pixcdust.readers

Readers for SWOT Pixel Cloud. They support the Netcdf official format and converted Zarr or Geopackage database.

Submodules

• pixcdust.readers.base_reader
• pixcdust.readers.gpkg
• pixcdust.readers.netcdf
• pixcdust.readers.zarr

Classes

NcSimpleReader	Class for reading SWOT Pixel cloud official format files.
GpkgReader	GeoPackage pixcdust database reader.
ZarrReader	Zarr pixcdust database reader.

Package Contents

class pixcdust.readers.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

class pixcdust.readers.GpkgReader(path: str | pathlib.Path, area_of_interest: geopandas.GeoDataFrame | None = None)

Bases: pixcdust.readers.base_reader.BaseReader

GeoPackage pixcdust database reader.

Read a database from a GeoPackage file . You can then request a xr.Dataset, pd.DataFrame or gpd.GeoDataFrame view of the database.

Attributes:

path: Path to read. variables: Not supported. area_of_interest: Optionally only read points in area_of_interest. MULTI_FILE_SUPPORT: False, only support one file.

_gdf_data: geopandas.GeoDataFrame | None = None

layers: list[str]

property data: xarray.Dataset

Return an xarray.Dataset view from the database loaded.

Equivalent to to_xarray.

Returns:

Dataset read

to_geodataframe() → geopandas.GeoDataFrame

Convert the database read to a gpd.GeoDataFrame. Only points in self.area_of_interest are included.

Returns:

GeoDataFrame read.

read_single_layer(layer: str) → geopandas.GeoDataFrame

Read and return a single layer of geopackage database.

Don’t load the read data into the class (can’t be then converted by the reader). Use read for more advanced usage.

Args:

layer : name of the geodataframe layer to read. Must be in self.layers

Returns:

Geodataframe containing data read from layer

read(layers: List[str] | None = None) → None

Load all layers, or subset of layers, from geopackage database. You can then access from data or with methods like to_xarray, to_dataframe or to_geodataframe.

Args:

layers: Optional list of layers to load. Default to all.

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

Bases: pixcdust.readers.base_reader.BaseReader

Zarr pixcdust database reader.

Read a database from a Zarr database (folder). You can then request a xr.Dataset, pd.DataFrame or gpd.GeoDataFrame view of the database.

Attributes:

path: Path to read. variables: Optionally only read these variables. area_of_interest: Optionally only read points in area_of_interest. MULTI_FILE_SUPPORT: False, only support one file.

read(date_interval: Tuple[datetime.datetime, datetime.datetime] | None | None = None) → None

Load a zarr database. You can then access from data or with methods like to_xarray, to_dataframe or to_geodataframe.

Args:

date_interval: Optional date filter on the database read.

Only load data dated within the interval.