[Fix] Simplify Depth adjustment (#386)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#386

epdb/legacy_api.py

@@ -1967,6 +1967,9 @@ def add_pathway_edge(request, package_uuid, pathway_uuid, e: Form[CreateEdge]):
             description=e.edgeReason,
         )
 
+        # Update depths as sideeffect of above operation
+        pw.update_depths()
+
         return redirect(new_e.url)
     except ValueError:
         return 403, {"message": "Adding Edge failed!"}

epdb/logic.py

@@ -995,52 +995,9 @@ class PackageManager(object):
 
         print("Fixing Node depths...")
         total_pws = Pathway.objects.filter(package=pack).count()
+
         for p, pw in enumerate(Pathway.objects.filter(package=pack)):
-            in_count = defaultdict(lambda: 0)
+            pw.update_depths()
-            out_count = defaultdict(lambda: 0)
-
-            for e in pw.edges:
-                # TODO check if this will remain
-                for react in e.start_nodes.all():
-                    out_count[str(react.uuid)] += 1
-
-                for prod in e.end_nodes.all():
-                    in_count[str(prod.uuid)] += 1
-
-            root_nodes = []
-            for n in pw.nodes:
-                num_parents = in_count[str(n.uuid)]
-                if num_parents == 0:
-                    # must be a root node or unconnected node
-                    if n.depth != 0:
-                        n.depth = 0
-                        n.save()
-
-                    # Only root node may have children
-                    if out_count[str(n.uuid)] > 0:
-                        root_nodes.append(n)
-
-            levels = [root_nodes]
-            seen = set()
-            # Do a bfs to determine depths starting with level 0 a.k.a. root nodes
-            for i, level_nodes in enumerate(levels):
-                new_level = []
-                for n in level_nodes:
-                    for e in n.out_edges.all():
-                        for prod in e.end_nodes.all():
-                            if str(prod.uuid) not in seen:
-                                old_depth = prod.depth
-                                if old_depth != i + 1:
-                                    prod.depth = i + 1
-                                    prod.save()
-
-                                new_level.append(prod)
-
-                    seen.add(str(n.uuid))
-
-                if new_level:
-                    levels.append(new_level)
-
             print(f"{p + 1}/{total_pws} fixed.", end="\r")
 
         return pack

epdb/migrations/0025_auto_20260511_2025.py

@@ -0,0 +1,56 @@
+# Generated by Django 6.0.3 on 2026-05-11 20:25
+
+from django.db import migrations
+from envipy_additional_information import HalfLife, HalfLifeModel, HalfLifeWS
+
+MAPPING = {
+    "": HalfLifeModel.OTHER,
+    "HS-SFO": HalfLifeModel.HS_SFO,
+    "FOMC": HalfLifeModel.FOMC,
+    "FOTC": HalfLifeModel.DFOP,
+    "FMOC": HalfLifeModel.FOMC,
+    "DFOP": HalfLifeModel.DFOP,
+    "SFO + SFO": HalfLifeModel.SFO_SFO,
+    "FOMC-SFO": HalfLifeModel.FOMC_SFO,
+    "first order kinetics": HalfLifeModel.SFO,
+    "SFO²": HalfLifeModel.SFO,
+    "HS": HalfLifeModel.HS,
+    "top down": HalfLifeModel.OTHER,
+    "SFO": HalfLifeModel.SFO,
+    "First Order": HalfLifeModel.SFO,
+    "SFO/SFO": HalfLifeModel.SFO_SFO,
+    "FOMC + SFO": HalfLifeModel.FOMC_SFO,
+    "true": HalfLifeModel.SFO,
+    "SFO-SFO": HalfLifeModel.SFO_SFO,
+    "DFOP-SFO": HalfLifeModel.DFOP_SFO,
+}
+
+
+def forward_func(apps, schema_editor):
+    AdditionalInformation = apps.get_model("epdb", "AdditionalInformation")
+
+    hls = AdditionalInformation.objects.filter(type="HalfLife")
+
+    for hl in hls:
+        data = hl.data
+        data["model"] = MAPPING[data["model"]].value
+        hl.data = HalfLife(**data).model_dump(mode="json")
+        hl.save()
+
+    hlws = AdditionalInformation.objects.filter(type="HalfLifeWS")
+
+    for hl in hlws:
+        data = hl.data
+        data["model"] = MAPPING[data["model"]].value
+        hl.data = HalfLifeWS(**data).model_dump(mode="json")
+        hl.save()
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ("epdb", "0024_user_contacted"),
+    ]
+
+    operations = [
+        migrations.RunPython(forward_func, reverse_code=migrations.RunPython.noop),
+    ]

