def initialize_pop(self):
p=POP(redirection="file", number_of_workers=8,redirect_stdout_file="pop.out")
cwd=os.getcwd()
p.change_directory(cwd)
popdatadir="/home/inti/code/amuse/trunk/sandbox/pelupes/pop/"
#set the grid we want to use
p.set_horiz_grid_file(popdatadir+'data/input/grid/horiz_grid_20010402.ieeer8')
p.set_vert_grid_file(popdatadir+'data/input/grid/in_depths.dat')
p.set_topography_file(popdatadir+'data/input/grid/topography_20010702.ieeei4')
#set the restart file
p.set_ts_file(popdatadir+'data/input/restart/r.x1_SAMOC_control.00750101')
#setup the forcing
p.set_shf_monthly_file(popdatadir+'data/input/shf_monthly/shf.normal_year+flux.mon')
p.set_sfwf_monthly_file(popdatadir+'data/input/sfwf/sfwf_phc0-50_ncarp_r46_flux.mon')
p.set_ws_monthly_file(popdatadir+'data/input/ws_monthly/ws.1958-2000.mon')
self.pop_grid=p.get_grid()
self.pop_forcings_grid=StaggeredGrid(p.elements, p.forcings, p._compute_cell_corners)
self.pop=p
self.timestep=self.timestep or self.pop.timestep/2
def test2(self):
#generate corners for a simple structured grid
shape = [5, 5]
ind = numpy.indices((shape[0] + 1, shape[1] + 1))
lats = numpy.array(ind[0], dtype=numpy.float)
lats = (0.5 - lats / shape[1]) * numpy.pi
lats[0] = lats[0] - 1e-14
lats = lats[::-1, :]
lons = numpy.array(ind[1], dtype=numpy.float)
lons = lons / ind.shape[1] * 2.0 * numpy.pi
corners = numpy.array([lons, lats])
elements = new_structured_grid(shape,
corners,
axes_names=['lon', 'lat'])
#let the north east corners of each cell be the position of the nodes
positions = numpy.array([lons[1:, 1:], lats[1:, 1:]])
nodes = StructuredGrid(*shape)
nodes.lat = (lats[1:, 1:] | units.rad)
nodes.lon = (lons[1:, 1:] | units.rad)
print(elements, nodes)
grid = StaggeredGrid(elements,
nodes,
get_corners=lambda: numpy.array([lons, lats]))
values = numpy.random.random(shape)
print(values)
elements.values = values
nodes.values = grid.map_elements_to_nodes(values)
print(nodes.values)
remapped_values = grid.map_nodes_to_elements(nodes.values)
print(remapped_values)
before_sum = values.sum()
after_sum = remapped_values.sum()
print('before', before_sum, 'after', after_sum)
self.assertEqual(
after_sum,
before_sum,
msg="Sum of values before and after remapping should be the same")
def test2(self):
#generate corners for a simple structured grid
shape = [5,5]
ind = numpy.indices( (shape[0]+1,shape[1]+1))
lats = numpy.array( ind[0] , dtype=numpy.float)
lats = (0.5-lats/shape[1]) * numpy.pi
lats[0] = lats[0] - 1e-14
lats = lats[::-1,:]
lons = numpy.array( ind[1] , dtype=numpy.float)
lons = lons/ind.shape[1] * 2.0*numpy.pi
corners = numpy.array([lons,lats])
elements = new_structured_grid(shape, corners, axes_names=['lon', 'lat'])
#let the north east corners of each cell be the position of the nodes
positions = numpy.array([lons[1:,1:], lats[1:,1:]])
nodes = StructuredGrid(*shape)
nodes.lat = (lats[1:,1:] | units.rad)
nodes.lon = (lons[1:,1:] | units.rad)
print elements, nodes
grid = StaggeredGrid(elements, nodes, get_corners=lambda: numpy.array([lons, lats]))
values = numpy.random.random(shape)
print values
elements.values = values
nodes.values = grid.map_elements_to_nodes(values)
print nodes.values
remapped_values = grid.map_nodes_to_elements(nodes.values)
print remapped_values
before_sum = values.sum()
after_sum = remapped_values.sum()
print 'before', before_sum, 'after', after_sum
self.assertEquals(after_sum, before_sum, msg="Sum of values before and after remapping should be the same")
def setUp(self):
try:
from omuse.community.cdo.interface import CDORemapper
except:
self.skip(
"conservative spherical remapper requires omuse.community.cdo.interface"
)
#this test creates a structured staggered grid and an unstructured staggered grid
#and then uses the conservative_spherical_remapper to remap values between the grids
#define nodal points and triangles of a small test grid
#got this grid from http://matplotlib.org/examples/pylab_examples/triplot_demo.html
xy = numpy.asarray([[-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888],
[-0.054, 0.890], [-0.045, 0.897], [-0.057, 0.895],
[-0.073, 0.900], [-0.087, 0.898], [-0.090, 0.904],
[-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
[-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942],
[-0.062, 0.949], [-0.054, 0.958], [-0.069, 0.954],
[-0.087, 0.952], [-0.087, 0.959], [-0.080, 0.966],
[-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
[-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980],
[-0.108, 0.980], [-0.104, 0.987], [-0.102, 0.993],
[-0.115, 1.001], [-0.099, 0.996], [-0.101, 1.007],
[-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
[-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010],
[-0.064, 1.005], [-0.048, 1.005], [-0.031, 1.005],
[-0.031, 0.996], [-0.040, 0.987], [-0.045, 0.980],
[-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
[-0.020, 0.954], [-0.006, 0.947], [0.003, 0.935],
[0.006, 0.926], [0.005, 0.921], [0.022, 0.923],
[0.033, 0.912], [0.029, 0.905], [0.017, 0.900],
[0.012, 0.895], [0.027, 0.893], [0.019, 0.886],
[0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883],
[-0.038, 0.879], [-0.057, 0.881], [-0.062, 0.876],
[-0.078, 0.876], [-0.087, 0.872], [-0.030, 0.907],
[-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
[-0.077, 0.990], [-0.059, 0.993]])
triangles = numpy.asarray([[67, 66, 1], [65, 2, 66], [1, 66, 2],
[64, 2, 65], [63, 3, 64], [60, 59, 57],
[2, 64, 3], [3, 63, 4], [0, 67, 1],
[62, 4, 63], [57, 59, 56], [59, 58, 56],
[61, 60, 69], [57, 69, 60], [4, 62, 68],
[6, 5, 9], [61, 68, 62], [69, 68, 61],
[9, 5, 70], [6, 8, 7], [4, 70, 5],
[8, 6, 9], [56, 69, 57], [69, 56, 52],
[70, 10, 9], [54, 53, 55], [56, 55, 53],
[68, 70, 4], [52, 56, 53], [11, 10, 12],
[69, 71, 68], [68, 13, 70], [10, 70, 13],
[51, 50, 52], [13, 68, 71], [52, 71, 69],
[12, 10, 13], [71, 52, 50], [71, 14, 13],
[50, 49, 71], [49, 48, 71], [14, 16, 15],
[14, 71, 48], [17, 19, 18], [17, 20, 19],
[48, 16, 14], [48, 47, 16], [47, 46, 16],
[16, 46, 45], [23, 22, 24], [21, 24, 22],
[17, 16, 45], [20, 17, 45], [21, 25, 24],
[27, 26, 28], [20, 72, 21], [25, 21, 72],
[45, 72, 20], [25, 28, 26], [44, 73, 45],
[72, 45, 73], [28, 25, 29], [29, 25, 31],
[43, 73, 44], [73, 43, 40], [72, 73, 39],
[72, 31, 25], [42, 40, 43], [31, 30, 29],
[39, 73, 40], [42, 41, 40], [72, 33, 31],
[32, 31, 33], [39, 38, 72], [33, 72, 38],
[33, 38, 34], [37, 35, 38], [34, 38, 35],
[35, 37, 36]])
num_elems = len(triangles)
elements = UnstructuredGrid(num_elems)
elements.n1 = triangles[:, 0] - 1
elements.n2 = triangles[:, 1] - 1
elements.n3 = triangles[:, 2] - 1
lons = numpy.zeros(num_elems, dtype=numpy.double)
lats = numpy.zeros(num_elems, dtype=numpy.double)
for i in range(num_elems):
for n in triangles[i]:
lons[i] += xy[n, 0] / 3.0
lats[i] += xy[n, 1] / 3.0
elements.lon = (lons | units.rad)
elements.lat = (lats | units.rad)
nodes = UnstructuredGrid(len(xy))
