Channels:
 - conda-forge
Platform: linux-64
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done


==> WARNING: A newer version of conda exists. <==
    current version: 24.3.0
    latest version: 24.7.1

Please update conda by running

    $ conda update -n base -c conda-forge conda


#
# To activate this environment, use
#
#     $ conda activate petals_env
#
# To deactivate an active environment, use
#
#     $ conda deactivate

pytest --junitxml=tests_xml/tests.xml --cov --cov-config=.coveragerc --cov-report html:coverage --cov-report xml:coverage.xml --cov-report term:skip-covered --ignore=climada_petals/test climada_petals/
============================= test session starts ==============================
platform linux -- Python 3.9.18, pytest-8.2.2, pluggy-1.5.0
rootdir: /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2
plugins: subtests-0.13.0, cov-5.0.0, Faker-26.0.0
collected 203 items

climada_petals/engine/test/test_supplychain.py ...............           [  7%]
climada_petals/engine/test/test_warn.py .......................          [ 18%]
climada_petals/entity/exposures/test/test_black_marble.py .............. [ 25%]
.                                                                        [ 26%]
climada_petals/entity/exposures/test/test_crop_production.py .......     [ 29%]
climada_petals/entity/exposures/test/test_gdp_asset.py ....              [ 31%]
climada_petals/entity/exposures/test/test_osm_dataloader.py ...........  [ 36%]
climada_petals/entity/exposures/test/test_spamagrar_unit.py .....        [ 39%]
climada_petals/entity/impact_funcs/test/test_fl.py .......               [ 42%]
climada_petals/entity/impact_funcs/test/test_wf.py ..                    [ 43%]
climada_petals/hazard/emulator/test/test_emulator.py ..                  [ 44%]
climada_petals/hazard/emulator/test/test_geo.py ....                     [ 46%]
climada_petals/hazard/emulator/test/test_random.py ..                    [ 47%]
climada_petals/hazard/emulator/test/test_stats.py .....                  [ 50%]
climada_petals/hazard/rf_glofas/test/test_cds_glofas_downloader.py ..... [ 52%]
.....                                                                    [ 55%]
climada_petals/hazard/rf_glofas/test/test_rf_glofas.py ....              [ 57%]
climada_petals/hazard/rf_glofas/test/test_river_flood_computation.py ... [ 58%]
...........                                                              [ 64%]
climada_petals/hazard/rf_glofas/test/test_transform_ops.py ............. [ 70%]
                                                                         [ 70%]
climada_petals/hazard/test/test_drought.py .                             [ 70%]
climada_petals/hazard/test/test_flood.py ........                        [ 74%]
climada_petals/hazard/test/test_landslide.py ......                      [ 77%]
climada_petals/hazard/test/test_low_flow.py ..........                   [ 82%]
climada_petals/hazard/test/test_relative_cropyield.py ...                [ 84%]
climada_petals/hazard/test/test_tc_rainfield.py ..........               [ 89%]
climada_petals/hazard/test/test_tc_surge_bathtub.py ...                  [ 90%]
climada_petals/hazard/test/test_tc_tracks_forecast.py FFFFF..            [ 94%]
climada_petals/hazard/test/test_wildfire.py ...........                  [ 99%]
climada_petals/util/test/test_config.py .                                [100%]

=================================== FAILURES ===================================
______________________ TestECMWF.test_ecmwf_multimessage _______________________

self = <climada_petals.hazard.test.test_tc_tracks_forecast.TestECMWF testMethod=test_ecmwf_multimessage>

    def test_ecmwf_multimessage(self):
        """Test ECMWF reader in multimessage format"""
        forecast = TCForecast()
>       forecast.fetch_ecmwf(files=TEST_BUFR_FILE_MULTIMESSAGE)

climada_petals/hazard/test/test_tc_tracks_forecast.py:91: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
climada_petals/hazard/tc_tracks_forecast.py:165: in fetch_ecmwf
    self.read_one_bufr_tc(file, id_no=i)
climada_petals/hazard/tc_tracks_forecast.py:415: in read_one_bufr_tc
    track = self._subset_to_track(
climada_petals/hazard/tc_tracks_forecast.py:517: in _subset_to_track
    track = xr.Dataset(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:713: in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:423: in merge_data_and_coords
    coords = create_coords_with_default_indexes(coords, data_vars)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/coordinates.py:1001: in create_coords_with_default_indexes
    variable = as_variable(obj, name=name, auto_convert=False)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:155: in as_variable
    data: T_DuckArray = as_compatible_data(obj)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:321: in as_compatible_data
    data = _possibly_convert_objects(data)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:235: in _possibly_convert_objects
    as_series = _as_nanosecond_precision(as_series)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:208: in _as_nanosecond_precision
    utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="datetime"))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

