def __get_dim_var_variables(self):
'''
:return dimension's variables and feature's variables
:return:
'''
dim_variables = OrderedDict()
var_variables = OrderedDict()
for v_name in self.variables.keys():
if v_name in self.dimensions.keys():
dim_variables[v_name] = self.variables[v_name]
else:
var_variables[v_name] = self.variables[v_name]
return dim_variables, var_variables
def main():
"""
Prepare sand clay and depth to bedrock in netcdf for MH
"""
gconfig_to_infile = OrderedDict([
# (default_domains.bc_mh_011, "/RESCUE/skynet3_rech1/huziy/directions_for_ManitobaHydro/20170320/rpn/geophys_CORDEX_NA_0.11deg_695x680_filled_grDes_barBor_Crop2Gras_peat"),
# (default_domains.bc_mh_022, "/RESCUE/skynet3_rech1/huziy/directions_for_ManitobaHydro/20170320/rpn/geophys_CORDEX_NA_0.22deg_filled_grDes_barBor_Crop2Gras_peat"),
(default_domains.bc_mh_044, "/RESCUE/skynet3_rech1/huziy/directions_for_ManitobaHydro/20170320/rpn/geophys_CORDEX_NA_0.44d_filled_grDes_barBor_Crop2Gras_peat"),
])
# gconfig_to_infile = {
# default_domains.bc_mh_011: "/HOME/data/Simulations/CRCM5/North_America/NorthAmerica_0.11deg_ERA40-Int0.75_B1/Samples/NorthAmerica_0.11deg_ERA40-Int0.75_B1_195801/pm1958010100_00000000p",
# default_domains.bc_mh_022: "/HOME/data/Simulations/CRCM5/North_America/NorthAmerica_0.22deg_ERA40-Int0.75_B1/Samples/NorthAmerica_0.22deg_ERA40-Int0.75_B1_195801/pm1958010100_00000000p",
# default_domains.bc_mh_044: "/HOME/data/Simulations/CRCM5/North_America/NorthAmerica_0.44deg_ERA40-Int0.75_B1/Samples/NorthAmerica_0.44deg_ERA40-Int0.75_B1_195801/pm1958010100_00000000p",
# }
gconfig_to_dirfile = {
# default_domains.bc_mh_011: "/HOME/huziy/skynet3_rech1/directions_for_ManitobaHydro/20170310/netcdf/directions_bc-mh_0.11deg_new_hsfix.nc",
# default_domains.bc_mh_022: "/HOME/huziy/skynet3_rech1/directions_for_ManitobaHydro/20170310/netcdf/directions_bc-mh_0.22deg_new_hsfix.nc",
default_domains.bc_mh_044: "/HOME/huziy/skynet3_rech1/directions_for_ManitobaHydro/20170310/netcdf/directions_bc-mh_0.44deg_new_hsfix.nc",
}
out_dir = Path("mh/sand_clay_dpth_fields_levels")
if not out_dir.is_dir():
out_dir.mkdir(parents=True)
# selection criteria
selection = {
"SAND": 1,
"CLAY": 1,
"DPTH": None
}
# ----
print(gconfig_to_infile)
print(gconfig_to_dirfile)
for gc, rpn_path in gconfig_to_infile.items():
select_data_to_nc(gc, in_rpn_path=rpn_path, in_directions_path=gconfig_to_dirfile[gc], selection=selection, out_dir=out_dir, levels=list(range(26)))
def __get_variables(self):
'''
get variables,OrderedDict
:return:
'''
group_variables = OrderedDict()
if len(self.groups) == 0: # 不包含group信息，只包含variables和groups
group_variables = self.dataset.variables
else:
for group in self.groups.keys():
variables = OrderedDict()
for variable in self.groups[group].variables:
variables[variable] = self.groups[group].variables[variable]
group_variables[group] = variables
return group_variables
def __get_dimensions(self):
'''
get dimensions,OrderedDict
:return:
'''
group_dimensions = OrderedDict()
if len(self.groups) == 0: # 不包含group信息，只包含variables和groups
group_dimensions = self.dataset.dimensions
else:
for group in self.groups.keys():
dimensions = OrderedDict()
for dimension in self.groups[group].dimensions:
dimensions[dimension] = self.groups[group].dimensions[dimension]
group_dimensions[group] = dimensions
return group_dimensions
def __get_hdf_variable_data_by_name(self, groupname):
'''
get variable data array by name,return OrderedDict
:param name:
:return:
'''
data_array_dict = OrderedDict()
for variable_name in self.groups[groupname].variables.keys():
variable = self.get_variable_by_group_variable(groupname, variable_name)
data_array_dict[variable_name] = variable[::]
return data_array_dict
def get_hdf_variables_data(self):
'''
return hdf all variables data array,return by OrderedDict
