Merge remote-tracking branch 'origin/dev' into dev

README.md

@@ -1,4 +1,4 @@
-# Climate Health Anlysis Platform (CHAP)
+# Climate Health Analysis Platform (CHAP)
 CHAP offers a platform for analysing the relationship between climate and health. The platform is designed to be modular and flexible, allowing for easy integration of new models and data sources. The platform is designed to be used by researchers and public health professionals to forecast and assess the impact of climate on health outcomes.
 
 # Installation

setup.py

@@ -38,6 +38,7 @@
                 'earthengine-api',
                 'mlflow',
                 'gluonts',
+                'xarray'
                 ]
 
 test_requirements = ['pytest>=3', "hypothesis"]

tests/external/test_external_models.py

@@ -10,8 +10,7 @@
 from climate_health.testing.external_model import sanity_check_external_model
 
 logging.basicConfig(level=logging.INFO)
-from climate_health.external.external_model import (get_model_from_yaml_file, run_command,
-                                                    ExternalCommandLineModel,
+from climate_health.external.external_model import (run_command,
                                                     get_model_from_directory_or_github_url)
 from ..data_fixtures import train_data, train_data_pop, future_climate_data
 from climate_health.util import conda_available, docker_available, pyenv_available

tests/test_external_model_evaluation_acceptance.py

@@ -75,16 +75,6 @@ def get_predictions(self, train_data: IsSpatioTemporalDataSet[ClimateHealthTimeS
         return DataSet(new_dict)
 
 
-# @pytest.mark.xfail
-@pytest.mark.slow
-@pytest.mark.skip(reason="Outdated")
-def test_external_model_evaluation(dataset_name, output_filename, load_data_func, external_predictive_model):
-    external_model = external_predictive_model
-    data_set = load_data_func(dataset_name)
-    report = evaluate_model(data_set, external_model)
-    report.save(output_filename)
-
-
 @pytest.mark.skip
 @pytest.mark.parametrize('mode', ['forecast'])
 def test_summary_model_evaluation(dataset_name, output_filename, load_data_func, mode):