mapclassify.JenksCaspall¶
-
class
mapclassify.
JenksCaspall
(y, k=5)[source]¶ Jenks Caspall Map Classification
- Parameters
- yarray
(n,1), values to classify
- kint
number of classes required
Examples
>>> import mapclassify as mc >>> cal = mc.load_example() >>> jc = mc.JenksCaspall(cal, k = 5) >>> jc.bins array([1.81000e+00, 7.60000e+00, 2.98200e+01, 1.81270e+02, 4.11145e+03]) >>> jc.counts array([14, 13, 14, 10, 7])
- 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
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