Plot seems weird

[Yigit Turgut]

On Dec 26, 11:28 am, Lie Ryan <lie.1... at gmail.com> wrote:
> On 12/26/2011 05:27 AM, Yigit Turgut wrote:
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> > On Dec 25, 7:06 pm, Rick Johnson<rantingrickjohn... at gmail.com>  wrote:
> >> On Dec 25, 9:33 am, Yigit Turgut<y.tur... at gmail.com>  wrote:
> >>> Hi all,
>
> >>> I have a text file as following;
>
> >>> 0.200047        0.000000
> >>> 0.200053        0.160000
> >>> 0.200059        0.000000
> >>> 0.200065        0.080000
> >>> 0.200072        0.000000
> >>> 0.200078        0.160000
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> >>> And I am trying to plot it with ;
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> >>> filenames = sys.argv[1:]
> >>> if len(filenames) == 0:
> >>>      filenames = [sys.stdin]
> >>> for filename in filenames:
> >>>      t,y1 = numpy.genfromtxt(filename, unpack=True)
> >>>      pyplot.plot(t,y1)
> >>>      pyplot.show()
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> >>> But graph seems weird, not as it supposed to be. Any ideas ?
>
> >> Interesting. Of course "weird" leaves a LOT to be desired. On a scale
> >> of 1-10, how "weird" is the result?
>
> > I apply a 1Khz test signal just to see if things run smoothly, but I
> > see spikes at lower and higher ends (logic 0,1) where I should see a
> > clean rectangle pwm signal. By the look of it I say weirdness is
> > around 3/10.
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> What are you expecting? Your data produces something that looks like the
> plot on the right of this screenshot
> (http://i44.tinypic.com/wwhlvp.jpg), I don't see anything weird with
> that; if you are expecting a square-wave-like plot (like on the left),
> then you should use a square-wave-like data, pyplot wouldn't magically
> transform a spiked-plot to squared-plot.
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> Here's what I use to convert the data on right plot to data on left
> plot, I don't know much about numpy so it might be possible to do it
> more efficiently or numpy might even have something like it already.
>
> from itertools import izip_longest
> def to_square(t, y1):
>      sq_data = [[], []]
>      for x,y, xn in izip_longest(data[0], data[1], data[0][1:]):
>          sq_data[0].append(x)
>          sq_data[1].append(y)
>          sq_data[0].append(xn)
>          sq_data[1].append(y)
>      return numpy.array(sq_data, dtype=float)

Thanks for the tip. I know that I feed a square wave signal and record
this data. Thus I believe the situation can be related to sampling
frequency.
Couldn't get your code working, maybe because I import the data from
file.