forked from enviPath/enviPy
work towards #120
This commit is contained in:
Liam Brydon
@ -2886,7 +2886,7 @@ class ApplicabilityDomain(EnviPathModel):
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rule_idx, probs, neighbour_datasets
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)
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neighbours_per_rule[rule_idx] = [
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CompoundStructure.objects.get(uuid=ds[1].structure_id())
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CompoundStructure.objects.get(uuid=ds.structure_id(1))
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for ds in neighbour_datasets
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]
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neighbor_probs_per_rule[rule_idx] = [
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@ -27,10 +27,11 @@ dependencies = [
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"scikit-learn>=1.6.1",
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"sentry-sdk[django]>=2.32.0",
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"setuptools>=80.8.0",
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"polars==1.35.1",
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]
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[tool.uv.sources]
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enviformer = { git = "ssh://git@git.envipath.com/enviPath/enviformer.git", rev = "v0.1.2" }
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enviformer = { git = "ssh://git@git.envipath.com/enviPath/enviformer.git", branch = "enhancement/dataset_support" }
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envipy-plugins = { git = "ssh://git@git.envipath.com/enviPath/enviPy-plugins.git", rev = "v0.1.0" }
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envipy-additional-information = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git", rev = "v0.1.7"}
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envipy-ambit = { git = "ssh://git@git.envipath.com/enviPath/enviPy-ambit.git" }
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@ -1,7 +1,7 @@
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from django.test import TestCase
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from epdb.logic import PackageManager
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from epdb.models import Reaction, Compound, User, Rule
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from epdb.models import Reaction, Compound, User, Rule, Package
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from utilities.ml import RuleBasedDataset
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@ -41,12 +41,88 @@ class DatasetTest(TestCase):
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super(DatasetTest, cls).setUpClass()
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cls.user = User.objects.get(username="anonymous")
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cls.package = PackageManager.create_package(cls.user, "Anon Test Package", "No Desc")
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cls.BBD_SUBSET = Package.objects.get(name="Fixtures")
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def test_smoke(self):
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def test_generate_dataset(self):
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"""Test generating dataset does not crash"""
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self.generate_dataset()
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def test_indexing(self):
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"""Test indexing a few different ways to check for crashes"""
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ds, reactions, rules = self.generate_dataset()
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print(ds[5])
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print(ds[2, 5])
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print(ds[3:6, 2:8])
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print(ds[:2, "structure_id"])
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def test_add_rows(self):
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"""Test adding one row and adding multiple rows"""
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ds, reactions, rules = self.generate_dataset()
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ds.add_row(list(ds.df.row(1)))
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ds.add_rows([list(ds.df.row(i)) for i in range(5)])
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def test_times_triggered(self):
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"""Check getting times triggered for a rule id"""
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ds, reactions, rules = self.generate_dataset()
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print(ds.times_triggered(rules[0].uuid))
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def test_block_indices(self):
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"""Test the usages of _block_indices"""
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ds, reactions, rules = self.generate_dataset()
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print(ds.struct_features())
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print(ds.triggered())
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print(ds.observed())
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def test_structure_id(self):
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"""Check getting a structure id from row index"""
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ds, reactions, rules = self.generate_dataset()
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print(ds.structure_id(0))
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def test_x(self):
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"""Test getting X portion of the dataframe"""
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ds, reactions, rules = self.generate_dataset()
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print(ds.X().df.head())
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def test_trig(self):
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"""Test getting the triggered portion of the dataframe"""
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ds, reactions, rules = self.generate_dataset()
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print(ds.trig().df.head())
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def test_y(self):
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"""Test getting the Y portion of the dataframe"""
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ds, reactions, rules = self.generate_dataset()
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print(ds.y().df.head())
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def test_classification_dataset(self):
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"""Test making the classification dataset"""
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ds, reactions, rules = self.generate_dataset()
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compounds = [c.default_structure for c in Compound.objects.filter(package=self.BBD_SUBSET)]
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class_ds, products = ds.classification_dataset(compounds, rules)
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print(class_ds.df.head(5))
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print(products[:5])
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def test_to_arff(self):
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"""Test exporting the arff version of the dataset"""
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ds, reactions, rules = self.generate_dataset()
