230 basic linting rule e

.pre-commit-config.yaml

@@ -11,4 +11,4 @@ repos:
   rev: v0.4.9
   hooks:
     - id: ruff-format
-    #- id: ruff
+    - id: ruff

alphadia/calibration/property.py

@@ -171,7 +171,7 @@ def fit(self, dataframe: pd.DataFrame, plot: bool = False, **kwargs):
             logging.error(f"Could not fit estimator {self.name}: {e}")
             return
 
-        if plot == True:
+        if plot is True:
             self.plot(dataframe, **kwargs)
 
     def predict(self, dataframe, inplace=True):
@@ -192,7 +192,7 @@ def predict(self, dataframe, inplace=True):
 
         """
 
-        if self.is_fitted == False:
+        if self.is_fitted is False:
             logging.warning(
                 f"{self.name} prediction was skipped as it has not been fitted yet"
             )

alphadia/cli.py

@@ -9,8 +9,6 @@
 import re
 import json
 
-logger = logging.getLogger()
-
 # alphadia imports
 import alphadia
 from alphadia.workflow import reporting
@@ -22,6 +20,10 @@
 # third party imports
 import argparse
 
+
+logger = logging.getLogger()
+
+
 parser = argparse.ArgumentParser(description="Search DIA experiments with alphaDIA")
 parser.add_argument(
     "--version",

alphadia/data/alpharaw.py

@@ -3,15 +3,15 @@
 import os
 import logging
 
-logger = logging.getLogger()
-
 # alphadia imports
 from alphadia import utils
 
 from alphadia.data.stats import log_stats
 
 # alpha family imports
-from alpharaw import thermo as alpharawthermo
+from alpharaw import (
+    thermo as alpharawthermo,
+)  # TODO fix: "import resolves to its containing file"
 from alpharaw import sciex as alpharawsciex
 from alpharaw import mzml as alpharawmzml
 
@@ -20,6 +20,8 @@
 import numba as nb
 import pandas as pd
 
+logger = logging.getLogger()
+
 
 @nb.njit(parallel=False, fastmath=True)
 def search_sorted_left(slice, value):

alphadia/data/bruker.py

@@ -3,8 +3,6 @@
 import os
 import logging
 
-logger = logging.getLogger()
-
 # alphadia imports
 from alphadia import utils
 from alphadia.data.stats import log_stats
@@ -20,6 +18,8 @@
 from numba.core import types
 from numba.experimental import jitclass
 
+logger = logging.getLogger()
+
 
 class TimsTOFTranspose(alphatims.bruker.TimsTOF):
     """Transposed TimsTOF data structure."""

alphadia/fdr.py

@@ -2,8 +2,6 @@
 import os
 import logging
 
-logger = logging.getLogger()
-
 # alphadia imports
 
 # alpha family imports
@@ -18,6 +16,8 @@
 
 from typing import Union, Optional, List
 
+logger = logging.getLogger()
+
 
 def perform_fdr(
     classifier: sklearn.base.BaseEstimator,

alphadia/fragcomp.py

@@ -4,9 +4,10 @@
 import numpy as np
 import pandas as pd
 
-logger = logging.getLogger(__name__)
 import numba as nb
 
+logger = logging.getLogger(__name__)
+
 
 @nb.njit
 def get_fragment_overlap(

alphadia/libtransform.py

@@ -2,7 +2,6 @@
 from pathlib import Path
 import logging
 
-logger = logging.getLogger()
 import os
 import typing
 
@@ -26,6 +25,8 @@
 import numpy as np
 import pandas as pd
 
+logger = logging.getLogger()
+
 
 class ProcessingStep:
     def __init__(self) -> None:

alphadia/numba/config.py

@@ -140,7 +140,7 @@ def __init__(self):
             if not isinstance(value, type(getattr(self, key))):
                 try:
                     value = type(getattr(self, key))(value)
-                except:
+                except Exception as e:
                     self.reporter.log_string(
                         f"Parameter {key} has wrong type {type(value)}",
                         verbosity="error",
@@ -151,7 +151,7 @@ def __init__(self):
                 if value.dtype != getattr(self, key).dtype:
                     try:
                         value = value.astype(getattr(self, key).dtype)
-                    except:
+                    except Exception as e:
                         self.reporter.log_string(
                             f"Parameter {key} has wrong dtype {value.dtype}",
                             verbosity="error",

alphadia/numba/fragments.py

@@ -9,6 +9,8 @@
 from numba.extending import overload_method
 import numpy as np
 
+import numba as nb
+
 
 @nb.experimental.jitclass()
 class FragmentContainer:
@@ -258,9 +260,6 @@ def slice_manual(inst, slices):
     return f
 
