diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 0b8b031..d9a3ae6 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -1,4 +1,3 @@
-
 name: build ⚙️
 
 on: [ push, pull_request ]
@@ -6,22 +5,32 @@ on: [ push, pull_request ]
 jobs:
   build:
 
+    name: Conda Build with Python${{ matrix.python-version }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11"]
+    defaults:
+      run:
+        shell: bash -l {0}
     steps:
-    - name: Checkout repository and submodules
-      uses: actions/checkout@v2
-      with:
-        submodules: recursive
+    - uses: actions/checkout@v3
     - name: Install packages
       run: |
-        sudo apt-get -y install pandoc
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+        sudo apt-get -y install pandoc graphviz
+    - name: Setup Conda (Micromamba) with Python${{ matrix.python-version }}
+      uses: mamba-org/setup-micromamba@v1
       with:
-        python-version: ${{ matrix.python-version }}
+        cache-downloads: true
+        cache-environment: true
+        environment-file: environment.yml
+        create-args: >-
+          conda
+          python=${{ matrix.python-version }}
+    - name: Conda and Mamba versions
+      run: |
+        conda --version
+        echo "micromamba: $(micromamba --version)"
     - name: Install requirements 📦
       run: |
         python -m pip install --upgrade pip
@@ -33,6 +42,9 @@ jobs:
     - name: Lint with flake8 ⚙️
       run: make lint
       if: matrix.python-version == 3.9
+    # - name: Check formatting with black ⚙️
+    #   run: black --check --target-version py39 rook tests
+    #   if: matrix.python-version == 3.9
     - name: Build docs 🏗️
       run: make docs
       if: matrix.python-version == 3.9
diff --git a/environment.yml b/environment.yml
index d236cd8..0d86d9a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -19,6 +19,8 @@ dependencies:
 - birdy
 - tensorflow=2.15.0
 - numpy=1.26.0
+- metview
+- eccodes
 # tests
 - pytest
 - pandoc
diff --git a/requirements.txt b/requirements.txt
index 71da141..ac0d939 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,4 +11,6 @@ birdy
 tensorflow
 scipy
 ipykernel
-numpy
\ No newline at end of file
+numpy
+metview
+eccodes
\ No newline at end of file
diff --git a/requirements_dev.txt b/requirements_dev.txt
index 765d88b..ac37539 100644
--- a/requirements_dev.txt
+++ b/requirements_dev.txt
@@ -14,8 +14,4 @@ bump2version
 Click
 cruft
 # Changing dependencies above this comment will create merge conflicts when updating the cookiecutter template with cruft. Add extra requirements below this line.
-birdy
-tensorflow
-scipy
-ipykernel
-numpy
\ No newline at end of file
+birdy
\ No newline at end of file
diff --git a/shearwater/processes/__init__.py b/shearwater/processes/__init__.py
index d880e28..c5aa82c 100644
--- a/shearwater/processes/__init__.py
+++ b/shearwater/processes/__init__.py
@@ -1,7 +1,5 @@
-# from .wps_say_hello import SayHello
 from .wps_cyclone import Cyclone
 
 processes = [
-#    SayHello(),
     Cyclone(),
 ]
diff --git a/shearwater/processes/wps_cyclone.py b/shearwater/processes/wps_cyclone.py
index 808c114..7f8e25f 100644
--- a/shearwater/processes/wps_cyclone.py
+++ b/shearwater/processes/wps_cyclone.py
@@ -1,154 +1,199 @@
-from pywps import Process, LiteralInput, ComplexOutput
+from pywps import Process, LiteralInput, LiteralOutput
 from pywps.app.Common import Metadata
+
 # from tensorflow.keras import models
 # import pickle
 import numpy as np
 import numpy
 import pandas as pd
+
+# import metview as mv
+
 # from datetime import datetime
 import os
-from pywps import FORMATS
+
+# from pywps import FORMATS
 from pathlib import Path
 import urllib.request
 
 # import intake
 
 import logging
+
 LOGGER = logging.getLogger("PYWPS")
 
 
 class Cyclone(Process):
-    """A process to forecast tropical cyclone activities."""
+    """A process to forecast tropical cyclone activity."""
+
     def __init__(self):
         inputs = [
             LiteralInput(
                 "start_day",
                 "Start Day",
                 data_type="string",
-                abstract="Enter the start date, like 2021-01-01",
-                default="2022-01-01"
+                abstract="Enter the start date, like 2024-05-01",
+                default="2024-05-01",
             ),
             LiteralInput(
                 "end_day",
                 "End Day",
                 data_type="string",
-                abstract="Enter the end date, like 2023-10-12",
-                default="2022-01-31"
+                abstract="Enter the end date, like 2024-05-28",
+                default="2024-05-28",
             ),
             LiteralInput(
                 "area",
                 "Area",
                 data_type="string",
                 abstract="Choose the region of your interest",
-                allowed_values=[
-                    "Sindian",
-                    "TBD"
-                ],
+                allowed_values=["Sindian", "TBD"],
                 default="Sindian",
-            )
+            ),
         ]
         outputs = [
             # LiteralOutput('output', 'Cyclone activity forecast',
             #               abstract='netcdf',
             #               # keywords=['output', 'result', 'response'],
             #               data_type='string'),
-            ComplexOutput('output_csv', 'Cyclone activity forecast',
-                          abstract='csv file',
-                          as_reference=True,
-                          # keywords=['output', 'result', 'response'],
-                          supported_formats=[FORMATS.CSV],)
+            LiteralOutput(
+                "output_png",
+                "Cyclone activity forecast",
+                abstract="png file",
+                data_type="string",
+            ),
+            # ComplexOutput('output_csv', 'Cyclone activity forecast',
+            #              abstract='csv file',
+            #              as_reference=True,
+            #              # keywords=['output', 'result', 'response'],
+            #              supported_formats=[FORMATS.CSV],)
         ]
 
         super(Cyclone, self).__init__(
             self._handler,
-            identifier='cyclone',
-            title='Cyclone',
-            abstract='A process to forecast tropical cyclone activities.',
+            identifier="cyclone",
+            title="Cyclone",
+            abstract="A process to forecast tropical cyclone activity.",
             # keywords=['hello', 'demo'],
             metadata=[
-                Metadata('PyWPS', 'https://pywps.org/'),
+                Metadata("PyWPS", "https://pywps.org/"),
             ],
-            version='0.1',
+            version="0.1",
             inputs=inputs,
             outputs=outputs,
             store_supported=True,
-            status_supported=True
+            status_supported=True,
         )
 
     def _handler(self, request, response):
         LOGGER.info("running cyclone ...")
-        # TODO: lazy load tensorflow ... issues with sphinx doc build
+        # TODO: lazy load for tensorflow and metview ... issues with sphinx doc build
         from tensorflow.keras import models
+        import metview as mv
 
-#         master_catalog = intake.open_catalog(["https://gitlab.dkrz.de/data-infrastructure-services/intake-esm/-/raw/master/esm-collections/cloud-access/dkrz_catalog.yaml"])  # noqa
-#         # master_catalog = intake.open_catalog('/pool/data/Catalogs/dkrz_catalog.yaml')
-#         era5_catalog = master_catalog['dkrz_era5_disk']
-
-#         query = {
-#             'era_id': 'ET',
-#             'level_type': 'surface',
-#             'table_id': 128,
-#             # 'frequency':'hourly',
-#             'code': 34,
-#             'dataType': 'an',
-#             'validation_date': '2023-06-27',
-#         }
-
-#         my_catalog = era5_catalog.search(**query)
-#         # my_catalog.df
-
-#         era_path = my_catalog.df['uri'].iloc[0]
-#         response.outputs['output'].data = f'netcdf {era_path}'
-#         response.outputs['output_csv'].data = 'csv ' + request.inputs['model'][0].data
-#         return response
-
-        start_date = request.inputs['start_day'][0].data
-        end_date = request.inputs['end_day'][0].data
+        start_date = request.inputs["start_day"][0].data
+        # end_date = request.inputs["end_day"][0].data
         # area = request.inputs['area'][0].data
 
-        # to be updated with data repository
-        data1 = pd.read_csv(
-            "https://github.com/climateintelligence/shearwater/raw/main/data/test_dailymeans_Sindian_1.zip")
-        # ("../shearwater/data/test_dailymeans_Sindian_1.zip")
-        data2 = pd.read_csv(
-            "https://github.com/climateintelligence/shearwater/raw/main/data/test_dailymeans_Sindian_2.zip")
-        # ("../shearwater/data/test_dailymeans_Sindian_2.zip")
-        data = pd.concat((data1, data2), ignore_index=True)
-        data = data.loc[(data.time >= start_date) & (data.time <= end_date)]
-
-        variables = ['vo', 'r', 'u_200', 'u_850', 'v_200', 'v_850', 'tcwv', 'sst', 'shear']
-        # with open("https://github.com/climateintelligence/shearwater/raw/main/data/full_statistics.pkl", 'rb') as f:
-        #     means, stds = pickle.load(f)
+        parameters = [
+            [138, "vo", [850]],
+            [157, "r", [700]],
+            [131, "u", [200, 850]],
+            [132, "v", [200, 850]],
+            [34, "sst", [0]],
+            [137, "tcwv", [0]],
+        ]
+        reso = 2.5
+        area_bbox = [0, 20, -30, 90]
+
+        data = pd.DataFrame()
+        for param1 in parameters:
+            path = f'/pool/data/ERA5/ET/{"sf" if param1[2]==[0] else "pl"}/an/1D/{str(param1[0]).zfill(3)}'
+            fs1_param = mv.read(
+                f"{path}/ET{'sf' if param1[2]==[0] else 'pl'}00_1D_{start_date[:7]}_{str(param1[0]).zfill(3)}.grb"
+            )
+            fs1_param = fs1_param.select(
+                date=start_date.replace("-", ""), level=param1[2]
+            )
+            fs1_param_interp = mv.read(
+                data=fs1_param,
+                grid=[reso, reso],
+                area=area_bbox,
+                interpolation='"--interpolation=grid-box-average"',
+            )
+            for level in param1[2]:
+                data.loc[
+                    :,
+                    f"{param1[1]}{'_'+str(level) if (param1[1]=='u' or param1[1]=='v') else ''}",
+                ] = (
+                    fs1_param_interp.select(level=level)
+                    .to_dataset()
+                    .to_dataframe()
+                    .reset_index(drop=True)[param1[1]]
+                )
+
+        data.loc[:, ["latitude", "longitude"]] = (
+            fs1_param_interp.select(level=level)
+            .to_dataset()
+            .to_dataframe()
+            .reset_index()[["latitude", "longitude"]]
+        )
+        data.loc[:, "time"] = start_date
+        data.loc[:, "shear"] = (
+            (data.u_200 - data.u_850) ** 2 + (data.v_200 - data.v_850) ** 2
+        ) ** 0.5
+        data.loc[:, "sst"] = data.sst.fillna(0)
+
+        variables = [
+            "vo",
+            "r",
+            "u_200",
+            "u_850",
+            "v_200",
+            "v_850",
+            "tcwv",
+            "sst",
+            "shear",
+        ]
         means, stds = pd.read_pickle(
-            "https://github.com/climateintelligence/shearwater/raw/main/data/full_statistics.zip")
+            "https://github.com/climateintelligence/shearwater/raw/main/data/full_statistics.zip"
+        )
 
-        data[variables] = (data[variables]-means[variables])/stds[variables]
+        data[variables] = (data[variables] - means[variables]) / stds[variables]
 
-        number_of_img, rows, cols = len(data.time.unique()), len(data.latitude.unique()), len(data.longitude.unique())
+        number_of_img, rows, cols = (
+            len(data.time.unique()),
+            len(data.latitude.unique()),
+            len(data.longitude.unique()),
+        )
         images = np.zeros((number_of_img, rows, cols, len(variables)))
-        df = data.sort_values(by=['time', 'latitude', 'longitude'])
+        df = data.sort_values(by=["time", "latitude", "longitude"])
         verbose = False
         k = 0
         for day in range(0, number_of_img):
 
-            a = df.iloc[377*day:377*(day+1)]
+            a = df.iloc[377 * day: 377 * (day + 1)]
             i = 0
             for var in variables:
-                images[day, :, :, i] = a.pivot(index='latitude', columns='longitude').sort_index(ascending=False)[var]
+                images[day, :, :, i] = a.pivot(
+                    index="latitude", columns="longitude"
+                ).sort_index(ascending=False)[var]
                 i += 1
             k += 1
-            if ((k % 100 == 0) & (verbose is True)):
+            if (k % 100 == 0) & (verbose is True):
                 print(k)
 
         test_img_std = images
 
-        test_img_std = np.pad(test_img_std, ((0, 0), (1, 2), (1, 2), (0, 0)), 'constant')
+        test_img_std = np.pad(
+            test_img_std, ((0, 0), (1, 2), (1, 2), (0, 0)), "constant"
+        )
 
         workdir = Path(self.workdir)
         model_path = os.path.join(workdir, "Unet_sevenAreas_fullStd_0lag_model.keras")
         urllib.request.urlretrieve(
             "https://github.com/climateintelligence/shearwater/raw/main/data/Unet_sevenAreas_fullStd_0lag_model.keras",
-            model_path  # "Unet_sevenAreas_fullStd_0lag_model.keras"
+            model_path,  # "Unet_sevenAreas_fullStd_0lag_model.keras"
         )
 
