Add anchor option to StdMean

.gitignore

@@ -11,3 +11,4 @@ mapclassify/tests/.ropeproject/
 .DS_Store
 .vscode/settings.json
 __pycache__
+/notebooks/.ipynb_checkpoints/

mapclassify/_classify_API.py

@@ -51,6 +51,7 @@ def classify(
     initial=100,
     bins=None,
     lowest=None,
+    anchor=False,
 ):
     """
 
@@ -94,6 +95,9 @@ def classify(
         Scalar minimum value of lowest class. Default is to set the minimum
         to ``-inf`` if  ``y.min()`` > first upper bound (which will override
         the default), otherwise minimum is set to ``y.min()``.
+    anchor : bool (default False)
+            Anchor upper bound of one class to the sample mean.
+
 
 
     Returns
@@ -182,7 +186,7 @@ def classify(
         classifier = _classifiers[scheme](y, pct)
 
     elif scheme == "stdmean":
-        classifier = _classifiers[scheme](y, multiples)
+        classifier = _classifiers[scheme](y, multiples, anchor)
 
     elif scheme == "jenkscaspallsampled":
         classifier = _classifiers[scheme](y, k, pct_sampled)

mapclassify/classifiers.py

@@ -1520,12 +1520,14 @@ class StdMean(MapClassifier):
 
     Parameters
     ----------
-
     y : numpy.array
-        :math:`(n,1)`, values to classify.
+        :math:`(n,1)`, values to classify
     multiples : numpy.array (default [-2, -1, 1, 2])
         The multiples of the standard deviation to add/subtract from
-        the sample mean to define the bins
+        the sample mean to define the bins.
+    anchor : bool (default False)
+        Anchor upper bound of one class to the sample mean.
+
 
     Attributes
     ----------
@@ -1539,6 +1541,17 @@ class StdMean(MapClassifier):
     counts : numpy.array
         :math:`(k,1)`, the number of observations falling in each class.
 
+    Notes
+    -----
+
+    If anchor is True, one of the intervals will have its closed upper bound
+    equal to the mean of y. Intermediate intervals will have widths equal to
+    the standard deviation of y. The first interval will be closed on the
+    minimum value of y, and the last interval will be closed on the maximum of
+    y. The first and last intervals may have widths different from the
+    intermediate intervals.
+
+
     Examples
     --------
 
@@ -1562,18 +1575,31 @@ class StdMean(MapClassifier):
 
     >>> list(st3.counts)
     [0, 0, 57, 0, 1]
-
+    >>> stda = mapclassify.StdMean(cal, anchor=True)
+    >>> stda.k
+    9
+    >>> stda.bins
+    array([ 125.92810345,  672.57333208, 1219.21856072, 1765.86378936,
+           2312.50901799, 2859.15424663, 3405.79947527, 3952.4447039 ,
+           4111.45      ])
+    >>> cal.mean(), cal.std(), cal.min(), cal.max()
+    (125.92810344827588, 546.6452286365233, 0.13, 4111.45)
     """
 
-    def __init__(self, y, multiples=[-2, -1, 1, 2]):
+    def __init__(self, y, multiples=[-2, -1, 1, 2], anchor=False):
         self.multiples = multiples
+        self.anchor = anchor
         MapClassifier.__init__(self, y)
         self.name = "StdMean"
 
     def _set_bins(self):
         y = self.y
         s = y.std(ddof=1)
         m = y.mean()
+        if self.anchor:
+            min_z = int((y.min() - m) / s)
+            max_z = int((y.max() - m) / s) + 1
+            self.multiples = list(range(min_z, max_z))
         cuts = [m + s * w for w in self.multiples]
         y_max = y.max()
         if cuts[-1] < y_max:

mapclassify/tests/test_mapclassify.py

@@ -631,6 +631,30 @@ def test_UserDefined_lowest(self):
         assert ud.get_legend_classes() == classes
 
 
+class TestStdMeanAnchor:
+    def setup_method(self):
+        self.V = load_example()
+
+    def test_StdMeanAnchor(self):
+        sm = StdMean(self.V, anchor=True)
+        bins = numpy.array(
+            [
+                125.92810345,
+                672.57333208,
+                1219.21856072,
+                1765.86378936,
+                2312.50901799,
+                2859.15424663,
+                3405.79947527,
+                3952.4447039,
+                4111.45,
+            ]
+        )
+        counts = numpy.array([50, 6, 1, 0, 0, 0, 0, 0, 1])
+        numpy.testing.assert_array_almost_equal(sm.bins, bins)
+        numpy.testing.assert_array_almost_equal(sm.counts, counts)
+
+
 class TestMaxP:
     def setup_method(self):
         self.V = load_example()