[Numpy-discussion] restrictions on fancy indexing
It's nice I can do: f = np.linspace (0, 1, 100) u[f.1] = 0 cool, this seems to work also: u[np.abs(f).1] = 0 cool! But exactly what kind of expressions are possible here? Certainly not arbitrary code. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] restrictions on fancy indexing
There's a tutorial here: http://www.scipy.org/Cookbook/Indexing Look down for the section on Fancy Indexing. On Fri, Sep 17, 2010 at 10:47 AM, Neal Becker ndbeck...@gmail.com wrote: It's nice I can do: f = np.linspace (0, 1, 100) u[f.1] = 0 cool, this seems to work also: u[np.abs(f).1] = 0 cool! But exactly what kind of expressions are possible here? Certainly not arbitrary code. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] restrictions on fancy indexing
On 17 September 2010 13:47, Neal Becker ndbeck...@gmail.com wrote: It's nice I can do: f = np.linspace (0, 1, 100) u[f.1] = 0 cool, this seems to work also: u[np.abs(f).1] = 0 cool! But exactly what kind of expressions are possible here? Certainly not arbitrary code. The short answer is, anything that yields a boolean or integer array. There's no syntactical magic here. It might be clearer to write it as: c = np.abs(f).1 u[c] = 0 As for what generates boolean arrays, well, they're just numpy arrays, you can mangle them any way you want. But in particular, == != are operators that take two arrays and yield a boolean array. Also useful are ~ | and , which are the logical operators on boolean arrays. Anne ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] restrictions on fancy indexing
On 09/17/2010 08:04 AM, Anne Archibald wrote: On 17 September 2010 13:47, Neal Beckerndbeck...@gmail.com wrote: It's nice I can do: f = np.linspace (0, 1, 100) u[f.1] = 0 cool, this seems to work also: u[np.abs(f).1] = 0 cool! But exactly what kind of expressions are possible here? Certainly not arbitrary code. The short answer is, anything that yields a boolean or integer array. There's no syntactical magic here. It might be clearer to write it as: c = np.abs(f).1 u[c] = 0 As for what generates boolean arrays, well, they're just numpy arrays, you can mangle them any way you want. But in particular,== != are operators that take two arrays and yield a boolean array. Also useful are ~ | and, which are the logical operators on boolean arrays. It can be important to bear in mind that they are not actually logical operators, they are bitwise operators pressed into service. Functionally, they substitute for logical operators on boolean arrays, but one must watch out for their high precedence. This typically requires using parentheses where they would not be needed for the true python logical operators: With python scalars: a b and c d With numpy arrays:(a b) (c d) Eric Anne ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
