Installation

ampycloud is available on pypi, which should make its installation straightforward. Typing the following in a terminal should take care of things:

pip install ampycloud

ampycloud uses semantic versioning. The latest stable version is 2.1.0.

The different releases of ampycloud are also available for download from its Github repository.

If you plan to do dev-work with ampycloud, you should instead clone/fork the develop branch of the ampycloud Github repository, in which case the install command becomes:

cd ./where/you/forked/ampycloud/
pip install -e .[dev]

Note

If you plan to do dev-work with ampycloud, you ought to read the contributing guidelines first.

Requirements

ampycloud is compatible with the following python versions:

    long_description_content_type="text/markdown",

Furthermore, ampycloud relies on a few external modules, which will be automatically installed by pip if required:

    python_requires='>=3.9.0',
    install_requires=[
        "matplotlib >= 3.7.2",
        "numpy >= 1.20.3",
        "scikit-learn >= 1.2.0",
        "scipy >= 1.7.3, < 1.14.1", # avoid conflict with statsmodels 0.14.1
        "statsmodels",
        "pandas >= 1.5",
        "pyyaml",
        "ruamel.yaml"

Testing the installation & Speed benchmark

ampycloud is shipped with a high-level entry point that allows to run a speed check from the command line. To see if your installation was successful, the command ampycloud_speed_test -h should return:

usage: ampycloud_speed_test [-h] [-niter int]

ampycloud 2.1.0 - Python package to characterize cloud layers using ceilometer measurements.
This entry point lets you measure the performance of ampycloud on your machine.

options:
  -h, --help  show this help message and exit
  -niter int  Number of ampycloud.demo() executions to trigger. Defaults to 10.

For details: https://MeteoSwiss.github.io/ampycloud
 

To actually run the speed test, simply call ampycloud_speed_test, optionally setting a different number of executions via the -niter argument. For comparison purposes, here are the performances on the machine that was responsible for compiling this documentation:

Test datetime: 2024-09-04 12:18:03.881727
Platform: Linux-6.5.0-1025-azure-x86_64-with-glibc2.35
CPU count: 4

ampycloud.demo() execution time from 10 runs:
 * mean [std]: 0.44s [0.02s]
 * median [min; max]: 0.44s [0.43s; 0.48s]