Organize UAI files into separate datasets/uais/ directory

This commit reorganizes the dataset structure to keep UAI files
separate from DEM files for better organization.

Changes:
- Move .uai files from datasets/dems/ to datasets/uais/
- Update generate_uai_from_circuit() to save in datasets/uais/
- Update documentation to reflect new folder structure
- Update datasets/README.md with dataset organization section
- Update examples/GETTING_STARTED.md with correct paths

Dataset structure:
- datasets/circuits/ - Circuit files (.stim)
- datasets/dems/ - Detector error models (.dem)
- datasets/uais/ - UAI format files (.uai)
- datasets/syndromes/ - Syndrome databases (.npz)

All tests passing (62/62)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Author: ChanceSiyuan

datasets/README.md

@@ -2,6 +2,18 @@
 
 Circuit-level surface-code memory experiments generated with Stim for **Belief Propagation (BP) decoding** demonstrations.
 
+## Dataset Organization
+
+The dataset is organized into subdirectories by file type:
+
+```
+datasets/
+├── circuits/     # Noisy quantum circuits (.stim)
+├── dems/         # Detector error models (.dem)
+├── uais/         # UAI format for probabilistic inference (.uai)
+└── syndromes/    # Syndrome databases (.npz)
+```
+
 ## Overview
 
 | Parameter | Value |
@@ -38,7 +50,7 @@ import stim
 import numpy as np
 
 # Load circuit
-circuit = stim.Circuit.from_file("datasets/noisy_circuits/sc_d3_r3_p0010_z.stim")
+circuit = stim.Circuit.from_file("datasets/circuits/sc_d3_r3_p0010_z.stim")
 
 # Extract DEM - this is what BP needs
 dem = circuit.detector_error_model(decompose_errors=True)
@@ -180,25 +192,27 @@ def evaluate_decoder(decoder_fn, circuit, n_shots=10000):
 uv sync
 
 # Generate circuits using the CLI
-uv run generate-noisy-circuits \
+python -m bpdecoderplus.cli \
   --distance 3 \
   --p 0.01 \
   --rounds 3 5 7 \
   --task z \
-  --output datasets/noisy_circuits
+  --generate-dem \
+  --generate-uai \
+  --generate-syndromes 10000
 ```
 
 ## Extending the Dataset
 
 ```bash
 # Different error rates
-uv run generate-noisy-circuits --p 0.005 --rounds 3 5 7
+python -m bpdecoderplus.cli --p 0.005 --rounds 3 5 7 --generate-dem --generate-uai
 
 # Different distances
-uv run generate-noisy-circuits --distance 5 --rounds 5 7 9
+python -m bpdecoderplus.cli --distance 5 --rounds 5 7 9 --generate-dem --generate-uai
 
 # X-memory experiment
-uv run generate-noisy-circuits --task x --rounds 3 5 7
+python -m bpdecoderplus.cli --task x --rounds 3 5 7 --generate-dem --generate-uai
 ```
 
 ## References

datasets/dems/sc_d3_r3_p0010_z.uai datasets/uais/sc_d3_r3_p0010_z.uaidatasets/dems/sc_d3_r3_p0010_z.uai renamed to datasets/uais/sc_d3_r3_p0010_z.uai

@@ -24,7 +24,7 @@ This creates files in organized subdirectories:
 datasets/
 ├── circuits/sc_d3_r3_p0010_z.stim    # Noisy quantum circuit
 ├── dems/sc_d3_r3_p0010_z.dem         # Detector error model
-├── dems/sc_d3_r3_p0010_z.uai         # UAI format for inference
+├── uais/sc_d3_r3_p0010_z.uai         # UAI format for inference
 └── syndromes/sc_d3_r3_p0010_z.npz    # 1000 syndrome samples
 ```
 
@@ -119,7 +119,7 @@ UAI (Uncertainty in Artificial Intelligence) is a standard format for representi
 python -m bpdecoderplus.cli --distance 3 --p 0.01 --rounds 3 --task z --generate-uai
 ```
 
-**Output:** `datasets/dems/sc_d3_r3_p0010_z.uai`
+**Output:** `datasets/uais/sc_d3_r3_p0010_z.uai`
 
 The `.uai` file structure:
 ```
@@ -234,10 +234,11 @@ datasets/
 │   └── sc_d3_r7_p0010_z.stim
 ├── dems/
 │   ├── sc_d3_r3_p0010_z.dem
-│   ├── sc_d3_r3_p0010_z.uai
 │   ├── sc_d3_r5_p0010_z.dem
+│   └── sc_d3_r7_p0010_z.dem
+├── uais/
+│   ├── sc_d3_r3_p0010_z.uai
 │   ├── sc_d3_r5_p0010_z.uai
-│   ├── sc_d3_r7_p0010_z.dem
 │   └── sc_d3_r7_p0010_z.uai
 └── syndromes/
     ├── sc_d3_r3_p0010_z.npz

datasets/dems/sc_d3_r5_p0010_z.uai datasets/uais/sc_d3_r5_p0010_z.uaidatasets/dems/sc_d3_r5_p0010_z.uai renamed to datasets/uais/sc_d3_r5_p0010_z.uai

@@ -249,7 +249,7 @@ def generate_uai_from_circuit(
 
     Args:
         circuit_path: Path to the circuit file (.stim).
-        output_path: Optional output path. If None, uses datasets/dems/ directory.
+        output_path: Optional output path. If None, uses datasets/uais/ directory.
         decompose_errors: Whether to decompose errors into components.
 
     Returns:
@@ -258,9 +258,9 @@ def generate_uai_from_circuit(
     circuit = stim.Circuit.from_file(str(circuit_path))
 
     if output_path is None:
-        dems_dir = pathlib.Path("datasets/dems")
-        dems_dir.mkdir(parents=True, exist_ok=True)
-        output_path = dems_dir / circuit_path.with_suffix(".uai").name
+        uais_dir = pathlib.Path("datasets/uais")
+        uais_dir.mkdir(parents=True, exist_ok=True)
+        output_path = uais_dir / circuit_path.with_suffix(".uai").name
 
     dem = extract_dem(circuit, decompose_errors=decompose_errors)
     save_uai(dem, output_path)