first commit

README

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+py_geo_voronoi 
+
+Created by Hohyon Ryu, Aug/19/2011
+
+* Dependencies
+
+ - shapely : sudo apt-get install python-shapely
+ - matplotlib (pylab) : sudo apt-get install python-matplotlib
+ - Built on Steve Fortune's Python Voronoi Code
+   Steve Fortune's homepage: http://netlib.bell-labs.com/cm/cs/who/sjf/index.html
+
+* Inputs:
+  1. Dictionary of the points: (You can use simple sequential number for the dictionary keys or some text to associate with.)
+    PointsMap={}
+    PointsMap["stationA"]=(-143.22, 38.22)
+    PointsMap["stationB"]=(-122.22, 56.22)
+    PointsMap[1]=(-122.22, 56.22)
+
+  2. The bounding box (left_top_x, left_top_y, bottom_right_x, bottom_right,y) to generate a voronoi lattice.
+    Bounding Box Options: You can either use the name or the coordinates
+    "AUSTIN" [30.8, -98.5, 29.535, -97.031]
+    "TX" [36.5, -106, 25, -93]
+    "US" [55, -130, 23, -60]
+    "GUS" [60, -140, 22, -50]
+    "KR" [45, 120, 32, 135] (Korea)
+    "W" [90, -180, -90, 180] (World, Default)
+
+  3. PlotMap: shows the voronoi lattice on a map. This may be extremely slow if you have more than 1M points. (Default is False, Not available for VoronoiLineEdges)
+
+
+* Output options:
+  1. Stations, Lines and edges
+  2. Polygons
+  3. GeoJson Polygons
+  4. GeoJson MultiPolygons
+
+  Example:
+  import voronoi_poly
+
+  vl=voronoi_poly.VoronoiLineEdges(PointsMap)
+  Output:
+    vertices, lines, edges, station_to_edge
+        (1) a list of 2-tuples, which are the x,y coordinates of the 
+            Voronoi diagram vertices
+        (2) a list of 3-tuples (a,b,c) which are the equations of the
+            lines in the Voronoi diagram: a*x + b*y = c
+        (3) a list of 3-tuples, (l, v1, v2) representing edges of the 
+            Voronoi diagram.  l is the index of the line, v1 and v2 are
+            the indices of the vetices at the end of the edge.  If 
+            v1 or v2 is -1, the line extends to infinity.    
+        (4) a dictionary, where keys are the station numbers(vertices), and the 
+            values are the number of edges surrounding the vertice.
+
+  vl=voronoi_poly.VoronoiPolygons(PointsMap, BoundingBox="AUSTIN", PlotMap=False)
+  Output:
+    185: {'coordinate': (3.04, 36.77), 'info': 'Algiers/Algeria', 'obj_polygon': <shapely.geometry.polygon.Polygon object at 0x3152190>}
+
+    - You can access the polygon coordinates by the following example:
+
+      from shapely.geometry import Polygon      
+      print list(polygon_data.exterior.coords)
+
+  vl=voronoi_poly.VoronoiGeoJson_Polygons(PointsMap, BoundingBox="US")
+
+  vl=voronoi_poly.VoronoiGeoJson_MultiPolygons(PointsMap, BoundingBox="KR")
+
+

main_example.py

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+#! /usr/local/bin/python
+
+import sys
+import voronoi_poly
+
+#Run this using the pipe: "cat sample_city_data | python main_example.py"
+if __name__=="__main__":
+
+  #Creating the PointsMap from the input data
+  PointsMap={}
+  for line in sys.stdin:
+    data=line.strip().split(",")
+    try:
+      PointsMap[data[0]]=(float(data[1]),float(data[2]))
+    except:
+      sys.stderr.write( "Invalid Input Line: "+line)
+
+  #vl=voronoi_poly.VoronoiLineEdges(PointsMap)
+  #vertices, lines, edges, station_to_edge
+  #print vl
+
+  #vl=voronoi_poly.VoronoiPolygons(PointsMap, BoundingBox="W", PlotMap=True)
+  #print vl
+
+  #cat sample_city_data | python main_example.py > out.geojson
+  #vl=voronoi_poly.VoronoiGeoJson_Polygons(PointsMap, BoundingBox="W", PlotMap=False)
+
+  vl=voronoi_poly.VoronoiGeoJson_MultiPolygons(PointsMap, BoundingBox="W", PlotMap=False)
+  print vl
+

sample_city_data

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+x,y
+Aba/Nigeria,7.35,5.1
+Abidjan/Ivory Coast,-4.03,5.33
+Accra/Ghana,-0.2,5.56
+Addis abeba/Ethiopia,38.74,9.03
+Agra/India,78.01,27.19
+Ahmadabad/India,72.58,23.03
+Al-Basrah/Iraq,47.82,30.53
+Aleppo/Syria,37.17,36.23
+Alexandria/Egypt,29.95,31.22
+Algiers/Algeria,3.04,36.77
+Al-MawSil/Iraq,43.14,36.34
+Ankara/Turkey,32.85,39.93
+Antananarivo/Madagascar,47.51,-18.89
+Bagdad/Iraq,44.44,33.33
+Bamako/Mali,-7.99,12.65
+Bandung/Indonesia,107.6,-6.91
+Bangalore/India,77.56,12.97
+Bangkok/Thailand,100.5,13.73
+Barcelona/Spain,2.17,41.4
+Bayrut/Lebanon,35.5,33.89
+Bekasi/Indonesia,106.97,-6.22
+Belem/Brazil,-48.5,-1.44
+Belo Horizonte/Brazil,-43.94,-19.92
+Benin/Nigeria,5.62,6.34
+Berlin/Germany,13.38,52.52
+Bhopal/India,77.4,23.24
+Bogota/Colombia,-74.09,4.63
+Bombay/India,72.82,18.96
+Brasilia/Brazil,-47.91,-15.78
+Brisbane/Australia,153.02,-27.46
+Bucharest/Romania,26.1,44.44
+Budapest/Hungary,19.08,47.51
+Buenos Aires/Argentina,-58.37,-34.61
+Bursa/Turkey,29.08,40.2
+Cairo/Egypt,31.25,30.06
+Calcutta/India,88.36,22.57
+Cali/Colombia,-76.52,3.44
+Cape Town/South Africa,18.46,-33.93
+Caracas/Venezuela,-66.93,10.54
+Casablanca/Morocco,-7.62,33.6
+Changchun/China,125.35,43.87
+Changsha/China,112.97,28.2
+Chattagam/Bangladesh,91.81,22.33
+Chengdu/China,104.07,30.67
+Chicago/United States of America,-87.68,41.84
+Chongqing/China,106.58,29.57
+Conakry/Guinea,-13.67,9.55
+Cordoba/Argentina,-64.19,-31.4
+Curitiba/Brazil,-49.29,-25.42
+Dakar/Senegal,-17.48,14.72
+Dalian/China,121.65,38.92
+Damascus/Syria,36.32,33.5
+Dar es Salaam/Tanzania,39.28,-6.82
+Davao/Philippines,125.63,7.11
+Delhi/India,77.21,28.67
+Dhaka/Bangladesh,90.39,23.7
+Douala/Cameroon,9.71,4.06
+Dubai/United Arab Emirates,55.33,25.27
+Durban/South Africa,30.99,-29.87
+Ecatepec/Mexico,-99.05,19.6
+Esfahan/Iran,51.68,32.68
+Faisalabad/Pakistan,73.11,31.41
+Fortaleza/Brazil,-38.59,-3.78
+Gaoxiong/Taiwan,120.27,22.63
+Gizeh/Egypt,31.21,30.01
+Guadalajara/Mexico,-103.35,20.67
+Guangzhou/China,113.25,23.12
+Guayaquil/Ecuador,-79.9,-2.21
+Gujranwala/Pakistan,74.18,32.16
+Hamburg/Germany,10.0,53.55
+Hangzhou/China,120.17,30.25
+Harare/Zimbabwe,31.05,-17.82
+Harbin/China,126.65,45.75
+Havanna/Cuba,-82.39,23.13
+Ho Chi Minh City/Vietnam,106.69,10.78
+Houston/United States of America,-95.39,29.77
+Hyderabad/India,78.48,17.4
+Hyderabad/Pakistan,68.37,25.38
+Ibadan/Nigeria,3.93,7.38
+Incheon/Korea (South),126.64,37.48
+Indore/India,75.86,22.72
+Istanbul/Turkey,29.0,41.1
+Izmir/Turkey,27.15,38.43
+Jaipur/India,75.8,26.92
+Jakarta/Indonesia,106.83,-6.18
+Jiddah/Saudi Arabia,39.17,21.5
+Jilin/China,126.55,43.85
+Jinan/China,117.0,36.67
+Jiulong/China,114.17,22.32
+Johannesburg/South Africa,28.04,-26.19
+Juarez/Mexico,-106.49,31.74
+Kabul/Afghanistan,69.17,34.53
+Kaduna/Nigeria,7.44,10.52
+Kano/Nigeria,8.52,12.0
+Kanpur/India,80.33,26.47
+Karachi/Pakistan,67.01,24.86
+Karaj/Iran,50.97,35.8
+Khartoum/Sudan,32.52,15.58
+Kiev/Ukraine,30.52,50.43
+Kinshasa/Congo (Dem. Rep.),15.32,-4.31
+Kobe/Japan,135.17,34.68
+Koyang/Korea (South),126.93,37.7
+Kumasi/Ghana,-1.63,6.69
+Lagos/Nigeria,3.35,6.5
+Lahore/Pakistan,74.35,31.56
+Lakhnau/India,80.92,26.85
+Lima/Peru,-77.05,-12.07
+Lome/Togo,1.35,6.17
+London/United Kingdom,-0.1,51.52
+Los Angeles/United States of America,-118.41,34.11
+Luanda/Angola,13.24,-8.82
+Lubumbashi/Congo (Dem. Rep.),27.48,-11.66
+Ludhiana/India,75.84,30.91
+Madras/India,80.27,13.09
+Madrid/Spain,-3.71,40.42
+Manaus/Brazil,-60.02,-3.12
+Manila/Philippines,120.97,14.62
+Maracaibo/Venezuela,-71.66,10.73
+Mashhad/Iran,59.57,36.27
+Mbuji-Mayi/Congo (Dem. Rep.),23.59,-6.13
+Medan/Indonesia,98.67,3.59
+Medellin/Colombia,-75.54,6.29
+Melbourne/Australia,144.96,-37.81
+Mexico City/Mexico,-99.14,19.43
+Minsk/Belarus,27.55,53.91
+Montreal/Canada,-73.57,45.52
+Moscow/Russia,37.62,55.75
+Multan/Pakistan,71.45,30.2
+Muqdisho/Somalia,45.33,2.05
+Nagoya/Japan,136.91,35.15
+Nagpur/India,79.08,21.16
+Nairobi/Kenya,36.82,-1.29
+Nanchang/China,115.88,28.68
+Nanjing/China,118.78,32.05
+Nashik/India,73.78,20.01
+New York/United States of America,-73.94,40.67
+osaka/Japan,135.5,34.68
+Paris/France,2.34,48.86
+Patna/India,85.13,25.62
+Peking/China,116.4,39.93
+Phoenix/United States of America,-112.07,33.54
+Pimpri/India,73.8,18.62
+Port Harcourt/Nigeria,7.01,4.81
+Pretoria/South Africa,28.22,-25.73
+Pune/India,73.84,18.53
+Pusan/Korea (South),129.03,35.11
+Pyeongyang/Korea (North),125.75,39.02
+Qingdao/China,120.32,36.07
+Quito/Ecuador,-78.5,-0.19
+Rabat/Morocco,-6.84,34.02
+Rangoon/Myanmar,96.15,16.79
+Rawalpindi/Pakistan,73.04,33.6
+Recife/Brazil,-34.92,-8.08
+Rio de Janeiro/Brazil,-43.2,-22.91
+Riyadh/Saudi Arabia,46.77,24.65
+Rome/Italy,12.5,41.89
+Rongcheng/China,116.34,23.54
+Saint Petersburg/Russia,30.32,59.93
+Salvador/Brazil,-38.5,-12.97
+Sana/Yemen,44.21,15.38
+Santa Cruz/Bolivia,-63.21,-17.77
+Santiago/Chile,-70.64,-33.46
+Santo Domingo/Dominican Republic,-69.91,18.48
+São Paulo/Brazil,-46.63,-23.53
+Sapporo/Japan,141.34,43.06
+Shanghai/China,121.47,31.23
+Shenyang/China,123.45,41.8
+Shijiazhuang/China,114.48,38.05
+Singapore/Singapore,103.85,1.3
+Seoul/Korea (South),126.99,37.56
+Soweto/South Africa,27.84,-26.28
+Subang Jaya/Malaysia,101.53,3.15
+Surabaya/Indonesia,112.74,-7.24
+Surat/India,72.82,21.2
+Suzhou/China,120.62,31.3
+Sydney/Australia,151.21,-33.87
+Taegu/Korea (South),128.6,35.87
+Taibei/Taiwan,121.45,25.02
+Taiyuan/China,112.55,37.87
+Tangshan/China,118.19,39.62
+Tashkent/Uzbekistan,69.3,41.31
+Tehran/Iran,51.43,35.67
+Thana/India,72.97,19.2
+Tianjin/China,117.2,39.13
+Tijuana/Mexico,-117.02,32.53
+Tokyo/Japan,139.77,35.67
+Toronto/Canada,-79.38,43.65
+Umm Durman/Sudan,32.48,15.65
+Urumqi/China,87.58,43.8
+Vadodara/India,73.18,22.31
+Valencia/Venezuela,-67.98,10.23
+Vancouver/Canada,-123.13,49.28
+Vienna/Austria,16.37,48.22
+Warsaw/Poland,21.02,52.26
+Wuhan/China,114.27,30.58
+Xian/China,108.9,34.27
+Xinyang/China,114.07,32.13
+Yaounde/Cameroon,11.52,3.87
+Yokohama/Japan,139.62,35.47
+Zhengzhou/China,113.67,34.75