Python, Let's Go Home. Quickly. (remote.python.pizza 2020-04-25)

When your daily commute, shopping tour, or visit to family or friends offers many possible routes and real-time online navigation services are of no use for your individual mean of transport, you can still learn to find the optimal route.

Transcript

1. Python, Let's Go Home. Quickly. Miroslav Šedivý   eumiro 1
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32. <?xml version='1.0' encoding='UTF-8' standalone='yes' ?> <gpx version="1.1" … > <metadata> <name>2020-04-25 remote.python.pizza</name> </metadata> <trk> <trkseg> <trkpt lat="49.013455" lon="8.4043583"> <ele>114.86</ele> <time>2020-04-25T09:23:33Z</time> <hdop>11.792</hdop> <extensions> <speed>4.61</speed> </extensions> </trkpt> <trkpt lat="49.0134691" lon="8.4041695"> <ele>113.86</ele> <time>2020-04-25T09:23:36Z</time> <hdop>11.792</hdop> <extensions> <speed>4.45</speed> </extensions> </trkpt> … </trkseg> </trk> </gpx>   eumiro 32
33. gpsbabel -t -i gpx -f 2020-04-25-pizza.gpx -o unicsv,grid=utm -F 2020-04-25-pizza.csv   eumiro 33
34. gpsbabel -t -i gpx -f 2020-04-25-pizza.gpx -o unicsv,grid=utm -F 2020-04-25-pizza.csv No, UTM-Zone, UTM-Ch, UTM-East, UTM-North, Altitude, HDOP, Date, Time 1, 32, U, 456445, 5429122, 114.9, 11.79, 2020/04/25, 09:23:33 2, 32, U, 456431, 5429124, 113.9, 11.79, 2020/04/25, 09:23:36 3, 32, U, 456421, 5429124, 112.9, 11.79, 2020/04/25, 09:23:38 4, 32, U, 456407, 5429115, 114.9, 11.79, 2020/04/25, 09:23:40 5, 32, U, 456400, 5429111, 113.9, 12.86, 2020/04/25, 09:23:42 6, 32, U, 456382, 5429101, 113.9, 11.79, 2020/04/25, 09:23:45 7, 32, U, 456373, 5429091, 113.9, 11.79, 2020/04/25, 09:23:48 …   eumiro 33
35. gpsbabel -t -i gpx -f 2020-04-25-pizza.gpx -o unicsv,grid=utm -F 2020-04-25-pizza.csv No, UTM-Zone, UTM-Ch, UTM-East, UTM-North, Altitude, HDOP, Date, Time 1, 32, U, 456445, 5429122, 114.9, 11.79, 2020/04/25, 09:23:33 2, 32, U, 456431, 5429124, 113.9, 11.79, 2020/04/25, 09:23:36 3, 32, U, 456421, 5429124, 112.9, 11.79, 2020/04/25, 09:23:38 4, 32, U, 456407, 5429115, 114.9, 11.79, 2020/04/25, 09:23:40 5, 32, U, 456400, 5429111, 113.9, 12.86, 2020/04/25, 09:23:42 6, 32, U, 456382, 5429101, 113.9, 11.79, 2020/04/25, 09:23:45 7, 32, U, 456373, 5429091, 113.9, 11.79, 2020/04/25, 09:23:48 … or gpxpy & utm, or GeoPandas   eumiro 33
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40. GPX tracks   eumiro 38
41. nd nodes   eumiro 39
42. simple crossings   eumiro 40
43. crossroads with turn restrictions and/or tra c lights   eumiro 41
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56. norm1 = statistics.NormalDist.from_samples([4., 5., 5., 6., 6., 6., 6., 7., 7., 8., …])   eumiro 54
57. norm2 = statistics.NormalDist.from_samples([6.9, 7.0, 7.1, …])   eumiro 55
58. norm1.inv_cdf(0.1)   eumiro 56
59. norm1.inv_cdf(0.9)   eumiro 57
60. # 0.2: 10:01 # 0.5: 11:08 # 0.8: 12:03   eumiro 58
61. Python, Let's Go Home. Quickly.   eumiro 59
62. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd   eumiro 59
63. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas   eumiro 59
64. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas   eumiro 59
65. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …)   eumiro 59
66. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights   eumiro 59
67. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights other crossings   eumiro 59
68. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights other crossings 4. split tracks into segments between nodes: pandas   eumiro 59
69. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights other crossings 4. split tracks into segments between nodes: pandas 5. build graph with nodes and elapsed times between them: networkx   eumiro 59
70. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights other crossings 4. split tracks into segments between nodes: pandas 5. build graph with nodes and elapsed times between them: networkx 6. find path using statistics: networkx, statistics   eumiro 59
71. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights other crossings 4. split tracks into segments between nodes: pandas 5. build graph with nodes and elapsed times between them: networkx 6. find path using statistics: networkx, statistics 7. output: matplotlib, ipyleaflet, OsmAnd plugin, whatever!   eumiro 59
72. Python, Let's Go Home. Quickly. 1. gather the GPS tracks: OsmAnd 2. cleanup, split into rides: manual / pandas 3. find nodes: pandas terminus nodes (home, work, shops, …) crossroads with turn restrictions and/or traffic lights other crossings 4. split tracks into segments between nodes: pandas 5. build graph with nodes and elapsed times between them: networkx 6. find path using statistics: networkx, statistics 7. output: matplotlib, ipyleaflet, OsmAnd plugin, whatever! Miroslav Šedivý   eumiro 59