nodes.lon = (xy[:, 0] | units.rad)
nodes.lat = (xy[:, 1] | units.rad)
self.unstructured = StaggeredGrid(elements, nodes)
#generate corners for a simple structured grid as source grid
shape = [5, 5]
lon_range = xy[:, 0].max() - xy[:, 0].min() + 0.025
lon_min = xy[:, 0].min() - 0.0125
lat_range = xy[:, 1].max() - xy[:, 1].min() + 0.025
lat_min = xy[:, 1].min() - 0.0125
ind = numpy.indices((shape[0] + 1, shape[1] + 1))
lats = numpy.array(ind[1], dtype=numpy.float)
lats = lat_min + lats / shape[1] * lat_range
lons = numpy.array(ind[0], dtype=numpy.float)
lons = lon_min + lons / shape[0] * lon_range
corners = numpy.array([lons, lats])
elements = new_structured_grid(shape,
corners,
axes_names=['lon', 'lat'])
nodes = StructuredGrid(*ind[0].shape)
nodes.lat = (lats | units.rad)
nodes.lon = (lons | units.rad)
self.structured = StaggeredGrid(elements, nodes)
corners += 0.01 #shift the grid
nodes = StructuredGrid(*ind[0].shape)
nodes.lat = (corners[1] | units.rad)
nodes.lon = (corners[0] | units.rad)
self.structured2 = StaggeredGrid(elements, nodes)
def test3(self):
#define nodal points and triangles of a small test grid
#got this grid from http://matplotlib.org/examples/pylab_examples/triplot_demo.html
xy = numpy.asarray([
[-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888], [-0.054, 0.890],
[-0.045, 0.897], [-0.057, 0.895], [-0.073, 0.900], [-0.087, 0.898],
[-0.090, 0.904], [-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
[-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942], [-0.062, 0.949],
[-0.054, 0.958], [-0.069, 0.954], [-0.087, 0.952], [-0.087, 0.959],
[-0.080, 0.966], [-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
[-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980], [-0.108, 0.980],
[-0.104, 0.987], [-0.102, 0.993], [-0.115, 1.001], [-0.099, 0.996],
[-0.101, 1.007], [-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
[-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010], [-0.064, 1.005],
[-0.048, 1.005], [-0.031, 1.005], [-0.031, 0.996], [-0.040, 0.987],
[-0.045, 0.980], [-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
[-0.020, 0.954], [-0.006, 0.947], [ 0.003, 0.935], [ 0.006, 0.926],
[ 0.005, 0.921], [ 0.022, 0.923], [ 0.033, 0.912], [ 0.029, 0.905],
[ 0.017, 0.900], [ 0.012, 0.895], [ 0.027, 0.893], [ 0.019, 0.886],
[ 0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883], [-0.038, 0.879],
[-0.057, 0.881], [-0.062, 0.876], [-0.078, 0.876], [-0.087, 0.872],
[-0.030, 0.907], [-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
[-0.077, 0.990], [-0.059, 0.993]])
triangles = numpy.asarray([
[67, 66, 1], [65, 2, 66], [ 1, 66, 2], [64, 2, 65], [63, 3, 64],
[60, 59, 57], [ 2, 64, 3], [ 3, 63, 4], [ 0, 67, 1], [62, 4, 63],
[57, 59, 56], [59, 58, 56], [61, 60, 69], [57, 69, 60], [ 4, 62, 68],
[ 6, 5, 9], [61, 68, 62], [69, 68, 61], [ 9, 5, 70], [ 6, 8, 7],
[ 4, 70, 5], [ 8, 6, 9], [56, 69, 57], [69, 56, 52], [70, 10, 9],
[54, 53, 55], [56, 55, 53], [68, 70, 4], [52, 56, 53], [11, 10, 12],
[69, 71, 68], [68, 13, 70], [10, 70, 13], [51, 50, 52], [13, 68, 71],
[52, 71, 69], [12, 10, 13], [71, 52, 50], [71, 14, 13], [50, 49, 71],
[49, 48, 71], [14, 16, 15], [14, 71, 48], [17, 19, 18], [17, 20, 19],
[48, 16, 14], [48, 47, 16], [47, 46, 16], [16, 46, 45], [23, 22, 24],
[21, 24, 22], [17, 16, 45], [20, 17, 45], [21, 25, 24], [27, 26, 28],
[20, 72, 21], [25, 21, 72], [45, 72, 20], [25, 28, 26], [44, 73, 45],
