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import getopt
import sys
from datetime import datetime
import gdal
from gdalconst import *
import numpy as np
import pandas as pd
import pyresample
import slimPre
from netCDF4 import Dataset, num2date
from scipy.spatial import cKDTree
import param
import prepro_private
if slimPre.partition_id() == "0":
print(" ___________ ____ _____________ ____")
print("\____ \_ __ \_/ __ \ \___ \_ __ \/ _ \ ")
print("| |_| | | \/\ ___/| |_| | | \( |_| )")
print("| __/|__| \_____| __/|__| \____/ ")
print("|__| |__| ")
print()
# --------- LOAD COMMAND LINE OPTIONS -----------------------------------------
opts = None
args = None
try:
opts, args = getopt.getopt(sys.argv[1:], "m:")
except getopt.GetoptError:
print("Argument error!")
sys.exit(1)
mesh_setup = "gbr_styx"
for opt, arg in opts:
if opt == "-m":
mesh_setup = arg
p = param.parameters(mesh_setup)
if slimPre.partition_id() == "0":
p.print_info()
# ------------------------------------------------------------------------------
pre_data_dir = p.prepro_dir
if slimPre.partition_nb() != "1":
pre_data_dir = pre_data_dir + "/%s" % slimPre.partition_id()
slimPre.make_directory(pre_data_dir)
# --- useful variables
fmt = "%Y-%m-%d %H:%M:%S"
d0 = datetime.strptime(p.initial_time, fmt)
d1 = datetime.strptime(p.final_time, fmt)
if d0 > d1:
raise ValueError(
"initial time (%s) must be before final time (%s) !"
% (p.initial_time, p.final_time)
)
if slimPre.partition_nb() == "1":
mesh_file_name = p.mesh_file
else:
mesh_file_name = p.mesh_file[:-4] + "_" + slimPre.partition_nb() + ".msh"
mesh = slimPre.Mesh(mesh_file_name, mesh_proj=p.mesh_proj)
region_global = slimPre.Region(mesh)
lonlat_global = slimPre.Coordinate_system(
region_global, data_proj="+proj=latlong +ellps=WGS84"
)
lonlat_global_degrees = lonlat_global.coordinates[:, :2] * 180.0 / np.pi
nElements = len(lonlat_global.coordinates)
time_tpxo = slimPre.Time(
initial_time=p.initial_time, final_time=p.final_time, time_step=900.0
)
time_wind = slimPre.Time(
initial_time=p.initial_time, final_time=p.final_time, time_step=3600.0
)
# export_xdmf = True if int(slimPre.partition_nb()) < 10 and nElements < 30_000 else False
export_xdmf = False
#################################
# 1. TIME INDEPENDENT VARIABLES #
#################################
if slimPre.partition_nb() == "1":
# --- bathymetry
if "bathymetry" in p.vec_prepro2D:
print("=== preprocessing bathymetry ===")
bath = prepro_private.interpolate_on_mesh(
lonlat_global_degrees,
None,
p.slimGBR_data_dir + "source/bathymetry/gbr100_10nov_v6.grd",
"z",
)
bath[:] = np.maximum(p.min_depth, -bath[:])
slimPre.write_file(
pre_data_dir + "/bathymetry.nc",
region=region_global,
time=None,
data=[("bathymetry", bath)],
)
slimPre.smooth_bathymetry(
mesh,
(pre_data_dir + "/bathymetry.nc", "bathymetry"),
output_file_name=pre_data_dir + "/bathymetry_smooth.nc",
coefficient=0.5,
transform_p0=False,
)
if export_xdmf:
slimPre.netcdf_to_xdmf(
mesh_file_name,
pre_data_dir + "/bathymetry_smooth.nc",
pre_data_dir + "/bathymetry.h5",
"bathymetry",
)
# --- coriolis
if "coriolis" in p.vec_prepro2D:
print("=== preprocessing Coriolis coefficient ===")
corio = 2 * 7.292e-5 * np.sin(lonlat_global.coordinates[:, 1])
