230 basic linting rule f remove unused

alphadia/data/bruker.py

@@ -693,9 +693,6 @@ def assemble_push_intensity(
             len(unique_precursor_index)
         )
 
-        relative_precursor_index = precursor_index_reverse[precursor_index]  # noqa: F841  # TODO check for potential bug then remove this line
-
-        n_precursor_indices = len(unique_precursor_index)  # noqa: F841  # TODO check for potential bug then remove this line
         n_tof_slices = len(tof_limits)
 
         # scan valuesa

alphadia/fdr.py

@@ -344,10 +344,8 @@ def plot_fdr(
     """
 
     y_test_proba = classifier.predict_proba(X_test)[:, 1]
-    y_test_pred = np.round(y_test_proba)  # noqa: F841  # TODO check for potential bug then remove this line
 
     y_train_proba = classifier.predict_proba(X_train)[:, 1]
-    y_train_pred = np.round(y_train_proba)  # noqa: F841  # TODO check for potential bug then remove this line
 
     fpr_test, tpr_test, thresholds_test = sklearn.metrics.roc_curve(
         y_test, y_test_proba

alphadia/features.py

@@ -465,14 +465,6 @@ def build_features(
         dense_precursors[0], p_expected_scan_center, p_expected_frame_center
     ).reshape(n_precursors, n_isotopes)
 
-    # sum precursor
-    sum_precursor_intensity = np.sum(  # noqa: F841  # TODO check for potential bug then remove this line
-        np.sum(dense_precursors[0], axis=-1), axis=-1
-    ).astype(np.float32)
-    sum_fragment_intensity = np.sum(  # noqa: F841  # TODO check for potential bug then remove this line
-        np.sum(dense_fragments[0], axis=-1), axis=-1
-    ).astype(np.float32)
-
     # (n_precursor, n_isotopes)
     mass_error_array = (observed_precursor_mz - isotope_mz) / isotope_mz * 1e6
 
@@ -724,10 +716,6 @@ def fragment_features(
     quant_window: nb.uint32 = 3,
     quant_all: nb.boolean = False,
 ):
-    fragment_feature_dict = nb.typed.Dict.empty(  # noqa: F841  # TODO check for potential bug then remove this line
-        key_type=nb.types.unicode_type, value_type=float_array
-    )
-
     n_observations = observation_importance.shape[0]
     n_fragments = dense_fragments.shape[1]
     feature_array[17] = float(n_observations)
@@ -780,7 +768,6 @@ def fragment_features(
 
     # (quant_window * 2 + 1)
     frame_rt_quant = frame_rt[center - quant_window : center + quant_window + 1]
-    quant_duration = frame_rt_quant[-1] - frame_rt_quant[0]  # noqa: F841  # TODO check for potential bug then remove this line
 
     # (quant_window * 2)
     delta_rt = frame_rt_quant[1:] - frame_rt_quant[:-1]
@@ -1019,8 +1006,6 @@ def profile_features(
     feature_array,
 ):
     n_observations = len(observation_importance)
-    # most intense observation across all observations
-    best_observation = np.argmax(observation_importance)  # noqa: F841  # TODO check for potential bug then remove this line
 
     fragment_idx_sorted = np.argsort(fragment_intensity)[::-1]
 
@@ -1203,8 +1188,6 @@ def reference_features(
         key_type=nb.types.unicode_type, value_type=nb.types.float32
     )
 
