diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 63 |
1 files changed, 37 insertions, 26 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 02e7f605e..14f02f797 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -143,6 +143,7 @@ def histogramdd(sample, bins=10, range=None, normed=False): H, edges = histogramdd(x, bins = (5, 6, 7)) See also: histogram + """ try: @@ -263,6 +264,7 @@ def average(a, axis=None, weights=None, returned=False): Raises ZeroDivisionError if appropriate. (The version in MA does not -- it returns masked values). + """ if axis is None: a = array(a).ravel() @@ -376,31 +378,32 @@ def piecewise(x, condlist, funclist, *args, **kw): def select(condlist, choicelist, default=0): """ Return an array composed of different elements of choicelist - depending on the list of conditions. - - condlist is a list of condition arrays containing ones or zeros - - choicelist is a list of choice arrays (of the "same" size as the - arrays in condlist). The result array has the "same" size as the - arrays in choicelist. If condlist is [c0, ..., cN-1] then choicelist - must be of length N. The elements of the choicelist can then be - represented as [v0, ..., vN-1]. The default choice if none of the - conditions are met is given as the default argument. - - The conditions are tested in order and the first one statisfied is - used to select the choice. In other words, the elements of the - output array are found from the following tree (notice the order of - the conditions matters): - - if c0: v0 - elif c1: v1 - elif c2: v2 - ... - elif cN-1: vN-1 - else: default - - Note that one of the condition arrays must be large enough to handle - the largest array in the choice list. + depending on the list of conditions. + + condlist is a list of condition arrays containing ones or zeros + + choicelist is a list of choice arrays (of the "same" size as the + arrays in condlist). The result array has the "same" size as the + arrays in choicelist. If condlist is [c0, ..., cN-1] then choicelist + must be of length N. The elements of the choicelist can then be + represented as [v0, ..., vN-1]. The default choice if none of the + conditions are met is given as the default argument. + + The conditions are tested in order and the first one statisfied is + used to select the choice. In other words, the elements of the + output array are found from the following tree (notice the order of + the conditions matters): + + if c0: v0 + elif c1: v1 + elif c2: v2 + ... + elif cN-1: vN-1 + else: default + + Note that one of the condition arrays must be large enough to handle + the largest array in the choice list. + """ n = len(condlist) n2 = len(choicelist) @@ -452,7 +455,8 @@ def gradient(f, *varargs): Outputs: N arrays of the same shape as f giving the derivative of f with respect - to each dimension. + to each dimension. + """ N = len(f.shape) # number of dimensions n = len(varargs) @@ -542,6 +546,7 @@ bins is monotonically increasing, or bins [i-1] > x >= bins[i] if bins is monotonically decreasing. Beyond the bounds of the bins 0 or len(bins) is returned as appropriate. + """) except RuntimeError: pass @@ -558,6 +563,7 @@ is specified, every occurrence of i at a position p contributes weights[p] instead of 1. See also: histogram, digitize, unique. + """) except RuntimeError: pass @@ -570,6 +576,7 @@ Add a docstring to a built-in obj if possible. If the obj already has a docstring raise a RuntimeError If this routine does not know how to add a docstring to the object raise a TypeError + """) except RuntimeError: pass @@ -612,6 +619,7 @@ def sort_complex(a): the imaginary part if the real part is equal (the default sort order for complex arrays). This function is a wrapper ensuring a complex return type. + """ b = array(a,copy=True) b.sort() @@ -633,6 +641,7 @@ def trim_zeros(filt, trim='fb'): >>> a = array((0, 0, 0, 1, 2, 3, 2, 1, 0)) >>> numpy.trim_zeros(a) array([1, 2, 3, 2, 1]) + """ first = 0 trim = trim.upper() @@ -657,6 +666,7 @@ def unique(x): Example: >>> unique([5,2,4,0,4,4,2,2,1]) array([0,1,2,4,5]) + """ try: tmp = x.flatten() @@ -682,6 +692,7 @@ def place(arr, mask, vals): """Similar to putmask arr[mask] = vals but the 1D array vals has the same number of elements as the non-zero values of mask. Inverse of extract. + """ return _insert(arr, mask, vals) |