blade.xmath¶

blade mathing operations.

blade.xmath.xaverage(iterable)¶

Discover average value of numbers in iterable.

Parameters:	iterable – iterable object
Returns:	a number

>>> from blade.xmath import xaverage
>>> xaverage([10, 40, 45])
31.666666666666668

blade.xmath.xcount(iterable)¶

Discover how common each item in iterable is and the overall count of each item in iterable.

Parameters:	iterable – iterable object
Returns:	Collects namedtuple Count(least=int, most=int, overall=[(thing1, int), (thing2, int), ...])

>>> from blade.xmath import xcount
>>> common = xcount([11, 3, 5, 11, 7, 3, 5, 11])
>>> # least common thing
>>> common.least
7
>>> # most common thing
>>> common.most
11
>>> # total count for every thing
>>> common.overall
[(11, 3), (3, 2), (5, 2), (7, 1)]

blade.xmath.xinterval(iterable)¶

Discover the length of the smallest interval that can contain the value of every items in iterable.

Parameters:	iterable – iterable object
Returns:	a number

>>> from blade.xmath import xinterval
>>> xinterval([3, 5, 7, 3, 11])
8

blade.xmath.xmedian(iterable)¶

Discover median value of numbers in iterable.

Parameters:	iterable – iterable object
Returns:	a number

>>> from blade.xmath import xmedian
>>> xmedian([4, 5, 7, 2, 1])
4
>>> xmedian([4, 5, 7, 2, 1, 8])
4.5

blade.xmath.xminmax(iterable)¶

Discover the minimum and maximum values among items in iterable.

Parameters:	iterable – iterable object
Returns:	namedtuple MinMAx(min=value, max=value).

>>> from blade.xmath import xminmax
>>> minmax = xminmax([1, 2, 4])
>>> minmax.min
1
>>> minmax.max
4

blade.xmath.xsum(iterable, start=0, precision=False)¶

Discover the total value of adding start and items in iterable together.

Parameters:	iterable – iterable object start (int or float) – starting number precision (bool) – add floats with extended precision

>>> from blade.xmath import xsum
>>> # default behavior
>>> xsum([1, 2, 3])
6
>>> # with a starting mumber
>>> xsum([1, 2, 3], start=1)
7
>>> # add floating points with extended precision
>>> xsum([.1, .1, .1, .1, .1, .1, .1, .1], precision=True)
0.8