lint: isort

pyproject.toml

@@ -35,6 +35,9 @@ line-length = 79
 lint.ignore = [
     "E712",  # Avoid equality comparisons to True/False
 ]
+lint.extend-select = [
+    "I",
+]
 
 [tool.tox]
 requires = ["tox>=4.19"]

src/ccompass/CCMPS.py

@@ -1,22 +1,16 @@
 """C-COMPASS main window."""
 
-from . import CCMPS_actions as action
-from . import PPMS
-from . import FDP
-from . import TPP
-from . import CM
-from . import TM
-from . import MOP
-from . import MOA
-from . import SM
-from . import RP
 import copy
+import os
 import pickle
+from pathlib import Path
 from tkinter import messagebox
+
 import FreeSimpleGUI as sg
 import pandas as pd
-import os
-from pathlib import Path
+
+from . import CM, FDP, MOA, MOP, PPMS, RP, SM, TM, TPP
+from . import CCMPS_actions as action
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 

src/ccompass/CCMPS_actions.py

@@ -1,12 +1,13 @@
 """Actions related to the main window."""
 
+import copy
+import random
+from datetime import datetime
+from tkinter import messagebox, simpledialog
+
 import FreeSimpleGUI as sg
 import numpy as np
 import pandas as pd
-from tkinter import messagebox, simpledialog
-from datetime import datetime
-import random
-import copy
 
 
 def fract_refreshtable(window, table):

src/ccompass/FDP.py

@@ -1,13 +1,14 @@
 """Fractionation data processing."""
 
 import copy
+from collections import Counter
+from tkinter import messagebox
+
+import FreeSimpleGUI as sg
 import numpy as np
 import pandas as pd
-import FreeSimpleGUI as sg
-from tkinter import messagebox
-from sklearn.preprocessing import MinMaxScaler
 from scipy.stats import pearsonr
-from collections import Counter
+from sklearn.preprocessing import MinMaxScaler
 
 
 def create_dataset(

src/ccompass/MOA.py

@@ -1,9 +1,9 @@
 """Multiple organelle analysis."""
 
-import pandas as pd
 import numpy as np
-from scipy.stats import ttest_ind
+import pandas as pd
 from scipy import stats
+from scipy.stats import ttest_ind
 
 
 def most_frequent_or_nan(row):

src/ccompass/MOP.py

@@ -1,24 +1,27 @@
 """Multiple organelle prediction."""
 
-import numpy as np
-import pandas as pd
-import random
 import copy
+import random
 from datetime import datetime
+
+import keras.backend as K
+import keras_tuner as kt
+import numpy as np
+import pandas as pd
+import tensorflow as tf
 from scipy import stats
 from sklearn import svm
 from sklearn.metrics import (
     accuracy_score,
+    confusion_matrix,
+    f1_score,
     precision_score,
     recall_score,
-    f1_score,
-    confusion_matrix,
 )
-import tensorflow as tf
 from tensorflow import keras
-import keras_tuner as kt
-import keras.backend as K
+
 from ._utils import get_data_directory
+
 # from tensorflow.keras.models import Model
 
 

src/ccompass/MOP_stats.py

@@ -3,8 +3,7 @@
 import numpy as np
 import pandas as pd
 from scipy import stats
-from scipy.stats import f
-from scipy.stats import ttest_ind
+from scipy.stats import f, ttest_ind
 
 
 def reduce_to_single_column(row):

src/ccompass/NPC.py

@@ -1,30 +1,31 @@
 """Normalized profile creation"""
 
 import copy
+from datetime import datetime
+from tkinter import messagebox, simpledialog
+
+import FreeSimpleGUI as sg
 import numpy as np
 import pandas as pd
-import FreeSimpleGUI as sg
-from tkinter import messagebox, simpledialog
+
 from .NPC_func import (
+    calculate_icorr,
+    calculate_ocorr,
+    create_concat,
     create_dataset,
-    pre_scaling,
-    filter_missing,
-    filter_empty,
+    create_median,
+    create_separate,
     filter_count,
+    filter_empty,
+    filter_missing,
+    implement_icorr,
     list_samples,
-    calculate_icorr,
+    pre_scaling,
     remove_worst,
-    implement_icorr,
-    create_median,
-    create_concat,
-    create_separate,
     remove_zeros,
-    calculate_ocorr,
     scale_area,
 )
 
-from datetime import datetime
-
 
 def create_data(path):  # creates initial dataset with corresponding data table
     data = pd.read_csv(path, sep="\t", header=0)

src/ccompass/NPC_func.py

@@ -1,6 +1,7 @@
+from collections import Counter
+
 import numpy as np
 import pandas as pd
-from collections import Counter
 from scipy.stats import pearsonr
 from sklearn.preprocessing import MinMaxScaler
 

src/ccompass/PPMS.py

@@ -1,8 +1,10 @@
 """Preparameters"""
 
+import copy
+
 import FreeSimpleGUI as sg
+
 from . import PPMS_actions as action
-import copy
 
 
 def fract_default():

src/ccompass/RP.py

@@ -1,15 +1,16 @@
 """Result plots"""
 
+import os
+from io import BytesIO
+
 import FreeSimpleGUI as sg
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
 import seaborn as sns
-import matplotlib.pyplot as plt
-from scipy.cluster.hierarchy import linkage, leaves_list
-from io import BytesIO
 from PIL import Image
-import matplotlib.colors as mcolors
-import os
-import numpy as np
+from scipy.cluster.hierarchy import leaves_list, linkage
 from scipy.stats import zscore
 
 

src/ccompass/SM.py

@@ -1,10 +1,11 @@
 """Show markers."""
 
+import os
+
 import FreeSimpleGUI as sg
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
-import os
 
 
 def draw_figure(canvas, figure):

src/ccompass/SOP.py

@@ -1,16 +1,17 @@
 """Single organelle prediction."""
 
+import copy
+
+import matplotlib.pyplot as plt
 import pandas as pd
 from sklearn import svm
 from sklearn.metrics import (
     accuracy_score,
+    confusion_matrix,
+    f1_score,
     precision_score,
     recall_score,
-    f1_score,
-    confusion_matrix,
 )
-import matplotlib.pyplot as plt
-import copy
 
 clf = svm.SVC(kernel="rbf", probability=True)
 

src/ccompass/TM.py

@@ -1,11 +1,12 @@
 """Test marker"""
 
+import os
+
 import FreeSimpleGUI as sg
-import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
-import os
 
 
 def draw_figure(canvas, figure):

src/ccompass/TPP.py

@@ -2,10 +2,11 @@
 
 import copy
 import math
+from tkinter import messagebox
+
+import FreeSimpleGUI as sg
 import numpy as np
 import pandas as pd
-import FreeSimpleGUI as sg
-from tkinter import messagebox
 from scipy.stats import pearsonr