File size: 11,277 Bytes
a16f583 | 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 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 |
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
from config import bands_list_order , time_before, LOADING_TIME_BEGINNING , window_size
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=window_size):
"""
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
#window = np.asarray(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 MultiRasterDatasetMultiYears(Dataset):
def __init__(self, samples_coordinates_array_subfolders , data_array_subfolders , dataframe, time_before = time_before):
"""
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.time_before = time_before
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)
# Initialize a dictionary to hold the tensors for each band
band_tensors = {band: [] for band in bands_list_order}
for subfolder in filtered_array:
subfolder = self.get_last_three_folders(subfolder)
# Check if the forelast subfolder is 'Elevation'
if subfolder.split(os.path.sep)[-1] == 'Elevation':
# Get the tensor for 'Elevation'
id_num, x, y = self.find_coordinates_index(subfolder, longitude, latitude)
elevation_tensor = self.datasets[subfolder].get_tensor_by_location(id_num, x, y)
if elevation_tensor is not None:
# Repeat the 'Elevation' tensor self.time_before times
for _ in range(self.time_before):
band_tensors['Elevation'].append(elevation_tensor)
else:
# Get the year from the last subfolder
year = int(subfolder.split(os.path.sep)[-1])
# Decrement the year by self.time_before
for decrement in range(self.time_before):
current_year = year - decrement
# Construct the subfolder with the decremented year
decremented_subfolder = os.path.sep.join(subfolder.split(os.path.sep)[:-1] + [str(current_year)])
id_num, x, y = self.find_coordinates_index(decremented_subfolder, longitude, latitude)
tensor = self.datasets[decremented_subfolder].get_tensor_by_location(id_num, x, y)
if tensor is not None:
# Append the tensor to the corresponding band in the dictionary
band = subfolder.split(os.path.sep)[-2]
band_tensors[band].append(tensor)
# Stack the tensors for each band
stacked_tensors = []
for band in bands_list_order:
if band_tensors[band]:
# Stack the tensors for the current band
stacked_tensor = torch.stack(band_tensors[band])
stacked_tensors.append(stacked_tensor)
# Concatenate all stacked tensors along the band dimension
final_tensor = torch.stack(stacked_tensors)
final_tensor = final_tensor.permute(0, 2, 3, 1)
return longitude, latitude, final_tensor, 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)
|