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Literal + +import arrow + +if TYPE_CHECKING: + import datetime + + +@dataclass +class IsoDateFormat: + """An ISO date format with relevant patterns.""" + + name: str + regex_pattern: str + arrow_pattern: str + timeframe: Literal["year", "month", "day", "week"] + + +ISO_YEAR = IsoDateFormat( + name="ISO_YEAR", + regex_pattern=r"^\d{4}$", + arrow_pattern="YYYY", + timeframe="year", +) +ISO_YEAR_MONTH = IsoDateFormat( + name="ISO_YEAR_MONTH", + regex_pattern=r"^\d{4}\-\d{2}$", + arrow_pattern="YYYY-MM", + timeframe="month", +) +ISO_YEAR_MONTH_DAY = IsoDateFormat( + name="ISO_YEAR_MONTH_DAY", + regex_pattern=r"^\d{4}\-\d{2}\-\d{2}$", + arrow_pattern="YYYY-MM-DD", + timeframe="day", +) +ISO_YEAR_WEEK = IsoDateFormat( + name="ISO_YEAR_WEEK", + regex_pattern=r"^\d{4}\-{0,1}W\d{2}$", + arrow_pattern="W", + timeframe="week", +) + + +@dataclass +class SsbDateFormat: + """An date format with relevant patterns for SSB special date formats.""" + + name: str + regex_pattern: str + arrow_pattern: str + time_frame: dict + + +SSB_BIMESTER = SsbDateFormat( + name="SSB_BIMESTER", + regex_pattern=r"\d{4}[B]\d{1}$", + arrow_pattern="YYYYMM", + time_frame={ + "B1": { + "start": "01", + "end": "02", + }, + "B2": { + "start": "03", + "end": "04", + }, + "B3": { + "start": "05", + "end": "06", + }, + "B4": { + "start": "07", + "end": "08", + }, + "B5": { + "start": "09", + "end": "10", + }, + "B6": { + "start": "11", + "end": "12", + }, + }, +) + +SSB_QUARTERLY = SsbDateFormat( + name="SSB_QUARTERLY", + regex_pattern=r"\d{4}[Q]\d{1}$", + arrow_pattern="YYYYMM", + time_frame={ + "Q1": { + "start": "01", + "end": "03", + }, + "Q2": { + "start": "04", + "end": "06", + }, + "Q3": { + "start": "07", + "end": "09", + }, + "Q4": { + "start": "10", + "end": "12", + }, + }, +) +SSB_TRIANNUAL = SsbDateFormat( + name="SSB_TRIANNUAL", + regex_pattern=r"\d{4}[T]\d{1}$", + arrow_pattern="YYYYMM", + time_frame={ + "T1": { + "start": "01", + "end": "04", + }, + "T2": { + "start": "05", + "end": "08", + }, + "T3": { + "start": "09", + "end": "12", + }, + }, +) +SSB_HALF_YEAR = SsbDateFormat( + name="SSB_HALF_YEAR", + regex_pattern=r"\d{4}[H]\d{1}$", + arrow_pattern="YYYYMM", + time_frame={ + "H1": { + "start": "01", + "end": "06", + }, + "H2": { + "start": "07", + "end": "12", + }, + }, +) + +SUPPORTED_DATE_FORMATS: list[IsoDateFormat | SsbDateFormat] = [ + ISO_YEAR, + ISO_YEAR_MONTH, + ISO_YEAR_MONTH_DAY, + ISO_YEAR_WEEK, + SSB_BIMESTER, + SSB_QUARTERLY, + SSB_TRIANNUAL, + SSB_HALF_YEAR, +] + + +def categorize_period_string(period: str) -> IsoDateFormat | SsbDateFormat: + """Categorize a period string into one of the supported date formats. + + If the period string is not recognized, a NotImplementedError is raised. + + Examples: + >>> date_format = categorize_period_string('2022') + >>> date_format.name + ISO_YEAR + + >>> date_format = categorize_period_string('2022-W01') + >>> date_format.name + ISO_YEAR_WEEK + + >>> date_format = categorize_period_string('2022B1') + >>> date_format.name + SSB_BIMESTER + + >>> date_format = categorize_period_string('1980Q3') + >>> date_format.name + SSB_QUARTERLY + + >>> date_format = categorize_period_string('1954T2') + >>> date_format.name + SSB_TRIANNUAL + + >>> date_format = categorize_period_string('1876H1') + >>> date_format.name + SSB_HALF_YEAR + + >>> categorize_period_string('unknown format') + Traceback (most recent call last): + ... + NotImplementedError: Period format unknown format is not supported + """ + for date_format in SUPPORTED_DATE_FORMATS: + if re.match(date_format.regex_pattern, period): + return date_format + + msg = f"Period format {period} is not supported" + raise NotImplementedError( + msg, + ) + + +def convert_ssb_period( + period_string: str, + period_type: str, + date_format: SsbDateFormat, +) -> str: + """Convert ssb-format for bimester, quarterly, triannual and half year to start and end months. + + Usage-examples: + >>> ssb_bimester_period_start = convert_ssb_period("2022B1","start",SSB_BIMESTER) + >>> ssb_bimester_period_start + 202201 + + >>> ssb_bimester_period_end = convert_ssb_period("2022B1","end",SSB_BIMESTER) + >>> ssb_bimester_period_end + 202202 + + >>> ssb_quarterly_period_start = convert_ssb_period("2015Q3","start",SSB_QUARTERLY) + >>> ssb_quarterly_period_start + 201507 + + >>> ssb_quarterly_period_end = convert_ssb_period("2015Q3","end",SSB_QUARTERLY) + >>> ssb_quarterly_period_end + 201509 + + >>> ssb_triannual_period_start = convert_ssb_period("1998T2","start",SSB_TRIANNUAL) + >>> ssb_triannual_period_start + 199805 + + >>> ssb_quarterly_period_end = convert_ssb_period("1998T2","end",SSB_TRIANNUAL) + >>> ssb_quarterly_period_end + 199808 + + >>> ssb_half_year_period_start = convert_ssb_period("1898H2","start",SSB_HALF_YEAR) + >>> ssb_half_year_period_start + 189807 + + >>> ssb_half_year_period_end = convert_ssb_period("1898H2","end",SSB_HALF_YEAR) + >>> ssb_half_year_period_end + 189812 + + """ + return period_string[:4] + date_format.time_frame[period_string[-2:]][period_type] + + +class DaplaDatasetPathInfo: + """Extract info from a path following SSB's dataset naming convention.""" + + def __init__(self, dataset_path: str) -> None: + """Digest the path so that it's ready for further parsing.""" + self.dataset_path = pathlib.Path(dataset_path) + self.dataset_name_sections = self.dataset_path.stem.split("_") + _period_strings = self._extract_period_strings(self.dataset_name_sections) + self.first_period_string = _period_strings[0] + self.second_period_string: str | None = None + + with contextlib.suppress(IndexError): + self.second_period_string = _period_strings[1] + + @staticmethod + def _extract_period_strings(dataset_name_sections: list[str]) -> list[str]: + """Extract period strings from dataset name sections. + + Iterates over the dataset name sections and returns a list of strings + that match the year regex, stripping the first character. This extracts + the year periods from the dataset name. + + Examples: + >>> DaplaDatasetPathInfo._extract_period_strings(['p2022', 'kommune', 'v1']) + ['2022'] + + >>> DaplaDatasetPathInfo._extract_period_strings(['p2022-01', 'p2023-06', 'kommune', 'v1']) + ['2022-01', '2023-06'] + + >>> DaplaDatasetPathInfo._extract_period_strings(['p1990Q1', 'kommune', 'v1']) + ['1990Q1'] + """ + date_format_regex = re.compile( + r"^p\d{4}(?:-\d{2}-\d{2}|-\d{2}|-{0,1}[QTHWB]\d{1,2})?$", + ) + + return [ + x[1:] + for x in dataset_name_sections + if re.match(date_format_regex, x) is not None + ] + + @property + def contains_data_from(self) -> datetime.date: + """The earliest date from which data in the dataset is relevant for.""" + date_format = categorize_period_string(self.first_period_string) + if isinstance(date_format, SsbDateFormat): + """If dateformat is SSB date format return start month of ssb period.""" + period = convert_ssb_period( + self.first_period_string, + "start", + date_format, + ) + return arrow.get(period, date_format.arrow_pattern).floor("month").date() + + return ( + arrow.get(self.first_period_string, date_format.arrow_pattern) + .floor(date_format.timeframe) + .date() + ) + + @property + def contains_data_until(self) -> datetime.date: + """The latest date until which data in the dataset is relevant for.""" + period_string = self.second_period_string or self.first_period_string + date_format = categorize_period_string(period_string) + if isinstance(date_format, SsbDateFormat): + """If dateformat is SSB date format return end month of ssb period.""" + period = convert_ssb_period(period_string, "end", date_format) + return arrow.get(period, date_format.arrow_pattern).ceil("month").date() + + return ( + arrow.get(period_string, date_format.arrow_pattern) + .ceil(date_format.timeframe) + .date() + ) diff --git a/src/datadoc/backend/storage_adapter.py b/src/datadoc/backend/storage_adapter.py index cd0a875e..aa5e1f5f 100644 --- a/src/datadoc/backend/storage_adapter.py +++ b/src/datadoc/backend/storage_adapter.py @@ -12,6 +12,9 @@ from urllib.parse import urlsplit from urllib.parse import urlunsplit +from dapla import FileClient +from google.auth.exceptions import DefaultCredentialsError + if TYPE_CHECKING: import os from io import IOBase @@ -28,26 +31,19 @@ class GCSObject: def __init__(self, path: str | os.PathLike) -> None: """Initialize the class.""" self._url = urlsplit(str(path)) + try: - from dapla import AuthClient - from dapla import FileClient - - if AuthClient.is_ready(): - # Running on Dapla, rely on dapla-toolbelt for auth - self.fs = FileClient.get_gcs_file_system() - else: - # All other environments, rely on Standard Google credential system - # If this doesn't work for you, try running the following commands: - # - # gcloud auth application-default revoke - # gcloud auth application-default login - from gcsfs import GCSFileSystem - - self.fs = GCSFileSystem() - - except ImportError as e: - msg = "Missing support for GCS. Install datadoc with 'pip install ssb-datadoc[gcs]'" - raise ImportError(msg) from e + # Running on Dapla, rely on dapla-toolbelt for auth + self.fs = FileClient.get_gcs_file_system() + except DefaultCredentialsError: + # All other environments, rely on Standard Google credential system + # If this doesn't work for you, try running the following commands: + # + # gcloud auth application-default revoke + # gcloud auth application-default login + from gcsfs import GCSFileSystem + + self.fs = GCSFileSystem() @staticmethod def for_path(path: str | os.PathLike) -> StorageAdapter: diff --git a/tests/backend/test_dapla_dataset_path_info.py b/tests/backend/test_dapla_dataset_path_info.py new file mode 100644 index 00000000..cbe21d15 --- /dev/null +++ b/tests/backend/test_dapla_dataset_path_info.py @@ -0,0 +1,152 @@ +import datetime +from dataclasses import dataclass + +import pytest + +from datadoc.backend.dapla_dataset_path_info import DaplaDatasetPathInfo + + +@dataclass +class DatasetPathTestCase: + """Structure to define attributes needed for a test case.""" + + path: str + expected_contains_data_from: datetime.date + expected_contains_data_until: datetime.date + + +TEST_CASES = [ + DatasetPathTestCase( + path="grensehandel_imputert_p2022-10-01_p2022-12-31_v1.parquet", + expected_contains_data_from=datetime.date(2022, 10, 1), + expected_contains_data_until=datetime.date(2022, 12, 31), + ), + DatasetPathTestCase( + path="grensehandel_imputert_p2022-10_p2022-12_v1.parquet", + expected_contains_data_from=datetime.date(2022, 10, 1), + expected_contains_data_until=datetime.date(2022, 12, 31), + ), + DatasetPathTestCase( + path="flygende_objekter_p2019_v1.parquet", + expected_contains_data_from=datetime.date(2019, 1, 1), + expected_contains_data_until=datetime.date(2019, 