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clients/python/test/Prosit/test_Prosit_2024_intensity_XL_NMS2.py

@@ -20,4 +20,4 @@ def test_available_grpc():
 
 
 def test_inference():
-    lib_test_inference(MODEL_NAME, SERVER_GRPC)
+    lib_test_inference(MODEL_NAME, SERVER_GRPC)

clients/python/test/Prosit/test_Prosit_2024_intensity_XL_NMS2_core.py

@@ -20,4 +20,4 @@ def test_available_grpc():
 
 
 def test_inference():
-    lib_test_inference(MODEL_NAME, SERVER_GRPC)
+    lib_test_inference(MODEL_NAME, SERVER_GRPC)

models/Prosit/XL_Prosit_NMS2_2024_intensity_postprocess/1/constants.py

@@ -44,7 +44,10 @@
     "Y": 20,
 }
 
-TERMINAL_ALPHABET = {"[]-": 30, "-[]": 31}  # unmodified n terminus  # unmodified c terminus
+TERMINAL_ALPHABET = {
+    "[]-": 30,
+    "-[]": 31,
+}  # unmodified n terminus  # unmodified c terminus
 
 ALPHABET_MODS = {
     "M[UNIMOD:35]": 21,
@@ -233,7 +236,14 @@
     "REVERSE",
 ]
 META_DATA_COLUMNS = SHARED_DATA_COLUMNS + META_DATA_ONLY_COLUMNS
-MZML_ONLY_DATA_COLUMNS = ["INTENSITIES", "MZ", "MZ_RANGE", "RETENTION_TIME", "MASS_ANALYZER", "FRAGMENTATION"]
+MZML_ONLY_DATA_COLUMNS = [
+    "INTENSITIES",
+    "MZ",
+    "MZ_RANGE",
+    "RETENTION_TIME",
+    "MASS_ANALYZER",
+    "FRAGMENTATION",
+]
 MZML_DATA_COLUMNS = SHARED_DATA_COLUMNS + MZML_ONLY_DATA_COLUMNS
 
 TMT_MODS = {
@@ -275,7 +285,9 @@
 
 IONS = ["y", "b"]  # limited to single character unicode string when array is created
 CHARGES = [1, 2, 3]  # limited to uint8 (0-255) when array is created
-POSITIONS = [x for x in range(1, 30)]  # fragment numbers 1-29 -- limited to uint8 (0-255) when array is created
+POSITIONS = [
+    x for x in range(1, 30)
+]  # fragment numbers 1-29 -- limited to uint8 (0-255) when array is created
 
 ANNOTATION_FRAGMENT_TYPE = []
 ANNOTATION_FRAGMENT_CHARGE = []
@@ -287,7 +299,11 @@
             ANNOTATION_FRAGMENT_CHARGE.append(charge)
             ANNOTATION_FRAGMENT_NUMBER.append(pos)
 
-ANNOTATION = [ANNOTATION_FRAGMENT_TYPE, ANNOTATION_FRAGMENT_CHARGE, ANNOTATION_FRAGMENT_NUMBER]
+ANNOTATION = [
+    ANNOTATION_FRAGMENT_TYPE,
+    ANNOTATION_FRAGMENT_CHARGE,
+    ANNOTATION_FRAGMENT_NUMBER,
+]
 
 
 ########################

models/Prosit/XL_Prosit_NMS2_2024_intensity_postprocess/1/model.py

@@ -162,9 +162,7 @@ def execute(self, requests):
             peptide_in_1 = [x[0].decode("utf-8") for x in peptide_in_1]
             peptide_in_2 = [x[0].decode("utf-8") for x in peptide_in_2]
 
-            peptide_2_sequence = [
-                s.replace("[UNIMOD:1898]", "") for s in peptide_in_2
-            ]
+            peptide_2_sequence = [s.replace("[UNIMOD:1898]", "") for s in peptide_in_2]
             peptide_in_2_mass = [compute_peptide_mass(s) for s in peptide_2_sequence]
 
             crosslinker_position = [find_crosslinker_position(x) for x in peptide_in_1]
@@ -177,10 +175,15 @@ def execute(self, requests):
             mask = create_masking(precursor_charges_in, peptide_length)
             masked_peaks = apply_masking(peaks_in, mask)
 
-            fragmentmz = [initialize_peaks(seq, chg, mass, pos) for seq, chg, mass, pos in zip(peptide_in_1, charge, peptide_in_2_mass, crosslinker_position)]
+            fragmentmz = [
+                initialize_peaks(seq, chg, mass, pos)
+                for seq, chg, mass, pos in zip(
+                    peptide_in_1, charge, peptide_in_2_mass, crosslinker_position
+                )
+            ]
             fragmentmz = np.array(fragmentmz)
             fragmentmz = fragmentmz.astype("float32")
-            
+
             fragmentmz[np.isnan(masked_peaks)] = -1
             masked_peaks[np.isnan(masked_peaks)] = -1
 

models/Prosit/XL_Prosit_NMS2_Helper_annotation/1/model.py

@@ -4,30 +4,33 @@
 import re
 
 
-
 def find_crosslinker_position(peptide_sequence: str):
     peptide_sequence = re.sub(r"\[UNIMOD:(?!1898).*?\]", "", peptide_sequence)
     crosslinker_position = re.search(r"K(?=\[UNIMOD:1898\])", peptide_sequence)
     crosslinker_position = crosslinker_position.start() + 1
     return crosslinker_position
 
+
 def gen_annotation_linear_pep(unmod_seq: str, precursor_charge: int):
     peptide_length = len(unmod_seq)
     ions = ["y", "b"]
     positions = [x for x in range(1, 30)]  # Generate positions 1 through 29
     annotation = []
     max_charge = 3  # Always consider three charges
-    
+
     for pos in positions:
         for ion in ions:
             for charge in range(1, max_charge + 1):
                 if charge > precursor_charge or pos >= peptide_length:
-                    annotation.append(None)  # Append None for invalid charges and positions beyond the sequence length
+                    annotation.append(
+                        None
+                    )  # Append None for invalid charges and positions beyond the sequence length
                 else:
                     annotation.append(f"{ion}{pos}+{charge}")
-                    
+
     return annotation
 
+
 def gen_annotation_xl(annotation, unmod_seq: str, crosslinker_position: int):
     peptide_length = len(unmod_seq)
 
@@ -39,21 +42,24 @@ def gen_annotation_xl(annotation, unmod_seq: str, crosslinker_position: int):
                 index = annotation.index(b_ion)
                 annotation[index] = f"b_xl{i}+{charge}"
 
-     # Correcting y-ion annotation to move backward from the end
+    # Correcting y-ion annotation to move backward from the end
     for i in range(peptide_length, 1, -1):
         for charge in ["1", "2", "3"]:
             y_ion = f"y{i}+{charge}"
             if y_ion in annotation:
                 index = annotation.index(y_ion)
-                
+
                 # Only annotate with '_xl' if within the crosslinker adjusted position
                 if i >= peptide_length - crosslinker_position + 1:
                     annotation[index] = f"y_xl{i}+{charge}"
                 else:
-                    annotation[index] = y_ion  # this line could be omitted if y_ions need no re-assignment
+                    annotation[index] = (
+                        y_ion  # this line could be omitted if y_ions need no re-assignment
+                    )
 
     return np.array(annotation).astype(np.object_)
 
+
 class TritonPythonModel:
     def initialize(self, args):
         self.model_config = json.loads(args["model_config"])
@@ -67,20 +73,28 @@ def execute(self, requests):
         annotation = np.empty((0, 174))
 
         for request in requests:
-            batchsize = pb_utils.get_input_tensor_by_name(request, "precursor_charges").as_numpy().shape[0]
-            precursor_charges = pb_utils.get_input_tensor_by_name(request, "precursor_charges").as_numpy()
+            batchsize = (
+                pb_utils.get_input_tensor_by_name(request, "precursor_charges")
+                .as_numpy()
+                .shape[0]
+            )
+            precursor_charges = pb_utils.get_input_tensor_by_name(
+                request, "precursor_charges"
+            ).as_numpy()
 
             peptide_sequences_1 = pb_utils.get_input_tensor_by_name(
                 request, "peptide_sequences_1"
             ).as_numpy()
-            
+
             for i in range(batchsize):
                 regular_sequence = peptide_sequences_1[i][0].decode("utf-8")
                 crosslinker_position = find_crosslinker_position(regular_sequence)
                 precursor_charge = int(precursor_charges[i][0])
                 unmod_seq = re.sub(r"\[.*?\]", "", regular_sequence)
                 annotation_lin = gen_annotation_linear_pep(unmod_seq, precursor_charge)
-                annotation_i = gen_annotation_xl(annotation_lin, unmod_seq, crosslinker_position)
+                annotation_i = gen_annotation_xl(
+                    annotation_lin, unmod_seq, crosslinker_position
+                )
                 annotation = np.vstack((annotation, annotation_i))
 
             t = pb_utils.Tensor("annotation", annotation)
@@ -89,4 +103,4 @@ def execute(self, requests):
         return responses
 
     def finalize(self):
-        pass
+        pass