vptstools

vptstools is a Python library to transfer and convert VPTS data. VPTS (vertical profile time series) express the density, speed and direction of biological signals such as birds, bats and insects within a weather radar volume, grouped into altitude layers (height) and measured over time (datetime).

Installation

Python 3.9+ is required.

pip install vptstools

If you need the tools/services to transfer data (SFTP, S3) install these additional dependencies:

pip install vptstools\[transfer\]

Usage

As a library user interested in working with ODIM HDF5 and VPTS files, the most important functions provided by the package are {py:func}vptstools.vpts.vp, {py:func}vptstools.vpts.vpts and {py:func}vptstools.vpts.vpts_to_csv, which can be used respectively to convert a single HDF5 file, a set of HDF5 files and save a VPTS DataFrame to a CSV file:

• Convert a single local ODIM HDF5 file to a VP DataFrame:

from vptstools.vpts import vp

# Download https://aloftdata.s3-eu-west-1.amazonaws.com/baltrad/hdf5/nldbl/2013/11/23/nldbl_vp_20131123T0000Z.h5
file_path_h5 = "./nldbl_vp_20131123T0000Z.h5"
df_vp = vp(file_path_h5)

• Convert a set of locally stored ODIM HDF5 files to a VPTS DataFrame:

from pathlib import Path
from vptstools.vpts import vpts

# Download files to data directory from e.g. https://aloftdata.eu/browse/?prefix=baltrad/hdf5/nldbl/2013/11/23/
file_paths = sorted(Path("./data").rglob("*.h5")) # Get all HDF5 files within the data directory
df_vpts = vpts(file_paths)

• Store a VP or VPTS DataFrame to a VPTS CSV file:

from vptstools.vpts import vpts_to_csv

vpts_to_csv(df_vpts, "vpts.csv")

Both {py:func}`vptstools.vpts.vp` and {py:func}`vptstools.vpts.vpts` have 2 other optional parameters related to the [VPTS CSV data exchange format](https://aloftdata.eu/vpts-csv/). The `vpts_csv_version` parameter defines the version of the VPTS CSV data exchange standard (default v1.0) whereas the `source_file` provides a way to define a custom [source_file](https://aloftdata.eu/vpts-csv/#source_file) field to reference the source from which the data were derived. 

To validate a VPTS DataFrame against the frictionless data schema as defined by the VPTS CSV data exchange format and return a report, use the {py:func}vptstools.vpts.validate_vpts:

from vptstools.vpts import validate_vpts

report = validate_vpts(df_vpts, schema_version="v1.0")
report.stats["errors"]

Other modules in the package are:

• {py:mod}vptstools.odimh5: This module extents the implementation of the original odimh5 package which is now deprecated.
• {py:mod}vptstools.vpts_csv: This module contains - for each version of the VPTS CSV exchange format - the corresponding implementation which can be used to generate a VP or VPTS DataFrame. For more information on how to support a new version of the VPTS CSV format, see contributing docs.
• {py:mod}vptstools.s3: This module contains the functions to manage the Aloft data repository S3 bucket.

CLI endpoints

In addition to using functions in Python scripts, two vptstools routines are available to be called from the command line after installing the package:

.. include:: click.rst

Development instructions

See contributing for a detailed overview and set of guidelines. If familiar with tox, the setup of a development environment boils down to:

tox -e dev   # Create development environment with venv and register an ipykernel. 
source venv/bin/activate  # Activate this environment to get started

Next, the following set of commands are available to support development:

tox              # Run the unit tests
tox -e docs      # Invoke sphinx-build to build the docs
tox -e format    # Run black code formatting

tox -e clean     # Remove old distribution files and temporary build artifacts (./build and ./dist)
tox -e build     # Build the package wheels and tar

tox -e linkcheck # Check for broken links in the documentation

tox -e publish   # Publish the package you have been developing to a package index server. By default, it uses testpypi. If you really want to publish your package to be publicly accessible in PyPI, use the `-- --repository pypi` option.
tox -av          # List all available tasks

To create a pinned requirements.txt set of dependencies, pip-tools is used:

pip-compile --extra transfer --resolver=backtracking`

Notes

• This project has been set up using PyScaffold 4.3.1. For details and usage information on PyScaffold see https://pyscaffold.org/.
• The odimh5 module was originally developed and released to pypi as a separate odimh5 package by Nicolas Noé (@niconoe). Version 0.1.0 has been included into this vptstools package.