slimPre.write_file(
pre_data_dir + "/coriolis.nc",
region=region_global,
time=None,
data=[("coriolis", corio)],
)
del corio
# --- manning
if "manning" in p.vec_prepro2D:
print("=== preprocessing Manning coefficient ===")
# Loading .tif file and structuring as a workable array
reefs_tiff = gdal.Open(
p.slimGBR_data_dir + "source/reef_map/reefs_as_raster.tif", GA_ReadOnly
)
nxReefs = reefs_tiff.RasterXSize
nyReefs = reefs_tiff.RasterYSize
(
oxReefs,
dxReefs,
t1Reefs,
oyReefs,
t2Reefs,
dyReefs,
) = reefs_tiff.GetGeoTransform()
reefs = np.array(reefs_tiff.GetRasterBand(1).ReadAsArray())
c = np.empty((nElements))
for i in range(nElements):
c[i] = slimPre.interpolate_from_structured_grid(
lonlat_global_degrees[i, 0],
lonlat_global_degrees[i, 1],
oxReefs,
oyReefs,
dxReefs,
dyReefs,
reefs,
)
def bottom_coef(off_reef, on_reef):
coef = np.ones(nElements) * off_reef
coef[c > 0.25] = on_reef
return coef
manning_025_25 = bottom_coef(0.025, 0.25)
manning_reduced = bottom_coef(0.025, 0.025 * np.sqrt(10))
bulk_0025_05 = bottom_coef(2.5e-3, 5e-2)
slimPre.write_file(
pre_data_dir + "/reef.nc",
region=region_global,
time=None,
data=[
("manning_025_25", manning_025_25),
("manning_reduced", manning_reduced),
("bulk_0025_05", bulk_0025_05),
],
)
if export_xdmf:
slimPre.netcdf_to_xdmf(
mesh_file_name,
pre_data_dir + "/reef.nc",
pre_data_dir + "/reef.h5",
["manning_025_25", "manning_reduced", "bulk_0025_05"],
)
del reefs_tiff, reefs
if "river_discharge" in p.vec_prepro2D:
print("=== preprocessing River discharge ===")
river_discharge_dir = pre_data_dir + "/discharge/"
slimPre.make_directory(river_discharge_dir)
streamflow_df = pd.read_csv(
f"{p.local_base_dir}source/streamflow.csv",
index_col="datetime",
parse_dates=True,
)
ts_streamflow = [t.timestamp() for t in streamflow_df.index]
time_streamflow = slimPre.Time(time_vector=ts_streamflow)
for c in streamflow_df.columns:
slimPre.write_file(
river_discharge_dir + "river_discharge_" + str(c) + ".nc",
region=None,
time=time_streamflow,
data=[("river_discharge", streamflow_df[c].values)],
)
###############################
# 2. TIME DEPENDENT VARIABLES #
###############################
if int(slimPre.partition_nb()) > 1:
# --- mercator and tides
if "mercator" in p.vec_prepro2D:
print("[%s] preprocessing mercator and tides" % slimPre.partition_id())
# mercator
print("[%s] read merc files" % slimPre.partition_id())
h_file = (
p.nc_data_dir
+ "/mercator_zos_"
+ d0.strftime("%Y%m%d")
+ "_"
+ d1.strftime("%Y%m%d")
+ ".nc"
)
u_file = (
p.nc_data_dir
+ "/mercator_uo_"
+ d0.strftime("%Y%m%d")
+ "_"
+ d1.strftime("%Y%m%d")
+ "_DA.nc"
)
v_file = (
p.nc_data_dir
+ "/mercator_vo_"
+ d0.strftime("%Y%m%d")
+ "_"
+ d1.strftime("%Y%m%d")
+ "_DA.nc"
)
print("[%s] interp merc on mesh: H" % slimPre.partition_id())
h_merc = prepro_private.interpolate_on_mesh(
lonlat_global_degrees, time_tpxo, h_file, "zos"
)
print("[%s] interp merc on mesh: U" % slimPre.partition_id())
u_merc = prepro_private.interpolate_on_mesh(
lonlat_global_degrees, time_tpxo, u_file, "uo"
)
print("[%s] interp merc on mesh: V" % slimPre.partition_id())
v_merc = prepro_private.interpolate_on_mesh(
lonlat_global_degrees, time_tpxo, v_file, "vo"
)
print("[%s] rotate merc" % slimPre.partition_id())
