From 36b7f950ac0e068a0a190bdca4a6f316014f9d26 Mon Sep 17 00:00:00 2001
From: Richard Sowers <r-sowers@illinois.edu>
Date: Wed, 7 Mar 2018 04:20:53 -0600
Subject: [PATCH] Combined Intervals files into one json file, Intervals.py. 
 Removed algorithm.py

---
 CONTRIBUTING.md       |   2 +-
 LICENSE => LICENSE.md |   0
 algorithm.ipynb       | 226 ++++++--------------------------------
 algorithm.py          | 249 ------------------------------------------
 intervals.py          |   6 +
 intervals_0.txt       |   1 -
 intervals_1.txt       |   1 -
 intervals_2.txt       |   1 -
 intervals_3.txt       |   1 -
 9 files changed, 41 insertions(+), 446 deletions(-)
 rename LICENSE => LICENSE.md (100%)
 delete mode 100644 algorithm.py
 create mode 100644 intervals.py
 delete mode 100644 intervals_0.txt
 delete mode 100644 intervals_1.txt
 delete mode 100644 intervals_2.txt
 delete mode 100644 intervals_3.txt

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index d83c65f..868c1fd 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,2 +1,2 @@
-Peter Maneykowski did some early versions of the algorithm
+Peter Maneykowski (ISE undergraduate) did some early versions of the algorithm.
 Nitin Srivastava (M.S. in ISE) wrote a thesis connected to this work.
\ No newline at end of file
diff --git a/LICENSE b/LICENSE.md
similarity index 100%
rename from LICENSE
rename to LICENSE.md
diff --git a/algorithm.ipynb b/algorithm.ipynb
index ffd5453..cbed481 100644
--- a/algorithm.ipynb
+++ b/algorithm.ipynb
@@ -66,11 +66,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['351554053682895', '353918057262822', '353918059182986', '869578020239930']\n",
+      "[Timestamp('2016-02-19 00:00:00'), Timestamp('2016-02-22 00:00:00')]\n"
+     ]
+    }
+   ],
    "source": [
     "#############code to get data\n",
     "class getData:\n",
@@ -97,25 +106,18 @@
     "\t\treturn reduced_data\n",
     "    \n",
     "    \n",
-    "gd=getData(fname)"
+    "gd=getData(fname)\n",
+    "print(sorted(gd.IMEISet))\n",
+    "print(gd.dateSet)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "351554053682895\n",
-      "2016-02-19 00:00:00\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_imei=gd.IMEISet[0]\n",
     "test_date=gd.dateSet[0]\n",
@@ -123,17 +125,6 @@
     "print(test_date)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -143,36 +134,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "B(0)= 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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82b66RNX7pB2P4gcu+JJk+29tvyTpFkn3Nz2fBXS7pG83PQmc1jJJL8+4/YoyC0YpbK+Q\ndIWkF5udSb1sj9iekPS6pGciYkensY0E3/YztvfMuOxtf/19SYqI+yJiuaQvS7qriTn2otv+tcf8\nlapfJLe9wamekZT9AwZJeznnK5L+bNYqwtCLiOmIWK1qteBq25d2GtvrefhnJCI+nDh0u6RvSRpb\nuNnUr9v+2b5V0vWSruvLhGo2j7+/HLwqafmM2+e378OQsD2qKvb/HBGnfFYoFxHx37afk7RO0o/m\nGjNwSzq2V864eaOqNbds2F4n6c8l3RAR/9v0fBZYDuv4OySttH2B7cWSNqr6wGFurDz+vubyuKQf\nRcSDTU+kbrbfZfuc9vVflfRhSR3fkB7Es3S+IukiVW/WHpb0yYj4SbOzqo/tSUmLJf1X+67vR8Sf\nNDilWtm+UdIXJL1L0lFJuyNifbOz6k37RfpBVQdIW3L74KDt7ZJaks6VdETS5ojY2uikamL7A5Ke\nl7RX1ZuZIekvI+LpRidWE9uXqfpNxSPty5MR8Xcdxw9a8AEAC2PglnQAAAuD4ANAIQg+ABSC4ANA\nIQg+ABSC4ANAIQg+ABSC4ANAIf4POUP8Mo9npB0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x286a34c51d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "class Box:\n",
     "    def __init__(self, width=1,height=1,shift=0):\n",
@@ -228,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -374,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -427,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -448,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -468,36 +434,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "size of data:  325\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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hxO+/B7bP3Ll6G6dPD+6cv/+u379WreDtDgU2m2/LKthW8tKlIgf/3TMh7Rot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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x286a37f3e80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "h0_latitude\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "IMEI=gd.IMEISet[0] #should be either 0,1,2, or 3\n",
     "\n",
@@ -530,37 +471,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of yvals_short:  34.179845825600005\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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sTlNMkniXWqqGhKvqe8DhIjIEeEREXlTVWmtMT5s2rep+UVERRUVFjXjZNJo2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\nyBhjjC8sARljjPHF/wedVF8MrUIk1QAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x286a35438d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "h0_reduced_boxexample\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "yvals=lats\n",
     "\n",
@@ -597,21 +513,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "making feature list\n",
-      "there are  1300 features\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "TVALS=numpy.array(data.index.get_level_values(\"locationTimestamp\"))\n",
     "SHIFTS=TVALS\n",
@@ -635,20 +542,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "mean of alpha_e 0.029380998406231047\n",
-      "stdev of alpha_e 0.9553821210312209\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "EML2=L0_EM(data,FEATURES,0)\n",
     "\n",
@@ -668,29 +566,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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goFUd+KgeNnbLpP3cGfQRwrr/MaDTuOe88hjT6XQM//RTxqCltVgDvAAMj45G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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x286a34de550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "h0_L2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "makeplot(EML2,alpha_e,startFlag=False)#,fname=\"fourthharvester\")\n",
     "print(\"h0_L2\")"
@@ -709,35 +589,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max(alpha_e): 34.17966762063247\n",
-      "min(alpha_e): -0.8828588614123873\n",
-      "mean(alpha_e) 0.029380998406231047\n",
-      "std(alpha_e) 0.9553821210312209\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEPCAYAAACzwehFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQpLUZFhIkpoMC0lSk2EhSWr6fwcEw18ERhYmAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x286a87b1320>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "h0_L2_hist\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"max(alpha_e):\",numpy.max(alpha_e))\n",
     "print(\"min(alpha_e):\",numpy.min(alpha_e))\n",
@@ -767,17 +619,7 @@
     "collapsed": false,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "L0_EM created\n",
-      "about to iterate\n",
-      "n= 0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "KAP=1E-10 #for box\n",
     "myEM=L0_EM(data,FEATURES,KAP)\n",
diff --git a/algorithm.py b/algorithm.py
deleted file mode 100644
index 3dacf5e..0000000
--- a/algorithm.py
+++ /dev/null
@@ -1,249 +0,0 @@
-import numpy
-import itertools
-import pandas
-import sys
-
-IMEI=int(sys.argv[1])
-out_fname="intervals_"+str(IMEI)+".txt"
-
-class getData:
-	def __init__(self,fname):
-		#sheetname="outdata_with_time"
-		#raw_data = pandas.read_excel("outdata_with_time.xlsx", sheetname="outdata_with_time", header=0)
-		raw_data=pandas.read_csv(str(fname)+".csv")
-		self.data=raw_data.loc[:,["IMEI","Latitude","Longitude","locationTimestamp", "timestamp"]]
-		self.data["IMEI"] = self.data["IMEI"].astype("str")
-		self.data = self.data.drop_duplicates()
-		#print(data)
-		self.IMEISet=sorted(list(frozenset(self.data["IMEI"])))
-		self.data["date"]=pandas.to_datetime(self.data.loc[:,"timestamp"]).dt.date
-
-		self.dateSet=sorted(list(frozenset(self.data["date"])))
-		print(self.IMEISet)
-		print([str(d) for d in self.dateSet])
-
-	def get(self,IMEI_index,date_index,outlist=["locationTimestamp","Latitude","Longitude","timestamp"]):
-		outlist=list(outlist)
-		self.data_idx={}
-		for n,d in enumerate(outlist):
-			self.data_idx[d]=n
-		date=self.dateSet[date_index]
-
-		imei=self.IMEISet[IMEI_index]
-		flags=(self.data["date"]==date) & (self.data["IMEI"]==imei)
-		reduced_data=self.data.loc[flags,outlist].as_matrix()
-		#temp=numpy.array(reduced_data).reshape([-1,len(outlist)])
-		temp=reduced_data.tolist()
-		#print("shape of data: ",temp.shape)
-		return temp
-
-
-
-class Box:
-    def __init__(self, width=1,height=1,shift=0):
-        self.width=float(width)
-        self.height=float(height)
-        self.shift=float(shift)
-        if (self.width<0):
-            raise ValueError('negative width in LeftBox')
-
-    def refBox(self,x):
-        x=float(x)
-        #width=1,height=1,shift=0
-        return 1 if 0<=x<=1 else 0
-
-    def eval(self, x):
-        if self.width<=0:
-            return numpy.inf
-        return self.height * self.refBox((x-self.shift)/self.width)
-    
-    def harvestFlag(self,x):
-        return 0<(x-self.shift)<self.width
-    
-    def __le__(self,other):
-        if not isinstance(other, Box):
-            return NotImplemented
-        #return ((other.shift<=self.shift) and ((self.shift+self.width)<=(other.shift+other.width)))
-        return (other.shift<=self.shift<=(other.shift+other.width))
-    
-    def __ge__(self,other):
-        return (other<=self)
-
-class L0_EM:
-    def __init__(self,data,feature_info,vkap,tau=0.01):
-        #data=[[time_1,y_1],[time_2,y_2],....]
-        data=list(data)
-        self.N_data=len(data)
-        self.times=numpy.array([line[0] for line in data])
-        self.y=numpy.matrix([line[1] for line in data]).transpose()
-        
-        N_finer=10
-        self.times_finer=numpy.linspace(min(self.times),max(self.times),N_finer*len(self.times))
-        
-        self.feature_info=list(feature_info)
-        features=[[f.eval(t) for t in self.times] for f in self.feature_info]
-        #features=[[feature_1(time_1),feature_1(time_2)..],[feature_2(time_1)..]..]
-        self.N_features=len(features)
-        self.Feat_e_T=numpy.matrix(features+[numpy.ones(self.N_data)])
-        self.Feat_e=self.Feat_e_T.transpose()
-        
-        dt=numpy.diff(self.times)
-        temp=(dt[1:]+dt[:-1])/2
-        D=numpy.concatenate(([dt[0]],temp,[dt[-1]]))
-        self.D=numpy.diag(D)
-
-        self.A=self.Feat_e_T.dot(self.D).dot(self.Feat_e)
-        self.b=self.Feat_e_T.dot(self.D).dot(self.y)
-    
-
-        self.Id=numpy.diag([float(vkap)]*self.N_features+[0])
-        self.alpha_e=None
-        self.stopFlag=False;
-
-        self.tau=float(tau)
-        self.feature_alpha_e=None
-        self.feature_count=None
-        self.feature_times=[]
-        self.feature_peaks=[]
-        self.flags=[]
-        self.dalpha=None
-
-
-    def initialize(self):
-        #print("rank(A): ",numpy.linalg.matrix_rank(self.A))
-        #print("shape of A: ",self.A.shape)
-        #self.alpha_e=numpy.linalg.solve(self.A,self.b)
-        self.alpha_e=numpy.linalg.pinv(self.A).dot(self.b)
-        #print("initial alpha: ",self.alpha_e)
-        return(self.alpha_e)
-
-    def iterate(self,alpha_e=None):
-
-        #alpha_e is external, self.alpha_e is class variable
-        alpha_e=numpy.matrix(alpha_e,dtype='float').reshape([-1,1]) if alpha_e is not None else self.alpha_e
-        temp=numpy.ravel(alpha_e)**2
-        temp[self.N_features]=1
-        S=numpy.diag(temp)
-
-        new_alpha_e=numpy.linalg.pinv(S.dot(self.A)+self.Id).dot(S.dot(self.b))
-
-        denom=numpy.linalg.norm(numpy.ravel(self.alpha_e),1)
-        num=numpy.linalg.norm(numpy.ravel(new_alpha_e-self.alpha_e),1)
-        self.dalpha=num/denom
-        print("dalpha/alpha=",self.dalpha)
-        self.stopFlag=(num<self.tau*denom)
-        self.alpha_e=new_alpha_e
-
-        self.feature_alpha_e=None
-        self.feature_count=None
-        self.feature_times=[]
-        self.feature_peaks=[]
-        self.flags=[]
-        self.intervals=[]
-        return(self.alpha_e)
-
-    def evaluate(self,alpha_e=None):
-        alpha_e=numpy.matrix(alpha_e,dtype='float').reshape([-1,1]) if alpha_e is not None else self.alpha_e
-        return self.Feat_e.dot(alpha_e)
-    
-    def evaluate_finer(self,alpha_e=None):
-        alpha_e=self.alpha_e if alpha_e is None else alpha_e
-        alpha_e=numpy.ravel(alpha_e)
-        constant=alpha_e[self.N_features]
-        temp=numpy.array([constant]*len(self.times_finer))
-        for n,f in enumerate(self.feature_info):
-            temp+=numpy.array([alpha_e[n]*f.eval(t) for t in self.times_finer])
-        return temp
-    
-    
-    def combine(self,a,b):
-        return (min(a[0],b[0]),max(a[1],b[1]))
-            
-    def findfeatures(self,alpha_e=None,delta=0.01,combineFlag=True):
-        alpha_e=numpy.matrix(alpha_e,dtype='float').reshape([-1,1]) if alpha_e is not None else self.alpha_e
-        alpha_e=numpy.ravel(alpha_e)
-        delta=0 if (delta is False) else float(delta) #feature threshold
-        self.feature_count=0
-        self.feature_times=[]
-        self.feature_peaks=[]
-        self.flags=[]
-        self.intervals=[(f.shift,f.shift+f.width) for aa,f in zip(alpha_e,self.feature_info)]
-            
-        
-        #threshold out the small features
-        alpha_e=numpy.array([aa if abs(aa)>=delta else 0 for aa in alpha_e])
-        
-        #combine features
-        if combineFlag:
-            for n in range(self.N_features-1,-1,-1):
-                int_n=self.intervals[n]
-                for nn in range(n-1,-1,-1):
-                    int_nn=self.intervals[nn]
-                    Flag=(alpha_e[n]!=0) and (alpha_e[nn]!=0)