[72, 45, 73], [28, 25, 29], [29, 25, 31], [43, 73, 44], [73, 43, 40],
[72, 73, 39], [72, 31, 25], [42, 40, 43], [31, 30, 29], [39, 73, 40],
[42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
[33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
num_elems = len(triangles)
elements = UnstructuredGrid(num_elems)
elements.n1 = triangles[:,0] - 1
elements.n2 = triangles[:,1] - 1
elements.n3 = triangles[:,2] - 1
nodes = UnstructuredGrid(len(xy))
nodes.lon = (xy[:,0] | units.rad)
nodes.lat = (xy[:,1] | units.rad)
grid = StaggeredGrid(elements, nodes)
values = numpy.random.random(num_elems)
print values
elements.values = values
nodes.values = grid.map_elements_to_nodes(values)
print nodes.values
remapped_values = grid.map_nodes_to_elements(nodes.values)
print remapped_values
before_sum = values.sum()
after_sum = remapped_values.sum()
print 'before', before_sum, 'after', after_sum
self.assertAlmostEquals(after_sum, before_sum, msg="Sum of values before and after remapping should be the same")
class POP_Adcirc(object):
def __init__(self, timestep=None, initialize_adcirc_state=False, adcirc_ramp=2. | units.day,
elev_boundary_sponge=False,layer_depth=500. | units.m, distributed=False, boundary_forcing="elevation"):
self.timestep=timestep
self.adcirc_ramp=adcirc_ramp
self.elev_boundary_sponge=elev_boundary_sponge
self.layer_depth=layer_depth
self.boundary_forcing=boundary_forcing
if distributed:
self.initialize_distributed_amuse()
self.initialize_pop()
self.initialize_adcirc()
if initialize_adcirc_state:
self.initialize_adcirc_state()
self.initialize_channels()
self.model_time=0. | units.s
self.pop_time_offset=self.pop.model_time
self.adcirc_time_offset=self.adcirc.model_time
def initialize_distributed_amuse(self):
da = init_local_only()
da.use_for_all_workers()
self.distributed_amuse=da
def initialize_pop(self):
p=POP(redirection="file", number_of_workers=8,redirect_stdout_file="pop.out")
cwd=os.getcwd()
p.change_directory(cwd)
popdatadir="/home/inti/code/amuse/trunk/sandbox/pelupes/pop/"
#set the grid we want to use
p.set_horiz_grid_file(popdatadir+'data/input/grid/horiz_grid_20010402.ieeer8')
p.set_vert_grid_file(popdatadir+'data/input/grid/in_depths.dat')
p.set_topography_file(popdatadir+'data/input/grid/topography_20010702.ieeei4')
#set the restart file
p.set_ts_file(popdatadir+'data/input/restart/r.x1_SAMOC_control.00750101')
#setup the forcing
p.set_shf_monthly_file(popdatadir+'data/input/shf_monthly/shf.normal_year+flux.mon')
p.set_sfwf_monthly_file(popdatadir+'data/input/sfwf/sfwf_phc0-50_ncarp_r46_flux.mon')
p.set_ws_monthly_file(popdatadir+'data/input/ws_monthly/ws.1958-2000.mon')
self.pop_grid=p.get_grid()
self.pop_forcings_grid=StaggeredGrid(p.elements, p.forcings, p._compute_cell_corners)
self.pop=p
self.timestep=self.timestep or self.pop.timestep/2
def read_adcirc_grid(self, grid_file):
gr=adcirc_grid_reader(grid_file,coordinates="spherical")
gr.read_grid()
nodes,elements,elev_boundaries,flow_boundaries=gr.get_sets()
if self.boundary_forcing=="flow":
# change elevation to flow boundary
for b in elev_boundaries:
b.type=22
flow_boundaries=elev_boundaries+flow_boundaries
elev_boundaries=[]
elements.n1=elements.nodes[:,0]
elements.n2=elements.nodes[:,1]
elements.n3=elements.nodes[:,2]
i=0
for e in elev_boundaries+flow_boundaries:
i+=1
nodes[e.nodes].vmark=i
# put maximum on depth to simulate top layer
nodes.depth=numpy.minimum(nodes.depth ,self.layer_depth )
# fix south east corner of bathymetry to prevent step right on edge
# this is actually counterproductive...
#~ lon=nodes.lon
#~ lat=nodes.lat
#~ a=numpy.where( (lat<11.5 |units.deg)*(lon>-61. | units.deg))[0]
#~ nodes[a].depth=numpy.minimum(nodes[a].depth, 100. | units.m)
# maybe increasing depth??
self.nodes=nodes
self.elements=elements
self._elev_boundaries=elev_boundaries
self._flow_boundaries=flow_boundaries
self._neta=gr.parameters["NETA"]
self._nflux=gr.parameters["NFLUX"]
def initialize_adcirc(self):
self.read_adcirc_grid("grid.input")
param=adcirc_parameter_reader("param.input")
param.read_parameters(NETA=self._neta, NFLUX=self._nflux)
#~ param.parameters['NBFR']=-1
param.parameters['NWS']=0
adcirc=Adcirc(coordinates="spherical", redirection="file",redirect_stdout_file="adcirc.out")
adcirc.set_rootdir(os.getcwd())
#~ adcirc._parameters=param.parameters
adcirc._parameters=get_default_parameter_set()
adcirc._parameters["NCOR"]=1 # set coriolis f internally
adcirc.assign_grid_and_boundary(self.nodes, self.elements, self._elev_boundaries, self._flow_boundaries)
adcirc.parameters.use_interface_parameters=True
adcirc.parameters.use_interface_grid=True
if self.boundary_forcing=="elevation":
adcirc.parameters.use_interface_elevation_boundary=True
elif self.boundary_forcing=="flow":
adcirc.parameters.use_interface_flow_boundary=True
adcirc.parameters.use_interface_met_forcing=True
adcirc.parameters.A_H=1000. | units.m**2/units.s
#~ adcirc.parameters.A_H=param.parameters["ESLM"] | units.m**2/units.s
timestep=self.timestep
n=1
#~ while timestep>abs(param.parameters["DTDP"]) | units.s:
while timestep>300. | units.s:
n+=1
timestep=timestep/n
adcirc.parameters.timestep=timestep
#~ adcirc.parameters.bottom_friction_law=["linear","quadratic","hybrid"][param.parameters["NOLIBF"]]
#~ adcirc.parameters.linear_bottom_friction_coeff=param.parameters["TAU"]| units.s**-1
adcirc.parameters.bottom_friction_law="hybrid"
adcirc.parameters.hybrid_bottom_friction_hbreak=50. | units.m
#~ adcirc.parameters.bottom_friction_law="linear"
#~ adcirc.parameters.linear_bottom_friction_coeff=0.00001| units.s**-1
adcirc.parameters.quadratic_bottom_friction_coeff=0.003#param.parameters["CF"]
adcirc.parameters.use_predictor_corrector=True#param.parameters["DTDP"]<0
adcirc.parameters.central_longitude=param.parameters["SLAM0"] | units.deg
adcirc.parameters.central_latitude=param.parameters["SFEA0"] | units.deg
adcirc.parameters.GWCE_weighting_factor=-1.#param.parameters["TAU0"]
#~ adcirc.parameters.GWCE_weighting_factor=0.005
#~ adcirc.parameters.spatial_derivative_advective_term_parameter=0
#~ adcirc.parameters.time_derivative_advective_term_parameter=0
#~ adcirc.parameters.finite_amplitude_term_parameter=0
adcirc.parameters.minimum_depth=5.| units.m
if self.adcirc_ramp:
adcirc.parameters.use_ramping=True
adcirc.parameters.ramping_time=self.adcirc_ramp
print(adcirc.parameters)
self.adcirc=adcirc
self.adcirc_grid=adcirc.get_grid()
self.adcirc_forcings_grid=StaggeredGrid(self.adcirc_grid.elements, nodes=adcirc.forcings)
elements=adcirc.elements.copy(filter_attributes=lambda x,y: y in ["lat","lon","n1","n2","n3"] )
nodes=adcirc.nodes.copy(filter_attributes=lambda x,y: y in ["lat","lon"] )
self.adcirc_memory_grid = StaggeredGrid( elements, nodes=nodes )
def initialize_adcirc_boundary_channel(self):