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ds.to_arff("dataset_arff_test.arff")
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def test_save_load(self):
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"""Test saving and loading dataset"""
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ds, reactions, rules = self.generate_dataset()
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def test_dataset_example(self):
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"""Test with a concrete example checking dataset size"""
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reactions = [r for r in Reaction.objects.filter(package=self.package)]
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applicable_rules = [self.rule1]
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ds = RuleBasedDataset.generate_dataset(reactions, applicable_rules)
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self.assertEqual(len(ds.y()), 1)
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self.assertEqual(sum(ds.y()[0]), 1)
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self.assertEqual(ds.y().df.item(), 1)
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def generate_dataset(self):
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"""Generate a RuleBasedDataset from test package data"""
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reactions = [r for r in Reaction.objects.filter(package=self.BBD_SUBSET)]
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applicable_rules = [r for r in Rule.objects.filter(package=self.BBD_SUBSET)]
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ds = RuleBasedDataset.generate_dataset(reactions, applicable_rules)
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return ds, reactions, applicable_rules
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335
utilities/ml.py
335
utilities/ml.py
@ -30,42 +30,47 @@ if TYPE_CHECKING:
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class Dataset(ABC):
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def __init__(self, columns: List[str] = None, data: List[List[str | int | float]] | pl.DataFrame = None):
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if isinstance(data, pl.DataFrame):
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if isinstance(data, pl.DataFrame): # Allows for re-creation of self in cases like indexing with __getitem__
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self.df = data
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else:
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# Build either an empty dataframe with columns or fill it with list of list data
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if data is not None and len(columns) != len(data[0]):
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raise ValueError(f"Header and Data are not aligned {len(columns)} vs. {len(data[0])}")
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raise ValueError(f"Header and Data are not aligned {len(columns)} columns vs. {len(data[0])} columns")
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if columns is None:
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raise ValueError("Columns can't be None if data is not already a DataFrame")
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self.df = pl.DataFrame(data=data, schema=columns)
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self.df = pl.DataFrame(data=data, schema=columns, orient="row", infer_schema_length=None)
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def add_rows(self, rows: List[List[str | int | float]]):
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"""Add rows to the dataset. Extends the polars dataframe stored in self"""
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if len(self.columns) != len(rows[0]):
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raise ValueError(f"Header and Data are not aligned {len(self.columns)} vs. {len(rows[0])}")
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new_rows = pl.DataFrame(data=rows, schema=self.columns)
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raise ValueError(f"Header and Data are not aligned {len(self.columns)} columns vs. {len(rows[0])} columns")
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new_rows = pl.DataFrame(data=rows, schema=self.columns, orient="row", infer_schema_length=None)
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self.df.extend(new_rows)
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def add_row(self, row: List[str | int | float]):
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"""See add_rows"""
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self.add_rows([row])
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def _block_indices(self, prefix) -> Tuple[int, int]:
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"""Find the start and end indexes in column labels that has the prefix"""
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indices: List[int] = []
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for i, feature in enumerate(self.columns):
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if feature.startswith(prefix):
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indices.append(i)
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return min(indices), max(indices)
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return min(indices, default=None), max(indices, default=None)
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@property
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def columns(self) -> List[str]:
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"""Use the polars dataframe columns"""
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return self.df.columns
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@abstractmethod
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def X(self):
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def X(self, **kwargs):
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pass
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@abstractmethod
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def y(self):
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def y(self, **kwargs):
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pass
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@staticmethod
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@ -73,11 +78,26 @@ class Dataset(ABC):
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def generate_dataset(reactions, *args, **kwargs):
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pass
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def at(self, position: int) -> RuleBasedDataset:
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return RuleBasedDataset(self.columns, self.num_labels, self.df[position])
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def at(self, position: int) -> Dataset:
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"""See __getitem__"""
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return self[position]
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def limit(self, limit: int) -> Dataset:
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"""See __getitem__"""
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return self[:limit]
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def __iter__(self):
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return (self.at(i) for i, _ in enumerate(self.data))
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"""Use polars iter_rows for iterating over the dataset"""
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return self.df.iter_rows()
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def __getitem__(self, item):
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"""Item is passed to polars allowing for advanced indexing.
