diff --git a/tests/quality_controll.ipynb b/tests/quality_controll.ipynb
index dda989fe5c25049b57ca700dd70351bafc76d4f2..cc93285e02bf8e40d0fe2d8c8fb355774ed5ce0b 100644
--- a/tests/quality_controll.ipynb
+++ b/tests/quality_controll.ipynb
@@ -14,14 +14,14 @@
    "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",
-    "    AnalysisService,\n",
-    "    COORDS,\n",
+    "    AnalysisServiceDownload,\n",
     "    STATION_LAT,\n",
     "    STATION_LON,\n",
     ")\n",
@@ -30,10 +30,14 @@
     "\n",
     "\n",
     "endpoint = \"https://toar-data.fz-juelich.de/api/v2/analysis/statistics/\"\n",
-    "analysis_service = AnalysisService(endpoint)\n",
-    "my_grid = RegularGrid(10, 10)\n",
+    "toargridding_base_path = Path(\"/home/simon/Projects/toar/toargridding/\")\n",
+    "cache_dir = toargridding_base_path / \"tests\" / \"results\"\n",
+    "data_download_dir = toargridding_base_path / \"tests\" / \"data\"\n",
     "\n",
-    "time = TimeSample(dt(2009,12,31), dt(2011,1,1), \"daily\")\n",
+    "analysis_service = AnalysisServiceDownload(endpoint, cache_dir, data_download_dir)\n",
+    "my_grid = RegularGrid(1.9, 2.5)\n",
+    "\n",
+    "time = TimeSample(dt(2016,1,1), dt(2016,12,31), \"daily\")\n",
     "metadata = Metadata.construct(\"mole_fraction_of_ozone_in_air\", \"mean\", time)\n",
     "\n",
     "with open(\"data/daily_2010-01-01_2011-01-01.zip\", \"r+b\") as sample_file:\n",
@@ -46,11 +50,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "timeseries, timeseries_metadata = analysis_service.load_data(response_content, metadata)\n",
-    "coords = analysis_service.get_clean_coords(timeseries_metadata)\n",
-    "timeseries = analysis_service.get_clean_timeseries(timeseries, metadata)\n",
-    "data = AnalysisRequestResult(timeseries, coords, metadata)\n",
-    "\n",
+    "data = analysis_service.get_data(metadata)\n",
     "ds = my_grid.as_xarray(data)"
    ]
   },
@@ -157,7 +157,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "timestep = 50\n",
+    "timestep = 2\n",
     "time = ds.time[timestep]\n",
     "data = ds.sel(time=time)\n",
     "\n",
@@ -171,6 +171,13 @@
     "plt.plot(ds.time, n_observations)\n",
     "print(np.unique(ds[\"n\"]))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -189,7 +196,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,