black format

src/baskerville/blocks.py

@@ -18,6 +18,7 @@
 
 from baskerville import layers
 
+
 ############################################################
 # Convolution
 ############################################################
@@ -892,7 +893,7 @@ def conv_tower(
     divisible_by=1,
     repeat=1,
     reprs=[],
-    **kwargs
+    **kwargs,
 ):
     """Construct a reducing convolution block.
 
@@ -943,7 +944,7 @@ def conv_tower_nac(
     divisible_by=1,
     repeat=1,
     reprs=[],
-    **kwargs
+    **kwargs,
 ):
     """Construct a reducing convolution block.
 
@@ -1000,7 +1001,7 @@ def res_tower(
     repeat=1,
     num_convs=2,
     reprs=[],
-    **kwargs
+    **kwargs,
 ):
     """Construct a reducing convolution block.
 
@@ -1087,7 +1088,7 @@ def convnext_tower(
     repeat=1,
     num_convs=2,
     reprs=[],
-    **kwargs
+    **kwargs,
 ):
     """Abc.
 
@@ -1129,7 +1130,7 @@ def _round(x):
             filters=rep_filters_int,
             kernel_size=kernel_size,
             dropout=dropout,
-            **kwargs
+            **kwargs,
         )
         current0 = current
 
@@ -1141,7 +1142,7 @@ def _round(x):
                 filters=rep_filters_int,
                 kernel_size=kernel_size,
                 dropout=dropout,
-                **kwargs
+                **kwargs,
             )
 
         # residual add
@@ -1187,7 +1188,7 @@ def transformer(
     qkv_width=1,
     mha_initializer="he_normal",
     kernel_initializer="he_normal",
-    **kwargs
+    **kwargs,
 ):
     """Construct a transformer block.
 
@@ -1255,7 +1256,7 @@ def transformer_split(
     qkv_width=1,
     mha_initializer="he_normal",
     kernel_initializer="he_normal",
-    **kwargs
+    **kwargs,
 ):
     """Construct a transformer block.
 
@@ -1393,7 +1394,7 @@ def transformer2(
     dropout=0.25,
     dense_expansion=2.0,
     qkv_width=1,
-    **kwargs
+    **kwargs,
 ):
     """Construct a transformer block, with length-wise pooling before
        returning to full length.
@@ -1416,7 +1417,7 @@ def transformer2(
         filters=min(4 * key_size, inputs.shape[-1]),
         kernel_size=3,
         pool_size=2,
-        **kwargs
+        **kwargs,
     )
 
     # layer norm
@@ -1517,7 +1518,7 @@ def squeeze_excite(
     additive=False,
     norm_type=None,
     bn_momentum=0.9,
-    **kwargs
+    **kwargs,
 ):
     return layers.SqueezeExcite(
         activation, additive, bottleneck_ratio, norm_type, bn_momentum
@@ -1545,7 +1546,7 @@ def dilated_dense(
     conv_type="standard",
     dropout=0,
     repeat=1,
-    **kwargs
+    **kwargs,
 ):
     """Construct a residual dilated dense block.
 
@@ -1570,7 +1571,7 @@ def dilated_dense(
             kernel_size=kernel_size,
             dilation_rate=int(np.round(dilation_rate)),
             conv_type=conv_type,
-            **kwargs
+            **kwargs,
         )
 
         # dense concat
@@ -1592,7 +1593,7 @@ def dilated_residual(
     conv_type="standard",
     norm_type=None,
     round=False,
-    **kwargs
+    **kwargs,
 ):
     """Construct a residual dilated convolution block.
 
@@ -1619,7 +1620,7 @@ def dilated_residual(
             conv_type=conv_type,
             norm_type=norm_type,
             norm_gamma="ones",
-            **kwargs
+            **kwargs,
         )
 
         # return
@@ -1629,7 +1630,7 @@ def dilated_residual(
             dropout=dropout,
             norm_type=norm_type,
             norm_gamma="zeros",
-            **kwargs
+            **kwargs,
         )
 
         # InitZero
@@ -1672,7 +1673,7 @@ def dilated_residual_nac(
             filters=filters,
             kernel_size=kernel_size,
             dilation_rate=int(np.round(dilation_rate)),
-            **kwargs
+            **kwargs,
         )
 
         # return
@@ -1697,7 +1698,7 @@ def dilated_residual_2d(
     dropout=0,
     repeat=1,
     symmetric=True,
-    **kwargs
+    **kwargs,
 ):
     """Construct a residual dilated convolution block."""
 
@@ -1717,7 +1718,7 @@ def dilated_residual_2d(
             kernel_size=kernel_size,
             dilation_rate=int(np.round(dilation_rate)),
             norm_gamma="ones",
-            **kwargs
+            **kwargs,
         )
 
         # return
@@ -1726,7 +1727,7 @@ def dilated_residual_2d(
             filters=rep_input.shape[-1],
             dropout=dropout,
             norm_gamma="zeros",
-            **kwargs
+            **kwargs,
         )
 
         # residual add
@@ -1818,7 +1819,7 @@ def dense_block(
     bn_momentum=0.99,
     norm_gamma=None,
     kernel_initializer="he_normal",
-    **kwargs
+    **kwargs,
 ):
     """Construct a single convolution block.
 
@@ -1909,7 +1910,7 @@ def dense_nac(
     bn_momentum=0.99,
     norm_gamma=None,
     kernel_initializer="he_normal",
-    **kwargs
+    **kwargs,
 ):
     """Construct a single convolution block.
 
@@ -1991,7 +1992,7 @@ def final(
     kernel_initializer="he_normal",
     l2_scale=0,
     l1_scale=0,
-    **kwargs
+    **kwargs,
 ):
     """Final simple transformation before comparison to targets.
 

src/baskerville/metrics.py

@@ -24,6 +24,7 @@
 for device in gpu_devices:
     tf.config.experimental.set_memory_growth(device, True)
 
+
 ################################################################################
 # Losses
 ################################################################################

src/baskerville/scripts/hound_eval_spec.py

@@ -35,6 +35,7 @@
 Test the accuracy of a trained model on targets/predictions normalized across targets.
 """
 
+
 ################################################################################
 # main
 ################################################################################

src/baskerville/scripts/hound_snp.py

@@ -25,6 +25,7 @@
 Compute variant effect predictions for SNPs in a VCF file.
 """
 
+
 ################################################################################
 # main
 ################################################################################

src/baskerville/scripts/hound_snp_slurm.py

@@ -33,6 +33,7 @@
 parallelized across a slurm cluster.
 """
 
+
 ################################################################################
 # main
 ################################################################################

src/baskerville/scripts/hound_train.py

@@ -163,7 +163,6 @@ def main():
         strategy = tf.distribute.MirroredStrategy()
 
         with strategy.scope():
-
             if not args.keras_fit:
                 # distribute data
                 for di in range(len(args.data_dirs)):

src/baskerville/seqnn.py

@@ -524,7 +524,7 @@ def predict(
         stream: bool = False,
         step: int = 1,
         dtype: str = "float32",
-        **kwargs
+        **kwargs,
     ):
         """Predict targets for SeqDataset, with more options.
 

src/baskerville/snps.py

@@ -224,7 +224,7 @@ def cluster_snps(snps, seq_len: int, center_pct: float):
 
 def initialize_output_h5(out_dir, snp_stats, snps, targets_length, targets_df):
     """Initialize an output HDF5 file for SAD stats.
-    
+
     Args:
         out_dir (str): Output directory.
         snp_stats [str]: List of SAD stats to compute.
@@ -287,7 +287,7 @@ def initialize_output_h5(out_dir, snp_stats, snps, targets_length, targets_df):
 
 def make_alt_1hot(ref_1hot, snp_seq_pos, ref_allele, alt_allele):
     """Return alternative allele one hot coding.
-    
+
     Args:
         ref_1hot (np.array): Reference allele one hot coding.
         snp_seq_pos (int): SNP position in sequence.
@@ -335,7 +335,7 @@ def make_alt_1hot(ref_1hot, snp_seq_pos, ref_allele, alt_allele):
 
 def make_strand_transform(targets_df, targets_strand_df):
     """Make a sparse matrix to sum strand pairs.
-    
+
     Args:
         targets_df (pd.DataFrame): Targets DataFrame.
         targets_strand_df (pd.DataFrame): Targets DataFrame, with strand pairs collapsed.
@@ -365,7 +365,7 @@ def make_strand_transform(targets_df, targets_strand_df):
 
 def write_pct(scores_out, snp_stats):
     """Compute percentile values for each target and write to HDF5.
-    
+
     Args:
         scores_out (h5py.File): Output HDF5 file.
         snp_stats [str]: List of SAD stats to compute.
@@ -395,7 +395,7 @@ def write_pct(scores_out, snp_stats):
 def write_snp(ref_preds_sum, alt_preds_sum, scores_out, si, snp_stats):
     """Write SNP predictions to HDF, assuming the length dimension has
     been collapsed.
-    
+
     Args:
         ref_preds_sum (np.array): Reference allele predictions.
         alt_preds_sum (np.array): Alternative allele predictions.
@@ -413,7 +413,7 @@ def write_snp(ref_preds_sum, alt_preds_sum, scores_out, si, snp_stats):
 def write_snp_len(ref_preds, alt_preds, scores_out, si, snp_stats):
     """Write SNP predictions to HDF, assuming the length dimension has
     been maintained.
-    
+
     Args:
         ref_preds (np.array): Reference allele predictions.
         alt_preds (np.array): Alternative allele predictions.

src/baskerville/trainer.py

@@ -783,7 +783,7 @@ def adaptive_clip_grad(
 ):
     """Adaptive gradient clipping."""
     new_grads = []
-    for (params, grads) in zip(parameters, gradients):
+    for params, grads in zip(parameters, gradients):
         p_norm = unitwise_norm(params)
         max_norm = tf.math.maximum(p_norm, eps) * clip_factor
         grad_norm = unitwise_norm(grads)

src/baskerville/vcf.py

@@ -219,13 +219,11 @@ def snp_seq1(snp, seq_len, genome_open):
     seq_ref = seq[left_len : left_len + len(snp.ref_allele)]
     ref_found = True
     if seq_ref != snp.ref_allele:
-
         # search for reference allele in alternatives
         ref_found = False
 
         # for each alternative allele
         for alt_al in snp.alt_alleles:
-
             # grab reference sequence matching alt length
             seq_ref_alt = seq[left_len : left_len + len(alt_al)]
             if seq_ref_alt == alt_al:
@@ -314,13 +312,11 @@ def snps_seq1(snps, seq_len, genome_fasta, return_seqs=False):
         # verify that ref allele matches ref sequence
         seq_ref = seq[left_len : left_len + len(snp.ref_allele)]
         if seq_ref != snp.ref_allele:
-
             # search for reference allele in alternatives
             ref_found = False
 
             # for each alternative allele
             for alt_al in snp.alt_alleles:
-
                 # grab reference sequence matching alt length
                 seq_ref_alt = seq[left_len : left_len + len(alt_al)]
                 if seq_ref_alt == alt_al: