Source code for

"""Generic tools for working with exogenous data sources."""
import logging
from abc import ABC, abstractmethod
from operator import itemgetter
from pathlib import Path
from typing import Any, Dict, Literal, Mapping, Optional, Tuple, Type

from genno import Computer, Key, Quantity, quote

from message_ix_models import ScenarioInfo
from message_ix_models.model.structure import get_codes
from message_ix_models.util import cached

__all__ = [

log = logging.getLogger(__name__)

#: Supported measures. Subclasses of :class:`.ExoDataSource` may provide support for
#: other measures.
#: .. todo:: Store this in a separate code list or concept scheme.

#: Known sources for data. Use :func:`register_source` to add to this collection.
SOURCES: Dict[str, Type["ExoDataSource"]] = {}

[docs]class ExoDataSource(ABC): """Base class for sources of exogenous data.""" #: Identifier for this particular source. id: str = "" #: Optional name for the returned :class:`.Key`/:class:`.Quantity`. If not set by #: :meth:`.__init__`, then the "measure" keyword is used. name: str = "" #: Optional additional dimensions for the returned :class:`.Key`/:class:`.Quantity`. #: If not set by :meth:`.__init__`, the dimensions are :math:`(n, y)`. extra_dims: Tuple[str, ...] = ()
[docs] @abstractmethod def __init__(self, source: str, source_kw: Mapping) -> None: """Handle `source` and `source_kw`. An implementation **must**: - Raise :class:`ValueError` if it does not recognize or cannot handle the arguments in `source` or `source_kw`. - Recognize and handle (if possible) a "measure" keyword in `source_kw` from :data:`MEASURES`. It **may**: - Transform these into other values, for instance by mapping certain values to others, applying regular expressions, or other operations. - Store those values as instance attributes for use in :meth:`__call__`. - Set :attr:`name` and/or :attr:`extra_dims` to control the behaviour of :func:`.prepare_computer`. - Log messages that give information that may help to debug a :class:`ValueError` for `source` or `source_kw` that cannot be handled. It **should not** actually load data or perform any time- or memory-intensive operations; these should only be triggered by :meth:`.__call__`. """ raise ValueError
[docs] @abstractmethod def __call__(self) -> Quantity: r"""Return the data. The Quantity returned by this method **must** have dimensions :math:`(n, y) \cup \text{extra_dims}`. If the original/upstream/raw data has different dimensionality (fewer or more dimensions; different dimension IDs), the code **must** transform these, make appropriate selections, etc. """ raise NotImplementedError
[docs] def transform(self, c: "Computer", base_key: Key) -> Key: """Prepare `c` to transform raw data from `base_key`. `base_key` identifies the :class:`.Quantity` that is returned by :meth:`.__call__`. Before the data is returned, :meth:`.transform` allows the data source to add additional tasks or computations to `c` that further transform the data. (These operations **may** be done in :meth:`.__call__` directly, but :meth:`.transform` allows use of other :mod:`genno` operators and conveniences.) The default implementation: 1. Aggregates the data (:func:`.genno.operator.aggregate`) on the |n| dimension using "n::groups". 2. Interpolates the data (:func:`.genno.operator.interpolate`) on the |y| dimension using "y::coords". """ # Aggregate c.add(base_key + "1", "aggregate", base_key, "n::groups", keep=False) # Interpolate to the desired set of periods kw = dict(fill_value="extrapolate") k2 = base_key + "2" c.add(k2, "interpolate", base_key + "1", "y::coords", kwargs=kw) return k2
[docs]def prepare_computer( context, c: "Computer", source="test", source_kw: Optional[Mapping] = None, *, strict: bool = True, ) -> Tuple[Key, ...]: """Prepare `c` to compute GDP, population, or other exogenous data. Check each :class:`ExoDataSource` in :data:`SOURCES` to determine whether it recognizes and can handle `source` and `source_kw`. If a source is identified, add tasks to `c` that retrieve and process data into a :class:`.Quantity` with, at least, dimensions :math:`(n, y)`. Parameters ---------- source : str Identifier of the source, possibly with other information to be handled by a :class:`ExoDataSource`. source_kw : dict, *optional* Keyword arguments for a Source class. These can include indexers, selectors, or other information needed by the source class to identify the data to be returned. If the key "measure" is present, it **should** be one of :data:`MEASURES`. strict : bool, *optional* Raise an exception if any of the keys to be added already exist. Returns ------- tuple of .Key Raises ------ ValueError if no source is registered which can handle `source` and `source_kw`. """ # Handle arguments source_kw = source_kw or dict() if measure := source_kw.get("measure"): if measure not in MEASURES: log.warning(f"source keyword {measure = } not in recognized {MEASURES}") else: measure = "UNKNOWN" # Look up input data flow source_obj = None for cls in SOURCES.values(): try: # Instantiate a Source object to provide this data source_obj = cls(source, source_kw or dict()) except Exception: pass # Class does not recognize the arguments if source_obj is None: raise ValueError(f"No source found that can handle {source!r}") # Add structural information to the Computer c.require_compat("") # Retrieve the node codelist c.add("n::codes", quote(get_codes(f"node/{context.model.regions}")), strict=strict) # Convert the codelist into a nested dict for aggregate() c.add("n::groups", "codelist_to_groups", "n::codes", strict=strict) # Add information about the list of periods if "y" not in c: info = ScenarioInfo() info.year_from_codes(get_codes(f"year/{context.model.years}")) c.add("y", quote(info.Y)) if "y0" not in c: c.add("y0", itemgetter(0), "y") # Above as coords/indexers c.add("y::coords", lambda years: dict(y=years), "y") c.add("y0::coord", lambda year: dict(y=year), "y0") # Retrieve the raw data k = Key( or measure.lower(), ("n", "y") + source_obj.extra_dims) k_raw = k + # Tagged with the source ID keys = [k] # Keys to return c.add(k_raw, source_obj) # Allow the class to further transform the data. See ExoDataSource.transform() for # the default: aggregate, then interpolate key = source_obj.transform(c, k_raw) # Alias `key` -> `k` c.add(k, key) # Index to y0 k_y0 = k + "y0_indexed" c.add(k_y0, "index_to", k, "y0::coord") keys.append(k_y0) # TODO also insert (1) index to a particular label on the "n" dimension (2) both return tuple(keys)
[docs]def register_source(cls: Type[ExoDataSource]) -> Type[ExoDataSource]: """Register :class:`.ExoDataSource` `cls` as a source of exogenous data.""" if in SOURCES: raise ValueError(f"{SOURCES[]} already registered for id {!r}") SOURCES[] = cls return cls
[docs]@register_source class DemoSource(ExoDataSource): """Example source of exogenous population and GDP data. Parameters ---------- source : str **Must** be like ``test s1``, where "s1" is a scenario ID from ("s0"…"s4"). source_kw : dict **Must** contain an element "measure", one of :data:`MEASURES`. """ id = "DEMO" def __init__(self, source, source_kw): if not source.startswith("test "): # Don't recognize this `source` string → can't provide data raise ValueError # Select the data according to the `source`; in this case, scenario *parts, scenario = source.partition("test ") self.indexers = dict(s=scenario) # Map from the measure ID to a variable name self.indexers.update( v={"POP": "Population", "GDP": "GDP"}[source_kw["measure"]] ) def __call__(self) -> Quantity: from genno.operator import select # - Retrieve the data. # - Apply the prepared indexers. return self.random_data().pipe(select, self.indexers, drop=True)
[docs] @staticmethod def random_data(): """Generate some random data with n, y, s, and v dimensions.""" from genno.operator import relabel from genno.testing import random_qty from pycountry import countries return random_qty(dict(n=len(countries), y=2, s=5, v=2), units="kg").pipe( relabel, n={f"n{i}": c.alpha_3 for i, c in enumerate(countries)}, v={"v0": "Population", "v1": "GDP"}, y={"y0": 2010, "y1": 2050}, )
[docs]@cached def iamc_like_data_for_query( path: Path, query: str, *, archive_member: Optional[str] = None, non_iso_3166: Literal["keep", "discard"] = "discard", replace: Optional[dict] = None, ) -> Quantity: """Load data from `path` in IAMC-like format and transform to :class:`.Quantity`. The steps involved are: 1. Read the data file; use pyarrow for better performance. 2. Immediately apply `query` to reduce the data to be handled in subsequent steps. 3. Assert that Model, Scenario, Variable, and Unit are unique; store the unique values. This means that `query` **must** result in data with unique values for these dimensions. 4. Transform "Region" labels to ISO 3166-1 alpha-3 codes using :func:`.iso_3166_alpha_3`. 5. Drop entire time series without such codes; for instance "World". 6. Transform to a pd.Series with "n" and "y" index levels; ensure the latter are int. 7. Transform to :class:`.Quantity` with units. The result is :obj:`.cached`. Parameters ---------- archive_member : bool, optional If given, `path` may be an archive with 2 or more members. The member named by `archive_member` is extracted and read. non_iso_3166 : bool, optional If "discard" (default), "region" labels that are not ISO 3166-1 country names are discarded, along with associated data. If "keep", such labels are kept. """ import pandas as pd from message_ix_models.util.pycountry import iso_3166_alpha_3 unique = dict() def drop_unique(df, names) -> pd.DataFrame: if len(df) == 0: raise RuntimeError(f"0 rows matching {query!r}") names_list = names.split() for name in names_list: values = df[name].unique() if len(values) > 1: raise RuntimeError(f"Not unique {name!r}: {values}") unique[name] = values[0] return df.drop(names_list, axis=1) def assign_n(df: pd.DataFrame) -> pd.DataFrame: if non_iso_3166 == "discard": return df.assign(n=df["REGION"].apply(iso_3166_alpha_3)) else: return df.assign(n=df["REGION"].apply(lambda v: iso_3166_alpha_3(v) or v)) # Identify the source object/buffer to read from if archive_member: # A single member in a ZIP archive that has >1 members import zipfile zf = zipfile.ZipFile(path) source: Any = else: # A direct path, possibly compressed source = path tmp = ( pd.read_csv(source, engine="pyarrow") .query(query) .replace(replace or {}) .rename(columns=lambda c: c.upper()) .pipe(drop_unique, "MODEL SCENARIO VARIABLE UNIT") .pipe(assign_n) .dropna(subset=["n"]) .drop("REGION", axis=1) .set_index("n") .rename(columns=lambda y: int(y)) .rename_axis(columns="y") .stack() .dropna() ) return Quantity(tmp, units=unique["UNIT"])