def line2graph(geo, isPlot=False):
"""create a graph from a collection of lines"""
gO = g_o.h3tree()
G = nx.MultiDiGraph()
# G = nx.Graph()
G.graph['crs'] = {'init': 'epsg:4326'}
G.graph['name'] = "local network"
for i, g in geo.iterrows():
seg = list(g['geometry'].coords)
start, end = seg[0], seg[-1]
dist = g_e.haversine(start[0], start[1], end[0], end[1])
j1, j2 = gO.encode(start[0], start[1], precision=13), gO.encode(end[0], end[1], precision=13)
G.add_edge(j1, j2, key=0, speed=g['maxspeed'], highway=g['fclass'], name=g['name'], length=dist)
G.add_node(j1, pos=start, x=start[0], y=start[1], key=0)
G.add_node(j2, pos=end, x=end[0], y=end[1], key=0)
if isPlot:
ox.plot_graph(G)
print("built the graph size %d" % (G.size()))
return G
def intersectGeom(poly1, poly2, id1, id2, precDigit=10):
"""return the intersection ids between two lists of polygons"""
gO = otree.h3tree()
idx = index.Index()
for i, p in enumerate(poly2):
idx.insert(i, p.bounds)
sectL = {}
numL = []
for i, p in zip(id1, poly1):
merged_cells = cascaded_union(
[poly2[i] for i in idx.intersection(p.bounds)])
pint = p.intersection(merged_cells)
if isinstance(pint, sh.geometry.Point):
l = [gO.encode(pint.x, pint.y, precision=precDigit)]
else:
l = [gO.encode(x.x, x.y, precision=precDigit) for x in pint]
sectL[i] = l
numL.append(len(l))
print("%.2f points per polygon" % (np.mean(numL)))
return sectL
def segment2graph(geo, isPlot=False):
"""create a graph from a collection of lines"""
print("building the graph")
gO = g_o.h3tree()
G = nx.MultiDiGraph()
# G = nx.Graph()
G.graph['crs'] = {'init': 'epsg:4326'}
G.graph['name'] = "local network"
for i, g in geo.iterrows():
seg = list(g['geometry'].coords)
for start, end, j in zip(seg[:-1], seg[1:], range(len(seg))):
# line = sh.geometry.LineString([start,end])
dist = g_e.haversine(start[0], start[1], end[0], end[1])
# j1, j2 = g['osm_id']+"_"+str(j), g['osm_id']+"_"+str(j+1)
j1, j2 = gO.encode(start[0], start[1], precision=13), gO.encode(end[0], end[1], precision=13)
G.add_edge(j1, j2, key=0, src=j1, trg=j2, speed=g['maxspeed'], highway=g['fclass'], name=g['name'],
length=dist) # ,geometry=line,osm_id=g['osm_id'])
G.add_node(j1, pos=start, x=start[0], y=start[1], key=0) # ,osmid=j1)
G.add_node(j2, pos=end, x=end[0], y=end[1], key=0) # ,osmid=j2)
if isPlot:
ox.plot_graph(G)
return G
import os, sys, gzip, random, csv, json, datetime, re
import numpy as np
import pandas as pd
import geopandas as gpd
import shapely as sh
import matplotlib.pyplot as plt
sys.path.append(os.environ['LAV_DIR'] + '/src/')
baseDir = os.environ['LAV_DIR']
import geomadi.geo_octree as g_o
import lernia.train_viz as t_v
import networkx as nx
import geomadi.geo_enrich as g_e
gO = g_o.h3tree(BoundBox=[5.866, 47.2704, 15.0377, 55.0574])
precDigit = 8
if False:
print('----------------------group-all-single-days----------------')
projDir = baseDir + "raw/traj/"
fL = os.listdir(projDir)
for precDigit in [8, 9, 10]:
trajL = []
for f in fL:
print(f)
traj = pd.read_csv(projDir + f,
compression="gzip",
converters={
"tx": eval,
"bound": eval,
"state": eval
})
"""
markov_chain:
creation of Markov chains and Bayesian inference
"""
import numpy as np
import pandas as pd
import scipy as sp
from scipy import spatial
import matplotlib.pyplot as plt
import geomadi.graph_viz as g_v
import geomadi.geo_octree as g_o
import networkx as nx
gO = g_o.h3tree()
class MarkovChain():
"""Monte Carlo for path optimization"""
def __init__(self, spotL, pairL=None, confM=None):
"""initialize quantities and Markov chains"""
self.spotL = spotL
if confM == None:
self.confM = {
"exp": 1,
"threshold": "row",
"leg": 7,
"doc": "threshold:[percentile,mean,len,row]"
}
else:
self.confM = confM
self.loadRouted(pairL)
import pandas as pd
import geopandas as gpd
import shapely as sh
from shapely.geometry.polygon import Polygon
import matplotlib.pyplot as plt
sys.path.append(os.environ['LAV_DIR'] + '/src/')
sys.path.append('/usr/local/lib/python3.6/dist-packages/')
baseDir = os.environ['LAV_DIR']
import lernia.train_viz as t_v
import albio.series_stat as s_s
import albio.series_interp as s_i
import geomadi.geo_octree as otree
import geomadi.geo_enrich as g_e
import geomadi.geo_ops as g_p
gO = otree.h3tree()
precDigit = 8
custD = "dep"
poi = pd.read_csv(baseDir + "raw/" + custD + "/poi_" + str(precDigit) +
".csv.gz",
compression="gzip")
if False:
print('----------------------------region------------------------')
poi = pd.read_csv(baseDir + "raw/" + custD + "/poi_" + str(precDigit) +
".csv.gz",
compression="gzip")
poi.loc[:, "region"] = g_e.addRegion(poi,
baseDir + "gis/geo/bundesland.shp")
poi.to_csv(baseDir + "raw/" + custD + "/poi_" + str(precDigit) + ".csv.gz",
