RunningCalcs 0.4

Description:

RunningCalcs 0.4

Introduction
Instances of the RunnincCalc classes in this library can be fed one input value at a time. This allows running several calculations in a single pass over an iterator. This isn’t possible with the built-in variants of most calculations, such as max() and heapq.nlargest().
RunningCalc instances can be fed values directly, for example:
mean_rc, stddev_rc = RunningMean(), RunningStdDev()
for x in values:
mean_rc.feed(x)
stddev_rc.feed(x)
mean, stddev = mean_rc.value, stddev_rc.value
Additionally, the apply_in_parallel() function is supplied, which makes performing several calculations in parallel easy (and fast!). For example:
mean, stddev = apply_in_parallel([RunningMean(), RunningStdDev()], values)
five_smallest, five_largest = apply_in_parallel([RunningNSmallest(5), RunningNLargest(5)], values)


Optimizations
In addition to the basic feed() method, some RunningCalc classes also implement an optimized feedMultiple() method, which accepts a sequence of values to be processed. This allows values to be processed in chunks, allowing for faster processing in many cases.
The apply_in_parallel() function automatically splits the given iterable of input values into chunks (chunk size can be controlled via the chunk_size keyword argument). Therefore using apply_in_parallel() is both fast and easy.


Writing Your Own RunningCalc Class

sub-class RunningCalc
implement the __init__() and feed() methods
make the calculation output value accessible via the value attribute
optionally implement an optimized feedMultiple() method
Note: the RunningCalc base class includes a default naive implementation of feedMultiple()