Improving docstrings (#1)

* Fix: Spelling fixes in README * First revision of improved docstrings for user-friendliness. * Second revision of improved docstrings for user-friendliness. * 1.0.1b1 --------- Co-authored-by: David Bickel <[email protected]>
Bio2Byte · Mar 11, 2024 · b38379c · b38379c
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diff --git a/README.md b/README.md
@@ -76,7 +76,6 @@ We recommend this installation for most users.
   ```
   pip install constava
   ```
-  (Takes about 1 minute)
 
 If the package requires to be uninstalled, run `pip uninstall constava`. 
 
@@ -124,7 +123,7 @@ We provide a couple of usage examples in a [Colab notebook](https://colab.resear
 
 ### Execution from the command line
 
-The software is subdevided in **three submodules**:
+The software is subdivided in **three submodules**:
 
 The `constava dihedrals` submodule provides a simple way to extract backbone 
 dihedral angles from MD simulations or PDB ensembles. For more information
@@ -516,8 +515,7 @@ https://orcid.org/0000-0002-4313-3845) -
 - Wim Vranken - 
 [![ORCID](https://orcid.org/sites/default/files/images/orcid_16x16.png)](https://orcid.org/0000-0001-7470-4324) - 
 [Personal page](https://researchportal.vub.be/en/persons/wim-vranken) - 
-[wim.
-  [email protected]](mailto:[email protected])
+[[email protected]](mailto:[email protected])
 
 <sup>&spades;</sup> Authors contributed equally to this work.
 

diff --git a/constava/wrapper/params.py b/constava/wrapper/params.py
@@ -40,41 +40,46 @@ class ConstavaParameters:
         input_files : List[str] or str
             Input file(s) that contain the dihedral angles.
         input_format : str
-            Format of the input file: {'auto', 'csv', 'xvg'}
+            Format of the input file. Options include:
+            - 'auto': Automatically detect the file format (default).
+            - 'csv': Comma-separated values format.
+            - 'xvg': XVG format used by GROMACS for graphing.
         output_file : str
             The file to write the output to.
         output_format : str
-            Format of output file: {'auto', 'csv', 'json', 'tsv'}
-
+            Format of the output file. Options include:
+            - 'auto': Automatically select the output format based on the input format or other criteria (default).
+            - 'csv': Comma-separated values format, suitable for spreadsheets and simple data analyses.
+            - 'json': JSON format, which is lightweight and easy for humans to read and write, and easy for machines
+            to parse and generate.
+            - 'tsv': Tab-separated values format, useful for tabular data that is less complex than CSV data.
         model_type : str
-            The probabilistic conformational state model used. Default is `kde`.
-            The alternative `grid` runs significantly faster while slightly 
-            sacrificing accuracy: {'kde', 'grid'}
+            Specifies the probabilistic conformational state model used. Options include:
+            - 'kde': Kernel Density Estimator (default).
+            - 'grid': A grid-based approximation of the KDE. Runs significantly faster with minor sacrifice to accuracy.
         model_load : str
             Load a conformational state model from the given pickled file.
         model_data : str
             Fit conformational state models to data provided in the given file.
         model_dump : str
-            Write the generated model to a pickled file, that can be loaded 
+            Write the generated model to a pickled file, that can be loaded
             again using `model_load`.
-
         window : List[int] or int
             Do inference using a moving reading-frame of <int> consecutive samples.
             Multiple values can be given as a list.
         window_series : List[int] or int
             Do inference using a moving reading-frame of <int> consecutive samples.
-            Return the results for every window rather than the average. Multiple 
+            Return the results for every window rather than the average. Multiple
             values can be given as a list.
         bootstrap : List[int] or int
             Do inference using <Int> samples obtained through bootstrapping.
             Multiple values can be given as a list.
         bootstrap_series : List[int] or int
             Do inference using <Int> samples obtained through bootstrapping.
-            Return the results for every subsample rather than the average. Multiple 
+            Return the results for every subsample rather than the average. Multiple
             values can be given as a list.
         bootstrap_samples : int
             When bootstrapping, sample <Int> times from the input data.
-
         input_degrees : bool
             Set `True` if input files are in degrees.
         model_data_degrees : bool