metoppy 0.1.0

Python package wrapper of MetopDatasets.jl Julia package for reading products from the METOP satellites.

MetopPy

Load native METOP products in Python. It's a Python wrapper around MetopDatasets.jl making it easy to install and use the package with Python.

MetopDatasets.jl is a package for reading products from the METOP satellites using the native binary format specified for each product. The METOP satellites are part of the EUMETSAT-POLAR-SYSTEM (EPS) and have produced near real-time, global weather and climate observation since 2007. Learn more METOP and the data access on EUMETSATs user-portal.

Status

MetopPy is under development and is not ready for use yet.

Copyright and License

This code is licensed under MIT license. See file LICENSE for details on the usage and distribution terms.

Authors

• Simon Kok Lupemba - Maintainer - EUMETSAT
• Francesco Murdaca- Contributor - EUMETSAT

Installation

(This still have to be implemented)

pip install metoppy

Dependencies

Dependency	Version	License	Home URL
juliacall	>=0.9.14	MIT License	https://juliapy.github.io/PythonCall.jl/stable/
juliapkg	>=0.1.22	MIT License	https://pypi.org/project/juliapkg/

Build/Edit/Test Dependencies

The following dependencies are only required for building/editing/testing the software, they are not distributed with it:

Dependency	Version	License	Home URL
pytest	8.4.1	MIT License (MIT)	https://docs.pytest.org/en/latest
pytest-cov	7.0	MIT License (MIT)	https://pytest-cov.readthedocs.io/en/latest
pytest-html	4.1.1	MIT License (MIT)	https://github.com/pytest-dev/pytest-html
pytest-mock	3.14.1	MIT License (MIT)	https://github.com/pytest-dev/pytest-mock
pre-commit	4.3.0	MIT License (MIT)	https://github.com/pre-commit/pre-commit
numpy	2.3.4	BSD License (BSD)	https://numpy.org/

Example

1. Get test file

from pathlib import Path
from metoppy.metopreader import MetopReader

metop_reader = MetopReader()
reduced_data_folder = metop_reader.get_test_data_artifact()
# ensure it's a Path object
reduced_data_folder = Path(reduced_data_folder)
reduced_data_files = [f for f in reduced_data_folder.iterdir() if f.is_file()]

test_file_name = next((s for s in reduced_data_files if s.name.startswith("ASCA_SZO")))
test_file_path = reduced_data_folder / test_file_name
ds = metop_reader.open_dataset(file_path=str(test_file_path), maskingvalue = float("nan"))

2. Check keys

keys = metop_reader.get_keys(ds)
print(list(keys))

Output of the print

['record_start_time', 'record_stop_time', 'degraded_inst_mdr', 'degraded_proc_mdr', 'utc_line_nodes', 'abs_line_number', 'sat_track_azi', 'as_des_pass', 'swath_indicator', 'latitude', 'longitude', 'sigma0_trip', 'kp', 'inc_angle_trip', 'azi_angle_trip', 'num_val_trip', 'f_kp', 'f_usable', 'f_land', 'lcr', 'flagfield']

3. Display variable information

print(ds['latitude'])

Output of the print

latitude (42 × 10)
  Datatype:    Float64 (Int32)
  Dimensions:  xtrack × atrack
  Attributes:
   description          = Latitude (-90 to 90 deg)
   missing_value        = Int32[-2147483648]
   scale_factor         = 1.0e-6

4. Read variable as neasted Python list

# Load CFVariable as a full Julia Array
latitude_julia = metop_reader.as_array(ds['latitude']) # preserves the 2D shape
latitude_shape = metop_reader.shape(latitude_julia)

# Convert to nested Python list
latitude_list = [
  [latitude_julia[i, j] for j in range(latitude_shape[1])]
  for i in range(latitude_shape[0])
]

# Print first 5x5 elements
for row in latitude_list[:5]:
    print(row[:5])

Output of the print

[71.101406, 70.99323199999999, 70.88307999999999, 70.770985, 70.65697999999999]
[71.298254, 71.18898999999999, 71.077743, 70.964546, 70.849434]
[71.494503, 71.384132, 71.271771, 71.157454, 71.041217]
[71.690134, 71.578638, 71.465144, 71.349689, 71.23231]
[71.885128, 71.772486, 71.65784099999999, 71.541231, 71.422691]

5. Read a slice of the variable (This is a good way to limit memory use for large variables)

longitude_slice = metop_reader.as_array(ds['longitude'][10:14,0:2])
print(metop_reader.shape(longitude_slice))

Output of the print

(4, 2)

6. View the julia array as numpy (requires that numpy is also installed)

import numpy as np
# View the julia array as a numpy array
longitude_slice_np = np.array(longitude_slice, copy = None)
print(longitude_slice_np)

Output of the print

[[233.685948 233.033544]
 [233.276564 232.620004]
 [232.858097 232.197423]
 [232.430274 231.765534]]

7. Close dataset and free file lock

metop_reader.close_dataset(ds)

Development

Pre-requisite: Install podman or docker in your machine.

1. Fork this repository and move into it

git clone XXX && cd XXX

2. Start container mounting the content

podman run -v ./:/usr/local/bin/metoppy -it python:3.12 /bin/bash

or

docker run -v ./:/usr/local/bin/metoppy -it python:3.12 /bin/bash

3. Move to the repository and install the package for testing

cd /usr/local/bin/metoppy && pip install -e .[test]

4. Modify the local code and test in the container.

pytest metoppy/tests

5. When you are happy, push code to your fork and open a PR (Github)