Update dependencies

[znicholls]

Continuation of #46 (don't merge both)

• [ ] Tests added
• [ ] Documentation added
• [ ] Example added (in the documentation, to an existing notebook, or in a new notebook)
• [ ] Description in CHANGELOG.rst added (single line such as: (`#XX <https://github.com/iiasa/climate-assessment/pull/XX>`_) Added feature which does something)

[znicholls]

@phackstock I got some of the way but then crashed into https://github.com/IAMconsortium/pyam/issues/793 which I think is the reason that the remaining tests are failing (because it causes a regression in the behaviour of reclassify_waste_and_other_co2_ar6)

[phackstock]

@jkikstra, if you could take this one that would be great. I'll need to update our Scenario Explorer processing container soon and that will require pandas 2.0 which currently breaks those tests.

[jkikstra]

Thanks for the initial work and figuring out the issue! Will try to tackle this at the end of this week.

[phackstock]

Perfect, thanks a lot @jkikstra!

[jkikstra]

Started working on it now, but stopping for today.

Starting a list here for changes that I think we need to be making:

• replace require_variable with require_data (in utils.py)
• check why pyam.concat is now failing in reclassify_waste_and_other_co2_ar6() (in checks.py), perhaps it is due to the changes for exclude? https://github.com/IAMconsortium/pyam/pull/759, meaning df.extra_cols is not the same for the two dataframes on line 843

@danielhuppmann I'm getting this:

def reclassify_waste_and_other_co2_ar6(df):
    """
    Reclassify waste and other CO2 into the energy and industrial processes category

    Reclassify CO2 emissions reported under Emissions|CO2|Other and
    Emissions|CO2|Waste, instead putting them under
    Emissions|CO2|Energy and Industrial Processes.

    Parameters
    ----------
    df : :class:`pyam.IamDataFrame`
        The original set of reported emissions

    Returns
    -------
    :class:`pyam.IamDataFrame`
        Reclassified set of emissions.
    """
    # filter out the scenarios that do not need changes
    df_nochange = df.copy()
    df_nochange.require_data(
        variable=["Emissions|CO2|Other", "Emissions|CO2|Waste"], exclude_on_fail=True
    )
    df_nochange.filter(exclude=True, inplace=True)
    df_nochange.reset_exclude()

    # select the scenarios that do need changes
    df_change = df.copy()
    df_change.require_data(
        variable=["Emissions|CO2|Other", "Emissions|CO2|Waste"], exclude_on_fail=True
    )
    df_change.filter(exclude=False, inplace=True)
    df_change.reset_exclude()
    if df_change.empty:
        return df_nochange

    # rename old CO2|Energy and Industrial Processes, to be replaced
    df_change.rename(
        variable={
            "Emissions|CO2|Energy and Industrial Processes": "Emissions|CO2|Energy and Industrial Processes|Incomplete"
        },
        inplace=True,
    )

    # use pandas to create new CO2|Energy and Industrial Processes by adding CO2|Other and CO2|Waste
    df_change_pd = df_change.as_pandas()
    varsum = [
        "Emissions|CO2|Waste",
        "Emissions|CO2|Other",
        "Emissions|CO2|Energy and Industrial Processes|Incomplete",
    ]
    df_change_notaffected_pd = df_change_pd[~df_change_pd.variable.isin(varsum)]
    df_change_notaffected_pyam = pyam.IamDataFrame(df_change_notaffected_pd)
    df_change_pd = df_change_pd[df_change_pd.variable.isin(varsum)]
    df_change_pd = df_change_pd.groupby(
        by=["model", "scenario", "year"], as_index=False
    )
    df_change_pd = df_change_pd.sum()
    df_change_pd["variable"] = "Emissions|CO2|Energy and Industrial Processes"
    df_change_pd["unit"] = "Mt CO2/yr"
    df_change_pd["region"] = "World"
    df_change_pyam = pyam.IamDataFrame(df_change_pd)
    df_change = pyam.concat([df_change_pyam, df_change_notaffected_pyam])

    # recombine dataframes
    df_new = pyam.concat([df_change, df_nochange], ignore_meta=True)

    return df_new

test results:

(ca-deps-311) C:\Users\kikstra\Documents\GitHub\climate-assessment>pytest tests/integration/test_units.py
======================================================= test session starts =======================================================
platform win32 -- Python 3.10.2, pytest-7.2.1, pluggy-1.0.0
rootdir: C:\Users\kikstra\Documents\GitHub\climate-assessment, configfile: setup.cfg
plugins: anyio-3.7.1
collected 23 items

tests\integration\test_units.py .....................FF                                                                      [100%]

============================================================ FAILURES =============================================================
_____________________________________________________ test_reclassify_co2_ar6 _____________________________________________________

    def test_reclassify_co2_ar6():
        input_emissions_file = os.path.join(TEST_DATA_DIR, "ex2.csv")
        processed_input_emissions_file = os.path.join(
            TEST_DATA_DIR, "ex2_adjusted-waste-other.csv"
        )
        # import pdb
        # pdb.set_trace()
        # pyam.compare(reclassify_waste_and_other_co2_ar6(pyam.IamDataFrame(input_emissions_file)), pyam.IamDataFrame(processed_input_emissions_file), )
>       assert reclassify_waste_and_other_co2_ar6(
            pyam.IamDataFrame(input_emissions_file)
        ).equals(pyam.IamDataFrame(processed_input_emissions_file))

tests\integration\test_units.py:194:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src\climate_assessment\checks.py:844: in reclassify_waste_and_other_co2_ar6
    df_new = pyam.concat([df_change, df_nochange], ignore_meta=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

objs = [<class 'pyam.core.IamDataFrame'>
Index:
 * model    : model12 (1)
 * scenario : 1point5 (1)
Timeseries data coordinat... year     : 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, ... 2100 (19)
Meta indicators:
   exclude (bool) False (1)]
ignore_meta_conflict = False, kwargs = {'ignore_meta': True}
as_iamdataframe = <function concat.<locals>.as_iamdataframe at 0x000001DB7FD2F1C0>
df = <class 'pyam.core.IamDataFrame'>
Index:
 * model    : model1, model10, model11, model13, model14, model2, ... model9 (...  year     : 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, ... 2100 (19)
Meta indicators:
   exclude (bool) False (1)
_merge_meta = True, index_names = FrozenList(['model', 'scenario']), extra_cols = ['exclude'], time_col = 'year'
consistent_time_domain = True

    def concat(objs, ignore_meta_conflict=False, **kwargs):
        """Concatenate a series of IamDataFrame-like objects

        Parameters
        ----------
        objs : iterable of IamDataFrames
            A list of objects castable to :class:`IamDataFrame`
        ignore_meta_conflict : bool, optional
            If False, raise an error if any meta columns present in `dfs` are not identical.
            If True, values in earlier elements of `dfs` take precedence.
        kwargs
            Passed to :class:`IamDataFrame(other, **kwargs) <IamDataFrame>`
            for any item of `dfs` which isn't already an IamDataFrame.

        Returns
        -------
        IamDataFrame

        Raises
        ------
        TypeError
            If `dfs` is not a list.
        ValueError
            If time domain or other timeseries data index dimension don't match.

        Notes
        -----
        The *meta* attributes are merged only for those objects of *objs* that are passed
        as :class:`IamDataFrame` instances.

        The :attr:`dimensions` and :attr:`index` names of all elements of *dfs* must be
        identical. The returned IamDataFrame inherits the dimensions and index names.
        """
        if not islistable(objs) or isinstance(objs, pd.DataFrame):
            raise TypeError(f"'{objs.__class__.__name__}' object is not iterable")

        objs = list(objs)
        if len(objs) < 1:
            raise ValueError("No objects to concatenate")

        def as_iamdataframe(df):
            if isinstance(df, IamDataFrame):
                return df, True
            else:
                return IamDataFrame(df, **kwargs), False

        # cast first item to IamDataFrame (if necessary)
        df, _merge_meta = as_iamdataframe(objs[0])
        index_names, extra_cols, time_col = df.index.names, df.extra_cols, df.time_col

        consistent_time_domain = True
        iam_dfs = [(df, _merge_meta)]

        # cast all items to IamDataFrame (if necessary) and check consistency of items
        for df in objs[1:]:
            df, _merge_meta = as_iamdataframe(df)
            if df.index.names != index_names:
                raise ValueError("Items have incompatible index dimensions.")
            if df.extra_cols != extra_cols:
>               raise ValueError("Items have incompatible timeseries data dimensions.")
E               ValueError: Items have incompatible timeseries data dimensions.

..\..\..\AppData\Local\Programs\Python\Python310\lib\site-packages\pyam\core.py:2931: ValueError
___________________________________________________ test_reclassify_co2_ar6_sum ___________________________________________________

    def test_reclassify_co2_ar6_sum():
        input_emissions_file = pyam.IamDataFrame(os.path.join(TEST_DATA_DIR, "ex2.csv"))

>       input_emissions_file_processed = reclassify_waste_and_other_co2_ar6(
            input_emissions_file
        )

tests\integration\test_units.py:202:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src\climate_assessment\checks.py:844: in reclassify_waste_and_other_co2_ar6
    df_new = pyam.concat([df_change, df_nochange], ignore_meta=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

objs = [<class 'pyam.core.IamDataFrame'>
Index:
 * model    : model12 (1)
 * scenario : 1point5 (1)
Timeseries data coordinat... year     : 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, ... 2100 (19)
Meta indicators:
   exclude (bool) False (1)]
ignore_meta_conflict = False, kwargs = {'ignore_meta': True}
as_iamdataframe = <function concat.<locals>.as_iamdataframe at 0x000001DB018A48B0>
df = <class 'pyam.core.IamDataFrame'>
Index:
 * model    : model1, model10, model11, model13, model14, model2, ... model9 (...  year     : 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, ... 2100 (19)
Meta indicators:
   exclude (bool) False (1)
_merge_meta = True, index_names = FrozenList(['model', 'scenario']), extra_cols = ['exclude'], time_col = 'year'
consistent_time_domain = True

    def concat(objs, ignore_meta_conflict=False, **kwargs):
        """Concatenate a series of IamDataFrame-like objects

