Add module-level docstring stubs, cleanup (#40)

Add module-level docstring stubs, cleanup

src/ccompass/CCMPS.py

@@ -1,4 +1,4 @@
-##### CELLCOMPASS
+"""C-COMPASS main window."""
 
 from . import CCMPS_actions as action
 from . import PPMS
@@ -10,34 +10,18 @@
 from . import MOA
 from . import SM
 from . import RP
-
-
 import copy
 import pickle
-
-# from NPC import NPC
-# from MLO_01_function import MLO, first_prediction
-# from CM_01_function import CM
-# from MM_01_function import MM
-
 from tkinter import messagebox
-
-
-# import runpy
-
 import FreeSimpleGUI as sg
 import pandas as pd
-
 import os
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
-# -----------------------------------------------------------------------------------------------------------------------------
-
-import os
-
 
 def main():
+    """The entry point for the C-COMPASS application."""
     fract_paths, fract_tables, fract_data, fract_pos = action.resetinput()
     fract_data, fract_std, fract_intermediate, fract_info, fract_conditions = (
         action.reset_fract()

src/ccompass/CCMPS_actions.py

@@ -1,3 +1,5 @@
+"""Actions related to the main window."""
+
 import FreeSimpleGUI as sg
 import numpy as np
 import pandas as pd
@@ -7,14 +9,6 @@
 import copy
 
 
-## parameters:
-# ------------------------------------------------------------------------------
-
-
-## internal functions:
-# ------------------------------------------------------------------------------
-
-
 def fract_refreshtable(window, table):
     window["-fractionation_table-"].Update(values=table)
     return
@@ -140,10 +134,6 @@ def tp_clearinput(window):
     return
 
 
-## external functions:
-# ------------------------------------------------------------------------------
-
-
 def reset_infract():
     fract_indata = {}
     fract_identifiers = {}

src/ccompass/CM.py

@@ -1,4 +1,4 @@
-##### CLASS MANAGER
+"""Class manager."""
 
 import FreeSimpleGUI as sg
 import numpy as np

src/ccompass/FDP.py

@@ -1,4 +1,4 @@
-##### FRACTIONATION DATA PROCESSING
+"""Fractionation data processing."""
 
 import copy
 import numpy as np
@@ -10,10 +10,6 @@
 from collections import Counter
 
 
-## internal functions:
-# ------------------------------------------------------------------------------
-
-
 def create_dataset(
     input_data, input_tables, identifiers, conditions, window_FDP, progress
 ):
@@ -471,10 +467,6 @@ def modify_structure(data_in):
     return data_out
 
 
-## run script::
-# ------------------------------------------------------------------------------
-
-
 def FDP_exec(
     window,
     input_tables,

src/ccompass/FSD.py

@@ -1,12 +1,8 @@
-##### FRACTIONATION SUMMARY DISPLAY
+"""Fractionation summary display."""
 
 import FreeSimpleGUI as sg
 
 
-## internal functions:
-# ------------------------------------------------------------------------------
-
-
 def FSD_exec(preparams, data_ways):
     class_string = ""
     for condition in data_ways["class"]:

src/ccompass/MOA.py

@@ -1,5 +1,4 @@
-### MULTIPLE ORGANELLE ANALYSIS
-
+"""Multiple organelle analysis."""
 
 import pandas as pd
 import numpy as np

src/ccompass/MOP.py

@@ -1,11 +1,11 @@
-##### MULTIPLE ORGANELLE PREDICTION
+"""Multiple organelle prediction."""
+
 import numpy as np
 import pandas as pd
 import random
 import copy
 from datetime import datetime
 from scipy import stats
-
 from sklearn import svm
 from sklearn.metrics import (
     accuracy_score,
@@ -14,7 +14,6 @@
     f1_score,
     confusion_matrix,
 )
-
 import tensorflow as tf
 from tensorflow import keras
 import keras_tuner as kt
@@ -23,16 +22,6 @@
 # from tensorflow.keras.models import Model
 
 
-import os
-
-# Define the path to the 'Classifier_Models' folder within the user's data directory
-classifier_directory = get_data_directory() / "CCOMPASS_Models"
-
-# Create the folder if it does not exist
-if not os.path.exists(classifier_directory):
-    os.makedirs(classifier_directory)
-
-
 optimizer_classes = {
     "adam": tf.keras.optimizers.Adam,
     "rmsprop": tf.keras.optimizers.RMSprop,
@@ -822,6 +811,10 @@ def multi_predictions(
     ]
     set_shapes = [np.shape(y_train_mixed_up)[1], np.shape(Z_train_mixed_up)[1]]
 
+    # Define the path to the 'Classifier_Models' folder within the user's data directory
+    classifier_directory = get_data_directory() / "CCOMPASS_Models"
+    classifier_directory.mkdir(exist_ok=True, parents=True)
+
     now = datetime.now()
     time = now.strftime("%Y%m%d%H%M%S")
     FNN_tuner = kt.Hyperband(
@@ -830,7 +823,7 @@ def multi_predictions(
         objective="val_mean_squared_error",
         max_epochs=NN_params["NN_epochs"],
         factor=3,
-        directory=classifier_directory,
+        directory=str(classifier_directory),
         project_name=time + "_Classifier_" + condition + "_" + str(roundn),
     )
 

src/ccompass/MOP_stats.py

@@ -1,4 +1,4 @@
-## MULTI ORGANELLE PREDICTION STATISTICS
+"""Multi-organelle prediction statistics."""
 
 import numpy as np
 import pandas as pd

src/ccompass/NPC.py

@@ -1,9 +1,10 @@
+"""Normalized profile creation"""
+
 import copy
 import numpy as np
 import pandas as pd
 import FreeSimpleGUI as sg
 from tkinter import messagebox, simpledialog
-
 from .NPC_func import (
     create_dataset,
     pre_scaling,
@@ -23,9 +24,6 @@
 )
 
 from datetime import datetime
-# -----------------------------------------------------------------------------
-## Local functions:
-# -----------------------------------------------------------------------------
 
 
 def create_data(path):  # creates initial dataset with corresponding data table

src/ccompass/NPC_func.py

@@ -10,11 +10,6 @@ def pre_process():
     return
 
 
-# -----------------------------------------------------------------------------
-## Local functions:
-# -----------------------------------------------------------------------------
-
-
 def create_condition_dataset(
     data_init, tables_init, conditions, idents
 ):  # creates dictionary containing data sorted by condition

src/ccompass/PPMS.py

@@ -1,4 +1,4 @@
-##### PREPARAMETERS
+"""Preparameters"""
 
 import FreeSimpleGUI as sg
 from . import PPMS_actions as action

src/ccompass/PPMS_actions.py

@@ -1,13 +1,4 @@
-## parameters:
-# ------------------------------------------------------------------------------
-
-
-## internal functions:
-# ------------------------------------------------------------------------------
-
-
-## external functions:
-# ------------------------------------------------------------------------------
+"""Preparameter GUI actions."""
 
 
 def set_combinations(params):

src/ccompass/RP.py

@@ -1,4 +1,5 @@
-### RESULT PLOTS
+"""Result plots"""
+
 import FreeSimpleGUI as sg
 import pandas as pd
 import seaborn as sns

src/ccompass/SM.py

@@ -1,5 +1,4 @@
-## SHOW MARKERS
-
+"""Show markers."""
 
 import FreeSimpleGUI as sg
 import pandas as pd

src/ccompass/SOP.py

@@ -1,4 +1,5 @@
-##### SINGLE ORGANELLE PREDICTION
+"""Single organelle prediction."""
+
 import pandas as pd
 from sklearn import svm
 from sklearn.metrics import (
@@ -8,11 +9,9 @@
     f1_score,
     confusion_matrix,
 )
-
 import matplotlib.pyplot as plt
 import copy
 
-
 clf = svm.SVC(kernel="rbf", probability=True)
 
 

src/ccompass/TM.py

@@ -1,4 +1,4 @@
-##### TEST MARKER
+"""Test marker"""
 
 import FreeSimpleGUI as sg
 import pandas as pd

src/ccompass/TPP.py

@@ -1,4 +1,4 @@
-##### TOTAL PROTEOME PROCESSING
+"""Total proteome profiling."""
 
 import copy
 import math
@@ -9,10 +9,6 @@
 from scipy.stats import pearsonr
 
 
-## internal functions:
-# ------------------------------------------------------------------------------
-
-
 def create_dataset(
     tp_indata, tp_tables, tp_identifiers, tp_conditions, window
 ):
@@ -169,12 +165,6 @@ def normalize_data(data, window):
     return data
 
 
-## run script::
-# ------------------------------------------------------------------------------
-
-# minrep = 2
-
-
 def TPP_exec(
     window,
     tp_data,