CNF posrec urlconfig fix

pyproject.toml

@@ -48,7 +48,7 @@ mistune = "==0.8.4"
 pymongo = "*"
 requests = "*"
 utilix = ">=0.11.0"
-xedocs = "*"
+xedocs = ">=0.2.32"
 commonmark = { version = "0.9.1", optional = true }
 nbsphinx = { version = "0.8.9", optional = true }
 recommonmark = { version = "0.7.1", optional = true }

straxen/config/protocols.py

@@ -28,7 +28,7 @@ def get_item_or_attr(obj, key, default=None):
 
 @URLConfig.register("resource")
 def get_resource(name: str, fmt: str = "text", readable: bool = False, **kwargs):
-    """Fetch a straxen resource Allow a direct download using <fmt='abs_path'> otherwise kwargs are
+    """Fetch a straxen resource, allow a direct download using <fmt='abs_path'> otherwise kwargs are
     passed directly to straxen.get_resource."""
     if fmt == "abs_path":
         downloader = utilix.mongo_storage.MongoDownloader()

straxen/plugins/peaklets/peaklet_positions_cnf.py

@@ -23,7 +23,7 @@ class PeakletPositionsCNF(PeakletPositionsBase):
     - sig: Confidence level of the contour
     - log_area_scale: Scaling parameter for log area
     - n_top_pmts: Number of top PMTs
-    - pred_function: Path to the compiled JAX function for predictions
+    - cnf_pred_function: Path to the compiled JAX function for predictions
 
     """
 
@@ -56,10 +56,11 @@ class PeakletPositionsCNF(PeakletPositionsBase):
         help="Scaling parameter for log area",
     )
 
-    pred_function = straxen.URLConfig(
+    cnf_pred_function = straxen.URLConfig(
         default=(
-            "jax://resource://flow_20240730.tar.gz?"
-            "n_poly=plugin.n_poly&sig=plugin.sig&fmt=abs_path"
+            "jax://resource://xedocs://posrec_models"
+            "?kind=cnf&attr=value&n_poly=plugin.n_poly&sig=plugin.sig&fmt=abs_path"
+            "&version=ONLINE&run_id=plugin.run_id"
         ),
         help="Compiled JAX function",
     )
@@ -150,7 +151,7 @@ def vectorized_prediction_chunk(self, flow_condition):
         else:
             inputs = np.zeros((self.N_chunk_max, self.n_top_pmts + 1))
             inputs[:N_entries] = flow_condition
-            xy, contour = self.pred_function(inputs)
+            xy, contour = self.cnf_pred_function(inputs)
             return xy[:N_entries], contour[:N_entries]
 
     def prediction_loop(self, flow_condition):