Usage

GeoRacoon provides high-level functions that allow you to do many things in just a few lines of code:

from riogrande.io import Source
from convster import parallel as cvpara
from convster.filters import bpgaussian  # border-preserving Gaussian

source = Source("landcover.tif")

# Kernel: 30 km sigma, 1 km resolution → 30 pixels
params_filter = dict(sigma=30, truncate=3, preserve_range=True)

cvpara.apply_filter(
    source=source,
    output_file="landcover_blurred.tif",
    block_size=(200, 200),
    img_filter=bpgaussian,
    filter_params=params_filter,
    data_as_dtype="float32",
    nbrcpu=4,
)

Minimal Examples

RioGrande — open a GeoTIFF and work with Sources and Bands

from riogrande.io import Source, Band

# Open a GeoTIFF as a Source
source = Source("elevation.tif")

# Import the file's profile (size, CRS, dtype, …)
profile = source.import_profile()

# Tag a band and retrieve it by tag later
source.set_tags(bidx=1, tags={"category": "elevation_mean"})
band = source.get_band(category="elevation_mean")

# Or just grab a band by index
band = source.get_band(bidx=1)
print(band.tags)

Convster — apply a spatial filter to a raster

from riogrande.io import Source
from convster import parallel as cvpara
from convster.filters import bpgaussian  # border-preserving Gaussian

source = Source("landcover.tif")

# Kernel: 30 km sigma, 1 km resolution → 30 pixels
params_filter = dict(sigma=30, truncate=3, preserve_range=True)

cvpara.apply_filter(
    source=source,
    output_file="landcover_blurred.tif",
    block_size=(200, 200),
    img_filter=bpgaussian,
    filter_params=params_filter,
    data_as_dtype="float32",
    nbrcpu=4,
)

CoonFit — fit a linear model and generate a prediction raster

import numpy as np
from riogrande.io import Source, Band
from coonfit import parallel as lfpara

# Set up response and predictor bands
response_band = Band(Source("lst.tif"), bidx=1)

pred_source = Source("elevation.tif")
pred_source.set_tags(bidx=1, tags={"category": "elevation"})
predictor_band = pred_source.get_band(category="elevation")

# Fit the model — returns a dict of {band: weight}
weights = lfpara.compute_weights(
    response=response_band,
    predictors=[predictor_band],
    block_size=(200, 200),
    include_intercept=True,
    no_data=np.nan,
    nbrcpu=4,
)
print(weights)

# Apply the fitted weights to produce a prediction raster
lfpara.compute_model(
    predictors=[predictor_band],
    optimal_weights=weights,
    output_file="lst_predicted.tif",
    block_size=(200, 200),
    nbrcpu=4,
)