Added logging interface and migrated most print statements.

Author: tboggs

spectral/algorithms/classifiers.py

@@ -32,6 +32,7 @@
 
 from __future__ import division, print_function, unicode_literals
 
+import logging
 import numpy
 import numpy as np
 
@@ -137,17 +138,18 @@ def train(self, training_data):
 
                 Data for the training classes.
         '''
+        logger = logging.getLogger('spectral')
         if not self.min_samples:
             # Set minimum number of samples to the number of bands in the image
             self.min_samples = training_data.nbands
-            print('Setting min samples to', self.min_samples)
+            logger.info('Setting min samples to %d', self.min_samples)
         self.classes = []
         for cl in training_data:
             if cl.size() >= self.min_samples:
                 self.classes.append(cl)
             else:
-                print('  Omitting class %3d : only %d samples present' % (
-                    cl.index, cl.size()))
+                logger.warn('Omitting class %3d : only %d samples present',
+                            cl.index, cl.size())
         for cl in self.classes:
             if not hasattr(cl, 'stats') or not cl.stats_valid():
                 cl.calc_stats()

spectral/algorithms/clustering.py

@@ -33,10 +33,12 @@
 
 from __future__ import division, print_function, unicode_literals
 
+import logging
 import numpy
+from warnings import warn
+
 from .classifiers import Classifier
 
-from warnings import warn
 
 
 def L1(v1, v2):
@@ -164,6 +166,8 @@ def kmeans(image, nclusters=10, max_iterations=20, **kwargs):
     import spectral
     import numpy
 
+    logger = logging.getLogger('spectral')
+
     if isinstance(image, numpy.ndarray):
         return kmeans_ndarray(*(image, nclusters, max_iterations), **kwargs)
 
@@ -203,7 +207,7 @@ def kmeans(image, nclusters=10, max_iterations=20, **kwargs):
         nclusters clusters in the startCenters array.'
         centers = numpy.array(start_clusters)
     else:
-        print('Initializing clusters along diagonal of N-dimensional bounding box.')
+        logging.debug('Initializing clusters along diagonal of N-dimensional bounding box.')
         centers = numpy.empty((nclusters, nbands), float)
         boxMin = image[0, 0]
         boxMax = image[0, 0]
@@ -272,8 +276,8 @@ def kmeans(image, nclusters=10, max_iterations=20, **kwargs):
             print("KeyboardInterrupt: Returning clusters from previous iteration")
             return (old_clusters, old_centers)
 
-    print('kmeans terminated with', len(set(old_clusters.ravel())), \
-        'clusters after', itnum - 1, 'iterations.', file=status)
+    logger.info('kmeans terminated with %d clusters after %d iterations',
+                len(set(old_clusters.ravel())), itnum - 1)
     return (old_clusters, centers)
 
 
@@ -344,11 +348,11 @@ def kmeans_ndarray(image, nclusters=10, max_iterations=20, **kwargs):
     import numpy as np
     from spectral.algorithms.spymath import has_nan, NaNValueError
 
+    logger = logging.getLogger('spectral')
+
     if has_nan(image):
         raise NaNValueError('Image data contains NaN values.')
 
-    status = spectral._status
-    
     # defaults for kwargs
     start_clusters = None
     compare = None
@@ -384,7 +388,8 @@ def kmeans_ndarray(image, nclusters=10, max_iterations=20, **kwargs):
         nclusters clusters in the startCenters array.'
         centers = numpy.array(start_clusters)
     else:
-        print('Initializing clusters along diagonal of N-dimensional bounding box.')
+        logger.debug('Initializing clusters along diagonal of N-dimensional' \
+                     ' bounding box.')
         boxMin = np.amin(image, 0)
         boxMax = np.amax(image, 0)
         delta = (boxMax - boxMin) / (nclusters - 1)
@@ -400,8 +405,6 @@ def kmeans_ndarray(image, nclusters=10, max_iterations=20, **kwargs):
     itnum = 1
     while (itnum <= max_iterations):
         try:
-            status.display_percentage('Iteration %d...' % itnum)
-
             # Assign all pixels
             for i in range(nclusters):
                 diffs = np.subtract(image, centers[i], out=diffs)
@@ -423,16 +426,13 @@ def kmeans_ndarray(image, nclusters=10, max_iterations=20, **kwargs):
                 iterations.append(clusters.reshape(nrows, ncols))
 
             if compare and compare(old_clusters, clusters):
-                status.end_percentage('done.')
                 break
             else:
                 nChanged = numpy.sum(clusters != old_clusters)
+                logger.info('k-means iteration {} - {} pixels reassigned.' \
+                            .format(itnum, nChanged))
                 if nChanged == 0:
-                    status.end_percentage('0 pixels reassigned.')
                     break
-                else:
-                    status.end_percentage('%d pixels reassigned.' \
-                                          % (nChanged))
 
             old_clusters[:] = clusters
             old_centers[:] = centers
@@ -442,7 +442,7 @@ def kmeans_ndarray(image, nclusters=10, max_iterations=20, **kwargs):
             print("KeyboardInterrupt: Returning clusters from previous iteration.")
             return (old_clusters.reshape(nrows, ncols), old_centers)
 
