Sampath Kumar msampathkumar

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Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Loosely ordered with the commands I use most towards the top. Sublime also offer full documentation.

Ctrl+C	copy current line (if no selection)
Ctrl+X	cut current line (if no selection)
Ctrl+⇧+K	delete line
Ctrl+↩	insert line after

	#
	# Copyright 201 Google Inc. All Rights Reserved.
	#
	# 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

	# Extracted using: $ unzip -p lib/pycharm.jar com/jetbrains/python/PyBundle.properties \| grep -B1 INSP.NAME \| grep '^#' \| sed 's\|Inspection\|\|g' \| sed -e 's\|#\s\{,1\}\|# noinspection \|'

	# noinspection PyPep8
	# noinspection PyPep8Naming
	# noinspection PyTypeChecker
	# noinspection PyAbstractClass
	# noinspection PyArgumentEqualDefault
	# noinspection PyArgumentList
	# noinspection PyAssignmentToLoopOrWithParameter
	# noinspection PyAttributeOutsideInit

	from airflow.models import Variable

	# Common (Not-so-nice way)
	# 3 DB connections when the file is parsed
	var1 = Variable.get("var1")
	var2 = Variable.get("var2")
	var3 = Variable.get("var3")

	# Recommended Way
	# Just 1 Database call

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	# A simple cheat sheet of Spark Dataframe syntax
	# Current for Spark 1.6.1

	# import statements
	from pyspark.sql import SQLContext
	from pyspark.sql.types import *
	from pyspark.sql.functions import *

	#creating dataframes
	df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data

	#!/usr/bin/python

	import subprocess
	import re

	# Get process info
	ps = subprocess.Popen(['ps', '-caxm', '-orss,comm'], stdout=subprocess.PIPE).communicate()[0]
	vm = subprocess.Popen(['vm_stat'], stdout=subprocess.PIPE).communicate()[0]

	# Iterate processes