gravpot dataset
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sCOCA_ML/dataset/gravpot_dataset.py
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sCOCA_ML/dataset/gravpot_dataset.py
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import os
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import random
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import numpy as np
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import torch
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from torch.utils.data import Dataset, DataLoader
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from glob import glob
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import re
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def read_cosmo_and_time_file(cosmo_and_time_file):
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with open(cosmo_and_time_file, 'r') as f:
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lines = f.readlines()
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cosmo_and_time_params = {}
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for line in lines:
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if line.strip(): # Skip empty lines
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key, value = line.split(':')
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if key.strip() == 'ID':
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cosmo_and_time_params['ID'] = value.strip()
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elif key.strip() == 'nforce':
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cosmo_and_time_params['nforce'] = int(value.strip())
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else:
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cosmo_and_time_params[key.strip()] = float(value.strip())
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return cosmo_and_time_params
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class GravPotDataset(Dataset):
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INITIAL_CONDITIONS_DIR = 'initial_conditions'
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TARGET_DIR = 'gravitational_potential'
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STYLE_DIR = 'cosmo_and_time'
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def __init__(self,
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root_dir:str,
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ids:list|None=None,
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N:int=128,
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N_full:int=768,
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match_str:str='train',
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device=torch.device('cpu'),
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initial_conditions_variables:tuple|list=['DM_delta', 'DM_phi'],
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target_variable:str='gravpot',
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style_files:str='cosmo_and_time_parameters',
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style_keys:list|None=["D1", "D2"],
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max_time:int=100):
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"""
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Dataset for gravitational potential data.
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Parameters:
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- root_dir: Directory containing the dataset.
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- ids: List of IDs to include in the dataset. If None, will discover IDs.
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- N: Size of the chunks to read (N x N x N).
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- N_full: Full size of the simulation box (N_full x N_full x N_full).
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- device: Device to load tensors onto (default is CPU)."""
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self.initial_conditions_variables = initial_conditions_variables
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self.target_variable = target_variable
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self.style_files = style_files
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self.style_keys = style_keys
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self.max_time = max_time
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self.root_dir = root_dir
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self.N = N
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self.N_full = N_full
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self.device = device
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# Compute how many chunks per dimension
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self.chunks_per_dim = N_full // N
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self.chunks_per_entry = self.chunks_per_dim ** 3
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# This will hold (ID, time, offsets)
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self.samples = []
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if ids is None:
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ids = self.discover_ids(match_str=match_str)
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self.ids = ids
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# Build indexable sample list
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self._prepare_samples()
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def discover_ids(self, match_str:str='train'):
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"""
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Discover IDs that contain match_str and have at least one valid time step
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with all required files present.
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"""
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pattern = os.path.join(self.root_dir, self.INITIAL_CONDITIONS_DIR, 'ICs_*')
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files = glob(pattern)
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valid_ids = []
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for file in files:
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match = re.search(r'ICs_(.+?)_'+f"{self.initial_conditions_variables[0]}.h5", os.path.basename(file))
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if not match:
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continue
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ID = match.group(1)
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if match_str not in ID:
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continue
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# Check if corresponding initial conditions file exists
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for var in self.initial_conditions_variables[1:]:
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ic_path = os.path.join(self.root_dir, self.INITIAL_CONDITIONS_DIR, f'ICs_{ID}_{var}.h5')
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if not os.path.exists(ic_path):
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continue
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# Check if at least one time has both target and style params
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found_valid_time = False
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for t in range(self.max_time):
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target_path = os.path.join(self.root_dir, self.TARGET_DIR, f'{self.target_variable}_{ID}_nforce{t}.h5')
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style_path = os.path.join(self.root_dir, self.STYLE_DIR, f'{self.style_files}_{ID}_nforce{t}.txt')
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if os.path.exists(target_path) and os.path.exists(style_path):
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found_valid_time = True
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break
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if found_valid_time:
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valid_ids.append(ID)
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return sorted(valid_ids)
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def _get_valid_times(self, ID):
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"""Returns valid time indices for which all required files exist."""
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valid_times = []
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for t in range(100): # arbitrary upper limit
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target_path = os.path.join(self.root_dir, self.TARGET_DIR, f'{self.target_variable}_{ID}_nforce{t}.h5')
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style_path = os.path.join(self.root_dir, self.STYLE_DIR, f'{self.style_files}_{ID}_nforce{t}.txt')
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if all(os.path.exists(p) for p in [target_path, style_path]):
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valid_times.append(t)
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return valid_times
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def _prepare_samples(self):
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self.samples.clear()
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for ID in self.ids:
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times = self._get_valid_times(ID)
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if not times:
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continue
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# Random offset for epoch start
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base_offset_x = random.randint(0, self.N_full)
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base_offset_y = random.randint(0, self.N_full)
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base_offset_z = random.randint(0, self.N_full)
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for i in range(self.chunks_per_dim):
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for j in range(self.chunks_per_dim):
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for k in range(self.chunks_per_dim):
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selected_time = random.choice(times)
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offset_x = (base_offset_x + i * self.N) % self.N_full
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offset_y = (base_offset_y + j * self.N) % self.N_full
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offset_z = (base_offset_z + k * self.N) % self.N_full
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self.samples.append((ID, selected_time, offset_x, offset_y, offset_z))
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def __len__(self):
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return len(self.samples)
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def __getitem__(self, idx):
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from pysbmy.field import read_field_chunk_3D_periodic
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ID, t, ox, oy, oz = self.samples[idx]
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# Filepaths
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input_paths = [
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os.path.join(self.root_dir, self.INITIAL_CONDITIONS_DIR, f'ICs_{ID}_{var}.h5')
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for var in self.initial_conditions_variables
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]
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target_path = os.path.join(self.root_dir, self.TARGET_DIR, f'{self.target_variable}_{ID}_nforce{t}.h5')
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style_path = os.path.join(self.root_dir, self.STYLE_DIR, f'{self.style_files}_{ID}_nforce{t}.txt')
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# Read 3D chunks
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input_arrays = [
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read_field_chunk_3D_periodic(file, self.N,self.N,self.N, ox,oy,oz, name=varname).array
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for file, varname in zip(input_paths, self.initial_conditions_variables)
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]
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target_array = read_field_chunk_3D_periodic(target_path, self.N, self.N, self.N, ox, oy, oz, name=self.target_variable).array
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# Stack the input arrays
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input_tensor = np.stack(input_arrays, axis=0)
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input_tensor = torch.tensor(input_tensor, dtype=torch.float32).to(self.device)
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# Target
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target_tensor = torch.tensor(target_array, dtype=torch.float32).unsqueeze(0).to(self.device)
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# Style parameters
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style_params = read_cosmo_and_time_file(style_path)
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# Select only the specified style keys
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if self.style_keys is not None:
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style_params = [style_params[key] for key in self.style_keys if key in style_params]
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else:
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style_params = list(style_params.values())
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# Convert to tensor
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style_tensor = torch.tensor(style_params, dtype=torch.float32).to(self.device)
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return {
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'input': input_tensor,
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'target': target_tensor,
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'style': style_tensor,
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'ID': ID,
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'time': t,
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'offset': (ox, oy, oz)
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}
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def on_epoch_end(self):
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"""Call this at the end of each epoch to regenerate offset + time choices."""
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self._prepare_samples()
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