play.py

# import numpy as np
# from scipy.optimize import minimize
#
# def rosen(x):
#     """The Rosenbrock function"""
#     return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)
#
# x0 = np.array([1.3, 0.7, 0.8, 1.9, 1.2])
# res = minimize(rosen, x0, method='nelder-mead',
#                options={'xtol': 1e-8, 'disp': True})
#
#


# fig = plt.figure(1, figsize=(5, 5), dpi=180)
# ax = fig.add_subplot(111)
# circ = Point(1,1).stack(1)
# circ2 = Point(2,2).stack(1)
#
# inter = circ.intersection(circ2)
#
# patch1 = PolygonPatch(circ, facecolor='#99ccff', edgecolor='#6699cc')
# ax.add_patch(patch1)
# patch2 = PolygonPatch(circ2, facecolor='#99ccff', edgecolor='#6699cc')
# ax.add_patch(patch2)
# patch3 = PolygonPatch(inter, facecolor='red', edgecolor='#6699cc')
# ax.add_patch(patch3)
# # x, y = line.xy
# plt.axis('equal')
# # ax.plot(x, y, color='#999999')
# plt.show()
#
#
# fig = plt.figure(1, figsize=(5, 5), dpi=180)
# ax = fig.add_subplot(111)
# feed_zones = [(0, 0, 0.5), (1, 1, 0.5), (2, 2, 0.5)]
# circs = [Point(zone[0], zone[1]).stack(zone[2]) for zone in feed_zones]
#
# patches = [PolygonPatch(circ) for circ in circs]
# for patch in patches:
#     ax.add_patch(patch)
# plt.axis('equal')
# plt.show()


from numba import jit
import numpy as np
import time

x = np.arange(100).reshape(10, 10)

@jit(nopython=True) # Set "nopython" mode for best performance, equivalent to @njit
def go_fast(a): # Function is compiled to machine code when called the first time
    trace = 0
    for i in range(a.shape[0]):   # Numba likes loops
        trace += np.tanh(a[i, i]) # Numba likes NumPy functions
    return a + trace              # Numba likes NumPy broadcasting

# DO NOT REPORT THIS... COMPILATION TIME IS INCLUDED IN THE EXECUTION TIME!
start = time.time()
go_fast(x)
end = time.time()
print("Elapsed (with compilation) = %s" % (end - start))

# NOW THE FUNCTION IS COMPILED, RE-TIME IT EXECUTING FROM CACHE
start = time.time()
go_fast(x)
end = time.time()
print("Elapsed (after compilation) = %s" % (end - start))


df = pd.DataFrame(res)