From dae7e8910146f9d118ab2732d976f64d09407f0c Mon Sep 17 00:00:00 2001 From: Danila Bredikhin Date: Fri, 13 Oct 2023 02:03:12 +0200 Subject: [PATCH 1/3] Fix precision in tests --- tests/test_anndata.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_anndata.py b/tests/test_anndata.py index 07d314e..6d97e1e 100644 --- a/tests/test_anndata.py +++ b/tests/test_anndata.py @@ -52,7 +52,7 @@ def test_multi_group(self, filepath_hdf5): ) mofa( - adata, groups_label="group", outfile=filepath_hdf5, expectations=["W", "Z"] + adata, groups_label="group", outfile=filepath_hdf5, expectations=["W", "Z"], ) adata.obs["true_group"] = [s.split("_")[1] for s in adata.obs["sample"]] From 1a5232c60ead30ad6bd5edc9b21158faea84f3df Mon Sep 17 00:00:00 2001 From: Danila Bredikhin Date: Fri, 13 Oct 2023 02:06:06 +0200 Subject: [PATCH 2/3] Fix precision in tests --- tests/test_anndata.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/test_anndata.py b/tests/test_anndata.py index 6d97e1e..9f0ce18 100644 --- a/tests/test_anndata.py +++ b/tests/test_anndata.py @@ -60,11 +60,11 @@ def test_multi_group(self, filepath_hdf5): assert all(adata.obs.group.values == adata.obs.true_group.values) for sample, value in ( - ("sample0_groupA", 0.1459154), - ("sample7_groupB", -0.1822545), + ("sample0_groupA", 0.145915), + ("sample7_groupB", -0.182254), ): si = np.where(adata.obs_names == sample)[0][0] - assert adata.obsm["X_mofa"][si, 0] == pytest.approx(value) + assert adata.obsm["X_mofa"][si, 0] == pytest.approx(value, 1e-5) if __name__ == "__main__": From 3b911b1ee77ced0928be33380e70a063abedd6fb Mon Sep 17 00:00:00 2001 From: Danila Bredikhin Date: Fri, 13 Oct 2023 02:08:01 +0200 Subject: [PATCH 3/3] Update README.md Add link to the pypi shield --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e1021f8..b0f0139 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Multi-Omics Factor Analysis -![PyPi version](https://img.shields.io/pypi/v/mofapy2) +[![PyPi version](https://img.shields.io/pypi/v/mofapy2)](https://pypi.org/project/mofapy2) MOFA is a factor analysis model that provides a general framework for the integration of multi-omic data sets in an unsupervised fashion. This repository contains `mofapy2` Python library source code.