41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from plotstyle import *
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fig, ax = plt.subplots( figsize=(3.5,3.7) )
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fig.subplots_adjust(**adjust_fs(fig, 4.0, 1.0, 2.8, 0.5))
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def gaussian(x, y) :
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return np.exp(-0.5*(x*x+y*y))
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def gaussiangradient(x, y) :
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return np.asarray([-x*np.exp(-0.5*(x*x+y*y)), -y*np.exp(-0.5*(x*x+y*y))])
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# Gauss contour lines:
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x1 = -2.0
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x2 = 2.0
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x = np.linspace(x1, x2, 200)
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y = np.linspace(x1, x2, 200)
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X, Y = np.meshgrid(x, y)
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Z = gaussian(X, Y)
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ax.set_xlabel('x')
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ax.set_ylabel('y', rotation='horizontal')
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ax.set_xticks(np.arange(x1, x2+0.1, 1.0))
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ax.set_yticks(np.arange(x1, x2+0.1, 1.0))
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ax.contour(X, Y, Z, linewidths=2, zorder=0)
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# gradients:
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xxg = [-1.1, 1.4, -1.0]
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yyg = [-1.1, 0.6, 0.75]
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for xg, yg in zip(xxg, yyg) :
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zg = gaussian(xg, yg)
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g = gaussiangradient(xg, yg)
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ax.scatter(xg, yg, c='k', s=100, zorder=10)
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ax.quiver([xg, xg], [yg, yg], [g[0], 0.0], [0.0, g[1]], units='xy', angles='uv', scale_units='x', scale=0.5, zorder=10)
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ax.quiver([xg], [yg], [g[0]], [g[1]], units='xy', angles='uv', scale_units='x', scale=0.5, zorder=10, lw=2)
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ax.text(-0.8, -1.5, '$\partial f/\partial x$', ha='center', zorder=20)
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ax.text(-1.55, -0.8, '$\partial f/\partial y$', va='center', rotation='vertical', zorder=20)
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ax.text(-0.4, -0.8, r'$\nabla f$', ha='left', zorder=20)
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plt.savefig('gradient.pdf')
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