diff --git a/stackstac/accumulate_metadata.py b/stackstac/accumulate_metadata.py deleted file mode 100644 index eb96404..0000000 --- a/stackstac/accumulate_metadata.py +++ /dev/null @@ -1,196 +0,0 @@ -from typing import ( - Container, - Dict, - Hashable, - Iterable, - Literal, - Mapping, - Sequence, - Union, - TypeVar, -) - -import numpy as np -import xarray as xr - - -# properties can contain lists; need some way to tell them a singleton list -# apart from the list of properties we're collecting -class _ourlist(list): - pass - - -def metadata_to_coords( - items: Iterable[Mapping[str, object]], - dim_name: str, - fields: Union[str, Sequence[str], Literal[True]] = True, - skip_fields: Container[str] = (), -) -> Dict[str, xr.Variable]: - return dict_to_coords( - accumulate_metadata( - items, - fields=[fields] if isinstance(fields, str) else fields, - skip_fields=skip_fields, - ), - dim_name, - ) - - -T = TypeVar("T", bound=Hashable) - - -def accumulate_metadata( - items: Iterable[Mapping[T, object]], - fields: Union[Sequence[T], Literal[True]] = True, - skip_fields: Container[T] = (), -) -> Dict[T, object]: - """ - Accumulate a sequence of multiple similar dicts into a single dict of lists. - - Each field will contain a list of all the values for that field (equal length to ``items``). - For items where the field didn't exist, None is used. - - Fields with only one unique value are flattened down to just that single value. - - Parameters - ---------- - items: - Iterable of dicts to accumulate - fields: - Only use these fields. If True, use all fields. - skip_fields: - Skip these fields. - """ - all_fields: Dict[T, object] = {} - for i, item in enumerate(items): - # Inductive case: update existing fields - for existing_field, existing_value in all_fields.items(): - new_value = item.get(existing_field, None) - if new_value == existing_value: - # leave fields that are the same for every item as singletons - continue - if isinstance(existing_value, _ourlist): - # we already have a list going; add to it - existing_value.append(new_value) - else: - # all prior values were the same; this is the first different one - all_fields[existing_field] = _ourlist( - [existing_value] * i + [new_value] - ) - - # Base case 1: add any never-before-seen fields, when inferring field names - if fields is True: - for new_field in item.keys() - all_fields.keys(): - if new_field in skip_fields: - continue - value = item[new_field] - all_fields[new_field] = ( - value if i == 0 else _ourlist([None] * i + [value]) - ) - # Base case 2: initialize with predefined fields - elif i == 0: - all_fields.update( - (field, item.get(field, None)) - for field in fields - if field not in skip_fields - ) - - return all_fields - - -def accumulate_metadata_only_allsame( - items: Iterable[Mapping[T, object]], - skip_fields: Container[T] = (), -) -> Dict[T, object]: - """ - Accumulate multiple similar dicts into a single flattened dict of only consistent values. - - If the value of a field differs between items, the field is dropped. - If the value of a field is the same for all items that contain that field, the field is kept. - - Note this means that missing fields are ignored, not treated as different. - - Parameters - ---------- - items: - Iterable of dicts to accumulate - skip_fields: - Skip these fields when ``fields`` is True. - """ - all_fields: Dict[T, object] = {} - for item in items: - for field, value in item.items(): - if field in skip_fields: - continue - if field not in all_fields: - all_fields[field] = value - else: - if value != all_fields[field]: - all_fields[field] = None - - return {field: value for field, value in all_fields.items() if value is not None} - - -def dict_to_coords( - metadata: Dict[str, object], dim_name: str -) -> Dict[str, xr.Variable]: - """ - Convert the output of `accumulate_metadata` into a dict of xarray Variables. - - 1-length lists and scalar values become 0D variables. - - Instances of ``_ourlist`` become 1D variables for ``dim_name``. - - Any other things with >= 1 dimension are dropped, because they probably don't line up - with the other dimensions of the final array. - """ - coords = {} - for field, props in metadata.items(): - while isinstance(props, list) and not isinstance(props, _ourlist): - # a list scalar (like `instruments = ['OLI', 'TIRS']`). - - # first, unpack (arbitrarily-nested) 1-element lists. - # keep re-checking if it's still a list - if len(props) == 1: - props = props[0] - continue - - # for now, treat multi-item lists as a set so xarray can interpret them as 0D variables. - # (numpy very much does not like to create object arrays containing python lists; - # `set` is basically a hack to make a 0D ndarray containing a Python object with multiple items.) - try: - props = set(props) - except TypeError: - # if it's not set-able, just give up - break - - props_arr = np.squeeze( - np.array( - props, - # Avoid DeprecationWarning creating ragged arrays when elements are lists/tuples of different lengths - dtype="object" - if ( - isinstance(props, _ourlist) - and len(set(len(x) if isinstance(x, (list, tuple)) else type(x) for x in props)) - > 1 - ) - else None, - ) - ) - - if ( - props_arr.ndim > 1 - or props_arr.ndim == 1 - and not isinstance(props, _ourlist) - ): - # probably a list-of-lists situation. the other dims likely don't correspond to - # our "bands", "y", and "x" dimensions, and xarray won't let us use unrelated - # dimensions. so just skip it for now. - continue - - coords[field] = xr.Variable( - (dim_name,) if props_arr.ndim == 1 else (), - props_arr, - ) - - return coords diff --git a/stackstac/coordinates.py b/stackstac/coordinates.py new file mode 100644 index 0000000..646d85a --- /dev/null +++ b/stackstac/coordinates.