refactor in gridding function

added a test implementation to retrieve names from the TOAR-DB for all contributors.

src/toargridding/contributors.py

@@ -3,6 +3,9 @@ from pandas import DataFrame
 from pandas.core.groupby import DataFrameGroupBy
 from typing import Iterable
 from pathlib import Path
+from time import sleep
+
+import requests
 
 #TODO: maybe create an abstract base class and separate the offline and service implementations
 class contributionsManager:
@@ -11,20 +14,30 @@ class contributionsManager:
         self.timeseriesIDs = set()
         self.endpoint = endpoint
         self.runsAsService = True
+        self.inline_mode = False
         if contributors_path is not None:
             self.runsAsService = False
             self.contributors_path = contributors_path
     def setup_contributors_endpoint_for_metadata(self):
+        """!create the contributors endpoint depending on the intended mode.
+
+        This either adds the timeseries IDs to the database and provides a get endpoint for the contributors or writes the timeseries IDs to a file and provides a curl command to upload the file to the database.
+        """
         if self.runsAsService:
             return self.setup_contributors_service()
         else:
-            return self.setup_contributors_id_file()
+            if self.inline_mode:
+                return self.setup_contributors_inline()
+            else:
+                return self.setup_contributors_id_file()
+    def setup_contributors_inline(self) -> str:
+        return f"{self.endpoint}" + "/?timeseriesids=" + ",".join([str(id) for id in self.timeseriesIDs])
     def setup_contributors_id_file(self) -> str:
         ext = "contributors"
         with open(self.contributors_path / f"{self.requestID}.{ext}", "w") as f:
             for id in self.timeseriesIDs:
                 f.write(f"{id}\n")
-        return f"curl {self.endpoint} -file {self.requestID}.{ext}"
+        return f"curl  -d @{self.requestID}.{ext} -X POST {self.endpoint}"
     def setup_contributors_service(self) -> str:
         # TODO: missing implementation
         raise NotImplementedError("This has not been implemented as this package is not yet operated as a service.")
@@ -39,5 +52,40 @@ class contributionsManager:
         for _, table in data_grouped_by_cell:
             self.add_timeseries_ids( table.index.to_list() )
 
+class contributions_manager_by_name(contributionsManager):
+    def add_timeseries_id(self, id : int) -> None:
+        names = self.id_to_names(id)
+        for name in names:
+            self.timeseriesIDs.add(name)
+    def add_timeseries_ids(self, ids : Iterable[int]) -> None:
+        for id in ids:
+            names = self.id_to_names(id)
+            for name in names:
+                self.timeseriesIDs.add(name)
+    def id_to_names(self, id : int) -> list[str]:
+        for _ in range(10):
+            req_res = requests.get(f"https://toar-data.fz-juelich.de/api/v2/timeseries/{id}")
+            try:
+                results = req_res.json()
+                break
+            except:
+                sleep(30)
+                pass
+        else:
+            raise RuntimeError(f"Could not get the response for the timeseries with id {id}.")
+
+
+        names = set()
+        for r in results["roles"]:
+            try:
+                #print("\t", r["contact"]["person"]["name"])
+                names.add( r["contact"]["person"]["name"] )
+            except:
+                try:
+                    #print("\t", r["contact"]["organisation"]["name"])
+                    names.add( r["contact"]["organisation"]["name"] )
+                except:
+                    raise RuntimeError("Could not find 'person' or 'organisation' in the response.\n" + str(r))
+        return names
 
 

src/toargridding/gridding.py

@@ -57,9 +57,9 @@ def get_gridded_toar_data(
         data = analysis_service.get_data(metadata)
         #TODO add processing of contributors
         # create contributors endpoint and write result to metadata
-        contrib = contributionsManager(metadata.get_id(), contributors_path)
-        contrib.extract_contributors_from_data_frame(data.stations_data)
-        metadata.contributors_metadata_field = contrib.setup_contributors_endpoint_for_metadata()
+        contributors_field = contributionsManager(metadata.get_id(), contributors_path)
+        contributors_field.extract_contributors_from_data_frame(data.stations_data)
+        metadata.contributors_metadata_field = contributors_field.setup_contributors_endpoint_for_metadata()
         ds = grid.as_xarray(data)
         datasets.append(ds)