Re: [Matplotlib-users] scatter plot with constant x

[Stan W. <sta...@nr...>]

> From: Eric Firing [mailto:ef...@ha...] 
> Sent: Wednesday, June 06, 2012 13:41
> To: mat...@li...
> Subject: Re: [Matplotlib-users] scatter plot with constant x
> 
> On 06/06/2012 06:42 AM, Ethan Gutmann wrote:
> >> ...
> >> No, but you can do this:
> >>
> >> plt.plot([3] * 4, [60, 80, 120, 180], ...)
> >
> > This started from a simple enough question, but it got me 
> thinking about what the fastest way to do this is (in case 
> you have HUGE arrays, or many loops over them).

[...]

> Since we end up needing float64 anyway:
> 
> In [3]: %timeit l=np.empty(10000,dtype=np.float64); l.fill(3)
> 100000 loops, best of 3: 14.1 us per loop
> 
> In [4]: %timeit l=np.zeros(10000,dtype=np.float64);l[:]=3
> 10000 loops, best of 3: 26.6 us per loop
> 
> Eric

Numpy's as_strided came to mind; it can make a large array that's really a
view of a one-element array:

    In [1]: as_strided = np.lib.stride_tricks.as_strided

    In [2]: s = as_strided(np.array([3], dtype=np.float64), shape=(10000,),
       ...: strides=(0,))

    In [3]: s[0] = 4

    In [4]: s[9999]  # all elements share data
    Out[4]: 4.0

It's somewhat slower to create the base array and the view than to create and
fill a 10000-element array:

    In [5]: %timeit l = np.empty(10000, dtype=np.float64); l.fill(3)
    100000 loops, best of 3: 10.1 us per loop

    In [6]: %timeit s = as_strided(np.array([3], dtype=np.float64), 
    shape=(10000,), strides=(0,))  # line broken for email
    10000 loops, best of 3: 21.6 us per loop

However, once created, its contents may be changed much more quickly:

    In [7]: l = np.empty(10000, dtype=np.float64)

    In [8]: %timeit l.fill(3)
    100000 loops, best of 3: 7.71 us per loop

    In [9]: %timeit s[0] = 3
    10000000 loops, best of 3: 116 ns per loop

Numpy's broadcast_arrays uses as_strided under the hood.  Code could look
like:

    x, y = np.broadcast_arrays(3, [60, 80, 120, 180])
    plt.plot(x, y, '+')
    x[0] = 21  # new x for all samples
    plt.plot(x, y, 'x')