pixcdust.readers.netcdf

Pre-conversion SWOT Pixel Cloud Netcdf reader.

Classes

NcSimpleConstants	Class setting defaults values in SWOT pixel cloud files such as name of attributes and variables
NcFormatCfg	Class configuring how a SWOT pixel cloud files is expected to be structured.
NcSimpleReader	Class for reading SWOT Pixel cloud official format files.

Module Contents

class pixcdust.readers.netcdf.NcSimpleConstants

Class setting defaults values in SWOT pixel cloud files such as name of attributes and variables

default_dim_name: str = 'points'

default_long_name: str = 'longitude'

default_lat_name: str = 'latitude'

default_cyc_num_name: str = 'cycle_number'

default_pass_num_name: str = 'pass_number'

default_tile_num_name: str = 'tile_number'

default_swath_side_name: str = 'swath_side'

default_time_start_name: str = 'time_granule_start'

default_time_format_filename: str = '%Y%m%dT%H%M%S'

default_time_format_attrs: str = '%Y-%m-%dT%H:%M:%S.%fZ'

default_added_time_name = 'time'

default_added_points_name = 'points'

class pixcdust.readers.netcdf.NcFormatCfg

Class configuring how a SWOT pixel cloud files is expected to be structured.

constants: NcSimpleConstants

trusted_group: str = 'pixel_cloud'

forbidden_variables: list[str] = ['pixc_line_qual', 'pixc_line_to_tvp', 'data_window_first_valid', 'data_window_last_valid',...

class pixcdust.readers.netcdf.NcSimpleReader(path: str | Iterable[str] | pathlib.Path | Iterable[pathlib.Path], variables: list[str] | None = None, area_of_interest: geopandas.GeoDataFrame | None = None, format_cfg: NcFormatCfg | None = None, conditions: dict[str, dict[str, str | float]] | None = None)

Bases: pixcdust.readers.base_reader.BaseReader

Class for reading SWOT Pixel cloud official format files.

It’s for simple uses cases as it only reads the pixel_cloud group.

Attributes:

path: Path or list of path to read. variables: Optionally only read these variables. area_of_interest: Optionally only read points in area_of_interest. MULTI_FILE_SUPPORT: True, this class can read multiple netcdf. conditions: Optionally pass conditions to filter variables. Example: { “sig0”:{‘operator’: “ge”, ‘threshold’: 20}, “classification”:{‘operator’: “ge”, ‘threshold’: 3}, }

MULTI_FILE_SUPPORT = True

forbidden_variables

trusted_group

cst

conditions = None

static extract_info_from_nc_attrs(filename: str) → Tuple[str, datetime.datetime, int, int, int, str]

Extracts orbit information from global attributes in a SWOT pixel cloud netcdf.

Args:

filename: path of SWOT PIXC Netcdf file

Returns:

(time of granule start as string, time of granule start as datetime, cycle number, pass number, tile number, swath size)

filter_variable() → None

Filters xarray dataset based on operator and threshold on specific variables.

Raises:

IOError: If the variable provided in conditions is not in the dataset. ValueError: If ‘operator’ or ‘threshold’ keys are not in conditions. AttributeError: If operator is not the function name of the operator module.

read(orbit_info: bool = False) → None

Load self.path file(s). You can then access from data or with methods like to_xarray, to_dataframe or to_geodataframe.

See self.open_mfdataset for more details on how multiple files are merged.

Args:

orbit_info: Option to extract the orbit information.

Only used if multiple files are read.

open_dataset() → None

Load the self.path file (need only one file in self.path). You can then access from data or with methods like to_xarray, to_dataframe or to_geodataframe.

open_mfdataset(orbit_info: bool = False) → None

Load self.path file(s) as a nested array. You can then access from data or with methods like to_xarray, to_dataframe or to_geodataframe.

Variables that are not one-dimensional along points dimension are not allowed and will be dropped:

• ‘pixc_line_qual’,

• ‘pixc_line_to_tvp’,

• ‘interferogram’

• etc.

Args:

orbit_info: option to extract the orbit information.

to_h3(variables: str | list[str] | None = None, resolution: int = 8, interp: bool = False, method: str = 'linear') → xarray.Dataset

Convert a Dataset with latitude and longitude coordinates into an H3-indexed grid.

Args:

variables: The variables you want to convert into the H3 grid, all variables by default. resolution: The resolution of the H3 grid. Valid values are from 0 (coarse) to 15 (fine). interp: True for interpolate data, could be more precise but take a lot of time, default is False. method: (‘nearest’, ‘linear’, ‘cubic’) The interpolation method used by`scippy.interpolate.griddata`.

Returns:

A new dataset with data variables interpolated onto the H3 grid

to_healpix(variables: str | list[str] | None = None, resolution: int = 8, interp: bool = False, method: str = 'linear') → xarray.Dataset

Convert a Dataset with latitude and longitude coordinates into an HEALPix-indexed grid.

Args:

variables: The variables you want to convert into the HEALpix grid, all by default. resolution: The resolution of the HEALPix grid. interp: True for interpolate data, could be more precise but take a lot of time, default is False. method: (‘nearest’, ‘linear’, ‘cubic’) The interpolation method used by`scippy.interpolate.griddata`.

Returns:

A new dataset with data variables interpolated onto the HEALPix grid.

__postprocess_points() → None

Adds a points coordinates containing shapely.Points (longitude, latitude) Useful for compatibility with xvec package and geographic manipulation.

__preprocess_types(ds: xarray.Dataset) → xarray.Dataset

Preprocessing function changing types in pixc dataset.

It cast the lon and lat to float32.

Args:

ds: pixc dataset read by xarray.open_dataset to preprocess

Returns:

dataset with cast types

__preprocess_types_and_add_orbit_info(ds: xarray.Dataset) → xarray.Dataset

Preprocessing function adding orbit information in pixc dataset.

It cast the lon and lat to float32.

Args:

ds: pixc dataset read by xarray.open_dataset to preprocess

Returns:

dataset augmented with orbit information for each index and with cast types