"""
	This program is designed to inspect an application environment
	This program should only be run on unix friendly systems

	We enable the engines to be able to run a several configurations
	Similarly to what a visitor design-pattern would do
"""
from __future__  import division
import os
import subprocess
from sets import Set
import re
import datetime
import Queue
from threading import Thread, RLock
import time
class Analysis:
	def __init__(self):
		self.logs = []
		pass
	def post(self,object):
		self.logs.append(object)
	def init(self):
		d = datetime.datetime.now()
		self.now = {"month":d.month,"year":d.year, "day":d.day,"hour":d.hour,"minute":d.minute}
	def getNow(self):
		d = datetime.datetime.now()
		return {"month":d.month,"year":d.year, "day":d.day,"hour":d.hour,"minute":d.minute}

"""
	This class is designed to analyze environment variables. Environment variables can either be folders, files or simple values
	The class returns a quantifiable assessment of the environment variables (expected 100%)
"""
class Env(Analysis):
	def __init__(self):
		Analysis.__init__(self)
	def init(self,values):
		#Analysis.init(self)
		self.values = values
	"""
		This function evaluate the validity of an environment variable by returning a 1 or 0 (computable)
		The function will use propositional logic (https://en.wikipedia.org/wiki/Propositional_calculus)
	"""
	def evaluate(self,id):
		
		if id in os.environ :
			#
			# We can inspect to make sure the environment variable is not a path or filename.
			# Using propositional logic we proceed as follows:
			# 	- (p) We determine if the value is an folder or file name (using regex)
			# 	- (q) In case of a file or folder we check for existance
			# The final result is a conjuction of p and q
			#
			value = os.environ[id]
			expressions = [os.sep,'(\\.\w+)$']
			p = sum([ re.search(xchar,value) is not None for xchar in expressions])
			q = os.path.exists(value)
			
			return int(p and q)
		else:
			return 0
	
	def composite (self):
		#Analysis.init(self)
		r = [ self.evaluate(id) for id in self.values] ;		
		N = len(r)
		n = sum(r)
		value = n/N
		missing = [self.values[i] for i in range(0,N) if r[i] == 0]
		return dict(self.getNow(),**{"value":value,"missing":missing})

class Sandbox(Analysis):
	def __init__(self):
		Analysis.__init__(self)
	def init(self,conf):
		#Analysis.init(self)
		self.sandbox_path = conf['sandbox']
		self.requirements_path = conf['requirements']
	def get_requirements (self):
		f = open(self.requirements_path)
		return [ name.replace('-',' ').replace('_',' ') for name in f.read().split('\n') if name != '']
	"""
		This function will return the modules installed in the sandbox (virtual environment)
	"""
	def get_sandbox_requirements(self):
		cmd = ['freeze']
		xchar = ''.join([os.sep]*2)
		pip_vm = ''.join([self.sandbox_path,os.sep,'bin',os.sep,'pip']).replace(xchar,os.sep)
		cmd = [pip_vm]+cmd
		r = subprocess.check_output(cmd).split('\n')
		return [row.replace('-',' ').replace('_',' ') for row in r if row.strip() != '']	
	def evaluate(self):
		pass
	"""
		This function returns the ratio of existing modules relative to the ones expected
	"""
	def composite(self):
		Analysis.init(self)
		required_modules= self.get_requirements()
		sandbox_modules	= self.get_sandbox_requirements()
		N = len(required_modules)
		n = len(Set(required_modules) - Set(sandbox_modules))
		value = 1 - (n/N)
		missing = list(Set(required_modules) - Set(sandbox_modules))
		
		return dict(self.getNow(),**{"value":value,"missing":missing})

"""
	This class performs the analysis of a list of processes and determines
	The class provides a quantifiable measure of how many processes it found over all
"""
class ProcessCounter(Analysis):
	def __init__(self):
		Analysis.__init__(self)
	def init(self,names):
		#Analysis.init(self)
		self.names = names
	def evaluate(self,name):
		cmd  = "".join(['ps -eo comm |grep ',name,' |wc -l'])
		handler = subprocess.Popen(cmd,shell=True,stdout=subprocess.PIPE)
		
		return int(handler.communicate()[0].replace("\n","") )
	def composite(self):
		#Analysis.init(self)
		r = {}
		for name in self.names :
			r[name] = self.evaluate(name)
		
