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Diffstat (limited to 'numpy/lib/arraysetops.py')
| -rw-r--r-- | numpy/lib/arraysetops.py | 206 |
1 files changed, 0 insertions, 206 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py deleted file mode 100644 index 7bd666029..000000000 --- a/numpy/lib/arraysetops.py +++ /dev/null @@ -1,206 +0,0 @@ -""" -Set operations for 1D numeric arrays based on sort() function. - -Contains: - ediff1d, - unique1d, - intersect1d, - intersect1d_nu, - setxor1d, - setmember1d, - union1d, - setdiff1d - -Concerning the speed, test_unique1d_speed() reveals that up to 10000000 -elements unique1d() is about 10 times faster than the standard dictionary-based -numpy.unique(). - -Limitations: Except unique1d, union1d and intersect1d_nu, all functions expect -inputs with unique elements. Speed could be gained in some operations by an -implementaion of sort(), that can provide directly the permutation vectors, -avoiding thus calls to argsort(). - -To do: Optionally return indices analogously to unique1d for all functions. - -Author: Robert Cimrman -""" -__all__ = ['unique1d', 'intersect1d', 'intersect1d_nu', 'setxor1d', - 'setmember1d', 'union1d', 'setdiff1d'] - - -# 02.11.2005, c -import time -import numpy - -## -# 03.11.2005, c -def ediff1d( ar1, to_end = None, to_begin = None ): - """Array difference with prefixed and/or appended value.""" - dar1 = ar1[1:] - ar1[:-1] - if to_end and to_begin: - shape = (ar1.shape[0] + 1,) + ar1.shape[1:] - ed = numpy.empty( shape, dtype = ar1.dtype ) - ed[0], ed[-1] = to_begin, to_end - ed[1:-1] = dar1 - elif to_end: - ed = numpy.empty( ar1.shape, dtype = ar1.dtype ) - ed[-1] = to_end - ed[:-1] = dar1 - elif to_begin: - ed = numpy.empty( ar1.shape, dtype = ar1.dtype ) - ed[0] = to_begin - ed[1:] = dar1 - else: - ed = dar1 - - return ed - - -## -# 01.11.2005, c -# 02.11.2005 -def unique1d( ar1, retindx = False ): - """Unique elements of 1D array. When ret_indx is True, return also the - indices indx such that ar1.flat[indx] is the resulting array of unique - elements.""" - if retindx: - ar = numpy.array(ar1).ravel() - perm = ar.argsort() - aux = ar.take(perm) - flag = ediff1d( aux, 1 ) != 0 - return perm.compress(flag), aux.compress(flag) - else: - ar = numpy.array( ar1 ).flatten() - ar.sort() - return ar.compress( ediff1d( ar, 1 ) != 0) - -## -# 01.11.2005, c -def intersect1d( ar1, ar2 ): - """Intersection of 1D arrays with unique elements.""" - aux = numpy.concatenate((ar1,ar2)) - aux.sort() - return aux.compress( (aux[1:] - aux[:-1]) == 0) - -## -# 01.11.2005, c -def intersect1d_nu( ar1, ar2 ): - """Intersection of 1D arrays with any elements.""" - # Might be faster then unique1d( intersect1d( ar1, ar2 ) )? - aux = numpy.concatenate((unique1d(ar1), unique1d(ar2))) - aux.sort() - return aux.compress( (aux[1:] - aux[:-1]) == 0) - -## -# 01.11.2005, c -def setxor1d( ar1, ar2 ): - """Set exclusive-or of 1D arrays with unique elements.""" - aux = numpy.concatenate( (ar1, ar2 ) ) - aux.sort() - flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0 - flag2 = ediff1d( flag, 0 ) == 0 - return aux.compress( flag2 ) - -## -# 03.11.2005, c -# 05.01.2006 -def setmember1d( ar1, ar2 ): - """Return an array of shape of ar1 containing 1 where the elements of - ar1 are in ar2 and 0 otherwise.""" - concat = numpy.concatenate - zlike = numpy.zeros_like - ar = concat( (ar1, ar2 ) ) - tt = concat( (zlike( ar1 ), - zlike( ar2 ) + 1) ) - perm = ar.argsort() - aux = ar.take(perm) - aux2 = tt.take(perm) - flag = ediff1d( aux, 1 ) == 0 - - ii = numpy.where( flag * aux2 )[0] - aux = perm[ii+1] - perm[ii+1] = perm[ii] - perm[ii] = aux - - indx = perm.argsort()[:len( ar1 )] - - return flag.take( indx ) - -## -# 03.11.2005, c -def union1d( ar1, ar2 ): - """Union of 1D arrays with unique elements.""" - return unique1d( numpy.concatenate( (ar1, ar2) ) ) - -## -# 03.11.2005, c -def setdiff1d( ar1, ar2 ): - """Set difference of 1D arrays with unique elements.""" - aux = setmember1d( ar1, ar2 ) - return ar1.compress(aux == 0) - -## -# 02.11.2005, c -def test_unique1d_speed( plot_results = False ): -# exponents = numpy.linspace( 2, 7, 9 ) - exponents = numpy.linspace( 2, 6, 9 ) - ratios = [] - nItems = [] - dt1s = [] - dt2s = [] - for ii in exponents: - - nItem = 10 ** ii - print 'using %d items:' % nItem - a = numpy.fix( nItem / 10 * numpy.random.random( nItem ) ) - - print 'dictionary:' - tt = time.clock() - b = numpy.unique( a ) - dt1 = time.clock() - tt - print dt1 - - print 'array:' - tt = time.clock() - c = unique1d( a ) - dt2 = time.clock() - tt - print dt2 - - - if dt1 < 1e-8: - ratio = 'ND' - else: - ratio = dt2 / dt1 - print 'ratio:', ratio - print 'nUnique: %d == %d\n' % (len( b ), len( c )) - - nItems.append( nItem ) - ratios.append( ratio ) - dt1s.append( dt1 ) - dt2s.append( dt2 ) - - assert numpy.alltrue( b == c ) - - - print nItems - print dt1s - print dt2s - print ratios - - if plot_results: - import pylab - - def plotMe( fig, fun, nItems, dt1s, dt2s ): - pylab.figure( fig ) - fun( nItems, dt1s, 'g-o', linewidth = 2, markersize = 8 ) - fun( nItems, dt2s, 'b-x', linewidth = 2, markersize = 8 ) - pylab.legend( ('dictionary', 'array' ) ) - pylab.xlabel( 'nItem' ) - pylab.ylabel( 'time [s]' ) - - plotMe( 1, pylab.loglog, nItems, dt1s, dt2s ) - plotMe( 2, pylab.plot, nItems, dt1s, dt2s ) - pylab.show() - -if (__name__ == '__main__'): - test_unique1d_speed( plot_results = True ) |