mirror of
https://github.com/smyalygames/FiniteVolumeGPU.git
synced 2025-11-29 17:28:03 +01:00
MPI prototype
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@@ -115,15 +115,17 @@ class IPEngine(object):
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# attach to a running cluster
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import ipyparallel
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cluster = ipyparallel.Client()#profile='mpi')
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while(len(cluster.ids) != n_engines):
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self.cluster = ipyparallel.Client()#profile='mpi')
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while(len(self.cluster.ids) != n_engines):
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time.sleep(0.5)
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self.logger.info("Waiting for cluster...")
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cluster = ipyparallel.Client()#profile='mpi')
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self.cluster = ipyparallel.Client()#profile='mpi')
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self.logger.info("Done")
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def __del__(self):
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self.cluster.shutdown(hub=True)
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self.e.terminate()
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try:
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self.e.communicate(timeout=3)
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@@ -137,6 +139,11 @@ class IPEngine(object):
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self.logger.info("IPEngine cerr: {:s}".format(cerr))
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while(len(self.cluster.ids) != 0):
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time.sleep(0.5)
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self.logger.info("Waiting for cluster to shutdown...")
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self.c.terminate()
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try:
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self.c.communicate(timeout=3)
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@@ -337,11 +344,19 @@ class CudaArray2D:
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"""
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Enables downloading data from GPU to Python
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"""
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def download(self, stream, async=False):
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def download(self, stream, async=False, extent=None):
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if (extent == None):
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x = self.x_halo
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y = self.y_halo
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nx = self.nx
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ny = self.ny
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else:
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x, y, nx, ny = extent
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#self.logger.debug("Downloading [%dx%d] buffer", self.nx, self.ny)
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#Allocate host memory
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#cpu_data = cuda.pagelocked_empty((self.ny, self.nx), np.float32)
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cpu_data = np.empty((self.ny, self.nx), dtype=np.float32)
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cpu_data = np.empty((ny, nx), dtype=np.float32)
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#Create copy object from device to host
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copy = cuda.Memcpy2D()
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@@ -349,20 +364,52 @@ class CudaArray2D:
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copy.set_dst_host(cpu_data)
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#Set offsets and pitch of source
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copy.src_x_in_bytes = self.x_halo*self.data.strides[1]
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copy.src_y = self.y_halo
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copy.src_x_in_bytes = x*self.data.strides[1]
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copy.src_y = y
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copy.src_pitch = self.data.strides[0]
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#Set width in bytes to copy for each row and
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#number of rows to copy
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copy.width_in_bytes = self.nx*cpu_data.itemsize
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copy.height = self.ny
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copy.width_in_bytes = nx*cpu_data.itemsize
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copy.height = ny
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copy(stream)
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if async==False:
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stream.synchronize()
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return cpu_data
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def upload(self, cpu_data, stream, extent=None):
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if (extent == None):
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x = self.x_halo
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y = self.y_halo
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nx = self.nx
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ny = self.ny
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else:
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x, y, nx, ny = extent
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assert(nx == cpu_data.shape[1])
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assert(ny == cpu_data.shape[0])
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assert(x+nx <= self.nx + 2*self.x_halo)
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assert(y+ny <= self.ny + 2*self.y_halo)
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#Create copy object from device to host
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copy = cuda.Memcpy2D()
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copy.set_dst_device(self.data.gpudata)
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copy.set_src_host(cpu_data)
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#Set offsets and pitch of source
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copy.dst_x_in_bytes = x*self.data.strides[1]
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copy.dst_y = y
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copy.dst_pitch = self.data.strides[0]
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#Set width in bytes to copy for each row and
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#number of rows to copy
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copy.width_in_bytes = nx*cpu_data.itemsize
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copy.height = ny
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copy(stream)
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@@ -129,9 +129,16 @@ class MagicLogger(Magics):
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logger.addHandler(ch)
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logger.log(args.level, "Console logger using level %s", logging.getLevelName(args.level))
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#Get the outfilename (try to evaluate if Python expression...)
