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Fill-the-Frames
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sid385 commited on
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44a0537
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1 Parent(s): 26c6915

Added main.py

Browse files

Initialized the structure

Files changed (16) hide show
  1. .gitignore +10 -0
  2. .python-version +1 -0
  3. README.md +0 -0
  4. main.py +6 -0
  5. pyproject.toml +13 -0
  6. src/__init__.py +0 -0
  7. src/data/__init__.py +0 -0
  8. src/data/dataset.py +0 -0
  9. src/data/s3_manager.py +82 -0
  10. src/data/transformes.py +0 -0
  11. src/model/ifnet.py +0 -0
  12. src/model/loss.py +0 -0
  13. src/model/refine.py +0 -0
  14. src/training/trainer.py +0 -0
  15. tests/test_s3_manager.py +44 -0
  16. uv.lock +354 -0
.gitignore ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ # Python-generated files
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+ __pycache__/
3
+ *.py[oc]
4
+ build/
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+ dist/
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+ wheels/
7
+ *.egg-info
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+
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+ # Virtual environments
10
+ .venv
.python-version ADDED
@@ -0,0 +1 @@
 
 
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+ 3.12
README.md ADDED
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main.py ADDED
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+ def main():
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+ print("Hello from satellite-video-feed!")
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+
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+
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+ if __name__ == "__main__":
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+ main()
pyproject.toml ADDED
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+ [project]
2
+ name = "satellite-video-feed"
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+ version = "0.1.0"
4
+ description = "Add your description here"
5
+ readme = "README.md"
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+ requires-python = ">=3.12"
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+ dependencies = [
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+ "boto3>=1.43.31",
9
+ "numpy>=2.4.6",
10
+ "pytest>=9.1.0",
11
+ "pyyaml>=6.0.3",
12
+ "xarray>=2026.4.0",
13
+ ]
src/__init__.py ADDED
File without changes
src/data/__init__.py ADDED
File without changes
src/data/dataset.py ADDED
File without changes
src/data/s3_manager.py ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import glob
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+ import logging
3
+ import os
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+
5
+ import boto3
6
+ import numpy as np
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+ import torch
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+ import xarray as xr
9
+ from botocore import UNSIGNED
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+ from botocore.config import Config
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+
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+ from src.config.settings import Settings
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+
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+ logger = logging.getLogger(__name__)
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+
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+
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+ class S3Manager:
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+ """Manages downloading GOES data from AWS S3, converting it, and purging raw files."""
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+
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+ def __init__(self, settings: Settings):
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+ self.settings = settings
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+ self.s3_client = boto3.client("s3", config=Config(signature_version=UNSIGNED))
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+ self.download_dir = settings.data.download_dir
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+ os.makedirs(self.download_dir, exist_ok=True)
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+
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+ def download_chunk(self, prefix: str) -> list[str]:
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+ """Downloads a chunk of .nc files from S3 based on a prefix."""
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+ logger.info(f"Downloading chunk with prefix: {prefix}")
29
+ # Implementation of boto3 list_objects and download_file goes here
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+ # Return list of downloaded file paths
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+ return []
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+
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+ def _convert_nc_to_bt_tensor(self, nc_path: str, pt_path: str) -> None:
34
+ """Converts raw radiance .nc file to normalized Brightness Temperature .pt tensor."""
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+ logger.debug(f"Processing physical BT for {nc_path}")
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+ ds = xr.open_dataset(nc_path)
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+
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+ # Simplified cropping logic assuming the data is already somewhat manageable
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+ # or cropping is handled by specific lat/lon boundaries.
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+ # For demonstration, selecting a 512x512 slice
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+ start_y, start_x = 500, 500
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+ try:
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+ ds_cropped = ds.isel(
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+ y=slice(start_y, start_y + 512), x=slice(start_x, start_x + 512)
45
+ )
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+ except (ValueError, IndexError):
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+ ds_cropped = ds # Fallback if dimensions are smaller
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+
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+ rad = ds_cropped["Rad"]
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+ fk1 = ds_cropped["planck_fk1"].values
51
+ fk2 = ds_cropped["planck_fk2"].values
52
+ bc1 = ds_cropped["planck_bc1"].values
53
+ bc2 = ds_cropped["planck_bc2"].values
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+
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+ rad_safe = rad.where(rad > 0)
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+ bt = (fk2 / np.log((fk1 / rad_safe) + 1) - bc1) / bc2
57
+
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+ # Normalize
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+ min_bt, max_bt = 180.0, 330.0
60
+ bt_norm = (bt - min_bt) / (max_bt - min_bt)
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+ bt_norm = bt_norm.clip(0, 1)
62
+
63
+ # Convert to tensor and save
64
+ tensor = (
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+ torch.from_numpy(bt_norm.values).float().unsqueeze(0)
66
+ ) # Shape: [1, H, W]
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+ torch.save(tensor, pt_path)
68
+ ds.close()
69
+
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+ def purge_raw_files(self) -> None:
71
+ """Deletes massive .nc files to save disk space."""
72
+ files = glob.glob(os.path.join(self.download_dir, "*.nc"))
73
+ for f in files:
74
+ os.remove(f)
75
+ logger.info(f"Purged {len(files)} raw .nc files.")
76
+
77
+ def purge_chunk(self) -> None:
78
+ """Deletes .pt files after training chunk is complete."""
79
+ files = glob.glob(os.path.join(self.download_dir, "*.pt"))
80
+ for f in files:
81
+ os.remove(f)
82
+ logger.info(f"Purged {len(files)} processed .pt files.")
src/data/transformes.py ADDED
File without changes
src/model/ifnet.py ADDED
File without changes
src/model/loss.py ADDED
File without changes
src/model/refine.py ADDED
File without changes
src/training/trainer.py ADDED
File without changes
tests/test_s3_manager.py ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from unittest.mock import MagicMock, patch
2
+
3
+ import numpy as np
4
+ import pytest
5
+ import xarray as xr
6
+
7
+ from src.config.settings import DataConfig, Settings, TrainingConfig
8
+ from src.data.s3_manager import S3Manager
9
+
10
+
11
+ @pytest.fixture
12
+ def mock_settings():
13
+ return Settings(
14
+ training=TrainingConfig(
15
+ epochs=1, batch_size=1, learning_rate=0.001, checkpoints_dir="chkpt"
16
+ ),
17
+ data=DataConfig(
18
+ s3_bucket="test-bucket", download_dir="test_dir", chunk_size_days=1
19
+ ),
20
+ )
21
+
22
+
23
+ def test_process_nc_to_pt(mock_settings, tmp_path):
24
+ manager = S3Manager(mock_settings)
25
+
26
+ # Create mock dataset
27
+ ds = xr.Dataset(
28
+ {"Rad": (("y", "x"), np.ones((512, 512)) * 10)},
29
+ coords={"y": np.arange(512), "x": np.arange(512)},
30
+ )
31
+ ds["planck_fk1"] = 1.0
32
+ ds["planck_fk2"] = 100.0
33
+ ds["planck_bc1"] = 0.1
34
+ ds["planck_bc2"] = 1.0
35
+
36
+ nc_path = tmp_path / "test.nc"
37
+ ds.to_netcdf(nc_path)
38
+
39
+ pt_path = tmp_path / "test.pt"
40
+
41
+ # The actual triplet extraction takes 3 files usually, testing just the converter here
42
+ manager._convert_nc_to_bt_tensor(str(nc_path), str(pt_path))
43
+
44
+ assert pt_path.exists()
uv.lock ADDED
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