This lines up the names in the conversion plugins with the [emotionML suggested vocab](https://www.w3.org/TR/emotion-voc/#dimensions).
emoml has different names for the 4-dimensional fsre scheme and the 3-dimensional vad scheme, which this pull request has added.
I've added the "unpredictability" dimension and mapped big6:surprise to it's maximum value. The assumption is that surprise varies between 5 and 10 to be in line with the other dimensions (no such thing as negative surprise, so no values less than 5). I see that arousal also has all values >5 (so no negative arousal). Ideally, surprise mappings for V, A and D should be calculated empirically - I think there'll be some arousal and possibly slightly lowered dominance.
I wonder if we should use another colon in the emoml names, eg: "emoml:fsredim:valence" or "emoml:big6:happiness", since the [emoml suggested vocab](https://www.w3.org/TR/emotion-voc/xml) only specifies names like "happiness" in a category "big6" (ie: it's hard to know which is the category in "big6happiness").
We'd have to go through the example plugins and make sure they also conform...
open to discussion on this btw...
ps: apologies for multiple changes in this one pr..
I've used euclidean metric instead of taxicab as I feel it makes more sense (taxicab has bizzare unintuitive effects for points far from the centroids).
Does a weighted average of centroids.
If intensity sums to zero for a category, a 'neutral' category is used or 0 if it's not present. I'm not 100% sure this is the best approach, and the name of the "neutral" category perhaps should use some convention?
Note that if there are no categories present, then no VAD (or other dimensional) estimate is returned. It may be better to use the neutral centroid if it's present in this case also.
Closes#12
* Shows only analysis plugins by default on /api/plugins
* Adds a plugin_type parameter to get other types of plugins
* default_plugin chosen from analysis plugins
* Changed the way modules are imported -> we can now use dotted
notation (e.g. senpy.plugins.conversion.centroids)
* Refactored ekman2vad's plugin -> generic centroids
* Added some basic tests
