Mon Feb 26 18:36:57 MST 2018

BenthicSubstrateMapping · Feb 27, 2018 · b9d7c1b · b9d7c1b
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diff --git a/README.md b/README.md
@@ -239,5 +239,10 @@ if __name__ == '__main__':
 
 ```
 
+## Version History
+
+v. 0.1. 2/26/2018. Initial public release
+
+
 
 
diff --git a/prism/__init__.py b/prism/__init__.py
@@ -17,8 +17,9 @@
 #   |b|y| |D|a|n|i|e|l| |B|u|s|c|o|m|b|e|
 #   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
 #   |d|a|n|i|e|l|.|b|u|s|c|o|m|b|e|@|n|a|u|.|e|d|u|
+# https://github.com/dbuscombe-usgs/prism
 
-__version__ = '0.0.21'
+__version__ = '0.1'
 
 #-----------------------------------------------------------------------------
 # Imports

diff --git a/prism/common_funcs.py b/prism/common_funcs.py
@@ -16,6 +16,7 @@
 #   |b|y| |D|a|n|i|e|l| |B|u|s|c|o|m|b|e|
 #   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
 #   |d|a|n|i|e|l|.|b|u|s|c|o|m|b|e|@|n|a|u|.|e|d|u|
+# https://github.com/dbuscombe-usgs/prism
 
 ##-------------------------------------------------------------
 from __future__ import division
@@ -24,7 +25,7 @@
 ##-------------------------------------------------------------
 def get_X(img, Lc):
    """
-   This function ...
+   This function computes distributions of backscatter according to each unique substrate
    """
    D = []
    counter = 0
@@ -51,7 +52,10 @@ def get_X(img, Lc):
 ##-------------------------------------------------------------
 def get_sparse_labels(bs, bed, buff):
    """
-   This function ...
+   This function generates a sparse label map by assigning
+   unique numeric codes to portions of a matrix of identical
+   size to the backscatter inputs, according to the locations
+   of bed observations and a buffer distance
    """
    ## make sparse labels
    Lc = np.zeros(np.shape(bs[0]['bs']))+99

diff --git a/prism/crf_funcs.py b/prism/crf_funcs.py
@@ -16,6 +16,7 @@
 #   |b|y| |D|a|n|i|e|l| |B|u|s|c|o|m|b|e|
 #   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
 #   |d|a|n|i|e|l|.|b|u|s|c|o|m|b|e|@|n|a|u|.|e|d|u|
+# https://github.com/dbuscombe-usgs/prism
 
 ##-------------------------------------------------------------
 from __future__ import division
@@ -28,7 +29,7 @@
 ##-------------------------------------------------------------
 def set_unary_from_labels(fp, lp, prob, labels):
    """
-   This function ...
+   This function sets unary potentials according to the label matrix
    """
    H = fp.shape[0]
    W = fp.shape[1]
@@ -43,7 +44,8 @@ def set_unary_from_labels(fp, lp, prob, labels):
 ##-------------------------------------------------------------
 def set_feats_both(d, fp, scol, compat_col, sspat, compat_spat):
    """
-   This function ...
+   This function generates features according to both spatial and 
+   amplitude kernels
    """
 
    d = set_feats_spat(d, sspat, compat_spat, mode)
@@ -54,7 +56,7 @@ def set_feats_both(d, fp, scol, compat_col, sspat, compat_spat):
 ##-------------------------------------------------------------
 def set_feats_spat(d, sspat, compat_spat):
    """
-   This function ...
+   This function generates features according to spatial kernel
    """
 
    d.addPairwiseGaussian(sxy=sspat, compat=compat_spat)
@@ -64,7 +66,7 @@ def set_feats_spat(d, sspat, compat_spat):
 ##-------------------------------------------------------------
 def set_feats_col(fp, d, scol, compat_col):
    """
-   This function ...
+   This function generates features according to amplitude kernel
    """
 
    scale = 1
@@ -88,7 +90,10 @@ def set_feats_col(fp, d, scol, compat_col):
 ##-------------------------------------------------------------
 def inference(d, n_iter, H, W, labels):
    """
-   This function ...
+   This function carries out the CRF inference, generating a MAP
+   substrate matrix, a conservative probabilistic map based on
+   frequentist principles, and a probability per substrate based on
+   the CRF inference
    """
    R = [] ; 
    Q, tmp1, tmp2 = d.startInference()
@@ -115,7 +120,7 @@ def inference(d, n_iter, H, W, labels):
 ##-------------------------------------------------------------
 def apply_CRF(fp, lp, labels, n_iter, prob_thres, scol, compat_col):
    """
-   This function ...
+   This function generates CRF-derived substrate map and probability map
    """
 
    prob = 0.51 # initial probability of unary labels

diff --git a/prism/data/README.md b/prism/data/README.md
@@ -0,0 +1,6 @@
+Two data sets are provided with the toolbox for users to experiment with. The data come from 1) Patricia Bay, British Columbia, Canada, and 2) lower Portsmouth Harbor, New Hampshire, USA. All 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, and L. 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.
diff --git a/prism/eval_funcs.py b/prism/eval_funcs.py
@@ -16,6 +16,7 @@
 #   |b|y| |D|a|n|i|e|l| |B|u|s|c|o|m|b|e|
 #   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
 #   |d|a|n|i|e|l|.|b|u|s|c|o|m|b|e|@|n|a|u|.|e|d|u|
+# https://github.com/dbuscombe-usgs/prism
 
 ##-------------------------------------------------------------
 import matplotlib.pyplot as plt
@@ -66,7 +67,7 @@ def plot_confusion_matrix(cm, classes,
 ##-------------------------------------------------------------
 def plot_confmatGMM(y_pred, Lc, bed, prefix):
    """
-   This function ...
+   This function generates and plots a confusion matrix for GMM model
    """
 
    #base = '..'+os.sep+'outputs'+os.sep+prefix+'_'
@@ -112,7 +113,7 @@ def plot_confmatGMM(y_pred, Lc, bed, prefix):
 ##-------------------------------------------------------------
 def plot_confmatCRF(y_pred, Lc, bed, prefix):
    """
-   This function ...
+   This function generates and plots a confusion matrix for CRF model
    """
 
    #base = '..'+os.sep+'outputs'+os.sep+prefix+'_'

diff --git a/prism/gmm_funcs.py b/prism/gmm_funcs.py
@@ -16,6 +16,7 @@
 #   |b|y| |D|a|n|i|e|l| |B|u|s|c|o|m|b|e|
 #   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
 #   |d|a|n|i|e|l|.|b|u|s|c|o|m|b|e|@|n|a|u|.|e|d|u|
+# https://github.com/dbuscombe-usgs/prism
 
 ##-------------------------------------------------------------
 from __future__ import division
@@ -31,7 +32,8 @@
 ##-------------------------------------------------------------
 def fit_GMM(img, Lc, test_size, covariance, tol):
    """
-   This function ...
+   This function fits a simple GMM model to the backscatter data
+   and associated labels
    """
    D, l = get_X(img, Lc)
 
@@ -55,8 +57,10 @@ def fit_GMM(img, Lc, test_size, covariance, tol):
 ##-------------------------------------------------------------
 def apply_GMM(g, img, prob_thres):
    """
-   This function ...
+   This function fits applies a GMM model to generate a substrate and
+   associated probability map
    """
+
    print('Estimating substrates ...')
    if np.ndim(img)>2: #multispectral
Original file line number	Diff line number	Diff line change
Expand Up		@@ -239,5 +239,10 @@ if __name__ == '__main__':

		```

		## Version History

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