update dataloader

dataloader.py

@@ -9,10 +9,7 @@ from torch.utils.data import Dataset
 from torchvision import transforms
 import torchvision.transforms.functional as TF
 
-<<<<<<< HEAD
 from celle.utils import replace_outliers
-=======
->>>>>>> 5d2263b456a117f5f8e81f9c45af75748d5a2c3d
 
 def simple_conversion(seq):
     """Create 26-dim embedding"""
@@ -59,23 +56,6 @@ def simple_conversion(seq):
     return torch.tensor([seqs_x]).long()
 
 
-<<<<<<< HEAD
-=======
-def replace_outliers(image, percentile=0.0001):
-
-    lower_bound, upper_bound = torch.quantile(image, percentile), torch.quantile(
-        image, 1 - percentile
-    )
-    mask = (image <= upper_bound) & (image >= lower_bound)
-
-    valid_pixels = image[mask]
-
-    image[~mask] = torch.clip(image[~mask], min(valid_pixels), max(valid_pixels))
-
-    return image
-
-
->>>>>>> 5d2263b456a117f5f8e81f9c45af75748d5a2c3d
 class CellLoader(Dataset):
     """imports mined opencell images with protein sequence"""