Source code for nes.nc_projections.points_nes_ghost
#!/usr/bin/env python
import sys
from warnings import warn
from numpy import float64, empty, ndarray, generic, array, issubdtype, character, concatenate, int64
from netCDF4 import date2num
from copy import deepcopy
from .points_nes import PointsNes
[docs]
class PointsNesGHOST(PointsNes):
"""
Attributes
----------
_qa : dict
Quality flags (GHOST checks) dictionary with the complete "data" key for all the values and the rest of the
attributes.
_flag : dict
Data flags (given by data provider) dictionary with the complete "data" key for all the values and the rest of
the attributes.
_qa : dict
Quality flags (GHOST checks) dictionary with the portion of "data" corresponding to the rank values.
_flag : dict
Data flags (given by data provider) dictionary with the portion of "data" corresponding to the rank values.
"""
def __init__(self, comm=None, path=None, info=False, dataset=None, parallel_method="X",
avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False,
balanced=False, times=None, **kwargs):
"""
Initialize the PointsNesGHOST class.
Parameters
----------
comm: MPI.COMM
MPI Communicator.
path: str
Path to the NetCDF to initialize the object.
info: bool
Indicates if you want to get reading/writing info.
dataset: Dataset
NetCDF4-python Dataset to initialize the class.
parallel_method : str
Indicates the parallelization method that you want. Default: "X".
Accepted values: ["X"].
avoid_first_hours : int
Number of hours to remove from first time steps.
avoid_last_hours : int
Number of hours to remove from last time steps.
first_level : int
Index of the first level to use.
last_level : int, None
Index of the last level to use. None if it is the last.
create_nes : bool
Indicates if you want to create the object from scratch (True) or through an existing file.
balanced : bool
Indicates if you want a balanced parallelization or not.
Balanced dataset cannot be written in chunking mode.
times : list, None
List of times to substitute the current ones while creation.
"""
super(PointsNesGHOST, self).__init__(comm=comm, path=path, info=info, dataset=dataset,
parallel_method=parallel_method,
avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours,
first_level=first_level, last_level=last_level, create_nes=create_nes,
times=times, balanced=balanced, **kwargs)
# Complete dimensions
self._flag = self._get_coordinate_dimension(["flag"])
self._qa = self._get_coordinate_dimension(["qa"])
# Dimensions screening
self.flag = self._get_coordinate_values(self._flag, "X")
self.qa = self._get_coordinate_values(self._qa, "X")
[docs]
@staticmethod
def new(comm=None, path=None, info=False, dataset=None, parallel_method="X",
avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False,
balanced=False, times=None, **kwargs):
"""
Initialize the PointsNesGHOST class.
Parameters
----------
comm: MPI.COMM
MPI Communicator.
path: str
Path to the NetCDF to initialize the object.
info: bool
Indicates if you want to get reading/writing info.
dataset: Dataset
NetCDF4-python Dataset to initialize the class.
parallel_method : str
Indicates the parallelization method that you want. Default: "X".
Accepted values: ["X"].
avoid_first_hours : int
Number of hours to remove from first time steps.
avoid_last_hours : int
Number of hours to remove from last time steps.
first_level : int
Index of the first level to use.
last_level : int, None
Index of the last level to use. None if it is the last.
create_nes : bool
Indicates if you want to create the object from scratch (True) or through an existing file.
balanced : bool
Indicates if you want a balanced parallelization or not.
Balanced dataset cannot be written in chunking mode.
times : list, None
List of times to substitute the current ones while creation.
"""
new = PointsNesGHOST(comm=comm, path=path, info=info, dataset=dataset,
parallel_method=parallel_method, avoid_first_hours=avoid_first_hours,
avoid_last_hours=avoid_last_hours, first_level=first_level, last_level=last_level,
create_nes=create_nes, balanced=balanced, times=times, **kwargs)
return new
def _create_dimensions(self, netcdf):
"""
Create "N_flag_codes" and "N_qa_codes" dimensions and the super dimensions
"time", "time_nv", "station", and "strlen".
Parameters
----------
netcdf : Dataset
NetCDF object.
"""
super(PointsNesGHOST, self)._create_dimensions(netcdf)
# Create N_flag_codes and N_qa_codes dimensions
netcdf.createDimension("N_flag_codes", self._flag["data"].shape[2])
netcdf.createDimension("N_qa_codes", self._qa["data"].shape[2])
return None
# noinspection DuplicatedCode
def _create_dimension_variables(self, netcdf):
"""
Create the "time", "time_bnds", "station", "lat", "lat_bnds", "lon" and "lon_bnds" variables.
Parameters
----------
netcdf : Dataset
NetCDF object.
"""
# TIMES
time_var = netcdf.createVariable("time", float64, ("time",), zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
time_var.units = "hours since {0}".format(
self.get_full_times()[self._get_time_id(self.hours_start, first=True)].strftime("%Y-%m-%d %H:%M:%S"))
time_var.standard_name = "time"
time_var.calendar = "standard"
time_var.long_name = "time"
if self.time_bnds is not None:
time_var.bounds = "time_bnds"
if self.size > 1:
time_var.set_collective(True)
time_var[:] = date2num(self.get_full_times()[self._get_time_id(self.hours_start, first=True):
self._get_time_id(self.hours_end, first=False)],
time_var.units, time_var.calendar)
# TIME BOUNDS
if self.time_bnds is not None:
time_bnds_var = netcdf.createVariable("time_bnds", float64, ("time", "time_nv",), zlib=self.zip_lvl,
complevel=self.zip_lvl)
if self.size > 1:
time_bnds_var.set_collective(True)
time_bnds_var[:] = date2num(self.get_full_time_bnds(), time_var.units, calendar="standard")
# STATIONS
stations = netcdf.createVariable("station", float64, ("station",), zlib=self.zip_lvl > 0,
complevel=self.zip_lvl)
stations.units = ""
stations.axis = "X"
stations.long_name = ""
stations.standard_name = "station"
if self.size > 1:
stations.set_collective(True)
stations[:] = self._station["data"]
# LATITUDES
lat = netcdf.createVariable("latitude", float64, self._lat_dim, zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
lat.units = "degrees_north"
lat.axis = "Y"
lat.long_name = "latitude coordinate"
lat.standard_name = "latitude"
if self.lat_bnds is not None:
lat.bounds = "lat_bnds"
if self.size > 1:
lat.set_collective(True)
lat[:] = self.get_full_latitudes()["data"]
# LONGITUDES
lon = netcdf.createVariable("longitude", float64, self._lon_dim, zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
lon.units = "degrees_east"
lon.axis = "X"
lon.long_name = "longitude coordinate"
lon.standard_name = "longitude"
if self.lon_bnds is not None:
lon.bounds = "lon_bnds"
if self.size > 1:
lon.set_collective(True)
lon[:] = self.get_full_longitudes()["data"]
[docs]
def erase_flags(self):
first_time_idx = self._get_time_id(self.hours_start, first=True)
last_time_idx = self._get_time_id(self.hours_end, first=False)
t_len = last_time_idx - first_time_idx
self._qa["data"] = empty((len(self.get_full_longitudes()["data"]), t_len, 0))
self._flag["data"] = empty((len(self.get_full_longitudes()["data"]), t_len, 0))
return None
# noinspection DuplicatedCode
def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False):
"""
Get the coordinate data of the current portion.
Parameters
----------
coordinate_info : dict, list
Dictionary with the "data" key with the coordinate variable values. and the attributes as other keys.
coordinate_axis : str
Name of the coordinate to extract. Accepted values: ["X"].
bounds : bool
Boolean variable to know if there are coordinate bounds.
Returns
-------
values : dict
Dictionary with the portion of data corresponding to the rank.