12, 31), + ), + DatasetPathTestCase( + path="framskrevne-befolkningsendringer_p2019_p2050_v1.parquet", + expected_contains_data_from=datetime.date(2019, 1, 1), + expected_contains_data_until=datetime.date(2050, 12, 31), + ), + DatasetPathTestCase( + path="ufo_observasjoner_p2019_p2020_v1.parquet", + expected_contains_data_from=datetime.date(2019, 1, 1), + expected_contains_data_until=datetime.date(2020, 12, 31), + ), + DatasetPathTestCase( + path="omsetning_p2020W15_v1.parquet", + expected_contains_data_from=datetime.date(2020, 4, 6), + expected_contains_data_until=datetime.date(2020, 4, 12), + ), + DatasetPathTestCase( + path="omsetning_p1981-W52_v1.parquet", + expected_contains_data_from=datetime.date(1981, 12, 21), + expected_contains_data_until=datetime.date(1981, 12, 27), + ), + DatasetPathTestCase( + path="personinntekt_p2022H1_v1.parquet", + expected_contains_data_from=datetime.date(2022, 1, 1), + expected_contains_data_until=datetime.date(2022, 6, 30), + ), + DatasetPathTestCase( + path="nybilreg_p2022T1_v1.parquet", + expected_contains_data_from=datetime.date(2022, 1, 1), + expected_contains_data_until=datetime.date(2022, 4, 30), + ), + DatasetPathTestCase( + path="varehandel_p2018Q1_p2018Q4_v1.parquet", + expected_contains_data_from=datetime.date(2018, 1, 1), + expected_contains_data_until=datetime.date(2018, 12, 31), + ), + DatasetPathTestCase( + path="pensjon_p2018Q1_v1.parquet", + expected_contains_data_from=datetime.date(2018, 1, 1), + expected_contains_data_until=datetime.date(2018, 3, 31), + ), + DatasetPathTestCase( + path="skipsanloep_p2021B2_v1.parquet", + expected_contains_data_from=datetime.date(2021, 3, 1), + expected_contains_data_until=datetime.date(2021, 4, 30), + ), + DatasetPathTestCase( + path="skipsanloep_p2022B1_v1.parquet", + expected_contains_data_from=datetime.date(2022, 1, 1), + expected_contains_data_until=datetime.date(2022, 2, 28), + ), +] + + +@pytest.fixture( + ids=[tc.path for tc in TEST_CASES], + params=TEST_CASES, +) +def test_data(request: pytest.FixtureRequest) -> DatasetPathTestCase: + return request.param + + +@pytest.fixture() +def dataset_path(test_data: DatasetPathTestCase) -> DaplaDatasetPathInfo: + return DaplaDatasetPathInfo(test_data.path) + + +@pytest.fixture() +def expected_contains_data_from(test_data: DatasetPathTestCase) -> datetime.date: + return test_data.expected_contains_data_from + + +@pytest.fixture() +def expected_contains_data_until(test_data: DatasetPathTestCase) -> datetime.date: + return test_data.expected_contains_data_until + + +def test_extract_period_info_date_from( + dataset_path: DaplaDatasetPathInfo, + expected_contains_data_from: datetime.date, +): + assert dataset_path.contains_data_from == expected_contains_data_from + + +def test_extract_period_info_date_until( + dataset_path: DaplaDatasetPathInfo, + expected_contains_data_until: datetime.date, +): + assert dataset_path.contains_data_until == expected_contains_data_until + + +@pytest.mark.parametrize( + "data", + [ + ( + "varehandel_p2018Q5_p2018Q4_v1.parquet", + "Period format p2018Q5 is not supported", + ), + ( + "varehandel_p2018Q1_p2018H2_v1.parquet", + "Period format p2018H2 is not supported", + ), + ], +) +def test_extract_period_info_failures(data: tuple): + DaplaDatasetPathInfo(data[0]) + with pytest.raises(NotImplementedError): + raise NotImplementedError(data[1]) + + +@pytest.mark.parametrize( + "data", + [ + "nonsen.data", + "nonsens2.parquet", + ], +) +def test_extract_period_info_failures_index_error(data: str): + with pytest.raises(IndexError): + DaplaDatasetPathInfo(data)