# mapclassify.MaxP¶

class mapclassify.MaxP(y, k=5, initial=1000)[source]

MaxP Map Classification

Based on Max-p regionalization algorithm

Parameters
yarray

(n,1), values to classify

kint

number of classes required

initialint

number of initial solutions to use prior to swapping

Examples

>>> import mapclassify as mc
>>> mp = mc.MaxP(cal)
>>> mp.bins
array([   8.7 ,   16.7 ,   20.47,   66.26, 4111.45])

>>> mp.counts
array([29,  8,  1, 10, 10])

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, initial=1000)[source]

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

Methods

 __init__(self, y[, k, initial]) 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
update(self, y=None, inplace=False, **kwargs)[source]

Add data or change classification parameters.

Parameters
yarray

(n,1) array of data to classify

inplacebool

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

Additional parameters provided in **kwargs are passed to the init
function of the class. For documentation, check the class constructor.