message = 'Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed ... DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.'
category = None

    def emit_user_level_warning(message, category=None) -> None:
        """Emit a warning at the user level by inspecting the stack trace."""
        stacklevel = find_stack_level()
>       return warnings.warn(message, category=category, stacklevel=stacklevel)
E       UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/utils.py:1105: UserWarning
----------------------------- Captured stderr call -----------------------------

Processing:   0%|          | 0/1 [00:00<?, ? files/s]
Processing:   0%|          | 0/1 [00:00<?, ? files/s]
_____________ TestECMWF.test_ecmwf_multimessage_missing_timeperiod _____________

self = <climada_petals.hazard.test.test_tc_tracks_forecast.TestECMWF testMethod=test_ecmwf_multimessage_missing_timeperiod>

    def test_ecmwf_multimessage_missing_timeperiod(self):
        """Test ECMWF reader should continue reading messages if one track misses timePeriod"""
        with self.assertLogs('climada_petals.hazard.tc_tracks_forecast', level='INFO') as cm:
            forecast = TCForecast()
>           forecast.fetch_ecmwf(files=TEST_BUFR_FILE_MULTIMESSAGE_MISSING_TIMEPERIOD)

climada_petals/hazard/test/test_tc_tracks_forecast.py:111: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
climada_petals/hazard/tc_tracks_forecast.py:165: in fetch_ecmwf
    self.read_one_bufr_tc(file, id_no=i)
climada_petals/hazard/tc_tracks_forecast.py:415: in read_one_bufr_tc
    track = self._subset_to_track(
climada_petals/hazard/tc_tracks_forecast.py:517: in _subset_to_track
    track = xr.Dataset(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:713: in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:423: in merge_data_and_coords
    coords = create_coords_with_default_indexes(coords, data_vars)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/coordinates.py:1001: in create_coords_with_default_indexes
    variable = as_variable(obj, name=name, auto_convert=False)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:155: in as_variable
    data: T_DuckArray = as_compatible_data(obj)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:321: in as_compatible_data
    data = _possibly_convert_objects(data)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:235: in _possibly_convert_objects
    as_series = _as_nanosecond_precision(as_series)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:208: in _as_nanosecond_precision
    utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="datetime"))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

message = 'Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed ... DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.'
category = None

    def emit_user_level_warning(message, category=None) -> None:
        """Emit a warning at the user level by inspecting the stack trace."""
        stacklevel = find_stack_level()
>       return warnings.warn(message, category=category, stacklevel=stacklevel)
E       UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/utils.py:1105: UserWarning
----------------------------- Captured stderr call -----------------------------

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Processing:   0%|          | 0/1 [00:00<?, ? files/s]
________________________ TestECMWF.test_equal_timestep _________________________

self = <climada_petals.hazard.test.test_tc_tracks_forecast.TestECMWF testMethod=test_equal_timestep>

    def test_equal_timestep(self):
        """Test equal timestep"""
        forecast = TCForecast()
>       forecast.fetch_ecmwf(files=TEST_BUFR_FILES)

climada_petals/hazard/test/test_tc_tracks_forecast.py:127: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
climada_petals/hazard/tc_tracks_forecast.py:165: in fetch_ecmwf
    self.read_one_bufr_tc(file, id_no=i)
climada_petals/hazard/tc_tracks_forecast.py:415: in read_one_bufr_tc
    track = self._subset_to_track(
climada_petals/hazard/tc_tracks_forecast.py:517: in _subset_to_track
    track = xr.Dataset(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:713: in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:423: in merge_data_and_coords
    coords = create_coords_with_default_indexes(coords, data_vars)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/coordinates.py:1001: in create_coords_with_default_indexes
    variable = as_variable(obj, name=name, auto_convert=False)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:155: in as_variable
    data: T_DuckArray = as_compatible_data(obj)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:321: in as_compatible_data
    data = _possibly_convert_objects(data)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:235: in _possibly_convert_objects
    as_series = _as_nanosecond_precision(as_series)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:208: in _as_nanosecond_precision
    utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="datetime"))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

message = 'Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed ... DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.'
category = None

    def emit_user_level_warning(message, category=None) -> None:
        """Emit a warning at the user level by inspecting the stack trace."""
        stacklevel = find_stack_level()
>       return warnings.warn(message, category=category, stacklevel=stacklevel)
E       UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/utils.py:1105: UserWarning
----------------------------- Captured stderr call -----------------------------