		#N = len(r)
		#n = sum(r)
		#return n/N
		return dict(self.getNow(),**r)
"""
	This class returns an application's both memory and cpu usage
"""
class DetailProcess(Analysis):
	def __init__(self):
		Analysis.__init__(self)
	def init (self,names):
		#Analysis.init(self)
		self.names = names;
	
	def split(self,name,stream):
		pattern = " (\d+.{0,1}\d*)\x20*(\d+.{0,1}\d*)\x20*(\d+.{0,1}\d*)\x20".replace(":name",name)
		g = re.match(pattern,stream)
		if g :
			return list(g.groups())+[name]
		else:
			return ''
	def evaluate(self,name) :
		cmd	= "ps -eo pmem,pcpu,vsize,comm|grep -E \":app\""
		handler = subprocess.Popen(cmd.replace(":app",name),shell=True,stdout=subprocess.PIPE)
		ostream = handler.communicate()[0].split('\n')
		
		ostream = [ self.split(name,row) for row in ostream if row != '']
		if len(ostream) == 0 or len(ostream[0]) < 4 :
			ostream = [['0','0','0',name]]
		r = []
		for row in ostream :
			#
			# Though the comm should only return the name as specified,
			# On OSX it has been observed that the fully qualified path is sometimes returned (go figure)
			#
			row =  [float(value) for value in row if value.strip() != '' and name not in value ] +[re.sub('\$|^','',name)]
			
			r.append(row)
		return r
	def status(self,row):
		x = row['memory_usage']
		y = row['cpu_usage']
		z = row['memory_available']
		if z :
			if y :
				return "running"
			return "idle"
		else:
			return "crash"
	def format(self,row):
		r= {"memory_usage":row[0],"cpu_usage":row[1],"memory_available":row[2]/1000,"label":row[3]}
		status = self.status(r)
		r['status'] = status
		return r
		#return dict(self.getNow(),**r)
	def composite(self):
		#Analysis.init(self)
		
		#value = self.evaluate(self.name)
		#row= {"memory_usage":value[0],"cpu_usage":value[1]}
		#return row
		#ma = [self.evaluate(name) for name in self.names]
		ma = []
		now = self.getNow()
		for name in self.names:
			
			matrix = self.evaluate(name)
			
			ma += [ dict(now, **self.format(row)) for row in matrix]
			
		#return [{"memory_usage":row[0],"cpu_usage":row[1],"memory_available":row[2]/1000,"label":row[3]} for row in ma]
		
		return ma

class Monitor (Thread):
	def __init__(self,pConfig,pWriter,id='processes') :
		Thread.__init__(self)
		
		self.config 	= pConfig[id]
		self.writer	= pWriter;
		self.logs	= []
		self.handler = self.config['class']
		self.mconfig = self.config['config']
		
	def stop(self):
		self.keep_running = False
	def run(self):
		r = {}
		self.keep_running = True
		lock = RLock()
		while self.keep_running:
			
			for label in self.mconfig:
				lock.acquire()
				self.handler.init(self.mconfig[label])
				r = self.handler.composite()
				self.writer.write(label=label,row = r)
				lock.release()		
			
			
			self.prune()
			HALF_HOUR = 60*1
			time.sleep(HALF_HOUR)
		print "Stopped ..."
	def prune(self) :
		MAX_ENTRIES = 100
		if len(self.logs) > MAX_ENTRIES :
			BEG = len(self.logs) - MAX_SIZE -1 
			self.logs = self.logs[BEG:]

class mapreducer:
	def __init__(self):
		self.store = {}
	def filter (self,key,dataset):
		return [row[key] for row in dataset if key in row]
	def run(self,dataset,mapper,reducer):
		r = None
		if mapper is not None:
			if isinstance(dataset,list) :
				[mapper(row,self.emit) for row in dataset]
			
			
		if reducer is not None:
			r = self.store
			# r = [reducer(self.store[key]) for key in self.store]
		else:
			r = self.store
		return r
	def mapper(self,row,emit):
		[emit(_matrix['label'],_matrix) for _matrix in row ]
	def reducer(self,values):
		beg = len(values)-101 if len(values) > 100 else 0
		return values[beg:]

	def emit(self,key,content):
		if key not in self.store:
			self.store[key] = []
		self.store[key].append(content)
# 	#
# 	# We need to generate the appropriate dataset here
# 	# map/reduce is a well documented technique for generating datasets
# 	#
# 	def map(self,key,id,rows):
		
# 		#r =  [row[key] for row in rows if key in row]
# 		for row in rows:
# 			if key in row :
# 				for xr in row[key]:
# 					self.emit(xr['label'],xr)
		
		

# def reduce(keys,values):
# 	print values[0]
# 	return r