label axes (albeit uninformatively), explain diagram in a boxed aside

[Ultimately_Untrue_Thought.git] / content / pages / ancillary / categories-scatterplot-source.md

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+Title: Source Code for Scatterplot Figure in "The Categories Were Made for Man to Make Predictions"
+Status: Hidden
+
+Requires [NumPy](http://www.numpy.org/), [Matplotlib](https://matplotlib.org/).
+
+```python
+import functools
+import itertools
+
+import matplotlib.pyplot as plot
+from matplotlib.colors import ListedColormap
+from mpl_toolkits.mplot3d import Axes3D
+from numpy import array
+from numpy.random import normal
+
+
+class Person:
+    def __init__(self, a, b, c):
+        self.a = a
+        self.b = b
+        self.c = c
+
+    def trait_vector(self):
+        return [self.a, self.b, self.c]
+
+    @classmethod
+    def generate_cis_male(cls):
+        a = normal(0, 1)
+        b = normal(0, 1)
+        c = normal(0, 1)
+        return cls(a, b, c)
+
+    @classmethod
+    def generate_cis_female(cls):
+        a = normal(3.5, 1)
+        b = normal(3.5, 1)
+        c = normal(3.5, 1)
+        return cls(a, b, c)
+
+    @classmethod
+    def generate_agp(cls):
+        a = normal(0, 1)
+        b = normal(0, 1)
+        c = normal(3.8, 1)
+        return cls(a, b, c)
+
+def my_plot():
+    group_size = 50
+
+    p1 = [Person.generate_cis_female().trait_vector()
+          for _ in range(group_size)]
+    p2 = [Person.generate_agp().trait_vector()
+          for _ in range(group_size)]
+    p3 = [Person.generate_cis_male().trait_vector()
+          for _ in range(group_size)]
+
+    p = array(p1 + p2 + p3)
+
+    target = list(functools.reduce(itertools.chain,
+                                   [itertools.repeat(i, group_size)
+                                    for i in range(1, 4)]))
+
+    figure = plot.figure(figsize=(6, 5))
+    axes = Axes3D(figure)
+
+    axes.scatter(p[:, 0], p[:, 1], p[:, 2],
+                 c=target,
+                 cmap=ListedColormap(["#FF1493", "#B000B0", "#1E90FF"]))
+    plot.show()
+
+
+if __name__ == "__main__":
+    my_plot()
+
+```
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