OrderedDict key:group name,value:variable data
:return:
'''
variables_data_dict = OrderedDict()
if not self.contains_group:
for variable_name in self.variables:
variables_data_dict[variable_name] = self.__get_hdf_variable_data_by_v_name(variable_name)
for g_name in self.groups.keys():
data_array_dict = self.__get_hdf_variable_data_by_name(g_name)
variables_data_dict[g_name] = data_array_dict
return variables_data_dict
def __get_variable_data_by_name(self, name):
'''
get variable data array by name,return OrderedDict
:param name:
:return:
'''
variable = self.get_variable_by_name(name)
t_variable = self.get_nc_variables_t_by_attributes()
data_array_dict = OrderedDict()
if t_variable == None:
# not have time variable
# print "not have time variable"
data_array_dict["None"] = variable[::]
else:
i = 0
for time in num2date(t_variable[::], t_variable.units):
now_time_data = variable[i:i + 1:]
data_array_dict[time] = now_time_data
i += 1
return data_array_dict
def main():
lon0, lat0 = -114.0708, 51.0486
surf_gz_path = "/RESCUE/skynet3_rech1/huziy/hail/erainterim_0.75d/surface_geopotential.nc"
gz_fields_path = "/HOME/data/Driving_data/Pilots/ERA-Interim_0.75/Pilots/era-interim_0.75d_198004"
# --
with Dataset(surf_gz_path) as ds:
lons, lats = ds.variables["longitude"][:], ds.variables["latitude"][:]
lats, lons = np.meshgrid(lats, lons)
surface_elev = get_closest_value_for_point(
lon0, lat0, lons, lats, ds.variables["z"][:].squeeze().T) / g_const
# --
with RPN(gz_fields_path) as r:
assert isinstance(r, RPN)
gz_data = r.get_4d_field("GZ", level_kind=level_kinds.PRESSURE)
lons_r, lats_r = r.get_longitudes_and_latitudes_for_the_last_read_rec()
dates = sorted(gz_data)
lev_to_field = gz_data[dates[0]]
levels = sorted(lev_to_field)
pelevs = [
get_closest_value_for_point(lon0, lat0, lons_r, lats_r,
lev_to_field[lev]) * 10
for lev in levels
]
print(OrderedDict(list(zip(levels, pelevs))))
print("surf elev = {}".format(surface_elev))
def main():
# nemo_manager = NemoYearlyFilesManager(folder="/home/huziy/skynet3_rech1/offline_glk_output_daily_1979-2012",
# suffix="icemod.nc")
# exp_label = "default-offline"
nemo_manager = NemoYearlyFilesManager(
folder="/home/huziy/skynet3_rech1/output_NEMO_offline_1979-2012_nosnow",
suffix="icemod.nc")
exp_label = "offline-nosnow"
# Study period
start_year = 2003
end_year = 2006
season_to_months = OrderedDict([("Winter", (12, 1, 2)),
("Spring", list(range(3, 6))),
("Summer", list(range(6, 9))),
("Fall", list(range(9, 12)))])
nemo_manager.plot_comparisons_of_seasonal_sst_with_homa_obs(
start_year=start_year,
end_year=end_year,
season_to_months=season_to_months,
exp_label=exp_label)
lake_to_folder_with_sim_data_1981_2000_seplakes_exp = {
"Superior": "/skynet3_rech1/huziy/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Superior",
"Michigan": "/skynet3_rech1/huziy/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Michigan",
"Erie": "/skynet3_rech1/huziy/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Erie",
"Ontario": "/skynet3_rech1/huziy/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Ontario",
"Huron": "/skynet3_rech1/huziy/NEMO_OFFICIAL/Simulations/1981-2000_Sim_per_lake_100yr_spinup_LIM3/Huron"
}
season_to_months_default = OrderedDict([
("Winter", [12, 1, 2]),
("Spring", [3, 4, 5]),
("Summer", [6, 7, 8]),
("Fall", [9, 10, 11]),
])
def get_clevs_and_cmap_for_name(name=""):
clevs = varname_to_colorlevels.get(name, [])
cmap = varname_to_cmapname.get(name, None)
if cmap is not None:
return clevs, cm.get_cmap(cmap, len(clevs) - 1)
return None, None
def __get_variables_data(self):
variables_data_dict = OrderedDict()
for v_name in self.variables.keys():
data_array_dict = self.__get_variable_data_by_name(v_name)
variables_data_dict[v_name] = data_array_dict
return variables_data_dict