pycnet package

Subpackages

• pycnet.cnet package
  • Module contents
• pycnet.file package
  • Submodules
  • pycnet.file.image module
    • ImageChecker
      • ImageChecker.in_queue
      • ImageChecker.bad_queue
      • ImageChecker.run()
    • checkImageFile()
    • checkImages()
    • findImages()
  • pycnet.file.wav module
    • WaveWorker
      • WaveWorker.in_queue
      • WaveWorker.done_queue
      • WaveWorker.output_dir
      • WaveWorker.run()
    • getAudioFileInfo()
    • getFlacLength()
    • getWavLength()
    • makeSoxCmds()
    • makeSpectroDirList()
  • Module contents
    • buildAudioFileDF()
    • buildFilePrefix()
    • buildFilename()
    • findFiles()
    • getFileSize()
    • getFolder()
    • inventoryFolder()
    • makeFileInventory()
    • massRenameFiles()
    • readInventoryFile()
    • removeSpectroDir()
    • renameFiles()
    • summarizeInventory()
• pycnet.process package
  • Module contents
    • batchImageData()
    • batchProcess()
    • blockMessage()
    • buildProcQueue()
    • combineOutputFiles()
    • generateClassScores()
    • generateEmbeddings()
    • generateSpectrograms()
    • getFullPaths()
    • logMessage()
    • parsePycnetArgs()
    • processFolder()
• pycnet.prog package
  • Module contents
    • ProgBarWorker
      • ProgBarWorker.done_queue
      • ProgBarWorker.total_size
      • ProgBarWorker.run()
    • makeProgBar()
• pycnet.review package
  • Module contents
    • buildClipDataFrame()
    • getApparentDetections()
    • getClipInfo()
    • getClipTimestamp()
    • getDefaultReviewSettings()
    • getReadableOffset()
    • getSourceFile()
    • getSourceFolders()
    • makeKscopeReviewTable()
    • makeReviewTable()
    • parseStrReviewCriteria()
    • readPredFile()
    • readReviewSettings()
    • summarizeDetections()
    • summarizeRecordingEffort()
    • tallyDetections()

Submodules

pycnet.TestPycnet module

Test the functionality of pycnet using synthetic data.

This script generates one audio file containing mostly random noise and a few examples of the synthetic tone played during northern spotted owl surveys (which is a target class in PNW-Cnet v5), then generates a set of spectrograms from the file, processes the spectrograms using PNW-Cnet v5, and checks that there are apparent detections of the Survey_Tone class at the correct timestamps.

Once the pycnet-audio package has been installed, just run

test_pycnet

and the script should run.

pycnet.TestPycnet.generateToneFile(output_dir)

Create a synthetic audio file containing three NSO survey tones.

Generate a 10 minute clip containing “pink noise” with NSO survey tones at 00:40, 03:25, and 07:50, normalized to -30 dB.

When the resulting audio file is processed using PNW-Cnet v5, there should be high-confidence detections of the Survey_Tone class in the part_004, part_018, and part_040 clips.

Parameters:

output_dir (str) – Path to the directory in which the temporary folder, audio file, and spectrograms will be generated.

Returns:

Path to the audio file that was synthesized.

Return type:

str

pycnet.TestPycnet.main()

Module contents

‘pycnet’ library for audio processing

The purpose of this library is to provide a Python-based API and command-line tools for the PNW-Cnet audio classification model for processing bioacoustics data on practical scales.

copyright:

3. 2024 by Zachary J. Ruff

license:

GNU General Public License v3; see LICENSE for details.