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See https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.__getitem__.html#polars.DataFrame.__getitem__"""
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res = self.df[item]
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if isinstance(res, pl.DataFrame): # If we get a dataframe back from indexing make new self with res dataframe
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return self.__class__(data=self.df[item])
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else: # If we don't get a dataframe back (likely base type, int, str, float etc.) return the item
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return res
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def save(self, path: "Path"):
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import pickle
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@ -86,35 +106,61 @@ class Dataset(ABC):
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pickle.dump(self, fh)
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@staticmethod
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def load(path: "str | Path") -> "RuleBasedDataset":
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def load(path: "str | Path") -> "Dataset":
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import pickle
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return pickle.load(open(path, "rb"))
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def __repr__(self):
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return (
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f"<{self.__class__.__name__} #rows={len(self.df)} #cols={len(self.columns)}>"
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)
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class NewRuleBasedDataset(Dataset):
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def __init__(self, num_labels, columns=None, data=None):
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def __len__(self):
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return len(self.df)
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class RuleBasedDataset(Dataset):
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def __init__(self, num_labels=None, columns=None, data=None):
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super().__init__(columns, data)
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self.num_labels: int = num_labels
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self.num_labels: int = num_labels if num_labels else sum([1 for c in self.columns if "trig_" in c])
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self.num_features: int = len(self.columns) - self.num_labels
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self._struct_features: Tuple[int, int] = self._block_indices("feature_")
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self._triggered: Tuple[int, int] = self._block_indices("trig_")
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self._observed: Tuple[int, int] = self._block_indices("obs_")
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def times_triggered(self, rule_uuid) -> int:
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return self.df.filter(pl.col(f"trig_{rule_uuid}") == 1).height
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def struct_features(self) -> Tuple[int, int]:
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return self._block_indices("feature_")
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return self._struct_features
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def triggered(self) -> Tuple[int, int]:
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return self._block_indices("trig_")
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return self._triggered
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def observed(self) -> Tuple[int, int]:
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return self._block_indices("obs_")
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return self._observed
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def X(self):
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pass
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def structure_id(self, index: int):
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return self.df.item(index, "structure_id")
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def y(self):
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pass
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def X(self, exclude_id_col=True, na_replacement=0):
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res = self[:, 1 if exclude_id_col else 0: len(self.columns) - self.num_labels]
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if na_replacement is not None:
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res.df = res.df.fill_null(na_replacement)
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return res
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def trig(self, na_replacement=0):
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res = self[:, self._triggered[0]: self._triggered[1]]
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if na_replacement is not None:
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res.df = res.df.fill_null(na_replacement)
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return res
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def y(self, na_replacement=0):
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res = self[:, len(self.columns) - self.num_labels:]
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if na_replacement is not None:
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res.df = res.df.fill_null(na_replacement)
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return res
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@staticmethod
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def generate_dataset(reactions, applicable_rules, educts_only=True):
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@ -178,7 +224,6 @@ class NewRuleBasedDataset(Dataset):
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[f"feature_{i}" for i, _ in enumerate(FormatConverter.maccs(compounds[0].smiles))] +
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[f"trig_{r.uuid}" for r in applicable_rules] +
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[f"obs_{r.uuid}" for r in applicable_rules])
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ds = NewRuleBasedDataset(len(applicable_rules), ds_columns)
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rows = []
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for i, comp in enumerate(compounds):
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@ -201,77 +246,18 @@ class NewRuleBasedDataset(Dataset):
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else:
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obs.append(0)
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rows.append([str(comp.uuid)] + feat + trig + obs)
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ds.add_rows(rows)
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ds = RuleBasedDataset(len(applicable_rules), ds_columns, data=rows)
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return ds
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def __getitem__(self, item):
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pass
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class RuleBasedDataset(Dataset):
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def __init__(
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self, columns: List[str], num_labels: int, data: List[List[str | int | float]] = None
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):
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self.columns: List[str] = columns
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self.num_labels: int = num_labels
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if data is None:
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self.data: List[List[str | int | float]] = list()
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else:
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self.data = data
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self.num_features: int = len(columns) - self.num_labels
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self._struct_features: Tuple[int, int] = self._block_indices("feature_")
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self._triggered: Tuple[int, int] = self._block_indices("trig_")
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self._observed: Tuple[int, int] = self._block_indices("obs_")
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def _block_indices(self, prefix) -> Tuple[int, int]:
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indices: List[int] = []
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for i, feature in enumerate(self.columns):
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if feature.startswith(prefix):
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indices.append(i)
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return min(indices), max(indices)
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def structure_id(self):
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return self.data[0][0]
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def add_row(self, row: List[str | int | float]):
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if len(self.columns) != len(row):
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raise ValueError(f"Header and Data are not aligned {len(self.columns)} vs. {len(row)}")
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self.data.append(row)
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def times_triggered(self, rule_uuid) -> int:
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idx = self.columns.index(f"trig_{rule_uuid}")
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times_triggered = 0
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for row in self.data:
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if row[idx] == 1:
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times_triggered += 1
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return times_triggered
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def struct_features(self) -> Tuple[int, int]:
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return self._struct_features
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def triggered(self) -> Tuple[int, int]:
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return self._triggered
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def observed(self) -> Tuple[int, int]:
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return self._observed
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def at(self, position: int) -> RuleBasedDataset:
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return RuleBasedDataset(self.columns, self.num_labels, [self.data[position]])
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def limit(self, limit: int) -> RuleBasedDataset:
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return RuleBasedDataset(self.columns, self.num_labels, self.data[:limit])
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def classification_dataset(
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self, structures: List[str | "CompoundStructure"], applicable_rules: List["Rule"]
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) -> Tuple[RuleBasedDataset, List[List[PredictionResult]]]:
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classify_data = []
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classify_products = []
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ds_columns = (["structure_id"] +
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[f"feature_{i}" for i, _ in enumerate(FormatConverter.maccs(structures[0].smiles))] +
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[f"trig_{r.uuid}" for r in applicable_rules] +
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[f"obs_{r.uuid}" for r in applicable_rules])
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for struct in structures:
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if isinstance(struct, str):
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struct_id = None
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@ -296,171 +282,8 @@ class RuleBasedDataset(Dataset):
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classify_data.append([struct_id] + features + trig + ([-1] * len(trig)))
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classify_products.append(prods)
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return RuleBasedDataset(
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columns=self.columns, num_labels=self.num_labels, data=classify_data
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), classify_products
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@staticmethod
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def generate_dataset(
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reactions: List["Reaction"], applicable_rules: List["Rule"], educts_only: bool = True
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) -> RuleBasedDataset:
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_structures = set()
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for r in reactions:
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for e in r.educts.all():
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_structures.add(e)
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if not educts_only:
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for e in r.products:
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_structures.add(e)
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compounds = sorted(_structures, key=lambda x: x.url)
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triggered: Dict[str, Set[str]] = defaultdict(set)
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observed: Set[str] = set()
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# Apply rules on collected compounds and store tps
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for i, comp in enumerate(compounds):
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logger.debug(f"{i + 1}/{len(compounds)}...")
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for rule in applicable_rules:
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product_sets = rule.apply(comp.smiles)
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if len(product_sets) == 0:
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continue
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key = f"{rule.uuid} + {comp.uuid}"
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if key in triggered:
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logger.info(f"{key} already present. Duplicate reaction?")
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for prod_set in product_sets:
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for smi in prod_set:
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try:
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smi = FormatConverter.standardize(smi, remove_stereo=True)
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except Exception:
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# :shrug:
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logger.debug(f"Standardizing SMILES failed for {smi}")
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pass
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triggered[key].add(smi)
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for i, r in enumerate(reactions):
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logger.debug(f"{i + 1}/{len(reactions)}...")
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if len(r.educts.all()) != 1:
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logger.debug(f"Skipping {r.url} as it has {len(r.educts.all())} substrates!")