epdb/models.py

@@ -2178,6 +2178,56 @@ class Pathway(EnviPathModel, AliasMixin, ScenarioMixin, AdditionalInformationMix
     ):
         return Edge.create(self, start_nodes, end_nodes, rule, name=name, description=description)
 
+    def update_depths(self):
+        # Collect number of in and out links per node
+        in_count = defaultdict(lambda: 0)
+        out_count = defaultdict(lambda: 0)
+
+        for e in self.edges:
+            for react in e.start_nodes.all():
+                out_count[str(react.uuid)] += 1
+
+            for prod in e.end_nodes.all():
+                in_count[str(prod.uuid)] += 1
+
+        depth_map = {}
+        depth_map[0] = list()
+
+        for n in self.nodes:
+            num_parents = in_count[str(n.uuid)]
+            if num_parents == 0:
+                # must be a root node or unconnected node
+                if n.depth != 0:
+                    n.depth = 0
+                    n.save()
+
+                # Only root node may have children
+                if out_count[str(n.uuid)] > 0:
+                    depth_map[0].append(n)
+
+        # At most depth len(nodes) is possible
+        for i in range(self.nodes.count()):
+            level_nodes = depth_map.get(i, [])
+
+            if len(level_nodes) == 0:
+                break
+
+            unique_next_level = set()
+            for n in level_nodes:
+                for e in self.edges:
+                    if n in e.start_nodes.all():
+                        for p in e.end_nodes.all():
+                            unique_next_level.add(p)
+
+            if len(unique_next_level) > 0:
+                depth_map[i + 1] = list(unique_next_level)
+
+        for depth, nodes in depth_map.items():
+            for n in nodes:
+                if n.depth != depth:
+                    n.depth = depth
+                    n.save()
+
 
 class Node(EnviPathModel, AliasMixin, ScenarioMixin, AdditionalInformationMixin):
     pathway = models.ForeignKey(

epdb/views.py

@@ -2506,6 +2506,9 @@ def package_pathway_edges(request, package_uuid, pathway_uuid):
             substrate_nodes, product_nodes, name=edge_name, description=edge_description
         )
 
+        # Update depths as sideeffect of above operation
+        current_pathway.update_depths()
+
         return redirect(current_pathway.url)
 
     else:

pepper/__init__.py

@@ -46,7 +46,7 @@ class PepperPrediction(PropertyPrediction):
 
         import matplotlib.patches as mpatches
         import numpy as np
-        from matplotlib import pyplot as plt
+        from matplotlib.figure import Figure
         from scipy import stats
 
         """
@@ -101,7 +101,8 @@ class PepperPrediction(PropertyPrediction):
         mask_red = x > vp
 
         # Plot
-        fig, ax = plt.subplots(figsize=(9, 5.5))
+        fig = Figure(figsize=(9, 5.5))
+        ax = fig.subplots()
         ax.plot(x, y, color="#1f4e79", lw=2, label="Lognormal PDF")
 
         if np.any(mask_green):
@@ -146,13 +147,12 @@ class PepperPrediction(PropertyPrediction):
         ]
         ax.legend(handles=patches, frameon=True)
 
-        plt.tight_layout()
+        fig.tight_layout()
 
