Ruff with safe fixes

deeprvat/annotations/annotations.py

@@ -7,7 +7,6 @@
 import sys
 import time
 from pathlib import Path
-from typing import Optional
 import dask.dataframe as dd
 import numpy as np
 import click
@@ -795,12 +794,12 @@ def deepsea_pca(
 
     del X_std
 
-    logger.info(f"Writing values to data frame")
+    logger.info("Writing values to data frame")
     pca_df = pd.DataFrame(
         X_pca, columns=[f"DeepSEA_PC_{i}" for i in range(1, n_components + 1)]
     )
     del X_pca
-    logger.info(f"adding key values to data frame")
+    logger.info("adding key values to data frame")
     pca_df = pd.concat([key_df, pca_df], axis=1)
 
     logger.info("Sanity check of results")

deeprvat/cv_utils.py

@@ -1,9 +1,5 @@
-import pandas as pd
 import yaml
-import os
 import sys
-from typing import Optional
-import re
 
 # import pickle
 import logging
@@ -202,7 +198,7 @@ def combine_test_set_burdens(
                 for col in range(this_y.shape[1]):
                     this_y[:, col] = standardize_series(this_y[:, col])
             elif y_transformation == "quantile_transform":
-                logger.info(f"  Quantile transforming combined target phenotype (y)")
+                logger.info("  Quantile transforming combined target phenotype (y)")
                 for col in range(this_y.shape[1]):
                     this_y[:, col] = my_quantile_transform(this_y[:, col])
             y[:] = this_y

deeprvat/data/rare.py

@@ -1,18 +1,12 @@
-import itertools
 import logging
-import random
 import sys
-from pathlib import Path
 from pprint import pformat
 from scipy.sparse import coo_matrix, vstack
 from typing import Dict, List, Optional, Union, Set
 import numpy as np
 import pandas as pd
 import copy
 import torch
-import torch.nn.functional as F
-import zarr
-from torch.utils.data import Dataset
 
 from deeprvat.utils import calculate_mean_std, standardize_series_with_params
 
@@ -86,7 +80,7 @@ def __init__(
             self.setup_metadata()
 
         if self.low_memory:
-            logger.info(f"  Cleaning up to save memory")
+            logger.info("  Cleaning up to save memory")
             self.annotation_df = None
             if not self.gene_specific_anno:
                 self.exploded_annotations = None
@@ -273,7 +267,7 @@ def setup_annotations(
             if self.gene_specific_anno
             else len(self.exploded_annotations)
         ) == 0:
-            raise RuntimeError(f"No rare variants found in provided genes")
+            raise RuntimeError("No rare variants found in provided genes")
 
     def apply_thresholds(self, thresholds: Optional[Dict[str, str]]):
         if thresholds is not None:
@@ -302,7 +296,7 @@ def apply_thresholds(self, thresholds: Optional[Dict[str, str]]):
             )
 
             if self.kept_variants.shape[0] == 0:
-                raise RuntimeError(f"  No variants passed thresholding")
+                raise RuntimeError("  No variants passed thresholding")
 
             logger.info(f" {self.kept_variants.shape[0]} variants passed thresholding")
 
@@ -320,7 +314,7 @@ def apply_thresholds(self, thresholds: Optional[Dict[str, str]]):
             self.variant_map[self.kept_variants] = np.arange(len(self.annotation_df))
 
             if len(self.annotation_df) == 0:
-                raise RuntimeError(f"  No variants passed thresholding")
+                raise RuntimeError("  No variants passed thresholding")
 
             logger.info(f" {len(self.annotation_df)} variants passed thresholding")
 
@@ -449,7 +443,7 @@ def __init__(
             self.setup_metadata()
 
         if self.low_memory:
-            logger.info(f"  Cleaning up to save memory")
+            logger.info("  Cleaning up to save memory")
             self.annotation_df = None
             if not self.gene_specific_anno:
                 self.exploded_annotations = None
@@ -594,7 +588,7 @@ def setup_annotations(
             ].astype({self.grouping_column: np.int32})
 
         if len(self.annotation_df) == 0:
-            raise RuntimeError(f"No rare variants found in provided genes")
+            raise RuntimeError("No rare variants found in provided genes")
 
     def apply_thresholds(self, thresholds: Optional[Dict[str, str]]):
         if self.gene_specific_anno:
@@ -614,7 +608,7 @@ def apply_thresholds(self, thresholds: Optional[Dict[str, str]]):
             )
 
             if self.kept_variants.shape[0] == 0:
-                raise RuntimeError(f"  No variants passed thresholding")
+                raise RuntimeError("  No variants passed thresholding")
 
             logger.info(f" {self.kept_variants.shape[0]} variants passed thresholding")
 

deeprvat/deeprvat/associate.py

@@ -10,7 +10,6 @@
 from typing import Dict, List, Optional, Tuple
 
 import click
-import dask.dataframe as dd
 import numpy as np
 import pandas as pd
 import pyranges as pr
@@ -1083,7 +1082,7 @@ def regress_(
     """
     assert len(gene_indices) == len(genes)
 
-    logger.info(f"Computing associations")
+    logger.info("Computing associations")
     logger.info(f"Covariates shape: {x_pheno.shape}, y shape: {y.shape}")
 
     regressed_genes = []
@@ -1288,7 +1287,7 @@ def combine_regression_results(
     :type model_name: Optional[str]
     :return: Concatenated regression results saved to a parquet file.
     """
-    logger.info(f"Concatenating results")
+    logger.info("Concatenating results")
     results = pd.concat([pd.read_parquet(f, engine="pyarrow") for f in result_files])
 
     if model_name is not None:
@@ -1530,7 +1529,7 @@ def regress_common_(
     assert len(gene_indices) == len(genes)
     logger.info(common_genotype_prefix)
 
-    logger.info(f"Computing associations")
+    logger.info("Computing associations")
     logger.info(f"Covariates shape: {x_pheno.shape}, y shape: {y.shape}")
 
     regressed_genes = []

deeprvat/deeprvat/common_variant_condition_utils.py

@@ -2,10 +2,7 @@
 
 import pandas as pd
 import pyranges as pr
-import pandas as pd
 from pyarrow.parquet import ParquetFile
-import scipy as sp
-import pickle
 import numpy as np
 import zarr
 from pathlib import Path

deeprvat/deeprvat/config.py

@@ -1,17 +1,14 @@
 import logging
 import pprint
 import sys
-from pprint import pprint
 from typing import Optional, Tuple
 
 import click
 import pandas as pd
-import torch.nn.functional as F
 import yaml
 
 from deeprvat.deeprvat.evaluate import pval_correction
 from pathlib import Path
-import os
 from copy import deepcopy
 
 logging.basicConfig(
@@ -575,7 +572,7 @@ def update_config(
             )
 
             baseline_columns = ["gene", "pval"]
-            logger.info(f"  Reading baseline results from:")
+            logger.info("  Reading baseline results from:")
             pprint(baseline_results)
             baseline_df = pd.concat(
                 [
@@ -616,7 +613,7 @@ def update_config(
                     baseline_df = baseline_df.query("significant")
             else:
                 if threshold is not None:
-                    baseline_temp = baseline_df.query(f"pval_corrected < @threshold")
+                    baseline_temp = baseline_df.query("pval_corrected < @threshold")
                     logger.info(
                         f"  {len(baseline_df)} genes "
                         "from baseline passed thresholding"

deeprvat/deeprvat/evaluate.py

@@ -2,8 +2,6 @@
 import sys
 from pathlib import Path
 from typing import Dict, Optional, Tuple
-from itertools import combinations
-import random
 import os
 
 import click
@@ -12,7 +10,7 @@
 import yaml
 from seak.cct import cct
 
-from deeprvat.utils import pval_correction, bfcorrect_df
+from deeprvat.utils import pval_correction
 
 logging.basicConfig(
     format="[%(asctime)s] %(levelname)s:%(name)s: %(message)s",
@@ -384,8 +382,8 @@ def evaluate(
     logger.info(significant.query('Method == "DeepRVAT"'))
     logger.info("Saving results")
     out_path = Path(out_dir)
-    significant.to_parquet(out_path / f"significant.parquet", engine="pyarrow")
-    all_pvals.to_parquet(out_path / f"all_results.parquet", engine="pyarrow")
+    significant.to_parquet(out_path / "significant.parquet", engine="pyarrow")
+    all_pvals.to_parquet(out_path / "all_results.parquet", engine="pyarrow")
 
 
 if __name__ == "__main__":

deeprvat/deeprvat/train.py

@@ -3,8 +3,6 @@
 import itertools
 import logging
 import pickle
-import random
-import shutil
 import sys
 from pathlib import Path
 from pprint import pformat, pprint
@@ -864,7 +862,7 @@ def run_bagging(
                 if str(e).find("CUDA out of memory") != -1:
                     if dm.hparams.batch_size > 4:
                         logging.error(
-                            f"Retrying training with half the original batch size"
+                            "Retrying training with half the original batch size"
                         )
                         gc.collect()
                         torch.cuda.empty_cache()

deeprvat/seed_gene_discovery/evaluate.py

@@ -52,13 +52,13 @@ def evaluate_(associations: Dict[str, pd.DataFrame], alpha: float):
             corrected_result = pval_correction(
                 result, alpha, correction_type=correction_type
             )
-            corrected_result[f"-log10pval_corrected"] = -np.log10(
-                corrected_result[f"pval_corrected"]
+            corrected_result["-log10pval_corrected"] = -np.log10(
+                corrected_result["pval_corrected"]
             )
             corrected_result["correction_method"] = correction_type
             corrected_results.append(corrected_result)
 
-            sig = corrected_result.query(f"significant")
+            sig = corrected_result.query("significant")
             n_sig = len(sig)
             logger.info(f"Significant genes: {n_sig}")
             metrics[f"significant{sig_col_suffix}"] = n_sig
@@ -70,7 +70,7 @@ def evaluate_(associations: Dict[str, pd.DataFrame], alpha: float):
         corrected_results = pd.concat(corrected_results)
         all_evaluations[pheno] = corrected_results
 
-        all_sig = corrected_results.query(f"significant")
+        all_sig = corrected_results.query("significant")
         all_significant[pheno] = all_sig
 
         print(all_sig)
@@ -128,7 +128,7 @@ def evaluate(
     out_dir = Path(out_dir)
     evaluations[pheno].to_parquet(out_file)
 
-    with open(out_dir / f"metrics.pkl", "wb") as f:
+    with open(out_dir / "metrics.pkl", "wb") as f:
         pickle.dump(metrics, f)
 
     all_associations.to_parquet(f"{out_dir}/all_associations.parquet")

deeprvat/seed_gene_discovery/seed_gene_discovery.py

@@ -15,7 +15,7 @@
 import pandas as pd
 import yaml
 from scipy.stats import beta
-from scipy.sparse import coo_matrix, spmatrix
+from scipy.sparse import spmatrix
 from torch.utils.data import DataLoader, Dataset
 from tqdm import tqdm
 
@@ -553,7 +553,7 @@ def make_dataset_(
         logger.info("Debug mode: Using only 1000 samples")
         batch_size = 1000
     else:
-        logger.info(f"Setting batch size to length of dataset")
+        logger.info("Setting batch size to length of dataset")
         batch_size = len(dataset)
 
     if "batch_size" in data_config["dataloader_config"].keys():
@@ -709,7 +709,7 @@ def run_association(
     n_genes = len(genes)
     if n_genes == 0:
         logger.info(
-            f"Number of chunks is too large. The pipeline will throw an error beacause there are no genes to test"
+            "Number of chunks is too large. The pipeline will throw an error beacause there are no genes to test"
         )
     logger.info(f"Processing genes in {genes} from {n_total_genes} in total")
     this_gene_ids = [gene_ids[i] for i in genes]

deeprvat/utils.py

@@ -5,8 +5,7 @@
 import shutil
 import sys
 import pickle
-from pathlib import Path
-from typing import Any, Callable, Dict, Iterable, Union
+from typing import Any, Callable, Dict, Iterable
 
 import optuna
 import numpy as np

pipelines/resources/absplice.py

@@ -14,7 +14,6 @@ def cli():
 @click.argument("input", type=click.Path(exists=True))
 @click.argument("output", type=click.Path(exists=False))
 def codign_genes(input, output):
-    import pandas as pd
     import pyranges as pr
 
     gr = pr.read_gtf(input["gtf_file"])

pipelines/resources/coding_genes.py

@@ -1,4 +1,3 @@
-import pandas as pd
 import pyranges as pr
 
 gr = pr.read_gtf(snakemake.input["gtf_file"])