docs: fix current API keywords in usage examples

README.md

@@ -116,18 +116,14 @@ from pyinterpolate import ordinary_kriging
 unknown_point = (20000, 65000)
 prediction = ordinary_kriging(theoretical_model=semivar,
                               known_locations=point_data,
-                              unknown_location=unknown_point,
+                              unknown_locations=unknown_point,
                               no_neighbors=32)
 ```
 
 **[5.]** Analyze error and uncertainty of predictions.
 
 ```python
-print(prediction)  # [predicted, variance error, lon, lat]
-```
-
-```bash
->> [211.23, 0.89, 20000, 60000]
+print(prediction[0])  # [predicted, variance error, lon, lat]
 ```
 
 With Pyinterpolate you can analyze and transform aggregated data. Here is the example of spatial disaggregation of areal data into point support using Poisson Kriging:
@@ -145,7 +141,7 @@ Setup with *conda*: `conda install -c conda-forge pyinterpolate`
 
 Setup with *pip*: `pip install pyinterpolate`
 
-Detailed instructions on how to install the package are presented in the file [SETUP.md](https://github.com/DataverseLabs/pyinterpolate/blob/main/SETUP.md). We pointed out there most common problems related to third-party packages.
+The package dependencies are listed in [pyproject.toml](https://github.com/DataverseLabs/pyinterpolate/blob/main/pyproject.toml).
 
 You may follow those setup steps to create a *conda* environment with the package for your work:
 
@@ -177,7 +173,7 @@ A world of advice, you should **always** use Virtual Environment for the install
 
 ## Tests and contribution
 
-All tests are grouped in the `test` directory. If you would like to contribute, then you won't avoid testing, but it is described step-by-step here: [CONTRIBUTING.md](https://github.com/DataverseLabs/pyinterpolate/blob/main/CONTRIBUTING.md)
+All tests are grouped in the `tests` directory. If you would like to contribute, then you won't avoid testing, but it is described step-by-step here: [CONTRIBUTING.md](https://github.com/DataverseLabs/pyinterpolate/blob/main/CONTRIBUTING.md)
 
 ## Use cases
 
@@ -213,7 +209,7 @@ Core requirements and dependencies are:
 * scipy
 * tqdm
 
-You may check a specific version of requirements in the `setup.cfg` file. Required packages versions are updated in a regular interval.
+The package dependencies are declared in `pyproject.toml`.
 
 ## Package structure
 

src/pyinterpolate/core/data_models/centroid_poisson_kriging.py

@@ -161,7 +161,7 @@ class CentroidPoissonKrigingInput:
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/core/data_models/point_support.py

@@ -142,7 +142,7 @@ class PointSupport:
     >>>
     >>> ps = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/core/data_models/point_support_distances.py

@@ -95,7 +95,7 @@ class PointSupportDistance:
     >>>
     >>> ps = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/core/data_models/poisson_kriging.py

@@ -181,7 +181,7 @@ class PoissonKrigingInput:
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/core/pipelines/block_filter.py

@@ -115,7 +115,7 @@ def filter_blocks(semivariogram_model: TheoreticalVariogram,
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )
@@ -239,7 +239,7 @@ def smooth_blocks(semivariogram_model: TheoreticalVariogram,
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )
@@ -369,7 +369,7 @@ class BlockPoissonKriging:
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/distance/block.py

@@ -253,7 +253,7 @@ def calc_block_to_block_distance(
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/idw/idw.py

@@ -86,7 +86,7 @@ def inverse_distance_weighting(unknown_location: ArrayLike,
     ...     [11, 9, 7]
     ... ])
     >>> pred = inverse_distance_weighting(
-    ...     unknown_locations=unknown_pos,
+    ...     unknown_location=unknown_pos,
     ...     known_values=locs[:, -1],
     ...     known_geometries=locs[:, :-1],
     ...     no_neighbors=2

src/pyinterpolate/kriging/block/area_to_area_poisson_kriging.py

@@ -104,7 +104,7 @@ def area_to_area_pk(semivariogram_model: TheoreticalVariogram,
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/kriging/block/area_to_point_poisson_kriging.py

@@ -110,7 +110,7 @@ def area_to_point_pk(semivariogram_model: TheoreticalVariogram,
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/kriging/block/centroid_based_poisson_kriging.py

@@ -114,7 +114,7 @@ def centroid_poisson_kriging(semivariogram_model: TheoreticalVariogram,
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/semivariogram/deconvolution/aggregated_variogram.py

@@ -213,7 +213,7 @@ class AggregatedVariogram:
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )
@@ -764,7 +764,7 @@ def regularize(blocks: Blocks,
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )

src/pyinterpolate/semivariogram/deconvolution/regularize.py

@@ -156,7 +156,7 @@ class Deconvolution:
     >>>
     >>> PS = PointSupport(
     ...     points=POINT_SUPPORT_DATA['ps'],
-    ...     ps_blocks=BLOCKS,
+    ...     blocks=BLOCKS,
     ...     points_value_column=POINT_SUPPORT_DATA['value_column_name'],
     ...     points_geometry_column=POINT_SUPPORT_DATA['geometry_column_name']
     ... )