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try:
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outfile = eval(args.out, self.shell.user_global_ns, self.shell.user_ns)
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except:
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outfile = args.out
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#Add log to file
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logger.log(args.level, "File logger using level %s to %s", logging.getLevelName(args.file_level), args.out)
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fh = logging.FileHandler(args.out)
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logger.log(args.level, "File logger using level %s to %s", logging.getLevelName(args.file_level), outfile)
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fh = logging.FileHandler(outfile)
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formatter = logging.Formatter('%(asctime)s:%(name)s:%(levelname)s: %(message)s')
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fh.setFormatter(formatter)
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fh.setLevel(args.file_level)
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@@ -161,6 +168,12 @@ class MagicMPI(Magics):
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self.cluster = None
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self.cluster = Common.IPEngine(args.num_engines)
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# Handle CUDA context when exiting python
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import atexit
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def exitfunc():
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self.logger.info("Exitfunc: Resetting MPI cluster")
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self.cluster = None
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atexit.register(exitfunc)
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@@ -143,4 +143,4 @@ class KP07 (Simulator.BaseSimulator):
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def computeDt(self):
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max_dt = gpuarray.min(self.cfl_data, stream=self.stream).get();
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return max_dt*0.5**self.order
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return max_dt*0.5**(self.order-1)
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@@ -138,4 +138,4 @@ class KP07_dimsplit(Simulator.BaseSimulator):
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def computeDt(self):
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max_dt = gpuarray.min(self.cfl_data, stream=self.stream).get();
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return max_dt*0.5
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return max_dt
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@@ -53,11 +53,11 @@ class BoundaryCondition(object):
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def __init__(self, types={ \
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'north': Type.Reflective, \
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'south': Type.Reflective, \
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'east': Type.Reflective, \
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'west': Type.Reflective \
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def __init__(self, types={
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'north': Type.Reflective,
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'south': Type.Reflective,
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'east': Type.Reflective,
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'west': Type.Reflective
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}):
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"""
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Constructor
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@@ -170,25 +170,24 @@ class BaseSimulator(object):
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t_start = self.simTime()
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t_end = t_start + t
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local_dt = dt
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if (local_dt == None):
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local_dt = self.computeDt()
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update_dt = False
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if (dt == None):
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update_dt = True
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while(self.simTime() < t_end):
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if (dt == None and self.simSteps() % 100 == 0):
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local_dt = self.computeDt()
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if (update_dt and (self.simSteps() % 100 == 0)):
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dt = self.computeDt()*self.cfl_scale
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# Compute timestep for "this" iteration (i.e., shorten last timestep)
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local_dt = np.float32(min(local_dt*self.cfl_scale, t_end-self.simTime()))
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dt = np.float32(min(dt, t_end-self.simTime()))
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# Stop if end reached (should not happen)
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if (local_dt <= 0.0):
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if (dt <= 0.0):
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self.logger.warning("Timestep size {:d} is less than or equal to zero!".format(self.simSteps()))
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break
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# Step forward in time
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self.step(local_dt)
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self.step(dt)
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#Print info
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print_string = printer.getPrintString(self.simTime() - t_start)
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@@ -197,7 +196,7 @@ class BaseSimulator(object):
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try:
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self.check()
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except AssertionError as e:
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e.args += ("Step={:d}, time={:f}".format(self.simSteps(), self.simTime()))
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e.args += ("Step={:d}, time={:f}".format(self.simSteps(), self.simTime()),)
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raise
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@@ -48,7 +48,11 @@ def downsample(highres_solution, x_factor, y_factor=None):
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def bump(nx, ny, width, height, bump_size=None, h_ref=0.5, h_amp=0.1, u_ref=0.0, u_amp=0.1, v_ref=0.0, v_amp=0.1, ref_nx=None, ref_ny=None):
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def bump(nx, ny, width, height,
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bump_size=None,
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ref_nx=None, ref_ny=None,
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x_center=0.5, y_center=0.5,
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h_ref=0.5, h_amp=0.1, u_ref=0.0, u_amp=0.1, v_ref=0.0, v_amp=0.1):
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if (ref_nx == None):
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ref_nx = nx
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@@ -64,8 +68,8 @@ def bump(nx, ny, width, height, bump_size=None, h_ref=0.5, h_amp=0.1, u_ref=0.0,
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ref_dx = width / float(ref_nx)
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ref_dy = height / float(ref_ny)
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x_center = ref_dx*ref_nx/2.0
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y_center = ref_dy*ref_ny/2.0
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x_center = ref_dx*ref_nx*x_center
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y_center = ref_dy*ref_ny*y_center
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x = ref_dx*(np.arange(0, ref_nx, dtype=np.float32)+0.5) - x_center
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y = ref_dy*(np.arange(0, ref_ny, dtype=np.float32)+0.5) - y_center
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