From 85bab7f4329ab0cf5934db1c8bc341684cf4ce88 Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Tue, 15 Oct 2024 19:39:02 +0200 Subject: [PATCH 1/9] wip: Initial commit. Some diagnostic setup. --- semantique/datacube.py | 12 ++++- semantique/processor/arrays.py | 4 +- semantique/processor/operators.py | 80 +++++++++++++++---------------- semantique/processor/utils.py | 10 +++- 4 files changed, 62 insertions(+), 44 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index 3ffbb16e..9cf7dbf0 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -159,6 +159,8 @@ class Opendatacube(Datacube): """ def __init__(self, layout = None, connection = None, tz = "UTC", **config): + # TODO: Put behind dask config flag check. + raise NotImplementedError("Not supported with Dask.") super(Opendatacube, self).__init__(layout) self.connection = connection self.tz = tz @@ -448,6 +450,8 @@ class GeotiffArchive(Datacube): """ def __init__(self, layout = None, src = None, tz = "UTC", **config): + # TODO: Put behind dask config flag check. + raise NotImplementedError("Not supported with Dask.") super(GeotiffArchive, self).__init__(layout) self.src = src self.tz = tz @@ -877,6 +881,8 @@ def retrieve(self, *reference, extent): # Trim the array if requested. # This will remove dimension coordinates with only missing or invalid data. if self.config["trim"]: + # TODO: Put behind dask config flag check. + raise NotImplementedError("Not supported with Dask.") data = data.sq.trim() return data @@ -958,7 +964,9 @@ def _load(self, metadata, extent): xy_coords="center", snap_bounds=False, ) - data = data.compute(**(self.config["dask_params"] or {})) + + # TODO: Remove (or put behind config flag) + # data = data.compute(**(self.config["dask_params"] or {})) # mosaicking in case of temporal grouping # convert datetimes to daily granularity - resample by day @@ -971,6 +979,8 @@ def _mosaic_ints(x, axis=0, na_value=np.nan): return np.where(all_na, na_array, chosen) if self.config["group_by_solar_day"]: + # TODO: Put behind dask config flag check. + raise NotImplementedError("Not supported with Dask.") if len(data.time): days = data.time.astype("datetime64[ns]").dt.floor("D") if data.dtype.kind == "f": diff --git a/semantique/processor/arrays.py b/semantique/processor/arrays.py index caf703a0..ec108b2b 100644 --- a/semantique/processor/arrays.py +++ b/semantique/processor/arrays.py @@ -768,7 +768,7 @@ def delineate(self, track_types = True, **kwargs): """ # Get and check array. - obj = xr.apply_ufunc(utils.null_as_zero, self._obj) + obj = utils.apply_ufunc(utils.null_as_zero, self._obj) if track_types: vtype = obj.sq.value_type if vtype is not None and vtype != "binary": @@ -816,7 +816,7 @@ def delineate(self, track_types = True, **kwargs): f"and/or '{SPACE}', not: {list(dims)}" ) # Delineate. - out = xr.apply_ufunc(lambda x, y: ndimage.label(x, y)[0], obj, nb) + out = utils.apply_ufunc(lambda x, y: ndimage.label(x, y)[0], obj, nb) # Post-process. out = out.where(pd.notnull(self._obj)) # Preserve nan. if track_types: diff --git a/semantique/processor/operators.py b/semantique/processor/operators.py index dbc8f7c2..c8dae63a 100644 --- a/semantique/processor/operators.py +++ b/semantique/processor/operators.py @@ -49,7 +49,7 @@ def not_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "not") promoter.check() f = lambda x: np.where(pd.notnull(x), np.logical_not(x), np.nan) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -105,7 +105,7 @@ def is_missing_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "is_missing") promoter.check() f = lambda x: pd.isnull(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -148,7 +148,7 @@ def not_missing_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "not_missing") promoter.check() f = lambda x: pd.notnull(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -191,7 +191,7 @@ def absolute_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "absolute") promoter.check() f = lambda x: np.absolute(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -236,7 +236,7 @@ def ceiling_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "ceiling") promoter.check() f = lambda x: np.ceil(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -281,7 +281,7 @@ def cosine_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "cosine") promoter.check() f = lambda x: np.cos(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -329,7 +329,7 @@ def f(x): sin = np.sin(x) sin_nozero = np.where(np.equal(sin, 0), np.nan, sin) return np.divide(1, sin_nozero) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -377,7 +377,7 @@ def f(x): tan = np.tan(x) tan_nozero = np.where(np.equal(tan, 0), np.nan, tan) return np.divide(1, tan_nozero) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -420,7 +420,7 @@ def cube_root_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "cube_root") promoter.check() f = lambda x: np.cbrt(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -467,7 +467,7 @@ def exponential_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "exponential") promoter.check() f = lambda x: np.exp(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -512,7 +512,7 @@ def floor_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "floor") promoter.check() f = lambda x: np.floor(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -559,7 +559,7 @@ def natural_logarithm_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "natural_logarithm") promoter.check() f = lambda x: np.where(np.equal(x, 0), np.nan, np.log(x)) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -607,7 +607,7 @@ def f(x): cos = np.cos(x) cos_nozero = np.where(np.equal(cos, 0), np.nan, cos) return np.divide(1, cos_nozero) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -652,7 +652,7 @@ def sine_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "sine") promoter.check() f = lambda x: np.sin(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -695,7 +695,7 @@ def square_root_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "square_root") promoter.check() f = lambda x: np.sqrt(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -740,7 +740,7 @@ def tangent_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "tangent") promoter.check() f = lambda x: np.tan(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -785,7 +785,7 @@ def to_degrees_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "to_degrees") promoter.check() f = lambda x: np.rad2deg(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -830,7 +830,7 @@ def to_radians_(x, track_types = True, **kwargs): promoter = TypePromoter(x, function = "to_radians") promoter.check() f = lambda x: np.deg2rad(x) - out = xr.apply_ufunc(f, x, keep_attrs = True) + out = utils.apply_ufunc(f, x, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -888,7 +888,7 @@ def add_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.add(x, y) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -942,7 +942,7 @@ def divide_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.divide(x, np.where(np.equal(y, 0), np.nan, y)) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -996,7 +996,7 @@ def multiply_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.multiply(x, y) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1050,7 +1050,7 @@ def power_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.power(x, y) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1104,7 +1104,7 @@ def subtract_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.subtract(x, y) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1162,7 +1162,7 @@ def normalized_difference_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.divide(np.subtract(x, y), np.add(x, y)) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1222,7 +1222,7 @@ def f(x, y): y = utils.null_as_zero(y) return np.where(pd.notnull(x), np.logical_and(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1285,7 +1285,7 @@ def f(x, y): y = utils.null_as_zero(y) return np.where(pd.notnull(x), np.logical_or(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1348,7 +1348,7 @@ def f(x, y): y = utils.null_as_zero(y) return np.where(pd.notnull(x), np.logical_xor(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1406,7 +1406,7 @@ def equal_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.equal(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1461,7 +1461,7 @@ def f(x, y): return np.where(pd.notnull(x), np.logical_and(a, b), np.nan) else: return np.where(pd.notnull(x), np.isin(x, y), np.nan) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1515,7 +1515,7 @@ def not_equal_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.not_equal(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1570,7 +1570,7 @@ def f(x, y): return np.where(pd.notnull(x), np.logical_or(a, b), np.nan) else: return np.where(pd.notnull(x), np.isin(x, y, invert = True), np.nan) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1628,7 +1628,7 @@ def greater_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.greater(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1682,7 +1682,7 @@ def greater_equal_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.greater_equal(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1736,7 +1736,7 @@ def less_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.less(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1790,7 +1790,7 @@ def less_equal_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.less_equal(x, y), np.nan) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1928,7 +1928,7 @@ def after_(x, y, track_types = True, **kwargs): promoter = TypePromoter(x, y, function = "after") promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.greater(x, np.nanmax(y)), np.nan) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -1991,7 +1991,7 @@ def before_(x, y, track_types = True, **kwargs): promoter = TypePromoter(x, y, function = "before") promoter.check() f = lambda x, y: np.where(pd.notnull(x), np.less(x, np.nanmin(y)), np.nan) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -2054,7 +2054,7 @@ def f(x, y): a = np.greater_equal(x, np.nanmin(y)) b = np.less_equal(x, np.nanmax(y)) return np.where(pd.notnull(x), np.logical_and(a, b), np.nan) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -2114,7 +2114,7 @@ def assign_(x, y, track_types = True, **kwargs): promoter.check() f = lambda x, y: np.where(pd.notnull(x), y, utils.get_null(y)) y = xr.DataArray(y).sq.align_with(x) - out = xr.apply_ufunc(f, x, y, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, keep_attrs = True) if track_types: out = promoter.promote(out) return out @@ -2173,7 +2173,7 @@ def assign_at_(x, y, z, track_types = True, **kwargs): f = lambda x, y, z: np.where(np.logical_and(pd.notnull(z), z), y, x) y = xr.DataArray(y).sq.align_with(x) z = z.sq.align_with(x) - out = xr.apply_ufunc(f, x, y, z, keep_attrs = True) + out = utils.apply_ufunc(f, x, y, z, keep_attrs = True) if track_types: out = promoter.promote(out) return out diff --git a/semantique/processor/utils.py b/semantique/processor/utils.py index 96e12447..2f694089 100644 --- a/semantique/processor/utils.py +++ b/semantique/processor/utils.py @@ -285,4 +285,12 @@ def parse_datetime_component(name, obj): } else: obj.sq.value_type = "discrete" - return obj \ No newline at end of file + return obj + + +def apply_ufunc(func, *args, **kwargs): + # TODO: Remove this. + print(f"Applying {func} with args {args}") + + # TODO: put behind config flag + return xr.apply_ufunc(func, *args, dask="parallelized", **kwargs) \ No newline at end of file From 916b0898b3f1e1804a2f436ef9836d1f59e63be5 Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Wed, 16 Oct 2024 08:34:38 +0200 Subject: [PATCH 2/9] Fix errors with the 'in' operator. Mostly ChatGPT-suggested. Not sure if behaviour is OK, need to check. --- semantique/processor/operators.py | 112 ++++++++++++++++-------------- semantique/processor/utils.py | 2 +- 2 files changed, 61 insertions(+), 53 deletions(-) diff --git a/semantique/processor/operators.py b/semantique/processor/operators.py index c8dae63a..2e815b9e 100644 --- a/semantique/processor/operators.py +++ b/semantique/processor/operators.py @@ -1,3 +1,4 @@ +import dask.array as da import pandas as pd import numpy as np import xarray as xr @@ -1411,60 +1412,67 @@ def equal_(x, y, track_types = True, **kwargs): out = promoter.promote(out) return out -def in_(x, y, track_types = True, **kwargs): - """Test if x is a member of set y. - Parameters - ---------- - x : :obj:`xarray.DataArray` - Array containing the operands at the left-hand side of each - expression. - y : :obj:`list` - Operands at the right-hand side of each expression. Should be a set of - values, which remains constant among all evaluated expressions. - track_types : :obj:`bool` - Should the operator promote the value type of the output object, based - on the value type of the input objects? +# TODO: Confirm behaviour is sensibe and lazy evaluation is kept +def in_(x, y, track_types=True, **kwargs): + """Test if x is a member of set y. + + Parameters + ---------- + x : xarray.DataArray + Array containing the operands at the left-hand side of each expression. + y : list or Interval + Operands at the right-hand side of each expression. Should be a set of + values, which remains constant among all evaluated expressions. + track_types : bool + Should the operator promote the value type of the output object, + based on the value type of the input objects? **kwargs: - Ignored. - - Returns - ------- - :obj:`xarray.DataArray` - An array with the same shape as ``x`` containing the results of all - evaluated expressions. - - Note - ----- - When tracking value types, this operator uses the following type promotion - manual, with the first layer of keys being the supported value types of - ``x``, the second layer of keys being the supported value types of ``y`` - given the value type of ``x``, and the corresponding value being the - promoted value type of the output. - - .. exec_code:: - :hide_code: - - from semantique.processor.types import TYPE_PROMOTION_MANUALS - obj = TYPE_PROMOTION_MANUALS["in"] - obj.pop("__preserve_labels__") - print(obj) - - """ - if track_types: - promoter = TypePromoter(x, y, function = "in") - promoter.check() - def f(x, y): - if isinstance(y, Interval): - a = np.greater_equal(x, y.lower) - b = np.less_equal(x, y.upper) - return np.where(pd.notnull(x), np.logical_and(a, b), np.nan) - else: - return np.where(pd.notnull(x), np.isin(x, y), np.nan) - out = utils.apply_ufunc(f, x, y, keep_attrs = True) - if track_types: - out = promoter.promote(out) - return out + Ignored. + + Returns + ------- + xarray.DataArray + An array with the same shape as ``x`` containing the results of all + evaluated expressions. + """ + if track_types: + promoter = TypePromoter(x, y, function="in") + promoter.check() + + def f(x, **kwargs): + y = kwargs['y'] + if isinstance(x, da.Array): + isnan = da.isnan + where = da.where + isin = da.isin + else: + isnan = np.isnan + where = np.where + isin = np.isin + + if isinstance(y, Interval): + a = x >= y.lower + b = x <= y.upper + c = a & b + not_null = ~isnan(x) + return where(not_null, c, np.nan) + else: + not_null = ~isnan(x) + in_y = isin(x, y) + return where(not_null, in_y, np.nan) + + out = utils.apply_ufunc( + f, + x, + keep_attrs=True, + kwargs={'y': y}, + output_dtypes=[x.dtype], + ) + + if track_types: + out = promoter.promote(out) + return out def not_equal_(x, y, track_types = True, **kwargs): """Test if x is not equal to y. diff --git a/semantique/processor/utils.py b/semantique/processor/utils.py index 2f694089..48f4ac61 100644 --- a/semantique/processor/utils.py +++ b/semantique/processor/utils.py @@ -290,7 +290,7 @@ def parse_datetime_component(name, obj): def apply_ufunc(func, *args, **kwargs): # TODO: Remove this. - print(f"Applying {func} with args {args}") + print(f"Applying {func} with args:\n{args}") # TODO: put behind config flag return xr.apply_ufunc(func, *args, dask="parallelized", **kwargs) \ No newline at end of file From e3576c3861b9fcf0d3a75207866f96cb5cf3add0 Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Thu, 9 Oct 2025 12:09:38 +0200 Subject: [PATCH 3/9] Further updates for dask compatibility. --- semantique/datacube.py | 26 ++++++++++++++---------- semantique/processor/core.py | 17 ++++++++-------- semantique/processor/reducers.py | 27 ++++++++++++++++++++----- semantique/processor/utils.py | 34 ++++++++------------------------ semantique/recipe.py | 22 ++++++++++++++++++++- 5 files changed, 76 insertions(+), 50 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index 9cf7dbf0..50a86b0b 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -864,20 +864,26 @@ def retrieve(self, *reference, extent): metadata = self.lookup(*reference) # Load the data values from the EO data cube. data = self._load(metadata, extent) - if data.sq.is_empty: - raise exceptions.EmptyDataError( - f"Data layer '{reference}' does not contain data within the " - "specified spatio-temporal extent" - ) + + # Removed for Dask. TODO: Put behind config flag. + # if data.sq.is_empty: + # raise exceptions.EmptyDataError( + # f"Data layer '{reference}' does not contain data within the " + # "specified spatio-temporal extent" + # ) + # Format loaded data. data = self._format(data, metadata, extent) # Mask invalid data. data = self._mask(data, metadata) - if data.sq.is_empty: - warnings.warn( - f"All values for data layer '{reference}' are invalid within the " - "specified spatio-temporal extent" - ) + + # Removed for Dask. TODO: Put behind config flag. + # if data.sq.is_empty: + # warnings.warn( + # f"All values for data layer '{reference}' are invalid within the " + # "specified spatio-temporal extent" + # ) + # Trim the array if requested. # This will remove dimension coordinates with only missing or invalid data. if self.config["trim"]: diff --git a/semantique/processor/core.py b/semantique/processor/core.py index 10a307e5..78b69792 100644 --- a/semantique/processor/core.py +++ b/semantique/processor/core.py @@ -1086,14 +1086,15 @@ def call_verb(self, name, params): verb = getattr(obj.sq, name) out = verb(**params) # Warn when output array is empty. - try: - is_empty = out.sq.is_empty - except AttributeError: - is_empty = out.is_empty - if is_empty: - warnings.warn( - f"Verb '{name}' returned an empty array" - ) + # Removed for Dask. TODO: Put behind config flag. + # try: + # is_empty = out.sq.is_empty + # except AttributeError: + # is_empty = out.is_empty + # if is_empty: + # warnings.warn( + # f"Verb '{name}' returned an empty array" + # ) logger.debug(f"Applied verb {name}:\n{out}") return out diff --git a/semantique/processor/reducers.py b/semantique/processor/reducers.py index e8a16ae0..0dcef06a 100644 --- a/semantique/processor/reducers.py +++ b/semantique/processor/reducers.py @@ -769,12 +769,29 @@ def percentage_(x, track_types = True, **kwargs): if track_types: promoter = TypePromoter(x, function = "percentage") promoter.check() - def f(x, axis = None): - part = np.count_nonzero(utils.null_as_zero(x), axis) - part = np.where(utils.allnull(x, axis), np.nan, part) - whole = np.sum(pd.notnull(x), axis) - return np.multiply(np.divide(part, whole), 100) + def f(x, axis=None): + # TODO: Tidy this up. + # All operations stay as dask/numpy arrays - no compute() calls + part = np.count_nonzero(utils.null_as_zero(x), axis=axis) + + # Check if all values are null along axis + all_null_mask = utils.allnull(x, axis=axis) + part = np.where(all_null_mask, np.nan, part) + + # Replace pd.notnull with numpy equivalent + whole = np.sum(~np.isnan(x), axis=axis) + + # Avoid division by zero - set to nan where whole == 0 + with np.errstate(divide='ignore', invalid='ignore'): + result = np.divide(part, whole) + result = np.multiply(result, 100) + + return result + # print(f"percentage_ args: {kwargs}") out = x.reduce(f, **kwargs) + # print(f"percentage_ out: {out}") + # print("percentage_ done.") + if track_types: out = promoter.promote(out) return out diff --git a/semantique/processor/utils.py b/semantique/processor/utils.py index 48f4ac61..2c6e5541 100644 --- a/semantique/processor/utils.py +++ b/semantique/processor/utils.py @@ -1,5 +1,6 @@ import rioxarray +import dask.array as da import numpy as np import pandas as pd import xarray as xr @@ -27,37 +28,18 @@ def get_null(x): else: return None -def allnull(x, axis): - """Test whether all elements along a given axis in an array are null. - Parameters - ---------- - x : :obj:`xarray.DataArray` or :obj:`numpy.array` - The input array. - axis : :obj:`int` - Axis along which the tests are performed. - - Return - ------- - :obj:`numpy.array` +def allnull(x, axis=None): + """Check if all values are null - stays lazy""" + # TODO: Try with np ops + return da.all(da.isnan(x), axis=axis) - """ - return np.equal(np.sum(pd.notnull(x), axis = axis), 0) def null_as_zero(x): - """Convert all null values in an array to 0. + """Replace nulls with zero - stays lazy""" + # TODO: Try with np ops + return da.where(da.isnan(x), 0, x) - Parameters - ----------- - x : :obj:`xarray.DataArray` or :obj:`numpy.array` - The input array. - - Return - ------ - :obj:`numpy.array` - - """ - return np.where(pd.isnull(x), 0, x) def inf_as_null(x): """Convert all infinite values in an array to null values. diff --git a/semantique/recipe.py b/semantique/recipe.py index 34d1d12a..cd3eae8b 100644 --- a/semantique/recipe.py +++ b/semantique/recipe.py @@ -100,7 +100,12 @@ def execute(self, datacube, mapping, space, time, run_preview = False, cache=cache, **config ) - return qp.optimize().execute() + + with ComputeTracer(): # TODO: Remove / put behind config flag + result = qp.optimize().execute() + + return result # qp.optimize().execute() + # return qp.optimize().execute() def visualise(self): """Visualise the recipe in a web browser. @@ -111,3 +116,18 @@ def visualise(self): to the browser. """ show(self) + + +# TODO: Remove / put behind config flag +from dask.callbacks import Callback +class ComputeTracer(Callback): + def __init__(self): + self.compute_count = 0 + + def _start(self, dsk): + self.compute_count += 1 + print(f"⚠️ COMPUTE #{self.compute_count} triggered!") + import traceback + traceback.print_stack() # Shows where compute was called + print(f" Graph size: {len(dsk)} tasks\n") + From a8d5ce4b4b2531e4a4761c8664c5fc425a061a12 Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Mon, 13 Oct 2025 15:40:04 +0200 Subject: [PATCH 4/9] Add dask config options and tidy logging/tracing --- semantique/datacube.py | 64 +++++++++++++++++++++-------------- semantique/processor/utils.py | 37 +++++++++++++++++--- semantique/recipe.py | 34 +++++++++---------- 3 files changed, 88 insertions(+), 47 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index 50a86b0b..f79e722d 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -117,11 +117,12 @@ class Opendatacube(Datacube): Trimming means that dimension coordinates for which all values are missing are removed from the array. The spatial dimensions are trimmed only at the edges, to maintain their regularity. Defaults to - :obj:`True`. + :obj:`True`. Note: Not currently supported with dask_lazy also enabled. * **group_by_solar_day** (:obj:`bool`): Should the time dimension be resampled to the day level, using solar day to keep scenes together? - Defaults to :obj:`True`. + Defaults to :obj:`True`. Note: Not currently supported with dask_lazy + also enabled. * **value_type_mapping** (:obj:`dict`): How do value type encodings in the layout map to the value types used by semantique? @@ -159,8 +160,6 @@ class Opendatacube(Datacube): """ def __init__(self, layout = None, connection = None, tz = "UTC", **config): - # TODO: Put behind dask config flag check. - raise NotImplementedError("Not supported with Dask.") super(Opendatacube, self).__init__(layout) self.connection = connection self.tz = tz @@ -450,8 +449,6 @@ class GeotiffArchive(Datacube): """ def __init__(self, layout = None, src = None, tz = "UTC", **config): - # TODO: Put behind dask config flag check. - raise NotImplementedError("Not supported with Dask.") super(GeotiffArchive, self).__init__(layout) self.src = src self.tz = tz @@ -708,9 +705,13 @@ class STACCube(Datacube): resampled to the day level, using solar day to keep scenes together? Defaults to :obj:`True`. + * **dask_lazy** (:obj:`bool`): Whether lazy computation with Dask is enabled (experimental). + + * **dask_chunk_size** (:obj:`int`): Dask chunks will be size*size*1 grid cells (x*y*time). + * **dask_params** (:obj:`dict`): Parameters passed to the .compute() function when fetching data via the stackstac API. Can be used to control the parallelism - in fetching data. Defaults to :obj:`None`, i.e. to use the threaded scheduler as + in fetching data. Ignored if dask_lazy is enabled. Defaults to :obj:`None`, i.e. to use the threaded scheduler as set by dask as a default for arrays. * **reauth_individual** (:obj:`bool`): Should the items be resigned/reauthenticated @@ -777,6 +778,8 @@ def _default_config(self): "trim": True, "group_by_solar_day": True, "dask_params": None, + "dask_lazy": False, + "dask_chunk_size": 2048, "reauth_individual": False, "value_type_mapping": { "nominal": "nominal", @@ -865,30 +868,39 @@ def retrieve(self, *reference, extent): # Load the data values from the EO data cube. data = self._load(metadata, extent) - # Removed for Dask. TODO: Put behind config flag. - # if data.sq.is_empty: - # raise exceptions.EmptyDataError( - # f"Data layer '{reference}' does not contain data within the " - # "specified spatio-temporal extent" - # ) + if not self.config["dask_lazy"]: + if data.sq.is_empty: + raise exceptions.EmptyDataError( + f"Data layer '{reference}' does not contain data within the " + "specified spatio-temporal extent" + ) + else: + # Can't do emptiness check without reading data. + # TODO: Alternative implementation (it's still useful to have an exception here.) + pass + # Format loaded data. data = self._format(data, metadata, extent) # Mask invalid data. data = self._mask(data, metadata) - # Removed for Dask. TODO: Put behind config flag. - # if data.sq.is_empty: - # warnings.warn( - # f"All values for data layer '{reference}' are invalid within the " - # "specified spatio-temporal extent" - # ) + if not self.config["dask_lazy"]: + if data.sq.is_empty: + warnings.warn( + f"All values for data layer '{reference}' are invalid within the " + "specified spatio-temporal extent" + ) + else: + # Can't do emptiness check without reading data. + # TODO: Alternative implementation (it's still useful to have a warning here.) + pass # Trim the array if requested. # This will remove dimension coordinates with only missing or invalid data. if self.config["trim"]: - # TODO: Put behind dask config flag check. - raise NotImplementedError("Not supported with Dask.") + if self.config["dask_lazy"]: + raise NotImplementedError("Not supported with Dask lazy computation.") data = data.sq.trim() return data @@ -969,10 +981,12 @@ def _load(self, metadata, extent): errors_as_nodata=(RasterioIOError(".*"),), xy_coords="center", snap_bounds=False, + chunksize=self.config["dask_chunk_size"] ) - # TODO: Remove (or put behind config flag) - # data = data.compute(**(self.config["dask_params"] or {})) + if not self.config["dask_lazy"]: + # Load data with dask (if available), even though full lazy computation isn't enabled. + data = data.compute(**(self.config["dask_params"] or {})) # mosaicking in case of temporal grouping # convert datetimes to daily granularity - resample by day @@ -985,8 +999,8 @@ def _mosaic_ints(x, axis=0, na_value=np.nan): return np.where(all_na, na_array, chosen) if self.config["group_by_solar_day"]: - # TODO: Put behind dask config flag check. - raise NotImplementedError("Not supported with Dask.") + if self.config["dask_lazy"]: + raise NotImplementedError("Group by solar day is not supported with Dask lazy computation.") if len(data.time): days = data.time.astype("datetime64[ns]").dt.floor("D") if data.dtype.kind == "f": diff --git a/semantique/processor/utils.py b/semantique/processor/utils.py index 2c6e5541..f6055c71 100644 --- a/semantique/processor/utils.py +++ b/semantique/processor/utils.py @@ -1,6 +1,8 @@ +import logging import rioxarray import dask.array as da +from dask.callbacks import Callback import numpy as np import pandas as pd import xarray as xr @@ -8,6 +10,8 @@ from semantique import components from semantique.dimensions import TIME, X, Y +logger = logging.getLogger(__name__) + def get_null(x): """Return the appropriate nodata value for an array. @@ -270,9 +274,34 @@ def parse_datetime_component(name, obj): return obj +# SHOULDDO: Find a better way to do this than with a global variable. +DASK_LAZY = False + +def set_global_dask_lazy(dask_lazy): + """Sets the global flag to enable or disable lazy dask computation.""" + global DASK_LAZY + if dask_lazy: + logger.info("Lazy dask computation enabled.") + else: + logger.info("Lazy dask computation disabled.") + DASK_LAZY = dask_lazy + + def apply_ufunc(func, *args, **kwargs): - # TODO: Remove this. - print(f"Applying {func} with args:\n{args}") + logger.debug(f"Applying {func} with args:\n{args}") - # TODO: put behind config flag - return xr.apply_ufunc(func, *args, dask="parallelized", **kwargs) \ No newline at end of file + if DASK_LAZY: + return xr.apply_ufunc(func, *args, dask="parallelized", **kwargs) + else: + return xr.apply_ufunc(func, *args, **kwargs) + +class ComputeTracer(Callback): + def __init__(self): + self.compute_count = 0 + + def _start(self, dsk): + self.compute_count += 1 + print(f"⚠️ COMPUTE #{self.compute_count} triggered!") + import traceback + traceback.print_stack() # Shows where compute was called + print(f" Graph size: {len(dsk)} tasks\n") diff --git a/semantique/recipe.py b/semantique/recipe.py index cd3eae8b..8bf17128 100644 --- a/semantique/recipe.py +++ b/semantique/recipe.py @@ -1,6 +1,11 @@ +import logging + +from semantique.processor.utils import ComputeTracer, set_global_dask_lazy from semantique.processor.core import QueryProcessor, FakeProcessor from semantique.visualiser.visualise import show +logger = logging.getLogger(__name__) + class QueryRecipe(dict): """Dict-like container to store instructions of a query recipe. @@ -100,12 +105,20 @@ def execute(self, datacube, mapping, space, time, run_preview = False, cache=cache, **config ) + + dask_lazy = datacube.config.get("dask_lazy") + if dask_lazy: + set_global_dask_lazy(True) + else: + set_global_dask_lazy(False) - with ComputeTracer(): # TODO: Remove / put behind config flag + if dask_lazy and logger.isEnabledFor(logging.DEBUG): + with ComputeTracer(): + result = qp.optimize().execute() + else: result = qp.optimize().execute() - return result # qp.optimize().execute() - # return qp.optimize().execute() + return result def visualise(self): """Visualise the recipe in a web browser. @@ -116,18 +129,3 @@ def visualise(self): to the browser. """ show(self) - - -# TODO: Remove / put behind config flag -from dask.callbacks import Callback -class ComputeTracer(Callback): - def __init__(self): - self.compute_count = 0 - - def _start(self, dsk): - self.compute_count += 1 - print(f"⚠️ COMPUTE #{self.compute_count} triggered!") - import traceback - traceback.print_stack() # Shows where compute was called - print(f" Graph size: {len(dsk)} tasks\n") - From d81e2f6da604e0edac566eca7f27e973f23d5353 Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Tue, 14 Oct 2025 11:59:45 +0200 Subject: [PATCH 5/9] Clean up operators - make closer to originals --- semantique/datacube.py | 6 ++ semantique/processor/operators.py | 121 +++++++++++++++--------------- semantique/processor/utils.py | 76 +++++++++++++++++-- 3 files changed, 137 insertions(+), 66 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index f79e722d..f88f0da2 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -25,6 +25,7 @@ from semantique import exceptions from semantique.dimensions import TIME, SPACE, X, Y +from semantique.processor.utils import set_global_dask_lazy class Datacube(): """Base class for EO data cube configurations. @@ -759,6 +760,11 @@ def __init__(self, layout=None, src=None, **config): params = self._default_config params.update(config) self.config = params + + if self.config["dask_lazy"]: + # Use dask lazy functions in subsequent operations + set_global_dask_lazy(True) + @property def src(self): diff --git a/semantique/processor/operators.py b/semantique/processor/operators.py index 2e815b9e..d5864ec1 100644 --- a/semantique/processor/operators.py +++ b/semantique/processor/operators.py @@ -1413,66 +1413,69 @@ def equal_(x, y, track_types = True, **kwargs): return out -# TODO: Confirm behaviour is sensibe and lazy evaluation is kept -def in_(x, y, track_types=True, **kwargs): - """Test if x is a member of set y. - - Parameters - ---------- - x : xarray.DataArray - Array containing the operands at the left-hand side of each expression. - y : list or Interval - Operands at the right-hand side of each expression. Should be a set of - values, which remains constant among all evaluated expressions. - track_types : bool - Should the operator promote the value type of the output object, - based on the value type of the input objects? +def in_(x, y, track_types = True, **kwargs): + """Test if x is a member of set y. + + Parameters + ---------- + x : :obj:`xarray.DataArray` + Array containing the operands at the left-hand side of each + expression. + y : :obj:`list` + Operands at the right-hand side of each expression. Should be a set of + values, which remains constant among all evaluated expressions. + track_types : :obj:`bool` + Should the operator promote the value type of the output object, based + on the value type of the input objects? **kwargs: - Ignored. - - Returns - ------- - xarray.DataArray - An array with the same shape as ``x`` containing the results of all - evaluated expressions. - """ - if track_types: - promoter = TypePromoter(x, y, function="in") - promoter.check() - - def f(x, **kwargs): - y = kwargs['y'] - if isinstance(x, da.Array): - isnan = da.isnan - where = da.where - isin = da.isin - else: - isnan = np.isnan - where = np.where - isin = np.isin - - if isinstance(y, Interval): - a = x >= y.lower - b = x <= y.upper - c = a & b - not_null = ~isnan(x) - return where(not_null, c, np.nan) - else: - not_null = ~isnan(x) - in_y = isin(x, y) - return where(not_null, in_y, np.nan) - - out = utils.apply_ufunc( - f, - x, - keep_attrs=True, - kwargs={'y': y}, - output_dtypes=[x.dtype], - ) - - if track_types: - out = promoter.promote(out) - return out + Ignored. + + Returns + ------- + :obj:`xarray.DataArray` + An array with the same shape as ``x`` containing the results of all + evaluated expressions. + + Note + ----- + When tracking value types, this operator uses the following type promotion + manual, with the first layer of keys being the supported value types of + ``x``, the second layer of keys being the supported value types of ``y`` + given the value type of ``x``, and the corresponding value being the + promoted value type of the output. + + .. exec_code:: + :hide_code: + + from semantique.processor.types import TYPE_PROMOTION_MANUALS + obj = TYPE_PROMOTION_MANUALS["in"] + obj.pop("__preserve_labels__") + print(obj) + + """ + if track_types: + promoter = TypePromoter(x, y, function = "in") + promoter.check() + def f(x, y): + if isinstance(y, Interval): + a = np.greater_equal(x, y.lower) + b = np.less_equal(x, y.upper) + + return np.where(utils.notnull(x), np.logical_and(a, b), np.nan) + else: + return np.where(utils.notnull(x), np.isin(x, y), np.nan) + + out = utils.apply_ufunc( + f, + x, + keep_attrs=True, + kwargs={'y': y}, + output_dtypes=[x.dtype], + ) + + if track_types: + out = promoter.promote(out) + return out def not_equal_(x, y, track_types = True, **kwargs): """Test if x is not equal to y. diff --git a/semantique/processor/utils.py b/semantique/processor/utils.py index f6055c71..c2ca030e 100644 --- a/semantique/processor/utils.py +++ b/semantique/processor/utils.py @@ -33,16 +33,77 @@ def get_null(x): return None -def allnull(x, axis=None): - """Check if all values are null - stays lazy""" - # TODO: Try with np ops - return da.all(da.isnan(x), axis=axis) +def isnull(x): + """Return a boolean array whose elements indicate whether the corresponding + input element is NaN, None etc.. + + Parameters + ---------- + x : :obj:`xarray.DataArray` or :obj:`numpy.array` + The input array. + + """ + + if DASK_LAZY: + # Doesn't trigger a computation, but may not work for all use cases + return da.isnull(x) + else: + # Continue to use this for backwards compatibility, for now + return pd.isnull(x) + + +def notnull(x): + """Return a boolean array whose elements indicate whether the corresponding + input element is *not* NaN, None etc.. + + Parameters + ---------- + x : :obj:`xarray.DataArray` or :obj:`numpy.array` + The input array. + + """ + + if DASK_LAZY: + # Doesn't trigger a computation, but may not work for all use cases + return da.notnull(x) + else: + # Continue to use this for backwards compatibility, for now + return pd.notnull(x) + + +def allnull(x, axis): + """Test whether all elements along a given axis in an array are null. + + Parameters + ---------- + x : :obj:`xarray.DataArray` or :obj:`numpy.array` + The input array. + axis : :obj:`int` + Axis along which the tests are performed. + + Return + ------- + :obj:`numpy.array` + + """ + + return np.equal(np.sum(notnull(x), axis = axis), 0) def null_as_zero(x): - """Replace nulls with zero - stays lazy""" - # TODO: Try with np ops - return da.where(da.isnan(x), 0, x) + """Convert all null values in an array to 0. + + Parameters + ----------- + x : :obj:`xarray.DataArray` or :obj:`numpy.array` + The input array. + + Return + ------ + :obj:`numpy.array` + + """ + return np.where(isnull(x), 0, x) def inf_as_null(x): @@ -64,6 +125,7 @@ def inf_as_null(x): return x return np.where(is_inf, get_null(x), x) + def datetime64_as_unix(x): """Convert datetime64 values in an array to unix time values. From 8e3a51088df3c716d98e1ec1d0fc14b0f91520aa Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Wed, 15 Oct 2025 19:59:11 +0200 Subject: [PATCH 6/9] Add Dask PoC demo notebook --- demo/dask-scl-poc.ipynb | 8698 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 8698 insertions(+) create mode 100644 demo/dask-scl-poc.ipynb diff --git a/demo/dask-scl-poc.ipynb b/demo/dask-scl-poc.ipynb new file mode 100644 index 00000000..a52412e3 --- /dev/null +++ b/demo/dask-scl-poc.ipynb @@ -0,0 +1,8698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "MJUe", + "metadata": {}, + "source": [ + "# Semantique with Dask:\n", + "# Sentinel-2 Scene Classification Layer PoC\n", + "\n", + "Simple cloud & snow time-series and filtering. Based on other semantique demo recipes." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "vblA", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.8.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if 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"\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if 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root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " 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!js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.8.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " 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true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = 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toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n })\n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n 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CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "import logging\n", + "import warnings\n", + "\n", + "import dask.distributed as dd\n", + "import geopandas as gpd\n", + "from pystac_client import Client\n", + "import shapely\n", + "\n", + "# For visualization\n", + "import holoviews as hv\n", + "import hvplot.xarray\n", + "\n", + "hv.extension(\"bokeh\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bkHC", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + } + ], + "source": [ + "import semantique as sq" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "lEQa", + "metadata": {}, + "outputs": [], + "source": [ + "# Configure logging\n", + "logging.basicConfig(\n", + " level=logging.WARNING,\n", + " format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',\n", + " force=True # This forces reconfiguration if already set\n", + ")\n", + "\n", + "logging.getLogger(\"semantique\").setLevel(logging.DEBUG)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "Xref", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://127.0.0.1:8787/status\n" + ] + } + ], + "source": [ + "# Start Dask LocalCluster and dashboard\n", + "# By default, dashboard will be available at http://localhost:8787\n", + "dask_client = dd.Client(n_workers=6, threads_per_worker=1)\n", + "print(dask_client.dashboard_link)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "BYtC", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found: 13 items.\n" + ] + }, + { + "data": { + "text/html": [ + "
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geometrycreatedplatformconstellationinstrumentseo:cloud_coverproj:epsgmgrs:utm_zonemgrs:latitude_bandmgrs:grid_square...s2:datastrip_ids2:granule_ids2:reflectance_conversion_factordatetimes2:sequenceearthsearch:s3_pathearthsearch:payload_idearthsearch:boa_offset_appliedprocessing:softwareupdated
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1POLYGON ((14.99974 48.75300, 16.49326 48.74333...2023-06-29T23:46:20.169Zsentinel-2asentinel-2[msi]6.5251283263333UWP...S2A_OPER_MSI_L2A_DS_2APS_20230629T175255_S2023...S2A_OPER_MSI_L2A_TL_2APS_20230629T175255_A0418...0.9678072023-06-29T10:07:23.808000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/d626...True{'sentinel2-to-stac': '0.1.0'}2023-06-29T23:46:20.169Z
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4POLYGON ((16.00142 48.74865, 16.49326 48.74333...2023-06-21T18:19:44.109Zsentinel-2bsentinel-2[msi]30.0043313263333UWP...S2B_OPER_MSI_L2A_DS_2BPS_20230621T113044_S2023...S2B_OPER_MSI_L2A_TL_2BPS_20230621T113044_A0328...0.9688352023-06-21T09:57:26.119000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/b647...True{'sentinel2-to-stac': '0.1.0'}2023-06-21T18:19:44.109Z
5POLYGON ((14.99974 48.75300, 16.49326 48.74333...2023-06-19T19:28:34.163Zsentinel-2asentinel-2[msi]37.6625573263333UWP...S2A_OPER_MSI_L2A_DS_2APS_20230619T160956_S2023...S2A_OPER_MSI_L2A_TL_2APS_20230619T160956_A0417...0.9691822023-06-19T10:07:23.655000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/e1eb...True{'sentinel2-to-stac': '0.1.0'}2023-06-19T19:28:34.163Z
6POLYGON ((15.99203 48.74873, 16.49326 48.74333...2023-06-16T19:59:17.938Zsentinel-2asentinel-2[msi]16.0895413263333UWP...S2A_OPER_MSI_L2A_DS_2APS_20230616T153853_S2023...S2A_OPER_MSI_L2A_TL_2APS_20230616T153853_A0416...0.9697722023-06-16T09:57:26.329000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/667b...True{'sentinel2-to-stac': '0.1.0'}2023-06-16T19:59:17.938Z
7POLYGON ((14.99974 48.75300, 16.49326 48.74333...2023-06-14T18:12:47.059Zsentinel-2bsentinel-2[msi]33.0782593263333UWP...S2B_OPER_MSI_L2A_DS_2BPS_20230614T113003_S2023...S2B_OPER_MSI_L2A_TL_2BPS_20230614T113003_A0327...0.9702082023-06-14T10:07:24.612000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/1ad4...True{'sentinel2-to-stac': '0.1.0'}2023-06-14T18:12:47.059Z
8POLYGON ((15.99516 48.74871, 16.49326 48.74333...2023-06-11T18:15:25.755Zsentinel-2bsentinel-2[msi]99.7405053263333UWP...S2B_OPER_MSI_L2A_DS_2BPS_20230611T113435_S2023...S2B_OPER_MSI_L2A_TL_2BPS_20230611T113435_A0327...0.9709292023-06-11T09:57:27.127000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/3e9b...True{'sentinel2-to-stac': '0.1.0'}2023-06-11T18:15:25.755Z
9POLYGON ((14.99974 48.75300, 16.49326 48.74333...2023-06-09T20:36:11.201Zsentinel-2asentinel-2[msi]97.9230173263333UWP...S2A_OPER_MSI_L2A_DS_2APS_20230609T161103_S2023...S2A_OPER_MSI_L2A_TL_2APS_20230609T161103_A0415...0.9714502023-06-09T10:07:23.354000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/ea4c...True{'sentinel2-to-stac': '0.1.0'}2023-06-09T20:36:11.201Z
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11POLYGON ((14.99974 48.75300, 16.49326 48.74333...2023-06-04T19:57:33.044Zsentinel-2bsentinel-2[msi]70.8311503263333UWP...S2B_OPER_MSI_L2A_DS_2BPS_20230604T113608_S2023...S2B_OPER_MSI_L2A_TL_2BPS_20230604T113608_A0326...0.9729022023-06-04T10:07:23.869000Z0s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W...roda-sentinel2/workflow-sentinel2-to-stac/6285...True{'sentinel2-to-stac': '0.1.0'}2023-06-04T19:57:33.044Z
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\n", + "

13 rows × 42 columns

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" + ], + "text/plain": [ + " geometry \\\n", + "0 POLYGON ((15.99815 48.74868, 16.49326 48.74333... \n", + "1 POLYGON ((14.99974 48.75300, 16.49326 48.74333... \n", + "2 POLYGON ((15.99666 48.74869, 16.49326 48.74333... \n", + "3 POLYGON ((14.99974 48.75300, 16.49326 48.74333... \n", + "4 POLYGON ((16.00142 48.74865, 16.49326 48.74333... \n", + "5 POLYGON ((14.99974 48.75300, 16.49326 48.74333... \n", + "6 POLYGON ((15.99203 48.74873, 16.49326 48.74333... \n", + "7 POLYGON ((14.99974 48.75300, 16.49326 48.74333... \n", + "8 POLYGON ((15.99516 48.74871, 16.49326 48.74333... \n", + "9 POLYGON ((14.99974 48.75300, 16.49326 48.74333... \n", + "10 POLYGON ((15.98986 48.74875, 16.49326 48.74333... \n", + "11 POLYGON ((14.99974 48.75300, 16.49326 48.74333... \n", + "12 POLYGON ((15.99951 48.74867, 16.49326 48.74333... \n", + "\n", + " created platform constellation instruments \\\n", + "0 2023-07-01T18:36:05.617Z sentinel-2b sentinel-2 [msi] \n", + "1 2023-06-29T23:46:20.169Z sentinel-2a sentinel-2 [msi] \n", + "2 2023-06-26T17:20:57.655Z sentinel-2a sentinel-2 [msi] \n", + "3 2023-06-24T18:11:36.093Z sentinel-2b sentinel-2 [msi] \n", + "4 2023-06-21T18:19:44.109Z sentinel-2b sentinel-2 [msi] \n", + "5 2023-06-19T19:28:34.163Z sentinel-2a sentinel-2 [msi] \n", + "6 2023-06-16T19:59:17.938Z sentinel-2a sentinel-2 [msi] \n", + "7 2023-06-14T18:12:47.059Z sentinel-2b sentinel-2 [msi] \n", + "8 2023-06-11T18:15:25.755Z sentinel-2b sentinel-2 [msi] \n", + "9 2023-06-09T20:36:11.201Z sentinel-2a sentinel-2 [msi] \n", + "10 2023-06-06T18:35:46.172Z sentinel-2a sentinel-2 [msi] \n", + "11 2023-06-04T19:57:33.044Z sentinel-2b sentinel-2 [msi] \n", + "12 2023-06-01T20:19:14.459Z sentinel-2b sentinel-2 [msi] \n", + "\n", + " eo:cloud_cover proj:epsg mgrs:utm_zone mgrs:latitude_band \\\n", + "0 99.629170 32633 33 U \n", + "1 6.525128 32633 33 U \n", + "2 11.215745 32633 33 U \n", + "3 99.978906 32633 33 U \n", + "4 30.004331 32633 33 U \n", + "5 37.662557 32633 33 U \n", + "6 16.089541 32633 33 U \n", + "7 33.078259 32633 33 U \n", + "8 99.740505 32633 33 U \n", + "9 97.923017 32633 33 U \n", + "10 93.600863 32633 33 U \n", + "11 70.831150 32633 33 U \n", + "12 2.554968 32633 33 U \n", + "\n", + " mgrs:grid_square ... s2:datastrip_id \\\n", + "0 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230701T113400_S2023... \n", + "1 WP ... S2A_OPER_MSI_L2A_DS_2APS_20230629T175255_S2023... \n", + "2 WP ... S2A_OPER_MSI_L2A_DS_2APS_20230626T140000_S2023... \n", + "3 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230624T113131_S2023... \n", + "4 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230621T113044_S2023... \n", + "5 WP ... S2A_OPER_MSI_L2A_DS_2APS_20230619T160956_S2023... \n", + "6 WP ... S2A_OPER_MSI_L2A_DS_2APS_20230616T153853_S2023... \n", + "7 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230614T113003_S2023... \n", + "8 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230611T113435_S2023... \n", + "9 WP ... S2A_OPER_MSI_L2A_DS_2APS_20230609T161103_S2023... \n", + "10 WP ... S2A_OPER_MSI_L2A_DS_2APS_20230606T140554_S2023... \n", + "11 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230604T113608_S2023... \n", + "12 WP ... S2B_OPER_MSI_L2A_DS_2BPS_20230601T143134_S2023... \n", + "\n", + " s2:granule_id \\\n", + "0 S2B_OPER_MSI_L2A_TL_2BPS_20230701T113400_A0329... \n", + "1 S2A_OPER_MSI_L2A_TL_2APS_20230629T175255_A0418... \n", + "2 S2A_OPER_MSI_L2A_TL_2APS_20230626T140000_A0418... \n", + "3 S2B_OPER_MSI_L2A_TL_2BPS_20230624T113131_A0328... \n", + "4 S2B_OPER_MSI_L2A_TL_2BPS_20230621T113044_A0328... \n", + "5 S2A_OPER_MSI_L2A_TL_2APS_20230619T160956_A0417... \n", + "6 S2A_OPER_MSI_L2A_TL_2APS_20230616T153853_A0416... \n", + "7 S2B_OPER_MSI_L2A_TL_2BPS_20230614T113003_A0327... \n", + "8 S2B_OPER_MSI_L2A_TL_2BPS_20230611T113435_A0327... \n", + "9 S2A_OPER_MSI_L2A_TL_2APS_20230609T161103_A0415... \n", + "10 S2A_OPER_MSI_L2A_TL_2APS_20230606T140554_A0415... \n", + "11 S2B_OPER_MSI_L2A_TL_2BPS_20230604T113608_A0326... \n", + "12 S2B_OPER_MSI_L2A_TL_2BPS_20230601T143134_A0325... \n", + "\n", + " s2:reflectance_conversion_factor datetime \\\n", + "0 0.967644 2023-07-01T09:57:27.326000Z \n", + "1 0.967807 2023-06-29T10:07:23.808000Z \n", + "2 0.968124 2023-06-26T09:57:25.568000Z \n", + "3 0.968380 2023-06-24T10:07:23.800000Z \n", + "4 0.968835 2023-06-21T09:57:26.119000Z \n", + "5 0.969182 2023-06-19T10:07:23.655000Z \n", + "6 0.969772 2023-06-16T09:57:26.329000Z \n", + "7 0.970208 2023-06-14T10:07:24.612000Z \n", + "8 0.970929 2023-06-11T09:57:27.127000Z \n", + "9 0.971450 2023-06-09T10:07:23.354000Z \n", + "10 0.972298 2023-06-06T09:57:25.707000Z \n", + "11 0.972902 2023-06-04T10:07:23.869000Z \n", + "12 0.973873 2023-06-01T09:57:25.756000Z \n", + "\n", + " s2:sequence earthsearch:s3_path \\\n", + "0 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "1 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "2 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "3 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "4 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "5 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "6 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "7 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "8 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "9 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "10 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "11 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "12 0 s3://sentinel-cogs/sentinel-s2-l2a-cogs/33/U/W... \n", + "\n", + " earthsearch:payload_id \\\n", + "0 roda-sentinel2/workflow-sentinel2-to-stac/5aec... \n", + "1 roda-sentinel2/workflow-sentinel2-to-stac/d626... \n", + "2 roda-sentinel2/workflow-sentinel2-to-stac/2195... \n", + "3 roda-sentinel2/workflow-sentinel2-to-stac/07e0... \n", + "4 roda-sentinel2/workflow-sentinel2-to-stac/b647... \n", + "5 roda-sentinel2/workflow-sentinel2-to-stac/e1eb... \n", + "6 roda-sentinel2/workflow-sentinel2-to-stac/667b... \n", + "7 roda-sentinel2/workflow-sentinel2-to-stac/1ad4... \n", + "8 roda-sentinel2/workflow-sentinel2-to-stac/3e9b... \n", + "9 roda-sentinel2/workflow-sentinel2-to-stac/ea4c... \n", + "10 roda-sentinel2/workflow-sentinel2-to-stac/30da... \n", + "11 roda-sentinel2/workflow-sentinel2-to-stac/6285... \n", + "12 roda-sentinel2/workflow-sentinel2-to-stac/899b... \n", + "\n", + " earthsearch:boa_offset_applied processing:software \\\n", + "0 True {'sentinel2-to-stac': '0.1.0'} \n", + "1 True {'sentinel2-to-stac': '0.1.0'} \n", + "2 True {'sentinel2-to-stac': '0.1.0'} \n", + "3 True {'sentinel2-to-stac': '0.1.0'} \n", + "4 True {'sentinel2-to-stac': '0.1.0'} \n", + "5 True {'sentinel2-to-stac': '0.1.0'} \n", + "6 True {'sentinel2-to-stac': '0.1.0'} \n", + "7 True {'sentinel2-to-stac': '0.1.0'} \n", + "8 True {'sentinel2-to-stac': '0.1.0'} \n", + "9 True {'sentinel2-to-stac': '0.1.0'} \n", + "10 True {'sentinel2-to-stac': '0.1.0'} \n", + "11 True {'sentinel2-to-stac': '0.1.0'} \n", + "12 True {'sentinel2-to-stac': '0.1.0'} \n", + "\n", + " updated \n", + "0 2023-07-01T18:36:05.617Z \n", + "1 2023-06-29T23:46:20.169Z \n", + "2 2023-06-26T17:20:57.655Z \n", + "3 2023-06-24T18:11:36.093Z \n", + "4 2023-06-21T18:19:44.109Z \n", + "5 2023-06-19T19:28:34.163Z \n", + "6 2023-06-16T19:59:17.938Z \n", + "7 2023-06-14T18:12:47.059Z \n", + "8 2023-06-11T18:15:25.755Z \n", + "9 2023-06-09T20:36:11.201Z \n", + "10 2023-06-06T18:35:46.172Z \n", + "11 2023-06-04T19:57:33.044Z \n", + "12 2023-06-01T20:19:14.459Z \n", + "\n", + "[13 rows x 42 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define area of interest and time period\n", + "\n", + "# Stockerau, Austria (small)\n", + "aoi = shapely.from_wkt(\"POLYGON ((16.17 48.33, 16.25 48.33, 16.25 48.41, 16.17 48.41, 16.17 48.33))\")\n", + "\n", + "# West of Vienna, 33UWP\n", + "# aoi = shapely.from_wkt(\"POLYGON ((15.00 48.75, 16.49 48.74, 16.46 47.75, 15.00 47.76, 15.00 48.75))\")\n", + "\n", + "aoi_gdf = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[aoi])\n", + "t_range = [\"2023-06-01\", \"2023-07-01\"] # 1 month\n", + "# t_range = [\"2023-03-01\", \"2023-07-01\"] # 4 months\n", + "epsg = 32633 # For MGRS-33UWP\n", + "\n", + "# STAC-based metadata retrieval\n", + "catalog = Client.open(\"https://earth-search.aws.element84.com/v1\")\n", + "query = catalog.search(\n", + " collections=\"sentinel-2-l2a\", datetime=t_range, limit=100, intersects=aoi\n", + ")\n", + "item_coll = query.item_collection()\n", + "\n", + "# list results - part I\n", + "items = list(query.items())\n", + "print(f\"Found: {len(items):d} items.\")\n", + "\n", + "# list results - part II\n", + "stac_json = query.item_collection_as_dict()\n", + "gdf = gpd.GeoDataFrame.from_features(stac_json, \"epsg:4326\")\n", + "gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "RGSE", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-10-15 17:55:53,239 - semantique.processor.utils - INFO - Lazy dask computation enabled.\n" + ] + } + ], + "source": [ + "# define datacube\n", + "with open(\"files/layout_stac.json\", \"r\") as file:\n", + " dc = sq.datacube.STACCube(\n", + " json.load(file),\n", + " src=item_coll,\n", + " group_by_solar_day=False, # Pending Dask support\n", + " trim=False, # Pending Dask support\n", + " dask_lazy=True,\n", + " dask_chunk_size=2048\n", + " )\n", + "\n", + "# use same extents as for STAC query to set up the context for the datacube\n", + "space = sq.SpatialExtent(aoi_gdf.to_crs(epsg))\n", + "time = sq.TemporalExtent(*t_range)\n", + "resolution=20" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "Kclp", + "metadata": {}, + "outputs": [], + "source": [ + "mapping = sq.mapping.Semantique()\n", + "mapping[\"entity\"] = {}\n", + "mapping[\"entity\"][\"cloud\"] = {\n", + " \"color\": sq.appearance(\"scl\").evaluate(\"in\", [8, 9, 10])\n", + "}\n", + "mapping[\"entity\"][\"snow\"] = {\n", + " \"color\": sq.appearance(\"scl\").evaluate(\"in\", [11])\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "emfo", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-10-15 17:55:53,267 - semantique.processor.core - INFO - Started parsing the semantic query\n", + "2025-10-15 17:55:53,325 - semantique.processor.core - DEBUG - Parsed the spatio-temporal extent:\n", + " Size: 2MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 16B 2023-06-01 2023-07-01\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " spatial_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " name: index\n", + " long_name: index\n", + " _FillValue: nan\n", + " value_type: nominal\n", + " value_labels: {1: 'feature_1'}\n", + "2025-10-15 17:55:53,326 - semantique.processor.core - INFO - Finished parsing the semantic query\n", + "2025-10-15 17:55:53,329 - semantique.processor.core - INFO - Started optimizing the semantic query\n", + "2025-10-15 17:55:53,330 - semantique.processor.core - INFO - Finished optimizing the semantic query\n", + "2025-10-15 17:55:53,331 - semantique.processor.core - INFO - Started executing the semantic query\n", + "2025-10-15 17:55:53,332 - semantique.processor.core - INFO - Started executing result: 'cloud_snow_share'\n", + "2025-10-15 17:55:53,333 - semantique.processor.core - DEBUG - Constructing collection of arrays\n", + "2025-10-15 17:55:53,334 - semantique.processor.core - DEBUG - Translating concept ('entity', 'cloud')\n", + "2025-10-15 17:55:53,341 - semantique.processor.core - DEBUG - Translated concept ('entity', 'cloud'):\n", + " Size: 2MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 16B 2023-06-01 2023-07-01\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " spatial_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " name: index\n", + " long_name: index\n", + " _FillValue: nan\n", + " value_type: nominal\n", + " value_labels: {1: 'feature_1'}\n", + "2025-10-15 17:55:53,342 - semantique.processor.core - DEBUG - Translating concept ('entity', 'snow')\n", + "2025-10-15 17:55:53,350 - semantique.processor.core - DEBUG - Translated concept ('entity', 'snow'):\n", + " Size: 2MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 16B 2023-06-01 2023-07-01\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " spatial_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " name: index\n", + " long_name: index\n", + " _FillValue: nan\n", + " value_type: nominal\n", + " value_labels: {1: 'feature_1'}\n", + "2025-10-15 17:55:53,351 - semantique.processor.core - DEBUG - Constructed collection of:\n", + "['cloud', 'snow']\n", + "2025-10-15 17:55:53,353 - semantique.processor.core - INFO - Finished executing result: 'cloud_snow_share'\n", + "2025-10-15 17:55:53,355 - semantique.processor.core - INFO - Finished executing the semantic query\n", + "2025-10-15 17:55:53,362 - semantique.processor.core - DEBUG - Responding:\n", + "{'cloud_snow_share': [ Size: 2MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 16B 2023-06-01 2023-07-01\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " spatial_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " name: index\n", + " long_name: index\n", + " _FillValue: nan\n", + " value_type: nominal\n", + " value_labels: {1: 'feature_1'}, Size: 2MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 16B 2023-06-01 2023-07-01\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " spatial_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " name: index\n", + " long_name: index\n", + " _FillValue: nan\n", + " value_type: nominal\n", + " value_labels: {1: 'feature_1'}]}\n", + "2025-10-15 17:55:53,363 - semantique.processor.core - INFO - Started parsing the semantic query\n", + "2025-10-15 17:55:53,405 - semantique.processor.core - DEBUG - Parsed the spatio-temporal extent:\n", + " Size: 2MB\n", + "array([[[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]],\n", + "\n", + " [[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 16B 2023-06-01 2023-07-01\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " spatial_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " name: index\n", + " long_name: index\n", + " _FillValue: nan\n", + " value_type: nominal\n", + " value_labels: {1: 'feature_1'}\n", + "2025-10-15 17:55:53,406 - semantique.processor.core - INFO - Finished parsing the semantic query\n", + "2025-10-15 17:55:53,407 - semantique.processor.utils - INFO - Lazy dask computation enabled.\n", + "2025-10-15 17:55:53,409 - semantique.processor.core - INFO - Started optimizing the semantic query\n", + "2025-10-15 17:55:53,410 - semantique.processor.core - INFO - Finished optimizing the semantic query\n", + "2025-10-15 17:55:53,411 - semantique.processor.core - INFO - Started executing the semantic query\n", + "2025-10-15 17:55:53,413 - semantique.processor.core - INFO - Started executing result: 'cloud_snow_share'\n", + "2025-10-15 17:55:53,414 - semantique.processor.core - DEBUG - Constructing collection of arrays\n", + "2025-10-15 17:55:53,415 - semantique.processor.core - DEBUG - Translating concept ('entity', 'cloud')\n", + "2025-10-15 17:55:53,416 - semantique.processor.core - DEBUG - Retrieving layer ('appearance', 'scl')\n", + "2025-10-15 17:55:53,474 - semantique.processor.core - DEBUG - Retrieved layer ('appearance', 'scl'):\n", + " Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0...\n", + " resolution: 20.0\n", + " value_type: ordinal\n", + " value_labels: {0: 'mask', 1: 'saturated', 2: 'dark', 3: 'shadow', 4: 've...\n", + "2025-10-15 17:55:53,476 - semantique.processor.core - DEBUG - Cache updated\n", + "2025-10-15 17:55:53,476 - semantique.processor.core - DEBUG - Sequence of layers: [('appearance', 'scl')]\n", + "2025-10-15 17:55:53,477 - semantique.processor.core - DEBUG - Currently cached layers: ['appearance_scl']\n", + "2025-10-15 17:55:53,481 - semantique.processor.utils - DEBUG - Applying .f at 0x7fe0606ff380> with args:\n", + "( Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0...\n", + " resolution: 20.0\n", + " value_type: ordinal\n", + " value_labels: {0: 'mask', 1: 'saturated', 2: 'dark', 3: 'shadow', 4: 've...,)\n", + "2025-10-15 17:55:53,513 - semantique.processor.core - DEBUG - Applied verb evaluate:\n", + " Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: binary\n", + "2025-10-15 17:55:53,519 - semantique.processor.core - DEBUG - Translated concept ('entity', 'cloud'):\n", + " Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: binary\n", + "2025-10-15 17:55:53,520 - semantique.processor.core - DEBUG - Translating concept ('entity', 'snow')\n", + "2025-10-15 17:55:53,521 - semantique.processor.core - DEBUG - Loading layer ('appearance', 'scl') from cache\n", + "2025-10-15 17:55:53,526 - semantique.processor.core - DEBUG - Retrieved layer ('appearance', 'scl'):\n", + " Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0...\n", + " resolution: 20.0\n", + " value_type: ordinal\n", + " value_labels: {0: 'mask', 1: 'saturated', 2: 'dark', 3: 'shadow', 4: 've...\n", + "2025-10-15 17:55:53,527 - semantique.processor.core - DEBUG - Cache updated\n", + "2025-10-15 17:55:53,528 - semantique.processor.core - DEBUG - Sequence of layers: []\n", + "2025-10-15 17:55:53,529 - semantique.processor.core - DEBUG - Currently cached layers: []\n", + "2025-10-15 17:55:53,535 - semantique.processor.utils - DEBUG - Applying .f at 0x7fe0697f0d60> with args:\n", + "( Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0...\n", + " resolution: 20.0\n", + " value_type: ordinal\n", + " value_labels: {0: 'mask', 1: 'saturated', 2: 'dark', 3: 'shadow', 4: 've...,)\n", + "2025-10-15 17:55:53,543 - semantique.processor.core - DEBUG - Applied verb evaluate:\n", + " Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: binary\n", + "2025-10-15 17:55:53,547 - semantique.processor.core - DEBUG - Translated concept ('entity', 'snow'):\n", + " Size: 7MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: binary\n", + "2025-10-15 17:55:53,548 - semantique.processor.core - DEBUG - Constructed collection of:\n", + "['cloud', 'snow']\n", + "2025-10-15 17:55:53,567 - semantique.processor.core - DEBUG - Applied verb merge:\n", + " Size: 13MB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 2...\n", + " * y (y) float64 4kB 5.363e+06 5.363e+06 ... 5.354e+06 5.354e+06\n", + " * x (x) float64 2kB 5.866e+05 5.866e+05 ... 5.926e+05 5.926e+05\n", + " temporal_ref int64 8B 0\n", + " spatial_feats (y, x) float64 1MB nan nan nan nan nan ... nan nan nan nan\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: binary\n", + "2025-10-15 17:55:53,608 - semantique.processor.core - DEBUG - Applied verb reduce:\n", + " Size: 96B\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 20...\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: continuous\n", + "2025-10-15 17:55:53,611 - semantique.processor.core - INFO - Finished executing result: 'cloud_snow_share'\n", + "2025-10-15 17:55:53,612 - semantique.processor.core - INFO - Finished executing the semantic query\n", + "2025-10-15 17:55:53,614 - semantique.processor.core - DEBUG - Responding:\n", + "{'cloud_snow_share': Size: 96B\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 20...\n", + " temporal_ref int64 8B 0\n", + "Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: continuous}\n" + ] + } + ], + "source": [ + "context = {\n", + " \"datacube\": dc,\n", + " \"mapping\": mapping,\n", + " \"space\": space,\n", + " \"time\": time,\n", + " \"crs\": epsg,\n", + " \"tz\": \"UTC\",\n", + " \"spatial_resolution\": [-resolution, resolution],\n", + " \"cache_data\": True\n", + " # \"cache_data\": False\n", + "}\n", + "\n", + "recipe = sq.QueryRecipe()\n", + "recipe[\"cloud_snow_share\"] = ( \n", + " sq.collection(\n", + " sq.entity(\"cloud\"), \n", + " sq.entity(\"snow\")\n", + " )\n", + " .merge(\"any\")\n", + " .reduce(\"percentage\", \"space\")\n", + ")\n", + "# Additional output - leave out for now for fairer comparisons\n", + "# (reintroduce when we have better data dependency management)\n", + "# recipe[\"cloud_snowfree\"] = ( \n", + "# sq.result(\"cloud_snow_share\")\n", + "# .evaluate(\"less\", 10)\n", + "# )\n", + "\n", + "# execute recipe\n", + "with warnings.catch_warnings():\n", + " warnings.simplefilter(\"ignore\", UserWarning)\n", + " warnings.simplefilter(\"ignore\", RuntimeWarning)\n", + " response = recipe.execute(**context)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "Hstk", + "metadata": { + "marimo": { + "config": { + "disabled": true + } + } + }, + "outputs": [], + "source": [ + "# Visualize task graph - to file. Can take a while!\n", + "# response[\"cloud_snow_share\"].data.visualize(filename=\"data/graph_cloud_snow_share.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "nWHF", + "metadata": { + "marimo": { + "config": { + "disabled": true + } + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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HighLevelGraph

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Layer1: asset-table

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Layer2: asset_table_to_reader_and_window

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Layer3: fetch_raster_window

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Layer4: getitem

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Layer5: astype

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Layer6: ne

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Layer7: where

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Layer8: ne

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Layer9: where

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Layer10: array

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layer_typeMaterializedLayer
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number of outputs1
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dtypebool
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Layer11: where

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Layer12: f

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Layer13: f_0

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Layer14: transpose

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Layer15: getitem

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(1, 12, 449, 303)
dtypefloat32
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on transpose-b228ed4a1924c4a2c9e7fd93df58b541
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Layer16: f

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\n", + " f-8f7f085e8083c2d4bdf5ab3f5ce9c631\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 449, 303)
dtypefloat32
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on where-012d1b457871ed908f86dfae7ff2a372
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Layer17: f_0

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12, 449, 303)
dtypefloat32
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on f-8f7f085e8083c2d4bdf5ab3f5ce9c631
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Layer18: transpose

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 449, 303)
dtypefloat32
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on f_0-8f7f085e8083c2d4bdf5ab3f5ce9c631
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Layer19: getitem

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(1, 12, 449, 303)
dtypefloat32
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on transpose-af9147d3fc0e17a557793bd6d2e6b921
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Layer20: concatenate

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs24
shape(2, 12, 449, 303)
dtypefloat32
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on getitem-0c2a425d2cb897a3373bd476af53d557
getitem-4ff5e35b82d12beca86ab3d2cde05904
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Layer21: asarray_isnull

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layer_typeBlockwise
is_materializedFalse
number of outputs24
shape(2, 12, 449, 303)
dtypebool
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on concatenate-47fe7c9b7cce4e45ab7948dbc55bb6f5
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\n", + " \n", + "

Layer22: where

\n", + " \n", + "

\n", + " where-76fd23fa20b6b65c45273ad0a58ccb78\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs24
shape(2, 12, 449, 303)
dtypefloat32
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on concatenate-47fe7c9b7cce4e45ab7948dbc55bb6f5
_asarray_isnull-f2914599207cb7b467abca1aa823ded7
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Layer23: any

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layer_typeBlockwise
is_materializedFalse
number of outputs24
shape(2, 12, 449, 303)
dtypebool
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on where-76fd23fa20b6b65c45273ad0a58ccb78
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Layer24: any-aggregate

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12, 449, 303)
dtypebool
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on any-40899ae0be794b92fdb7b44b29af93f2
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Layer25: invert

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layer_typeBlockwise
is_materializedFalse
number of outputs24
shape(2, 12, 449, 303)
dtypebool
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on _asarray_isnull-f2914599207cb7b467abca1aa823ded7
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Layer26: sum

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\n", + " sum-3a4edc4ce40eeb7afc08a8296b3ee5da\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs24
shape(2, 12, 449, 303)
dtypeint64
chunksize(1, 1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on invert-40ffe2b24ef14a7f6b1ebcd3804110ae
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Layer27: sum-aggregate

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\n", + " sum-aggregate-92e1671557dba32b4b4cacdd642f9353\n", + "

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12, 449, 303)
dtypeint64
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on sum-3a4edc4ce40eeb7afc08a8296b3ee5da
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Layer28: equal

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\n", + " equal-149eb22bd15bc21327c8eb44dd97e804\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 449, 303)
dtypebool
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on sum-aggregate-92e1671557dba32b4b4cacdd642f9353
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Layer29: where

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\n", + " where-9facb6c6362e96179415849e2275fde1\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 449, 303)
dtypefloat64
chunksize(1, 449, 303)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on any-aggregate-06500e64b5323cff380bd9d3fdd3cb2c
equal-149eb22bd15bc21327c8eb44dd97e804
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Layer30: reshape

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12, 136047)
dtypefloat64
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on where-9facb6c6362e96179415849e2275fde1
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Layer31: isnan

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\n", + " isnan-8c19b454cfb3a9334151f05fbb99ab43\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypebool
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on reshape-d4dc45bbd02c34cade2c12fc106b3404
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Layer32: invert

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypebool
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on isnan-8c19b454cfb3a9334151f05fbb99ab43
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Layer33: sum

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypeint64
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on invert-9c512d5db9536b92574a5cc1a0d36483
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Layer34: sum-aggregate

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\n", + " sum-aggregate-f9d95b2f5f93afa27ca5b22415e74348\n", + "

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12,)
dtypeint64
chunksize(1,)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on sum-0d04510c46f2c83deeebb5a2c78afda2
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Layer35: asarray_isnull

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypebool
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on reshape-d4dc45bbd02c34cade2c12fc106b3404
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Layer36: where

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\n", + " where-a483c2e8b857759aad440899c5bd24f2\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypefloat64
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on _asarray_isnull-ff57c21eb03908c85088523f90262be2
reshape-d4dc45bbd02c34cade2c12fc106b3404
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Layer37: astype

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypebool
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on where-a483c2e8b857759aad440899c5bd24f2
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Layer38: astype

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypeint64
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on astype-096e9adad4444606f6727d0d07c4bd38
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Layer39: sum

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\n", + " sum-8e375fe22ddc4d5441dd7066544ea9b4\n", + "

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypeint64
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on astype-bff2f6d0fbdc57637fa2eb61a67d7b94
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Layer40: sum-aggregate

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12,)
dtypeint64
chunksize(1,)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on sum-8e375fe22ddc4d5441dd7066544ea9b4
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Layer41: invert

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypebool
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on _asarray_isnull-ff57c21eb03908c85088523f90262be2
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Layer42: sum

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12, 136047)
dtypeint64
chunksize(1, 136047)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on invert-e68a9c9bf801a0362997f48d02b1c623
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Layer43: sum-aggregate

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layer_typeMaterializedLayer
is_materializedTrue
number of outputs12
shape(12,)
dtypeint64
chunksize(1,)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on sum-775593baa171724aaab0bb511f1bd770
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Layer44: equal

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12,)
dtypebool
chunksize(1,)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on sum-aggregate-f20de339ec6af101f100085967970a45
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Layer45: where

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layer_typeBlockwise
is_materializedFalse
number of outputs12
shape(12,)
dtypefloat64
chunksize(1,)
typedask.array.core.Array
chunk_typenumpy.ndarray
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equal-9f0174a48b38c8fb9c64cf6ce449a2ac
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Layer46: divide

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Layer47: multiply

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" + ], + "text/plain": [ + "HighLevelGraph with 47 layers.\n", + "\n", + " 0. asset-table-d430f26086d18353d050b6810c2ec64b\n", + " 1. asset_table_to_reader_and_window-a478deb1b5eaa9ee27c6b02f19e7abea\n", + " 2. fetch_raster_window-0a5ef31258203b0ddbde185ea9f2c4c1\n", + " 3. getitem-5b21e533707affbd8ff8d863dd805cfb\n", + " 4. astype-27a5caa3566b401a267c4098d3731341\n", + " 5. ne-0c20b400983a18ca46a49c24220631c8\n", + " 6. where-1ea4d0552e1498b920d445cc702e0342\n", + " 7. ne-8d47136e1ec05dd7130a1f6bbb2e9dfb\n", + " 8. where-ad631554a952eb40e7a85778b72dacb1\n", + " 9. array-7582cc4aa0f7a9cf493233c2a4dd49f5\n", + " 10. where-012d1b457871ed908f86dfae7ff2a372\n", + " 11. f-a84a4d99e3fbb68755ce5dadb5094103\n", + " 12. f_0-a84a4d99e3fbb68755ce5dadb5094103\n", + " 13. transpose-b228ed4a1924c4a2c9e7fd93df58b541\n", + " 14. getitem-4ff5e35b82d12beca86ab3d2cde05904\n", + " 15. f-8f7f085e8083c2d4bdf5ab3f5ce9c631\n", + " 16. f_0-8f7f085e8083c2d4bdf5ab3f5ce9c631\n", + " 17. transpose-af9147d3fc0e17a557793bd6d2e6b921\n", + " 18. getitem-0c2a425d2cb897a3373bd476af53d557\n", + " 19. concatenate-47fe7c9b7cce4e45ab7948dbc55bb6f5\n", + " 20. _asarray_isnull-f2914599207cb7b467abca1aa823ded7\n", + " 21. where-76fd23fa20b6b65c45273ad0a58ccb78\n", + " 22. any-40899ae0be794b92fdb7b44b29af93f2\n", + " 23. any-aggregate-06500e64b5323cff380bd9d3fdd3cb2c\n", + " 24. invert-40ffe2b24ef14a7f6b1ebcd3804110ae\n", + " 25. sum-3a4edc4ce40eeb7afc08a8296b3ee5da\n", + " 26. sum-aggregate-92e1671557dba32b4b4cacdd642f9353\n", + " 27. equal-149eb22bd15bc21327c8eb44dd97e804\n", + " 28. where-9facb6c6362e96179415849e2275fde1\n", + " 29. reshape-d4dc45bbd02c34cade2c12fc106b3404\n", + " 30. isnan-8c19b454cfb3a9334151f05fbb99ab43\n", + " 31. invert-9c512d5db9536b92574a5cc1a0d36483\n", + " 32. sum-0d04510c46f2c83deeebb5a2c78afda2\n", + " 33. sum-aggregate-f9d95b2f5f93afa27ca5b22415e74348\n", + " 34. _asarray_isnull-ff57c21eb03908c85088523f90262be2\n", + " 35. where-a483c2e8b857759aad440899c5bd24f2\n", + " 36. astype-096e9adad4444606f6727d0d07c4bd38\n", + " 37. astype-bff2f6d0fbdc57637fa2eb61a67d7b94\n", + " 38. sum-8e375fe22ddc4d5441dd7066544ea9b4\n", + " 39. sum-aggregate-72fa3ecb06baad1e174ca33c3208f95c\n", + " 40. invert-e68a9c9bf801a0362997f48d02b1c623\n", + " 41. sum-775593baa171724aaab0bb511f1bd770\n", + " 42. sum-aggregate-f20de339ec6af101f100085967970a45\n", + " 43. equal-9f0174a48b38c8fb9c64cf6ce449a2ac\n", + " 44. where-36b943d3072a058cc3c655ffb56c464b\n", + " 45. divide-fa3758df75d1ae1a154d5d1490de253c\n", + " 46. multiply-b9566f3ecab806f868ff5dcea533a521" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Alternative, lighter task graph explorer\n", + "response[\"cloud_snow_share\"].data.__dask_graph__()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "iLit", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'cloud_snow_share': Size: 96B\n", + " dask.array\n", + " Coordinates:\n", + " * time (time) datetime64[ns] 96B 2023-06-01T09:57:25.756000 ... 20...\n", + " temporal_ref int64 8B 0\n", + " Attributes:\n", + " spec: RasterSpec(epsg=32633, bounds=(586580.0, 5353640.0, 592640.0...\n", + " crs: epsg:32633\n", + " transform: | 20.00, 0.00, 586580.00|\\n| 0.00,-20.00, 5362620.00|\\n| 0.0...\n", + " resolution: 20.0\n", + " value_type: continuous}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inspect the response - is it still a lazy dask array or has it been materialized?\n", + "response" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ZHCJ", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", + "/jupyter/semantique/semantique/__init__.py:12: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + } + ], + "source": [ + "# Run the computation!\n", + "result_cs_pc = response[\"cloud_snow_share\"].compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ROlb", + "metadata": { + "marimo": { + "config": { + "disabled": true + } + } + }, + "outputs": [], + "source": [ + "# Compute secondary output - ignore for now.\n", + "# result_csfree_mask = response[\"cloud_snowfree\"].compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "qnkX", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Scatter [time] (cloud_snow_share)" + ] + }, + "execution_count": 16, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "553c9767-6673-49b7-a1e7-454fcc6765dc" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "result_cs_pc.hvplot.scatter(\n", + " x=\"time\", y=\"cloud_snow_share\", marker=\"x\", color=\"blue\"\n", + ").opts(title=\"Cloud & snow percentage\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "TqIu", + "metadata": { + "marimo": { + "config": { + "disabled": true + } + } + }, + "outputs": [], + "source": [ + "# Results of secondary output - ignore for now.\n", + "# result_csfree_mask.hvplot.scatter(\n", + "# x=\"time\", y=\"cloud_snowfree\", marker=\"x\"\n", + "# ).opts(color=\"red\", title=\"Cloud-free images\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Vxnm", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e66e1f8c7799542bf5802979a4bd1520b0729088 Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Thu, 8 Jan 2026 12:24:34 +0100 Subject: [PATCH 7/9] Revert changes to STACCube, introduce DaskCube (wip) --- semantique/datacube.py | 529 +++++++++++++++++++++++++++++++++++++---- 1 file changed, 482 insertions(+), 47 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index 27f5902b..af421910 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -706,13 +706,9 @@ class STACCube(Datacube): resampled to the day level, using solar day to keep scenes together? Defaults to :obj:`True`. - * **dask_lazy** (:obj:`bool`): Whether lazy computation with Dask is enabled (experimental). - - * **dask_chunk_size** (:obj:`int`): Dask chunks will be size*size*1 grid cells (x*y*time). - * **dask_params** (:obj:`dict`): Parameters passed to the .compute() function when fetching data via the stackstac API. Can be used to control the parallelism - in fetching data. Ignored if dask_lazy is enabled. Defaults to :obj:`None`, i.e. to use the threaded scheduler as + in fetching data. Defaults to :obj:`None`, i.e. to use the threaded scheduler as set by dask as a default for arrays. * **reauth_individual** (:obj:`bool`): Should the items be resigned/reauthenticated @@ -762,11 +758,6 @@ def __init__(self, layout=None, src=None, **config): params = self._default_config params.update(config) self.config = params - - if self.config["dask_lazy"]: - # Use dask lazy functions in subsequent operations - set_global_dask_lazy(True) - @property def src(self): @@ -786,8 +777,6 @@ def _default_config(self): "trim": True, "group_by_solar_day": True, "dask_params": None, - "dask_lazy": False, - "dask_chunk_size": 2048, "reauth_individual": False, "access_token": "", "value_type_mapping": { @@ -876,40 +865,23 @@ def retrieve(self, *reference, extent): metadata = self.lookup(*reference) # Load the data values from the EO data cube. data = self._load(metadata, extent) - - if not self.config["dask_lazy"]: - if data.sq.is_empty: - raise exceptions.EmptyDataError( - f"Data layer '{reference}' does not contain data within the " - "specified spatio-temporal extent" - ) - else: - # Can't do emptiness check without reading data. - # TODO: Alternative implementation (it's still useful to have an exception here.) - pass - - + if data.sq.is_empty: + raise exceptions.EmptyDataError( + f"Data layer '{reference}' does not contain data within the " + "specified spatio-temporal extent" + ) # Format loaded data. data = self._format(data, metadata, extent) # Mask invalid data. data = self._mask(data, metadata) - - if not self.config["dask_lazy"]: - if data.sq.is_empty: - warnings.warn( - f"All values for data layer '{reference}' are invalid within the " - "specified spatio-temporal extent" - ) - else: - # Can't do emptiness check without reading data. - # TODO: Alternative implementation (it's still useful to have a warning here.) - pass - + if data.sq.is_empty: + warnings.warn( + f"All values for data layer '{reference}' are invalid within the " + "specified spatio-temporal extent" + ) # Trim the array if requested. # This will remove dimension coordinates with only missing or invalid data. if self.config["trim"]: - if self.config["dask_lazy"]: - raise NotImplementedError("Not supported with Dask lazy computation.") data = data.sq.trim() return data @@ -985,7 +957,6 @@ def _load(self, metadata, extent): "errors_as_nodata": (RasterioIOError(".*"),), "xy_coords": "center", "snap_bounds": False, - "chunksize": self.config["dask_chunk_size"] } # reauth @@ -1006,10 +977,7 @@ def _load(self, metadata, extent): item_coll, **stackstac_inputs ) - - if not self.config["dask_lazy"]: - # Load data with dask (if available), even though full lazy computation isn't enabled. - data = data.compute(**(self.config["dask_params"] or {})) + data = data.compute(**(self.config["dask_params"] or {})) # mosaicking in case of temporal grouping # convert datetimes to daily granularity - resample by day @@ -1022,8 +990,6 @@ def _mosaic_ints(x, axis=0, na_value=np.nan): return np.where(all_na, na_array, chosen) if self.config["group_by_solar_day"]: - if self.config["dask_lazy"]: - raise NotImplementedError("Group by solar day is not supported with Dask lazy computation.") if len(data.time): days = data.time.astype("datetime64[ns]").dt.floor("D") if data.dtype.kind == "f": @@ -1205,4 +1171,473 @@ def _sign_metadata(items): else: updated_items.extend(curr_colls[coll]["items"]) # return signed items - return pystac.ItemCollection(updated_items) \ No newline at end of file + return pystac.ItemCollection(updated_items) + +class DaskCube(Datacube): + """ + A variant of `STACCube` that creates a lazy Dask-backed xarray.DataArray from a STAC item collection. + + Experimental. Functionality is limited to operations that do not require intermediate data reads. + + Limitations include: + * No trim support. + * No warnings if an intermediate step returns an empty dataset. + * Only really effective for a single output (others are supported but will trigger a recompute). + * Expect output to be slightly different, due to implementation differences\*. + * Some operators may not work as expected. + * May not work with really big jobs, due to task graph size issues. + + \*Uses `odc.stac.load` (which supports a lazy group-by-solar-day functionality), rather than `stackstac.load`. + + Parameters + ---------- + layout : :obj:`dict` + The layout file describing the EO data cube. If :obj:`None`, an empty + EO data cube is constructed. + src : :obj:`pystac.item_collection.ItemCollection` or `list of pystac.item.Item` + The item search result from a previous STAC search as a src to build the datacube + **config: + Additional keyword arguments tuning the data retrieval configuration. + Valid options are: + + * ~**trim** (:obj:`bool`):~ Unsupported. + + * **group_by_solar_day** (:obj:`bool`): Should the time dimension be + resampled to the day level, using solar day to keep scenes together? + Defaults to :obj:`True`. + + * **dask_chunk_size** (:obj:`int`): Dask chunks will be size × size × 1 grid cells (x × y × time). + + * **dask_params** (:obj:`dict`): Parameters passed to the .compute() function. + + * **reauth_individual** (:obj:`bool`): Should the items be resigned/reauthenticated + before loading them? Defaults to False. + + * **access_token** (:obj:`str`): Access token string (OAuth2) to be used in accessing the STAC href. + + * **value_type_mapping** (:obj:`dict`): How do value type encodings in + the layout map to the value types used by semantique? + Defaults to a one-to-one mapping: :: + + { + "nominal": "nominal", + "ordinal": "ordinal", + "binary": "binary", + "continuous": "continuous", + "discrete": "discrete" + } + + * **resamplers** (:obj:`dict`): When data need to be resampled to a + different spatial and/or temporal resolution, what resampling technique + should be used? Should be specified separately for each possible value + type in the layout. Valid techniques are: :: + + 'nearest', 'average', 'bilinear', 'cubic', 'cubic_spline', + 'lanczos', 'mode', 'gauss', 'max', 'min', 'med', 'q1', 'q3' + + Defaults to: :: + + { + "nominal": "nearest", + "ordinal": "nearest", + "binary": "nearest", + "continuous": "bilinear", + "discrete": "nearest" + } + + """ + + def __init__(self, layout=None, src=None, **config): + super(DaskCube, self).__init__(layout) + self.src = src + # Timezone of the temporal coordinates is infered from the pystac search results + # & automatically converted to UTC internally - result is given back as datetime64[ns] + self.tz = "UTC" + # Update default configuration parameters with provided ones. + params = self._default_config + params.update(config) + self.config = params + + # Use dask lazy functions in subsequent operations + set_global_dask_lazy(True) + + + @property + def src(self): + """:obj:`pystac.item_collection.ItemCollection` or :obj:`list of pystac.item.Item`: + The item search result from a previous STAC search.""" + return self._src + + @src.setter + def src(self, value): + if value is not None: + assert np.all([isinstance(x, pystac.item.Item) for x in value]) + self._src = value + + @property + def _default_config(self): + return { + "trim": False, + "group_by_solar_day": True, + "dask_params": None, + "dask_chunk_size": 2048, + "reauth_individual": False, + "access_token": "", + "value_type_mapping": { + "nominal": "nominal", + "ordinal": "ordinal", + "binary": "binary", + "continuous": "continuous", + "discrete": "discrete", + }, + "resamplers": { + "nominal": "nearest", + "ordinal": "nearest", + "binary": "nearest", + "continuous": "bilinear", + "discrete": "nearest", + }, + } + + @property + def config(self): + """:obj:`dict`: Configuration settings for data retrieval.""" + return self._config + + @config.setter + def config(self, value): + assert isinstance(value, dict) + self._config = value + + @property + def layout(self): + """:obj:`dict`: The layout file of the EO data cube.""" + return self._layout + + @layout.setter + def layout(self, value): + self._layout = {} if value is None else self._parse_layout(value) + + def _parse_layout(self, obj): + # Function to recursively parse and metadata objects to make them autocomplete friendly + def _parse(current_obj, ref_path): + if "type" in current_obj and "values" in current_obj: + current_obj["reference"] = copy.deepcopy(ref_path) + if isinstance(current_obj["values"], list): + current_obj["labels"] = { + item["label"]: item["id"] for item in current_obj["values"] + } + current_obj["descriptions"] = { + item["description"]: item["id"] + for item in current_obj["values"] + } + return + + # If not a "layer", traverse deeper into the object. + for key, value in current_obj.items(): + if isinstance(value, dict): + new_ref_path = ref_path + [key] + _parse(value, new_ref_path) + + # Start parsing from the root object. + for key, value in obj.items(): + if isinstance(value, dict): + _parse(value, [key]) + return obj + + def retrieve(self, *reference, extent): + """Retrieve a data layer from the EO data cube. + + Parameters + ---------- + *reference: + The index of the data layer in the layout of the EO data cube. + extent : :obj:`xarray.DataArray` + Spatio-temporal extent in which the data should be retrieved. Should be + given as an array with a temporal dimension and two spatial dimensions, + such as returned by + :func:`parse_extent `. + The retrieved subset of the EO data cube will have the same extent. + + Returns + ------- + :obj:`xarray.DataArray` + The retrieved subset of the EO data cube. + + """ + # Solve the reference by obtaining the corresponding metadata object. + metadata = self.lookup(*reference) + # Load the data values from the EO data cube. + data = self._load(metadata, extent) + + + # Format loaded data. + data = self._format(data, metadata, extent) + # Mask invalid data. + data = self._mask(data, metadata) + + if self.config["trim"]: + raise NotImplementedError("Not supported with Dask lazy computation.") + + return data + + def _load(self, metadata, extent): + # check if extent is valid + if TIME not in extent.dims: + raise exceptions.MissingDimensionError( + "Cannot retrieve data in an extent without a temporal dimension" + ) + if X not in extent.dims and Y not in extent.dims: + raise exceptions.MissingDimensionError( + "Cannot retrieve data in an extent without spatial dimensions" + ) + + # retrieve spatial bounds, resolution & epsg + # note: round to avoid binary format <-> floating-point number inconsistencies + s_bounds = tuple(np.array(extent.rio.bounds()).round(8)) + res = tuple(np.abs(extent.rio.resolution()).round(8)) + epsg = int(str(extent.rio.crs)[5:]) + + # retrieve resampler + resampler_name = self.config["resamplers"][metadata["type"]] + resampler_func = getattr(rasterio.enums.Resampling, resampler_name) + + # retrieve layer specific information + lyr_dtype, lyr_na = self._get_dtype_na(metadata) + + # subset temporally and spatially + if "spatial_feats" in extent.coords: + extent = extent.drop_vars("spatial_feats") + t_bounds = extent.sq.tz_convert(self.tz).time.values + item_coll = DaskCube.filter_spatio_temporal( + self.src, + extent.rio.bounds(), + epsg, + t_bounds[0], + t_bounds[1] + ) + + # subset according to layer key + filtered_items = [] + for item in item_coll: + has_no_key = True + has_conformant_key = False + for asset_key, asset in item.assets.items(): + if 'semantique:key' in asset.extra_fields: + has_no_key = False + asset_key = asset.extra_fields['semantique:key'] + ref_key = metadata['reference'] + if "_".join(asset_key) == "_".join(ref_key): + has_conformant_key = True + break + else: + continue + if has_no_key or has_conformant_key: + filtered_items.append(item) + item_coll = filtered_items + + # return extent array as NaN in case of no data + if not len(item_coll): + empty_arr = xr.full_like(extent, np.nan) + return empty_arr + + # TODO: Replace with odc.stac.load equivalents. + stackstac_inputs = { + "assets": [metadata["name"]], + "resampling": resampler_func, + "bounds": s_bounds, + "epsg": epsg, + "resolution": res, + "fill_value": lyr_na, + "dtype": lyr_dtype, + "rescale": False, + "errors_as_nodata": (RasterioIOError(".*"),), + "xy_coords": "center", + "snap_bounds": False, + "chunksize": self.config["dask_chunk_size"] + } + + # reauth + if self.config["reauth_individual"]: + item_coll = DaskCube._sign_metadata(item_coll) + + # auth via token + if self.config["access_token"]: + gdal_env = stackstac.rio_env.LayeredEnv( + always=dict( + GDAL_HTTP_AUTH="BEARER", + GDAL_HTTP_BEARER=self.config["access_token"], + ), ) + + stackstac_inputs["gdal_env"] = gdal_env + + + # TODO: use odc.stac.load; Pass in group_by_solar_day + data = stackstac.stack( + item_coll, + **stackstac_inputs + ) + + return data + + def _get_dtype_na(self, metadata): + # retrieve dtype + try: + lyr_dtype = np.dtype(metadata["dtype"]) + except: + lyr_dtype = "float32" + # retrieve na_value + try: + lyr_na = np.array([metadata["na_value"]], dtype=lyr_dtype)[0] + except: + if isinstance(np.array([1], dtype=lyr_dtype)[0], np.floating): + lyr_na = np.nan + else: + lyr_na = 0 + # return both + return lyr_dtype, lyr_na + + def _format(self, data, metadata, extent): + # Step I: Set band as array name. + data.name = str(data["band"][0].values) + data = data.squeeze(dim="band", drop=True) + # Step II: Drop unnecessary dimensions & coordinates. + keep_coords = ["time", data.rio.x_dim, data.rio.y_dim] + drop_coords = [x for x in list(data.coords) if x not in keep_coords] + data = data.drop_vars(drop_coords) + # Step III: Format temporal coordinates. + # --> Make sure time dimension has the correct name. + # --> Convert time coordinates back into the original timezone. + data = data.sq.rename_dims({"time": TIME}) + data = data.sq.write_tz(self.tz) + data = data.sq.tz_convert(extent.sq.tz) + # Step IV: Format spatial coordinates. + # --> Make sure X and Y dims have the correct names. + # --> Store resolution as an attribute of the spatial coordinate dimensions. + # --> Add spatial feature indices as a non-dimension coordinate. + data = data.sq.rename_dims({data.rio.y_dim: Y, data.rio.x_dim: X}) + data = data.sq.write_spatial_resolution(extent.sq.spatial_resolution) + data.coords["spatial_feats"] = ([Y, X], extent["spatial_feats"].data) + # Step V: Write semantique specific attributes. + # --> Value types for the data and all dimension coordinates. + # --> Mapping from category labels to indices for all categorical data. + data.sq.value_type = self.config["value_type_mapping"][metadata["type"]] + if isinstance(metadata["values"], list): + value_labels = {} + for x in metadata["values"]: + try: + label = x["label"] + except KeyError: + label = None + value_labels[x["id"]] = label + data.sq.value_labels = value_labels + data[TIME].sq.value_type = "datetime" + data[Y].sq.value_type = "continuous" + data[X].sq.value_type = "continuous" + data["spatial_feats"].sq.value_type = extent["spatial_feats"].sq.value_type + data["spatial_feats"].sq.value_labels = extent["spatial_feats"].sq.value_labels + return data + + def _mask(self, data, metadata): + # Step I: Mask nodata values. + _, lyr_na = self._get_dtype_na(metadata) + data = data.where(data != lyr_na) + data = data.where(data != data.rio.nodata) + # Step II: Mask values outside of the spatial extent. + # This is needed since data are initially loaded for the bbox of the extent. + data = data.where(data["spatial_feats"].notnull()) + return data + + @staticmethod + def _divide_chunks(lst, k): + return [lst[i : i + k] for i in range(0, len(lst), k)] + + @staticmethod + def filter_spatio_temporal(item_collection, bbox, bbox_crs, start_datetime, end_datetime): + """ + Filter item collection by spatio-temporal extent. + + Args: + item_collection (pystac.ItemCollection): The item collection to filter. + bbox (tuple): The bounding box in WGS84 coordinates to filter by. + bbox_crs (str): The CRS of the bounding box. + start_datetime (np.datetime64): The start datetime to filter by. + end_datetime (np.datetime64): The end datetime to filter by. + """ + min_lon, min_lat, max_lon, max_lat = bbox + spatial_filter = box(min_lon, min_lat, max_lon, max_lat) + source_crs = pyproj.CRS("EPSG:4326") + target_crs = pyproj.CRS(bbox_crs) + transformer = ( + pyproj.Transformer + .from_crs(source_crs, target_crs, always_xy=True) + .transform + ) + filtered_items = [] + for item in item_collection: + item_geom = shape(item.geometry) + item_geom = transform(transformer, item_geom) + item_datetime = np.datetime64(item.datetime) + if not spatial_filter.intersects(item_geom): + continue + if not (start_datetime <= item_datetime < end_datetime): + continue + filtered_items.append(item) + return filtered_items + + @staticmethod + def _sign_metadata(items): + # retrieve collections root & item ids + roots = [x.get_root_link().href for x in items] + # create dictionary grouped by collection + curr_colls = {} + for c, item in zip(roots, items): + if c not in curr_colls: + curr_colls[c] = {"items": []} + curr_colls[c]["items"].append(item) + # define collections requiring authentication + # dict with collection and modifier + auth_colls = {} + auth_colls = { + "https://planetarycomputer.microsoft.com/api/stac/v1": pc.sign_inplace + } + # update signature for items + updated_items = [] + for coll in curr_colls.keys(): + if coll in auth_colls.keys(): + # perform search again to renew authentification + retry = Retry( + total=5, + backoff_factor=1, + status_forcelist=[408, 502, 503, 504], + allowed_methods=None, + ) + client = pystac_client.Client.open( + coll, + modifier=auth_colls[coll], + stac_io=StacApiIO(max_retries=retry, timeout=1800), + ) + item_chunks = STACCube._divide_chunks(curr_colls[coll]["items"], 100) + for chunk in item_chunks: + item_search = client.search( + ids=[x.id for x in chunk], + collections=[x.get_collection() for x in chunk], + ) + for item in item_search.items(): + original_item = next( + (i for i in chunk if i.id == item.id), None + ) + if original_item is not None: + # create a deep copy of the original item + # aim: keep original attributes and assets + new_item = original_item.clone() + # imprinting of the updated hrefs with new tokens + for asset_key in item.assets: + if asset_key in new_item.assets: + new_href = item.assets[asset_key].href + new_item.assets[asset_key].href = new_href + updated_items.append(new_item) + else: + updated_items.extend(curr_colls[coll]["items"]) + # return signed items + return pystac.ItemCollection(updated_items) From f9b87eedda16e8e8334face87416c9aae3087578 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 8 Jan 2026 11:46:07 +0000 Subject: [PATCH 8/9] Replace stackstac with odc.stac in DaskCube Use odc.stac.load instead of stackstac.stack for the DaskCube class to leverage odc.stac's native lazy group-by-solar-day functionality. Changes: - Add odc.stac import - Replace stackstac.stack with odc.stac.load in DaskCube._load - Update _format to handle odc.stac Dataset output structure - Add odc-stac>=0.3.9 to dependencies --- semantique/datacube.py | 65 ++++++++++++++++++++---------------------- setup.py | 1 + 2 files changed, 32 insertions(+), 34 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index af421910..b9a1c749 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -4,6 +4,7 @@ import copy import datacube import datetime +import odc.stac import os import planetary_computer as pc import pyproj @@ -1398,9 +1399,8 @@ def _load(self, metadata, extent): res = tuple(np.abs(extent.rio.resolution()).round(8)) epsg = int(str(extent.rio.crs)[5:]) - # retrieve resampler + # retrieve resampler name (odc.stac uses string names) resampler_name = self.config["resamplers"][metadata["type"]] - resampler_func = getattr(rasterio.enums.Resampling, resampler_name) # retrieve layer specific information lyr_dtype, lyr_na = self._get_dtype_na(metadata) @@ -1441,41 +1441,36 @@ def _load(self, metadata, extent): empty_arr = xr.full_like(extent, np.nan) return empty_arr - # TODO: Replace with odc.stac.load equivalents. - stackstac_inputs = { - "assets": [metadata["name"]], - "resampling": resampler_func, - "bounds": s_bounds, - "epsg": epsg, - "resolution": res, - "fill_value": lyr_na, - "dtype": lyr_dtype, - "rescale": False, - "errors_as_nodata": (RasterioIOError(".*"),), - "xy_coords": "center", - "snap_bounds": False, - "chunksize": self.config["dask_chunk_size"] - } - # reauth if self.config["reauth_individual"]: item_coll = DaskCube._sign_metadata(item_coll) - # auth via token - if self.config["access_token"]: - gdal_env = stackstac.rio_env.LayeredEnv( - always=dict( - GDAL_HTTP_AUTH="BEARER", - GDAL_HTTP_BEARER=self.config["access_token"], - ), ) + # determine groupby strategy + groupby = "solar_day" if self.config["group_by_solar_day"] else "time" + + # odc.stac.load parameters + chunk_size = self.config["dask_chunk_size"] + odc_stac_inputs = { + "bands": [metadata["name"]], + "resampling": resampler_name, + "bbox": s_bounds, + "crs": f"EPSG:{epsg}", + "resolution": res[0], # odc.stac uses single resolution value + "nodata": lyr_na, + "dtype": lyr_dtype, + "chunks": {"x": chunk_size, "y": chunk_size}, + "groupby": groupby, + "fail_on_error": False, + } - stackstac_inputs["gdal_env"] = gdal_env + # auth via token - set GDAL environment variables + if self.config["access_token"]: + os.environ["GDAL_HTTP_AUTH"] = "BEARER" + os.environ["GDAL_HTTP_BEARER"] = self.config["access_token"] - - # TODO: use odc.stac.load; Pass in group_by_solar_day - data = stackstac.stack( + data = odc.stac.load( item_coll, - **stackstac_inputs + **odc_stac_inputs ) return data @@ -1498,10 +1493,12 @@ def _get_dtype_na(self, metadata): return lyr_dtype, lyr_na def _format(self, data, metadata, extent): - # Step I: Set band as array name. - data.name = str(data["band"][0].values) - data = data.squeeze(dim="band", drop=True) - # Step II: Drop unnecessary dimensions & coordinates. + # Step I: Extract the band variable from the Dataset. + # odc.stac.load returns a Dataset with band names as variables. + band_name = metadata["name"] + data = data[band_name] + data.name = band_name + # Step II: Drop unnecessary coordinates. keep_coords = ["time", data.rio.x_dim, data.rio.y_dim] drop_coords = [x for x in list(data.coords) if x not in keep_coords] data = data.drop_vars(drop_coords) diff --git a/setup.py b/setup.py index 09e3b654..e817cb7b 100644 --- a/setup.py +++ b/setup.py @@ -10,6 +10,7 @@ 'geocube>=0.4.1', 'geopandas>=0.11,<1', 'numpy>=1.21', + 'odc-stac>=0.3.9', 'pandas>=2.0', 'planetary-computer', 'pyproj>=3.0', From d1122ab38300bd3805d0b75a02c6fc93ee11aebd Mon Sep 17 00:00:00 2001 From: lukesdm <2824165+lukesdm@users.noreply.github.com> Date: Thu, 8 Jan 2026 14:53:52 +0100 Subject: [PATCH 9/9] Fix bounding box calc and dask config check --- semantique/datacube.py | 5 ++++- semantique/recipe.py | 3 ++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/semantique/datacube.py b/semantique/datacube.py index b9a1c749..b697967f 100644 --- a/semantique/datacube.py +++ b/semantique/datacube.py @@ -1261,6 +1261,7 @@ def __init__(self, layout=None, src=None, **config): # Use dask lazy functions in subsequent operations set_global_dask_lazy(True) + @property @@ -1453,7 +1454,9 @@ def _load(self, metadata, extent): odc_stac_inputs = { "bands": [metadata["name"]], "resampling": resampler_name, - "bbox": s_bounds, + # Bounding box in input CRS ('bbox' is EPSG:4326 only) + "x": (s_bounds[0], s_bounds[2]), + "y": (s_bounds[1], s_bounds[3]), "crs": f"EPSG:{epsg}", "resolution": res[0], # odc.stac uses single resolution value "nodata": lyr_na, diff --git a/semantique/recipe.py b/semantique/recipe.py index 8bf17128..47586d55 100644 --- a/semantique/recipe.py +++ b/semantique/recipe.py @@ -1,5 +1,6 @@ import logging +from semantique.datacube import DaskCube from semantique.processor.utils import ComputeTracer, set_global_dask_lazy from semantique.processor.core import QueryProcessor, FakeProcessor from semantique.visualiser.visualise import show @@ -106,7 +107,7 @@ def execute(self, datacube, mapping, space, time, run_preview = False, **config ) - dask_lazy = datacube.config.get("dask_lazy") + dask_lazy = isinstance(datacube, DaskCube) if dask_lazy: set_global_dask_lazy(True) else: