def test_all_even(self):
"""
@summary: This test creates a surface and runs each implementation of the
algorithm and checks that the results are the same
"""
SURFACE_WIDTH = 1000
SURFACE_HEIGHT = 1000
STEP_SIZE = .2
CELL_SIZE = .005
TILE_SIZE = 1.0
NUM_CONES = 100
NUM_ELLIPSOIDS = 100
MAX_HEIGHT = 50
MAX_RADIUS = 200
NUM_WORKERS = 1
# Generate surface
sg = SurfaceGenerator(SURFACE_HEIGHT, SURFACE_WIDTH, 0.0, 0.0, CELL_SIZE,
defVal=0)
sg.addRandom(numCones=NUM_CONES, numEllipsoids=NUM_ELLIPSOIDS,
maxHeight=MAX_HEIGHT, maxRad=MAX_RADIUS)
# Make sure that we have at least one source cell
if np.min(sg.grid) > 0:
sg.grid[0,0] = 0
# Write out grid
sg.writeGrid(self.surfaceFn)
inputGrid = np.loadtxt(self.surfaceFn, dtype=int, comments='', skiprows=6)
# Run Dijkstra
serialInstance = SingleTileSerialDijkstraLCP(self.surfaceFn,
self.serialCostFn,
seaLevelRiseCostFn)
serialInstance.findSourceCells()
serialInstance.calculate()
# Verify grid
serialAry = np.loadtxt(self.serialCostFn, dtype=int, comments='', skiprows=6)
self._verifyGrid(serialAry, inputGrid)
# Run parallel Dijkstra
parInstance = SingleTileParallelDijkstraLCP(self.surfaceFn,
self.parCostFn,
seaLevelRiseCostFn)
parInstance.setMaxWorkers(16)
parInstance.setStepSize(.20)
parInstance.findSourceCells()
parInstance.calculate()
# Compare serial and parallel single tile
parAry = np.loadtxt(self.parCostFn, dtype=int, comments='', skiprows=6)
# Verify parallel run
self._verifyGrid(parAry, inputGrid)
#print len(np.where(serialAry != parAry))
assert np.array_equal(serialAry, parAry)
# Split surface
splitTile(self.surfaceFn, TILE_SIZE, self.mtSurfaceDir)
# Split cost surface
splitTile(self.parCostFn, TILE_SIZE, self.mtCmpDir)
# Run multi-tile
mtInstance = MultiTileWqParallelDijkstraLCP(self.mtSurfaceDir,
self.mtCostDir,
self.mtOutDir,
TILE_SIZE,
STEP_SIZE,
summaryFn=self.mtSummaryFn)
# Run
print "Starting workers"
mtInstance.startWorkers(NUM_WORKERS)
mtInstance.calculate()
# Only on success
print "Stopping workers"
mtInstance.stopWorkers()
# Compare output directories
assert self._checkOutputs(self.mtCostDir, self.mtCmpDir)
outF.write("%s\n" % ','.join(headers))
for ss in STEP_SIZES:
for fn in glob.glob(searchPath):
print "Step size:", ss
print "Filename:", fn
tileStatsFn = os.path.join(args.outputDir, '%s-%s.csv' % (ss, os.path.basename(fn)))
if not os.path.exists(tileStatsFn):
try:
costFn = 'cost.asc'
atime = time.time()
tile = SingleTileParallelDijkstraLCP(fn, costFn, seaLevelRiseCostFn)
tile.setMaxWorkers(50)
tile.findSourceCells()
tile.setStepSize(ss)
tile.calculate()
btime = time.time()
tile.writeStats(os.path.join(args.outputDir, '%s-%s.csv' % (ss, os.path.basename(fn))))
dtime = btime - atime
mtx = np.loadtxt(fn, dtype=int, skiprows=6)
rows, cols = mtx.shape
outF.write("%s," % fn) # File name
outF.write("%s," % rows) # Rows
outF.write("%s," % cols) # Columns
outF.write("%s," % ss) # Step size
outF.write("%s," % dtime) # Running time
outF.write("%s," % mtx.min()) # Minimum