Thanks for your reply I suppose, variable length signals are split on equal parts and dominant harmonic is extracted. Then scatter plot shows this pattern, which has some low correlation, but I can't abstract what could be concluded from grid pattern, as I lack statistical knowledge. Maybe it's saying that data is quantized, which can't be easily seen from single sample bar chart, but perhaps scatter plot suggests that? That's only my wild guess On Thu, Nov 1, 2012 at 1:17 AM, wrote:

I don't have much of an idea what we are supposed to see, except that there might not be much autocorrelation.

Is this grided data and some scatter points might actually be many points on top of each other so we don't see all points and not the frequencey distribution? Is y on a continuous, metric scale or are all grid points different categories, observations.

On Wednesday, October 31, 2012, wrote:

On Wed, Oct 31, 2012 at 8:59 PM, klo uo javascript:;> wrote:

...

Thanks for your reply

I suppose, variable length signals are split on equal parts and dominant harmonic is extracted. Then scatter plot shows this pattern, which has some low correlation, but I can't abstract what could be concluded from grid pattern, as I lack statistical knowledge. Maybe it's saying that data is quantized, which can't be easily seen from single sample bar chart, but perhaps scatter plot suggests that? That's only my wild guess

http://pandasplotting.blogspot.ca/2012/06/lag-plot.html In general you would see a lag autocorrelation structure in the plot.

My guess is that even if there is a pattern in your data we might not see it because we don't see plots that are plotted on top of each other. We only see the support of the y_t, y_{t+1} transition (points that are at least once in the sample), but not the frequencies (or conditional distribution).

If that's the case, then reduce alpha level so many points on top of each other are darker, or colorcode the histogram for each y_t: bincount for each y_t and normalize, or use np.histogram directly for each y_t, then assign to each point a colorscale depending on it's frequency.

Did you calculate the correlation? (But maybe linear correlation won't show much.)

Josef

The answer is hexbin() in matplotlib when you have many points laying on or near each other. Cheers! Ben Root

Obviously there are some real patterns there, but when interpreting low-resolution plots visually, be careful of Moire effects: view the following image at multiple zoom levels as an example. http://upload.wikimedia.org/wikipedia/commons/4/42/Divers_-_Illustrated_Lond... My own data is extremely deceptive when viewed on a computer monitor at typical resolutions, and physical printouts or zoomed-in variants show dramatically different patterns. Jonathan On Wed, Oct 31, 2012 at 7:43 PM, klo uo wrote:

OK, thanks guys for your suggestions, which I'll try tomorrow

I did correlation first, but no significant values Then I did linear regression, one sample to rest and while there I spotted this grid pattern I was using pandas lag_plot, but it's same plot when I do MPL scatter one sample on others

_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion