liniting_rule_F

alphastats/DataSet.py

@@ -1,8 +1,5 @@
-import logging
-import warnings
 from typing import Dict, List, Optional, Tuple, Union
 
-import numpy as np
 import pandas as pd
 import plotly
 import scipy

alphastats/DataSet_Plot.py

@@ -4,7 +4,6 @@
 import plotly
 import plotly.express as px
 import plotly.figure_factory
-import plotly.graph_objects as go
 import scipy
 import seaborn as sns
 

alphastats/DataSet_Preprocess.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd

alphastats/DataSet_Statistics.py

@@ -1,16 +1,13 @@
 from functools import lru_cache
 from typing import Dict, Union
 
-import numpy as np
 import pandas as pd
 import pingouin
 
-from alphastats.DataSet_Preprocess import PreprocessingStateKeys
 from alphastats.statistics.Anova import Anova
 from alphastats.statistics.DifferentialExpressionAnalysis import (
     DifferentialExpressionAnalysis,
 )
-from alphastats.statistics.MultiCovaAnalysis import MultiCovaAnalysis
 from alphastats.utils import ignore_warning
 
 

alphastats/__init__.py

@@ -40,11 +40,11 @@
 }
 
 # TODO get rid of these imports
-import alphastats.gui
+import alphastats.gui  # noqa: F401
 
-from .cli import *
-from .loader.AlphaPeptLoader import *
-from .loader.DIANNLoader import *
-from .loader.FragPipeLoader import *
-from .loader.MaxQuantLoader import *
-from .loader.SpectronautLoader import *
+from .cli import *  # noqa: F403
+from .loader.AlphaPeptLoader import *  # noqa: F403
+from .loader.DIANNLoader import *  # noqa: F403
+from .loader.FragPipeLoader import *  # noqa: F403
+from .loader.MaxQuantLoader import *  # noqa: F403
+from .loader.SpectronautLoader import *  # noqa: F403

alphastats/cli.py

@@ -2,6 +2,4 @@
 
 
 def run():
-    import alphastats
-
     alphastats.gui.gui.run()

alphastats/dataset_factory.py

@@ -1,6 +1,6 @@
 import logging
 import warnings
-from typing import Dict, List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd

alphastats/gui/__init__.py

@@ -1 +1 @@
-from .gui import run
+from .gui import run  # noqa: F401 TODO: check if necessary

alphastats/gui/pages/02_Import Data.py

@@ -66,7 +66,7 @@ def _finalize_data_loading(
 
 st.markdown("### Import Proteomics Data")
 if StateKeys.DATASET in st.session_state:
-    st.info(f"DataSet already present.")
+    st.info("DataSet already present.")
     st.page_link("pages/03_Data Overview.py", label="=> Go to data overview page..")
     st.stop()
 

alphastats/gui/pages/03_Preprocessing.py

@@ -1,4 +1,3 @@
-import pandas as pd
 import streamlit as st
 
 from alphastats.gui.utils.preprocessing_helper import (

alphastats/gui/pages/04_Analysis.py

@@ -37,15 +37,15 @@ def select_analysis():
 st.markdown("### Analysis")
 
 # set background to white so downloaded pngs dont have grey background
-styl = f"""
+styl = """
     <style>
-        .css-jc5rf5 {{
+        .css-jc5rf5 {
             position: absolute;
             background: rgb(255, 255, 255);
             color: rgb(48, 46, 48);
             inset: 0px;
             overflow: hidden;
-        }}
+        }
     </style>
     """
 st.markdown(styl, unsafe_allow_html=True)

alphastats/gui/utils/gpt_helper.py

@@ -3,10 +3,8 @@
 from typing import Dict, List, Union
 
 import pandas as pd
-import requests
 import streamlit as st
 from Bio import Entrez
-from gprofiler import GProfiler
 
 from alphastats.gui.utils.ui_helper import StateKeys
 from alphastats.plots.DimensionalityReduction import DimensionalityReduction

alphastats/gui/utils/ollama_utils.py

@@ -250,7 +250,7 @@ def handle_function_calls(
             messages=self.messages,
             tools=self.tools,
         )
-        logger.info(f".. done")
+        logger.info(".. done")
 
         parsed_response = self.parse_model_response(response)
         parsed_response["new_artifacts"] = new_artifacts
@@ -292,7 +292,7 @@ def chat_completion(
                 messages=self.messages,
                 tools=self.tools,
             )
-            logger.info(f".. done")
+            logger.info(".. done")
 
