for example I read this: On Pythons prior to 2.7 and 3.1, once you start experimenting with floating-point numbers, you're likely to stumble across something that may look a bit odd at first glance: >>> 3.1415 * 2 # repr: as code (Pythons < 2.7 and 3.1) 6.2830000000000004 >>> print(3.1415 * 2) # str: user-friendly 6.283 The first result isn't a bug; it's a display issue. It turns out that there are two ways to print every object in Python--with full precision (as in the first result shown here), and in a user-friendly form (as in the second). Formally, the first form is known as an object's as-code repr, and the second is its user-friendly str. In older Pythons, the floating-point repr sometimes displays more precision than you might expect. The difference can also matter when we step up to using classes. For now, if something looks odd, try showing it with a print built-in function call statement. Better yet, upgrade to Python 2.7 and the latest 3.X, where floating-point numbers display themselves more intelligently, usually with fewer extraneous digits--since this book is based on Pythons 2.7 and 3.3, this is the display form I'll be showing throughout this book for floating-point numbers: >>> 3.1415 * 2 # repr: as code (Pythons >= 2.7 and 3.1) 6.283 Besides expressions, there are a handful of useful numeric modules that ship with Python--modules are just packages of additional tools that we import to use: >>> import math >>> math.pi 3.141592653589793 >>> math.sqrt(85) 9.219544457292887 The math module contains more advanced numeric tools as functions, while the ran dom module performs random-number generation and random selections (here, from a Python list coded in square brackets--an ordered collection of other objects to be introduced later in this chapter): >>> import random >>> random.random() 0.7082048489415967 >>> random.choice([1, 2, 3, 4]) 1
Could the problem be something like this? thanks Gabriele 2014-03-03 22:44 GMT-05:00 Gabriele Brambilla < gb.gabrielebrambi...@gmail.com>: > ok, > the things I get is very specific. > my results differs from the other guy's ones with which I compare them for > slightly details. > We fit the data with a function and we obtain different but similar > values. our fit routines on the same data return the same results. So we > produce different data. > the values in www are like 1.01e-134 and also smaller, and I sum values > like this: there could be any problem summing so small numbers? is there a > Python guide that helps in using strange values numbers? > > thanks > > Gabriele > > > > > 2014-03-03 21:02 GMT-05:00 Steven D'Aprano <st...@pearwood.info>: > > On Mon, Mar 03, 2014 at 08:00:52PM -0500, Gabriele Brambilla wrote: >> > Hi, >> > >> > I'm doing a sum in a for loop: >> > >> > www is the quantity I add. >> > >> > MYMAP[i, j, k] = MYMAP[i, j, k] + www >> > >> > MYMAP is a numpy array >> > >> > I have strong reasons to think that in this operation happens some >> > numerical error...Have you suggestions to discover where it is? >> >> You are almost certainly correct. Unless all the numbers are exact >> integers, nearly every floating point operation will add some error. >> >> Without seeing your code, and some representative sample data, it is >> impossible to tell whether the error is tiny or huge. >> >> Tiny error: >> >> MYMAP[1, j, k] = 1.001 >> www = 0.25 >> >> >> Huge error: >> >> MYMAP[1, j, k] = 1.001e19 >> www = 0.25 >> >> >> >> -- >> Steven >> _______________________________________________ >> Tutor maillist - Tutor@python.org >> To unsubscribe or change subscription options: >> https://mail.python.org/mailman/listinfo/tutor >> > >
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