diff --git a/Get-elements.py b/Get-elements.py
deleted file mode 100644
index 68d6f83..0000000
--- a/Get-elements.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-
-img1 = cv2.imread('Gstar.png')
-img=cv2.medianBlur(img1,7)
-hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
-print hsv.shape
-#cv2.namedWindow('img', cv2.WINDOW_NORMAL)
-#cv2.namedWindow('graph', cv2.WINDOW_NORMAL)
-print "starting frequency"
-start=input()
-print "Final frequency"
-end=input()
-adlinesfinal=[]
-specgraph = np.zeros(img.shape, np.uint8)
-adred=[]
-adblue=[]
-adgreen=[]
-wavelength=[]
-for i in range (3,img.shape[1]-3):
-    redval=img[0,i,2]
-    redhieght=(redval*181)/255
-    greenval=img[0,i,1]
-    greenhieght=(greenval*181)/255
-    blueval=img[0,i,0]
-    bluehieght=(blueval*181)/255
-    redprevdif=int(img[0,i,2])-int(img[0,i-3,2])
-    redaheadvdif=int(img[0,i,2])-int(img[0,i+3,2])
-    greenprevdif=int(img[0,i,1])-int(img[0,i-3,1])
-    greenaheadvdif=int(img[0,i,1])-int(img[0,i+3,1])
-    blprevdif=int(img[0,i,0])-int(img[0,i-3,0])
-    blaheadvdif=int(img[0,i,0])-int(img[0,i+3,0])
-
-    y=0
-    if redprevdif < y and redaheadvdif < y:
-        adred.append(i)
-        #adred.append(i+1)
-        #adred.append(i+2)
-        #adred.append(i-1)
-        #adred.append(i-2)
-
-    if greenprevdif < y and greenaheadvdif < y:
-        adgreen.append(i)
-        #adgreen.append(i+1)
-        #adgreen.append(i+2)
-        #adgreen.append(i-1)
-        #adgreen.append(i-2)
-
-
-    if blprevdif < y and blaheadvdif < y:
-        adblue.append(i)
-        #adblue.append(i+1)
-        #adblue.append(i+2)
-        #adblue.append(i-1)
-        #adblue.append(i-2)
-
-    specgraph = cv2.circle(specgraph,(i,181-redhieght), 1, (0,0,255), -1)
-    specgraph = cv2.circle(specgraph,(i,181-greenhieght), 1, (0,255,0), -1)
-    specgraph = cv2.circle(specgraph,(i,181-bluehieght), 1, (255,0,0), -1)
-
-for i in range (2,img.shape[1]-2):
-    if i in adgreen and i in adred:
-        adlinesfinal.append(i)
-    if i in adgreen and i in adblue:
-        adlinesfinal.append(i)
-    if i in adblue and i in adred:
-        adlinesfinal.append(i)
-    if (img[0,i,2]+img[0,i,1])<10 and i in adblue:
-        adlinesfinal.append(i)
-    if (img[0,i,0]+img[0,i,1])<10 and i in adred:
-        adlinesfinal.append(i)
-    if (img[0,i,2]+img[0,i,0])<10 and i in adgreen:
-        adlinesfinal.append(i)
-H=0
-Fe=0
-Mn=0
-He=0
-Heion=0
-CN=0
-Na=0
-TiO=0
-CaH=0
-for i in range(len(adlinesfinal)):
-    wavelenght=((end-start)*adlinesfinal[i])/int(img1.shape[1])
-    wavelenght+=start
-    print wavelenght,' ',adlinesfinal[i]
-    wavelength.append(wavelenght)
-
-for i in range(len(wavelength)):
