From 166fed09934716499bd2c2dc96956d0076201e2a Mon Sep 17 00:00:00 2001
From: burtonrj <burtonrossj@gmail.com>
Date: Mon, 30 Oct 2023 19:40:54 +0000
Subject: [PATCH] PR corrections - inplace instead of copy on flowsom function

---
 .pre-commit-config.yaml                |  2 +-
 docs/tutorials/flowsom.ipynb           | 10 +++-------
 pytometry/tools/clustering/_flowsom.py |  6 +++---
 3 files changed, 7 insertions(+), 11 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c256d00..470d542 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -27,7 +27,7 @@ repos:
         language_version: python3
         args:
           - --max-line-length=88
-          - --ignore=E203
+          - --ignore=E203,TYP001
         exclude: |
           (?x)(
               __init__.py
diff --git a/docs/tutorials/flowsom.ipynb b/docs/tutorials/flowsom.ipynb
index d7474d9..966fe24 100644
--- a/docs/tutorials/flowsom.ipynb
+++ b/docs/tutorials/flowsom.ipynb
@@ -14,11 +14,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "fa01f5013e68d3d2",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import scanpy as sc\n",
@@ -163,7 +159,7 @@
    "metadata": {},
    "source": [
     "We can now cluster our data with the `flowsom_clustering` function from the `tl` (tools) module of `pytometry`. The FlowSOM algorithm works in the following stages:\n",
-    "1. A self-organsing map is trained that learns a low dimensional embedding of the single cell feature space\n",
+    "1. A self-organising map is trained that learns a low dimensional embedding of the single cell feature space\n",
     "2. Consensus clustering (https://github.com/burtonrj/consensusclustering) of the SOM nodes identifies the optimal number of clusters to retain\n",
     "3. Each observation is assigned to a cluster according to its nearest meta-cluster in the SOM\n",
     "\n",
@@ -202,7 +198,7 @@
     "    max_clusters=20,\n",
     "    return_clustering_objs=True,\n",
     "    verbose=True,\n",
-    "    copy=True,\n",
+    "    inplace=True,\n",
     "    agglomerative_clustering_kwargs={\"metric\": \"euclidean\", \"linkage\": \"ward\"},\n",
     ")"
    ]
diff --git a/pytometry/tools/clustering/_flowsom.py b/pytometry/tools/clustering/_flowsom.py
index cda1137..71dcb73 100644
--- a/pytometry/tools/clustering/_flowsom.py
+++ b/pytometry/tools/clustering/_flowsom.py
@@ -141,7 +141,7 @@ def flowsom_clustering(
     max_clusters: int = 10,
     n_resamples: int = 100,
     resample_frac: float = 0.9,
-    copy: bool = False,
+    inplace: bool = True,
     verbose: bool = False,
     agglomerative_clustering_kwargs: dict | None = None,
     return_clustering_objs: bool = False,
@@ -169,7 +169,7 @@ def flowsom_clustering(
         max_clusters (int, default=10): maximum number of clusters to consider
         n_resamples (int, default=100): number of resamples for consensus clustering
         resample_frac (float, default=0.5): fraction of samples to resample for
-        copy (bool, default=False): whether to copy the AnnData object or modify it
+        inplace (bool, default=False): whether to copy the AnnData object or modify it
         verbose (bool, default=False)
         agglomerative_clustering_kwargs (dict, default=None): keyword arguments for
         sklearn.cluster.AgglomerativeClustering. If None, defaults to
@@ -183,7 +183,7 @@ def flowsom_clustering(
         containing the annotated data matrix and the clustering objects is returned.
 
     """
-    adata = adata.copy() if copy else adata
+    adata = adata if inplace else adata.copy()
     logger.info("Running FlowSOM clustering")
     logger.info("Training SOM")
     som = som_clustering(