subsample factor 1
2 changed files+4−4
python/ephys_compression_tests/algorithms/ans/__init__.pymodified+2−2View file
@@ -318,7 +318,7 @@ for a in algorithm_dicts_base:
318318 return reconstructed
319319 algorithm_dicts.append({
320320 "name": a["name"] + f"-ar{order}",
321- "version": a["version"] + f".1",
321+ "version": a["version"] + f".2",
322322 "encode": encode0_ar,
323323 "decode": decode0_ar,
324324 "description": a["description"] + f" with auto-regressive prediction encoding of order {order}",
@@ -361,7 +361,7 @@ for ar_order in [2, 8]:
361361 return decode0_ar_lossy
362362 algorithm_dicts.append({
363363 "name": f"ans-ar{ar_order}-lossy-tol{tolerance}",
364- "version": "10",
364+ "version": "11",
365365 "encode": make_encode_ar_lossy(),
366366 "decode": make_decode_ar_lossy(),
367367 "description": f"ANS with lossy auto-regressive prediction encoding of order {ar_order} and tolerance {tolerance}",
python/ephys_compression_tests/algorithms/ans/ar_numba.pymodified+2−2View file
@@ -70,7 +70,7 @@ def _fit_ar_model_channel(channel_data: np.ndarray, k: int, subsample_factor: in
7070 return coefficients
7171
7272
73-def fit_ar_model(data: np.ndarray, k: int, subsample_factor: int = 100,
73+def fit_ar_model(data: np.ndarray, k: int, subsample_factor: int = 1,
7474 min_samples: int = 1000) -> tuple[np.ndarray, np.ndarray]:
7575 """
7676 Fit an autoregressive model of order k to multi-channel time series data.
@@ -78,7 +78,7 @@ def fit_ar_model(data: np.ndarray, k: int, subsample_factor: int = 100,
7878 Args:
7979 data: 2D array of shape (timepoints, channels) with dtype int16
8080 k: Order of the autoregressive model
81- subsample_factor: Use every Nth sample for fitting (default: 100)
81+ subsample_factor: Use every Nth sample for fitting (default: 1)
8282 min_samples: Minimum number of samples to use for fitting (default: 1000)
8383
8484 Returns: