Skip to content

gcages.cmip7_scenariomip.pre_processing.reaggregation.basic#

Basic reaggregation

This is called 'basic' because it's the first one we thought about. It's also, in some ways, the simplest. It assumes that domestic aviation is reported at the model region level.

Classes:

Name Description
GriddingSectorComponents

Definition of the components of a gridding sector for reporting
GriddingSectorComponentsCarbonRemovalReporting

Definition of the components of a carbon removal gridding sector for reporting
ReaggregatorBasic

Reaggregator that follows this module's logic

Functions:

Name Description
aggregate_cols

Aggregate columns
assert_has_all_required_timeseries

Assert that the data has all the required timeseries
assert_is_internally_consistent

Assert that the data is internally consistent
get_complete_timeseries_index

Get the index of complete data
get_default_internal_conistency_checking_tolerances

Get default tolerances used when checking the internal consistency of data
get_example_input

Get example input data
get_internal_consistency_checking_index

Get the index which selects only data relevant for checking internal consistency
get_required_timeseries_index

Get the index of required data
to_complete

Convert the raw data to complete data
to_gridding_sectors

Re-aggregate data to the sectors used for gridding

Attributes:

Name Type Description
COMPLETE_MODEL_REGION_VARIABLES tuple[str, ...]

Complete set of variables at the model region level
COMPLETE_WORLD_VARIABLES tuple[str, ...]

Complete set of variables at the world level
OPTIONAL_MODEL_REGION_VARIABLES tuple[str, ...]

Optional set of variables at the model region level
OPTIONAL_WORLD_VARIABLES tuple[str, ...]

Optional set of variables at the world level
REQUIRED_MODEL_REGION_VARIABLES tuple[str, ...]

Required set of variables at the model region level
REQUIRED_WORLD_VARIABLES tuple[str, ...]

Required set of variables at the world level
SECTOR_DOMESTIC_AVIATION

Domestic aviation sector
gridding_sectors_reporting

The reporting sector component definitions

COMPLETE_MODEL_REGION_VARIABLES module-attribute #

COMPLETE_MODEL_REGION_VARIABLES: tuple[str, ...] = tuple(
    v
    for gs in gridding_sectors_reporting
    if spatial_resolution == MODEL_REGION
    for v in to_complete_variables()
)

Complete set of variables at the model region level

COMPLETE_WORLD_VARIABLES module-attribute #

COMPLETE_WORLD_VARIABLES: tuple[str, ...] = tuple(
    v
    for gs in gridding_sectors_reporting
    if spatial_resolution == WORLD
    for v in to_complete_variables()
)

Complete set of variables at the world level

OPTIONAL_MODEL_REGION_VARIABLES module-attribute #

OPTIONAL_MODEL_REGION_VARIABLES: tuple[str, ...] = tuple(
    set(COMPLETE_MODEL_REGION_VARIABLES)
    - set(REQUIRED_MODEL_REGION_VARIABLES)
)

Optional set of variables at the model region level

OPTIONAL_WORLD_VARIABLES module-attribute #

OPTIONAL_WORLD_VARIABLES: tuple[str, ...] = tuple(
    set(COMPLETE_WORLD_VARIABLES)
    - set(REQUIRED_WORLD_VARIABLES)
)

Optional set of variables at the world level

REQUIRED_MODEL_REGION_VARIABLES module-attribute #

REQUIRED_MODEL_REGION_VARIABLES: tuple[str, ...] = tuple(
    v
    for gs in gridding_sectors_reporting
    if spatial_resolution == MODEL_REGION
    for v in to_required_variables()
)

Required set of variables at the model region level

REQUIRED_WORLD_VARIABLES module-attribute #

REQUIRED_WORLD_VARIABLES: tuple[str, ...] = tuple(
    v
    for gs in gridding_sectors_reporting
    if spatial_resolution == WORLD
    for v in to_required_variables()
)

Required set of variables at the world level

SECTOR_DOMESTIC_AVIATION module-attribute #

SECTOR_DOMESTIC_AVIATION = (
    "Energy|Demand|Transportation|Domestic Aviation"
)

Domestic aviation sector

gridding_sectors_reporting module-attribute #

gridding_sectors_reporting = (
    GriddingSectorComponents(
        gridding_sector="Agricultural Waste Burning",
        spatial_resolution=MODEL_REGION,
        input_sectors=("AFOLU|Agricultural Waste Burning",),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=("CO2",),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Agriculture",
        spatial_resolution=MODEL_REGION,
        input_sectors=(
            "AFOLU|Agriculture",
            "AFOLU|Land|Harvested Wood Products",
            "AFOLU|Land|Land Use and Land-Use Change",
            "AFOLU|Land|Other",
            "AFOLU|Land|Wetlands",
        ),
        input_sectors_optional=(
            "AFOLU|Land|Harvested Wood Products",
            "AFOLU|Land|Land Use and Land-Use Change",
            "AFOLU|Land|Other",
            "AFOLU|Land|Wetlands",
        ),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(
            "BC",
            "CO",
            "OC",
            "CO2",
            "Sulfur",
        ),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Aircraft",
        spatial_resolution=WORLD,
        input_sectors=(
            "Energy|Demand|Bunkers|International Aviation",
        ),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=("CH4",),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Domestic aviation headache",
        spatial_resolution=MODEL_REGION,
        input_sectors=(SECTOR_DOMESTIC_AVIATION,),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=("CH4",),
        reporting_only=True,
    ),
    GriddingSectorComponents(
        gridding_sector="Transportation Sector",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Energy|Demand|Transportation",),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Energy Sector",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Energy|Supply",),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Forest Burning",
        spatial_resolution=MODEL_REGION,
        input_sectors=("AFOLU|Land|Fires|Forest Burning",),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=("CO2",),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Grassland Burning",
        spatial_resolution=MODEL_REGION,
        input_sectors=(
            "AFOLU|Land|Fires|Grassland Burning",
        ),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=("CO2",),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Industrial Sector",
        spatial_resolution=MODEL_REGION,
        input_sectors=(
            "Energy|Demand|Industry",
            "Energy|Demand|Other Sector",
            "Industrial Processes",
            "Other",
        ),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_sectors_optional=(
            "Energy|Demand|Other Sector",
            "Other",
        ),
        input_species_optional=(),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="International Shipping",
        spatial_resolution=WORLD,
        input_sectors=(
            "Energy|Demand|Bunkers|International Shipping",
        ),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Peat Burning",
        spatial_resolution=MODEL_REGION,
        input_sectors=("AFOLU|Land|Fires|Peat Burning",),
        input_sectors_optional=(
            "AFOLU|Land|Fires|Peat Burning",
        ),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=COMPLETE_GRIDDING_SPECIES,
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Residential Commercial Other",
        spatial_resolution=MODEL_REGION,
        input_sectors=(
            "Energy|Demand|Residential and Commercial and AFOFI",
        ),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Solvents Production and Application",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Product Use",),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(
            "BC",
            "CH4",
            "CO",
            "NOx",
            "OC",
            "Sulfur",
        ),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Waste",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Waste",),
        input_sectors_optional=(),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(),
        reporting_only=False,
    ),
    GriddingSectorComponents(
        gridding_sector="Other CDR",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Other Capture and Removal",),
        input_sectors_optional=(
            "Other Capture and Removal",
        ),
        all_species=COMPLETE_GRIDDING_SPECIES,
        input_species_optional=(
            "CH4",
            "N2O",
            "BC",
            "CO",
            "NH3",
            "OC",
            "NOx",
            "Sulfur",
            "VOC",
        ),
        reporting_only=False,
    ),
    GriddingSectorComponentsCarbonRemovalReporting(
        gridding_sector="BECCS",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Geological Storage|Biomass",),
        input_sectors_optional=(
            "Geological Storage|Biomass",
        ),
        reporting_only=False,
    ),
    GriddingSectorComponentsCarbonRemovalReporting(
        gridding_sector="Enhanced Weathering",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Enhanced Weathering",),
        input_sectors_optional=("Enhanced Weathering",),
        reporting_only=False,
    ),
    GriddingSectorComponentsCarbonRemovalReporting(
        gridding_sector="Direct Air Capture",
        spatial_resolution=MODEL_REGION,
        input_sectors=(
            "Geological Storage|Direct Air Capture",
        ),
        input_sectors_optional=(
            "Geological Storage|Direct Air Capture",
        ),
        reporting_only=False,
    ),
    GriddingSectorComponentsCarbonRemovalReporting(
        gridding_sector="Ocean",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Ocean",),
        input_sectors_optional=("Ocean",),
        reporting_only=False,
    ),
    GriddingSectorComponentsCarbonRemovalReporting(
        gridding_sector="Biochar",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Land Use|Biochar",),
        input_sectors_optional=("Land Use|Biochar",),
        reporting_only=False,
    ),
    GriddingSectorComponentsCarbonRemovalReporting(
        gridding_sector="Soil Carbon Management",
        spatial_resolution=MODEL_REGION,
        input_sectors=("Land Use|Soil Carbon Management",),
        input_sectors_optional=(
            "Land Use|Soil Carbon Management",
        ),
        reporting_only=False,
    ),
)

The reporting sector component definitions

Note that this only defines the reporting sectors. The sectors used for aggregation are defined in to_gridding_sectors because the logic for how we do the re-aggregation is more complex than a straight mapping.

GriddingSectorComponents #

Definition of the components of a gridding sector for reporting

This is meant for internal use only.

OR logic is applied to the exclusions i.e. a variable will not be required if the sector is in input_sectors_optional or the species is in input_species_optional (i.e. we are maximally relaxed about optional reporting, instead of using AND logic and being restrictive).

Methods:

Name Description
to_complete_variables

Convert to the complete set of variables for this gridding sector
to_required_variables

Convert to the required set of variables for this gridding sector

Attributes:

Name Type Description
all_species tuple[str, ...]

The input species
gridding_sector str

The gridding sector
input_sectors tuple[str, ...]

The input sectors
input_sectors_optional tuple[str, ...]

The input sectors that are optional
input_species_optional tuple[str, ...]

The input species that are optional
reporting_only bool

Is this definition only used for reporting, not aggregating?
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
@define
class GriddingSectorComponents:
    """
    Definition of the components of a gridding sector for reporting

    This is meant for internal use only.

    OR logic is applied to the exclusions
    i.e. a variable will not be required
    if the sector is in `input_sectors_optional`
    or the species is in `input_species_optional`
    (i.e. we are maximally relaxed about optional reporting,
    instead of using AND logic and being restrictive).
    """

    gridding_sector: str
    """The gridding sector"""

    spatial_resolution: SpatialResolutionOption

    input_sectors: tuple[str, ...]
    """
    The input sectors
    """

    input_sectors_optional: tuple[str, ...]
    """
    The input sectors that are optional
    """

    all_species: tuple[str, ...]
    """The input species"""

    input_species_optional: tuple[str, ...]
    """The input species that are optional"""

    reporting_only: bool
    """Is this definition only used for reporting, not aggregating?"""

    def to_complete_variables(self) -> tuple[str, ...]:
        """
        Convert to the complete set of variables for this gridding sector
        """
        return tuple(
            f"Emissions|{species}|{sector}"
            for species in self.all_species
            for sector in self.input_sectors
        )

    def to_required_variables(self) -> tuple[str, ...]:
        """
        Convert to the required set of variables for this gridding sector
        """
        return tuple(
            f"Emissions|{species}|{sector}"
            for species in self.all_species
            for sector in self.input_sectors
            if not (
                sector in self.input_sectors_optional
                or species in self.input_species_optional
            )
        )

all_species instance-attribute #

all_species: tuple[str, ...]

The input species

gridding_sector instance-attribute #

gridding_sector: str

The gridding sector

input_sectors instance-attribute #

input_sectors: tuple[str, ...]

The input sectors

input_sectors_optional instance-attribute #

input_sectors_optional: tuple[str, ...]

The input sectors that are optional

input_species_optional instance-attribute #

input_species_optional: tuple[str, ...]

The input species that are optional

reporting_only instance-attribute #

reporting_only: bool

Is this definition only used for reporting, not aggregating?

to_complete_variables #

to_complete_variables() -> tuple[str, ...]

Convert to the complete set of variables for this gridding sector

Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
88
89
90
91
92
93
94
95
96
def to_complete_variables(self) -> tuple[str, ...]:
    """
    Convert to the complete set of variables for this gridding sector
    """
    return tuple(
        f"Emissions|{species}|{sector}"
        for species in self.all_species
        for sector in self.input_sectors
    )

to_required_variables #

to_required_variables() -> tuple[str, ...]

Convert to the required set of variables for this gridding sector

Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
 98
 99
100
101
102
103
104
105
106
107
108
109
110
def to_required_variables(self) -> tuple[str, ...]:
    """
    Convert to the required set of variables for this gridding sector
    """
    return tuple(
        f"Emissions|{species}|{sector}"
        for species in self.all_species
        for sector in self.input_sectors
        if not (
            sector in self.input_sectors_optional
            or species in self.input_species_optional
        )
    )

GriddingSectorComponentsCarbonRemovalReporting #

Definition of the components of a carbon removal gridding sector for reporting

This is for carbon removal i.e. is for CO2 only.

This is meant for internal use only.

Methods:

Name Description
to_complete_variables

Convert to the complete set of variables for this gridding sector
to_required_variables

Convert to the required set of variables for this gridding sector

Attributes:

Name Type Description
gridding_sector str

The gridding sector
input_sectors tuple[str, ...]

The input sectors
input_sectors_optional tuple[str, ...]

The input sectors that are optional
reporting_only bool

Is this definition only used for reporting, not aggregating?
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
@define
class GriddingSectorComponentsCarbonRemovalReporting:
    """
    Definition of the components of a carbon removal gridding sector for reporting

    This is for carbon removal i.e. is for CO2 only.

    This is meant for internal use only.
    """

    gridding_sector: str
    """The gridding sector"""

    spatial_resolution: SpatialResolutionOption

    input_sectors: tuple[str, ...]
    """
    The input sectors
    """

    input_sectors_optional: tuple[str, ...]
    """
    The input sectors that are optional
    """

    reporting_only: bool
    """Is this definition only used for reporting, not aggregating?"""

    def to_complete_variables(self) -> tuple[str, ...]:
        """
        Convert to the complete set of variables for this gridding sector
        """
        return tuple(f"Carbon Removal|{sector}" for sector in self.input_sectors)

    def to_required_variables(self) -> tuple[str, ...]:
        """
        Convert to the required set of variables for this gridding sector
        """
        return tuple(
            f"Carbon Removal|{sector}"
            for sector in self.input_sectors
            if sector not in self.input_sectors_optional
        )

gridding_sector instance-attribute #

gridding_sector: str

The gridding sector

input_sectors instance-attribute #

input_sectors: tuple[str, ...]

The input sectors

input_sectors_optional instance-attribute #

input_sectors_optional: tuple[str, ...]

The input sectors that are optional

reporting_only instance-attribute #

reporting_only: bool

Is this definition only used for reporting, not aggregating?

to_complete_variables #

to_complete_variables() -> tuple[str, ...]

Convert to the complete set of variables for this gridding sector

Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
141
142
143
144
145
def to_complete_variables(self) -> tuple[str, ...]:
    """
    Convert to the complete set of variables for this gridding sector
    """
    return tuple(f"Carbon Removal|{sector}" for sector in self.input_sectors)

to_required_variables #

to_required_variables() -> tuple[str, ...]

Convert to the required set of variables for this gridding sector

Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
147
148
149
150
151
152
153
154
155
def to_required_variables(self) -> tuple[str, ...]:
    """
    Convert to the required set of variables for this gridding sector
    """
    return tuple(
        f"Carbon Removal|{sector}"
        for sector in self.input_sectors
        if sector not in self.input_sectors_optional
    )

ReaggregatorBasic #

Reaggregator that follows this module's logic

Methods:

Name Description
assert_has_all_required_timeseries

Assert that the data has all the required timeseries
assert_is_internally_consistent

Assert that the data is internally consistent
default_tols_internal_consistency

Get default tolerances for internal consistency checks
get_internal_consistency_checking_index

Get the index which selects only data relevant for checking internal consistency
to_complete

Convert the raw data to complete data
to_gridding_sectors

Re-aggregate data to the sectors used for gridding

Attributes:

Name Type Description
internal_consistency_tolerances Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]

Tolerances to apply when checking the internal consistency of the data
model_regions tuple[str, ...]

Model regions to use while reaggregating
region_level str

Region level in the data index
unit_level str

Unit level in the data index
variable_level str

Variable level in the data index
world_region str

The value used when the data represents the sum over all regions
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
@define
class ReaggregatorBasic:
    """
    Reaggregator that follows this module's logic
    """

    model_regions: tuple[str, ...]
    """Model regions to use while reaggregating"""

    region_level: str = "region"
    """Region level in the data index"""

    unit_level: str = "unit"
    """Unit level in the data index"""

    variable_level: str = "variable"
    """Variable level in the data index"""

    world_region: str = "World"
    """
    The value used when the data represents the sum over all regions

    (Having a value for this is odd,
    there should really just be no region level when data is the sum,
    but this is the data format used so we have to follow this convention.)
    """

    internal_consistency_tolerances: (
        Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]
    ) = field()
    """
    Tolerances to apply when checking the internal consistency of the data
    """

    @internal_consistency_tolerances.default
    def default_tols_internal_consistency(
        self,
    ) -> Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]:
        """
        Get default tolerances for internal consistency checks
        """
        return get_default_internal_conistency_checking_tolerances()

    def assert_has_all_required_timeseries(self, indf: pd.DataFrame) -> None:
        """
        Assert that the data has all the required timeseries

        Parameters
        ----------
        indf
            Data to check

        Raises
        ------
        NotCompleteError
            `indf` is not complete
        """
        assert_has_all_required_timeseries(
            indf,
            model_regions=self.model_regions,
            world_region=self.world_region,
            region_level=self.region_level,
            variable_level=self.variable_level,
        )

    def assert_is_internally_consistent(self, indf: pd.DataFrame) -> None:
        """
        Assert that the data is internally consistent

        Parameters
        ----------
        indf
            Data to check

        Raises
        ------
        InternalConsistencyError
            The data is not internally consistent
        """
        assert_is_internally_consistent(
            indf,
            model_regions=self.model_regions,
            tolerances=self.internal_consistency_tolerances,
            world_region=self.world_region,
            region_level=self.region_level,
            unit_level=self.unit_level,
            variable_level=self.variable_level,
        )

    def get_internal_consistency_checking_index(self) -> pd.MultiIndex:
        """
        Get the index which selects only data relevant for checking internal consistency

        Returns
        -------
        :
            Internal consistency checking index
        """
        return get_internal_consistency_checking_index(
            model_regions=self.model_regions,
            world_region=self.world_region,
            region_level=self.region_level,
            variable_level=self.variable_level,
        )

    def to_complete(self, raw: pd.DataFrame) -> ToCompleteResult:
        """
        Convert the raw data to complete data

        Parameters
        ----------
        raw
            Raw data

        Returns
        -------
        :
            To complete result
        """
        return to_complete(
            indf=raw,
            model_regions=self.model_regions,
            unit_level=self.unit_level,
            variable_level=self.variable_level,
            region_level=self.region_level,
            world_region=self.world_region,
        )

    def to_gridding_sectors(self, indf: pd.DataFrame) -> pd.DataFrame:
        """
        Re-aggregate data to the sectors used for gridding

        Parameters
        ----------
        indf
            Data to re-aggregate

        Returns
        -------
        :
            Data re-aggregated to the gridding sectors
        """
        return to_gridding_sectors(
            indf=indf, region_level=self.region_level, world_region=self.world_region
        )

internal_consistency_tolerances class-attribute instance-attribute #

internal_consistency_tolerances: (
    Mapping[str, Mapping[str, float]]
    | Mapping[str, Mapping[str, PINT_SCALAR]]
) = field()

Tolerances to apply when checking the internal consistency of the data

model_regions instance-attribute #

model_regions: tuple[str, ...]

Model regions to use while reaggregating

region_level class-attribute instance-attribute #

region_level: str = 'region'

Region level in the data index

unit_level class-attribute instance-attribute #

unit_level: str = 'unit'

Unit level in the data index

variable_level class-attribute instance-attribute #

variable_level: str = 'variable'

Variable level in the data index

world_region class-attribute instance-attribute #

world_region: str = 'World'

The value used when the data represents the sum over all regions

(Having a value for this is odd, there should really just be no region level when data is the sum, but this is the data format used so we have to follow this convention.)

assert_has_all_required_timeseries #

assert_has_all_required_timeseries(indf: DataFrame) -> None

Assert that the data has all the required timeseries

Parameters:

Name Type Description Default
indf DataFrame

Data to check

 required

Raises:

Type Description
NotCompleteError

indf is not complete
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
def assert_has_all_required_timeseries(self, indf: pd.DataFrame) -> None:
    """
    Assert that the data has all the required timeseries

    Parameters
    ----------
    indf
        Data to check

    Raises
    ------
    NotCompleteError
        `indf` is not complete
    """
    assert_has_all_required_timeseries(
        indf,
        model_regions=self.model_regions,
        world_region=self.world_region,
        region_level=self.region_level,
        variable_level=self.variable_level,
    )

assert_is_internally_consistent #

assert_is_internally_consistent(indf: DataFrame) -> None

Assert that the data is internally consistent

Parameters:

Name Type Description Default
indf DataFrame

Data to check

 required

Raises:

Type Description
InternalConsistencyError

The data is not internally consistent
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
def assert_is_internally_consistent(self, indf: pd.DataFrame) -> None:
    """
    Assert that the data is internally consistent

    Parameters
    ----------
    indf
        Data to check

    Raises
    ------
    InternalConsistencyError
        The data is not internally consistent
    """
    assert_is_internally_consistent(
        indf,
        model_regions=self.model_regions,
        tolerances=self.internal_consistency_tolerances,
        world_region=self.world_region,
        region_level=self.region_level,
        unit_level=self.unit_level,
        variable_level=self.variable_level,
    )

default_tols_internal_consistency #

default_tols_internal_consistency() -> (
    Mapping[str, Mapping[str, float]]
    | Mapping[str, Mapping[str, PINT_SCALAR]]
)

Get default tolerances for internal consistency checks

Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1465
1466
1467
1468
1469
1470
1471
1472
@internal_consistency_tolerances.default
def default_tols_internal_consistency(
    self,
) -> Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]:
    """
    Get default tolerances for internal consistency checks
    """
    return get_default_internal_conistency_checking_tolerances()

get_internal_consistency_checking_index #

get_internal_consistency_checking_index() -> MultiIndex

Get the index which selects only data relevant for checking internal consistency

Returns:

Type Description
MultiIndex

Internal consistency checking index
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
def get_internal_consistency_checking_index(self) -> pd.MultiIndex:
    """
    Get the index which selects only data relevant for checking internal consistency

    Returns
    -------
    :
        Internal consistency checking index
    """
    return get_internal_consistency_checking_index(
        model_regions=self.model_regions,
        world_region=self.world_region,
        region_level=self.region_level,
        variable_level=self.variable_level,
    )

to_complete #

to_complete(raw: DataFrame) -> ToCompleteResult

Convert the raw data to complete data

Parameters:

Name Type Description Default
raw DataFrame

Raw data

 required

Returns:

Type Description
ToCompleteResult

To complete result
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
def to_complete(self, raw: pd.DataFrame) -> ToCompleteResult:
    """
    Convert the raw data to complete data

    Parameters
    ----------
    raw
        Raw data

    Returns
    -------
    :
        To complete result
    """
    return to_complete(
        indf=raw,
        model_regions=self.model_regions,
        unit_level=self.unit_level,
        variable_level=self.variable_level,
        region_level=self.region_level,
        world_region=self.world_region,
    )

to_gridding_sectors #

to_gridding_sectors(indf: DataFrame) -> DataFrame

Re-aggregate data to the sectors used for gridding

Parameters:

Name Type Description Default
indf DataFrame

Data to re-aggregate

 required

Returns:

Type Description
DataFrame

Data re-aggregated to the gridding sectors
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
def to_gridding_sectors(self, indf: pd.DataFrame) -> pd.DataFrame:
    """
    Re-aggregate data to the sectors used for gridding

    Parameters
    ----------
    indf
        Data to re-aggregate

    Returns
    -------
    :
        Data re-aggregated to the gridding sectors
    """
    return to_gridding_sectors(
        indf=indf, region_level=self.region_level, world_region=self.world_region
    )

aggregate_cols #

aggregate_cols(
    df: DataFrame, aggregations: dict[str, list[str]]
) -> DataFrame

Aggregate columns

This is a helper function for to_gridding_sectors.

It does the aggregation in place so we can check that all the columns were used.

Parameters:

Name Type Description Default
df DataFrame

Starting pd.DataFrame

 required
aggregations dict[str, list[str]]

Aggregations to apply

Each key is the output column, each value is the components that contribute to the output column.

 required

Returns:

Type Description
DataFrame

df with the aggregations applied
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
def aggregate_cols(
    df: pd.DataFrame, aggregations: dict[str, list[str]]
) -> pd.DataFrame:
    """
    Aggregate columns

    This is a helper function for [to_gridding_sectors][(m).].

    It does the aggregation in place so we can check that all the columns were used.

    Parameters
    ----------
    df
        Starting [pd.DataFrame][pandas.DataFrame]

    aggregations
        Aggregations to apply

        Each key is the output column,
        each value is the components that contribute to the output column.

    Returns
    -------
    :
        `df` with the aggregations applied
    """
    for aggregate, components in aggregations.items():
        df[aggregate] = df[components].sum(axis="columns")
        df = df.drop(
            # Subtract aggregate in case the aggregate and component have the same name
            list(set(components) - {aggregate}),
            axis="columns",
        )

    return df

assert_has_all_required_timeseries #

assert_has_all_required_timeseries(
    df: DataFrame,
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> None

Assert that the data has all the required timeseries

Parameters:

Name Type Description Default
df DataFrame

Data to check

 required
model_regions tuple[str, ...]

Model regions to use while reaggregating

 required
world_region str

The value used when the data represents the sum over all regions

 'World'
region_level str

Region level in the data index

 'region'
variable_level str

Variable level in the data index

 'variable'

Raises:

Type Description
NotCompleteError

indf is not complete
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
def assert_has_all_required_timeseries(
    df: pd.DataFrame,
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> None:
    """
    Assert that the data has all the required timeseries

    Parameters
    ----------
    df
        Data to check

    model_regions
        Model regions to use while reaggregating

    world_region
        The value used when the data represents the sum over all regions

    region_level
        Region level in the data index

    variable_level
        Variable level in the data index

    Raises
    ------
    NotCompleteError
        `indf` is not complete
    """
    assert_all_groups_are_complete(
        df,
        complete_index=get_required_timeseries_index(
            model_regions=model_regions,
            world_region=world_region,
            region_level=region_level,
            variable_level=variable_level,
        ),
    )

assert_is_internally_consistent #

assert_is_internally_consistent(
    df: DataFrame,
    model_regions: tuple[str, ...],
    tolerances: Mapping[str, Mapping[str, float]]
    | Mapping[str, Mapping[str, PINT_SCALAR]],
    world_region: str = "World",
    region_level: str = "region",
    unit_level: str = "unit",
    variable_level: str = "variable",
) -> None

Assert that the data is internally consistent

Parameters:

Name Type Description Default
df DataFrame

Data to check

 required
model_regions tuple[str, ...]

Model regions to use while reaggregating

 required
tolerances Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]

Tolerances to apply while checking internal consistency

Each key should be a variable up to species info e.g. "Emission|CH4" and each value should be the tolerance arguments to pass to np.isclose. These tolerance arguments can be pint quantities, in which case they are converted to the data's units before passing to np.isclose.

 required
world_region str

The value used when the data represents the sum over all regions

 'World'
region_level str

Region level in the data index

 'region'
unit_level str

Unit level in the data index

 'unit'
variable_level str

Variable level in the data index

 'variable'

Raises:

Type Description
InternalConsistencyError

The data is not internally consistent at the given tolerances
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
def assert_is_internally_consistent(  # noqa: PLR0913
    df: pd.DataFrame,
    model_regions: tuple[str, ...],
    tolerances: Mapping[str, Mapping[str, float]]
    | Mapping[str, Mapping[str, PINT_SCALAR]],
    world_region: str = "World",
    region_level: str = "region",
    unit_level: str = "unit",
    variable_level: str = "variable",
) -> None:
    """
    Assert that the data is internally consistent

    Parameters
    ----------
    df
        Data to check

    model_regions
        Model regions to use while reaggregating

    tolerances
        Tolerances to apply while checking internal consistency

        Each key should be a variable up to species info
        e.g. "Emission|CH4"
        and each value should be the tolerance arguments to pass
        to [np.isclose][numpy.isclose].
        These tolerance arguments can be pint quantities,
        in which case they are converted to the data's units
        before passing to [np.isclose][numpy.isclose].

    world_region
        The value used when the data represents the sum over all regions

    region_level
        Region level in the data index

    unit_level
        Unit level in the data index

    variable_level
        Variable level in the data index

    Raises
    ------
    InternalConsistencyError
        The data is not internally consistent at the given tolerances
    """
    try:
        import pint
    except ModuleNotFoundError:
        pint = None  # type: ignore

    internal_consistency_checking_index = get_internal_consistency_checking_index(
        model_regions=model_regions,
        world_region=world_region,
        region_level=region_level,
        variable_level=variable_level,
    )

    # Hard-code the logic here
    # because that's what is needed for consistency with the rest of the module.
    # This is one of the issues with the data model used by ScmRun, pyam etc.:
    # the convention is that the first two levels are the species total
    # but this is only implied and not explicit at all
    # (and not even followed in all cases e.g. Emissions|HFC|HFC23).
    def get_species_total_variable(v: str) -> str:
        return "|".join(v.split("|")[:2])

    for species_total_variable, df_species in df.groupby(
        df.index.get_level_values(variable_level).map(get_species_total_variable)
    ):
        df_species_total_reported = df_species.loc[
            (
                df_species.index.get_level_values(variable_level)
                == species_total_variable
            )
            & (df_species.index.get_level_values(region_level) == world_region)
        ]
        if df_species_total_reported.empty:
            # Nothing reported so can move on
            continue

        df_species_internal_consistency_checking_relevant = multi_index_lookup(
            df_species, internal_consistency_checking_index
        )
        if df_species_internal_consistency_checking_relevant.empty:
            # Nothing relevant for checking internal consistency
            continue

        # Note: what we're checking here is that if you sum over the sectors
        # and regions using the index which provides internal consistency,
        # you should get the reported totals.
        # This is different to checking that the implied hierarchy
        # from the variable names
        # (i.e. that levels are separated by "|"
        # and each level should be the sum of its components)
        # is not what we're checking.

        # TODO: split out a function like
        # assert_reported_matches_sum_of_components
        # Would have to be very specific to this setup
        # because of how many different ways it can go wrong
        # e.g. you can have extra components that aren't used,
        # incorrect aggregation

        df_species_aggregate = get_region_sector_sum(
            df_species_internal_consistency_checking_relevant,
            region_level=region_level,
            world_region=world_region,
        ).reorder_levels(df_species.index.names)

        tolerances_species = {}
        for kwarg, value in tolerances[species_total_variable].items():
            if pint is not None and isinstance(value, pint.Quantity):
                if kwarg == "atol":
                    species_units = df_species.index.get_level_values(
                        unit_level
                    ).unique()
                    if len(species_units) > 1:
                        msg = (
                            "Cannot use pint conversion "
                            "if your data contains different units. "
                            f"For {species_total_variable=}, we have {species_units=}"
                        )
                        raise ValueError(msg)

                    tolerances_species[kwarg] = value.to(species_units[0]).m

                elif kwarg == "rtol":
                    tolerances_species[kwarg] = value.to("dimensionless").m

                else:
                    raise NotImplementedError(kwarg)

            else:
                tolerances_species[kwarg] = value

        comparison_species = compare_close(
            left=df_species_total_reported,
            right=df_species_aggregate,
            left_name="reported_total",
            right_name="derived_from_input",
            **tolerances_species,
        )

        if not comparison_species.empty:
            raise InternalConsistencyError(
                differences=comparison_species,
                data_that_was_summed=df_species,
                # Would need something like this to give full details
                # data_that_was_not_summed=data_that_was_not_summed,
                tolerances=tolerances_species,
            )

get_complete_timeseries_index #

get_complete_timeseries_index(
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> MultiIndex

Get the index of complete data

Parameters:

Name Type Description Default
model_regions tuple[str, ...]

Model regions to use while reaggregating

 required
world_region str

The value used when the data represents the sum over all regions

 'World'
region_level str

Region level in the data index

 'region'
variable_level str

Variable level in the data index

 'variable'

Returns:

Type Description
MultiIndex

Index of complete data
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
def get_complete_timeseries_index(
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> pd.MultiIndex:
    """
    Get the index of complete data

    Parameters
    ----------
    model_regions
        Model regions to use while reaggregating

    world_region
        The value used when the data represents the sum over all regions

    region_level
        Region level in the data index

    variable_level
        Variable level in the data index

    Returns
    -------
    :
        Index of complete data
    """
    world_required = pd.MultiIndex.from_product(
        [COMPLETE_WORLD_VARIABLES, [world_region]], names=[variable_level, region_level]
    )

    model_region_required = pd.MultiIndex.from_product(
        [COMPLETE_MODEL_REGION_VARIABLES, model_regions],
        names=[variable_level, region_level],
    )

    res: pd.MultiIndex = world_required.append(model_region_required)  # type: ignore

    return res

get_default_internal_conistency_checking_tolerances #

get_default_internal_conistency_checking_tolerances() -> (
    Mapping[str, Mapping[str, float]]
    | Mapping[str, Mapping[str, PINT_SCALAR]]
)

Get default tolerances used when checking the internal consistency of data

If openscm_units is available, we use pint quantities for the tolerances to add unit awareness. If not, we return plain floats.

Returns:

Type Description
Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]

Tolerances to use when checking the internal consistency of the data
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
def get_default_internal_conistency_checking_tolerances() -> (
    Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]
):
    """
    Get default tolerances used when checking the internal consistency of data

    If [openscm_units][] is available,
    we use [pint](https://pint.readthedocs.io)
    quantities for the tolerances to add unit awareness.
    If not, we return plain floats.

    Returns
    -------
    :
        Tolerances to use when checking the internal consistency of the data
    """
    try:
        import openscm_units

        Q = openscm_units.unit_registry.Quantity

        default_tolerances: (
            Mapping[str, Mapping[str, float]] | Mapping[str, Mapping[str, PINT_SCALAR]]
        ) = {  # type: ignore # some issue with openscm-units type hints
            "Emissions|BC": dict(rtol=1e-3, atol=Q(1e-3, "Mt BC/yr")),
            "Emissions|CH4": dict(rtol=1e-3, atol=Q(1e-2, "Mt CH4/yr")),
            "Emissions|CO": dict(rtol=1e-3, atol=Q(1e-1, "Mt CO/yr")),
            "Emissions|CO2": dict(rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")),
            "Emissions|NH3": dict(rtol=1e-3, atol=Q(1e-2, "Mt NH3/yr")),
            "Emissions|NOx": dict(rtol=1e-3, atol=Q(1e-2, "Mt NO2/yr")),
            "Emissions|OC": dict(rtol=1e-3, atol=Q(1e-3, "Mt OC/yr")),
            "Emissions|Sulfur": dict(rtol=1e-3, atol=Q(1e-2, "Mt SO2/yr")),
            "Emissions|VOC": dict(rtol=1e-3, atol=Q(1e-2, "Mt VOC/yr")),
            "Emissions|N2O": dict(rtol=1e-3, atol=Q(1e-1, "kt N2O/yr")),
            "Carbon Removal|Enhanced Weathering": dict(
                rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")
            ),
            "Carbon Removal|Geological Storage": dict(
                rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")
            ),
            "Carbon Removal|Long-Lived Materials": dict(
                rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")
            ),
            "Carbon Removal|Ocean": dict(rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")),
            "Carbon Removal|Land Use": dict(rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")),
            "Carbon Removal|Other": dict(rtol=1e-3, atol=Q(1e0, "Mt CO2/yr")),
        }

    except ImportError:
        default_tolerances = {
            "Emissions|BC": dict(rtol=1e-3, atol=1e-6),
            "Emissions|CH4": dict(rtol=1e-3, atol=1e-6),
            "Emissions|CO": dict(rtol=1e-3, atol=1e-6),
            "Emissions|CO2": dict(rtol=1e-3, atol=1e-6),
            "Emissions|NH3": dict(rtol=1e-3, atol=1e-6),
            "Emissions|NOx": dict(rtol=1e-3, atol=1e-6),
            "Emissions|OC": dict(rtol=1e-3, atol=1e-6),
            "Emissions|Sulfur": dict(rtol=1e-3, atol=1e-6),
            "Emissions|VOC": dict(rtol=1e-3, atol=1e-6),
            "Emissions|N2O": dict(rtol=1e-3, atol=1e-6),
            "Carbon Removal|Enhanced Weathering": dict(rtol=1e-3, atol=1e-6),
            "Carbon Removal|Geological Storage": dict(rtol=1e-3, atol=1e-6),
            "Carbon Removal|Long-Lived Materials": dict(rtol=1e-3, atol=1e-6),
            "Carbon Removal|Ocean": dict(rtol=1e-3, atol=1e-6),
            "Carbon Removal|Land Use|Biochar": dict(rtol=1e-3, atol=1e-6),
            "Carbon Removal|Land Use|Soil Carbon Management": dict(
                rtol=1e-3, atol=1e-6
            ),
            "Carbon Removal|Other": dict(rtol=1e-3, atol=1e-6),
        }

    return default_tolerances

get_example_input #

get_example_input(
    model_regions: tuple[str, ...],
    global_only_variables: tuple[tuple[str, str], ...] = (
        ("Emissions|HFC|HFC23", "kt HFC23/yr"),
        ("Emissions|HFC", "kt HFC134a-equiv/yr"),
        ("Emissions|HFC|HFC134a", "kt HFC134a/yr"),
        ("Emissions|HFC|HFC43-10", "kt HFC43-10/yr"),
        ("Emissions|PFC", "kt CF4-equiv/yr"),
        ("Emissions|F-Gases", "Mt CO2-equiv/yr"),
        ("Emissions|SF6", "kt SF6/yr"),
        ("Emissions|CF4", "kt CF4/yr"),
        ("Emissions|C2F6", "kt C2F6/yr"),
        ("Emissions|C6F14", "kt C6F14/yr"),
    ),
    timepoints: NP_ARRAY_OF_FLOAT_OR_INT = arange(
        2010, 2100 + 1, 10.0
    ),
    get_variable_unit: Callable[
        [str], str
    ] = get_variable_unit_default,
    rng: Generator = default_rng(),
    world_region: str = "World",
    model: str = "model",
    scenario: str = "scenario",
    region_level: str = "region",
    variable_level: str = "variable",
    model_level: str = "model",
    scenario_level: str = "scenario",
    unit_level: str = "unit",
    columns_name: str = "year",
) -> DataFrame

Get example input data

Parameters:

Name Type Description Default
model_regions tuple[str, ...]

Model regions to use in the example data

 required
global_only_variables tuple[tuple[str, str], ...]

Variables to include only at the global total level

 (('Emissions|HFC|HFC23', 'kt HFC23/yr'), ('Emissions|HFC', 'kt HFC134a-equiv/yr'), ('Emissions|HFC|HFC134a', 'kt HFC134a/yr'), ('Emissions|HFC|HFC43-10', 'kt HFC43-10/yr'), ('Emissions|PFC', 'kt CF4-equiv/yr'), ('Emissions|F-Gases', 'Mt CO2-equiv/yr'), ('Emissions|SF6', 'kt SF6/yr'), ('Emissions|CF4', 'kt CF4/yr'), ('Emissions|C2F6', 'kt C2F6/yr'), ('Emissions|C6F14', 'kt C6F14/yr'))
timepoints NP_ARRAY_OF_FLOAT_OR_INT

Timepoints to use in the example

 arange(2010, 2100 + 1, 10.0)
get_variable_unit Callable[[str], str]

Function to use to get the unit for each variable

 get_variable_unit_default
rng Generator

Random number generator

 default_rng()
world_region str

The value used when the data represents the sum over all regions

 'World'
model str

Model metadata value

 'model'
scenario str

Scenario metadata value

 'scenario'
region_level str

Region level in the data index

 'region'
variable_level str

Variable level in the data index

 'variable'
model_level str

Model level in the data index

 'model'
scenario_level str

Scenario level in the data index

 'scenario'
unit_level str

Unit level in the data index

 'unit'
columns_name str

Name of the columns in the output

 'year'

Returns:

Type Description
DataFrame

Example input expected by the re-aggregator
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
def get_example_input(  # noqa: PLR0913
    model_regions: tuple[str, ...],
    global_only_variables: tuple[tuple[str, str], ...] = (
        ("Emissions|HFC|HFC23", "kt HFC23/yr"),
        ("Emissions|HFC", "kt HFC134a-equiv/yr"),
        ("Emissions|HFC|HFC134a", "kt HFC134a/yr"),
        ("Emissions|HFC|HFC43-10", "kt HFC43-10/yr"),
        ("Emissions|PFC", "kt CF4-equiv/yr"),
        ("Emissions|F-Gases", "Mt CO2-equiv/yr"),
        ("Emissions|SF6", "kt SF6/yr"),
        ("Emissions|CF4", "kt CF4/yr"),
        ("Emissions|C2F6", "kt C2F6/yr"),
        ("Emissions|C6F14", "kt C6F14/yr"),
    ),
    timepoints: NP_ARRAY_OF_FLOAT_OR_INT = np.arange(2010, 2100 + 1, 10.0),
    get_variable_unit: Callable[[str], str] = get_variable_unit_default,
    rng: np.random.Generator = np.random.default_rng(),
    world_region: str = "World",
    model: str = "model",
    scenario: str = "scenario",
    region_level: str = "region",
    variable_level: str = "variable",
    model_level: str = "model",
    scenario_level: str = "scenario",
    unit_level: str = "unit",
    columns_name: str = "year",
) -> pd.DataFrame:
    """
    Get example input data

    Parameters
    ----------
    model_regions
        Model regions to use in the example data

    global_only_variables
        Variables to include only at the global total level

    timepoints
        Timepoints to use in the example

    get_variable_unit
        Function to use to get the unit for each variable

    rng
        Random number generator

    world_region
        The value used when the data represents the sum over all regions

    model
        Model metadata value

    scenario
        Scenario metadata value

    region_level
        Region level in the data index

    variable_level
        Variable level in the data index

    model_level
        Model level in the data index

    scenario_level
        Scenario level in the data index

    unit_level
        Unit level in the data index

    columns_name
        Name of the columns in the output

    Returns
    -------
    :
        Example input expected by the re-aggregator
    """
    # Hard-coded because the example input
    # is tightly coupled to the idea of completeness
    starting_variables = (*COMPLETE_MODEL_REGION_VARIABLES, *COMPLETE_WORLD_VARIABLES)

    # Get our starting point
    start_index = pd.MultiIndex.from_product(
        [starting_variables, model_regions, [model], [scenario]],
        names=[variable_level, region_level, model_level, scenario_level],
    )
    start = pd.DataFrame(
        rng.random((start_index.shape[0], timepoints.size)),
        columns=pd.Index(timepoints, name=columns_name),
        index=start_index,
    )

    # Aggregate up the sectors
    start_sector_full = aggregate_df_level(
        start, level="variable", on_clash="overwrite"
    )
    # Aggregate up the regions
    start_sector_full_region_sum = set_index_levels_func(
        groupby_except(start_sector_full, region_level).sum(),
        {region_level: world_region},
    )

    # Put it altogether
    all_info = pd.concat(
        [
            start_sector_full,
            start_sector_full_region_sum.reorder_levels(start_sector_full.index.names),
        ]
    )

    # Keep only the bits required for completeness
    complete_index = get_complete_timeseries_index(
        model_regions=model_regions,
        world_region=world_region,
        region_level=region_level,
        variable_level=variable_level,
    )
    res_gridding = multi_index_lookup(all_info, complete_index)

    # Add unit info
    res_gridding.index = update_levels_from_other(
        res_gridding.index,  # type: ignore # fix when moving to pandas-openscm
        {unit_level: (variable_level, get_variable_unit)},
    )

    if global_only_variables:
        global_only = pd.DataFrame(
            rng.random((len(global_only_variables), timepoints.size)),
            columns=res_gridding.columns,
            index=pd.MultiIndex.from_tuples(
                [
                    (variable, unit, world_region, model, scenario)
                    for (variable, unit) in global_only_variables
                ],
                names=[
                    variable_level,
                    unit_level,
                    region_level,
                    model_level,
                    scenario_level,
                ],
            ),
        )

        res = pd.concat(
            [res_gridding, global_only.reorder_levels(res_gridding.index.names)]
        )

    else:
        res = res_gridding

    return res

get_internal_consistency_checking_index #

get_internal_consistency_checking_index(
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> MultiIndex

Get the index which selects only data relevant for checking internal consistency

Parameters:

Name Type Description Default
model_regions tuple[str, ...]