-                    #Flag = Flag and (numpy.sign(alpha_e[n])==numpy.sign(alpha_e[nn]))
-                    Flag = Flag and (self.feature_info[nn]>=self.feature_info[n])
-                    if (Flag):
-                        alpha_e[nn]+=alpha_e[n]
-                        alpha_e[n]=0
-                        self.intervals[nn]=self.combine(int_n,int_nn)
-        
-        for aa,f in zip(alpha_e,self.feature_info):
-            if abs(aa)==0:
-                continue
-            tempflags=numpy.array([f.harvestFlag(tt) for tt in self.times],dtype='bool')
-            self.flags.append(tempflags)
-            self.feature_times.append(f.shift)
-            self.feature_peaks.append(f.height*aa+alpha_e[self.N_features])
-            self.feature_count+=1
-        self.feature_times=numpy.array(self.feature_times)
-        self.feature_peaks=numpy.array(self.feature_peaks)
-        self.intervals=[ival for aa,ival in zip(alpha_e,self.intervals) if abs(aa)!=0]
-        return alpha_e
-
-
-fname="outdata_with_time"
-gd=getData(fname)
-DATE=0
-HEIGHT=0.0002
-WIDTHS=[200,300,400,500]
-
-
-#IMEI=2
-raw_data=gd.get(IMEI,DATE)
-data=raw_data#[0:500]
-TVALS=numpy.array([line[gd.data_idx["locationTimestamp"]] for line in data])
-SHIFTS=TVALS
-N_ITER=30
-
-print("making feature list",flush=True)
-FEATURES=[]
-for s,w in itertools.product(sorted(SHIFTS),sorted(WIDTHS,reverse=True)):
-    FEATURES.append(Box(height=HEIGHT,width=w,shift=s))    
-print("there are ",len(FEATURES), "features", flush=True)
-
-
-KAP=1.5E-7 #for box
-myEM=L0_EM(data,FEATURES,KAP)
-
-print("L0_EM created", flush=True)
-alpha_e=myEM.initialize()
-print("about to iterate", flush=True)
-N_iter=20
-for n in range(N_iter):
-    print("n=",n,flush=True)
-    alpha_e=myEM.iterate(alpha_e)
-    myEM.findfeatures()
-    if (myEM.stopFlag):
-        break
-        
-print("done")
-
-DELTA=0.01 #don't threshold
-COMBINEFLAG=True #don't combine features
-alpha_e_uncombined=myEM.findfeatures(alpha_e=alpha_e,delta=DELTA,combineFlag=COMBINEFLAG)
-print("there are ",myEM.feature_count,"features", flush=True)
-
-print(myEM.intervals)
-
-with open(out_fname,'w') as f:
-	f.write(str(myEM.intervals))
diff --git a/intervals.py b/intervals.py
new file mode 100644
index 0000000..2389007
--- /dev/null
+++ b/intervals.py
@@ -0,0 +1,6 @@
+Intervals={
+'351554053682895':[(1455901052.0, 1455901925.0), (1455902880.0, 1455903380.0), (1455903382.0, 1455903973.0), (1455903987.0, 1455904387.0), (1455904491.0, 1455904991.0), (1455905055.0, 1455905255.0), (1455905777.0, 1455907652.0), (1455907743.0, 1455908925.0), (1455908951.0, 1455909690.0), (1455910298.0, 1455911149.0), (1455911381.0, 1455911881.0), (1455912123.0, 1455912958.0), (1455915030.0, 1455915330.0), (1455915371.0, 1455916250.0), (1455916469.0, 1455916669.0), (1455917074.0, 1455917911.0)],
+'353918057262822':[(1455901334.0, 1455901856.0), (1455901905.0, 1455902404.0), (1455903541.0, 1455904542.0), (1455904610.0, 1455905196.0), (1455905223.0, 1455906007.0), (1455906065.0, 1455906595.0), (1455906723.0, 1455908210.0), (1455908285.0, 1455908955.0), (1455909139.0, 1455909639.0), (1455910149.0, 1455911169.0), (1455911300.0, 1455912032.0), (1455912103.0, 1455912967.0), (1455915249.0, 1455917584.0)]