        Parameters
        ----------
        objs : iterable of IamDataFrames
            A list of objects castable to :class:`IamDataFrame`
        ignore_meta_conflict : bool, optional
            If False, raise an error if any meta columns present in `dfs` are not identical.
            If True, values in earlier elements of `dfs` take precedence.
        kwargs
            Passed to :class:`IamDataFrame(other, **kwargs) <IamDataFrame>`
            for any item of `dfs` which isn't already an IamDataFrame.

        Returns
        -------
        IamDataFrame

        Raises
        ------
        TypeError
            If `dfs` is not a list.
        ValueError
            If time domain or other timeseries data index dimension don't match.

        Notes
        -----
        The *meta* attributes are merged only for those objects of *objs* that are passed
        as :class:`IamDataFrame` instances.

        The :attr:`dimensions` and :attr:`index` names of all elements of *dfs* must be
        identical. The returned IamDataFrame inherits the dimensions and index names.
        """
        if not islistable(objs) or isinstance(objs, pd.DataFrame):
            raise TypeError(f"'{objs.__class__.__name__}' object is not iterable")

        objs = list(objs)
        if len(objs) < 1:
            raise ValueError("No objects to concatenate")

        def as_iamdataframe(df):
            if isinstance(df, IamDataFrame):
                return df, True
            else:
                return IamDataFrame(df, **kwargs), False

        # cast first item to IamDataFrame (if necessary)
        df, _merge_meta = as_iamdataframe(objs[0])
        index_names, extra_cols, time_col = df.index.names, df.extra_cols, df.time_col

        consistent_time_domain = True
        iam_dfs = [(df, _merge_meta)]

        # cast all items to IamDataFrame (if necessary) and check consistency of items
        for df in objs[1:]:
            df, _merge_meta = as_iamdataframe(df)
            if df.index.names != index_names:
                raise ValueError("Items have incompatible index dimensions.")
            if df.extra_cols != extra_cols:
>               raise ValueError("Items have incompatible timeseries data dimensions.")
E               ValueError: Items have incompatible timeseries data dimensions.

..\..\..\AppData\Local\Programs\Python\Python310\lib\site-packages\pyam\core.py:2931: ValueError
======================================================== warnings summary =========================================================
..\..\..\AppData\Roaming\Python\Python310\site-packages\jupyter_client\connect.py:20
  C:\Users\kikstra\AppData\Roaming\Python\Python310\site-packages\jupyter_client\connect.py:20: DeprecationWarning: Jupyter is migrating its paths to use standard platformdirs
  given by the platformdirs library.  To remove this warning and
  see the appropriate new directories, set the environment variable
  `JUPYTER_PLATFORM_DIRS=1` and then run `jupyter --paths`.
  The use of platformdirs will be the default in `jupyter_core` v6
    from jupyter_core.paths import jupyter_data_dir, jupyter_runtime_dir, secure_write

..\..\..\AppData\Local\Programs\Python\Python310\lib\site-packages\openscm_runner\adapters\ciceroscm_adapter\ciceroscm_wrapper.py:10
  C:\Users\kikstra\AppData\Local\Programs\Python\Python310\lib\site-packages\openscm_runner\adapters\ciceroscm_adapter\ciceroscm_wrapper.py:10: DeprecationWarning: The distutils package is deprecated and slated for removal in Python 3.12. Use setuptools or check PEP 632 for potential alternatives
    from distutils import dir_util

tests/integration/test_units.py::test_reclassify_co2_ar6
tests/integration/test_units.py::test_reclassify_co2_ar6_sum
  c:\users\kikstra\documents\github\climate-assessment\src\climate_assessment\checks.py:836: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
    df_change_pd = df_change_pd.sum()

tests/integration/test_units.py::test_reclassify_co2_ar6
tests/integration/test_units.py::test_reclassify_co2_ar6_sum
  C:\Users\kikstra\AppData\Local\Programs\Python\Python310\lib\site-packages\pyam\core.py:2958: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.
    pd.concat(ret_data, verify_integrity=False),

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
===================================================== short test summary info =====================================================
FAILED tests/integration/test_units.py::test_reclassify_co2_ar6 - ValueError: Items have incompatible timeseries data dimensions.
FAILED tests/integration/test_units.py::test_reclassify_co2_ar6_sum - ValueError: Items have incompatible timeseries data dimensions.
============================================ 2 failed, 21 passed, 6 warnings in 6.20s =============================================

(ca-deps-311) C:\Users\kikstra\Documents\GitHub\climate-assessment>

[znicholls]

Closing in favour of #50