u_merc[:], v_merc[:] = lonlat_global.rotate(u_merc, v_merc)
# tides
print("[%s] preprocessing tides" % slimPre.partition_id())
h_tides, u_tides, v_tides = slimPre.tpxo_tide(
region_global,
time_tpxo,
h_file=p.nc_data_dir + "/h_tpxo9_zone.nc",
u_file=p.nc_data_dir + "/u_tpxo9_zone.nc",
export_as_transport=False,
)
# sum the two components
print("[%s] sum mercator and tides" % slimPre.partition_id())
h = h_merc[:] + h_tides[:]
u = u_merc[:] + u_tides[:]
v = v_merc[:] + v_tides[:]
print("[%s] merc+tides: write files" % slimPre.partition_id())
slimPre.write_file(
pre_data_dir + "/mercator_and_tides.nc",
region=region_global,
time=time_tpxo,
data=[("h", h), ("u", u), ("v", v)],
)
if export_xdmf:
print("[%s] export merc+tides to xdmf" % slimPre.partition_id())
slimPre.netcdf_to_xdmf(
mesh_file_name,
pre_data_dir + "/mercator_and_tides.nc",
p.prepro_dir + "/mercator_and_tides.h5",
["h", ("u", "v")],
time_tpxo._time[0],
time_tpxo._time[-1],
len(time_tpxo._time) // 4,
)
del h, u, v
del h_tides, u_tides, v_tides
del h_merc, u_merc, v_merc
# --- eReefs wind
if "wind_ereefs" in p.vec_prepro2D:
print("[%s] preprocessing eReefs wind" % slimPre.partition_id())
eReefs_file = (
p.nc_data_dir
+ "/eReefs_wind."
+ d0.strftime("%Y%m%d")
+ "."
+ d1.strftime("%Y%m%d")
+ ".nc"
)
print("[%s] reading eReefs data" % slimPre.partition_id())
eReefs = Dataset(eReefs_file)
tSource = np.ma.array(eReefs.variables["time"][:])
tSourceUnits = eReefs.variables["time"].units
tSourceDatetime = num2date(tSource, units=tSourceUnits, calendar="gregorian")
tSourceDelta = tSourceDatetime - datetime(1970, 1, 1)
tSourceSeconds = [elt.total_seconds() for elt in tSourceDelta]
tEReefs = slimPre.Time(np.float64(tSourceSeconds), "1970-01-01 00:00:00")
lonSource = np.ma.array(eReefs.variables["longitude"][:])
latSource = np.ma.array(eReefs.variables["latitude"][:])
uWindSource = np.ma.array(eReefs.variables["wspeed_u"][:])
vWindSource = np.ma.array(eReefs.variables["wspeed_v"][:])
nTimeSource = len(tSourceSeconds)
uWindSource = prepro_private.fill_mask(uWindSource)
vWindSource = prepro_private.fill_mask(vWindSource)
lonSource.mask = uWindSource[0, :, :].mask
latSource.mask = uWindSource[0, :, :].mask
lonTarget = lonlat_global_degrees[:, 0]
latTarget = lonlat_global_degrees[:, 1]
print("[%s] ereefs wind: building cKDTree" % slimPre.partition_id())
tree = cKDTree(np.c_[lonSource.ravel(), latSource.ravel()])
print("[%s] ereefs wind: queering cKDTree" % slimPre.partition_id())
dd, ii = tree.query(np.c_[lonTarget, latTarget], k=4)
print("[%s] ereefs wind: resampling" % slimPre.partition_id())
# get indexes
r, c = np.unravel_index(ii, np.shape(lonSource))
# mask distances corresponding to masked values
dd = np.ma.array(dd, mask=lonSource.mask[r, c])
# compute weights (inverse distance weighting)
weights = 1.0 / dd
weights = weights / np.sum(weights, axis=-1)[:, np.newaxis]
# compute resampled data
def resamp_idw(data):
data_resamp = np.reshape(
np.sum(data[:, r, c] * weights, axis=-1),
# [nTimeSource, len(y), len(x)],
[nTimeSource, len(lonTarget)],
)
return data_resamp
uWindResamp = resamp_idw(uWindSource)
vWindResamp = resamp_idw(vWindSource)
uWindResamp[:], vWindResamp[:] = lonlat_global.rotate(uWindResamp, vWindResamp)
print("[%s] eReefs wind: write files" % slimPre.partition_id())
slimPre.write_file(
pre_data_dir + "/eReefs_wind.nc",
region=region_global,
time=tEReefs,
data=[("u", uWindResamp), ("v", vWindResamp)],
)
if export_xdmf:
print("[%s] eReefs wind: export to xdmf" % slimPre.partition_id())
slimPre.netcdf_to_xdmf(
mesh_file_name,
pre_data_dir + "/eReefs_wind.nc",
p.prepro_dir + "/eReefs_wind.h5",
["u", "v", ("u", "v")],
tEReefs._time[0],
tEReefs._time[-1],
len(tEReefs._time),
)
del uWindSource, vWindSource
del uWindResamp, vWindResamp
# --- wind
if "wind" in p.vec_prepro2D:
print("[%s] preprocessing wind" % slimPre.partition_id())
wind_file = (
p.nc_data_dir
+ "/wind_"
+ d0.strftime("%Y%m%d")
+ "_"
+ d1.strftime("%Y%m%d")
+ ".nc"
)
print("[%s] interp wind on mesh: msl" % slimPre.partition_id())
pa = prepro_private.interpolate_on_mesh(
lonlat_global_degrees, time_wind, wind_file, "msl"
)
print("[%s] interp wind on mesh: u" % slimPre.partition_id())
u = prepro_private.interpolate_on_mesh(
lonlat_global_degrees, time_wind, wind_file, "u10"
)
print("[%s] interp wind on mesh: v" % slimPre.partition_id())
v = prepro_private.interpolate_on_mesh(
lonlat_global_degrees, time_wind, wind_file, "v10"
)
print("[%s] rotate wind" % slimPre.partition_id())
u[:], v[:] = lonlat_global.rotate(u, v)
print("[%s] wind: write files" % slimPre.partition_id())
slimPre.write_file(
pre_data_dir + "/wind.nc",
region=region_global,
time=time_wind,
data=[("windx", u), ("windy", v), ("pa", pa)],
)
if export_xdmf:
slimPre.netcdf_to_xdmf(
mesh_file_name,
pre_data_dir + "/wind.nc",
p.prepro_dir + "/wind.h5",
["pa", ("windx", "windy")],
time_wind._time[0],
time_wind._time[-1],
len(time_wind._time),
)
del pa, u, v
# --- barocline gradient
if "baroclinic_gradient" in p.vec_prepro2D:
print("[%s] preprocessing barocline gradient" % slimPre.partition_id())
th_file = (
p.nc_data_dir
+ "/mercator_thetao_"
+ d0.strftime("%Y%m%d")
+ "_"
+ d1.strftime("%Y%m%d")
+ ".nc"
)
s_file = (
p.nc_data_dir
+ "/mercator_so_"
+ d0.strftime("%Y%m%d")
+ "_"
+ d1.strftime("%Y%m%d")
+ ".nc"
)
with Dataset(th_file, "r") as f:
Th = prepro_private.fill_mask3(np.ma.array(f.variables["thetao"][:]))
lon = np.array(f.variables["longitude"][:], dtype=np.float)
lat = np.array(f.variables["latitude"][:], dtype=np.float)
time = prepro_private.convert_time(
np.array(f.variables["time"][:]), f.variables["time"].units
)
depth = np.array(f.variables["depth"][:])
with Dataset(s_file, "r") as f:
S = prepro_private.fill_mask3(np.ma.array(f.variables["so"][:]))
rho = prepro_private.jackett(Th, S, p.rho_mean)
del Th, S
grad_x, grad_y = prepro_private.compuWindResampe_baroclinic_gradient(
lonlat_global_degrees, p.mesh_proj, rho, time, depth, lon, lat
)
f = slimPre.slim_private._load_function(
(p.prepro_dir + "/bathymetry_smooth.nc", "bathymetry"), mesh._groups
)
bath = region_global._evaluateFunctor(f, 1)[None, :, 0]
f_x = -(p.g / p.rho_mean) * grad_x / bath
f_y = -(p.g / p.rho_mean) * grad_y / bath
del grad_x, grad_y
time_grad = slimPre.Time(time_vector=time)
slimPre.write_file(
pre_data_dir + "/baroclinic_forcing.nc",
region=region_global,
time=time_grad,
data=[("fx", f_x), ("fy", f_y)],
)
print("[%s] DONE!" % slimPre.partition_id())
slimPre.exit(0)