# -*- coding: utf-8 -*-
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import numpy as np
from netCDF4 import Dataset, date2num, num2date
from smos.grid import EASE25CellGrid
from smos.interface import SMOSImg, SMOSDs
[docs]
class SMOS_IC_Img(SMOSImg):
"""
Class for reading one SMOS IC netcdf image file.
Parameters
----------
filename: str
filename of the SMOS nc image file
mode: str, optional (default: 'r')
mode of opening the file, only 'r' is implemented at the moment
parameters : str or list, optional (default: None)
one or list of parameters to read. We add 'Quality_Flags' if 'read_flags'
is not None. All parameters are described in docs/varnames.rst. If
None are passed, all parameters are read.
flatten: bool, optional (default: False)
If set then the data is read into 1D arrays. This is used to e.g
reshuffle the data.
grid : pygeogrids.CellGrid, optional (default: EASE25CellGrid)
Grid that the image data is organised on, by default the global EASE25
grid is used.
read_flags : tuple or None, optional (default: (0, 1))
Filter values to read based on the selected QUALITY_FLAGS.
Values for locations that are not assigned any of the here passed flags
are replaces with NaN (by default only the missing-data, i.e. flag=2,
locations are filtered out). If None is passed, no flags are considered.
float_fillval : float or None, optional (default: np.nan)
Fill Value for masked pixels, this is only applied to float variables.
Therefore e.g. mask variables are never filled but use the fill value
as in the data.
"""
def __init__(self, filename, mode='r', parameters=None, flatten=False,
grid=EASE25CellGrid(bbox=None), read_flags=(0, 1), float_fillval=np.nan):
super(SMOSImg, self).__init__(filename, mode=mode)
if parameters is None:
parameters = []
if type(parameters) != list:
parameters = [parameters]
self.read_flags = read_flags
self.parameters = parameters
self.flatten = flatten
self.grid = grid
self.image_missing = False
self.img = None # to be loaded
self.glob_attrs = None
self.float_fillval = float_fillval
def _read_img(self) -> (dict, dict):
# Read a netcdf image and metadata
ds = Dataset(self.filename)
self.glob_attrs = ds.__dict__
param_img = {}
param_meta = {}
if len(self.parameters) == 0:
# all data vars, exclude coord vars
self.parameters = [k for k in ds.variables.keys() if
ds.variables[k].ndim != 1]
parameters = list(self.parameters)
if (self.read_flags is not None) and ('Quality_Flag' not in parameters):
parameters.append('Quality_Flag')
for parameter in parameters:
metadata = {}
param = ds.variables[parameter]
data = param[:]
# read long name, FillValue and unit
for attr in param.ncattrs():
metadata[attr] = param.getncattr(attr)
data.mask = ~np.isfinite(data)
np.ma.set_fill_value(data, metadata['_FillValue'])
metadata['image_missing'] = 0
param_img[parameter] = data
param_meta[parameter] = metadata
# filter with the flags (this excludes non-land points as well)
if self.read_flags is not None:
flag_mask = ~np.isin(param_img['Quality_Flag'], self.read_flags)
else:
flag_mask = np.full(param_img[parameters[0]].shape, False)
for param, data in param_img.items():
param_img[param].mask = (data.mask | flag_mask)
if self.float_fillval is not None:
if issubclass(data.dtype.type, np.floating):
param_img[param] = data.filled(fill_value=self.float_fillval)
param_img[param] = param_img[param].flatten()[self.grid.activegpis]
if ('Quality_Flag' in param_img.keys()) and \
('Quality_Flag' not in self.parameters):
param_img.pop('Quality_Flag')
param_meta.pop('Quality_Flag')
return param_img, param_meta
[docs]
class SMOS_IC_Ds(SMOSDs):
"""
Class for reading SMOS IC images in nc format. Images are orgnaised in subdirs
for each year.
Parameters
----------
data_path : str
Path to the nc files
parameter : str or list, optional (default: None)
one or list of parameters to read, see SMOS documentation
for more information (default: 'Soil_Moisture').
flatten: bool, optional (default: False)
If set then the data is read into 1D arrays. This is used to e.g
reshuffle the data.
grid : pygeogrids.CellGrid, optional (default: EASE25CellGrid)
Grid that the image data is organised on, by default the global EASE25
grid is used.
read_flags : tuple or None, optional (default: (0, 1))
Filter values to read based on the selected QUALITY_FLAGS.
Values for locations that are not assigned any of the here passed flags
are replaces with NaN (by default only the missing-data, i.e. flag=2,
locations are filtered out).
float_fillval : float or None, optional (default: np.nan)
Fill Value for masked pixels, this is only applied to float variables.
Therefore e.g. mask variables are never filled but use the fill value
as in the data.
"""
default_fname_template = "SM_RE06_MIR_CDF3S*_{datetime}T000000_{datetime}T235959_105_*_8.DBL.nc"
def __init__(self, data_path, parameters=None, flatten=False,
grid=EASE25CellGrid(bbox=None), filename_templ=None,
read_flags=(0, 1), float_fillval=np.nan):
if filename_templ is None:
filename_templ = self.default_fname_template
super().__init__(data_path, ioclass=SMOS_IC_Img,
parameters=parameters,
flatten=flatten,
grid=grid,
filename_templ=filename_templ,
read_flags=read_flags,
float_fillval=float_fillval
)