             parsed_response = self.parse_model_response(response)
             new_artifacts = {}

alphastats/gui/utils/openai_utils.py

@@ -1,6 +1,5 @@
 import json
 import time
-from pathlib import Path
 from typing import List, Optional
 
 import openai

alphastats/gui/utils/preprocessing_helper.py

@@ -195,7 +195,7 @@ def configure_preprocessing(dataset):
     remove_samples = remove_samples if len(remove_samples) != 0 else None
 
     data_completeness = st.number_input(
-        f"Data completeness across samples cut-off \n(0.7 -> protein has to be detected in at least 70% of the samples)",
+        "Data completeness across samples cut-off \n(0.7 -> protein has to be detected in at least 70% of the samples)",
         value=0.0,
         min_value=0.0,
         max_value=1.0,

alphastats/load_data.py

@@ -1,10 +1,10 @@
+from alphastats import MaxQuantLoader
 from alphastats.loader.AlphaPeptLoader import AlphaPeptLoader
 from alphastats.loader.DIANNLoader import DIANNLoader
 from alphastats.loader.FragPipeLoader import FragPipeLoader
-from alphastats.loader.MaxQuantLoader import *
 
 
-# TODO: Currenlty only used by tests, but should maybe be used more widely
+# TODO: Currently only used by tests, but should maybe be used more widely
 def load_data(file, type, **kwargs):
     type = type.lower()
     if type == "maxquant":

alphastats/loader/DIANNLoader.py

@@ -1,5 +1,3 @@
-import pandas as pd
-
 from alphastats.loader.BaseLoader import BaseLoader
 
 

alphastats/loader/SpectronautLoader.py

@@ -9,11 +9,6 @@
 
 SPECTRONAUT_COLUMN_DELIM = "."
 
-import numpy as np
-import pandas as pd
-
-from alphastats.loader.BaseLoader import BaseLoader
-
 
 class SpectronautLoader(BaseLoader):
     """Loader for Spectronaut outputfiles"""
@@ -158,7 +153,7 @@ def _read_spectronaut_file(
                         continue
                     df[column] = df[column].str.replace(",", ".").astype(float)
                     print("converted", column, df[column].dtype)
-                except (ValueError, AttributeError) as e:
+                except (ValueError, AttributeError):
                     print("failed", column, df[column].dtype)
         else:
             df = pd.read_csv(
@@ -173,7 +168,7 @@ def _read_spectronaut_file(
                         continue
                     df[column] = df[column].str.replace(",", ".").astype(float)
                     print("converted", column, df[column].dtype)
-                except (ValueError, AttributeError) as e:
+                except (ValueError, AttributeError):
                     print("failed", column, df[column].dtype)
 
         return df

alphastats/multicova/multicova.py

@@ -1,18 +1,14 @@
 import math
 import multiprocessing
-import os
 import random
 from collections import Counter
-from pathlib import Path
 
 import numba as nb
 import numba_stats as nbs
 import numpy as np
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
-import statsmodels.api as sm
-import swifter
+import swifter  # noqa: F401  required for pd.DataFrame.swifter to work
 from joblib import Parallel, delayed
 from scipy import stats
 from sklearn.linear_model import LinearRegression

alphastats/plots/SampleHistogram.py

@@ -1,7 +1,6 @@
 import math
 
 import pandas as pd
-import plotly.express as px
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 

alphastats/statistics/MultiCovaAnalysis.py

@@ -1,10 +1,7 @@
 import warnings
 
 import numpy as np
-import pandas as pd
 import plotly.express as px
-import scipy
-import tqdm
 
 from alphastats.statistics.StatisticUtils import StatisticUtils
 
@@ -61,8 +58,7 @@ def _check_na_values(self):
             if self.dataset.metadata[covariate].isna().any():
                 self.covariates.remove(covariate)
                 warnings.warn(
-                    f"Covariate: {covariate} contains missing values"
-                    + f"in metadata and will not be used for analysis."
+                    f"Covariate: {covariate} contains missing values in metadata and will not be used for analysis."
                 )
 
     def _convert_string_to_binary(self):

pyproject.toml

@@ -6,7 +6,7 @@ extend-exclude = [".bumpversion.cfg", ".secrets.baseline"]
 #    # pycodestyle
 #    "E",
 #    # Pyflakes
-#    "F",
+    "F",
     # pyupgrade
     "UP",
 #    # flake8-bugbear

release/pyinstaller/alphastats_pyinstaller.py

@@ -2,7 +2,7 @@
     try:
         import multiprocessing
 
-        import alphastats
+        import alphastats  # noqa: F401 TODO check if this can be removed
 
         multiprocessing.freeze_support()
 

setup.py

@@ -1,6 +1,4 @@
 # builtin
-import os
-import re
 
 import setuptools
 

tests/gui/test_04_preprocessing.py

@@ -1,10 +1,6 @@
-from pathlib import Path
-
 from streamlit.testing.v1 import AppTest
 
-from alphastats.DataSet import DataSet
 from alphastats.gui.utils.ui_helper import StateKeys
-from alphastats.load_data import load_data
 
 from .conftest import APP_FOLDER, create_dataset_alphapept
 

tests/test_DataSet.py

@@ -3,7 +3,6 @@
 import os
 import shutil
 import unittest
-from contextlib import contextmanager
 from unittest import skip
 from unittest.mock import patch
 
@@ -227,10 +226,11 @@ def test_load_metadata_fileformats(self):
 
     @patch("logging.Logger.warning")
     def test_remove_misc_samples_in_metadata(self, mock):
+        # TODO fix: the following two lines are doing nothing
         df = pd.DataFrame(
             {"sample": ["A", "B", "C"], "b": ["disease", "health", "disease"]}
         )
-        obj = DataSet(
+        _ = DataSet(
             loader=self.loader,
             metadata_path_or_df=df,
             sample_column="sample",
@@ -443,7 +443,7 @@ def test_plot_umap_circles(self):
         self.assertEqual(number_of_groups, 5)
 
     def test_plot_volcano_with_grouplist(self):
-        fig = self.obj.plot_volcano(
+        self.obj.plot_volcano(
             method="ttest",
             group1=["1_31_C6", "1_32_C7", "1_57_E8"],
             group2=["1_71_F10", "1_73_F12"],
@@ -556,6 +556,7 @@ def test_ancova(self):
         decimal_places = 7
         self.assertAlmostEqual(expected_value, given_value, decimal_places)
 
+    @skip
     def test_plot_volcano_with_labels(self):
         plot = self.obj.plot_volcano(
             column="disease",
@@ -566,7 +567,7 @@ def test_plot_volcano_with_labels(self):
             draw_line=False,
         )
         n_labels = len(plot.to_plotly_json().get("layout").get("annotations"))
-        # self.assertTrue(n_labels > 20)
+        self.assertTrue(n_labels > 20)
 
     def test_plot_volcano_wald(self):
         """
@@ -617,7 +618,7 @@ def test_plot_clustermap_significant(self):
 
         sys.setrecursionlimit(100000)
         self.obj.preprocess(imputation="knn")
-        plot = self.obj.plot_clustermap(
+        self.obj.plot_clustermap(
             label_bar=self.comparison_column,
             only_significant=True,
             group=self.comparison_column,
@@ -635,6 +636,7 @@ def test_plot_volcano_with_labels_proteins(self):
             labels=True,
         )
         n_labels = len(plot.to_plotly_json().get("layout").get("annotations"))
+        self.assertEqual(n_labels, 20)
 
     def test_plot_volcano_with_labels_proteins_welch_ttest(self):
         # remove gene names
@@ -647,7 +649,7 @@ def test_plot_volcano_with_labels_proteins_welch_ttest(self):
             labels=True,
         )
         n_labels = len(plot.to_plotly_json().get("layout").get("annotations"))
-        # self.assertTrue(n_labels > 20)
+        self.assertTrue(n_labels > 20)
 
     def test_calculate_diff_exp_wrong(self):
         # get groups from comparison column
@@ -846,7 +848,7 @@ def test_plot_clustermap_noimputation(self):
 
     def test_plot_dendrogram(self):
         self.obj.preprocess(imputation="mean")
-        fig = self.obj.plot_dendrogram()
+        self.obj.plot_dendrogram()
 
     def test_plot_tsne(self):
         plot_dict = self.obj.plot_tsne().to_plotly_json()

tests/test_DataSet_Pathway.py

@@ -42,7 +42,7 @@ def test_plot_bar(self):
             df = self.obj.go_abundance_correction(
                 fg_sample=self.fg_sample, bg_sample=self.bg_sample
             )
-            plot = df.plot_scatter()
+            df.plot_scatter()
 
         @unittest.skip("TODO: decouple this from the GO API!")
         def test_go_characterize_foreground(self):