Looking back on 2015, I would like to summarize the mathematical functions I found in scipy.

scipy.stats.dirichlet It is a function of Dirichlet distribution used in LDA etc.

import numpy as np
import scipy.stats as ss
import matplotlib.pyplot as plt
import matplotlib.tri as tri

alphas = np.array([3.0, 4.0, 5.0])
dc = ss.dirichlet(alphas)

corners = np.array([[0.0, 0.0], [1.0, 0.0], [0.5, np.sqrt(3.0) / 2.0]])
midpoints = [(corners[(i + 1) % 3] + corners[(i + 2) % 3]) / 2.0 for i in range(3)]

def xy2bc(xy):
    s = [(corners[i] - midpoints[i]).dot(xy - midpoints[i]) / 0.75 for i in range(3)]
    return np.clip(s, 0.0, 1.0)
refiner = tri.UniformTriRefiner(tri.Triangulation(corners[:, 0], corners[:, 1]))
trimesh = refiner.refine_triangulation(subdiv=8)
pvals = [dc.pdf(xy2bc(xy)) for xy in zip(trimesh.x, trimesh.y)]
plt.tricontourf(trimesh, pvals, 200)
plt.axis('equal')
plt.show()

scipy.stats.wishert, scipy.stats.invwishert This is also the Wishart distribution and the inverse Wishart distribution that are sometimes used in MCMC. The following example will generate 10 random numbers for each of the 2D variance matrices.

import numpy as np
import scipy.stats as ss

w = ss.wishart(df=3, scale=np.matrix([[1.0, 0.5], [0.5, 1.0]]))
print w.rvs(10)
iw = ss.invwishart(df=3, scale=np.matrix([[1.0, 0.5], [0.5, 1.0]]))
print iw.rvs(10)

scipy.special.comb, scipy.special.perm Calculation of permutations and combinations.

>>> import scipy.special as ss
>>> ss.comb(6, 3) # 6C3
20.0
>>> ss.perm(6, 3) # 6P3
120.0

scipy.optimize.rosen Rosenblock function used in the subject of optimization.

from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt

from scipy.optimize import rosen
import numpy as np

fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y = np.meshgrid(np.arange(-3., 3., 0.1), np.arange(-3., 3., 0.1))
Z = [[rosen((x, y)) for x, y in zip(xx, yy)] for xx, yy in zip(X, Y)]
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
plt.show()

scipy.signal.lti It is a Linear Time Invariant that is taken care of in control engineering. The following example creates a linear time-invariant system and draws its Bode plot.

import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

# 1 / s^2 + 0.1s + 1
s = signal.lti([1], [1.0, 0.1, 1.0])
w, mag, phase = signal.bode(s, np.arange(0.1, 5.0, 0.01).tolist())

fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True)
ax0.semilogx(w, mag, 'b-')
ax0.set_ylabel("Magnitude")
ax1.semilogx(w, phase, 'r-')
ax1.set_ylabel("Phase")
plt.show()

scipy.spatial.KDTree It is a KD tree of the space division algorithm. In the following example, points are scattered in a grid pattern in 3D space, and all points within a distance range of 1.0 from points (20,20,20) are extracted.

import numpy as np
import scipy.spatial as ss                                      

x, y, z= np.mgrid[0:100, 0:100, 0:100]
points = zip(x.ravel(), y.ravel(), z.ravel())
tree = ss.KDTree(points)
a = tree.query_ball_point([20, 20, 20], 1.0)
print [points[i] for i in a]

[(19, 20, 20), (20, 19, 20), (20, 20, 19), (20, 20, 20), (20, 20, 21), (20, 21, 20), (21, 20, 20)]

scipy.cluster.vq.kmeans2 The k-means method of the clustering algorithm.

import numpy
from scipy.cluster.vq import kmeans2, whiten

features = numpy.array([[ 1.9,2.3],
                        [ 1.5,2.5],
                        [ 0.8,0.6],
                        [ 0.4,1.8],
                        [ 0.1,0.1],
                        [ 0.2,1.8],
                        [ 2.0,0.5],
                        [ 0.3,1.5],
                        [ 1.0,1.0]])
wf = whiten(features)  #Normalization
print kmeans2(wf, k=2)

You will get a label for the center and each value.

(array([[ 1.40584568,  0.69587293],
       [ 1.24002799,  2.50514254]]), array([1, 1, 0, 1, 0, 1, 0, 1, 0]))

scipy.constants.g It's not a function, but it's gravitational acceleration. Various other physical parameters are defined.

>>> import scipy.constants
>>> scipy.constants.g
9.80665

scipy.ndimage.gaussian_filter A filter algorithm used in image processing.

import scipy
from scipy import ndimage
import matplotlib.pyplot as plt

img = scipy.misc.lena().astype(float)
fimg = ndimage.gaussian_filter(img, 3)

plt.figure(figsize=(8, 4))
plt.subplot(121)
plt.imshow(img, cmap=plt.cm.gray)
plt.subplot(122)
plt.imshow(fimg, cmap=plt.cm.gray)
plt.show()

Summary

scipy implements various functions and algorithms in various fields, and it's fun just looking at the reference. I will be indebted to you next year as well.

Mathematical functions found in Scipy

Summary