Model regions to use while reaggregating

 required
world_region str

The value used when the data represents the sum over all regions

 'World'
region_level str

Region level in the data index

 'region'
variable_level str

Variable level in the data index

 'variable'

Returns:

Type Description
MultiIndex

Internal consistency checking index
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
def get_internal_consistency_checking_index(
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> pd.MultiIndex:
    """
    Get the index which selects only data relevant for checking internal consistency

    Parameters
    ----------
    model_regions
        Model regions to use while reaggregating

    world_region
        The value used when the data represents the sum over all regions

    region_level
        Region level in the data index

    variable_level
        Variable level in the data index

    Returns
    -------
    :
        Internal consistency checking index
    """
    world_internal_consistency_checking = pd.MultiIndex.from_product(
        [COMPLETE_WORLD_VARIABLES, [world_region]], names=[variable_level, region_level]
    )
    model_region_consistency_checking_variables = [
        v
        for v in COMPLETE_MODEL_REGION_VARIABLES
        if not (
            # Avoid double counting with "Energy|Demand|Transportation"
            SECTOR_DOMESTIC_AVIATION in v
            or (v.startswith("Carbon Removal") and v.count("|") <= 1)
        )
    ]
    # Add components of the tree that are needed for consistency checking
    # but we don't otherwise use.
    # This is a nasty hack that we should clean up in future
    # so the logic of this is more obvious.
    model_region_consistency_checking_variables.append(
        "Carbon Removal|Land Use|Agroforestry"
    )
    model_region_consistency_checking_variables.append(
        "Carbon Removal|Land Use|Re/Afforestation"
    )
    model_region_consistency_checking_variables.append(
        "Carbon Removal|Land Use|Forest Management"
    )
    model_region_consistency_checking_variables.append("Carbon Removal|Land Use|Other")
    model_region_consistency_checking_variables.append(
        "Carbon Removal|Geological Storage|Other Sources"
    )
    model_region_consistency_checking_variables.append(
        "Carbon Removal|Geological Storage|Synthetic Fuels"
    )
    model_region_consistency_checking = pd.MultiIndex.from_product(
        [model_region_consistency_checking_variables, model_regions],
        names=[variable_level, region_level],
    )

    res: pd.MultiIndex = world_internal_consistency_checking.append(  # type: ignore
        model_region_consistency_checking
    )

    return res

get_required_timeseries_index #

get_required_timeseries_index(
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> MultiIndex

Get the index of required data

Parameters:

Name Type Description Default
model_regions tuple[str, ...]

Model regions to use while reaggregating

 required
world_region str

The value used when the data represents the sum over all regions

 'World'
region_level str

Region level in the data index

 'region'
variable_level str

Variable level in the data index

 'variable'

Returns:

Type Description
MultiIndex

Index for required data
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
def get_required_timeseries_index(
    model_regions: tuple[str, ...],
    world_region: str = "World",
    region_level: str = "region",
    variable_level: str = "variable",
) -> pd.MultiIndex:
    """
    Get the index of required data

    Parameters
    ----------
    model_regions
        Model regions to use while reaggregating

    world_region
        The value used when the data represents the sum over all regions

    region_level
        Region level in the data index

    variable_level
        Variable level in the data index

    Returns
    -------
    :
        Index for required data
    """
    world_required = pd.MultiIndex.from_product(
        [REQUIRED_WORLD_VARIABLES, [world_region]], names=[variable_level, region_level]
    )

    model_region_required = pd.MultiIndex.from_product(
        [REQUIRED_MODEL_REGION_VARIABLES, model_regions],
        names=[variable_level, region_level],
    )

    res: pd.MultiIndex = world_required.append(model_region_required)  # type: ignore

    return res

to_complete #

to_complete(
    indf: DataFrame,
    model_regions: tuple[str, ...],
    unit_level: str = "unit",
    variable_level: str = "variable",
    region_level: str = "region",
    world_region: str = "World",
) -> ToCompleteResult

Convert the raw data to complete data

Parameters:

Name Type Description Default
indf DataFrame

Data to process

 required
model_regions tuple[str, ...]

Model regions to use while reaggregating

 required
unit_level str

Unit level in the data index

 'unit'
variable_level str

Variable level in the data index

 'variable'
region_level str

Region level in the data index

 'region'
world_region str

The value used when the data represents the sum over all regions

 'World'

Returns:

Type Description
ToCompleteResult

To complete result
Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
def to_complete(  # noqa: PLR0913
    indf: pd.DataFrame,
    model_regions: tuple[str, ...],
    unit_level: str = "unit",
    variable_level: str = "variable",
    region_level: str = "region",
    world_region: str = "World",
) -> ToCompleteResult:
    """
    Convert the raw data to complete data

    Parameters
    ----------
    indf
        Data to process

    model_regions
        Model regions to use while reaggregating

    unit_level
        Unit level in the data index

    variable_level
        Variable level in the data index

    region_level
        Region level in the data index

    world_region
        The value used when the data represents the sum over all regions

    Returns
    -------
    :
        To complete result
    """
    assert_only_working_on_variable_unit_region_variations(indf)

    complete_index = get_complete_timeseries_index(
        model_regions=model_regions,
        region_level=region_level,
        variable_level=variable_level,
        world_region=world_region,
    )

    keep = multi_index_lookup(indf, complete_index)
    missing_indexes = get_missing_levels(
        keep.index,  # type: ignore # pandas-stubs confused
        complete_index=complete_index,
        unit_col=unit_level,
    )

    if missing_indexes.empty:
        res = ToCompleteResult(complete=keep, assumed_zero=None)
    else:
        keep_split = split_sectors(keep, middle_level="species")

        species_unit_map = {
            species: unit
            for species, unit in keep_split.index.droplevel(
                keep_split.index.names.difference(["species", unit_level])  # type: ignore #pandas-stubs confused
            )
            .drop_duplicates()
            .reorder_levels(["species", unit_level])
        }

        emissions_mask = missing_indexes.get_level_values(
            variable_level
        ).str.startswith("Emissions")
        missing_indexes_emissions = missing_indexes[emissions_mask]
        missing_indexes_carbon_removal = missing_indexes[~emissions_mask]

        if not missing_indexes_emissions.empty:
            missing_indexes_emissions_split = split_sectors(missing_indexes_emissions)  # type: ignore # type hint is wrong upstream (fix when moving to pandas-openscm)
            zeros_index_split = update_levels_from_other(
                missing_indexes_emissions_split,  # type: ignore # type hint is wrong upstream (fix when moving to pandas-openscm)
                {unit_level: ("species", species_unit_map)},
            )
            zeros_index_emissions: pd.MultiIndex = combine_sectors(  # type: ignore # need to think through type hints for combine_sectors more carefully
                zeros_index_split,  # type: ignore # need to think through type hints for combine_sectors more carefully
                middle_level="species",
            )

        if not missing_indexes_carbon_removal.empty:
            existing_carbon_removal = keep_split[
                keep_split.index.get_level_values("table").str.startswith(
                    "Carbon Removal"
                )
            ]
            if existing_carbon_removal.empty:
                unit = species_unit_map["CO2"]
            else:
                # Use the first, as good as any
                unit = existing_carbon_removal.index.get_level_values("unit")[0]

            zeros_index_carbon_removal = set_levels(
                missing_indexes_carbon_removal, {unit_level: unit}
            )

        if (
            not missing_indexes_emissions.empty
            and not missing_indexes_carbon_removal.empty
        ):
            zeros_index = zeros_index_emissions.append(  # type: ignore # not supported by pandas-stubs
                zeros_index_carbon_removal.reorder_levels(zeros_index_emissions.names)  # type: ignore # not supported by pandas-stubs
            )

        elif not missing_indexes_emissions.empty:
            zeros_index = zeros_index_emissions

        elif not missing_indexes_carbon_removal.empty:
            zeros_index = zeros_index_carbon_removal

        else:
            raise AssertionError

        other_levels_deduped = indf.index.droplevel(
            [variable_level, unit_level, region_level]
        ).drop_duplicates()
        if other_levels_deduped.shape[0] != 1:
            msg = f"Multiple values in other levels:\n{other_levels_deduped=}"
            raise AssertionError(msg)

        extra_levels = {
            level: value
            for level, value in zip(other_levels_deduped.names, other_levels_deduped[0])
        }
        assumed_zero = set_index_levels_func(
            pd.DataFrame(
                np.zeros((zeros_index.shape[0], keep.shape[1])),
                columns=keep.columns,
                index=zeros_index,
            ),
            extra_levels,
            copy=False,
        )
        complete = pd.concat([keep, assumed_zero.reorder_levels(keep.index.names)])
        res = ToCompleteResult(complete=complete, assumed_zero=assumed_zero)

    return res

to_gridding_sectors #

to_gridding_sectors(
    indf: DataFrame,
    region_level: str = "region",
    world_region: str = "World",
) -> DataFrame

Re-aggregate data to the sectors used for gridding

Parameters#

indf Data to re-aggregate

region_level Region level in the data index

world_region The value used when the data represents the sum over all regions

Returns#

: Data re-aggregated to the gridding sectors

Source code in src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py 
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
def to_gridding_sectors(
    indf: pd.DataFrame, region_level: str = "region", world_region: str = "World"
) -> pd.DataFrame:
    """
    Re-aggregate data to the sectors used for gridding

    Parameters
    ----------
    indf
        Data to re-aggregate

    region_level
        Region level in the data index

    world_region
        The value used when the data represents the sum over all regions

    Returns
    -------
    :
        Data re-aggregated to the gridding sectors
    """
    # Split off the carbon removal tree first
    carbon_removal_mask = indf.index.get_level_values("variable").str.startswith(
        "Carbon Removal"
    )
    carbon_removal = indf[carbon_removal_mask]
    emissions = indf[~carbon_removal_mask]

    # Processing is way easier if we process the DataFrame's a bit first
    emissions_world_mask = (
        emissions.index.get_level_values(region_level) == world_region
    )

    # Data that is at the world level i.e. has no region information
    emissions_sector_df = (
        split_sectors(
            emissions.loc[emissions_world_mask].reset_index("region", drop=True),
            bottom_level="sectors",
        )
        .stack()
        .unstack("sectors")
    )

    # Data with region information
    emissions_region_sector_df = (
        split_sectors(emissions.loc[~emissions_world_mask], bottom_level="sectors")
        .stack()
        .unstack("sectors")
    )

    # CDR information
    emissions_cdr = -1 * update_index_levels_func(
        carbon_removal,
        {"variable": lambda x: x.replace("Carbon Removal", "Emissions|CO2|CDR")},
    )
    emissions_cdr_world_mask = (
        emissions_cdr.index.get_level_values(region_level) == world_region
    )
    if emissions_cdr_world_mask.any():
        raise NotImplementedError

    emissions_region_sector_df_co2_mask = (
        emissions_region_sector_df.index.get_level_values("species") == "CO2"
    )

    emissions_cdr_region_sector_df = convert_unit_like(
        split_sectors(  # type: ignore # pandas-stubs confused
            emissions_cdr.loc[~emissions_cdr_world_mask], bottom_level="sectors"
        )
        .stack()
        .unstack("sectors"),
        emissions_region_sector_df.loc[emissions_region_sector_df_co2_mask],  # type: ignore # pandas-stubs confused
    )

    # Move domestic aviation to the global level,
    # remove it from regional transport
    # and drop the levels we no longer use.
    domestic_aviation_sum = groupby_except(
        emissions_region_sector_df[SECTOR_DOMESTIC_AVIATION],
        region_level,
    ).sum()
    emissions_sector_df["Aircraft"] = (
        emissions_sector_df["Energy|Demand|Bunkers|International Aviation"]
        + domestic_aviation_sum
    )
    emissions_region_sector_df["Energy|Demand|Transportation"] = (
        emissions_region_sector_df["Energy|Demand|Transportation"]
        - emissions_region_sector_df[SECTOR_DOMESTIC_AVIATION]
    )
    emissions_sector_df = emissions_sector_df.drop(
        ["Energy|Demand|Bunkers|International Aviation"], axis="columns"
    )
    emissions_region_sector_df = emissions_region_sector_df.drop(
        [SECTOR_DOMESTIC_AVIATION], axis="columns"
    )

    # Handle the CDR sectors
    carbon_removal_map = {
        "CDR|Enhanced Weathering": "Other Capture and Removal",
        "CDR|Geological Storage|Biomass": "Energy|Supply",
        "CDR|Geological Storage|Direct Air Capture": "Other Capture and Removal",
        "CDR|Ocean": "Other Capture and Removal",
        "CDR|Land Use|Biochar": "AFOLU|Land|Other",
        "CDR|Land Use|Soil Carbon Management": (
            "AFOLU|Land|Land Use and Land-Use Change"
        ),
        # See note above
        # "CDR|Other": "Other Capture and Removal",
    }
    for cdr_sector, emissions_sector in carbon_removal_map.items():
        row_loc = emissions_region_sector_df_co2_mask
        col_loc = emissions_sector
        emissions_region_sector_df.loc[row_loc, col_loc] = (
            emissions_region_sector_df.loc[row_loc, col_loc]
            - emissions_cdr_region_sector_df[cdr_sector]
        )

    # Aggregate
    sector_df_gridding = aggregate_cols(
        emissions_sector_df,  # type: ignore # need to cast first or something
        {
            "International Shipping": ["Energy|Demand|Bunkers|International Shipping"],
            # Not the same as the reporting sector as we have done manipulations above
            "Aircraft": ["Aircraft"],
        },
    )
    region_sector_df_gridding = aggregate_cols(
        emissions_region_sector_df,  # type: ignore # need to cast first or something
        {
            "Agricultural Waste Burning": [
                "AFOLU|Agricultural Waste Burning",
            ],
            "Agriculture": [
                "AFOLU|Agriculture",
                "AFOLU|Land|Harvested Wood Products",
                "AFOLU|Land|Land Use and Land-Use Change",
                "AFOLU|Land|Other",
                "AFOLU|Land|Wetlands",
            ],
            "Energy Sector": ["Energy|Supply"],
            "Forest Burning": ["AFOLU|Land|Fires|Forest Burning"],
            "Grassland Burning": ["AFOLU|Land|Fires|Grassland Burning"],
            "Industrial Sector": [
                "Energy|Demand|Industry",
                "Energy|Demand|Other Sector",
                "Industrial Processes",
                "Other",
            ],
            "Other CDR": [
                "Other Capture and Removal",
            ],
            "Peat Burning": ["AFOLU|Land|Fires|Peat Burning"],
            "Residential Commercial Other": [
                "Energy|Demand|Residential and Commercial and AFOFI"
            ],
            "Solvents Production and Application": ["Product Use"],
            "Transportation Sector": ["Energy|Demand|Transportation"],
            "Waste": ["Waste"],
        },
    )

    emissions_cdr_region_sector_df_gridding = aggregate_cols(
        emissions_cdr_region_sector_df,
        {
            "BECCS": [
                "CDR|Geological Storage|Biomass",
            ],
            "Enhanced Weathering": [
                "CDR|Enhanced Weathering",
            ],
            "Direct Air Capture": [
                "CDR|Geological Storage|Direct Air Capture",
            ],
            "Ocean": [
                "CDR|Ocean",
            ],
            "Biochar": [
                "CDR|Land Use|Biochar",
            ],
            "Soil Carbon Management": [
                "CDR|Land Use|Soil Carbon Management",
            ],
        },
    )

    sector_df_gridding_like_input = combine_sectors(
        set_index_levels_func(
            sector_df_gridding.unstack().stack("sectors", future_stack=True),  # type: ignore # pandas-stubs confused
            {region_level: world_region},
        ),
        bottom_level="sectors",
    )
    region_sector_df_gridding_like_input = combine_sectors(
        region_sector_df_gridding.unstack().stack("sectors", future_stack=True),  # type: ignore # pandas-stubs confused
        bottom_level="sectors",
    )
    emissions_cdr_region_sector_df_gridding_like_input = combine_sectors(
        emissions_cdr_region_sector_df_gridding.unstack().stack(
            "sectors",  # type: ignore # pandas-stubs confused
            future_stack=True,
        ),
        bottom_level="sectors",
    )

    res = pd.concat(
        [
            df.reorder_levels(indf.index.names)
            for df in [
                sector_df_gridding_like_input,
                region_sector_df_gridding_like_input,
                emissions_cdr_region_sector_df_gridding_like_input,
            ]
        ]
    )

    return res