worked on documentation.

removed manual creation of request from get_sample_data.ipynb
this is handled in a different example (get_sample_data_manual.ipynb)

README.md

@@ -22,6 +22,7 @@ TBD, see pyproject.toml
 Move to the folder you want to create download this project to.
 We now need to download the source code (https://gitlab.jsc.fz-juelich.de/esde/toar-public/toargridding/-/tree/dev?ref_type=heads). Either as ZIP folder or via git:
 
+## Download with GIT
 Clone the project from its git repository:
 ```
 git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/toargridding.git 
@@ -32,6 +33,7 @@ cd toargridding
 git checkout dev
 ```
 
+## Installing Dependencies
 
 The handling of required packages is done with poetry. So run poetry in the project directory:
 ```
@@ -46,17 +48,21 @@ There are at the moment three example provided as jupyter notebooks (https://jup
 tests/produce_data.ipynb 
 ```
 Provides an example on how to download data, apply gridding and save the results as netCDF files.
+A possible improvement for is the exchange of the AnalysisService with AnalysisServiceDownload, which caches requests from the TOARDB. 
+This allows different griddings without the necessity to repeat the request to the TOARDB and subsequent download.
+
 ## Retrieving data
 ```
 get_sample_data.ipynb 
 ```
-Downloads data from the TOAR database. 
+Downloads data from the TOAR database. The extracted data are written to disc. No further processing or gridding is done. 
 
 ## Retrieving data
 ```
 get_sample_data_manual.ipynb 
 ```
 Downloads data from the TOAR database with a manual creation of the request to the TOAR database.
+This example does not perform any gridding.
 
 ## Retriving data and visualization
 ```

tests/get_sample_data.ipynb

@@ -43,70 +43,12 @@
     "\n",
     "analysis_service = AnalysisServiceDownload(statistics_endpoint, cache_dir, download_dir)\n",
     "\n",
-    "analysis_service.get_data(metadata)\n",
+    "results = analysis_service.get_data(metadata)\n",
     "\n",
     "end_time = datetime.now()\n",
     "print(end_time-start_time)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %%script false --no-error\n",
-    "\n",
-    "# manual access to data\n",
-    "\n",
-    "import requests\n",
-    "\n",
-    "end_with_padding = end + timedelta(1)\n",
-    "\n",
-    "response = requests.get(\n",
-    "    statistics_endpoint,\n",
-    "    params={\n",
-    "        \"daterange\": f\"{start.isoformat()},{end_with_padding.isoformat()}\",  # 1-year\n",
-    "        \"variable_id\": 5,\n",
-    "        \"statistics\": statistic,\n",
-    "        \"sampling\": sampling,\n",
-    "        \"min_data_capture\": 0,\n",
-    "        \"limit\": \"None\",  # get all timeseries\n",
-    "        \"format\": \"by_statistic\",\n",
-    "        \"metadata_scheme\": \"basic\",\n",
-    "    },\n",
-    ")\n",
-    "\n",
-    "print(response)\n",
-    "print(response.headers)\n",
-    "print(response.json())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(response.headers[\"Content-Type\"] == \"application/json\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "#this hard coded link does not work.\n",
-    "#status_endpoint = \"https://toar-data.fz-juelich.de/api/v2/analysis/status/5ec3a54c-322c-4bce-a3f5-fdf485a56514\"\n",
-    "\n",
-    "#response = requests.get(status_endpoint)\n",
-    "#print(response.headers)\n",
-    "#print(response.json())"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

toargridding/toar_rest_client.py

@@ -267,6 +267,8 @@ class AnalysisService:
         ----------
         metadata:
             meta data for the request.
+        return:
+            Requested data and statistics, station coordinates and metadata of the request
         """
         
         timeseries, timeseries_metadata = self.get_timeseries_and_metadata(metadata)