API PES

PW interactions
minor
2026-05-12 13:16:39 +02:00 · 2026-05-12 13:16:39 +02:00 · 2026-05-12 13:16:39 +02:00 · 2026-05-12 13:16:39 +02:00 · 2026-05-12 13:16:39 +02:00 · 2026-05-12 21:04:56 +12:00
9 changed files with 146 additions and 77 deletions
--- a/epdb/legacy_api.py
+++ b/epdb/legacy_api.py
@ -2074,6 +2074,9 @@ 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!"}
--- a/epdb/logic.py
+++ b/epdb/logic.py
@ -1065,52 +1065,9 @@ 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)
-            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)
-
+            pw.update_depths()
            print(f"{p + 1}/{total_pws} fixed.", end="\r")

        return pack
--- a/epdb/migrations/0025_auto_20260511_2025.py
+++ b/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,
+    "SFOÂ²": 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),
+    ]
--- a/epdb/models.py
+++ b/epdb/models.py
@ -2186,6 +2186,56 @@ 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(
--- a/epdb/views.py
+++ b/epdb/views.py
@ -2539,6 +2539,9 @@ 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:
--- a/pepper/init.py
+++ b/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
--- a/pepper/impl/pepper.py
+++ b/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:
-            results = Parallel(n_jobs=os.environ["N_PEPPER_THREADS"])(
+        n_threads = int(os.environ.get("N_PEPPER_THREADS", 1))
+        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")
            )
--- a/utilities/legacy.py
+++ b/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 != "":
-                    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")
+            if model:
+                model = HalfLifeModel(model.upper())

            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)
--- a/uv.lock
+++ b/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-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:fbe2e149c5174ef90d29a5f84a554dfaf28e003cb4f61fa2c8c024c17ec7ca58" },
-    { 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-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", 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", 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-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_x86_64.whl", hash = "sha256:cb9a8ba8137ab24e36bf1742cb79a1294bd374db570f09fc15a5e1318160db4e" },
-    { 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_arm64.whl", hash = "sha256:99fc421a5d234580e45957a7b02effbf3e1c884a5dd077afc85352c77bf41434" },
-    { 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-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_x86_64.whl", hash = "sha256:8f81dedb4c6076ec325acc3b47525f9c550e5284a18eae1d9061c543f7b6e7de" },
-    { 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_arm64.whl", hash = "sha256:64c187345509f2b1bb334feed4666e2c781ca381874bde589182f81247e61f88" },
-    { 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_x86_64.whl", hash = "sha256:a9dbb6f64f63258bc811e2c0c99640a81e5af93c531ad96e95c5ec777ea46dab" },
-    { 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-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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", upload-time = "2025-10-01T23:34:58Z" },
 ]

 [[package]]
Author	SHA1	Message	Date
Tim Lorsbach	3b5d299128	API PES Some checks failed CI / test (pull_request) Failing after 14s Details API CI / api-tests (pull_request) Failing after 20s Details	2026-05-12 13:16:39 +02:00
Tim Lorsbach	38e901a51e	PW interactions	2026-05-12 13:16:39 +02:00
Tim Lorsbach	c92fccaf8e	minor	2026-05-12 13:16:39 +02:00
Tim Lorsbach	5eb3ebac89	Wip	2026-05-12 13:16:39 +02:00
Tim Lorsbach	d9530ce755	adjusted migration Initial bayer app Show Pack Classification Adjusted docker compose to bayer specifics Adjusted Dockerfile for Bayer Adding secret flags to group, add secret pools to packages Adjusted View for Package creation Prep configs, added Package Create Modal wip More on PES wip wip	2026-05-12 13:16:39 +02:00
jebus	1e43c298d2	[Fix] Simplify Depth adjustment (#386 ) Co-authored-by: Tim Lorsbach <tim@lorsba.ch> Reviewed-on: enviPath/enviPy#386	2026-05-12 21:04:56 +12:00
jebus	b39fc7eaf8	[Fix] Update Node depth when adding new Edges to a Pathway (#384 ) Co-authored-by: Tim Lorsbach <tim@lorsba.ch> Reviewed-on: enviPath/enviPy#384	2026-05-12 09:40:35 +12:00
jebus	a2fc9f72cb	[Feature] Make use of HalfLifeModel Enum (#383 ) Co-authored-by: Tim Lorsbach <tim@lorsba.ch> Reviewed-on: enviPath/enviPy#383	2026-05-12 09:23:56 +12:00
Tobias O	734b02767e	[Fix] Update plotting imports and thread handling in Pepper class (#382 ) - plt.subplot does not work reliably with async/ threads. - Bug in thread run that would fail with env set (string to number) Reviewed-on: enviPath/enviPy#382 Co-authored-by: Tobias O <tobias.olenyi@envipath.com> Co-committed-by: Tobias O <tobias.olenyi@envipath.com>	2026-05-12 06:43:26 +12:00