update to pysits 2.0

README.md

@@ -2,7 +2,7 @@
 
 Python wrapper for the [sits](https://github.com/e-sensing/sits) R package.
 
-## 📦 Installation
+## Installation
 
 To install `pysits` with pip:
 
@@ -18,7 +18,7 @@ pip install git+https://github.com/e-sensing/pysits.git
 
 > To use `pysits`, you must have [sits](https://github.com/e-sensing/sits) installed. For more information, refer to the [official sits documentation](https://e-sensing.github.io/sitsbook/setup.html).
 
-## 🛠 Development setup (for contributors)
+## Development setup (for contributors)
 
 To set up a local development environment:
 
@@ -42,23 +42,23 @@ source .venv/bin/activate  # or .venv\Scripts\activate on Windows
 uv pip install -e ".[dev]"
 ```
 
-### 🔍 Run tests
+### Run tests
 
 We use `pytest` for testing:
 
 ```bash
 pytest
 ```
 
-### 🧹 Code formatting
+### Code formatting
 
 To keep the codebase clean and consistent we use [ruff](https://github.com/astral-sh/ruff):
 
 ```bash
 ruff format .
 ```
 
-### 🧪 Linting
+### Linting
 
 We use [ruff](https://github.com/astral-sh/ruff) for static analysis:
 
@@ -68,18 +68,18 @@ ruff check .
 
 > The `examples/` directory is excluded from linting.
 
-## 📚 Learn more
+## Learn more
 
 To learn all about `sits`, including its concepts, API, and real-world examples, we recommend accessing the [official sits book](https://e-sensing.github.io/sitsbook/). The book provides examples in both R and Python.
 
-## 🤝 Contributing
+## Contributing
 
 We welcome contributions! Please:
 
 - Fork the repository
 - Create a feature branch
 - Submit a pull request with a clear description
 
-## 📄 License
+## License
 
 `pysits` is distributed under the GPL-2.0 license. See [LICENSE](./LICENSE) for more details.

pyproject.toml

@@ -77,7 +77,10 @@ xarray = [
     "xarray>=2025.3.0",
     "dask>=2025.3.0",
     "rioxarray>=0.18.2",
-    "affine>=2.4.0"
+    "affine>=2.4.0",
+    "odc-stac>=0.5.3",
+    "pystac>=1.15.0",
+    "planetary-computer>=1.0.0"
 ]
 
 dev = [

pysits/__init__.py

@@ -27,7 +27,13 @@
     sits_colors_set,
     sits_colors_show,
 )
-from .sits.config import sits_config, sits_config_show, sits_config_user_file
+from .sits.config import (
+    sits_config,
+    sits_config_show,
+    sits_config_user_file,
+    sits_config_value,
+    sits_parallel,
+)
 from .sits.context import (
     cerrado_2classes,
     point_mt_6bands,
@@ -72,30 +78,36 @@
     impute_mean,
     impute_mean_window,
     impute_median,
-    sits_impute,
 )
 from .sits.ml import (
+    sits_barlow_twins,
+    sits_contrastive_learning,
     sits_formula_linear,
     sits_formula_logref,
     sits_kfold_validate,
     sits_lightgbm,
     sits_lighttae,
     sits_mlp,
     sits_model_export,
+    sits_pre_train,
     sits_resnet,
     sits_rfor,
+    sits_ssl_lejepa,
+    sits_ssl_mae,
+    sits_ssl_vicreg,
     sits_svm,
     sits_tae,
     sits_tempcnn,
     sits_train,
     sits_xgboost,
 )
 from .sits.segment import sits_segment, sits_slic, sits_snic
-from .sits.tiles import sits_mgrs_to_roi, sits_roi_to_tiles, sits_tiles_to_roi
+from .sits.tiles import sits_roi_to_tiles, sits_tiles_to_roi
 from .sits.ts import (
     sits_cluster_clean,
     sits_cluster_dendro,
     sits_cluster_frequency,
+    sits_encode,
     sits_geo_dist,
     sits_get_class,
     sits_get_data,
@@ -109,15 +121,13 @@
     sits_reduce_imbalance,
     sits_sample,
     sits_sampling_design,
-    sits_sgolay,
     sits_show_prediction,
     sits_som_clean_samples,
     sits_som_evaluate_cluster,
     sits_som_map,
     sits_stats,
     sits_stratified_sampling,
     sits_validate,
-    sits_whittaker,
 )
 from .sits.tuning import sits_tuning, sits_tuning_hparams
 from .sits.utils import (
@@ -158,6 +168,8 @@
     "sits_config",
     "sits_config_show",
     "sits_config_user_file",
+    "sits_config_value",
+    "sits_parallel",
     # Data management
     "sits_bands",
     "sits_timeline",
@@ -190,16 +202,19 @@
     "sits_model_export",
     "sits_formula_linear",
     "sits_formula_logref",
+    "sits_ssl_mae",
+    "sits_ssl_lejepa",
+    "sits_ssl_vicreg",
+    "sits_barlow_twins",
+    "sits_contrastive_learning",
+    "sits_pre_train",
     # Impute
-    "sits_impute",
     "impute_linear",
     "impute_mean",