-    n_observation = reference_observation_importance.shape[0]  # noqa: F841  # TODO check for potential bug then remove this line
-    n_fragments = reference_fragments_scan_profile.shape[0]  # noqa: F841  # TODO check for potential bug then remove this line
     fragment_idx_sorted = np.argsort(fragment_lib_intensity)[::-1]
 
     if (

alphadia/outputtransform.py

@@ -400,9 +400,9 @@ def build_transfer_model(self):
         tune_mgr = FinetuneManager(
             device=device, settings=self.config["transfer_learning"]
         )
-        stats = tune_mgr.finetune_rt(transfer_lib.precursor_df)  # noqa: F841  # TODO check for potential bug then remove this line
-        stats = tune_mgr.finetune_charge(transfer_lib.precursor_df)  # noqa: F841  # TODO check for potential bug then remove this line
-        stats = tune_mgr.finetune_ms2(  # noqa: F841  # TODO check for potential bug then remove this line
+        tune_mgr.finetune_rt(transfer_lib.precursor_df)
+        tune_mgr.finetune_charge(transfer_lib.precursor_df)
+        tune_mgr.finetune_ms2(
             transfer_lib.precursor_df.copy(), transfer_lib.fragment_intensity_df.copy()
         )
 

alphadia/utils.py

@@ -423,9 +423,6 @@ def fourier_filter(dense_stack, kernel):
 
     """
 
-    k0 = kernel.shape[0]  # noqa: F841  # TODO check for potential bug then remove this line
-    k1 = kernel.shape[1]  # noqa: F841  # TODO check for potential bug then remove this line
-
     # make sure both dimensions are even
     scan_mod = dense_stack.shape[3] % 2
     frame_mod = dense_stack.shape[4] % 2
@@ -453,8 +450,10 @@ def fourier_filter(dense_stack, kernel):
 
     # with nb.objmode(smooth_output='float32[:,:,:,:]'):
     #    # roll back to original position
+    #    k0 = kernel.shape[0]
+    #    k1 = kernel.shape[1]
     #    smooth_output = np.roll(smooth_output, -k0//2, axis=2)
-    #     smooth_output = np.roll(smooth_output, -k1//2, axis=3)
+    #    smooth_output = np.roll(smooth_output, -k1//2, axis=3)
 
     return smooth_output
 

tests/unit_tests/test_fdr.py

@@ -258,11 +258,11 @@ def test_feed_forward():
     # assert classifier.metrics["test_accuracy"][-1] > 0.99
     # assert classifier.metrics["train_accuracy"][-1] > 0.99
 
-    y_pred = classifier.predict(x)  # noqa: F841  # TODO check for potential bug then remove this line
-    # assert np.all(y_pred == y)  # TODO fix this test
+    y_pred = classifier.predict(x)  # noqa: F841  # TODO fix this test
+    # assert np.all(y_pred == y)
 
-    y_proba = classifier.predict_proba(x)[:, 1]  # noqa: F841  # TODO check for potential bug then remove this line
-    # assert np.all(np.round(y_proba) == y)  # TODO fix this test
+    y_proba = classifier.predict_proba(x)[:, 1]  # noqa: F841  # TODO fix this test
+    # assert np.all(np.round(y_proba) == y)
 
 
 def test_feed_forward_save():
@@ -285,5 +285,5 @@ def test_feed_forward_save():
         torch.load(os.path.join(tempfolder, "test_feed_forward_save.pth"))
     )
 
-    y_pred = new_classifier.predict(x)  # noqa: F841  # TODO check for potential bug then remove this line
-    # assert np.all(y_pred == y)  # TODO fix this test
+    y_pred = new_classifier.predict(x)  # noqa: F841  # TODO fix this test
+    # assert np.all(y_pred == y)

tests/unit_tests/test_grouping.py

@@ -1,4 +1,3 @@
-import time
 import pytest
 import numpy as np
 import pandas as pd
@@ -194,8 +193,5 @@ def test_grouping_fuzz(expected_time: int = 10):
         {"precursor_idx": precursor_idx, "proteins": proteins, "decoy": decoys}
     )
 
-    grouping_start_time = time.time()
     _ = grouping.perform_grouping(simulated_psm_data, genes_or_proteins="proteins")
-    grouping_end_time = time.time()
-    elapsed_time = grouping_end_time - grouping_start_time  # noqa: F841  # TODO check for potential bug then remove this line
     assert True  # TODO fix this test