Dimension Reduction Functions

Functions

class xr_fresh.dimension_reduction.ExtendedGeoWombatAccessor(xarray_obj)[source]
Attributes:
affine

Get the affine transform object.

altitude

Get satellite altitudes (in km)

array_is_dask

Get whether the array is a Dask array.

avail_sensors

Get supported sensors.

band_chunks

Get the band chunk size.

bottom

Get the array bounding box bottom coordinate.

bounds

Get the array bounding box (left, bottom, right, top)

bounds_as_namedtuple

Get the array bounding box as a rasterio.coords.BoundingBox

cellx

Get the cell size in the x direction.

cellxh

Get the half width of the cell size in the x direction.

celly

Get the cell size in the y direction.

cellyh

Get the half width of the cell size in the y direction.

central_um

Get a dictionary of central wavelengths (in micrometers)

chunk_grid

Get the image chunk grid.

col_chunks

Get the column chunk size.

crs_to_pyproj

Get the CRS as a pyproj.CRS object.

data_are_separate

Checks whether the data are loaded separately.

data_are_stacked

Checks whether the data are stacked.

dtype

Get the data type of the DataArray.

filenames

Gets the data filenames.

footprint_grid

Get the image footprint grid.

geodataframe

Get a geopandas.GeoDataFrame of the array bounds.

geometry

Get the polygon geometry of the array bounding box.

has_band

Check whether the DataArray has a band attribute.

has_band_coord

Check whether the DataArray has a band coordinate.

has_band_dim

Check whether the DataArray has a band dimension.

has_time

Check whether the DataArray has a time attribute.

has_time_coord

Check whether the DataArray has a time coordinate.

has_time_dim

Check whether the DataArray has a time dimension.

left

Get the array bounding box left coordinate.

meta

Get the array metadata.

nbands

Get the number of array bands.

ncols

Get the number of array columns.

ndims

Get the number of array dimensions.

nodataval

Get the ‘no data’ value from the attributes.

nrows

Get the number of array rows.

ntime

Get the number of time dimensions.

offsetval

Get the offset value.

pydatetime

Get Python datetime objects from the time dimension.

right

Get the array bounding box right coordinate.

row_chunks

Get the row chunk size.

scaleval

Get the scale factor value.

sensor_names

Get sensor full names.

time_chunks

Get the time chunk size.

top

Get the array bounding box top coordinate.

transform

Get the data transform (cell x, 0, left, 0, cell y, top)

unary_union

Get a representation of the union of the image bounds.

wavelengths

Get a dictionary of sensor wavelengths.

Methods

apply(filename, user_func[, n_jobs])

Applies a user function to an Xarray Dataset or DataArray and writes to file.

assign_nodata_attrs(nodata)

Assigns 'no data' attributes.

avi([nodata, mask, sensor, scale_factor])

Calculates the advanced vegetation index

band_mask(valid_bands[, src_nodata, ...])

Creates a mask from band nonzeros.

bounds_overlay(bounds[, how])

Checks whether the bounds overlay the image bounds.

calc_area(values[, op, units, row_chunks, ...])

Calculates the area of data values.

check_chunksize(chunksize, array_size)

Asserts that the chunk size fits within intervals of 16 and is smaller than the array.

clip(df[, query, mask_data, expand_by])

Clips a DataArray by vector polygon geometry.

clip_by_polygon(df[, query, mask_data, ...])

Clips a DataArray by vector polygon geometry.

compare(op, b[, return_binary])

Comparison operation.

compute(**kwargs)

Computes data.

evi([nodata, mask, sensor, scale_factor])

Calculates the enhanced vegetation index

evi2([nodata, mask, sensor, scale_factor])

Calculates the two-band modified enhanced vegetation index

extract(aoi[, bands, time_names, ...])

Extracts data within an area or points of interest.

gcvi([nodata, mask, sensor, scale_factor])

Calculates the green chlorophyll vegetation index

imshow([mask, nodata, flip, text_color, rot])

Shows an image on a plot.

k_pca(gamma, n_components, n_workers, chunk_size)

Applies Kernel PCA to the dataset and returns a DataArray with the components as bands.

kndvi([nodata, mask, sensor, scale_factor])

Calculates the kernel normalized difference vegetation index

mask(df[, query, keep])

Masks a DataArray.

mask_nodata()

Masks 'no data' values with nans.

match_data(data, band_names)

Coerces the xarray.DataArray to match another xarray.DataArray.

moving([stat, perc, w, nodata, weights])

Applies a moving window function to the DataArray.

n_windows([row_chunks, col_chunks])

Calculates the number of windows in a row/column iteration.

nbr([nodata, mask, sensor, scale_factor])

Calculates the normalized burn ratio

ndvi([nodata, mask, sensor, scale_factor])

Calculates the normalized difference vegetation index

norm_brdf(solar_za, solar_az, sensor_za, ...)

Applies Bidirectional Reflectance Distribution Function (BRDF) normalization.

norm_diff(b1, b2[, nodata, mask, sensor, ...])

Calculates the normalized difference band ratio.

read(band, **kwargs)

Reads data for a band or bands.

recode(polygon, to_replace[, num_workers])

Recodes a DataArray with polygon mappings.

replace(to_replace)

Replace values given in to_replace with value.

sample([method, band, n, strata, spacing, ...])

Generates samples from a raster.

save(filename[, mode, nodata, overwrite, ...])

Saves a DataArray to raster using rasterio/dask.

set_nodata([src_nodata, dst_nodata, ...])

Sets 'no data' values and applies scaling to an xarray.DataArray.

subset([left, top, right, bottom, rows, ...])

Subsets a DataArray.

tasseled_cap([nodata, sensor, scale_factor])

Applies a tasseled cap transformation

to_netcdf(filename, *args, **kwargs)

Writes an Xarray DataArray to a NetCDF file.

to_polygon([mask, connectivity])

Converts a dask array to a GeoDataFrame

to_raster(filename[, readxsize, readysize, ...])

Writes an Xarray DataArray to a raster file.

to_vector(filename[, mask, connectivity])

Writes an Xarray DataArray to a vector file.

to_vrt(filename[, overwrite, resampling, ...])

Writes a file to a VRT file.

transform_crs([dst_crs, dst_res, dst_width, ...])

Transforms an xarray.DataArray to a new coordinate reference system.

wi([nodata, mask, sensor, scale_factor])

Calculates the woody vegetation index

windows([row_chunks, col_chunks, ...])

Generates windows for a row/column iteration.
k_pca(gamma: float, n_components: int, n_workers: int, chunk_size: int) → DataArray[source]

Applies Kernel PCA to the dataset and returns a DataArray with the components as bands.

Parameters:

  • gamma (float) – The gamma parameter for the RBF kernel.

  • n_components (int) – The number of components to keep.

  • n_workers (int) – The number of parallel jobs for KernelPCA and ParallelTask.

  • chunk_size (int) – The size of the chunks for processing.

Returns:

A DataArray with the Kernel PCA components as bands.

Return type:

xr.DataArray

Examples: .. code-block:: python

import xr_fresh.dimension_reduction # This registers the accessor

# Initialize Ray with ray.init(num_cpus=8) as rays:

# Example usage with gw.open(

sorted(
[

“./tests/data/RadT_tavg_202301.tif”, “./tests/data/RadT_tavg_202302.tif”, “./tests/data/RadT_tavg_202304.tif”, “./tests/data/RadT_tavg_202305.tif”,

]

), stack_dim=”band”, band_names=[0, 1, 2, 3],

) as src:

# get 3 k principal components - base zero counting transformed_dataarray = src.gw_ext.k_pca(

gamma=15, n_components=3, n_workers=8, chunk_size=256

) transformed_dataarray.plot.imshow(col=’component’, col_wrap=1, figsize=(8, 12)) plt.show()