'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.07,
'n_estimators': 75}
K562_4f2m_prob_lightgbm = {'max_depth': 13, 'num_leaves': 281, 'max_bin': 25, 'min_child_samples': 80,
'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 60, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.75, 'n_estimators': 175}
K562_6f5m_prob_rf = {'n_estimators': 180, 'max_depth': 35, 'min_samples_leaf': 7, 'min_samples_split': 5,
'max_features': 'log2'}
K562_4f2m_prob_rf = {'n_estimators': 80, 'max_depth': 130, 'min_samples_leaf': 6, 'min_samples_split': 5,
'max_features': 'log2'}
K562_6f5m_prob_svm = {'C': 0.5, 'gamma': 0.0625, 'kernel': 'rbf'}
K562_4f2m_prob_svm = {'C': 1.0, 'gamma': 0.0625, 'kernel': 'rbf'}
K562_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 6, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01,
'learning_rate': 0.1}
K562_4f2m_prob_xgboost = {'n_estimators': 50, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0,
'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 0.01,
'learning_rate': 0.01}
################# NHEK #########################
NHEK_6f5m_prob_mlp = {'batch_size': 128, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs',
'activation': 'identity', 'hidden_layer_sizes': 32}
NHEK_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd',
'activation': 'relu', 'hidden_layer_sizes': (16, 32)}
NHEK_6f5m_prob_logistic = {'C': 0.9000000000000001}
NHEK_4f2m_prob_logistic = {'C': 0.1}
NHEK_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 50}
NHEK_4f2m_prob_deepforest = {'max_layers': 20, 'n_estimators': 10, 'n_trees': 50}
NHEK_6f5m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 291, 'max_bin': 45, 'min_child_samples': 140,
'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 70, 'reg_alpha': 1.0,
'reg_lambda': 0.7, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
NHEK_4f2m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 331, 'max_bin': 35, 'min_child_samples': 100,
'colsample_bytree': 0.8, 'subsample': 0.9, 'subsample_freq': 60, 'reg_alpha': 0.0,
'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.07, 'n_estimators': 100}
NHEK_6f5m_prob_rf = {'n_estimators': 70, 'max_depth': 106, 'min_samples_leaf': 10, 'min_samples_split': 9,
'max_features': 'log2'}
NHEK_4f2m_prob_rf = {'n_estimators': 130, 'max_depth': 9, 'min_samples_leaf': 7, 'min_samples_split': 4,
'max_features': 'sqrt'}
NHEK_6f5m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'}
NHEK_4f2m_prob_svm = {'C': 2.0, 'gamma': 16.0, 'kernel': 'rbf'}
NHEK_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0,
'colsample_bytree': 0.9, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0,
'learning_rate': 0.07}
NHEK_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0.4,
'colsample_bytree': 0.6, 'subsample': 0.7, 'reg_alpha': 0.05, 'reg_lambda': 1,
'learning_rate': 1.0}
if __name__ == '_main_':
print(getattr(EPIconst.BaseModelParams, "NHEK_tpcp_deepforest"))
| class Epiconst:
class Featurename:
pseknc = 'pseknc'
cksnap = 'cksnap'
dpcp = 'dpcp'
eiip = 'eiip'
kmer = 'kmer'
tpcp = 'tpcp'
all = sorted([pseknc, cksnap, dpcp, eiip, kmer, tpcp])
class Cellname:
k562 = 'K562'
nhek = 'NHEK'
imr90 = 'IMR90'
he_la_s3 = 'HeLa-S3'
huvec = 'HUVEC'
gm12878 = 'GM12878'
all = sorted([GM12878, HeLa_S3, HUVEC, IMR90, K562, NHEK])
class Methodname:
ensemble = 'meta'
xgboost = 'xgboost'
svm = 'svm'
deepforest = 'deepforest'
lightgbm = 'lightgbm'
rf = 'rf'
all = sorted([lightgbm, rf, xgboost, svm, deepforest])
class Modelinitparams:
logistic = {'n_jobs': 13}
mlp = {}
deepforest = {'n_jobs': 13, 'use_predictor': False, 'random_state': 1, 'predictor': 'forest', 'verbose': 0}
lightgbm = {'n_jobs': 13, 'max_depth': -1, 'num_leaves': 31, 'min_child_samples': 20, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'objective': None, 'n_estimators': 100, 'learning_rate': 0.1, 'device': 'gpu', 'boosting_type': 'gbdt', 'class_weight': None, 'importance_type': 'split', 'min_child_weight': 0.001, 'random_state': None, 'subsample_for_bin': 200000, 'silent': True}
rf = {'n_jobs': 13, 'n_estimators': 100, 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'auto'}
svm = {'probability': True}
xgboost = {'learning_rate': 0.1, 'n_estimators': 500, 'max_depth': 5, 'min_child_weight': 1, 'seed': 0, 'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1, 'use_label_encoder': False, 'eval_metric': 'logloss', 'tree_method': 'gpu_hist'}
class Basemodelparams:
gm12878_cksnap_deepforest = {'max_layers': 20, 'n_estimators': 5, 'n_trees': 250}
gm12878_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 125, 'min_child_samples': 90, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
gm12878_cksnap_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
gm12878_cksnap_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1}
gm12878_cksnap_rf = {'n_estimators': 340, 'max_depth': 114, 'min_samples_leaf': 3, 'min_samples_split': 2, 'max_features': 'sqrt'}
'----------------------------------------------'
gm12878_dpcp_deepforest = {'max_layers': 20, 'n_estimators': 2, 'n_trees': 300}
gm12878_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 331, 'max_bin': 135, 'min_child_samples': 190, 'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.9, 'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
gm12878_dpcp_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'}
gm12878_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 3, 'reg_lambda': 3, 'learning_rate': 0.1}
gm12878_dpcp_rf = {'n_estimators': 150, 'max_depth': 88, 'min_samples_leaf': 1, 'min_samples_split': 3, 'max_features': 'sqrt'}
'----------------------------------------------'
gm12878_eiip_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300}
gm12878_eiip_lightgbm = {'max_depth': 12, 'num_leaves': 291, 'max_bin': 115, 'min_child_samples': 40, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
gm12878_eiip_rf = {'n_estimators': 280, 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 7, 'max_features': 'sqrt'}
gm12878_eiip_svm = {'C': 1.0, 'gamma': 2048.0, 'kernel': 'rbf'}
gm12878_eiip_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
gm12878_kmer_deepforest = {'max_layers': 25, 'n_estimators': 5, 'n_trees': 400}
gm12878_kmer_lightgbm = {'max_depth': 12, 'num_leaves': 291, 'max_bin': 115, 'min_child_samples': 40, 'colsample_bytree': 1.0, 'subsample': 0.8, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
gm12878_kmer_rf = {'n_estimators': 170, 'max_depth': 41, 'min_samples_leaf': 3, 'min_samples_split': 2, 'max_features': 'sqrt'}
gm12878_kmer_svm = {'C': 2.0, 'gamma': 128.0, 'kernel': 'rbf'}
gm12878_kmer_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
gm12878_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400}
gm12878_pseknc_lightgbm = {'max_depth': 11, 'num_leaves': 291, 'max_bin': 185, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 40, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
gm12878_pseknc_rf = {'n_estimators': 250, 'max_depth': 41, 'min_samples_leaf': 2, 'min_samples_split': 6, 'max_features': 'log2'}
gm12878_pseknc_svm = {'C': 0.5, 'gamma': 1024.0, 'kernel': 'rbf'}
gm12878_pseknc_xgboost = {'n_estimators': 950, 'max_depth': 6, 'min_child_weight': 1, 'gamma': 0.1, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.1}
'----------------------------------------------'
gm12878_tpcp_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 100}
gm12878_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 321, 'max_bin': 175, 'min_child_samples': 80, 'colsample_bytree': 0.9, 'subsample': 1.0, 'subsample_freq': 20, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
gm12878_tpcp_rf = {'n_estimators': 250, 'max_depth': 89, 'min_samples_leaf': 2, 'min_samples_split': 9, 'max_features': 'log2'}
gm12878_tpcp_svm = {'C': 16.0, 'gamma': 64.0, 'kernel': 'rbf'}
gm12878_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'=============================================='
he_la_s3_cksnap_deepforest = {'max_layers': 20, 'n_estimators': 2, 'n_trees': 300}
he_la_s3_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 341, 'max_bin': 105, 'min_child_samples': 80, 'colsample_bytree': 0.9, 'subsample': 0.9, 'subsample_freq': 40, 'reg_alpha': 0.1, 'reg_lambda': 0.1, 'min_split_gain': 0.4, 'learning_rate': 0.1, 'n_estimators': 150}
he_la_s3_cksnap_svm = {'C': 128.0, 'gamma': 128.0, 'kernel': 'rbf'}
he_la_s3_cksnap_rf = {'n_estimators': 340, 'max_depth': 44, 'min_samples_leaf': 1, 'min_samples_split': 5, 'max_features': 'sqrt'}
he_la_s3_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.7, 'subsample': 0.7, 'reg_alpha': 3, 'reg_lambda': 0.5, 'learning_rate': 0.1}
'----------------------------------------------'
he_la_s3_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400}
he_la_s3_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 221, 'max_bin': 155, 'min_child_samples': 180, 'colsample_bytree': 0.7, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 200}
he_la_s3_dpcp_rf = {'n_estimators': 70, 'max_depth': 32, 'min_samples_leaf': 1, 'min_samples_split': 8, 'max_features': 'sqrt'}
he_la_s3_dpcp_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'}
he_la_s3_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
he_la_s3_eiip_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200}
he_la_s3_eiip_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 5, 'min_child_samples': 110, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 100}
he_la_s3_eiip_rf = {'n_estimators': 180, 'max_depth': 138, 'min_samples_leaf': 6, 'min_samples_split': 10, 'max_features': 'sqrt'}
he_la_s3_eiip_svm = {'C': 2.0, 'gamma': 1024.0, 'kernel': 'rbf'}
he_la_s3_eiip_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
he_la_s3_kmer_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200}
he_la_s3_kmer_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 165, 'min_child_samples': 90, 'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 70, 'reg_alpha': 0.001, 'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 125}
he_la_s3_kmer_rf = {'n_estimators': 240, 'max_depth': 77, 'min_samples_leaf': 2, 'min_samples_split': 2, 'max_features': 'sqrt'}
he_la_s3_kmer_svm = {'C': 8.0, 'gamma': 128.0, 'kernel': 'rbf'}
he_la_s3_kmer_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
he_la_s3_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 200}
he_la_s3_pseknc_lightgbm = {'max_depth': 12, 'num_leaves': 261, 'max_bin': 25, 'min_child_samples': 90, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
he_la_s3_pseknc_rf = {'n_estimators': 330, 'max_depth': 118, 'min_samples_leaf': 1, 'min_samples_split': 8, 'max_features': 'log2'}
he_la_s3_pseknc_svm = {'C': 1.0, 'gamma': 256.0, 'kernel': 'rbf'}
he_la_s3_pseknc_xgboost = {'n_estimators': 750, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.1, 'reg_lambda': 2, 'learning_rate': 0.1}
'----------------------------------------------'
he_la_s3_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 250}
he_la_s3_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 341, 'max_bin': 45, 'min_child_samples': 10, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 1e-05, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 250}
he_la_s3_tpcp_rf = {'n_estimators': 320, 'max_depth': 99, 'min_samples_leaf': 1, 'min_samples_split': 10, 'max_features': 'sqrt'}
he_la_s3_tpcp_svm = {'C': 4.0, 'gamma': 32.0, 'kernel': 'rbf'}
he_la_s3_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 7, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'=============================================='
huvec_cksnap_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200}
huvec_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 271, 'max_bin': 45, 'min_child_samples': 10, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 0.5, 'reg_lambda': 0.5, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 175}
huvec_cksnap_rf = {'n_estimators': 270, 'max_depth': 38, 'min_samples_leaf': 2, 'min_samples_split': 2, 'max_features': 'auto'}
huvec_cksnap_svm = {'C': 8.0, 'gamma': 64.0, 'kernel': 'rbf'}
huvec_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.7, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
huvec_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400}
huvec_dpcp_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 245, 'min_child_samples': 30, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.5, 'reg_lambda': 0.3, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
huvec_dpcp_rf = {'n_estimators': 300, 'max_depth': 61, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'log2'}
huvec_dpcp_svm = {'C': 4.0, 'gamma': 16.0, 'kernel': 'rbf'}
huvec_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 3, 'reg_lambda': 3, 'learning_rate': 0.1}
'----------------------------------------------'
huvec_eiip_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 300}
huvec_eiip_lightgbm = {'max_depth': -1, 'num_leaves': 281, 'max_bin': 25, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
huvec_eiip_rf = {'n_estimators': 310, 'max_depth': 28, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 'sqrt'}
huvec_eiip_svm = {'C': 4.0, 'gamma': 512.0, 'kernel': 'rbf'}
huvec_eiip_xgboost = {'n_estimators': 600, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1}
'----------------------------------------------'
huvec_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300}
huvec_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 251, 'max_bin': 5, 'min_child_samples': 170, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 0.5, 'reg_lambda': 0.7, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 125}
huvec_kmer_rf = {'n_estimators': 230, 'max_depth': 59, 'min_samples_leaf': 1, 'min_samples_split': 4, 'max_features': 'auto'}
huvec_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
huvec_kmer_xgboost = {'n_estimators': 600, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1}
'----------------------------------------------'
huvec_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 400}
huvec_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 311, 'max_bin': 115, 'min_child_samples': 190, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 175}