"""
if coordinate_info is None:
return None
if not isinstance(coordinate_info, dict):
values = {"data": deepcopy(coordinate_info)}
else:
values = deepcopy(coordinate_info)
coordinate_len = len(values["data"].shape)
if bounds:
coordinate_len -= 1
if coordinate_axis == "X":
if coordinate_len == 1:
values["data"] = values["data"][self.read_axis_limits["x_min"]:self.read_axis_limits["x_max"]]
elif coordinate_len == 2:
values["data"] = values["data"][self.read_axis_limits["x_min"]:self.read_axis_limits["x_max"],
self.read_axis_limits["t_min"]:self.read_axis_limits["t_max"]]
elif coordinate_len == 3:
values["data"] = values["data"][self.read_axis_limits["x_min"]:self.read_axis_limits["x_max"],
self.read_axis_limits["t_min"]:self.read_axis_limits["t_max"], :]
else:
raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format(
dim=values["data"].shape))
return values
# noinspection DuplicatedCode
def _read_variable(self, var_name):
"""
Read the corresponding variable data according to the current rank.
Parameters
----------
var_name : str
Name of the variable to read.
Returns
-------
data: array
Portion of the variable data corresponding to the rank.
"""
nc_var = self.dataset.variables[var_name]
var_dims = nc_var.dimensions
# Read data in 1 or 2 dimensions
if len(var_dims) < 2:
data = nc_var[self.read_axis_limits["x_min"]:self.read_axis_limits["x_max"]]
elif len(var_dims) == 2:
data = nc_var[self.read_axis_limits["x_min"]:self.read_axis_limits["x_max"],
self.read_axis_limits["t_min"]:self.read_axis_limits["t_max"]]
elif len(var_dims) == 3:
data = nc_var[self.read_axis_limits["x_min"]:self.read_axis_limits["x_max"],
self.read_axis_limits["t_min"]:self.read_axis_limits["t_max"],
:]
else:
raise NotImplementedError("Error with {0}. Only can be read netCDF with 3 dimensions or less".format(
var_name))
# Unmask array
data = self._unmask_array(data)
return data
# noinspection DuplicatedCode
def _create_variables(self, netcdf, chunking=False):
"""
Create the netCDF file variables.
Parameters
----------
netcdf : Dataset
netcdf4-python open Dataset.
chunking : bool
Indicates if you want to chunk the output netCDF.
"""
if self.variables is not None:
for i, (var_name, var_dict) in enumerate(self.variables.items()):
# Get data type
if "dtype" in var_dict.keys():
var_dtype = var_dict["dtype"]
if (var_dict["data"] is not None) and (var_dtype != var_dict["data"].dtype):
msg = "WARNING!!! "
msg += "Different data types for variable {0}. ".format(var_name)
msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict["data"].dtype)
warn(msg)
sys.stderr.flush()
try:
var_dict["data"] = var_dict["data"].astype(var_dtype)
except Exception:
raise TypeError("It was not possible to cast the data to the input dtype.")
