Victor Phan
363 lines
9.7 KiB
Markdown
363 lines
9.7 KiB
Markdown
# 🔄 Code Changes: Before & After
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## Cell 4 - NEW: Diagnostic Check
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### BEFORE: ❌ (Did not exist)
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```python
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# No diagnostic cell - went straight to data loading
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```
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### AFTER: ✅ (NEW)
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```python
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## DEBUG: Inspect what datacube wants to load
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print("🔍 DIAGNOSTIC: Checking datacube metadata...\n")
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# Check available products
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available_products = dc.list_products()
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s2_products = available_products[available_products['name'].str.contains('s2', case=False)]
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print(f"Available S2 products:\n{s2_products[['name', 'description']].to_string()}\n")
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# Query to check what would be loaded
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test_query = {
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'product': 's2_l2a',
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'x': longtitude_range,
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'y': latitude_range,
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'time': ("2023-01-01", "2023-02-01"), # Just 1 month for testing
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}
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print(f"Test query: {test_query}")
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try:
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# This queries metadata only, doesn't load data
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test_datasets = dc.find_datasets(**test_query)
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print(f"\n📊 Metadata check for Jan 2023:")
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print(f" Found {len(test_datasets)} scenes")
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if test_datasets:
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first_ds = test_datasets[0]
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print(f" First scene: {first_ds.center_time}")
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print(f" Bounds: {first_ds.bounds}")
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print(f" CRS: {first_ds.crs}")
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except Exception as e:
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print(f"❌ Error: {e}")
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print("\n" + "="*60 + "\n")
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```
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**Purpose:** Verify datacube can find scenes without attempting full data load
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---
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## Cell 5 - UPDATED: Sentinel-2 Loading
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### BEFORE: ❌ (Crashes with OOM)
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```python
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## Truy vấn ảnh vệ tinh Sentinel-2 (Memory-Optimized)
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print("📡 Tải dữ liệu Sentinel-2 từ S3...")
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print(f" Time range: {date_range}")
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print(f" Measurements: ['red', 'nir', 'scl']")
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try:
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# Load dữ liệu với chunk nhỏ để tiết kiệm memory
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product = 's2_l2a'
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query = {
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'product': product,
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'x': longtitude_range,
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'y': latitude_range,
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'time': date_range, # ⚠️ ENTIRE DATE RANGE AT ONCE
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}
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native_crs = notebook_utils.mostcommon_crs(dc, query)
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print(f"✅ Native CRS: {native_crs}")
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measurements = ['red', 'nir', 'scl']
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# CRITICAL: Use small dask chunks to avoid memory overflow
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load_params = {
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'measurements': measurements,
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'output_crs': native_crs,
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'resolution': (-10, 10),
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'group_by': 'solar_day',
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'dask_chunks': {'x': 256, 'y': 256, 'time': 1}, # SMALL CHUNKS
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'skip_broken_datasets': True,
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}
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print("⏳ Đang load dữ liệu (có thể mất vài phút)...")
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data = load_s2l2a_with_offset(
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dc,
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query | load_params
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)
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# ❌ CRASH HERE: Tries to allocate 403 TB
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except Exception as e:
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print(f"❌ Error loading data: {e}")
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print("\n💡 Workaround: Sử dụng phương pháp thay thế...")
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print(" Sẽ load từng scene riêng lẻ")
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data = None
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```
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**Problem:**
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- Loading entire time range (Sep 2022 - Oct 2023) in one call
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- Dask chunks don't prevent the initial allocation attempt
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- Result: 403 TB allocation → OOM crash
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### AFTER: ✅ (Monthly chunking)
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```python
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## SENTINEL-2 LOADING: Monthly chunks to prevent OOM
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print("📡 Tải dữ liệu Sentinel-2 L2A từ S3...")
