Merge branch 'master' of github.com:ivargr/climate_health

README.md

@@ -24,7 +24,7 @@ The following shows basic usage of the platform. Follow the link to the document
 CHAP supports evaluating models that are defined using the MLflow specification for machine learning models (link coming). Such models can e.g. exist in Github repositories. CHAP also has some built-in example data that can be used to evaluate models. The following example shows how to evaluate an Ewars model located on Github ([https://github.com/sandvelab/chap_auto_ewars](https://github.com/sandvelab/chap_auto_ewars))  using the ISMIP dataset:
 
 ```bash
-chap-cli evaluate --model-name https://github.com/sandvelab/chap_auto_ewars --dataset-name ISIMIP_dengue_harmonized --dataset-country brazil
+chap evaluate --model-name https://github.com/sandvelab/chap_auto_ewars --dataset-name ISIMIP_dengue_harmonized --dataset-country brazil
 ```
 
 The above example requires that you have installed chap with pip and also that you have Docker available.

chap_core/__init__.py

@@ -2,7 +2,7 @@
 
 __author__ = """Sandvelab"""
 __email__ = "[email protected]"
-__version__ = "0.0.5"
+__version__ = "0.0.7"
 
 from . import fetch
 from . import data

chap_core/chap_cli.py

@@ -48,7 +48,7 @@ def harmonize(input_filename: Path, output_filename: Path):
     with open(input_filename, "r") as f:
         text = f.read()
     request_data = RequestV1.model_validate_json(text)
-    dataset = dataset_from_request_v1(request_data, target_name="disease")
+    dataset = dataset_from_request_v1(request_data, target_name="disease", usecwd_for_credentials=True)
     dataset.to_csv(output_filename)
 
 

chap_core/google_earth_engine/gee_era5.py

@@ -167,13 +167,15 @@ def gee_properties_to_fields(property_dicts: list[dict]) -> dict[str, DataSet]:
 
 
 class Era5LandGoogleEarthEngine:
-    def __init__(self):
+    def __init__(self, usecwd=False):
         self.gee_helper = Era5LandGoogleEarthEngineHelperFunctions()
         self.is_initialized = False
+        self._usecwd = usecwd
         self._initialize_client()
 
+
     def _initialize_client(self):
-        load_dotenv(find_dotenv())
+        load_dotenv(find_dotenv(usecwd=self._usecwd))
         # read environment variables
         account = os.environ.get("GOOGLE_SERVICE_ACCOUNT_EMAIL")
         private_key = os.environ.get("GOOGLE_SERVICE_ACCOUNT_PRIVATE_KEY")

chap_core/rest_api.py

@@ -99,7 +99,8 @@ async def favicon() -> FileResponse:
 @app.post('/predict')
 async def predict(data: PredictionRequest) -> dict:
     """
-    Start a prediction task using the given data as training data
+    Start a prediction task using the given data as training data.
+    Results can be retrieved using the get-results endpoint.
     """
     json_data = data.model_dump()
     str_data = json.dumps(json_data)
@@ -132,11 +133,9 @@ async def get_results() -> FullPredictionResponse:
     Retrieve results made by the model
     """
     cur_job = internal_state.current_job
-    print(cur_job)
     if not (cur_job and cur_job.is_finished):
         raise HTTPException(status_code=400, detail="No response available")
     result = cur_job.result
-    print(result)
     return result
 
 
@@ -167,5 +166,4 @@ async def get_status() -> State:
 
 def main_backend():
     import uvicorn
-
     uvicorn.run(app, host="0.0.0.0", port=8000)

chap_core/rest_api_src/worker_functions.py

@@ -23,8 +23,8 @@
 }
 
 
-def initialize_gee_client():
-    gee_client = Era5LandGoogleEarthEngine()
+def initialize_gee_client(usecwd=False):
+    gee_client = Era5LandGoogleEarthEngine(usecwd=usecwd)
     return gee_client
 
 
@@ -98,7 +98,7 @@ def get_target_id(json_data, target_names):
 
 
 def dataset_from_request_v1(
-        json_data: RequestV1, target_name="diseases"
+        json_data: RequestV1, target_name="diseases", usecwd_for_credentials=False
 ) -> DataSet[FullData]:
     translations = {target_name: "disease_cases"}
     data = {
@@ -107,7 +107,7 @@ def dataset_from_request_v1(
         )
         for feature in json_data.features
     }
-    gee_client = initialize_gee_client()
+    gee_client = initialize_gee_client(usecwd=usecwd_for_credentials)
     period_range = data["disease_cases"].period_range
     locations = list(data["disease_cases"].keys())
     climate_data = gee_client.get_historical_era5(

chap_core/spatio_temporal_data/temporal_dataclass.py

@@ -200,9 +200,6 @@ def locations(self) -> Iterable[Location]:
     def data(self) -> Iterable[FeaturesT]:
         return self._data_dict.values()
 
-    # def items(self) -> Iterable[Tuple[Location, FeaturesT]]:
-    #    return self._data_dict.items()
-
     def _add_location_to_dataframe(self, df, location):
         df["location"] = location
         return df

chap_core/worker/rq_worker.py

@@ -18,6 +18,8 @@
 
 
 class RedisJob(Generic[ReturnType]):
+    '''Wrapper for a Redis Job'''
+
     def __init__(self, job: Job):
         self._job = job
 
@@ -38,17 +40,16 @@ def cancel(self):
 
     @property
     def is_finished(self) -> bool:
-        print(self._job.is_finished)
         if self._job.get_status() == "queued":
             logger.warning(
                 "Job is queued, maybe no worker is set up? Run `$ rq worker`"
             )
-        print(self._job.get_status())
-
         return self._job.is_finished
 
 
 class RedisQueue:
+    '''Simple abstraction for a Redis Queue'''
+
     def __init__(self):
         host, port = self.read_environment_variables()
         self.q = Queue(connection=Redis(host=host, port=int(port)))

docs_source/tutorials/downloaded_json_data.rst

@@ -1,23 +1,41 @@
-Running on JSON data downloaded from the CHAP-app
+Running on JSON data downloaded from the CHAP-app (when CHAP Core is not installed on your DHIS2-instance)
 =================================================
-For this example you will need to have the CHAP-app installed on DHIS2 and Chap Core installed on your local computer, in addition to Docker installed and credentials for Google Earth Engine.
-Chap Core can be installed by running the following command:
+
+Requriments: 
+    - Docker is installed on your computer (Installation instructions can be found at https://docs.docker.com/get-started/get-docker/).
+    - CHAP-app is installed on your DHIS2-instance (Instruction for CHAP-app installation could be found at https://github.com/dhis2/chap-app)
+    - Access to credentials for Google Earth Engine. (Private Key and Service Account Email)
+
+Install CHAP Core
+-----------------
+We recommend you run CHAP Core with Conda. If you don't have Conda, you could install Miniconda, 
+(a minimal installer for Conda) from https://docs.anaconda.com/miniconda/#latest-miniconda-installer-links
+
+Windows: After installation open Anaconda Prompt, search for "Anaconda Prompt" in the Windows Start menu.
+Linux: Conda should work in your default terminal after installation.
+
+We recommend you to create a new conda environment by running the following commands:
+
+    $ conda create -n chap-core python=3.11
+    $ conda activate chap-core
+
+In the same shell, install CHAP Core, by runing the following commands (this take 5-20 minutes):
 
     $ pip install git+https://github.com/dhis2/chap-core.git
 
+After installation, chap command line interface (CLI) should be available in your terminal.
+
 Credentials for Google Earth Engine
 ------------------------------------------
-Credentials for Google Earth Engine needs to be put as environment variables on your local computer.
-The easiest way to do this is to create a file called ".env" in the root of the chap-core repository with the two environment variables: 
-"GOOGLE_SERVICE_ACCOUNT_EMAIL" and "GOOGLE_SERVICE_ACCOUNT_PRIVATE_KEY". The file should look similar to the following content:
+Credentials for Google Earth Engine need to be set as environment variables on your local computer. We recommend you create a new folder where you will later run CHAP Core. Inside 
+this folder, create a new file named ".env" with the two environment variables: "GOOGLE_SERVICE_ACCOUNT_EMAIL" and "GOOGLE_SERVICE_ACCOUNT_PRIVATE_KEY". 
+The file should look similar to the following content:
 
 .. code-block:: bash
-    
+
     GOOGLE_SERVICE_ACCOUNT_EMAIL="[email protected]"
     GOOGLE_SERVICE_ACCOUNT_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----  <your-private-key>  -----END PRIVATE KEY-----"
 
-
-
 Convert the JSON data into a CHAP-DataSet
 ------------------------------------------
 

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.0.5
+current_version = 0.0.7
 commit = True
 tag = True
 

setup.py

@@ -80,6 +80,6 @@
     test_suite="tests",
     tests_require=test_requirements,
     url="https://github.com/dhis2/chap-core",
-    version="0.0.5",
+    version="0.0.7",
     zip_safe=False,
 )