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +from typing import Any, Collection, Dict, List, Literal, Tuple + +import numpy as np +import pandas as pd +import xarray as xr + +from stackstac.coordinates_utils import ( + Coordinates, + items_to_coords, + unnested_items, + unpacked_per_band_asset_fields, +) + +from .raster_spec import RasterSpec +from .stac_types import ItemSequence + +ASSET_TABLE_DT = np.dtype( + [("url", object), ("bounds", "float64", 4), ("scale_offset", "float64", 2)] +) + +# Asset fields which are a list with one item per band in the asset. +# For one-band assets, they should be a list of length 1. +# We'll unpack those 1-length lists, so the subfields can be flattened into +# top-level coordinates. +# This is how we get `eo:bands_common_name` or `raster:bands_scale` coordinates. +PER_BAND_ASSET_FIELDS = { + "eo:bands", + "raster:bands", +} + + +def to_coords( + items: ItemSequence, + asset_ids: List[str], + spec: RasterSpec, + xy_coords: Literal["center", "topleft", False] = "topleft", + properties: bool = True, + band_coords: bool = True, +) -> Tuple[Coordinates, List[str]]: + times = pd.to_datetime( + [item["properties"]["datetime"] for item in items], + infer_datetime_format=True, + errors="coerce", + ) + if times.tz is not None: + # xarray can't handle tz-aware DatetimeIndexes, so we convert to UTC and drop the timezone + # https://github.com/pydata/xarray/issues/3291. + # The `tz is None` case is typically a manifestation of https://github.com/pandas-dev/pandas/issues/41047. + # Since all STAC timestamps should be UTC (https://github.com/radiantearth/stac-spec/issues/1095), + # we feel safe assuming that any tz-naive datetimes are already in UTC. + times = times.tz_convert(None) + + dims = ["time", "band", "y", "x"] + coords = { + "time": times, + "id": xr.Variable("time", [item["id"] for item in items]), + "band": asset_ids, + } + + if xy_coords is not False: + if xy_coords == "center": + pixel_center = True + elif xy_coords == "topleft": + pixel_center = False + else: + raise ValueError( + f"xy_coords must be 'center', 'topleft', or False, not {xy_coords!r}" + ) + + transform = spec.transform + # We generate the transform ourselves in `RasterSpec`, and it's always constructed to be rectilinear. + # Someday, this should not always be the case, in order to support non-rectilinear data without warping. + assert ( + transform.is_rectilinear + ), f"Non-rectilinear transform generated: {transform}" + minx, miny, maxx, maxy = spec.bounds + xres, yres = spec.resolutions_xy + + if pixel_center: + half_xpixel, half_ypixel = xres / 2, yres / 2 + minx, miny, maxx, maxy = ( + minx + half_xpixel, + miny - half_ypixel, + maxx + half_xpixel, + maxy - half_ypixel, + ) + + height, width = spec.shape + # Wish pandas had an RangeIndex that supported floats... + # https://github.com/pandas-dev/pandas/issues/46484 + xs = pd.Index(np.linspace(minx, maxx, width, endpoint=False), dtype="float64") + ys = pd.Index(np.linspace(maxy, miny, height, endpoint=False), dtype="float64") + + coords["x"] = xs + coords["y"] = ys + + if properties: + assert properties is True, ( + "Passing specific properties is no longer supported. " + "The `properties` argument must only be True or False. " + "If you have a use case for this, please open an issue." + ) + coords.update(items_to_property_coords(items)) + + if band_coords: + coords.update(items_to_band_coords(items, asset_ids)) + + # Add `epsg` last in case it's also a field in properties; our data model assumes it's a coordinate + coords["epsg"] = spec.epsg + + return coords, dims + + +def items_to_property_coords( + items: ItemSequence, + skip_fields: Collection[str] = frozenset(["datetime", "id"]), +) -> Coordinates: + return items_to_coords( + ( + ((i,), k, v) + for i, item in enumerate(items) + # TODO: should we unnest properties? + for k, v in item["properties"].items() + if k not in skip_fields + ), + shape=(len(items),), + dims=("time",), + ) + + +def items_to_band_coords( + items: ItemSequence, + asset_ids: List[str], + skip_fields: Collection[str] = frozenset(["href", "type"]), +) -> Coordinates: + def fields_values_generator(): + for ii, item in enumerate(items): + for ai, id in enumerate(asset_ids): + try: + asset = item["assets"][id] + except KeyError: + continue + + for field, value in unnested_items( + unpacked_per_band_asset_fields(asset.items(), PER_BAND_ASSET_FIELDS) + ): + field = rename_some_band_fields(field) + if field not in skip_fields: + yield (ii, ai), field, value + + return items_to_coords( + fields_values_generator(), + shape=(len(items), len(asset_ids)), + dims=("time", "band"), + ) + + +def rename_some_band_fields(field: str) -> str: + """ + Apply renamings to band fields for "convenience". + + This is just for backwards compatibility. + These renamings should probably be removed for simplicity and consistency. + """ + if field == "sar:polarizations": + return "polarization" + return field.removeprefix("eo:bands_") + + +def spec_to_attrs(spec: RasterSpec) -> Dict[str, Any]: + attrs = {"spec": spec, "crs": f"epsg:{spec.epsg}", "transform": spec.transform} + + resolutions = spec.resolutions_xy + if resolutions[0] == resolutions[1]: + attrs["resolution"] = resolutions[0] + else: + attrs["resolution_xy"] = resolutions + return attrs diff --git a/stackstac/coordinates_utils.py b/stackstac/coordinates_utils.py new file mode 100644 index 0000000..67d57b4 --- /dev/null +++ b/stackstac/coordinates_utils.py @@ -0,0 +1,328 @@ +from __future__ import annotations +from collections import defaultdict +import datetime + +from typing import Any, Callable, Container, Iterable, Iterator, Mapping, TypeVar, Union + +import numpy as np +import pandas as pd +import xarray as xr + +Coordinates = Mapping[str, Union[pd.Index, np.ndarray, xr.Variable, list]] + + +def items_to_coords( + items: Iterable[tuple[tuple[int, ...], str, object]], + *, + shape: tuple[int, ...], + dims: tuple[str, ...], +) -> dict[str, xr.Variable]: + """ + Create coordinates from ``(index, field, value)`` tuples. + + Say you want to make coordinates for a 2x2 DataArray with dimensions ``time, band``. + Your metadata might look like:: + + ((0, 0), "color", "red"), + ((0, 0), "day", "mon"), + ((0, 0), "href", "0/red"), + + ((0, 1), "color", "green"), + ((0, 1), "day", "mon"), + ((0, 1), "href", "0/green"), + + ((1, 0), "color", "red"), + ((1, 0), "day", "tues"), + ((1, 0), "href", "1/red"), + + ((1, 1), "color", "green"), + ((1, 1), "day", "tues"), + # ((1, 1), "href", "1/green") skip this to see how missing values are handled + + This would produce a dict of coordinates like:: + + { + "color": xr.Variable( + dims=["band"], data=["red", "green"], + ), + "day": xr.Variable( + dims=["time"], data=["mon", "tues"], + ), + "href": xr.Variable( + dims=["time", "band"], + data=[ + ["0/red", "0/green"], + ["1/red", None], + ], + ), + } + + Each ``item`` in the iterator gives the value for a particular field at a particular + coordinate in the array. Per field, those are combined and de-duplicated: you can + see how "color" only labels the "band" dimension, because it's the same across all + times; "day" only labels the time dimension, because it's the same across all bands, + and "href" labels both, because it varies across both dimensions. + + Parameters + ---------- + items: + Iterable of ``(index, field, value)`` tuples. ``index`` is a tuple of + integers for the position in the array that this item labels. + ``field`` is the name of a coordinate, and ``value`` is its value. + shape: + The shape of the data being labeled. If a coordinate covered all of the + dimensions, this is the shape it would have. Each ``index`` in ``items`` + must be the same length (same number of dimensions) as ``shape``. + dims: + Dimension names corresponding to ``shape``. Must be the same length + as ``shape``. + + Returns + ------- + coords: dict of xarray Variables, one per unique ``field`` in the items. + """ + assert len(shape) == len( + dims + ), f"{shape=} has {len(shape)} dimensions; {dims=} has {len(dims)}" + + fields = defaultdict(lambda: DtypeUpdatingArray(shape)) + # {field: + # np.array([ + # [v_asset_0, v_asset_1, ...], # item 0 + # [v_asset_0, v_asset_1, ...], # item 1 + # ..., + # ]) + # } + for idx, field, value in items: + values = fields[field] + values[idx] = value + + deduped = {field: deduplicate_axes(arr.arr()) for field, arr in fields.items()} + + return {field: xr.Variable(dims, arr).squeeze() for field, arr in deduped.items()} + + +class DtypeUpdatingArray: + _arr: np.ndarray | None + _shape: tuple[int, ...] + _postprocess: Callable[[np.ndarray], np.ndarray] | None + + def __init__(self, shape: tuple[int, ...]) -> None: + self._arr = None + self._shape = shape + self._postprocess = None + + def __setitem__(self, idx, value) -> None: + assert len(idx) == len( + self._shape + ), f"Expected {len(self._shape)}-dimensional index, got {idx}" + if self._arr is None: + # Based on this first value, we guess whether the dtype will be numeric, or + # str/object. + dtype, fill, self._postprocess = self.dtype_fill_postprocess_for(value) + self._arr = np.full(self._shape, fill, dtype=dtype) + try: + self._arr[idx] = value + except (TypeError, ValueError): + # If our dtype guess was wrong, or a field has values of multiple types, + # promote the whole array to a more generic dtype. + # A `ValueError` might be "could not convert string to float". + # (so if there did happen to be string values that could be parsed as numbers, + # we'd do that, which is probably ok?) + try: + new_dtype = np.result_type(value, self._arr) + except TypeError: + # Thrown when "no common DType exists for the given inputs. + # For example they cannot be stored in a single array unless the + # dtype is `object`" + new_dtype = np.dtype(object) + + if new_dtype.kind == "O": + # Replace previous NaN fill values with None when converting to object array. + # Of course, this would convert values that were actually None in the data... + assert self._arr.dtype.kind != "O", self._arr.dtype + isnan = np.isnan(self._arr) + self._arr = np.where(isnan, None, self._arr) # type: ignore + else: + self._arr = self._arr.astype(new_dtype) + + self._postprocess = None # postprocess is invalidated if we convert dtypes + self._arr[idx] = value # redo insertion with new dtype + + @staticmethod + def dtype_fill_postprocess_for( + x, + ) -> tuple[type | np.dtype, object, Callable[[np.ndarray], np.ndarray] | None]: + if isinstance(x, bool): + # `bool` is a subclass of `int` in Python. + def postprocess(arr: np.ndarray) -> np.ndarray: + if not (arr == None).any(): # noqa: E711 + return arr.astype(bool) + return arr + + return (object, None, postprocess) + # Sadly, we have to use an object array so we can represent missing values. + if