 
-import numba as nb
-
-
 @nb.njit
 def get_ion_group_mapping(
     ion_precursor,

alphadia/outputtransform.py

@@ -2,7 +2,6 @@
 import logging
 import os
 
-logger = logging.getLogger()
 
 from alphadia import grouping, libtransform, utils
 from alphadia import fdr

alphadia/peakgroup/kernel.py

@@ -1,7 +1,6 @@
 # native imports
 import logging
 
-logger = logging.getLogger()
 import typing
 
 # alphadia imports
@@ -14,6 +13,9 @@
 import numba as nb
 
 
+logger = logging.getLogger()
+
+
 @nb.njit()
 def multivariate_normal(x: np.ndarray, mu: np.ndarray, sigma: np.ndarray):
     """multivariate normal distribution, probability density function

alphadia/peakgroup/search.py

@@ -1,7 +1,6 @@
 # native imports
 import logging
 
-logger = logging.getLogger()
 import os
 
 # alphadia imports
@@ -20,6 +19,9 @@
 import pandas as pd
 
 
+logger = logging.getLogger()
+
+
 @nb.experimental.jitclass()
 class HybridCandidateConfigJIT:
     """

alphadia/planning.py

@@ -1,7 +1,6 @@
 # native imports
 import logging
 
-logger = logging.getLogger()
 import socket
 from pathlib import Path
 import os
@@ -28,6 +27,9 @@
 
 from alphabase.constants import modification
 
+
+logger = logging.getLogger()
+
 modification.add_new_modifications(
     {
         "Dimethyl:d12@Protein N-term": {"composition": "H(-2)2H(8)13C(2)"},

alphadia/plexscoring.py

@@ -1,7 +1,6 @@
 # native imports
 import logging
 
-logger = logging.getLogger()
 import typing
 
 # alphadia imports
@@ -24,6 +23,11 @@
 import numpy as np
 import numba as nb
 
+
+from alphadia.numba import config
+
+logger = logging.getLogger()
+
 NUM_FEATURES = 46
 
 
@@ -154,9 +158,6 @@ def multiplex_candidates(
     return multiplexed_candidates_df
 
 
-from alphadia.numba import config
-
-
 @nb.experimental.jitclass()
 class CandidateConfigJIT:
     collect_fragments: nb.boolean
@@ -584,14 +585,14 @@ def process(
         for i in range(_dense_precursors.shape[1]):
             dense_precursors[0, i, 0] = np.sum(_dense_precursors[0, i], axis=0)
             for k in range(_dense_precursors.shape[3]):
-                for l in range(_dense_precursors.shape[4]):
+                for ll in range(_dense_precursors.shape[4]):
                     sum = 0
                     count = 0
                     for j in range(_dense_precursors.shape[2]):
-                        sum += _dense_precursors[1, i, j, k, l]
-                        if _dense_precursors[1, i, j, k, l] > 0:
+                        sum += _dense_precursors[1, i, j, k, ll]
+                        if _dense_precursors[1, i, j, k, ll] > 0:
                             count += 1
-                    dense_precursors[1, i, 0, k, l] = sum / (count + 1e-6)
+                    dense_precursors[1, i, 0, k, ll] = sum / (count + 1e-6)
 
         # DEBUG only used for debugging
         # self.dense_precursors = dense_precursors

alphadia/plotting/utils.py

@@ -35,7 +35,7 @@ def lighten_color(color, amount=0.5):
 
     try:
         c = mc.cnames[color]
-    except:
+    except KeyError:
         c = color
     c = colorsys.rgb_to_hls(*mc.to_rgb(c))
     return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])

alphadia/test_data_downloader.py

@@ -74,7 +74,7 @@ def _get_filename(self) -> str:  # pragma: no cover
         """Get filename from url."""
         try:
             remotefile = urlopen(self._encoded_url)
-        except:
+        except Exception:
             print(f"Could not open {self._url} for reading filename")
             raise ValueError(
                 f"Could not open {self._url} for reading filename"

alphadia/utils.py

@@ -6,7 +6,6 @@
 import platform
 import torch
 
-logger = logging.getLogger()
 
 # alphadia imports
 
@@ -20,6 +19,8 @@
 import numba as nb
 import matplotlib.patches as patches
 
+logger = logging.getLogger()
+
 
 ISOTOPE_DIFF = 1.0032999999999674
 
@@ -307,7 +308,7 @@ def get_isotope_columns(colnames):
         if col[:2] == "i_":
             try:
                 isotopes.append(int(col[2:]))
-            except:
+            except Exception:
                 logging.warning(
                     f"Column {col} does not seem to be a valid isotope column"
                 )
@@ -655,10 +656,10 @@ def merge_missing_columns(
         Merged left dataframe
 
     """
-    if type(on) == str:
+    if isinstance(on, str):
         on = [on]
 
-    if type(right_columns) == str:
+    if isinstance(right_columns, str):
         right_columns = [right_columns]
 
     missing_from_left = list(set(right_columns) - set(left_df.columns))

alphadia/validate.py

@@ -1,8 +1,6 @@
 # native imports
 import logging
 
-logger = logging.getLogger()
-
 # alphadia imports
 
 # alpha family imports
@@ -11,6 +9,8 @@
 import pandas as pd
 import numpy as np
 
+logger = logging.getLogger()
+
 
 class Property:
     """Column property base class"""

alphadia/workflow/base.py

@@ -2,7 +2,6 @@
 import os
 import logging
 
-logger = logging.getLogger()
 import typing
 
 # alphadia imports
@@ -14,6 +13,8 @@
 
 # third party imports
 
+logger = logging.getLogger()
+
 TEMP_FOLDER = ".progress"