mapclassify.FisherJenks

class mapclassify.FisherJenks(y, k=5)[source]

Fisher Jenks optimal classifier - mean based

Parameters
yarray

(n,1), values to classify

kint

number of classes required

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> fj = mc.FisherJenks(cal)
>>> fj.adcm
799.24
>>> fj.bins
array([  75.29,  192.05,  370.5 ,  722.85, 4111.45])
>>> fj.counts
array([49,  3,  4,  1,  1])
>>>
Attributes
ybarray

(n,1), bin ids for observations

binsarray

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

kint

the number of classes

countsarray

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

__init__(self, y, k=5)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, y[, k])

Initialize self.

find_bin(self, x)

Sort input or inputs according to the current bin estimate

get_adcm(self)

Absolute deviation around class median (ADCM).

get_fmt(self)

get_gadf(self)

Goodness of absolute deviation of fit

get_legend_classes(self[, fmt])

Format the strings for the classes on the legend

get_tss(self)

Total sum of squares around 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(self, gdf[, border_color, …])

Plot Mapclassiifer NOTE: Requires matplotlib, and implicitly requires geopandas dataframe as input.

set_fmt(self, fmt)

table(self)

update(self[, y, inplace])

Add data or change classification parameters.

Attributes

fmt