Collection.query

from datetime import datetime
from tilebox.datasets import field
from tilebox.datasets.query import TimeInterval

# querying a specific time
time = "2023-05-01 12:45:33.423"
data = collection.query(temporal_extent=time)

# querying a time interval
interval = ("2023-05-01", "2023-08-01")
data = collection.query(temporal_extent=interval)

# displaying a progress bar while querying
data = collection.query(
  temporal_extent=interval,
  show_progress=True,
)

# querying a spatio-temporal collection with a spatial filter
data = collection.query(
  temporal_extent=interval,
  spatial_extent=geometry,
  filter=(field("cloud_cover") < 10) & (field("platform") == "sentinel-2c"),
)

# querying a time interval with TimeInterval
interval = TimeInterval(
    start=datetime(2023, 5, 1),
    end=datetime(2023, 8, 1),
    start_exclusive=False,
    end_inclusive=False,
)
data = collection.query(temporal_extent=interval)

# querying with an iterable
datapoints = collection.query(
  temporal_extent=interval,
  skip_data=True,  # only fetch datapoint IDs and time
)
first_50 = collection.query(temporal_extent=datapoints.time[:50])

from datetime import datetime
from tilebox.datasets import field
from tilebox.datasets.query import TimeInterval

# querying a specific time
time = "2023-05-01 12:45:33.423"
data = collection.query(temporal_extent=time)

# querying a time interval
interval = ("2023-05-01", "2023-08-01")
data = collection.query(temporal_extent=interval)

# displaying a progress bar while querying
data = collection.query(
  temporal_extent=interval,
  show_progress=True,
)

# querying a spatio-temporal collection with a spatial filter
data = collection.query(
  temporal_extent=interval,
  spatial_extent=geometry,
  filter=(field("cloud_cover") < 10) & (field("platform") == "sentinel-2c"),
)

# querying a time interval with TimeInterval
interval = TimeInterval(
    start=datetime(2023, 5, 1),
    end=datetime(2023, 8, 1),
    start_exclusive=False,
    end_inclusive=False,
)
data = collection.query(temporal_extent=interval)

# querying with an iterable
datapoints = collection.query(
  temporal_extent=interval,
  skip_data=True,  # only fetch datapoint IDs and time
)
first_50 = collection.query(temporal_extent=datapoints.time[:50])

def Collection.query(
    *,
    temporal_extent: TimeIntervalLike,
    filter: Expression | None = None,
    spatial_extent: SpatialFilterLike | None = None,
    skip_data: bool = False,
    show_progress: bool | Callable[[float], None] = False,
) -> xarray.Dataset

Query a range of data points in this collection in a specified temporal extent. If no data exists for the requested time or interval, an empty xarray.Dataset is returned.

Parameters

TimeIntervalLike

The time or time interval for which to query data. This can be a single time scalar, a tuple of two time scalars, or an array of time scalars.Valid time scalars are: datetime.datetime objects, strings in ISO 8601 format, or Unix timestamps in seconds.Behavior for each input type:

TimeScalar: If a single time scalar is provided, query returns all data points for that exact millisecond.
TimeInterval: If a time interval is provided, query returns all data points in that interval. Intervals can be a tuple of two TimeScalars or a TimeInterval object. Tuples are interpreted as a half-open interval [start, end). With a TimeInterval object, the start_exclusive and end_inclusive parameters control whether the start and end time are inclusive or exclusive.
Iterable[TimeScalar]: If an array of time scalars is provided, query constructs a time interval from the first and last time scalar in the array. Here, both the start and end times are inclusive.

Expression | None

Optional expression over fields marked queryable in the dataset schema. Build expressions with field() and combine them with &, |, and ~. See Filter by custom fields.

SpatialFilterLike | None

Optional spatial filter for spatio-temporal datasets. Pass a geometry to use the default intersection behavior, or configure a spatial filter mode.

bool

If True, only required datapoint fields are returned, such as time, id, and ingestion_time. Defaults to False.

bool | Callable[[float], None]

If True, display a progress bar when pagination is required. You can also pass a callback to receive progress values between 0 and 1. Defaults to False.

Returns

An xarray.Dataset containing the requested data points.

from datetime import datetime
from tilebox.datasets import field
from tilebox.datasets.query import TimeInterval

# querying a specific time
time = "2023-05-01 12:45:33.423"
data = collection.query(temporal_extent=time)

# querying a time interval
interval = ("2023-05-01", "2023-08-01")
data = collection.query(temporal_extent=interval)

# displaying a progress bar while querying
data = collection.query(
  temporal_extent=interval,
  show_progress=True,
)

# querying a spatio-temporal collection with a spatial filter
data = collection.query(
  temporal_extent=interval,
  spatial_extent=geometry,
  filter=(field("cloud_cover") < 10) & (field("platform") == "sentinel-2c"),
)

# querying a time interval with TimeInterval
interval = TimeInterval(
    start=datetime(2023, 5, 1),
    end=datetime(2023, 8, 1),
    start_exclusive=False,
    end_inclusive=False,
)
data = collection.query(temporal_extent=interval)

# querying with an iterable
datapoints = collection.query(
  temporal_extent=interval,
  skip_data=True,  # only fetch datapoint IDs and time
)
first_50 = collection.query(temporal_extent=datapoints.time[:50])

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