# two things remain to be fixed:
# - subgrid (as generated by pop nodes slicing) is not a structuredgrid yet
# so remapping doesn't work directly (proper fix is to fix subgridding)
# - the definitions of lat and lon are inconsistent between codes, leading
# to problems with remapping. Various possible fixes: make def consistent, or
# define something in the remapper (coordinate trasnform keyword, or is the channel
# transform (not implemented yet for remapping channel) something tobe used here??
elev_boundaries=list(self.adcirc.elevation_boundaries())
flow_boundaries=list(self.adcirc.flow_boundaries())
remapper=functools.partial(interpolating_2D_remapper, axes_names=["lon","lat"])
if self.boundary_forcing=="elevation":
boundary_=elev_boundaries[0]
elif self.boundary_forcing=="flow":
boundary_=flow_boundaries[0]
boundary=boundary_.empty_copy()
boundary.node=boundary_.node
boundary.lat=self.adcirc.nodes[boundary.node].lat
boundary.lon=self.adcirc.nodes[boundary.node].lon + (2*numpy.pi| units.rad)
#~ from amuse.io import write_set_to_file
#~ write_set_to_file(elev_boundaries[0],"elev_boundary","amuse")
#~ raise
# correction for grid mismatch (??) :
boundary.lat+= (0.25 | units.deg)
boundary.lon+= (0.25 | units.deg)
# hardcoded subregion
pop_region=self.pop.nodes[280:310,218:317]
pop_region_copy=pop_region.copy()
self._adcirc_boundary=boundary
self.boundary_channel=pop_region_copy.new_remapping_channel_to(boundary, remapper)
self._pop_boundary_region=pop_region
self._pop_boundary_region_copy=pop_region_copy
self._aux_boundary_channel1=pop_region.new_channel_to(self._pop_boundary_region_copy)
self._aux_boundary_channel2=self._adcirc_boundary.new_channel_to(boundary_)
def update_adcirc_boundary(self):
if self.boundary_forcing=="elevation":
self.update_elevation_boundary()
elif self.boundary_forcing=="flow":
self.update_flow_boundary()
def update_elevation_boundary(self):
self._aux_boundary_channel1.copy_attributes(["ssh"])
self.boundary_channel.copy_attributes(["ssh"])
if self.elev_boundary_sponge:
n=self.elev_nsponge
sponge=numpy.arange(n)/(1.*n)
self._adcirc_boundary[:n].ssh*=sponge
self._adcirc_boundary[-n:].ssh*=sponge[::-1]
self._aux_boundary_channel2.transform(["eta"],None,["ssh"])
def update_flow_boundary(self):
self._aux_boundary_channel1.copy_attributes(["vx_barotropic","vy_barotropic"])
self.boundary_channel.copy_attributes(["depth","vx_barotropic","vy_barotropic"])
self._aux_boundary_channel2.transform(["flux_x","flux_y"],lambda x,y,z : (x*y, x*z), ["depth","vx_barotropic","vy_barotropic"])
def initialize_channels(self):
self.initialize_adcirc_boundary_channel()
self.forcings_channel = self.pop_forcings_grid.new_remapping_channel_to(self.adcirc_forcings_grid, conservative_spherical_remapper)
#~ self.memory_channel = self.pop_grid.new_remapping_channel_to(self.adcirc_memory_grid, conservative_spherical_remapper)
def update_adcirc_forcings(self):
self.forcings_channel.copy_attributes(["tau_x","tau_y"])
#~ tau_x=self.adcirc_forcings_grid.nodes.tau_x
#~ tau_y=self.adcirc_forcings_grid.nodes.tau_y
#~ coriolis_f=self.adcirc_forcings_grid.nodes.coriolis_f
#~ print "tau_x (min,max,mean):", tau_x.min(),tau_x.max(),tau_x.mean()
#~ print "tau_y (min,max,mean):", tau_y.min(),tau_y.max(),tau_y.mean()
#~ print "coriolis_f (min,max,mean):", coriolis_f.min(),coriolis_f.max(),coriolis_f.mean()
def initialize_adcirc_state(self):
channel1 = self.pop_grid.new_remapping_channel_to(self.adcirc_memory_grid, conservative_spherical_remapper)
channel2 = self.adcirc_memory_grid.nodes.new_channel_to(self.adcirc_grid.nodes)
channel1.copy_attributes(["vx_barotropic", "vy_barotropic","ssh"])
def f(vx,vy,eta):
return vx,vy,eta, 0. | units.m/units.s
channel2.transform(["vx","vy","eta","deta_dt"], f, ["vx_barotropic", "vy_barotropic","ssh"])
def evolve_model(self, tend, timestep=None):
timestep=timestep or self.timestep or tend-self.model_time
while self.model_time<tend-timestep/2:
next_timestep=self.pop.timestep_next
print("update boundary..")
self.update_adcirc_boundary()
print("update forcings..")
self.update_adcirc_forcings()
print("evolve pop...")
self.pop.evolve_model(self.model_time+next_timestep + self.pop_time_offset)
print("evolve adcirc..")