     "impute_median",
     "impute_mean_window",
     # Time-series
     "sits_show_prediction",
-    "sits_sgolay",
-    "sits_whittaker",
     "sits_get_data",
     "sits_get_class",
     "sits_get_probs",
@@ -219,8 +234,8 @@
     "sits_reduce_imbalance",
     "sits_sampling_design",
     "sits_stratified_sampling",
+    "sits_encode",
     # Tiles
-    "sits_mgrs_to_roi",
     "sits_tiles_to_roi",
     "sits_roi_to_tiles",
     # Segments

pysits/backend/functions.py

@@ -17,7 +17,10 @@
 
 """backend functions."""
 
-from pysits.backend.loaders import load_function_from_package
+from pysits.backend.loaders import (
+    load_function_from_package,
+    load_internal_function_from_package,
+)
 
 # Base - plot
 r_fnc_plot = load_function_from_package("base::plot")
@@ -40,6 +43,9 @@
 # Base - class (base)
 r_fnc_class = load_function_from_package("base::class")
 
+# Base - eval (base)
+r_fnc_eval = load_function_from_package("base::eval")
+
 # Base - as.data.frame (base)
 r_fnc_as_data_frame = load_function_from_package("base::as_data_frame")
 
@@ -48,3 +54,6 @@
 
 # Base - rownames (base)
 r_fnc_rownames = load_function_from_package("base::rownames")
+
+# sits - configuration (internal)
+r_fnc_sits_conf = load_internal_function_from_package("sits:::.conf")

pysits/backend/loaders.py

@@ -105,3 +105,42 @@ def load_function_from_package(name: str) -> Callable[..., Any]:
 
     # Return function
     return getattr(pkg, func_name)
+
+
+def load_internal_function_from_package(name: str) -> Callable[..., Any]:
+    """Load an internal R function from a specified package.
+
+    Internal functions are those not exported by a package. They are addressed
+    in R with the ``package:::function`` notation.
+
+    Args:
+        name (str): The fully qualified name of the R function in the format
+                    'package:::function'. For example, 'sits:::.conf'.
+
+    Returns:
+        Callable[..., Any]: A Python callable that wraps the R function.
+                            The exact signature depends on the underlying R function.
+
+    Raises:
+        ValueError: If the ``name`` doesn't follow the 'package:::function' format.
+
+        PackageNotFoundError: If the specified R package is not installed.
+
+    Examples:
+        >>> conf = load_internal_function_from_package('sits:::.conf')
+        >>> scale = conf('sources', 'BDC', 'collections', 'MOD13Q1-6.1')
+    """
+    # Parse package and function
+    package_name, _, func_name = name.partition(":::")
+
+    if not package_name or not func_name:
+        raise ValueError(
+            f"Invalid function name format: {name}. "
+            "Expected format: 'package:::function'"
+        )
+
+    # Import the package
+    importr(package_name, on_conflict="warn")
+
+    # Return function
+    return rpy2_r_interface(name)

pysits/conversions/common.py

@@ -25,8 +25,10 @@
 from pandas import DataFrame as PandasDataFrame
 from rpy2.rinterface_lib.sexp import NULLType
 from rpy2.robjects import pandas2ri
+from rpy2.robjects.language import LangVector
 from rpy2.robjects.robject import RObjectMixin
 
+from pysits.backend.functions import r_fnc_eval
 from pysits.backend.pkgs import r_pkg_tibble
 from pysits.conversions.dsl.base import DSLObject
 from pysits.conversions.tibble import geopandas_to_tibble, pandas_to_tibble
@@ -179,6 +181,22 @@ def convert_to_r(obj):
     raise TypeError(f"Cannot convert object of type {obj_type} to R format")
 
 
+def eval_r_language(obj):
+    """Evaluate an unevaluated R expression.
+
+    Args:
+        obj: The R object to evaluate.
+
+    Returns:
+        The evaluated R object. Objects that are not R expressions are returned
+        unchanged.
+    """
+    if isinstance(obj, LangVector):
+        return r_fnc_eval(obj)
+
+    return obj
+
+
 def convert_to_python(obj, as_type="str"):
     """Convert an R object to a Python representation.
 
@@ -206,6 +224,9 @@ def convert_to_python(obj, as_type="str"):
     def _convert(value, type_):
         result = []
 
+        # R expressions must be evaluated to be converted
+        value = eval_r_language(value)
+
         if isinstance(value, ro.ListVector):
             for k, v in value.items():
                 result.append(