isinstance(x, (int, float)): + # TODO bool, complex + return (float, np.nan, None) + # NOTE: we don't use int64, even for ints, because there'd be no + # way to represent missing values. Using pandas nullable arrays + # could be interesting at some point. + if isinstance(x, complex): + return (complex, np.nan, None) + # TODO: datetimes. Handling datetime objects will take some special logic + # (you can't just assign them), plus not sure about timezones. + + return (object, None, None) + + def arr(self) -> np.ndarray: + assert self._arr is not None, "Accessing array with no values" + if self._postprocess: + return self._postprocess(self._arr) + return self._arr + + +def deduplicate_axes(arr: np.ndarray) -> np.ndarray: + "Flatten dimensions to length 1 where all values are duplicated" + if arr.dtype.kind in ("f", "c"): + arr_nan = np.isnan(arr) + return _nandeduplicate_axes(arr, arr_nan, np.where(arr_nan, True, arr)) + return _deduplicate_axes(arr) + + +def _deduplicate_axes(arr: np.ndarray) -> np.ndarray: + if arr.size <= 1: + return arr + + for axis in range(arr.ndim): + if arr.shape[axis] <= 1: + continue + first = arr.take([0], axis=axis) + # ^ note `[0]` instead of `0`: that keeps the dimension + # as length 1 instead of dropping it + allsame = (arr == first).all(axis=axis) + if allsame.all(): + return deduplicate_axes(first) + return arr + + +def _nandeduplicate_axes( + arr: np.ndarray, arr_nan: np.ndarray, arr_filled: np.ndarray +) -> np.ndarray: + if arr.size <= 1: + return arr + + for axis in range(arr.ndim): + if arr.shape[axis] <= 1: + continue + first_nan = arr_nan.take([0], axis=axis) + first_filled = arr_filled.take([0], axis=axis) + # ^ note `[0]` instead of `0`: that keeps the dimension + # as length 1 instead of dropping it + allsame = (arr_filled == first_filled).all(axis=axis) & ( + arr_nan == first_nan + ).all(axis=axis) + if allsame.all(): + return _nandeduplicate_axes( + arr.take([0], axis=axis), first_nan, first_filled + ) + return arr + + +VT = TypeVar("VT") + + +def unnested_items( + items: Iterable[tuple[str, VT]], prefix: tuple[str, ...] = (), sep: str = "_" +) -> Iterator[tuple[str, VT]]: + """ + Iterate over flattened dicts, prefixing sub-keys with the name of their parent key. + + Example + ------- + >>> list(unnested_dict_items({ + ... "foo": 1, + ... "bar": { + ... "a": 2, + ... "foo": 3, + ... }, + ... }.items())) + [ + ("foo", 1), + ("bar_a", 2), + ("bar_foo", 3), + ] + """ + for k, v in items: + if isinstance(v, dict) and v: + yield from unnested_items(v.items(), prefix=prefix + (k,), sep=sep) + else: + yield sep.join(prefix + (k,)) if prefix else k, v + + # Note that we don't descend into lists/tuples. For the purposes of STAC metadata, + # there'd be no reason to do this: we're not going to make an xarray coordinate like + # `classification:bitfields_0_name`, `classification:bitfields_0_fill`, ..., + # `classification:bitfields_8_name`, `classification:bitfields_8_fill` + # and unpack a separate coordinate for every field in a sequence. Rather, we + # `preserve anything that's a sequence as a single element in an object array. + + +def scalar_sequence(x): + """ + Convert sequence inputs into NumPy scalars. + + Use this to wrap inputs to `np.array` that you don't want to be treated as + additional axes in the array. + + Example + ------- + >>> s = scalar_sequence([1, 2]) + >>> s + array(list([1, 2]), dtype=object) + >>> s.shape + () + >>> s.item() + [1, 2] + >>> arr = np.array([s]) + >>> arr + >>> arr.shape + (1,) + >>> # for comparision, if we hadn't wrapped it: + >>> np.array([[1, 2]]).shape + >>> (1, 2) + """ + if not isinstance(x, (list, tuple)): + return x + + scalar = np.empty((), dtype=object) # basically a pointer + scalar[()] = x + return scalar + + +def unpacked_per_band_asset_fields( + asset: Iterable[tuple[str, Any]], fields: Container +) -> Iterator[tuple[str, Any]]: + """ + Unpack 1-length list/tuple values for the given ``fields``. + + For keys of ``asset`` in ``fields``, if the value is a 1-length + list or tuple, use its single value. Otherwise, use an empty dict. + """ + # NOTE: this will have to change a lot when we support multi-band assets; + # this is predicated on each asset having exactly 1 band. + for k, v in asset: + if k in fields: + if isinstance(v, (list, tuple)): + if len(v) == 1: + v = v[0] + else: + # For >1 band, drop metadata entirely (you can't use the data anyway). + # Otherwise, coordinates would be a mess: both unpacked `eo:bands` + # fields like `eo:bands_common_name`, and plain `eo:bands` which would + # be None for all 1-band assets, and contain the dicts for multi-band + # assets. + continue + elif v is None: + continue + + yield k, v diff --git a/stackstac/prepare.py b/stackstac/prepare.py index 704fd3e..59ee86f 100644 --- a/stackstac/prepare.py +++ b/stackstac/prepare.py @@ -3,29 +3,23 @@ import collections from typing import ( AbstractSet, - Literal, NamedTuple, - Sequence, Optional, Set, Union, Tuple, List, - Dict, - Any, ) import warnings import affine import numpy as np -import pandas as pd -import xarray as xr from .raster_spec import IntFloat, Bbox, Resolutions, RasterSpec from .stac_types import ItemSequence -from . import accumulate_metadata, geom_utils +from . import geom_utils ASSET_TABLE_DT = np.dtype( [("url", object), ("bounds", "float64", 4), ("scale_offset", "float64", 2)] @@ -153,10 +147,12 @@ def prepare_items( if raster_bands is not None: if len(raster_bands) != 1: raise ValueError( - f"raster:bands has {len(raster_bands)} elements for asset {asset_id!r}. " + f"raster:bands has {len(raster_bands)} elements for asset {id!r}. " "Multi-band rasters are not currently supported.