self.adcirc.evolve_model(self.model_time+next_timestep + self.adcirc_time_offset)
print("done")
self.model_time+=next_timestep
def initialize_adcirc(self):
self.read_adcirc_grid("grid.input")
param=adcirc_parameter_reader("param.input")
param.read_parameters(NETA=self._neta, NFLUX=self._nflux)
#~ param.parameters['NBFR']=-1
param.parameters['NWS']=0
adcirc=Adcirc(coordinates="spherical", redirection="file",redirect_stdout_file="adcirc.out")
adcirc.set_rootdir(os.getcwd())
#~ adcirc._parameters=param.parameters
adcirc._parameters=get_default_parameter_set()
adcirc._parameters["NCOR"]=1 # set coriolis f internally
adcirc.assign_grid_and_boundary(self.nodes, self.elements, self._elev_boundaries, self._flow_boundaries)
adcirc.parameters.use_interface_parameters=True
adcirc.parameters.use_interface_grid=True
if self.boundary_forcing=="elevation":
adcirc.parameters.use_interface_elevation_boundary=True
elif self.boundary_forcing=="flow":
adcirc.parameters.use_interface_flow_boundary=True
adcirc.parameters.use_interface_met_forcing=True
adcirc.parameters.A_H=1000. | units.m**2/units.s
#~ adcirc.parameters.A_H=param.parameters["ESLM"] | units.m**2/units.s
timestep=self.timestep
n=1
#~ while timestep>abs(param.parameters["DTDP"]) | units.s:
while timestep>300. | units.s:
n+=1
timestep=timestep/n
adcirc.parameters.timestep=timestep
#~ adcirc.parameters.bottom_friction_law=["linear","quadratic","hybrid"][param.parameters["NOLIBF"]]
#~ adcirc.parameters.linear_bottom_friction_coeff=param.parameters["TAU"]| units.s**-1
adcirc.parameters.bottom_friction_law="hybrid"
adcirc.parameters.hybrid_bottom_friction_hbreak=50. | units.m
#~ adcirc.parameters.bottom_friction_law="linear"
#~ adcirc.parameters.linear_bottom_friction_coeff=0.00001| units.s**-1
adcirc.parameters.quadratic_bottom_friction_coeff=0.003#param.parameters["CF"]
adcirc.parameters.use_predictor_corrector=True#param.parameters["DTDP"]<0
adcirc.parameters.central_longitude=param.parameters["SLAM0"] | units.deg
adcirc.parameters.central_latitude=param.parameters["SFEA0"] | units.deg
adcirc.parameters.GWCE_weighting_factor=-1.#param.parameters["TAU0"]
#~ adcirc.parameters.GWCE_weighting_factor=0.005
#~ adcirc.parameters.spatial_derivative_advective_term_parameter=0
#~ adcirc.parameters.time_derivative_advective_term_parameter=0
#~ adcirc.parameters.finite_amplitude_term_parameter=0
adcirc.parameters.minimum_depth=5.| units.m
if self.adcirc_ramp:
adcirc.parameters.use_ramping=True
adcirc.parameters.ramping_time=self.adcirc_ramp
print(adcirc.parameters)
self.adcirc=adcirc
self.adcirc_grid=adcirc.get_grid()
self.adcirc_forcings_grid=StaggeredGrid(self.adcirc_grid.elements, nodes=adcirc.forcings)
elements=adcirc.elements.copy(filter_attributes=lambda x,y: y in ["lat","lon","n1","n2","n3"] )
nodes=adcirc.nodes.copy(filter_attributes=lambda x,y: y in ["lat","lon"] )
self.adcirc_memory_grid = StaggeredGrid( elements, nodes=nodes )
def test3(self):
#define nodal points and triangles of a small test grid
#got this grid from http://matplotlib.org/examples/pylab_examples/triplot_demo.html
xy = numpy.asarray([[-0.101, 0.872], [-0.080, 0.883], [-0.069, 0.888],
[-0.054, 0.890], [-0.045, 0.897], [-0.057, 0.895],
[-0.073, 0.900], [-0.087, 0.898], [-0.090, 0.904],
[-0.069, 0.907], [-0.069, 0.921], [-0.080, 0.919],
[-0.073, 0.928], [-0.052, 0.930], [-0.048, 0.942],
[-0.062, 0.949], [-0.054, 0.958], [-0.069, 0.954],
[-0.087, 0.952], [-0.087, 0.959], [-0.080, 0.966],
[-0.085, 0.973], [-0.087, 0.965], [-0.097, 0.965],
[-0.097, 0.975], [-0.092, 0.984], [-0.101, 0.980],
[-0.108, 0.980], [-0.104, 0.987], [-0.102, 0.993],
[-0.115, 1.001], [-0.099, 0.996], [-0.101, 1.007],
[-0.090, 1.010], [-0.087, 1.021], [-0.069, 1.021],
[-0.052, 1.022], [-0.052, 1.017], [-0.069, 1.010],
[-0.064, 1.005], [-0.048, 1.005], [-0.031, 1.005],
[-0.031, 0.996], [-0.040, 0.987], [-0.045, 0.980],
[-0.052, 0.975], [-0.040, 0.973], [-0.026, 0.968],
[-0.020, 0.954], [-0.006, 0.947], [0.003, 0.935],
[0.006, 0.926], [0.005, 0.921], [0.022, 0.923],
[0.033, 0.912], [0.029, 0.905], [0.017, 0.900],
[0.012, 0.895], [0.027, 0.893], [0.019, 0.886],
[0.001, 0.883], [-0.012, 0.884], [-0.029, 0.883],
[-0.038, 0.879], [-0.057, 0.881], [-0.062, 0.876],
[-0.078, 0.876], [-0.087, 0.872], [-0.030, 0.907],
[-0.007, 0.905], [-0.057, 0.916], [-0.025, 0.933],
[-0.077, 0.990], [-0.059, 0.993]])
triangles = numpy.asarray([[67, 66, 1], [65, 2, 66], [1, 66, 2],
[64, 2, 65], [63, 3, 64], [60, 59, 57],
[2, 64, 3], [3, 63, 4], [0, 67, 1],
[62, 4, 63], [57, 59, 56], [59, 58, 56],
[61, 60, 69], [57, 69, 60], [4, 62, 68],
[6, 5, 9], [61, 68, 62], [69, 68, 61],
[9, 5, 70], [6, 8, 7], [4, 70, 5],
[8, 6, 9], [56, 69, 57], [69, 56, 52],
[70, 10, 9], [54, 53, 55], [56, 55, 53],
[68, 70, 4], [52, 56, 53], [11, 10, 12],
[69, 71, 68], [68, 13, 70], [10, 70, 13],
[51, 50, 52], [13, 68, 71], [52, 71, 69],
[12, 10, 13], [71, 52, 50], [71, 14, 13],
[50, 49, 71], [49, 48, 71], [14, 16, 15],
[14, 71, 48], [17, 19, 18], [17, 20, 19],
[48, 16, 14], [48, 47, 16], [47, 46, 16],
[16, 46, 45], [23, 22, 24], [21, 24, 22],
[17, 16, 45], [20, 17, 45], [21, 25, 24],
[27, 26, 28], [20, 72, 21], [25, 21, 72],
[45, 72, 20], [25, 28, 26], [44, 73, 45],
[72, 45, 73], [28, 25, 29], [29, 25, 31],
[43, 73, 44], [73, 43, 40], [72, 73, 39],
[72, 31, 25], [42, 40, 43], [31, 30, 29],
[39, 73, 40], [42, 41, 40], [72, 33, 31],
[32, 31, 33], [39, 38, 72], [33, 72, 38],
[33, 38, 34], [37, 35, 38], [34, 38, 35],
[35, 37, 36]])
num_elems = len(triangles)
elements = UnstructuredGrid(num_elems)
elements.n1 = triangles[:, 0] - 1
elements.n2 = triangles[:, 1] - 1
elements.n3 = triangles[:, 2] - 1
nodes = UnstructuredGrid(len(xy))
nodes.lon = (xy[:, 0] | units.rad)
nodes.lat = (xy[:, 1] | units.rad)
grid = StaggeredGrid(elements, nodes)
values = numpy.random.random(num_elems)
print(values)
elements.values = values
nodes.values = grid.map_elements_to_nodes(values)
print(nodes.values)
remapped_values = grid.map_nodes_to_elements(nodes.values)
print(remapped_values)
before_sum = values.sum()
after_sum = remapped_values.sum()
print('before', before_sum, 'after', after_sum)
self.assertAlmostEqual(
after_sum,
before_sum,
msg="Sum of values before and after remapping should be the same")
def get_grid(self):
return StaggeredGrid(self.elements, self.nodes,
self._compute_cell_corners)
def get_grid(self):
return StaggeredGrid(self.elements, self.nodes)
