[Numpysvn] r8584  branches/1.5.x/numpy/lib
numpysvn@scip...
numpysvn@scip...
Sun Aug 1 06:21:57 CDT 2010
Author: rgommers
Date: 20100801 06:21:57 0500 (Sun, 01 Aug 2010)
New Revision: 8584
Modified:
branches/1.5.x/numpy/lib/scimath.py
branches/1.5.x/numpy/lib/shape_base.py
branches/1.5.x/numpy/lib/stride_tricks.py
branches/1.5.x/numpy/lib/twodim_base.py
Log:
DOC: wiki merge, twodim_base and a few loose ones.
Modified: branches/1.5.x/numpy/lib/scimath.py
===================================================================
 branches/1.5.x/numpy/lib/scimath.py 20100801 11:21:38 UTC (rev 8583)
+++ branches/1.5.x/numpy/lib/scimath.py 20100801 11:21:57 UTC (rev 8584)
@@ 3,16 +3,17 @@
whose output datatype is different than the input datatype in certain
domains of the input.
For example, for functions like log() with branch cuts, the versions in this
module provide the mathematically valid answers in the complex plane:
+For example, for functions like `log` with branch cuts, the versions in this
+module provide the mathematically valid answers in the complex plane::
>>> import math
>>> from numpy.lib import scimath
>>> scimath.log(math.exp(1)) == (1+1j*math.pi)
True
+ >>> import math
+ >>> from numpy.lib import scimath
+ >>> scimath.log(math.exp(1)) == (1+1j*math.pi)
+ True
Similarly, sqrt(), other base logarithms, power() and trig functions are
+Similarly, `sqrt`, other base logarithms, `power` and trig functions are
correctly handled. See their respective docstrings for specific examples.
+
"""
__all__ = ['sqrt', 'log', 'log2', 'logn','log10', 'power', 'arccos',
Modified: branches/1.5.x/numpy/lib/shape_base.py
===================================================================
 branches/1.5.x/numpy/lib/shape_base.py 20100801 11:21:38 UTC (rev 8583)
+++ branches/1.5.x/numpy/lib/shape_base.py 20100801 11:21:57 UTC (rev 8584)
@@ 126,27 +126,26 @@
`func` is called as `res = func(a, axis)`, where `axis` is the first
element of `axes`. The result `res` of the function call must have
 either the same dimensions as `a` or one less dimension. If `res` has one
 less dimension than `a`, a dimension is inserted before `axis`.
 The call to `func` is then repeated for each axis in `axes`,
+ either the same dimensions as `a` or one less dimension. If `res`
+ has one less dimension than `a`, a dimension is inserted before
+ `axis`. The call to `func` is then repeated for each axis in `axes`,
with `res` as the first argument.
Parameters

func : function
This function must take two arguments, `func(a, axis)`.
 a : ndarray
+ a : array_like
Input array.
axes : array_like
 Axes over which `func` is applied, the elements must be
 integers.
+ Axes over which `func` is applied; the elements must be integers.
Returns

val : ndarray
 The output array. The number of dimensions is the same as `a`, but
 the shape can be different. This depends on whether `func` changes
 the shape of its output with respect to its input.
+ The output array. The number of dimensions is the same as `a`,
+ but the shape can be different. This depends on whether `func`
+ changes the shape of its output with respect to its input.
See Also

Modified: branches/1.5.x/numpy/lib/stride_tricks.py
===================================================================
 branches/1.5.x/numpy/lib/stride_tricks.py 20100801 11:21:38 UTC (rev 8583)
+++ branches/1.5.x/numpy/lib/stride_tricks.py 20100801 11:21:57 UTC (rev 8584)
@@ 33,16 +33,16 @@
Parameters

 `*args` : arrays
+ `*args` : array_likes
The arrays to broadcast.
Returns

broadcasted : list of arrays
 These arrays are views on the original arrays. They are typically not
 contiguous. Furthermore, more than one element of a broadcasted array
 may refer to a single memory location. If you need to write to the
 arrays, make copies first.
+ These arrays are views on the original arrays. They are typically
+ not contiguous. Furthermore, more than one element of a
+ broadcasted array may refer to a single memory location. If you
+ need to write to the arrays, make copies first.
Examples

Modified: branches/1.5.x/numpy/lib/twodim_base.py
===================================================================
 branches/1.5.x/numpy/lib/twodim_base.py 20100801 11:21:38 UTC (rev 8583)
+++ branches/1.5.x/numpy/lib/twodim_base.py 20100801 11:21:57 UTC (rev 8584)
@@ 172,20 +172,22 @@
M : int, optional
Number of columns in the output. If None, defaults to `N`.
k : int, optional
 Index of the diagonal: 0 refers to the main diagonal, a positive value
 refers to an upper diagonal, and a negative value to a lower diagonal.
 dtype : dtype, optional
+ Index of the diagonal: 0 (the default) refers to the main diagonal,
+ a positive value refers to an upper diagonal, and a negative value
+ to a lower diagonal.
+ dtype : datatype, optional
Datatype of the returned array.
Returns

