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
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