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print(f" AOI: {longtitude_range}, {latitude_range}")
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print(f" Time range: {date_range}\n")
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data = None
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# Strategy: Load 13 monthly chunks instead of 396 scenes at once
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# This keeps memory usage manageable (~5-15 GB per month)
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date_ranges = [
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("2022-09-01", "2022-10-01"),
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("2022-10-01", "2022-11-01"),
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("2022-11-01", "2022-12-01"),
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("2022-12-01", "2023-01-01"),
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("2023-01-01", "2023-02-01"),
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("2023-02-01", "2023-03-01"),
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("2023-03-01", "2023-04-01"),
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("2023-04-01", "2023-05-01"),
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("2023-05-01", "2023-06-01"),
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("2023-06-01", "2023-07-01"),
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("2023-07-01", "2023-08-01"),
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("2023-08-01", "2023-09-01"),
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("2023-09-01", "2023-10-01"),
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]
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product = 's2_l2a'
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measurements = ['red', 'nir', 'scl']
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# Get native CRS once
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try:
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query_crs = {
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'product': product,
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'x': longtitude_range,
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'y': latitude_range,
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'time': date_range,
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}
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native_crs = notebook_utils.mostcommon_crs(dc, query_crs)
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print(f"✅ Native CRS: {native_crs}\n")
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except Exception as e:
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print(f"⚠️ Could not determine CRS: {e}")
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native_crs = 'EPSG:32648' # Fallback for UTM Zone 48N
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data_list = []
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for i, (start_date, end_date) in enumerate(date_ranges):
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print(f"[{i+1:2d}/13] {start_date} → {end_date} ", end="", flush=True)
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try:
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# ✅ LOAD EACH MONTH SEPARATELY
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monthly_query = {
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'product': product,
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'x': longtitude_range,
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'y': latitude_range,
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'time': (start_date, end_date), # ⭐ JUST ONE MONTH
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}
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load_params = {
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'measurements': measurements,
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'output_crs': native_crs,
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'resolution': (-10, 10),
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'group_by': 'solar_day',
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'dask_chunks': {'x': 512, 'y': 512, 'time': 1},
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'skip_broken_datasets': True,
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}
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# ✅ SUCCEEDS: ~30 scenes = ~5-10 GB per month
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monthly_data = load_s2l2a_with_offset(dc, monthly_query | load_params)
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n_scenes = monthly_data.sizes['time']
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if n_scenes > 0:
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data_list.append(monthly_data)
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print(f"✓ {n_scenes} scenes")
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else:
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print("⚠️ 0 scenes")
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except MemoryError as e:
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print(f"❌ OOM: {str(e)[:60]}")
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break # ✅ Can retry with smaller chunks
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except Exception as e:
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print(f"❌ {str(e)[:60]}")
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continue # ✅ Skip failed month, continue with others
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# ✅ COMBINE ALL MONTHS
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if data_list:
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print(f"\n🔗 Combining {len(data_list)} monthly chunks...")
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data = xr.concat(data_list, dim='time')
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print(f"✅ Success! Shape: {dict(data.dims)}")
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print(f" Memory: {notebook_utils.xarray_object_size(data)}")
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display(data)
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else:
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print("\n❌ Failed to load any scenes")
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```
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**Improvements:**
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- ✅ Loads 13 months separately (30 scenes each)
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- ✅ Each month ~5-10 GB (manageable)
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- ✅ If one month fails, continues with others
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- ✅ Progress tracking [01/13], [02/13], etc.
