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Diffstat (limited to 'doc/source/reference/arrays.classes.rst')
| -rw-r--r-- | doc/source/reference/arrays.classes.rst | 48 |
1 files changed, 27 insertions, 21 deletions
diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst index 9dcbb6267..6b6f366df 100644 --- a/doc/source/reference/arrays.classes.rst +++ b/doc/source/reference/arrays.classes.rst @@ -6,6 +6,10 @@ Standard array subclasses .. currentmodule:: numpy +.. for doctests + >>> import numpy as np + >>> np.random.seed(1) + .. note:: Subclassing a ``numpy.ndarray`` is possible but if your goal is to create @@ -404,23 +408,25 @@ alias for "matrix "in NumPy. Example 1: Matrix creation from a string ->>> a=mat('1 2 3; 4 5 3') ->>> print (a*a.T).I -[[ 0.2924 -0.1345] - [-0.1345 0.0819]] +>>> a = np.mat('1 2 3; 4 5 3') +>>> print((a*a.T).I) + [[ 0.29239766 -0.13450292] + [-0.13450292 0.08187135]] + Example 2: Matrix creation from nested sequence ->>> mat([[1,5,10],[1.0,3,4j]]) +>>> np.mat([[1,5,10],[1.0,3,4j]]) matrix([[ 1.+0.j, 5.+0.j, 10.+0.j], [ 1.+0.j, 3.+0.j, 0.+4.j]]) Example 3: Matrix creation from an array ->>> mat(random.rand(3,3)).T -matrix([[ 0.7699, 0.7922, 0.3294], - [ 0.2792, 0.0101, 0.9219], - [ 0.3398, 0.7571, 0.8197]]) +>>> np.mat(np.random.rand(3,3)).T +matrix([[4.17022005e-01, 3.02332573e-01, 1.86260211e-01], + [7.20324493e-01, 1.46755891e-01, 3.45560727e-01], + [1.14374817e-04, 9.23385948e-02, 3.96767474e-01]]) + Memory-mapped file arrays ========================= @@ -451,15 +457,15 @@ array actually get written to disk. Example: ->>> a = memmap('newfile.dat', dtype=float, mode='w+', shape=1000) +>>> a = np.memmap('newfile.dat', dtype=float, mode='w+', shape=1000) >>> a[10] = 10.0 >>> a[30] = 30.0 >>> del a ->>> b = fromfile('newfile.dat', dtype=float) ->>> print b[10], b[30] +>>> b = np.fromfile('newfile.dat', dtype=float) +>>> print(b[10], b[30]) 10.0 30.0 ->>> a = memmap('newfile.dat', dtype=float) ->>> print a[10], a[30] +>>> a = np.memmap('newfile.dat', dtype=float) +>>> print(a[10], a[30]) 10.0 30.0 @@ -590,9 +596,9 @@ This default iterator selects a sub-array of dimension :math:`N-1` from the array. This can be a useful construct for defining recursive algorithms. To loop over the entire array requires :math:`N` for-loops. ->>> a = arange(24).reshape(3,2,4)+10 +>>> a = np.arange(24).reshape(3,2,4)+10 >>> for val in a: -... print 'item:', val +... print('item:', val) item: [[10 11 12 13] [14 15 16 17]] item: [[18 19 20 21] @@ -614,7 +620,7 @@ an iterator that will cycle over the entire array in C-style contiguous order. >>> for i, val in enumerate(a.flat): -... if i%5 == 0: print i, val +... if i%5 == 0: print(i, val) 0 10 5 15 10 20 @@ -636,8 +642,8 @@ N-dimensional enumeration Sometimes it may be useful to get the N-dimensional index while iterating. The ndenumerate iterator can achieve this. ->>> for i, val in ndenumerate(a): -... if sum(i)%5 == 0: print i, val +>>> for i, val in np.ndenumerate(a): +... if sum(i)%5 == 0: print(i, val) (0, 0, 0) 10 (1, 1, 3) 25 (2, 0, 3) 29 @@ -658,8 +664,8 @@ objects as inputs and returns an iterator that returns tuples providing each of the input sequence elements in the broadcasted result. ->>> for val in broadcast([[1,0],[2,3]],[0,1]): -... print val +>>> for val in np.broadcast([[1,0],[2,3]],[0,1]): +... print(val) (1, 0) (0, 1) (2, 0) |