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 still in an early development phase. Some features from MetopDatasets.jl are not yet implemented, and the public API may undergo breaking changes in the near future. That said, the package is already useful in its current immature state, and we appreciate curious users trying it out and providing feedback through GitHub issues.

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

pip install metoppy

Documentaion

MetopPy does not yet have its own dedicated documentation page. The best resource for now is the Examples section later in this README.

Another useful resource is the MetopDatasets.jl documentaion page, which provides concrete examples of how to use the Julia version of the package, supported data formats, and additional information.

Dependencies

Build/Edit/Test Dependencies

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

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 = str(reduced_data_folder / test_file_name)
ds = metop_reader.open_dataset(file_path=test_file_path, maskingvalue = float("nan"))

2. Check keys

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

['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'])

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])

[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))

(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)

[[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)

8. It is recommended to use the with syntax to automaticlly close the file

latitude = None

with metop_reader.dataset(test_file_path, maskingvalue = float("nan")) as ds:
  latitude = metop_reader.as_array(ds['latitude'])
  latitude = np.array(latitude, copy = None)

print(latitude.shape)

(42, 10)

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)