         # model_trained = models.load_model(
@@ -160,10 +205,65 @@ def _handler(self, request, response):
         prediction = model_trained.predict(test_img_std)
 
         data = data[["latitude", "longitude", "time"]]
-        data['predictions_lag0'] = prediction.reshape(-1, 1)
+        data["predictions_lag0"] = prediction.reshape(-1, 1)
+
+        lag = 0
+        workdir = Path(self.workdir)
+        outfilename = os.path.join(
+            workdir, f'tcactivity_48_17_{start_date.replace("-","")}_lag{lag}_Sindian'
+        )
+
+        if True:
+            predscol = f"predictions_lag{lag}"
+            gpt = mv.create_geo(
+                latitudes=data["latitude"].values,
+                longitudes=data["longitude"].values,
+                values=data[predscol].values,
+            ).set_dates([pd.Timestamp(start_date)] * data.shape[0])
+            fs = mv.geo_to_grib(geopoints=gpt, grid=[2.5, 2.5], tolerance=1.5) * 1e2
+
+            # cont_gen = mv.mcont(
+            #     legend="on",
+            #     contour_line_colour="avocado",
+            #     contour_shade="on",
+            #     contour_shade_technique="grid_shading",
+            #     contour_shade_max_level_colour="red",
+            #     contour_shade_min_level_colour="blue",
+            #     contour_shade_colour_direction="clockwise",
+            # )
+            # cont_tc = mv.mcont(
+            #     legend="on",
+            #     contour_line_colour="avocado",
+            #     contour_shade="on",
+            #     contour_max_level=105,
+            #     contour_min_level=0,
+            #     contour_shade_technique="grid_shading",
+            #     contour_shade_max_level_colour="red",
+            #     contour_shade_min_level_colour="blue",
+            #     contour_shade_colour_direction="clockwise",
+            # )
+
+            cont_oper = mv.mcont(
+                contour_automatic_setting="style_name",
+                contour_style_name="prob_green2yellow",
+                legend="on",
+            )
+            coastlines = mv.mcoast(
+                map_coastline_land_shade="on", map_coastline_land_shade_colour="grey"
+            )
+
+            gview = mv.geoview(
+                map_area_definition="corners", area=area_bbox, coastlines=coastlines
+            )
+            legend = mv.mlegend(
+                legend_text_font_size=0.5,
+            )
 
-        prediction_path = os.path.join(workdir, "prediction_Sindian.csv")
-        data.to_csv(prediction_path)
+            mv.setoutput(mv.png_output(output_name=outfilename + ".png"))
+            mv.plot(gview, fs, cont_oper, legend)
+            response.outputs["output_png"].data = outfilename + ".png"
+        else:
+            data.to_csv(outfilename + ".csv")
+            response.outputs["output_csv"].data = outfilename + ".csv"
 
-        response.outputs['output_csv'].file = prediction_path
         return response
diff --git a/spec-list.txt b/spec-list.txt
index 88e3fc4..483d9d2 100644
--- a/spec-list.txt
+++ b/spec-list.txt
@@ -9,56 +9,67 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_3.conda
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_6.conda
 https://conda.anaconda.org/conda-forge/linux-64/libboost-headers-1.85.0-ha770c72_1.conda
 https://conda.anaconda.org/conda-forge/noarch/poppler-data-0.4.12-hd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_8.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_10.conda
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_8.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_10.conda
+https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.13-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda
 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.28.1-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.10-h36c2ea0_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda
 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/json-c-0.17-h7ab15ed_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda
 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.19-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda
 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-h3d2ce59_8.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-h3d2ce59_10.conda
 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda
 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.4-h7f98852_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.18-h36c2ea0_1.tar.bz2
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_8.conda
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_10.conda
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-hd590300_1001.conda
+https://conda.anaconda.org/conda-forge/linux-64/m4-1.4.18-h516909a_1001.tar.bz2
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+https://conda.anaconda.org/conda-forge/linux-64/freeglut-3.2.2-hac7e632_2.conda
 https://conda.anaconda.org/conda-forge/linux-64/geotiff-1.7.1-h6b2125f_15.conda
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+https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.4-haf2f30d_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda
 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2023.12.1-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/kealib-1.5.3-hee9dde6_1.conda
+https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.3-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.17.0-h884d01b_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/libnetcdf-4.9.2-nompi_h135f659_114.conda
+https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda
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 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.0-py311h64a7726_0.conda
@@ -287,27 +341,33 @@ https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.10.0-
 https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/cftime-1.6.4-py311h18e1886_0.conda
 https://conda.anaconda.org/conda-forge/noarch/distributed-2023.3.0-pyhd8ed1ab_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/h5py-3.11.0-nompi_py311h439e445_102.conda
 https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.1.1-py311hd0e15ba_2.conda
 https://conda.anaconda.org/conda-forge/noarch/ipython-8.25.0-pyh707e725_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/jasper-4.2.4-h536e39c_0.conda
 https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.22.0-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.2-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.17.0-hc7a4891_1.conda
+https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda
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 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py311h14de704_1.conda
+https://conda.anaconda.org/conda-forge/linux-64/pango-1.54.0-h84a9a3c_0.conda
 https://conda.anaconda.org/conda-forge/noarch/pyopenssl-24.0.0-pyhd8ed1ab_0.conda
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 https://conda.anaconda.org/conda-forge/linux-64/shapely-2.0.4-py311h0bed3d6_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.267-h5606698_1.conda
 https://conda.anaconda.org/conda-forge/noarch/birdy-0.8.7-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/dask-2023.3.0-pyhd8ed1ab_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/eccodes-2.35.0-he84ddb8_0.conda
 https://conda.anaconda.org/conda-forge/noarch/google-auth-2.30.0-pyhff2d567_0.conda
 https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.29.4-pyh3099207_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.6.5-nompi_py311h74118c1_102.conda
+https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda
 https://conda.anaconda.org/conda-forge/noarch/requests-oauthlib-2.0.0-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.5.22-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/xarray-2024.3.0-pyhd8ed1ab_0.conda
@@ -315,10 +375,13 @@ https://conda.anaconda.org/conda-forge/noarch/zarr-2.12.0-pyhd8ed1ab_0.tar.bz2
 https://conda.anaconda.org/conda-forge/noarch/google-auth-oauthlib-1.2.0-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/intake-esm-2023.6.14-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/noarch/python-geotiff-0.2.10-pyh6eeeb26_0.conda
+https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-hc9dc06e_21.conda
 https://conda.anaconda.org/conda-forge/linux-64/tiledb-2.20.1-hd75ad12_1.conda
 https://conda.anaconda.org/conda-forge/linux-64/libgdal-3.8.4-hab4ef92_3.conda
+https://conda.anaconda.org/conda-forge/linux-64/magics-metview-4.15.3-he53f960_0.conda
 https://conda.anaconda.org/conda-forge/noarch/tensorboard-2.15.2-pyhd8ed1ab_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/gdal-3.8.4-py311h8be719e_3.conda
+https://conda.anaconda.org/conda-forge/linux-64/metview-5.22.0-h3a65212_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/tensorflow-base-2.15.0-cpu_py311h6aa969b_2.conda
 https://conda.anaconda.org/conda-forge/linux-64/fiona-1.9.6-py311hf8e0aa6_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/tensorflow-estimator-2.15.0-cpu_py311ha26c8b9_2.conda
diff --git a/tests/test_wps_caps.py b/tests/test_wps_caps.py
index e0ce636..8ac5612 100644
--- a/tests/test_wps_caps.py
+++ b/tests/test_wps_caps.py
@@ -6,12 +6,10 @@
 
 def test_wps_caps():
     client = client_for(Service(processes=processes))
-    resp = client.get(service='wps', request='getcapabilities', version='1.0.0')
-    names = resp.xpath_text('/wps:Capabilities'
-                            '/wps:ProcessOfferings'
-                            '/wps:Process'
-                            '/ows:Identifier')
+    resp = client.get(service="wps", request="getcapabilities", version="1.0.0")
+    names = resp.xpath_text(
+        "/wps:Capabilities" "/wps:ProcessOfferings" "/wps:Process" "/ows:Identifier"
+    )
     assert sorted(names.split()) == [
-        'cyclone',
-#        'hello',
+        "cyclone",
     ]