mapclassify.StdMean

class mapclassify.StdMean(y, multiples=[- 2, - 1, 1, 2])[source]

Standard Deviation and Mean Map Classification

Parameters
yarray

(n,1), values to classify

multiplesarray

the multiples of the standard deviation to add/subtract from the sample mean to define the bins, default=[-2,-1,1,2]

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> st = mc.StdMean(cal)
>>> st.k
5
>>> st.bins
array([-967.36235382, -420.71712519,  672.57333208, 1219.21856072,
       4111.45      ])
>>> st.counts
array([ 0,  0, 56,  1,  1])
>>>
>>> st3 = mc.StdMean(cal, multiples = [-3, -1.5, 1.5, 3])
>>> st3.bins
array([-1514.00758246,  -694.03973951,   945.8959464 ,  1765.86378936,
        4111.45      ])
>>> st3.counts
array([ 0,  0, 57,  0,  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, multiples=[- 2, - 1, 1, 2])[source]

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

Methods

__init__(self, y[, multiples])

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.