lint

Author: berombau

CHANGELOG.md

@@ -12,4 +12,4 @@ and this project adheres to [Semantic Versioning][].
 
 ### Added
 
--   Basic tool, preprocessing and plotting functions
+- Basic tool, preprocessing and plotting functions

README.md

@@ -13,9 +13,9 @@ The complete FlowSOM package known from R, now available in Python!
 
 Please refer to the [documentation][link-docs]. In particular, the following resources are available:
 
--   [Example FlowSOM notebook][link-docs-example]
--   [API documentation][link-api]
--   [FlowSOM Python Cheatsheet][cheatsheet]
+- [Example FlowSOM notebook][link-docs-example]
+- [API documentation][link-api]
+- [FlowSOM Python Cheatsheet][cheatsheet]
 
 ## Installation
 

docs/api.md

@@ -8,11 +8,11 @@ import flowsom as fs
 
 The functionality is organised in subpackages:
 
--   `io` for reading and writing FCS files
--   `pp` for preprocessing
--   `models` for models
--   `tl` for tools
--   `pl` for plotting
+- `io` for reading and writing FCS files
+- `pp` for preprocessing
+- `models` for models
+- `tl` for tools
+- `pl` for plotting
 
 The central class is `FlowSOM`, which can be used to read in data and directly perform the FlowSOM algorithm {cite:p}`van_gassen_flowsom_2015`. There is also a convenience function `flowsom_clustering` which performs the same steps but directly on an existing AnnData object. For more low-level control, the `FlowSOMEstimator` class with a scikit-learn Estimator API can be used.
 

docs/contributing.md

@@ -99,11 +99,11 @@ Specify `vX.X.X` as a tag name and create a release. For more information, see [
 
 Please write documentation for new or changed features and use-cases. This project uses [sphinx][] with the following features:
 
--   the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
--   [Numpy-style docstrings][numpydoc] (through the [napoleon][numpydoc-napoleon] extension).
--   Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
--   [Sphinx autodoc typehints][], to automatically reference annotated input and output types
--   Citations (like {cite:p}`van_gassen_flowsom_2015`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
+- the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
+- [Numpy-style docstrings][numpydoc] (through the [napoleon][numpydoc-napoleon] extension).
+- Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
+- [Sphinx autodoc typehints][], to automatically reference annotated input and output types
+- Citations (like {cite:p}`van_gassen_flowsom_2015`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
 
 See the [scanpy developer docs](https://scanpy.readthedocs.io/en/latest/dev/documentation.html) for more information
 on how to write documentation.
@@ -120,10 +120,10 @@ repository.
 
 #### Hints
 
--   If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
-    if you do so can sphinx automatically create a link to the external documentation.
--   If building the documentation fails because of a missing link that is outside your control, you can add an entry to
-    the `nitpick_ignore` list in `docs/conf.py`
+- If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
+  if you do so can sphinx automatically create a link to the external documentation.
+- If building the documentation fails because of a missing link that is outside your control, you can add an entry to
+  the `nitpick_ignore` list in `docs/conf.py`
 
 #### Building the docs locally
 

src/flowsom/io/read_fcs.py

@@ -17,8 +17,8 @@ def read_FCS(filepath):
         f = readfcs.read(filepath, reindex=True)
         f.var.n = f.var.n.astype(int)
         f.var = f.var.sort_values(by="n")
-        #f.uns["meta"]["channels"].index = f.uns["meta"]["channels"].index.astype(int)
-        #f.uns["meta"]["channels"] = f.uns["meta"]["channels"].sort_index()
+        # f.uns["meta"]["channels"].index = f.uns["meta"]["channels"].index.astype(int)
+        # f.uns["meta"]["channels"] = f.uns["meta"]["channels"].sort_index()
     except ValueError:
         f = readfcs.read(filepath, reindex=False)
         markers = {
@@ -27,12 +27,10 @@ def read_FCS(filepath):
             if re.match("^p[0-9]+s$", string)
         }
         fluo_channels = list(markers.keys())
-        non_fluo_channels = {
-            i: f.var["channel"][i] for i in f.var.index if i not in fluo_channels
-        }
+        non_fluo_channels = {i: f.var["channel"][i] for i in f.var.index if i not in fluo_channels}
         index_markers = dict(markers, **non_fluo_channels)
         f.var.rename(index=index_markers, inplace=True)
-        #f.uns["meta"]["channels"]["$PnS"] = [index_markers[key] for key in f.uns["meta"]["channels"].index]
+        # f.uns["meta"]["channels"]["$PnS"] = [index_markers[key] for key in f.uns["meta"]["channels"].index]
     return f
 
 

src/flowsom/main.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import copy
-
 import anndata as ad
 import igraph as ig
 import numpy as np
@@ -425,17 +423,18 @@ def get_cell_data(self):
     def get_cluster_data(self):
         """Get the cluster data."""
         return self.mudata["cluster_data"]
-    
+
     def copy(self):
         """
         Returns a copy of the FlowSOM instance, leveraging MuData's built-in copy method.
-            
-        Returns:
+
+        Returns
+        -------
             FlowSOM: A new instance of FlowSOM with all data copied.
         """
         # Create a new instance without calling __init__
         fsom_copy = self.__class__.__new__(self.__class__)
-        
+
         # Copy attributes
         fsom_copy.cols_to_use = self.cols_to_use
         fsom_copy.mad_allowed = self.mad_allowed
@@ -451,7 +450,6 @@ def copy(self):
         return fsom_copy
 
 
-
 def flowsom_clustering(inp: ad.AnnData, cols_to_use=None, n_clusters=10, xdim=10, ydim=10, **kwargs):
     """Perform FlowSOM clustering on an anndata object and returns the anndata object.