File size: 1,826 Bytes
1d072df 102f324 1d072df 88d8002 1d072df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | import os
import numpy as np
import random
import torch
from torch.utils.data import Dataset, DataLoader
class Hg38VariableDataset(Dataset):
def __init__(self, data_dir='data', window_size=10_000):
self.data_dir = data_dir
self.window_size = window_size
self.chromosomes = self._load_chromosomes()
self.chromosome_lengths = {k: v.shape[-1] for k, v in self.chromosomes.items()}
self.chromosome_names = list(self.chromosomes.keys())
self.nucleotides = np.array(['A', 'T', 'G', 'C', 'N'])
def _load_chromosomes(self):
chromosomes = {}
for filename in sorted(os.listdir(self.data_dir)):
if filename.endswith(".npz"):
chrom_name = filename.split(".")[0] # Use file name (e.g., "chr1")
matrix = np.load(os.path.join(self.data_dir, filename))['data']
chromosomes[chrom_name] = matrix
return chromosomes
def select_nucleotide_fast(self, matrix):
choices = np.argmax(matrix * np.random.rand(*matrix.shape), axis=0)
return ''.join(self.nucleotides[choices])
def __len__(self):
return sum(self.chromosome_lengths.values())
def __getitem__(self, idx):
chrom = random.choice(self.chromosome_names)
chrom_length = self.chromosome_lengths[chrom]
start = np.random.randint(0, chrom_length - self.window_size)
sequence = self.select_nucleotide_fast(self.chromosomes[chrom][:, start:start + self.window_size])
return {
'chromosome': chrom,
'start': start,
'sequence': sequence
}
# # # Example usage
# dataset = Hg38VariableDataset()
# data_loader = DataLoader(dataset, batch_size=32, shuffle=True)
# # Iterate over batches
# for batch in data_loader:
# break
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