doc: expand coding standards

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labguide/research/coding_standards.md

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 # Coding standards
 
-- Code should be readable
+Code is a core research product of the lab.
+We expect that lab members write code with the intention of it being reviewed (and potentially re-used) by other lab members.
 
-- All lab members should be
-familiar with principles of readable coding:
+To help in this, all lab members should be familiar with principles of clean code.
+Dr. Poldrack has a [presentation on this topic](https://github.com/poldrack/clean_coding/blob/master/CleanCoding_Python.pdf) that we encourage you to review.
+For a more in-depth introduction, two particularly useful recommendations here are:
    - [Art of Readable Code](https://www.oreilly.com/library/view/the-art-of/9781449318482/)
    - [Clean Code](https://www.oreilly.com/library/view/clean-code-a/9780136083238/)
 
-- Code should be modular
-   - Functions should do a single
-    thing that is clearly expressed in the name of the function
-   - Functions should include a
-    docstring that clearly specifies input and output
+When writing readable code, many different design patterns can be followed.
+We therefore provide some general purpose recommendations below as well as some Python examples taken from [Dr. Poldrack's clean code tutorial](https://github.com/poldrack/clean_coding/tree/master/python_example).
 
-- Code should be portable
-   - Any absolute paths should be
-    specified as a variable in a single location, or preferably as a
-    command line argument
-   - Any required environment
-    variables should be clearly described
-   - Any non-standard requirements
-    (e.g. Python libraries not available through PYPI) should be
-    described with instructions on how to install
+We also recommend checking out relevant tutorials, like the [Good Research Code Handbook](https://goodresearch.dev/index.html). 
 
-- Important functions should be
-tested
+## Code should be modular
 
+Write code such that it can be reviewed as individual "units," each of which have one well-scoped function.
 
-
+- Functions should do a single thing that is clearly expressed in the name of the function
+- Functions should include a docstring that clearly specifies input and output
+
+```python
+sc=[]
+for i in range(data.shape[1]):
+    if data.columns[i].split('.')[0][-7:] == '_survey':
+        sc=sc+[data.columns[i]]
+data=data[sc]
+```
+
+Compare this with a modularized refactoring:
+
+```python
+def extract_surveys_from_behavioral_data(behavioral_data_raw):
+   """
+   Extract survey data from behavioral data.
+   survey variables are labeled <survey_name>_survey.<variable name>
+   so filter on variables that include "_survey" in their name
+
+   Parameters
+   ----------
+   behavioral_data_raw : pandas.DataFrame
+   """
+    survey_variables = [i for i in behavioral_data_raw.columns if i.find('_survey') > -1]
+    return(behavioral_data_raw[survey_variables])
+```
+
+## Code should be portable
+
+Aim to be able to execute your code on a new machine.
+
+- Any absolute paths should be specified as a variable in a single location, or preferably as a command line argument
+- Any required environment variables should be clearly described
+- Any non-standard requirements  (e.g. Python libraries not available through PyPI) should be described with instructions on how to install
+
+Here is an example of what _not_ to do:
+
+```python
+h=read_csv('https://raw.githubusercontent.com/poldrack/clean_coding/master/data/health.csv',index_col=0)[hc].dropna().mean(1)
+```
+
+Compare this with a modular, portable refactoring:
+
+```python
+# load health data
+def load_health_data(datadir, filename='health.csv'):
+    return(pd.read_csv(os.path.join(datadir, filename), index_col=0))
+```
+
+## Important functions should be tested
+
+Functions that are critical for correct outputs should be tested.
+At a minimum, unit tests should be written to check that the correctness of outputs.
+For example, here is a minimal unit test to ensure that two data frames have the same index:
+
+```python
+def confirm_data_frame_index_alignment(df1, df2):
+    assert all(df1.index == df2.index)
+```
+
+More detailed recommendations on testing---including testing frameworks such as PyTest---are available in the [Hitchhiker's Guide to Python](https://docs.python-guide.org/writing/tests/).
+
+## Python packaging
+
+For projects that aim to develop pip-installable packages should follow current best-practices in Python Packing.
+As of May 2024, this is outlined in [this blog post](https://effigies.gitlab.io/posts/python-packaging-2023/) by lab member Chris Markiewicz.