| """Utilitaries to process data tiles.""" |
|
|
| import numpy as np |
| import xarray as xr |
|
|
|
|
| def normalize_array(inarr: xr.DataArray, percentile: float = 0.99) -> xr.DataArray: |
| """Performs normalization between 0.0 and 1.0 using the given |
| percentile. |
| """ |
| quantile_value = inarr.quantile(percentile, dim=["x", "y", "t"]) |
| minimum = inarr.min(dim=["x", "y", "t"]) |
|
|
| inarr = (inarr - minimum) / (quantile_value - minimum) |
|
|
| |
| return inarr.where(inarr < 1.0, 1.0) |
|
|
|
|
| def select_optical_bands(inarr: xr.DataArray) -> xr.DataArray: |
| """Filters and keep only the optical bands for a given array.""" |
| return inarr.sel( |
| bands=[ |
| band |
| for band in inarr.coords["bands"].to_numpy() |
| if band.startswith("S2-L2A-B") |
| ] |
| ) |
|
|
|
|
| def select_sar_bands(inarr: xr.DataArray) -> xr.DataArray: |
| """Filters and keep only the SAR bands for a given array.""" |
| return inarr.sel( |
| bands=[ |
| band |
| for band in inarr.coords["bands"].to_numpy() |
| if band in ["S1-SIGMA0-VV", "S1-SIGMA0-VH", "S1-SIGMA0-HH", "S1-SIGMA0-HV"] |
| ] |
| ) |
|
|
|
|
| def array_bounds(inarr: xr.DataArray) -> tuple: |
| """Returns the 4 bounds values for the x and y coordinates of the tile""" |
| return ( |
| inarr.coords["x"].min().item(), |
| inarr.coords["y"].min().item(), |
| inarr.coords["x"].max().item(), |
| inarr.coords["y"].max().item(), |
| ) |
|
|
|
|
| def arrays_cosine_similarity( |
| first_array: xr.DataArray, second_array: xr.DataArray |
| ) -> float: |
| """Returns a similarity score based on normalized cosine distance. The |
| input arrays must have similar ranges to obtain a valid score. |
| 1.0 represents the best score (same tiles), while 0.0 is the worst score. |
| """ |
| dot_product = np.sum(first_array * second_array) |
| first_norm = np.linalg.norm(first_array) |
| second_norm = np.linalg.norm(second_array) |
| similarity = (dot_product / (first_norm * second_norm)).item() |
|
|
| return similarity |
|
|
