Added trophic flow analytical tool and test

tools/python/animal/test_trophic_mass_flow.py

@@ -0,0 +1,97 @@
+"""Test file for TrophicFlowAnalysis class in trophic_mass_flow.py."""
+
+import pandas as pd
+import pytest
+
+# Import class (adjust path when needed)
+from trophic_mass_flow import TrophicFlowAnalysis  # Change filename when different
+
+
+@pytest.fixture
+def analysis():
+    """Fixture for flow analysis used in tests.
+
+    Create a clean object with these parameters for every test we run for every method.
+    """
+    # which data file to use
+    test_file = "animal_trophic_interactions.csv"
+    # create new instance of the class
+    return TrophicFlowAnalysis(
+        file_path=test_file,
+        config={"convert_to_grams": False, "value_to_sum": "C", "n_cols": 2},
+    )
+
+
+def test_class_initialisation(analysis):
+    """Test that the class can be initialized correctly."""
+    # check if object is an instance of our class.
+    # Assert means "if this is false, fail the test."
+    assert isinstance(analysis, TrophicFlowAnalysis)
+    assert analysis.file_path == "animal_trophic_interactions.csv"
+    assert isinstance(analysis.config, dict)
+    # is None make sure the dataframe start as none
+    assert analysis.df is None
+    assert analysis.pivoted_df is None
+
+
+def test_load_data(analysis):
+    """Test load_data method."""
+    # call the method and store what it returns.
+    result = analysis.load_data()
+    # check method returns self (For method chaining)
+    assert result is analysis
+    # check whether data below was loaded
+    assert isinstance(analysis.df, pd.DataFrame)
+    assert len(analysis.df) > 0
+    assert "time" in analysis.df.columns
+    assert "resource_kind" in analysis.df.columns
+
+
+def test_process_data(analysis):
+    """Test process_data pipeline."""
+    # call load_data() method
+    analysis.load_data()
+    # calls process_data() running convert_units, group_and_aggregate(),pivot_data()
+    # store what method returns in "result" variable
+    result = analysis.process_data()
+    # checks if "result" is the same object as what we started
+    assert result is analysis
+    # checks again if its returns a dataframe that is populated
+    assert analysis.group_df is not None
+    assert analysis.pivoted_df is not None
+    assert len(analysis.pivoted_df) > 0
+
+
+def test_plot_faceted_saves_file(analysis, tmp_path):
+    """Test plot_faceted saves a file without showing a plot.
+
+    Args:
+        analysis: class method
+        tmp_path: a temporary folder to save file during testing.
+
+    """
+    # call load_data() and process_data method
+    analysis.load_data().process_data()
+    # create file path with pathlib to join files using "/"
+    output_path = tmp_path / "test_plot.png"
+    # calls plot_faceted() method, save plot into path, dont show graph
+    analysis.plot_faceted(
+        save_path=str(output_path), show=False, title="Test Plot Title"
+    )
+    # assert whether a PNG file is created.
+    # if fails, shows the message "....was not saved"
+    assert output_path.is_file(), "Plot file was not saved"
+    # check if file is bigger than 10KB, smaller than that would indicate failure
+    assert output_path.stat().st_size > 10000, "Plot file might be too small"
+
+
+# ================================================================
+# running the test
+# ================================================================
+
+if __name__ == "__main__":
+    print("Running tests")
+    test_class_initialisation(analysis())
+    test_load_data(analysis())
+    test_process_data(analysis())
+    print("All tests passed")