API PES

PW interactions
minor
2026-05-07 11:19:30 +02:00 · 2026-05-07 09:07:36 +02:00 · 2026-05-05 13:04:03 +02:00 · 2026-04-22 22:22:07 +02:00 · 2026-04-21 22:53:30 +02:00
9 changed files with 77 additions and 146 deletions
--- a/epdb/legacy_api.py
+++ b/epdb/legacy_api.py
@ -2074,9 +2074,6 @@ 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
@ -1065,9 +1065,52 @@ 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)):
-            pw.update_depths()
+            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)
+
            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
@ -1,56 +0,0 @@
-# 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
@ -2186,56 +2186,6 @@ 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
@ -2539,9 +2539,6 @@ 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.figure import Figure
+        from matplotlib import pyplot as plt
        from scipy import stats

        """
@ -101,8 +101,7 @@ class PepperPrediction(PropertyPrediction):
        mask_red = x > vp

        # Plot
-        fig = Figure(figsize=(9, 5.5))
-        ax = fig.subplots()
+        fig, ax = plt.subplots(figsize=(9, 5.5))
        ax.plot(x, y, color="#1f4e79", lw=2, label="Lognormal PDF")

        if np.any(mask_green):
@ -147,12 +146,13 @@ class PepperPrediction(PropertyPrediction):
        ]
        ax.legend(handles=patches, frameon=True)

-        fig.tight_layout()
+        plt.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
@ -187,9 +187,8 @@ class Pepper:
        groups = [group for group in dataset.group_by("structure_id")]

        # Unless explicitly set compute everything serial
-        n_threads = int(os.environ.get("N_PEPPER_THREADS", 1))
-        if n_threads > 1:
-            results = Parallel(n_jobs=n_threads)(
+        if os.environ.get("N_PEPPER_THREADS", 1) > 1:
+            results = Parallel(n_jobs=os.environ["N_PEPPER_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, HalfLifeModel
+from envipy_additional_information import HalfLife, HalfLifeWS
 from envipy_additional_information.information import Interval
 from envipy_additional_information.parsers import (
    AcidityParser,
@ -473,12 +473,17 @@ 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 model:
-                model = HalfLifeModel(model.upper())
+            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")

            return HalfLife(model=model, fit=fit, comment=comment, dt50=i, source=source)

@ -503,10 +508,6 @@ 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#676dae1c5678539beac637b87e49b9dadfdfd85a" }
+source = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?branch=develop#0a608c85c73a6ef5c38afea87d2b57fb43f01a70" }
 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", 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" },
+    { 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" },
 ]

 [[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", 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" },
+    { 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" },
 ]

 [[package]]
Author	SHA1	Message	Date
Tim Lorsbach	e3876ac945	API PES Some checks failed API CI / api-tests (pull_request) Failing after 14s Details CI / test (pull_request) Failing after 29s Details	2026-05-07 11:19:30 +02:00
Tim Lorsbach	ad1e575e4c	PW interactions Some checks failed CI / test (pull_request) Failing after 15s Details API CI / api-tests (pull_request) Failing after 31s Details	2026-05-07 09:07:36 +02:00
Tim Lorsbach	15c23a2151	minor Some checks failed API CI / api-tests (pull_request) Failing after 15s Details CI / test (pull_request) Failing after 33s Details	2026-05-05 13:04:03 +02:00
Tim Lorsbach	72399b16b3	Wip Some checks failed API CI / api-tests (pull_request) Failing after 14s Details CI / test (pull_request) Failing after 30s Details	2026-04-22 22:22:07 +02:00
Tim Lorsbach	54056c654d	adjusted migration Some checks failed API CI / api-tests (pull_request) Failing after 21s Details CI / test (pull_request) Failing after 22s Details 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	2026-04-21 22:53:30 +02:00