apply unsafe auto-fixes for ruff rule SIM

.pre-commit-config.yaml

@@ -12,7 +12,7 @@ repos:
   hooks:
     - id: ruff-format
     - id: ruff
-      args: ['--fix']
+      args: ['--unsafe-fixes', '--fix']
 -   repo: https://github.com/Yelp/detect-secrets
     rev: v1.5.0
     hooks:

alphastats/DataSet_Preprocess.py

@@ -128,9 +128,8 @@ def _remove_na_values(self, cut_off):
             count = column.isna().sum()
             try:
                 count = count.item()
-                if isinstance(count, int):
-                    if count < limit:
-                        keep_list += [column_name]
+                if isinstance(count, int) and count < limit:
+                    keep_list += [column_name]
 
             except ValueError:
                 invalid += 1
@@ -402,7 +401,7 @@ def preprocess(
             imputation (str, optional):  method to impute data: either "mean", "median", "knn" or "randomforest". Defaults to None.
             subset (bool, optional): filter matrix so only samples that are described in metadata found in matrix. Defaults to False.
         """
-        for k in kwargs.keys():
+        for k in kwargs:
             if k not in [
                 "batch",
             ]:

alphastats/gui/pages/04_Analysis.py

@@ -64,13 +64,13 @@ def select_analysis():
     with c1:
         method = select_analysis()
 
-        if method in st.session_state.plotting_options.keys():
+        if method in st.session_state.plotting_options:
             analysis_result = get_analysis(
                 method=method, options_dict=st.session_state.plotting_options
             )
             plot_to_display = True
 
-        elif method in st.session_state.statistic_options.keys():
+        elif method in st.session_state.statistic_options:
             analysis_result = get_analysis(
                 method=method, options_dict=st.session_state.statistic_options
             )

alphastats/gui/pages/05_GPT.py

@@ -1,3 +1,4 @@
+import contextlib
 import os
 
 import pandas as pd
@@ -105,10 +106,8 @@ def select_analysis():
 
     try_to_set_api_key(api_key)
 
-    try:
+    with contextlib.suppress(OpenAIError):
         client = OpenAI(api_key=st.secrets["openai_api_key"])
-    except OpenAIError:
-        pass
     method = st.selectbox(
         "Differential Analysis using:",
         options=["ttest", "anova", "wald", "sam", "paired-ttest", "welch-ttest"],

alphastats/gui/utils/analysis_helper.py

@@ -91,7 +91,7 @@ def st_general(method_dict):
         for parameter in settings_dict:
             parameter_dict = settings_dict[parameter]
 
-            if "options" in parameter_dict.keys():
+            if "options" in parameter_dict:
                 chosen_parameter = st.selectbox(
                     parameter_dict.get("label"), options=parameter_dict.get("options")
                 )
@@ -200,7 +200,7 @@ def get_analysis_options_from_dict(method, options_dict):
     elif method == "UMAP Plot":
         return st_plot_umap(method_dict)
 
-    elif "settings" not in method_dict.keys():
+    elif "settings" not in method_dict:
         if st.session_state.dataset.mat.isna().values.any() == True:
             st.error(
                 "Data contains missing values impute your data before plotting (Preprocessing - Imputation)."
@@ -342,7 +342,7 @@ def helper_compare_two_groups():
 
 
 def get_analysis(method, options_dict):
-    if method in options_dict.keys():
+    if method in options_dict:
         obj = get_analysis_options_from_dict(method, options_dict=options_dict)
         return obj
 

alphastats/gui/utils/ui_helper.py

@@ -72,7 +72,7 @@ def empty_session_state():
     """
     remove all variables to avoid conflicts
     """
-    for key in st.session_state.keys():
+    for key in st.session_state:
         del st.session_state[key]
     st.empty()
 

alphastats/loader/AlphaPeptLoader.py

@@ -77,10 +77,7 @@ def standardize_protein_group_column(entry: str) -> str:
         protein_id_list = []
         for protein in proteins:
             # 'sp|P0DMV9|HS71B_HUMAN,sp|P0DMV8|HS71A_HUMAN',
-            if "|" in protein:
-                fasta_header_split = protein.split("|")
-            else:
-                fasta_header_split = protein
+            fasta_header_split = protein.split("|") if "|" in protein else protein
             if isinstance(fasta_header_split, str):
                 #  'ENSEMBL:ENSBTAP00000007350',
                 if "ENSEMBL:" in fasta_header_split:

alphastats/multicova/multicova.py

@@ -145,10 +145,7 @@ def get_tstat_cutoff(res_real, t_perm_avg, delta):
     # between the observed and average random t-stat is
     # larger than the selected delta.
     t_max = t_real_abs[t_diff > delta]
-    if t_max.shape[0] == 0:
-        t_max = np.ceil(np.max(t_real_abs))
-    else:
-        t_max = np.min(t_max)
+    t_max = np.ceil(np.max(t_real_abs)) if t_max.shape[0] == 0 else np.min(t_max)
     return t_max
 
 
@@ -329,10 +326,9 @@ def get_fdr_line(
     for i in np.arange(0, len(fc_s)):
         for j in np.arange(0, len(s_s)):
             res_s = perform_ttest_getMaxS(fc=fc_s[i], s=s_s[j], s0=s0, n_x=n_x, n_y=n_y)
-            if res_s[3] >= t_limit:
-                if svals[i] < s_s[j]:
-                    svals[i] = s_s[j]
-                    pvals[i] = res_s[2]
+            if res_s[3] >= t_limit and svals[i] < s_s[j]:
+                svals[i] = s_s[j]
+                pvals[i] = res_s[2]
 
     s_df = pd.DataFrame(
         np.array([fc_s, svals, pvals]).T, columns=["fc_s", "svals", "pvals"]

tests/test_DataSet.py

@@ -369,7 +369,7 @@ def test_plot_sampledistribution_group(self):
         # check if there are two groups control and disease
         self.assertEqual(plot_dict.get("data")[0].get("legendgroup"), "control")
         #  check that it is boxplot and not violinplot
-        is_boxplot = "boxmode" in plot_dict.get("layout").keys()
+        is_boxplot = "boxmode" in plot_dict.get("layout")
         self.assertTrue(is_boxplot)
 
     def test_plot_correlation_matrix(self):
@@ -826,7 +826,7 @@ def test_plot_intensity_box(self):
         plot_dict = plot.to_plotly_json()
         #  log scale
         self.assertEqual(plot_dict.get("layout").get("yaxis").get("type"), "log")
-        is_boxplot = "boxmode" in plot_dict.get("layout").keys()
+        is_boxplot = "boxmode" in plot_dict.get("layout")
         self.assertTrue(is_boxplot)
 
     def test_plot_intensity_scatter(self):