fix

cas9on.py

@@ -203,28 +203,28 @@ def process_gene(gene_symbol, model_path):
 #     df.to_csv(output_path, index=False)
 
 
-def create_bigwig(predictions, bigwig_path):
-    # Convert predictions to DataFrame if it's a list of lists
-    if isinstance(predictions, list):
-        import pandas as pd
-        df = pd.DataFrame(predictions, columns=["Chr", "Start Pos", "End Pos", "Strand", "Transcript", "Exon", "Target", "gRNA", "Prediction"])
-    else:
-        df = predictions  # Assuming predictions is already a DataFrame
+def create_bigwig(df, bigwig_path):
+    import pandas as pd
+    import pyBigWig
+
+    if isinstance(df, list):
+        df = pd.DataFrame(df, columns=["Chr", "Start Pos", "End Pos", "Strand", "Transcript", "Exon", "Target", "gRNA", "Prediction"])
 
     # Calculate chromosome sizes as the maximum end position per chromosome
-    chrom_sizes = df.groupby('Chr')['End Pos'].max().to_dict()
+    # Ensure the sizes are integers
+    chrom_sizes = df.groupby('Chr')['End Pos'].max().astype(int).to_dict()
 
     # Create a BigWig file
     with pyBigWig.open(bigwig_path, "w") as bw:
-        # Add chromosome sizes to the header
-        bw.addHeader(list(chrom_sizes.items()))
+        # Add chromosome sizes to the header, ensuring sizes are integers
+        bw.addHeader([(chr, size) for chr, size in chrom_sizes.items()])
 
         # Add entries for each prediction
-        for index, row in df.iterrows():
-            chrom = row['Chr']
-            start = int(row['Start Pos']) - 1  # BigWig positions are 0-based
-            end = int(row['End Pos'])
-            score = float(row['Prediction'])
-
-            # Add the entry to the BigWig file
-            bw.addEntries([chrom], [start], ends=[end], values=[score])
+        for chrom in df['Chr'].unique():
+            chrom_df = df[df['Chr'] == chrom]
+            bw.addEntries(
+                chrom,
+                chrom_df['Start Pos'].astype(int).tolist(),
+                ends=chrom_df['End Pos'].astype(int).tolist(),
+                values=chrom_df['Prediction'].astype(float).tolist()
+            )