Skip to content
Probabilistic Acoustic Sediment Mapping toolbox
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
prism
LICENSE
MANIFEST
PKG-INFO
README.md
prism_logo.png
prism_logo_blk.png
prism_logo_blk_sm.png
setup.cfg
setup.py

README.md

prism_logo_blk_sm

A Python framework for Probabilistic Acoustic Sediment Mapping. See project website here for more details

Contributing & Credits

Software Developer: Dr. Daniel Buscombe, Northern Arizona University, Flagstaff, AZ 86011, daniel.buscombe@nau.edu

Example backscatter data (.tiff files) originate from data collected by R2Sonic and distributed for use as part of the R2Sonic 2017 Multispectral Backscatter competition.

Bed observation data from Patricia Bay are digitized from data presented in: B.~Biffard. Seabed remote sensing by single-beam echosounder: models, methods and applications. Doctoral dissertation, University of Victoria, Canada, 2011.

Bed observation data from Portsmouth (NEWBEX) are digitized from data presented in: T.~Weber, L.~and Ward. Observations of backscatter from sand and gravel seafloors between 170 and 250 kHz. Journal of the Acoustical Society of America, vol.~138, no.~4, pp.~2169 - 2180, 2015.

CRF subfunctions using the pydensecrf wrapper

Setup

Installing in a conda virtual env (recommended)

PriSM is designed for use with Python 3

Windows:

conda create --name prism_test python=3
activate prism_test
pip install Cython
conda install gdal rasterio shapely fiona pyproj -y
conda config --add channels conda-forge
conda install pydensecrf
conda config --remove channels conda-forge

win1

win2

pip install git+https://github.com/dbuscombe-usgs/prism.git

win3

Linux:

conda create --name prism_test python=3
source activate prism_test
pip install numpy Cython
pip install pydensecrf
pip install git+https://github.com/dbuscombe-usgs/prism.git

finally deactivate the venv ::

deactivate

Linux:

source deactivate

Installing as a library accessible outside of virtual env

  1. the latest 'bleeding edge' (pre-release) version directly from github::
pip install git+https://github.com/dbuscombe-usgs/prism.git

(Windows users) install git from here: https://git-scm.com/download/win

  1. from github repo clone::
git clone git@github.com:dbuscombe-usgs/prism.git
cd prism
python setup.py install

or a local installation:

python setup.py install --user
  1. linux users, using a virtual environment:
virtualenv venv
source venv/bin/activate
pip install numpy Cython
pip install git+https://github.com/dbuscombe-usgs/prism.git
deactivate ##(or source venv/bin/deactivate)

Running the test

Then run the test ::

python -c "import prism; prism.test.dotest()" 

win6

win7

Using the GUI

run the GUI ::

python -c "import prism; prism.gui_funcs.gui()" 

win8

or alternatively from within the python console like so:

win9

More info

Download the user manual

Using prism within python scripts

A full worked example using the NEWBEX data set


def run_prism():

   #==================================================================================
   ##GMM parameters
   covariance = 'full'
   tol = 1e-2

   ##CRF parameters
   theta = 300 
   mu = 100 
   n_iter = 15

   # general settings
   gridres = 1  # grid size in m
   buff = 10 # buffer distance on each bed observation
   prob_thres = 0.1 #probability threshold
   chambolle = 0.0  #chambolle filter
   test_size = 0.5

   ## update this with the full file path
   bs100 = 'newbex_mosaic_100.tiff'
   bs200 = 'newbex_mosaic_200.tiff'
   bs400 = 'newbex_mosaic_400.tiff'
   refs_file = 'newbex_bed.shp'

   prefix = 'newbex'


   #======== READ
   #==================================================================================
   input = [bs100, bs200, bs400]
   img, bs = read_geotiff(input, gridres, chambolle)

   bed = read_shpfile(refs_file, bs)
   Lc = get_sparse_labels(bs, bed, buff)

   if np.ndim(img)>2:
      mask = img[:,:,0]==0
   else:
      mask = img==0

   #======== GMM
   #==================================================================================
   g = fit_GMM(img, Lc, test_size, covariance, tol)
   y_pred_gmm, y_prob_gmm, y_gmm_prob_per_class = apply_GMM(g, img, prob_thres)

   #======== CRF
   #==================================================================================
   y_pred_crf, y_prob_crf, y_crf_prob_per_class = apply_CRF(img, Lc, bed['labels'], n_iter, prob_thres, theta, mu)

   #======== PLOT
   #==================================================================================
   cmap = plt.get_cmap('tab20b',len(bed['labels'])-1).colors

   cmap1 = []
   cmap1.append('gray')
   for k in cmap:
      cmap1.append(colors.rgb2hex(k))

   plot_dists_per_sed(Lc, img, bed, cmap1, prefix)

   plot_gmm(mask, y_pred_gmm, y_prob_gmm, bs, bed, cmap, prefix)
   plot_crf(mask, y_pred_crf, y_prob_crf, bs, bed, cmap, prefix)

   plot_gmm_image(mask, y_pred_gmm, y_prob_gmm, bs, bed, cmap, prefix)
   plot_crf_image(mask, y_pred_crf, y_prob_crf, bs, bed, cmap, prefix)

   plot_gmm_crf(mask, y_pred_gmm, y_prob_gmm, y_pred_crf, y_prob_crf, bs, bed, cmap, prefix)
   plot_gmm_crf_images(mask, y_pred_gmm, y_prob_gmm, y_pred_crf, y_prob_crf, bs, bed, cmap, prefix)
   plot_bs_maps(img, bed, bs, cmap, prefix)

   plot_confmatCRF(y_pred_crf, Lc, bed, prefix)
   plot_confmatGMM(y_pred_gmm, Lc, bed, prefix)

   #======== EXPORT
   #==================================================================================
   export_bed_data(bed, prefix)
   export_gmm_gtiff(mask, y_pred_gmm.copy(), y_prob_gmm.copy(), bs, prefix)
   export_crf_gtiff(mask, y_pred_crf.copy(), y_prob_crf.copy(), bs, prefix)

if __name__ == '__main__':
   run_prism()

Version History

v. 0.1. 2/26/2018. Initial public release

You can’t perform that action at this time.