-    print('kmeans terminated with', len(set(old_clusters.ravel())), \
-        'clusters after', itnum - 1, 'iterations.', file=status)
+    logger.info('kmeans terminated with %d clusters after %d iterations.',
+                len(set(old_clusters.ravel())), itnum - 1)
     return (old_clusters.reshape(nrows, ncols), centers)
 

spectral/algorithms/resampling.py

@@ -34,6 +34,9 @@
 
 from __future__ import division, print_function, unicode_literals
 
+import logging
+import numpy as np
+
 def erf_local(x):
     import math
     # save the sign of x
@@ -106,7 +109,7 @@ def create_resampling_matrix(centers1, fwhm1, centers2, fwhm2):
     to another.  Arguments are the band centers and full-width half maximum
     spectral response for the original and new band discretizations.
     '''
-    import numpy
+    logger = logging.getLogger('spectral')
 
     sqrt_8log2 = 2.3548200450309493
 
@@ -117,7 +120,7 @@ def create_resampling_matrix(centers1, fwhm1, centers2, fwhm2):
     bounds2 = [[centers2[i] - fwhm2[i] / 2.0, centers2[i] + fwhm2[i] /
                 2.0] for i in range(N2)]
 
-    M = numpy.zeros([N2, N1])
+    M = np.zeros([N2, N1])
 
     jStart = 0
     nan = float('nan')
@@ -130,7 +133,7 @@ def create_resampling_matrix(centers1, fwhm1, centers2, fwhm2):
             j += 1
 
         if j == N1:
-            print(('No overlap for target band %d (%f / %f)' % (
+            logger.info(('No overlap for target band %d (%f / %f)' % (
                 i, centers2[i], fwhm2[i])))
             M[i, 0] = nan
             continue
@@ -146,8 +149,8 @@ def create_resampling_matrix(centers1, fwhm1, centers2, fwhm2):
         # Put NaN in first element of any row that doesn't produce a band in
         # the new schema.
         if len(matches) == 0:
-            print(('No overlap for target band %d (%f / %f)' % (
-                i, centers2[i], fwhm2[i])))
+            logger.info('No overlap for target band %d (%f / %f)',
+                         i, centers2[i], fwhm2[i])
             M[i, 0] = nan
             continue
 
@@ -157,7 +160,7 @@ def create_resampling_matrix(centers1, fwhm1, centers2, fwhm2):
         # case can be handled.
 
         overlaps = [overlap(bounds1[k], bounds2[i]) for k in matches]
-        contribs = numpy.zeros(len(matches))
+        contribs = np.zeros(len(matches))
         A = 0.
         for k in range(len(matches)):
             #endNorms = [normal(centers2[i], stdev, x) for x in overlaps[k]]
@@ -262,5 +265,4 @@ def __call__(self, spectrum):
 
         Any target bands that do not have at lease one overlapping source band
         will contain `float('nan')` as the resampled band value.'''
-        import numpy
-        return numpy.dot(self.matrix, spectrum)
+        return np.dot(self.matrix, spectrum)

spectral/database/aster.py

@@ -426,10 +426,10 @@ def get_signature(self, spectrumID):
         sig.sample_name = results[0][0]
         sig.sample_id = results[0][1]
         x = array.array(arraytypecode)
-        x.fromstring(results[0][2])
+        frombytes(x, results[0][2])
         sig.x = list(x)
         y = array.array(arraytypecode)
-        y.fromstring(results[0][3])
+        frombytes(y, results[0][3])
         sig.y = list(y)
         return sig
 

spectral/database/ecostress.py

@@ -32,6 +32,7 @@
 from __future__ import division, print_function, unicode_literals
 
 import itertools
+import logging
 
 from spectral.utilities.python23 import IS_PYTHON3
 from .aster import AsterDatabase, Signature
@@ -47,6 +48,7 @@
 def read_ecostress_file(filename):
     '''Reads an ECOSTRESS v1 spectrum file.'''
 
+    logger = logging.getLogger('spectral')
     lines = open_file(filename).readlines()
     if not IS_PYTHON3:
         lines = [line.decode('iso-8859-1') for line in lines]
@@ -63,7 +65,7 @@ def read_ecostress_file(filename):
         try:
             s.sample[pair[0].lower()] = pair[1]
         except:
-            print('line {}: {}'.format(i, lines[i]))
+            logger.error('Failed to parse line: {}: {}'.format(i, lines[i]))
             raise
 
     # Read measurment metadata
@@ -84,21 +86,23 @@ def read_ecostress_file(filename):
 
         # Try to handle invalid values on signature lines
         if nItems == 1:
-            print('single item (%s) on signature line, %s' \
-                  %  (pair[0], filename))
+            logger.info('Skipping single item (%s) on signature line for %s',
+                  pair[0], filename)
             continue
         elif nItems > 2:
-            print('more than 2 values on signature line,', filename)
+            logger.info('Skipping more than 2 values on signature line for %s',
+                        filename)
             continue
         try:
             x = float(pair[0])
         except:
-            print('corrupt signature line,', filename)
+            logger.info('Corrupt signature line in file %s', filename)
         if x == 0:
-#           print 'Zero wavelength value', filename
+            logger.info('Skipping zero wavelength value in file %s', filename)
             continue
         elif x < 0:
-            print('Negative wavelength value,', filename)
+            logger.info('Skipping negative wavelength value in file %s',
+                        filename)
             continue
 
         pairs.append(pair)
@@ -173,6 +177,7 @@ def _import_files(self, data_dir, ignore=None):
         import numpy
         import os
 
+        logger = logging.getLogger('spectral')
         if not os.path.isdir(data_dir):
             raise Exception('Error: Invalid directory name specified.')
         if ignore is not None:
@@ -193,7 +198,7 @@ class Sig:
             if f in filesToIgnore:
                 numIgnored += 1
                 continue
-            print('Importing %s.' % f)
+            logger.info('Importing ECOSTRESS file %s.', f)
             numFiles += 1
             sig = self.read_file(f)
             s = sig.sample
@@ -233,13 +238,14 @@ class Sig:
                                 m['x units'], yUnit, m['first x value'],
                                 m['last x value'], sig.x, sig.y)
         if numFiles == 0:
-            print('No data files were found in directory "%s".' \
-                  % data_dir)
+            logger.warning('No ECOSTRESS data files were found in "%s".',
+                           data_dir)
         else:
-            print('Processed %d files.' % numFiles)
+            logger.info('Processed %d ECOSTRESS files.', numFiles)
         if numIgnored > 0:
-            print('Ignored the following %d bad files:' % (numIgnored))
+            msg = 'Ignored the following %d bad files:' % (numIgnored)
             for f in filesToIgnore:
-                print('\t' + f)
+                msg += '\n\t' + f
+            logger.info(msg)
 
         return sigs

spectral/graphics/rasterwindow.py

@@ -1,9 +1,11 @@
 
 from __future__ import division, print_function, unicode_literals
 
+import logging
 import wx
 from spectral.graphics.graphics import SpyWindow
 
+logger = logging.getLogger('spectral')
 
 class RasterWindow(wx.Frame, SpyWindow):
     '''
@@ -44,7 +46,7 @@ def paint(self, dc):
     def left_double_click(self, evt):
         from spectral import settings
         if "data source" in self.kwargs:
-            print('(%d,%d)' % (evt.GetY(), evt.GetX()))
+            logger.info('{}'.format((evt.GetY(), evt.GetX()))
             settings.plotter.plot(self.kwargs["data source"]
                                   [evt.GetY(), evt.GetX()],
                                   source=self.kwargs["data source"])

spectral/graphics/spywxpythonthread.py

@@ -40,10 +40,12 @@
 DEFAULT_X_SIZE = 600
 DEFAULT_Y_SIZE = 600
 
+import logging
 from wx import *
 #from Numeric import *
 from spectral.graphics import *
 
+logger = logging.getLogger('spectral')
 
 #---------------------------------------------------------------------------
 #wxEVT_VIEW_IMAGE = wxID_HIGHEST + 1
@@ -130,7 +132,7 @@ def paint(self, dc):
         dc.EndDrawing()
 
     def left_double_click(self, evt):
-        print((evt.m_y, evt.m_x))
+        logger.debug('LEFT DOUBLE-CLICK at {}'.format((evt.m_y, evt.m_x)))
         from spectral import settings
         if "data source" in self.kwargs:
             settings.plotter.plot(self.kwargs["data source"][evt.m_y, evt.m_x],

spectral/io/aviris.py

@@ -34,9 +34,6 @@
 
 from __future__ import division, print_function, unicode_literals
 
-from warnings import warn
-
-
 def open(file, band_file=None):
     '''
     Returns a SpyFile object for an AVIRIS image file.
@@ -89,19 +86,7 @@ class Params:
 
     if band_file:
         img.bands = read_aviris_bands(find_file_path(band_file))
-    else:
-        # Let user know if band cal files are available
-        fileDir = os.path.split(p.filename)[0]
-        calFiles = glob.glob(fileDir + '/*.spc')
-        if len(calFiles) > 0:
-            print('\nThe following band calibration files are located in ' \
-                'the same directory as the opened AVIRIS file:\n')
-            for f in calFiles:
-                print("    " + os.path.split(f)[1])
-            print('\nTo associate a band calibration file with an AVIRIS ' \
-                  'data file, re-open the AVIRIS file with the following ' \
-                  'syntax:\n')
-            print('    >>> img = aviris.open(fileName, calFileName)\n')
+
     return img