diff --git a/src/embkit/encoding/genome.py b/src/embkit/encoding/genome.py
index 5038952..084a1db 100644
--- a/src/embkit/encoding/genome.py
+++ b/src/embkit/encoding/genome.py
@@ -1,4 +1,5 @@
 
+import pandas as pd
 
 # chromosome lengths (GRCh38)
 chromosome_length={'chr1':248956422, 
@@ -27,6 +28,24 @@
                    'chrY':57227415}
 
 
+def row_format(row):
+    return {
+        "chr" : row.CHROM,
+        "pos" : row.POS,
+        "ref" : row.REF,
+        "alt" : ",".join( str(i) for i in row.ALT)
+    }
+
+def vcf_to_dataframe(vcf_reader, row_filter=None):
+    """
+    vcf_to_dataframe take a PyVCF reader and create a pandas DataFrame
+    """
+    vals = []
+    for record in vcf_reader:
+        if row_filter is None or row_filter(record):
+            vals.append( row_format(record) )
+    return pd.DataFrame(vals)
+
 def vectorize_variant_count( variant_df, bin_size=1000000, seq_col="chr", pos_col="pos"):
     """
     Vectorize the variant count in bins of specified size across the genome.
@@ -41,7 +60,7 @@ def vectorize_variant_count( variant_df, bin_size=1000000, seq_col="chr", pos_co
     counter=0
     for chrom, length in chromosome_length.items():
         for i in range(1, length, bin_size): # last bin for each chromosome might not equal 1MB depending on the chr length
-            bin_label = f'{chrom}_{counter}'
+            bin_label = f'{chrom}_{counter:04d}'
             bins.append((chrom, i, min(i + bin_size, length), bin_label))
             bin_labels.append(bin_label)
             counter+=1
diff --git a/src/embkit/optimize/__init__.py b/src/embkit/optimize/__init__.py
index 6e77e79..18bf9cf 100644
--- a/src/embkit/optimize/__init__.py
+++ b/src/embkit/optimize/__init__.py
@@ -14,6 +14,8 @@
 from torch.optim import Adam
 from torch.utils.data import DataLoader, TensorDataset, Dataset
 from tqdm.autonotebook import tqdm
+import numpy as np
+from ..losses import net_vae_loss
 
 from .. import get_device, dataframe_loader
 
@@ -360,9 +362,6 @@ def fit_net_vae(
     unify this implementation with ``fit_vae`` to avoid long-term duplication.
     """
 
-    import numpy as np
-    from ..losses import net_vae_loss
-
     if isinstance(X, torch.Tensor):
         if not getattr(model, "features", None):
             raise ValueError("Tensor input requires model.features to be defined.")