README.md

@@ -11,10 +11,11 @@
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/psf/black)
 [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/charliermarsh/ruff)
 [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8176570.svg)](https://doi.org/10.5281/zenodo.8176570)
 
 ## About
 
-junifer is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data.
+`junifer` is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data.
 
 It is currently being developed and maintained at the [Applied Machine Learning](https://www.fz-juelich.de/en/inm/inm-7/research-groups/applied-machine-learning-aml) group at [Forschungszentrum Juelich](https://www.fz-juelich.de/en), Germany. Although the library is designed for people working at [Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7)](https://www.fz-juelich.de/en/inm/inm-7), it is designed to be as modular as possible thus enabling others to extend it easily.
 
@@ -55,12 +56,12 @@ conda install -c conda-forge junifer
 
 ## Citation
 
-If you use junifer in a scientific publication, we would appreciate if you cite our work. Currently, we do not have a publication, so feel free to use the project [URL](https://juaml.github.io/junifer).
+If you use `junifer` in a scientific publication, we would appreciate if you cite our work. Currently, we do not have a publication, so feel free to use the project's [Zenodo URL](https://doi.org/10.5281/zenodo.8176569).
 
 ## Funding
 
 We thank the [Helmholtz Imaging Platform](https://helmholtz-imaging.de/) and
-[SMHB](https://www.fz-juelich.de/en/smhb) for supporting development of Junifer.
+[SMHB](https://www.fz-juelich.de/en/smhb) for supporting development of `junifer`.
 (The funding sources had no role in the design, implementation and evaluation of the pipeline.)
 
 ## Contribution
@@ -72,7 +73,7 @@ Contributions are welcome and greatly appreciated. Please read the [guidelines](
 junifer is released under the AGPL v3 license:
 
 junifer, FZJuelich AML neuroimaging feature extraction library.
-Copyright (C) 2022, authors of junifer.
+Copyright (C) 2023, authors of junifer.
 
 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU Affero General Public License as published by

docs/changes/newsfragments/247.doc

@@ -0,0 +1 @@
+Add Zenodo badge in ``README`` and improve general documentation by `Synchon Mandal`_

docs/contribution.rst

@@ -2,8 +2,8 @@
 
 .. _contribution_guidelines:
 
-Contributing to junifer
+Contributing
-=======================
+============
 
 Setting up the local development environment
 --------------------------------------------

docs/maintaining.rst

@@ -70,7 +70,7 @@ to generate the proper changelog that should be reflected in
 
    .. code-block:: bash
 
-    SETUPTOOLS_SCM_DEBUG=1 python -m build --source --binary --out-dir dist/ .
+       SETUPTOOLS_SCM_DEBUG=1 python -m build --outdir dist/ .
 
 #. Push the tag
 

junifer/data/masks.py

@@ -213,7 +213,7 @@ def get_mask(
     all_masks = []
     for t_mask in true_masks:
         if isinstance(t_mask, dict):
-            mask_name = list(t_mask.keys())[0]
+            mask_name = next(iter(t_mask.keys()))
             mask_params = t_mask[mask_name]
         else:
             mask_name = t_mask

junifer/data/tests/test_masks.py

@@ -411,7 +411,7 @@ def test_get_mask_multiple(
         computed = get_mask(masks=junifer_masks, target_data=input["BOLD"])
 
         masks_names = [
-            list(x.keys())[0] if isinstance(x, dict) else x for x in masks
+            next(iter(x.keys())) if isinstance(x, dict) else x for x in masks
         ]
 
         mask_funcs = [

junifer/markers/tests/test_collection.py

@@ -197,7 +197,7 @@ def test_marker_collection_storage(tmp_path: Path) -> None:
 
     features = storage.list_features()
     assert len(features) == 3
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     t_feature = storage.read_df(feature_md5=feature_md5)
     fname = "gmd_schaefer100x7_mean"
     t_data = out[fname]["VBM_GM"]["data"]  # type: ignore

junifer/storage/pandas_base.py

@@ -114,7 +114,8 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
         if len(elem_idx) == 1:
             # Create normal index for vector
             index = pd.Index(
-                data=list(elem_idx.values())[0], name=list(elem_idx.keys())[0]
+                data=next(iter(elem_idx.values())),
+                name=next(iter(elem_idx.keys())),
             )
         else:
             # Create multiindex for timeseries

junifer/storage/tests/test_hdf5.py

@@ -175,7 +175,7 @@ def test_store_metadata_and_list_features(tmp_path: Path) -> None:
     # List the stored features
     features = storage.list_features()
     # Get the first MD5
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     # Check the MD5
     assert meta_md5 == feature_md5
 

junifer/storage/tests/test_sqlite.py

@@ -335,7 +335,7 @@ def test_store_df_and_read_df(tmp_path: Path) -> None:
     with pytest.raises(ValueError, match="Only one"):
         storage.read_df(feature_name="wrong_name", feature_md5="wrong_md5")
     # Get MD5 hash of features
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "feature_md5" == feature_md5
     # Check for key
     assert "BOLD_markername" == features[feature_md5]["name"]
@@ -373,7 +373,7 @@ def test_store_metadata(tmp_path: Path) -> None:
         meta_md5=meta_md5, element=element_to_store, meta=meta_to_store
     )
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert meta_md5 == feature_md5
 
 
@@ -474,7 +474,7 @@ def test_store_matrix(tmp_path: Path) -> None:
     stored_names = [f"{i}~{j}" for i in row_names for j in col_names]
 
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "BOLD_fc" == features[feature_md5]["name"]
 
     read_df = storage.read_df(feature_md5=feature_md5)
@@ -498,7 +498,7 @@ def test_store_matrix(tmp_path: Path) -> None:
         for j in range(data.shape[1])
     ]
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "BOLD_fc" == features[feature_md5]["name"]
     read_df = storage.read_df(feature_md5=feature_md5)
     assert list(read_df.columns) == stored_names
@@ -563,7 +563,7 @@ def test_store_matrix(tmp_path: Path) -> None:
     ]
 
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "BOLD_fc" == features[feature_md5]["name"]
     read_df = storage.read_df(feature_md5=feature_md5)
     assert list(read_df.columns) == stored_names
@@ -595,7 +595,7 @@ def test_store_matrix(tmp_path: Path) -> None:
     ]
 
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "BOLD_fc" == features[feature_md5]["name"]
     read_df = storage.read_df(feature_md5=feature_md5)
     assert list(read_df.columns) == stored_names
@@ -632,7 +632,7 @@ def test_store_matrix(tmp_path: Path) -> None:
     ]
 
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "BOLD_fc" == features[feature_md5]["name"]
     read_df = storage.read_df(feature_md5=feature_md5)
     assert list(read_df.columns) == stored_names
@@ -663,7 +663,7 @@ def test_store_matrix(tmp_path: Path) -> None:
     ]
 
     features = storage.list_features()
-    feature_md5 = list(features.keys())[0]
+    feature_md5 = next(iter(features.keys()))
     assert "BOLD_fc" == features[feature_md5]["name"]
     read_df = storage.read_df(feature_md5=feature_md5)
     assert list(read_df.columns) == stored_names