Processing:   0%|          | 0/2 [00:00<?, ? files/s]
Processing:   0%|          | 0/2 [00:00<?, ? files/s]
__________________________ TestECMWF.test_fetch_ecmwf __________________________

self = <climada_petals.hazard.test.test_tc_tracks_forecast.TestECMWF testMethod=test_fetch_ecmwf>

    def test_fetch_ecmwf(self):
        """Test ECMWF reader with static files"""
        forecast = TCForecast()
>       forecast.fetch_ecmwf(files=TEST_BUFR_FILES)

climada_petals/hazard/test/test_tc_tracks_forecast.py:62: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
climada_petals/hazard/tc_tracks_forecast.py:165: in fetch_ecmwf
    self.read_one_bufr_tc(file, id_no=i)
climada_petals/hazard/tc_tracks_forecast.py:415: in read_one_bufr_tc
    track = self._subset_to_track(
climada_petals/hazard/tc_tracks_forecast.py:517: in _subset_to_track
    track = xr.Dataset(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:713: in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:423: in merge_data_and_coords
    coords = create_coords_with_default_indexes(coords, data_vars)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/coordinates.py:1001: in create_coords_with_default_indexes
    variable = as_variable(obj, name=name, auto_convert=False)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:155: in as_variable
    data: T_DuckArray = as_compatible_data(obj)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:321: in as_compatible_data
    data = _possibly_convert_objects(data)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:235: in _possibly_convert_objects
    as_series = _as_nanosecond_precision(as_series)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:208: in _as_nanosecond_precision
    utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="datetime"))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

message = 'Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed ... DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.'
category = None

    def emit_user_level_warning(message, category=None) -> None:
        """Emit a warning at the user level by inspecting the stack trace."""
        stacklevel = find_stack_level()
>       return warnings.warn(message, category=category, stacklevel=stacklevel)
E       UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/utils.py:1105: UserWarning
----------------------------- Captured stderr call -----------------------------

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Processing:   0%|          | 0/2 [00:00<?, ? files/s]
____________________________ TestECMWF.test_hdf5_io ____________________________

self = <climada_petals.hazard.test.test_tc_tracks_forecast.TestECMWF testMethod=test_hdf5_io>

    def test_hdf5_io(self):
        """Test writing and reading hdf5 TCTracks instances"""
        tc_track = TCForecast()
>       tc_track.fetch_ecmwf(files=TEST_BUFR_FILES)

climada_petals/hazard/test/test_tc_tracks_forecast.py:142: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
climada_petals/hazard/tc_tracks_forecast.py:165: in fetch_ecmwf
    self.read_one_bufr_tc(file, id_no=i)
climada_petals/hazard/tc_tracks_forecast.py:415: in read_one_bufr_tc
    track = self._subset_to_track(
climada_petals/hazard/tc_tracks_forecast.py:517: in _subset_to_track
    track = xr.Dataset(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:713: in __init__
    variables, coord_names, dims, indexes, _ = merge_data_and_coords(
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/dataset.py:423: in merge_data_and_coords
    coords = create_coords_with_default_indexes(coords, data_vars)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/coordinates.py:1001: in create_coords_with_default_indexes
    variable = as_variable(obj, name=name, auto_convert=False)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:155: in as_variable
    data: T_DuckArray = as_compatible_data(obj)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:321: in as_compatible_data
    data = _possibly_convert_objects(data)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:235: in _possibly_convert_objects
    as_series = _as_nanosecond_precision(as_series)
../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/variable.py:208: in _as_nanosecond_precision
    utils.emit_user_level_warning(NON_NANOSECOND_WARNING.format(case="datetime"))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

message = 'Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed ... DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.'
category = None

    def emit_user_level_warning(message, category=None) -> None:
        """Emit a warning at the user level by inspecting the stack trace."""
        stacklevel = find_stack_level()
>       return warnings.warn(message, category=category, stacklevel=stacklevel)
E       UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/xarray/core/utils.py:1105: UserWarning
----------------------------- Captured stderr call -----------------------------

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=============================== warnings summary ===============================
../../../../climada_install_env/workspace/climada/util/__init__.py:25
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/util/__init__.py:25: FionaDeprecationWarning: This function will be removed in version 2.0. Please use CRS.from_epsg() instead.
    from .constants import *

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:15
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:15: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
    warnings.warn(

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:11
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:11: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PANDAS_VERSION = LooseVersion(pd.__version__)

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:13
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:13: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PANDAS_0210 = PANDAS_VERSION >= LooseVersion("0.21.0")

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:14
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:14: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PANDAS_0220 = PANDAS_VERSION >= LooseVersion("0.22.0")

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:15
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/pandas_datareader/compat/__init__.py:15: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    PANDAS_0230 = PANDAS_VERSION >= LooseVersion("0.23.0")

../../../../../miniforge3/envs/petals_env/lib/python3.9/site-packages/cdsapi/api.py:17
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/cdsapi/api.py:17: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
    import pkg_resources

../../../../climada_install_env/workspace/climada/hazard/centroids/centr.py:979
climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/hazard/centroids/centr.py:979: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
    latitude = np.array(data.get('lat'))

../../../../climada_install_env/workspace/climada/hazard/centroids/centr.py:980
climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/hazard/centroids/centr.py:980: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
    longitude = np.array(data.get('lon'))

../../../../climada_install_env/workspace/climada/hazard/centroids/centr.py:998: 5 warnings
climada_petals/engine/test/test_warn.py: 5 warnings
climada_petals/hazard/emulator/test/test_geo.py: 5 warnings
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/hazard/centroids/centr.py:998: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
    values = np.array(data.get(centr_name))

climada_petals/hazard/test/test_wildfire.py:55
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_wildfire.py:55: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(coord[:, 0], coord[:, 1])

climada_petals/hazard/test/test_wildfire.py:56
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_wildfire.py:56: DeprecatedWarning: set_area_approx is deprecated. This method is futile and will be removed in a future version. `Centroids.get_area_pixel` can be run without initialization.
    centroids.set_area_approx()

climada_petals/hazard/test/test_wildfire.py:58
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_wildfire.py:58: DeprecatedWarning: empty_geometry_points is deprecated. This method has no effect and will be removed in a future version. In the current version of climada the geometry points of a `Centroids` object cannot be removed as they are the backbone of the Centroids' GeoDataFrame.
    centroids.empty_geometry_points()

climada_petals/engine/test/test_supplychain.py: 16 warnings
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/engine/supplychain.py:878: UserWarning: No known unit was provided. It is assumed that values do not need to be converted.
    warnings.warn(

climada_petals/engine/test/test_supplychain.py: 21 warnings
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/numpy/matrixlib/defmatrix.py:70: PendingDeprecationWarning: the matrix subclass is not the recommended way to represent matrices or deal with linear algebra (see https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). Please adjust your code to use regular ndarray.
    return matrix(data, dtype=dtype, copy=False)

climada_petals/engine/test/test_supplychain.py::TestSupplyChain::test_calc_shock_to_sectors
climada_petals/engine/test/test_supplychain.py::TestSupplyChain::test_calc_shock_to_sectors
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_missing
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_no_param
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_sep
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_recovery_no_param
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_unknown
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/engine/supplychain.py:550: UserWarning: No impacted sectors were specified. It is assumed that the exposure is representative of all sectors in the IO table
    warnings.warn(

climada_petals/engine/test/test_supplychain.py::TestSupplyChain::test_map_exp_to_mriot
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/engine/supplychain.py:897: UserWarning: For a correct calculation the format of regions' names in exposure and the IO table must match.
    warnings.warn(

climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_missing
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_missing
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_no_param
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_sep
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/engine/supplychain.py:763: UserWarning: BoARIO 'model' parameters were not specified and default values are used. This is not recommended and likely undesired.
    warnings.warn(f"""BoARIO '{boario_param_type}' parameters were not specified and default values are used. This is not recommended and likely undesired."""

climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_missing
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_missing
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_no_param
climada_petals/engine/test/test_supplychain.py::TestSupplyChain_boario::test_calc_impacts_boario_rebuilding_sep
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/engine/supplychain.py:763: UserWarning: BoARIO 'sim' parameters were not specified and default values are used. This is not recommended and likely undesired.
    warnings.warn(f"""BoARIO '{boario_param_type}' parameters were not specified and default values are used. This is not recommended and likely undesired."""

climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/hazard/io.py:1074: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
    hazard_kwargs[var_name] = np.array(hf_data.get(var_name))

climada_petals/engine/test/test_warn.py::TestWarn::test_from_hazard
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/hazard/io.py:1088: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
    u_hdf5.to_string, np.array(hf_data.get(var_name)).tolist())]

climada_petals/entity/exposures/test/test_black_marble.py::TestProvinces::test_filter_admin1_pass
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/entity/exposures/black_marble.py:406: ShapelyDeprecationWarning: The 'cascaded_union()' function is deprecated. Use 'unary_union()' instead.
    all_geom = shapely.ops.cascaded_union(admin1_geom)

climada_petals/entity/exposures/test/test_black_marble.py::TestEconIndices::test_fill_econ_indicators_na_pass
climada_petals/entity/exposures/test/test_black_marble.py::TestEconIndices::test_fill_econ_indicators_pass
climada_petals/entity/exposures/test/test_black_marble.py::TestEconIndices::test_fill_econ_indicators_pass
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/util/finance.py:199: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
    close_val = float(close_val.iloc[0].values)

climada_petals/entity/exposures/test/test_crop_production.py::TestCropProduction::test_from_area_and_yield_nc4
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/cfgrib/xarray_plugin.py:10: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    if LooseVersion(xr.__version__) <= "0.17.0":

climada_petals/entity/exposures/test/test_crop_production.py::TestCropProduction::test_from_area_and_yield_nc4
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/setuptools/_distutils/version.py:337: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
    other = LooseVersion(other)

climada_petals/entity/exposures/test/test_crop_production.py: 99 warnings
climada_petals/entity/exposures/test/test_gdp_asset.py: 12 warnings
climada_petals/hazard/test/test_flood.py: 27 warnings
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/util/hdf5_handler.py:65: DeprecationWarning: `product` is deprecated as of NumPy 1.25.0, and will be removed in NumPy 2.0. Please use `prod` instead.
    contents[name] = np.array(obj)

climada_petals/entity/exposures/test/test_crop_production.py: 15 warnings
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.
    return _methods._mean(a, axis=axis, dtype=dtype,

climada_petals/entity/exposures/test/test_crop_production.py: 15 warnings
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
    ret = ret.dtype.type(ret / rcount)

climada_petals/entity/exposures/test/test_gdp_asset.py: 4 warnings
climada_petals/hazard/test/test_flood.py: 9 warnings
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/util/coordinates.py:824: RuntimeWarning: invalid value encountered in cast
    region_id = hdf5_f['NatIdGrid'].reshape(grid_shape).astype(int)

climada_petals/hazard/emulator/test/test_geo.py::TestGeo::test_hazregion_centroids
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/emulator/geo.py:109: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(*latlon)

climada_petals/hazard/emulator/test/test_stats.py::TestStats::test_haz_max_events
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/emulator/test/test_stats.py:114: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    hazard.centroids = Centroids.from_lat_lon(np.array([1, 3, 5]), np.array([2, 4, 6]))

climada_petals/hazard/test/test_drought.py::TestReader::test
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/drought.py:227: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    self.centroids = Centroids.from_lat_lon(lat_1d, lon_1d)

climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_centroids_flood
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_flood.py:147: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    rand_centroids = Centroids.from_lat_lon(lat.flatten(), lon.flatten())

climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_exact_area_selection_country
climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_exact_area_selection_region
climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_flooded_area
climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_flooded_area
climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_flooded_area
climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_flooded_area
climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_isimip_country_flood
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/river_flood.py:398: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(lat, lon)

climada_petals/hazard/test/test_flood.py::TestRiverFlood::test_flooded_area
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/river_flood.py:317: DeprecatedWarning: set_area_pixel is deprecated. This method is futile and will be removed in a future version. `Centroids.get_area_pixel` can be run without initialization.
    self.centroids.set_area_pixel()

climada_petals/hazard/test/test_low_flow.py::TestLowFlowDummyData::test_events_from_clusters_default
climada_petals/hazard/test/test_low_flow.py::TestLowFlowDummyData::test_events_from_clusters_parameter
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_low_flow.py:77: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(np.array(lat), np.array(lon))

climada_petals/hazard/test/test_low_flow.py::TestLowFlowDummyData::test_events_from_clusters_default
climada_petals/hazard/test/test_low_flow.py::TestLowFlowDummyData::test_events_from_clusters_parameter
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_low_flow.py:78: DeprecatedWarning: set_lat_lon_to_meta is deprecated. This method has no effect and will be removed in a future version.
    centroids.set_lat_lon_to_meta()

climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_filter_events
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_load_FR_all
climada_petals/hazard/test/test_low_flow.py::TestDischargeDataHandling::test_compute_threshold_grid
climada_petals/hazard/test/test_low_flow.py::TestDischargeDataHandling::test_compute_threshold_grid
  /var/lib/jenkins/miniforge3/envs/petals_env/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1563: RuntimeWarning: All-NaN slice encountered
    return function_base._ureduce(a,

climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_filter_events
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_filter_events
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_load_FR_all
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_load_FR_all
  <string>:6: FutureWarning: 'M' is deprecated and will be removed in a future version. Please use 'ME' instead of 'M'.

climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_filter_events
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_load_FR_all
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/low_flow.py:643: DeprecatedWarning: set_area_approx is deprecated. This method is futile and will be removed in a future version. `Centroids.get_area_pixel` can be run without initialization.
    centroids.set_area_approx()

climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_combine_nc
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_filter_events
climada_petals/hazard/test/test_low_flow.py::TestLowFlowNETCDF::test_load_FR_all
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/low_flow.py:645: DeprecatedWarning: empty_geometry_points is deprecated. This method has no effect and will be removed in a future version. In the current version of climada the geometry points of a `Centroids` object cannot be removed as they are the backbone of the Centroids' GeoDataFrame.
    centroids.empty_geometry_points()

climada_petals/hazard/test/test_tc_rainfield.py::TestReader::test_cross_antimeridian
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_tc_rainfield.py:120: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    cen = Centroids.from_lat_lon([-16.95, -16.8, -16.8], [179.9, 180.1, -179.9])

climada_petals/hazard/test/test_tc_rainfield.py::TestReader::test_cross_antimeridian
climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_cross_antimeridian
  /var/lib/jenkins/jobs/climada_install_env/workspace/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
    if ibtracs_ds.dims['storm'] == 0:

climada_petals/hazard/test/test_tc_rainfield.py::TestReader::test_set_one_pass
climada_petals/hazard/test/test_tc_rainfield.py::TestReader::test_tcr
climada_petals/hazard/test/test_tc_rainfield.py::TestModel::test_compute_rain_pass
climada_petals/hazard/test/test_tc_rainfield.py::TestModel::test_rainfield_diff_time_steps
climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_surge_from_track
  <string>:6: FutureWarning: 'H' is deprecated and will be removed in a future version. Please use 'h' instead of 'H'.

climada_petals/hazard/test/test_tc_rainfield.py::TestModel::test_rainfield_diff_time_steps
  <string>:6: FutureWarning: 'T' is deprecated and will be removed in a future version. Please use 'min' instead of 'T'.

climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_cross_antimeridian
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_tc_surge_bathtub.py:184: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    cen = Centroids.from_lat_lon([-16.95, -16.8, -16.8], [179.9, 180.1, -179.9])

climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_cross_antimeridian
climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_fraction_on_land
climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_surge_from_track
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_tc_surge_bathtub.py:52: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(*[ar.ravel() for ar in np.meshgrid(lon, lat)][::-1])

climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_fraction_on_land
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_tc_surge_bathtub.py:97: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(lat, lon)

climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_surge_from_track
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_tc_surge_bathtub.py:121: UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
    track = xr.Dataset({

climada_petals/hazard/test/test_tc_surge_bathtub.py::TestTCSurgeBathtub::test_surge_from_track
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/test/test_tc_surge_bathtub.py:156: DeprecatedWarning: from_lat_lon is deprecated. This method will be removed in a future version. Simply use the constructor instead.
    centroids = Centroids.from_lat_lon(lat, lon)

climada_petals/hazard/test/test_tc_tracks_forecast.py::TestCXML::test_custom_xsl
climada_petals/hazard/test/test_tc_tracks_forecast.py::TestCXML::test_default_xsl
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/tc_tracks_forecast.py:641: FutureWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
    all_storms_df = pd.read_csv(

climada_petals/hazard/test/test_wildfire.py::TestMethodsFirms::test_centroids_pass
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/wildfire.py:658: DeprecatedWarning: set_area_approx is deprecated. This method is futile and will be removed in a future version. `Centroids.get_area_pixel` can be run without initialization.
    centroids.set_area_approx()

climada_petals/hazard/test/test_wildfire.py::TestMethodsFirms::test_centroids_pass
  /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/climada_petals/hazard/wildfire.py:660: DeprecatedWarning: empty_geometry_points is deprecated. This method has no effect and will be removed in a future version. In the current version of climada the geometry points of a `Centroids` object cannot be removed as they are the backbone of the Centroids' GeoDataFrame.
    centroids.empty_geometry_points()

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
- generated xml file: /var/lib/jenkins/jobs/petals_branches/branches/develop/workspace@2/tests_xml/tests.xml -

---------- coverage: platform linux, python 3.9.18-final-0 -----------
Name                                                       Stmts   Miss  Cover
------------------------------------------------------------------------------
climada_petals/__init__.py                                    13      2    85%
climada_petals/engine/supplychain.py                         246     56    77%
climada_petals/engine/warn.py                                155     15    90%
climada_petals/entity/exposures/black_marble.py              179     61    66%
climada_petals/entity/exposures/crop_production.py           458    213    53%
climada_petals/entity/exposures/gdp_asset.py                 102     15    85%
climada_petals/entity/exposures/osm_dataloader.py            128     12    91%
climada_petals/entity/exposures/spam_agrar.py                172    119    31%
climada_petals/entity/impact_funcs/drought.py                 67     45    33%
climada_petals/entity/impact_funcs/relative_cropyield.py      28     15    46%
climada_petals/entity/impact_funcs/river_flood.py            102     16    84%
climada_petals/hazard/drought.py                             277     69    75%
climada_petals/hazard/emulator/emulator.py                   116      8    93%
climada_petals/hazard/emulator/geo.py                         83      1    99%
climada_petals/hazard/emulator/random.py                      52      3    94%
climada_petals/hazard/emulator/stats.py                       97      4    96%
climada_petals/hazard/landslide.py                            76      3    96%
climada_petals/hazard/low_flow.py                            361     45    88%
climada_petals/hazard/relative_cropyield.py                  416    310    25%
climada_petals/hazard/rf_glofas/cds_glofas_downloader.py      72      4    94%
climada_petals/hazard/rf_glofas/setup.py                      75     54    28%
climada_petals/hazard/rf_glofas/transform_ops.py             160     18    89%
climada_petals/hazard/river_flood.py                         152     45    70%
climada_petals/hazard/tc_rainfield.py                        343     56    84%
climada_petals/hazard/tc_tracks_forecast.py                  262     80    69%
climada_petals/hazard/wildfire.py                            495    292    41%
------------------------------------------------------------------------------
TOTAL                                                       4969   1561    69%

15 files skipped due to complete coverage.
Coverage HTML written to dir coverage
Coverage XML written to file coverage.xml

=========================== short test summary info ============================
FAILED climada_petals/hazard/test/test_tc_tracks_forecast.py::TestECMWF::test_ecmwf_multimessage - UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
FAILED climada_petals/hazard/test/test_tc_tracks_forecast.py::TestECMWF::test_ecmwf_multimessage_missing_timeperiod - UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
FAILED climada_petals/hazard/test/test_tc_tracks_forecast.py::TestECMWF::test_equal_timestep - UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
FAILED climada_petals/hazard/test/test_tc_tracks_forecast.py::TestECMWF::test_fetch_ecmwf - UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
FAILED climada_petals/hazard/test/test_tc_tracks_forecast.py::TestECMWF::test_hdf5_io - UserWarning: Converting non-nanosecond precision datetime values to nanosecond precision. This behavior can eventually be relaxed in xarray, as it is an artifact from pandas which is now beginning to support non-nanosecond precision values. This warning is caused by passing non-nanosecond np.datetime64 or np.timedelta64 values to the DataArray or Variable constructor; it can be silenced by converting the values to nanosecond precision ahead of time.
=========== 5 failed, 198 passed, 329 warnings in 232.35s (0:03:52) ============
make: *** [Makefile:23: unit_test] Error 1