else:
var_dtype = var_dict["data"].dtype
if var_dtype is object:
raise TypeError("Data dtype is object. Define dtype explicitly as dictionary key 'dtype'")
# Get dimensions when reading datasets
if "dimensions" in var_dict.keys():
var_dims = var_dict["dimensions"]
# Get dimensions when creating new datasets
else:
if len(var_dict["data"].shape) == 1:
# For data that depends only on station (e.g. station_code)
var_dims = self._var_dim
else:
# For data that is dependent on time and station (e.g. PM10)
var_dims = self._var_dim + ("time",)
if var_dict["data"] is not None:
# Ensure data is of type numpy array (to create NES)
if not isinstance(var_dict["data"], (ndarray, generic)):
try:
var_dict["data"] = array(var_dict["data"])
except AttributeError:
raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name))
# Convert list of strings to chars for parallelization
if issubdtype(var_dtype, character):
var_dict["data_aux"] = self._str2char(var_dict["data"])
var_dims += ("strlen",)
var_dtype = "S1"
if self.info:
print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1,
len(self.variables)))
if not chunking:
var = netcdf.createVariable(var_name, var_dtype, var_dims,
zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
else:
if self.master:
chunk_size = var_dict["data"].shape
else:
chunk_size = None
chunk_size = self.comm.bcast(chunk_size, root=0)
var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0,
complevel=self.zip_lvl, chunksizes=chunk_size)
if self.info:
print("Rank {0:03d}: Var {1} created ({2}/{3})".format(
self.rank, var_name, i + 1, len(self.variables)))
if self.size > 1:
var.set_collective(True)
if self.info:
print("Rank {0:03d}: Var {1} collective ({2}/{3})".format(
self.rank, var_name, i + 1, len(self.variables)))
for att_name, att_value in var_dict.items():
if att_name == "data":
if self.info:
print("Rank {0:03d}: Filling {1})".format(self.rank, var_name))
if "data_aux" in var_dict.keys():
att_value = var_dict["data_aux"]
if len(att_value.shape) == 1:
try:
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"]] = att_value
except IndexError:
raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"]].shape,
att_value.shape))
except ValueError:
raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"]].shape,
att_value.shape))
elif len(att_value.shape) == 2:
if "strlen" in var_dims:
try:
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"], :] = att_value
except IndexError:
raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"], :].shape,
att_value.shape))
except ValueError:
raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"], :].shape,
att_value.shape))
else:
try:
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
self.write_axis_limits["t_min"]:self.write_axis_limits["t_max"]] = att_value
except IndexError:
raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
self.write_axis_limits["t_min"]:self.write_axis_limits["t_max"]].shape,
att_value.shape))
except ValueError:
out_shape = var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
self.write_axis_limits["t_min"]:self.write_axis_limits["t_max"]].shape
raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format(
out_shape, att_value.shape))
elif len(att_value.shape) == 3:
try:
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
self.write_axis_limits["t_min"]:self.write_axis_limits["t_max"],
:] = att_value
except IndexError:
raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
self.write_axis_limits["t_min"]:self.write_axis_limits["t_max"],
:].shape,
att_value.shape))
except ValueError:
raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format(
var[self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
self.write_axis_limits["t_min"]:self.write_axis_limits["t_max"],
:].shape,
att_value.shape))
if self.info:
print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1,
len(self.variables)))
elif att_name not in ["chunk_size", "var_dims", "dimensions", "dtype", "data_aux"]:
var.setncattr(att_name, att_value)
if "data_aux" in var_dict.keys():
del var_dict["data_aux"]
self._set_var_crs(var)
if self.info:
print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1,
len(self.variables)))
return None
# noinspection DuplicatedCode
def _gather_data(self, data_to_gather):
"""
Gather all the variable data into the MPI rank 0 to perform a serial write.
Returns
-------
data_to_gather: dict
Variables to gather.
"""
data_list = deepcopy(data_to_gather)
for var_name, var_info in data_list.items():
try:
# noinspection PyArgumentList
data_aux = self.comm.gather(data_list[var_name]["data"], root=0)
if self.rank == 0:
shp_len = len(data_list[var_name]["data"].shape)
# concatenate over station
if self.parallel_method == "X":
if shp_len == 1:
# dimensions = (station)
axis = 0
elif shp_len == 2:
# dimensions = (station, strlen) or
# dimensions = (station, time)
axis = 0
else:
msg = "The points NetCDF must have "
msg += "surface values (without levels)."
raise NotImplementedError(msg)
elif self.parallel_method == "T":
# concatenate over time
if shp_len == 1:
# dimensions = (station)
axis = None
elif shp_len == 2:
if "strlen" in var_info["dimensions"]:
# dimensions = (station, strlen)
axis = None
else:
# dimensions = (station, time)
axis = 1
else:
msg = "The points NetCDF must have "
msg += "surface values (without levels)."
raise NotImplementedError(msg)
else:
raise NotImplementedError(
"Parallel method '{meth}' is not implemented. Use one of these: {accept}".format(
meth=self.parallel_method, accept=["X", "T"]))
data_list[var_name]["data"] = concatenate(data_aux, axis=axis)
except Exception as e:
msg = f"**ERROR** an error has occurred while gathering the '{var_name}' variable.\n"
print(msg)
sys.stderr.write(msg)
print(e)
sys.stderr.write(str(e))
sys.stderr.flush()
self.comm.Abort(1)
return data_list
def _create_metadata(self, netcdf):
"""
Create metadata variables.
Parameters
----------
netcdf : Dataset
NetCDF object.
"""
# N FLAG CODES
flag = netcdf.createVariable("flag", int64, ("station", "time", "N_flag_codes",),
zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
flag.units = ""
flag.axis = ""
flag.long_name = ""
flag.standard_name = "flag"
if self.size > 1:
flag.set_collective(True)
flag[:] = self._flag["data"]
# N QA CODES
qa = netcdf.createVariable("qa", int64, ("station", "time", "N_qa_codes",),
zlib=self.zip_lvl > 0, complevel=self.zip_lvl)
qa.units = ""
qa.axis = ""
qa.long_name = ""
qa.standard_name = "N_qa_codes"
if self.size > 1:
qa.set_collective(True)
qa[:] = self._qa["data"]
return None
[docs]
def to_netcdf(self, path, compression_level=0, serial=False, info=False, chunking=False, nc_type="NES",
keep_open=False):
"""
Write the netCDF output file.
Parameters
----------
keep_open : bool
nc_type : str
path : str
Path to the output netCDF file.
compression_level : int
Level of compression (0 to 9) Default: 0 (no compression).
serial : bool
Indicates if you want to write in serial or not. Default: False.
info : bool
Indicates if you want to print the information of each writing step by stdout Default: False.
chunking : bool
Indicates if you want a chunked netCDF output. Only available with non-serial writes. Default: False.
"""
if (not serial) and (self.size > 1):
msg = "WARNING!!! "
msg += "GHOST datasets cannot be written in parallel yet. "
msg += "Changing to serial mode."
warn(msg)
sys.stderr.flush()
super(PointsNesGHOST, self).to_netcdf(path, compression_level=compression_level,
serial=True, info=info, chunking=chunking)
return None
[docs]
def to_points(self):
"""
Transform a PointsNesGHOST into a PointsNes object
Returns
----------
points_nes : nes.Nes
Points Nes Object (without GHOST metadata variables)
"""
points_nes = PointsNes(comm=self.comm,
info=self.info,
balanced=self.balanced,
parallel_method=self.parallel_method,
avoid_first_hours=self.hours_start,
avoid_last_hours=self.hours_end,
first_level=self.first_level,
last_level=self.last_level,
create_nes=True,
lat=self.lat["data"],
lon=self.lon["data"],
times=self.time
)
# The version attribute in GHOST files prior to 1.3.3 is called data_version, after it is version
if "version" in self.global_attrs:
ghost_version = self.global_attrs["version"]
elif "data_version" in self.global_attrs:
ghost_version = self.global_attrs["data_version"]
else:
ghost_version = "0.0.0"
metadata_variables = self.get_standard_metadata(ghost_version)
self.free_vars(metadata_variables)
self.free_vars("station")
points_nes.variables = deepcopy(self.variables)
return points_nes
[docs]
@staticmethod
def get_standard_metadata(ghost_version):
"""
Get all possible GHOST variables for each version.
Parameters
----------
ghost_version : str
Version of GHOST file.
