# -*- coding: utf-8 -*-
import os
import json
import datetime
import time
import itertools
import logging
logging.basicConfig(level=logging.DEBUG)

import numpy

import bottle 

import ebus.datastore

datastore = ebus.datastore.Datastore("hdf-data")
app = bottle.Bottle("ebus")

lastflush = -1

@app.route('/')
def index_file():
    return static_files("index.html")

@app.route('/static/:filename#.+#')
def static_files(filename):
    return bottle.static_file(filename, root=os.path.join(os.path.dirname(__file__),"static"))


@app.get('/sensor/:name')
def sensor_data_get(name):
    try:
        table = datastore.getTable(name)
        with datastore:
            data = table.readSorted(sortby="timestamp", checkCSI=True, start=0, stop=1,step=-1).tolist()[0]
            return {'sensor':name,'error':None,'data':data}
    except Exception,e:
        return {'sensor':name,'data':None, 'error':str(e)}

@app.put('/sensor/:name')
@app.put('/sensor/:name/:timestamp')
def sensor_data_put(name,timestamp=None):
    FLUSH_INTERVAL=30 #sec
    global lastflush

    if not timestamp: timestamp = int(time.time())
    try:
        value = bottle.request.POST.value
        type = bottle.request.POST.type

        if type == "int":
            klass = ebus.datastore.ValueInt
        elif type == "float":
            klass = ebus.datastore.ValueFloat
        elif type == "string":
            klass = ebus.datastore.ValueString
	elif type == "":
	    klass = None
        else:
            return {'error':'INVALID_TYPE', msg:'Type {0} is invalid'.format(type)}

	msg = "Storing {0} of type {1} with timestamp {2} to {3}".format(value,type,timestamp,name)
	logging.info(msg)

        datastore.addValue(name, timestamp, value, klass, flush=False)

        if time.time() - FLUSH_INTERVAL > lastflush:
	    logging.info("call datastore.flush()")
            datastore.flush()
	    lastflush = time.time()

        return {'error':None,'msg':msg}
    except Exception,e:
	logging.error("Error: " + "{0} name={1} value={2} type={3}".format(e, name, value, type))
        return {'error':str(e),'msg':str(e)}



@app.route('/sensor/:name/:startdate/:enddate')
def sensor_name_start_end(name,startdate,enddate):
    SAMPLING_STEPSIZE=500
    NUMBER_OF_VALUES=250
    try:
        startdate, enddate = int(startdate), int(enddate)
	logging.info("/sensor/ start={0} end={1}".format(startdate, enddate))
        table=datastore.getTable(name)
        with datastore:
	    i = table.where("(timestamp >= startdate) & (timestamp <= enddate)",
			    condvars={'startdate':startdate,'enddate':enddate,'timestamp':table.cols.timestamp},
			    step=SAMPLING_STEPSIZE)
	    timestamps = []
	    try:
	        for x in range(20):
		    i.next()
		    timestamps.append(i['timestamp'])
	    except: pass

	    if len(timestamps) > 10:
		    diff = map(lambda (x1,x2): (x2-x1)/SAMPLING_STEPSIZE, zip(timestamps[:-1], timestamps[1:]))
		    diff_avg = numpy.average(diff)
		    time_period = enddate - startdate
		    samples = time_period / diff_avg
		    step = numpy.ceil(samples / float(NUMBER_OF_VALUES))

		    data = [(x['timestamp'], x['value']) 
			    for x in table.where("(timestamp >= startdate) & (timestamp <= enddate)", step=step)]
		    logging.info("diff={0} samples={1} step={2} len={3} ({4})".format(diff_avg, samples, step, len(data),name))
	    else:
		    # Not enough data, sampling approach useless, deliver all data
		    data = [(x['timestamp'], x['value']) for x in table.where("(timestamp >= startdate) & (timestamp <= enddate)")]
            return {'sensor':name, 'error':None,'data':data}
    except Exception,e:
	logging.error("Error: " + str(e) + str(type(e)))
        return {'sensor':name,'data':None, 'error':str(e)}

@app.route('/avg/:name/:startdate')
@app.route('/avg/:name/:startdate/:period')
def sensor_avg_start(name, startdate, period=60*15): #15min
    AVG_STEPSIZE=50
    try:
        startdate, enddate = int(startdate), int(time.time())
	logging.info("/avg/ start={0} end={1}".format(startdate, enddate))
        table=datastore.getTable(name)
        with datastore:
            sel_rows = table.where("(timestamp >= startdate) & (timestamp <=enddate)", step=AVG_STEPSIZE)
            f_group = range(startdate, enddate, period)
            data = map(lambda (group_id, grouped_rows): (group_id, numpy.average([row['value'] for row in grouped_rows])),
                    itertools.groupby(sel_rows, lambda t: (t['timestamp']/period)*period))
            data = map(lambda (timestamp,value): (timestamp, value), data)
            return {'sensor':name, 'error':None,'data':data}
    except Exception,e:
            return {'sensor':name, 'error':str(e), 'data':None}