-    if wavelength[i]<=661 and wavelength[i]>=649: #656
-        H+=1
-        print(H)
-    if wavelength[i]<=409 and wavelength[i]>=400: #404.5
-        Fe+=1
-        Mn+=1
-    if wavelength[i]<=425 and wavelength[i]>=415:
-        He+=1
-    if wavelength[i]<=445 and wavelength[i]>=435:
-        Heion+=1
-    if wavelength[i]<=430 and wavelength[i]>=420:
-        CN+=1
-    if wavelength[i]<=594 and wavelength[i]>=584:
-        Na+=1
-    if wavelength[i]<=634 and wavelength[i]>=624:
-        TiO+=1
-    if wavelength[i]<=639 and wavelength[i]>=632:
-        CaH+=1
-if  H>=2:
-    print"Hydogen present"
-if  He>=2:
-    print"Helium present"
-if  Fe>=2:
-    print "Iron present"
-if  Mn>=2:
-    print "Mn present"
-if  Heion>=2:
-    print"Helium ion present"
-if  CN>=2:
-    print "CN radical present"
-
-if  TiO>=2:
-    print "TiO present"
-if CaH>=2:
-    print "CaH present"
-
-cv2.imshow("graph",specgraph)
-cv2.imshow("img",img)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
diff --git a/Get-rotation.py b/Get-rotation.py
deleted file mode 100644
index ec4ce2d..0000000
--- a/Get-rotation.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-import math
-cap = cv2.VideoCapture('latest_1024_0304.mp4')
-
-# take first frame of the video
-ret,frame = cap.read()
-frame = cv2.flip(frame, 0)
-img=cv2.imread('latest_512_0304.jpg',0)
-#current = cv2.imread('Cstar.png')
-blur = cv2.GaussianBlur(img,(35,35),0)
-previousimg = cv2.imread('latest_512_0304.jpg')
-img1 = cv2.imread('latest_512_0304.jpg',1)
-ret,thresh = cv2.threshold(blur,20,255,0)
-thresh1 =cv2.bitwise_not(thresh)
-contours,hierarchy = cv2.findContours(thresh1, 1, 2)
-x=3.9
-cnt = contours[0]
-leftmost = tuple(cnt[cnt[:,:,0].argmin()][0])
-rightmost = tuple(cnt[cnt[:,:,0].argmax()][0])
-topmost = tuple(cnt[cnt[:,:,1].argmin()][0])
-bottommost = tuple(cnt[cnt[:,:,1].argmax()][0])
-leftmost=(int(leftmost[0]*(x+0.1)),int(leftmost[1]*x))
-rightmost=(int(rightmost[0]*(x+0.1)),int(rightmost[1]*x))
-bottommost=(int(bottommost[0]*(x+0.1)),int(bottommost[1]*x))
-topmost=(int(topmost[0]*(x+0.1)),int(topmost[1]*x))
-
-
-
-print leftmost,' ',rightmost,' ',topmost,' ',bottommost
-img1 = cv2.circle(img1,leftmost, 12, (0,255,255), -1)
-img1 = cv2.circle(img1,rightmost, 12, (0,255,255), -1)
-img1 = cv2.circle(img1,topmost, 12, (0,255,255), -1)
-img1 = cv2.circle(img1,bottommost, 12, (0,255,255), -1)
-
-
-# setup initial location of window
-r,h,c,w = 400,100,300,100  # simply hardcoded the values
-track_window = (c,r,w,h)
-
-# set up the ROI for tracking
-roi = frame[r:r+h, c:c+w]
-hsv_roi =  cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-mask = cv2.inRange(hsv_roi, np.array((0., 60.,32.)), np.array((180.,255.,255.)))
-roi_hist = cv2.calcHist([hsv_roi],[0],mask,[180],[0,180])
-cv2.normalize(roi_hist,roi_hist,0,255,cv2.NORM_MINMAX)
-cv2.namedWindow('image', cv2.WINDOW_NORMAL)
-# Setup the termination criteria, either 10 iteration or move by atleast 1 pt
-term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
-i=1
-s=0
-while(1):
-    ret ,frame = cap.read()
-    frame = cv2.flip(frame, 0)
-
-    if ret == True:
-        hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-        dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
-
-        # apply meanshift to get the new location
-        ret, track_window = cv2.meanShift(dst, track_window, term_crit)
-
-        # Draw it on image
-        x,y,w,h = track_window
-        if s==0:
-            x1,y1=x,y
-            s+=1
-        img2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2)
-        img2 = cv2.circle(img2,leftmost, 12, (0,255,255), -1)
-        img2 = cv2.circle(img2,rightmost, 12, (0,255,255), -1)
-        img2 = cv2.circle(img2,topmost, 12, (0,255,255), -1)
-        img2 = cv2.circle(img2,bottommost, 12, (0,255,255), -1)
-        cv2.imshow('image',img2)
-        k = cv2.waitKey(4) & 0xff
-        if k == 27:
-            break
-    else:
-        break
-x2,y2=x,y
-distance=math.sqrt((x1-x2)**2)
-distance1=math.sqrt((leftmost[0]-rightmost[0])**2)
-time=(48*distance1*3.14)/distance
-print time
-cv2.imshow("images",img2)
-cv2.imshow("image06",img1)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
diff --git a/Get-weightandtemp.py b/Get-weightandtemp.py
deleted file mode 100644
index 2e35a58..0000000
--- a/Get-weightandtemp.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-
-
-star=cv2.imread("M.png")
-Gstar=cv2.imread("Gstar.png")
-Astar=cv2.imread("Astar.png")
-Kstar=cv2.imread("Kstar.png")
-Ostar=cv2.imread("Ostar.png")
-Fstar=cv2.imread("Fstar.png")
-Mstar=cv2.imread("M.png")
-Bstar=cv2.imread("Bstar.png")
-tempG1=Gstar[20:140:,20:300,:]
-tempG2=Gstar[20:140:,500:600,:]
-tempA1=Astar[20:140,20:300,:]
-tempA2=Astar[20:140:,500:600,:]
-tempF1=Fstar[20:140:,20:300,:]
-tempF2=Fstar[20:140:,500:600,:]
-tempO1=Ostar[20:140:,20:300,:]
-tempO2=Ostar[20:140:,500:600,:]
-tempM1=Mstar[20:140:,20:300,:]
-tempM2=Mstar[20:140:,500:600,:]
-tempB1=Bstar[20:140,20:300,:]
-tempB2=Bstar[20:140:,500:600,:]
-tempK1=Kstar[20:140:,20:300,:]
-tempK2=Kstar[20:140:,500:600,:]
-method = 'cv2.TM_CCOEFF_NORMED'
-method = eval(method)
-
-# Apply template Matching
-resG1 = cv2.matchTemplate(star,tempG1,method)
-min_val, max_valG1, min_loc, max_loc = cv2.minMaxLoc(resG1)
-resG2 = cv2.matchTemplate(star,tempG2,method)
-min_val, max_valG2, min_loc, max_loc = cv2.minMaxLoc(resG2)
-resA1 = cv2.matchTemplate(star,tempA1,method)
-min_val, max_valA1, min_loc, max_loc = cv2.minMaxLoc(resA1)
-resA2 = cv2.matchTemplate(star,tempA2,method)
-min_val, max_valA2, min_loc, max_loc = cv2.minMaxLoc(resA2)
-resM1 = cv2.matchTemplate(star,tempM1,method)
-min_val, max_valM1, min_loc, max_loc = cv2.minMaxLoc(resM1)
-resM2 = cv2.matchTemplate(star,tempM2,method)
-min_val, max_valM2, min_loc, max_loc = cv2.minMaxLoc(resM2)
-resF1 = cv2.matchTemplate(star,tempF1,method)
