another stab at getting barcodes into the mix

[DeliciousHair]

I noticed that the current PR #22 for a barcode visualisation is failing, so I thought I'd try to fix up whatever the problem is, and in the process decided make the thing a little more to my tastes. Main change is that the H0 and H1 barcodes are combined in a single plot with finer lines than previous (since this is the most common scenario to examine) and individual plots for each dimension are produced if dim != 2.

As well, the optional export_png keyword argument in the plot_bacode() method will cause a PNG version of the barcode to be returned as an io.BytesIO() object.

Example use is included in the docstring for the Barcode class:

n = 300
t = np.linspace(0, 2 * np.pi, n)
noise = np.random.normal(0, 0.1, size=n)

data = np.vstack([((3+d) * np.cos(t[i]+d), (3+d) * np.sin(t[i]+d)) for i, d in enumerate(noise)])
diagrams = ripser(data)

bc = Barcode(diagrams['dgms'])
bc.plot_barcode()

[codecov-io]

Codecov Report

Merging #24 (4702251) into master (c35fa04) will decrease coverage by 3.49%. The diff coverage is 15.27%.

@@            Coverage Diff             @@
##           master      #24      +/-   ##
==========================================
- Coverage   75.90%   72.41%   -3.50%     
==========================================
  Files          20       20              
  Lines        1457     1526      +69     
  Branches      298      317      +19     
==========================================
- Hits         1106     1105       -1     
- Misses        293      360      +67     
- Partials       58       61       +3     

Impacted Files	Coverage Δ
persim/visuals.py	44.08% <15.27%> (-25.15%)	:arrow_down:
persim/bottleneck.py	95.77% <0.00%> (-2.82%)	:arrow_down:

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[DeliciousHair]

I see, failure due to lack of tests written. Any ideas how I can address this?

[sauln]

Thanks for this PR!!

I wouldn't worry too much about the codecov results. Testing plotting code can be pretty nebulous.

Could you post come screenshots of the output this produces into a comment?

[DeliciousHair]

Sure, easy! Here is the output from the sample code provided:

[sauln]

Awesome! Thanks again for picking this up!

Does it look the same if you plot above $H_1$?

Would it be easy to make the x-axes the same for all of the sets? I could see it being helpful for comparisons, but also annoying if each have different scales. What do you think?

In the $H_1$ barcode, the bars are so thin it was hard to see. How difficult is it to make the width of each bar determined by the number of bars shown?

[DeliciousHair]

Awesome! Thanks again for picking this up!

No problem, I was using the feature after all

Does it look the same if you plot above $H_1$?

No, if dim != 2 then individual plots are produced for each dimension. I can extend this up to $H_2$ trivially, but beyond that things get a bit large, so I figured that combining $H_0$ and $H_1$ was a good idea, everything else left on and individual basis.

Would it be easy to make the x-axes the same for all of the sets? I could see it being helpful for comparisons, but also annoying if each have different scales. What do you think?

Very easy, but the question arises about the number of bars for small $\epsilon$ values. If you want to share the axis and still have it readable, I should also make it possible for the user to specify the number of bars they want to to see so that the low-$\epsilon$ junk is truncated off, optionally of course.

In the $H_1$ barcode, the bars are so thin it was hard to see. How difficult is it to make the width of each bar determined by the number of bars shown?

The linewidth is already controllable via the linewidth keyword argument, but making that a function of n_bars would be a bit tricky. See above comment for controlling what is displayed; if that is added then the user could specify the linewidth as well in order to get something visually appealing.

Would you like me to do that?

[DeliciousHair]

Sorry for abandoning this, was far too busy at the time to deal with it, then forgot all about it. Saw it today and thought I should fix it up. Better late than never!