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Copy pathSciPy.py
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174 lines (104 loc) · 2.71 KB
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# import scipy
# print(scipy.__version__)
# from scipy import constants
# print(constants.liter)
# from scipy import constants
# print(constants.pi)
# from scipy import constants
# print(dir(constants))
# from scipy.optimize import root
# from math import cos
# def eqn(x):
# return x + cos(x)
# myroot = root(eqn, 0)
# print(myroot)
# print(myroot.x)
# from scipy.optimize import minimize
# def eqn(x):
# return x**2 + x + 2
# mymin = minimize(eqn, 0, method='BFGS')
# print(mymin)
# import numpy as np
# from scipy.sparse import csr_matrix
# arr = np.array([0, 0, 0, 0, 0, 1, 1, 0, 2])
# print(csr_matrix(arr))
# import numpy as np
# from scipy.sparse import csr_matrix
# arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
# print(csr_matrix(arr).data)
# import numpy as np
# from scipy.sparse import csr_matrix
# arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
# print(csr_matrix(arr).count_nonzero())
# import numpy as np
# from scipy.sparse import csr_matrix
# arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
# mat = csr_matrix(arr)
# mat.eliminate_zeros()
# print(mat)
# import numpy as np
# from scipy.sparse import csr_matrix
# arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
# mat = csr_matrix(arr)
# mat.sum_duplicates()
# print(mat)
# import numpy as np
# from scipy.sparse import csr_matrix
# arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
# newarr = csr_matrix(arr).tocsc()
# print(newarr)
# import numpy as np
# from scipy.sparse.csgraph import connected_components
# from scipy.sparse import csr_matrix
# arr = np.array([
# [0, 1, 2],
# [1, 0, 0],
# [2, 0, 0]
# ])
# newarr = csr_matrix(arr)
# print(connected_components(newarr))
# import numpy as np
# from scipy.sparse.csgraph import dijkstra
# from scipy.sparse import csr_matrix
# arr = np.array([
# [0, 1, 2],
# [1, 0, 0],
# [2, 0, 0]
# ])
# newarr = csr_matrix(arr)
# print(dijkstra(newarr, return_predecessors=True, indices=0))
# import numpy as np
# from scipy.spatial import Delaunay
# import matplotlib.pyplot as plt
# points = np.array([
# [2, 4],
# [3, 4],
# [3, 0],
# [2, 2],
# [4, 1]
# ])
# simplices = Delaunay(points).simplices
# plt.triplot(points[:, 0], points[:, 1], simplices)
# plt.scatter(points[:, 0], points[:, 1], color='r')
# plt.show()
# import numpy as np
# from scipy.spatial import ConvexHull
# import matplotlib.pyplot as plt
# points = np.array([
# [2, 4],
# [3, 4],
# [3, 0],
# [2, 2],
# [4, 1],
# [1, 2],
# [5, 0],
# [3, 1],
# [1, 2],
# [0, 2]
# ])
# hull = ConvexHull(points)
# hull_points = hull.simplices
# plt.scatter(points[:,0], points[:,1])
# for simplex in hull_points:
# plt.plot(points[simplex,0], points[simplex,1], 'k-')
# plt.show()