justxor

## doc.sh

#
# Always install local overrides first
#
/app/docker/docker-bootstrap.sh

STEP_CNT=4

echo_step() {
cat <<EOF

## sds.py
# import html
import html

s = '<html><head></head><body><h1>GeeksForGeeks</h1></body></html>'
# Using html.escape() method
gfg = html.escape(s)

print(gfg)

#Вывод:

## pg.py
>>> import phonenumbers
>>> x = phonenumbers.parse("+442083661177", None)
>>> print(x)
Country Code: 44 National Number: 2083661177 Leading Zero: False
>>> type(x)
<class 'phonenumbers.phonenumber.PhoneNumber'>
>>> y = phonenumbers.parse("020 8366 1177", "GB")
>>> print(y)
Country Code: 44 National Number: 2083661177 Leading Zero: False
>>> x == y

## dec.py
import time
def slow(seconds):
  def decorator_slow(func):
  def wrapper( * args, ** kwargs):
  print(f '{func.__name__} sleeping {seconds} second')
time.sleep(seconds)
result_value = func( * args, ** kwargs)
return result_value
return wrapper
return decorator_slow

## dsd.yml
# This is a sample config file that you can use as a reference for building your own.

# The "features" section describes how to map your input events into ML features that Metarank understands.
# See doc/feature_extractors.md for a general overview of possible things you can do, and doc/features
# for detailed per-extractor configuration options.

# These features can be shared between multiple models, so if you have a model A using features 1-2-3 and
# a model B using features 1-2, then all three features will be computed only once.
# You need to explicitely include a feture in the model configuration for Metarank to use it.
features:

## mat.js
Math.fround(0);     // 0
Math.fround(1);     // 1
Math.fround(1.337); // 1.3370000123977661
Math.fround(1.5);   // 1.5
Math.fround(NaN);   // NaN

## dssd.py
list_ = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
print(list_)

# lambda function
lambda_list = list(map(lambda x: x * 2, list_))

# Map basically iterates every element
# in the list_ and returns the lambda
# function result
print(lambda_list)

## sta.py
from datetime import date

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    # a class method to create a Person object by birth year.
    @classmethod
    def fromBirthYear(cls, name, year):

## sdd.js
var numObj = 5.123456;

console.log(numObj.toPrecision());    // выведет '5.123456'
console.log(numObj.toPrecision(5));   // выведет '5.1235'
console.log(numObj.toPrecision(2));   // выведет '5.1'
console.log(numObj.toPrecision(1));   // выведет '5'

numObj = 0.000123;

console.log(numObj.toPrecision());    // выведет '0.000123'

## ds.py
howdoi print stack trace python
> import traceback
>
> try:
>     1/0
> except:
>     print '>>> traceback <<<'
>     traceback.print_exc()
>     print '>>> end of traceback <<<'
> traceback.print_exc()

	#
	# Always install local overrides first
	#
	/app/docker/docker-bootstrap.sh

	STEP_CNT=4

	echo_step() {
	cat <<EOF
	# import html
	import html

	s = '<html><head></head><body><h1>GeeksForGeeks</h1></body></html>'
	# Using html.escape() method
	gfg = html.escape(s)

	print(gfg)

	#Вывод:
	>>> import phonenumbers
	>>> x = phonenumbers.parse("+442083661177", None)
	>>> print(x)
	Country Code: 44 National Number: 2083661177 Leading Zero: False
	>>> type(x)
	<class 'phonenumbers.phonenumber.PhoneNumber'>
	>>> y = phonenumbers.parse("020 8366 1177", "GB")
	>>> print(y)
	Country Code: 44 National Number: 2083661177 Leading Zero: False
	>>> x == y
	import time
	def slow(seconds):
	def decorator_slow(func):
	def wrapper( * args, ** kwargs):
	print(f '{func.__name__} sleeping {seconds} second')
	time.sleep(seconds)
	result_value = func( * args, ** kwargs)
	return result_value
	return wrapper
	return decorator_slow
	# This is a sample config file that you can use as a reference for building your own.

	# The "features" section describes how to map your input events into ML features that Metarank understands.
	# See doc/feature_extractors.md for a general overview of possible things you can do, and doc/features
	# for detailed per-extractor configuration options.

	# These features can be shared between multiple models, so if you have a model A using features 1-2-3 and
	# a model B using features 1-2, then all three features will be computed only once.
	# You need to explicitely include a feture in the model configuration for Metarank to use it.
	features:
	Math.fround(0); // 0
	Math.fround(1); // 1
	Math.fround(1.337); // 1.3370000123977661
	Math.fround(1.5); // 1.5
	Math.fround(NaN); // NaN
	list_ = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
	print(list_)

	# lambda function
	lambda_list = list(map(lambda x: x * 2, list_))

	# Map basically iterates every element
	# in the list_ and returns the lambda
	# function result
	print(lambda_list)
	from datetime import date

	class Person:
	def __init__(self, name, age):
	self.name = name
	self.age = age

	# a class method to create a Person object by birth year.
	@classmethod
	def fromBirthYear(cls, name, year):
	var numObj = 5.123456;

	console.log(numObj.toPrecision()); // выведет '5.123456'
	console.log(numObj.toPrecision(5)); // выведет '5.1235'
	console.log(numObj.toPrecision(2)); // выведет '5.1'
	console.log(numObj.toPrecision(1)); // выведет '5'

	numObj = 0.000123;

	console.log(numObj.toPrecision()); // выведет '0.000123'
	howdoi print stack trace python
	> import traceback
	>
	> try:
	> 1/0
	> except:
	> print '>>> traceback <<<'
	> traceback.print_exc()
	> print '>>> end of traceback <<<'
	> traceback.print_exc()