added rotate column in table

metagenlab · Oct 25, 2024 · b5fa3a8 · b5fa3a8
1 parent 9af1fae
commit b5fa3a8
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Showing 2 changed files with 11 additions and 9 deletions.
diff --git a/mess/workflow/scripts/calculate_cov.py b/mess/workflow/scripts/calculate_cov.py
@@ -93,7 +93,7 @@ def get_even_dist(table):
     df["tax_abundance"] = df["proportion"] / df["count"]
     df["genome_bases"] = df["total_sequence_length"] * df["tax_abundance"]
     df["sum_genome_bases"] = df.groupby("samplename")["genome_bases"].transform("sum")
-    df["cov_obtained"] = bases / df["sum_genome_bases"] 
+    df["cov_obtained"] = bases / df["sum_genome_bases"]
     df["cov_sim"] = df["tax_abundance"] * df["cov_obtained"]
     df["sum_cov"] = df.groupby("samplename")["cov_sim"].transform("sum")
     df["bases"] = df["cov_sim"] * df["total_sequence_length"]
@@ -117,7 +117,7 @@ def get_even_dist(table):
         df["sum_genome_bases"] = df.groupby("samplename")["genome_bases"].transform(
             "sum"
         )
-        df["cov_obtained"] = bases / df["sum_genome_bases"] 
+        df["cov_obtained"] = bases / df["sum_genome_bases"]
         df["cov_sim"] = df["tax_abundance"] * df["cov_obtained"]
         df["sum_cov"] = df.groupby("samplename")["cov_sim"].transform("sum")
         df["bases"] = df["cov_sim"] * df["total_sequence_length"]
@@ -173,6 +173,8 @@ def get_even_dist(table):
     "tax_abundance",
     "seed",
 ]
+if "rotate" in entry_df.columns:
+    cols.append("rotate")
 
 df["reads"] = df["reads"].apply(lambda x: int(round(x)))
 df["bases"] = df["bases"].apply(lambda x: int(round(x)))
@@ -182,4 +184,4 @@ def get_even_dist(table):
 df = df.astype(
     {"seed": int, "tax_id": int, "total_sequence_length": int, "number_of_contigs": int}
 )
-df[cols].set_index(["samplename", "fasta"]).to_csv(snakemake.output[0], sep="\t")  # type: ignore
+df[cols].to_csv(snakemake.output[0], sep="\t", index=False)  # type: ignore
diff --git a/mess/workflow/scripts/split_contigs.py b/mess/workflow/scripts/split_contigs.py
@@ -35,17 +35,17 @@ def split_fasta(fa, outdir):
 
 cols = ["samplename", "fasta", "contig", "contig_length", "tax_id", "seed", "cov_sim"]
 
-if snakemake.params.rotate > 1:
-    cols += ["n", "random_start"]
+if snakemake.params.circular:
+    cols += ["n", "random_start", "rotate"]
+    if "rotate" not in df.columns:
+        df.loc[:, "rotate"] = [snakemake.params.rotate] * len(df)
     df["random_start"] = df.apply(
-        lambda row: get_random_start(
-            row["seed"], row["contig_length"], snakemake.params.rotate
-        ),
+        lambda row: get_random_start(row["seed"], row["contig_length"], row["rotate"]),
         axis=1,
     )
     df_expanded = df.explode("random_start").reset_index(drop=True)
     df_expanded["n"] = df_expanded.groupby(["samplename", "contig"]).cumcount() + 1
     df = df_expanded
-    df["cov_sim"] = df["cov_sim"] / snakemake.params.rotate
+    df["cov_sim"] = df["cov_sim"] / df["rotate"]
 
 df[cols].to_csv(snakemake.output.tsv, sep="\t", index=False)