Add dots to alternating tabular lines

[krzentner]

Tables with keys of varying lengths are hard to read, since some of the key names end up far from their values. This change adds a sequence of dots on all odd lines, so that lines are easier to match up with their keys.

[codecov[bot]]

Codecov Report

Merging #37 into master will increase coverage by 0.26%. The diff coverage is 100%.

@@            Coverage Diff             @@
##           master      #37      +/-   ##
==========================================
+ Coverage    94.2%   94.47%   +0.26%     
==========================================
  Files           7        7              
  Lines         328      344      +16     
  Branches       48       53       +5     
==========================================
+ Hits          309      325      +16     
  Misses         12       12              
  Partials        7        7

Impacted Files	Coverage Δ
src/dowel/tabular_input.py	100% <100%> (ø)	:arrow_up:

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[krzentner]

I definitely think that alternating lines is easier to scan than adding dots on every line, especially when text is scrolling quickly. I don't have a very strong opinion on sparse-dots vs dense dots, but sparse dots looks a little less distracting to me. A realistic example with the current implementation looks like this:

-------------------------------------  ------------
AverageDiscountedReturn                -1.83889
AverageReturn  . . . . . . . . . . .   -6.90437
Entropy                                 4.18283
EnvExecTime  . . . . . . . . . . . .    1.5736
Extras/EpisodeRewardMean               -6.96981
GaussianMLPBaseline/ExplainedVariance   0.326456
GaussianMLPBaseline/LossAfter           1.19292
GaussianMLPBaseline/LossBefore . . .    1.26723
GaussianMLPBaseline/MeanKL              0.00447475
GaussianMLPBaseline/dLoss  . . . . .    0.074309
GaussianMLPPolicy/Entropy               4.17182
GaussianMLPPolicy/KL . . . . . . . .    0.00811469
GaussianMLPPolicy/KLBefore              0
GaussianMLPPolicy/LossAfter  . . . .   -0.011946
GaussianMLPPolicy/LossBefore            5.96046e-09
GaussianMLPPolicy/dLoss  . . . . . .    0.0119461
Iteration                               3
MaxReturn  . . . . . . . . . . . . .   -6.56137
MinReturn                              -7.28088
NumTrajs . . . . . . . . . . . . . .   12
Perplexity                             65.5508
PolicyExecTime . . . . . . . . . . .    0.703327
ProcessExecTime                         0.056324
StdReturn  . . . . . . . . . . . . .    0.230479
-------------------------------------  ------------

With denser dots, it looks like this:

-------------------------------------  ------------
AverageDiscountedReturn                -1.83889
AverageReturn .......................  -6.90437
Entropy                                 4.18283
EnvExecTime .........................   1.5736
Extras/EpisodeRewardMean               -6.96981
GaussianMLPBaseline/ExplainedVariance   0.326456
GaussianMLPBaseline/LossAfter           1.19292
GaussianMLPBaseline/LossBefore ......   1.26723
GaussianMLPBaseline/MeanKL              0.00447475
GaussianMLPBaseline/dLoss ...........   0.074309
GaussianMLPPolicy/Entropy               4.17182
GaussianMLPPolicy/KL ................   0.00811469
GaussianMLPPolicy/KLBefore              0
GaussianMLPPolicy/LossAfter .........  -0.011946
GaussianMLPPolicy/LossBefore            5.96046e-09
GaussianMLPPolicy/dLoss .............   0.0119461
Iteration                               3
MaxReturn ...........................  -6.56137
MinReturn                              -7.28088
NumTrajs ............................  12
Perplexity                             65.5508
PolicyExecTime ......................   0.703327
ProcessExecTime                         0.056324
StdReturn ...........................   0.230479
-------------------------------------  ------------

With even denser dots, it looks like this (which I think isn't really easier to read than no dots):

-------------------------------------  ------------
AverageDiscountedReturn .............  -1.83889
AverageReturn .......................  -6.90437
Entropy .............................   4.18283
EnvExecTime .........................   1.5736
Extras/EpisodeRewardMean ............  -6.96981
GaussianMLPBaseline/ExplainedVariance   0.326456
GaussianMLPBaseline/LossAfter .......   1.19292
GaussianMLPBaseline/LossBefore ......   1.26723
GaussianMLPBaseline/MeanKL ..........   0.00447475
GaussianMLPBaseline/dLoss ...........   0.074309
GaussianMLPPolicy/Entropy ...........   4.17182
GaussianMLPPolicy/KL ................   0.00811469
GaussianMLPPolicy/KLBefore ..........   0
GaussianMLPPolicy/LossAfter .........  -0.011946
GaussianMLPPolicy/LossBefore ........   5.96046e-09
GaussianMLPPolicy/dLoss  ............   0.0119461
Iteration ...........................   3
MaxReturn ...........................  -6.56137
MinReturn ...........................  -7.28088
NumTrajs ............................  12
Perplexity ..........................  65.5508
PolicyExecTime ......................   0.703327
ProcessExecTime .....................   0.056324
StdReturn ...........................   0.230479
-------------------------------------  ------------

[ryanjulian]

full density

-------------------------------------  ---
a....................................  100
bbbbbbb..............................   55
ccccc................................   55
d....................................   55
ThisIsAnnoylinglyLong/LikeActuallyWTF   23
ee...................................   55
ff...................................   55
-------------------------------------  ---

half vertical

-------------------------------------  ---
a                                      100
bbbbbbb..............................   55
ccccc                                   55
d....................................   55
ThisIsAnnoylinglyLong/LikeActuallyWTF   23
ee...................................   55
ff                                      55
-------------------------------------  ---

half horizontal

-------------------------------------  ---
a . . . . . . . . . . . . . . . . . .  100
bbbbbbb . . . . . . . . . . . . . . .   55
ccccc . . . . . . . . . . . . . . . .   55
d . . . . . . . . . . . . . . . . . .   55
ThisIsAnnoylinglyLong/LikeActuallyWTF   23
ee  . . . . . . . . . . . . . . . . .   55
ff  . . . . . . . . . . . . . . . . .   55
-------------------------------------  ---

half both

-------------------------------------  ---
a                                      100
bbbbbbb . . . . . . . . . . . . . . .   55
ccccc                                   55
d . . . . . . . . . . . . . . . . . .   55
ThisIsAnnoylinglyLong/LikeActuallyWTF   23
ee  . . . . . . . . . . . . . . . . .   55
ff                                      55
-------------------------------------  ---

For my eyes, full-density and half-horizontal are much more readable than the vertical-skipping options. I think keys with wildly-varying lengths make the horizontal-skipping options hard to read if you get unlucky with where you are skipping lines (i.e. if you end up skipping dots on a bunch of short lines but keeping them on longer lines)

[ryanjulian]

I don't want to flame -- how about we put this to a poll on the RESL slack channel? I generated some GIFs.

[ryanjulian]

Poll results from RESL (n=12):

I realized I should have done a ranking poll instead...

H-density	V-density	%	Votes
100%	100%	50%	6
100%	50%	33%	4
50%	100%	17%	2
50%	50%	0%	0

Here's how how Louise ranked them (best-to-worst):

1. 100% / 100%
2. 50% / 100%
3. 50% / 50 %
4. 100% / 50 %

People seem to overwhelmingly prefer dense dots (88%). They are more split on vertical density (67%).

You've spent the most time staring at this so I'll let you pick what to do and won't comment any more. I hope the surveys were helpful.