Returns
----------
metadata_variables[GHOST_version] : list
A List of metadata variables for a certain GHOST version
"""
# This metadata variables are
metadata_variables = {"1.4": ["GHOST_version", "station_reference", "station_timezone", "latitude", "longitude",
"altitude", "sampling_height", "measurement_altitude", "ellipsoid",
"horizontal_datum", "vertical_datum", "projection", "distance_to_building",
"distance_to_kerb", "distance_to_junction", "distance_to_source", "street_width",
"street_type", "daytime_traffic_speed", "daily_passing_vehicles", "data_level",
"climatology", "station_name", "city", "country",
"administrative_country_division_1", "administrative_country_division_2",
"population", "representative_radius", "network", "associated_networks",
"area_classification", "station_classification", "main_emission_source",
"land_use", "terrain", "measurement_scale",
"ESDAC_Iwahashi_landform_classification",
"ESDAC_modal_Iwahashi_landform_classification_5km",
"ESDAC_modal_Iwahashi_landform_classification_25km",
"ESDAC_Meybeck_landform_classification",
"ESDAC_modal_Meybeck_landform_classification_5km",
"ESDAC_modal_Meybeck_landform_classification_25km",
"GHSL_settlement_model_classification",
"GHSL_modal_settlement_model_classification_5km",
"GHSL_modal_settlement_model_classification_25km",
"Joly-Peuch_classification_code", "Koppen-Geiger_classification",
"Koppen-Geiger_modal_classification_5km",
"Koppen-Geiger_modal_classification_25km",
"MODIS_MCD12C1_v6_IGBP_land_use", "MODIS_MCD12C1_v6_modal_IGBP_land_use_5km",
"MODIS_MCD12C1_v6_modal_IGBP_land_use_25km", "MODIS_MCD12C1_v6_UMD_land_use",
"MODIS_MCD12C1_v6_modal_UMD_land_use_5km",
"MODIS_MCD12C1_v6_modal_UMD_land_use_25km", "MODIS_MCD12C1_v6_LAI",
"MODIS_MCD12C1_v6_modal_LAI_5km", "MODIS_MCD12C1_v6_modal_LAI_25km",
"WMO_region", "WWF_TEOW_terrestrial_ecoregion", "WWF_TEOW_biogeographical_realm",
"WWF_TEOW_biome", "UMBC_anthrome_classification",
"UMBC_modal_anthrome_classification_5km",
"UMBC_modal_anthrome_classification_25km",
"EDGAR_v4.3.2_annual_average_BC_emissions",
"EDGAR_v4.3.2_annual_average_CO_emissions",
"EDGAR_v4.3.2_annual_average_NH3_emissions",
"EDGAR_v4.3.2_annual_average_NMVOC_emissions",
"EDGAR_v4.3.2_annual_average_NOx_emissions",
"EDGAR_v4.3.2_annual_average_OC_emissions",
"EDGAR_v4.3.2_annual_average_PM10_emissions",
"EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions",
"EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions",
"EDGAR_v4.3.2_annual_average_SO2_emissions", "ASTER_v3_altitude",
"ETOPO1_altitude", "ETOPO1_max_altitude_difference_5km",
"GHSL_built_up_area_density", "GHSL_average_built_up_area_density_5km",
"GHSL_average_built_up_area_density_25km", "GHSL_max_built_up_area_density_5km",
"GHSL_max_built_up_area_density_25km", "GHSL_population_density",
"GHSL_average_population_density_5km", "GHSL_average_population_density_25km",
"GHSL_max_population_density_5km", "GHSL_max_population_density_25km",
"GPW_population_density", "GPW_average_population_density_5km",
"GPW_average_population_density_25km", "GPW_max_population_density_5km",
"GPW_max_population_density_25km",
"NOAA-DMSP-OLS_v4_nighttime_stable_lights",
"NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km",
"NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km",
"NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km",
"NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km",
"OMI_level3_column_annual_average_NO2",
"OMI_level3_column_cloud_screened_annual_average_NO2",
"OMI_level3_tropospheric_column_annual_average_NO2",
"OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2",
"GSFC_coastline_proximity", "primary_sampling_type",
"primary_sampling_instrument_name",
"primary_sampling_instrument_documented_flow_rate",
"primary_sampling_instrument_reported_flow_rate",
"primary_sampling_process_details", "primary_sampling_instrument_manual_name",
"primary_sampling_further_details", "sample_preparation_types",
"sample_preparation_techniques", "sample_preparation_process_details",
"sample_preparation_further_details", "measurement_methodology",
"measuring_instrument_name", "measuring_instrument_sampling_type",
"measuring_instrument_documented_flow_rate",
"measuring_instrument_reported_flow_rate", "measuring_instrument_process_details",
"measuring_instrument_process_details", "measuring_instrument_manual_name",
"measuring_instrument_further_details", "measuring_instrument_reported_units",
"measuring_instrument_reported_lower_limit_of_detection",
"measuring_instrument_documented_lower_limit_of_detection",
"measuring_instrument_reported_upper_limit_of_detection",
"measuring_instrument_documented_upper_limit_of_detection",
"measuring_instrument_reported_uncertainty",
"measuring_instrument_documented_uncertainty",
"measuring_instrument_reported_accuracy",
"measuring_instrument_documented_accuracy",
"measuring_instrument_reported_precision",
"measuring_instrument_documented_precision",
"measuring_instrument_reported_zero_drift",
"measuring_instrument_documented_zero_drift",
"measuring_instrument_reported_span_drift",
"measuring_instrument_documented_span_drift",
"measuring_instrument_reported_zonal_drift",
"measuring_instrument_documented_zonal_drift",
"measuring_instrument_reported_measurement_resolution",
"measuring_instrument_documented_measurement_resolution",
"measuring_instrument_reported_absorption_cross_section",
"measuring_instrument_documented_absorption_cross_section",
"measuring_instrument_inlet_information",
"measuring_instrument_calibration_scale",
"network_provided_volume_standard_temperature",
"network_provided_volume_standard_pressure", "retrieval_algorithm",
"principal_investigator_name", "principal_investigator_institution",
"principal_investigator_email_address", "contact_name",
"contact_institution", "contact_email_address", "meta_update_stamp",
"data_download_stamp", "data_revision_stamp", "network_sampling_details",
"network_uncertainty_details", "network_maintenance_details",
"network_qa_details", "network_miscellaneous_details", "data_licence",
"process_warnings", "temporal_resolution",
"reported_lower_limit_of_detection_per_measurement",
"reported_upper_limit_of_detection_per_measurement",
"reported_uncertainty_per_measurement", "derived_uncertainty_per_measurement",
"day_night_code", "weekday_weekend_code", "season_code",
"hourly_native_representativity_percent", "hourly_native_max_gap_percent",
"daily_native_representativity_percent", "daily_representativity_percent",
"daily_native_max_gap_percent", "daily_max_gap_percent",
"monthly_native_representativity_percent", "monthly_representativity_percent",
"monthly_native_max_gap_percent", "monthly_max_gap_percent",
"annual_native_representativity_percent", "annual_native_max_gap_percent",
"all_representativity_percent", "all_max_gap_percent"],
}
return metadata_variables[ghost_version]
# noinspection DuplicatedCode
[docs]
def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0):
"""
Add variables data to shapefile.
var_list : list, str
List (or single string) of the variables to be loaded and saved in the shapefile.
idx_lev : int
Index of vertical level for which the data will be saved in the shapefile.
idx_time : int
Index of time for which the data will be saved in the shapefile.
"""
if idx_lev != 0:
msg = "Error: Points dataset has no level (Level: {0}).".format(idx_lev)
raise ValueError(msg)
for var_name in var_list:
# station as dimension
if len(self.variables[var_name]["dimensions"]) == 1:
self.shapefile[var_name] = self.variables[var_name]["data"][:].ravel()
# station and time as dimensions
else:
self.shapefile[var_name] = self.variables[var_name]["data"][:, idx_time].ravel()
return None
@staticmethod
def _get_axis_index_(axis):
if axis == "T":
value = 1
elif axis == "X":
value = 0
else:
raise ValueError("Unknown axis: {0}".format(axis))
return value
@staticmethod
def _set_var_crs(var):
"""
Set the grid_mapping
Parameters
----------
var : Variable
netCDF4-python variable object.
"""
return None