 I : ndarray (N,M)
+ I : ndarray of shape (N,M)
An array where all elements are equal to zero, except for the `k`th
diagonal, whose values are equal to one.
See Also

 diag : Return a diagonal 2D array using a 1D array specified by the user.
+ identity : (almost) equivalent function
+ diag : diagonal 2D array from a 1D array specified by the user.
Examples

@@ 294,7 +296,9 @@
Input data, which is flattened and set as the `k`th
diagonal of the output.
k : int, optional
 Diagonal to set. The default is 0.
+ Diagonal to set; 0, the default, corresponds to the "main" diagonal,
+ a positive (negative) `k` giving the number of the diagonal above
+ (below) the main.
Returns

@@ 303,7 +307,7 @@
See Also

 diag : Matlab workalike for 1D and 2D arrays.
+ diag : MATLAB workalike for 1D and 2D arrays.
diagonal : Return specified diagonals.
trace : Sum along diagonals.
@@ 342,7 +346,7 @@
def tri(N, M=None, k=0, dtype=float):
"""
 Construct an array filled with ones at and below the given diagonal.
+ An array with ones at and below the given diagonal and zeros elsewhere.
Parameters

@@ 352,7 +356,7 @@
Number of columns in the array.
By default, `M` is taken equal to `N`.
k : int, optional
 The subdiagonal below which the array is filled.
+ The subdiagonal at and below which the array is filled.
`k` = 0 is the main diagonal, while `k` < 0 is below it,
and `k` > 0 is above. The default is 0.
dtype : dtype, optional
@@ 360,9 +364,9 @@
Returns

 T : (N,M) ndarray
 Array with a lower triangle filled with ones, in other words
 ``T[i,j] == 1`` for ``i <= j + k``.
+ T : ndarray of shape (N, M)
+ Array with its lower triangle filled with ones and zero elsewhere;
+ in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.
Examples

@@ 391,9 +395,9 @@

m : array_like, shape (M, N)
Input array.
 k : int
 Diagonal above which to zero elements.
 `k = 0` is the main diagonal, `k < 0` is below it and `k > 0` is above.
+ k : int, optional
+ Diagonal above which to zero elements. `k = 0` (the default) is the
+ main diagonal, `k < 0` is below it and `k > 0` is above.
Returns

@@ 402,7 +406,7 @@
See Also

 triu
+ triu : same thing, only for the upper triangle
Examples

@@ 421,13 +425,14 @@
"""
Upper triangle of an array.
 Construct a copy of a matrix with elements below the kth diagonal zeroed.
+ Return a copy of a matrix with the elements below the `k`th diagonal
+ zeroed.
 Please refer to the documentation for `tril`.
+ Please refer to the documentation for `tril` for further details.
See Also

 tril
+ tril : lower triangle of an array
Examples

@@ 448,17 +453,17 @@
Generate a Van der Monde matrix.
The columns of the output matrix are decreasing powers of the input
 vector. Specifically, the ith output column is the input vector to
 the power of ``N  i  1``. Such a matrix with a geometric progression
 in each row is named Van Der Monde, or Vandermonde matrix, from
 AlexandreTheophile Vandermonde.
+ vector. Specifically, the `i`th output column is the input vector
+ raised elementwise to the power of ``N  i  1``. Such a matrix with
+ a geometric progression in each row is named for AlexandreTheophile
+ Vandermonde.
Parameters

x : array_like
1D input array.
N : int, optional
 Order of (number of columns in) the output. If `N` is not specified,
+ Order of (number of columns in) the output. If `N` is not specified,
a square array is returned (``N = len(x)``).
Returns
@@ 467,11 +472,6 @@
Van der Monde matrix of order `N`. The first column is ``x^(N1)``,
the second ``x^(N2)`` and so forth.
 References
 
 .. [1] Wikipedia, "Vandermonde matrix",
 http://en.wikipedia.org/wiki/Vandermonde_matrix

Examples

>>> x = np.array([1, 2, 3, 5])
@@ 586,10 +586,12 @@
We can now use the Matplotlib to visualize this 2dimensional histogram:
 >>> extent = [xedges[0], xedges[1], yedges[0], yedges[1]]
+ >>> extent = [yedges[0], yedges[1], xedges[1], xedges[0]]
>>> import matplotlib.pyplot as plt
 >>> plt.imshow(H, extent=extent)
+ >>> plt.imshow(H, extent=extent, interpolation='nearest')
<matplotlib.image.AxesImage object at ...>
+ >>> plt.colorbar()
+ <matplotlib.colorbar.Colorbar instance at ...>
>>> plt.show()
"""
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