mapclassify.MaximumBreaks

class mapclassify.MaximumBreaks(y, k=5, mindiff=0)

Maximum Breaks Map Classification.

Parameters:

ynumpy.array

\((n,1)\), values to classify.

kint (default 5)

The number of classes required.

mindifffloat (default 0)

The minimum difference between class breaks.

Examples

>>> import mapclassify
>>> cal = mapclassify.load_example()
>>> mb = mapclassify.MaximumBreaks(cal, k=5)
>>> mb.k
5

>>> mb.bins
array([ 146.005,  228.49 ,  546.675, 2417.15 , 4111.45 ])

>>> mb.counts.tolist()
[50, 2, 4, 1, 1]

Attributes:

ybnumpy.array

\((n,1)\), bin IDs for observations.

binsnumpy.array

\((k,1)\), the upper bounds of each class.

kint

The number of classes.

countsnumpy.array

\((k,1)\), the number of observations falling in each class.

__init__(y, k=5, mindiff=0)

Methods

__init__(y[, k, mindiff])
find_bin(x)	Sort input or inputs according to the current bin estimate.
get_adcm()	Absolute deviation around class median (ADCM).
get_fmt()
get_gadf()	Goodness of absolute deviation of fit.
get_legend_classes([fmt])	Format the strings for the classes on the legend.
get_tss()	Returns sum of squares over all class means.
make(*args, **kwargs)	Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function.
plot(gdf[, border_color, border_width, ...])	Plot a mapclassifier object.
set_fmt(fmt)
table()
update([y, inplace])	Add data or change classification parameters.

Attributes

fmt

update(y=None, inplace=False, **kwargs)

Add data or change classification parameters.

Parameters:

ynumpy.array (default None)

\((n,1)\), array of data to classify.

inplacebool (default False)

Whether to conduct the update in place or to return a copy estimated from the additional specifications.

**kwargsdict

Additional parameters that are passed to the __init__ function of the class. For documentation, check the class constructor.