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[Chore] Linted Files (#150)

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Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: enviPath/enviPy#150
This commit is contained in:
jebus
2025-10-09 07:25:13 +13:00
parent 22f0bbe10b
commit afeb56622c
50 changed files with 5616 additions and 4408 deletions
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168
epdb/management/commands/bootstrap.py
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@ -5,32 +5,49 @@ from django.core.management.base import BaseCommand
from django.db import transaction
from epdb.logic import UserManager, GroupManager, PackageManager, SettingManager
from epdb.models import UserSettingPermission, MLRelativeReasoning, EnviFormer, Permission, User, ExternalDatabase
from epdb.models import (
UserSettingPermission,
MLRelativeReasoning,
EnviFormer,
Permission,
User,
ExternalDatabase,
)
class Command(BaseCommand):
def create_users(self):
# Anonymous User
if not User.objects.filter(email='anon@envipath.com').exists():
anon = UserManager.create_user("anonymous", "anon@envipath.com", "SuperSafe",
is_active=True, add_to_group=False, set_setting=False)
if not User.objects.filter(email="anon@envipath.com").exists():
anon = UserManager.create_user(
"anonymous",
"anon@envipath.com",
"SuperSafe",
is_active=True,
add_to_group=False,
set_setting=False,
)
else:
anon = User.objects.get(email='anon@envipath.com')
anon = User.objects.get(email="anon@envipath.com")
# Admin User
if not User.objects.filter(email='admin@envipath.com').exists():
admin = UserManager.create_user("admin", "admin@envipath.com", "SuperSafe",
is_active=True, add_to_group=False, set_setting=False)
if not User.objects.filter(email="admin@envipath.com").exists():
admin = UserManager.create_user(
"admin",
"admin@envipath.com",
"SuperSafe",
is_active=True,
add_to_group=False,
set_setting=False,
)
admin.is_staff = True
admin.is_superuser = True
admin.save()
else:
admin = User.objects.get(email='admin@envipath.com')
admin = User.objects.get(email="admin@envipath.com")
# System Group
g = GroupManager.create_group(admin, 'enviPath Users', 'All enviPath Users')
g = GroupManager.create_group(admin, "enviPath Users", "All enviPath Users")
g.public = True
g.save()
@ -43,14 +60,20 @@ class Command(BaseCommand):
admin.default_group = g
admin.save()
if not User.objects.filter(email='user0@envipath.com').exists():
user0 = UserManager.create_user("user0", "user0@envipath.com", "SuperSafe",
is_active=True, add_to_group=False, set_setting=False)
if not User.objects.filter(email="user0@envipath.com").exists():
user0 = UserManager.create_user(
"user0",
"user0@envipath.com",
"SuperSafe",
is_active=True,
add_to_group=False,
set_setting=False,
)
user0.is_staff = True
user0.is_superuser = True
user0.save()
else:
user0 = User.objects.get(email='user0@envipath.com')
user0 = User.objects.get(email="user0@envipath.com")
g.user_member.add(user0)
g.save()
@ -61,18 +84,20 @@ class Command(BaseCommand):
return anon, admin, g, user0
def import_package(self, data, owner):
return PackageManager.import_legacy_package(data, owner, keep_ids=True, add_import_timestamp=False, trust_reviewed=True)
return PackageManager.import_legacy_package(
data, owner, keep_ids=True, add_import_timestamp=False, trust_reviewed=True
)
def create_default_setting(self, owner, packages):
s = SettingManager.create_setting(
owner,
name='Global Default Setting',
description='Global Default Setting containing BBD Rules and Max 30 Nodes and Max Depth of 8',
name="Global Default Setting",
description="Global Default Setting containing BBD Rules and Max 30 Nodes and Max Depth of 8",
max_nodes=30,
max_depth=5,
rule_packages=packages,
model=None,
model_threshold=None
model_threshold=None,
)
return s
@ -84,54 +109,51 @@ class Command(BaseCommand):
"""
databases = [
{
'name': 'PubChem Compound',
'full_name': 'PubChem Compound Database',
'description': 'Chemical database of small organic molecules',
'base_url': 'https://pubchem.ncbi.nlm.nih.gov',
'url_pattern': 'https://pubchem.ncbi.nlm.nih.gov/compound/{id}'
"name": "PubChem Compound",
"full_name": "PubChem Compound Database",
"description": "Chemical database of small organic molecules",
"base_url": "https://pubchem.ncbi.nlm.nih.gov",
"url_pattern": "https://pubchem.ncbi.nlm.nih.gov/compound/{id}",
},
{
'name': 'PubChem Substance',
'full_name': 'PubChem Substance Database',
'description': 'Database of chemical substances',
'base_url': 'https://pubchem.ncbi.nlm.nih.gov',
'url_pattern': 'https://pubchem.ncbi.nlm.nih.gov/substance/{id}'
"name": "PubChem Substance",
"full_name": "PubChem Substance Database",
"description": "Database of chemical substances",
"base_url": "https://pubchem.ncbi.nlm.nih.gov",
"url_pattern": "https://pubchem.ncbi.nlm.nih.gov/substance/{id}",
},
{
'name': 'ChEBI',
'full_name': 'Chemical Entities of Biological Interest',
'description': 'Dictionary of molecular entities',
'base_url': 'https://www.ebi.ac.uk/chebi',
'url_pattern': 'https://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI:{id}'
"name": "ChEBI",
"full_name": "Chemical Entities of Biological Interest",
"description": "Dictionary of molecular entities",
"base_url": "https://www.ebi.ac.uk/chebi",
"url_pattern": "https://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI:{id}",
},
{
'name': 'RHEA',
'full_name': 'RHEA Reaction Database',
'description': 'Comprehensive resource of biochemical reactions',
'base_url': 'https://www.rhea-db.org',
'url_pattern': 'https://www.rhea-db.org/rhea/{id}'
"name": "RHEA",
"full_name": "RHEA Reaction Database",
"description": "Comprehensive resource of biochemical reactions",
"base_url": "https://www.rhea-db.org",
"url_pattern": "https://www.rhea-db.org/rhea/{id}",
},
{
'name': 'KEGG Reaction',
'full_name': 'KEGG Reaction Database',
'description': 'Database of biochemical reactions',
'base_url': 'https://www.genome.jp',
'url_pattern': 'https://www.genome.jp/entry/{id}'
"name": "KEGG Reaction",
"full_name": "KEGG Reaction Database",
"description": "Database of biochemical reactions",
"base_url": "https://www.genome.jp",
"url_pattern": "https://www.genome.jp/entry/{id}",
},
{
'name': 'UniProt',
'full_name': 'MetaCyc Metabolic Pathway Database',
'description': 'UniProt is a freely accessible database of protein sequence and functional information',
'base_url': 'https://www.uniprot.org',
'url_pattern': 'https://www.uniprot.org/uniprotkb?query="{id}"'
}
"name": "UniProt",
"full_name": "MetaCyc Metabolic Pathway Database",
"description": "UniProt is a freely accessible database of protein sequence and functional information",
"base_url": "https://www.uniprot.org",
"url_pattern": 'https://www.uniprot.org/uniprotkb?query="{id}"',
},
]
for db_info in databases:
ExternalDatabase.objects.get_or_create(
name=db_info['name'],
defaults=db_info
)
ExternalDatabase.objects.get_or_create(name=db_info["name"], defaults=db_info)
@transaction.atomic
def handle(self, *args, **options):
@ -142,20 +164,24 @@ class Command(BaseCommand):
# Import Packages
packages = [
'EAWAG-BBD.json',
'EAWAG-SOIL.json',
'EAWAG-SLUDGE.json',
'EAWAG-SEDIMENT.json',
"EAWAG-BBD.json",
"EAWAG-SOIL.json",
"EAWAG-SLUDGE.json",
"EAWAG-SEDIMENT.json",
]
mapping = {}
for p in packages:
print(f"Importing {p}...")
package_data = json.loads(open(s.BASE_DIR / 'fixtures' / 'packages' / '2025-07-18' / p, encoding='utf-8').read())
package_data = json.loads(
open(
s.BASE_DIR / "fixtures" / "packages" / "2025-07-18" / p, encoding="utf-8"
).read()
)
imported_package = self.import_package(package_data, admin)
mapping[p.replace('.json', '')] = imported_package
mapping[p.replace(".json", "")] = imported_package
setting = self.create_default_setting(admin, [mapping['EAWAG-BBD']])
setting = self.create_default_setting(admin, [mapping["EAWAG-BBD"]])
setting.public = True
setting.save()
setting.make_global_default()
@ -171,26 +197,28 @@ class Command(BaseCommand):
usp.save()
# Create Model Package
pack = PackageManager.create_package(admin, "Public Prediction Models",
"Package to make Prediction Models publicly available")
pack = PackageManager.create_package(
admin,
"Public Prediction Models",
"Package to make Prediction Models publicly available",
)
pack.reviewed = True
pack.save()
# Create RR
ml_model = MLRelativeReasoning.create(
package=pack,
rule_packages=[mapping['EAWAG-BBD']],
data_packages=[mapping['EAWAG-BBD']],
rule_packages=[mapping["EAWAG-BBD"]],
data_packages=[mapping["EAWAG-BBD"]],
eval_packages=[],
threshold=0.5,
name='ECC - BBD - T0.5',
description='ML Relative Reasoning',
name="ECC - BBD - T0.5",
description="ML Relative Reasoning",
)
ml_model.build_dataset()
ml_model.build_model()
# ml_model.evaluate_model()
# If available, create EnviFormerModel
if s.ENVIFORMER_PRESENT:
enviFormer_model = EnviFormer.create(pack, 'EnviFormer - T0.5', 'EnviFormer Model with Threshold 0.5', 0.5)
EnviFormer.create(pack, "EnviFormer - T0.5", "EnviFormer Model with Threshold 0.5", 0.5)