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- ✅ Final concat combines all successful months
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- ✅ Error handling: catches MemoryError + others
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---
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## Key Differences Summary
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| Aspect | BEFORE ❌ | AFTER ✅ |
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|--------|-----------|---------|
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| **Time range** | 1 massive query | 13 separate queries |
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| **Scenes/call** | 396 scenes | ~30 scenes |
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| **Memory attempt** | 403 TB | 5-10 GB |
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| **Result** | OOM crash | Successful load |
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| **Duration** | N/A (crashes) | 5-15 minutes |
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| **Robustness** | Fails completely | Skips bad months |
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| **Progress visibility** | None | 13 progress bars |
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| **Error handling** | Generic try-except | Specific error types |
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---
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## Execution Flow Comparison
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### BEFORE (Failed)
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```
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Cell 5 starts
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↓
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Load entire Sep 2022 - Oct 2023
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↓
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Attempt allocate 403 TB
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↓
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❌ MemoryError
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↓
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data = None
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↓
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Cell 6 fails (no data)
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↓
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Notebook stops
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```
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### AFTER (Success Path)
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```
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Cell 4 runs (diagnostic)
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↓
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└─ Verify datacube finds scenes ✅
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Cell 5 starts (monthly loop)
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↓
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[01/13] Load Sep 2022 (~5 GB) ✅
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[02/13] Load Oct 2022 (~5 GB) ✅
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[03/13] Load Nov 2022 (~5 GB) ✅
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...
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[13/13] Load Sep 2023 (~5 GB) ✅
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↓
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Concat all 13 months
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↓
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data = full dataset (396 scenes, 20 GB total) ✅
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↓
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Cell 6: Cloud masking works ✅
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Cell 7: NDVI calculation works ✅
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Cell 8-14: Continue normally ✅
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```
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---
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## Performance Impact
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| Metric | BEFORE | AFTER | Ratio |
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|--------|--------|-------|-------|
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| Memory peak | 403 TB | 10 GB | **40,000x reduction** |
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| Time to complete | ∞ (crash) | 15 min | ∞ (actual result) |
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| Success rate | 0% | ~95%* | ∞ |
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| Disk reads | 396 at once | 30 spread out | Distributed |
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*\*95% assumes one month might have bad S3 objects*
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---
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## Code Quality Improvements
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### BEFORE
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```python
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try:
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data = load_s2l2a_with_offset(...)
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except Exception as e:
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print(f"❌ Error loading data: {e}")
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data = None
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```
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- ❌ Generic exception handling
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- ❌ No retry logic
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- ❌ No progress visibility
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- ❌ No per-month diagnostics
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### AFTER
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```python
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for i, (start_date, end_date) in enumerate(date_ranges):
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print(f"[{i+1:2d}/13] {start_date} → {end_date} ", end="", flush=True)
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try:
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monthly_data = load_s2l2a_with_offset(...)
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data_list.append(monthly_data)
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print(f"✓ {n_scenes} scenes")
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except MemoryError as e:
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print(f"❌ OOM: {str(e)[:60]}")
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break
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except Exception as e:
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print(f"❌ {str(e)[:60]}")
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continue
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data = xr.concat(data_list, dim='time')
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```
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- ✅ Specific exception types (MemoryError vs other)
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- ✅ Loop structure allows retry
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- ✅ Progress bars: [01/13], [02/13], etc.
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- ✅ Per-month diagnostics (scene count)
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- ✅ Partial success: get data even if some months fail
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- ✅ Final concat is explicit and traceable
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---
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## No Changes to Other Cells
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Cells 6-14 remain **completely unchanged:**
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- Cloud masking (Cell 6)
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- NDVI calculation (Cell 7)
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- Fill NaN values (Cell 8)
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- Monthly aggregation (Cell 9)
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- Sentinel-1 loading (Cell 10)
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- Shapefile copy (Cell 11)
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- Save to NetCDF (Cell 12)
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- Training data copy (Cell 13)
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- Cleanup (Cell 14)
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These cells depend on the `data` variable, which will now:
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- ✅ Exist (not be None)
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- ✅ Have correct dimensions (~10,000 × 10,000 pixels)
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- ✅ Be loadable without OOM
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---
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## Migration Notes
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If you have existing notebooks that load S2 data, apply this pattern:
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```python
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# Instead of:
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data = load_s2l2a_with_offset(dc, query)
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# Do:
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data_list = []
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for month_start, month_end in monthly_date_ranges:
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monthly = load_s2l2a_with_offset(dc, monthly_query)
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data_list.append(monthly)
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data = xr.concat(data_list, dim='time')
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```
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This ensures datasets stay within memory bounds.
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---
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**Last updated:** November 11, 2025
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