huvec_pseknc_rf = {'n_estimators': 310, 'max_depth': 42, 'min_samples_leaf': 2, 'min_samples_split': 7, 'max_features': 'sqrt'}
huvec_pseknc_svm = {'C': 1.0, 'gamma': 256.0, 'kernel': 'rbf'}
huvec_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
huvec_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 150}
huvec_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 251, 'max_bin': 35, 'min_child_samples': 190, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
huvec_tpcp_rf = {'n_estimators': 330, 'max_depth': 121, 'min_samples_leaf': 2, 'min_samples_split': 5, 'max_features': 'sqrt'}
huvec_tpcp_svm = {'C': 2.0, 'gamma': 32.0, 'kernel': 'rbf'}
huvec_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.9, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'=============================================='
imr90_cksnap_deepforest = {'max_layers': 20, 'n_estimators': 2, 'n_trees': 250}
imr90_cksnap_lightgbm = {'max_depth': 0, 'num_leaves': 271, 'max_bin': 95, 'min_child_samples': 60, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.3, 'learning_rate': 0.1, 'n_estimators': 225}
imr90_cksnap_rf = {'n_estimators': 280, 'max_depth': 124, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 'auto'}
imr90_cksnap_svm = {'C': 16.0, 'gamma': 16.0, 'kernel': 'rbf'}
imr90_cksnap_xgboost = {'n_estimators': 900, 'max_depth': 10, 'min_child_weight': 2, 'gamma': 0.4, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0.1, 'learning_rate': 0.1}
'----------------------------------------------'
imr90_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200}
imr90_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 281, 'max_bin': 115, 'min_child_samples': 20, 'colsample_bytree': 0.7, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.5, 'learning_rate': 0.1, 'n_estimators': 125}
imr90_dpcp_rf = {'n_estimators': 70, 'max_depth': 116, 'min_samples_leaf': 1, 'min_samples_split': 9, 'max_features': 'log2'}
imr90_dpcp_svm = {'C': 1.0, 'gamma': 32.0, 'kernel': 'rbf'}
imr90_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.05, 'reg_lambda': 0.1, 'learning_rate': 0.1}
'----------------------------------------------'
imr90_eiip_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 350}
imr90_eiip_lightgbm = {'max_depth': 13, 'num_leaves': 331, 'max_bin': 55, 'min_child_samples': 50, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 80, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.4, 'learning_rate': 0.2, 'n_estimators': 200}
imr90_eiip_rf = {'n_estimators': 240, 'max_depth': 78, 'min_samples_leaf': 1, 'min_samples_split': 2, 'max_features': 'auto'}
imr90_eiip_svm = {'C': 4.0, 'gamma': 512.0, 'kernel': 'rbf'}
imr90_eiip_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
imr90_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 250}
imr90_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 271, 'max_bin': 175, 'min_child_samples': 120, 'colsample_bytree': 0.8, 'subsample': 1.0, 'subsample_freq': 30, 'reg_alpha': 0.7, 'reg_lambda': 0.9, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
imr90_kmer_rf = {'n_estimators': 280, 'max_depth': 79, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'auto'}
imr90_kmer_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'}
imr90_kmer_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 2, 'gamma': 0.2, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
imr90_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300}
imr90_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 15, 'min_child_samples': 50, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
imr90_pseknc_rf = {'n_estimators': 240, 'max_depth': 96, 'min_samples_leaf': 3, 'min_samples_split': 4, 'max_features': 'auto'}
imr90_pseknc_svm = {'C': 4.0, 'gamma': 1024.0, 'kernel': 'rbf'}
imr90_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0.2, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
imr90_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 300}
imr90_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 35, 'min_child_samples': 60, 'colsample_bytree': 0.6, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.5, 'min_split_gain': 0.1, 'learning_rate': 0.1, 'n_estimators': 100}
imr90_tpcp_rf = {'n_estimators': 290, 'max_depth': 71, 'min_samples_leaf': 5, 'min_samples_split': 4, 'max_features': 'auto'}
imr90_tpcp_svm = {'C': 1.0, 'gamma': 512.0, 'kernel': 'rbf'}
imr90_tpcp_xgboost = {'n_estimators': 950, 'max_depth': 7, 'min_child_weight': 5, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.5, 'learning_rate': 0.1}
'=============================================='
k562_cksnap_deepforest = {'max_layers': 20, 'n_estimators': 2, 'n_trees': 400}
k562_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 311, 'max_bin': 225, 'min_child_samples': 60, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 250}
k562_cksnap_rf = {'n_estimators': 330, 'max_depth': 109, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'sqrt'}
k562_cksnap_svm = {'C': 16.0, 'gamma': 32.0, 'kernel': 'rbf'}
k562_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 10, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 2, 'reg_lambda': 0.05, 'learning_rate': 0.1}
'----------------------------------------------'
k562_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 150}
k562_dpcp_lightgbm = {'colsample_bytree': 0.7, 'subsample': 0.7, 'subsample_freq': 80, 'reg_alpha': 1e-05, 'reg_lambda': 0.001, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 225}
k562_dpcp_rf = {'n_estimators': 240, 'max_depth': 127, 'min_samples_leaf': 1, 'min_samples_split': 6, 'max_features': 'sqrt'}
k562_dpcp_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'}
k562_dpcp_xgboost = {'n_estimators': 950, 'max_depth': 10, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.05, 'learning_rate': 0.1}
'----------------------------------------------'
k562_eiip_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 150}
k562_eiip_lightgbm = {'max_depth': 0, 'num_leaves': 321, 'max_bin': 225, 'min_child_samples': 110, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.1, 'learning_rate': 0.1, 'n_estimators': 150}
k562_eiip_rf = {'n_estimators': 120, 'max_depth': 93, 'min_samples_leaf': 3, 'min_samples_split': 3, 'max_features': 'auto'}
k562_eiip_svm = {'C': 2.0, 'gamma': 1024.0, 'kernel': 'rbf'}
k562_eiip_xgboost = {'n_estimators': 650, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0, 'learning_rate': 0.1}
'----------------------------------------------'
k562_kmer_deepforest = {'max_layers': 15, 'n_estimators': 5, 'n_trees': 150}
k562_kmer_lightgbm = {'max_depth': 0, 'num_leaves': 321, 'max_bin': 5, 'min_child_samples': 70, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
k562_kmer_rf = {'n_estimators': 290, 'max_depth': 137, 'min_samples_leaf': 10, 'min_samples_split': 7, 'max_features': 'auto'}
k562_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
k562_kmer_xgboost = {'n_estimators': 650, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.6, 'reg_alpha': 0.5, 'reg_lambda': 0, 'learning_rate': 0.1}
'----------------------------------------------'
k562_pseknc_deepforest = {'max_layers': 15, 'n_estimators': 2, 'n_trees': 300}
k562_pseknc_lightgbm = {'max_depth': -1, 'num_leaves': 241, 'max_bin': 65, 'min_child_samples': 200, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
k562_pseknc_rf = {'n_estimators': 250, 'max_depth': 50, 'min_samples_leaf': 1, 'min_samples_split': 6, 'max_features': 'log2'}
k562_pseknc_svm = {'C': 0.5, 'gamma': 512.0, 'kernel': 'rbf'}
k562_pseknc_xgboost = {'n_estimators': 1000, 'max_depth': 8, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.7, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1, 'learning_rate': 0.1}
'----------------------------------------------'
k562_tpcp_deepforest = {'max_layers': 20, 'n_estimators': 2, 'n_trees': 300}
k562_tpcp_lightgbm = {'max_depth': -1, 'num_leaves': 241, 'max_bin': 105, 'min_child_samples': 130, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
k562_tpcp_rf = {'n_estimators': 280, 'max_depth': 143, 'min_samples_leaf': 5, 'min_samples_split': 2, 'max_features': 'sqrt'}
k562_tpcp_svm = {'C': 2.0, 'gamma': 64.0, 'kernel': 'rbf'}
k562_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 12, 'min_child_weight': 4, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 2, 'reg_lambda': 1, 'learning_rate': 0.1}
'=============================================='
nhek_cksnap_deepforest = {'max_layers': 20, 'n_estimators': 5, 'n_trees': 400}
nhek_cksnap_lightgbm = {'max_depth': -1, 'num_leaves': 291, 'max_bin': 205, 'min_child_samples': 90, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 75}
nhek_cksnap_rf = {'n_estimators': 300, 'max_depth': 76, 'min_samples_leaf': 3, 'min_samples_split': 3, 'max_features': 'auto'}
nhek_cksnap_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
nhek_cksnap_xgboost = {'n_estimators': 1000, 'max_depth': 5, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
nhek_dpcp_deepforest = {'max_layers': 10, 'n_estimators': 8, 'n_trees': 200}
nhek_dpcp_lightgbm = {'max_depth': 0, 'num_leaves': 301, 'max_bin': 145, 'min_child_samples': 70, 'colsample_bytree': 0.7, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.9, 'reg_lambda': 1.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 150}
nhek_dpcp_rf = {'n_estimators': 300, 'max_depth': 138, 'min_samples_leaf': 1, 'min_samples_split': 5, 'max_features': 'auto'}
nhek_dpcp_svm = {'C': 8.0, 'gamma': 16.0, 'kernel': 'rbf'}
nhek_dpcp_xgboost = {'n_estimators': 1000, 'max_depth': 9, 'min_child_weight': 3, 'gamma': 0.5, 'colsample_bytree': 0.7, 'subsample': 0.7, 'reg_alpha': 0, 'reg_lambda': 1, 'learning_rate': 0.1}
'----------------------------------------------'
nhek_eiip_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 100}
nhek_eiip_lightgbm = {'max_depth': 11, 'num_leaves': 231, 'max_bin': 255, 'min_child_samples': 70, 'colsample_bytree': 1.0, 'subsample': 0.6, 'subsample_freq': 0, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
nhek_eiip_rf = {'n_estimators': 230, 'max_depth': 56, 'min_samples_leaf': 2, 'min_samples_split': 6, 'max_features': 'log2'}
nhek_eiip_svm = {'C': 8.0, 'gamma': 512.0, 'kernel': 'rbf'}
nhek_eiip_xgboost = {'n_estimators': 850, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1, 'learning_rate': 0.1}
'----------------------------------------------'
nhek_kmer_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200}
nhek_kmer_lightgbm = {'max_depth': 13, 'num_leaves': 261, 'max_bin': 115, 'min_child_samples': 60, 'colsample_bytree': 0.9, 'subsample': 0.9, 'subsample_freq': 40, 'reg_alpha': 0.0, 'reg_lambda': 0.001, 'min_split_gain': 1.0, 'learning_rate': 0.1, 'n_estimators': 150}
nhek_kmer_rf = {'n_estimators': 60, 'max_depth': 117, 'min_samples_leaf': 3, 'min_samples_split': 3, 'max_features': 'auto'}
nhek_kmer_svm = {'C': 4.0, 'gamma': 64.0, 'kernel': 'rbf'}
nhek_kmer_xgboost = {'n_estimators': 850, 'max_depth': 9, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 1, 'reg_lambda': 0.1, 'learning_rate': 0.1}
'----------------------------------------------'
nhek_pseknc_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 150}
nhek_pseknc_lightgbm = {'max_depth': 12, 'num_leaves': 271, 'max_bin': 155, 'min_child_samples': 20, 'colsample_bytree': 0.9, 'subsample': 0.8, 'subsample_freq': 60, 'reg_alpha': 0.1, 'reg_lambda': 1e-05, 'min_split_gain': 0.7, 'learning_rate': 0.1, 'n_estimators': 75}
nhek_pseknc_rf = {'n_estimators': 190, 'max_depth': 85, 'min_samples_leaf': 1, 'min_samples_split': 10, 'max_features': 'auto'}
nhek_pseknc_svm = {'C': 0.5, 'gamma': 512.0, 'kernel': 'rbf'}
nhek_pseknc_xgboost = {'n_estimators': 950, 'max_depth': 6, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0.1, 'reg_lambda': 3, 'learning_rate': 0.1}
'----------------------------------------------'
nhek_tpcp_deepforest = {'max_layers': 10, 'n_estimators': 2, 'n_trees': 200}
nhek_tpcp_lightgbm = {'max_depth': 0, 'num_leaves': 241, 'max_bin': 15, 'min_child_samples': 90, 'colsample_bytree': 0.7, 'subsample': 0.8, 'subsample_freq': 40, 'reg_alpha': 0.001, 'reg_lambda': 0.001, 'min_split_gain': 0.2, 'learning_rate': 0.1, 'n_estimators': 100}
nhek_tpcp_rf = {'n_estimators': 120, 'max_depth': 115, 'min_samples_leaf': 1, 'min_samples_split': 4, 'max_features': 'auto'}
nhek_tpcp_svm = {'C': 1.0, 'gamma': 128.0, 'kernel': 'rbf'}
nhek_tpcp_xgboost = {'n_estimators': 1000, 'max_depth': 7, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.8, 'subsample': 0.8, 'reg_alpha': 0.01, 'reg_lambda': 0.01, 'learning_rate': 0.1}
class Metamodelparams:
gm12878_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'identity', 'hidden_layer_sizes': 32}
gm12878_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'identity', 'hidden_layer_sizes': 8}
gm12878_6f5m_prob_logistic = {'C': 2.900000000000001}
gm12878_4f2m_prob_logistic = {'C': 0.9000000000000001}
gm12878_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 400}
gm12878_4f2m_prob_deepforest = {'max_layers': 20, 'n_estimators': 10, 'n_trees': 200}
gm12878_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 331, 'max_bin': 55, 'min_child_samples': 200, 'colsample_bytree': 0.7, 'subsample': 0.8, 'subsample_freq': 30, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 50}
gm12878_4f2m_prob_lightgbm = {'max_depth': 11, 'num_leaves': 311, 'max_bin': 85, 'min_child_samples': 150, 'colsample_bytree': 0.8, 'subsample': 1.0, 'subsample_freq': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 75}
gm12878_6f5m_prob_rf = {'n_estimators': 250, 'max_depth': 50, 'min_samples_leaf': 9, 'min_samples_split': 5, 'max_features': 'auto'}
gm12878_4f2m_prob_rf = {'n_estimators': 140, 'max_depth': 53, 'min_samples_leaf': 6, 'min_samples_split': 7, 'max_features': 'log2'}
gm12878_6f5m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'}
gm12878_4f2m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'}
gm12878_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.1}
gm12878_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.05}
he_la_s3_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'relu', 'hidden_layer_sizes': 32}
he_la_s3_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'relu', 'hidden_layer_sizes': (16, 32)}
he_la_s3_6f5m_prob_logistic = {'C': 1.9000000000000004}
he_la_s3_4f2m_prob_logistic = {'C': 0.5000000000000001}
he_la_s3_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 10, 'n_trees': 400}
he_la_s3_4f2m_prob_deepforest = {'max_layers': 15, 'n_estimators': 13, 'n_trees': 400}
he_la_s3_6f5m_prob_lightgbm = {'max_depth': 5, 'num_leaves': 281, 'max_bin': 175, 'min_child_samples': 180, 'colsample_bytree': 1.0, 'subsample': 0.7, 'subsample_freq': 80, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 150}
he_la_s3_4f2m_prob_lightgbm = {'max_depth': 3, 'num_leaves': 311, 'max_bin': 35, 'min_child_samples': 20, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 70, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 1.0, 'n_estimators': 125}
he_la_s3_6f5m_prob_rf = {'n_estimators': 130, 'max_depth': 20, 'min_samples_leaf': 2, 'min_samples_split': 3, 'max_features': 'sqrt'}
he_la_s3_4f2m_prob_rf = {'n_estimators': 210, 'max_depth': 117, 'min_samples_leaf': 2, 'min_samples_split': 5, 'max_features': 'auto'}
he_la_s3_6f5m_prob_svm = {'C': 0.125, 'gamma': 0.0625, 'kernel': 'rbf'}
he_la_s3_4f2m_prob_svm = {'C': 0.25, 'gamma': 0.0625, 'kernel': 'rbf'}
he_la_s3_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.7, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'learning_rate': 0.1}
he_la_s3_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.05, 'learning_rate': 0.1}
huvec_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'relu', 'hidden_layer_sizes': 8}
huvec_4f2m_prob_mlp = {'batch_size': 128, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'tanh', 'hidden_layer_sizes': (8, 16)}
huvec_6f5m_prob_logistic = {'C': 2.900000000000001}
huvec_4f2m_prob_logistic = {'C': 0.9000000000000001}
huvec_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 250}
huvec_4f2m_prob_deepforest = {'max_layers': 15, 'n_estimators': 13, 'n_trees': 400}
huvec_6f5m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 311, 'max_bin': 45, 'min_child_samples': 170, 'colsample_bytree': 0.7, 'subsample': 0.6, 'subsample_freq': 10, 'reg_alpha': 0.0, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 100}
huvec_4f2m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 261, 'max_bin': 45, 'min_child_samples': 180, 'colsample_bytree': 0.9, 'subsample': 0.8, 'subsample_freq': 10, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 200}
huvec_6f5m_prob_rf = {'n_estimators': 290, 'max_depth': 105, 'min_samples_leaf': 5, 'min_samples_split': 2, 'max_features': 'log2'}
huvec_4f2m_prob_rf = {'n_estimators': 140, 'max_depth': 76, 'min_samples_leaf': 3, 'min_samples_split': 2, 'max_features': 'log2'}
huvec_6f5m_prob_svm = {'C': 0.125, 'gamma': 0.0625, 'kernel': 'rbf'}
huvec_4f2m_prob_svm = {'C': 1.0, 'gamma': 64.0, 'kernel': 'rbf'}
huvec_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.01, 'reg_lambda': 0.02, 'learning_rate': 0.05}
huvec_4f2m_prob_xgboost = {'n_estimators': 50, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0.05, 'reg_lambda': 0.02, 'learning_rate': 0.01}
imr90_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'identity', 'hidden_layer_sizes': (16, 32)}
imr90_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 5e-06, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'tanh', 'hidden_layer_sizes': (8, 16)}
imr90_6f5m_prob_logistic = {'C': 2.5000000000000004}
imr90_4f2m_prob_logistic = {'C': 2.5000000000000004}
imr90_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 8, 'n_trees': 300}
imr90_4f2m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 200}
imr90_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 341, 'max_bin': 85, 'min_child_samples': 70, 'colsample_bytree': 0.9, 'subsample': 1.0, 'subsample_freq': 40, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 250}
imr90_4f2m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 321, 'max_bin': 55, 'min_child_samples': 60, 'colsample_bytree': 0.7, 'subsample': 0.9, 'subsample_freq': 30, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.2, 'n_estimators': 175}
imr90_6f5m_prob_rf = {'n_estimators': 340, 'max_depth': 9, 'min_samples_leaf': 7, 'min_samples_split': 3, 'max_features': 'log2'}
imr90_4f2m_prob_rf = {'n_estimators': 270, 'max_depth': 120, 'min_samples_leaf': 10, 'min_samples_split': 7, 'max_features': 'log2'}
imr90_6f5m_prob_svm = {'C': 1.0, 'gamma': 32.0, 'kernel': 'rbf'}
imr90_4f2m_prob_svm = {'C': 2.0, 'gamma': 32.0, 'kernel': 'rbf'}
imr90_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.9, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.05}
imr90_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.07}
k562_6f5m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'logistic', 'hidden_layer_sizes': (8, 16)}
k562_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'tanh', 'hidden_layer_sizes': 8}
k562_6f5m_prob_logistic = {'C': 2.900000000000001}
k562_4f2m_prob_logistic = {'C': 0.1}
k562_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 400}
k562_4f2m_prob_deepforest = {'max_layers': 10, 'n_estimators': 5, 'n_trees': 300}
k562_6f5m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 301, 'max_bin': 65, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 1.0, 'subsample_freq': 30, 'reg_alpha': 1e-05, 'reg_lambda': 1e-05, 'min_split_gain': 0.0, 'learning_rate': 0.07, 'n_estimators': 75}
k562_4f2m_prob_lightgbm = {'max_depth': 13, 'num_leaves': 281, 'max_bin': 25, 'min_child_samples': 80, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 60, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.75, 'n_estimators': 175}
k562_6f5m_prob_rf = {'n_estimators': 180, 'max_depth': 35, 'min_samples_leaf': 7, 'min_samples_split': 5, 'max_features': 'log2'}
k562_4f2m_prob_rf = {'n_estimators': 80, 'max_depth': 130, 'min_samples_leaf': 6, 'min_samples_split': 5, 'max_features': 'log2'}
k562_6f5m_prob_svm = {'C': 0.5, 'gamma': 0.0625, 'kernel': 'rbf'}
k562_4f2m_prob_svm = {'C': 1.0, 'gamma': 0.0625, 'kernel': 'rbf'}
k562_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 6, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.1}
k562_4f2m_prob_xgboost = {'n_estimators': 50, 'max_depth': 3, 'min_child_weight': 3, 'gamma': 0, 'colsample_bytree': 0.6, 'subsample': 0.6, 'reg_alpha': 0, 'reg_lambda': 0.01, 'learning_rate': 0.01}
nhek_6f5m_prob_mlp = {'batch_size': 128, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'lbfgs', 'activation': 'identity', 'hidden_layer_sizes': 32}
nhek_4f2m_prob_mlp = {'batch_size': 64, 'learning_rate_init': 0.0001, 'max_iter': 300, 'solver': 'sgd', 'activation': 'relu', 'hidden_layer_sizes': (16, 32)}
nhek_6f5m_prob_logistic = {'C': 0.9000000000000001}
nhek_4f2m_prob_logistic = {'C': 0.1}
nhek_6f5m_prob_deepforest = {'max_layers': 10, 'n_estimators': 13, 'n_trees': 50}
nhek_4f2m_prob_deepforest = {'max_layers': 20, 'n_estimators': 10, 'n_trees': 50}
nhek_6f5m_prob_lightgbm = {'max_depth': 0, 'num_leaves': 291, 'max_bin': 45, 'min_child_samples': 140, 'colsample_bytree': 1.0, 'subsample': 0.9, 'subsample_freq': 70, 'reg_alpha': 1.0, 'reg_lambda': 0.7, 'min_split_gain': 0.0, 'learning_rate': 0.1, 'n_estimators': 200}
nhek_4f2m_prob_lightgbm = {'max_depth': -1, 'num_leaves': 331, 'max_bin': 35, 'min_child_samples': 100, 'colsample_bytree': 0.8, 'subsample': 0.9, 'subsample_freq': 60, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'min_split_gain': 0.0, 'learning_rate': 0.07, 'n_estimators': 100}
nhek_6f5m_prob_rf = {'n_estimators': 70, 'max_depth': 106, 'min_samples_leaf': 10, 'min_samples_split': 9, 'max_features': 'log2'}
nhek_4f2m_prob_rf = {'n_estimators': 130, 'max_depth': 9, 'min_samples_leaf': 7, 'min_samples_split': 4, 'max_features': 'sqrt'}
nhek_6f5m_prob_svm = {'C': 0.0625, 'gamma': 0.0625, 'kernel': 'rbf'}
nhek_4f2m_prob_svm = {'C': 2.0, 'gamma': 16.0, 'kernel': 'rbf'}
nhek_6f5m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 1, 'gamma': 0, 'colsample_bytree': 0.9, 'subsample': 0.8, 'reg_alpha': 0, 'reg_lambda': 0, 'learning_rate': 0.07}
nhek_4f2m_prob_xgboost = {'n_estimators': 100, 'max_depth': 3, 'min_child_weight': 2, 'gamma': 0.4, 'colsample_bytree': 0.6, 'subsample': 0.7, 'reg_alpha': 0.05, 'reg_lambda': 1, 'learning_rate': 1.0}
if __name__ == '_main_':
print(getattr(EPIconst.BaseModelParams, 'NHEK_tpcp_deepforest')) |
# Functions can encapsulate functionality you want to reuse:
def even_odd(x):
if x % 2 == 0:
print("even")
else:
print("odd")
# reused
even_odd(2)
even_odd(4)
even_odd(7)
even_odd(22)
even_odd(8)
# output should be >>> even, even, odd, even, even
| def even_odd(x):
if x % 2 == 0:
print('even')
else:
print('odd')
even_odd(2)
even_odd(4)
even_odd(7)
even_odd(22)
even_odd(8) |
version = '0.11.0'
version_cmd = 'confd -version'
download_url = 'https://github.com/kelseyhightower/confd/releases/download/vVERSION/confd-VERSION-linux-amd64'
install_script = """
chmod +x confd-VERSION-linux-amd64
mv -f confd-VERSION-linux-amd64 /usr/local/bin/confd
"""
| version = '0.11.0'
version_cmd = 'confd -version'
download_url = 'https://github.com/kelseyhightower/confd/releases/download/vVERSION/confd-VERSION-linux-amd64'
install_script = '\nchmod +x confd-VERSION-linux-amd64\nmv -f confd-VERSION-linux-amd64 /usr/local/bin/confd\n' |
class Sensors:
def __init__(self, **kwargs):
self.sensor_data_dictionary = kwargs
def update(self, **kwargs):
self.sensor_data_dictionary = kwargs
def get_value(self, key):
return (self.sensor_data_dictionary.get(key))
| class Sensors:
def __init__(self, **kwargs):
self.sensor_data_dictionary = kwargs
def update(self, **kwargs):
self.sensor_data_dictionary = kwargs
def get_value(self, key):
return self.sensor_data_dictionary.get(key) |
# https://www.google.com/webhp?sourceid=chrome-
# instant&ion=1&espv=2&ie=UTF-8#q=dp%20coin%20change
def coin_change_recur(coins,n,change_sum):
# If sum is 0 there exists a solution with no coins
if change_sum == 0:
return 1
# if sum is less then 0 no solution exists
if change_sum < 0:
return 0
# if there is no coins left and sum is not 0 then no solution
# exists
if n <= 0 and change_sum > 0:
return 0
# counts the solution including the coins[n-1] and excluding the coins[n-1]
return (coin_change_recur(coins,n-1,change_sum) +
coin_change_recur(coins,n,change_sum - coins[n-1]))
# To hold the results that has been already computed
memo_dict = {}
def coin_change_memo(coins,n,change_sum):
# Check if we have already computed for the current change_sum
if change_sum in memo_dict:
return memo_dict[change_sum]
# If sum is 0 there exists a solution with no coins
if change_sum == 0:
return 1
# if sum is less then 0 no solution exists
if change_sum < 0:
return 0
# if thhere are no coins left and sum is not 0 then no solution exists
if n <= 0 and change_sum > 0:
return 0
# count the solution inclusding coins[n-1] and excluding coins[n-1]
count = (coin_change_memo(coins,n-1,change_sum) +
coin_change_memo(coins,n,change_sum - coins[n-1]))
#memo_dict[change_sum] = count
return count
def coin_change_bottom_up(coins,change_sum):
coins_len = len(coins)
T = [[0] * (coins_len) for i in range(change_sum + 1)]
# Initialize the base case : getting sum 0
for i in range(coins_len):
T[0][i] = 1
for i in range(1, change_sum + 1):
for j in range(coins_len):
# Solutions including coins[j]
x = T[i - coins[j]][j] if i >= coins[j] else 0
# Solutions excluding coins[j]
y = T[i][j-1] if j >= 1 else 0
# total count
T[i][j] = x + y
return T[change_sum][coins_len - 1]
if __name__ == "__main__":
coins = [1,2,3]
print("Number of ways to make change: ", coin_change_recur(coins,len(coins),4))
print("Number of ways to make change: ", coin_change_memo(coins,len(coins),4))
print("Number of ways to make change: ", coin_change_bottom_up(coins,4))
| def coin_change_recur(coins, n, change_sum):
if change_sum == 0:
return 1
if change_sum < 0:
return 0
if n <= 0 and change_sum > 0:
return 0
return coin_change_recur(coins, n - 1, change_sum) + coin_change_recur(coins, n, change_sum - coins[n - 1])
memo_dict = {}
def coin_change_memo(coins, n, change_sum):
if change_sum in memo_dict:
return memo_dict[change_sum]
if change_sum == 0:
return 1
if change_sum < 0:
return 0
if n <= 0 and change_sum > 0:
return 0
count = coin_change_memo(coins, n - 1, change_sum) + coin_change_memo(coins, n, change_sum - coins[n - 1])
return count
def coin_change_bottom_up(coins, change_sum):
coins_len = len(coins)
t = [[0] * coins_len for i in range(change_sum + 1)]
for i in range(coins_len):
T[0][i] = 1
for i in range(1, change_sum + 1):
for j in range(coins_len):
x = T[i - coins[j]][j] if i >= coins[j] else 0
y = T[i][j - 1] if j >= 1 else 0
T[i][j] = x + y
return T[change_sum][coins_len - 1]
if __name__ == '__main__':
coins = [1, 2, 3]
print('Number of ways to make change: ', coin_change_recur(coins, len(coins), 4))
print('Number of ways to make change: ', coin_change_memo(coins, len(coins), 4))
print('Number of ways to make change: ', coin_change_bottom_up(coins, 4)) |
def calc_fuel(mass: int):
return max(mass // 3 - 2, 0)
def calc_fuel_rec(mass: int):
fuel = calc_fuel(mass)
if fuel == 0:
return fuel
else:
return fuel + calc_fuel_rec(fuel)
| def calc_fuel(mass: int):
return max(mass // 3 - 2, 0)
def calc_fuel_rec(mass: int):
fuel = calc_fuel(mass)
if fuel == 0:
return fuel
else:
return fuel + calc_fuel_rec(fuel) |
MODEL_TYPE = {
"PointRend" : 1,
"MobileNetV3Large" : 2,
"MobileNetV3Small" : 3
}
TASK_TYPE = {
"Object Detection" : 1,
"Instance Segmentation (Map)" : 2,
"Instance Segmentation (Blend)" : 3
}
"""
0 : No Ml model to run
1 : Object Detection : PointRend
2 : Instance Detection (Map) : MobileV3Large
3 : Instance Detection (Blend) : MobileV3Large
""" | model_type = {'PointRend': 1, 'MobileNetV3Large': 2, 'MobileNetV3Small': 3}
task_type = {'Object Detection': 1, 'Instance Segmentation (Map)': 2, 'Instance Segmentation (Blend)': 3}
'\n0 : No Ml model to run \n1 : Object Detection : PointRend\n2 : Instance Detection (Map) : MobileV3Large \n3 : Instance Detection (Blend) : MobileV3Large \n' |
class Cls:
x = "a"
d = {"a": "ab"}
cl = Cls()
cl.x = "b"
d[cl.x]
| class Cls:
x = 'a'
d = {'a': 'ab'}
cl = cls()
cl.x = 'b'
d[cl.x] |
#Copyright 2018 Infosys Ltd.