-min_val, max_valF1, min_loc, max_loc = cv2.minMaxLoc(resF1)
-resF2 = cv2.matchTemplate(star,tempF2,method)
-min_val, max_valF2, min_loc, max_loc = cv2.minMaxLoc(resF2)
-resO1 = cv2.matchTemplate(star,tempO1,method)
-min_val, max_valO1, min_loc, max_loc = cv2.minMaxLoc(resO1)
-resO2 = cv2.matchTemplate(star,tempO2,method)
-min_val, max_valO2, min_loc, max_loc = cv2.minMaxLoc(resO2)
-resB1 = cv2.matchTemplate(star,tempB1,method)
-min_val, max_valB1, min_loc, max_loc = cv2.minMaxLoc(resB1)
-resB2 = cv2.matchTemplate(star,tempB2,method)
-min_val, max_valB2, min_loc, max_loc = cv2.minMaxLoc(resB2)
-resK1 = cv2.matchTemplate(star,tempK1,method)
-min_val, max_valK1, min_loc, max_loc = cv2.minMaxLoc(resK1)
-resK2 = cv2.matchTemplate(star,tempK2,method)
-min_val, max_valK2, min_loc, max_loc = cv2.minMaxLoc(resK2)
-
-
-top_left = max_loc
-#bottom_right = (top_left[0] + w, top_left[1] + hA1)
-
-if max_valG1 + max_valG2  >=1.999:
-    print "G"
-    print "1.4 to 2.1 times the mass of the Sun surface temperatures between 7600 and 10,000 K"
-
-
-
-if max_valA1 + max_valA2  >=1.999:
-    print "A1"
-    print ""
-
-if max_valM1 + max_valM2  >=1.999:
-    print "M1"
-    print "	2,400–3,700 K	0.08–0.45 sun mass""
-
-
-if max_valF1 + max_valF2  >=1.999:
-    print "F1"
-    print "	6,000–7,500 K 1.04–1.4 sun mass"
-
-
-if max_valO1 + max_valO2  >=1.999:
-    print "O1"
-    print "temperatures in excess of 30,000 kelvin O-type stars range from 10,000 times the Sun to around 1,000,000 times, giants from 100,000 times the Sun to over 1,000,000, and supergiants from about 200,000 times the Sun to several million times"
-
-
-if max_valB1 + max_valB2  >=1.999:
-    print "B1"
-    print " to 16 times the mass of the Sun surface temperatures between 10,000 and 30,000 K"
-
-if max_valK1 + max_valK2  >=1.999:
-    print "K1"
-    print "3,700–5,200 K 0.45–0.8 sun mass"
diff --git a/Getaddsorptionline.py b/Getaddsorptionline.py
deleted file mode 100644
index 0708731..0000000
--- a/Getaddsorptionline.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-
-img1 = cv2.imread('Gstar.png')
-img=cv2.medianBlur(img1,7)
-#cv2.namedWindow('img', cv2.WINDOW_NORMAL)
-#cv2.namedWindow('graph', cv2.WINDOW_NORMAL)
-specgraph = np.zeros(img.shape, np.uint8)
-adred=[]
-adblue=[]
-adgreen=[]
-for i in range (3,img.shape[1]-3):
-    redval=img[0,i,2]
-    redhieght=(redval*181)/255
-    greenval=img[0,i,1]
-    greenhieght=(greenval*181)/255
-    blueval=img[0,i,0]
-    bluehieght=(blueval*181)/255
-    redprevdif=int(img[0,i,2])-int(img[0,i-3,2])
-    redaheadvdif=int(img[0,i,2])-int(img[0,i+3,2])
-    greenprevdif=int(img[0,i,1])-int(img[0,i-3,1])
-    greenaheadvdif=int(img[0,i,1])-int(img[0,i+3,1])
-    blprevdif=int(img[0,i,0])-int(img[0,i-3,0])
-    blaheadvdif=int(img[0,i,0])-int(img[0,i+3,0])
-
-    y=0
-    if redprevdif < y and redaheadvdif < y:
-        adred.append(i)
-        #adred.append(i+1)
-        #adred.append(i+2)
-        #adred.append(i-1)
-        #adred.append(i-2)
-
-    if greenprevdif < y and greenaheadvdif < y:
-        adgreen.append(i)
-        #adgreen.append(i+1)
-        #adgreen.append(i+2)
-        #adgreen.append(i-1)
-        #adgreen.append(i-2)
-
-
-    if blprevdif < y and blaheadvdif < y:
-        adblue.append(i)
-        #adblue.append(i+1)
-        #adblue.append(i+2)
-        #adblue.append(i-1)
-        #adblue.append(i-2)
-
-    specgraph = cv2.circle(specgraph,(i,181-redhieght), 1, (0,0,255), -1)
-    specgraph = cv2.circle(specgraph,(i,181-greenhieght), 1, (0,255,0), -1)
-    specgraph = cv2.circle(specgraph,(i,181-bluehieght), 1, (255,0,0), -1)
-print adred
-print adgreen
-for i in range (2,img.shape[1]-2):
-    if i in adgreen and i in adred:
-        specgraph = cv2.line(specgraph,(i,0),(i,181),(0,255,255),1)
-    if i in adgreen and i in adblue:
-        specgraph = cv2.line(specgraph,(i,0),(i,181),(0,255,255),1)
-    if i in adblue and i in adred:
-        specgraph = cv2.line(specgraph,(i,0),(i,181),(0,255,255),1)
-    if (img[0,i,2]+img[0,i,1])<10 and i in adblue:
-        specgraph = cv2.line(specgraph,(i,0),(i,181),(0,255,255),1)
-
-cv2.imshow("graph",specgraph)
-cv2.imshow("img",img)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
diff --git a/Getspectrumdata.py b/Getspectrumdata.py
deleted file mode 100644
index d9711b6..0000000
--- a/Getspectrumdata.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-def spectrum(filename):
-    print("reached")
-    print(filename)
-    img = cv2.imread(filename)
-    specgraph = np.zeros(img.shape, np.uint8)
-    print(img.shape[1])
-    print()
-    for i in range (img.shape[1]):
-        print(i)
-        redval=img[int(img.shape[0]/2)][i][2]
-        redhieght=(redval*181)/255
-        greenval=img[int(img.shape[0]/2)][i][1]
-        greenhieght=(greenval*181)/255
-        blueval=img[int(img.shape[0]/2)][i][0]
-        bluehieght=(blueval*181)/255
-        specgraph = cv2.circle(specgraph,(i,int(181-redhieght)), 1, (0,0,255), -1)
-        specgraph = cv2.circle(specgraph,(i,int(181-greenhieght)), 1, (0,255,0), -1)
-        specgraph = cv2.circle(specgraph,(i,int(181-bluehieght)), 1, (255,0,0), -1)
-    print("reached")
-    return specgraph
diff --git a/get-speed.py b/get-speed.py
deleted file mode 100644
index db78c10..0000000
--- a/get-speed.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-
-img1 = cv2.imread('Astar.png')
-img=cv2.medianBlur(img1,7)
-hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
-print hsv.shape
-#cv2.namedWindow('img', cv2.WINDOW_NORMAL)
-#cv2.namedWindow('graph', cv2.WINDOW_NORMAL)
-print "starting frequency"
-start=input()
-print "Final frequency"
-end=input()
-adlinesfinal=[]
-specgraph = np.zeros(img.shape, np.uint8)
-adred=[]
-adblue=[]
-adgreen=[]
-wavelength=[]
-for i in range (3,img.shape[1]-3):
-    redval=img[0,i,2]
-    redhieght=(redval*181)/255
-    greenval=img[0,i,1]
-    greenhieght=(greenval*181)/255
-    blueval=img[0,i,0]
-    bluehieght=(blueval*181)/255
-    redprevdif=int(img[0,i,2])-int(img[0,i-3,2])
-    redaheadvdif=int(img[0,i,2])-int(img[0,i+3,2])
-    greenprevdif=int(img[0,i,1])-int(img[0,i-3,1])
-    greenaheadvdif=int(img[0,i,1])-int(img[0,i+3,1])
-    blprevdif=int(img[0,i,0])-int(img[0,i-3,0])
-    blaheadvdif=int(img[0,i,0])-int(img[0,i+3,0])
-
-    y=0
-    if redprevdif < y and redaheadvdif < y:
-        adred.append(i)
-        #adred.append(i+1)
-        #adred.append(i+2)
-        #adred.append(i-1)
-        #adred.append(i-2)
-
-    if greenprevdif < y and greenaheadvdif < y:
-        adgreen.append(i)
-        #adgreen.append(i+1)
-        #adgreen.append(i+2)
-        #adgreen.append(i-1)
-        #adgreen.append(i-2)
-
-
-    if blprevdif < y and blaheadvdif < y:
-        adblue.append(i)
-        #adblue.append(i+1)
-        #adblue.append(i+2)
-        #adblue.append(i-1)
-        #adblue.append(i-2)
-
-    specgraph = cv2.circle(specgraph,(i,181-redhieght), 1, (0,0,255), -1)
-    specgraph = cv2.circle(specgraph,(i,181-greenhieght), 1, (0,255,0), -1)
-    specgraph = cv2.circle(specgraph,(i,181-bluehieght), 1, (255,0,0), -1)
-
-for i in range (2,img.shape[1]-2):
-    if i in adgreen and i in adred:
-        adlinesfinal.append(i)
-    if i in adgreen and i in adblue:
-        adlinesfinal.append(i)
-    if i in adblue and i in adred:
-        adlinesfinal.append(i)
-    if (img[0,i,2]+img[0,i,1])<10 and i in adblue:
-        adlinesfinal.append(i)
-    if (img[0,i,0]+img[0,i,1])<10 and i in adred:
-        adlinesfinal.append(i)
-    if (img[0,i,2]+img[0,i,0])<10 and i in adgreen:
-        adlinesfinal.append(i)
-H=[]
-Fe=[]
-Mn=[]
-He=[]
-Heion=[]
-CN=[]
-Na=[]
-TiO=[]
-CaH=[]
-dopplershift=0
-for i in range(len(adlinesfinal)):
-    wavelenght=((end-start)*adlinesfinal[i])/int(img1.shape[1])
-    wavelenght+=start
-    wavelength.append(wavelenght)
-for i in range(len(wavelength)):
-    if wavelength[i]<=660 and wavelength[i]>=652: #656
-        H.append(wavelength[i])
-    if wavelength[i]<=408 and wavelength[i]>=400: #404.5
-        Fe.append(wavelength[i])
-        Mn.append(wavelength[i])
-    if wavelength[i]<=425 and wavelength[i]>=417:
-        He.append(wavelength[i])
-    if wavelength[i]<=444 and wavelength[i]>=434:
-        Heion.append(wavelength[i])
-    if wavelength[i]<=429 and wavelength[i]>=421:
-        CN.append(wavelength[i])
-    if wavelength[i]<=593 and wavelength[i]>=585:
-        Na.append(wavelength[i])
-    if wavelength[i]<=635 and wavelength[i]>=629:
-        TiO.append(wavelength[i])
-    if wavelength[i]<=639 and wavelength[i]>=633:
-        CaH.append(wavelength[i])
-val=max(len(CaH),len(H),len(Fe),len(Heion),len(CN),len(Na),len(TiO))
-if val==len(TiO):
-    avg=sum(TiO)/len(TiO)
-    dopplershift=(avg-632)/632
-if val==len(H):
-    avg=sum(H)/len(H)
-    dopplershift=(avg-656.3)/656.3
-if val==len(He):
-    avg=sum(He)/len(He)
-    dopplershift=(avg-421)/421
-if val==len(Fe):
-    avg=sum(Fe)/len(Fe)
-    dopplershift=(avg-404.5)/404.5
-if val==len(Heion):
-    avg=sum(Heion)/len(Heion)
-    dopplershift=(avg-440)/440
-if val==len(Na):
-    avg=sum(Na)/len(Na)
-    dopplershift=(avg-588.9)/588.9
-if val==len(CaH):
-    avg=sum(CaH)/len(CaH)
-    dopplershift=(avg-634)/634
-if val==len(CN):
-    avg=sum(CN)/len(CN)
-    dopplershift=(avg-420)/420
-print dopplershift
-c=300000000
-vs=c*dopplershift
-print vs
-cv2.imshow("graph",specgraph)
-cv2.imshow("img",img)
-cv2.waitKey(0)
-cv2.destroyAllWindows()