Release 0.9.14

.gitignore

@@ -141,6 +141,9 @@ venv.bak/
 *.code-workspace
 .vscode
 
+# Zed
+.zed/
+
 # mkdocs documentation
 /site
 

HOW_TO_RELEASE.md

@@ -9,62 +9,10 @@ The document describes how to release `gpsea` to *PyPi*.
 - remove deprecated methods targeted for removal in this version. The `TODO` markers are labeled using 
   the target version (e.g. `TODO[v0.3.0]`)
 - bump versions to a release:
-  - `src/gpsea/__init__.py`
+  - `pyproject.toml`
 - ensure the CI passes
-- deploy to PyPi (described below)
 - merge to `main`
-- create a GitHub release from the latest `main` commit, including a new tag 
+- create a GitHub release from the latest `main` commit, including a new tag. The `release.yml` workflow takes over and deploys the new release to PyPi.
 - merge `main` to `develop`
 - bump versions to a `dev` version to begin next development iteration
 
-## Deploy to PyPi
-
-### Virtual environment for deployment
-
-As an optional one-time step, consider creating a dedicated virtual environment with the packages required 
-for testing, building, and deployment:
-
-```shell
-# Create and activate the virtual environment
-python3 -m venv build
-source build/bin/activate
-
-# Install the build packages
-python3 -m pip install build twine
-```
-
-### Setup PyPi credentials
-
-As another one-time action, you must create a profile on PyPi and generate an access token. 
-The token is used to upload the packages.
-
-First, create an account (e.g. associated with your GitHub account), configure 2FA, store recovery codes, etc.
-Then, generate a token to upload the packages. You can generate a token per project or for all projects. 
-Store the token into `$HOME/.pypirc` file with `-rw-------` permissions. The file should look like:
-
-```
-[pypi]
-  username = __token__
-  password = <YOUR-TOKEN-HERE>
-```
-
-Now we're ready to publish packages!
-
-### Deploy
-Run the following to deploy `gpsea` to PyPi:  
-
-```bash
-# Ensure you're on the release branch
-cd gpsea
-
-# Build the package
-python3 -m build
-
-# Deploy
-python3 -m twine upload dist/*
-
-# Clear the built and deployed files
-rm -rf build dist
-```
-
-The commands will build source distribution and a wheel, and deploy the source distribution and wheel to PyPi.

docs/conf.py

@@ -53,7 +53,7 @@
 
 # General information about the project.
 project = u'GPSEA'
-copyright = u'2025'
+copyright = u'2026'
 author = u'Lauren Rekerle, Daniel Danis, Peter N Robinson'
 
 # The version info for the project you're documenting, acts as replacement for

docs/tutorial.rst

@@ -332,11 +332,12 @@ Statistical testing
 
 Now we can perform the testing and evaluate the results.
 
->>> result = analysis.compare_genotype_vs_phenotypes(
+>>> result = analysis.compare_genotype_vs_phenotypes( # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
 ...     cohort=cohort,
 ...     gt_clf=gt_clf,
 ...     pheno_clfs=pheno_clfs,
 ... )
+HPO terms processed: ...
 >>> result.total_tests
 30
 

docs/user-guide/analyses/phenotype-classes.rst

@@ -287,11 +287,12 @@ Analysis
 
 We can now test associations between the genotype classes and the HPO terms:
 
->>> result = analysis.compare_genotype_vs_phenotypes(
+>>> result = analysis.compare_genotype_vs_phenotypes( # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
 ...     cohort=cohort,
 ...     gt_clf=gt_clf,
 ...     pheno_clfs=pheno_clfs,
 ... )
+HPO terms processed: ...
 >>> len(result.phenotypes)
 369
 >>> result.total_tests

pyproject.toml

@@ -4,10 +4,10 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "gpsea"
-version = "0.9.13"
+version = "0.9.14"
 authors = [
     { name = "Lauren Rekerle", email = "lauren.rekerle@jax.org" },
-    { name = "Daniel Danis", email = "daniel.danis@bih-charite.de" },
+    { name = "Daniel Danis", email = "daniel.gordon.danis@protonmail.com" },
     { name = "Peter Robinson", email = "peter.robinson@bih-charite.de" },
 ]
 description = "Discover genotype-phenotype correlations with GA4GH phenopackets"
@@ -54,6 +54,7 @@ test = [
     "phenopacket-store-toolkit>=0.1.2",
     "pytest>=7.0.0,<8.0.0",
     "ruff==0.12.1",
+    "basedpyright==v1.39.10",
 ]
 docs = [
     "sphinx>=7.0.0",
@@ -70,6 +71,3 @@ bugtracker = "https://github.com/P2GX/gpsea/issues"
 
 [tool.setuptools]
 package-dir = { "" = "src" }
-
-[tool.setuptools.dynamic]
-version = { attr = "gpsea.__version__" }

src/gpsea/analysis/clf/_pheno.py

@@ -76,9 +76,6 @@ def test(
     ) -> typing.Optional[PhenotypeCategorization[hpotk.TermId]]:
         self._check_patient(patient)
 
-        if len(patient.phenotypes) == 0:
-            return None
-
         for phenotype in patient.phenotypes:
             if phenotype.is_present:
                 if self._query == phenotype.identifier or any(
@@ -94,7 +91,10 @@ def test(
                     ):
                         return self._phenotype_excluded
 
-        return None
+        if self._missing_implies_phenotype_excluded:
+            return self._phenotype_excluded
+        else:
+            return None
 
     def __eq__(self, value: object) -> bool:
         return (

src/gpsea/analysis/clf/_util.py

@@ -4,7 +4,8 @@
 
 import hpotk
 
-from ._pheno import PhenotypeClassifier, HpoClassifier
+from ._api import PhenotypeClassifier
+from ._pheno import HpoClassifier
 
 from gpsea.model import Patient
 

src/gpsea/analysis/pcats/_impl.py

@@ -1,24 +1,22 @@
 import abc
+import collections.abc
 import os
+import sys
 import typing
-
 from collections import Counter
 
 import hpotk
 import numpy as np
 import pandas as pd
-
+import tqdm
 from statsmodels.stats import multitest
 
 from gpsea.model import Patient
 
-from ..clf import GenotypeClassifier
-from ..clf import P, PhenotypeClassifier
+from .._base import MultiPhenotypeAnalysisResult, StatisticResult
+from ..clf import GenotypeClassifier, P, PhenotypeClassifier
 from ..mtc_filter import PhenotypeMtcFilter, PhenotypeMtcResult
-
 from .stats import CountStatistic
-from .._base import MultiPhenotypeAnalysisResult, StatisticResult
-
 
 DEFAULT_MTC_PROCEDURE = "fdr_bh"
 """
@@ -27,12 +25,12 @@
 
 
 def apply_classifiers_on_individuals(
-    individuals: typing.Iterable[Patient],
+    individuals: collections.abc.Iterable[Patient],
     gt_clf: GenotypeClassifier,
-    pheno_clfs: typing.Sequence[PhenotypeClassifier[P]],
-) -> typing.Tuple[
-    typing.Sequence[int],
-    typing.Sequence[pd.DataFrame],
+    pheno_clfs: collections.abc.Sequence[PhenotypeClassifier[P]],
+) -> tuple[
+    collections.abc.Sequence[int],
+    collections.abc.Sequence[pd.DataFrame],
 ]:
     """
     Classify individuals with the genotype and phenotype classifiers.
@@ -57,7 +55,12 @@ def apply_classifiers_on_individuals(
 
     # Apply genotype and phenotype predicates
     count_dict = {}
-    for ph_predicate in pheno_clfs:
+    for ph_predicate in tqdm.tqdm(
+        pheno_clfs,
+        desc="HPO terms processed",
+        file=sys.stdout,
+        unit=" terms",
+    ):
         if ph_predicate.phenotype not in count_dict:
             # Make an empty frame for keeping track of the counts.
             count_dict[ph_predicate.phenotype] = pd.DataFrame(
@@ -83,7 +86,7 @@ def apply_classifiers_on_individuals(
     # Convert dicts to numpy arrays
     n_usable_patients = [n_usable_patient_counter[ph_predicate.phenotype] for ph_predicate in pheno_clfs]
 
-    counts = [count_dict[ph_predicate.phenotype] for ph_predicate in pheno_clfs]
+    counts: list[pd.DataFrame] = [count_dict[ph_predicate.phenotype] for ph_predicate in pheno_clfs]
 
     return n_usable_patients, counts
 

tests/analysis/clf/test_disease.py

@@ -0,0 +1,36 @@
+import hpotk
+import pytest
+
+from gpsea.analysis.clf import DiseasePresenceClassifier
+from gpsea.model import Cohort, Patient
+
+
+class TestDiseasePresencePredicate:
+    @pytest.mark.parametrize(
+        "patient_id, patient_category",
+        [
+            ("HetSingleVar", "Yes"),
+            ("HomoVar", "No"),
+        ],
+    )
+    def test_disease_predicate(
+        self,
+        patient_id: str,
+        patient_category: str,
+        toy_cohort: Cohort,
+    ):
+        patient = find_patient(patient_id, toy_cohort)
+        disease_id = hpotk.TermId.from_curie("OMIM:148050")
+        predicate = DiseasePresenceClassifier(disease_id)
+        actual = predicate.test(patient)
+
+        assert actual is not None
+        assert actual.phenotype == disease_id
+        assert actual.category.name == patient_category
+
+
+def find_patient(pat_id: str, cohort: Cohort) -> Patient:
+    for pat in cohort.all_patients:
+        if pat.patient_id == pat_id:
+            return pat
+    raise ValueError(f"Could not find patient {pat_id}")