count_by and endpoints grouping modes

[mathemancer]

Fixes #384

This adds two grouping mode: count_by for Number typed columns, and endpoints for any sets of columns which can be ordered.

Technical details

endpoints

This mode is meant to be internal for now. That said, it can be called from the API if desired. The reason for making it internal is that some validation of the parameters (specifically the ordering of the endpoint tuples described below) isn't performant without multiple queries per transaction, and we don't currently have that included in our execute_query function. To avoid duplicating work, I'm deferring implementing that validation till we have that functionality.

The way this mode works is that you give an ascending-order array of arrays where each inner array represents a tuple of values from the columns chosen for the grouping. The values do not need to exist in the columns, but they do need to be of appropriate type. That is, the bounds can be chosen between values, as long as there is space between those values for that type. Order for tuples is defined in the same way that PostgreSQL orders rows by a set of columns. This means that if you give the columns in a different order, the order of the tuples in the given array needs to change correspondingly.

The value defining the mode is "endpoints", and the extra parameter "bound_tuples" is required (and is an array of arrays where each inner array has the same number of elements as the number of columns).

count_by

This mode lets a user specify a global_min, global_max and count_by parameters, each of which should be a number (ideally with count_by < global_max - global_min). This will return groups satisfying those parameters in the following way:

Given [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], if the user chooses global_min = 2, global_max = 17, and count_by = 3, then the resulting groups will be: [2, 3, 4], [5, 6, 7], [8, 9, 10], [11, 12, 13], [14, 15]. The parameters do not need to be integers. Internally, this sets up bounds by choosing the global_min as the lowest tuple, then adding count_by iteratively until the global_max is reached, then using the endpoints mode internally. Note that for continuous data, this means the intervals will be greater than or equal to their lower bound, but strictly less than their upper bound.

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