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import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import os
from pathlib import Path
import re
import glob
class RasterTensorDatasetMapping(Dataset):
def __init__(self, base_path):
"""
Initialize the dataset
Parameters:
base_path: str, base path to RasterTensorData directory
subfolder: str, name of the subfolder (e.g., 'Elevation')
"""
# Replace "OC_LUCAS_LFU_LfL_Coordinates" with "RasterTensorData" in the base path
self.base_path = Path(base_path.replace("Coordinates1Mil", "RasterTensorData"))
self.folder_path = self.base_path
# Create ID to filename mapping
self.id_to_file = self._create_id_mapping()
# Load all numpy arrays into memory (optional, can be modified to load on demand)
self.data_cache = {}
for id_num, filepath in self.id_to_file.items():
self.data_cache[id_num] = np.load(filepath)
def _create_id_mapping(self):
"""Create a dictionary mapping IDs to their corresponding file paths"""
id_to_file = {}
for file_path in self.folder_path.glob("*.npy"):
# Extract ID number from filename
match = re.search(r'ID(\d+)N', file_path.name)
if match:
id_num = int(match.group(1))
id_to_file[id_num] = file_path
return id_to_file
def get_tensor_by_location(self, id_num, x, y, window_size=17):
"""
Get a window_size x window_size square around the specified x,y coordinates
Parameters:
id_num: int, ID number from filename
x: int, x coordinate
y: int, y coordinate
window_size: int, size of the square window (default 17)
Returns:
torch.Tensor: window_size x window_size tensor
"""
if id_num not in self.id_to_file:
raise ValueError(f"ID {id_num} not found in dataset")
# Get the data array
if id_num in self.data_cache:
data = self.data_cache[id_num]
else:
data = np.load(self.id_to_file[id_num])
# Calculate window boundaries
half_window = window_size // 2
x_start = int(max(0, x - half_window))
x_end = int(min(data.shape[0], x + half_window + 1))
y_start = int(max(0, y - half_window))
y_end = int(min(data.shape[1], y + half_window + 1))
# Extract window
window = data[x_start:x_end, y_start:y_end]
# Pad if necessary
if window.shape != (window_size, window_size):
padded_window = np.zeros((window_size, window_size))
x_offset = half_window - (x - x_start)
y_offset = half_window - (y - y_start)
padded_window[
x_offset:x_offset+window.shape[0],
y_offset:y_offset+window.shape[1]
] = window
window = padded_window
return torch.from_numpy(window).float()
def __len__(self):
return len(self.id_to_file)
def __getitem__(self, idx):
# This is a placeholder implementation
# Modify according to your specific needs
id_num = list(self.id_to_file.keys())[idx]
return self.data_cache[id_num]
# Example usage:
"""
# Initialize the dataset
base_path = "/content/drive/MyDrive/Colab Notebooks/MappingSOC/Data/RasterTensorData"
dataset = RasterTensorDataset(base_path, "Elevation")
# Get the dictionary mapping IDs to filenames
id_mapping = dataset.id_to_file
print("ID to filename mapping:", id_mapping)
# Get a 17x17 window for a specific location
id_num = 10 # example ID
x, y = 100, 100 # example coordinates
window = dataset.get_tensor_by_location(id_num, x, y)
print("Window shape:", window.shape)
# Create a DataLoader if needed
from torch.utils.data import DataLoader
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
"""
from torch.utils.data import Dataset
import numpy as np
import torch
from pathlib import Path
import os
class MultiRasterDatasetMapping(Dataset):
def __init__(self, subfolders, dataframe, bands_list_order=None):
"""
Parameters:
subfolders: list of str, paths to directories containing raster data and coordinates.npy (e.g., /path/to/LAI/2015)
dataframe: pandas.DataFrame, contains columns 'longitude' and 'latitude'
bands_list_order: list of str, order of bands (e.g., ['Elevation', 'LAI', ...])
"""
self.subfolders = subfolders
self.dataframe = dataframe
self.bands_list_order = bands_list_order if bands_list_order is not None else []
self.datasets = {}
self.coordinates = {}
# Initialize datasets and coordinates with error checking
for subfolder in subfolders:
coord_file = f"{subfolder}/coordinates.npy"
if not Path(coord_file).exists():
raise FileNotFoundError(f"Coordinates file not found: {coord_file}")
self.coordinates[subfolder] = np.load(coord_file)
self.datasets[subfolder] = RasterTensorDatasetMapping(subfolder) # Assuming this class exists
def find_coordinates_index(self, subfolder, longitude, latitude):
"""
Finds the index of the matching coordinates in the subfolder's coordinates.npy file.
Parameters:
subfolder: str, path to the subfolder (e.g., /path/to/LAI/2015)
longitude: float, longitude to match
latitude: float, latitude to match
Returns:
tuple: (id_num, x, y) if match is found, otherwise raises an error
"""
coords = self.coordinates[subfolder]
match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0]
if match.size == 0:
raise ValueError(f"Coordinates ({longitude}, {latitude}) not found in {subfolder}")
return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4]
def filter_by_year(self, inference_year):
"""
Filter subfolders for a single inference year.
Parameters:
inference_year: str, the year for inference (e.g., '2015')
Returns:
dict: {band: subfolder path for the specified year}
"""
filtered_paths = {band: None for band in self.bands_list_order}
for subfolder in self.subfolders:
parts = subfolder.split('/')
band = parts[-2] if 'Elevation' not in subfolder else 'Elevation'
subfolder_year = parts[-1] if 'Elevation' not in subfolder else None
if band in self.bands_list_order:
if band == 'Elevation':
filtered_paths['Elevation'] = subfolder
elif subfolder_year == inference_year:
filtered_paths[band] = subfolder
return filtered_paths
def __getitem__(self, index):
"""
Retrieve tensor and coordinates for a given index for a single year.
Parameters:
index: int, index of the row in the dataframe
Returns:
tuple: (tensor, coordinates), where tensor is (num_bands, height, width)
and coordinates are (longitude, latitude)
"""
row = self.dataframe.iloc[index]
longitude, latitude = row["longitude"], row["latitude"]
inference_year = "2015" # Hardcoded to INFERENCE_TIME; adjust if needed in dataframe
# Filter subfolders by year
filtered_paths = self.filter_by_year(inference_year)
# Initialize band tensors
band_tensors = []
window_size = 33 # From your config
# Collect tensors for each band for the single year
for band in self.bands_list_order:
subfolder = filtered_paths[band]
if subfolder:
id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude)
tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y)
band_tensors.append(tensor if tensor is not None else torch.zeros(window_size, window_size))
else:
band_tensors.append(torch.zeros(window_size, window_size))
if len(band_tensors) != len(self.bands_list_order):
raise ValueError(f"Expected {len(self.bands_list_order)} bands, but got {len(band_tensors)}")
final_tensor = torch.stack(band_tensors) # Shape: (num_bands, height, width)
return final_tensor, torch.tensor([longitude, latitude], dtype=torch.float32)
def __len__(self):
"""
Return the number of samples in the dataset.
"""
return len(self.dataframe)
def get_tensor_by_location(self, subfolder, id_num, x, y):
"""Get tensor from specific subfolder dataset"""
return self.datasets[subfolder].get_tensor_by_location(id_num, x, y) |