Here is what I get with the orriginal trapz function: IDLE 1.2.2 >>> import numpy as np >>> np.__version__ '1.1.0' >>> y = np.arange(24).reshape(6,4) >>> x = np.arange(6) >>> np.trapz(y, x, axis=0)
Traceback (most recent call last):
File "<pyshell#4>", line 1, in <module>
np.trapz(y, x, axis=0)
File "C:\Python25\Lib\site-packages\numpy\lib\function_base.py", line 1536,
in trapz
return add.reduce(d * (y[slice1]+y[slice2])/2.0,axis)
ValueError: shape mismatch: objects cannot be broadcast to a single shape
>>>
Nadav.
-----הודעה מקורית-----
מאת: [EMAIL PROTECTED] בשם Ryan May
נשלח: ש 12-יולי-08 18:31
אל: Discussion of Numerical Python
נושא: Re: [Numpy-discussion] A correction to numpy trapz function
Nadav Horesh wrote:
> The function trapz accepts x axis vector only for axis=-1. Here is my
> modification (correction?) to let it accept a vector x for integration along
> any axis:
>
> def trapz(y, x=None, dx=1.0, axis=-1):
> """
> Integrate y(x) using samples along the given axis and the composite
> trapezoidal rule. If x is None, spacing given by dx is assumed. If x
> is an array, it must have either the dimensions of y, or a vector of
> length matching the dimension of y along the integration axis.
> """
> y = asarray(y)
> nd = y.ndim
> slice1 = [slice(None)]*nd
> slice2 = [slice(None)]*nd
> slice1[axis] = slice(1,None)
> slice2[axis] = slice(None,-1)
> if x is None:
> d = dx
> else:
> x = asarray(x)
> if x.ndim == 1:
> if len(x) != y.shape[axis]:
> raise ValueError('x length (%d) does not match y axis %d
> length (%d)' % (len(x), axis, y.shape[axis]))
> d = diff(x)
> return tensordot(d, (y[slice1]+y[slice2])/2.0,(0, axis))
> d = diff(x, axis=axis)
> return add.reduce(d * (y[slice1]+y[slice2])/2.0,axis)
>
What version were you working with originally? With 1.1, this is what I
have:
def trapz(y, x=None, dx=1.0, axis=-1):
"""Integrate y(x) using samples along the given axis and the composite
trapezoidal rule. If x is None, spacing given by dx is assumed.
"""
y = asarray(y)
if x is None:
d = dx
else:
d = diff(x,axis=axis)
nd = len(y.shape)
slice1 = [slice(None)]*nd
slice2 = [slice(None)]*nd
slice1[axis] = slice(1,None)
slice2[axis] = slice(None,-1)
return add.reduce(d * (y[slice1]+y[slice2])/2.0,axis)
For me, this works fine with supplying x for axis != -1.
Ryan
--
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma
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