Update README.rst

CONTRIBUTING.rst

@@ -256,3 +256,160 @@ To generate the documentation locally:
   make html
 
 - Open `_build/html/index.html` in a browser.
+
+Use ChatGPT!
+============
+Leverage the power of ChatGPT to optimize various facets of your software development process,
+including code checking, interface optimization, and effective testing.
+
+Simply use the templates provided below and fill in the blank spaces between
+``START OF CODE`` and ``END OF CODE`` with the specifics of your task.
+
+Due to the token limit of ChatGPT, the answers might not be complete. Use this prompt to continue the answer of ChatGPT:
+
+.. code-block:: none
+
+    "Continue from where you left off."
+
+Code checking
+-------------
+For reviewing your code's logic, ensuring conciseness, and promoting clarity, try the following prompt:
+
+.. code-block:: none
+
+    "
+    Analyze and evaluate the provided TARGET CODE to ensure it adheres to best coding practices and is free from logical errors.
+
+    Inputs:
+    TARGET CODE:
+    - START OF CODE
+    -------------------------------------
+    your code
+    -------------------------------------
+    - END OF CODE
+
+    **Key Directive**: Identify vulnerabilities, inefficiencies, and areas of improvement. This is crucial.
+
+    Requirements:
+
+    1. Syntax and Formatting:
+    - Ensure consistent indentation and formatting throughout.
+    - Use meaningful variable and function names.
+    - Avoid hard-coded values; suggest constants or configuration inputs if necessary.
+
+    2. Logic and Flow:
+    - Confirm that the logic flows correctly and efficiently.
+    - Identify any potential issues like infinite loops, off-by-one errors, or misused conditions.
+
+    3. Error Handling:
+    - Suggest robust error handling mechanisms.
+    - Highlight potential areas where exceptions might arise and are not currently handled.
+
+    4. General Guidelines:
+    - Suggest improvements for readability and maintainability.
+    - Do not leave any placeholders like "TODO", "Fix this", "Add ..." without suggestions.
+    - Offer potential refactorings if they simplify the code without losing clarity.
+
+    Output Expectations:
+    - Detailed feedback on the TARGET CODE with line references.
+    - Suggestions for improvements and potential refactorings.
+    - Highlighted vulnerabilities and their proposed resolutions.
+    "
+
+
+
+Interface optimization
+----------------------
+To enhance the public interface or signature of your functions and classes, employ the following prompt:
+
+.. code-block:: none
+
+    "
+    Review and suggest improvements for the interface of the given TARGET FUNCTION to enhance its usability, clarity, and integration capabilities.
+
+    Inputs:
+    TARGET FUNCTION:
+    - START OF CODE
+    -------------------------------------
+    your code
+    -------------------------------------
+    - END OF CODE
+
+    **Key Directive**: The interface should be intuitive, versatile, and easy for other developers to integrate and use.
+
+    Requirements:
+
+    1. Signature Clarity:
+    - Ensure function/method names are descriptive and concise.
+    - Parameters should have clear names and, if possible, default values that make sense.
+
+    2. Documentation:
+    - Suggest comprehensive docstrings for the function.
+    - Propose comments for complex code blocks to aid understanding.
+
+    3. Return Values and Types:
+    - Advise on consistent return types, considering scenarios like error or null conditions.
+    - Recommend clear naming for returned objects, especially if using data structures like dictionaries or tuples.
+
+    4. General Guidelines:
+    - Avoid overloading the interface with too many parameters; suggest alternatives if needed.
+    - Consider common use cases and ensure they are easily achievable with the proposed interface.
+    - The interface should promote good coding practices and be resistant to misuse.
+
+    Output Expectations:
+    - Feedback and suggestions on function/method signatures.
+    - Proposed docstrings and comments.
+    - Recommendations for ensuring a consistent and intuitive interface.
+    "
+
+Testing
+-------
+For generating efficient tests with extensive coverage and considering edge cases, utilize the prompt template below:
+
+.. code-block:: none
+
+    "
+    Generate test functions for a given TARGET FUNCTION using the style of the provided TESTING TEMPLATE.
+
+    Inputs:
+    TARGET FUNCTION:
+    - START OF CODE
+    -------------------------------------
+    your code
+    -------------------------------------
+    - END OF CODE
+
+    TESTING TEMPLATE:
+    - START OF CODE
+    -------------------------------------
+    your code
+    -------------------------------------
+    - END OF CODE
+
+    **Key Directive**: For the Normal Cases Test Class, EACH function MUST test ONLY ONE individual parameter of the TARGET FUNCTION using Hypothesis for property-based testing. This is crucial.
+
+    Requirements:
+
+    1. Normal Cases Test Class:
+    - Name: 'Test[TARGET FUNCTION NAME]'.
+    - Objective: Test EACH parameter *INDIVIDUALLY*.
+    - Tests: For EACH parameter, at least 10 positive and 10 negative tests.
+
+    2. Complex Cases Test Class:
+    - Name: 'Test[TARGET FUNCTION NAME]Complex'.
+    - Objective: Test combinations of the TARGET FUNCTION parameters.
+    - Tests: At least 5 positive and 5 negative that intricately challenge the TARGET FUNCTION.
+
+    3. General Guidelines:
+    - Use Hypothesis for property-based testing, but test parameters individually for the Normal Cases Test Class .
+    - Tests should be clear, concise, and non-redundant.
+    - Do not leave any placeholders like "TODO", "Fill this", "Add ..." incomplete.
+    - Expose potential issues in the TARGET FUNCTION.
+
+    Output Expectations:
+    - Two test classes: one for normal cases (individual parameters) and one for complex cases (combinations).
+    - In Normal Cases, one function = one parameter tested.
+    - Total: at least 30 unique tests, 150+ lines of code.
+
+    Reminder: In Normal Cases, it's crucial to test parameters individually.
+    "