         # --- Export to SVG string ---
         buf = io.StringIO()
         fig.savefig(buf, format="svg", bbox_inches="tight")
         svg = buf.getvalue()
-        plt.close(fig)
         buf.close()
 
         return svg

pepper/impl/pepper.py

@@ -187,8 +187,9 @@ class Pepper:
         groups = [group for group in dataset.group_by("structure_id")]
 
         # Unless explicitly set compute everything serial
-        if os.environ.get("N_PEPPER_THREADS", 1) > 1:
+        n_threads = int(os.environ.get("N_PEPPER_THREADS", 1))
-            results = Parallel(n_jobs=os.environ["N_PEPPER_THREADS"])(
+        if n_threads > 1:
+            results = Parallel(n_jobs=n_threads)(
                 delayed(compute_bayes_per_group)(group[1])
                 for group in dataset.group_by("structure_id")
             )

utilities/legacy.py

@@ -64,7 +64,7 @@
 
 import logging
 
-from envipy_additional_information import HalfLife, HalfLifeWS
+from envipy_additional_information import HalfLife, HalfLifeWS, HalfLifeModel
 from envipy_additional_information.information import Interval
 from envipy_additional_information.parsers import (
     AcidityParser,
@@ -473,17 +473,12 @@ def build_additional_information_from_request(request, type_):
 
             comment = get_parameter_or_empty_string(request, "comment")
             source = get_parameter_or_empty_string(request, "source")
-            first_order = get_parameter_or_empty_string(request, "firstOrder")
+            # first_order = get_parameter_or_empty_string(request, "firstOrder")
             model = get_parameter_or_empty_string(request, "model")
             fit = get_parameter_or_empty_string(request, "fit")
 
-            if first_order != "":
+            if model:
-                if model != "":
+                model = HalfLifeModel(model.upper())
-                    raise ValueError("not both, model and firstOrder can be set!")
-                if first_order == "true":
-                    model = "SFO"
-                else:
-                    logger.info("firstOrder is set to false which is not meaningful")
 
             return HalfLife(model=model, fit=fit, comment=comment, dt50=i, source=source)
 
@@ -508,6 +503,10 @@ def build_additional_information_from_request(request, type_):
             comment_ws = get_parameter_or_empty_string(request, "comment_ws")
             source_ws = get_parameter_or_empty_string(request, "source_ws")
             model_ws = get_parameter_or_empty_string(request, "model_ws")
+
+            if model_ws:
+                model_ws = HalfLifeModel(model_ws.upper())
+
             fit_ws = get_parameter_or_empty_string(request, "fit_ws")
 
             dt50_total = IntervalParser.from_string(hl_ws_total)

uv.lock

@@ -894,7 +894,7 @@ provides-extras = ["ms-login", "dev", "pepper-plugin"]
 [[package]]
 name = "envipy-additional-information"
 version = "0.4.2"
-source = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?branch=develop#0a608c85c73a6ef5c38afea87d2b57fb43f01a70" }
+source = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?branch=develop#676dae1c5678539beac637b87e49b9dadfdfd85a" }
 dependencies = [
     { name = "pydantic" },
 ]
@@ -2763,9 +2763,9 @@ dependencies = [
     { name = "typing-extensions", marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
 ]
 wheels = [
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:a47b7986bee3f61ad217d8a8ce24605809ab425baf349f97de758815edd2ef54" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:a47b7986bee3f61ad217d8a8ce24605809ab425baf349f97de758815edd2ef54", upload-time = "2025-10-01T23:35:50Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:fbe2e149c5174ef90d29a5f84a554dfaf28e003cb4f61fa2c8c024c17ec7ca58" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:fbe2e149c5174ef90d29a5f84a554dfaf28e003cb4f61fa2c8c024c17ec7ca58", upload-time = "2025-10-01T23:35:52Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:057efd30a6778d2ee5e2374cd63a63f63311aa6f33321e627c655df60abdd390" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:057efd30a6778d2ee5e2374cd63a63f63311aa6f33321e627c655df60abdd390", upload-time = "2025-10-01T23:35:55Z" },
 ]
 
 [[package]]
@@ -2785,19 +2785,19 @@ dependencies = [
     { name = "typing-extensions", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 wheels = [
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-linux_s390x.whl", hash = "sha256:0e34e276722ab7dd0dffa9e12fe2135a9b34a0e300c456ed7ad6430229404eb5" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-linux_s390x.whl", hash = "sha256:0e34e276722ab7dd0dffa9e12fe2135a9b34a0e300c456ed7ad6430229404eb5", upload-time = "2025-10-01T23:33:41Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:610f600c102386e581327d5efc18c0d6edecb9820b4140d26163354a99cd800d" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:610f600c102386e581327d5efc18c0d6edecb9820b4140d26163354a99cd800d", upload-time = "2025-10-01T23:33:45Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:cb9a8ba8137ab24e36bf1742cb79a1294bd374db570f09fc15a5e1318160db4e" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:cb9a8ba8137ab24e36bf1742cb79a1294bd374db570f09fc15a5e1318160db4e", upload-time = "2025-10-01T23:33:48Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:2be20b2c05a0cce10430cc25f32b689259640d273232b2de357c35729132256d" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:2be20b2c05a0cce10430cc25f32b689259640d273232b2de357c35729132256d", upload-time = "2025-10-01T23:33:52Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_arm64.whl", hash = "sha256:99fc421a5d234580e45957a7b02effbf3e1c884a5dd077afc85352c77bf41434" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_arm64.whl", hash = "sha256:99fc421a5d234580e45957a7b02effbf3e1c884a5dd077afc85352c77bf41434", upload-time = "2025-10-01T23:34:10Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-linux_s390x.whl", hash = "sha256:8b5882276633cf91fe3d2d7246c743b94d44a7e660b27f1308007fdb1bb89f7d" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-linux_s390x.whl", hash = "sha256:8b5882276633cf91fe3d2d7246c743b94d44a7e660b27f1308007fdb1bb89f7d", upload-time = "2025-10-01T23:34:15Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a5064b5e23772c8d164068cc7c12e01a75faf7b948ecd95a0d4007d7487e5f25" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a5064b5e23772c8d164068cc7c12e01a75faf7b948ecd95a0d4007d7487e5f25", upload-time = "2025-10-01T23:34:19Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:8f81dedb4c6076ec325acc3b47525f9c550e5284a18eae1d9061c543f7b6e7de" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:8f81dedb4c6076ec325acc3b47525f9c550e5284a18eae1d9061c543f7b6e7de", upload-time = "2025-10-01T23:34:23Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:e1ee1b2346ade3ea90306dfbec7e8ff17bc220d344109d189ae09078333b0856" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:e1ee1b2346ade3ea90306dfbec7e8ff17bc220d344109d189ae09078333b0856", upload-time = "2025-10-01T23:34:28Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-win_arm64.whl", hash = "sha256:64c187345509f2b1bb334feed4666e2c781ca381874bde589182f81247e61f88" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-win_arm64.whl", hash = "sha256:64c187345509f2b1bb334feed4666e2c781ca381874bde589182f81247e61f88", upload-time = "2025-10-01T23:34:45Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:af81283ac671f434b1b25c95ba295f270e72db1fad48831eb5e4748ff9840041" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:af81283ac671f434b1b25c95ba295f270e72db1fad48831eb5e4748ff9840041", upload-time = "2025-10-01T23:34:50Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:a9dbb6f64f63258bc811e2c0c99640a81e5af93c531ad96e95c5ec777ea46dab" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:a9dbb6f64f63258bc811e2c0c99640a81e5af93c531ad96e95c5ec777ea46dab", upload-time = "2025-10-01T23:34:53Z" },
-    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:6d93a7165419bc4b2b907e859ccab0dea5deeab261448ae9a5ec5431f14c0e64" },
+    { url = "https://download-r2.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:6d93a7165419bc4b2b907e859ccab0dea5deeab261448ae9a5ec5431f14c0e64", upload-time = "2025-10-01T23:34:58Z" },
 ]
 
 [[package]]