speasy.core.direct_archive_downloader.direct_archive_downloader module

class speasy.core.direct_archive_downloader.direct_archive_downloader.RandomSplitDirectDownload

Bases: object

static get_product(url_pattern: str, variable: str, start_time: str | ~datetime.datetime | ~numpy.float64 | float | ~numpy.datetime64, stop_time: str | ~datetime.datetime | ~numpy.float64 | float | ~numpy.datetime64, fname_regex: str, split_frequency: str = 'daily', date_format=None, file_reader: ~typing.Callable[[...], ~speasy.products.variable.SpeasyVariable | None] = <function _read_cdf>, **kwargs) → SpeasyVariable | None

static list_files(split_frequency, url_pattern: str, start_time: str | datetime | float64 | float | datetime64, stop_time: str | datetime | float64 | float | datetime64, fname_regex: str, date_format=None, force_refresh=False) → List[str]

static overlaps_range(range_start, range_stop, start, stop, version=None)

class speasy.core.direct_archive_downloader.direct_archive_downloader.RegularSplitDirectDownload

Bases: object

static get_product(url_pattern: str, variable: str, start_time: str | ~datetime.datetime | ~numpy.float64 | float | ~numpy.datetime64, stop_time: str | ~datetime.datetime | ~numpy.float64 | float | ~numpy.datetime64, use_file_list: bool = False, split_frequency: str = 'daily', file_reader: ~typing.Callable[[...], ~speasy.products.variable.SpeasyVariable | None] = <function _read_cdf>, **kwargs) → SpeasyVariable | None

speasy.core.direct_archive_downloader.direct_archive_downloader.apply_date_format(txt: str, date: datetime) → str

speasy.core.direct_archive_downloader.direct_archive_downloader.filter_ranges(ranges: List[Tuple[str, Tuple[datetime, datetime]]], start: str | datetime | float64 | float | datetime64, stop: str | datetime | float64 | float | datetime64) → List[str]

Given a list of (file, (start, stop)) tuples, filter those that overlap with the specified time range. Parameters ———- ranges : List[Tuple[str, Tuple[datetime, datetime]]]

List of (file, (start, stop)) tuples sorted by start time of each range in ascending order.

startAnyDateTimeType

Time range start

stopAnyDateTimeType

Time range stop

Returns

List[str]

List of files that overlap with the specified time range.

speasy.core.direct_archive_downloader.direct_archive_downloader.get_product(url_pattern: str, split_rule: str, variable: str, start_time: str | ~datetime.datetime | ~numpy.float64 | float | ~numpy.datetime64, stop_time: str | ~datetime.datetime | ~numpy.float64 | float | ~numpy.datetime64, use_file_list: bool = False, file_reader: ~typing.Callable[[...], ~speasy.products.variable.SpeasyVariable | None] = <function _read_cdf>, codec: str | None = None, **kwargs) → SpeasyVariable | None

speasy.core.direct_archive_downloader.direct_archive_downloader.map_ranges(url, fname_regex: str | Pattern, date_format: str | None = None) → List[Tuple[str, Tuple[datetime, datetime]]]

speasy.core.direct_archive_downloader.direct_archive_downloader.spilt_range(split_frequency: str, start_time: str | datetime | float64 | float | datetime64, stop_time: str | datetime | float64 | float | datetime64)

Given a split frequency (daily, yearly) and a time range, generate the list of start time of each fragment given a split frequency (daily, yearly) and a time range to split.

Parameters:

split_frequencystr

Fragments spilt frequency (daily, monthly, yearly)

start_timeAnyDateTimeType

Time range start

stop_timeAnyDateTimeType

Time range stop

Returns:

List[datetime]

Ordered list of start time of each fragment composing the given input range

Examples

>>> spilt_range('daily', "2018-01-02", "2018-01-03")
[datetime.datetime(2018, 1, 2, 0, 0, tzinfo=datetime.timezone.utc), datetime.datetime(2018, 1, 3, 0, 0, tzinfo=datetime.timezone.utc)]