README.rst

@@ -48,6 +48,8 @@ providing the following algorithms:
 Moreover, AAanalysis provides functions for loading protein benchmark datasets (**load_data**),
 amino acid scale sets (**load_scales**), and their in-depth two-level classification (**AAontology**).
 
+If you are only looking for making publication-ready plots easily, see our `Plotting Prelude <https://https://aaanalysis.readthedocs.io/en/latest/generated/plotting_prelude.htmll>`.
+
 Install
 =======
 **AAanalysis** can be installed either from `PyPi <https://pypi.org/project/aaanalysis>`_ or
@@ -61,14 +63,13 @@ Install
 
 Contributions
 =============
-
-We welcome bug reports, feature requests, or updates on documentations and code. For details, refer to our
-`CONTRIBUTING.rst <https://github.com/breimanntools/aaanalysis/blob/master/CONTRIBUTING.rst>`_.
-Please email stephanbreimann@gmail.com for further questions or suggestions?
+We appreciate bug reports, feature requests, or updates on documentation and code. For details, please refer to our
+`CONTRIBUTING.rst <https://github.com/breimanntools/aaanalysis/blob/master/CONTRIBUTING.rst>`_. This guide includes
+not only specifics related to  `AAanalysis`, but also general software development hacks such as handy ChatGPT prompts.
+For further questions or suggestions, please email stephanbreimann@gmail.com.
 
 Citations
 =========
-
 If you use 'AAanalysis' in your work, please cite the respective publication as follows:
 
 **AAclust**:

aaanalysis/explainer/__init__.py

@@ -1,5 +0,0 @@
-from aaanalysis.data_loader import load_dataset, load_scales
-from aaanalysis.aaclust import AAclust
-from aaanalysis.cpp import CPP, CPPPlot, SequenceFeature, SplitRange
-from aaanalysis.dpulearn import dPULearn
-from aaanalysis.plotting import plot_settings, plot_set_legend, plot_gcfs, plot_get_cmap, plot_get_cdict

aaanalysis/explainer/tree_based.py

@@ -16,7 +16,7 @@
 # COL_FEAT_IMPACT = "feat_impact"
 
