[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

CHANGELOG.md

@@ -5,13 +5,13 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## \[Unreleased\]
+## [Unreleased]
 
 ## Notes
 
 Format:
 
-- Title: \[version\] - YYYY-MM-DD
+- Title: [version] - YYYY-MM-DD
 - Subtitle: tags (see below)
 - Items: Description of the change with link to issue/pr if available
 

README.md

@@ -167,7 +167,7 @@ DANCE 2.0 tackles this challenge by transforming preprocessing from a black-box
 The full installation process might be a bit tedious and could involve some debugging when using CUDA enabled packages.
 Thus, we provide an `install.sh` script that simplifies the installation process, assuming the user have [conda](https://conda.io/projects/conda/en/latest/index.html) set up on their machines.
 The installation script creates a conda environment `dance` and install the DANCE package along with all its dependencies with a apseicifc CUDA version.
-Currently, two options are accepted: `cpu` and  `cu118`.
+Currently, two options are accepted: `cpu` and `cu118`.
 For example, to install the DANCE package using CUDA 11.8 in a `dance-env` conda environment, simply run:
 
 ```bash
@@ -340,7 +340,7 @@ pip install -e .
 | ------------------------ | ----------------- | ---------------------------- | ----------------------------- |
 | ScMoGCN                  | Accuracy          | 0.0827 / 0.0810              | 0.0600 / 0.0630               |
 | SCMM                     | Accuracy          | 0.005 / N/A                  | 5e-5 / N/A                    |
-| Cross-modal autoencoders | Accuracy          | 0.0002 / N/A                 | 0.0002 /  N/A                 |
+| Cross-modal autoencoders | Accuracy          | 0.0002 / N/A                 | 0.0002 / N/A                  |
 
 #### 3) Joint Embedding
 

examples/atlas/sc_similarity_examples/readme.md

@@ -1,11 +1,11 @@
-\[scGPT->metadatas\]+\[get_result_web->sweep_results\]+\[data_processing/merge_result_metadata.py\]->\[data/cell_type_annotation_atlas.xlsx\]
-\[data/cell_type_annotation_atlas.xlsx\]+\[similarity/analyze_atlas_accuracy.py\]->\[data/in_atlas_datas\]
-\[similarity/example_usage_anndata.py\]+\[data/in_atlas_datas\]+\[data/cell_type_annotation_atlas.xlsx\]->\[data/dataset_similarity\]
-\[data/dataset_similarity\]+\[similarity/process_tissue_similarity_matrices.py\]->\[data/new_sim\]
+[scGPT->metadatas]+[get_result_web->sweep_results]+[data_processing/merge_result_metadata.py]->[data/cell_type_annotation_atlas.xlsx]
+[data/cell_type_annotation_atlas.xlsx]+[similarity/analyze_atlas_accuracy.py]->[data/in_atlas_datas]
+[similarity/example_usage_anndata.py]+[data/in_atlas_datas]+[data/cell_type_annotation_atlas.xlsx]->[data/dataset_similarity]
+[data/dataset_similarity]+[similarity/process_tissue_similarity_matrices.py]->[data/new_sim]
 
 #run_similarity_optimization.sh
-\[data/new_sim\]+\[similarity/optimize_similarity_weights.py\]+\[cache/sweep_cache.json\]->\[data/similarity_weights_results\]
-\[data/similarity_weights_results\]+\[similarity/visualize_atlas_performance.py\]+\[cache/sweep_cache.json\]->\[data/imgs\]
+[data/new_sim]+[similarity/optimize_similarity_weights.py]+[cache/sweep_cache.json]->[data/similarity_weights_results]
+[data/similarity_weights_results]+[similarity/visualize_atlas_performance.py]+[cache/sweep_cache.json]->[data/imgs]
 
 #注意additional sweep_id的问题
 #sweep_cache.json需要注意additional_sweep_id的问题， sweep_cache_data.pkl不需要。

examples/tuning/README.md

@@ -4,7 +4,7 @@ First create a sweep and then optionally spawn multiple agents given the sweep i
 $ python main.py
 ```
 
-Record the sweep id ("\[\*\] Sweep ID: \<sweep_id>") and use it to spawn another agent
+Record the sweep id ("[\*] Sweep ID: \<sweep_id>") and use it to spawn another agent
 
 ```bash
 $ python main.py --sweep_id <sweep_id>
@@ -15,5 +15,5 @@ $ python main.py --sweep_id <sweep_id>
 Currently there seem to be an issue with wandb sweep agent *might be* throwing segfault at the end of the sweep.
 This error might carry over to new runs. To fix this, user need to remove the old data and redownload from scratch.
 
-\[Update 2023-06-04\] The segfault seems to happen when writing to the source code
+[Update 2023-06-04] The segfault seems to happen when writing to the source code
 (even with "no changes", e.g., adding blank lines) when runing the sweep agent?..