Victor Phan
170 lines
4.6 KiB
Markdown
170 lines
4.6 KiB
Markdown
# 📋 Quick Troubleshooting: Sentinel-2 Loading
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## Before You Run
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✅ Verify dask cluster is running:
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```python
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# Cell 2 output should show:
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# Scheduler: 127.0.0.1:8786 (or gateway address)
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# Workers: 4 (or your configured number)
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```
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✅ Verify S3 access is configured:
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```python
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# Cell 2 should complete without errors
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# If you see authentication errors, check credentials
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```
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## Running Notebook 01
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### Step-by-step execution:
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**Cell 1:** Introduction (markdown, no action)
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**Cell 2:** Initialize Dask + Datacube
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- Wait for cluster to initialize (10-30 seconds)
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- Should show worker status
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**Cell 3:** Set coordinates
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- Automatic, takes <1 second
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**Cell 4:** Diagnostic Check ⭐ RUN THIS FIRST
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- **Purpose:** Verify datacube can find scenes without loading
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- **Expected output:**
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```
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Available S2 products:
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name description
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s2_l2a Sentinel-2 L2A Data
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📊 Metadata check for Jan 2023:
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Found 28 scenes
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First scene: 2023-01-15 10:30:45
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Bounds: BoundingBox(...)
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CRS: EPSG:32648
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```
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- **If fails:** S3 connection issue - check credentials in Cell 2
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**Cell 5:** Load Sentinel-2 Data (THE FIXED CELL)
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- **Expected duration:** 5-15 minutes (depending on workers)
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- **Watch for:** Monthly progress bars
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```
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[01/13] 2022-09-01 → 2022-10-01 ✓ 32 scenes
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[02/13] 2022-10-01 → 2022-11-01 ✓ 28 scenes
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...
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```
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- **Expected final output:**
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```
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✅ Success! Shape: {'time': 396, 'y': ~10000, 'x': ~10000}
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Memory: 15-20 GB
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```
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## Common Issues & Fixes
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### ❌ "Still getting huge dimensions error"
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**Symptom:**
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```
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Error: shape (396, 563539, 992108)
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```
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**Cause:** Datacube function is still loading full tiles
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**Fixes (in order):**
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1. Run Cell 4 diagnostic → Check actual bounds returned
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2. Verify `load_s2l2a_with_offset()` in `new_import_ODC.py` includes spatial subsetting
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3. Add manual clipping:
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```python
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# After line: monthly_data = load_s2l2a_with_offset(...)
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# Add this:
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if monthly_data.sizes['y'] > 15000:
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print(f"⚠️ Clipping oversized data: {monthly_data.dims}")
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monthly_data = monthly_data.sel(
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x=slice(longtitude_range[0], longtitude_range[1]),
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y=slice(latitude_range[0], latitude_range[1]),
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)
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```
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### ❌ "Cell 4 says 0 scenes found"
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**Cause:** S3 data may not exist for your region/dates
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**Fixes:**
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1. Check if S3 bucket/path is correct
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2. Try different time range (e.g., "2023-01-01" to "2023-12-31")
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3. Verify coordinates are in correct order: (longitude_min, longitude_max), (latitude_min, latitude_max)
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### ❌ "Dask workers running out of memory"
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**Symptom:**
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```
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MemoryError during ...
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Killed process (out of memory)
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```
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**Quick fix:**
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1. Reduce chunk size in Cell 5:
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```python
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'dask_chunks': {'x': 256, 'y': 256, 'time': 1} # Smaller chunks
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```
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2. Or reduce number of workers in Cell 2:
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```python
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cluster, client = notebook_utils.initialize_dask(
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use_gateway=True,
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workers=(1, 5) # Reduce from (1, 10)
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)
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```
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3. Or load fewer months at once - split Cell 5 manually
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### ❌ "One month loaded but then it fails"
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**Cause:** One S3 object is corrupted/missing
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**Expected behavior:**
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- Code continues to next month (has try-except)
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- Check output logs for which month failed
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- You can manually skip it by removing from `date_ranges` list
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**Is this okay?**
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✅ Yes! If 12/13 months load, you have 380+ scenes (good dataset)
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### ⚠️ "Still taking too long / worker still slow"
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**Cause:** Network latency from S3, or insufficient workers
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**Options:**
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1. **Increase workers:** Cell 2 `workers=(1, 15)` (if hardware allows)
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2. **Enable rechunking:** Add to Cell 5:
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```python
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monthly_data = monthly_data.rechunk({'x': 'auto', 'y': 'auto'})
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```
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3. **Check Dask dashboard:** Ask instructor for URL (port 8787)
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## Success Criteria ✅
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After Cell 5 completes, you should have:
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1. **Variable `data` exists** and is not None
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2. **Dimensions match AOI:**
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```
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time: 396 (or close - some months may have 0 scenes)
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y: ~10000 pixels (±10%)
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x: ~10000 pixels (±10%)
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```
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3. **No memory errors** (or only 1-2 skipped months)
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4. **Dask workers still healthy** (can continue to next cells)
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## Next: Cells 6-10
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Once Cell 5 succeeds, remaining cells should work automatically:
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- **Cell 6:** Cloud masking
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- **Cell 7:** NDVI calculation
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- **Cell 8:** Fill NaN values
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- **Cell 9:** Monthly aggregation
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- **Cell 10:** Sentinel-1 loading
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These cells don't involve loading new data, just processing the `data` variable.
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---
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**Need help?** Check:
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1. `/home/x79/CSIROBoeingPhase5-Vietnam/MEMORY_FIX_EXPLAINED.md` (detailed explanation)
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2. Dask dashboard if available
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3. Datacube documentation: `dc.list_products()`
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