fixes for tdx

src/coincident/datasets/csda.py

@@ -14,6 +14,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
+from typing import Any
 
 from coincident.datasets.general import Dataset
 
@@ -53,3 +54,19 @@ class TDX(Dataset):
     end: str | None = None
     type: str = "sar"
     provider: str = "csda"
+    stac_kwargs: dict[str, Any] = field(
+        default_factory=lambda: {
+            "limit": 1000,
+            "filter": {
+                "op": "and",
+                "args": [
+                    # exclude PAZ, only SSC products
+                    {
+                        "op": "in",
+                        "args": [{"property": "platform"}, ["TDX-1", "TSX-1"]],
+                    },
+                    {"op": "=", "args": [{"property": "sar:product_type"}, "SSC"]},
+                ],
+            },
+        }
+    )

src/coincident/search/main.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import warnings
 from typing import Any
 
 import geopandas as gpd
@@ -51,10 +52,10 @@ def search(
         try:
             dataset = _alias_to_Dataset[dataset]
         except KeyError as e:
-            message = (
+            msg_unsupported = (
                 f"{dataset} is not a supported dataset: {_alias_to_Dataset.keys()}"
             )
-            raise ValueError(message) from e
+            raise ValueError(msg_unsupported) from e
 
     # Validate Datetimes
     _validate_temporal_bounds(dataset, datetime)
@@ -81,6 +82,11 @@ def search(
             shapely_geometry = shapely_geometry.reverse()
         aoi = _pystac_client._format_intersects(shapely_geometry)  # to JSON geometry
     else:
+        if "bbox" not in kwargs:
+            msg_unconstrained = (
+                "Neither `bbox` nor `intersects` provided... search will be global"
+            )
+            warnings.warn(msg_unconstrained, stacklevel=2)
         aoi = None
 
     # STAC API Searches
@@ -94,8 +100,8 @@ def search(
         if dataset.provider == "maxar":
             # NOTE: not sure how to avoid incompatible type "str | None"; expected "str" for Dataset.attrs
             client = stac.configure_maxar_client(dataset.area_based_calc)  # type: ignore[attr-defined]
-            results = stac.search(client, **stac_api_kwargs)
-            gf = stac.to_geopandas(results)
+            item_collection = stac.search(client, **stac_api_kwargs)
+            gf = stac.to_geopandas(item_collection)
             # Client-side reduce to only acquisitions having stereo pairs
             gf = gf.loc[gf.stereo_pair_identifiers.str[0].dropna().index]
 
@@ -110,8 +116,26 @@ def search(
             # Generic STAC endpoint w/o additional config
             else:
                 client = stac.configure_stac_client(dataset.search)  # type: ignore[arg-type]
-            results = stac.search(client, **stac_api_kwargs)
-            gf = stac.to_geopandas(results)
+            item_collection = stac.search(client, **stac_api_kwargs)
+
+            # Per-dataset munging
+            # https://github.com/uw-cryo/coincident/issues/8#issuecomment-2449810481
+            if dataset.alias == "tdx":
+                # Drop columns with messy schema
+                dropcols = [
+                    "sceneInfo",
+                    "missionInfo",
+                    "previewInfo",
+                    "imageDataInfo",
+                    "generationInfo",
+                    "acquisitionInfo",
+                    "productVariantInfo",
+                ]
+                for item in item_collection:
+                    for col in dropcols:
+                        item.properties.pop(col)
+
+            gf = stac.to_geopandas(item_collection)
 
     # Non-STAC Searches
     elif dataset.alias == "3dep":

src/coincident/search/stac.py

@@ -30,7 +30,32 @@
 def to_geopandas(
     collection: pystac.item_collection.ItemCollection,
 ) -> gpd.GeoDataFrame:
-    """Convert returned from STAC API to geodataframe via arrow"""
+    """
+    Convert a STAC ItemCollection to a GeoDataFrame.
+    This function converts a given STAC ItemCollection to a GeoDataFrame using the
+    `stac_geoparquet.arrow.parse_stac_items_to_arrow` method. It also adds an additional
+    column 'dayofyear' for convenience.
+
+    Parameters
+    ----------
+    collection : pystac.item_collection.ItemCollection
+        The STAC ItemCollection to be converted.
+
+    Returns
+    -------
+    gpd.GeoDataFrame
+        A GeoDataFrame containing the data from the STAC ItemCollection.
+
+    Raises
+    ------
+    ValueError
+        If the provided ItemCollection is empty.
+    """
+    # Catch if no items are passed
+    if len(collection) == 0:
+        message = "ItemCollection is empty, cannot convert to GeoDataFrame"
+        raise ValueError(message)
+
     record_batch_reader = stac_geoparquet.arrow.parse_stac_items_to_arrow(collection)
     gf = gpd.GeoDataFrame.from_arrow(record_batch_reader)  # doesn't keep arrow dtypes
 
@@ -65,6 +90,8 @@ def search(
     client: pystac_client.client.Client, **kwargs: dict[str, Any] | None
 ) -> pystac_client.item_search.ItemSearch:
     """Search any STAC API (e.g. https://github.com/nasa/cmr-stac)"""
+    # NOTE: add logging for kwargs?
+    # print(kwargs)
     results = client.search(
         **kwargs,
     )

tests/test_search.py

@@ -42,6 +42,16 @@ def test_polygon_invalid_type():
         m.search.search(dataset="3dep", intersects="-120, 40, -121, 41")
 
 
+def test_to_geopandas_empty_search_result():
+    with pytest.raises(ValueError, match="ItemCollection is empty"):
+        m.search.stac.to_geopandas([])
+
+
+def test_unconstrained_search_warns():
+    with pytest.warns(match="Neither `bbox` nor `intersects` provided"):
+        m.search.search(dataset="tdx")
+
+
 # TODO: add more assertions / tests for this section
 @network
 @pytest.mark.filterwarnings("ignore:Server does not conform")
@@ -98,6 +108,7 @@ def test_gedi_search(aoi):
 def test_tdx_search(aoi):
     gf = m.search.search(dataset="tdx", intersects=aoi, datetime=["2009", "2020"])
     assert len(gf) == 48
+    assert gf["sar:product_type"].unique() == "SSC"
 
 
 # MS PLANETARY COMPUTER