def genlandroadclassmap(roadclassmap, landclassmap, header):
# make the default class value to be a large number and then take
# the minimum value of the maps.
roadclassmap = roadclassmap.replace(to_replace=-1, value=999)
landroadclassmap = np.minimum(roadclassmap, landclassmap)
outputmap(landroadclassmap, header, LANDROADCLASSMAP)
def main():
speedmap = pd.read_csv(SPEEDMAP, skiprows=6, header=None, sep=r"\s+")
header = extractheader(HEADER)
attinxcol = speedmap.shape
indexlen = attinxcol[0]
columnlen = attinxcol[1]
attrmap = pd.DataFrame(index=range(indexlen), columns=range(columnlen)) #initialize costmap with nan
attrmap = attrmap.fillna(0) #initialize costmap with 0
with open(CENTERLIST, 'r') as p:
centerlist = p.readlines()
for i in range(99):
(disW, disN, weight) = centerlist[i].strip('\n').split(',')
costmapfile = outfilename(disW, disN, TRAVELCOSTPATH, TRAVELCOSTMAP, "NW", 100)
try:
newattrmap = costmap2attrmap(costmapfile)
except IOError:
print "file not found: ", outfilename
continue
print "\nstart adding...\n"
start = time.time()
attrmap = attrmap + (int)(weight)*newattrmap
end = time.time()
print "done map using time: "
print (end-start)
#normalizer = np.matrix(attrmap).max()
#attrmap /= normalizer
attrmap.round() # round to integer
outputmap(attrmap, header, ATTRACTIVEMAP)
def main():
speedmap = pd.read_csv(SPEEDMAP, skiprows=6, header=None, sep=r"\s+")
header = extractheader(HEADER)
attinxcol = speedmap.shape
indexlen = attinxcol[0]
columnlen = attinxcol[1]
attrmap = pd.DataFrame(
index=range(indexlen),
columns=range(columnlen)) #initialize costmap with nan
attrmap = attrmap.fillna(0) #initialize costmap with 0
with open(CENTERLIST, 'r') as p:
centerlist = p.readlines()
for i in range(99):
(disW, disN, weight) = centerlist[i].strip('\n').split(',')
costmapfile = outfilename(disW, disN, TRAVELCOSTPATH, TRAVELCOSTMAP,
"NW", 100)
try:
newattrmap = costmap2attrmap(costmapfile)
except IOError:
print "file not found: ", outfilename
continue
print "\nstart adding...\n"
start = time.time()
attrmap = attrmap + (int)(weight) * newattrmap
end = time.time()
print "done map using time: "
print(end - start)
#normalizer = np.matrix(attrmap).max()
#attrmap /= normalizer
attrmap.round() # round to integer
outputmap(attrmap, header, ATTRACTIVEMAP)
def walk8directions(self, travelcostpath, travelcostmap, repeattimes, dirP,
dirnearP, dirsideP, diropP):
"""Walk in each of the 8 directions.
"""
self.walkeachdirection("N", travelcostpath, travelcostmap, repeattimes,
dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("NE", travelcostpath, travelcostmap,
repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("E", travelcostpath, travelcostmap, repeattimes,
dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("SE", travelcostpath, travelcostmap,
repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("S", travelcostpath, travelcostmap, repeattimes,
dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("SW", travelcostpath, travelcostmap,
repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("W", travelcostpath, travelcostmap, repeattimes,
dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("NW", travelcostpath, travelcostmap,
repeattimes, dirP, dirnearP, dirsideP, diropP)
#self.costmap[self.costmap < 20] = 20
outcostfilename = outfilename(self.cellx, self.celly, travelcostpath,
travelcostmap, "NW", 100)
outputmap(self.costmap, self.outfileheader, outcostfilename)
for (x, y), val in self.visited_dict.iteritems():
print "(", x, ",", y, ")", val
def genlandroadclassmap(roadclassmap, landclassmap, header):
# make the default class value to be a large number and then take
# the minimum value of the maps.
roadclassmap = roadclassmap.replace(to_replace=-1, value=999)
landroadclassmap = np.minimum(roadclassmap, landclassmap)
outputmap(landroadclassmap, header, LANDROADCLASSMAP)
def __init__(self, landcover_matrix, road_matrix, speedchart, speedmap,
header):
self.cat_list = []
self.speed_list = []
self.projectioninfo_list = []
# read the speedchart to be a speed dictionary
self.speedchart2dict(speedchart)
# replace the landcover catogory values with speed values
self.landcoverspeed_matrix = self.cat2speedmap(landcover_matrix)
self.roadspeed_matrix = self.cat2speedmap(road_matrix)
self.finalspeed_matrix = self.overlapspeedmap(
self.landcoverspeed_matrix, self.roadspeed_matrix)
#self.landcoverspeed_matrix = self.landcoverspeed_matrix.apply(np.sqrt)
outputmap(self.landcoverspeed_matrix, header,
"./Data/landcoverspeedmap.txt")
def walk8directions(self, travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP):
"""Walk in each of the 8 directions.
"""
self.walkeachdirection("N",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("NE",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("E",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("SE",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("S",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("SW",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("W",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
self.walkeachdirection("NW",travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
#self.costmap[self.costmap < 20] = 20
outcostfilename = outfilename(self.cellx, self.celly, travelcostpath, travelcostmap, "NW", 100)
outputmap(self.costmap, self.outfileheader, outcostfilename)
for (x,y) , val in self.visited_dict.iteritems():
print "(" ,x, ",",y,")",val
def __init__(self, landcover_matrix, road_matrix, speedchart, speedmap, header):
self.cat_list = []
self.speed_list = []
self.projectioninfo_list = []
# read the speedchart to be a speed dictionary
self.speedchart2dict(speedchart)
# replace the landcover catogory values with speed values
self.landcoverspeed_matrix = self.cat2speedmap(landcover_matrix)
self.roadspeed_matrix = self.cat2speedmap(road_matrix)
self.finalspeed_matrix = self.overlapspeedmap(self.landcoverspeed_matrix, self.roadspeed_matrix)
#self.landcoverspeed_matrix = self.landcoverspeed_matrix.apply(np.sqrt)
outputmap(self.landcoverspeed_matrix, header, "./Data/landcoverspeedmap.txt")
outputmap(self.roadspeed_matrix, header, "./Data/roadspeedmap.txt")
outputmap(self.finalspeed_matrix, header, speedmap)