repo: update dev branch

pyproject.toml

@@ -17,7 +17,7 @@
 
 [project]
 name = "pysits"
-version = "1.5.4"
+version = "2.0.0.dev2"
 description = "Python wrapper for the sits R package"
 readme = "README.md"
 requires-python = ">=3.10,<4"

pysits/__init__.py

@@ -20,7 +20,6 @@
 from .conversions.dsl.mask import MaskValue
 from .conversions.dsl.tuning import hparam
 from .settings import __version__
-from .sits.classification import sits_classify, sits_label_classification, sits_smooth
 from .sits.colors import (
     sits_colors,
     sits_colors_reset,
@@ -62,17 +61,27 @@
     sits_apply,
     sits_bands,
     sits_bbox,
+    sits_classify,
+    sits_encode,
+    sits_label_classification,
     sits_labels,
     sits_labels_summary,
     sits_list_collections,
     sits_merge,
     sits_mixture_model,
     sits_reduce,
     sits_select,
+    sits_smooth,
     sits_timeline,
 )
 from .sits.data import sits_summary as summary
-from .sits.exporters import sits_as_geopandas, sits_as_xarray, sits_to_csv, sits_to_xlsx
+from .sits.exporters import (
+    sits_as_geopandas,
+    sits_as_xarray,
+    sits_timeseries_to_csv,
+    sits_to_csv,
+    sits_to_xlsx,
+)
 from .sits.impute import (
     impute_linear,
     impute_mean,
@@ -87,6 +96,7 @@
     sits_kfold_validate,
     sits_lightgbm,
     sits_lighttae,
+    sits_lstm_fcn,
     sits_mlp,
     sits_model_export,
     sits_pre_train,
@@ -107,7 +117,6 @@
     sits_cluster_clean,
     sits_cluster_dendro,
     sits_cluster_frequency,
-    sits_encode,
     sits_geo_dist,
     sits_get_class,
     sits_get_data,
@@ -118,13 +127,15 @@
     sits_pred_references,
     sits_pred_sample,
     sits_predictors,
+    sits_random_sampling,
     sits_reduce_imbalance,
     sits_sample,
     sits_sampling_design,
     sits_show_prediction,
     sits_som_clean_samples,
     sits_som_evaluate_cluster,
     sits_som_map,
+    sits_som_remove_samples,
     sits_stats,
     sits_stratified_sampling,
     sits_validate,
@@ -137,13 +148,21 @@
     read_rds,
 )
 from .sits.visualization import sits_plot as plot
-from .sits.visualization import sits_view
+from .sits.visualization import sits_sankey, sits_view
 
 __all__ = (
     # Classification
     "sits_classify",
     "sits_smooth",
     "sits_label_classification",
+    # Embeddings
+    "sits_pre_train",
+    "sits_encode",
+    "sits_ssl_mae",
+    "sits_ssl_lejepa",
+    "sits_ssl_vicreg",
+    "sits_barlow_twins",
+    "sits_contrastive_learning",
     # Cube
     "sits_cube",
     "sits_clean",
@@ -170,7 +189,7 @@
     "sits_config_user_file",
     "sits_config_value",
     "sits_parallel",
-    # Data management
+    # Data
     "sits_bands",
     "sits_timeline",
     "sits_labels",
@@ -194,6 +213,7 @@
     "sits_resnet",
     "sits_tempcnn",
     "sits_lighttae",
+    "sits_lstm_fcn",
     "sits_svm",
     "sits_xgboost",
     "sits_lightgbm",
@@ -202,12 +222,6 @@
     "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
     "impute_linear",
     "impute_mean",
@@ -228,13 +242,14 @@
     "sits_som_map",
     "sits_som_evaluate_cluster",
     "sits_som_clean_samples",
+    "sits_som_remove_samples",
     "sits_geo_dist",
     "sits_patterns",
     "sits_sample",
     "sits_reduce_imbalance",
     "sits_sampling_design",
     "sits_stratified_sampling",
-    "sits_encode",
+    "sits_random_sampling",
     # Tiles
     "sits_tiles_to_roi",
     "sits_roi_to_tiles",
@@ -249,12 +264,14 @@
     "sits_as_xarray",
     "sits_as_geopandas",
     "sits_to_xlsx",
+    "sits_timeseries_to_csv",
     # Tuning
     "sits_tuning_hparams",
     "sits_tuning",
     # Visualization
     "plot",
     "sits_view",
+    "sits_sankey",
     # DSL Variables
     "MaskValue",
     "hparam",

pysits/conversions/tibble.py

@@ -480,7 +480,8 @@ def geopandas_to_tibble(data: GeoPandasDataFrame) -> RDataFrame:
             f"Warning: Dropping columns with embedded DataFrames: {dropped_columns}"
         )
 
-    data_safe = data[safe_columns].copy()
+    # Geometries are transferred as WKT, so the result is a plain data frame
+    data_safe = PandasDataFrame(data[safe_columns].copy())
 
     if isinstance(data, GeoPandasDataFrame):
         geom_col = data.geometry.name

pysits/docs/content/impute_linear.md

@@ -3,7 +3,8 @@ Replace NA values by linear interpolation
 Remove NA by linear interpolation
 
 Args:
-    data (list | pandas.DataFrame): A time series vector or matrix.
+    data (list): A time series vector or matrix.
 
 Returns:
     R: A set of filtered time series using the imputation function.
+

pysits/docs/content/impute_mean.md

@@ -3,7 +3,7 @@ Remove NA using mean
 Remove NA using mean
 
 Args:
-    data (list[float] | pandas.DataFrame): A time series or matrix.
+    data (list[float] | SITSMatrix): A time series vector or matrix.
 
 Returns:
     R: A set of filtered time series using the imputation function.

pysits/docs/content/impute_mean_window.md

@@ -3,14 +3,14 @@ Remove NA using weighted moving average
 Remove NA using weighted moving average
 
 Args:
-    data (list): A time series vector or matrix.
+    data (list[float]): A time series vector or matrix.
     k (int): Width of the moving average window. Expands to both sides
         of the center element e.g. k = 2 means 4 observations (2 left,
         2 right) are taken into account. If all observations in the
         current window are NA, the window size is automatically
         increased until there are at least 2 non-NA values present.
-    weighting (str): The weighting strategy to be used. More details
-        below (default is "simple").
+    weighting (str): Weighting strategy to be used. More details below
+        (default is "simple").
 
 Returns:
     R: A set of filtered time series using the imputation function.

pysits/docs/content/impute_median.md

@@ -3,7 +3,7 @@ Remove NA using median
 Remove NA using median
 
 Args:
-    data (list[float] | SITSMatrix): A time series vector or matrix.
+    data (list | SITSMatrix): A time series vector or matrix.
 
 Returns:
     R: A set of filtered time series using the imputation function.