mapclassify.NaturalBreaks¶
-
class
mapclassify.
NaturalBreaks
(y, k=5, initial=10)[source]¶ Natural Breaks Map Classification
- Parameters
- yarray
(n,1), values to classify
- kint
number of classes required
- initialint, default: 10
Number of initial solutions generated with different centroids. Best of initial results is returned.
Examples
>>> import numpy as np >>> import mapclassify as mc >>> np.random.seed(123456) >>> cal = mc.load_example() >>> nb = mc.NaturalBreaks(cal, k=5) >>> nb.k 5 >>> nb.counts array([49, 3, 4, 1, 1]) >>> nb.bins array([ 75.29, 192.05, 370.5 , 722.85, 4111.45]) >>> x = np.array([1] * 50) >>> x[-1] = 20 >>> nb = mc.NaturalBreaks(x, k = 5)
Warning: Not enough unique values in array to form k classes Warning: setting k to 2
>>> nb.bins array([ 1, 20]) >>> nb.counts array([49, 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, k=5, initial=10)[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.