def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
array: xarray.DataArray = cube.get_array()
array += 1000
# Shape (#dates, #bands, #rows, #cols)
array.loc[:, 'red'] += 10
array.loc[:, 'nir'] += 100
return XarrayDataCube(array)
def apply_hypercube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
from scipy.signal import savgol_filter
array: xarray.DataArray = cube.get_array()
filled = array.interpolate_na(dim='t')
smoothed_array = savgol_filter(filled.values, 5, 2, axis=0)
return XarrayDataCube(xarray.DataArray(smoothed_array, dims=array.dims, coords=array.coords))
def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
inarr = cube.get_array()
B4 = inarr.loc[:, 'bandzero']
B8 = inarr.loc[:, 'bandone']
ndvi = (B8 - B4) / (B8 + B4)
ndvi = ndvi.expand_dims(dim='bands', axis=-3).assign_coords(bands=['ndvi'])
return XarrayDataCube(ndvi)
def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
"""
Apply Savitzky-Golay smoothing to a timeseries datacube.
This UDF preserves dimensionality, and assumes an input
datacube with a temporal dimension 't' as input.
"""
array: xarray.DataArray = cube.get_array()
filled = array.interpolate_na(dim='t')
smoothed_array = savgol_filter(filled.values, 5, 2, axis=0)
return XarrayDataCube(array=xarray.DataArray(
smoothed_array, dims=array.dims, coords=array.coords))
def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
"""Compute the NDVI based on sentinel2 tiles"""
array: xarray.DataArray = cube.get_array()
red = array.sel(bands="TOC-B04_10M")
nir = array.sel(bands="TOC-B08_10M")
ndvi = (nir - red) / (nir + red)
import os
statinfo = os.stat("/data/users/Public")
print(statinfo)
return XarrayDataCube(ndvi)
def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
# access the underlying xarray
inarr=cube.get_array()
# ndvi
B4=inarr.loc[:,'B04']
B8=inarr.loc[:,'B08']
ndvi=(B8-B4)/(B8+B4)
# extend bands dim
ndvi=ndvi.expand_dims(dim='bands', axis=-3).assign_coords(bands=['ndvi'])
# wrap back to datacube and return
return XarrayDataCube(ndvi)
def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
"""
Applies a rolling window median composite to a timeseries datacube.
This UDF preserves dimensionality, and assumes a datacube with a temporal dimension 't' as input.
"""
array: xarray.DataArray = cube.get_array()
#this method computes dekad's, can be used to resample data to desired frequency
time_dimension_index = array.get_index('t')
d = time_dimension_index.day - np.clip((time_dimension_index.day - 1) // 10, 0, 2) * 10 - 1
date = time_dimension_index.values - np.array(d, dtype="timedelta64[D]")
#replace each value with 30-day window median
#first median rolling window to fill gaps on all dates
composited = array.rolling(t=30,min_periods=1, center=True).median().dropna("t")
#resample rolling window medians to dekads
ten_daily_composite = composited.groupby_bins("t",date).median()
return XarrayDataCube(ten_daily_composite)
def apply_datacube(cube: XarrayDataCube, context: dict) -> XarrayDataCube:
array: xarray.DataArray = cube.get_array()
array += 60
return XarrayDataCube(array)