🔥 remove comments

src/move/tasks/identify_associations.py

@@ -484,9 +484,6 @@ def _bayes_approach_parallel(
         f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. "
         f"Bayes_abs shape is {bayes_abs_shape}"
     )
-    # file_path = output_path / "bayes_abs_multi.tsv"
-    # logger.debug(f"Saving bayes_abs to {file_path}")
-    # np.savetxt(file_path, bayes_abs, delimiter='\t')
 
     bayes_p = np.exp(bayes_abs) / (
         1 + np.exp(bayes_abs)
@@ -662,74 +659,32 @@ def _bayes_approach(
     mask = diff.mask  # Extract the mask from the masked array
     # Replace masked values with a placeholder (e.g., np.nan)
     data[mask] = np.nan
-    # Define the file path to save the TSV file
-    # output_path = Path(config.data.results_path) / "identify_associations"
-    # file_path = output_path / "diff_normal.tsv"
-    # Save the data to the TSV fil
-    # logger.debug(f"Saving diff to {file_path}")
-    # np.savetxt(file_path, diff, delimiter="\t")
-
-    # data = prob.data  # Extract the data from the masked array
-    # mask = prob.mask  # Extract the mask from the masked array
-    # Replace masked values with a placeholder (e.g., np.nan)
-    # data[mask] = np.nan
-    # Define the file path to save the TSV file
-    # output_path = Path(config.data.results_path) / "identify_associations"
-    # file_path = output_path / "prob_original_script.tsv"
-    # # Save the data to the TSV fil
-    # logger.debug(f"Saving prob to {file_path}")
-    # np.savetxt(file_path, prob, delimiter="\t")
-    # logger.debug(f"prob is {prob}")
-
-    # file_path = output_path / "bayes_k_original_all.tsv"
-    # logger.debug(f"Saving bayes_k (not worker, all) to {file_path}")
-    # np.savetxt(file_path, bayes_k, delimiter="\t")
+
     bayes_mask[bayes_mask != 0] = 1
     bayes_mask = np.array(bayes_mask, dtype=bool)
 
     # Calculate Bayes probabilities
     bayes_abs = np.abs(bayes_k)
-    # file_path = output_path / "bayes_abs_original.tsv"
-    # logger.debug(f"Saving bayes_abs to {file_path}")
-    # np.savetxt(file_path, bayes_abs, delimiter="\t")
 
     bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs))  # 2D: N x C
-    # file_path = output_path / "bayes_p_original.tsv"
-    # logger.debug(f"Saving bayes_p to {file_path}")
-    # np.savetxt(file_path, bayes_p, delimiter="\t")
 
     # ! Marc said this is needed to not identify the features with themselves
     bayes_abs[bayes_mask] = np.min(
         bayes_abs
     )  # Bring feature_i feature_i associations to minimum
     sort_ids = np.argsort(bayes_abs, axis=None)[::-1]  # 1D: N x C
-    # file_path = output_path / "sort_ids_original_script.tsv"
-    # logger.debug(f"Saving sort_ids to {file_path}")
-    # np.savetxt(file_path, sort_ids, delimiter="\t")
     logger.debug(f"sort_ids are {sort_ids}")
 
     prob = np.take(bayes_p, sort_ids)  # 1D: N x C
-    # file_path = output_path / "prob_original_final.tsv"
-    # logger.debug(f"Saving prob to {file_path}")
-    # np.savetxt(file_path, prob, delimiter="\t")
     logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]")
 
     # Sort Bayes
     bayes_k = np.take(bayes_k, sort_ids)  # 1D: N x C
-    # file_path = output_path / "sorted_bayes_k_original_script.tsv"
-    # logger.debug(f"Saving sorted_bayes_k to {file_path}")
-    # np.savetxt(file_path, bayes_k, delimiter="\t")
 
     # Calculate FDR
     fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1)  # 1D
-    # file_path = output_path / "fdr_original_script.tsv"
-    # logger.debug(f"Saving fdr to {file_path}")
-    # np.savetxt(file_path, fdr, delimiter="\t")
     idx = np.argmin(np.abs(fdr - task_config.sig_threshold))
     logger.debug(f"Index is {idx}")
-    # file_path = output_path / "idx_original_script.tsv"
-    # logger.debug(f"Saving idx to {file_path}")
-    # np.savetxt(file_path, idx, delimiter='\t')
     logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]")
 
     return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx]