LIC : change license from GPL -> 3 clause BSD

They promised me cookies!

examples/legacy_link_usage.py

@@ -1,20 +1,3 @@
-#Copyright 2012 Thomas A Caswell
-#[email protected]
-#http://jfi.uchicago.edu/~tcaswell
-#
-#This program is free software; you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation; either version 3 of the License, or (at
-#your option) any later version.
-#
-#This program is distributed in the hope that it will be useful, but
-#WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-#General Public License for more details.
-#
-#You should have received a copy of the GNU General Public License
-#along with this program; if not, see <http://www.gnu.org/licenses>.
-
 from __future__ import division
 import matplotlib
 matplotlib.use('qt4agg')

trackpy/__init__.py

@@ -1,22 +1,3 @@
-# Copyright 2012 Daniel B. Allan
-# [email protected], [email protected]
-# http://pha.jhu.edu/~dallan
-# http://www.danallan.com
-#
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation; either version 3 of the License, or (at
-# your option) any later version.
-#
-# This program is distributed in the hope that it will be useful, but
-# WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, see <http://www.gnu.org/licenses>.
-
-# Configure logging for all modules in this package.
 import logging
 FORMAT = "%(name)s.%(funcName)s:  %(message)s"
 logging.basicConfig(level=logging.WARN, format=FORMAT)

trackpy/feature.py

@@ -1,21 +1,3 @@
-# Copyright 2012 Daniel B. Allan
-# [email protected], [email protected]
-# http://pha.jhu.edu/~dallan
-# http://www.danallan.com
-#
-# This program is free software; you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation; either version 3 of the License, or (at
-# your option) any later version.
-#
-# This program is distributed in the hope that it will be useful, but
-# WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program; if not, see <http://www.gnu.org/licenses>.
-
 
 from __future__ import division
 import warnings
@@ -120,7 +102,7 @@ def refine(raw_image, image, radius, coords, max_iterations=10, engine='auto',
     Characterize the neighborhood of a local maximum, and iteratively
     hone in on its center-of-brightness. Return its coordinates, integrated
     brightness, size (Rg), eccentricity (0=circular), and signal strength.
-    
+
     Parameters
     ----------
     raw_image : array (any dimensions)
@@ -146,15 +128,15 @@ def refine(raw_image, image, radius, coords, max_iterations=10, engine='auto',
             engine = 'python'
     if engine == 'python':
         coords = np.array(coords)  # a copy, will not modify in place
-        results = _refine(raw_image, image, radius, coords, max_iterations, 
+        results = _refine(raw_image, image, radius, coords, max_iterations,
                           characterize, walkthrough)
     elif engine == 'numba':
         if not NUMBA_AVAILABLE:
             warnings.warn("numba could not be imported. Without it, the "
                           "'numba' engine runs very slow. Use the 'python' "
                           "engine or install numba.", UserWarning)
         if image.ndim != 2:
-            raise NotImplementedError("The numba engine only supports 2D " 
+            raise NotImplementedError("The numba engine only supports 2D "
                                       "images. You can extend it if you feel "
                                       "like a hero.")
         if walkthrough:
@@ -225,7 +207,7 @@ def _refine(raw_image, image, radius, coords, max_iterations,
             # If we're off by less than half a pixel, interpolate.
             else:
                 # Here, coord is a float. We are off the grid.
-                neighborhood = ndimage.shift(neighborhood, -off_center, 
+                neighborhood = ndimage.shift(neighborhood, -off_center,
                                              order=2, mode='constant', cval=0)
                 new_coord = coord + off_center
                 # Disallow any whole-pixels moves on future iterations.
@@ -357,7 +339,7 @@ def _numba_refine(raw_image, image, radius, coords, max_iterations,
                 # TODO Implement this for numba.
                 # Remember to zero cm_n somewhere in here.
                 # Here, coord is a float. We are off the grid.
-                # neighborhood = ndimage.shift(neighborhood, -off_center, 
+                # neighborhood = ndimage.shift(neighborhood, -off_center,
                 #                              order=2, mode='constant', cval=0)
                 # new_coord = np.float_(coord) + off_center
                 # Disallow any whole-pixels moves on future iterations.
@@ -420,7 +402,7 @@ def _numba_refine(raw_image, image, radius, coords, max_iterations,
 def locate(raw_image, diameter, minmass=100., maxsize=None, separation=None,
            noise_size=1, smoothing_size=None, threshold=1, invert=False,
            percentile=64, topn=None, preprocess=True, max_iterations=10,
-           filter_before=True, filter_after=True, 
+           filter_before=True, filter_after=True,
            characterize=True, engine='auto'):
     """Locate Gaussian-like blobs of a given approximate size.
 
@@ -449,7 +431,7 @@ def locate(raw_image, diameter, minmass=100., maxsize=None, separation=None,
         default.
     percentile : Features must have a peak brighter than pixels in this
         percentile. This helps eliminate spurrious peaks.
-    topn : Return only the N brightest features above minmass. 
+    topn : Return only the N brightest features above minmass.
         If None (default), return all features above minmass.
 
     Returns
@@ -554,7 +536,7 @@ def locate(raw_image, diameter, minmass=100., maxsize=None, separation=None,
         if maxsize is not None:
             approx_size = np.empty(count_maxima)
             for i in range(count_maxima):
-                approx_size[i] = estimate_size(image, radius, coords[i], 
+                approx_size[i] = estimate_size(image, radius, coords[i],
                                                approx_mass[i])
             condition &= approx_size < maxsize
         coords = coords[condition]
@@ -644,7 +626,7 @@ def batch(frames, diameter, minmass=100, maxsize=None, separation=None,
         default.
     percentile : Features must have a peak brighter than pixels in this
         percentile. This helps eliminate spurrious peaks.
-    topn : Return only the N brightest features above minmass. 
+    topn : Return only the N brightest features above minmass.
         If None (default), return all features above minmass.
 
     Returns
@@ -688,7 +670,7 @@ def batch(frames, diameter, minmass=100, maxsize=None, separation=None,
     References
     ----------
     .. [1] Crocker, J.C., Grier, D.G. http://dx.doi.org/10.1006/jcis.1996.0217
-    
+
     """
     # Gather meta information and save as YAML in current directory.
     timestamp = pd.datetime.utcnow().strftime('%Y-%m-%d-%H%M%S')
@@ -698,12 +680,13 @@ def batch(frames, diameter, minmass=100, maxsize=None, separation=None,
         source = None
     meta_info = dict(timestamp=timestamp,
                      trackpy_version=trackpy.__version__,
-                     source=source, diameter=diameter, minmass=minmass, 
-                     maxsize=maxsize, separation=separation, 
-                     noise_size=noise_size, smoothing_size=smoothing_size, 
-                     invert=invert, percentile=percentile, topn=topn, 
+                     source=source, diameter=diameter, minmass=minmass,
+                     maxsize=maxsize, separation=separation,
+                     noise_size=noise_size, smoothing_size=smoothing_size,
+                     invert=invert, percentile=percentile, topn=topn,
                      preprocess=preprocess, max_iterations=max_iterations,
                      filter_before=filter_before, filter_after=filter_after)
+
     if meta:
         if isinstance(meta, str):
             filename = meta

trackpy/linking.py

@@ -1,19 +1,3 @@
-#Copyright 2012 Thomas A Caswell
-#[email protected]
-#http://jfi.uchicago.edu/~tcaswell
-#
-#This program is free software; you can redistribute it and/or modify
-#it under the terms of the GNU General Public License as published by
-#the Free Software Foundation; either version 3 of the License, or (at
-#your option) any later version.
-#
-#This program is distributed in the hope that it will be useful, but
-#WITHOUT ANY WARRANTY; without even the implied warranty of
-#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-#General Public License for more details.
-#
-#You should have received a copy of the GNU General Public License
-#along with this program; if not, see <http://www.gnu.org/licenses>.
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
@@ -333,7 +317,7 @@ def __str__(self):
     def __repr__(self):
         coords = '(' + (', '.join(["{:.3f}"]*len(self.pos))).format(*self.pos) + ')'
         track = " in Track %d" % self.track.indx if self.track else ""
-        return "<%s at %d, " % (self.__class__.__name__, self.t) + coords + track + ">" 
+        return "<%s at %d, " % (self.__class__.__name__, self.t) + coords + track + ">"
 
 class IndexedPointND(PointND):
 
@@ -366,7 +350,7 @@ def link(levels, search_range, hash_generator, memory=0, track_cls=None,
         algorithm used to resolve subnetworks of nearby particles
         'auto' uses numba if available
 
-    Returns  
+    Returns
     -------
     tracks : list of Track (or track_cls) objects
 
@@ -384,7 +368,7 @@ def link(levels, search_range, hash_generator, memory=0, track_cls=None,
     label_generator = link_iter(iter(levels), search_range, memory=memory,
                                 neighbor_strategy=neighbor_strategy,
                                 link_strategy=link_strategy,
-                                track_cls=track_cls, 
+                                track_cls=track_cls,
                                 hash_generator=hash_generator)
     labels = list(label_generator)
     points = sum(map(list, levels), [])  # flatten levels: a list of poits
@@ -421,7 +405,7 @@ def link_df(features, search_range, memory=0,
         algorithm used to resolve subnetworks of nearby particles
         'auto' uses numba if available
 
-    Returns  
+    Returns
     -------
     trajectories : DataFrame
         This is the input features DataFrame, now with a new column labeling
@@ -437,7 +421,7 @@ def link_df(features, search_range, memory=0,
         For 'BTree' mode only. Define the shape of the search region.
         If None (default), infer shape from range of data.
     box_size : sequence
-        For 'BTree' mode only. Define the parition size to optimize 
+        For 'BTree' mode only. Define the parition size to optimize
         performance. If None (default), the search_range is used, which is
         a reasonable guess for best performance.
     verify_integrity : boolean
@@ -640,7 +624,7 @@ def link_iter(levels, search_range, memory=0,
         For 'BTree' mode only. Define the shape of the search region.
         (Higher-level wrappers of link infer this from the data.)
     box_size : sequence
-        For 'BTree' mode only. Define the parition size to optimize 
+        For 'BTree' mode only. Define the parition size to optimize
         performance. If None (default), the search_range is used, which is
         a reasonable guess for best performance.
     track_cls : class (optional)
@@ -692,7 +676,7 @@ def link_iter(levels, search_range, memory=0,
 
     try:
         # Start ID numbers from zero, incompatible with multithreading.
-        track_cls.reset_counter()  
+        track_cls.reset_counter()
     except AttributeError:
         # must be using a custom Track class without this method
         pass
@@ -728,7 +712,7 @@ def link_iter(levels, search_range, memory=0,
             p.forward_cands = []
 
         # Sort out what can go to what.
-        assign_candidates(cur_level, prev_hash, search_range, 
+        assign_candidates(cur_level, prev_hash, search_range,
                           neighbor_strategy)
 
         # sort the candidate lists by distance
@@ -790,7 +774,7 @@ def link_iter(levels, search_range, memory=0,
 
             spl, dpl = subnet_linker(s_sn, len(d_sn), search_range)
 
-            # Identify the particles in the destination set that 
+            # Identify the particles in the destination set that
             # were not linked to.
             d_remain = set(d for d in d_sn if d is not None)  # TODO DAN
             d_remain -= set(d for d in dpl if d is not None)
@@ -872,6 +856,7 @@ def assign_candidates(cur_level, prev_hash, search_range, neighbor_strategy):
                 p.back_cands.append((wp, dists[i,j]))
                 wp.forward_cands.append((p, dists[i,j]))
 
+
 class SubnetOversizeException(Exception):
     '''An :py:exc:`Exception` to be raised when the sub-nets are too
     big to be efficiently linked.  If you get this then either reduce your search range