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 ascii2raster(layername):
filename = './Data/' + layername + '.txt'
grassheader = extractheader(GRASSHEADER)
os.system('sed -i -e "1,6d" '+ filename) #delete first 6 lines
lines = grassheader.rstrip().split('\n') #insert header line by line
for line in reversed(lines):
os.system("sed -i 1i\ '" + line+"' " +filename) #insert header to the 1st line
if grass.run_command('r.in.ascii', input=filename, output=layername):
raise RuntimeError('unable to read ascii map to raster map ' + layername )
def main():
#convert road speed map to road class type matrix
roadclass_matrix = roadspeed2class(ROAD, ROADSPEEDCHART, ROADCLASSMAP, HEADER)
landcover_matrix = asciiMap2DataFrame(LANDCOVER)
# generate landroadclassmap
header= extractheader(HEADER)
genlandroadclassmap(roadclass_matrix, landcover_matrix, header)
# generate speedmap and dirprobmap
speedmap = SpeedMap(landcover_matrix, roadclass_matrix, SPEEDCHART, SPEEDMAP, header)
dirprobmap = SpeedMap(landcover_matrix, roadclass_matrix, DIRPROBCHART, DIRPROBMAP, header)
def main():
#convert road speed map to road class type matrix
roadclass_matrix = roadspeed2class(ROAD, ROADSPEEDCHART, ROADCLASSMAP,
HEADER)
landcover_matrix = asciiMap2DataFrame(LANDCOVER)
# generate landroadclassmap
header = extractheader(HEADER)
genlandroadclassmap(roadclass_matrix, landcover_matrix, header)
# generate speedmap and dirprobmap
speedmap = SpeedMap(landcover_matrix, roadclass_matrix, SPEEDCHART,
SPEEDMAP, header)
dirprobmap = SpeedMap(landcover_matrix, roadclass_matrix, DIRPROBCHART,
DIRPROBMAP, header)
def __init__(self, cellx, celly, speedmap=SPEEDMAP, dirprobmap=DIRPROBMAP,
travelcostpath=TRAVELCOSTPATH, travelcostmap=TRAVELCOSTMAP,
maxcost=MAXCOST, maxmove=MAXMOVE, repeattimes=REPEATTIMES, cellsize=CELLSIZE,
dirP=DIRP, dirnearP = DIRNEARP, dirsideP=DIRSIDEP, diropP=DIROPP):
""" Random Walk from one cell on a given map
Update dirlist for walking in each direction.
Update repeatcost and costaccumulated for each 2hrs move.
@param: cellx and celly is the curretn position x and y indexies in speedmap.
speedmap is a matrix with speed value meters/min in each cell and each cell is 30 meters.
cellsize is the length of each cell.
maxcost is the termiantion cost from current cell doing stocastic greedy random walk.
maxmove is the maximum number of cells to move before termination.
repeattimes is the times for each cell to conduct maxcost walks in each direction.
dirP is the probabilty that goes for a pre-selected direction.
dirsideP is the probabilty that goes for the two directions near pre-selected direction.
diropP is the proabiltiy taht goes for the opposite direction of pre-selected direction.
"""
# W: west, N: north, E: east, S:South
#read in values
self.speedmatrix=pd.read_csv(speedmap, skiprows=6, header=None, sep=r"\s+")
self.dirprobmatrix=pd.read_csv(dirprobmap, skiprows=6, header=None, sep=r"\s+")
self.cellx=cellx #initial starting cell x index
self.celly=celly #initial starting cell y index
self.distN = cellx #current cell x index
self.distW = celly #current cell y index #(cellx, celly) = (0, 1) = (disN, disW)
self.maxmove = maxmove
self.distancetuple = self.speedmatrix.shape
self.xmax = self.distancetuple[0]
self.ymax = self.distancetuple[1]
self.maxcost=maxcost
self.cellsize=cellsize
self.outfileheader = extractheader(HEADER)
#initiliaze parameters
self.costmap = pd.DataFrame(index=range(self.xmax), columns=range(self.ymax)) #initialize costmap with nan
self.costmap = self.costmap.fillna(999) #initialize costmap with 999
self.costmap.iloc[self.cellx, self.celly] = 0 # set the starting point cost to be 0
self.costaccumulated = 0
self.travelpathlist = []
self.visited_dict = {}
# select a seed for this cell to parallize the travelcost maps for each cell.
# The seed of this cell is the product of the x and y axis positions of this cell.
seedbase = self.cellx * self.celly
np.random.seed(seedbase)
self.walk8directions(travelcostpath, travelcostmap, repeattimes, dirP, dirnearP, dirsideP, diropP)
def main():
header = extractheader(HEADER)
speed_df = pd.read_csv(SPEEDMAP, sep=' ', skiprows=6, header=None, dtype=np.int)
(nrows, ncols) = speed_df.shape
travelcostmap = gettravelcostmap(nrows, ncols, header)
costmap_df = pd.read_csv(INPUT, sep=' ', skiprows=6, header=None, dtype=np.float)
costmap = np.asarray(costmap_df).round()
weightarray = np.fromfile(WEIGHTMAP, sep=' ', dtype=np.float)
smoothcost = SmoothCost(costmap, weightarray, speed_df)
costmap = smoothcost.smoothedmap
outputmap(costmap, header, OUTPUT)
if ISEMP == 0:
INPUT="./Data/attrmap-pop.txt"
OUTPUT="./Data/attrmap-pop-interpolated.txt"
else:
INPUT="./Data/attrmap-emp.txt"
OUTPUT="./Data/attrmap-emp-interpolated.txt"
HEADER="./Input/arcGISheader.txt"
WEIGHTMAP = "./Input/weightmap.txt"
DIRPROBMAP = "./Data/dirprobmap.txt"
DIRPROBMAX = 200
#THREADNUM = 32
REPEATNUM = 5
header = extractheader(HEADER)
class SmoothAttr():
def __init__(self, matrix, weightarray, dirprobmatrix, repeattimes=REPEATNUM, dirprobmax=DIRPROBMAX):
start = time.time()
self.weightarray = weightarray
self.matrix = matrix
(self.rows, self.cols) = matrix.shape
self.maxrow = self.rows-2
self.maxcol = self.cols-2
self.dirprobmatrix = dirprobmatrix
self.dirprobmax = dirprobmax
self.smoothedmap = np.zeros((self.rows, self.cols), dtype=np.int)
self.smooth2max(repeattimes)
def smoothrow(self, rowidx):
ISEMP = 1
if ISEMP == 0:
INPUT = "./Data/attrmap-pop.txt"
OUTPUT = "./Data/attrmap-pop-interpolated.txt"
else:
INPUT = "./Data/attrmap-emp.txt"
OUTPUT = "./Data/attrmap-emp-interpolated.txt"
HEADER = "./Input/arcGISheader.txt"
WEIGHTMAP = "./Input/weightmap.txt"
DIRPROBMAP = "./Data/dirprobmap.txt"
DIRPROBMAX = 200
#THREADNUM = 32
REPEATNUM = 5
header = extractheader(HEADER)
class SmoothAttr():
def __init__(self,
matrix,
weightarray,
dirprobmatrix,
repeattimes=REPEATNUM,
dirprobmax=DIRPROBMAX):
start = time.time()
self.weightarray = weightarray
self.matrix = matrix
(self.rows, self.cols) = matrix.shape
self.maxrow = self.rows - 2
self.maxcol = self.cols - 2
