diff --git a/README.md b/README.md index 008f87d..da7b4dc 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,9 @@ Follow https://twitter.com/marenbuettner to learn about a first public release. For beta users: Read the [docs](https://pytometry.netlify.app). -You can install `pytometry` via [pip](https://pip.pypa.io/) from [PyPI](PyPI): +## Installation + +You can install `pytometry` via [pip](https://pip.pypa.io/) from [PyPI](https://pypi.org/): ``` pip install pytometry diff --git a/docs/index.md b/docs/index.md index 4369532..81242bd 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,6 +1,6 @@ ```{include} ../README.md :start-line: 0 -:end-line: 17 +:end-line: 35 ``` ```{toctree} diff --git a/docs/tutorials/quickstart.ipynb b/docs/tutorials/quickstart.ipynb index 5bd91ac..440f27b 100644 --- a/docs/tutorials/quickstart.ipynb +++ b/docs/tutorials/quickstart.ipynb @@ -78,6 +78,16 @@ "pm.pl.plotdata(adata)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fb73d4d", + "metadata": {}, + "outputs": [], + "source": [ + "pm.pl.scatter_density(adata)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/pytometry/__init__.py b/pytometry/__init__.py index 8dfc6f0..b7b3bb8 100644 --- a/pytometry/__init__.py +++ b/pytometry/__init__.py @@ -20,7 +20,7 @@ """ -__version__ = "0.1.1" # denote a pre-release for 0.1.0 with 0.1a1 +__version__ = "0.1.2" # denote a pre-release for 0.1.0 with 0.1a1 from . import plotting as pl from . import preprocessing as pp diff --git a/pytometry/plotting/__init__.py b/pytometry/plotting/__init__.py index af1e536..8cd3dcb 100644 --- a/pytometry/plotting/__init__.py +++ b/pytometry/plotting/__init__.py @@ -1 +1,2 @@ from ._histogram import plotdata +from ._scatter_density import scatter_density diff --git a/pytometry/preprocessing/_process_data.py b/pytometry/preprocessing/_process_data.py index 7672885..0ffce56 100644 --- a/pytometry/preprocessing/_process_data.py +++ b/pytometry/preprocessing/_process_data.py @@ -171,9 +171,8 @@ def compensate( compens = compens.iloc[query_idx, query_idx] # sort compensation matrix by adata.var_names compens = compens.iloc[idx_sort, idx_sort] - X_comp = np.linalg.solve(compens, adata.X[:, ref_idx].T).T + X_comp = np.linalg.solve(compens.T, adata.X[:, ref_idx].T).T adata.X[:, ref_idx] = X_comp - # check for nan values nan_val = np.isnan(adata.X[:, ref_idx]).sum() if nan_val > 0: