added notebook for example of different versions to include contributors

edited formatting for contributors endpoint with names

examples/contributor_metadata.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get Dataset from request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime as dt\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "from toargridding.grids import RegularGrid\n",
+    "from toargridding.toar_rest_client import (\n",
+    "    AnalysisServiceDownload,\n",
+    "    STATION_LAT,\n",
+    "    STATION_LON,\n",
+    ")\n",
+    "from toargridding.metadata import Metadata, TimeSample, AnalysisRequestResult, Coordinates\n",
+    "from toargridding.variables import Coordinate\n",
+    "\n",
+    "from toargridding.contributors import contributionsManager, contributions_manager_by_name\n",
+    "\n",
+    "import logging\n",
+    "from toargridding.defaultLogging import toargridding_defaultLogging\n",
+    "#setup of logging\n",
+    "logger = toargridding_defaultLogging()\n",
+    "logger.addShellLogger(logging.DEBUG)\n",
+    "logger.logExceptions()\n",
+    "\n",
+    "endpoint = \"https://toar-data.fz-juelich.de/api/v2/analysis/statistics/\"\n",
+    "#starts in directory [path/to/toargridding]/tests\n",
+    "#maybe adopt the toargridding_base_path for your machine.\n",
+    "toargridding_base_path = Path(\".\")\n",
+    "cache_dir = toargridding_base_path / \"cache\"\n",
+    "data_download_dir = toargridding_base_path / \"results\"\n",
+    "\n",
+    "analysis_service = AnalysisServiceDownload(endpoint, cache_dir, data_download_dir, use_downloaded=True)\n",
+    "my_grid = RegularGrid(1.9, 2.5)\n",
+    "\n",
+    "time = TimeSample(dt(2016,1,1), dt(2016,2,28), \"daily\")\n",
+    "metadata = Metadata.construct(\"mole_fraction_of_ozone_in_air\", time, \"mean\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contributors as dedicated file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this cell can runs longer than 30minutes\n",
+    "data = analysis_service.get_data(metadata)\n",
+    "\n",
+    "# create contributors endpoint and write result to metadata\n",
+    "contrib = contributionsManager(metadata.get_id(), data_download_dir)\n",
+    "contrib.extract_contributors_from_data_frame(data.stations_data)\n",
+    "metadata.contributors_metadata_field = contrib.setup_contributors_endpoint_for_metadata()\n",
+    "ds = my_grid.as_xarray(data)\n",
+    "#store dataset\n",
+    "ds.to_netcdf(data_download_dir / f\"curl+id-file_{metadata.get_id()}_by_names_inline_{my_grid.get_id()}.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contributors as single line request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this cell can runs longer than 30minutes\n",
+    "data = analysis_service.get_data(metadata)\n",
+    "\n",
+    "# create contributors endpoint and write result to metadata\n",
+    "contrib = contributionsManager(metadata.get_id(), data_download_dir)\n",
+    "contrib.inline_mode = True\n",
+    "contrib.extract_contributors_from_data_frame(data.stations_data)\n",
+    "metadata.contributors_metadata_field = contrib.setup_contributors_endpoint_for_metadata()\n",
+    "ds = my_grid.as_xarray(data)\n",
+    "#store dataset\n",
+    "ds.to_netcdf(data_download_dir / f\"request_in_field_{metadata.get_id()}_by_names_inline_{my_grid.get_id()}.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contributors by name within field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this cell can runs longer than 30minutes\n",
+    "data = analysis_service.get_data(metadata)\n",
+    "\n",
+    "# create contributors endpoint and write result to metadata\n",
+    "contrib = contributions_manager_by_name(metadata.get_id(), data_download_dir)\n",
+    "contrib.inline_mode = True\n",
+    "contrib.extract_contributors_from_data_frame(data.stations_data)\n",
+    "metadata.contributors_metadata_field = contrib.setup_contributors_endpoint_for_metadata()\n",
+    "ds = my_grid.as_xarray(data)\n",
+    "#store dataset\n",
+    "ds.to_netcdf(data_download_dir / f\"by_name_{metadata.get_id()}_by_names_inline_{my_grid.get_id()}.nc\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "toargridding-g-KQ1Hyq-py3.10",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

src/toargridding/contributors.py

@@ -64,7 +64,7 @@ class contributions_manager_by_name(contributionsManager):
                 self.timeseriesIDs.add(name)
     def id_to_names(self, id : int) -> list[str]:
         for pos in range(10):
-            req_res = requests.get(f"https://toar-data.fz-juelich.de/api/v2/timeseries/{id}")
+            req_res = requests.get(f"https://toar-data.fz-juelich.de/api/v2/timeseries/{id}", timeout=40)
             try:
                 results = req_res.json()
                 break
@@ -86,5 +86,12 @@ class contributions_manager_by_name(contributionsManager):
                 except:
                     raise RuntimeError("Could not find 'person' or 'organisation' in the response.\n" + str(r))
         return names
-
+    def setup_contributors_inline(self) -> str:
+        return ", ".join([str(id) for id in sorted(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'see file {self.requestID}.{ext}"'