Edited contributing guidelines, multiconstructor for yaml loader

CONTRIBUTING.md

@@ -54,8 +54,8 @@ for each new feature simplifies the development, review and merge processes by
 maintining logical separation. To create a feature branch:
 
 ```bash
-  git fetch agrifoodpy
-  git checkout -b <your-branch-name> agrifoodpy/main
+  git fetch afp
+  git checkout -b <your-branch-name> afp/main
 ```
 
 ### Hack away!

agrifoodpy/pipeline/pipeline.py

@@ -32,6 +32,20 @@ def _load_function(path):
         module = importlib.import_module(module_path)
         return getattr(module, func_name)
 
+    @staticmethod
+    def _is_supported_yaml_function(path):
+        """Return True for dotted numpy/xarray function paths."""
+        if not isinstance(path, str) or "." not in path:
+            return False
+
+        module_path, _ = path.rsplit(".", 1)
+        return (
+            module_path == "numpy"
+            or module_path.startswith("numpy.")
+            or module_path == "xarray"
+            or module_path.startswith("xarray.")
+        )
+
     @classmethod
     def read(cls, filename):
         """Read a pipeline configuration from a YAML file
@@ -47,6 +61,52 @@ def read(cls, filename):
             The pipeline object.
         """
 
+        def dynamic_call_constructor(package_name):
+            """Build a multi-constructor for supported package functions."""
+
+            def constructor(loader, suffix, node):
+                func_path = f"{package_name}.{suffix}" if suffix else package_name
+
+                # Check if the function path is supported
+                if not cls._is_supported_yaml_function(func_path):
+                    raise yaml.constructor.ConstructorError(
+                        None,
+                        None,
+                        f"Unsupported YAML function tag '!{func_path}'.",
+                        node.start_mark,
+                    )
+
+                func = cls._load_function(func_path)
+
+                if isinstance(node, yaml.ScalarNode):
+                    return func
+                if isinstance(node, yaml.SequenceNode):
+                    args = loader.construct_sequence(node, deep=True)
+                    return func(*args)
+                if isinstance(node, yaml.MappingNode):
+                    kwargs = loader.construct_mapping(node, deep=True)
+                    return func(**kwargs)
+
+                raise yaml.constructor.ConstructorError(
+                    None,
+                    None,
+                    f"Unsupported YAML node type for '!{func_path}'.",
+                    node.start_mark,
+                )
+
+            return constructor
+
+        yaml.add_multi_constructor(
+            "!numpy.",
+            dynamic_call_constructor("numpy"),
+            Loader=yaml.FullLoader,
+        )
+        yaml.add_multi_constructor(
+            "!xarray.",
+            dynamic_call_constructor("xarray"),
+            Loader=yaml.FullLoader,
+        )
+
         with open(filename, "r") as f:
             config = yaml.load(f, Loader=yaml.FullLoader)
 

agrifoodpy/pipeline/tests/data/test_config_numpy_array.yaml

@@ -0,0 +1,6 @@
+nodes:
+  - function: agrifoodpy.utils.nodes.write_to_datablock
+    name: Numpy Array
+    params:
+      key: "test_numpy_array"
+      value: !numpy.array [[1, 2, 3]]

agrifoodpy/pipeline/tests/data/test_config_numpy_array_kwargs.yaml

@@ -0,0 +1,6 @@
+nodes:
+  - function: agrifoodpy.utils.nodes.write_to_datablock
+    name: Numpy Array
+    params:
+      key: "test_numpy_array"
+      value: !numpy.array {object: [1, 2, 3]}

agrifoodpy/pipeline/tests/data/test_config_unsupported_function.yaml

@@ -0,0 +1,6 @@
+nodes:
+  - function: agrifoodpy.utils.nodes.write_to_datablock
+    name: Unsupported Function
+    params:
+      key: "test_pandas_array"
+      value: !pandas.DataFrame {data: [1, 2, 3], columns: ["A", "B", "C"]}

agrifoodpy/pipeline/tests/data/test_config_xarray_dataarray.yaml

@@ -0,0 +1,6 @@
+nodes:
+  - function: agrifoodpy.utils.nodes.write_to_datablock
+    name: Xarray DataArray
+    params:
+      key: "test_value"
+      value: !xarray.DataArray [[1,2,3] , {Year: [2020, 2021, 2022]}, "Year"]

agrifoodpy/pipeline/tests/data/test_config_xarray_dataarray_kwargs.yaml

@@ -0,0 +1,6 @@
+nodes:
+  - function: agrifoodpy.utils.nodes.write_to_datablock
+    name: Xarray DataArray
+    params:
+      key: "test_value"
+      value: !xarray.DataArray {data: [1, 2, 3], dims: ["Year"], coords: {Year: [2020, 2021, 2022]}}

agrifoodpy/pipeline/tests/test_pipeline.py

@@ -1,5 +1,8 @@
 from agrifoodpy.pipeline import Pipeline, standalone
+import numpy as np
+import xarray as xr
 import pytest
+import os
 
 def test_init():
     pipeline = Pipeline()
@@ -359,3 +362,56 @@ def reserved_param_node(x, datablock=None):
         @pipeline_node(['wrong_key'])
         def unknown_input_node(right_key):
             pass
+
+
+# Test reading YAML config with numpy array parameters and values
+def test_read_yaml_numpy_array():
+
+    script_dir = os.path.dirname(__file__)
+    config_path = os.path.join(script_dir, "data/test_config_numpy_array.yaml")
+
+    pipeline = Pipeline.read(str(config_path))
+    pipeline.run()
+
+    assert np.array_equal(pipeline.params[0]['value'], np.array([1, 2, 3]))
+    assert np.array_equal(pipeline.datablock["test_numpy_array"], np.array([1, 2, 3]))
+
+def test_read_yaml_numpy_array_kwargs():
+    script_dir = os.path.dirname(__file__)
+    config_path = os.path.join(script_dir, "data/test_config_numpy_array_kwargs.yaml")
+
+    pipeline = Pipeline.read(str(config_path))
+    pipeline.run()
+
+    assert np.array_equal(pipeline.params[0]['value'], np.array([1, 2, 3]))
+    assert np.array_equal(pipeline.datablock["test_numpy_array"], np.array([1, 2, 3]))
+
+def test_read_yaml_xarray_dataarray():
+    script_dir = os.path.dirname(__file__)
+    config_path = os.path.join(script_dir, "data/test_config_xarray_dataarray.yaml")
+
+    pipeline = Pipeline.read(str(config_path))
+    pipeline.run()
+
+    expected_array = xr.DataArray([1, 2, 3], coords={"Year": [2020, 2021, 2022]}, dims=["Year"])
+    xr.testing.assert_equal(pipeline.params[0]['value'], expected_array)
+    xr.testing.assert_equal(pipeline.datablock["test_value"], expected_array)
+
+def test_read_yaml_xarray_dataarray_kwargs():
+    script_dir = os.path.dirname(__file__)
+    config_path = os.path.join(script_dir, "data/test_config_xarray_dataarray_kwargs.yaml")
+
+    pipeline = Pipeline.read(str(config_path))
+    pipeline.run()
+
+    expected_array = xr.DataArray([1, 2, 3], coords={"Year": [2020, 2021, 2022]}, dims=["Year"])
+    xr.testing.assert_equal(pipeline.params[0]['value'], expected_array)
+    xr.testing.assert_equal(pipeline.datablock["test_value"], expected_array)
+
+def test_read_yaml_unsupported_function():
+    from yaml.constructor import ConstructorError
+    script_dir = os.path.dirname(__file__)
+    config_path = os.path.join(script_dir, "data/test_config_unsupported_function.yaml")
+
+    with pytest.raises(ConstructorError):
+        pipeline = Pipeline.read(str(config_path))

docs/config_file.rst

@@ -4,7 +4,7 @@ Command line tool
 =================
 
 The ``agrifoodpy`` command line tool allows you to run a pipeline of functions
-defined in a configuration file. This is useful for automating workflows and
+defined in a YAML configuration file. This is useful for automating workflows and
 reproducibility. You can specify the configuration file and an output file for
 the results.