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##############################################################
#
#  read_totem_data.R
#
#  Import script
#
#  Parses raw Totem Sensor accelerometer data in R
#  And looks for peaks (steps, cycles, repeats) 
#

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# -*- coding: utf-8 -*-

## IPython Reset

#from IPython import get_ipython
#get_ipython().magic('reset -sf')

##############################################################################

import numpy as np

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# -*- coding: utf-8 -*-

#from IPython import get_ipython
#get_ipython().magic('reset -sf')

import numpy as np
import matplotlib.pyplot as plt
import json

##############################################################################

robinvanemden

# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import urllib,json,codecs

#change to pymongo for greater compatibility
import iopro 

def getobs( x, max = 5, err=0 ):
    if (err==0):

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library(ggplot2)
library(jsonlite)
library(rmongodb)

setwd("C:/Users/User/Desktop/LiF")

BASE_URL =    "http://localhost:8080"
key =         "51fc58011"    
question_nr = 2626
customers <-  4000

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library(Cairo)
library(ggplot2)
library(animation)

############################## configuration ##############################


# set working directory

setwd("C:/Users/robin/Desktop/Monopolist")

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# -*- coding: utf-8 -*-

#from IPython import get_ipython
#get_ipython().magic('reset -sf')


import sys,random
import ujson as json
import asyncio,aiohttp,pymongo
import numpy as np

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# Implementation of Lock in Feedback.
# -*- coding: utf-8 -*-
from libs.base import *
import numpy as np
import ujson as json
import sys

class Lif:

    def __init__(self, theta, x0=1.0, A=1.4, T=100, gamma=.004, omega=0.8, lifversion=2):

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import numpy as np

def matrixpush(m, row):
    if not np.all(np.isfinite(values[:,0])):
        i = np.count_nonzero(np.logical_not(np.isnan(values[:,0])))
        m[i,] = row
    else:
        m = np.vstack([m,row])
        m = m[1:,]
    return(m)

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for t in range(0,stream):
    x = x0 + amplitude*np.cos(omega * t)
    y = amplitude*np.cos(omega * t)*getobs(x,5,variance)
    values = matrixpush(values, np.array([t,x,y]))
    if np.all(np.isfinite(values[:,0])):
        x0 = x0 + learnrate * sum( values[:,2] ) / inttime