Port ddrs ddr_match corrections: outflow_idx fix, neg-solve counter, signed tau, β + Cunge X

benchmarks/src/ddr_benchmarks/benchmark.py

@@ -23,10 +23,11 @@
 from tqdm import tqdm
 
 # Reuse ALL DDR imports
-from ddr import ddr_functions, dmc, kan, streamflow
+from ddr import dmc, kan, streamflow
 from ddr._version import __version__
 from ddr.geodatazoo.dataclasses import Dates, RoutingDataclass
 from ddr.io.readers import read_zarr
+from ddr.scripts_utils import compute_daily_runoff
 from ddr.validation import Config, Metrics, plot_box_fig, plot_cdf, plot_gauge_map, plot_time_series, utils
 from ddr.validation.enums import GeoDataset
 
@@ -784,19 +785,10 @@ def benchmark(
     diffroute_predictions = diffroute_predictions[non_headwater]
 
     # === EVALUATION (same as test.py) ===
-    num_days = len(ddr_predictions[0][13 : (-11 + cfg.params.tau)]) // 24
+    ddr_daily = compute_daily_runoff(torch.tensor(ddr_predictions), cfg.params.tau)
+    diffroute_daily = compute_daily_runoff(torch.tensor(diffroute_predictions), cfg.params.tau)
 
-    ddr_daily = ddr_functions.downsample(
-        torch.tensor(ddr_predictions[:, (13 + cfg.params.tau) : (-11 + cfg.params.tau)]),
-        rho=num_days,
-    ).numpy()
-
-    diffroute_daily = ddr_functions.downsample(
-        torch.tensor(diffroute_predictions[:, (13 + cfg.params.tau) : (-11 + cfg.params.tau)]),
-        rho=num_days,
-    ).numpy()
-
-    daily_obs = observations[:, 1:-1]
+    daily_obs = observations[:, :-2]  # Pooled day i ↔ obs day i
 
     # Compute metrics using DDR's Metrics class
     log.info("=" * 50)

docs/datasets.md

@@ -29,13 +29,13 @@ ds = xr.Dataset(
         "Qr": (["time", "divide_id"], qr_data),  # Lateral inflow in m³/s
     },
     coords={
-        "time": time_index,           # Daily timestamps
-        "divide_id": divide_ids,      # Catchment identifiers
+        "time": time_index,  # Daily timestamps
+        "divide_id": divide_ids,  # Catchment identifiers
     },
     attrs={
         "units": "m^3/s",
         "source": "your_model_name",
-    }
+    },
 )
 ```
 
@@ -88,13 +88,13 @@ The NOAA-OWP Hydrofabric v2.2 is the recommended geospatial dataset for CONUS ap
 ```python
 # Flowpath attributes
 flowpath_attrs = [
-    "id",              # Waterbody identifier (wb-XXXXX)
-    "toid",            # Downstream identifier
-    "Length_m",        # Channel length in meters
-    "So",              # Channel slope (dimensionless)
-    "TopWdth",         # Top width in meters
-    "ChSlp",           # Channel side slope
-    "MusX",            # Muskingum X parameter
+    "id",  # Waterbody identifier (wb-XXXXX)
+    "toid",  # Downstream identifier
+    "Length_m",  # Channel length in meters
+    "So",  # Channel slope (dimensionless)
+    "TopWdth",  # Top width in meters
+    "ChSlp",  # Channel side slope
+    "MusX",  # Muskingum X parameter
 ]
 ```
 
@@ -203,7 +203,7 @@ ds = xr.Dataset(
     },
     coords={
         "divide_id": divide_ids,  # Format: "cat-XXXXX"
-    }
+    },
 )
 ```
 
@@ -241,7 +241,7 @@ ds = xr.Dataset(
     coords={
         "time": time_index,
         "gage_id": gage_ids,  # 8-digit zero-padded strings
-    }
+    },
 )
 ```
 

docs/engine/binsparse.md

@@ -71,12 +71,15 @@ print(list_geodatasets())  # ['hydrofabric_v2.2', 'lynker', 'merit']
 ```python
 from ddr_engine import register_converter
 
+
 class MyConverter:
     def to_zarr(self, ids):
         return np.array(ids, dtype=np.int32)
+
     def from_zarr(self, order):
         return order.tolist()
 
+
 register_converter("my_geodataset", MyConverter())
 ```
 

docs/engine/index.md

@@ -23,12 +23,11 @@ The `ddr_engine` package exports I/O functions at the package level:
 ```python
 from ddr_engine import (
     # Primary API (recommended)
-    coo_to_zarr,      # Write COO matrix (pass geodataset name)
-    coo_from_zarr,    # Read COO matrix (auto-detects geodataset)
+    coo_to_zarr,  # Write COO matrix (pass geodataset name)
+    coo_from_zarr,  # Read COO matrix (auto-detects geodataset)
     coo_to_zarr_group,  # Write gauge subset
-
     # Converter registry
-    list_geodatasets,    # List available geodatasets
+    list_geodatasets,  # List available geodatasets
     register_converter,  # Register custom geodataset
 )
 ```

docs/gpu.md

@@ -42,9 +42,9 @@ DDR stores adjacency matrices in Coordinate (COO) format, which efficiently repr
 
 ```python
 # COO representation
-row_indices = [1, 2, 3, 3]      # Downstream segment indices
-col_indices = [0, 1, 1, 2]      # Upstream segment indices
-values = [1, 1, 1, 1]           # Connection weights (always 1)
+row_indices = [1, 2, 3, 3]  # Downstream segment indices
+col_indices = [0, 1, 1, 2]  # Upstream segment indices
+values = [1, 1, 1, 1]  # Connection weights (always 1)
 shape = (n_segments, n_segments)
 
 # Matrix interpretation:

docs/startup.md

@@ -79,6 +79,7 @@ ddr --help
 ```python
 # Or verify from Python
 import ddr
+
 print(ddr.__version__)
 ```