├── .gitattributes
├── .gitignore
└── St-dbscan.py


/.gitattributes:
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 1 | # Auto detect text files and perform LF normalization
 2 | * text=auto
 3 | 
 4 | # Custom for Visual Studio
 5 | *.cs     diff=csharp
 6 | *.sln    merge=union
 7 | *.csproj merge=union
 8 | *.vbproj merge=union
 9 | *.fsproj merge=union
10 | *.dbproj merge=union
11 | 
12 | # Standard to msysgit
13 | *.doc	 diff=astextplain
14 | *.DOC	 diff=astextplain
15 | *.docx diff=astextplain
16 | *.DOCX diff=astextplain
17 | *.dot  diff=astextplain
18 | *.DOT  diff=astextplain
19 | *.pdf  diff=astextplain
20 | *.PDF	 diff=astextplain
21 | *.rtf	 diff=astextplain
22 | *.RTF	 diff=astextplain
23 | 


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/.gitignore:
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 1 | # Windows image file caches
 2 | Thumbs.db
 3 | ehthumbs.db
 4 | 
 5 | # Folder config file
 6 | Desktop.ini
 7 | 
 8 | # Recycle Bin used on file shares
 9 | $RECYCLE.BIN/
10 | 
11 | # Windows Installer files
12 | *.cab
13 | *.msi
14 | *.msm
15 | *.msp
16 | 
17 | # =========================
18 | # Operating System Files
19 | # =========================
20 | 
21 | # OSX
22 | # =========================
23 | 
24 | .DS_Store
25 | .AppleDouble
26 | .LSOverride
27 | 
28 | # Icon must end with two \r
29 | Icon

30 | 
31 | # Thumbnails
32 | ._*
33 | 
34 | # Files that might appear on external disk
35 | .Spotlight-V100
36 | .Trashes
37 | 
38 | # Directories potentially created on remote AFP share
39 | .AppleDB
40 | .AppleDesktop
41 | Network Trash Folder
42 | Temporary Items
43 | .apdisk
44 | 


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/St-dbscan.py:
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  1 | import math
  2 | from xlrd import *
  3 | Noise = 999999
  4 | Unmarked = 99999
  5 | filename = input('Enter file location:')
  6 | book = open_workbook(filename)
  7 | csheet = book.sheet_by_index(0)
  8 | rw = csheet.nrows
  9 | #rw = 30
 10 | D = {}
 11 | C = {}
 12 | X = {}
 13 | T = {}
 14 | K = {}
 15 | Cluster_Label = 0
 16 | Minpts = math.log(rw)
 17 | #print(Minpts)
 18 | count = 0
 19 | stack = 0
 20 | 
 21 | print(Minpts)
 22 | 
 23 | def eps1(x, y):
 24 |     return math.sqrt((x.lat - y.lat)**2 + (x.lon - y.lon)**2)
 25 | 
 26 | def eps2(x, y):
 27 |     return math.sqrt((x.y1 - y.y1)**2 + (x.y2 - y.y2)**2)
 28 | 
 29 | def Retrieve_Neighbours(x, n):
 30 |     T = {}
 31 |     Y = {}
 32 |     count = 0
 33 |     for r in range(rw):
 34 |         if n == r:
 35 |             continue
 36 |         else:
 37 |             e1 = eps1(x, D[r])
 38 |             e2 = eps2(x, D[r])
 39 |             print(e1, e2)
 40 |             if(e1 < 0.8 and e2 < 0.4):
 41 |                 Y[count] = Data(D[r].acode, D[r].lat, D[r].lon,  D[r].y1, D[r].y2, D[r].value)
 42 |                 T[count] = r
 43 |                 count += 1
 44 |                # print(count)
 45 |     return Y
 46 |     
 47 | 
 48 | def push(x):
 49 |      Y = {}
 50 |      for j in range(count):
 51 |          Y[stack] = Data(D[T[j]].acode, D[T[j]].lat, D[T[j]].lon,  D[T[j]].y1, D[T[j]].y2, D[T[j]].value)
 52 |          K[stack] = T[j]
 53 |          stack = stack + 1
 54 | 
 55 | def pop():
 56 |     stack = stack - 1
 57 |     return Y[stack]
 58 | 
 59 | class Data:
 60 |     acode = 0.0
 61 |     lat = 0.0
 62 |     lon = 0.0
 63 |     y1 = 0.0
 64 |     y2 = 0.0
 65 |     value = 0.0
 66 |     clabel = Unmarked
 67 |     def __init__(self, acode, lat, lon, y1, y2, value):
 68 |         self.lat = lat
 69 |         self.lon = lon
 70 |         self.y1 = y1
 71 |         self.y2 = y2
 72 |         self.acode = acode
 73 |         self.value = value
 74 |         
 75 | for r in range(rw):
 76 |     r0 = float(csheet.cell(r,0).value)
 77 |     r1 = float(csheet.cell(r,3).value)
 78 |     r2 = float(csheet.cell(r,4).value)
 79 |     r3 = float(csheet.cell(r,1).value)
 80 |     r4 = float(csheet.cell(r,2).value)
 81 |     r5 = float(csheet.cell(r,5).value)
 82 |     D[r] = Data(r0, r1, r2, r3, r4, r5)
 83 |     
 84 | for r in range(rw):
 85 |     if D[r].clabel == Unmarked:
 86 |         X = Retrieve_Neighbours(D[r], r)
 87 |         if count < Minpts:
 88 |             D[r].clabel = Noise
 89 |         else :
 90 |             Cluster_label = Cluster_label + 1
 91 |             for j in range(count):
 92 |                 D[T[j]].clabel = Cluster_label
 93 |                 #print(D[T[j]].acode, D[T[j]].clabel)
 94 |                 push(D[T[j]])
 95 | 
 96 |                 while(stack < 0):
 97 |                     CurrentObj = pop()
 98 |                     X = Retrieve_Neighbors(CurrentObj, K[stack])
 99 | 
100 |                     if count < Minpts:
101 |                         for j in range(count):
102 |                             if D[T[j]].clabel != Noise or D[T[j]].clabel == Unmarked:
103 |                                 D[T[j]].clabel = Cluster_label
104 |                                 Push(D[T[j]])
105 |                     
106 |                 
107 | 
108 |             
109 |                 
110 | 
111 | 
112 | 
113 | 
114 |                 
115 | 
116 | 
117 | 
118 |     
119 | 


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