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continue
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for comp in r.educts.all():
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for rule in applicable_rules:
|
||||
key = f"{rule.uuid} + {comp.uuid}"
|
||||
|
||||
if key not in triggered:
|
||||
continue
|
||||
|
||||
# standardize products from reactions for comparison
|
||||
standardized_products = []
|
||||
for cs in r.products.all():
|
||||
smi = cs.smiles
|
||||
|
||||
try:
|
||||
smi = FormatConverter.standardize(smi, remove_stereo=True)
|
||||
except Exception as e:
|
||||
# :shrug:
|
||||
logger.debug(f"Standardizing SMILES failed for {smi}")
|
||||
pass
|
||||
|
||||
standardized_products.append(smi)
|
||||
|
||||
if len(set(standardized_products).difference(triggered[key])) == 0:
|
||||
observed.add(key)
|
||||
else:
|
||||
pass
|
||||
|
||||
ds = None
|
||||
|
||||
for i, comp in enumerate(compounds):
|
||||
# Features
|
||||
feat = FormatConverter.maccs(comp.smiles)
|
||||
trig = []
|
||||
obs = []
|
||||
|
||||
for rule in applicable_rules:
|
||||
key = f"{rule.uuid} + {comp.uuid}"
|
||||
|
||||
# Check triggered
|
||||
if key in triggered:
|
||||
trig.append(1)
|
||||
else:
|
||||
trig.append(0)
|
||||
|
||||
# Check obs
|
||||
if key in observed:
|
||||
obs.append(1)
|
||||
elif key not in triggered:
|
||||
obs.append(None)
|
||||
else:
|
||||
obs.append(0)
|
||||
|
||||
if ds is None:
|
||||
header = (
|
||||
["structure_id"]
|
||||
+ [f"feature_{i}" for i, _ in enumerate(feat)]
|
||||
+ [f"trig_{r.uuid}" for r in applicable_rules]
|
||||
+ [f"obs_{r.uuid}" for r in applicable_rules]
|
||||
)
|
||||
ds = RuleBasedDataset(header, len(applicable_rules))
|
||||
|
||||
ds.add_row([str(comp.uuid)] + feat + trig + obs)
|
||||
|
||||
return ds
|
||||
|
||||
def X(self, exclude_id_col=True, na_replacement=0):
|
||||
res = self.__getitem__(
|
||||
(slice(None), slice(1 if exclude_id_col else 0, len(self.columns) - self.num_labels))
|
||||
)
|
||||
if na_replacement is not None:
|
||||
res = [[x if x is not None else na_replacement for x in row] for row in res]
|
||||
return res
|
||||
|
||||
def trig(self, na_replacement=0):
|
||||
res = self.__getitem__((slice(None), slice(self._triggered[0], self._triggered[1])))
|
||||
if na_replacement is not None:
|
||||
res = [[x if x is not None else na_replacement for x in row] for row in res]
|
||||
return res
|
||||
|
||||
def y(self, na_replacement=0):
|
||||
res = self.__getitem__((slice(None), slice(len(self.columns) - self.num_labels, None)))
|
||||
if na_replacement is not None:
|
||||
res = [[x if x is not None else na_replacement for x in row] for row in res]
|
||||
return res
|
||||
|
||||
def __getitem__(self, key):
|
||||
if not isinstance(key, tuple):
|
||||
raise TypeError("Dataset must be indexed with dataset[rows, columns]")
|
||||
|
||||
row_key, col_key = key
|
||||
|
||||
# Normalize rows
|
||||
if isinstance(row_key, int):
|
||||
rows = [self.data[row_key]]
|
||||
else:
|
||||
rows = self.data[row_key]
|
||||
|
||||
# Normalize columns
|
||||
if isinstance(col_key, int):
|
||||
res = [row[col_key] for row in rows]
|
||||
else:
|
||||
res = [
|
||||
[row[i] for i in range(*col_key.indices(len(row)))]
|
||||
if isinstance(col_key, slice)
|
||||
else [row[i] for i in col_key]
|
||||
for row in rows
|
||||
]
|
||||
|
||||
return res
|
||||
ds = RuleBasedDataset(len(applicable_rules), ds_columns, data=classify_data)
|
||||
return ds, classify_products
|
||||
|
||||
def to_arff(self, path: "Path"):
|
||||
arff = f"@relation 'enviPy-dataset: -C {self.num_labels}'\n"
|
||||
@ -472,7 +295,7 @@ class RuleBasedDataset(Dataset):
|
||||
arff += f"@attribute {c} {{0,1}}\n"
|
||||
|
||||
arff += "\n@data\n"
|
||||
for d in self.data:
|
||||
for d in self:
|
||||
ys = ",".join([str(v if v is not None else "?") for v in d[-self.num_labels :]])
|
||||
xs = ",".join([str(v if v is not None else "?") for v in d[: self.num_features]])
|
||||
arff += f"{ys},{xs}\n"
|
||||
@ -481,14 +304,10 @@ class RuleBasedDataset(Dataset):
|
||||
fh.write(arff)
|
||||
fh.flush()
|
||||
|
||||
def __repr__(self):
|
||||
return (
|
||||
f"<Dataset #rows={len(self.data)} #cols={len(self.columns)} #labels={self.num_labels}>"
|
||||
)
|
||||
|
||||
|
||||
class EnviFormerDataset(Dataset):
|
||||
def __init__(self, educts, products):
|
||||
super().__init__()
|
||||
assert len(educts) == len(products), "Can't have unequal length educts and products"
|
||||
|
||||
@staticmethod
|
||||
|
||||
115
uv.lock
generated
115
uv.lock
generated
@ -1,5 +1,5 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
revision = 2
|
||||
requires-python = ">=3.12"
|
||||
|
||||
[[package]]
|
||||
@ -176,6 +176,19 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/c9/af/0dcccc7fdcdf170f9a1585e5e96b6fb0ba1749ef6be8c89a6202284759bd/celery-5.5.3-py3-none-any.whl", hash = "sha256:0b5761a07057acee94694464ca482416b959568904c9dfa41ce8413a7d65d525", size = 438775, upload-time = "2025-06-01T11:08:09.94Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "celery-stubs"
|
||||
version = "0.1.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "mypy" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/98/14/b853ada8706a3a301396566b6dd405d1cbb24bff756236a12a01dbe766a4/celery-stubs-0.1.3.tar.gz", hash = "sha256:0fb5345820f8a2bd14e6ffcbef2d10181e12e40f8369f551d7acc99d8d514919", size = 46583, upload-time = "2023-02-10T02:20:11.837Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1c/7a/4ab2347d13f1f59d10a7337feb9beb002664119f286036785284c6bec150/celery_stubs-0.1.3-py3-none-any.whl", hash = "sha256:dfb9ad27614a8af028b2055bb4a4ae99ca5e9a8d871428a506646d62153218d7", size = 89085, upload-time = "2023-02-10T02:20:09.409Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.10.5"
|
||||
@ -525,7 +538,7 @@ wheels = [
|
||||
[[package]]
|
||||
name = "enviformer"
|
||||
version = "0.1.0"
|
||||
source = { git = "ssh://git@git.envipath.com/enviPath/enviformer.git?rev=v0.1.2#3f28f60cfa1df814cf7559303b5130933efa40ae" }
|
||||
source = { git = "ssh://git@git.envipath.com/enviPath/enviformer.git?branch=enhancement%2Fdataset_support#8edc3f30c46749af30c033a4faf9abe062b47970" }
|
||||
dependencies = [
|
||||
{ name = "joblib" },
|
||||
{ name = "lightning" },
|
||||
@ -546,7 +559,6 @@ dependencies = [
|
||||
{ name = "django-ninja" },
|
||||
{ name = "django-oauth-toolkit" },
|
||||
{ name = "django-polymorphic" },
|
||||
{ name = "django-stubs" },
|
||||
{ name = "enviformer" },
|
||||
{ name = "envipy-additional-information" },
|
||||
{ name = "envipy-ambit" },
|
||||
@ -554,6 +566,7 @@ dependencies = [
|
||||
{ name = "epam-indigo" },
|
||||
{ name = "gunicorn" },
|
||||
{ name = "networkx" },
|
||||
{ name = "polars" },
|
||||
{ name = "psycopg2-binary" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "rdkit" },
|
||||
@ -566,6 +579,8 @@ dependencies = [
|
||||
|
||||
[package.optional-dependencies]
|
||||
dev = [
|
||||
{ name = "celery-stubs" },
|
||||
{ name = "django-stubs" },
|
||||
{ name = "poethepoet" },
|
||||
{ name = "pre-commit" },
|
||||
{ name = "ruff" },
|
||||
@ -577,15 +592,16 @@ ms-login = [
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "celery", specifier = ">=5.5.2" },
|
||||
{ name = "celery-stubs", marker = "extra == 'dev'", specifier = "==0.1.3" },
|
||||
{ name = "django", specifier = ">=5.2.1" },
|
||||
{ name = "django-extensions", specifier = ">=4.1" },
|
||||
{ name = "django-model-utils", specifier = ">=5.0.0" },
|
||||
{ name = "django-ninja", specifier = ">=1.4.1" },
|
||||
{ name = "django-oauth-toolkit", specifier = ">=3.0.1" },
|
||||
{ name = "django-polymorphic", specifier = ">=4.1.0" },
|
||||
{ name = "django-stubs", specifier = ">=5.2.4" },
|
||||
{ name = "enviformer", git = "ssh://git@git.envipath.com/enviPath/enviformer.git?rev=v0.1.2" },
|
||||
{ name = "envipy-additional-information", git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?rev=v0.1.4" },
|
||||
{ name = "django-stubs", marker = "extra == 'dev'", specifier = ">=5.2.4" },
|
||||
{ name = "enviformer", git = "ssh://git@git.envipath.com/enviPath/enviformer.git?branch=enhancement%2Fdataset_support" },
|
||||
{ name = "envipy-additional-information", git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?rev=v0.1.7" },
|
||||
{ name = "envipy-ambit", git = "ssh://git@git.envipath.com/enviPath/enviPy-ambit.git" },
|
||||
{ name = "envipy-plugins", git = "ssh://git@git.envipath.com/enviPath/enviPy-plugins.git?rev=v0.1.0" },
|
||||
{ name = "epam-indigo", specifier = ">=1.30.1" },
|
||||
@ -593,6 +609,7 @@ requires-dist = [
|
||||
{ name = "msal", marker = "extra == 'ms-login'", specifier = ">=1.33.0" },
|
||||
{ name = "networkx", specifier = ">=3.4.2" },
|
||||
{ name = "poethepoet", marker = "extra == 'dev'", specifier = ">=0.37.0" },
|
||||
{ name = "polars", specifier = "==1.34.0" },
|
||||
{ name = "pre-commit", marker = "extra == 'dev'", specifier = ">=4.3.0" },
|
||||
{ name = "psycopg2-binary", specifier = ">=2.9.10" },
|
||||
{ name = "python-dotenv", specifier = ">=1.1.0" },
|
||||
@ -608,8 +625,8 @@ provides-extras = ["ms-login", "dev"]
|
||||
|
||||
[[package]]
|
||||
name = "envipy-additional-information"
|
||||
version = "0.1.0"
|
||||
source = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?rev=v0.1.4#4da604090bf7cf1f3f552d69485472dbc623030a" }
|
||||
version = "0.1.7"
|
||||
source = { git = "ssh://git@git.envipath.com/enviPath/enviPy-additional-information.git?rev=v0.1.7#d02a5d5e6a931e6565ea86127813acf7e4b33a30" }
|
||||
dependencies = [
|
||||
{ name = "pydantic" },
|
||||
]
|
||||
@ -1074,6 +1091,47 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/b7/da/7d22601b625e241d4f23ef1ebff8acfc60da633c9e7e7922e24d10f592b3/multidict-6.7.0-py3-none-any.whl", hash = "sha256:394fc5c42a333c9ffc3e421a4c85e08580d990e08b99f6bf35b4132114c5dcb3", size = 12317, upload-time = "2025-10-06T14:52:29.272Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "mypy"
|
||||
version = "1.18.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "mypy-extensions" },
|
||||
{ name = "pathspec" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c0/77/8f0d0001ffad290cef2f7f216f96c814866248a0b92a722365ed54648e7e/mypy-1.18.2.tar.gz", hash = "sha256:06a398102a5f203d7477b2923dda3634c36727fa5c237d8f859ef90c42a9924b", size = 3448846, upload-time = "2025-09-19T00:11:10.519Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/07/06/dfdd2bc60c66611dd8335f463818514733bc763e4760dee289dcc33df709/mypy-1.18.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:33eca32dd124b29400c31d7cf784e795b050ace0e1f91b8dc035672725617e34", size = 12908273, upload-time = "2025-09-19T00:10:58.321Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/81/14/6a9de6d13a122d5608e1a04130724caf9170333ac5a924e10f670687d3eb/mypy-1.18.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a3c47adf30d65e89b2dcd2fa32f3aeb5e94ca970d2c15fcb25e297871c8e4764", size = 11920910, upload-time = "2025-09-19T00:10:20.043Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/5f/a9/b29de53e42f18e8cc547e38daa9dfa132ffdc64f7250e353f5c8cdd44bee/mypy-1.18.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d6c838e831a062f5f29d11c9057c6009f60cb294fea33a98422688181fe2893", size = 12465585, upload-time = "2025-09-19T00:10:33.005Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/77/ae/6c3d2c7c61ff21f2bee938c917616c92ebf852f015fb55917fd6e2811db2/mypy-1.18.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01199871b6110a2ce984bde85acd481232d17413868c9807e95c1b0739a58914", size = 13348562, upload-time = "2025-09-19T00:10:11.51Z" },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/9f/83/abcb3ad9478fca3ebeb6a5358bb0b22c95ea42b43b7789c7fb1297ca44f4/mypy-1.18.2-cp312-cp312-win_amd64.whl", hash = "sha256:d8068d0afe682c7c4897c0f7ce84ea77f6de953262b12d07038f4d296d547074", size = 9828828, upload-time = "2025-09-19T00:10:28.203Z" },
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||||
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||||
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||||
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||||
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Reference in New Issue
Block a user