API documentation

The following is API docs for the current functionality of chap-core that is available for use.

Data Fetching

Functionality for fetching data

chap_core.fetch.gee_era5(credentials: GEECredentials | dict[str, str], polygons: FeatureCollectionModel | str, start_period: str, end_period: str, band_names=list[str], reducer: str = 'mean') → list[ERA5Entry]

class chap_core.data.DataSet(data_dict: dict[str, FeaturesT], polygon_dict: dict[str, Polygon] = None)

Class representing severeal time series at different locations.

classmethod from_pandas(df: DataFrame, dataclass: Type[FeaturesT], fill_missing=False) → DataSet[FeaturesT]

Create a SpatioTemporalDict from a pandas dataframe. The dataframe needs to have a ‘location’ column, and a ‘time_period’ column. The time_period columnt needs to have strings that can be parsed into a period. All fields in the dataclass needs to be present in the dataframe. If ‘fill_missing’ is True, missing values will be filled with np.nan. Else all the time series needs to be consecutive.

Parameters

dfpd.DataFrame

The dataframe

dataclassType[FeaturesT]

The dataclass to use for the time series

fill_missingbool, optional

If missing values should be filled, by default False

Returns

DataSet[FeaturesT]

The SpatioTemporalDict

Examples

>>> import pandas as pd
>>> from chap_core.spatio_temporal_data.temporal_dataclass import DataSet
>>> from chap_core.datatypes import HealthData
>>> df = pd.DataFrame(
...     {
...         "location": ["Oslo", "Oslo", "Bergen", "Bergen"],
...         "time_period": ["2020-01", "2020-02", "2020-01", "2020-02"],
...         "disease_cases": [10, 20, 30, 40],
...     }
... )
>>> DataSet.from_pandas(df, HealthData)

classmethod from_period_observations(observation_dict: dict[str, list[PeriodObservation]]) → DataSet[TimeSeriesData]

Create a SpatioTemporalDict from a dictionary of PeriodObservations. The keys are the location names, and the values are lists of PeriodObservations.

Parameters

observation_dictdict[str, list[PeriodObservation]]

The dictionary of observations

Returns

DataSet[TimeSeriesData]

The SpatioTemporalDict

Examples

>>> from chap_core.spatio_temporal_data.temporal_dataclass import DataSet
>>> from chap_core.api_types import PeriodObservation
>>> class HealthObservation(PeriodObservation):
...     disease_cases: int
>>> observations = {
...     "Oslo": [
...         HealthObservation(time_period="2020-01", disease_cases=10),
...         HealthObservation(time_period="2020-02", disease_cases=20),
...     ]
... }
>>> DataSet.from_period_observations(observations)
>>> DataSet.to_pandas()

to_pandas() → DataFrame

Join the pandas frame for all locations with locations as column

class chap_core.data.PeriodObservation(*, time_period: str)