+'353918059182986':[(1455901176.0, 1455901897.0), (1455902110.0, 1455902410.0), (1455903567.0, 1455904534.0), (1455904647.0, 1455905147.0), (1455905337.0, 1455905882.0), (1455906117.0, 1455906632.0), (1455906701.0, 1455907567.0), (1455907737.0, 1455908237.0), (1455908397.0, 1455909091.0), (1455909147.0, 1455909578.0), (1455910156.0, 1455911162.0), (1455911298.0, 1455912972.0), (1455915341.0, 1455916086.0), (1455916090.0, 1455917587.0)]
+'869578020239930':[(1455901393.0, 1455901818.0), (1455902046.0, 1455902709.0), (1455903709.0, 1455904869.0), (1455904877.0, 1455905077.0), (1455905153.0, 1455905653.0), (1455905657.0, 1455906157.0), (1455906248.0, 1455906793.0), (1455907628.0, 1455908962.0), (1455908988.0, 1455910082.0), (1455910341.0, 1455911281.0), (1455911283.0, 1455911803.0), (1455912036.0, 1455912653.0), (1455912666.0, 1455913478.0), (1455914616.0, 1455915016.0), (1455915124.0, 1455916136.0), (1455916199.0, 1455917576.0)]
+}
diff --git a/intervals_0.txt b/intervals_0.txt
deleted file mode 100644
index deefb14..0000000
--- a/intervals_0.txt
+++ /dev/null
@@ -1 +0,0 @@
-[(1455901052.0, 1455901925.0), (1455902880.0, 1455903380.0), (1455903382.0, 1455903973.0), (1455903987.0, 1455904387.0), (1455904491.0, 1455904991.0), (1455905055.0, 1455905255.0), (1455905777.0, 1455907652.0), (1455907743.0, 1455908925.0), (1455908951.0, 1455909690.0), (1455910298.0, 1455911149.0), (1455911381.0, 1455911881.0), (1455912123.0, 1455912958.0), (1455915030.0, 1455915330.0), (1455915371.0, 1455916250.0), (1455916469.0, 1455916669.0), (1455917074.0, 1455917911.0)]
\ No newline at end of file
diff --git a/intervals_1.txt b/intervals_1.txt
deleted file mode 100644
index f16a23a..0000000
--- a/intervals_1.txt
+++ /dev/null
@@ -1 +0,0 @@
-[(1455901334.0, 1455901856.0), (1455901905.0, 1455902404.0), (1455903541.0, 1455904542.0), (1455904610.0, 1455905196.0), (1455905223.0, 1455906007.0), (1455906065.0, 1455906595.0), (1455906723.0, 1455908210.0), (1455908285.0, 1455908955.0), (1455909139.0, 1455909639.0), (1455910149.0, 1455911169.0), (1455911300.0, 1455912032.0), (1455912103.0, 1455912967.0), (1455915249.0, 1455917584.0)]
\ No newline at end of file
diff --git a/intervals_2.txt b/intervals_2.txt
deleted file mode 100644
index f1d9901..0000000
--- a/intervals_2.txt
+++ /dev/null
@@ -1 +0,0 @@
-[(1455901176.0, 1455901897.0), (1455902110.0, 1455902410.0), (1455903567.0, 1455904534.0), (1455904647.0, 1455905147.0), (1455905337.0, 1455905882.0), (1455906117.0, 1455906632.0), (1455906701.0, 1455907567.0), (1455907737.0, 1455908237.0), (1455908397.0, 1455909091.0), (1455909147.0, 1455909578.0), (1455910156.0, 1455911162.0), (1455911298.0, 1455912972.0), (1455915341.0, 1455916086.0), (1455916090.0, 1455917587.0)]
\ No newline at end of file
diff --git a/intervals_3.txt b/intervals_3.txt
deleted file mode 100644
index 54fd91c..0000000
--- a/intervals_3.txt
+++ /dev/null
@@ -1 +0,0 @@
-[(1455901393.0, 1455901818.0), (1455902046.0, 1455902709.0), (1455903709.0, 1455904869.0), (1455904877.0, 1455905077.0), (1455905153.0, 1455905653.0), (1455905657.0, 1455906157.0), (1455906248.0, 1455906793.0), (1455907628.0, 1455908962.0), (1455908988.0, 1455910082.0), (1455910341.0, 1455911281.0), (1455911283.0, 1455911803.0), (1455912036.0, 1455912653.0), (1455912666.0, 1455913478.0), (1455914616.0, 1455915016.0), (1455915124.0, 1455916136.0), (1455916199.0, 1455917576.0)]
\ No newline at end of file
-- 
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