 # II Main Functions
-class Tree:
+class TreeModel:
     """A wrapper for Tree based prediction models and Tree explainer from SHAP package to
     explain prediction (typically binary classification) results at global and individual level"""
     def __init__(self, model=None):
@@ -32,7 +32,7 @@ def add_feat_import(self, df_feat=None):
         """"""
 
 
-class TreeSHAP:
+class ShapModel:
     """A wrapper for Tree explainer from SHAP package"""
     def __init__(self, model=None):
         """"""

aaanalysis/plotting/plotting_functions.py

@@ -9,20 +9,14 @@
 from typing import List, Union, Tuple
 import warnings
 
-STR_CMAP_CPP = "CPP"
-STR_CMAP_SHAP = "SHAP"
-STR_CMAP_TAB = "TAB"
-STR_DICT_COLOR = "DICT_COLOR"
-STR_DICT_CAT = "DICT_CAT"
-
 LIST_FONTS = ['Arial', 'Avant Garde',
               'Bitstream Vera Sans', 'Computer Modern Sans Serif',
               'DejaVu Sans', 'Geneva',
               'Helvetica', 'Lucid',
               'Lucida Grande', 'Verdana']
 
 
-# Helper functions
+# I Helper functions
 # Check plot_settings
 def check_font(font="Arial"):
     """"""
@@ -132,9 +126,10 @@ def _get_cmap_with_gap(n_colors=100, pct_gap=10, pct_center=None,
     return cmap
 
 
-# Default plotting function
+# II Main functions
+# Plotting colors
 def plot_get_cmap(name: str = "CPP",
-                  n_colors: int = 100,
+                  n_colors: int = 101,
                   facecolor_dark: bool = False
                   ) -> Union[List[Tuple[float, float, float]], List[str]]:
     """
@@ -147,17 +142,17 @@ def plot_get_cmap(name: str = "CPP",
 
          - ``CPP``: Continuous color map for CPP plots.
          - ``SHAP``: Continuous color map for CPP-SHP plots.
-         - ``TAB``: List of Tableau (tab) colors for appealing visualization of categories.
+         - ``CAT``: Color list for appealing visualization of categories.
 
     n_colors
-        Number of colors in the color map. Must be >=2 for 'CPP' and 'SHAP' and 2-9 for 'TAB'.
+        Number of colors in the color map. Must be >=2 for 'CPP' and 'SHAP' and 2-9 for 'CAT'.
     facecolor_dark
         Whether central color in 'CPP' and 'SHAP' is black (if ``True``) or white.
 
     Returns
     -------
     list
-        List with colors given as RGB tuples (for 'CPP' and 'SHAP') or matplotlib color names (for 'TAB').
+        List with colors given as RGB tuples (for 'CPP' and 'SHAP') or matplotlib color names (for 'CAT').
 
     Examples
     --------
@@ -167,7 +162,7 @@ def plot_get_cmap(name: str = "CPP",
         >>> import matplotlib.pyplot as plt
         >>> import seaborn as sns
         >>> import aaanalysis as aa
-        >>> colors = aa.plot_get_cmap(name="TAB", n_colors=4)
+        >>> colors = aa.plot_get_cmap(name="CAT", n_colors=4)
         >>> data = {'Classes': ['Class A', 'Class B', 'Class C', "Class D"], 'Values': [23, 27, 43, 38]}
         >>> aa.plot_settings(no_ticks_x=True, font_scale=1.2)
         >>> sns.barplot(x='Classes', y='Values', data=data, palette=colors)
@@ -181,19 +176,19 @@ def plot_get_cmap(name: str = "CPP",
     * `Matplotlib color names <https://matplotlib.org/stable/gallery/color/named_colors.html>`_
     """
     # Check input
-    list_names = [STR_CMAP_CPP, STR_CMAP_SHAP, STR_CMAP_TAB]
+    list_names = [ut.STR_CMAP_CPP, ut.STR_CMAP_SHAP, ut.STR_CMAP_CAT]
     if name not in list_names:
         raise ValueError(f"'name' must be one of following: {list_names}")
     ut.check_bool(name="facecolor_dark", val=facecolor_dark)
 
     # Get color maps
-    if name == STR_CMAP_SHAP:
+    if name == ut.STR_CMAP_SHAP:
         ut.check_non_negative_number(name="n_colors", val=n_colors, min_val=3, just_int=True)
         return _get_shap_cmap(n_colors=n_colors, facecolor_dark=facecolor_dark)
-    elif name == STR_CMAP_CPP:
+    elif name == ut.STR_CMAP_CPP:
         ut.check_non_negative_number(name="n_colors", val=n_colors, min_val=3, just_int=True)
         return _get_cpp_cmap(n_colors=n_colors, facecolor_dark=facecolor_dark)
-    elif name == STR_CMAP_TAB:
+    elif name == ut.STR_CMAP_CAT:
         ut.check_non_negative_number(name="n_colors", val=n_colors, min_val=2, max_val=9, just_int=True)
         return _get_tab_color(n_colors=n_colors)
 
@@ -221,15 +216,16 @@ def plot_get_cdict(name: str = "DICT_COLOR") -> dict:
     >>> dict_color = aa.plot_get_cdict(name="DICT_COLOR")
 
     """
-    list_names = [STR_DICT_COLOR, STR_DICT_CAT]
+    list_names = [ut.STR_DICT_COLOR, ut.STR_DICT_CAT]
     if name not in list_names:
         raise ValueError(f"'name' must be one of following: {list_names}")
-    if name == STR_DICT_COLOR:
+    if name == ut.STR_DICT_COLOR:
         return ut.DICT_COLOR
     else:
         return ut.DICT_COLOR_CAT
 
-# TODO test
+
+# Plotting adjuster
 def plot_settings(font_scale: float = 1,
                   font: str = "Arial",
                   fig_format: str = "pdf",
@@ -310,7 +306,7 @@ def plot_settings(font_scale: float = 1,
         >>> import seaborn as sns
         >>> import aaanalysis as aa
         >>> data = {'Classes': ['Class A', 'Class B', 'Class C'], 'Values': [23, 27, 43]}
-        >>> colors = aa.plot_get_cmap(name="TAB", n_colors=3)
+        >>> colors = aa.plot_get_cmap(name="CAT", n_colors=3)
         >>> aa.plot_settings()
         >>> sns.barplot(x='Classes', y='Values', data=data, palette=colors)
         >>> sns.despine()

aaanalysis/utils.py

@@ -122,6 +122,13 @@ def _folder_path(super_folder, folder_name):
                   "Shape": "tab:cyan",
                   "Structure-Activity": "tab:brown"}
 
+# Parameter options for cmaps and color dicts
+STR_CMAP_CPP = "CPP"
+STR_CMAP_SHAP = "SHAP"
+STR_CMAP_CAT = "CAT"
+STR_DICT_COLOR = "DICT_COLOR"
+STR_DICT_CAT = "DICT_CAT"
+
 
 # II MAIN FUNCTIONS
 # Main Helper functions

docs/build/html/_downloads/2a58656d4cb61bad4f791aca80114c58/aaanalysis-plot_settings-2.py

@@ -2,7 +2,7 @@
 import seaborn as sns
 import aaanalysis as aa
 data = {'Classes': ['Class A', 'Class B', 'Class C'], 'Values': [23, 27, 43]}
-colors = aa.plot_get_cmap(name="TAB", n_colors=3)
+colors = aa.plot_get_cmap(name="CAT", n_colors=3)
 aa.plot_settings()
 sns.barplot(x='Classes', y='Values', data=data, palette=colors)
 sns.despine()

docs/build/html/_downloads/b89a5a5e2e370853db1a08a17e57d011/aaanalysis-plot_get_cmap-1.py

@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 import aaanalysis as aa
-colors = aa.plot_get_cmap(name="TAB", n_colors=4)
+colors = aa.plot_get_cmap(name="CAT", n_colors=4)
 data = {'Classes': ['Class A', 'Class B', 'Class C', "Class D"], 'Values': [23, 27, 43, 38]}
 aa.plot_settings(no_ticks_x=True, font_scale=1.2)
 sns.barplot(x='Classes', y='Values', data=data, palette=colors)