#Use of this source code is governed by Apache 2.0 license that can be found in the LICENSE file or at
#http://www.apache.org/licenses/LICENSE-2.0 .
####DATABASE QUERY STATUS CODES####
CON000 = 'CON000' # Successfull database connection
CON001 = 'CON001' # Failed to connect to database
EXE000 = 'EXE000' # Successful query execution
EXE001 = 'EXE001' # Query Execution failure | con000 = 'CON000'
con001 = 'CON001'
exe000 = 'EXE000'
exe001 = 'EXE001' |
#a = int(input())
#b = int(input())
entrada = input()
a, b = entrada.split(" ")
a = int(a)
b = int(b)
if(a > b):
if(a%b == 0):
print ("Sao Multiplos")
else:
print("Nao sao Multiplos")
else:
if(b%a == 0):
print("Sao Multiplos")
else:
print("Nao sao Multiplos")
| entrada = input()
(a, b) = entrada.split(' ')
a = int(a)
b = int(b)
if a > b:
if a % b == 0:
print('Sao Multiplos')
else:
print('Nao sao Multiplos')
elif b % a == 0:
print('Sao Multiplos')
else:
print('Nao sao Multiplos') |
class Solution:
def orangesRotting(self, grid: List[List[int]]) -> int:
d=0
while True:
c=False
old=[]
for i in range(len(grid)):
s=[]
for j in range(len(grid[0])):
s.append(grid[i][j])
old.append(s)
for i in range(len(grid)):
for j in range(len(grid[0])):
if old[i][j]==2:
f=self.change(grid,i,j)
if f:
c=True
if c==False:
break
else:
d=d+1
for i in range(len(grid)):
for j in range(len(grid[0])):
if grid[i][j]==1:
return -1
return d
def change(self,grid,i,j):
r=False
for ti,tj in zip([-1,0,0,1],[0,-1,1,0]):
if ti+i>=0 and ti+i<len(grid) and tj+j>=0 and tj+j<len(grid[0]):
if grid[ti+i][tj+j]==1:
grid[ti+i][tj+j]=2
r=True
return r
| class Solution:
def oranges_rotting(self, grid: List[List[int]]) -> int:
d = 0
while True:
c = False
old = []
for i in range(len(grid)):
s = []
for j in range(len(grid[0])):
s.append(grid[i][j])
old.append(s)
for i in range(len(grid)):
for j in range(len(grid[0])):
if old[i][j] == 2:
f = self.change(grid, i, j)
if f:
c = True
if c == False:
break
else:
d = d + 1
for i in range(len(grid)):
for j in range(len(grid[0])):
if grid[i][j] == 1:
return -1
return d
def change(self, grid, i, j):
r = False
for (ti, tj) in zip([-1, 0, 0, 1], [0, -1, 1, 0]):
if ti + i >= 0 and ti + i < len(grid) and (tj + j >= 0) and (tj + j < len(grid[0])):
if grid[ti + i][tj + j] == 1:
grid[ti + i][tj + j] = 2
r = True
return r |
class Solution:
def binaryGap(self, n: int) -> int:
a = str(bin(n))
a = a[2:]
dis = []
c = 0
for i in range(len(a)):
if a[i] == "1":
dis.append(c)
c = 0
c +=1
return max(dis)
| class Solution:
def binary_gap(self, n: int) -> int:
a = str(bin(n))
a = a[2:]
dis = []
c = 0
for i in range(len(a)):
if a[i] == '1':
dis.append(c)
c = 0
c += 1
return max(dis) |
class Suggestion:
def __init__(self, obj=None):
"""
See "https://smartystreets.com/docs/cloud/us-autocomplete-api#http-response"
"""
self.text = obj.get('text', None)
self.street_line = obj.get('street_line', None)
self.city = obj.get('city', None)
self.state = obj.get('state', None)
| class Suggestion:
def __init__(self, obj=None):
"""
See "https://smartystreets.com/docs/cloud/us-autocomplete-api#http-response"
"""
self.text = obj.get('text', None)
self.street_line = obj.get('street_line', None)
self.city = obj.get('city', None)
self.state = obj.get('state', None) |
# coding: utf-8
# In[1]:
#num01_SwethaMJ.py
sum_ = 0
for i in range(1,1000):
if i%3==0 or i%5==0:
sum_ += i
print(sum_)
| sum_ = 0
for i in range(1, 1000):
if i % 3 == 0 or i % 5 == 0:
sum_ += i
print(sum_) |
# M6 #2
str = 'inet addr:127.0.0.1 Mask:255.0.0.0'
index = str.find(':')
if index > 0:
# clip off the front
str1 = str[index+1:]
i = str1.find(' ')
addr = str1[:i].rstrip()
# addr is the inet address
print('Address: ', addr)
| str = 'inet addr:127.0.0.1 Mask:255.0.0.0'
index = str.find(':')
if index > 0:
str1 = str[index + 1:]
i = str1.find(' ')
addr = str1[:i].rstrip()
print('Address: ', addr) |
# (C) Datadog, Inc. 2010-2016
# All rights reserved
# Licensed under Simplified BSD License (see LICENSE)
class Singleton(type):
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls]
| class Singleton(type):
_instances = {}
def __call__(cls, *args, **kwargs):
if cls not in cls._instances:
cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
return cls._instances[cls] |
class Solution:
def romanToInt(self, s: str) -> int:
dic = {"I":1,"V":5,"X":10,"L":50,"C":100,"D":500,"M":1000}
res = 0
while s:
letter = s[0]
if len(s)==1:
res+=dic[letter]
return res
if dic[letter]>=dic[s[1]]:
res+=dic[letter]
s = s[1:]
elif dic[letter]<dic[s[1]]:
res +=dic[s[1]]-dic[letter]
s=s[2:]
return res | class Solution:
def roman_to_int(self, s: str) -> int:
dic = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
res = 0
while s:
letter = s[0]
if len(s) == 1:
res += dic[letter]
return res
if dic[letter] >= dic[s[1]]:
res += dic[letter]
s = s[1:]
elif dic[letter] < dic[s[1]]:
res += dic[s[1]] - dic[letter]
s = s[2:]
return res |
class blank (object):
def __init__ (self):
object.__init__ (self)
deployment_settings = blank ()
# Web2py Settings
deployment_settings.web2py = blank ()
deployment_settings.web2py.port = 8000
# Database settings
deployment_settings.database = blank ()
deployment_settings.database.db_type = "sqlite"
deployment_settings.database.host = "localhost"
deployment_settings.database.port = None # use default
deployment_settings.database.database = "healthscapes"
deployment_settings.database.username = "hs"
deployment_settings.database.password = "hs"
deployment_settings.database.pool_size = 30
# MongoDB Settings
deployment_settings.mongodb = blank ()
deployment_settings.mongodb.host = None
deployment_settings.mongodb.port = 27017
deployment_settings.mongodb.db = 'mongo_db'
deployment_settings.mongodb.username = 'mongo'
deployment_settings.mongodb.password = 'mongo'
# PostGIS Settings
deployment_settings.postgis = blank ()
deployment_settings.postgis.host = None
deployment_settings.postgis.port = 5432
deployment_settings.postgis.database = "geodata"
deployment_settings.postgis.username = "postgis"
deployment_settings.postgis.password = "postgis"
deployment_settings.postgis.pool_size = 10
deployment_settings.geoserver_sources = []
# Upload Geoserver Settings
deployment_settings.geoserver = blank ()
deployment_settings.geoserver.host = 'http://localhost'
deployment_settings.geoserver.port = 8888
deployment_settings.geoserver.username = "admin"
deployment_settings.geoserver.password = "geoserver"
deployment_settings.geoserver.workspace = 'hsd'
deployment_settings.geoserver.pgis_store = 'test'
# NPR Settings
deployment_settings.npr = blank ()
deployment_settings.npr.key = 'MDA2OTc4ODY2MDEyOTc0NTMyMjFmZGNjZg001'
deployment_settings.data = blank ()
deployment_settings.data.base_table = 'datatypes'
# Development Mode
deployment_settings.dev_mode = blank ()
deployment_settings.dev_mode.enabled = False
deployment_settings.dev_mode.firstname = 'First'
deployment_settings.dev_mode.lastname = 'Last'
deployment_settings.dev_mode.email = 'fake@gmail.com'
# ExtJS Settings
deployment_settings.extjs = blank ()
deployment_settings.extjs.location = 'http://skapes.org/media/js/ext'
| class Blank(object):
def __init__(self):
object.__init__(self)
deployment_settings = blank()
deployment_settings.web2py = blank()
deployment_settings.web2py.port = 8000
deployment_settings.database = blank()
deployment_settings.database.db_type = 'sqlite'
deployment_settings.database.host = 'localhost'
deployment_settings.database.port = None
deployment_settings.database.database = 'healthscapes'
deployment_settings.database.username = 'hs'
deployment_settings.database.password = 'hs'
deployment_settings.database.pool_size = 30
deployment_settings.mongodb = blank()
deployment_settings.mongodb.host = None
deployment_settings.mongodb.port = 27017
deployment_settings.mongodb.db = 'mongo_db'
deployment_settings.mongodb.username = 'mongo'
deployment_settings.mongodb.password = 'mongo'
deployment_settings.postgis = blank()
deployment_settings.postgis.host = None
deployment_settings.postgis.port = 5432
deployment_settings.postgis.database = 'geodata'
deployment_settings.postgis.username = 'postgis'
deployment_settings.postgis.password = 'postgis'
deployment_settings.postgis.pool_size = 10
deployment_settings.geoserver_sources = []
deployment_settings.geoserver = blank()
deployment_settings.geoserver.host = 'http://localhost'
deployment_settings.geoserver.port = 8888
deployment_settings.geoserver.username = 'admin'
deployment_settings.geoserver.password = 'geoserver'
deployment_settings.geoserver.workspace = 'hsd'
deployment_settings.geoserver.pgis_store = 'test'
deployment_settings.npr = blank()
deployment_settings.npr.key = 'MDA2OTc4ODY2MDEyOTc0NTMyMjFmZGNjZg001'
deployment_settings.data = blank()
deployment_settings.data.base_table = 'datatypes'
deployment_settings.dev_mode = blank()
deployment_settings.dev_mode.enabled = False
deployment_settings.dev_mode.firstname = 'First'
deployment_settings.dev_mode.lastname = 'Last'
deployment_settings.dev_mode.email = 'fake@gmail.com'
deployment_settings.extjs = blank()
deployment_settings.extjs.location = 'http://skapes.org/media/js/ext' |
def insertion_sort(to_sort):
i=0
while i <= len(to_sort)-1:
hole = i;
item = to_sort[i]
while hole > 0 and to_sort[hole-1] > item:
to_sort[hole] = to_sort[hole-1]
hole-=1
to_sort[hole] = item
i+=1
return to_sort
| def insertion_sort(to_sort):
i = 0
while i <= len(to_sort) - 1:
hole = i
item = to_sort[i]
while hole > 0 and to_sort[hole - 1] > item:
to_sort[hole] = to_sort[hole - 1]
hole -= 1
to_sort[hole] = item
i += 1
return to_sort |
load_modules = {
'hw_USBtin': {'port':'auto', 'speed':500}, # IO hardware module # Module for sniff and replay
'mod_stat': {"bus":'mod_stat','debug':2},'mod_stat~2': {"bus":'mod_stat'},
'mod_firewall': {}, 'mod_fuzz1':{'debug':2},
'gen_replay': {'debug': 1}# Stats
}
# Now let's describe the logic of this test
actions = [
{'hw_USBtin': {'action': 'read','pipe': 1}}, # Read to PIPE 1
{'mod_stat': {'pipe': 1}}, # Write generated packets (pings)
{'mod_firewall': {'white_bus': ["mod_stat"]}},
{'gen_replay': {}},
{'mod_stat~2': {'pipe': 1}},
{'hw_USBtin': {'action': 'write','pipe': 1}},
]
| load_modules = {'hw_USBtin': {'port': 'auto', 'speed': 500}, 'mod_stat': {'bus': 'mod_stat', 'debug': 2}, 'mod_stat~2': {'bus': 'mod_stat'}, 'mod_firewall': {}, 'mod_fuzz1': {'debug': 2}, 'gen_replay': {'debug': 1}}
actions = [{'hw_USBtin': {'action': 'read', 'pipe': 1}}, {'mod_stat': {'pipe': 1}}, {'mod_firewall': {'white_bus': ['mod_stat']}}, {'gen_replay': {}}, {'mod_stat~2': {'pipe': 1}}, {'hw_USBtin': {'action': 'write', 'pipe': 1}}] |
def dragon_lives_for(sequence):
dragon_size = 50
sheep = 0
squeezed_for = 0
days = 0
while True:
sheep += sequence.pop(0)
if dragon_size <= sheep:
sheep -= dragon_size
dragon_size += 1
squeezed_for = 0
else:
sheep = 0
dragon_size -= 1
squeezed_for += 1
if squeezed_for >= 5:
return days
days += 1
def test_dragon_lives_for():
assert dragon_lives_for([50, 52, 52, 49, 50, 47, 45, 43, 50, 55]) == 7
if __name__ == '__main__':
with open('input/01') as f:
l = [int(v) for v in f.read().split(', ')]
print(dragon_lives_for(l))
| def dragon_lives_for(sequence):
dragon_size = 50
sheep = 0
squeezed_for = 0
days = 0
while True:
sheep += sequence.pop(0)
if dragon_size <= sheep:
sheep -= dragon_size
dragon_size += 1
squeezed_for = 0
else:
sheep = 0
dragon_size -= 1
squeezed_for += 1
if squeezed_for >= 5:
return days
days += 1
def test_dragon_lives_for():
assert dragon_lives_for([50, 52, 52, 49, 50, 47, 45, 43, 50, 55]) == 7
if __name__ == '__main__':
with open('input/01') as f:
l = [int(v) for v in f.read().split(', ')]
print(dragon_lives_for(l)) |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------
# Copyright (C) 2009 StatPro Italia s.r.l.
#
# StatPro Italia
# Via G. B. Vico 4
# I-20123 Milano
# ITALY
#
# phone: +39 02 96875 1
# fax: +39 02 96875 605
#
# email: info@riskmap.net
#
# This program is distributed in the hope that it will be
# useful, but WITHOUT ANY WARRANTY; without even the
# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
# PURPOSE. See the license for more details.
# -----------------------------------------------------------
#
# Author: Enrico Sirola <enrico.sirola@statpro.com>
# $Id$
"""drmaa constants"""
# drmaa_get_attribute()
ATTR_BUFFER = 1024
# drmaa_get_contact()
CONTACT_BUFFER = 1024
# drmaa_get_DRM_system()
DRM_SYSTEM_BUFFER = 1024
# drmaa_get_DRM_system()
DRMAA_IMPLEMENTATION_BUFFER = 1024
# Agreed buffer length constants
# these are recommended minimum values
ERROR_STRING_BUFFER = 1024
JOBNAME_BUFFER = 1024
SIGNAL_BUFFER = 32
# Agreed constants
TIMEOUT_WAIT_FOREVER = -1
TIMEOUT_NO_WAIT = 0
JOB_IDS_SESSION_ANY = "DRMAA_JOB_IDS_SESSION_ANY"
JOB_IDS_SESSION_ALL = "DRMAA_JOB_IDS_SESSION_ALL"
SUBMISSION_STATE_ACTIVE = "drmaa_active"
SUBMISSION_STATE_HOLD = "drmaa_hold"
# Agreed placeholder names
PLACEHOLDER_INCR = "$drmaa_incr_ph$"
PLACEHOLDER_HD = "$drmaa_hd_ph$"
PLACEHOLDER_WD = "$drmaa_wd_ph$"
# Agreed names of job template attributes
REMOTE_COMMAND = "drmaa_remote_command"
JS_STATE = "drmaa_js_state"
WD = "drmaa_wd"
JOB_CATEGORY = "drmaa_job_category"
NATIVE_SPECIFICATION = "drmaa_native_specification"
BLOCK_EMAIL = "drmaa_block_email"
START_TIME = "drmaa_start_time"
JOB_NAME = "drmaa_job_name"
INPUT_PATH = "drmaa_input_path"
OUTPUT_PATH = "drmaa_output_path"
ERROR_PATH = "drmaa_error_path"
JOIN_FILES = "drmaa_join_files"
TRANSFER_FILES = "drmaa_transfer_files"
DEADLINE_TIME = "drmaa_deadline_time"
WCT_HLIMIT = "drmaa_wct_hlimit"
WCT_SLIMIT = "drmaa_wct_slimit"
DURATION_HLIMIT = "drmaa_duration_hlimit"
DURATION_SLIMIT = "drmaa_duration_slimit"
# names of job template vector attributes
V_ARGV = "drmaa_v_argv"
V_ENV = "drmaa_v_env"
V_EMAIL = "drmaa_v_email"
NO_MORE_ELEMENTS = 25
def job_state(code):
return _JOB_PS[code]
class JobState(object):
UNDETERMINED = 'undetermined'
QUEUED_ACTIVE = 'queued_active'
SYSTEM_ON_HOLD = 'system_on_hold'
USER_ON_HOLD = 'user_on_hold'
USER_SYSTEM_ON_HOLD = 'user_system_on_hold'
RUNNING = 'running'
SYSTEM_SUSPENDED = 'system_suspended'
USER_SUSPENDED = 'user_suspended'
USER_SYSTEM_SUSPENDED = 'user_system_suspended'
DONE = 'done'
FAILED = 'failed'
# Job control action
class JobControlAction(object):
SUSPEND = 'suspend'
RESUME = 'resume'
HOLD = 'hold'
RELEASE = 'release'
TERMINATE = 'terminate'
_JOB_CONTROL = [
JobControlAction.SUSPEND,
JobControlAction.RESUME,
JobControlAction.HOLD,
JobControlAction.RELEASE,
JobControlAction.TERMINATE
]
def string_to_control_action(operation):
return _JOB_CONTROL.index(operation)
def control_action_to_string(code):
return _JOB_CONTROL[code]
def status_to_string(status):
return _JOB_PS[status]
_JOB_PS = {
0x00: JobState.UNDETERMINED,
0x10: JobState.QUEUED_ACTIVE,
0x11: JobState.SYSTEM_ON_HOLD,
0x12: JobState.USER_ON_HOLD,
0x13: JobState.USER_SYSTEM_ON_HOLD,
0x20: JobState.RUNNING,
0x21: JobState.SYSTEM_SUSPENDED,
0x22: JobState.USER_SUSPENDED,
0x23: JobState.USER_SYSTEM_SUSPENDED,
0x30: JobState.DONE,
0x40: JobState.FAILED,
}
# State at submission time
class JobSubmissionState(object):
HOLD_STATE = SUBMISSION_STATE_HOLD
ACTIVE_STATE = SUBMISSION_STATE_ACTIVE
_SUBMISSION_STATE = [
JobSubmissionState.HOLD_STATE,
JobSubmissionState.ACTIVE_STATE
]
def submission_state(code):
return _SUBMISSION_STATE[code]
| """drmaa constants"""
attr_buffer = 1024
contact_buffer = 1024
drm_system_buffer = 1024
drmaa_implementation_buffer = 1024
error_string_buffer = 1024
jobname_buffer = 1024
signal_buffer = 32
timeout_wait_forever = -1
timeout_no_wait = 0
job_ids_session_any = 'DRMAA_JOB_IDS_SESSION_ANY'
job_ids_session_all = 'DRMAA_JOB_IDS_SESSION_ALL'
submission_state_active = 'drmaa_active'
submission_state_hold = 'drmaa_hold'
placeholder_incr = '$drmaa_incr_ph$'
placeholder_hd = '$drmaa_hd_ph$'
placeholder_wd = '$drmaa_wd_ph$'
remote_command = 'drmaa_remote_command'
js_state = 'drmaa_js_state'
wd = 'drmaa_wd'
job_category = 'drmaa_job_category'
native_specification = 'drmaa_native_specification'
block_email = 'drmaa_block_email'
start_time = 'drmaa_start_time'
job_name = 'drmaa_job_name'
input_path = 'drmaa_input_path'
output_path = 'drmaa_output_path'
error_path = 'drmaa_error_path'
join_files = 'drmaa_join_files'
transfer_files = 'drmaa_transfer_files'
deadline_time = 'drmaa_deadline_time'
wct_hlimit = 'drmaa_wct_hlimit'
wct_slimit = 'drmaa_wct_slimit'
duration_hlimit = 'drmaa_duration_hlimit'
duration_slimit = 'drmaa_duration_slimit'
v_argv = 'drmaa_v_argv'
v_env = 'drmaa_v_env'
v_email = 'drmaa_v_email'
no_more_elements = 25
def job_state(code):
return _JOB_PS[code]
class Jobstate(object):
undetermined = 'undetermined'
queued_active = 'queued_active'
system_on_hold = 'system_on_hold'
user_on_hold = 'user_on_hold'
user_system_on_hold = 'user_system_on_hold'
running = 'running'
system_suspended = 'system_suspended'
user_suspended = 'user_suspended'
user_system_suspended = 'user_system_suspended'
done = 'done'
failed = 'failed'
class Jobcontrolaction(object):
suspend = 'suspend'
resume = 'resume'
hold = 'hold'
release = 'release'
terminate = 'terminate'
_job_control = [JobControlAction.SUSPEND, JobControlAction.RESUME, JobControlAction.HOLD, JobControlAction.RELEASE, JobControlAction.TERMINATE]
def string_to_control_action(operation):
return _JOB_CONTROL.index(operation)
def control_action_to_string(code):
return _JOB_CONTROL[code]
def status_to_string(status):
return _JOB_PS[status]
_job_ps = {0: JobState.UNDETERMINED, 16: JobState.QUEUED_ACTIVE, 17: JobState.SYSTEM_ON_HOLD, 18: JobState.USER_ON_HOLD, 19: JobState.USER_SYSTEM_ON_HOLD, 32: JobState.RUNNING, 33: JobState.SYSTEM_SUSPENDED, 34: JobState.USER_SUSPENDED, 35: JobState.USER_SYSTEM_SUSPENDED, 48: JobState.DONE, 64: JobState.FAILED}
class Jobsubmissionstate(object):
hold_state = SUBMISSION_STATE_HOLD
active_state = SUBMISSION_STATE_ACTIVE
_submission_state = [JobSubmissionState.HOLD_STATE, JobSubmissionState.ACTIVE_STATE]
def submission_state(code):
return _SUBMISSION_STATE[code] |
load("//tools/bzl:maven_jar.bzl", "maven_jar")
def external_plugin_deps():
AUTO_VALUE_VERSION = "1.7.4"
maven_jar(
name = "auto-value",
artifact = "com.google.auto.value:auto-value:" + AUTO_VALUE_VERSION,
sha1 = "6b126cb218af768339e4d6e95a9b0ae41f74e73d",
)
maven_jar(
name = "auto-value-annotations",
artifact = "com.google.auto.value:auto-value-annotations:" + AUTO_VALUE_VERSION,
sha1 = "eff48ed53995db2dadf0456426cc1f8700136f86",
)
| load('//tools/bzl:maven_jar.bzl', 'maven_jar')
def external_plugin_deps():
auto_value_version = '1.7.4'
maven_jar(name='auto-value', artifact='com.google.auto.value:auto-value:' + AUTO_VALUE_VERSION, sha1='6b126cb218af768339e4d6e95a9b0ae41f74e73d')
maven_jar(name='auto-value-annotations', artifact='com.google.auto.value:auto-value-annotations:' + AUTO_VALUE_VERSION, sha1='eff48ed53995db2dadf0456426cc1f8700136f86') |
# -*- coding: utf-8 -*-
class AzureShellCache:
__inst = None
__cache = {}
@staticmethod
def Instance():
if AzureShellCache.__inst == None:
AzureShellCache()
return AzureShellCache.__inst
def __init__(self):
if AzureShellCache.__inst != None:
raise Exception("This must not be called!!")
AzureShellCache.__inst = self
def set(self, k, v):
self.__cache[k] = v
def get(self, k):
return self.__cache.get(k)
| class Azureshellcache:
__inst = None
__cache = {}
@staticmethod
def instance():
if AzureShellCache.__inst == None:
azure_shell_cache()
return AzureShellCache.__inst
def __init__(self):
if AzureShellCache.__inst != None:
raise exception('This must not be called!!')
AzureShellCache.__inst = self
def set(self, k, v):
self.__cache[k] = v
def get(self, k):
return self.__cache.get(k) |
solutions = []
maxAllowed = 10**1000
value = 1
base = 1
while value * value <= maxAllowed:
while value < base * 10:
solutions.append(value)
value += base
base = value
solutions.append(value)
while True:
num = int(input())
if num == 0:
break
# Binary search
# start is inclusive, end is not
start = 0
end = len(solutions)
while start + 1 < end:
middle = (start + end) // 2
# Too high
if solutions[middle] * solutions[middle] > num:
end = middle
else:
start = middle;
print(solutions[start]) | solutions = []
max_allowed = 10 ** 1000
value = 1
base = 1
while value * value <= maxAllowed:
while value < base * 10:
solutions.append(value)
value += base
base = value
solutions.append(value)
while True:
num = int(input())
if num == 0:
break
start = 0
end = len(solutions)
while start + 1 < end:
middle = (start + end) // 2
if solutions[middle] * solutions[middle] > num:
end = middle
else:
start = middle
print(solutions[start]) |
# template_parsetab.py
# This file is automatically generated. Do not edit.
# pylint: disable=W,C,R
_tabversion = '3.10'
_lr_method = 'LALR'
_lr_signature = 'template_validateALL ARROW ARROWPARENS ARROW_PRE ASSIGN ASSIGNBAND ASSIGNBOR ASSIGNBXOR ASSIGNDIVIDE ASSIGNLLSHIFT ASSIGNLSHIFT ASSIGNMINUS ASSIGNMOD ASSIGNPLUS ASSIGNRRSHIFT ASSIGNRSHIFT ASSIGNTIMES AWAIT BACKSLASH BAND BITINV BNEGATE BOR BREAK BXOR BYTE CASE CATCH CHAR CLASS CLOSECOM COLON COMMA COMMENT COND_DOT CONST CONTINUE DEC DEFAULT DELETE DIVIDE DO DOT DOUBLE ELSE EMPTYLINE EQUAL EQUAL_STRICT EXPONENT EXPORT EXTENDS FINALLY FOR FROM FUNCTION GET GLOBAL GTHAN GTHANEQ ID IF IMPORT IN INC INFERRED INSTANCEOF LAND LBRACKET LET LLSHIFT LOR LPAREN LSBRACKET LSHIFT LTHAN LTHANEQ MINUS MLSTRLIT MOD NATIVE NEW NOT NOTEQUAL NOTEQUAL_STRICT NUMBER OF OPENCOM PLUS QEST RBRACKET REGEXPR RETURN RPAREN RRSHIFT RSBRACKET RSHIFT SEMI SET SHORT SIGNED SLASHR STATIC STRINGLIT SWITCH TEMPLATE TEMPLATE_STR TGTHAN THROW TIMES TLTHAN TRIPLEDOT TRY TYPED TYPEOF VAR VARIABLE VAR_TYPE_PREC WHILE WITH YIELD newlinelthan : LTHAN\n | TLTHAN\n gthan : GTHAN\n | TGTHAN\n id : ID\n | GET\n | SET\n | STATIC\n | CATCH\n | GLOBAL\n | AWAIT\n left_id : id id_opt : id\n |\n id_var_type : id \n id_var_decl : id \n var_type : var_type id_var_type\n | id_var_type\n | SHORT\n | DOUBLE\n | CHAR\n | BYTE\n | INFERRED\n | var_type template_ref\n \n templatedeflist : var_type\n | var_type ASSIGN var_type\n | templatedeflist COMMA var_type\n | templatedeflist COMMA var_type ASSIGN var_type\n template : lthan templatedeflist gthan\n typeof_opt : TYPEOF\n |\n \n simple_templatedeflist : typeof_opt var_type\n | simple_templatedeflist COMMA typeof_opt var_type\n template_ref : lthan simple_templatedeflist gthan\n template_ref_validate : lthan simple_templatedeflist gthan\n template_validate : template\n | template_ref_validate\n '
_lr_action_items = {'LTHAN':([0,9,11,12,13,14,15,16,18,19,20,21,22,23,24,25,28,29,33,34,36,37,39,42,43,44,],[5,5,-18,-19,-20,-21,-22,-23,-15,-5,-6,-7,-8,-9,-10,-11,-3,-4,-17,-24,5,5,5,5,-34,5,]),'TLTHAN':([0,9,11,12,13,14,15,16,18,19,20,21,22,23,24,25,28,29,33,34,36,37,39,42,43,44,],[6,6,-18,-19,-20,-21,-22,-23,-15,-5,-6,-7,-8,-9,-10,-11,-3,-4,-17,-24,6,6,6,6,-34,6,]),'$end':([1,2,3,26,28,29,30,],[0,-36,-37,-29,-3,-4,-35,]),'SHORT':([4,5,6,10,17,27,31,32,35,38,41,],[12,-1,-2,12,-30,12,-31,12,-31,12,12,]),'DOUBLE':([4,5,6,10,17,27,31,32,35,38,41,],[13,-1,-2,13,-30,13,-31,13,-31,13,13,]),'CHAR':([4,5,6,10,17,27,31,32,35,38,41,],[14,-1,-2,14,-30,14,-31,14,-31,14,14,]),'BYTE':([4,5,6,10,17,27,31,32,35,38,41,],[15,-1,-2,15,-30,15,-31,15,-31,15,15,]),'INFERRED':([4,5,6,10,17,27,31,32,35,38,41,],[16,-1,-2,16,-30,16,-31,16,-31,16,16,]),'TYPEOF':([4,5,6,31,35,],[17,-1,-2,17,17,]),'ID':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[19,-1,-2,19,19,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,19,-3,-4,-31,19,-17,-24,-31,19,19,19,19,19,19,-34,19,]),'GET':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[20,-1,-2,20,20,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,20,-3,-4,-31,20,-17,-24,-31,20,20,20,20,20,20,-34,20,]),'SET':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[21,-1,-2,21,21,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,21,-3,-4,-31,21,-17,-24,-31,21,21,21,21,21,21,-34,21,]),'STATIC':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[22,-1,-2,22,22,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,22,-3,-4,-31,22,-17,-24,-31,22,22,22,22,22,22,-34,22,]),'CATCH':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[23,-1,-2,23,23,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,23,-3,-4,-31,23,-17,-24,-31,23,23,23,23,23,23,-34,23,]),'GLOBAL':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[24,-1,-2,24,24,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,24,-3,-4,-31,24,-17,-24,-31,24,24,24,24,24,24,-34,24,]),'AWAIT':([4,5,6,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,29,31,32,33,34,35,36,37,38,39,41,42,43,44,],[25,-1,-2,25,25,-18,-19,-20,-21,-22,-23,-30,-15,-5,-6,-7,-8,-9,-10,-11,25,-3,-4,-31,25,-17,-24,-31,25,25,25,25,25,25,-34,25,]),'COMMA':([7,8,9,11,12,13,14,15,16,18,19,20,21,22,23,24,25,28,29,33,34,36,37,39,40,42,43,44,],[27,31,-25,-18,-19,-20,-21,-22,-23,-15,-5,-6,-7,-8,-9,-10,-11,-3,-4,-17,-24,-32,-27,-26,31,-33,-34,-28,]),'GTHAN':([7,8,9,11,12,13,14,15,16,18,19,20,21,22,23,24,25,28,29,33,34,36,37,39,40,42,43,44,],[28,28,-25,-18,-19,-20,-21,-22,-23,-15,-5,-6,-7,-8,-9,-10,-11,-3,-4,-17,-24,-32,-27,-26,28,-33,-34,-28,]),'TGTHAN':([7,8,9,11,12,13,14,15,16,18,19,20,21,22,23,24,25,28,29,33,34,36,37,39,40,42,43,44,],[29,29,-25,-18,-19,-20,-21,-22,-23,-15,-5,-6,-7,-8,-9,-10,-11,-3,-4,-17,-24,-32,-27,-26,29,-33,-34,-28,]),'ASSIGN':([9,11,12,13,14,15,16,18,19,20,21,22,23,24,25,28,29,33,34,37,43,],[32,-18,-19,-20,-21,-22,-23,-15,-5,-6,-7,-8,-9,-10,-11,-3,-4,-17,-24,41,-34,]),}
_lr_action = {}
for _k, _v in _lr_action_items.items():
for _x,_y in zip(_v[0],_v[1]):
if not _x in _lr_action: _lr_action[_x] = {}
_lr_action[_x][_k] = _y
del _lr_action_items
_lr_goto_items = {'template_validate':([0,],[1,]),'template':([0,],[2,]),'template_ref_validate':([0,],[3,]),'lthan':([0,9,36,37,39,42,44,],[4,35,35,35,35,35,35,]),'templatedeflist':([4,],[7,]),'simple_templatedeflist':([4,35,],[8,40,]),'var_type':([4,10,27,32,38,41,],[9,36,37,39,42,44,]),'typeof_opt':([4,31,35,],[10,38,10,]),'id_var_type':([4,9,10,27,32,36,37,38,39,41,42,44,],[11,33,11,11,11,33,33,11,33,11,33,33,]),'id':([4,9,10,27,32,36,37,38,39,41,42,44,],[18,18,18,18,18,18,18,18,18,18,18,18,]),'gthan':([7,8,40,],[26,30,43,]),'template_ref':([9,36,37,39,42,44,],[34,34,34,34,34,34,]),}
_lr_goto = {}
for _k, _v in _lr_goto_items.items():
for _x, _y in zip(_v[0], _v[1]):
if not _x in _lr_goto: _lr_goto[_x] = {}
_lr_goto[_x][_k] = _y
del _lr_goto_items
_lr_productions = [
("S' -> template_validate","S'",1,None,None,None),
('lthan -> LTHAN','lthan',1,'p_lthan','js_parse_template.py',12),
('lthan -> TLTHAN','lthan',1,'p_lthan','js_parse_template.py',13),
('gthan -> GTHAN','gthan',1,'p_gthan','js_parse_template.py',18),
('gthan -> TGTHAN','gthan',1,'p_gthan','js_parse_template.py',19),
('id -> ID','id',1,'p_id','js_parse_template.py',24),
('id -> GET','id',1,'p_id','js_parse_template.py',25),
('id -> SET','id',1,'p_id','js_parse_template.py',26),
('id -> STATIC','id',1,'p_id','js_parse_template.py',27),
('id -> CATCH','id',1,'p_id','js_parse_template.py',28),
('id -> GLOBAL','id',1,'p_id','js_parse_template.py',29),
('id -> AWAIT','id',1,'p_id','js_parse_template.py',30),
('left_id -> id','left_id',1,'p_left_id','js_parse_template.py',35),
('id_opt -> id','id_opt',1,'p_id_opt','js_parse_template.py',39),
('id_opt -> <empty>','id_opt',0,'p_id_opt','js_parse_template.py',40),
('id_var_type -> id','id_var_type',1,'p_id_var_type','js_parse_template.py',46),
('id_var_decl -> id','id_var_decl',1,'p_id_var_decl','js_parse_template.py',51),
('var_type -> var_type id_var_type','var_type',2,'p_var_type','js_parse_template.py',56),
('var_type -> id_var_type','var_type',1,'p_var_type','js_parse_template.py',57),
('var_type -> SHORT','var_type',1,'p_var_type','js_parse_template.py',58),
('var_type -> DOUBLE','var_type',1,'p_var_type','js_parse_template.py',59),
('var_type -> CHAR','var_type',1,'p_var_type','js_parse_template.py',60),
('var_type -> BYTE','var_type',1,'p_var_type','js_parse_template.py',61),
('var_type -> INFERRED','var_type',1,'p_var_type','js_parse_template.py',62),
('var_type -> var_type template_ref','var_type',2,'p_var_type','js_parse_template.py',63),
('templatedeflist -> var_type','templatedeflist',1,'p_templatedeflist','js_parse_template.py',69),
('templatedeflist -> var_type ASSIGN var_type','templatedeflist',3,'p_templatedeflist','js_parse_template.py',70),
('templatedeflist -> templatedeflist COMMA var_type','templatedeflist',3,'p_templatedeflist','js_parse_template.py',71),
('templatedeflist -> templatedeflist COMMA var_type ASSIGN var_type','templatedeflist',5,'p_templatedeflist','js_parse_template.py',72),
('template -> lthan templatedeflist gthan','template',3,'p_template','js_parse_template.py',77),
('typeof_opt -> TYPEOF','typeof_opt',1,'p_typeof_opt','js_parse_template.py',82),
('typeof_opt -> <empty>','typeof_opt',0,'p_typeof_opt','js_parse_template.py',83),
('simple_templatedeflist -> typeof_opt var_type','simple_templatedeflist',2,'p_simple_templatedeflist','js_parse_template.py',93),
('simple_templatedeflist -> simple_templatedeflist COMMA typeof_opt var_type','simple_templatedeflist',4,'p_simple_templatedeflist','js_parse_template.py',94),
('template_ref -> lthan simple_templatedeflist gthan','template_ref',3,'p_template_ref','js_parse_template.py',99),
('template_ref_validate -> lthan simple_templatedeflist gthan','template_ref_validate',3,'p_template_ref_validate','js_parse_template.py',104),
('template_validate -> template','template_validate',1,'p_template_validate','js_parse_template.py',109),
('template_validate -> template_ref_validate','template_validate',1,'p_template_validate','js_parse_template.py',110),
]
| _tabversion = '3.10'
_lr_method = 'LALR'
_lr_signature = 'template_validateALL ARROW ARROWPARENS ARROW_PRE ASSIGN ASSIGNBAND ASSIGNBOR ASSIGNBXOR ASSIGNDIVIDE ASSIGNLLSHIFT ASSIGNLSHIFT ASSIGNMINUS ASSIGNMOD ASSIGNPLUS ASSIGNRRSHIFT ASSIGNRSHIFT ASSIGNTIMES AWAIT BACKSLASH BAND BITINV BNEGATE BOR BREAK BXOR BYTE CASE CATCH CHAR CLASS CLOSECOM COLON COMMA COMMENT COND_DOT CONST CONTINUE DEC DEFAULT DELETE DIVIDE DO DOT DOUBLE ELSE EMPTYLINE EQUAL EQUAL_STRICT EXPONENT EXPORT EXTENDS FINALLY FOR FROM FUNCTION GET GLOBAL GTHAN GTHANEQ ID IF IMPORT IN INC INFERRED INSTANCEOF LAND LBRACKET LET LLSHIFT LOR LPAREN LSBRACKET LSHIFT LTHAN LTHANEQ MINUS MLSTRLIT MOD NATIVE NEW NOT NOTEQUAL NOTEQUAL_STRICT NUMBER OF OPENCOM PLUS QEST RBRACKET REGEXPR RETURN RPAREN RRSHIFT RSBRACKET RSHIFT SEMI SET SHORT SIGNED SLASHR STATIC STRINGLIT SWITCH TEMPLATE TEMPLATE_STR TGTHAN THROW TIMES TLTHAN TRIPLEDOT TRY TYPED TYPEOF VAR VARIABLE VAR_TYPE_PREC WHILE WITH YIELD newlinelthan : LTHAN\n | TLTHAN\n gthan : GTHAN\n | TGTHAN\n id : ID\n | GET\n | SET\n | STATIC\n | CATCH\n | GLOBAL\n | AWAIT\n left_id : id id_opt : id\n |\n id_var_type : id \n id_var_decl : id \n var_type : var_type id_var_type\n | id_var_type\n | SHORT\n | DOUBLE\n | CHAR\n | BYTE\n | INFERRED\n | var_type template_ref\n \n templatedeflist : var_type\n | var_type ASSIGN var_type\n | templatedeflist COMMA var_type\n | templatedeflist COMMA var_type ASSIGN var_type\n template : lthan templatedeflist gthan\n typeof_opt : TYPEOF\n |\n \n simple_templatedeflist : typeof_opt var_type\n | simple_templatedeflist COMMA typeof_opt var_type\n template_ref : lthan simple_templatedeflist gthan\n template_ref_validate : lthan simple_templatedeflist gthan\n template_validate : template\n | template_ref_validate\n '
_lr_action_items = {'LTHAN': ([0, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 33, 34, 36, 37, 39, 42, 43, 44], [5, 5, -18, -19, -20, -21, -22, -23, -15, -5, -6, -7, -8, -9, -10, -11, -3, -4, -17, -24, 5, 5, 5, 5, -34, 5]), 'TLTHAN': ([0, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 33, 34, 36, 37, 39, 42, 43, 44], [6, 6, -18, -19, -20, -21, -22, -23, -15, -5, -6, -7, -8, -9, -10, -11, -3, -4, -17, -24, 6, 6, 6, 6, -34, 6]), '$end': ([1, 2, 3, 26, 28, 29, 30], [0, -36, -37, -29, -3, -4, -35]), 'SHORT': ([4, 5, 6, 10, 17, 27, 31, 32, 35, 38, 41], [12, -1, -2, 12, -30, 12, -31, 12, -31, 12, 12]), 'DOUBLE': ([4, 5, 6, 10, 17, 27, 31, 32, 35, 38, 41], [13, -1, -2, 13, -30, 13, -31, 13, -31, 13, 13]), 'CHAR': ([4, 5, 6, 10, 17, 27, 31, 32, 35, 38, 41], [14, -1, -2, 14, -30, 14, -31, 14, -31, 14, 14]), 'BYTE': ([4, 5, 6, 10, 17, 27, 31, 32, 35, 38, 41], [15, -1, -2, 15, -30, 15, -31, 15, -31, 15, 15]), 'INFERRED': ([4, 5, 6, 10, 17, 27, 31, 32, 35, 38, 41], [16, -1, -2, 16, -30, 16, -31, 16, -31, 16, 16]), 'TYPEOF': ([4, 5, 6, 31, 35], [17, -1, -2, 17, 17]), 'ID': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [19, -1, -2, 19, 19, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 19, -3, -4, -31, 19, -17, -24, -31, 19, 19, 19, 19, 19, 19, -34, 19]), 'GET': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [20, -1, -2, 20, 20, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 20, -3, -4, -31, 20, -17, -24, -31, 20, 20, 20, 20, 20, 20, -34, 20]), 'SET': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [21, -1, -2, 21, 21, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 21, -3, -4, -31, 21, -17, -24, -31, 21, 21, 21, 21, 21, 21, -34, 21]), 'STATIC': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [22, -1, -2, 22, 22, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 22, -3, -4, -31, 22, -17, -24, -31, 22, 22, 22, 22, 22, 22, -34, 22]), 'CATCH': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [23, -1, -2, 23, 23, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 23, -3, -4, -31, 23, -17, -24, -31, 23, 23, 23, 23, 23, 23, -34, 23]), 'GLOBAL': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [24, -1, -2, 24, 24, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 24, -3, -4, -31, 24, -17, -24, -31, 24, 24, 24, 24, 24, 24, -34, 24]), 'AWAIT': ([4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44], [25, -1, -2, 25, 25, -18, -19, -20, -21, -22, -23, -30, -15, -5, -6, -7, -8, -9, -10, -11, 25, -3, -4, -31, 25, -17, -24, -31, 25, 25, 25, 25, 25, 25, -34, 25]), 'COMMA': ([7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 33, 34, 36, 37, 39, 40, 42, 43, 44], [27, 31, -25, -18, -19, -20, -21, -22, -23, -15, -5, -6, -7, -8, -9, -10, -11, -3, -4, -17, -24, -32, -27, -26, 31, -33, -34, -28]), 'GTHAN': ([7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 33, 34, 36, 37, 39, 40, 42, 43, 44], [28, 28, -25, -18, -19, -20, -21, -22, -23, -15, -5, -6, -7, -8, -9, -10, -11, -3, -4, -17, -24, -32, -27, -26, 28, -33, -34, -28]), 'TGTHAN': ([7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 33, 34, 36, 37, 39, 40, 42, 43, 44], [29, 29, -25, -18, -19, -20, -21, -22, -23, -15, -5, -6, -7, -8, -9, -10, -11, -3, -4, -17, -24, -32, -27, -26, 29, -33, -34, -28]), 'ASSIGN': ([9, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 28, 29, 33, 34, 37, 43], [32, -18, -19, -20, -21, -22, -23, -15, -5, -6, -7, -8, -9, -10, -11, -3, -4, -17, -24, 41, -34])}
_lr_action = {}
for (_k, _v) in _lr_action_items.items():
for (_x, _y) in zip(_v[0], _v[1]):
if not _x in _lr_action:
_lr_action[_x] = {}
_lr_action[_x][_k] = _y
del _lr_action_items
_lr_goto_items = {'template_validate': ([0], [1]), 'template': ([0], [2]), 'template_ref_validate': ([0], [3]), 'lthan': ([0, 9, 36, 37, 39, 42, 44], [4, 35, 35, 35, 35, 35, 35]), 'templatedeflist': ([4], [7]), 'simple_templatedeflist': ([4, 35], [8, 40]), 'var_type': ([4, 10, 27, 32, 38, 41], [9, 36, 37, 39, 42, 44]), 'typeof_opt': ([4, 31, 35], [10, 38, 10]), 'id_var_type': ([4, 9, 10, 27, 32, 36, 37, 38, 39, 41, 42, 44], [11, 33, 11, 11, 11, 33, 33, 11, 33, 11, 33, 33]), 'id': ([4, 9, 10, 27, 32, 36, 37, 38, 39, 41, 42, 44], [18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18]), 'gthan': ([7, 8, 40], [26, 30, 43]), 'template_ref': ([9, 36, 37, 39, 42, 44], [34, 34, 34, 34, 34, 34])}
_lr_goto = {}
for (_k, _v) in _lr_goto_items.items():
for (_x, _y) in zip(_v[0], _v[1]):
if not _x in _lr_goto:
_lr_goto[_x] = {}
_lr_goto[_x][_k] = _y
del _lr_goto_items
_lr_productions = [("S' -> template_validate", "S'", 1, None, None, None), ('lthan -> LTHAN', 'lthan', 1, 'p_lthan', 'js_parse_template.py', 12), ('lthan -> TLTHAN', 'lthan', 1, 'p_lthan', 'js_parse_template.py', 13), ('gthan -> GTHAN', 'gthan', 1, 'p_gthan', 'js_parse_template.py', 18), ('gthan -> TGTHAN', 'gthan', 1, 'p_gthan', 'js_parse_template.py', 19), ('id -> ID', 'id', 1, 'p_id', 'js_parse_template.py', 24), ('id -> GET', 'id', 1, 'p_id', 'js_parse_template.py', 25), ('id -> SET', 'id', 1, 'p_id', 'js_parse_template.py', 26), ('id -> STATIC', 'id', 1, 'p_id', 'js_parse_template.py', 27), ('id -> CATCH', 'id', 1, 'p_id', 'js_parse_template.py', 28), ('id -> GLOBAL', 'id', 1, 'p_id', 'js_parse_template.py', 29), ('id -> AWAIT', 'id', 1, 'p_id', 'js_parse_template.py', 30), ('left_id -> id', 'left_id', 1, 'p_left_id', 'js_parse_template.py', 35), ('id_opt -> id', 'id_opt', 1, 'p_id_opt', 'js_parse_template.py', 39), ('id_opt -> <empty>', 'id_opt', 0, 'p_id_opt', 'js_parse_template.py', 40), ('id_var_type -> id', 'id_var_type', 1, 'p_id_var_type', 'js_parse_template.py', 46), ('id_var_decl -> id', 'id_var_decl', 1, 'p_id_var_decl', 'js_parse_template.py', 51), ('var_type -> var_type id_var_type', 'var_type', 2, 'p_var_type', 'js_parse_template.py', 56), ('var_type -> id_var_type', 'var_type', 1, 'p_var_type', 'js_parse_template.py', 57), ('var_type -> SHORT', 'var_type', 1, 'p_var_type', 'js_parse_template.py', 58), ('var_type -> DOUBLE', 'var_type', 1, 'p_var_type', 'js_parse_template.py', 59), ('var_type -> CHAR', 'var_type', 1, 'p_var_type', 'js_parse_template.py', 60), ('var_type -> BYTE', 'var_type', 1, 'p_var_type', 'js_parse_template.py', 61), ('var_type -> INFERRED', 'var_type', 1, 'p_var_type', 'js_parse_template.py', 62), ('var_type -> var_type template_ref', 'var_type', 2, 'p_var_type', 'js_parse_template.py', 63), ('templatedeflist -> var_type', 'templatedeflist', 1, 'p_templatedeflist', 'js_parse_template.py', 69), ('templatedeflist -> var_type ASSIGN var_type', 'templatedeflist', 3, 'p_templatedeflist', 'js_parse_template.py', 70), ('templatedeflist -> templatedeflist COMMA var_type', 'templatedeflist', 3, 'p_templatedeflist', 'js_parse_template.py', 71), ('templatedeflist -> templatedeflist COMMA var_type ASSIGN var_type', 'templatedeflist', 5, 'p_templatedeflist', 'js_parse_template.py', 72), ('template -> lthan templatedeflist gthan', 'template', 3, 'p_template', 'js_parse_template.py', 77), ('typeof_opt -> TYPEOF', 'typeof_opt', 1, 'p_typeof_opt', 'js_parse_template.py', 82), ('typeof_opt -> <empty>', 'typeof_opt', 0, 'p_typeof_opt', 'js_parse_template.py', 83), ('simple_templatedeflist -> typeof_opt var_type', 'simple_templatedeflist', 2, 'p_simple_templatedeflist', 'js_parse_template.py', 93), ('simple_templatedeflist -> simple_templatedeflist COMMA typeof_opt var_type', 'simple_templatedeflist', 4, 'p_simple_templatedeflist', 'js_parse_template.py', 94), ('template_ref -> lthan simple_templatedeflist gthan', 'template_ref', 3, 'p_template_ref', 'js_parse_template.py', 99), ('template_ref_validate -> lthan simple_templatedeflist gthan', 'template_ref_validate', 3, 'p_template_ref_validate', 'js_parse_template.py', 104), ('template_validate -> template', 'template_validate', 1, 'p_template_validate', 'js_parse_template.py', 109), ('template_validate -> template_ref_validate', 'template_validate', 1, 'p_template_validate', 'js_parse_template.py', 110)] |
def f():
print('f executed from module 1')
if __name__ == '__main__':
print('We are in module 1')
| def f():
print('f executed from module 1')
if __name__ == '__main__':
print('We are in module 1') |
class ParserError(Exception):
"""
Base parser exception class.
Throws when any error occurs.
"""
pass
| class Parsererror(Exception):
"""
Base parser exception class.
Throws when any error occurs.
"""
pass |
n=int(input("Enter a number : "))
r=int(input("Enter range of table : "))
print("Multiplication Table of",n,"is")
for i in range(0,r):
i=i+1
print(n,"X",i,"=",n*i)
print("Loop completed") | n = int(input('Enter a number : '))
r = int(input('Enter range of table : '))
print('Multiplication Table of', n, 'is')
for i in range(0, r):
i = i + 1
print(n, 'X', i, '=', n * i)
print('Loop completed') |
PRACTICE = False
N_DAYS = 80
with open("test.txt" if PRACTICE else "input.txt", "r") as f:
content = f.read().strip()
state = list(map(int, content.split(",")))
def next_day(state):
new_state = []
num_new = 0
for fish in state:
if fish == 0:
new_state.append(6)
num_new += 1
else:
new_state.append(fish - 1)
return new_state + [8] * num_new
for days in range(N_DAYS):
state = next_day(state)
# print("After {:2} days: ".format(days + 1) + ",".join(map(str, state)))
print(len(state))
| practice = False
n_days = 80
with open('test.txt' if PRACTICE else 'input.txt', 'r') as f:
content = f.read().strip()
state = list(map(int, content.split(',')))
def next_day(state):
new_state = []
num_new = 0
for fish in state:
if fish == 0:
new_state.append(6)
num_new += 1
else:
new_state.append(fish - 1)
return new_state + [8] * num_new
for days in range(N_DAYS):
state = next_day(state)
print(len(state)) |
# This program says hello
print("Hello World!")
# Ask the user to input their name and assign it to the name variable
print("What is your name? ")
myName = input()
# Print out greet followed by name
print("It is good to meet you, " + myName)
# Print out the length of the name
print("The length of your name " + str(len(myName)))
# Ask for your age and show how old you will be next year
print("What is your age?")
myAge = input()
print("You will be " + str(int(myAge)+1) + "in a year. ")
| print('Hello World!')
print('What is your name? ')
my_name = input()
print('It is good to meet you, ' + myName)
print('The length of your name ' + str(len(myName)))
print('What is your age?')
my_age = input()
print('You will be ' + str(int(myAge) + 1) + 'in a year. ') |
N = int(input())
x, y = 0, 0
for _ in range(N):
T, S = input().split()
T = int(T)
x += min(int(12 * T / 1000), len(S))
y += max(len(S) - int(12 * T / 1000), 0)
print(x, y)
| n = int(input())
(x, y) = (0, 0)
for _ in range(N):
(t, s) = input().split()
t = int(T)
x += min(int(12 * T / 1000), len(S))
y += max(len(S) - int(12 * T / 1000), 0)
print(x, y) |
# -*- coding: utf-8 -*-
# tomolab
# Michele Scipioni
# Harvard University, Martinos Center for Biomedical Imaging
# University of Pisa
LIGHT_BLUE = "rgb(200,228,246)"
BLUE = "rgb(47,128,246)"
LIGHT_RED = "rgb(246,228,200)"
RED = "rgb(246,128,47)"
LIGHT_GRAY = "rgb(246,246,246)"
GRAY = "rgb(200,200,200)"
GREEN = "rgb(0,100,0)" | light_blue = 'rgb(200,228,246)'
blue = 'rgb(47,128,246)'
light_red = 'rgb(246,228,200)'
red = 'rgb(246,128,47)'
light_gray = 'rgb(246,246,246)'
gray = 'rgb(200,200,200)'
green = 'rgb(0,100,0)' |
__all__ = [
'manager', \
'node', \
'feature', \
'python_utils'
]
| __all__ = ['manager', 'node', 'feature', 'python_utils'] |
TEST_LAT=-12
TEST_LONG=60
TEST_LOCATION_HIERARCHY_FOR_GEO_CODE=['madagascar']
class DummyLocationTree(object):
def get_location_hierarchy_for_geocode(self, lat, long ):
return TEST_LOCATION_HIERARCHY_FOR_GEO_CODE
def get_centroid(self, location_name, level):
if location_name=="jalgaon" and level==2:
return None
return TEST_LONG, TEST_LAT
def get_location_hierarchy(self,lowest_level_location_name):
if lowest_level_location_name=='pune':
return ['pune','mh','india']
| test_lat = -12
test_long = 60
test_location_hierarchy_for_geo_code = ['madagascar']
class Dummylocationtree(object):
def get_location_hierarchy_for_geocode(self, lat, long):
return TEST_LOCATION_HIERARCHY_FOR_GEO_CODE
def get_centroid(self, location_name, level):
if location_name == 'jalgaon' and level == 2:
return None
return (TEST_LONG, TEST_LAT)
def get_location_hierarchy(self, lowest_level_location_name):
if lowest_level_location_name == 'pune':
return ['pune', 'mh', 'india'] |
#
# PySNMP MIB module HUAWEI-PGI-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/HUAWEI-PGI-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 19:35:58 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
SingleValueConstraint, ConstraintsIntersection, ValueSizeConstraint, ConstraintsUnion, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ConstraintsIntersection", "ValueSizeConstraint", "ConstraintsUnion", "ValueRangeConstraint")
hwDatacomm, = mibBuilder.importSymbols("HUAWEI-MIB", "hwDatacomm")
ModuleCompliance, ObjectGroup, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "ObjectGroup", "NotificationGroup")
Counter64, Gauge32, Integer32, TimeTicks, MibScalar, MibTable, MibTableRow, MibTableColumn, ObjectIdentity, iso, ModuleIdentity, MibIdentifier, Bits, Unsigned32, Counter32, IpAddress, NotificationType = mibBuilder.importSymbols("SNMPv2-SMI", "Counter64", "Gauge32", "Integer32", "TimeTicks", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ObjectIdentity", "iso", "ModuleIdentity", "MibIdentifier", "Bits", "Unsigned32", "Counter32", "IpAddress", "NotificationType")
DisplayString, RowStatus, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "RowStatus", "TextualConvention")
hwPortGroupIsolation = ModuleIdentity((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144))
if mibBuilder.loadTexts: hwPortGroupIsolation.setLastUpdated('200701010000Z')
if mibBuilder.loadTexts: hwPortGroupIsolation.setOrganization('Huawei Technologies Co. Ltd.')
hwPortGroupIsolationMibObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1))
hwPortGroupIsolationConfigTable = MibTable((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1), )
if mibBuilder.loadTexts: hwPortGroupIsolationConfigTable.setStatus('current')
hwPortGroupIsolationConfigEntry = MibTableRow((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1), ).setIndexNames((0, "HUAWEI-PGI-MIB", "hwPortGroupIsolationIndex"))
if mibBuilder.loadTexts: hwPortGroupIsolationConfigEntry.setStatus('current')
hwPortGroupIsolationIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 1024)))
if mibBuilder.loadTexts: hwPortGroupIsolationIndex.setStatus('current')
hwPortGroupIsolationIfName = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 11), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: hwPortGroupIsolationIfName.setStatus('current')
hwPortGroupIsolationGroupID = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 12), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 255))).setMaxAccess("readcreate")
if mibBuilder.loadTexts: hwPortGroupIsolationGroupID.setStatus('current')
hwPortGroupIsolationConfigRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 51), RowStatus()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: hwPortGroupIsolationConfigRowStatus.setStatus('current')
hwPortGroupIsolationConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3))
hwPortGroupIsolationCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 1))
hwPortGroupIsolationCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 1, 1)).setObjects(("HUAWEI-PGI-MIB", "hwPortGroupIsolationObjectGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hwPortGroupIsolationCompliance = hwPortGroupIsolationCompliance.setStatus('current')
hwPortGroupIsolationGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 3))
hwPortGroupIsolationObjectGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 3, 1)).setObjects(("HUAWEI-PGI-MIB", "hwPortGroupIsolationIfName"), ("HUAWEI-PGI-MIB", "hwPortGroupIsolationGroupID"), ("HUAWEI-PGI-MIB", "hwPortGroupIsolationConfigRowStatus"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hwPortGroupIsolationObjectGroup = hwPortGroupIsolationObjectGroup.setStatus('current')
mibBuilder.exportSymbols("HUAWEI-PGI-MIB", PYSNMP_MODULE_ID=hwPortGroupIsolation, hwPortGroupIsolation=hwPortGroupIsolation, hwPortGroupIsolationIfName=hwPortGroupIsolationIfName, hwPortGroupIsolationCompliance=hwPortGroupIsolationCompliance, hwPortGroupIsolationConformance=hwPortGroupIsolationConformance, hwPortGroupIsolationConfigTable=hwPortGroupIsolationConfigTable, hwPortGroupIsolationIndex=hwPortGroupIsolationIndex, hwPortGroupIsolationGroups=hwPortGroupIsolationGroups, hwPortGroupIsolationConfigEntry=hwPortGroupIsolationConfigEntry, hwPortGroupIsolationMibObjects=hwPortGroupIsolationMibObjects, hwPortGroupIsolationObjectGroup=hwPortGroupIsolationObjectGroup, hwPortGroupIsolationGroupID=hwPortGroupIsolationGroupID, hwPortGroupIsolationCompliances=hwPortGroupIsolationCompliances, hwPortGroupIsolationConfigRowStatus=hwPortGroupIsolationConfigRowStatus)
| (octet_string, integer, object_identifier) = mibBuilder.importSymbols('ASN1', 'OctetString', 'Integer', 'ObjectIdentifier')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(single_value_constraint, constraints_intersection, value_size_constraint, constraints_union, value_range_constraint) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'SingleValueConstraint', 'ConstraintsIntersection', 'ValueSizeConstraint', 'ConstraintsUnion', 'ValueRangeConstraint')
(hw_datacomm,) = mibBuilder.importSymbols('HUAWEI-MIB', 'hwDatacomm')
(module_compliance, object_group, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'ObjectGroup', 'NotificationGroup')
(counter64, gauge32, integer32, time_ticks, mib_scalar, mib_table, mib_table_row, mib_table_column, object_identity, iso, module_identity, mib_identifier, bits, unsigned32, counter32, ip_address, notification_type) = mibBuilder.importSymbols('SNMPv2-SMI', 'Counter64', 'Gauge32', 'Integer32', 'TimeTicks', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'ObjectIdentity', 'iso', 'ModuleIdentity', 'MibIdentifier', 'Bits', 'Unsigned32', 'Counter32', 'IpAddress', 'NotificationType')
(display_string, row_status, textual_convention) = mibBuilder.importSymbols('SNMPv2-TC', 'DisplayString', 'RowStatus', 'TextualConvention')
hw_port_group_isolation = module_identity((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144))
if mibBuilder.loadTexts:
hwPortGroupIsolation.setLastUpdated('200701010000Z')
if mibBuilder.loadTexts:
hwPortGroupIsolation.setOrganization('Huawei Technologies Co. Ltd.')
hw_port_group_isolation_mib_objects = mib_identifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1))
hw_port_group_isolation_config_table = mib_table((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1))
if mibBuilder.loadTexts:
hwPortGroupIsolationConfigTable.setStatus('current')
hw_port_group_isolation_config_entry = mib_table_row((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1)).setIndexNames((0, 'HUAWEI-PGI-MIB', 'hwPortGroupIsolationIndex'))
if mibBuilder.loadTexts:
hwPortGroupIsolationConfigEntry.setStatus('current')
hw_port_group_isolation_index = mib_table_column((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 1), integer32().subtype(subtypeSpec=value_range_constraint(1, 1024)))
if mibBuilder.loadTexts:
hwPortGroupIsolationIndex.setStatus('current')
hw_port_group_isolation_if_name = mib_table_column((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 11), octet_string().subtype(subtypeSpec=value_size_constraint(0, 255))).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
hwPortGroupIsolationIfName.setStatus('current')
hw_port_group_isolation_group_id = mib_table_column((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 12), integer32().subtype(subtypeSpec=value_range_constraint(1, 255))).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
hwPortGroupIsolationGroupID.setStatus('current')
hw_port_group_isolation_config_row_status = mib_table_column((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 1, 1, 1, 51), row_status()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
hwPortGroupIsolationConfigRowStatus.setStatus('current')
hw_port_group_isolation_conformance = mib_identifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3))
hw_port_group_isolation_compliances = mib_identifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 1))
hw_port_group_isolation_compliance = module_compliance((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 1, 1)).setObjects(('HUAWEI-PGI-MIB', 'hwPortGroupIsolationObjectGroup'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hw_port_group_isolation_compliance = hwPortGroupIsolationCompliance.setStatus('current')
hw_port_group_isolation_groups = mib_identifier((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 3))
hw_port_group_isolation_object_group = object_group((1, 3, 6, 1, 4, 1, 2011, 5, 25, 144, 3, 3, 1)).setObjects(('HUAWEI-PGI-MIB', 'hwPortGroupIsolationIfName'), ('HUAWEI-PGI-MIB', 'hwPortGroupIsolationGroupID'), ('HUAWEI-PGI-MIB', 'hwPortGroupIsolationConfigRowStatus'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hw_port_group_isolation_object_group = hwPortGroupIsolationObjectGroup.setStatus('current')
mibBuilder.exportSymbols('HUAWEI-PGI-MIB', PYSNMP_MODULE_ID=hwPortGroupIsolation, hwPortGroupIsolation=hwPortGroupIsolation, hwPortGroupIsolationIfName=hwPortGroupIsolationIfName, hwPortGroupIsolationCompliance=hwPortGroupIsolationCompliance, hwPortGroupIsolationConformance=hwPortGroupIsolationConformance, hwPortGroupIsolationConfigTable=hwPortGroupIsolationConfigTable, hwPortGroupIsolationIndex=hwPortGroupIsolationIndex, hwPortGroupIsolationGroups=hwPortGroupIsolationGroups, hwPortGroupIsolationConfigEntry=hwPortGroupIsolationConfigEntry, hwPortGroupIsolationMibObjects=hwPortGroupIsolationMibObjects, hwPortGroupIsolationObjectGroup=hwPortGroupIsolationObjectGroup, hwPortGroupIsolationGroupID=hwPortGroupIsolationGroupID, hwPortGroupIsolationCompliances=hwPortGroupIsolationCompliances, hwPortGroupIsolationConfigRowStatus=hwPortGroupIsolationConfigRowStatus) |
class Solution:
def frequencySort(self, s: str) -> str:
freq = {}
for ch in s:
if(ch in freq):
freq[ch] += 1
else:
freq[ch] = 1
out = ""
for k,v in sorted(freq.items(), key=lambda x: x[1], reverse=True):
out += (k * v)
return out | class Solution:
def frequency_sort(self, s: str) -> str:
freq = {}
for ch in s:
if ch in freq:
freq[ch] += 1
else:
freq[ch] = 1
out = ''
for (k, v) in sorted(freq.items(), key=lambda x: x[1], reverse=True):
out += k * v
return out |
psys_game_rain = 0
psys_game_snow = 1
psys_game_blood = 2
psys_game_blood_2 = 3
psys_game_hoof_dust = 4
psys_game_hoof_dust_mud = 5
psys_game_water_splash_1 = 6
psys_game_water_splash_2 = 7
psys_game_water_splash_3 = 8
psys_torch_fire = 9
psys_fire_glow_1 = 10
psys_fire_glow_fixed = 11
psys_torch_smoke = 12
psys_flue_smoke_short = 13
psys_flue_smoke_tall = 14
psys_war_smoke_tall = 15
psys_ladder_dust_6m = 16
psys_ladder_dust_8m = 17
psys_ladder_dust_10m = 18
psys_ladder_dust_12m = 19
psys_ladder_dust_14m = 20
psys_ladder_straw_6m = 21
psys_ladder_straw_8m = 22
psys_ladder_straw_10m = 23
psys_ladder_straw_12m = 24
psys_ladder_straw_14m = 25
psys_torch_fire_sparks = 26
psys_fire_sparks_1 = 27
psys_pistol_smoke = 28
psys_brazier_fire_1 = 29
psys_cooking_fire_1 = 30
psys_cooking_smoke = 31
psys_food_steam = 32
psys_candle_light = 33
psys_candle_light_small = 34
psys_lamp_fire = 35
psys_dummy_smoke = 36
psys_dummy_smoke_big = 37
psys_gourd_smoke = 38
psys_gourd_piece_1 = 39
psys_gourd_piece_2 = 40
psys_fire_fly_1 = 41
psys_bug_fly_1 = 42
psys_moon_beam_1 = 43
psys_moon_beam_paricle_1 = 44
psys_night_smoke_1 = 45
psys_fireplace_fire_small = 46
psys_fireplace_fire_big = 47
psys_village_fire_big = 48
psys_village_fire_smoke_big = 49
psys_map_village_fire = 50
psys_map_village_fire_smoke = 51
psys_map_village_looted_smoke = 52
psys_dungeon_water_drops = 53
psys_wedding_rose = 54
psys_sea_foam_a = 55
psys_fall_leafs_a = 56
psys_desert_storm = 57
psys_blizzard = 58
psys_rain = 59
psys_oil = 60
psys_ship_shrapnel = 61
psys_lanse = 62
psys_lanse_straw = 63
psys_dummy_straw = 64
psys_dummy_straw_big = 65
psys_lanse_blood = 66
psys_blood_decapitation = 67
| psys_game_rain = 0
psys_game_snow = 1
psys_game_blood = 2
psys_game_blood_2 = 3
psys_game_hoof_dust = 4
psys_game_hoof_dust_mud = 5
psys_game_water_splash_1 = 6
psys_game_water_splash_2 = 7
psys_game_water_splash_3 = 8
psys_torch_fire = 9
psys_fire_glow_1 = 10
psys_fire_glow_fixed = 11
psys_torch_smoke = 12
psys_flue_smoke_short = 13
psys_flue_smoke_tall = 14
psys_war_smoke_tall = 15
psys_ladder_dust_6m = 16
psys_ladder_dust_8m = 17
psys_ladder_dust_10m = 18
psys_ladder_dust_12m = 19
psys_ladder_dust_14m = 20
psys_ladder_straw_6m = 21
psys_ladder_straw_8m = 22
psys_ladder_straw_10m = 23
psys_ladder_straw_12m = 24
psys_ladder_straw_14m = 25
psys_torch_fire_sparks = 26
psys_fire_sparks_1 = 27
psys_pistol_smoke = 28
psys_brazier_fire_1 = 29
psys_cooking_fire_1 = 30
psys_cooking_smoke = 31
psys_food_steam = 32
psys_candle_light = 33
psys_candle_light_small = 34
psys_lamp_fire = 35
psys_dummy_smoke = 36
psys_dummy_smoke_big = 37
psys_gourd_smoke = 38
psys_gourd_piece_1 = 39
psys_gourd_piece_2 = 40
psys_fire_fly_1 = 41
psys_bug_fly_1 = 42
psys_moon_beam_1 = 43
psys_moon_beam_paricle_1 = 44
psys_night_smoke_1 = 45
psys_fireplace_fire_small = 46
psys_fireplace_fire_big = 47
psys_village_fire_big = 48
psys_village_fire_smoke_big = 49
psys_map_village_fire = 50
psys_map_village_fire_smoke = 51
psys_map_village_looted_smoke = 52
psys_dungeon_water_drops = 53
psys_wedding_rose = 54
psys_sea_foam_a = 55
psys_fall_leafs_a = 56
psys_desert_storm = 57
psys_blizzard = 58
psys_rain = 59
psys_oil = 60
psys_ship_shrapnel = 61
psys_lanse = 62
psys_lanse_straw = 63
psys_dummy_straw = 64
psys_dummy_straw_big = 65
psys_lanse_blood = 66
psys_blood_decapitation = 67 |
# config:utf-8
"""
production settings file.
"""
| """
production settings file.
""" |
def isEven(num):
num1 = num / 2
num2 = num // 2
if num1 == num2:
return True
else:
return False
# pi = (4/1) - (4/3) + (4/5) - (4/7) + (4/9) - (4/11) + (4/13) - (4/15)
pi = 0.0
for index, i in enumerate(range(1, 100), start=1):
thing = (4/((2 * index) - 1))
if isEven(index):
pi -= thing
else:
pi += thing
print(pi)
| def is_even(num):
num1 = num / 2
num2 = num // 2
if num1 == num2:
return True
else:
return False
pi = 0.0
for (index, i) in enumerate(range(1, 100), start=1):
thing = 4 / (2 * index - 1)
if is_even(index):
pi -= thing
else:
pi += thing
print(pi) |
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