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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
import pandas as pd
def get_metadata(self, id_num):
"""Get metadata from filename"""
if id_num not in self.id_to_file:
raise ValueError(f"ID {id_num} not found")
filename = self.id_to_file[id_num].name
pattern = r'ID(\d+)N(\d+\.\d+)S(\d+\.\d+)W(\d+\.\d+)E(\d+\.\d+)'
match = re.search(pattern, filename)
if match:
return {
'id': int(match.group(1)),
'north': float(match.group(2)),
'south': float(match.group(3)),
'west': float(match.group(4)),
'east': float(match.group(5))
}
return None
def get_available_ids(self):
"""Return list of available IDs"""
return list(self.id_to_file.keys())
class RasterTensorDataset(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')
"""
self.folder_path = 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 glob.glob(os.path.join(self.folder_path, "*.npy")):
# Extract ID number from filename
match = re.search(r'ID(\d+)N', file_path)
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)
"""
class MultiRasterDataset(Dataset):
def __init__(self, samples_coordinates_array_subfolders , data_array_subfolders , dataframe):
"""
Parameters:
subfolders: list of str, names of subfolders to include
dataframe: pandas.DataFrame, contains columns GPS_LONG, GPS_LAT, and OC (target variable)
"""
self.data_array_subfolders = data_array_subfolders
self.seasonalityBased = self.check_seasonality(data_array_subfolders)
self.samples_coordinates_array_subfolders = samples_coordinates_array_subfolders
self.dataframe = dataframe
self.datasets = {
self.get_last_three_folders(subfolder): RasterTensorDataset(subfolder)
for subfolder in self.data_array_subfolders
}
self.coordinates = {
self.get_last_three_folders(subfolder): np.load(f"{subfolder}/coordinates.npy")#[np.isfinite(np.load(f"{subfolder}/coordinates.npy"))]
for subfolder in self.samples_coordinates_array_subfolders
}
def check_seasonality(self,data_array_subfolders):
seasons = ['winter', 'spring', 'summer', 'autumn']
# Check if any subfolder contains a season name
is_seasonal = any(
any(season in subfolder.lower() for season in seasons)
for subfolder in data_array_subfolders
)
return 1 if is_seasonal else 0
def get_last_three_folders(self,path):
# Split the path into components
parts = path.rstrip('/').split('/')
# Return last 3 components, or all if less than 3
return '/'.join(parts[-2:])
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, name of the subfolder
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]
# Assuming the first two columns of `coordinates.npy` are longitude and latitude
match = np.where((coords[:, 1] == longitude) & (coords[:, 0] == latitude))[0]
if match.size == 0:
raise ValueError(f"{coords} Coordinates ({longitude}, {latitude}) not found in {subfolder}")
# Return id_num, x, y from the same row
return coords[match[0], 2], coords[match[0], 3], coords[match[0], 4]
def __getitem__(self, index):
"""
Retrieve tensor and target value for a given index.
Parameters:
index: int, index of the row in the dataframe
Returns:
tuple: (tensor, OC), where tensor is the data and OC is the target variable
"""
row = self.dataframe.iloc[index]
longitude, latitude, oc = row["GPS_LONG"], row["GPS_LAT"], row["OC"]
tensors = []
filtered_array = self.filter_by_season_or_year(row['season'],row['year'],self.seasonalityBased)
for subfolder in filtered_array:
subfolder = self.get_last_three_folders(subfolder)
id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude)
tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y)
if tensor is not None:
tensors.append(tensor)
# Combine all tensors into a single tensor
return longitude, latitude, torch.stack(tensors), oc
def filter_by_season_or_year(self, season,year,Season_or_year):
if Season_or_year:
filtered_array = [
path for path in self.samples_coordinates_array_subfolders
if ('Elevation' in path) or
('MODIS_NPP' in path and path.endswith(str(year))) or
(not 'Elevation' in path and not 'MODIS_NPP' in path and path.endswith(season))
]
else:
filtered_array = [
path for path in self.samples_coordinates_array_subfolders
if ('Elevation' in path) or
(not 'Elevation' in path and path.endswith(str(year)))
]
return filtered_array
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)
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