def d3d(tmpdir):
# initialize a model
rpath = str(tmpdir) + "/d3d/"
case.update({"rpath": rpath + "/20181001.00/"}) # use tmpdir for running the model
b = pyposeidon.model(**case)
b.execute()
# creating a time sequence of the runs
start_date = pd.to_datetime("2018-10-1 0:0:0")
end_date = pd.to_datetime("2018-10-2 0:0:0")
date_list = pd.date_range(start_date, end_date, freq="12H")
# creating a sequence of folder to store the runs. In this case we name them after the date attribute.
# NOTE that the first folder is the fisrt run already perfomed!!
rpaths = [rpath + datetime.datetime.strftime(x, "%Y%m%d.%H") + "/" for x in date_list]
# set meteo files
meteo = []
for date in date_list:
end_date = pd.to_datetime(date) + pd.to_timedelta("12H")
end_date = end_date.strftime(format="%Y-%m-%d %H:%M:%S")
dr = pd.date_range(date, end_date, freq="12H")
names = ["uvp_" + datetime.datetime.strftime(x, "%Y%m%d%H") + ".grib" for x in dr]
dur = [(DATA_DIR / name).as_posix() for name in names]
meteo.append(dur)
# set cast
for l in range(len(rpaths) - 1):
h = cast.cast(
solver="d3d", model=b, ppath=rpaths[l], cpath=rpaths[l + 1], meteo=meteo[l + 1], date=date_list[l + 1]
)
h.set(execute=True) # execute
# Run check case - Total duration
check.update({"rpath": rpath + "check/"}) # use tmpdir for running the model
c = pyposeidon.model(**check)
c.execute()
# COMPARE
output = data.data(folders=rpaths, solver="d3d")
total = data.data(folders=[rpath + "check/"], solver="d3d")
test = True
rb = []
for var in total.Dataset.data_vars:
if not total.Dataset[var].equals(output.Dataset[var]):
rb.append(var)
if np.abs(total.Dataset[var].values - output.Dataset[var].values).max() > 1.0e-6:
test = False
print(rb)
return test
def d3d(tmpdir,dic):
#initialize a model
rpath = str(tmpdir)+'/'
dic.update({'rpath':rpath}) # use tmpdir for running the model
b = pyposeidon.model(**dic)
try:
b.execute()
out = data.data(**dic)
a = pyposeidon.read_model(rpath+'d3d_model.json') # read model
a.execute()
out = data.data(**dic)
return True
except:
return False
def schism(tmpdir, dic):
#initialize a model
rpath = str(tmpdir) + '/'
dic.update({'rpath': rpath}) # use tmpdir for running the model
b = pyposeidon.model(**dic)
try:
b.execute()
b.results()
a = pyposeidon.read_model(rpath + 'test_model.json') # read model
a.execute()
a.results()
return True
except:
return False
def schism(tmpdir, case):
#initialize a model
dic = {
'solver': 'schism',
'geometry': case,
'manning': .12,
'windrot': 0.00001,
'tag': 'test',
'start_date': '2017-10-1 0:0:0',
'time_frame': '12H',
'meteo_source': [DATA_DIR / 'erai.grib'], #meteo file
'meteo_engine': 'cfgrib',
'dem_source': DEM_FILE,
'ncores': NCORES, #number of cores
'update': ['all'], #update only meteo, keep dem
'parameters': {
'dt': 400,
'rnday': 0.3,
'nhot': 0,
'ihot': 0,
'nspool': 9,
'ihfskip': 36,
'nhot_write': 108
}
}
rpath = str(tmpdir) + '/'
dic.update({'rpath': rpath}) # use tmpdir for running the model
b = pyposeidon.model(**dic)
try:
b.execute()
b.results()
return True
except:
return False
def schism(tmpdir, case):
# initialize a model
dic = {
"solver": "schism",
"geometry": case,
"manning": 0.12,
"windrot": 0.00001,
"tag": "test",
"start_date": "2017-10-1 0:0:0",
"time_frame": "12H",
"meteo_source": [DATA_DIR / "erai.grib"], # meteo file
"meteo_engine": "cfgrib",
"dem_source": DEM_FILE,
"ncores": NCORES, # number of cores
"update": ["all"], # update only meteo, keep dem
"parameters": {
"dt": 400,
"rnday": 0.3,
"nhot": 0,
"ihot": 0,
"nspool": 9,
"ihfskip": 36,
"nhot_write": 108
},
}
rpath = str(tmpdir) + "/"
dic.update({"rpath": rpath}) # use tmpdir for running the model
b = pyposeidon.model(**dic)
try:
b.execute()
b.results()
return True
except:
return False
def schism(tmpdir):
#initialize a model
rpath = str(tmpdir) + '/schism/'
case.update({'rpath':
rpath + '20181001.00/'}) # use tmpdir for running the model
b = pyposeidon.model(**case)
b.execute()
#creating a time sequence of the runs
start_date = pd.to_datetime('2018-10-1 0:0:0')
end_date = pd.to_datetime('2018-10-2 0:0:0')
date_list = pd.date_range(start_date, end_date, freq='12H')
#creating a sequence of folder to store the runs. In this case we name them after the date attribute.
#NOTE that the first folder is the fisrt run already perfomed!!
rpaths = [
rpath + datetime.datetime.strftime(x, '%Y%m%d.%H') + '/'
for x in date_list
]
#creating a sequence of folder from which we read the meteo.
meteo = []
for date in date_list:
end_date = pd.to_datetime(date) + pd.to_timedelta('12H')
end_date = end_date.strftime(format='%Y-%m-%d %H:%M:%S')
dr = pd.date_range(date, end_date, freq='12H')
names = [
'uvp_' + datetime.datetime.strftime(x, '%Y%m%d%H') + '.grib'
for x in dr
]
dur = [(DATA_DIR / name).as_posix() for name in names]
meteo.append(dur)
#set cast
for l in range(len(rpaths) - 1):
h = cast.cast(solver='schism',
model=b,
ppath=rpaths[l],
cpath=rpaths[l + 1],
meteo=meteo[l + 1],
date=date_list[l + 1])
h.set(execute=True) # execute
# Run check case - Total duration
check.update({'rpath':
rpath + 'check/'}) # use tmpdir for running the model
c = pyposeidon.model(**check)
c.execute()
# COMPARE
output = data.data(folders=rpaths, solver='schism')
total = data.data(folders=[rpath + 'check/'], solver='schism')
rb = []
for var in total.Dataset.data_vars:
if not total.Dataset[var].equals(output.Dataset[var]):
rb.append(var)
print(rb)
# flag = True TODO
# for var in rb:
# flag = False
# mdif = np.abs(total.results.Dataset[var].values - output.results.Dataset[var].values).max()
# if mdif < 1.e-14 :
# flag = True
# print(mdif)
if (rb == ['zcor']) or rb == []:
return True
else:
return False
def __init__(self, **kwargs):
for attr, value in kwargs.iteritems():
setattr(self, attr, value)
logging.basicConfig(filename=self.path + self.case + '.log',
level=logging.INFO)
parent = kwargs.get('parent', '.')
# load model
with open(parent + 'info.pkl', 'r') as f:
info = pickle.load(f)
info['minlon'] = kwargs.get('minlon', None)
info['maxlon'] = kwargs.get('maxlon', None)
info['minlat'] = kwargs.get('minlat', None)
info['maxlat'] = kwargs.get('maxlat', None)
info['rpath'] = kwargs.get('rpath', info['rpath'] + './nested/')
rpath = info['rpath'] #save for later
info['resolution'] = kwargs.get('resolution', None)
info['atm'] = False
#create new case
nest = model(**info)
nest.set() #setup nested run
nest.output() #output to run folder
check = [
os.path.exists(parent + f) for f in ['u.amu', 'v.amv', 'p.amp']
]
if np.any(check) == False:
nest.force()
nest.uvp() #write u,v,p files
else: #link u,v,p
for filename in ['u.amu', 'v.amv', 'p.amp']:
os.symlink(parent + filename, rpath + filename)
# modify mdf file
inp, order = mdf.read(rpath + nest.impl.tag + '.mdf')
# adjust variables
#create the ini file
if 'Filic' not in order: order.append('Filic')
inp['Filic'] = nest.impl.tag + '.ini'
pdata = data([parent])
s1 = pdata.get_data('S1', step=1)
u1 = pdata.get_data('U1', step=1)
v1 = pdata.get_data('V1', step=1)
xz = pdata.get_data('XZ')
yz = pdata.get_data('YZ')
orig = pyresample.geometry.SwathDefinition(lons=xz,
lats=yz) # original points
targ = pyresample.geometry.SwathDefinition(
lons=nest.impl.grid.x, lats=nest.impl.grid.y) # target grid
s_ini = pyresample.kd_tree.resample_nearest(orig,
s1,
targ,
radius_of_influence=100000,
fill_value=0)
u_ini = pyresample.kd_tree.resample_nearest(orig,
u1,
targ,
radius_of_influence=100000,
fill_value=0)
v_ini = pyresample.kd_tree.resample_nearest(orig,
v1,
targ,
radius_of_influence=100000,
fill_value=0)
# Write .ini file
with open(rpath + nest.impl.tag + '.ini', 'w') as f:
np.savetxt(f, s_ini)
np.savetxt(f, u_ini)
np.savetxt(f, v_ini)
#create the bc file
if 'Filbnd' not in order: order.append('Filbnd')
if 'Filana' not in order: order.append('Filana')
inp['Filbnd'] = nest.impl.tag + '.bnd'
inp['Filana'] = nest.impl.tag + '.bca'
# bca =
inp['Restid'] = '##' #no restart file
# update mdf
mdf.write(inp, rpath + nest.impl.tag + '.mdf', selection=order)
# run case
sys.stdout.write('executing\n')
sys.stdout.flush()
os.chdir(rpath)
#subprocess.call(rpath+'run_flow2d3d.sh',shell=True)
nest.run()
nest.save()
logging.info('nesting run done for date :' +
datetime.datetime.strftime(date, '%Y%m%d.%H'))
def __init__(self, **kwargs):
for attr, value in kwargs.iteritems():
setattr(self, attr, value)
logging.basicConfig(filename=self.path + self.case + ".log", level=logging.INFO)
parent = kwargs.get("parent", ".")
# load model
with open(parent + "info.pkl", "r") as f:
info = pickle.load(f)
info["minlon"] = kwargs.get("minlon", None)
info["maxlon"] = kwargs.get("maxlon", None)
info["minlat"] = kwargs.get("minlat", None)
info["maxlat"] = kwargs.get("maxlat", None)
info["rpath"] = kwargs.get("rpath", info["rpath"] + "./nested/")
rpath = info["rpath"] # save for later
info["resolution"] = kwargs.get("resolution", None)
info["atm"] = False
# create new case
nest = model(**info)
nest.set() # setup nested run
nest.output() # output to run folder
check = [os.path.exists(parent + f) for f in ["u.amu", "v.amv", "p.amp"]]
if np.any(check) == False:
nest.force()
nest.uvp() # write u,v,p files
else: # link u,v,p
for filename in ["u.amu", "v.amv", "p.amp"]:
os.symlink(parent + filename, rpath + filename)
# modify mdf file
inp, order = mdf.read(rpath + nest.impl.tag + ".mdf")
# adjust variables
# create the ini file
if "Filic" not in order:
order.append("Filic")
inp["Filic"] = nest.impl.tag + ".ini"
pdata = data([parent])
s1 = pdata.get_data("S1", step=1)
u1 = pdata.get_data("U1", step=1)
v1 = pdata.get_data("V1", step=1)
xz = pdata.get_data("XZ")
yz = pdata.get_data("YZ")
orig = pyresample.geometry.SwathDefinition(lons=xz, lats=yz) # original points
targ = pyresample.geometry.SwathDefinition(lons=nest.impl.grid.x, lats=nest.impl.grid.y) # target grid
s_ini = pyresample.kd_tree.resample_nearest(orig, s1, targ, radius_of_influence=100000, fill_value=0)
u_ini = pyresample.kd_tree.resample_nearest(orig, u1, targ, radius_of_influence=100000, fill_value=0)
v_ini = pyresample.kd_tree.resample_nearest(orig, v1, targ, radius_of_influence=100000, fill_value=0)
# Write .ini file
with open(rpath + nest.impl.tag + ".ini", "w") as f:
np.savetxt(f, s_ini)
np.savetxt(f, u_ini)
np.savetxt(f, v_ini)
# create the bc file
if "Filbnd" not in order:
order.append("Filbnd")
if "Filana" not in order:
order.append("Filana")
inp["Filbnd"] = nest.impl.tag + ".bnd"
inp["Filana"] = nest.impl.tag + ".bca"
# bca =
inp["Restid"] = "##" # no restart file
# update mdf
mdf.write(inp, rpath + nest.impl.tag + ".mdf", selection=order)
# run case
sys.stdout.write("executing\n")
sys.stdout.flush()
os.chdir(rpath)
# subprocess.call(rpath+'run_flow2d3d.sh',shell=True)
nest.run()
nest.save()
logging.info("nesting run done for date :" + datetime.datetime.strftime(date, "%Y%m%d.%H"))
def schism(tmpdir):
# initialize a model
rpath = str(tmpdir) + "/schism/"
case.update({"rpath":
rpath + "20181001.00/"}) # use tmpdir for running the model
b = pyposeidon.model(**case)
b.execute()
# creating a time sequence of the runs
start_date = pd.to_datetime("2018-10-1 0:0:0")
end_date = pd.to_datetime("2018-10-2 0:0:0")
date_list = pd.date_range(start_date, end_date, freq="12H")
# creating a sequence of folder to store the runs. In this case we name them after the date attribute.
# NOTE that the first folder is the fisrt run already perfomed!!
rpaths = [
rpath + datetime.datetime.strftime(x, "%Y%m%d.%H") + "/"
for x in date_list
]
# creating a sequence of folder from which we read the meteo.
meteo = []
for date in date_list:
prev_date = pd.to_datetime(date) - pd.to_timedelta("12H")
prev_date = prev_date.strftime(format="%Y-%m-%d %H:%M:%S")
dr = pd.date_range(prev_date, date, freq="12H")
names = [
"uvp_" + datetime.datetime.strftime(x, "%Y%m%d%H") + ".grib"
for x in dr
]
dur = [(DATA_DIR / name).as_posix() for name in names]
meteo.append(dur)
# set cast
for l in range(len(rpaths) - 1):
h = cast.cast(solver="schism",
model=b,
ppath=rpaths[l],
cpath=rpaths[l + 1],
meteo=meteo[l + 1],
date=date_list[l + 1])
h.set(execute=True) # execute
# Run check case - Total duration
check.update({"rpath":
rpath + "check/"}) # use tmpdir for running the model
# Combine meteo appropriately
m1 = pm.meteo(meteo_source=METEO_FILES_2[0], meteo_engine="cfgrib")
m2 = pm.meteo(meteo_source=METEO_FILES_2[1], meteo_engine="cfgrib")
m3 = pm.meteo(meteo_source=METEO_FILES_2[2], meteo_engine="cfgrib")
m4 = pm.meteo(meteo_source=METEO_FILES_2[3], meteo_engine="cfgrib")
# extract correct chunk
w1 = m1.Dataset.isel(time=slice(0, 13))
w2 = m2.Dataset.isel(time=slice(
1, 13)) # note that we keep the 12 hour from the previous file
w3 = m3.Dataset.isel(time=slice(1, 13))
w4 = m4.Dataset.isel(time=slice(1, 13))
# combine
meteo = xr.combine_by_coords([w1, w2, w3, w4], combine_attrs="override")
# saving
check.update({"meteo_source": meteo})
c = pyposeidon.model(**check)
c.execute()
# COMPARE
output = data.data(folders=rpaths, solver="schism")
total = data.data(folders=[rpath + "check/"], solver="schism")
r = output.Dataset.isel(time=slice(0, 36))
rb = []
for var in total.Dataset.data_vars:
if not total.Dataset[var].equals(r[var]):
rb.append(var)
print(rb)
# flag = True TODO
# for var in rb:
# flag = False
# mdif = np.abs(total.results.Dataset[var].values - output.results.Dataset[var].values).max()
# if mdif < 1.e-14 :
# flag = True
# print(mdif)
if (rb == ["zcor"]) or rb == []:
return True
else:
return False