mapclassify.JenksCaspallForced

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

Jenks Caspall Map Classification with forced movements

Parameters
yarray

(n,1), values to classify

kint

number of classes required

Examples

>>> import mapclassify as mc
>>> cal = mc.load_example()
>>> jcf = mc.JenksCaspallForced(cal, k = 5)
>>> jcf.k
5
>>> jcf.bins
array([1.34000e+00, 5.90000e+00, 1.67000e+01, 5.06500e+01, 4.11145e+03])
>>> jcf.counts
array([12, 12, 13,  9, 12])
>>> jcf4 = mc.JenksCaspallForced(cal, k = 4)
>>> jcf4.k
4
>>> jcf4.bins
array([2.51000e+00, 8.70000e+00, 3.66800e+01, 4.11145e+03])
>>> jcf4.counts
array([15, 14, 14, 15])
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