\n" "If you don't care about this asset, you can skip it by giving a list " "of asset IDs you *do* want in `assets=`, and leaving this one out." + "For example:\n" + f"`assets={[x for x in asset_ids if x != id]!r}`" ) asset_scale = raster_bands[0].get("scale", 1) asset_offset = raster_bands[0].get("offset", 0) @@ -394,182 +390,3 @@ def prepare_items( items = [item for item, isnan in zip(items, item_isnan) if not isnan] return asset_table, spec, asset_ids, items - - -def to_coords( - items: ItemSequence, - asset_ids: List[str], - spec: RasterSpec, - xy_coords: Literal["center", "topleft", False] = "topleft", - properties: Union[bool, str, Sequence[str]] = True, - band_coords: bool = True, -) -> Tuple[Dict[str, Union[pd.Index, np.ndarray, list]], List[str]]: - - times = pd.to_datetime( - [item["properties"]["datetime"] for item in items], - infer_datetime_format=True, - errors="coerce", - ) - if times.tz is not None: - # xarray can't handle tz-aware DatetimeIndexes, so we convert to UTC and drop the timezone - # https://github.com/pydata/xarray/issues/3291. - # The `tz is None` case is typically a manifestation of https://github.com/pandas-dev/pandas/issues/41047. - # Since all STAC timestamps should be UTC (https://github.com/radiantearth/stac-spec/issues/1095), - # we feel safe assuming that any tz-naive datetimes are already in UTC. - times = times.tz_convert(None) - - dims = ["time", "band", "y", "x"] - coords = { - "time": times, - "id": xr.Variable("time", [item["id"] for item in items]), - "band": asset_ids, - } - - if xy_coords is not False: - if xy_coords == "center": - pixel_center = True - elif xy_coords == "topleft": - pixel_center = False - else: - raise ValueError( - f"xy_coords must be 'center', 'topleft', or False, not {xy_coords!r}" - ) - - transform = spec.transform - # We generate the transform ourselves in `RasterSpec`, and it's always constructed to be rectilinear. - # Someday, this should not always be the case, in order to support non-rectilinear data without warping. - assert ( - transform.is_rectilinear - ), f"Non-rectilinear transform generated: {transform}" - minx, miny, maxx, maxy = spec.bounds - xres, yres = spec.resolutions_xy - - if pixel_center: - half_xpixel, half_ypixel = xres / 2, yres / 2 - minx, miny, maxx, maxy = ( - minx + half_xpixel, - miny - half_ypixel, - maxx + half_xpixel, - maxy - half_ypixel, - ) - - height, width = spec.shape - # Wish pandas had an RangeIndex that supported floats... - # https://github.com/pandas-dev/pandas/issues/46484 - xs = pd.Index(np.linspace(minx, maxx, width, endpoint=False), dtype="float64") - ys = pd.Index(np.linspace(maxy, miny, height, endpoint=False), dtype="float64") - - coords["x"] = xs - coords["y"] = ys - - if properties: - coords.update( - accumulate_metadata.metadata_to_coords( - (item["properties"] for item in items), - "time", - fields=properties, - skip_fields={"datetime"}, - # skip_fields={"datetime", "providers"}, - ) - ) - - # Property-merging code using awkward array. Slightly shorter, not sure if it's faster, - # probably not worth the dependency - - # import awkward as ak - - # awk_props = ak.Array([item._data for item in items]).properties - # for field, props in zip(ak.fields(awk_props), ak.unzip(awk_props)): - # if field == "datetime": - # continue - - # # if all values are the same, collapse to a 0D coordinate - # try: - # if len(ak.run_lengths(props)) == 1: - # props = ak.to_list(props[0]) - # # ^ NOTE: `to_list` because `ak.to_numpy` on string scalars (`ak.CharBehavior`) - # # turns them into an int array of the characters! - # except NotImplementedError: - # # generally because it's an OptionArray (so there's >1 value anyway) - # pass - - # try: - # props = np.squeeze(ak.to_numpy(props)) - # except ValueError: - # continue - - # coords[field] = xr.Variable( - # (("time",) + tuple(f"dim_{i}" for i in range(1, props.ndim))) - # if np.ndim(props) > 0 - # else (), - # props, - # ) - # else: - # # For now don't use awkward when the field names are already known, - # # mostly so users don't have to have it installed. - # if isinstance(properties, str): - # properties = (properties,) - # for prop in properties: # type: ignore (`properties` cannot be True at this point) - # coords[prop] = xr.Variable( - # "time", [item["properties"].get(prop) for item in items] - # ) - - if band_coords: - flattened_metadata_by_asset = [ - accumulate_metadata.accumulate_metadata_only_allsame( - (item["assets"].get(asset_id, {}) for item in items), - skip_fields={"href", "type", "roles"}, - ) - for asset_id in asset_ids - ] - - eo_by_asset = [] - for meta in flattened_metadata_by_asset: - # NOTE: we look for `eo:bands` in each Asset's metadata, not as an Item-level list. - # This only became available in STAC 1.0.0-beta.1, so we'll fail on older collections. - # See https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#item-fields - eo = meta.pop("eo:bands", {}) - if isinstance(eo, list): - eo = eo[0] if len(eo) == 1 else {} - # ^ `eo:bands` should be a list when present, but >1 item means it's probably a multi-band asset, - # which we can't currently handle, so we ignore it. we don't error here, because - # as long as you don't actually _use_ that asset, everything will be fine. we could - # warn, but that would probably just get annoying. - eo_by_asset.append(eo) - try: - meta["polarization"] = meta.pop("sar:polarizations") - except KeyError: - pass - - coords.update( - accumulate_metadata.metadata_to_coords( - flattened_metadata_by_asset, - "band", - skip_fields={"href"}, - # skip_fields={"href", "title", "description", "type", "roles"}, - ) - ) - if any(eo_by_asset): - coords.update( - accumulate_metadata.metadata_to_coords( - eo_by_asset, - "band", - fields=["common_name", "center_wavelength", "full_width_half_max"], - ) - ) - - # Add `epsg` last in case it's also a field in properties; our data model assumes it's a coordinate - coords["epsg"] = spec.epsg - - return coords, dims - - -def to_attrs(spec: RasterSpec) -> Dict[str, Any]: - attrs = {"spec": spec, "crs": f"epsg:{spec.epsg}", "transform": spec.transform} - - resolutions = spec.resolutions_xy - if resolutions[0] == resolutions[1]: - attrs["resolution"] = resolutions[0] - else: - attrs["resolution_xy"] = resolutions - return attrs diff --git a/stackstac/stac_types.py b/stackstac/stac_types.py index 738c4c5..63279ad 100644 --- a/stackstac/stac_types.py +++ b/stackstac/stac_types.py @@ -91,7 +91,7 @@ class EOBand(TypedDict, total=False): Tuple[int, int, int, int, int, int], Tuple[int, int, int, int, int, int, int, int, int], ], - "eo:bands": EOBand, + "eo:bands": List[EOBand], "sar:polarizations": List[str], }, total=False, @@ -131,13 +131,12 @@ class ItemDict(TypedDict): ItemCollectionIsh = Union[ SatstacItemCollection, PystacCatalog, PystacItemCollection, ItemSequence ] +ItemsIsh = Union[ + ItemCollectionIsh, ItemIsh, Sequence[PystacItem], Sequence[SatstacItem] +] -def items_to_plain( - items: Union[ - ItemCollectionIsh, ItemIsh, Sequence[PystacItem], Sequence[SatstacItem] - ] -) -> ItemSequence: +def items_to_plain(items: ItemsIsh) -> ItemSequence: """ Convert something like a collection/Catalog of STAC items into a list of plain dicts diff --git a/stackstac/stack.py b/stackstac/stack.py index dc85fd9..c172b37 100644 --- a/stackstac/stack.py +++ b/stackstac/stack.py @@ -1,32 +1,28 @@ from __future__ import annotations -from typing import AbstractSet, List, Literal, Optional, Sequence, Tuple, Type, Union +from typing import AbstractSet, List, Literal, Optional, Tuple, Type, Union import numpy as np import xarray as xr -import dask +import dask.base from rasterio import RasterioIOError from rasterio.enums import Resampling -from .prepare import prepare_items, to_attrs, to_coords +from .prepare import prepare_items +from .coordinates import spec_to_attrs, to_coords from .raster_spec import Bbox, IntFloat, Resolutions from .reader_protocol import Reader from .rio_env import LayeredEnv from .rio_reader import AutoParallelRioReader from .stac_types import ( - ItemCollectionIsh, - ItemIsh, - PystacItem, - SatstacItem, + ItemsIsh, items_to_plain, ) from .to_dask import items_to_dask, ChunksParam def stack( - items: Union[ - ItemCollectionIsh, ItemIsh, Sequence[PystacItem], Sequence[SatstacItem] - ], + items: ItemsIsh, assets: Optional[Union[List[str], AbstractSet[str]]] = frozenset( ["image/tiff", "image/x.geotiff", "image/vnd.stac.geotiff", "image/jp2"] ), @@ -42,7 +38,7 @@ def stack( rescale: bool = True, sortby_date: Literal["asc", "desc", False] = "asc", xy_coords: Literal["center", "topleft", False] = "topleft", - properties: Union[bool, str, Sequence[str]] = True, + properties: bool = True, band_coords: bool = True, gdal_env: Optional[LayeredEnv] = None, errors_as_nodata: Tuple[Exception, ...] = ( @@ -228,18 +224,14 @@ def stack( If False, ``x`` and ``y`` will just be indexed by row/column numbers, saving a small amount of time and local memory. properties: - Which fields from each STAC Item's ``properties`` to add as coordinates to the DataArray, indexing the "time" - dimension. + Whether to use each STAC Item's ``properties`` as coordinates for the DataArray, indexing the "time" dimension. - If None (default), all properties will be used. If a string or sequence of strings, only those fields - will be used. For each Item missing a particular field, its value for that Item will be None. - - If False, no properties will be added. + If False, only ``time`` and ``id`` will be added. band_coords: Whether to include Asset-level metadata as coordinates for the ``bands`` dimension. - If True (default), for each asset ID, the field(s) that have the same value across all Items - will be added as coordinates. + Fields that have the same value across all Assets in an Item, or all Items, will be + deduplicated and collapsed down as appropriate. The ``eo:bands`` field is also unpacked if present, and ``sar:polarizations`` is renamed to ``polarization`` for convenience. @@ -321,6 +313,6 @@ def stack( properties=properties, band_coords=band_coords, ), - attrs=to_attrs(spec), + attrs=spec_to_attrs(spec), name="stackstac-" + dask.base.tokenize(arr), ) diff --git a/stackstac/tests/items-landsat-c2-l2.json b/stackstac/tests/items-landsat-c2-l2.json new file mode 100644 index 0000000..5eae714 --- /dev/null +++ b/stackstac/tests/items-landsat-c2-l2.json @@ -0,0 +1 @@ +[{"type": "Feature", "stac_version": "1.0.0", "id": "LC09_L2SR_089113_20231008_02_T2", "properties": {"gsd": 30, "created": "2023-10-10T09:16:37.761406Z", "sci:doi": "10.5066/P9OGBGM6", "datetime": "2023-10-08T23:55:50.758410Z", "platform": "landsat-9", "proj:epsg": 3031, "proj:shape": [8521, 8561], "description": "Landsat Collection 2 Level-2", "instruments": ["oli", "tirs"], "eo:cloud_cover": 100.0, "proj:transform": [30.0, 0.0, 1281585.0, 0.0, -30.0, -768885.0], "view:off_nadir": 0, "landsat:wrs_row": "113", "landsat:scene_id": "LC90891132023281LGN00", "landsat:wrs_path": "089", "landsat:wrs_type": "2", "view:sun_azimuth": 57.77711452, "landsat:correction": "L2SR", "view:sun_elevation": 14.52884281, "landsat:cloud_cover_land": 100.0, "landsat:collection_number": "02", "landsat:collection_category": "T2"}, "geometry": {"type": "Polygon", "coordinates": [[[117.85828738204097, -74.91546031511366], [123.45165341938181, -75.90706707244007], [126.92671495037585, -74.4169985484992], [121.6271365631418, -73.50930124810642], [117.85828738204097, -74.91546031511366]]]}, "links": [{"rel": "collection", "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2", "type": "application/json"}, {"rel": "parent", "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2", "type": "application/json"}, {"rel": "root", "href": "https://planetarycomputer.microsoft.com/api/stac/v1/", "type": "application/json", "title": "Microsoft Planetary Computer STAC API"}, {"rel": "self", "href": "https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2/items/LC09_L2SR_089113_20231008_02_T2", "type": "application/geo+json"}, {"rel": "cite-as", "href": "https://doi.org/10.5066/P9OGBGM6", "title": "Landsat 8-9 OLI/TIRS Collection 2 Level-2"}, {"rel": "via", "href": "https://landsatlook.usgs.gov/stac-server/collections/landsat-c2l2-sr/items/LC09_L2SR_089113_20231008_20231009_02_T2_SR", "type": "application/json", "title": "USGS STAC Item"}, {"rel": "preview", "href": "https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=landsat-c2-l2&item=LC09_L2SR_089113_20231008_02_T2", "type": "text/html", "title": "Map of item"}], "assets": {"ang": {"href": "https://landsateuwest.blob.core.windows.net/landsat-c2/level-2/standard/oli-tirs/2023/089/113/LC09_L2SR_089113_20231008_20231009_02_T2/LC09_L2SR_089113_20231008_20231009_02_T2_ANG.txt", "type": "text/plain", "title": "Angle Coefficients File", "description": "Collection 2 Level-1 Angle Coefficients File", "roles": ["metadata"]}, "red": {"href": "https://landsateuwest.blob.core.windows.net/landsat-c2/level-2/standard/oli-tirs/2023/089/113/LC09_L2SR_089113_20231008_20231009_02_T2/LC09_L2SR_089113_20231008_20231009_02_T2_SR_B4.TIF", "type": "image/tiff; application=geotiff; profile=cloud-optimized", "title": "Red Band", "description": "Collection 2 Level-2 Red Band (SR_B4) Surface Reflectance", "eo:bands": [{"name": "OLI_B4", "center_wavelength": 0.65, "full_width_half_max": 0.04, "common_name": "red", "description": "Visible red"}], "raster:bands": [{"scale": 2.75e-05, "nodata": 0, "offset": -0.2, "data_type": "uint16", "spatial_resolution": 30}], "roles": ["data", "reflectance"]}, "blue": {"href": 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{"name": "dilated_cloud", "length": 1, "offset": 1, "classes": [{"name": "not_dilated", "value": 0, "description": "Cloud is not dilated or no cloud"}, {"name": "dilated", "value": 1, "description": "Cloud dilation"}], "description": "Dilated cloud"}, {"name": "cirrus", "length": 1, "offset": 2, "classes": [{"name": "not_cirrus", "value": 0, "description": "Cirrus confidence is not high"}, {"name": "cirrus", "value": 1, "description": "High confidence cirrus"}], "description": "Cirrus mask"}, {"name": "cloud", "length": 1, "offset": 3, "classes": [{"name": "not_cloud", "value": 0, "description": "Cloud confidence is not high"}, {"name": "cloud", "value": 1, "description": "High confidence cloud"}], "description": "Cloud mask"}, {"name": "cloud_shadow", "length": 1, "offset": 4, "classes": [{"name": "not_shadow", "value": 0, "description": "Cloud shadow confidence is not high"}, {"name": "shadow", "value": 1, "description": "High confidence cloud shadow"}], "description": "Cloud shadow 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"interpolated", "value": 1, "description": "Pixel is interpolated aerosol"}], "description": "Aerosol interpolation"}, {"name": "level", "length": 2, "offset": 6, "classes": [{"name": "climatology", "value": 0, "description": "No aerosol correction applied"}, {"name": "low", "value": 1, "description": "Low aerosol level"}, {"name": "medium", "value": 2, "description": "Medium aerosol level"}, {"name": "high", "value": 3, "description": "High aerosol level"}], "description": "Aerosol level"}], "roles": ["data-mask", "water-mask"]}, "tilejson": {"href": "https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=landsat-c2-l2&item=LC09_L2SR_089073_20231008_02_T2&assets=red&assets=green&assets=blue&color_formula=gamma+RGB+2.7%2C+saturation+1.5%2C+sigmoidal+RGB+15+0.55&format=png", "type": "application/json", "title": "TileJSON with default rendering", "roles": ["tiles"]}, "rendered_preview": {"href": "https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=landsat-c2-l2&item=LC09_L2SR_089073_20231008_02_T2&assets=red&assets=green&assets=blue&color_formula=gamma+RGB+2.7%2C+saturation+1.5%2C+sigmoidal+RGB+15+0.55&format=png", "type": "image/png", "title": "Rendered preview", "rel": "preview", "roles": ["overview"]}}, "bbox": [154.23732681235387, -19.861255510506375, 156.4631214114148, -17.715294489493626], "stac_extensions": ["https://stac-extensions.github.io/raster/v1.1.0/schema.json", "https://stac-extensions.github.io/eo/v1.0.0/schema.json", "https://stac-extensions.github.io/view/v1.0.0/schema.json", "https://stac-extensions.github.io/projection/v1.1.0/schema.json", "https://landsat.usgs.gov/stac/landsat-extension/v1.1.1/schema.json", "https://stac-extensions.github.io/classification/v1.0.0/schema.json", "https://stac-extensions.github.io/scientific/v1.0.0/schema.json"], "collection": "landsat-c2-l2"}] \ No newline at end of file diff --git a/stackstac/tests/test_coordinates.py b/stackstac/tests/test_coordinates.py new file mode 100644 index 0000000..6196af4 --- /dev/null +++ b/stackstac/tests/test_coordinates.py @@ -0,0 +1,196 @@ +import json + +import pytest +import xarray as xr +import numpy as np + +from stackstac.coordinates import ( + items_to_band_coords, + rename_some_band_fields, +) +from stackstac.coordinates_utils import scalar_sequence + + +@pytest.fixture +def landsat_c2_l2_json(): + with open("stackstac/tests/items-landsat-c2-l2.json") as f: + return json.load(f) + + +def test_band_coords(landsat_c2_l2_json): + ids = ["red", "green", "qa_pixel", "qa_radsat"] + coords = items_to_band_coords(landsat_c2_l2_json, ids, skip_fields=set()) + + # Note that we intentionally keep some coordinates that would normally be dropped, + # since they're handy for testing + + # 0D coordinate + type = coords["type"] + assert isinstance(type, xr.Variable) + assert type.shape == () + assert ( + type.values.item() == "image/tiff; application=geotiff; profile=cloud-optimized" + ) + + # 1D coordinate along bands + title = coords["title"] + assert isinstance(title, xr.Variable) + np.testing.assert_equal( + title.values, + [ + "Red Band", + "Green Band", + "Pixel Quality Assessment Band", + "Radiometric Saturation and Terrain Occlusion Quality Assessment Band", + ], + ) + + # 1D coordinate along bands, where each element is a variable-length list + roles = coords["roles"] + assert isinstance(roles, xr.Variable) + assert roles.shape == (len(ids),) + # `roles` is an array of lists: + # + # array([list(['data', 'reflectance']), list(['data', 'reflectance']), + # list(['cloud', 'cloud-shadow', 'snow-ice', 'water-mask']), + # list(['saturation'])], dtype=object) + # + # Actually working with this in xarray is awkward. I'm not sure how users + # would ever usefully interact with this besides printing it, because + # just an equality check requires wrapping your list in `scalar_sequence`, + # and if you wanted to do some sort of set operation (only 'data' roles, say), + # I don't think it's even possible in xarray. + assert roles[0] == scalar_sequence(["data", "reflectance"]) + assert roles[2] == scalar_sequence( + ["cloud", "cloud-shadow", "snow-ice", "water-mask"] + ) + assert roles[3] == scalar_sequence(["saturation"]) + + # Here's a 2D coordinate along both time and band + href = coords["href"] + assert isinstance(href, xr.Variable) + assert href.dims == ("time", "band") + assert href.shape == (len(landsat_c2_l2_json), len(ids)) + + # `eo:bands` should be unpacked + # NOTE: for backwards compatibility, we de-prefix `eo:bands`. + # I'd like to remove this; `eo:bands` doesn't deserve more special + # treatment than `raster:bands`, for instance. + assert "eo:bands" not in coords + assert "description" in coords + common_name = coords["common_name"] + assert isinstance(common_name, xr.Variable) + assert common_name.dims == ("band",) + np.testing.assert_equal(common_name.values, ["red", "green", None, None]) + + # `raster:bands` is also unpacked + assert "raster:bands" not in coords + data_type = coords["raster:bands_data_type"] + assert isinstance(data_type, xr.Variable) + assert data_type.shape == () + assert data_type == "uint16" + + # missing values in `raster:bands_unit` are imputed with None + unit = coords["raster:bands_unit"] + assert isinstance(unit, xr.Variable) + np.testing.assert_equal(unit.values, [None, None, "bit index", "bit index"]) + + # `classification:bitfields` contains scalar sequences of dicts. + # Again, quite awkward to work with in xarray, but at least it's properly there? + bitfields = coords["classification:bitfields"] + assert isinstance(bitfields, xr.Variable) + assert bitfields.dims == ("band",) + assert bitfields[0].values.item() is None + c2 = bitfields[2].values.item() + assert isinstance(c2, list) + assert len(c2) == 12 + assert all([isinstance(c, dict) for c in c2]) + + +def test_band_coords_type_promotion(): + stac = [ + { + "assets": { + "foo": { + "complex_field": 0, + "numeric": 0, + "mixed_numeric_object": 0, + "mixed_numeric_str": 0, + } + } + }, + { + "assets": { + "foo": { + "complex_field": 2 - 4.0j, + "numeric": None, + "mixed_numeric_object": [], + "mixed_numeric_str": "baz", + "partially_missing_str": "woof", + "partially_missing_numeric": -1, + "partially_missing_object": {}, + } + } + }, + ] + coords = items_to_band_coords(stac, ["foo"]) + + complex = coords["complex_field"] + assert isinstance(complex, xr.Variable) + assert complex.dtype.kind == "c" + np.testing.assert_equal(complex.values, [0, 2 - 4.0j]) + + numeric = coords["numeric"] + assert isinstance(numeric, xr.Variable) + assert numeric.dtype == np.dtype(float) + assert numeric[0] == 0 + assert np.isnan(numeric[1]) + + mixed_numeric_object = coords["mixed_numeric_object"] + assert isinstance(mixed_numeric_object, xr.Variable) + assert mixed_numeric_object.dtype == np.dtype(object) + np.testing.assert_equal(mixed_numeric_object.values, [0, []]) + + mixed_numeric_str = coords["mixed_numeric_str"] + assert isinstance(mixed_numeric_str, xr.Variable) + assert mixed_numeric_str.dtype == np.dtype(object) + np.testing.assert_equal( + mixed_numeric_str.values, np.array([0.0, "baz"], dtype=object) + ) + # ^ without explicitly specifying `dtype=object`, NumPy would weirdly have + # turned our list into a string array (`