The statistics module is one of the fundamental parts of Yatel. It’s designed to support decision making through extraction of measure of positions, variation, skewness and peak analysis of arc weights in a given environment.

The features of this module are divided into 2 distinct groups:

  • Transformation Functions: Is responsible for converting an environment of a given network into a numpy array to accelerate the calculation of statistics.
  • Calculation Functions: Are used for calculating statistical measures on a haplotypes environment.

While all calculation functions use internally the transformation functions, it is often critical to the performance of processing to precalculate in an array with the values ​​of the distances of an environment.

Transformation Functions

The transformation functions are two:

  • weights2array: given a dom.Edges iterable this function returns a numpy.ndarray with all weight values ​​of said arcs.
>>> from yatel import dom, db, stats

# Our classic network example
>>> nw = db.YatelNetwork("memory", mode="w")

>>> nw.add_elements([
...     dom.Haplotype(0, name="Cordoba", clima="calor", edad=200, frio=True), # left
...     dom.Haplotype(1, name="Cordoba", poblacion=12), # right
...     dom.Haplotype(2, name="Cordoba"), # bottom

...     dom.Edge(6599, (0, 1)),
...     dom.Edge(8924, (1, 2)),
...     dom.Edge(9871, (2, 0)),

...     dom.Fact(0, name="Andalucia", lang="sp", timezone="utc-3"),
...     dom.Fact(1, lang="sp"),
...     dom.Fact(1, timezone="utc-6"),
...     dom.Fact(2, name="Andalucia", lang="sp", timezone="utc")
... ])
... nw.confirm_changes()

# we extract all edges
edges = nw.edges()
array([ 6599.,  8924.,  9871.])
  • env2weightarray: This function is responsible for converting a db.YatelNetwork instance into an array with all weights of the edges contained; or any of them filtered by environments. Also for reasons of implementations can receive any iterable and turn it into a numpy array.
>>> stats.env2weightarray(nw)
array([ 6599.,  8924.,  9871.])

# with an environment
>>> stats.env2weightarray(nw, name="Andalucia")
array([ 9871.])

Calculation Functions

Calculation functions are responsible for efficiently calculating statistics on the variability of a network or a network environment. The full list of functions can be found on the reference module yatel.stats

# we could calculate for example, the mean (or average) in a network
>>> stats.average(nw)

# or in a environment
>>> stats.average(nw, name="Andalucia")

For performance reasons is desirable to calculate all weights from an environment before before making many calculations (this can speed up to several hundred times the data analysis)

# we get the array with it's values
>>> arr = stats.env2weightarray(nw, lang="sp")

# calculate the deviation
>>> stats.std(arr)

The functions also support python iterables such as lists or tuples

>>> stats.average([1, 2, 3])

# this wont return a number
>>> stats.average([])

A More Advanced Example

While Yatel provides for the calculation of common statistics, stats module for its architecture facilitates data analysis of more complex environments easily integrating itself with the functionality of SciPy.

For example if we wanted to calculate One-Way ANOVA with two environments of our network.

# import the one-way ANOVA
>>> from scipy.stats import f_oneway

# first sample
>>> arr0 = stats.env2weightarray(nw, lang="sp")

# second sample
>>> arr1 = stats.env2weightarray(nw, name="Andalucia")

>>> f, p = f_oneway(arr0, arr1)

# value of F
>>> f

# value of P
>>> p