Skip to main content

Tools for interacting with GT's LF AWESOME Receiver data

Project description

LF Logo

LF Data

This project provides a set of useful tools for interacting with data taken from the LF AWESOME receivers maintained by the LF Radio Lab at Georgia Tech. This data is available publicly at Waldo World.

Installation

Run the following to install:

pip install lf

Example Usage

import lf

# Load an entire .mat file
data = lf.data.rx.load_rx_data("path_to_mat_file")

# Load a specific variable or set of variables
variables = ["station_name", "call_sign", "data"]
data = lf.data.rx.load_rx_data("path_to_mat_file", variables)

# Create an LFData object from two mat files or dictionaries
mat_files = ["amplitudeNS.mat", "phaseNS.mat", "amplitudeEW.mat", "phaseEW.mat"]
data = lf.data.rx.LFData(mat_files)

# Evaluate the quality of your data with the EvalLF object
data_eval = lf.data.rxquality.EvalLF(data)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lf-0.1.3.tar.gz (28.2 kB view details)

Uploaded Source

Built Distribution

lf-0.1.3-py3-none-any.whl (30.6 kB view details)

Uploaded Python 3

File details

Details for the file lf-0.1.3.tar.gz.

File metadata

  • Download URL: lf-0.1.3.tar.gz
  • Upload date:
  • Size: 28.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.7

File hashes

Hashes for lf-0.1.3.tar.gz
Algorithm Hash digest
SHA256 58bf43667b1bc20ee287591f24a61a28d60a360f163df28d72182f48dbee73e2
MD5 0e356773179a4b25248ff85b69d053a9
BLAKE2b-256 23cb1ba74bb9b99c76e0f43f161dad9fc93f553300a087b8f3916170ad46039e

See more details on using hashes here.

File details

Details for the file lf-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: lf-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 30.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.7

File hashes

Hashes for lf-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 eabb16cb9b6bbf826a5949aad6f7630d7470031c58b7718264735dac7077fb46
MD5 dadb1c619c08529e81fe5cd501b801c9
BLAKE2b-256 ec806378e545d9b5e91c3b65d6a338aff3581c7feb529ac894e8b7a62ba7fa5f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page